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Repository: mbeyeler/opencv-python-blueprints
Branch: master
Commit: 15d7c3d7a067
Files: 19168
Total size: 106.1 MB
Directory structure:
gitextract__p8st6ul/
├── .gitignore
├── CONTRIBUTING.md
├── LICENSE
├── README.md
├── chapter1/
│ ├── chapter1.py
│ ├── filters.py
│ └── gui.py
├── chapter2/
│ ├── chapter2.py
│ ├── gestures.py
│ └── gui.py
├── chapter3/
│ ├── chapter3.py
│ ├── feature_matching.py
│ └── gui.py
├── chapter4/
│ ├── calibrate.py
│ ├── chapter4.py
│ ├── gui.py
│ └── scene3D.py
├── chapter5/
│ ├── chapter5.py
│ ├── saliency.py
│ └── tracking.py
├── chapter6/
│ ├── chapter6.py
│ ├── classifiers.py
│ └── datasets/
│ ├── __init__.py
│ ├── gtsrb.py
│ └── gtsrb_training/
│ ├── 00000/
│ │ ├── 00000_00000.ppm
│ │ ├── 00000_00001.ppm
│ │ ├── 00000_00002.ppm
│ │ ├── 00000_00003.ppm
│ │ ├── 00000_00004.ppm
│ │ ├── 00000_00005.ppm
│ │ ├── 00000_00006.ppm
│ │ ├── 00000_00007.ppm
│ │ ├── 00000_00008.ppm
│ │ ├── 00000_00009.ppm
│ │ ├── 00000_00010.ppm
│ │ ├── 00000_00011.ppm
│ │ ├── 00000_00012.ppm
│ │ ├── 00000_00013.ppm
│ │ ├── 00000_00014.ppm
│ │ ├── 00000_00015.ppm
│ │ ├── 00000_00016.ppm
│ │ ├── 00000_00017.ppm
│ │ ├── 00000_00018.ppm
│ │ ├── 00000_00019.ppm
│ │ ├── 00000_00020.ppm
│ │ ├── 00000_00021.ppm
│ │ ├── 00000_00022.ppm
│ │ ├── 00000_00023.ppm
│ │ ├── 00000_00024.ppm
│ │ ├── 00000_00025.ppm
│ │ ├── 00000_00026.ppm
│ │ ├── 00000_00027.ppm
│ │ ├── 00000_00028.ppm
│ │ ├── 00000_00029.ppm
│ │ ├── 00001_00000.ppm
│ │ ├── 00001_00001.ppm
│ │ ├── 00001_00002.ppm
│ │ ├── 00001_00003.ppm
│ │ ├── 00001_00004.ppm
│ │ ├── 00001_00005.ppm
│ │ ├── 00001_00006.ppm
│ │ ├── 00001_00007.ppm
│ │ ├── 00001_00008.ppm
│ │ ├── 00001_00009.ppm
│ │ ├── 00001_00010.ppm
│ │ ├── 00001_00011.ppm
│ │ ├── 00001_00012.ppm
│ │ ├── 00001_00013.ppm
│ │ ├── 00001_00014.ppm
│ │ ├── 00001_00015.ppm
│ │ ├── 00001_00016.ppm
│ │ ├── 00001_00017.ppm
│ │ ├── 00001_00018.ppm
│ │ ├── 00001_00019.ppm
│ │ ├── 00001_00020.ppm
│ │ ├── 00001_00021.ppm
│ │ ├── 00001_00022.ppm
│ │ ├── 00001_00023.ppm
│ │ ├── 00001_00024.ppm
│ │ ├── 00001_00025.ppm
│ │ ├── 00001_00026.ppm
│ │ ├── 00001_00027.ppm
│ │ ├── 00001_00028.ppm
│ │ ├── 00001_00029.ppm
│ │ ├── 00002_00000.ppm
│ │ ├── 00002_00001.ppm
│ │ ├── 00002_00002.ppm
│ │ ├── 00002_00003.ppm
│ │ ├── 00002_00004.ppm
│ │ ├── 00002_00005.ppm
│ │ ├── 00002_00006.ppm
│ │ ├── 00002_00007.ppm
│ │ ├── 00002_00008.ppm
│ │ ├── 00002_00009.ppm
│ │ ├── 00002_00010.ppm
│ │ ├── 00002_00011.ppm
│ │ ├── 00002_00012.ppm
│ │ ├── 00002_00013.ppm
│ │ ├── 00002_00014.ppm
│ │ ├── 00002_00015.ppm
│ │ ├── 00002_00016.ppm
│ │ ├── 00002_00017.ppm
│ │ ├── 00002_00018.ppm
│ │ ├── 00002_00019.ppm
│ │ ├── 00002_00020.ppm
│ │ ├── 00002_00021.ppm
│ │ ├── 00002_00022.ppm
│ │ ├── 00002_00023.ppm
│ │ ├── 00002_00024.ppm
│ │ ├── 00002_00025.ppm
│ │ ├── 00002_00026.ppm
│ │ ├── 00002_00027.ppm
│ │ ├── 00002_00028.ppm
│ │ ├── 00002_00029.ppm
│ │ ├── 00003_00000.ppm
│ │ ├── 00003_00001.ppm
│ │ ├── 00003_00002.ppm
│ │ ├── 00003_00003.ppm
│ │ ├── 00003_00004.ppm
│ │ ├── 00003_00005.ppm
│ │ ├── 00003_00006.ppm
│ │ ├── 00003_00007.ppm
│ │ ├── 00003_00008.ppm
│ │ ├── 00003_00009.ppm
│ │ ├── 00003_00010.ppm
│ │ ├── 00003_00011.ppm
│ │ ├── 00003_00012.ppm
│ │ ├── 00003_00013.ppm
│ │ ├── 00003_00014.ppm
│ │ ├── 00003_00015.ppm
│ │ ├── 00003_00016.ppm
│ │ ├── 00003_00017.ppm
│ │ ├── 00003_00018.ppm
│ │ ├── 00003_00019.ppm
│ │ ├── 00003_00020.ppm
│ │ ├── 00003_00021.ppm
│ │ ├── 00003_00022.ppm
│ │ ├── 00003_00023.ppm
│ │ ├── 00003_00024.ppm
│ │ ├── 00003_00025.ppm
│ │ ├── 00003_00026.ppm
│ │ ├── 00003_00027.ppm
│ │ ├── 00003_00028.ppm
│ │ ├── 00003_00029.ppm
│ │ ├── 00004_00000.ppm
│ │ ├── 00004_00001.ppm
│ │ ├── 00004_00002.ppm
│ │ ├── 00004_00003.ppm
│ │ ├── 00004_00004.ppm
│ │ ├── 00004_00005.ppm
│ │ ├── 00004_00006.ppm
│ │ ├── 00004_00007.ppm
│ │ ├── 00004_00008.ppm
│ │ ├── 00004_00009.ppm
│ │ ├── 00004_00010.ppm
│ │ ├── 00004_00011.ppm
│ │ ├── 00004_00012.ppm
│ │ ├── 00004_00013.ppm
│ │ ├── 00004_00014.ppm
│ │ ├── 00004_00015.ppm
│ │ ├── 00004_00016.ppm
│ │ ├── 00004_00017.ppm
│ │ ├── 00004_00018.ppm
│ │ ├── 00004_00019.ppm
│ │ ├── 00004_00020.ppm
│ │ ├── 00004_00021.ppm
│ │ ├── 00004_00022.ppm
│ │ ├── 00004_00023.ppm
│ │ ├── 00004_00024.ppm
│ │ ├── 00004_00025.ppm
│ │ ├── 00004_00026.ppm
│ │ ├── 00004_00027.ppm
│ │ ├── 00004_00028.ppm
│ │ ├── 00004_00029.ppm
│ │ ├── 00005_00000.ppm
│ │ ├── 00005_00001.ppm
│ │ ├── 00005_00002.ppm
│ │ ├── 00005_00003.ppm
│ │ ├── 00005_00004.ppm
│ │ ├── 00005_00005.ppm
│ │ ├── 00005_00006.ppm
│ │ ├── 00005_00007.ppm
│ │ ├── 00005_00008.ppm
│ │ ├── 00005_00009.ppm
│ │ ├── 00005_00010.ppm
│ │ ├── 00005_00011.ppm
│ │ ├── 00005_00012.ppm
│ │ ├── 00005_00013.ppm
│ │ ├── 00005_00014.ppm
│ │ ├── 00005_00015.ppm
│ │ ├── 00005_00016.ppm
│ │ ├── 00005_00017.ppm
│ │ ├── 00005_00018.ppm
│ │ ├── 00005_00019.ppm
│ │ ├── 00005_00020.ppm
│ │ ├── 00005_00021.ppm
│ │ ├── 00005_00022.ppm
│ │ ├── 00005_00023.ppm
│ │ ├── 00005_00024.ppm
│ │ ├── 00005_00025.ppm
│ │ ├── 00005_00026.ppm
│ │ ├── 00005_00027.ppm
│ │ ├── 00005_00028.ppm
│ │ ├── 00005_00029.ppm
│ │ ├── 00006_00000.ppm
│ │ ├── 00006_00001.ppm
│ │ ├── 00006_00002.ppm
│ │ ├── 00006_00003.ppm
│ │ ├── 00006_00004.ppm
│ │ ├── 00006_00005.ppm
│ │ ├── 00006_00006.ppm
│ │ ├── 00006_00007.ppm
│ │ ├── 00006_00008.ppm
│ │ ├── 00006_00009.ppm
│ │ ├── 00006_00010.ppm
│ │ ├── 00006_00011.ppm
│ │ ├── 00006_00012.ppm
│ │ ├── 00006_00013.ppm
│ │ ├── 00006_00014.ppm
│ │ ├── 00006_00015.ppm
│ │ ├── 00006_00016.ppm
│ │ ├── 00006_00017.ppm
│ │ ├── 00006_00018.ppm
│ │ ├── 00006_00019.ppm
│ │ ├── 00006_00020.ppm
│ │ ├── 00006_00021.ppm
│ │ ├── 00006_00022.ppm
│ │ ├── 00006_00023.ppm
│ │ ├── 00006_00024.ppm
│ │ ├── 00006_00025.ppm
│ │ ├── 00006_00026.ppm
│ │ ├── 00006_00027.ppm
│ │ ├── 00006_00028.ppm
│ │ ├── 00006_00029.ppm
│ │ └── GT-00000.csv
│ ├── 00002/
│ │ ├── 00000_00000.ppm
│ │ ├── 00000_00001.ppm
│ │ ├── 00000_00002.ppm
│ │ ├── 00000_00003.ppm
│ │ ├── 00000_00004.ppm
│ │ ├── 00000_00005.ppm
│ │ ├── 00000_00006.ppm
│ │ ├── 00000_00007.ppm
│ │ ├── 00000_00008.ppm
│ │ ├── 00000_00009.ppm
│ │ ├── 00000_00010.ppm
│ │ ├── 00000_00011.ppm
│ │ ├── 00000_00012.ppm
│ │ ├── 00000_00013.ppm
│ │ ├── 00000_00014.ppm
│ │ ├── 00000_00015.ppm
│ │ ├── 00000_00016.ppm
│ │ ├── 00000_00017.ppm
│ │ ├── 00000_00018.ppm
│ │ ├── 00000_00019.ppm
│ │ ├── 00000_00020.ppm
│ │ ├── 00000_00021.ppm
│ │ ├── 00000_00022.ppm
│ │ ├── 00000_00023.ppm
│ │ ├── 00000_00024.ppm
│ │ ├── 00000_00025.ppm
│ │ ├── 00000_00026.ppm
│ │ ├── 00000_00027.ppm
│ │ ├── 00000_00028.ppm
│ │ ├── 00000_00029.ppm
│ │ ├── 00001_00000.ppm
│ │ ├── 00001_00001.ppm
│ │ ├── 00001_00002.ppm
│ │ ├── 00001_00003.ppm
│ │ ├── 00001_00004.ppm
│ │ ├── 00001_00005.ppm
│ │ ├── 00001_00006.ppm
│ │ ├── 00001_00007.ppm
│ │ ├── 00001_00008.ppm
│ │ ├── 00001_00009.ppm
│ │ ├── 00001_00010.ppm
│ │ ├── 00001_00011.ppm
│ │ ├── 00001_00012.ppm
│ │ ├── 00001_00013.ppm
│ │ ├── 00001_00014.ppm
│ │ ├── 00001_00015.ppm
│ │ ├── 00001_00016.ppm
│ │ ├── 00001_00017.ppm
│ │ ├── 00001_00018.ppm
│ │ ├── 00001_00019.ppm
│ │ ├── 00001_00020.ppm
│ │ ├── 00001_00021.ppm
│ │ ├── 00001_00022.ppm
│ │ ├── 00001_00023.ppm
│ │ ├── 00001_00024.ppm
│ │ ├── 00001_00025.ppm
│ │ ├── 00001_00026.ppm
│ │ ├── 00001_00027.ppm
│ │ ├── 00001_00028.ppm
│ │ ├── 00001_00029.ppm
│ │ ├── 00002_00000.ppm
│ │ ├── 00002_00001.ppm
│ │ ├── 00002_00002.ppm
│ │ ├── 00002_00003.ppm
│ │ ├── 00002_00004.ppm
│ │ ├── 00002_00005.ppm
│ │ ├── 00002_00006.ppm
│ │ ├── 00002_00007.ppm
│ │ ├── 00002_00008.ppm
│ │ ├── 00002_00009.ppm
│ │ ├── 00002_00010.ppm
│ │ ├── 00002_00011.ppm
│ │ ├── 00002_00012.ppm
│ │ ├── 00002_00013.ppm
│ │ ├── 00002_00014.ppm
│ │ ├── 00002_00015.ppm
│ │ ├── 00002_00016.ppm
│ │ ├── 00002_00017.ppm
│ │ ├── 00002_00018.ppm
│ │ ├── 00002_00019.ppm
│ │ ├── 00002_00020.ppm
│ │ ├── 00002_00021.ppm
│ │ ├── 00002_00022.ppm
│ │ ├── 00002_00023.ppm
│ │ ├── 00002_00024.ppm
│ │ ├── 00002_00025.ppm
│ │ ├── 00002_00026.ppm
│ │ ├── 00002_00027.ppm
│ │ ├── 00002_00028.ppm
│ │ ├── 00002_00029.ppm
│ │ ├── 00003_00000.ppm
│ │ ├── 00003_00001.ppm
│ │ ├── 00003_00002.ppm
│ │ ├── 00003_00003.ppm
│ │ ├── 00003_00004.ppm
│ │ ├── 00003_00005.ppm
│ │ ├── 00003_00006.ppm
│ │ ├── 00003_00007.ppm
│ │ ├── 00003_00008.ppm
│ │ ├── 00003_00009.ppm
│ │ ├── 00003_00010.ppm
│ │ ├── 00003_00011.ppm
│ │ ├── 00003_00012.ppm
│ │ ├── 00003_00013.ppm
│ │ ├── 00003_00014.ppm
│ │ ├── 00003_00015.ppm
│ │ ├── 00003_00016.ppm
│ │ ├── 00003_00017.ppm
│ │ ├── 00003_00018.ppm
│ │ ├── 00003_00019.ppm
│ │ ├── 00003_00020.ppm
│ │ ├── 00003_00021.ppm
│ │ ├── 00003_00022.ppm
│ │ ├── 00003_00023.ppm
│ │ ├── 00003_00024.ppm
│ │ ├── 00003_00025.ppm
│ │ ├── 00003_00026.ppm
│ │ ├── 00003_00027.ppm
│ │ ├── 00003_00028.ppm
│ │ ├── 00003_00029.ppm
│ │ ├── 00004_00000.ppm
│ │ ├── 00004_00001.ppm
│ │ ├── 00004_00002.ppm
│ │ ├── 00004_00003.ppm
│ │ ├── 00004_00004.ppm
│ │ ├── 00004_00005.ppm
│ │ ├── 00004_00006.ppm
│ │ ├── 00004_00007.ppm
│ │ ├── 00004_00008.ppm
│ │ ├── 00004_00009.ppm
│ │ ├── 00004_00010.ppm
│ │ ├── 00004_00011.ppm
│ │ ├── 00004_00012.ppm
│ │ ├── 00004_00013.ppm
│ │ ├── 00004_00014.ppm
│ │ ├── 00004_00015.ppm
│ │ ├── 00004_00016.ppm
│ │ ├── 00004_00017.ppm
│ │ ├── 00004_00018.ppm
│ │ ├── 00004_00019.ppm
│ │ ├── 00004_00020.ppm
│ │ ├── 00004_00021.ppm
│ │ ├── 00004_00022.ppm
│ │ ├── 00004_00023.ppm
│ │ ├── 00004_00024.ppm
│ │ ├── 00004_00025.ppm
│ │ ├── 00004_00026.ppm
│ │ ├── 00004_00027.ppm
│ │ ├── 00004_00028.ppm
│ │ ├── 00004_00029.ppm
│ │ ├── 00005_00000.ppm
│ │ ├── 00005_00001.ppm
│ │ ├── 00005_00002.ppm
│ │ ├── 00005_00003.ppm
│ │ ├── 00005_00004.ppm
│ │ ├── 00005_00005.ppm
│ │ ├── 00005_00006.ppm
│ │ ├── 00005_00007.ppm
│ │ ├── 00005_00008.ppm
│ │ ├── 00005_00009.ppm
│ │ ├── 00005_00010.ppm
│ │ ├── 00005_00011.ppm
│ │ ├── 00005_00012.ppm
│ │ ├── 00005_00013.ppm
│ │ ├── 00005_00014.ppm
│ │ ├── 00005_00015.ppm
│ │ ├── 00005_00016.ppm
│ │ ├── 00005_00017.ppm
│ │ ├── 00005_00018.ppm
│ │ ├── 00005_00019.ppm
│ │ ├── 00005_00020.ppm
│ │ ├── 00005_00021.ppm
│ │ ├── 00005_00022.ppm
│ │ ├── 00005_00023.ppm
│ │ ├── 00005_00024.ppm
│ │ ├── 00005_00025.ppm
│ │ ├── 00005_00026.ppm
│ │ ├── 00005_00027.ppm
│ │ ├── 00005_00028.ppm
│ │ ├── 00005_00029.ppm
│ │ ├── 00006_00000.ppm
│ │ ├── 00006_00001.ppm
│ │ ├── 00006_00002.ppm
│ │ ├── 00006_00003.ppm
│ │ ├── 00006_00004.ppm
│ │ ├── 00006_00005.ppm
│ │ ├── 00006_00006.ppm
│ │ ├── 00006_00007.ppm
│ │ ├── 00006_00008.ppm
│ │ ├── 00006_00009.ppm
│ │ ├── 00006_00010.ppm
│ │ ├── 00006_00011.ppm
│ │ ├── 00006_00012.ppm
│ │ ├── 00006_00013.ppm
│ │ ├── 00006_00014.ppm
│ │ ├── 00006_00015.ppm
│ │ ├── 00006_00016.ppm
│ │ ├── 00006_00017.ppm
│ │ ├── 00006_00018.ppm
│ │ ├── 00006_00019.ppm
│ │ ├── 00006_00020.ppm
│ │ ├── 00006_00021.ppm
│ │ ├── 00006_00022.ppm
│ │ ├── 00006_00023.ppm
│ │ ├── 00006_00024.ppm
│ │ ├── 00006_00025.ppm
│ │ ├── 00006_00026.ppm
│ │ ├── 00006_00027.ppm
│ │ ├── 00006_00028.ppm
│ │ ├── 00006_00029.ppm
│ │ ├── 00007_00000.ppm
│ │ ├── 00007_00001.ppm
│ │ ├── 00007_00002.ppm
│ │ ├── 00007_00003.ppm
│ │ ├── 00007_00004.ppm
│ │ ├── 00007_00005.ppm
│ │ ├── 00007_00006.ppm
│ │ ├── 00007_00007.ppm
│ │ ├── 00007_00008.ppm
│ │ ├── 00007_00009.ppm
│ │ ├── 00007_00010.ppm
│ │ ├── 00007_00011.ppm
│ │ ├── 00007_00012.ppm
│ │ ├── 00007_00013.ppm
│ │ ├── 00007_00014.ppm
│ │ ├── 00007_00015.ppm
│ │ ├── 00007_00016.ppm
│ │ ├── 00007_00017.ppm
│ │ ├── 00007_00018.ppm
│ │ ├── 00007_00019.ppm
│ │ ├── 00007_00020.ppm
│ │ ├── 00007_00021.ppm
│ │ ├── 00007_00022.ppm
│ │ ├── 00007_00023.ppm
│ │ ├── 00007_00024.ppm
│ │ ├── 00007_00025.ppm
│ │ ├── 00007_00026.ppm
│ │ ├── 00007_00027.ppm
│ │ ├── 00007_00028.ppm
│ │ ├── 00007_00029.ppm
│ │ ├── 00008_00000.ppm
│ │ ├── 00008_00001.ppm
│ │ ├── 00008_00002.ppm
│ │ ├── 00008_00003.ppm
│ │ ├── 00008_00004.ppm
│ │ ├── 00008_00005.ppm
│ │ ├── 00008_00006.ppm
│ │ ├── 00008_00007.ppm
│ │ ├── 00008_00008.ppm
│ │ ├── 00008_00009.ppm
│ │ ├── 00008_00010.ppm
│ │ ├── 00008_00011.ppm
│ │ ├── 00008_00012.ppm
│ │ ├── 00008_00013.ppm
│ │ ├── 00008_00014.ppm
│ │ ├── 00008_00015.ppm
│ │ ├── 00008_00016.ppm
│ │ ├── 00008_00017.ppm
│ │ ├── 00008_00018.ppm
│ │ ├── 00008_00019.ppm
│ │ ├── 00008_00020.ppm
│ │ ├── 00008_00021.ppm
│ │ ├── 00008_00022.ppm
│ │ ├── 00008_00023.ppm
│ │ ├── 00008_00024.ppm
│ │ ├── 00008_00025.ppm
│ │ ├── 00008_00026.ppm
│ │ ├── 00008_00027.ppm
│ │ ├── 00008_00028.ppm
│ │ ├── 00008_00029.ppm
│ │ ├── 00009_00000.ppm
│ │ ├── 00009_00001.ppm
│ │ ├── 00009_00002.ppm
│ │ ├── 00009_00003.ppm
│ │ ├── 00009_00004.ppm
│ │ ├── 00009_00005.ppm
│ │ ├── 00009_00006.ppm
│ │ ├── 00009_00007.ppm
│ │ ├── 00009_00008.ppm
│ │ ├── 00009_00009.ppm
│ │ ├── 00009_00010.ppm
│ │ ├── 00009_00011.ppm
│ │ ├── 00009_00012.ppm
│ │ ├── 00009_00013.ppm
│ │ ├── 00009_00014.ppm
│ │ ├── 00009_00015.ppm
│ │ ├── 00009_00016.ppm
│ │ ├── 00009_00017.ppm
│ │ ├── 00009_00018.ppm
│ │ ├── 00009_00019.ppm
│ │ ├── 00009_00020.ppm
│ │ ├── 00009_00021.ppm
│ │ ├── 00009_00022.ppm
│ │ ├── 00009_00023.ppm
│ │ ├── 00009_00024.ppm
│ │ ├── 00009_00025.ppm
│ │ ├── 00009_00026.ppm
│ │ ├── 00009_00027.ppm
│ │ ├── 00009_00028.ppm
│ │ ├── 00009_00029.ppm
│ │ ├── 00010_00000.ppm
│ │ ├── 00010_00001.ppm
│ │ ├── 00010_00002.ppm
│ │ ├── 00010_00003.ppm
│ │ ├── 00010_00004.ppm
│ │ ├── 00010_00005.ppm
│ │ ├── 00010_00006.ppm
│ │ ├── 00010_00007.ppm
│ │ ├── 00010_00008.ppm
│ │ ├── 00010_00009.ppm
│ │ ├── 00010_00010.ppm
│ │ ├── 00010_00011.ppm
│ │ ├── 00010_00012.ppm
│ │ ├── 00010_00013.ppm
│ │ ├── 00010_00014.ppm
│ │ ├── 00010_00015.ppm
│ │ ├── 00010_00016.ppm
│ │ ├── 00010_00017.ppm
│ │ ├── 00010_00018.ppm
│ │ ├── 00010_00019.ppm
│ │ ├── 00010_00020.ppm
│ │ ├── 00010_00021.ppm
│ │ ├── 00010_00022.ppm
│ │ ├── 00010_00023.ppm
│ │ ├── 00010_00024.ppm
│ │ ├── 00010_00025.ppm
│ │ ├── 00010_00026.ppm
│ │ ├── 00010_00027.ppm
│ │ ├── 00010_00028.ppm
│ │ ├── 00010_00029.ppm
│ │ ├── 00011_00000.ppm
│ │ ├── 00011_00001.ppm
│ │ ├── 00011_00002.ppm
│ │ ├── 00011_00003.ppm
│ │ ├── 00011_00004.ppm
│ │ ├── 00011_00005.ppm
│ │ ├── 00011_00006.ppm
│ │ ├── 00011_00007.ppm
│ │ ├── 00011_00008.ppm
│ │ ├── 00011_00009.ppm
│ │ ├── 00011_00010.ppm
│ │ ├── 00011_00011.ppm
│ │ ├── 00011_00012.ppm
│ │ ├── 00011_00013.ppm
│ │ ├── 00011_00014.ppm
│ │ ├── 00011_00015.ppm
│ │ ├── 00011_00016.ppm
│ │ ├── 00011_00017.ppm
│ │ ├── 00011_00018.ppm
│ │ ├── 00011_00019.ppm
│ │ ├── 00011_00020.ppm
│ │ ├── 00011_00021.ppm
│ │ ├── 00011_00022.ppm
│ │ ├── 00011_00023.ppm
│ │ ├── 00011_00024.ppm
│ │ ├── 00011_00025.ppm
│ │ ├── 00011_00026.ppm
│ │ ├── 00011_00027.ppm
│ │ ├── 00011_00028.ppm
│ │ ├── 00011_00029.ppm
│ │ ├── 00012_00000.ppm
│ │ ├── 00012_00001.ppm
│ │ ├── 00012_00002.ppm
│ │ ├── 00012_00003.ppm
│ │ ├── 00012_00004.ppm
│ │ ├── 00012_00005.ppm
│ │ ├── 00012_00006.ppm
│ │ ├── 00012_00007.ppm
│ │ ├── 00012_00008.ppm
│ │ ├── 00012_00009.ppm
│ │ ├── 00012_00010.ppm
│ │ ├── 00012_00011.ppm
│ │ ├── 00012_00012.ppm
│ │ ├── 00012_00013.ppm
│ │ ├── 00012_00014.ppm
│ │ ├── 00012_00015.ppm
│ │ ├── 00012_00016.ppm
│ │ ├── 00012_00017.ppm
│ │ ├── 00012_00018.ppm
│ │ ├── 00012_00019.ppm
│ │ ├── 00012_00020.ppm
│ │ ├── 00012_00021.ppm
│ │ ├── 00012_00022.ppm
│ │ ├── 00012_00023.ppm
│ │ ├── 00012_00024.ppm
│ │ ├── 00012_00025.ppm
│ │ ├── 00012_00026.ppm
│ │ ├── 00012_00027.ppm
│ │ ├── 00012_00028.ppm
│ │ ├── 00012_00029.ppm
│ │ ├── 00013_00000.ppm
│ │ ├── 00013_00001.ppm
│ │ ├── 00013_00002.ppm
│ │ ├── 00013_00003.ppm
│ │ ├── 00013_00004.ppm
│ │ ├── 00013_00005.ppm
│ │ ├── 00013_00006.ppm
│ │ ├── 00013_00007.ppm
│ │ ├── 00013_00008.ppm
│ │ ├── 00013_00009.ppm
│ │ ├── 00013_00010.ppm
│ │ ├── 00013_00011.ppm
│ │ ├── 00013_00012.ppm
│ │ ├── 00013_00013.ppm
│ │ ├── 00013_00014.ppm
│ │ ├── 00013_00015.ppm
│ │ ├── 00013_00016.ppm
│ │ ├── 00013_00017.ppm
│ │ ├── 00013_00018.ppm
│ │ ├── 00013_00019.ppm
│ │ ├── 00013_00020.ppm
│ │ ├── 00013_00021.ppm
│ │ ├── 00013_00022.ppm
│ │ ├── 00013_00023.ppm
│ │ ├── 00013_00024.ppm
│ │ ├── 00013_00025.ppm
│ │ ├── 00013_00026.ppm
│ │ ├── 00013_00027.ppm
│ │ ├── 00013_00028.ppm
│ │ ├── 00013_00029.ppm
│ │ ├── 00014_00000.ppm
│ │ ├── 00014_00001.ppm
│ │ ├── 00014_00002.ppm
│ │ ├── 00014_00003.ppm
│ │ ├── 00014_00004.ppm
│ │ ├── 00014_00005.ppm
│ │ ├── 00014_00006.ppm
│ │ ├── 00014_00007.ppm
│ │ ├── 00014_00008.ppm
│ │ ├── 00014_00009.ppm
│ │ ├── 00014_00010.ppm
│ │ ├── 00014_00011.ppm
│ │ ├── 00014_00012.ppm
│ │ ├── 00014_00013.ppm
│ │ ├── 00014_00014.ppm
│ │ ├── 00014_00015.ppm
│ │ ├── 00014_00016.ppm
│ │ ├── 00014_00017.ppm
│ │ ├── 00014_00018.ppm
│ │ ├── 00014_00019.ppm
│ │ ├── 00014_00020.ppm
│ │ ├── 00014_00021.ppm
│ │ ├── 00014_00022.ppm
│ │ ├── 00014_00023.ppm
│ │ ├── 00014_00024.ppm
│ │ ├── 00014_00025.ppm
│ │ ├── 00014_00026.ppm
│ │ ├── 00014_00027.ppm
│ │ ├── 00014_00028.ppm
│ │ ├── 00014_00029.ppm
│ │ ├── 00015_00000.ppm
│ │ ├── 00015_00001.ppm
│ │ ├── 00015_00002.ppm
│ │ ├── 00015_00003.ppm
│ │ ├── 00015_00004.ppm
│ │ ├── 00015_00005.ppm
│ │ ├── 00015_00006.ppm
│ │ ├── 00015_00007.ppm
│ │ ├── 00015_00008.ppm
│ │ ├── 00015_00009.ppm
│ │ ├── 00015_00010.ppm
│ │ ├── 00015_00011.ppm
│ │ ├── 00015_00012.ppm
│ │ ├── 00015_00013.ppm
│ │ ├── 00015_00014.ppm
│ │ ├── 00015_00015.ppm
│ │ ├── 00015_00016.ppm
│ │ ├── 00015_00017.ppm
│ │ ├── 00015_00018.ppm
│ │ ├── 00015_00019.ppm
│ │ ├── 00015_00020.ppm
│ │ ├── 00015_00021.ppm
│ │ ├── 00015_00022.ppm
│ │ ├── 00015_00023.ppm
│ │ ├── 00015_00024.ppm
│ │ ├── 00015_00025.ppm
│ │ ├── 00015_00026.ppm
│ │ ├── 00015_00027.ppm
│ │ ├── 00015_00028.ppm
│ │ ├── 00015_00029.ppm
│ │ ├── 00016_00000.ppm
│ │ ├── 00016_00001.ppm
│ │ ├── 00016_00002.ppm
│ │ ├── 00016_00003.ppm
│ │ ├── 00016_00004.ppm
│ │ ├── 00016_00005.ppm
│ │ ├── 00016_00006.ppm
│ │ ├── 00016_00007.ppm
│ │ ├── 00016_00008.ppm
│ │ ├── 00016_00009.ppm
│ │ ├── 00016_00010.ppm
│ │ ├── 00016_00011.ppm
│ │ ├── 00016_00012.ppm
│ │ ├── 00016_00013.ppm
│ │ ├── 00016_00014.ppm
│ │ ├── 00016_00015.ppm
│ │ ├── 00016_00016.ppm
│ │ ├── 00016_00017.ppm
│ │ ├── 00016_00018.ppm
│ │ ├── 00016_00019.ppm
│ │ ├── 00016_00020.ppm
│ │ ├── 00016_00021.ppm
│ │ ├── 00016_00022.ppm
│ │ ├── 00016_00023.ppm
│ │ ├── 00016_00024.ppm
│ │ ├── 00016_00025.ppm
│ │ ├── 00016_00026.ppm
│ │ ├── 00016_00027.ppm
│ │ ├── 00016_00028.ppm
│ │ ├── 00016_00029.ppm
│ │ ├── 00017_00000.ppm
│ │ ├── 00017_00001.ppm
│ │ ├── 00017_00002.ppm
│ │ ├── 00017_00003.ppm
│ │ ├── 00017_00004.ppm
│ │ ├── 00017_00005.ppm
│ │ ├── 00017_00006.ppm
│ │ ├── 00017_00007.ppm
│ │ ├── 00017_00008.ppm
│ │ ├── 00017_00009.ppm
│ │ ├── 00017_00010.ppm
│ │ ├── 00017_00011.ppm
│ │ ├── 00017_00012.ppm
│ │ ├── 00017_00013.ppm
│ │ ├── 00017_00014.ppm
│ │ ├── 00017_00015.ppm
│ │ ├── 00017_00016.ppm
│ │ ├── 00017_00017.ppm
│ │ ├── 00017_00018.ppm
│ │ ├── 00017_00019.ppm
│ │ ├── 00017_00020.ppm
│ │ ├── 00017_00021.ppm
│ │ ├── 00017_00022.ppm
│ │ ├── 00017_00023.ppm
│ │ ├── 00017_00024.ppm
│ │ ├── 00017_00025.ppm
│ │ ├── 00017_00026.ppm
│ │ ├── 00017_00027.ppm
│ │ ├── 00017_00028.ppm
│ │ ├── 00017_00029.ppm
│ │ ├── 00018_00000.ppm
│ │ ├── 00018_00001.ppm
│ │ ├── 00018_00002.ppm
│ │ ├── 00018_00003.ppm
│ │ ├── 00018_00004.ppm
│ │ ├── 00018_00005.ppm
│ │ ├── 00018_00006.ppm
│ │ ├── 00018_00007.ppm
│ │ ├── 00018_00008.ppm
│ │ ├── 00018_00009.ppm
│ │ ├── 00018_00010.ppm
│ │ ├── 00018_00011.ppm
│ │ ├── 00018_00012.ppm
│ │ ├── 00018_00013.ppm
│ │ ├── 00018_00014.ppm
│ │ ├── 00018_00015.ppm
│ │ ├── 00018_00016.ppm
│ │ ├── 00018_00017.ppm
│ │ ├── 00018_00018.ppm
│ │ ├── 00018_00019.ppm
│ │ ├── 00018_00020.ppm
│ │ ├── 00018_00021.ppm
│ │ ├── 00018_00022.ppm
│ │ ├── 00018_00023.ppm
│ │ ├── 00018_00024.ppm
│ │ ├── 00018_00025.ppm
│ │ ├── 00018_00026.ppm
│ │ ├── 00018_00027.ppm
│ │ ├── 00018_00028.ppm
│ │ ├── 00018_00029.ppm
│ │ ├── 00019_00000.ppm
│ │ ├── 00019_00001.ppm
│ │ ├── 00019_00002.ppm
│ │ ├── 00019_00003.ppm
│ │ ├── 00019_00004.ppm
│ │ ├── 00019_00005.ppm
│ │ ├── 00019_00006.ppm
│ │ ├── 00019_00007.ppm
│ │ ├── 00019_00008.ppm
│ │ ├── 00019_00009.ppm
│ │ ├── 00019_00010.ppm
│ │ ├── 00019_00011.ppm
│ │ ├── 00019_00012.ppm
│ │ ├── 00019_00013.ppm
│ │ ├── 00019_00014.ppm
│ │ ├── 00019_00015.ppm
│ │ ├── 00019_00016.ppm
│ │ ├── 00019_00017.ppm
│ │ ├── 00019_00018.ppm
│ │ ├── 00019_00019.ppm
│ │ ├── 00019_00020.ppm
│ │ ├── 00019_00021.ppm
│ │ ├── 00019_00022.ppm
│ │ ├── 00019_00023.ppm
│ │ ├── 00019_00024.ppm
│ │ ├── 00019_00025.ppm
│ │ ├── 00019_00026.ppm
│ │ ├── 00019_00027.ppm
│ │ ├── 00019_00028.ppm
│ │ ├── 00019_00029.ppm
│ │ ├── 00020_00000.ppm
│ │ ├── 00020_00001.ppm
│ │ ├── 00020_00002.ppm
│ │ ├── 00020_00003.ppm
│ │ ├── 00020_00004.ppm
│ │ ├── 00020_00005.ppm
│ │ ├── 00020_00006.ppm
│ │ ├── 00020_00007.ppm
│ │ ├── 00020_00008.ppm
│ │ ├── 00020_00009.ppm
│ │ ├── 00020_00010.ppm
│ │ ├── 00020_00011.ppm
│ │ ├── 00020_00012.ppm
│ │ ├── 00020_00013.ppm
│ │ ├── 00020_00014.ppm
│ │ ├── 00020_00015.ppm
│ │ ├── 00020_00016.ppm
│ │ ├── 00020_00017.ppm
│ │ ├── 00020_00018.ppm
│ │ ├── 00020_00019.ppm
│ │ ├── 00020_00020.ppm
│ │ ├── 00020_00021.ppm
│ │ ├── 00020_00022.ppm
│ │ ├── 00020_00023.ppm
│ │ ├── 00020_00024.ppm
│ │ ├── 00020_00025.ppm
│ │ ├── 00020_00026.ppm
│ │ ├── 00020_00027.ppm
│ │ ├── 00020_00028.ppm
│ │ ├── 00020_00029.ppm
│ │ ├── 00021_00000.ppm
│ │ ├── 00021_00001.ppm
│ │ ├── 00021_00002.ppm
│ │ ├── 00021_00003.ppm
│ │ ├── 00021_00004.ppm
│ │ ├── 00021_00005.ppm
│ │ ├── 00021_00006.ppm
│ │ ├── 00021_00007.ppm
│ │ ├── 00021_00008.ppm
│ │ ├── 00021_00009.ppm
│ │ ├── 00021_00010.ppm
│ │ ├── 00021_00011.ppm
│ │ ├── 00021_00012.ppm
│ │ ├── 00021_00013.ppm
│ │ ├── 00021_00014.ppm
│ │ ├── 00021_00015.ppm
│ │ ├── 00021_00016.ppm
│ │ ├── 00021_00017.ppm
│ │ ├── 00021_00018.ppm
│ │ ├── 00021_00019.ppm
│ │ ├── 00021_00020.ppm
│ │ ├── 00021_00021.ppm
│ │ ├── 00021_00022.ppm
│ │ ├── 00021_00023.ppm
│ │ ├── 00021_00024.ppm
│ │ ├── 00021_00025.ppm
│ │ ├── 00021_00026.ppm
│ │ ├── 00021_00027.ppm
│ │ ├── 00021_00028.ppm
│ │ ├── 00021_00029.ppm
│ │ ├── 00022_00000.ppm
│ │ ├── 00022_00001.ppm
│ │ ├── 00022_00002.ppm
│ │ ├── 00022_00003.ppm
│ │ ├── 00022_00004.ppm
│ │ ├── 00022_00005.ppm
│ │ ├── 00022_00006.ppm
│ │ ├── 00022_00007.ppm
│ │ ├── 00022_00008.ppm
│ │ ├── 00022_00009.ppm
│ │ ├── 00022_00010.ppm
│ │ ├── 00022_00011.ppm
│ │ ├── 00022_00012.ppm
│ │ ├── 00022_00013.ppm
│ │ ├── 00022_00014.ppm
│ │ ├── 00022_00015.ppm
│ │ ├── 00022_00016.ppm
│ │ ├── 00022_00017.ppm
│ │ ├── 00022_00018.ppm
│ │ ├── 00022_00019.ppm
│ │ ├── 00022_00020.ppm
│ │ ├── 00022_00021.ppm
│ │ ├── 00022_00022.ppm
│ │ ├── 00022_00023.ppm
│ │ ├── 00022_00024.ppm
│ │ ├── 00022_00025.ppm
│ │ ├── 00022_00026.ppm
│ │ ├── 00022_00027.ppm
│ │ ├── 00022_00028.ppm
│ │ ├── 00022_00029.ppm
│ │ ├── 00023_00000.ppm
│ │ ├── 00023_00001.ppm
│ │ ├── 00023_00002.ppm
│ │ ├── 00023_00003.ppm
│ │ ├── 00023_00004.ppm
│ │ ├── 00023_00005.ppm
│ │ ├── 00023_00006.ppm
│ │ ├── 00023_00007.ppm
│ │ ├── 00023_00008.ppm
│ │ ├── 00023_00009.ppm
│ │ ├── 00023_00010.ppm
│ │ ├── 00023_00011.ppm
│ │ ├── 00023_00012.ppm
│ │ ├── 00023_00013.ppm
│ │ ├── 00023_00014.ppm
│ │ ├── 00023_00015.ppm
│ │ ├── 00023_00016.ppm
│ │ ├── 00023_00017.ppm
│ │ ├── 00023_00018.ppm
│ │ ├── 00023_00019.ppm
│ │ ├── 00023_00020.ppm
│ │ ├── 00023_00021.ppm
│ │ ├── 00023_00022.ppm
│ │ ├── 00023_00023.ppm
│ │ ├── 00023_00024.ppm
│ │ ├── 00023_00025.ppm
│ │ ├── 00023_00026.ppm
│ │ ├── 00023_00027.ppm
│ │ ├── 00023_00028.ppm
│ │ ├── 00023_00029.ppm
│ │ ├── 00024_00000.ppm
│ │ ├── 00024_00001.ppm
│ │ ├── 00024_00002.ppm
│ │ ├── 00024_00003.ppm
│ │ ├── 00024_00004.ppm
│ │ ├── 00024_00005.ppm
│ │ ├── 00024_00006.ppm
│ │ ├── 00024_00007.ppm
│ │ ├── 00024_00008.ppm
│ │ ├── 00024_00009.ppm
│ │ ├── 00024_00010.ppm
│ │ ├── 00024_00011.ppm
│ │ ├── 00024_00012.ppm
│ │ ├── 00024_00013.ppm
│ │ ├── 00024_00014.ppm
│ │ ├── 00024_00015.ppm
│ │ ├── 00024_00016.ppm
│ │ ├── 00024_00017.ppm
│ │ ├── 00024_00018.ppm
│ │ ├── 00024_00019.ppm
│ │ ├── 00024_00020.ppm
│ │ ├── 00024_00021.ppm
│ │ ├── 00024_00022.ppm
│ │ ├── 00024_00023.ppm
│ │ ├── 00024_00024.ppm
│ │ ├── 00024_00025.ppm
│ │ ├── 00024_00026.ppm
│ │ ├── 00024_00027.ppm
│ │ ├── 00024_00028.ppm
│ │ ├── 00024_00029.ppm
│ │ ├── 00025_00000.ppm
│ │ ├── 00025_00001.ppm
│ │ ├── 00025_00002.ppm
│ │ ├── 00025_00003.ppm
│ │ ├── 00025_00004.ppm
│ │ ├── 00025_00005.ppm
│ │ ├── 00025_00006.ppm
│ │ ├── 00025_00007.ppm
│ │ ├── 00025_00008.ppm
│ │ ├── 00025_00009.ppm
│ │ ├── 00025_00010.ppm
│ │ ├── 00025_00011.ppm
│ │ ├── 00025_00012.ppm
│ │ ├── 00025_00013.ppm
│ │ ├── 00025_00014.ppm
│ │ ├── 00025_00015.ppm
│ │ ├── 00025_00016.ppm
│ │ ├── 00025_00017.ppm
│ │ ├── 00025_00018.ppm
│ │ ├── 00025_00019.ppm
│ │ ├── 00025_00020.ppm
│ │ ├── 00025_00021.ppm
│ │ ├── 00025_00022.ppm
│ │ ├── 00025_00023.ppm
│ │ ├── 00025_00024.ppm
│ │ ├── 00025_00025.ppm
│ │ ├── 00025_00026.ppm
│ │ ├── 00025_00027.ppm
│ │ ├── 00025_00028.ppm
│ │ ├── 00025_00029.ppm
│ │ ├── 00026_00000.ppm
│ │ ├── 00026_00001.ppm
│ │ ├── 00026_00002.ppm
│ │ ├── 00026_00003.ppm
│ │ ├── 00026_00004.ppm
│ │ ├── 00026_00005.ppm
│ │ ├── 00026_00006.ppm
│ │ ├── 00026_00007.ppm
│ │ ├── 00026_00008.ppm
│ │ ├── 00026_00009.ppm
│ │ ├── 00026_00010.ppm
│ │ ├── 00026_00011.ppm
│ │ ├── 00026_00012.ppm
│ │ ├── 00026_00013.ppm
│ │ ├── 00026_00014.ppm
│ │ ├── 00026_00015.ppm
│ │ ├── 00026_00016.ppm
│ │ ├── 00026_00017.ppm
│ │ ├── 00026_00018.ppm
│ │ ├── 00026_00019.ppm
│ │ ├── 00026_00020.ppm
│ │ ├── 00026_00021.ppm
│ │ ├── 00026_00022.ppm
│ │ ├── 00026_00023.ppm
│ │ ├── 00026_00024.ppm
│ │ ├── 00026_00025.ppm
│ │ ├── 00026_00026.ppm
│ │ ├── 00026_00027.ppm
│ │ ├── 00026_00028.ppm
│ │ ├── 00026_00029.ppm
│ │ ├── 00027_00000.ppm
│ │ ├── 00027_00001.ppm
│ │ ├── 00027_00002.ppm
│ │ ├── 00027_00003.ppm
│ │ ├── 00027_00004.ppm
│ │ ├── 00027_00005.ppm
│ │ ├── 00027_00006.ppm
│ │ ├── 00027_00007.ppm
│ │ ├── 00027_00008.ppm
│ │ ├── 00027_00009.ppm
│ │ ├── 00027_00010.ppm
│ │ ├── 00027_00011.ppm
│ │ ├── 00027_00012.ppm
│ │ ├── 00027_00013.ppm
│ │ ├── 00027_00014.ppm
│ │ ├── 00027_00015.ppm
│ │ ├── 00027_00016.ppm
│ │ ├── 00027_00017.ppm
│ │ ├── 00027_00018.ppm
│ │ ├── 00027_00019.ppm
│ │ ├── 00027_00020.ppm
│ │ ├── 00027_00021.ppm
│ │ ├── 00027_00022.ppm
│ │ ├── 00027_00023.ppm
│ │ ├── 00027_00024.ppm
│ │ ├── 00027_00025.ppm
│ │ ├── 00027_00026.ppm
│ │ ├── 00027_00027.ppm
│ │ ├── 00027_00028.ppm
│ │ ├── 00027_00029.ppm
│ │ ├── 00028_00000.ppm
│ │ ├── 00028_00001.ppm
│ │ ├── 00028_00002.ppm
│ │ ├── 00028_00003.ppm
│ │ ├── 00028_00004.ppm
│ │ ├── 00028_00005.ppm
│ │ ├── 00028_00006.ppm
│ │ ├── 00028_00007.ppm
│ │ ├── 00028_00008.ppm
│ │ ├── 00028_00009.ppm
│ │ ├── 00028_00010.ppm
│ │ ├── 00028_00011.ppm
│ │ ├── 00028_00012.ppm
│ │ ├── 00028_00013.ppm
│ │ ├── 00028_00014.ppm
│ │ ├── 00028_00015.ppm
│ │ ├── 00028_00016.ppm
│ │ ├── 00028_00017.ppm
│ │ ├── 00028_00018.ppm
│ │ ├── 00028_00019.ppm
│ │ ├── 00028_00020.ppm
│ │ ├── 00028_00021.ppm
│ │ ├── 00028_00022.ppm
│ │ ├── 00028_00023.ppm
│ │ ├── 00028_00024.ppm
│ │ ├── 00028_00025.ppm
│ │ ├── 00028_00026.ppm
│ │ ├── 00028_00027.ppm
│ │ ├── 00028_00028.ppm
│ │ ├── 00028_00029.ppm
│ │ ├── 00029_00000.ppm
│ │ ├── 00029_00001.ppm
│ │ ├── 00029_00002.ppm
│ │ ├── 00029_00003.ppm
│ │ ├── 00029_00004.ppm
│ │ ├── 00029_00005.ppm
│ │ ├── 00029_00006.ppm
│ │ ├── 00029_00007.ppm
│ │ ├── 00029_00008.ppm
│ │ ├── 00029_00009.ppm
│ │ ├── 00029_00010.ppm
│ │ ├── 00029_00011.ppm
│ │ ├── 00029_00012.ppm
│ │ ├── 00029_00013.ppm
│ │ ├── 00029_00014.ppm
│ │ ├── 00029_00015.ppm
│ │ ├── 00029_00016.ppm
│ │ ├── 00029_00017.ppm
│ │ ├── 00029_00018.ppm
│ │ ├── 00029_00019.ppm
│ │ ├── 00029_00020.ppm
│ │ ├── 00029_00021.ppm
│ │ ├── 00029_00022.ppm
│ │ ├── 00029_00023.ppm
│ │ ├── 00029_00024.ppm
│ │ ├── 00029_00025.ppm
│ │ ├── 00029_00026.ppm
│ │ ├── 00029_00027.ppm
│ │ ├── 00029_00028.ppm
│ │ ├── 00029_00029.ppm
│ │ ├── 00030_00000.ppm
│ │ ├── 00030_00001.ppm
│ │ ├── 00030_00002.ppm
│ │ ├── 00030_00003.ppm
│ │ ├── 00030_00004.ppm
│ │ ├── 00030_00005.ppm
│ │ ├── 00030_00006.ppm
│ │ ├── 00030_00007.ppm
│ │ ├── 00030_00008.ppm
│ │ ├── 00030_00009.ppm
│ │ ├── 00030_00010.ppm
│ │ ├── 00030_00011.ppm
│ │ ├── 00030_00012.ppm
│ │ ├── 00030_00013.ppm
│ │ ├── 00030_00014.ppm
│ │ ├── 00030_00015.ppm
│ │ ├── 00030_00016.ppm
│ │ ├── 00030_00017.ppm
│ │ ├── 00030_00018.ppm
│ │ ├── 00030_00019.ppm
│ │ ├── 00030_00020.ppm
│ │ ├── 00030_00021.ppm
│ │ ├── 00030_00022.ppm
│ │ ├── 00030_00023.ppm
│ │ ├── 00030_00024.ppm
│ │ ├── 00030_00025.ppm
│ │ ├── 00030_00026.ppm
│ │ ├── 00030_00027.ppm
│ │ ├── 00030_00028.ppm
│ │ ├── 00030_00029.ppm
│ │ ├── 00031_00000.ppm
│ │ ├── 00031_00001.ppm
│ │ ├── 00031_00002.ppm
│ │ ├── 00031_00003.ppm
│ │ ├── 00031_00004.ppm
│ │ ├── 00031_00005.ppm
│ │ ├── 00031_00006.ppm
│ │ ├── 00031_00007.ppm
│ │ ├── 00031_00008.ppm
│ │ ├── 00031_00009.ppm
│ │ ├── 00031_00010.ppm
│ │ ├── 00031_00011.ppm
│ │ ├── 00031_00012.ppm
│ │ ├── 00031_00013.ppm
│ │ ├── 00031_00014.ppm
│ │ ├── 00031_00015.ppm
│ │ ├── 00031_00016.ppm
│ │ ├── 00031_00017.ppm
│ │ ├── 00031_00018.ppm
│ │ ├── 00031_00019.ppm
│ │ ├── 00031_00020.ppm
│ │ ├── 00031_00021.ppm
│ │ ├── 00031_00022.ppm
│ │ ├── 00031_00023.ppm
│ │ ├── 00031_00024.ppm
│ │ ├── 00031_00025.ppm
│ │ ├── 00031_00026.ppm
│ │ ├── 00031_00027.ppm
│ │ ├── 00031_00028.ppm
│ │ ├── 00031_00029.ppm
│ │ ├── 00032_00000.ppm
│ │ ├── 00032_00001.ppm
│ │ ├── 00032_00002.ppm
│ │ ├── 00032_00003.ppm
│ │ ├── 00032_00004.ppm
│ │ ├── 00032_00005.ppm
│ │ ├── 00032_00006.ppm
│ │ ├── 00032_00007.ppm
│ │ ├── 00032_00008.ppm
│ │ ├── 00032_00009.ppm
│ │ ├── 00032_00010.ppm
│ │ ├── 00032_00011.ppm
│ │ ├── 00032_00012.ppm
│ │ ├── 00032_00013.ppm
│ │ ├── 00032_00014.ppm
│ │ ├── 00032_00015.ppm
│ │ ├── 00032_00016.ppm
│ │ ├── 00032_00017.ppm
│ │ ├── 00032_00018.ppm
│ │ ├── 00032_00019.ppm
│ │ ├── 00032_00020.ppm
│ │ ├── 00032_00021.ppm
│ │ ├── 00032_00022.ppm
│ │ ├── 00032_00023.ppm
│ │ ├── 00032_00024.ppm
│ │ ├── 00032_00025.ppm
│ │ ├── 00032_00026.ppm
│ │ ├── 00032_00027.ppm
│ │ ├── 00032_00028.ppm
│ │ ├── 00032_00029.ppm
│ │ ├── 00033_00000.ppm
│ │ ├── 00033_00001.ppm
│ │ ├── 00033_00002.ppm
│ │ ├── 00033_00003.ppm
│ │ ├── 00033_00004.ppm
│ │ ├── 00033_00005.ppm
│ │ ├── 00033_00006.ppm
│ │ ├── 00033_00007.ppm
│ │ ├── 00033_00008.ppm
│ │ ├── 00033_00009.ppm
│ │ ├── 00033_00010.ppm
│ │ ├── 00033_00011.ppm
│ │ ├── 00033_00012.ppm
│ │ ├── 00033_00013.ppm
│ │ ├── 00033_00014.ppm
│ │ ├── 00033_00015.ppm
│ │ ├── 00033_00016.ppm
│ │ ├── 00033_00017.ppm
│ │ ├── 00033_00018.ppm
│ │ ├── 00033_00019.ppm
│ │ ├── 00033_00020.ppm
│ │ ├── 00033_00021.ppm
│ │ ├── 00033_00022.ppm
│ │ ├── 00033_00023.ppm
│ │ ├── 00033_00024.ppm
│ │ ├── 00033_00025.ppm
│ │ ├── 00033_00026.ppm
│ │ ├── 00033_00027.ppm
│ │ ├── 00033_00028.ppm
│ │ ├── 00033_00029.ppm
│ │ ├── 00034_00000.ppm
│ │ ├── 00034_00001.ppm
│ │ ├── 00034_00002.ppm
│ │ ├── 00034_00003.ppm
│ │ ├── 00034_00004.ppm
│ │ ├── 00034_00005.ppm
│ │ ├── 00034_00006.ppm
│ │ ├── 00034_00007.ppm
│ │ ├── 00034_00008.ppm
│ │ ├── 00034_00009.ppm
│ │ ├── 00034_00010.ppm
│ │ ├── 00034_00011.ppm
│ │ ├── 00034_00012.ppm
│ │ ├── 00034_00013.ppm
│ │ ├── 00034_00014.ppm
│ │ ├── 00034_00015.ppm
│ │ ├── 00034_00016.ppm
│ │ ├── 00034_00017.ppm
│ │ ├── 00034_00018.ppm
│ │ ├── 00034_00019.ppm
│ │ ├── 00034_00020.ppm
│ │ ├── 00034_00021.ppm
│ │ ├── 00034_00022.ppm
│ │ ├── 00034_00023.ppm
│ │ ├── 00034_00024.ppm
│ │ ├── 00034_00025.ppm
│ │ ├── 00034_00026.ppm
│ │ ├── 00034_00027.ppm
│ │ ├── 00034_00028.ppm
│ │ ├── 00034_00029.ppm
│ │ ├── 00035_00000.ppm
│ │ ├── 00035_00001.ppm
│ │ ├── 00035_00002.ppm
│ │ ├── 00035_00003.ppm
│ │ ├── 00035_00004.ppm
│ │ ├── 00035_00005.ppm
│ │ ├── 00035_00006.ppm
│ │ ├── 00035_00007.ppm
│ │ ├── 00035_00008.ppm
│ │ ├── 00035_00009.ppm
│ │ ├── 00035_00010.ppm
│ │ ├── 00035_00011.ppm
│ │ ├── 00035_00012.ppm
│ │ ├── 00035_00013.ppm
│ │ ├── 00035_00014.ppm
│ │ ├── 00035_00015.ppm
│ │ ├── 00035_00016.ppm
│ │ ├── 00035_00017.ppm
│ │ ├── 00035_00018.ppm
│ │ ├── 00035_00019.ppm
│ │ ├── 00035_00020.ppm
│ │ ├── 00035_00021.ppm
│ │ ├── 00035_00022.ppm
│ │ ├── 00035_00023.ppm
│ │ ├── 00035_00024.ppm
│ │ ├── 00035_00025.ppm
│ │ ├── 00035_00026.ppm
│ │ ├── 00035_00027.ppm
│ │ ├── 00035_00028.ppm
│ │ ├── 00035_00029.ppm
│ │ ├── 00036_00000.ppm
│ │ ├── 00036_00001.ppm
│ │ ├── 00036_00002.ppm
│ │ ├── 00036_00003.ppm
│ │ ├── 00036_00004.ppm
│ │ ├── 00036_00005.ppm
│ │ ├── 00036_00006.ppm
│ │ ├── 00036_00007.ppm
│ │ ├── 00036_00008.ppm
│ │ ├── 00036_00009.ppm
│ │ ├── 00036_00010.ppm
│ │ ├── 00036_00011.ppm
│ │ ├── 00036_00012.ppm
│ │ ├── 00036_00013.ppm
│ │ ├── 00036_00014.ppm
│ │ ├── 00036_00015.ppm
│ │ ├── 00036_00016.ppm
│ │ ├── 00036_00017.ppm
│ │ ├── 00036_00018.ppm
│ │ ├── 00036_00019.ppm
│ │ ├── 00036_00020.ppm
│ │ ├── 00036_00021.ppm
│ │ ├── 00036_00022.ppm
│ │ ├── 00036_00023.ppm
│ │ ├── 00036_00024.ppm
│ │ ├── 00036_00025.ppm
│ │ ├── 00036_00026.ppm
│ │ ├── 00036_00027.ppm
│ │ ├── 00036_00028.ppm
│ │ ├── 00036_00029.ppm
│ │ ├── 00037_00000.ppm
│ │ ├── 00037_00001.ppm
│ │ ├── 00037_00002.ppm
│ │ ├── 00037_00003.ppm
│ │ ├── 00037_00004.ppm
│ │ ├── 00037_00005.ppm
│ │ ├── 00037_00006.ppm
│ │ ├── 00037_00007.ppm
│ │ ├── 00037_00008.ppm
│ │ ├── 00037_00009.ppm
│ │ ├── 00037_00010.ppm
│ │ ├── 00037_00011.ppm
│ │ ├── 00037_00012.ppm
│ │ ├── 00037_00013.ppm
│ │ ├── 00037_00014.ppm
│ │ ├── 00037_00015.ppm
│ │ ├── 00037_00016.ppm
│ │ ├── 00037_00017.ppm
│ │ ├── 00037_00018.ppm
│ │ ├── 00037_00019.ppm
│ │ ├── 00037_00020.ppm
│ │ ├── 00037_00021.ppm
│ │ ├── 00037_00022.ppm
│ │ ├── 00037_00023.ppm
│ │ ├── 00037_00024.ppm
│ │ ├── 00037_00025.ppm
│ │ ├── 00037_00026.ppm
│ │ ├── 00037_00027.ppm
│ │ ├── 00037_00028.ppm
│ │ ├── 00037_00029.ppm
│ │ ├── 00038_00000.ppm
│ │ ├── 00038_00001.ppm
│ │ ├── 00038_00002.ppm
│ │ ├── 00038_00003.ppm
│ │ ├── 00038_00004.ppm
│ │ ├── 00038_00005.ppm
│ │ ├── 00038_00006.ppm
│ │ ├── 00038_00007.ppm
│ │ ├── 00038_00008.ppm
│ │ ├── 00038_00009.ppm
│ │ ├── 00038_00010.ppm
│ │ ├── 00038_00011.ppm
│ │ ├── 00038_00012.ppm
│ │ ├── 00038_00013.ppm
│ │ ├── 00038_00014.ppm
│ │ ├── 00038_00015.ppm
│ │ ├── 00038_00016.ppm
│ │ ├── 00038_00017.ppm
│ │ ├── 00038_00018.ppm
│ │ ├── 00038_00019.ppm
│ │ ├── 00038_00020.ppm
│ │ ├── 00038_00021.ppm
│ │ ├── 00038_00022.ppm
│ │ ├── 00038_00023.ppm
│ │ ├── 00038_00024.ppm
│ │ ├── 00038_00025.ppm
│ │ ├── 00038_00026.ppm
│ │ ├── 00038_00027.ppm
│ │ ├── 00038_00028.ppm
│ │ ├── 00038_00029.ppm
│ │ ├── 00039_00000.ppm
│ │ ├── 00039_00001.ppm
│ │ ├── 00039_00002.ppm
│ │ ├── 00039_00003.ppm
│ │ ├── 00039_00004.ppm
│ │ ├── 00039_00005.ppm
│ │ ├── 00039_00006.ppm
│ │ ├── 00039_00007.ppm
│ │ ├── 00039_00008.ppm
│ │ ├── 00039_00009.ppm
│ │ ├── 00039_00010.ppm
│ │ ├── 00039_00011.ppm
│ │ ├── 00039_00012.ppm
│ │ ├── 00039_00013.ppm
│ │ ├── 00039_00014.ppm
│ │ ├── 00039_00015.ppm
│ │ ├── 00039_00016.ppm
│ │ ├── 00039_00017.ppm
│ │ ├── 00039_00018.ppm
│ │ ├── 00039_00019.ppm
│ │ ├── 00039_00020.ppm
│ │ ├── 00039_00021.ppm
│ │ ├── 00039_00022.ppm
│ │ ├── 00039_00023.ppm
│ │ ├── 00039_00024.ppm
│ │ ├── 00039_00025.ppm
│ │ ├── 00039_00026.ppm
│ │ ├── 00039_00027.ppm
│ │ ├── 00039_00028.ppm
│ │ ├── 00039_00029.ppm
│ │ ├── 00040_00000.ppm
│ │ ├── 00040_00001.ppm
│ │ ├── 00040_00002.ppm
│ │ ├── 00040_00003.ppm
│ │ ├── 00040_00004.ppm
│ │ ├── 00040_00005.ppm
│ │ ├── 00040_00006.ppm
│ │ ├── 00040_00007.ppm
│ │ ├── 00040_00008.ppm
│ │ ├── 00040_00009.ppm
│ │ ├── 00040_00010.ppm
│ │ ├── 00040_00011.ppm
│ │ ├── 00040_00012.ppm
│ │ ├── 00040_00013.ppm
│ │ ├── 00040_00014.ppm
│ │ ├── 00040_00015.ppm
│ │ ├── 00040_00016.ppm
│ │ ├── 00040_00017.ppm
│ │ ├── 00040_00018.ppm
│ │ ├── 00040_00019.ppm
│ │ ├── 00040_00020.ppm
│ │ ├── 00040_00021.ppm
│ │ ├── 00040_00022.ppm
│ │ ├── 00040_00023.ppm
│ │ ├── 00040_00024.ppm
│ │ ├── 00040_00025.ppm
│ │ ├── 00040_00026.ppm
│ │ ├── 00040_00027.ppm
│ │ ├── 00040_00028.ppm
│ │ ├── 00040_00029.ppm
│ │ ├── 00041_00000.ppm
│ │ ├── 00041_00001.ppm
│ │ ├── 00041_00002.ppm
│ │ ├── 00041_00003.ppm
│ │ ├── 00041_00004.ppm
│ │ ├── 00041_00005.ppm
│ │ ├── 00041_00006.ppm
│ │ ├── 00041_00007.ppm
│ │ ├── 00041_00008.ppm
│ │ ├── 00041_00009.ppm
│ │ ├── 00041_00010.ppm
│ │ ├── 00041_00011.ppm
│ │ ├── 00041_00012.ppm
│ │ ├── 00041_00013.ppm
│ │ ├── 00041_00014.ppm
│ │ ├── 00041_00015.ppm
│ │ ├── 00041_00016.ppm
│ │ ├── 00041_00017.ppm
│ │ ├── 00041_00018.ppm
│ │ ├── 00041_00019.ppm
│ │ ├── 00041_00020.ppm
│ │ ├── 00041_00021.ppm
│ │ ├── 00041_00022.ppm
│ │ ├── 00041_00023.ppm
│ │ ├── 00041_00024.ppm
│ │ ├── 00041_00025.ppm
│ │ ├── 00041_00026.ppm
│ │ ├── 00041_00027.ppm
│ │ ├── 00041_00028.ppm
│ │ ├── 00041_00029.ppm
│ │ ├── 00042_00000.ppm
│ │ ├── 00042_00001.ppm
│ │ ├── 00042_00002.ppm
│ │ ├── 00042_00003.ppm
│ │ ├── 00042_00004.ppm
│ │ ├── 00042_00005.ppm
│ │ ├── 00042_00006.ppm
│ │ ├── 00042_00007.ppm
│ │ ├── 00042_00008.ppm
│ │ ├── 00042_00009.ppm
│ │ ├── 00042_00010.ppm
│ │ ├── 00042_00011.ppm
│ │ ├── 00042_00012.ppm
│ │ ├── 00042_00013.ppm
│ │ ├── 00042_00014.ppm
│ │ ├── 00042_00015.ppm
│ │ ├── 00042_00016.ppm
│ │ ├── 00042_00017.ppm
│ │ ├── 00042_00018.ppm
│ │ ├── 00042_00019.ppm
│ │ ├── 00042_00020.ppm
│ │ ├── 00042_00021.ppm
│ │ ├── 00042_00022.ppm
│ │ ├── 00042_00023.ppm
│ │ ├── 00042_00024.ppm
│ │ ├── 00042_00025.ppm
│ │ ├── 00042_00026.ppm
│ │ ├── 00042_00027.ppm
│ │ ├── 00042_00028.ppm
│ │ ├── 00042_00029.ppm
│ │ ├── 00043_00000.ppm
│ │ ├── 00043_00001.ppm
│ │ ├── 00043_00002.ppm
│ │ ├── 00043_00003.ppm
│ │ ├── 00043_00004.ppm
│ │ ├── 00043_00005.ppm
│ │ ├── 00043_00006.ppm
│ │ ├── 00043_00007.ppm
│ │ ├── 00043_00008.ppm
│ │ ├── 00043_00009.ppm
│ │ ├── 00043_00010.ppm
│ │ ├── 00043_00011.ppm
│ │ ├── 00043_00012.ppm
│ │ ├── 00043_00013.ppm
│ │ ├── 00043_00014.ppm
│ │ ├── 00043_00015.ppm
│ │ ├── 00043_00016.ppm
│ │ ├── 00043_00017.ppm
│ │ ├── 00043_00018.ppm
│ │ ├── 00043_00019.ppm
│ │ ├── 00043_00020.ppm
│ │ ├── 00043_00021.ppm
│ │ ├── 00043_00022.ppm
│ │ ├── 00043_00023.ppm
│ │ ├── 00043_00024.ppm
│ │ ├── 00043_00025.ppm
│ │ ├── 00043_00026.ppm
│ │ ├── 00043_00027.ppm
│ │ ├── 00043_00028.ppm
│ │ ├── 00043_00029.ppm
│ │ ├── 00044_00000.ppm
│ │ ├── 00044_00001.ppm
│ │ ├── 00044_00002.ppm
│ │ ├── 00044_00003.ppm
│ │ ├── 00044_00004.ppm
│ │ ├── 00044_00005.ppm
│ │ ├── 00044_00006.ppm
│ │ ├── 00044_00007.ppm
│ │ ├── 00044_00008.ppm
│ │ ├── 00044_00009.ppm
│ │ ├── 00044_00010.ppm
│ │ ├── 00044_00011.ppm
│ │ ├── 00044_00012.ppm
│ │ ├── 00044_00013.ppm
│ │ ├── 00044_00014.ppm
│ │ ├── 00044_00015.ppm
│ │ ├── 00044_00016.ppm
│ │ ├── 00044_00017.ppm
│ │ ├── 00044_00018.ppm
│ │ ├── 00044_00019.ppm
│ │ ├── 00044_00020.ppm
│ │ ├── 00044_00021.ppm
│ │ ├── 00044_00022.ppm
│ │ ├── 00044_00023.ppm
│ │ ├── 00044_00024.ppm
│ │ ├── 00044_00025.ppm
│ │ ├── 00044_00026.ppm
│ │ ├── 00044_00027.ppm
│ │ ├── 00044_00028.ppm
│ │ ├── 00044_00029.ppm
│ │ ├── 00045_00000.ppm
│ │ ├── 00045_00001.ppm
│ │ ├── 00045_00002.ppm
│ │ ├── 00045_00003.ppm
│ │ ├── 00045_00004.ppm
│ │ ├── 00045_00005.ppm
│ │ ├── 00045_00006.ppm
│ │ ├── 00045_00007.ppm
│ │ ├── 00045_00008.ppm
│ │ ├── 00045_00009.ppm
│ │ ├── 00045_00010.ppm
│ │ ├── 00045_00011.ppm
│ │ ├── 00045_00012.ppm
│ │ ├── 00045_00013.ppm
│ │ ├── 00045_00014.ppm
│ │ ├── 00045_00015.ppm
│ │ ├── 00045_00016.ppm
│ │ ├── 00045_00017.ppm
│ │ ├── 00045_00018.ppm
│ │ ├── 00045_00019.ppm
│ │ ├── 00045_00020.ppm
│ │ ├── 00045_00021.ppm
│ │ ├── 00045_00022.ppm
│ │ ├── 00045_00023.ppm
│ │ ├── 00045_00024.ppm
│ │ ├── 00045_00025.ppm
│ │ ├── 00045_00026.ppm
│ │ ├── 00045_00027.ppm
│ │ ├── 00045_00028.ppm
│ │ ├── 00045_00029.ppm
│ │ ├── 00046_00000.ppm
│ │ ├── 00046_00001.ppm
│ │ ├── 00046_00002.ppm
│ │ ├── 00046_00003.ppm
│ │ ├── 00046_00004.ppm
│ │ ├── 00046_00005.ppm
│ │ ├── 00046_00006.ppm
│ │ ├── 00046_00007.ppm
│ │ ├── 00046_00008.ppm
│ │ ├── 00046_00009.ppm
│ │ ├── 00046_00010.ppm
│ │ ├── 00046_00011.ppm
│ │ ├── 00046_00012.ppm
│ │ ├── 00046_00013.ppm
│ │ ├── 00046_00014.ppm
│ │ ├── 00046_00015.ppm
│ │ ├── 00046_00016.ppm
│ │ ├── 00046_00017.ppm
│ │ ├── 00046_00018.ppm
│ │ ├── 00046_00019.ppm
│ │ ├── 00046_00020.ppm
│ │ ├── 00046_00021.ppm
│ │ ├── 00046_00022.ppm
│ │ ├── 00046_00023.ppm
│ │ ├── 00046_00024.ppm
│ │ ├── 00046_00025.ppm
│ │ ├── 00046_00026.ppm
│ │ ├── 00046_00027.ppm
│ │ ├── 00046_00028.ppm
│ │ ├── 00046_00029.ppm
│ │ ├── 00047_00000.ppm
│ │ ├── 00047_00001.ppm
│ │ ├── 00047_00002.ppm
│ │ ├── 00047_00003.ppm
│ │ ├── 00047_00004.ppm
│ │ ├── 00047_00005.ppm
│ │ ├── 00047_00006.ppm
│ │ ├── 00047_00007.ppm
│ │ ├── 00047_00008.ppm
│ │ ├── 00047_00009.ppm
│ │ ├── 00047_00010.ppm
│ │ ├── 00047_00011.ppm
│ │ ├── 00047_00012.ppm
│ │ ├── 00047_00013.ppm
│ │ ├── 00047_00014.ppm
│ │ ├── 00047_00015.ppm
│ │ ├── 00047_00016.ppm
│ │ ├── 00047_00017.ppm
│ │ ├── 00047_00018.ppm
│ │ ├── 00047_00019.ppm
│ │ ├── 00047_00020.ppm
│ │ ├── 00047_00021.ppm
│ │ ├── 00047_00022.ppm
│ │ ├── 00047_00023.ppm
│ │ ├── 00047_00024.ppm
│ │ ├── 00047_00025.ppm
│ │ ├── 00047_00026.ppm
│ │ ├── 00047_00027.ppm
│ │ ├── 00047_00028.ppm
│ │ ├── 00047_00029.ppm
│ │ ├── 00048_00000.ppm
│ │ ├── 00048_00001.ppm
│ │ ├── 00048_00002.ppm
│ │ ├── 00048_00003.ppm
│ │ ├── 00048_00004.ppm
│ │ ├── 00048_00005.ppm
│ │ ├── 00048_00006.ppm
│ │ ├── 00048_00007.ppm
│ │ ├── 00048_00008.ppm
│ │ ├── 00048_00009.ppm
│ │ ├── 00048_00010.ppm
│ │ ├── 00048_00011.ppm
│ │ ├── 00048_00012.ppm
│ │ ├── 00048_00013.ppm
│ │ ├── 00048_00014.ppm
│ │ ├── 00048_00015.ppm
│ │ ├── 00048_00016.ppm
│ │ ├── 00048_00017.ppm
│ │ ├── 00048_00018.ppm
│ │ ├── 00048_00019.ppm
│ │ ├── 00048_00020.ppm
│ │ ├── 00048_00021.ppm
│ │ ├── 00048_00022.ppm
│ │ ├── 00048_00023.ppm
│ │ ├── 00048_00024.ppm
│ │ ├── 00048_00025.ppm
│ │ ├── 00048_00026.ppm
│ │ ├── 00048_00027.ppm
│ │ ├── 00048_00028.ppm
│ │ ├── 00048_00029.ppm
│ │ ├── 00049_00000.ppm
│ │ ├── 00049_00001.ppm
│ │ ├── 00049_00002.ppm
│ │ ├── 00049_00003.ppm
│ │ ├── 00049_00004.ppm
│ │ ├── 00049_00005.ppm
│ │ ├── 00049_00006.ppm
│ │ ├── 00049_00007.ppm
│ │ ├── 00049_00008.ppm
│ │ ├── 00049_00009.ppm
│ │ ├── 00049_00010.ppm
│ │ ├── 00049_00011.ppm
│ │ ├── 00049_00012.ppm
│ │ ├── 00049_00013.ppm
│ │ ├── 00049_00014.ppm
│ │ ├── 00049_00015.ppm
│ │ ├── 00049_00016.ppm
│ │ ├── 00049_00017.ppm
│ │ ├── 00049_00018.ppm
│ │ ├── 00049_00019.ppm
│ │ ├── 00049_00020.ppm
│ │ ├── 00049_00021.ppm
│ │ ├── 00049_00022.ppm
│ │ ├── 00049_00023.ppm
│ │ ├── 00049_00024.ppm
│ │ ├── 00049_00025.ppm
│ │ ├── 00049_00026.ppm
│ │ ├── 00049_00027.ppm
│ │ ├── 00049_00028.ppm
│ │ ├── 00049_00029.ppm
│ │ ├── 00050_00000.ppm
│ │ ├── 00050_00001.ppm
│ │ ├── 00050_00002.ppm
│ │ ├── 00050_00003.ppm
│ │ ├── 00050_00004.ppm
│ │ ├── 00050_00005.ppm
│ │ ├── 00050_00006.ppm
│ │ ├── 00050_00007.ppm
│ │ ├── 00050_00008.ppm
│ │ ├── 00050_00009.ppm
│ │ ├── 00050_00010.ppm
│ │ ├── 00050_00011.ppm
│ │ ├── 00050_00012.ppm
│ │ ├── 00050_00013.ppm
│ │ ├── 00050_00014.ppm
│ │ ├── 00050_00015.ppm
│ │ ├── 00050_00016.ppm
│ │ ├── 00050_00017.ppm
│ │ ├── 00050_00018.ppm
│ │ ├── 00050_00019.ppm
│ │ ├── 00050_00020.ppm
│ │ ├── 00050_00021.ppm
│ │ ├── 00050_00022.ppm
│ │ ├── 00050_00023.ppm
│ │ ├── 00050_00024.ppm
│ │ ├── 00050_00025.ppm
│ │ ├── 00050_00026.ppm
│ │ ├── 00050_00027.ppm
│ │ ├── 00050_00028.ppm
│ │ ├── 00050_00029.ppm
│ │ ├── 00051_00000.ppm
│ │ ├── 00051_00001.ppm
│ │ ├── 00051_00002.ppm
│ │ ├── 00051_00003.ppm
│ │ ├── 00051_00004.ppm
│ │ ├── 00051_00005.ppm
│ │ ├── 00051_00006.ppm
│ │ ├── 00051_00007.ppm
│ │ ├── 00051_00008.ppm
│ │ ├── 00051_00009.ppm
│ │ ├── 00051_00010.ppm
│ │ ├── 00051_00011.ppm
│ │ ├── 00051_00012.ppm
│ │ ├── 00051_00013.ppm
│ │ ├── 00051_00014.ppm
│ │ ├── 00051_00015.ppm
│ │ ├── 00051_00016.ppm
│ │ ├── 00051_00017.ppm
│ │ ├── 00051_00018.ppm
│ │ ├── 00051_00019.ppm
│ │ ├── 00051_00020.ppm
│ │ ├── 00051_00021.ppm
│ │ ├── 00051_00022.ppm
│ │ ├── 00051_00023.ppm
│ │ ├── 00051_00024.ppm
│ │ ├── 00051_00025.ppm
│ │ ├── 00051_00026.ppm
│ │ ├── 00051_00027.ppm
│ │ ├── 00051_00028.ppm
│ │ ├── 00051_00029.ppm
│ │ ├── 00052_00000.ppm
│ │ ├── 00052_00001.ppm
│ │ ├── 00052_00002.ppm
│ │ ├── 00052_00003.ppm
│ │ ├── 00052_00004.ppm
│ │ ├── 00052_00005.ppm
│ │ ├── 00052_00006.ppm
│ │ ├── 00052_00007.ppm
│ │ ├── 00052_00008.ppm
│ │ ├── 00052_00009.ppm
│ │ ├── 00052_00010.ppm
│ │ ├── 00052_00011.ppm
│ │ ├── 00052_00012.ppm
│ │ ├── 00052_00013.ppm
│ │ ├── 00052_00014.ppm
│ │ ├── 00052_00015.ppm
│ │ ├── 00052_00016.ppm
│ │ ├── 00052_00017.ppm
│ │ ├── 00052_00018.ppm
│ │ ├── 00052_00019.ppm
│ │ ├── 00052_00020.ppm
│ │ ├── 00052_00021.ppm
│ │ ├── 00052_00022.ppm
│ │ ├── 00052_00023.ppm
│ │ ├── 00052_00024.ppm
│ │ ├── 00052_00025.ppm
│ │ ├── 00052_00026.ppm
│ │ ├── 00052_00027.ppm
│ │ ├── 00052_00028.ppm
│ │ ├── 00052_00029.ppm
│ │ ├── 00053_00000.ppm
│ │ ├── 00053_00001.ppm
│ │ ├── 00053_00002.ppm
│ │ ├── 00053_00003.ppm
│ │ ├── 00053_00004.ppm
│ │ ├── 00053_00005.ppm
│ │ ├── 00053_00006.ppm
│ │ ├── 00053_00007.ppm
│ │ ├── 00053_00008.ppm
│ │ ├── 00053_00009.ppm
│ │ ├── 00053_00010.ppm
│ │ ├── 00053_00011.ppm
│ │ ├── 00053_00012.ppm
│ │ ├── 00053_00013.ppm
│ │ ├── 00053_00014.ppm
│ │ ├── 00053_00015.ppm
│ │ ├── 00053_00016.ppm
│ │ ├── 00053_00017.ppm
│ │ ├── 00053_00018.ppm
│ │ ├── 00053_00019.ppm
│ │ ├── 00053_00020.ppm
│ │ ├── 00053_00021.ppm
│ │ ├── 00053_00022.ppm
│ │ ├── 00053_00023.ppm
│ │ ├── 00053_00024.ppm
│ │ ├── 00053_00025.ppm
│ │ ├── 00053_00026.ppm
│ │ ├── 00053_00027.ppm
│ │ ├── 00053_00028.ppm
│ │ ├── 00053_00029.ppm
│ │ ├── 00054_00000.ppm
│ │ ├── 00054_00001.ppm
│ │ ├── 00054_00002.ppm
│ │ ├── 00054_00003.ppm
│ │ ├── 00054_00004.ppm
│ │ ├── 00054_00005.ppm
│ │ ├── 00054_00006.ppm
│ │ ├── 00054_00007.ppm
│ │ ├── 00054_00008.ppm
│ │ ├── 00054_00009.ppm
│ │ ├── 00054_00010.ppm
│ │ ├── 00054_00011.ppm
│ │ ├── 00054_00012.ppm
│ │ ├── 00054_00013.ppm
│ │ ├── 00054_00014.ppm
│ │ ├── 00054_00015.ppm
│ │ ├── 00054_00016.ppm
│ │ ├── 00054_00017.ppm
│ │ ├── 00054_00018.ppm
│ │ ├── 00054_00019.ppm
│ │ ├── 00054_00020.ppm
│ │ ├── 00054_00021.ppm
│ │ ├── 00054_00022.ppm
│ │ ├── 00054_00023.ppm
│ │ ├── 00054_00024.ppm
│ │ ├── 00054_00025.ppm
│ │ ├── 00054_00026.ppm
│ │ ├── 00054_00027.ppm
│ │ ├── 00054_00028.ppm
│ │ ├── 00054_00029.ppm
│ │ ├── 00055_00000.ppm
│ │ ├── 00055_00001.ppm
│ │ ├── 00055_00002.ppm
│ │ ├── 00055_00003.ppm
│ │ ├── 00055_00004.ppm
│ │ ├── 00055_00005.ppm
│ │ ├── 00055_00006.ppm
│ │ ├── 00055_00007.ppm
│ │ ├── 00055_00008.ppm
│ │ ├── 00055_00009.ppm
│ │ ├── 00055_00010.ppm
│ │ ├── 00055_00011.ppm
│ │ ├── 00055_00012.ppm
│ │ ├── 00055_00013.ppm
│ │ ├── 00055_00014.ppm
│ │ ├── 00055_00015.ppm
│ │ ├── 00055_00016.ppm
│ │ ├── 00055_00017.ppm
│ │ ├── 00055_00018.ppm
│ │ ├── 00055_00019.ppm
│ │ ├── 00055_00020.ppm
│ │ ├── 00055_00021.ppm
│ │ ├── 00055_00022.ppm
│ │ ├── 00055_00023.ppm
│ │ ├── 00055_00024.ppm
│ │ ├── 00055_00025.ppm
│ │ ├── 00055_00026.ppm
│ │ ├── 00055_00027.ppm
│ │ ├── 00055_00028.ppm
│ │ ├── 00055_00029.ppm
│ │ ├── 00056_00000.ppm
│ │ ├── 00056_00001.ppm
│ │ ├── 00056_00002.ppm
│ │ ├── 00056_00003.ppm
│ │ ├── 00056_00004.ppm
│ │ ├── 00056_00005.ppm
│ │ ├── 00056_00006.ppm
│ │ ├── 00056_00007.ppm
│ │ ├── 00056_00008.ppm
│ │ ├── 00056_00009.ppm
│ │ ├── 00056_00010.ppm
│ │ ├── 00056_00011.ppm
│ │ ├── 00056_00012.ppm
│ │ ├── 00056_00013.ppm
│ │ ├── 00056_00014.ppm
│ │ ├── 00056_00015.ppm
│ │ ├── 00056_00016.ppm
│ │ ├── 00056_00017.ppm
│ │ ├── 00056_00018.ppm
│ │ ├── 00056_00019.ppm
│ │ ├── 00056_00020.ppm
│ │ ├── 00056_00021.ppm
│ │ ├── 00056_00022.ppm
│ │ ├── 00056_00023.ppm
│ │ ├── 00056_00024.ppm
│ │ ├── 00056_00025.ppm
│ │ ├── 00056_00026.ppm
│ │ ├── 00056_00027.ppm
│ │ ├── 00056_00028.ppm
│ │ ├── 00056_00029.ppm
│ │ ├── 00057_00000.ppm
│ │ ├── 00057_00001.ppm
│ │ ├── 00057_00002.ppm
│ │ ├── 00057_00003.ppm
│ │ ├── 00057_00004.ppm
│ │ ├── 00057_00005.ppm
│ │ ├── 00057_00006.ppm
│ │ ├── 00057_00007.ppm
│ │ ├── 00057_00008.ppm
│ │ ├── 00057_00009.ppm
│ │ ├── 00057_00010.ppm
│ │ ├── 00057_00011.ppm
│ │ ├── 00057_00012.ppm
│ │ ├── 00057_00013.ppm
│ │ ├── 00057_00014.ppm
│ │ ├── 00057_00015.ppm
│ │ ├── 00057_00016.ppm
│ │ ├── 00057_00017.ppm
│ │ ├── 00057_00018.ppm
│ │ ├── 00057_00019.ppm
│ │ ├── 00057_00020.ppm
│ │ ├── 00057_00021.ppm
│ │ ├── 00057_00022.ppm
│ │ ├── 00057_00023.ppm
│ │ ├── 00057_00024.ppm
│ │ ├── 00057_00025.ppm
│ │ ├── 00057_00026.ppm
│ │ ├── 00057_00027.ppm
│ │ ├── 00057_00028.ppm
│ │ ├── 00057_00029.ppm
│ │ ├── 00058_00000.ppm
│ │ ├── 00058_00001.ppm
│ │ ├── 00058_00002.ppm
│ │ ├── 00058_00003.ppm
│ │ ├── 00058_00004.ppm
│ │ ├── 00058_00005.ppm
│ │ ├── 00058_00006.ppm
│ │ ├── 00058_00007.ppm
│ │ ├── 00058_00008.ppm
│ │ ├── 00058_00009.ppm
│ │ ├── 00058_00010.ppm
│ │ ├── 00058_00011.ppm
│ │ ├── 00058_00012.ppm
│ │ ├── 00058_00013.ppm
│ │ ├── 00058_00014.ppm
│ │ ├── 00058_00015.ppm
│ │ ├── 00058_00016.ppm
│ │ ├── 00058_00017.ppm
│ │ ├── 00058_00018.ppm
│ │ ├── 00058_00019.ppm
│ │ ├── 00058_00020.ppm
│ │ ├── 00058_00021.ppm
│ │ ├── 00058_00022.ppm
│ │ ├── 00058_00023.ppm
│ │ ├── 00058_00024.ppm
│ │ ├── 00058_00025.ppm
│ │ ├── 00058_00026.ppm
│ │ ├── 00058_00027.ppm
│ │ ├── 00058_00028.ppm
│ │ ├── 00058_00029.ppm
│ │ ├── 00059_00000.ppm
│ │ ├── 00059_00001.ppm
│ │ ├── 00059_00002.ppm
│ │ ├── 00059_00003.ppm
│ │ ├── 00059_00004.ppm
│ │ ├── 00059_00005.ppm
│ │ ├── 00059_00006.ppm
│ │ ├── 00059_00007.ppm
│ │ ├── 00059_00008.ppm
│ │ ├── 00059_00009.ppm
│ │ ├── 00059_00010.ppm
│ │ ├── 00059_00011.ppm
│ │ ├── 00059_00012.ppm
│ │ ├── 00059_00013.ppm
│ │ ├── 00059_00014.ppm
│ │ ├── 00059_00015.ppm
│ │ ├── 00059_00016.ppm
│ │ ├── 00059_00017.ppm
│ │ ├── 00059_00018.ppm
│ │ ├── 00059_00019.ppm
│ │ ├── 00059_00020.ppm
│ │ ├── 00059_00021.ppm
│ │ ├── 00059_00022.ppm
│ │ ├── 00059_00023.ppm
│ │ ├── 00059_00024.ppm
│ │ ├── 00059_00025.ppm
│ │ ├── 00059_00026.ppm
│ │ ├── 00059_00027.ppm
│ │ ├── 00059_00028.ppm
│ │ ├── 00059_00029.ppm
│ │ ├── 00060_00000.ppm
│ │ ├── 00060_00001.ppm
│ │ ├── 00060_00002.ppm
│ │ ├── 00060_00003.ppm
│ │ ├── 00060_00004.ppm
│ │ ├── 00060_00005.ppm
│ │ ├── 00060_00006.ppm
│ │ ├── 00060_00007.ppm
│ │ ├── 00060_00008.ppm
│ │ ├── 00060_00009.ppm
│ │ ├── 00060_00010.ppm
│ │ ├── 00060_00011.ppm
│ │ ├── 00060_00012.ppm
│ │ ├── 00060_00013.ppm
│ │ ├── 00060_00014.ppm
│ │ ├── 00060_00015.ppm
│ │ ├── 00060_00016.ppm
│ │ ├── 00060_00017.ppm
│ │ ├── 00060_00018.ppm
│ │ ├── 00060_00019.ppm
│ │ ├── 00060_00020.ppm
│ │ ├── 00060_00021.ppm
│ │ ├── 00060_00022.ppm
│ │ ├── 00060_00023.ppm
│ │ ├── 00060_00024.ppm
│ │ ├── 00060_00025.ppm
│ │ ├── 00060_00026.ppm
│ │ ├── 00060_00027.ppm
│ │ ├── 00060_00028.ppm
│ │ ├── 00060_00029.ppm
│ │ ├── 00061_00000.ppm
│ │ ├── 00061_00001.ppm
│ │ ├── 00061_00002.ppm
│ │ ├── 00061_00003.ppm
│ │ ├── 00061_00004.ppm
│ │ ├── 00061_00005.ppm
│ │ ├── 00061_00006.ppm
│ │ ├── 00061_00007.ppm
│ │ ├── 00061_00008.ppm
│ │ ├── 00061_00009.ppm
│ │ ├── 00061_00010.ppm
│ │ ├── 00061_00011.ppm
│ │ ├── 00061_00012.ppm
│ │ ├── 00061_00013.ppm
│ │ ├── 00061_00014.ppm
│ │ ├── 00061_00015.ppm
│ │ ├── 00061_00016.ppm
│ │ ├── 00061_00017.ppm
│ │ ├── 00061_00018.ppm
│ │ ├── 00061_00019.ppm
│ │ ├── 00061_00020.ppm
│ │ ├── 00061_00021.ppm
│ │ ├── 00061_00022.ppm
│ │ ├── 00061_00023.ppm
│ │ ├── 00061_00024.ppm
│ │ ├── 00061_00025.ppm
│ │ ├── 00061_00026.ppm
│ │ ├── 00061_00027.ppm
│ │ ├── 00061_00028.ppm
│ │ ├── 00061_00029.ppm
│ │ ├── 00062_00000.ppm
│ │ ├── 00062_00001.ppm
│ │ ├── 00062_00002.ppm
│ │ ├── 00062_00003.ppm
│ │ ├── 00062_00004.ppm
│ │ ├── 00062_00005.ppm
│ │ ├── 00062_00006.ppm
│ │ ├── 00062_00007.ppm
│ │ ├── 00062_00008.ppm
│ │ ├── 00062_00009.ppm
│ │ ├── 00062_00010.ppm
│ │ ├── 00062_00011.ppm
│ │ ├── 00062_00012.ppm
│ │ ├── 00062_00013.ppm
│ │ ├── 00062_00014.ppm
│ │ ├── 00062_00015.ppm
│ │ ├── 00062_00016.ppm
│ │ ├── 00062_00017.ppm
│ │ ├── 00062_00018.ppm
│ │ ├── 00062_00019.ppm
│ │ ├── 00062_00020.ppm
│ │ ├── 00062_00021.ppm
│ │ ├── 00062_00022.ppm
│ │ ├── 00062_00023.ppm
│ │ ├── 00062_00024.ppm
│ │ ├── 00062_00025.ppm
│ │ ├── 00062_00026.ppm
│ │ ├── 00062_00027.ppm
│ │ ├── 00062_00028.ppm
│ │ ├── 00062_00029.ppm
│ │ ├── 00063_00000.ppm
│ │ ├── 00063_00001.ppm
│ │ ├── 00063_00002.ppm
│ │ ├── 00063_00003.ppm
│ │ ├── 00063_00004.ppm
│ │ ├── 00063_00005.ppm
│ │ ├── 00063_00006.ppm
│ │ ├── 00063_00007.ppm
│ │ ├── 00063_00008.ppm
│ │ ├── 00063_00009.ppm
│ │ ├── 00063_00010.ppm
│ │ ├── 00063_00011.ppm
│ │ ├── 00063_00012.ppm
│ │ ├── 00063_00013.ppm
│ │ ├── 00063_00014.ppm
│ │ ├── 00063_00015.ppm
│ │ ├── 00063_00016.ppm
│ │ ├── 00063_00017.ppm
│ │ ├── 00063_00018.ppm
│ │ ├── 00063_00019.ppm
│ │ ├── 00063_00020.ppm
│ │ ├── 00063_00021.ppm
│ │ ├── 00063_00022.ppm
│ │ ├── 00063_00023.ppm
│ │ ├── 00063_00024.ppm
│ │ ├── 00063_00025.ppm
│ │ ├── 00063_00026.ppm
│ │ ├── 00063_00027.ppm
│ │ ├── 00063_00028.ppm
│ │ ├── 00063_00029.ppm
│ │ ├── 00064_00000.ppm
│ │ ├── 00064_00001.ppm
│ │ ├── 00064_00002.ppm
│ │ ├── 00064_00003.ppm
│ │ ├── 00064_00004.ppm
│ │ ├── 00064_00005.ppm
│ │ ├── 00064_00006.ppm
│ │ ├── 00064_00007.ppm
│ │ ├── 00064_00008.ppm
│ │ ├── 00064_00009.ppm
│ │ ├── 00064_00010.ppm
│ │ ├── 00064_00011.ppm
│ │ ├── 00064_00012.ppm
│ │ ├── 00064_00013.ppm
│ │ ├── 00064_00014.ppm
│ │ ├── 00064_00015.ppm
│ │ ├── 00064_00016.ppm
│ │ ├── 00064_00017.ppm
│ │ ├── 00064_00018.ppm
│ │ ├── 00064_00019.ppm
│ │ ├── 00064_00020.ppm
│ │ ├── 00064_00021.ppm
│ │ ├── 00064_00022.ppm
│ │ ├── 00064_00023.ppm
│ │ ├── 00064_00024.ppm
│ │ ├── 00064_00025.ppm
│ │ ├── 00064_00026.ppm
│ │ ├── 00064_00027.ppm
│ │ ├── 00064_00028.ppm
│ │ ├── 00064_00029.ppm
│ │ ├── 00065_00000.ppm
│ │ ├── 00065_00001.ppm
│ │ ├── 00065_00002.ppm
│ │ ├── 00065_00003.ppm
│ │ ├── 00065_00004.ppm
│ │ ├── 00065_00005.ppm
│ │ ├── 00065_00006.ppm
│ │ ├── 00065_00007.ppm
│ │ ├── 00065_00008.ppm
│ │ ├── 00065_00009.ppm
│ │ ├── 00065_00010.ppm
│ │ ├── 00065_00011.ppm
│ │ ├── 00065_00012.ppm
│ │ ├── 00065_00013.ppm
│ │ ├── 00065_00014.ppm
│ │ ├── 00065_00015.ppm
│ │ ├── 00065_00016.ppm
│ │ ├── 00065_00017.ppm
│ │ ├── 00065_00018.ppm
│ │ ├── 00065_00019.ppm
│ │ ├── 00065_00020.ppm
│ │ ├── 00065_00021.ppm
│ │ ├── 00065_00022.ppm
│ │ ├── 00065_00023.ppm
│ │ ├── 00065_00024.ppm
│ │ ├── 00065_00025.ppm
│ │ ├── 00065_00026.ppm
│ │ ├── 00065_00027.ppm
│ │ ├── 00065_00028.ppm
│ │ ├── 00065_00029.ppm
│ │ ├── 00066_00000.ppm
│ │ ├── 00066_00001.ppm
│ │ ├── 00066_00002.ppm
│ │ ├── 00066_00003.ppm
│ │ ├── 00066_00004.ppm
│ │ ├── 00066_00005.ppm
│ │ ├── 00066_00006.ppm
│ │ ├── 00066_00007.ppm
│ │ ├── 00066_00008.ppm
│ │ ├── 00066_00009.ppm
│ │ ├── 00066_00010.ppm
│ │ ├── 00066_00011.ppm
│ │ ├── 00066_00012.ppm
│ │ ├── 00066_00013.ppm
│ │ ├── 00066_00014.ppm
│ │ ├── 00066_00015.ppm
│ │ ├── 00066_00016.ppm
│ │ ├── 00066_00017.ppm
│ │ ├── 00066_00018.ppm
│ │ ├── 00066_00019.ppm
│ │ ├── 00066_00020.ppm
│ │ ├── 00066_00021.ppm
│ │ ├── 00066_00022.ppm
│ │ ├── 00066_00023.ppm
│ │ ├── 00066_00024.ppm
│ │ ├── 00066_00025.ppm
│ │ ├── 00066_00026.ppm
│ │ ├── 00066_00027.ppm
│ │ ├── 00066_00028.ppm
│ │ ├── 00066_00029.ppm
│ │ ├── 00067_00000.ppm
│ │ ├── 00067_00001.ppm
│ │ ├── 00067_00002.ppm
│ │ ├── 00067_00003.ppm
│ │ ├── 00067_00004.ppm
│ │ ├── 00067_00005.ppm
│ │ ├── 00067_00006.ppm
│ │ ├── 00067_00007.ppm
│ │ ├── 00067_00008.ppm
│ │ ├── 00067_00009.ppm
│ │ ├── 00067_00010.ppm
│ │ ├── 00067_00011.ppm
│ │ ├── 00067_00012.ppm
│ │ ├── 00067_00013.ppm
│ │ ├── 00067_00014.ppm
│ │ ├── 00067_00015.ppm
│ │ ├── 00067_00016.ppm
│ │ ├── 00067_00017.ppm
│ │ ├── 00067_00018.ppm
│ │ ├── 00067_00019.ppm
│ │ ├── 00067_00020.ppm
│ │ ├── 00067_00021.ppm
│ │ ├── 00067_00022.ppm
│ │ ├── 00067_00023.ppm
│ │ ├── 00067_00024.ppm
│ │ ├── 00067_00025.ppm
│ │ ├── 00067_00026.ppm
│ │ ├── 00067_00027.ppm
│ │ ├── 00067_00028.ppm
│ │ ├── 00067_00029.ppm
│ │ ├── 00068_00000.ppm
│ │ ├── 00068_00001.ppm
│ │ ├── 00068_00002.ppm
│ │ ├── 00068_00003.ppm
│ │ ├── 00068_00004.ppm
│ │ ├── 00068_00005.ppm
│ │ ├── 00068_00006.ppm
│ │ ├── 00068_00007.ppm
│ │ ├── 00068_00008.ppm
│ │ ├── 00068_00009.ppm
│ │ ├── 00068_00010.ppm
│ │ ├── 00068_00011.ppm
│ │ ├── 00068_00012.ppm
│ │ ├── 00068_00013.ppm
│ │ ├── 00068_00014.ppm
│ │ ├── 00068_00015.ppm
│ │ ├── 00068_00016.ppm
│ │ ├── 00068_00017.ppm
│ │ ├── 00068_00018.ppm
│ │ ├── 00068_00019.ppm
│ │ ├── 00068_00020.ppm
│ │ ├── 00068_00021.ppm
│ │ ├── 00068_00022.ppm
│ │ ├── 00068_00023.ppm
│ │ ├── 00068_00024.ppm
│ │ ├── 00068_00025.ppm
│ │ ├── 00068_00026.ppm
│ │ ├── 00068_00027.ppm
│ │ ├── 00068_00028.ppm
│ │ ├── 00068_00029.ppm
│ │ ├── 00069_00000.ppm
│ │ ├── 00069_00001.ppm
│ │ ├── 00069_00002.ppm
│ │ ├── 00069_00003.ppm
│ │ ├── 00069_00004.ppm
│ │ ├── 00069_00005.ppm
│ │ ├── 00069_00006.ppm
│ │ ├── 00069_00007.ppm
│ │ ├── 00069_00008.ppm
│ │ ├── 00069_00009.ppm
│ │ ├── 00069_00010.ppm
│ │ ├── 00069_00011.ppm
│ │ ├── 00069_00012.ppm
│ │ ├── 00069_00013.ppm
│ │ ├── 00069_00014.ppm
│ │ ├── 00069_00015.ppm
│ │ ├── 00069_00016.ppm
│ │ ├── 00069_00017.ppm
│ │ ├── 00069_00018.ppm
│ │ ├── 00069_00019.ppm
│ │ ├── 00069_00020.ppm
│ │ ├── 00069_00021.ppm
│ │ ├── 00069_00022.ppm
│ │ ├── 00069_00023.ppm
│ │ ├── 00069_00024.ppm
│ │ ├── 00069_00025.ppm
│ │ ├── 00069_00026.ppm
│ │ ├── 00069_00027.ppm
│ │ ├── 00069_00028.ppm
│ │ ├── 00069_00029.ppm
│ │ ├── 00070_00000.ppm
│ │ ├── 00070_00001.ppm
│ │ ├── 00070_00002.ppm
│ │ ├── 00070_00003.ppm
│ │ ├── 00070_00004.ppm
│ │ ├── 00070_00005.ppm
│ │ ├── 00070_00006.ppm
│ │ ├── 00070_00007.ppm
│ │ ├── 00070_00008.ppm
│ │ ├── 00070_00009.ppm
│ │ ├── 00070_00010.ppm
│ │ ├── 00070_00011.ppm
│ │ ├── 00070_00012.ppm
│ │ ├── 00070_00013.ppm
│ │ ├── 00070_00014.ppm
│ │ ├── 00070_00015.ppm
│ │ ├── 00070_00016.ppm
│ │ ├── 00070_00017.ppm
│ │ ├── 00070_00018.ppm
│ │ ├── 00070_00019.ppm
│ │ ├── 00070_00020.ppm
│ │ ├── 00070_00021.ppm
│ │ ├── 00070_00022.ppm
│ │ ├── 00070_00023.ppm
│ │ ├── 00070_00024.ppm
│ │ ├── 00070_00025.ppm
│ │ ├── 00070_00026.ppm
│ │ ├── 00070_00027.ppm
│ │ ├── 00070_00028.ppm
│ │ ├── 00070_00029.ppm
│ │ ├── 00071_00000.ppm
│ │ ├── 00071_00001.ppm
│ │ ├── 00071_00002.ppm
│ │ ├── 00071_00003.ppm
│ │ ├── 00071_00004.ppm
│ │ ├── 00071_00005.ppm
│ │ ├── 00071_00006.ppm
│ │ ├── 00071_00007.ppm
│ │ ├── 00071_00008.ppm
│ │ ├── 00071_00009.ppm
│ │ ├── 00071_00010.ppm
│ │ ├── 00071_00011.ppm
│ │ ├── 00071_00012.ppm
│ │ ├── 00071_00013.ppm
│ │ ├── 00071_00014.ppm
│ │ ├── 00071_00015.ppm
│ │ ├── 00071_00016.ppm
│ │ ├── 00071_00017.ppm
│ │ ├── 00071_00018.ppm
│ │ ├── 00071_00019.ppm
│ │ ├── 00071_00020.ppm
│ │ ├── 00071_00021.ppm
│ │ ├── 00071_00022.ppm
│ │ ├── 00071_00023.ppm
│ │ ├── 00071_00024.ppm
│ │ ├── 00071_00025.ppm
│ │ ├── 00071_00026.ppm
│ │ ├── 00071_00027.ppm
│ │ ├── 00071_00028.ppm
│ │ ├── 00071_00029.ppm
│ │ ├── 00072_00000.ppm
│ │ ├── 00072_00001.ppm
│ │ ├── 00072_00002.ppm
│ │ ├── 00072_00003.ppm
│ │ ├── 00072_00004.ppm
│ │ ├── 00072_00005.ppm
│ │ ├── 00072_00006.ppm
│ │ ├── 00072_00007.ppm
│ │ ├── 00072_00008.ppm
│ │ ├── 00072_00009.ppm
│ │ ├── 00072_00010.ppm
│ │ ├── 00072_00011.ppm
│ │ ├── 00072_00012.ppm
│ │ ├── 00072_00013.ppm
│ │ ├── 00072_00014.ppm
│ │ ├── 00072_00015.ppm
│ │ ├── 00072_00016.ppm
│ │ ├── 00072_00017.ppm
│ │ ├── 00072_00018.ppm
│ │ ├── 00072_00019.ppm
│ │ ├── 00072_00020.ppm
│ │ ├── 00072_00021.ppm
│ │ ├── 00072_00022.ppm
│ │ ├── 00072_00023.ppm
│ │ ├── 00072_00024.ppm
│ │ ├── 00072_00025.ppm
│ │ ├── 00072_00026.ppm
│ │ ├── 00072_00027.ppm
│ │ ├── 00072_00028.ppm
│ │ ├── 00072_00029.ppm
│ │ ├── 00073_00000.ppm
│ │ ├── 00073_00001.ppm
│ │ ├── 00073_00002.ppm
│ │ ├── 00073_00003.ppm
│ │ ├── 00073_00004.ppm
│ │ ├── 00073_00005.ppm
│ │ ├── 00073_00006.ppm
│ │ ├── 00073_00007.ppm
│ │ ├── 00073_00008.ppm
│ │ ├── 00073_00009.ppm
│ │ ├── 00073_00010.ppm
│ │ ├── 00073_00011.ppm
│ │ ├── 00073_00012.ppm
│ │ ├── 00073_00013.ppm
│ │ ├── 00073_00014.ppm
│ │ ├── 00073_00015.ppm
│ │ ├── 00073_00016.ppm
│ │ ├── 00073_00017.ppm
│ │ ├── 00073_00018.ppm
│ │ ├── 00073_00019.ppm
│ │ ├── 00073_00020.ppm
│ │ ├── 00073_00021.ppm
│ │ ├── 00073_00022.ppm
│ │ ├── 00073_00023.ppm
│ │ ├── 00073_00024.ppm
│ │ ├── 00073_00025.ppm
│ │ ├── 00073_00026.ppm
│ │ ├── 00073_00027.ppm
│ │ ├── 00073_00028.ppm
│ │ ├── 00073_00029.ppm
│ │ ├── 00074_00000.ppm
│ │ ├── 00074_00001.ppm
│ │ ├── 00074_00002.ppm
│ │ ├── 00074_00003.ppm
│ │ ├── 00074_00004.ppm
│ │ ├── 00074_00005.ppm
│ │ ├── 00074_00006.ppm
│ │ ├── 00074_00007.ppm
│ │ ├── 00074_00008.ppm
│ │ ├── 00074_00009.ppm
│ │ ├── 00074_00010.ppm
│ │ ├── 00074_00011.ppm
│ │ ├── 00074_00012.ppm
│ │ ├── 00074_00013.ppm
│ │ ├── 00074_00014.ppm
│ │ ├── 00074_00015.ppm
│ │ ├── 00074_00016.ppm
│ │ ├── 00074_00017.ppm
│ │ ├── 00074_00018.ppm
│ │ ├── 00074_00019.ppm
│ │ ├── 00074_00020.ppm
│ │ ├── 00074_00021.ppm
│ │ ├── 00074_00022.ppm
│ │ ├── 00074_00023.ppm
│ │ ├── 00074_00024.ppm
│ │ ├── 00074_00025.ppm
│ │ ├── 00074_00026.ppm
│ │ ├── 00074_00027.ppm
│ │ ├── 00074_00028.ppm
│ │ ├── 00074_00029.ppm
│ │ └── GT-00002.csv
│ ├── 00004/
│ │ ├── 00000_00000.ppm
│ │ ├── 00000_00001.ppm
│ │ ├── 00000_00002.ppm
│ │ ├── 00000_00003.ppm
│ │ ├── 00000_00004.ppm
│ │ ├── 00000_00005.ppm
│ │ ├── 00000_00006.ppm
│ │ ├── 00000_00007.ppm
│ │ ├── 00000_00008.ppm
│ │ ├── 00000_00009.ppm
│ │ ├── 00000_00010.ppm
│ │ ├── 00000_00011.ppm
│ │ ├── 00000_00012.ppm
│ │ ├── 00000_00013.ppm
│ │ ├── 00000_00014.ppm
│ │ ├── 00000_00015.ppm
│ │ ├── 00000_00016.ppm
│ │ ├── 00000_00017.ppm
│ │ ├── 00000_00018.ppm
│ │ ├── 00000_00019.ppm
│ │ ├── 00000_00020.ppm
│ │ ├── 00000_00021.ppm
│ │ ├── 00000_00022.ppm
│ │ ├── 00000_00023.ppm
│ │ ├── 00000_00024.ppm
│ │ ├── 00000_00025.ppm
│ │ ├── 00000_00026.ppm
│ │ ├── 00000_00027.ppm
│ │ ├── 00000_00028.ppm
│ │ ├── 00000_00029.ppm
│ │ ├── 00001_00000.ppm
│ │ ├── 00001_00001.ppm
│ │ ├── 00001_00002.ppm
│ │ ├── 00001_00003.ppm
│ │ ├── 00001_00004.ppm
│ │ ├── 00001_00005.ppm
│ │ ├── 00001_00006.ppm
│ │ ├── 00001_00007.ppm
│ │ ├── 00001_00008.ppm
│ │ ├── 00001_00009.ppm
│ │ ├── 00001_00010.ppm
│ │ ├── 00001_00011.ppm
│ │ ├── 00001_00012.ppm
│ │ ├── 00001_00013.ppm
│ │ ├── 00001_00014.ppm
│ │ ├── 00001_00015.ppm
│ │ ├── 00001_00016.ppm
│ │ ├── 00001_00017.ppm
│ │ ├── 00001_00018.ppm
│ │ ├── 00001_00019.ppm
│ │ ├── 00001_00020.ppm
│ │ ├── 00001_00021.ppm
│ │ ├── 00001_00022.ppm
│ │ ├── 00001_00023.ppm
│ │ ├── 00001_00024.ppm
│ │ ├── 00001_00025.ppm
│ │ ├── 00001_00026.ppm
│ │ ├── 00001_00027.ppm
│ │ ├── 00001_00028.ppm
│ │ ├── 00001_00029.ppm
│ │ ├── 00002_00000.ppm
│ │ ├── 00002_00001.ppm
│ │ ├── 00002_00002.ppm
│ │ ├── 00002_00003.ppm
│ │ ├── 00002_00004.ppm
│ │ ├── 00002_00005.ppm
│ │ ├── 00002_00006.ppm
│ │ ├── 00002_00007.ppm
│ │ ├── 00002_00008.ppm
│ │ ├── 00002_00009.ppm
│ │ ├── 00002_00010.ppm
│ │ ├── 00002_00011.ppm
│ │ ├── 00002_00012.ppm
│ │ ├── 00002_00013.ppm
│ │ ├── 00002_00014.ppm
│ │ ├── 00002_00015.ppm
│ │ ├── 00002_00016.ppm
│ │ ├── 00002_00017.ppm
│ │ ├── 00002_00018.ppm
│ │ ├── 00002_00019.ppm
│ │ ├── 00002_00020.ppm
│ │ ├── 00002_00021.ppm
│ │ ├── 00002_00022.ppm
│ │ ├── 00002_00023.ppm
│ │ ├── 00002_00024.ppm
│ │ ├── 00002_00025.ppm
│ │ ├── 00002_00026.ppm
│ │ ├── 00002_00027.ppm
│ │ ├── 00002_00028.ppm
│ │ ├── 00002_00029.ppm
│ │ ├── 00003_00000.ppm
│ │ ├── 00003_00001.ppm
│ │ ├── 00003_00002.ppm
│ │ ├── 00003_00003.ppm
│ │ ├── 00003_00004.ppm
│ │ ├── 00003_00005.ppm
│ │ ├── 00003_00006.ppm
│ │ ├── 00003_00007.ppm
│ │ ├── 00003_00008.ppm
│ │ ├── 00003_00009.ppm
│ │ ├── 00003_00010.ppm
│ │ ├── 00003_00011.ppm
│ │ ├── 00003_00012.ppm
│ │ ├── 00003_00013.ppm
│ │ ├── 00003_00014.ppm
│ │ ├── 00003_00015.ppm
│ │ ├── 00003_00016.ppm
│ │ ├── 00003_00017.ppm
│ │ ├── 00003_00018.ppm
│ │ ├── 00003_00019.ppm
│ │ ├── 00003_00020.ppm
│ │ ├── 00003_00021.ppm
│ │ ├── 00003_00022.ppm
│ │ ├── 00003_00023.ppm
│ │ ├── 00003_00024.ppm
│ │ ├── 00003_00025.ppm
│ │ ├── 00003_00026.ppm
│ │ ├── 00003_00027.ppm
│ │ ├── 00003_00028.ppm
│ │ ├── 00003_00029.ppm
│ │ ├── 00004_00000.ppm
│ │ ├── 00004_00001.ppm
│ │ ├── 00004_00002.ppm
│ │ ├── 00004_00003.ppm
│ │ ├── 00004_00004.ppm
│ │ ├── 00004_00005.ppm
│ │ ├── 00004_00006.ppm
│ │ ├── 00004_00007.ppm
│ │ ├── 00004_00008.ppm
│ │ ├── 00004_00009.ppm
│ │ ├── 00004_00010.ppm
│ │ ├── 00004_00011.ppm
│ │ ├── 00004_00012.ppm
│ │ ├── 00004_00013.ppm
│ │ ├── 00004_00014.ppm
│ │ ├── 00004_00015.ppm
│ │ ├── 00004_00016.ppm
│ │ ├── 00004_00017.ppm
│ │ ├── 00004_00018.ppm
│ │ ├── 00004_00019.ppm
│ │ ├── 00004_00020.ppm
│ │ ├── 00004_00021.ppm
│ │ ├── 00004_00022.ppm
│ │ ├── 00004_00023.ppm
│ │ ├── 00004_00024.ppm
│ │ ├── 00004_00025.ppm
│ │ ├── 00004_00026.ppm
│ │ ├── 00004_00027.ppm
│ │ ├── 00004_00028.ppm
│ │ ├── 00004_00029.ppm
│ │ ├── 00005_00000.ppm
│ │ ├── 00005_00001.ppm
│ │ ├── 00005_00002.ppm
│ │ ├── 00005_00003.ppm
│ │ ├── 00005_00004.ppm
│ │ ├── 00005_00005.ppm
│ │ ├── 00005_00006.ppm
│ │ ├── 00005_00007.ppm
│ │ ├── 00005_00008.ppm
│ │ ├── 00005_00009.ppm
│ │ ├── 00005_00010.ppm
│ │ ├── 00005_00011.ppm
│ │ ├── 00005_00012.ppm
│ │ ├── 00005_00013.ppm
│ │ ├── 00005_00014.ppm
│ │ ├── 00005_00015.ppm
│ │ ├── 00005_00016.ppm
│ │ ├── 00005_00017.ppm
│ │ ├── 00005_00018.ppm
│ │ ├── 00005_00019.ppm
│ │ ├── 00005_00020.ppm
│ │ ├── 00005_00021.ppm
│ │ ├── 00005_00022.ppm
│ │ ├── 00005_00023.ppm
│ │ ├── 00005_00024.ppm
│ │ ├── 00005_00025.ppm
│ │ ├── 00005_00026.ppm
│ │ ├── 00005_00027.ppm
│ │ ├── 00005_00028.ppm
│ │ ├── 00005_00029.ppm
│ │ ├── 00006_00000.ppm
│ │ ├── 00006_00001.ppm
│ │ ├── 00006_00002.ppm
│ │ ├── 00006_00003.ppm
│ │ ├── 00006_00004.ppm
│ │ ├── 00006_00005.ppm
│ │ ├── 00006_00006.ppm
│ │ ├── 00006_00007.ppm
│ │ ├── 00006_00008.ppm
│ │ ├── 00006_00009.ppm
│ │ ├── 00006_00010.ppm
│ │ ├── 00006_00011.ppm
│ │ ├── 00006_00012.ppm
│ │ ├── 00006_00013.ppm
│ │ ├── 00006_00014.ppm
│ │ ├── 00006_00015.ppm
│ │ ├── 00006_00016.ppm
│ │ ├── 00006_00017.ppm
│ │ ├── 00006_00018.ppm
│ │ ├── 00006_00019.ppm
│ │ ├── 00006_00020.ppm
│ │ ├── 00006_00021.ppm
│ │ ├── 00006_00022.ppm
│ │ ├── 00006_00023.ppm
│ │ ├── 00006_00024.ppm
│ │ ├── 00006_00025.ppm
│ │ ├── 00006_00026.ppm
│ │ ├── 00006_00027.ppm
│ │ ├── 00006_00028.ppm
│ │ ├── 00006_00029.ppm
│ │ ├── 00007_00000.ppm
│ │ ├── 00007_00001.ppm
│ │ ├── 00007_00002.ppm
│ │ ├── 00007_00003.ppm
│ │ ├── 00007_00004.ppm
│ │ ├── 00007_00005.ppm
│ │ ├── 00007_00006.ppm
│ │ ├── 00007_00007.ppm
│ │ ├── 00007_00008.ppm
│ │ ├── 00007_00009.ppm
│ │ ├── 00007_00010.ppm
│ │ ├── 00007_00011.ppm
│ │ ├── 00007_00012.ppm
│ │ ├── 00007_00013.ppm
│ │ ├── 00007_00014.ppm
│ │ ├── 00007_00015.ppm
│ │ ├── 00007_00016.ppm
│ │ ├── 00007_00017.ppm
│ │ ├── 00007_00018.ppm
│ │ ├── 00007_00019.ppm
│ │ ├── 00007_00020.ppm
│ │ ├── 00007_00021.ppm
│ │ ├── 00007_00022.ppm
│ │ ├── 00007_00023.ppm
│ │ ├── 00007_00024.ppm
│ │ ├── 00007_00025.ppm
│ │ ├── 00007_00026.ppm
│ │ ├── 00007_00027.ppm
│ │ ├── 00007_00028.ppm
│ │ ├── 00007_00029.ppm
│ │ ├── 00008_00000.ppm
│ │ ├── 00008_00001.ppm
│ │ ├── 00008_00002.ppm
│ │ ├── 00008_00003.ppm
│ │ ├── 00008_00004.ppm
│ │ ├── 00008_00005.ppm
│ │ ├── 00008_00006.ppm
│ │ ├── 00008_00007.ppm
│ │ ├── 00008_00008.ppm
│ │ ├── 00008_00009.ppm
│ │ ├── 00008_00010.ppm
│ │ ├── 00008_00011.ppm
│ │ ├── 00008_00012.ppm
│ │ ├── 00008_00013.ppm
│ │ ├── 00008_00014.ppm
│ │ ├── 00008_00015.ppm
│ │ ├── 00008_00016.ppm
│ │ ├── 00008_00017.ppm
│ │ ├── 00008_00018.ppm
│ │ ├── 00008_00019.ppm
│ │ ├── 00008_00020.ppm
│ │ ├── 00008_00021.ppm
│ │ ├── 00008_00022.ppm
│ │ ├── 00008_00023.ppm
│ │ ├── 00008_00024.ppm
│ │ ├── 00008_00025.ppm
│ │ ├── 00008_00026.ppm
│ │ ├── 00008_00027.ppm
│ │ ├── 00008_00028.ppm
│ │ ├── 00008_00029.ppm
│ │ ├── 00009_00000.ppm
│ │ ├── 00009_00001.ppm
│ │ ├── 00009_00002.ppm
│ │ ├── 00009_00003.ppm
│ │ ├── 00009_00004.ppm
│ │ ├── 00009_00005.ppm
│ │ ├── 00009_00006.ppm
│ │ ├── 00009_00007.ppm
│ │ ├── 00009_00008.ppm
│ │ ├── 00009_00009.ppm
│ │ ├── 00009_00010.ppm
│ │ ├── 00009_00011.ppm
│ │ ├── 00009_00012.ppm
│ │ ├── 00009_00013.ppm
│ │ ├── 00009_00014.ppm
│ │ ├── 00009_00015.ppm
│ │ ├── 00009_00016.ppm
│ │ ├── 00009_00017.ppm
│ │ ├── 00009_00018.ppm
│ │ ├── 00009_00019.ppm
│ │ ├── 00009_00020.ppm
│ │ ├── 00009_00021.ppm
│ │ ├── 00009_00022.ppm
│ │ ├── 00009_00023.ppm
│ │ ├── 00009_00024.ppm
│ │ ├── 00009_00025.ppm
│ │ ├── 00009_00026.ppm
│ │ ├── 00009_00027.ppm
│ │ ├── 00009_00028.ppm
│ │ ├── 00009_00029.ppm
│ │ ├── 00010_00000.ppm
│ │ ├── 00010_00001.ppm
│ │ ├── 00010_00002.ppm
│ │ ├── 00010_00003.ppm
│ │ ├── 00010_00004.ppm
│ │ ├── 00010_00005.ppm
│ │ ├── 00010_00006.ppm
│ │ ├── 00010_00007.ppm
│ │ ├── 00010_00008.ppm
│ │ ├── 00010_00009.ppm
│ │ ├── 00010_00010.ppm
│ │ ├── 00010_00011.ppm
│ │ ├── 00010_00012.ppm
│ │ ├── 00010_00013.ppm
│ │ ├── 00010_00014.ppm
│ │ ├── 00010_00015.ppm
│ │ ├── 00010_00016.ppm
│ │ ├── 00010_00017.ppm
│ │ ├── 00010_00018.ppm
│ │ ├── 00010_00019.ppm
│ │ ├── 00010_00020.ppm
│ │ ├── 00010_00021.ppm
│ │ ├── 00010_00022.ppm
│ │ ├── 00010_00023.ppm
│ │ ├── 00010_00024.ppm
│ │ ├── 00010_00025.ppm
│ │ ├── 00010_00026.ppm
│ │ ├── 00010_00027.ppm
│ │ ├── 00010_00028.ppm
│ │ ├── 00010_00029.ppm
│ │ ├── 00011_00000.ppm
│ │ ├── 00011_00001.ppm
│ │ ├── 00011_00002.ppm
│ │ ├── 00011_00003.ppm
│ │ ├── 00011_00004.ppm
│ │ ├── 00011_00005.ppm
│ │ ├── 00011_00006.ppm
│ │ ├── 00011_00007.ppm
│ │ ├── 00011_00008.ppm
│ │ ├── 00011_00009.ppm
│ │ ├── 00011_00010.ppm
│ │ ├── 00011_00011.ppm
│ │ ├── 00011_00012.ppm
│ │ ├── 00011_00013.ppm
│ │ ├── 00011_00014.ppm
│ │ ├── 00011_00015.ppm
│ │ ├── 00011_00016.ppm
│ │ ├── 00011_00017.ppm
│ │ ├── 00011_00018.ppm
│ │ ├── 00011_00019.ppm
│ │ ├── 00011_00020.ppm
│ │ ├── 00011_00021.ppm
│ │ ├── 00011_00022.ppm
│ │ ├── 00011_00023.ppm
│ │ ├── 00011_00024.ppm
│ │ ├── 00011_00025.ppm
│ │ ├── 00011_00026.ppm
│ │ ├── 00011_00027.ppm
│ │ ├── 00011_00028.ppm
│ │ ├── 00011_00029.ppm
│ │ ├── 00012_00000.ppm
│ │ ├── 00012_00001.ppm
│ │ ├── 00012_00002.ppm
│ │ ├── 00012_00003.ppm
│ │ ├── 00012_00004.ppm
│ │ ├── 00012_00005.ppm
│ │ ├── 00012_00006.ppm
│ │ ├── 00012_00007.ppm
│ │ ├── 00012_00008.ppm
│ │ ├── 00012_00009.ppm
│ │ ├── 00012_00010.ppm
│ │ ├── 00012_00011.ppm
│ │ ├── 00012_00012.ppm
│ │ ├── 00012_00013.ppm
│ │ ├── 00012_00014.ppm
│ │ ├── 00012_00015.ppm
│ │ ├── 00012_00016.ppm
│ │ ├── 00012_00017.ppm
│ │ ├── 00012_00018.ppm
│ │ ├── 00012_00019.ppm
│ │ ├── 00012_00020.ppm
│ │ ├── 00012_00021.ppm
│ │ ├── 00012_00022.ppm
│ │ ├── 00012_00023.ppm
│ │ ├── 00012_00024.ppm
│ │ ├── 00012_00025.ppm
│ │ ├── 00012_00026.ppm
│ │ ├── 00012_00027.ppm
│ │ ├── 00012_00028.ppm
│ │ ├── 00012_00029.ppm
│ │ ├── 00013_00000.ppm
│ │ ├── 00013_00001.ppm
│ │ ├── 00013_00002.ppm
│ │ ├── 00013_00003.ppm
│ │ ├── 00013_00004.ppm
│ │ ├── 00013_00005.ppm
│ │ ├── 00013_00006.ppm
│ │ ├── 00013_00007.ppm
│ │ ├── 00013_00008.ppm
│ │ ├── 00013_00009.ppm
│ │ ├── 00013_00010.ppm
│ │ ├── 00013_00011.ppm
│ │ ├── 00013_00012.ppm
│ │ ├── 00013_00013.ppm
│ │ ├── 00013_00014.ppm
│ │ ├── 00013_00015.ppm
│ │ ├── 00013_00016.ppm
│ │ ├── 00013_00017.ppm
│ │ ├── 00013_00018.ppm
│ │ ├── 00013_00019.ppm
│ │ ├── 00013_00020.ppm
│ │ ├── 00013_00021.ppm
│ │ ├── 00013_00022.ppm
│ │ ├── 00013_00023.ppm
│ │ ├── 00013_00024.ppm
│ │ ├── 00013_00025.ppm
│ │ ├── 00013_00026.ppm
│ │ ├── 00013_00027.ppm
│ │ ├── 00013_00028.ppm
│ │ ├── 00013_00029.ppm
│ │ ├── 00014_00000.ppm
│ │ ├── 00014_00001.ppm
│ │ ├── 00014_00002.ppm
│ │ ├── 00014_00003.ppm
│ │ ├── 00014_00004.ppm
│ │ ├── 00014_00005.ppm
│ │ ├── 00014_00006.ppm
│ │ ├── 00014_00007.ppm
│ │ ├── 00014_00008.ppm
│ │ ├── 00014_00009.ppm
│ │ ├── 00014_00010.ppm
│ │ ├── 00014_00011.ppm
│ │ ├── 00014_00012.ppm
│ │ ├── 00014_00013.ppm
│ │ ├── 00014_00014.ppm
│ │ ├── 00014_00015.ppm
│ │ ├── 00014_00016.ppm
│ │ ├── 00014_00017.ppm
│ │ ├── 00014_00018.ppm
│ │ ├── 00014_00019.ppm
│ │ ├── 00014_00020.ppm
│ │ ├── 00014_00021.ppm
│ │ ├── 00014_00022.ppm
│ │ ├── 00014_00023.ppm
│ │ ├── 00014_00024.ppm
│ │ ├── 00014_00025.ppm
│ │ ├── 00014_00026.ppm
│ │ ├── 00014_00027.ppm
│ │ ├── 00014_00028.ppm
│ │ ├── 00014_00029.ppm
│ │ ├── 00015_00000.ppm
│ │ ├── 00015_00001.ppm
│ │ ├── 00015_00002.ppm
│ │ ├── 00015_00003.ppm
│ │ ├── 00015_00004.ppm
│ │ ├── 00015_00005.ppm
│ │ ├── 00015_00006.ppm
│ │ ├── 00015_00007.ppm
│ │ ├── 00015_00008.ppm
│ │ ├── 00015_00009.ppm
│ │ ├── 00015_00010.ppm
│ │ ├── 00015_00011.ppm
│ │ ├── 00015_00012.ppm
│ │ ├── 00015_00013.ppm
│ │ ├── 00015_00014.ppm
│ │ ├── 00015_00015.ppm
│ │ ├── 00015_00016.ppm
│ │ ├── 00015_00017.ppm
│ │ ├── 00015_00018.ppm
│ │ ├── 00015_00019.ppm
│ │ ├── 00015_00020.ppm
│ │ ├── 00015_00021.ppm
│ │ ├── 00015_00022.ppm
│ │ ├── 00015_00023.ppm
│ │ ├── 00015_00024.ppm
│ │ ├── 00015_00025.ppm
│ │ ├── 00015_00026.ppm
│ │ ├── 00015_00027.ppm
│ │ ├── 00015_00028.ppm
│ │ ├── 00015_00029.ppm
│ │ ├── 00016_00000.ppm
│ │ ├── 00016_00001.ppm
│ │ ├── 00016_00002.ppm
│ │ ├── 00016_00003.ppm
│ │ ├── 00016_00004.ppm
│ │ ├── 00016_00005.ppm
│ │ ├── 00016_00006.ppm
│ │ ├── 00016_00007.ppm
│ │ ├── 00016_00008.ppm
│ │ ├── 00016_00009.ppm
│ │ ├── 00016_00010.ppm
│ │ ├── 00016_00011.ppm
│ │ ├── 00016_00012.ppm
│ │ ├── 00016_00013.ppm
│ │ ├── 00016_00014.ppm
│ │ ├── 00016_00015.ppm
│ │ ├── 00016_00016.ppm
│ │ ├── 00016_00017.ppm
│ │ ├── 00016_00018.ppm
│ │ ├── 00016_00019.ppm
│ │ ├── 00016_00020.ppm
│ │ ├── 00016_00021.ppm
│ │ ├── 00016_00022.ppm
│ │ ├── 00016_00023.ppm
│ │ ├── 00016_00024.ppm
│ │ ├── 00016_00025.ppm
│ │ ├── 00016_00026.ppm
│ │ ├── 00016_00027.ppm
│ │ ├── 00016_00028.ppm
│ │ ├── 00016_00029.ppm
│ │ ├── 00017_00000.ppm
│ │ ├── 00017_00001.ppm
│ │ ├── 00017_00002.ppm
│ │ ├── 00017_00003.ppm
│ │ ├── 00017_00004.ppm
│ │ ├── 00017_00005.ppm
│ │ ├── 00017_00006.ppm
│ │ ├── 00017_00007.ppm
│ │ ├── 00017_00008.ppm
│ │ ├── 00017_00009.ppm
│ │ ├── 00017_00010.ppm
│ │ ├── 00017_00011.ppm
│ │ ├── 00017_00012.ppm
│ │ ├── 00017_00013.ppm
│ │ ├── 00017_00014.ppm
│ │ ├── 00017_00015.ppm
│ │ ├── 00017_00016.ppm
│ │ ├── 00017_00017.ppm
│ │ ├── 00017_00018.ppm
│ │ ├── 00017_00019.ppm
│ │ ├── 00017_00020.ppm
│ │ ├── 00017_00021.ppm
│ │ ├── 00017_00022.ppm
│ │ ├── 00017_00023.ppm
│ │ ├── 00017_00024.ppm
│ │ ├── 00017_00025.ppm
│ │ ├── 00017_00026.ppm
│ │ ├── 00017_00027.ppm
│ │ ├── 00017_00028.ppm
│ │ ├── 00017_00029.ppm
│ │ ├── 00018_00000.ppm
│ │ ├── 00018_00001.ppm
│ │ ├── 00018_00002.ppm
│ │ ├── 00018_00003.ppm
│ │ ├── 00018_00004.ppm
│ │ ├── 00018_00005.ppm
│ │ ├── 00018_00006.ppm
│ │ ├── 00018_00007.ppm
│ │ ├── 00018_00008.ppm
│ │ ├── 00018_00009.ppm
│ │ ├── 00018_00010.ppm
│ │ ├── 00018_00011.ppm
│ │ ├── 00018_00012.ppm
│ │ ├── 00018_00013.ppm
│ │ ├── 00018_00014.ppm
│ │ ├── 00018_00015.ppm
│ │ ├── 00018_00016.ppm
│ │ ├── 00018_00017.ppm
│ │ ├── 00018_00018.ppm
│ │ ├── 00018_00019.ppm
│ │ ├── 00018_00020.ppm
│ │ ├── 00018_00021.ppm
│ │ ├── 00018_00022.ppm
│ │ ├── 00018_00023.ppm
│ │ ├── 00018_00024.ppm
│ │ ├── 00018_00025.ppm
│ │ ├── 00018_00026.ppm
│ │ ├── 00018_00027.ppm
│ │ ├── 00018_00028.ppm
│ │ ├── 00018_00029.ppm
│ │ ├── 00019_00000.ppm
│ │ ├── 00019_00001.ppm
│ │ ├── 00019_00002.ppm
│ │ ├── 00019_00003.ppm
│ │ ├── 00019_00004.ppm
│ │ ├── 00019_00005.ppm
│ │ ├── 00019_00006.ppm
│ │ ├── 00019_00007.ppm
│ │ ├── 00019_00008.ppm
│ │ ├── 00019_00009.ppm
│ │ ├── 00019_00010.ppm
│ │ ├── 00019_00011.ppm
│ │ ├── 00019_00012.ppm
│ │ ├── 00019_00013.ppm
│ │ ├── 00019_00014.ppm
│ │ ├── 00019_00015.ppm
│ │ ├── 00019_00016.ppm
│ │ ├── 00019_00017.ppm
│ │ ├── 00019_00018.ppm
│ │ ├── 00019_00019.ppm
│ │ ├── 00019_00020.ppm
│ │ ├── 00019_00021.ppm
│ │ ├── 00019_00022.ppm
│ │ ├── 00019_00023.ppm
│ │ ├── 00019_00024.ppm
│ │ ├── 00019_00025.ppm
│ │ ├── 00019_00026.ppm
│ │ ├── 00019_00027.ppm
│ │ ├── 00019_00028.ppm
│ │ ├── 00019_00029.ppm
│ │ ├── 00020_00000.ppm
│ │ ├── 00020_00001.ppm
│ │ ├── 00020_00002.ppm
│ │ ├── 00020_00003.ppm
│ │ ├── 00020_00004.ppm
│ │ ├── 00020_00005.ppm
│ │ ├── 00020_00006.ppm
│ │ ├── 00020_00007.ppm
│ │ ├── 00020_00008.ppm
│ │ ├── 00020_00009.ppm
│ │ ├── 00020_00010.ppm
│ │ ├── 00020_00011.ppm
│ │ ├── 00020_00012.ppm
│ │ ├── 00020_00013.ppm
│ │ ├── 00020_00014.ppm
│ │ ├── 00020_00015.ppm
│ │ ├── 00020_00016.ppm
│ │ ├── 00020_00017.ppm
│ │ ├── 00020_00018.ppm
│ │ ├── 00020_00019.ppm
│ │ ├── 00020_00020.ppm
│ │ ├── 00020_00021.ppm
│ │ ├── 00020_00022.ppm
│ │ ├── 00020_00023.ppm
│ │ ├── 00020_00024.ppm
│ │ ├── 00020_00025.ppm
│ │ ├── 00020_00026.ppm
│ │ ├── 00020_00027.ppm
│ │ ├── 00020_00028.ppm
│ │ ├── 00020_00029.ppm
│ │ ├── 00021_00000.ppm
│ │ ├── 00021_00001.ppm
│ │ ├── 00021_00002.ppm
│ │ ├── 00021_00003.ppm
│ │ ├── 00021_00004.ppm
│ │ ├── 00021_00005.ppm
│ │ ├── 00021_00006.ppm
│ │ ├── 00021_00007.ppm
│ │ ├── 00021_00008.ppm
│ │ ├── 00021_00009.ppm
│ │ ├── 00021_00010.ppm
│ │ ├── 00021_00011.ppm
│ │ ├── 00021_00012.ppm
│ │ ├── 00021_00013.ppm
│ │ ├── 00021_00014.ppm
│ │ ├── 00021_00015.ppm
│ │ ├── 00021_00016.ppm
│ │ ├── 00021_00017.ppm
│ │ ├── 00021_00018.ppm
│ │ ├── 00021_00019.ppm
│ │ ├── 00021_00020.ppm
│ │ ├── 00021_00021.ppm
│ │ ├── 00021_00022.ppm
│ │ ├── 00021_00023.ppm
│ │ ├── 00021_00024.ppm
│ │ ├── 00021_00025.ppm
│ │ ├── 00021_00026.ppm
│ │ ├── 00021_00027.ppm
│ │ ├── 00021_00028.ppm
│ │ ├── 00021_00029.ppm
│ │ ├── 00022_00000.ppm
│ │ ├── 00022_00001.ppm
│ │ ├── 00022_00002.ppm
│ │ ├── 00022_00003.ppm
│ │ ├── 00022_00004.ppm
│ │ ├── 00022_00005.ppm
│ │ ├── 00022_00006.ppm
│ │ ├── 00022_00007.ppm
│ │ ├── 00022_00008.ppm
│ │ ├── 00022_00009.ppm
│ │ ├── 00022_00010.ppm
│ │ ├── 00022_00011.ppm
│ │ ├── 00022_00012.ppm
│ │ ├── 00022_00013.ppm
│ │ ├── 00022_00014.ppm
│ │ ├── 00022_00015.ppm
│ │ ├── 00022_00016.ppm
│ │ ├── 00022_00017.ppm
│ │ ├── 00022_00018.ppm
│ │ ├── 00022_00019.ppm
│ │ ├── 00022_00020.ppm
│ │ ├── 00022_00021.ppm
│ │ ├── 00022_00022.ppm
│ │ ├── 00022_00023.ppm
│ │ ├── 00022_00024.ppm
│ │ ├── 00022_00025.ppm
│ │ ├── 00022_00026.ppm
│ │ ├── 00022_00027.ppm
│ │ ├── 00022_00028.ppm
│ │ ├── 00022_00029.ppm
│ │ ├── 00023_00000.ppm
│ │ ├── 00023_00001.ppm
│ │ ├── 00023_00002.ppm
│ │ ├── 00023_00003.ppm
│ │ ├── 00023_00004.ppm
│ │ ├── 00023_00005.ppm
│ │ ├── 00023_00006.ppm
│ │ ├── 00023_00007.ppm
│ │ ├── 00023_00008.ppm
│ │ ├── 00023_00009.ppm
│ │ ├── 00023_00010.ppm
│ │ ├── 00023_00011.ppm
│ │ ├── 00023_00012.ppm
│ │ ├── 00023_00013.ppm
│ │ ├── 00023_00014.ppm
│ │ ├── 00023_00015.ppm
│ │ ├── 00023_00016.ppm
│ │ ├── 00023_00017.ppm
│ │ ├── 00023_00018.ppm
│ │ ├── 00023_00019.ppm
│ │ ├── 00023_00020.ppm
│ │ ├── 00023_00021.ppm
│ │ ├── 00023_00022.ppm
│ │ ├── 00023_00023.ppm
│ │ ├── 00023_00024.ppm
│ │ ├── 00023_00025.ppm
│ │ ├── 00023_00026.ppm
│ │ ├── 00023_00027.ppm
│ │ ├── 00023_00028.ppm
│ │ ├── 00023_00029.ppm
│ │ ├── 00024_00000.ppm
│ │ ├── 00024_00001.ppm
│ │ ├── 00024_00002.ppm
│ │ ├── 00024_00003.ppm
│ │ ├── 00024_00004.ppm
│ │ ├── 00024_00005.ppm
│ │ ├── 00024_00006.ppm
│ │ ├── 00024_00007.ppm
│ │ ├── 00024_00008.ppm
│ │ ├── 00024_00009.ppm
│ │ ├── 00024_00010.ppm
│ │ ├── 00024_00011.ppm
│ │ ├── 00024_00012.ppm
│ │ ├── 00024_00013.ppm
│ │ ├── 00024_00014.ppm
│ │ ├── 00024_00015.ppm
│ │ ├── 00024_00016.ppm
│ │ ├── 00024_00017.ppm
│ │ ├── 00024_00018.ppm
│ │ ├── 00024_00019.ppm
│ │ ├── 00024_00020.ppm
│ │ ├── 00024_00021.ppm
│ │ ├── 00024_00022.ppm
│ │ ├── 00024_00023.ppm
│ │ ├── 00024_00024.ppm
│ │ ├── 00024_00025.ppm
│ │ ├── 00024_00026.ppm
│ │ ├── 00024_00027.ppm
│ │ ├── 00024_00028.ppm
│ │ ├── 00024_00029.ppm
│ │ ├── 00025_00000.ppm
│ │ ├── 00025_00001.ppm
│ │ ├── 00025_00002.ppm
│ │ ├── 00025_00003.ppm
│ │ ├── 00025_00004.ppm
│ │ ├── 00025_00005.ppm
│ │ ├── 00025_00006.ppm
│ │ ├── 00025_00007.ppm
│ │ ├── 00025_00008.ppm
│ │ ├── 00025_00009.ppm
│ │ ├── 00025_00010.ppm
│ │ ├── 00025_00011.ppm
│ │ ├── 00025_00012.ppm
│ │ ├── 00025_00013.ppm
│ │ ├── 00025_00014.ppm
│ │ ├── 00025_00015.ppm
│ │ ├── 00025_00016.ppm
│ │ ├── 00025_00017.ppm
│ │ ├── 00025_00018.ppm
│ │ ├── 00025_00019.ppm
│ │ ├── 00025_00020.ppm
│ │ ├── 00025_00021.ppm
│ │ ├── 00025_00022.ppm
│ │ ├── 00025_00023.ppm
│ │ ├── 00025_00024.ppm
│ │ ├── 00025_00025.ppm
│ │ ├── 00025_00026.ppm
│ │ ├── 00025_00027.ppm
│ │ ├── 00025_00028.ppm
│ │ ├── 00025_00029.ppm
│ │ ├── 00026_00000.ppm
│ │ ├── 00026_00001.ppm
│ │ ├── 00026_00002.ppm
│ │ ├── 00026_00003.ppm
│ │ ├── 00026_00004.ppm
│ │ ├── 00026_00005.ppm
│ │ ├── 00026_00006.ppm
│ │ ├── 00026_00007.ppm
│ │ ├── 00026_00008.ppm
│ │ ├── 00026_00009.ppm
│ │ ├── 00026_00010.ppm
│ │ ├── 00026_00011.ppm
│ │ ├── 00026_00012.ppm
│ │ ├── 00026_00013.ppm
│ │ ├── 00026_00014.ppm
│ │ ├── 00026_00015.ppm
│ │ ├── 00026_00016.ppm
│ │ ├── 00026_00017.ppm
│ │ ├── 00026_00018.ppm
│ │ ├── 00026_00019.ppm
│ │ ├── 00026_00020.ppm
│ │ ├── 00026_00021.ppm
│ │ ├── 00026_00022.ppm
│ │ ├── 00026_00023.ppm
│ │ ├── 00026_00024.ppm
│ │ ├── 00026_00025.ppm
│ │ ├── 00026_00026.ppm
│ │ ├── 00026_00027.ppm
│ │ ├── 00026_00028.ppm
│ │ ├── 00026_00029.ppm
│ │ ├── 00027_00000.ppm
│ │ ├── 00027_00001.ppm
│ │ ├── 00027_00002.ppm
│ │ ├── 00027_00003.ppm
│ │ ├── 00027_00004.ppm
│ │ ├── 00027_00005.ppm
│ │ ├── 00027_00006.ppm
│ │ ├── 00027_00007.ppm
│ │ ├── 00027_00008.ppm
│ │ ├── 00027_00009.ppm
│ │ ├── 00027_00010.ppm
│ │ ├── 00027_00011.ppm
│ │ ├── 00027_00012.ppm
│ │ ├── 00027_00013.ppm
│ │ ├── 00027_00014.ppm
│ │ ├── 00027_00015.ppm
│ │ ├── 00027_00016.ppm
│ │ ├── 00027_00017.ppm
│ │ ├── 00027_00018.ppm
│ │ ├── 00027_00019.ppm
│ │ ├── 00027_00020.ppm
│ │ ├── 00027_00021.ppm
│ │ ├── 00027_00022.ppm
│ │ ├── 00027_00023.ppm
│ │ ├── 00027_00024.ppm
│ │ ├── 00027_00025.ppm
│ │ ├── 00027_00026.ppm
│ │ ├── 00027_00027.ppm
│ │ ├── 00027_00028.ppm
│ │ ├── 00027_00029.ppm
│ │ ├── 00028_00000.ppm
│ │ ├── 00028_00001.ppm
│ │ ├── 00028_00002.ppm
│ │ ├── 00028_00003.ppm
│ │ ├── 00028_00004.ppm
│ │ ├── 00028_00005.ppm
│ │ ├── 00028_00006.ppm
│ │ ├── 00028_00007.ppm
│ │ ├── 00028_00008.ppm
│ │ ├── 00028_00009.ppm
│ │ ├── 00028_00010.ppm
│ │ ├── 00028_00011.ppm
│ │ ├── 00028_00012.ppm
│ │ ├── 00028_00013.ppm
│ │ ├── 00028_00014.ppm
│ │ ├── 00028_00015.ppm
│ │ ├── 00028_00016.ppm
│ │ ├── 00028_00017.ppm
│ │ ├── 00028_00018.ppm
│ │ ├── 00028_00019.ppm
│ │ ├── 00028_00020.ppm
│ │ ├── 00028_00021.ppm
│ │ ├── 00028_00022.ppm
│ │ ├── 00028_00023.ppm
│ │ ├── 00028_00024.ppm
│ │ ├── 00028_00025.ppm
│ │ ├── 00028_00026.ppm
│ │ ├── 00028_00027.ppm
│ │ ├── 00028_00028.ppm
│ │ ├── 00028_00029.ppm
│ │ ├── 00029_00000.ppm
│ │ ├── 00029_00001.ppm
│ │ ├── 00029_00002.ppm
│ │ ├── 00029_00003.ppm
│ │ ├── 00029_00004.ppm
│ │ ├── 00029_00005.ppm
│ │ ├── 00029_00006.ppm
│ │ ├── 00029_00007.ppm
│ │ ├── 00029_00008.ppm
│ │ ├── 00029_00009.ppm
│ │ ├── 00029_00010.ppm
│ │ ├── 00029_00011.ppm
│ │ ├── 00029_00012.ppm
│ │ ├── 00029_00013.ppm
│ │ ├── 00029_00014.ppm
│ │ ├── 00029_00015.ppm
│ │ ├── 00029_00016.ppm
│ │ ├── 00029_00017.ppm
│ │ ├── 00029_00018.ppm
│ │ ├── 00029_00019.ppm
│ │ ├── 00029_00020.ppm
│ │ ├── 00029_00021.ppm
│ │ ├── 00029_00022.ppm
│ │ ├── 00029_00023.ppm
│ │ ├── 00029_00024.ppm
│ │ ├── 00029_00025.ppm
│ │ ├── 00029_00026.ppm
│ │ ├── 00029_00027.ppm
│ │ ├── 00029_00028.ppm
│ │ ├── 00029_00029.ppm
│ │ ├── 00030_00000.ppm
│ │ ├── 00030_00001.ppm
│ │ ├── 00030_00002.ppm
│ │ ├── 00030_00003.ppm
│ │ ├── 00030_00004.ppm
│ │ ├── 00030_00005.ppm
│ │ ├── 00030_00006.ppm
│ │ ├── 00030_00007.ppm
│ │ ├── 00030_00008.ppm
│ │ ├── 00030_00009.ppm
│ │ ├── 00030_00010.ppm
│ │ ├── 00030_00011.ppm
│ │ ├── 00030_00012.ppm
│ │ ├── 00030_00013.ppm
│ │ ├── 00030_00014.ppm
│ │ ├── 00030_00015.ppm
│ │ ├── 00030_00016.ppm
│ │ ├── 00030_00017.ppm
│ │ ├── 00030_00018.ppm
│ │ ├── 00030_00019.ppm
│ │ ├── 00030_00020.ppm
│ │ ├── 00030_00021.ppm
│ │ ├── 00030_00022.ppm
│ │ ├── 00030_00023.ppm
│ │ ├── 00030_00024.ppm
│ │ ├── 00030_00025.ppm
│ │ ├── 00030_00026.ppm
│ │ ├── 00030_00027.ppm
│ │ ├── 00030_00028.ppm
│ │ ├── 00030_00029.ppm
│ │ ├── 00031_00000.ppm
│ │ ├── 00031_00001.ppm
│ │ ├── 00031_00002.ppm
│ │ ├── 00031_00003.ppm
│ │ ├── 00031_00004.ppm
│ │ ├── 00031_00005.ppm
│ │ ├── 00031_00006.ppm
│ │ ├── 00031_00007.ppm
│ │ ├── 00031_00008.ppm
│ │ ├── 00031_00009.ppm
│ │ ├── 00031_00010.ppm
│ │ ├── 00031_00011.ppm
│ │ ├── 00031_00012.ppm
│ │ ├── 00031_00013.ppm
│ │ ├── 00031_00014.ppm
│ │ ├── 00031_00015.ppm
│ │ ├── 00031_00016.ppm
│ │ ├── 00031_00017.ppm
│ │ ├── 00031_00018.ppm
│ │ ├── 00031_00019.ppm
│ │ ├── 00031_00020.ppm
│ │ ├── 00031_00021.ppm
│ │ ├── 00031_00022.ppm
│ │ ├── 00031_00023.ppm
│ │ ├── 00031_00024.ppm
│ │ ├── 00031_00025.ppm
│ │ ├── 00031_00026.ppm
│ │ ├── 00031_00027.ppm
│ │ ├── 00031_00028.ppm
│ │ ├── 00031_00029.ppm
│ │ ├── 00032_00000.ppm
│ │ ├── 00032_00001.ppm
│ │ ├── 00032_00002.ppm
│ │ ├── 00032_00003.ppm
│ │ ├── 00032_00004.ppm
│ │ ├── 00032_00005.ppm
│ │ ├── 00032_00006.ppm
│ │ ├── 00032_00007.ppm
│ │ ├── 00032_00008.ppm
│ │ ├── 00032_00009.ppm
│ │ ├── 00032_00010.ppm
│ │ ├── 00032_00011.ppm
│ │ ├── 00032_00012.ppm
│ │ ├── 00032_00013.ppm
│ │ ├── 00032_00014.ppm
│ │ ├── 00032_00015.ppm
│ │ ├── 00032_00016.ppm
│ │ ├── 00032_00017.ppm
│ │ ├── 00032_00018.ppm
│ │ ├── 00032_00019.ppm
│ │ ├── 00032_00020.ppm
│ │ ├── 00032_00021.ppm
│ │ ├── 00032_00022.ppm
│ │ ├── 00032_00023.ppm
│ │ ├── 00032_00024.ppm
│ │ ├── 00032_00025.ppm
│ │ ├── 00032_00026.ppm
│ │ ├── 00032_00027.ppm
│ │ ├── 00032_00028.ppm
│ │ ├── 00032_00029.ppm
│ │ ├── 00033_00000.ppm
│ │ ├── 00033_00001.ppm
│ │ ├── 00033_00002.ppm
│ │ ├── 00033_00003.ppm
│ │ ├── 00033_00004.ppm
│ │ ├── 00033_00005.ppm
│ │ ├── 00033_00006.ppm
│ │ ├── 00033_00007.ppm
│ │ ├── 00033_00008.ppm
│ │ ├── 00033_00009.ppm
│ │ ├── 00033_00010.ppm
│ │ ├── 00033_00011.ppm
│ │ ├── 00033_00012.ppm
│ │ ├── 00033_00013.ppm
│ │ ├── 00033_00014.ppm
│ │ ├── 00033_00015.ppm
│ │ ├── 00033_00016.ppm
│ │ ├── 00033_00017.ppm
│ │ ├── 00033_00018.ppm
│ │ ├── 00033_00019.ppm
│ │ ├── 00033_00020.ppm
│ │ ├── 00033_00021.ppm
│ │ ├── 00033_00022.ppm
│ │ ├── 00033_00023.ppm
│ │ ├── 00033_00024.ppm
│ │ ├── 00033_00025.ppm
│ │ ├── 00033_00026.ppm
│ │ ├── 00033_00027.ppm
│ │ ├── 00033_00028.ppm
│ │ ├── 00033_00029.ppm
│ │ ├── 00034_00000.ppm
│ │ ├── 00034_00001.ppm
│ │ ├── 00034_00002.ppm
│ │ ├── 00034_00003.ppm
│ │ ├── 00034_00004.ppm
│ │ ├── 00034_00005.ppm
│ │ ├── 00034_00006.ppm
│ │ ├── 00034_00007.ppm
│ │ ├── 00034_00008.ppm
│ │ ├── 00034_00009.ppm
│ │ ├── 00034_00010.ppm
│ │ ├── 00034_00011.ppm
│ │ ├── 00034_00012.ppm
│ │ ├── 00034_00013.ppm
│ │ ├── 00034_00014.ppm
│ │ ├── 00034_00015.ppm
│ │ ├── 00034_00016.ppm
│ │ ├── 00034_00017.ppm
│ │ ├── 00034_00018.ppm
│ │ ├── 00034_00019.ppm
│ │ ├── 00034_00020.ppm
│ │ ├── 00034_00021.ppm
│ │ ├── 00034_00022.ppm
│ │ ├── 00034_00023.ppm
│ │ ├── 00034_00024.ppm
│ │ ├── 00034_00025.ppm
│ │ ├── 00034_00026.ppm
│ │ ├── 00034_00027.ppm
│ │ ├── 00034_00028.ppm
│ │ ├── 00034_00029.ppm
│ │ ├── 00035_00000.ppm
│ │ ├── 00035_00001.ppm
│ │ ├── 00035_00002.ppm
│ │ ├── 00035_00003.ppm
│ │ ├── 00035_00004.ppm
│ │ ├── 00035_00005.ppm
│ │ ├── 00035_00006.ppm
│ │ ├── 00035_00007.ppm
│ │ ├── 00035_00008.ppm
│ │ ├── 00035_00009.ppm
│ │ ├── 00035_00010.ppm
│ │ ├── 00035_00011.ppm
│ │ ├── 00035_00012.ppm
│ │ ├── 00035_00013.ppm
│ │ ├── 00035_00014.ppm
│ │ ├── 00035_00015.ppm
│ │ ├── 00035_00016.ppm
│ │ ├── 00035_00017.ppm
│ │ ├── 00035_00018.ppm
│ │ ├── 00035_00019.ppm
│ │ ├── 00035_00020.ppm
│ │ ├── 00035_00021.ppm
│ │ ├── 00035_00022.ppm
│ │ ├── 00035_00023.ppm
│ │ ├── 00035_00024.ppm
│ │ ├── 00035_00025.ppm
│ │ ├── 00035_00026.ppm
│ │ ├── 00035_00027.ppm
│ │ ├── 00035_00028.ppm
│ │ ├── 00035_00029.ppm
│ │ ├── 00036_00000.ppm
│ │ ├── 00036_00001.ppm
│ │ ├── 00036_00002.ppm
│ │ ├── 00036_00003.ppm
│ │ ├── 00036_00004.ppm
│ │ ├── 00036_00005.ppm
│ │ ├── 00036_00006.ppm
│ │ ├── 00036_00007.ppm
│ │ ├── 00036_00008.ppm
│ │ ├── 00036_00009.ppm
│ │ ├── 00036_00010.ppm
│ │ ├── 00036_00011.ppm
│ │ ├── 00036_00012.ppm
│ │ ├── 00036_00013.ppm
│ │ ├── 00036_00014.ppm
│ │ ├── 00036_00015.ppm
│ │ ├── 00036_00016.ppm
│ │ ├── 00036_00017.ppm
│ │ ├── 00036_00018.ppm
│ │ ├── 00036_00019.ppm
│ │ ├── 00036_00020.ppm
│ │ ├── 00036_00021.ppm
│ │ ├── 00036_00022.ppm
│ │ ├── 00036_00023.ppm
│ │ ├── 00036_00024.ppm
│ │ ├── 00036_00025.ppm
│ │ ├── 00036_00026.ppm
│ │ ├── 00036_00027.ppm
│ │ ├── 00036_00028.ppm
│ │ ├── 00036_00029.ppm
│ │ ├── 00037_00000.ppm
│ │ ├── 00037_00001.ppm
│ │ ├── 00037_00002.ppm
│ │ ├── 00037_00003.ppm
│ │ ├── 00037_00004.ppm
│ │ ├── 00037_00005.ppm
│ │ ├── 00037_00006.ppm
│ │ ├── 00037_00007.ppm
│ │ ├── 00037_00008.ppm
│ │ ├── 00037_00009.ppm
│ │ ├── 00037_00010.ppm
│ │ ├── 00037_00011.ppm
│ │ ├── 00037_00012.ppm
│ │ ├── 00037_00013.ppm
│ │ ├── 00037_00014.ppm
│ │ ├── 00037_00015.ppm
│ │ ├── 00037_00016.ppm
│ │ ├── 00037_00017.ppm
│ │ ├── 00037_00018.ppm
│ │ ├── 00037_00019.ppm
│ │ ├── 00037_00020.ppm
│ │ ├── 00037_00021.ppm
│ │ ├── 00037_00022.ppm
│ │ ├── 00037_00023.ppm
│ │ ├── 00037_00024.ppm
│ │ ├── 00037_00025.ppm
│ │ ├── 00037_00026.ppm
│ │ ├── 00037_00027.ppm
│ │ ├── 00037_00028.ppm
│ │ ├── 00037_00029.ppm
│ │ ├── 00038_00000.ppm
│ │ ├── 00038_00001.ppm
│ │ ├── 00038_00002.ppm
│ │ ├── 00038_00003.ppm
│ │ ├── 00038_00004.ppm
│ │ ├── 00038_00005.ppm
│ │ ├── 00038_00006.ppm
│ │ ├── 00038_00007.ppm
│ │ ├── 00038_00008.ppm
│ │ ├── 00038_00009.ppm
│ │ ├── 00038_00010.ppm
│ │ ├── 00038_00011.ppm
│ │ ├── 00038_00012.ppm
│ │ ├── 00038_00013.ppm
│ │ ├── 00038_00014.ppm
│ │ ├── 00038_00015.ppm
│ │ ├── 00038_00016.ppm
│ │ ├── 00038_00017.ppm
│ │ ├── 00038_00018.ppm
│ │ ├── 00038_00019.ppm
│ │ ├── 00038_00020.ppm
│ │ ├── 00038_00021.ppm
│ │ ├── 00038_00022.ppm
│ │ ├── 00038_00023.ppm
│ │ ├── 00038_00024.ppm
│ │ ├── 00038_00025.ppm
│ │ ├── 00038_00026.ppm
│ │ ├── 00038_00027.ppm
│ │ ├── 00038_00028.ppm
│ │ ├── 00038_00029.ppm
│ │ ├── 00039_00000.ppm
│ │ ├── 00039_00001.ppm
│ │ ├── 00039_00002.ppm
│ │ ├── 00039_00003.ppm
│ │ ├── 00039_00004.ppm
│ │ ├── 00039_00005.ppm
│ │ ├── 00039_00006.ppm
│ │ ├── 00039_00007.ppm
│ │ ├── 00039_00008.ppm
│ │ ├── 00039_00009.ppm
│ │ ├── 00039_00010.ppm
│ │ ├── 00039_00011.ppm
│ │ ├── 00039_00012.ppm
│ │ ├── 00039_00013.ppm
│ │ ├── 00039_00014.ppm
│ │ ├── 00039_00015.ppm
│ │ ├── 00039_00016.ppm
│ │ ├── 00039_00017.ppm
│ │ ├── 00039_00018.ppm
│ │ ├── 00039_00019.ppm
│ │ ├── 00039_00020.ppm
│ │ ├── 00039_00021.ppm
│ │ ├── 00039_00022.ppm
│ │ ├── 00039_00023.ppm
│ │ ├── 00039_00024.ppm
│ │ ├── 00039_00025.ppm
│ │ ├── 00039_00026.ppm
│ │ ├── 00039_00027.ppm
│ │ ├── 00039_00028.ppm
│ │ ├── 00039_00029.ppm
│ │ ├── 00040_00000.ppm
│ │ ├── 00040_00001.ppm
│ │ ├── 00040_00002.ppm
│ │ ├── 00040_00003.ppm
│ │ ├── 00040_00004.ppm
│ │ ├── 00040_00005.ppm
│ │ ├── 00040_00006.ppm
│ │ ├── 00040_00007.ppm
│ │ ├── 00040_00008.ppm
│ │ ├── 00040_00009.ppm
│ │ ├── 00040_00010.ppm
│ │ ├── 00040_00011.ppm
│ │ ├── 00040_00012.ppm
│ │ ├── 00040_00013.ppm
│ │ ├── 00040_00014.ppm
│ │ ├── 00040_00015.ppm
│ │ ├── 00040_00016.ppm
│ │ ├── 00040_00017.ppm
│ │ ├── 00040_00018.ppm
│ │ ├── 00040_00019.ppm
│ │ ├── 00040_00020.ppm
│ │ ├── 00040_00021.ppm
│ │ ├── 00040_00022.ppm
│ │ ├── 00040_00023.ppm
│ │ ├── 00040_00024.ppm
│ │ ├── 00040_00025.ppm
│ │ ├── 00040_00026.ppm
│ │ ├── 00040_00027.ppm
│ │ ├── 00040_00028.ppm
│ │ ├── 00040_00029.ppm
│ │ ├── 00041_00000.ppm
│ │ ├── 00041_00001.ppm
│ │ ├── 00041_00002.ppm
│ │ ├── 00041_00003.ppm
│ │ ├── 00041_00004.ppm
│ │ ├── 00041_00005.ppm
│ │ ├── 00041_00006.ppm
│ │ ├── 00041_00007.ppm
│ │ ├── 00041_00008.ppm
│ │ ├── 00041_00009.ppm
│ │ ├── 00041_00010.ppm
│ │ ├── 00041_00011.ppm
│ │ ├── 00041_00012.ppm
│ │ ├── 00041_00013.ppm
│ │ ├── 00041_00014.ppm
│ │ ├── 00041_00015.ppm
│ │ ├── 00041_00016.ppm
│ │ ├── 00041_00017.ppm
│ │ ├── 00041_00018.ppm
│ │ ├── 00041_00019.ppm
│ │ ├── 00041_00020.ppm
│ │ ├── 00041_00021.ppm
│ │ ├── 00041_00022.ppm
│ │ ├── 00041_00023.ppm
│ │ ├── 00041_00024.ppm
│ │ ├── 00041_00025.ppm
│ │ ├── 00041_00026.ppm
│ │ ├── 00041_00027.ppm
│ │ ├── 00041_00028.ppm
│ │ ├── 00041_00029.ppm
│ │ ├── 00042_00000.ppm
│ │ ├── 00042_00001.ppm
│ │ ├── 00042_00002.ppm
│ │ ├── 00042_00003.ppm
│ │ ├── 00042_00004.ppm
│ │ ├── 00042_00005.ppm
│ │ ├── 00042_00006.ppm
│ │ ├── 00042_00007.ppm
│ │ ├── 00042_00008.ppm
│ │ ├── 00042_00009.ppm
│ │ ├── 00042_00010.ppm
│ │ ├── 00042_00011.ppm
│ │ ├── 00042_00012.ppm
│ │ ├── 00042_00013.ppm
│ │ ├── 00042_00014.ppm
│ │ ├── 00042_00015.ppm
│ │ ├── 00042_00016.ppm
│ │ ├── 00042_00017.ppm
│ │ ├── 00042_00018.ppm
│ │ ├── 00042_00019.ppm
│ │ ├── 00042_00020.ppm
│ │ ├── 00042_00021.ppm
│ │ ├── 00042_00022.ppm
│ │ ├── 00042_00023.ppm
│ │ ├── 00042_00024.ppm
│ │ ├── 00042_00025.ppm
│ │ ├── 00042_00026.ppm
│ │ ├── 00042_00027.ppm
│ │ ├── 00042_00028.ppm
│ │ ├── 00042_00029.ppm
│ │ ├── 00043_00000.ppm
│ │ ├── 00043_00001.ppm
│ │ ├── 00043_00002.ppm
│ │ ├── 00043_00003.ppm
│ │ ├── 00043_00004.ppm
│ │ ├── 00043_00005.ppm
│ │ ├── 00043_00006.ppm
│ │ ├── 00043_00007.ppm
│ │ ├── 00043_00008.ppm
│ │ ├── 00043_00009.ppm
│ │ ├── 00043_00010.ppm
│ │ ├── 00043_00011.ppm
│ │ ├── 00043_00012.ppm
│ │ ├── 00043_00013.ppm
│ │ ├── 00043_00014.ppm
│ │ ├── 00043_00015.ppm
│ │ ├── 00043_00016.ppm
│ │ ├── 00043_00017.ppm
│ │ ├── 00043_00018.ppm
│ │ ├── 00043_00019.ppm
│ │ ├── 00043_00020.ppm
│ │ ├── 00043_00021.ppm
│ │ ├── 00043_00022.ppm
│ │ ├── 00043_00023.ppm
│ │ ├── 00043_00024.ppm
│ │ ├── 00043_00025.ppm
│ │ ├── 00043_00026.ppm
│ │ ├── 00043_00027.ppm
│ │ ├── 00043_00028.ppm
│ │ ├── 00043_00029.ppm
│ │ ├── 00044_00000.ppm
│ │ ├── 00044_00001.ppm
│ │ ├── 00044_00002.ppm
│ │ ├── 00044_00003.ppm
│ │ ├── 00044_00004.ppm
│ │ ├── 00044_00005.ppm
│ │ ├── 00044_00006.ppm
│ │ ├── 00044_00007.ppm
│ │ ├── 00044_00008.ppm
│ │ ├── 00044_00009.ppm
│ │ ├── 00044_00010.ppm
│ │ ├── 00044_00011.ppm
│ │ ├── 00044_00012.ppm
│ │ ├── 00044_00013.ppm
│ │ ├── 00044_00014.ppm
│ │ ├── 00044_00015.ppm
│ │ ├── 00044_00016.ppm
│ │ ├── 00044_00017.ppm
│ │ ├── 00044_00018.ppm
│ │ ├── 00044_00019.ppm
│ │ ├── 00044_00020.ppm
│ │ ├── 00044_00021.ppm
│ │ ├── 00044_00022.ppm
│ │ ├── 00044_00023.ppm
│ │ ├── 00044_00024.ppm
│ │ ├── 00044_00025.ppm
│ │ ├── 00044_00026.ppm
│ │ ├── 00044_00027.ppm
│ │ ├── 00044_00028.ppm
│ │ ├── 00044_00029.ppm
│ │ ├── 00045_00000.ppm
│ │ ├── 00045_00001.ppm
│ │ ├── 00045_00002.ppm
│ │ ├── 00045_00003.ppm
│ │ ├── 00045_00004.ppm
│ │ ├── 00045_00005.ppm
│ │ ├── 00045_00006.ppm
│ │ ├── 00045_00007.ppm
│ │ ├── 00045_00008.ppm
│ │ ├── 00045_00009.ppm
│ │ ├── 00045_00010.ppm
│ │ ├── 00045_00011.ppm
│ │ ├── 00045_00012.ppm
│ │ ├── 00045_00013.ppm
│ │ ├── 00045_00014.ppm
│ │ ├── 00045_00015.ppm
│ │ ├── 00045_00016.ppm
│ │ ├── 00045_00017.ppm
│ │ ├── 00045_00018.ppm
│ │ ├── 00045_00019.ppm
│ │ ├── 00045_00020.ppm
│ │ ├── 00045_00021.ppm
│ │ ├── 00045_00022.ppm
│ │ ├── 00045_00023.ppm
│ │ ├── 00045_00024.ppm
│ │ ├── 00045_00025.ppm
│ │ ├── 00045_00026.ppm
│ │ ├── 00045_00027.ppm
│ │ ├── 00045_00028.ppm
│ │ ├── 00045_00029.ppm
│ │ ├── 00046_00000.ppm
│ │ ├── 00046_00001.ppm
│ │ ├── 00046_00002.ppm
│ │ ├── 00046_00003.ppm
│ │ ├── 00046_00004.ppm
│ │ ├── 00046_00005.ppm
│ │ ├── 00046_00006.ppm
│ │ ├── 00046_00007.ppm
│ │ ├── 00046_00008.ppm
│ │ ├── 00046_00009.ppm
│ │ ├── 00046_00010.ppm
│ │ ├── 00046_00011.ppm
│ │ ├── 00046_00012.ppm
│ │ ├── 00046_00013.ppm
│ │ ├── 00046_00014.ppm
│ │ ├── 00046_00015.ppm
│ │ ├── 00046_00016.ppm
│ │ ├── 00046_00017.ppm
│ │ ├── 00046_00018.ppm
│ │ ├── 00046_00019.ppm
│ │ ├── 00046_00020.ppm
│ │ ├── 00046_00021.ppm
│ │ ├── 00046_00022.ppm
│ │ ├── 00046_00023.ppm
│ │ ├── 00046_00024.ppm
│ │ ├── 00046_00025.ppm
│ │ ├── 00046_00026.ppm
│ │ ├── 00046_00027.ppm
│ │ ├── 00046_00028.ppm
│ │ ├── 00046_00029.ppm
│ │ ├── 00047_00000.ppm
│ │ ├── 00047_00001.ppm
│ │ ├── 00047_00002.ppm
│ │ ├── 00047_00003.ppm
│ │ ├── 00047_00004.ppm
│ │ ├── 00047_00005.ppm
│ │ ├── 00047_00006.ppm
│ │ ├── 00047_00007.ppm
│ │ ├── 00047_00008.ppm
│ │ ├── 00047_00009.ppm
│ │ ├── 00047_00010.ppm
│ │ ├── 00047_00011.ppm
│ │ ├── 00047_00012.ppm
│ │ ├── 00047_00013.ppm
│ │ ├── 00047_00014.ppm
│ │ ├── 00047_00015.ppm
│ │ ├── 00047_00016.ppm
│ │ ├── 00047_00017.ppm
│ │ ├── 00047_00018.ppm
│ │ ├── 00047_00019.ppm
│ │ ├── 00047_00020.ppm
│ │ ├── 00047_00021.ppm
│ │ ├── 00047_00022.ppm
│ │ ├── 00047_00023.ppm
│ │ ├── 00047_00024.ppm
│ │ ├── 00047_00025.ppm
│ │ ├── 00047_00026.ppm
│ │ ├── 00047_00027.ppm
│ │ ├── 00047_00028.ppm
│ │ ├── 00047_00029.ppm
│ │ ├── 00048_00000.ppm
│ │ ├── 00048_00001.ppm
│ │ ├── 00048_00002.ppm
│ │ ├── 00048_00003.ppm
│ │ ├── 00048_00004.ppm
│ │ ├── 00048_00005.ppm
│ │ ├── 00048_00006.ppm
│ │ ├── 00048_00007.ppm
│ │ ├── 00048_00008.ppm
│ │ ├── 00048_00009.ppm
│ │ ├── 00048_00010.ppm
│ │ ├── 00048_00011.ppm
│ │ ├── 00048_00012.ppm
│ │ ├── 00048_00013.ppm
│ │ ├── 00048_00014.ppm
│ │ ├── 00048_00015.ppm
│ │ ├── 00048_00016.ppm
│ │ ├── 00048_00017.ppm
│ │ ├── 00048_00018.ppm
│ │ ├── 00048_00019.ppm
│ │ ├── 00048_00020.ppm
│ │ ├── 00048_00021.ppm
│ │ ├── 00048_00022.ppm
│ │ ├── 00048_00023.ppm
│ │ ├── 00048_00024.ppm
│ │ ├── 00048_00025.ppm
│ │ ├── 00048_00026.ppm
│ │ ├── 00048_00027.ppm
│ │ ├── 00048_00028.ppm
│ │ ├── 00048_00029.ppm
│ │ ├── 00049_00000.ppm
│ │ ├── 00049_00001.ppm
│ │ ├── 00049_00002.ppm
│ │ ├── 00049_00003.ppm
│ │ ├── 00049_00004.ppm
│ │ ├── 00049_00005.ppm
│ │ ├── 00049_00006.ppm
│ │ ├── 00049_00007.ppm
│ │ ├── 00049_00008.ppm
│ │ ├── 00049_00009.ppm
│ │ ├── 00049_00010.ppm
│ │ ├── 00049_00011.ppm
│ │ ├── 00049_00012.ppm
│ │ ├── 00049_00013.ppm
│ │ ├── 00049_00014.ppm
│ │ ├── 00049_00015.ppm
│ │ ├── 00049_00016.ppm
│ │ ├── 00049_00017.ppm
│ │ ├── 00049_00018.ppm
│ │ ├── 00049_00019.ppm
│ │ ├── 00049_00020.ppm
│ │ ├── 00049_00021.ppm
│ │ ├── 00049_00022.ppm
│ │ ├── 00049_00023.ppm
│ │ ├── 00049_00024.ppm
│ │ ├── 00049_00025.ppm
│ │ ├── 00049_00026.ppm
│ │ ├── 00049_00027.ppm
│ │ ├── 00049_00028.ppm
│ │ ├── 00049_00029.ppm
│ │ ├── 00050_00000.ppm
│ │ ├── 00050_00001.ppm
│ │ ├── 00050_00002.ppm
│ │ ├── 00050_00003.ppm
│ │ ├── 00050_00004.ppm
│ │ ├── 00050_00005.ppm
│ │ ├── 00050_00006.ppm
│ │ ├── 00050_00007.ppm
│ │ ├── 00050_00008.ppm
│ │ ├── 00050_00009.ppm
│ │ ├── 00050_00010.ppm
│ │ ├── 00050_00011.ppm
│ │ ├── 00050_00012.ppm
│ │ ├── 00050_00013.ppm
│ │ ├── 00050_00014.ppm
│ │ ├── 00050_00015.ppm
│ │ ├── 00050_00016.ppm
│ │ ├── 00050_00017.ppm
│ │ ├── 00050_00018.ppm
│ │ ├── 00050_00019.ppm
│ │ ├── 00050_00020.ppm
│ │ ├── 00050_00021.ppm
│ │ ├── 00050_00022.ppm
│ │ ├── 00050_00023.ppm
│ │ ├── 00050_00024.ppm
│ │ ├── 00050_00025.ppm
│ │ ├── 00050_00026.ppm
│ │ ├── 00050_00027.ppm
│ │ ├── 00050_00028.ppm
│ │ ├── 00050_00029.ppm
│ │ ├── 00051_00000.ppm
│ │ ├── 00051_00001.ppm
│ │ ├── 00051_00002.ppm
│ │ ├── 00051_00003.ppm
│ │ ├── 00051_00004.ppm
│ │ ├── 00051_00005.ppm
│ │ ├── 00051_00006.ppm
│ │ ├── 00051_00007.ppm
│ │ ├── 00051_00008.ppm
│ │ ├── 00051_00009.ppm
│ │ ├── 00051_00010.ppm
│ │ ├── 00051_00011.ppm
│ │ ├── 00051_00012.ppm
│ │ ├── 00051_00013.ppm
│ │ ├── 00051_00014.ppm
│ │ ├── 00051_00015.ppm
│ │ ├── 00051_00016.ppm
│ │ ├── 00051_00017.ppm
│ │ ├── 00051_00018.ppm
│ │ ├── 00051_00019.ppm
│ │ ├── 00051_00020.ppm
│ │ ├── 00051_00021.ppm
│ │ ├── 00051_00022.ppm
│ │ ├── 00051_00023.ppm
│ │ ├── 00051_00024.ppm
│ │ ├── 00051_00025.ppm
│ │ ├── 00051_00026.ppm
│ │ ├── 00051_00027.ppm
│ │ ├── 00051_00028.ppm
│ │ ├── 00051_00029.ppm
│ │ ├── 00052_00000.ppm
│ │ ├── 00052_00001.ppm
│ │ ├── 00052_00002.ppm
│ │ ├── 00052_00003.ppm
│ │ ├── 00052_00004.ppm
│ │ ├── 00052_00005.ppm
│ │ ├── 00052_00006.ppm
│ │ ├── 00052_00007.ppm
│ │ ├── 00052_00008.ppm
│ │ ├── 00052_00009.ppm
│ │ ├── 00052_00010.ppm
│ │ ├── 00052_00011.ppm
│ │ ├── 00052_00012.ppm
│ │ ├── 00052_00013.ppm
│ │ ├── 00052_00014.ppm
│ │ ├── 00052_00015.ppm
│ │ ├── 00052_00016.ppm
│ │ ├── 00052_00017.ppm
│ │ ├── 00052_00018.ppm
│ │ ├── 00052_00019.ppm
│ │ ├── 00052_00020.ppm
│ │ ├── 00052_00021.ppm
│ │ ├── 00052_00022.ppm
│ │ ├── 00052_00023.ppm
│ │ ├── 00052_00024.ppm
│ │ ├── 00052_00025.ppm
│ │ ├── 00052_00026.ppm
│ │ ├── 00052_00027.ppm
│ │ ├── 00052_00028.ppm
│ │ ├── 00052_00029.ppm
│ │ ├── 00053_00000.ppm
│ │ ├── 00053_00001.ppm
│ │ ├── 00053_00002.ppm
│ │ ├── 00053_00003.ppm
│ │ ├── 00053_00004.ppm
│ │ ├── 00053_00005.ppm
│ │ ├── 00053_00006.ppm
│ │ ├── 00053_00007.ppm
│ │ ├── 00053_00008.ppm
│ │ ├── 00053_00009.ppm
│ │ ├── 00053_00010.ppm
│ │ ├── 00053_00011.ppm
│ │ ├── 00053_00012.ppm
│ │ ├── 00053_00013.ppm
│ │ ├── 00053_00014.ppm
│ │ ├── 00053_00015.ppm
│ │ ├── 00053_00016.ppm
│ │ ├── 00053_00017.ppm
│ │ ├── 00053_00018.ppm
│ │ ├── 00053_00019.ppm
│ │ ├── 00053_00020.ppm
│ │ ├── 00053_00021.ppm
│ │ ├── 00053_00022.ppm
│ │ ├── 00053_00023.ppm
│ │ ├── 00053_00024.ppm
│ │ ├── 00053_00025.ppm
│ │ ├── 00053_00026.ppm
│ │ ├── 00053_00027.ppm
│ │ ├── 00053_00028.ppm
│ │ ├── 00053_00029.ppm
│ │ ├── 00054_00000.ppm
│ │ ├── 00054_00001.ppm
│ │ ├── 00054_00002.ppm
│ │ ├── 00054_00003.ppm
│ │ ├── 00054_00004.ppm
│ │ ├── 00054_00005.ppm
│ │ ├── 00054_00006.ppm
│ │ ├── 00054_00007.ppm
│ │ ├── 00054_00008.ppm
│ │ ├── 00054_00009.ppm
│ │ ├── 00054_00010.ppm
│ │ ├── 00054_00011.ppm
│ │ ├── 00054_00012.ppm
│ │ ├── 00054_00013.ppm
│ │ ├── 00054_00014.ppm
│ │ ├── 00054_00015.ppm
│ │ ├── 00054_00016.ppm
│ │ ├── 00054_00017.ppm
│ │ ├── 00054_00018.ppm
│ │ ├── 00054_00019.ppm
│ │ ├── 00054_00020.ppm
│ │ ├── 00054_00021.ppm
│ │ ├── 00054_00022.ppm
│ │ ├── 00054_00023.ppm
│ │ ├── 00054_00024.ppm
│ │ ├── 00054_00025.ppm
│ │ ├── 00054_00026.ppm
│ │ ├── 00054_00027.ppm
│ │ ├── 00054_00028.ppm
│ │ ├── 00054_00029.ppm
│ │ ├── 00055_00000.ppm
│ │ ├── 00055_00001.ppm
│ │ ├── 00055_00002.ppm
│ │ ├── 00055_00003.ppm
│ │ ├── 00055_00004.ppm
│ │ ├── 00055_00005.ppm
│ │ ├── 00055_00006.ppm
│ │ ├── 00055_00007.ppm
│ │ ├── 00055_00008.ppm
│ │ ├── 00055_00009.ppm
│ │ ├── 00055_00010.ppm
│ │ ├── 00055_00011.ppm
│ │ ├── 00055_00012.ppm
│ │ ├── 00055_00013.ppm
│ │ ├── 00055_00014.ppm
│ │ ├── 00055_00015.ppm
│ │ ├── 00055_00016.ppm
│ │ ├── 00055_00017.ppm
│ │ ├── 00055_00018.ppm
│ │ ├── 00055_00019.ppm
│ │ ├── 00055_00020.ppm
│ │ ├── 00055_00021.ppm
│ │ ├── 00055_00022.ppm
│ │ ├── 00055_00023.ppm
│ │ ├── 00055_00024.ppm
│ │ ├── 00055_00025.ppm
│ │ ├── 00055_00026.ppm
│ │ ├── 00055_00027.ppm
│ │ ├── 00055_00028.ppm
│ │ ├── 00055_00029.ppm
│ │ ├── 00056_00000.ppm
│ │ ├── 00056_00001.ppm
│ │ ├── 00056_00002.ppm
│ │ ├── 00056_00003.ppm
│ │ ├── 00056_00004.ppm
│ │ ├── 00056_00005.ppm
│ │ ├── 00056_00006.ppm
│ │ ├── 00056_00007.ppm
│ │ ├── 00056_00008.ppm
│ │ ├── 00056_00009.ppm
│ │ ├── 00056_00010.ppm
│ │ ├── 00056_00011.ppm
│ │ ├── 00056_00012.ppm
│ │ ├── 00056_00013.ppm
│ │ ├── 00056_00014.ppm
│ │ ├── 00056_00015.ppm
│ │ ├── 00056_00016.ppm
│ │ ├── 00056_00017.ppm
│ │ ├── 00056_00018.ppm
│ │ ├── 00056_00019.ppm
│ │ ├── 00056_00020.ppm
│ │ ├── 00056_00021.ppm
│ │ ├── 00056_00022.ppm
│ │ ├── 00056_00023.ppm
│ │ ├── 00056_00024.ppm
│ │ ├── 00056_00025.ppm
│ │ ├── 00056_00026.ppm
│ │ ├── 00056_00027.ppm
│ │ ├── 00056_00028.ppm
│ │ ├── 00056_00029.ppm
│ │ ├── 00057_00000.ppm
│ │ ├── 00057_00001.ppm
│ │ ├── 00057_00002.ppm
│ │ ├── 00057_00003.ppm
│ │ ├── 00057_00004.ppm
│ │ ├── 00057_00005.ppm
│ │ ├── 00057_00006.ppm
│ │ ├── 00057_00007.ppm
│ │ ├── 00057_00008.ppm
│ │ ├── 00057_00009.ppm
│ │ ├── 00057_00010.ppm
│ │ ├── 00057_00011.ppm
│ │ ├── 00057_00012.ppm
│ │ ├── 00057_00013.ppm
│ │ ├── 00057_00014.ppm
│ │ ├── 00057_00015.ppm
│ │ ├── 00057_00016.ppm
│ │ ├── 00057_00017.ppm
│ │ ├── 00057_00018.ppm
│ │ ├── 00057_00019.ppm
│ │ ├── 00057_00020.ppm
│ │ ├── 00057_00021.ppm
│ │ ├── 00057_00022.ppm
│ │ ├── 00057_00023.ppm
│ │ ├── 00057_00024.ppm
│ │ ├── 00057_00025.ppm
│ │ ├── 00057_00026.ppm
│ │ ├── 00057_00027.ppm
│ │ ├── 00057_00028.ppm
│ │ ├── 00057_00029.ppm
│ │ ├── 00058_00000.ppm
│ │ ├── 00058_00001.ppm
│ │ ├── 00058_00002.ppm
│ │ ├── 00058_00003.ppm
│ │ ├── 00058_00004.ppm
│ │ ├── 00058_00005.ppm
│ │ ├── 00058_00006.ppm
│ │ ├── 00058_00007.ppm
│ │ ├── 00058_00008.ppm
│ │ ├── 00058_00009.ppm
│ │ ├── 00058_00010.ppm
│ │ ├── 00058_00011.ppm
│ │ ├── 00058_00012.ppm
│ │ ├── 00058_00013.ppm
│ │ ├── 00058_00014.ppm
│ │ ├── 00058_00015.ppm
│ │ ├── 00058_00016.ppm
│ │ ├── 00058_00017.ppm
│ │ ├── 00058_00018.ppm
│ │ ├── 00058_00019.ppm
│ │ ├── 00058_00020.ppm
│ │ ├── 00058_00021.ppm
│ │ ├── 00058_00022.ppm
│ │ ├── 00058_00023.ppm
│ │ ├── 00058_00024.ppm
│ │ ├── 00058_00025.ppm
│ │ ├── 00058_00026.ppm
│ │ ├── 00058_00027.ppm
│ │ ├── 00058_00028.ppm
│ │ ├── 00058_00029.ppm
│ │ ├── 00059_00000.ppm
│ │ ├── 00059_00001.ppm
│ │ ├── 00059_00002.ppm
│ │ ├── 00059_00003.ppm
│ │ ├── 00059_00004.ppm
│ │ ├── 00059_00005.ppm
│ │ ├── 00059_00006.ppm
│ │ ├── 00059_00007.ppm
│ │ ├── 00059_00008.ppm
│ │ ├── 00059_00009.ppm
│ │ ├── 00059_00010.ppm
│ │ ├── 00059_00011.ppm
│ │ ├── 00059_00012.ppm
│ │ ├── 00059_00013.ppm
│ │ ├── 00059_00014.ppm
│ │ ├── 00059_00015.ppm
│ │ ├── 00059_00016.ppm
│ │ ├── 00059_00017.ppm
│ │ ├── 00059_00018.ppm
│ │ ├── 00059_00019.ppm
│ │ ├── 00059_00020.ppm
│ │ ├── 00059_00021.ppm
│ │ ├── 00059_00022.ppm
│ │ ├── 00059_00023.ppm
│ │ ├── 00059_00024.ppm
│ │ ├── 00059_00025.ppm
│ │ ├── 00059_00026.ppm
│ │ ├── 00059_00027.ppm
│ │ ├── 00059_00028.ppm
│ │ ├── 00059_00029.ppm
│ │ ├── 00060_00000.ppm
│ │ ├── 00060_00001.ppm
│ │ ├── 00060_00002.ppm
│ │ ├── 00060_00003.ppm
│ │ ├── 00060_00004.ppm
│ │ ├── 00060_00005.ppm
│ │ ├── 00060_00006.ppm
│ │ ├── 00060_00007.ppm
│ │ ├── 00060_00008.ppm
│ │ ├── 00060_00009.ppm
│ │ ├── 00060_00010.ppm
│ │ ├── 00060_00011.ppm
│ │ ├── 00060_00012.ppm
│ │ ├── 00060_00013.ppm
│ │ ├── 00060_00014.ppm
│ │ ├── 00060_00015.ppm
│ │ ├── 00060_00016.ppm
│ │ ├── 00060_00017.ppm
│ │ ├── 00060_00018.ppm
│ │ ├── 00060_00019.ppm
│ │ ├── 00060_00020.ppm
│ │ ├── 00060_00021.ppm
│ │ ├── 00060_00022.ppm
│ │ ├── 00060_00023.ppm
│ │ ├── 00060_00024.ppm
│ │ ├── 00060_00025.ppm
│ │ ├── 00060_00026.ppm
│ │ ├── 00060_00027.ppm
│ │ ├── 00060_00028.ppm
│ │ ├── 00060_00029.ppm
│ │ ├── 00061_00000.ppm
│ │ ├── 00061_00001.ppm
│ │ ├── 00061_00002.ppm
│ │ ├── 00061_00003.ppm
│ │ ├── 00061_00004.ppm
│ │ ├── 00061_00005.ppm
│ │ ├── 00061_00006.ppm
│ │ ├── 00061_00007.ppm
│ │ ├── 00061_00008.ppm
│ │ ├── 00061_00009.ppm
│ │ ├── 00061_00010.ppm
│ │ ├── 00061_00011.ppm
│ │ ├── 00061_00012.ppm
│ │ ├── 00061_00013.ppm
│ │ ├── 00061_00014.ppm
│ │ ├── 00061_00015.ppm
│ │ ├── 00061_00016.ppm
│ │ ├── 00061_00017.ppm
│ │ ├── 00061_00018.ppm
│ │ ├── 00061_00019.ppm
│ │ ├── 00061_00020.ppm
│ │ ├── 00061_00021.ppm
│ │ ├── 00061_00022.ppm
│ │ ├── 00061_00023.ppm
│ │ ├── 00061_00024.ppm
│ │ ├── 00061_00025.ppm
│ │ ├── 00061_00026.ppm
│ │ ├── 00061_00027.ppm
│ │ ├── 00061_00028.ppm
│ │ ├── 00061_00029.ppm
│ │ ├── 00062_00000.ppm
│ │ ├── 00062_00001.ppm
│ │ ├── 00062_00002.ppm
│ │ ├── 00062_00003.ppm
│ │ ├── 00062_00004.ppm
│ │ ├── 00062_00005.ppm
│ │ ├── 00062_00006.ppm
│ │ ├── 00062_00007.ppm
│ │ ├── 00062_00008.ppm
│ │ ├── 00062_00009.ppm
│ │ ├── 00062_00010.ppm
│ │ ├── 00062_00011.ppm
│ │ ├── 00062_00012.ppm
│ │ ├── 00062_00013.ppm
│ │ ├── 00062_00014.ppm
│ │ ├── 00062_00015.ppm
│ │ ├── 00062_00016.ppm
│ │ ├── 00062_00017.ppm
│ │ ├── 00062_00018.ppm
│ │ ├── 00062_00019.ppm
│ │ ├── 00062_00020.ppm
│ │ ├── 00062_00021.ppm
│ │ ├── 00062_00022.ppm
│ │ ├── 00062_00023.ppm
│ │ ├── 00062_00024.ppm
│ │ ├── 00062_00025.ppm
│ │ ├── 00062_00026.ppm
│ │ ├── 00062_00027.ppm
│ │ ├── 00062_00028.ppm
│ │ ├── 00062_00029.ppm
│ │ ├── 00063_00000.ppm
│ │ ├── 00063_00001.ppm
│ │ ├── 00063_00002.ppm
│ │ ├── 00063_00003.ppm
│ │ ├── 00063_00004.ppm
│ │ ├── 00063_00005.ppm
│ │ ├── 00063_00006.ppm
│ │ ├── 00063_00007.ppm
│ │ ├── 00063_00008.ppm
│ │ ├── 00063_00009.ppm
│ │ ├── 00063_00010.ppm
│ │ ├── 00063_00011.ppm
│ │ ├── 00063_00012.ppm
│ │ ├── 00063_00013.ppm
│ │ ├── 00063_00014.ppm
│ │ ├── 00063_00015.ppm
│ │ ├── 00063_00016.ppm
│ │ ├── 00063_00017.ppm
│ │ ├── 00063_00018.ppm
│ │ ├── 00063_00019.ppm
│ │ ├── 00063_00020.ppm
│ │ ├── 00063_00021.ppm
│ │ ├── 00063_00022.ppm
│ │ ├── 00063_00023.ppm
│ │ ├── 00063_00024.ppm
│ │ ├── 00063_00025.ppm
│ │ ├── 00063_00026.ppm
│ │ ├── 00063_00027.ppm
│ │ ├── 00063_00028.ppm
│ │ ├── 00063_00029.ppm
│ │ ├── 00064_00000.ppm
│ │ ├── 00064_00001.ppm
│ │ ├── 00064_00002.ppm
│ │ ├── 00064_00003.ppm
│ │ ├── 00064_00004.ppm
│ │ ├── 00064_00005.ppm
│ │ ├── 00064_00006.ppm
│ │ ├── 00064_00007.ppm
│ │ ├── 00064_00008.ppm
│ │ ├── 00064_00009.ppm
│ │ ├── 00064_00010.ppm
│ │ ├── 00064_00011.ppm
│ │ ├── 00064_00012.ppm
│ │ ├── 00064_00013.ppm
│ │ ├── 00064_00014.ppm
│ │ ├── 00064_00015.ppm
│ │ ├── 00064_00016.ppm
│ │ ├── 00064_00017.ppm
│ │ ├── 00064_00018.ppm
│ │ ├── 00064_00019.ppm
│ │ ├── 00064_00020.ppm
│ │ ├── 00064_00021.ppm
│ │ ├── 00064_00022.ppm
│ │ ├── 00064_00023.ppm
│ │ ├── 00064_00024.ppm
│ │ ├── 00064_00025.ppm
│ │ ├── 00064_00026.ppm
│ │ ├── 00064_00027.ppm
│ │ ├── 00064_00028.ppm
│ │ ├── 00064_00029.ppm
│ │ ├── 00065_00000.ppm
│ │ ├── 00065_00001.ppm
│ │ ├── 00065_00002.ppm
│ │ ├── 00065_00003.ppm
│ │ ├── 00065_00004.ppm
│ │ ├── 00065_00005.ppm
│ │ ├── 00065_00006.ppm
│ │ ├── 00065_00007.ppm
│ │ ├── 00065_00008.ppm
│ │ ├── 00065_00009.ppm
│ │ ├── 00065_00010.ppm
│ │ ├── 00065_00011.ppm
│ │ ├── 00065_00012.ppm
│ │ ├── 00065_00013.ppm
│ │ ├── 00065_00014.ppm
│ │ ├── 00065_00015.ppm
│ │ ├── 00065_00016.ppm
│ │ ├── 00065_00017.ppm
│ │ ├── 00065_00018.ppm
│ │ ├── 00065_00019.ppm
│ │ ├── 00065_00020.ppm
│ │ ├── 00065_00021.ppm
│ │ ├── 00065_00022.ppm
│ │ ├── 00065_00023.ppm
│ │ ├── 00065_00024.ppm
│ │ ├── 00065_00025.ppm
│ │ ├── 00065_00026.ppm
│ │ ├── 00065_00027.ppm
│ │ ├── 00065_00028.ppm
│ │ ├── 00065_00029.ppm
│ │ └── GT-00004.csv
│ ├── 00006/
│ │ ├── 00000_00000.ppm
│ │ ├── 00000_00001.ppm
│ │ ├── 00000_00002.ppm
│ │ ├── 00000_00003.ppm
│ │ ├── 00000_00004.ppm
│ │ ├── 00000_00005.ppm
│ │ ├── 00000_00006.ppm
│ │ ├── 00000_00007.ppm
│ │ ├── 00000_00008.ppm
│ │ ├── 00000_00009.ppm
│ │ ├── 00000_00010.ppm
│ │ ├── 00000_00011.ppm
│ │ ├── 00000_00012.ppm
│ │ ├── 00000_00013.ppm
│ │ ├── 00000_00014.ppm
│ │ ├── 00000_00015.ppm
│ │ ├── 00000_00016.ppm
│ │ ├── 00000_00017.ppm
│ │ ├── 00000_00018.ppm
│ │ ├── 00000_00019.ppm
│ │ ├── 00000_00020.ppm
│ │ ├── 00000_00021.ppm
│ │ ├── 00000_00022.ppm
│ │ ├── 00000_00023.ppm
│ │ ├── 00000_00024.ppm
│ │ ├── 00000_00025.ppm
│ │ ├── 00000_00026.ppm
│ │ ├── 00000_00027.ppm
│ │ ├── 00000_00028.ppm
│ │ ├── 00000_00029.ppm
│ │ ├── 00001_00000.ppm
│ │ ├── 00001_00001.ppm
│ │ ├── 00001_00002.ppm
│ │ ├── 00001_00003.ppm
│ │ ├── 00001_00004.ppm
│ │ ├── 00001_00005.ppm
│ │ ├── 00001_00006.ppm
│ │ ├── 00001_00007.ppm
│ │ ├── 00001_00008.ppm
│ │ ├── 00001_00009.ppm
│ │ ├── 00001_00010.ppm
│ │ ├── 00001_00011.ppm
│ │ ├── 00001_00012.ppm
│ │ ├── 00001_00013.ppm
│ │ ├── 00001_00014.ppm
│ │ ├── 00001_00015.ppm
│ │ ├── 00001_00016.ppm
│ │ ├── 00001_00017.ppm
│ │ ├── 00001_00018.ppm
│ │ ├── 00001_00019.ppm
│ │ ├── 00001_00020.ppm
│ │ ├── 00001_00021.ppm
│ │ ├── 00001_00022.ppm
│ │ ├── 00001_00023.ppm
│ │ ├── 00001_00024.ppm
│ │ ├── 00001_00025.ppm
│ │ ├── 00001_00026.ppm
│ │ ├── 00001_00027.ppm
│ │ ├── 00001_00028.ppm
│ │ ├── 00001_00029.ppm
│ │ ├── 00002_00000.ppm
│ │ ├── 00002_00001.ppm
│ │ ├── 00002_00002.ppm
│ │ ├── 00002_00003.ppm
│ │ ├── 00002_00004.ppm
│ │ ├── 00002_00005.ppm
│ │ ├── 00002_00006.ppm
│ │ ├── 00002_00007.ppm
│ │ ├── 00002_00008.ppm
│ │ ├── 00002_00009.ppm
│ │ ├── 00002_00010.ppm
│ │ ├── 00002_00011.ppm
│ │ ├── 00002_00012.ppm
│ │ ├── 00002_00013.ppm
│ │ ├── 00002_00014.ppm
│ │ ├── 00002_00015.ppm
│ │ ├── 00002_00016.ppm
│ │ ├── 00002_00017.ppm
│ │ ├── 00002_00018.ppm
│ │ ├── 00002_00019.ppm
│ │ ├── 00002_00020.ppm
│ │ ├── 00002_00021.ppm
│ │ ├── 00002_00022.ppm
│ │ ├── 00002_00023.ppm
│ │ ├── 00002_00024.ppm
│ │ ├── 00002_00025.ppm
│ │ ├── 00002_00026.ppm
│ │ ├── 00002_00027.ppm
│ │ ├── 00002_00028.ppm
│ │ ├── 00002_00029.ppm
│ │ ├── 00003_00000.ppm
│ │ ├── 00003_00001.ppm
│ │ ├── 00003_00002.ppm
│ │ ├── 00003_00003.ppm
│ │ ├── 00003_00004.ppm
│ │ ├── 00003_00005.ppm
│ │ ├── 00003_00006.ppm
│ │ ├── 00003_00007.ppm
│ │ ├── 00003_00008.ppm
│ │ ├── 00003_00009.ppm
│ │ ├── 00003_00010.ppm
│ │ ├── 00003_00011.ppm
│ │ ├── 00003_00012.ppm
│ │ ├── 00003_00013.ppm
│ │ ├── 00003_00014.ppm
│ │ ├── 00003_00015.ppm
│ │ ├── 00003_00016.ppm
│ │ ├── 00003_00017.ppm
│ │ ├── 00003_00018.ppm
│ │ ├── 00003_00019.ppm
│ │ ├── 00003_00020.ppm
│ │ ├── 00003_00021.ppm
│ │ ├── 00003_00022.ppm
│ │ ├── 00003_00023.ppm
│ │ ├── 00003_00024.ppm
│ │ ├── 00003_00025.ppm
│ │ ├── 00003_00026.ppm
│ │ ├── 00003_00027.ppm
│ │ ├── 00003_00028.ppm
│ │ ├── 00003_00029.ppm
│ │ ├── 00004_00000.ppm
│ │ ├── 00004_00001.ppm
│ │ ├── 00004_00002.ppm
│ │ ├── 00004_00003.ppm
│ │ ├── 00004_00004.ppm
│ │ ├── 00004_00005.ppm
│ │ ├── 00004_00006.ppm
│ │ ├── 00004_00007.ppm
│ │ ├── 00004_00008.ppm
│ │ ├── 00004_00009.ppm
│ │ ├── 00004_00010.ppm
│ │ ├── 00004_00011.ppm
│ │ ├── 00004_00012.ppm
│ │ ├── 00004_00013.ppm
│ │ ├── 00004_00014.ppm
│ │ ├── 00004_00015.ppm
│ │ ├── 00004_00016.ppm
│ │ ├── 00004_00017.ppm
│ │ ├── 00004_00018.ppm
│ │ ├── 00004_00019.ppm
│ │ ├── 00004_00020.ppm
│ │ ├── 00004_00021.ppm
│ │ ├── 00004_00022.ppm
│ │ ├── 00004_00023.ppm
│ │ ├── 00004_00024.ppm
│ │ ├── 00004_00025.ppm
│ │ ├── 00004_00026.ppm
│ │ ├── 00004_00027.ppm
│ │ ├── 00004_00028.ppm
│ │ ├── 00004_00029.ppm
│ │ ├── 00005_00000.ppm
│ │ ├── 00005_00001.ppm
│ │ ├── 00005_00002.ppm
│ │ ├── 00005_00003.ppm
│ │ ├── 00005_00004.ppm
│ │ ├── 00005_00005.ppm
│ │ ├── 00005_00006.ppm
│ │ ├── 00005_00007.ppm
│ │ ├── 00005_00008.ppm
│ │ ├── 00005_00009.ppm
│ │ ├── 00005_00010.ppm
│ │ ├── 00005_00011.ppm
│ │ ├── 00005_00012.ppm
│ │ ├── 00005_00013.ppm
│ │ ├── 00005_00014.ppm
│ │ ├── 00005_00015.ppm
│ │ ├── 00005_00016.ppm
│ │ ├── 00005_00017.ppm
│ │ ├── 00005_00018.ppm
│ │ ├── 00005_00019.ppm
│ │ ├── 00005_00020.ppm
│ │ ├── 00005_00021.ppm
│ │ ├── 00005_00022.ppm
│ │ ├── 00005_00023.ppm
│ │ ├── 00005_00024.ppm
│ │ ├── 00005_00025.ppm
│ │ ├── 00005_00026.ppm
│ │ ├── 00005_00027.ppm
│ │ ├── 00005_00028.ppm
│ │ ├── 00005_00029.ppm
│ │ ├── 00006_00000.ppm
│ │ ├── 00006_00001.ppm
│ │ ├── 00006_00002.ppm
│ │ ├── 00006_00003.ppm
│ │ ├── 00006_00004.ppm
│ │ ├── 00006_00005.ppm
│ │ ├── 00006_00006.ppm
│ │ ├── 00006_00007.ppm
│ │ ├── 00006_00008.ppm
│ │ ├── 00006_00009.ppm
│ │ ├── 00006_00010.ppm
│ │ ├── 00006_00011.ppm
│ │ ├── 00006_00012.ppm
│ │ ├── 00006_00013.ppm
│ │ ├── 00006_00014.ppm
│ │ ├── 00006_00015.ppm
│ │ ├── 00006_00016.ppm
│ │ ├── 00006_00017.ppm
│ │ ├── 00006_00018.ppm
│ │ ├── 00006_00019.ppm
│ │ ├── 00006_00020.ppm
│ │ ├── 00006_00021.ppm
│ │ ├── 00006_00022.ppm
│ │ ├── 00006_00023.ppm
│ │ ├── 00006_00024.ppm
│ │ ├── 00006_00025.ppm
│ │ ├── 00006_00026.ppm
│ │ ├── 00006_00027.ppm
│ │ ├── 00006_00028.ppm
│ │ ├── 00006_00029.ppm
│ │ ├── 00007_00000.ppm
│ │ ├── 00007_00001.ppm
│ │ ├── 00007_00002.ppm
│ │ ├── 00007_00003.ppm
│ │ ├── 00007_00004.ppm
│ │ ├── 00007_00005.ppm
│ │ ├── 00007_00006.ppm
│ │ ├── 00007_00007.ppm
│ │ ├── 00007_00008.ppm
│ │ ├── 00007_00009.ppm
│ │ ├── 00007_00010.ppm
│ │ ├── 00007_00011.ppm
│ │ ├── 00007_00012.ppm
│ │ ├── 00007_00013.ppm
│ │ ├── 00007_00014.ppm
│ │ ├── 00007_00015.ppm
│ │ ├── 00007_00016.ppm
│ │ ├── 00007_00017.ppm
│ │ ├── 00007_00018.ppm
│ │ ├── 00007_00019.ppm
│ │ ├── 00007_00020.ppm
│ │ ├── 00007_00021.ppm
│ │ ├── 00007_00022.ppm
│ │ ├── 00007_00023.ppm
│ │ ├── 00007_00024.ppm
│ │ ├── 00007_00025.ppm
│ │ ├── 00007_00026.ppm
│ │ ├── 00007_00027.ppm
│ │ ├── 00007_00028.ppm
│ │ ├── 00007_00029.ppm
│ │ ├── 00008_00000.ppm
│ │ ├── 00008_00001.ppm
│ │ ├── 00008_00002.ppm
│ │ ├── 00008_00003.ppm
│ │ ├── 00008_00004.ppm
│ │ ├── 00008_00005.ppm
│ │ ├── 00008_00006.ppm
│ │ ├── 00008_00007.ppm
│ │ ├── 00008_00008.ppm
│ │ ├── 00008_00009.ppm
│ │ ├── 00008_00010.ppm
│ │ ├── 00008_00011.ppm
│ │ ├── 00008_00012.ppm
│ │ ├── 00008_00013.ppm
│ │ ├── 00008_00014.ppm
│ │ ├── 00008_00015.ppm
│ │ ├── 00008_00016.ppm
│ │ ├── 00008_00017.ppm
│ │ ├── 00008_00018.ppm
│ │ ├── 00008_00019.ppm
│ │ ├── 00008_00020.ppm
│ │ ├── 00008_00021.ppm
│ │ ├── 00008_00022.ppm
│ │ ├── 00008_00023.ppm
│ │ ├── 00008_00024.ppm
│ │ ├── 00008_00025.ppm
│ │ ├── 00008_00026.ppm
│ │ ├── 00008_00027.ppm
│ │ ├── 00008_00028.ppm
│ │ ├── 00008_00029.ppm
│ │ ├── 00009_00000.ppm
│ │ ├── 00009_00001.ppm
│ │ ├── 00009_00002.ppm
│ │ ├── 00009_00003.ppm
│ │ ├── 00009_00004.ppm
│ │ ├── 00009_00005.ppm
│ │ ├── 00009_00006.ppm
│ │ ├── 00009_00007.ppm
│ │ ├── 00009_00008.ppm
│ │ ├── 00009_00009.ppm
│ │ ├── 00009_00010.ppm
│ │ ├── 00009_00011.ppm
│ │ ├── 00009_00012.ppm
│ │ ├── 00009_00013.ppm
│ │ ├── 00009_00014.ppm
│ │ ├── 00009_00015.ppm
│ │ ├── 00009_00016.ppm
│ │ ├── 00009_00017.ppm
│ │ ├── 00009_00018.ppm
│ │ ├── 00009_00019.ppm
│ │ ├── 00009_00020.ppm
│ │ ├── 00009_00021.ppm
│ │ ├── 00009_00022.ppm
│ │ ├── 00009_00023.ppm
│ │ ├── 00009_00024.ppm
│ │ ├── 00009_00025.ppm
│ │ ├── 00009_00026.ppm
│ │ ├── 00009_00027.ppm
│ │ ├── 00009_00028.ppm
│ │ ├── 00009_00029.ppm
│ │ ├── 00010_00000.ppm
│ │ ├── 00010_00001.ppm
│ │ ├── 00010_00002.ppm
│ │ ├── 00010_00003.ppm
│ │ ├── 00010_00004.ppm
│ │ ├── 00010_00005.ppm
│ │ ├── 00010_00006.ppm
│ │ ├── 00010_00007.ppm
│ │ ├── 00010_00008.ppm
│ │ ├── 00010_00009.ppm
│ │ ├── 00010_00010.ppm
│ │ ├── 00010_00011.ppm
│ │ ├── 00010_00012.ppm
│ │ ├── 00010_00013.ppm
│ │ ├── 00010_00014.ppm
│ │ ├── 00010_00015.ppm
│ │ ├── 00010_00016.ppm
│ │ ├── 00010_00017.ppm
│ │ ├── 00010_00018.ppm
│ │ ├── 00010_00019.ppm
│ │ ├── 00010_00020.ppm
│ │ ├── 00010_00021.ppm
│ │ ├── 00010_00022.ppm
│ │ ├── 00010_00023.ppm
│ │ ├── 00010_00024.ppm
│ │ ├── 00010_00025.ppm
│ │ ├── 00010_00026.ppm
│ │ ├── 00010_00027.ppm
│ │ ├── 00010_00028.ppm
│ │ ├── 00010_00029.ppm
│ │ ├── 00011_00000.ppm
│ │ ├── 00011_00001.ppm
│ │ ├── 00011_00002.ppm
│ │ ├── 00011_00003.ppm
│ │ ├── 00011_00004.ppm
│ │ ├── 00011_00005.ppm
│ │ ├── 00011_00006.ppm
│ │ ├── 00011_00007.ppm
│ │ ├── 00011_00008.ppm
│ │ ├── 00011_00009.ppm
│ │ ├── 00011_00010.ppm
│ │ ├── 00011_00011.ppm
│ │ ├── 00011_00012.ppm
│ │ ├── 00011_00013.ppm
│ │ ├── 00011_00014.ppm
│ │ ├── 00011_00015.ppm
│ │ ├── 00011_00016.ppm
│ │ ├── 00011_00017.ppm
│ │ ├── 00011_00018.ppm
│ │ ├── 00011_00019.ppm
│ │ ├── 00011_00020.ppm
│ │ ├── 00011_00021.ppm
│ │ ├── 00011_00022.ppm
│ │ ├── 00011_00023.ppm
│ │ ├── 00011_00024.ppm
│ │ ├── 00011_00025.ppm
│ │ ├── 00011_00026.ppm
│ │ ├── 00011_00027.ppm
│ │ ├── 00011_00028.ppm
│ │ ├── 00011_00029.ppm
│ │ ├── 00012_00000.ppm
│ │ ├── 00012_00001.ppm
│ │ ├── 00012_00002.ppm
│ │ ├── 00012_00003.ppm
│ │ ├── 00012_00004.ppm
│ │ ├── 00012_00005.ppm
│ │ ├── 00012_00006.ppm
│ │ ├── 00012_00007.ppm
│ │ ├── 00012_00008.ppm
│ │ ├── 00012_00009.ppm
│ │ ├── 00012_00010.ppm
│ │ ├── 00012_00011.ppm
│ │ ├── 00012_00012.ppm
│ │ ├── 00012_00013.ppm
│ │ ├── 00012_00014.ppm
│ │ ├── 00012_00015.ppm
│ │ ├── 00012_00016.ppm
│ │ ├── 00012_00017.ppm
│ │ ├── 00012_00018.ppm
│ │ ├── 00012_00019.ppm
│ │ ├── 00012_00020.ppm
│ │ ├── 00012_00021.ppm
│ │ ├── 00012_00022.ppm
│ │ ├── 00012_00023.ppm
│ │ ├── 00012_00024.ppm
│ │ ├── 00012_00025.ppm
│ │ ├── 00012_00026.ppm
│ │ ├── 00012_00027.ppm
│ │ ├── 00012_00028.ppm
│ │ ├── 00012_00029.ppm
│ │ ├── 00013_00000.ppm
│ │ ├── 00013_00001.ppm
│ │ ├── 00013_00002.ppm
│ │ ├── 00013_00003.ppm
│ │ ├── 00013_00004.ppm
│ │ ├── 00013_00005.ppm
│ │ ├── 00013_00006.ppm
│ │ ├── 00013_00007.ppm
│ │ ├── 00013_00008.ppm
│ │ ├── 00013_00009.ppm
│ │ ├── 00013_00010.ppm
│ │ ├── 00013_00011.ppm
│ │ ├── 00013_00012.ppm
│ │ ├── 00013_00013.ppm
│ │ ├── 00013_00014.ppm
│ │ ├── 00013_00015.ppm
│ │ ├── 00013_00016.ppm
│ │ ├── 00013_00017.ppm
│ │ ├── 00013_00018.ppm
│ │ ├── 00013_00019.ppm
│ │ ├── 00013_00020.ppm
│ │ ├── 00013_00021.ppm
│ │ ├── 00013_00022.ppm
│ │ ├── 00013_00023.ppm
│ │ ├── 00013_00024.ppm
│ │ ├── 00013_00025.ppm
│ │ ├── 00013_00026.ppm
│ │ ├── 00013_00027.ppm
│ │ ├── 00013_00028.ppm
│ │ ├── 00013_00029.ppm
│ │ └── GT-00006.csv
│ ├── 00008/
│ │ ├── 00000_00000.ppm
│ │ ├── 00000_00001.ppm
│ │ ├── 00000_00002.ppm
│ │ ├── 00000_00003.ppm
│ │ ├── 00000_00004.ppm
│ │ ├── 00000_00005.ppm
│ │ ├── 00000_00006.ppm
│ │ ├── 00000_00007.ppm
│ │ ├── 00000_00008.ppm
│ │ ├── 00000_00009.ppm
│ │ ├── 00000_00010.ppm
│ │ ├── 00000_00011.ppm
│ │ ├── 00000_00012.ppm
│ │ ├── 00000_00013.ppm
│ │ ├── 00000_00014.ppm
│ │ ├── 00000_00015.ppm
│ │ ├── 00000_00016.ppm
│ │ ├── 00000_00017.ppm
│ │ ├── 00000_00018.ppm
│ │ ├── 00000_00019.ppm
│ │ ├── 00000_00020.ppm
│ │ ├── 00000_00021.ppm
│ │ ├── 00000_00022.ppm
│ │ ├── 00000_00023.ppm
│ │ ├── 00000_00024.ppm
│ │ ├── 00000_00025.ppm
│ │ ├── 00000_00026.ppm
│ │ ├── 00000_00027.ppm
│ │ ├── 00000_00028.ppm
│ │ ├── 00000_00029.ppm
│ │ ├── 00001_00000.ppm
│ │ ├── 00001_00001.ppm
│ │ ├── 00001_00002.ppm
│ │ ├── 00001_00003.ppm
│ │ ├── 00001_00004.ppm
│ │ ├── 00001_00005.ppm
│ │ ├── 00001_00006.ppm
│ │ ├── 00001_00007.ppm
│ │ ├── 00001_00008.ppm
│ │ ├── 00001_00009.ppm
│ │ ├── 00001_00010.ppm
│ │ ├── 00001_00011.ppm
│ │ ├── 00001_00012.ppm
│ │ ├── 00001_00013.ppm
│ │ ├── 00001_00014.ppm
│ │ ├── 00001_00015.ppm
│ │ ├── 00001_00016.ppm
│ │ ├── 00001_00017.ppm
│ │ ├── 00001_00018.ppm
│ │ ├── 00001_00019.ppm
│ │ ├── 00001_00020.ppm
│ │ ├── 00001_00021.ppm
│ │ ├── 00001_00022.ppm
│ │ ├── 00001_00023.ppm
│ │ ├── 00001_00024.ppm
│ │ ├── 00001_00025.ppm
│ │ ├── 00001_00026.ppm
│ │ ├── 00001_00027.ppm
│ │ ├── 00001_00028.ppm
│ │ ├── 00001_00029.ppm
│ │ ├── 00002_00000.ppm
│ │ ├── 00002_00001.ppm
│ │ ├── 00002_00002.ppm
│ │ ├── 00002_00003.ppm
│ │ ├── 00002_00004.ppm
│ │ ├── 00002_00005.ppm
│ │ ├── 00002_00006.ppm
│ │ ├── 00002_00007.ppm
│ │ ├── 00002_00008.ppm
│ │ ├── 00002_00009.ppm
│ │ ├── 00002_00010.ppm
│ │ ├── 00002_00011.ppm
│ │ ├── 00002_00012.ppm
│ │ ├── 00002_00013.ppm
│ │ ├── 00002_00014.ppm
│ │ ├── 00002_00015.ppm
│ │ ├── 00002_00016.ppm
│ │ ├── 00002_00017.ppm
│ │ ├── 00002_00018.ppm
│ │ ├── 00002_00019.ppm
│ │ ├── 00002_00020.ppm
│ │ ├── 00002_00021.ppm
│ │ ├── 00002_00022.ppm
│ │ ├── 00002_00023.ppm
│ │ ├── 00002_00024.ppm
│ │ ├── 00002_00025.ppm
│ │ ├── 00002_00026.ppm
│ │ ├── 00002_00027.ppm
│ │ ├── 00002_00028.ppm
│ │ ├── 00002_00029.ppm
│ │ ├── 00003_00000.ppm
│ │ ├── 00003_00001.ppm
│ │ ├── 00003_00002.ppm
│ │ ├── 00003_00003.ppm
│ │ ├── 00003_00004.ppm
│ │ ├── 00003_00005.ppm
│ │ ├── 00003_00006.ppm
│ │ ├── 00003_00007.ppm
│ │ ├── 00003_00008.ppm
│ │ ├── 00003_00009.ppm
│ │ ├── 00003_00010.ppm
│ │ ├── 00003_00011.ppm
│ │ ├── 00003_00012.ppm
│ │ ├── 00003_00013.ppm
│ │ ├── 00003_00014.ppm
│ │ ├── 00003_00015.ppm
│ │ ├── 00003_00016.ppm
│ │ ├── 00003_00017.ppm
│ │ ├── 00003_00018.ppm
│ │ ├── 00003_00019.ppm
│ │ ├── 00003_00020.ppm
│ │ ├── 00003_00021.ppm
│ │ ├── 00003_00022.ppm
│ │ ├── 00003_00023.ppm
│ │ ├── 00003_00024.ppm
│ │ ├── 00003_00025.ppm
│ │ ├── 00003_00026.ppm
│ │ ├── 00003_00027.ppm
│ │ ├── 00003_00028.ppm
│ │ ├── 00003_00029.ppm
│ │ ├── 00004_00000.ppm
│ │ ├── 00004_00001.ppm
│ │ ├── 00004_00002.ppm
│ │ ├── 00004_00003.ppm
│ │ ├── 00004_00004.ppm
│ │ ├── 00004_00005.ppm
│ │ ├── 00004_00006.ppm
│ │ ├── 00004_00007.ppm
│ │ ├── 00004_00008.ppm
│ │ ├── 00004_00009.ppm
│ │ ├── 00004_00010.ppm
│ │ ├── 00004_00011.ppm
│ │ ├── 00004_00012.ppm
│ │ ├── 00004_00013.ppm
│ │ ├── 00004_00014.ppm
│ │ ├── 00004_00015.ppm
│ │ ├── 00004_00016.ppm
│ │ ├── 00004_00017.ppm
│ │ ├── 00004_00018.ppm
│ │ ├── 00004_00019.ppm
│ │ ├── 00004_00020.ppm
│ │ ├── 00004_00021.ppm
│ │ ├── 00004_00022.ppm
│ │ ├── 00004_00023.ppm
│ │ ├── 00004_00024.ppm
│ │ ├── 00004_00025.ppm
│ │ ├── 00004_00026.ppm
│ │ ├── 00004_00027.ppm
│ │ ├── 00004_00028.ppm
│ │ ├── 00004_00029.ppm
│ │ ├── 00005_00000.ppm
│ │ ├── 00005_00001.ppm
│ │ ├── 00005_00002.ppm
│ │ ├── 00005_00003.ppm
│ │ ├── 00005_00004.ppm
│ │ ├── 00005_00005.ppm
│ │ ├── 00005_00006.ppm
│ │ ├── 00005_00007.ppm
│ │ ├── 00005_00008.ppm
│ │ ├── 00005_00009.ppm
│ │ ├── 00005_00010.ppm
│ │ ├── 00005_00011.ppm
│ │ ├── 00005_00012.ppm
│ │ ├── 00005_00013.ppm
│ │ ├── 00005_00014.ppm
│ │ ├── 00005_00015.ppm
│ │ ├── 00005_00016.ppm
│ │ ├── 00005_00017.ppm
│ │ ├── 00005_00018.ppm
│ │ ├── 00005_00019.ppm
│ │ ├── 00005_00020.ppm
│ │ ├── 00005_00021.ppm
│ │ ├── 00005_00022.ppm
│ │ ├── 00005_00023.ppm
│ │ ├── 00005_00024.ppm
│ │ ├── 00005_00025.ppm
│ │ ├── 00005_00026.ppm
│ │ ├── 00005_00027.ppm
│ │ ├── 00005_00028.ppm
│ │ ├── 00005_00029.ppm
│ │ ├── 00006_00000.ppm
│ │ ├── 00006_00001.ppm
│ │ ├── 00006_00002.ppm
│ │ ├── 00006_00003.ppm
│ │ ├── 00006_00004.ppm
│ │ ├── 00006_00005.ppm
│ │ ├── 00006_00006.ppm
│ │ ├── 00006_00007.ppm
│ │ ├── 00006_00008.ppm
│ │ ├── 00006_00009.ppm
│ │ ├── 00006_00010.ppm
│ │ ├── 00006_00011.ppm
│ │ ├── 00006_00012.ppm
│ │ ├── 00006_00013.ppm
│ │ ├── 00006_00014.ppm
│ │ ├── 00006_00015.ppm
│ │ ├── 00006_00016.ppm
│ │ ├── 00006_00017.ppm
│ │ ├── 00006_00018.ppm
│ │ ├── 00006_00019.ppm
│ │ ├── 00006_00020.ppm
│ │ ├── 00006_00021.ppm
│ │ ├── 00006_00022.ppm
│ │ ├── 00006_00023.ppm
│ │ ├── 00006_00024.ppm
│ │ ├── 00006_00025.ppm
│ │ ├── 00006_00026.ppm
│ │ ├── 00006_00027.ppm
│ │ ├── 00006_00028.ppm
│ │ ├── 00006_00029.ppm
│ │ ├── 00007_00000.ppm
│ │ ├── 00007_00001.ppm
│ │ ├── 00007_00002.ppm
│ │ ├── 00007_00003.ppm
│ │ ├── 00007_00004.ppm
│ │ ├── 00007_00005.ppm
│ │ ├── 00007_00006.ppm
│ │ ├── 00007_00007.ppm
│ │ ├── 00007_00008.ppm
│ │ ├── 00007_00009.ppm
│ │ ├── 00007_00010.ppm
│ │ ├── 00007_00011.ppm
│ │ ├── 00007_00012.ppm
│ │ ├── 00007_00013.ppm
│ │ ├── 00007_00014.ppm
│ │ ├── 00007_00015.ppm
│ │ ├── 00007_00016.ppm
│ │ ├── 00007_00017.ppm
│ │ ├── 00007_00018.ppm
│ │ ├── 00007_00019.ppm
│ │ ├── 00007_00020.ppm
│ │ ├── 00007_00021.ppm
│ │ ├── 00007_00022.ppm
│ │ ├── 00007_00023.ppm
│ │ ├── 00007_00024.ppm
│ │ ├── 00007_00025.ppm
│ │ ├── 00007_00026.ppm
│ │ ├── 00007_00027.ppm
│ │ ├── 00007_00028.ppm
│ │ ├── 00007_00029.ppm
│ │ ├── 00008_00000.ppm
│ │ ├── 00008_00001.ppm
│ │ ├── 00008_00002.ppm
│ │ ├── 00008_00003.ppm
│ │ ├── 00008_00004.ppm
│ │ ├── 00008_00005.ppm
│ │ ├── 00008_00006.ppm
│ │ ├── 00008_00007.ppm
│ │ ├── 00008_00008.ppm
│ │ ├── 00008_00009.ppm
│ │ ├── 00008_00010.ppm
│ │ ├── 00008_00011.ppm
│ │ ├── 00008_00012.ppm
│ │ ├── 00008_00013.ppm
│ │ ├── 00008_00014.ppm
│ │ ├── 00008_00015.ppm
│ │ ├── 00008_00016.ppm
│ │ ├── 00008_00017.ppm
│ │ ├── 00008_00018.ppm
│ │ ├── 00008_00019.ppm
│ │ ├── 00008_00020.ppm
│ │ ├── 00008_00021.ppm
│ │ ├── 00008_00022.ppm
│ │ ├── 00008_00023.ppm
│ │ ├── 00008_00024.ppm
│ │ ├── 00008_00025.ppm
│ │ ├── 00008_00026.ppm
│ │ ├── 00008_00027.ppm
│ │ ├── 00008_00028.ppm
│ │ ├── 00008_00029.ppm
│ │ ├── 00009_00000.ppm
│ │ ├── 00009_00001.ppm
│ │ ├── 00009_00002.ppm
│ │ ├── 00009_00003.ppm
│ │ ├── 00009_00004.ppm
│ │ ├── 00009_00005.ppm
│ │ ├── 00009_00006.ppm
│ │ ├── 00009_00007.ppm
│ │ ├── 00009_00008.ppm
│ │ ├── 00009_00009.ppm
│ │ ├── 00009_00010.ppm
│ │ ├── 00009_00011.ppm
│ │ ├── 00009_00012.ppm
│ │ ├── 00009_00013.ppm
│ │ ├── 00009_00014.ppm
│ │ ├── 00009_00015.ppm
│ │ ├── 00009_00016.ppm
│ │ ├── 00009_00017.ppm
│ │ ├── 00009_00018.ppm
│ │ ├── 00009_00019.ppm
│ │ ├── 00009_00020.ppm
│ │ ├── 00009_00021.ppm
│ │ ├── 00009_00022.ppm
│ │ ├── 00009_00023.ppm
│ │ ├── 00009_00024.ppm
│ │ ├── 00009_00025.ppm
│ │ ├── 00009_00026.ppm
│ │ ├── 00009_00027.ppm
│ │ ├── 00009_00028.ppm
│ │ ├── 00009_00029.ppm
│ │ ├── 00010_00000.ppm
│ │ ├── 00010_00001.ppm
│ │ ├── 00010_00002.ppm
│ │ ├── 00010_00003.ppm
│ │ ├── 00010_00004.ppm
│ │ ├── 00010_00005.ppm
│ │ ├── 00010_00006.ppm
│ │ ├── 00010_00007.ppm
│ │ ├── 00010_00008.ppm
│ │ ├── 00010_00009.ppm
│ │ ├── 00010_00010.ppm
│ │ ├── 00010_00011.ppm
│ │ ├── 00010_00012.ppm
│ │ ├── 00010_00013.ppm
│ │ ├── 00010_00014.ppm
│ │ ├── 00010_00015.ppm
│ │ ├── 00010_00016.ppm
│ │ ├── 00010_00017.ppm
│ │ ├── 00010_00018.ppm
│ │ ├── 00010_00019.ppm
│ │ ├── 00010_00020.ppm
│ │ ├── 00010_00021.ppm
│ │ ├── 00010_00022.ppm
│ │ ├── 00010_00023.ppm
│ │ ├── 00010_00024.ppm
│ │ ├── 00010_00025.ppm
│ │ ├── 00010_00026.ppm
│ │ ├── 00010_00027.ppm
│ │ ├── 00010_00028.ppm
│ │ ├── 00010_00029.ppm
│ │ ├── 00011_00000.ppm
│ │ ├── 00011_00001.ppm
│ │ ├── 00011_00002.ppm
│ │ ├── 00011_00003.ppm
│ │ ├── 00011_00004.ppm
│ │ ├── 00011_00005.ppm
│ │ ├── 00011_00006.ppm
│ │ ├── 00011_00007.ppm
│ │ ├── 00011_00008.ppm
│ │ ├── 00011_00009.ppm
│ │ ├── 00011_00010.ppm
│ │ ├── 00011_00011.ppm
│ │ ├── 00011_00012.ppm
│ │ ├── 00011_00013.ppm
│ │ ├── 00011_00014.ppm
│ │ ├── 00011_00015.ppm
│ │ ├── 00011_00016.ppm
│ │ ├── 00011_00017.ppm
│ │ ├── 00011_00018.ppm
│ │ ├── 00011_00019.ppm
│ │ ├── 00011_00020.ppm
│ │ ├── 00011_00021.ppm
│ │ ├── 00011_00022.ppm
│ │ ├── 00011_00023.ppm
│ │ ├── 00011_00024.ppm
│ │ ├── 00011_00025.ppm
│ │ ├── 00011_00026.ppm
│ │ ├── 00011_00027.ppm
│ │ ├── 00011_00028.ppm
│ │ ├── 00011_00029.ppm
│ │ ├── 00012_00000.ppm
│ │ ├── 00012_00001.ppm
│ │ ├── 00012_00002.ppm
│ │ ├── 00012_00003.ppm
│ │ ├── 00012_00004.ppm
│ │ ├── 00012_00005.ppm
│ │ ├── 00012_00006.ppm
│ │ ├── 00012_00007.ppm
│ │ ├── 00012_00008.ppm
│ │ ├── 00012_00009.ppm
│ │ ├── 00012_00010.ppm
│ │ ├── 00012_00011.ppm
│ │ ├── 00012_00012.ppm
│ │ ├── 00012_00013.ppm
│ │ ├── 00012_00014.ppm
│ │ ├── 00012_00015.ppm
│ │ ├── 00012_00016.ppm
│ │ ├── 00012_00017.ppm
│ │ ├── 00012_00018.ppm
│ │ ├── 00012_00019.ppm
│ │ ├── 00012_00020.ppm
│ │ ├── 00012_00021.ppm
│ │ ├── 00012_00022.ppm
│ │ ├── 00012_00023.ppm
│ │ ├── 00012_00024.ppm
│ │ ├── 00012_00025.ppm
│ │ ├── 00012_00026.ppm
│ │ ├── 00012_00027.ppm
│ │ ├── 00012_00028.ppm
│ │ ├── 00012_00029.ppm
│ │ ├── 00013_00000.ppm
│ │ ├── 00013_00001.ppm
│ │ ├── 00013_00002.ppm
│ │ ├── 00013_00003.ppm
│ │ ├── 00013_00004.ppm
│ │ ├── 00013_00005.ppm
│ │ ├── 00013_00006.ppm
│ │ ├── 00013_00007.ppm
│ │ ├── 00013_00008.ppm
│ │ ├── 00013_00009.ppm
│ │ ├── 00013_00010.ppm
│ │ ├── 00013_00011.ppm
│ │ ├── 00013_00012.ppm
│ │ ├── 00013_00013.ppm
│ │ ├── 00013_00014.ppm
│ │ ├── 00013_00015.ppm
│ │ ├── 00013_00016.ppm
│ │ ├── 00013_00017.ppm
│ │ ├── 00013_00018.ppm
│ │ ├── 00013_00019.ppm
│ │ ├── 00013_00020.ppm
│ │ ├── 00013_00021.ppm
│ │ ├── 00013_00022.ppm
│ │ ├── 00013_00023.ppm
│ │ ├── 00013_00024.ppm
│ │ ├── 00013_00025.ppm
│ │ ├── 00013_00026.ppm
│ │ ├── 00013_00027.ppm
│ │ ├── 00013_00028.ppm
│ │ ├── 00013_00029.ppm
│ │ ├── 00014_00000.ppm
│ │ ├── 00014_00001.ppm
│ │ ├── 00014_00002.ppm
│ │ ├── 00014_00003.ppm
│ │ ├── 00014_00004.ppm
│ │ ├── 00014_00005.ppm
│ │ ├── 00014_00006.ppm
│ │ ├── 00014_00007.ppm
│ │ ├── 00014_00008.ppm
│ │ ├── 00014_00009.ppm
│ │ ├── 00014_00010.ppm
│ │ ├── 00014_00011.ppm
│ │ ├── 00014_00012.ppm
│ │ ├── 00014_00013.ppm
│ │ ├── 00014_00014.ppm
│ │ ├── 00014_00015.ppm
│ │ ├── 00014_00016.ppm
│ │ ├── 00014_00017.ppm
│ │ ├── 00014_00018.ppm
│ │ ├── 00014_00019.ppm
│ │ ├── 00014_00020.ppm
│ │ ├── 00014_00021.ppm
│ │ ├── 00014_00022.ppm
│ │ ├── 00014_00023.ppm
│ │ ├── 00014_00024.ppm
│ │ ├── 00014_00025.ppm
│ │ ├── 00014_00026.ppm
│ │ ├── 00014_00027.ppm
│ │ ├── 00014_00028.ppm
│ │ ├── 00014_00029.ppm
│ │ ├── 00015_00000.ppm
│ │ ├── 00015_00001.ppm
│ │ ├── 00015_00002.ppm
│ │ ├── 00015_00003.ppm
│ │ ├── 00015_00004.ppm
│ │ ├── 00015_00005.ppm
│ │ ├── 00015_00006.ppm
│ │ ├── 00015_00007.ppm
│ │ ├── 00015_00008.ppm
│ │ ├── 00015_00009.ppm
│ │ ├── 00015_00010.ppm
│ │ ├── 00015_00011.ppm
│ │ ├── 00015_00012.ppm
│ │ ├── 00015_00013.ppm
│ │ ├── 00015_00014.ppm
│ │ ├── 00015_00015.ppm
│ │ ├── 00015_00016.ppm
│ │ ├── 00015_00017.ppm
│ │ ├── 00015_00018.ppm
│ │ ├── 00015_00019.ppm
│ │ ├── 00015_00020.ppm
│ │ ├── 00015_00021.ppm
│ │ ├── 00015_00022.ppm
│ │ ├── 00015_00023.ppm
│ │ ├── 00015_00024.ppm
│ │ ├── 00015_00025.ppm
│ │ ├── 00015_00026.ppm
│ │ ├── 00015_00027.ppm
│ │ ├── 00015_00028.ppm
│ │ ├── 00015_00029.ppm
│ │ ├── 00016_00000.ppm
│ │ ├── 00016_00001.ppm
│ │ ├── 00016_00002.ppm
│ │ ├── 00016_00003.ppm
│ │ ├── 00016_00004.ppm
│ │ ├── 00016_00005.ppm
│ │ ├── 00016_00006.ppm
│ │ ├── 00016_00007.ppm
│ │ ├── 00016_00008.ppm
│ │ ├── 00016_00009.ppm
│ │ ├── 00016_00010.ppm
│ │ ├── 00016_00011.ppm
│ │ ├── 00016_00012.ppm
│ │ ├── 00016_00013.ppm
│ │ ├── 00016_00014.ppm
│ │ ├── 00016_00015.ppm
│ │ ├── 00016_00016.ppm
│ │ ├── 00016_00017.ppm
│ │ ├── 00016_00018.ppm
│ │ ├── 00016_00019.ppm
│ │ ├── 00016_00020.ppm
│ │ ├── 00016_00021.ppm
│ │ ├── 00016_00022.ppm
│ │ ├── 00016_00023.ppm
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================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
# C extensions
*.so
# Distribution / packaging
.Python
env/
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
*.egg-info/
.installed.cfg
*.egg
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*,cover
# Translations
*.mo
*.pot
# Django stuff:
*.log
# Sphinx documentation
docs/_build/
# PyBuilder
target/
*.pkl
================================================
FILE: CONTRIBUTING.md
================================================
# Contributing to OpenCV with Python Blueprints
**Note: This document is a 'getting started' summary for contributing code,
documentation, testing, and filing issues.**
How to contribute
-----------------
The preferred workflow for contributing to OpenCV with Python Blueprints is to fork the
[main repository](https://github.com/mbeyeler/opencv-python-blueprints) on
GitHub, clone, and develop on a branch. Steps:
1. Fork the [project repository](https://github.com/mbeyeler/opencv-python-blueprints)
by clicking on the 'Fork' button near the top right of the page. This creates
a copy of the code under your GitHub user account.
2. Clone your fork of the OpenCV with Python Blueprints repo from your GitHub account to your local disk:
```bash
$ git clone https://github.com/YourLogin/opencv-python-blueprints.git
$ cd opencv-python-blueprints
```
3. Create a ``feature`` branch to hold your development changes:
```bash
$ git checkout -b my-feature
```
Always use a ``feature`` branch. It's good practice to never work on the ``master`` branch!
4. Develop the feature on your feature branch. Add changed files using ``git add`` and then ``git commit`` files:
```bash
$ git add modified_files
$ git commit
```
to record your changes in Git, then push the changes to your GitHub account with:
```bash
$ git push -u origin my-feature
```
5. Go to the GitHub web page of your fork of the CARLsim 3 repo.
Click the 'Pull request' button to send your changes to the project's maintainers for
review. This will send an email to the committers.
(If any of the above seems like magic to you, please look up the
[Git documentation](https://git-scm.com/documentation) on the web, or ask a friend or another contributor for help.)
Pull Request Checklist
----------------------
We recommended that your contribution complies with the
following rules before you submit a pull request:
- If your pull request addresses an issue, please use the pull request title
to describe the issue and mention the issue number in the pull request description.
This will make sure a link back to the original issue is created.
- Please prefix the title of your pull request with `[MRG]` (Ready for
Merge), if the contribution is complete and ready for a detailed review.
uhAn incomplete contribution -- where you expect to do more work before
receiving a full review -- should be prefixed `[WIP]` (to indicate a work
in progress) and changed to `[MRG]` when it matures. WIPs may be useful
to: indicate you are working on something to avoid duplicated work,
request broad review of functionality or API, or seek collaborators.
WIPs often benefit from the inclusion of a
[task list](https://github.com/blog/1375-task-lists-in-gfm-issues-pulls-comments)
in the PR description.
- Documentation is necessary for enhancements to be accepted.
Filing bugs
-----------
We use Github issues to track all bugs and feature requests; feel free to
open an issue if you have found a bug or wish to see a feature implemented.
It is recommended to check that your issue complies with the
following rules before submitting:
- Verify that your issue is not being currently addressed by other
[issues](https://github.com/mbeyeler/opencv-python-blueprints/issues?q=)
or [pull requests](https://github.com/mbeyeler/opencv-python-blueprints/pulls?q=).
- Please include your operating system type and version number, as well as your Python,
NumPy, SciPy, and OpenCV versions.
This information can be found by runnning the following code snippet:
```
import platform; print(platform.platform())
import sys; print("Python", sys.version)
import numpy; print("NumPy", numpy.__version__)
import scipy; print("SciPy", scipy.__version__)
import cv2; print("OpenCV", cv2.__version__)
================================================
FILE: LICENSE
================================================
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================================================
FILE: README.md
================================================
# OpenCV with Python Blueprints
[](https://doi.org/10.5281/zenodo.154060)
[](https://groups.google.com/d/forum/opencv-python-blueprints)
[](http://www.gnu.org/licenses/gpl-3.0)
This repository contains all up-to-date source code for the following book:
<img src="https://2.bp.blogspot.com/-0kv2Un_wtT4/VlX2XOazp3I/AAAAAAAAACE/bmZ6AsPfRKY8D6Btr10SObc6QiD8Hi0bQ/s200/2690OS_OpenCV%2Bwith%2BPython%2BBlueprints_.jpg" align="left" style="width: 220px; margin-right: 5px"/>
Michael Beyeler <br/>
<a href="http://www.amazon.com/OpenCV-Python-Blueprints-Michael-Beyeler/dp/1785282697"><b>OpenCV with Python Blueprints: Design and develop advanced computer vision projects using OpenCV with Python</b></a>
Packt Publishing Ltd., London, England <br/>
Paperback: 230 pages <br/>
ISBN 978-178528269-0
<br clear="both"/>
This book demonstrates how to develop a series of intermediate to advanced projects using OpenCV and Python,
rather than teaching the core concepts of OpenCV in theoretical lessons. Instead, the working projects
developed in this book teach the reader how to apply their theoretical knowledge to topics such as
image manipulation, augmented reality, object tracking, 3D scene reconstruction, statistical learning,
and object categorization.
By the end of this book, readers will be OpenCV experts whose newly gained experience allows them to develop their own advanced computer vision applications.
If you use either book or code in a scholarly publication, please cite as:
> M. Beyeler, (2015). OpenCV with Python Blueprints: Design and develop advanced computer vision projects using OpenCV with Python. Packt Publishing Ltd., London, England, 230 pages, ISBN 978-
178528269-0.
Or use the following bibtex:
```
@book{OpenCVWithPythonBlueprints,
title = {{OpenCV with Python Blueprints}},
subtitle = {Design and develop advanced computer vision projects using {OpenCV} with {Python}},
author = {Michael Beyeler},
year = {2015},
pages = {230},
publisher = {Packt Publishing Ltd.},
isbn = {978-178528269-0}
}
```
Scholarly work referencing this book:
- B Zhang et al. (2018). Automatic matching of construction onsite resources under camera views. *Automation in Construction*.
- A Jakubović & J Velagić (2018). Image Feature Matching and Object Detection Using Brute-Force Matchers. *International Symposium ELMAR*.
- B Zhang et al. (2018). Multi-View Matching for Onsite Construction Resources with Combinatorial Optimization. *International Symposium on Automation and Robotics in Construction (ISARC)* 35:1-7.
- LA Marcomini (2018). Identificação automática do comportamento do tráfego a partir de imagens de vídeo. *Escola de Engenharia de São Carlos*, Master's Thesis.
- G Laica et al. (2018). Diseño y construcción de un andador inteligente para el desplazamiento autónomo de los adultos mayores con visión reducida y problemas de movilidad del hogar de vida "Luis Maldonado Tamayo" mediante la investigación de técnicas de visión artificial. *Departamento de Ciencias de la Energía y Mecánica, Universidad de las Fuerzas Armadas ESPE*, Master's Thesis.
- I Huitzil-Velasco et al. (2017). Test of a Myo Armband. *Revista de Ciencias Ambientales y Recursos Naturales* 3(10): 48-56.
- Y Güçlütürk et al. (2016). Convolutional sketch inversion. *European Conference on Computer Vision (ECCV)* 810-824.
All code was tested with OpenCV 2.4.9 and Python 2.7 on Ubuntu 14.04 and Windows 8.1, and is available from:
https://github.com/mbeyeler/opencv-python-blueprints.
For questions, discussions, and more detailed help please refer to the Google group:
https://groups.google.com/d/forum/opencv-python-blueprints
## Critical Reception
<img src="https://3.bp.blogspot.com/-m8yl8xCrM3Q/V9yFYMAj3YI/AAAAAAAAAq8/5IzGqAeUp9cCwq13j1EL7aunfUvvre5bQCLcB/s640/opencv-python-blueprints-amazon-new.png" style="width: 70%; margin-left: 15%"/>
What readers on Amazon have to say:
> The author does a great job explaining the concepts needed to understand what's happening in the application without
> the need of going into too many details. <br/>
– [Sebastian Montabone](http://www.samontab.com)
> Excellent book to build practical OpenCV projects! I'm still relatively new to OpenCV, but all examples are well
> laid out and easy to follow. The author does a good job explaining the concepts in detail and shows how they apply
> in real life. As a professional programmer, I especially love that you can just fork the code from GitHub and follow
> along. Strongly recommend to readers with basic knowledge of computer vision, machine learning, and Python!
– Amazon Customer
> Usually I'm not a big fan of technical books because they are too dull, but this one is written in an engaging
> manner with a few dry jokes here and there. Can only recommend! <br/>
– lakesouth
## Who This Book Is for
As part of Packt's Blueprints series, this book is for intermediate users of OpenCV who aim to master their skills
by developing advanced practical applications. You should already have some
experience of building simple applications, and you are expected to be familiar with
OpenCV's concepts and Python libraries. Basic knowledge of Python programming
is expected and assumed.
By the end of this book, you will be an OpenCV expert, and your newly gained
experience will allow you to develop your own advanced computer vision
applications.
## Software Requirements
All projects can run on Windows, Mac, or Linux, and require the following software packages:
* OpenCV 2.4.9 or later: Recent 32-bit and 64-bit versions as well as installation instructions are available at
http://opencv.org/downloads.html. Platform-specific installation instructions can be found at
http://docs.opencv.org/doc/tutorials/introduction/table_of_content_introduction/table_of_content_introduction.html.
* Python 2.7 or later: Recent 32-bit and 64-bit installers are available at https://www.python.org/downloads. The
installation instructions can be found at https://wiki.python.org/moin/BeginnersGuide/Download.
* NumPy 1.9.2 or later: This package for scientific computing officially comes in 32-bit format only, and can be
obtained from http://www.scipy.org/scipylib/download.html. The installation instructions can be found at
http://www.scipy.org/scipylib/building/index.html#building.
In addition, some chapters require the following free Python modules:
* wxPython 2.8 or later (Chapters 1 to 4, 7): This GUI programming toolkit can be obtained from
http://www.wxpython.org/download.php.
Its installation instructions are given at http://wxpython.org/builddoc.php.
If you are using Max OS 10.11 (El Capitan), try:
```
$ sudo pip install --upgrade --trusted-host wxpython.org --pre -f http://wxpython.org/Phoenix/snapshot-builds/ wxPython_Phoenix
```
See [this bug](https://github.com/mbeyeler/opencv-python-blueprints/issues/9) for context.
Thanks to @KaroAntonio for the fix!
* matplotlib 1.4.3 or later (Chapters 4 to 7): This 2D plotting library can be obtained from
http://matplotlib.org/downloads.html. Its installation instructions can be found by going to
http://matplotlib.org/faq/installing_faq.html#how-to-install.
* SciPy 0.16.0 or later (Chapter 1): This scientific Python library officially comes in 32-bit only, and can be
obtained from http://www.scipy.org/scipylib/download.html. The installation instructions can be found at
http://www.scipy.org/scipylib/building/index.html#building.
* libfreenect 0.5.2 or later (Chapter 2): The libfreenect module by the OpenKinect project (http://www.openkinect.org)
provides drivers and libraries for the Microsoft Kinect hardware, and can be obtained from
https://github.com/OpenKinect/libfreenect. Its installation instructions can be found at
http://openkinect.org/wiki/Getting_Started.
Furthermore, the use of iPython (http://ipython.org/install.html) is highly recommended as it provides a flexible,
interactive console interface.
## License
The software is released under the GNU General Public License (GPL), which is the most commonly used free software
license according to Wikipedia. GPL allows for commercial use, distribution, modification, patent use, and private use.
The GPL is a copyleft license, which means that derived works can only be distributed under the same license terms.
For more information, please see the license file.
================================================
FILE: chapter1/chapter1.py
================================================
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""OpenCV with Python Blueprints
Chapter 1: Fun with Filters
An app to apply three different image filter effects to the video stream
of a webcam in real-time.
The three effects are:
* Black-and-white pencil sketch
* Warming/cooling filters
* Cartoonizer
"""
import numpy as np
import wx
import cv2
from gui import BaseLayout
from filters import PencilSketch, WarmingFilter, CoolingFilter, Cartoonizer
__author__ = "Michael Beyeler"
__license__ = "GNU GPL 3.0 or later"
class FilterLayout(BaseLayout):
"""Custom layout for filter effects
This class implements a custom layout for applying diverse filter
effects to a camera feed. The layout is based on an abstract base
class BaseLayout. It displays the camera feed (passed to the class as
a cv2.VideoCapture object) in the variable self.panels_vertical.
Additional layout elements can be added by using the Add method (e.g.,
self.panels_vertical(wx.Panel(self, -1))).
"""
def _init_custom_layout(self):
"""Initializes image filter effects"""
self.pencil_sketch = PencilSketch((self.imgWidth, self.imgHeight))
self.warm_filter = WarmingFilter()
self.cool_filter = CoolingFilter()
self.cartoonizer = Cartoonizer()
def _create_custom_layout(self):
"""Layout showing a row of radio buttons below the camera feed"""
# create a horizontal layout with all filter modes as radio buttons
pnl = wx.Panel(self, -1)
self.mode_warm = wx.RadioButton(pnl, -1, 'Warming Filter', (10, 10),
style=wx.RB_GROUP)
self.mode_cool = wx.RadioButton(pnl, -1, 'Cooling Filter', (10, 10))
self.mode_sketch = wx.RadioButton(pnl, -1, 'Pencil Sketch', (10, 10))
self.mode_cartoon = wx.RadioButton(pnl, -1, 'Cartoon', (10, 10))
hbox = wx.BoxSizer(wx.HORIZONTAL)
hbox.Add(self.mode_warm, 1)
hbox.Add(self.mode_cool, 1)
hbox.Add(self.mode_sketch, 1)
hbox.Add(self.mode_cartoon, 1)
pnl.SetSizer(hbox)
# add panel with radio buttons to existing panels in a vertical
# arrangement
self.panels_vertical.Add(pnl, flag=wx.EXPAND | wx.BOTTOM | wx.TOP,
border=1)
def _process_frame(self, frame_rgb):
"""Processes the current RGB camera frame
:returns: The processed RGB frame to be displayed
"""
# choose filter effect based on radio buttons setting
if self.mode_warm.GetValue():
frame = self.warm_filter.render(frame_rgb)
elif self.mode_cool.GetValue():
frame = self.cool_filter.render(frame_rgb)
elif self.mode_sketch.GetValue():
frame = self.pencil_sketch.render(frame_rgb)
elif self.mode_cartoon.GetValue():
frame = self.cartoonizer.render(frame_rgb)
return frame
def main():
# open webcam
capture = cv2.VideoCapture(0)
if not(capture.isOpened()):
capture.open()
if hasattr(cv2, 'cv'):
capture.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH, 640)
capture.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, 480)
else:
capture.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
# start graphical user interface
app = wx.App()
layout = FilterLayout(capture, title='Fun with Filters')
layout.Center()
layout.Show()
app.MainLoop()
if __name__ == '__main__':
main()
================================================
FILE: chapter1/filters.py
================================================
#!/usr/bin/env python
# -*- coding: utf-8 -*-
""" A module containing a number of interesting image filter effects,
such as:
* Black-and-white pencil sketch
* Warming/cooling filters
* Cartoonizer
"""
import numpy as np
import cv2
from scipy.interpolate import UnivariateSpline
__author__ = "Michael Beyeler"
__license__ = "GNU GPL 3.0 or later"
class PencilSketch:
"""Pencil sketch effect
A class that applies a pencil sketch effect to an image.
The processed image is overlayed over a background image for visual
effect.
"""
def __init__(self, (width, height), bg_gray='pencilsketch_bg.jpg'):
"""Initialize parameters
:param (width, height): Image size.
:param bg_gray: Optional background image to improve the illusion
that the pencil sketch was drawn on a canvas.
"""
self.width = width
self.height = height
# try to open background canvas (if it exists)
self.canvas = cv2.imread(bg_gray, cv2.CV_8UC1)
if self.canvas is not None:
self.canvas = cv2.resize(self.canvas, (self.width, self.height))
def render(self, img_rgb):
"""Applies pencil sketch effect to an RGB image
:param img_rgb: RGB image to be processed
:returns: Processed RGB image
"""
img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2GRAY)
img_blur = cv2.GaussianBlur(img_gray, (21, 21), 0, 0)
img_blend = cv2.divide(img_gray, img_blur, scale=256)
# if available, blend with background canvas
if self.canvas is not None:
img_blend = cv2.multiply(img_blend, self.canvas, scale=1. / 256)
return cv2.cvtColor(img_blend, cv2.COLOR_GRAY2RGB)
class WarmingFilter:
"""Warming filter
A class that applies a warming filter to an image.
The class uses curve filters to manipulate the perceived color
temparature of an image. The warming filter will shift the image's
color spectrum towards red, away from blue.
"""
def __init__(self):
"""Initialize look-up table for curve filter"""
# create look-up tables for increasing and decreasing a channel
self.incr_ch_lut = self._create_LUT_8UC1([0, 64, 128, 192, 256],
[0, 70, 140, 210, 256])
self.decr_ch_lut = self._create_LUT_8UC1([0, 64, 128, 192, 256],
[0, 30, 80, 120, 192])
def render(self, img_rgb):
"""Applies warming filter to an RGB image
:param img_rgb: RGB image to be processed
:returns: Processed RGB image
"""
# warming filter: increase red, decrease blue
c_r, c_g, c_b = cv2.split(img_rgb)
c_r = cv2.LUT(c_r, self.incr_ch_lut).astype(np.uint8)
c_b = cv2.LUT(c_b, self.decr_ch_lut).astype(np.uint8)
img_rgb = cv2.merge((c_r, c_g, c_b))
# increase color saturation
c_h, c_s, c_v = cv2.split(cv2.cvtColor(img_rgb, cv2.COLOR_RGB2HSV))
c_s = cv2.LUT(c_s, self.incr_ch_lut).astype(np.uint8)
return cv2.cvtColor(cv2.merge((c_h, c_s, c_v)), cv2.COLOR_HSV2RGB)
def _create_LUT_8UC1(self, x, y):
"""Creates a look-up table using scipy's spline interpolation"""
spl = UnivariateSpline(x, y)
return spl(xrange(256))
class CoolingFilter:
"""Cooling filter
A class that applies a cooling filter to an image.
The class uses curve filters to manipulate the perceived color
temparature of an image. The warming filter will shift the image's
color spectrum towards blue, away from red.
"""
def __init__(self):
"""Initialize look-up table for curve filter"""
# create look-up tables for increasing and decreasing a channel
self.incr_ch_lut = self._create_LUT_8UC1([0, 64, 128, 192, 256],
[0, 70, 140, 210, 256])
self.decr_ch_lut = self._create_LUT_8UC1([0, 64, 128, 192, 256],
[0, 30, 80, 120, 192])
def render(self, img_rgb):
"""Applies pencil sketch effect to an RGB image
:param img_rgb: RGB image to be processed
:returns: Processed RGB image
"""
# cooling filter: increase blue, decrease red
c_r, c_g, c_b = cv2.split(img_rgb)
c_r = cv2.LUT(c_r, self.decr_ch_lut).astype(np.uint8)
c_b = cv2.LUT(c_b, self.incr_ch_lut).astype(np.uint8)
img_rgb = cv2.merge((c_r, c_g, c_b))
# decrease color saturation
c_h, c_s, c_v = cv2.split(cv2.cvtColor(img_rgb, cv2.COLOR_RGB2HSV))
c_s = cv2.LUT(c_s, self.decr_ch_lut).astype(np.uint8)
return cv2.cvtColor(cv2.merge((c_h, c_s, c_v)), cv2.COLOR_HSV2RGB)
def _create_LUT_8UC1(self, x, y):
"""Creates a look-up table using scipy's spline interpolation"""
spl = UnivariateSpline(x, y)
return spl(xrange(256))
class Cartoonizer:
"""Cartoonizer effect
A class that applies a cartoon effect to an image.
The class uses a bilateral filter and adaptive thresholding to create
a cartoon effect.
"""
def __init__(self):
pass
def render(self, img_rgb):
numDownSamples = 2 # number of downscaling steps
numBilateralFilters = 7 # number of bilateral filtering steps
# -- STEP 1 --
# downsample image using Gaussian pyramid
img_color = img_rgb
for _ in xrange(numDownSamples):
img_color = cv2.pyrDown(img_color)
# repeatedly apply small bilateral filter instead of applying
# one large filter
for _ in xrange(numBilateralFilters):
img_color = cv2.bilateralFilter(img_color, 9, 9, 7)
# upsample image to original size
for _ in xrange(numDownSamples):
img_color = cv2.pyrUp(img_color)
# make sure resulting image has the same dims as original
img_color = cv2.resize(img_color, img_rgb.shape[:2])
# -- STEPS 2 and 3 --
# convert to grayscale and apply median blur
img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2GRAY)
img_blur = cv2.medianBlur(img_gray, 7)
# -- STEP 4 --
# detect and enhance edges
img_edge = cv2.adaptiveThreshold(img_blur, 255,
cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY, 9, 2)
# -- STEP 5 --
# convert back to color so that it can be bit-ANDed with color image
img_edge = cv2.cvtColor(img_edge, cv2.COLOR_GRAY2RGB)
return cv2.bitwise_and(img_color, img_edge)
================================================
FILE: chapter1/gui.py
================================================
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""A module containing simple GUI layouts using wxPython"""
import abc
import six
import time
import wx
import cv2
__author__ = "Michael Beyeler"
__license__ = "GNU GPL 3.0 or later"
class Meta1(wx.Frame):
pass
@six.add_metaclass(abc.ABCMeta)
class BaseLayout(Meta1):
"""Abstract base class for all layouts
A custom layout needs to implement at least three methods:
* _init_custom_layout: A method to initialize all relevant
parameters. This method will be called in the
class constructor, after initializing common
parameters, right before creating the GUI
layout.
* _create_custom_layout: A method to create a custom GUI layout. This
method will be called in the class
constructor, after initializing common
parameters.
Every GUI contains the camera feed in the
variable self.pnl.
Additional layout elements can be added below
the camera feed by means of the method
self.panels_vertical.Add.
* _process_frame: A method to process the current RGB camera
frame. It needs to return the processed RGB
frame to be displayed.
"""
def __init__(self, capture, title=None, parent=None, id=-1, fps=10):
"""Class constructor
This method initializes all necessary parameters and generates a
basic GUI layout that can then be modified by
self.init_custom_layout() and self.create_custom_layout().
:param parent: A wx.Frame parent (often Null). If it is non-Null,
the frame will be minimized when its parent is minimized and
restored when it is restored.
:param id: The window identifier. Value -1 indicates default value.
:param title: The caption to be displayed on the frame's title bar.
:param capture: A cv2.VideoCapture object to be used as camera
feed.
:param fps: frames per second at which to display camera feed
"""
self.capture = capture
self.fps = fps
# determine window size and init wx.Frame
success, frame = self._acquire_frame()
if not success:
print "Could not acquire frame from camera."
raise SystemExit
self.imgHeight, self.imgWidth = frame.shape[:2]
self.bmp = wx.BitmapFromBuffer(self.imgWidth, self.imgHeight, frame)
wx.Frame.__init__(self, parent, id, title,
size=(self.imgWidth, self.imgHeight))
self._init_base_layout()
self._create_base_layout()
def _init_base_layout(self):
"""Initialize parameters
This method performs initializations that are common to all GUIs,
such as the setting up of a timer.
It then calls an abstract method self.init_custom_layout() that
allows for additional, application-specific initializations.
"""
# set up periodic screen capture
self.timer = wx.Timer(self)
self.timer.Start(1000. / self.fps)
self.Bind(wx.EVT_TIMER, self._on_next_frame)
# allow for custom modifications
self._init_custom_layout()
def _create_base_layout(self):
"""Create generic layout
This method sets up a basic layout that is common to all GUIs, such
as a live stream of the camera (capture device). This stream is
assigned to the variable self.pnl, and arranged in a vertical
layout self.panels_vertical.
Additional layout elements can be added below the livestream by
means of the method self.panels_vertical.Add.
"""
# set up video stream
self.pnl = wx.Panel(self, size=(self.imgWidth, self.imgHeight))
self.pnl.SetBackgroundColour(wx.BLACK)
self.pnl.Bind(wx.EVT_PAINT, self._on_paint)
# display the button layout beneath the video stream
self.panels_vertical = wx.BoxSizer(wx.VERTICAL)
self.panels_vertical.Add(self.pnl, 1, flag=wx.EXPAND | wx.TOP,
border=1)
# allow for custom layout modifications
self._create_custom_layout()
# round off the layout by expanding and centering
self.SetMinSize((self.imgWidth, self.imgHeight))
self.SetSizer(self.panels_vertical)
self.Centre()
def _on_next_frame(self, event):
"""
This method captures a new frame from the camera (or capture
device) and sends an RGB version to the method self.process_frame.
The latter will then apply task-specific post-processing and return
an image to be displayed.
"""
success, frame = self._acquire_frame()
if success:
# process current frame
frame = self._process_frame(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
# update buffer and paint (EVT_PAINT triggered by Refresh)
self.bmp.CopyFromBuffer(frame)
self.Refresh(eraseBackground=False)
def _on_paint(self, event):
"""
This method draws the camera frame stored in the bitmap self.bmp
onto the panel self.pnl. Make sure self.pnl exists and is at least
the size of the camera frame.
This method is called whenever an event wx.EVT_PAINT is triggered.
"""
# read and draw buffered bitmap
deviceContext = wx.BufferedPaintDC(self.pnl)
deviceContext.DrawBitmap(self.bmp, 0, 0)
def _acquire_frame(self):
"""
This method is called whenever a new frame needs to be acquired.
:returns: (success, frame), whether acquiring was successful
(via Boolean success) and current frame
"""
return self.capture.read()
@abc.abstractmethod
def _init_custom_layout(self):
"""
This method is called in the class constructor, after setting up
relevant event callbacks, and right before creation of the GUI
layout.
"""
pass
@abc.abstractmethod
def _create_custom_layout(self):
"""
This method is responsible for creating the GUI layout.
It is called in the class constructor, after setting up relevant
event callbacks and self.init_layout, and creates the layout.
Every GUI contains the camera feed in the variable self.pnl.
Additional layout elements can be added below the camera feed by
adding them to self.panels_vertical.
"""
pass
@abc.abstractmethod
def _process_frame(self, frame_rgb):
"""
This method is responsible for any post-processing that needs to be
applied to the current frame of the camera (capture device) stream.
:param frame_rgb: The RGB camera frame to be processed.
:returns: The processed RGB camera frame to be displayed.
"""
pass
================================================
FILE: chapter2/chapter2.py
================================================
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""OpenCV with Python Blueprints
Chapter 2: Hand Gesture Recognition Using a Kinect Depth Sensor
An app to detect and track simple hand gestures in real-time using the
output of a Microsoft Kinect 3D Sensor.
"""
import numpy as np
import wx
import cv2
import freenect
from gui import BaseLayout
from gestures import HandGestureRecognition
__author__ = "Michael Beyeler"
__license__ = "GNU GPL 3.0 or later"
class KinectLayout(BaseLayout):
"""Custom layout for Kinect display
A plain GUI layout for Kinect output. We overwrite the BaseLayout's
_acquire_frame method to acquire a new frame from the depth sensor
instead.
"""
def _init_custom_layout(self):
"""Initializes hand gesture recognition"""
self.hand_gestures = HandGestureRecognition()
def _create_custom_layout(self):
"""Use plain layout"""
pass
def _acquire_frame(self):
"""Acquire frame from depth sensor using freenect library"""
frame, _ = freenect.sync_get_depth()
# return success if frame size is valid
if frame is not None:
return (True, frame)
else:
return (False, frame)
def _process_frame(self, frame):
"""Recognizes hand gesture in a frame of the depth sensor"""
# clip max depth to 1023, convert to 8-bit grayscale
np.clip(frame, 0, 2**10 - 1, frame)
frame >>= 2
frame = frame.astype(np.uint8)
# recognize hand gesture
num_fingers, img_draw = self.hand_gestures.recognize(frame)
# draw some helpers for correctly placing hand
height, width = frame.shape[:2]
cv2.circle(img_draw, (width / 2, height / 2), 3, [255, 102, 0], 2)
cv2.rectangle(img_draw, (width / 3, height / 3), (width * 2 / 3, height * 2 / 3),
[255, 102, 0], 2)
# print number of fingers on image
cv2.putText(img_draw, str(num_fingers), (30, 30),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255))
return img_draw
def main():
device = cv2.cv.CV_CAP_OPENNI
capture = cv2.VideoCapture()
if not(capture.isOpened()):
capture.open(device)
if hasattr(cv2, 'cv'):
capture.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH, 640)
capture.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, 480)
else:
capture.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
# start graphical user interface
app = wx.App()
layout = KinectLayout(capture, title='Kinect Hand Gesture Recognition')
layout.Show(True)
app.MainLoop()
if __name__ == '__main__':
main()
================================================
FILE: chapter2/gestures.py
================================================
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""A module containing an algorithm for hand gesture recognition"""
import numpy as np
import cv2
__author__ = "Michael Beyeler"
__license__ = "GNU GPL 3.0 or later"
class HandGestureRecognition:
"""Hand gesture recognition class
This class implements an algorithm for hand gesture recognition
based on a single-channel input image showing the segmented arm region,
where pixel values stand for depth. The easiest way to acquire
such an image is with a depth sensor such as Microsoft Kinect 3D.
The algorithm will then find the hull of the segmented hand
region and convexity defects therein. Based on this information,
an estimate on the number of extended fingers is derived.
"""
def __init__(self):
"""Class constructor
This method initializes all necessary parameters.
"""
# maximum depth deviation for a pixel to be considered within range
self.abs_depth_dev = 14
# cut-off angle (deg): everything below this is a convexity point that
# belongs to two extended fingers
self.thresh_deg = 80.0
def recognize(self, img_gray):
"""Recognizes hand gesture in a single-channel depth image
This method estimates the number of extended fingers based on
a single-channel depth image showing a hand and arm region.
:param img_gray: single-channel depth image
:returns: (num_fingers, img_draw) The estimated number of
extended fingers and an annotated RGB image
"""
self.height, self.width = img_gray.shape[:2]
# segment arm region
segment = self._segment_arm(img_gray)
# find the hull of the segmented area, and based on that find the
# convexity defects
(contours, defects) = self._find_hull_defects(segment)
# detect the number of fingers depending on the contours and convexity
# defects, then draw defects that belong to fingers green, others red
img_draw = cv2.cvtColor(img_gray, cv2.COLOR_GRAY2RGB)
(num_fingers, img_draw) = self._detect_num_fingers(contours,
defects, img_draw)
return (num_fingers, img_draw)
def _segment_arm(self, frame):
"""Segments arm region
This method accepts a single-channel depth image of an arm and
hand region and extracts the segmented arm region.
It is assumed that the hand is placed in the center of the image.
:param frame: single-channel depth image
:returns: binary image (mask) of segmented arm region, where
arm=255, else=0
"""
# find center (21x21 pixel) region of image frame
center_half = 10 # half-width of 21 is 21/2-1
center = frame[self.height/2-center_half:self.height/2+center_half,
self.width/2-center_half:self.width/2+center_half]
# find median depth value of center region
med_val = np.median(center)
# try this instead:
frame = np.where(abs(frame-med_val) <= self.abs_depth_dev,
128, 0).astype(np.uint8)
# morphological
kernel = np.ones((3, 3), np.uint8)
frame = cv2.morphologyEx(frame, cv2.MORPH_CLOSE, kernel)
# connected component
small_kernel = 3
frame[self.height/2-small_kernel:self.height/2+small_kernel,
self.width/2-small_kernel:self.width/2+small_kernel] = 128
mask = np.zeros((self.height+2, self.width+2), np.uint8)
flood = frame.copy()
cv2.floodFill(flood, mask, (self.width/2, self.height/2), 255,
flags=4 | (255 << 8))
ret, flooded = cv2.threshold(flood, 129, 255, cv2.THRESH_BINARY)
return flooded
def _find_hull_defects(self, segment):
"""Find hull defects
This method finds all defects in the hull of a segmented arm
region.
:param segment: a binary image (mask) of a segmented arm region,
where arm=255, else=0
:returns: (max_contour, defects) the largest contour in the image
and all corresponding defects
"""
contours, hierarchy = cv2.findContours(segment, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
# find largest area contour
max_contour = max(contours, key=cv2.contourArea)
epsilon = 0.01*cv2.arcLength(max_contour, True)
max_contour = cv2.approxPolyDP(max_contour, epsilon, True)
# find convexity hull and defects
hull = cv2.convexHull(max_contour, returnPoints=False)
defects = cv2.convexityDefects(max_contour, hull)
return (max_contour, defects)
def _detect_num_fingers(self, contours, defects, img_draw):
"""Detects the number of extended fingers
This method determines the number of extended fingers based on a
contour and convexity defects.
It will annotate an RGB color image of the segmented arm region
with all relevant defect points and the hull.
:param contours: a list of contours
:param defects: a list of convexity defects
:param img_draw: an RGB color image to be annotated
:returns: (num_fingers, img_draw) the estimated number of extended
fingers and an annotated RGB color image
"""
# if there are no convexity defects, possibly no hull found or no
# fingers extended
if defects is None:
return [0, img_draw]
# we assume the wrist will generate two convexity defects (one on each
# side), so if there are no additional defect points, there are no
# fingers extended
if len(defects) <= 2:
return [0, img_draw]
# if there is a sufficient amount of convexity defects, we will find a
# defect point between two fingers so to get the number of fingers,
# start counting at 1
num_fingers = 1
for i in range(defects.shape[0]):
# each defect point is a 4-tuple
start_idx, end_idx, farthest_idx, _ = defects[i, 0]
start = tuple(contours[start_idx][0])
end = tuple(contours[end_idx][0])
far = tuple(contours[farthest_idx][0])
# draw the hull
cv2.line(img_draw, start, end, [0, 255, 0], 2)
# if angle is below a threshold, defect point belongs to two
# extended fingers
if angle_rad(np.subtract(start, far),
np.subtract(end, far)) < deg2rad(self.thresh_deg):
# increment number of fingers
num_fingers = num_fingers + 1
# draw point as green
cv2.circle(img_draw, far, 5, [0, 255, 0], -1)
else:
# draw point as red
cv2.circle(img_draw, far, 5, [255, 0, 0], -1)
# make sure we cap the number of fingers
return (min(5, num_fingers), img_draw)
def angle_rad(v1, v2):
"""Angle in radians between two vectors
This method returns the angle (in radians) between two array-like
vectors using the cross-product method, which is more accurate for
small angles than the dot-product-acos method.
"""
return np.arctan2(np.linalg.norm(np.cross(v1, v2)), np.dot(v1, v2))
def deg2rad(angle_deg):
"""Convert degrees to radians
This method converts an angle in radians e[0,2*np.pi) into degrees
e[0,360)
"""
return angle_deg/180.0*np.pi
================================================
FILE: chapter2/gui.py
================================================
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""A module containing simple GUI layouts using wxPython"""
import abc
import six
import time
import wx
import cv2
__author__ = "Michael Beyeler"
__license__ = "GNU GPL 3.0 or later"
class Meta1(wx.Frame):
pass
@six.add_metaclass(abc.ABCMeta)
class BaseLayout(Meta1):
"""Abstract base class for all layouts
A custom layout needs to implement at least three methods:
* _init_custom_layout: A method to initialize all relevant
parameters. This method will be called in the
class constructor, after initializing common
parameters, right before creating the GUI
layout.
* _create_custom_layout: A method to create a custom GUI layout. This
method will be called in the class
constructor, after initializing common
parameters.
Every GUI contains the camera feed in the
variable self.pnl.
Additional layout elements can be added below
the camera feed by means of the method
self.panels_vertical.Add.
* _process_frame: A method to process the current RGB camera
frame. It needs to return the processed RGB
frame to be displayed.
"""
def __init__(self, capture, title=None, parent=None, id=-1, fps=10):
"""Class constructor
This method initializes all necessary parameters and generates a
basic GUI layout that can then be modified by
self.init_custom_layout() and self.create_custom_layout().
:param parent: A wx.Frame parent (often Null). If it is non-Null,
the frame will be minimized when its parent is minimized and
restored when it is restored.
:param id: The window identifier. Value -1 indicates default value.
:param title: The caption to be displayed on the frame's title bar.
:param capture: A cv2.VideoCapture object to be used as camera
feed.
:param fps: frames per second at which to display camera feed
"""
self.capture = capture
self.fps = fps
# determine window size and init wx.Frame
success, frame = self._acquire_frame()
if not success:
print "Could not acquire frame from camera."
raise SystemExit
self.imgHeight, self.imgWidth = frame.shape[:2]
self.bmp = wx.BitmapFromBuffer(self.imgWidth, self.imgHeight, frame)
wx.Frame.__init__(self, parent, id, title,
size=(self.imgWidth, self.imgHeight))
self._init_base_layout()
self._create_base_layout()
def _init_base_layout(self):
"""Initialize parameters
This method performs initializations that are common to all GUIs,
such as the setting up of a timer.
It then calls an abstract method self.init_custom_layout() that
allows for additional, application-specific initializations.
"""
# set up periodic screen capture
self.timer = wx.Timer(self)
self.timer.Start(1000. / self.fps)
self.Bind(wx.EVT_TIMER, self._on_next_frame)
# allow for custom modifications
self._init_custom_layout()
def _create_base_layout(self):
"""Create generic layout
This method sets up a basic layout that is common to all GUIs, such
as a live stream of the camera (capture device). This stream is
assigned to the variable self.pnl, and arranged in a vertical
layout self.panels_vertical.
Additional layout elements can be added below the livestream by
means of the method self.panels_vertical.Add.
"""
# set up video stream
self.pnl = wx.Panel(self, size=(self.imgWidth, self.imgHeight))
self.pnl.SetBackgroundColour(wx.BLACK)
self.pnl.Bind(wx.EVT_PAINT, self._on_paint)
# display the button layout beneath the video stream
self.panels_vertical = wx.BoxSizer(wx.VERTICAL)
self.panels_vertical.Add(self.pnl, 1, flag=wx.EXPAND | wx.TOP,
border=1)
# allow for custom layout modifications
self._create_custom_layout()
# round off the layout by expanding and centering
self.SetMinSize((self.imgWidth, self.imgHeight))
self.SetSizer(self.panels_vertical)
self.Centre()
def _on_next_frame(self, event):
"""
This method captures a new frame from the camera (or capture
device) and sends an RGB version to the method self.process_frame.
The latter will then apply task-specific post-processing and return
an image to be displayed.
"""
success, frame = self._acquire_frame()
if success:
# process current frame
frame = self._process_frame(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
# update buffer and paint (EVT_PAINT triggered by Refresh)
self.bmp.CopyFromBuffer(frame)
self.Refresh(eraseBackground=False)
def _on_paint(self, event):
"""
This method draws the camera frame stored in the bitmap self.bmp
onto the panel self.pnl. Make sure self.pnl exists and is at least
the size of the camera frame.
This method is called whenever an event wx.EVT_PAINT is triggered.
"""
# read and draw buffered bitmap
deviceContext = wx.BufferedPaintDC(self.pnl)
deviceContext.DrawBitmap(self.bmp, 0, 0)
def _acquire_frame(self):
"""
This method is called whenever a new frame needs to be acquired.
:returns: (success, frame), whether acquiring was successful
(via Boolean success) and current frame
"""
return self.capture.read()
@abc.abstractmethod
def _init_custom_layout(self):
"""
This method is called in the class constructor, after setting up
relevant event callbacks, and right before creation of the GUI
layout.
"""
pass
@abc.abstractmethod
def _create_custom_layout(self):
"""
This method is responsible for creating the GUI layout.
It is called in the class constructor, after setting up relevant
event callbacks and self.init_layout, and creates the layout.
Every GUI contains the camera feed in the variable self.pnl.
Additional layout elements can be added below the camera feed by
adding them to self.panels_vertical.
"""
pass
@abc.abstractmethod
def _process_frame(self, frame_rgb):
"""
This method is responsible for any post-processing that needs to be
applied to the current frame of the camera (capture device) stream.
:param frame_rgb: The RGB camera frame to be processed.
:returns: The processed RGB camera frame to be displayed.
"""
pass
================================================
FILE: chapter3/chapter3.py
================================================
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""OpenCV with Python Blueprints
Chapter 3: Finding Objects Via Feature Matching and Perspective Transforms
An app to detect and track an object of interest in the video stream of a
webcam, even if the object is viewed at different angles, distances, or
under partial occlusion.
"""
import cv2
import wx
from gui import BaseLayout
from feature_matching import FeatureMatching
class FeatureMatchingLayout(BaseLayout):
"""A custom layout for feature matching display
A plain GUI layout for feature matching output.
Each captured frame is passed to the FeatureMatching class, so that an
object of interest can be tracked.
"""
def _init_custom_layout(self):
"""Initializes feature matching class"""
self.matching = FeatureMatching(train_image='salinger.jpg')
def _create_custom_layout(self):
"""Use plain layout"""
pass
def _process_frame(self, frame):
"""Processes each captured frame"""
# if object detected, display new frame, else old one
success, new_frame = self.matching.match(frame)
if success:
return new_frame
else:
return frame
def main():
capture = cv2.VideoCapture(0)
if not(capture.isOpened()):
capture.open()
if hasattr(cv2, 'cv'):
capture.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH, 640)
capture.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, 480)
else:
capture.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
# start graphical user interface
app = wx.App()
layout = FeatureMatchingLayout(capture, title='Feature Matching')
layout.Show(True)
app.MainLoop()
if __name__ == '__main__':
main()
================================================
FILE: chapter3/feature_matching.py
================================================
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""A module containing an algorithm for feature matching"""
import numpy as np
import cv2
__author__ = "Michael Beyeler"
__license__ = "GNU GPL 3.0 or later"
class FeatureMatching:
"""Feature matching class
This class implements an algorithm for feature matching and tracking.
A SURF descriptor is obtained from a training or template image
(train_image) that shows the object of interest from the front and
upright.
The algorithm will then search for this object in every image frame
passed to the method FeatureMatching.match. The matching is performed
with a FLANN based matcher.
Note: If you want to use this code (including SURF) in a non-commercial
application, you will need to acquire a SURF license.
"""
def __init__(self, train_image="salinger.jpg"):
"""Constructor
This method initializes the SURF descriptor, FLANN matcher, and the
tracking algorithm.
:param train_image: training or template image showing the object
of interest
"""
# initialize SURF
self.min_hessian = 400
self.SURF = cv2.SURF(self.min_hessian)
# template image: "train" image
# later on compared ot each video frame: "query" image
self.img_obj = cv2.imread(train_image, cv2.CV_8UC1)
if self.img_obj is None:
print "Could not find train image " + train_image
raise SystemExit
self.sh_train = self.img_obj.shape[:2]
self.key_train, self.desc_train = \
self.SURF.detectAndCompute(self.img_obj, None)
# initialize FLANN
FLANN_INDEX_KDTREE = 0
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=50)
self.flann = cv2.FlannBasedMatcher(index_params, search_params)
# initialize tracking
self.last_hinv = np.zeros((3, 3))
self.num_frames_no_success = 0
self.max_frames_no_success = 5
self.max_error_hinv = 50.
def match(self, frame):
"""Detects and tracks an object of interest in a video frame
This method detects and tracks an object of interest (of which a
SURF descriptor was obtained upon initialization) in a video frame.
Correspondence is established with a FLANN based matcher.
The algorithm then applies a perspective transform on the frame in
order to project the object of interest to the frontal plane.
Outlier rejection is applied to improve the tracking of the object
from frame to frame.
:param frame: input (query) image in which to detect the object
:returns: (success, frame) whether the detection was successful and
and the perspective-transformed frame
"""
# create a working copy (grayscale) of the frame
# and store its shape for convenience
img_query = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
sh_query = img_query.shape[:2] # rows,cols
# --- feature extraction
# detect keypoints in the query image (video frame)
# using SURF descriptor
key_query, desc_query = self._extract_features(img_query)
# --- feature matching
# returns a list of good matches using FLANN
# based on a scene and its feature descriptor
good_matches = self._match_features(desc_query)
# early outlier detection and rejection
if len(good_matches) < 4:
self.num_frames_no_success = self.num_frames_no_success + 1
return False, frame
# --- corner point detection
# calculates the homography matrix needed to convert between
# keypoints from the train image and the query image
dst_corners = self._detect_corner_points(key_query, good_matches)
# early outlier detection and rejection
# if any corners lie significantly outside the image, skip frame
if np.any(filter(lambda x: x[0] < -20 or x[1] < -20 or
x[0] > sh_query[1]+20 or x[1] > sh_query[0] + 20,
dst_corners)):
self.num_frames_no_success = self.num_frames_no_success + 1
return False, frame
# early outlier detection and rejection
# find the area of the quadrilateral that the four corner points spans
area = 0
for i in xrange(0, 4):
next_i = (i + 1) % 4
area = area + (dst_corners[i][0]*dst_corners[next_i][1] -
dst_corners[i][1]*dst_corners[next_i][0])/2.
# early outlier detection and rejection
# reject corner points if area is unreasonable
if area < np.prod(sh_query)/16. or area > np.prod(sh_query)/2.:
self.num_frames_no_success = self.num_frames_no_success + 1
return False, frame
# adjust x-coordinate (col) of corner points so that they can be drawn
# next to the train image (add self.sh_train[1])
dst_corners = [(np.int(dst_corners[i][0] + self.sh_train[1]),
np.int(dst_corners[i][1]))
for i in xrange(len(dst_corners))]
# outline corner points of train image in query image
img_flann = draw_good_matches(self.img_obj, self.key_train, img_query,
key_query, good_matches)
for i in xrange(0, len(dst_corners)):
cv2.line(img_flann, dst_corners[i], dst_corners[(i + 1) % 4],
(0, 255, 0), 3)
# --- bring object of interest to frontal plane
[Hinv, dst_size] = self._warp_keypoints(good_matches, key_query,
sh_query)
# outlier rejection
# if last frame recent: new Hinv must be similar to last one
# else: accept whatever Hinv is found at this point
recent = self.num_frames_no_success < self.max_frames_no_success
similar = np.linalg.norm(Hinv - self.last_hinv) < self.max_error_hinv
if recent and not similar:
self.num_frames_no_success = self.num_frames_no_success + 1
return False, frame
# reset counters and update Hinv
self.num_frames_no_success = 0
self.last_h = Hinv
img_out = cv2.warpPerspective(img_query, Hinv, dst_size)
img_out = cv2.cvtColor(img_out, cv2.COLOR_GRAY2RGB)
return True, img_out
def _extract_features(self, frame):
"""Detects keypoints using the SURF descriptor
:param frame: the input image
:returns: (keypoints, descriptor)
"""
return self.SURF.detectAndCompute(frame, None)
def _match_features(self, desc_frame):
"""Feature matching between train and query image
This method finds matches between the descriptor of an input
(query) frame and the stored template (train) image.
The ratio test is applied to distinguish between good matches and
outliers.
:param desc_frame: descriptor of input (query) image
:returns: list of good matches
"""
# find 2 best matches (kNN with k=2)
matches = self.flann.knnMatch(self.desc_train, desc_frame, k=2)
# discard bad matches, ratio test as per Lowe's paper
good_matches = filter(lambda x: x[0].distance < 0.7*x[1].distance,
matches)
good_matches = [good_matches[i][0] for i in xrange(len(good_matches))]
return good_matches
def _detect_corner_points(self, key_frame, good_matches):
"""Detects corner points in an input (query) image
This method finds the homography matrix to go from the template
(train) image to the input (query) image, and finds the coordinates
of the good matches (from the train image) in the query image.
:param key_frame: keypoints of the query image
:param good_matches: list of good matches
:returns: coordinates of good matches in transformed query image
"""
# find homography using RANSAC
src_points = [self.key_train[good_matches[i].queryIdx].pt
for i in xrange(len(good_matches))]
dst_points = [key_frame[good_matches[i].trainIdx].pt
for i in xrange(len(good_matches))]
H, _ = cv2.findHomography(np.array(src_points), np.array(dst_points),
cv2.RANSAC)
# outline train image in query image
src_corners = np.array([(0, 0), (self.sh_train[1], 0),
(self.sh_train[1], self.sh_train[0]),
(0, self.sh_train[0])], dtype=np.float32)
dst_corners = cv2.perspectiveTransform(src_corners[None, :, :], H)
# convert to tuple
dst_corners = map(tuple, dst_corners[0])
return dst_corners
def _warp_keypoints(self, good_matches, key_frame, sh_frame):
"""Projects keypoints to the frontal plane
This method computes the homography matrix that is required to
project a list of keypoints to the frontal plane.
:param good_matches: list of good matches
:param key_frame: list of keypoints in the input (query) image
:param sh_frame: shape of the input (query) image
:returns: [Hinv, dst_size] homography matrix and size of resulting
image
"""
# bring object to frontoparallel plane: centered, up-right
dst_size = (sh_frame[1], sh_frame[0]) # cols,rows
scale_row = 1./self.sh_train[0]*dst_size[1]/2.
bias_row = dst_size[0]/4.
scale_col = 1./self.sh_train[1]*dst_size[0]*3/4.
bias_col = dst_size[1]/8.
# source points are the ones in the train image
src_points = [key_frame[good_matches[i].trainIdx].pt
for i in xrange(len(good_matches))]
# destination points are the ones in the query image
# off-set in space so that the image is centered
dst_points = [self.key_train[good_matches[i].queryIdx].pt
for i in xrange(len(good_matches))]
dst_points = [[x*scale_row+bias_row, y*scale_col+bias_col]
for x, y in dst_points]
# find homography
Hinv, _ = cv2.findHomography(np.array(src_points),
np.array(dst_points), cv2.RANSAC)
return [Hinv, dst_size]
def draw_good_matches(img1, kp1, img2, kp2, matches):
"""Visualizes a list of good matches
This function visualizes a list of good matches. It is only required in
OpenCV releases that do not ship with the function drawKeypoints.
The function draws two images (img1 and img2) side-by-side,
highlighting a list of keypoints in both, and connects matching
keypoints in the two images with blue lines.
:param img1: first image
:param kp1: list of keypoints for first image
:param img2: second image
:param kp2: list of keypoints for second image
:param matches: list of good matches
:returns: annotated output image
"""
# Create a new output image that concatenates the two images together
# (a.k.a) a montage
rows1, cols1 = img1.shape[:2]
rows2, cols2 = img2.shape[:2]
out = np.zeros((max([rows1, rows2]), cols1+cols2, 3), dtype='uint8')
# Place the first image to the left, copy 3x to make it RGB
out[:rows1, :cols1, :] = np.dstack([img1, img1, img1])
# Place the next image to the right of it, copy 3x to make it RGB
out[:rows2, cols1:cols1+cols2, :] = np.dstack([img2, img2, img2])
radius = 4
BLUE = (255, 0, 0)
thickness = 1
# For each pair of points we have between both images
# draw circles, then connect a line between them
for m in matches:
# Get the matching keypoints for each of the images
c1, r1 = kp1[m.queryIdx].pt
c2, r2 = kp2[m.trainIdx].pt
# Draw a small circle at both co-ordinates
# radius 4
# colour blue
# thickness = 1
cv2.circle(out, (int(c1), int(r1)), radius, BLUE, thickness)
cv2.circle(out, (int(c2)+cols1, int(r2)), radius, BLUE, thickness)
# Draw a line in between the two points
# thickness = 1
# colour blue
cv2.line(out, (int(c1), int(r1)), (int(c2)+cols1, int(r2)), BLUE,
thickness)
return out
================================================
FILE: chapter3/gui.py
================================================
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""A module containing simple GUI layouts using wxPython"""
import abc
import six
import time
import wx
import cv2
__author__ = "Michael Beyeler"
__license__ = "GNU GPL 3.0 or later"
class Meta1(wx.Frame):
pass
@six.add_metaclass(abc.ABCMeta)
class BaseLayout(Meta1):
"""Abstract base class for all layouts
A custom layout needs to implement at least three methods:
* _init_custom_layout: A method to initialize all relevant
parameters. This method will be called in the
class constructor, after initializing common
parameters, right before creating the GUI
layout.
* _create_custom_layout: A method to create a custom GUI layout. This
method will be called in the class
constructor, after initializing common
parameters.
Every GUI contains the camera feed in the
variable self.pnl.
Additional layout elements can be added below
the camera feed by means of the method
self.panels_vertical.Add.
* _process_frame: A method to process the current RGB camera
frame. It needs to return the processed RGB
frame to be displayed.
"""
def __init__(self, capture, title=None, parent=None, id=-1, fps=10):
"""Class constructor
This method initializes all necessary parameters and generates a
basic GUI layout that can then be modified by
self.init_custom_layout() and self.create_custom_layout().
:param parent: A wx.Frame parent (often Null). If it is non-Null,
the frame will be minimized when its parent is minimized and
restored when it is restored.
:param id: The window identifier. Value -1 indicates default value.
:param title: The caption to be displayed on the frame's title bar.
:param capture: A cv2.VideoCapture object to be used as camera
feed.
:param fps: frames per second at which to display camera feed
"""
self.capture = capture
self.fps = fps
# determine window size and init wx.Frame
success, frame = self._acquire_frame()
if not success:
print "Could not acquire frame from camera."
raise SystemExit
self.imgHeight, self.imgWidth = frame.shape[:2]
self.bmp = wx.BitmapFromBuffer(self.imgWidth, self.imgHeight, frame)
wx.Frame.__init__(self, parent, id, title,
size=(self.imgWidth, self.imgHeight))
self._init_base_layout()
self._create_base_layout()
def _init_base_layout(self):
"""Initialize parameters
This method performs initializations that are common to all GUIs,
such as the setting up of a timer.
It then calls an abstract method self.init_custom_layout() that
allows for additional, application-specific initializations.
"""
# set up periodic screen capture
self.timer = wx.Timer(self)
self.timer.Start(1000. / self.fps)
self.Bind(wx.EVT_TIMER, self._on_next_frame)
# allow for custom modifications
self._init_custom_layout()
def _create_base_layout(self):
"""Create generic layout
This method sets up a basic layout that is common to all GUIs, such
as a live stream of the camera (capture device). This stream is
assigned to the variable self.pnl, and arranged in a vertical
layout self.panels_vertical.
Additional layout elements can be added below the livestream by
means of the method self.panels_vertical.Add.
"""
# set up video stream
self.pnl = wx.Panel(self, size=(self.imgWidth, self.imgHeight))
self.pnl.SetBackgroundColour(wx.BLACK)
self.pnl.Bind(wx.EVT_PAINT, self._on_paint)
# display the button layout beneath the video stream
self.panels_vertical = wx.BoxSizer(wx.VERTICAL)
self.panels_vertical.Add(self.pnl, 1, flag=wx.EXPAND | wx.TOP,
border=1)
# allow for custom layout modifications
self._create_custom_layout()
# round off the layout by expanding and centering
self.SetMinSize((self.imgWidth, self.imgHeight))
self.SetSizer(self.panels_vertical)
self.Centre()
def _on_next_frame(self, event):
"""
This method captures a new frame from the camera (or capture
device) and sends an RGB version to the method self.process_frame.
The latter will then apply task-specific post-processing and return
an image to be displayed.
"""
success, frame = self._acquire_frame()
if success:
# process current frame
frame = self._process_frame(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
# update buffer and paint (EVT_PAINT triggered by Refresh)
self.bmp.CopyFromBuffer(frame)
self.Refresh(eraseBackground=False)
def _on_paint(self, event):
"""
This method draws the camera frame stored in the bitmap self.bmp
onto the panel self.pnl. Make sure self.pnl exists and is at least
the size of the camera frame.
This method is called whenever an event wx.EVT_PAINT is triggered.
"""
# read and draw buffered bitmap
deviceContext = wx.BufferedPaintDC(self.pnl)
deviceContext.DrawBitmap(self.bmp, 0, 0)
def _acquire_frame(self):
"""
This method is called whenever a new frame needs to be acquired.
:returns: (success, frame), whether acquiring was successful
(via Boolean success) and current frame
"""
return self.capture.read()
@abc.abstractmethod
def _init_custom_layout(self):
"""
This method is called in the class constructor, after setting up
relevant event callbacks, and right before creation of the GUI
layout.
"""
pass
@abc.abstractmethod
def _create_custom_layout(self):
"""
This method is responsible for creating the GUI layout.
It is called in the class constructor, after setting up relevant
event callbacks and self.init_layout, and creates the layout.
Every GUI contains the camera feed in the variable self.pnl.
Additional layout elements can be added below the camera feed by
adding them to self.panels_vertical.
"""
pass
@abc.abstractmethod
def _process_frame(self, frame_rgb):
"""
This method is responsible for any post-processing that needs to be
applied to the current frame of the camera (capture device) stream.
:param frame_rgb: The RGB camera frame to be processed.
:returns: The processed RGB camera frame to be displayed.
"""
pass
================================================
FILE: chapter4/calibrate.py
================================================
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""A module for camera calibration using a chessboard"""
import cv2
import numpy as np
import wx
from gui import BaseLayout
class CameraCalibration(BaseLayout):
"""Camera calibration
This class performs camera calibration on a webcam video feed using
the chessboard approach described here:
http://docs.opencv.org/doc/tutorials/calib3d/camera_calibration/camera_calibration.html
"""
def _init_custom_layout(self):
"""Initializes camera calibration"""
# setting chessboard size
self.chessboard_size = (9, 6)
# prepare object points
self.objp = np.zeros((np.prod(self.chessboard_size), 3),
dtype=np.float32)
self.objp[:, :2] = np.mgrid[0:self.chessboard_size[0],
0:self.chessboard_size[1]].T.reshape(-1, 2)
# prepare recording
self.recording = False
self.record_min_num_frames = 20
self._reset_recording()
def _create_custom_layout(self):
"""Creates a horizontal layout with a single button"""
pnl = wx.Panel(self, -1)
self.button_calibrate = wx.Button(pnl, label='Calibrate Camera')
self.Bind(wx.EVT_BUTTON, self._on_button_calibrate)
hbox = wx.BoxSizer(wx.HORIZONTAL)
hbox.Add(self.button_calibrate)
pnl.SetSizer(hbox)
self.panels_vertical.Add(pnl, flag=wx.EXPAND | wx.BOTTOM | wx.TOP,
border=1)
def _process_frame(self, frame):
"""Processes each frame
If recording mode is on (self.recording==True), this method will
perform all the hard work of the camera calibration process:
- for every frame, until enough frames have been processed:
- find the chessboard corners
- refine the coordinates of the detected corners
- after enough frames have been processed:
- estimate the intrinsic camera matrix and distortion
coefficients
:param frame: current RGB video frame
:returns: annotated video frame showing detected chessboard corners
"""
# if we are not recording, just display the frame
if not self.recording:
return frame
# else we're recording
img_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY).astype(np.uint8)
if self.record_cnt < self.record_min_num_frames:
# need at least some number of chessboard samples before we can
# calculate the intrinsic matrix
ret, corners = cv2.findChessboardCorners(img_gray,
self.chessboard_size,
None)
if ret:
cv2.drawChessboardCorners(frame, self.chessboard_size, corners,
ret)
# refine found corners
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER,
30, 0.01)
cv2.cornerSubPix(img_gray, corners, (9, 9), (-1, -1), criteria)
self.obj_points.append(self.objp)
self.img_points.append(corners)
self.record_cnt += 1
else:
# we have already collected enough frames, so now we want to
# calculate the intrinsic camera matrix (K) and the distortion
# vector (dist)
print "Calibrating..."
ret, K, dist, rvecs, tvecs = cv2.calibrateCamera(self.obj_points,
self.img_points,
(self.imgHeight,
self.imgWidth),
None, None)
print "K=", K
print "dist=", dist
# double-check reconstruction error (should be as close to zero as
# possible)
mean_error = 0
for i in xrange(len(self.obj_points)):
img_points2, _ = cv2.projectPoints(self.obj_points[i],
rvecs[i], tvecs[i], K, dist)
error = cv2.norm(self.img_points[i], img_points2,
cv2.NORM_L2) / len(img_points2)
mean_error += error
print "mean error=", mean_error
self.recording = False
self._reset_recording()
self.button_calibrate.Enable()
return frame
def _on_button_calibrate(self, event):
"""Enable recording mode upon pushing the button"""
self.button_calibrate.Disable()
self.recording = True
self._reset_recording()
def _reset_recording(self):
"""Disable recording mode and reset data structures"""
self.record_cnt = 0
self.obj_points = []
self.img_points = []
def main():
capture = cv2.VideoCapture(0)
if not(capture.isOpened()):
capture.open()
if hasattr(cv2, 'cv'):
capture.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH, 640)
capture.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, 480)
else:
capture.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
# start graphical user interface
app = wx.App()
layout = CameraCalibration(capture, title='Camera Calibration')
layout.Show(True)
app.MainLoop()
if __name__ == '__main__':
main()
================================================
FILE: chapter4/chapter4.py
================================================
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""OpenCV with Python Blueprints
Chapter 4: 3D Scene Reconstruction Using Structure From Motion
An app to detect and extract structure from motion on a pair of images
using stereo vision. We will assume that the two images have been taken
with the same camera, of which we know the internal camera parameters. If
these parameters are not known, use calibrate.py to estimate them.
The result is a point cloud that shows the 3D real-world coordinates
of points in the scene.
"""
import numpy as np
from scene3D import SceneReconstruction3D
def main():
# camera matrix and distortion coefficients
# can be recovered with calibrate.py
# but the examples used here are already undistorted, taken with a camera
# of known K
K = np.array([[2759.48/4, 0, 1520.69/4, 0, 2764.16/4,
1006.81/4, 0, 0, 1]]).reshape(3, 3)
d = np.array([0.0, 0.0, 0.0, 0.0, 0.0]).reshape(1, 5)
scene = SceneReconstruction3D(K, d)
# load a pair of images for which to perform SfM
scene.load_image_pair("fountain_dense/0004.png", "fountain_dense/0005.png")
# draw 3D point cloud of fountain
# use "pan axes" button in pyplot to inspect the cloud (rotate and zoom
# to convince you of the result)
scene.plot_point_cloud()
if __name__ == '__main__':
main()
================================================
FILE: chapter4/gui.py
================================================
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""A module containing simple GUI layouts using wxPython"""
import abc
import six
import time
import wx
import cv2
__author__ = "Michael Beyeler"
__license__ = "GNU GPL 3.0 or later"
class Meta1(wx.Frame):
pass
@six.add_metaclass(abc.ABCMeta)
class BaseLayout(Meta1):
"""Abstract base class for all layouts
A custom layout needs to implement at least three methods:
* _init_custom_layout: A method to initialize all relevant
parameters. This method will be called in the
class constructor, after initializing common
parameters, right before creating the GUI
layout.
* _create_custom_layout: A method to create a custom GUI layout. This
method will be called in the class
constructor, after initializing common
parameters.
Every GUI contains the camera feed in the
variable self.pnl.
Additional layout elements can be added below
the camera feed by means of the method
self.panels_vertical.Add.
* _process_frame: A method to process the current RGB camera
frame. It needs to return the processed RGB
frame to be displayed.
"""
def __init__(self, capture, title=None, parent=None, id=-1, fps=10):
"""Class constructor
This method initializes all necessary parameters and generates a
basic GUI layout that can then be modified by
self.init_custom_layout() and self.create_custom_layout().
:param parent: A wx.Frame parent (often Null). If it is non-Null,
the frame will be minimized when its parent is minimized and
restored when it is restored.
:param id: The window identifier. Value -1 indicates default value.
:param title: The caption to be displayed on the frame's title bar.
:param capture: A cv2.VideoCapture object to be used as camera
feed.
:param fps: frames per second at which to display camera feed
"""
self.capture = capture
self.fps = fps
# determine window size and init wx.Frame
success, frame = self._acquire_frame()
if not success:
print "Could not acquire frame from camera."
raise SystemExit
self.imgHeight, self.imgWidth = frame.shape[:2]
self.bmp = wx.BitmapFromBuffer(self.imgWidth, self.imgHeight, frame)
wx.Frame.__init__(self, parent, id, title,
size=(self.imgWidth, self.imgHeight))
self._init_base_layout()
self._create_base_layout()
def _init_base_layout(self):
"""Initialize parameters
This method performs initializations that are common to all GUIs,
such as the setting up of a timer.
It then calls an abstract method self.init_custom_layout() that
allows for additional, application-specific initializations.
"""
# set up periodic screen capture
self.timer = wx.Timer(self)
self.timer.Start(1000. / self.fps)
self.Bind(wx.EVT_TIMER, self._on_next_frame)
# allow for custom modifications
self._init_custom_layout()
def _create_base_layout(self):
"""Create generic layout
This method sets up a basic layout that is common to all GUIs, such
as a live stream of the camera (capture device). This stream is
assigned to the variable self.pnl, and arranged in a vertical
layout self.panels_vertical.
Additional layout elements can be added below the livestream by
means of the method self.panels_vertical.Add.
"""
# set up video stream
self.pnl = wx.Panel(self, size=(self.imgWidth, self.imgHeight))
self.pnl.SetBackgroundColour(wx.BLACK)
self.pnl.Bind(wx.EVT_PAINT, self._on_paint)
# display the button layout beneath the video stream
self.panels_vertical = wx.BoxSizer(wx.VERTICAL)
self.panels_vertical.Add(self.pnl, 1, flag=wx.EXPAND | wx.TOP,
border=1)
# allow for custom layout modifications
self._create_custom_layout()
# round off the layout by expanding and centering
self.SetMinSize((self.imgWidth, self.imgHeight))
self.SetSizer(self.panels_vertical)
self.Centre()
def _on_next_frame(self, event):
"""
This method captures a new frame from the camera (or capture
device) and sends an RGB version to the method self.process_frame.
The latter will then apply task-specific post-processing and return
an image to be displayed.
"""
success, frame = self._acquire_frame()
if success:
# process current frame
frame = self._process_frame(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
# update buffer and paint (EVT_PAINT triggered by Refresh)
self.bmp.CopyFromBuffer(frame)
self.Refresh(eraseBackground=False)
def _on_paint(self, event):
"""
This method draws the camera frame stored in the bitmap self.bmp
onto the panel self.pnl. Make sure self.pnl exists and is at least
the size of the camera frame.
This method is called whenever an event wx.EVT_PAINT is triggered.
"""
# read and draw buffered bitmap
deviceContext = wx.BufferedPaintDC(self.pnl)
deviceContext.DrawBitmap(self.bmp, 0, 0)
def _acquire_frame(self):
"""
This method is called whenever a new frame needs to be acquired.
:returns: (success, frame), whether acquiring was successful
(via Boolean success) and current frame
"""
return self.capture.read()
@abc.abstractmethod
def _init_custom_layout(self):
"""
This method is called in the class constructor, after setting up
relevant event callbacks, and right before creation of the GUI
layout.
"""
pass
@abc.abstractmethod
def _create_custom_layout(self):
"""
This method is responsible for creating the GUI layout.
It is called in the class constructor, after setting up relevant
event callbacks and self.init_layout, and creates the layout.
Every GUI contains the camera feed in the variable self.pnl.
Additional layout elements can be added below the camera feed by
adding them to self.panels_vertical.
"""
pass
@abc.abstractmethod
def _process_frame(self, frame_rgb):
"""
This method is responsible for any post-processing that needs to be
applied to the current frame of the camera (capture device) stream.
:param frame_rgb: The RGB camera frame to be processed.
:returns: The processed RGB camera frame to be displayed.
"""
pass
================================================
FILE: chapter4/scene3D.py
================================================
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""A module that contains an algorithm for 3D scene reconstruction """
import cv2
import numpy as np
import sys
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
class SceneReconstruction3D:
"""3D scene reconstruction
This class implements an algorithm for 3D scene reconstruction using
stereo vision and structure-from-motion techniques.
A 3D scene is reconstructed from a pair of images that show the same
real-world scene from two different viewpoints. Feature matching is
performed either with rich feature descriptors or based on optic flow.
3D coordinates are obtained via triangulation.
Note that a complete structure-from-motion pipeline typically includes
bundle adjustment and geometry fitting, which are out of scope for
this project.
"""
def __init__(self, K, dist):
"""Constructor
This method initializes the scene reconstruction algorithm.
:param K: 3x3 intrinsic camera matrix
:param dist: vector of distortion coefficients
"""
self.K = K
self.K_inv = np.linalg.inv(K) # store inverse for fast access
self.d = dist
def load_image_pair(self, img_path1, img_path2, use_pyr_down=True):
"""Loads pair of images
This method loads the two images for which the 3D scene should be
reconstructed. The two images should show the same real-world scene
from two different viewpoints.
:param img_path1: path to first image
:param img_path2: path to second image
:param use_pyr_down: flag whether to downscale the images to
roughly 600px width (True) or not (False)
"""
self.img1 = cv2.imread(img_path1, cv2.CV_8UC3)
self.img2 = cv2.imread(img_path2, cv2.CV_8UC3)
# make sure images are valid
if self.img1 is None:
sys.exit("Image " + img_path1 + " could not be loaded.")
if self.img2 is None:
sys.exit("Image " + img_path2 + " could not be loaded.")
if len(self.img1.shape) == 2:
self.img1 = cv2.cvtColor(self.img1, cv2.COLOR_GRAY2BGR)
self.img2 = cv2.cvtColor(self.img2, cv2.COLOR_GRAY2BGR)
# scale down image if necessary
# to something close to 600px wide
target_width = 600
if use_pyr_down and self.img1.shape[1] > target_width:
while self.img1.shape[1] > 2*target_width:
self.img1 = cv2.pyrDown(self.img1)
self.img2 = cv2.pyrDown(self.img2)
# undistort the images
self.img1 = cv2.undistort(self.img1, self.K, self.d)
self.img2 = cv2.undistort(self.img2, self.K, self.d)
def plot_optic_flow(self):
"""Plots optic flow field
This method plots the optic flow between the first and second
image.
"""
self._extract_keypoints("flow")
img = self.img1
for i in xrange(len(self.match_pts1)):
cv2.line(img, tuple(self.match_pts1[i]), tuple(self.match_pts2[i]),
color=(255, 0, 0))
theta = np.arctan2(self.match_pts2[i][1] - self.match_pts1[i][1],
self.match_pts2[i][0] - self.match_pts1[i][0])
cv2.line(img, tuple(self.match_pts2[i]),
(np.int(self.match_pts2[i][0] - 6*np.cos(theta+np.pi/4)),
np.int(self.match_pts2[i][1] - 6*np.sin(theta+np.pi/4))),
color=(255, 0, 0))
cv2.line(img, tuple(self.match_pts2[i]),
(np.int(self.match_pts2[i][0] - 6*np.cos(theta-np.pi/4)),
np.int(self.match_pts2[i][1] - 6*np.sin(theta-np.pi/4))),
color=(255, 0, 0))
cv2.imshow("imgFlow", img)
cv2.waitKey()
def draw_epipolar_lines(self, feat_mode="SURF"):
"""Draws epipolar lines
This method computes and draws the epipolar lines of the two
loaded images.
:param feat_mode: whether to use rich descriptors for feature
matching ("surf") or optic flow ("flow")
"""
self._extract_keypoints(feat_mode)
self._find_fundamental_matrix()
# Find epilines corresponding to points in right image (second image)
# and drawing its lines on left image
pts2re = self.match_pts2.reshape(-1, 1, 2)
lines1 = cv2.computeCorrespondEpilines(pts2re, 2, self.F)
lines1 = lines1.reshape(-1, 3)
img3, img4 = self._draw_epipolar_lines_helper(self.img1, self.img2,
lines1, self.match_pts1,
self.match_pts2)
# Find epilines corresponding to points in left image (first image) and
# drawing its lines on right image
pts1re = self.match_pts1.reshape(-1, 1, 2)
lines2 = cv2.computeCorrespondEpilines(pts1re, 1, self.F)
lines2 = lines2.reshape(-1, 3)
img1, img2 = self._draw_epipolar_lines_helper(self.img2, self.img1,
lines2, self.match_pts2,
self.match_pts1)
cv2.imshow("left", img1)
cv2.imshow("right", img3)
cv2.waitKey()
def plot_rectified_images(self, feat_mode="SURF"):
"""Plots rectified images
This method computes and plots a rectified version of the two
images side by side.
:param feat_mode: whether to use rich descriptors for feature
matching ("surf") or optic flow ("flow")
"""
self._extract_keypoints(feat_mode)
self._find_fundamental_matrix()
self._find_essential_matrix()
self._find_camera_matrices_rt()
R = self.Rt2[:, :3]
T = self.Rt2[:, 3]
#perform the rectification
R1, R2, P1, P2, Q, roi1, roi2 = cv2.stereoRectify(self.K, self.d,
self.K, self.d,
self.img1.shape[:2],
R, T, alpha=1.0)
mapx1, mapy1 = cv2.initUndistortRectifyMap(self.K, self.d, R1, self.K,
self.img1.shape[:2],
cv2.CV_32F)
mapx2, mapy2 = cv2.initUndistortRectifyMap(self.K, self.d, R2, self.K,
self.img2.shape[:2],
cv2.CV_32F)
img_rect1 = cv2.remap(self.img1, mapx1, mapy1, cv2.INTER_LINEAR)
img_rect2 = cv2.remap(self.img2, mapx2, mapy2, cv2.INTER_LINEAR)
# draw the images side by side
total_size = (max(img_rect1.shape[0], img_rect2.shape[0]),
img_rect1.shape[1] + img_rect2.shape[1], 3)
img = np.zeros(total_size, dtype=np.uint8)
img[:img_rect1.shape[0], :img_rect1.shape[1]] = img_rect1
img[:img_rect2.shape[0], img_rect1.shape[1]:] = img_rect2
# draw horizontal lines every 25 px accross the side by side image
for i in range(20, img.shape[0], 25):
cv2.line(img, (0, i), (img.shape[1], i), (255, 0, 0))
cv2.imshow('imgRectified', img)
cv2.waitKey()
def plot_point_cloud(self, feat_mode="SURF"):
"""Plots 3D point cloud
This method generates and plots a 3D point cloud of the recovered
3D scene.
:param feat_mode: whether to use rich descriptors for feature
matching ("surf") or optic flow ("flow")
"""
self._extract_keypoints(feat_mode)
self._find_fundamental_matrix()
self._find_essential_matrix()
self._find_camera_matrices_rt()
# triangulate points
first_inliers = np.array(self.match_inliers1).reshape(-1, 3)[:, :2]
second_inliers = np.array(self.match_inliers2).reshape(-1, 3)[:, :2]
pts4D = cv2.triangulatePoints(self.Rt1, self.Rt2, first_inliers.T,
second_inliers.T).T
# convert from homogeneous coordinates to 3D
pts3D = pts4D[:, :3]/np.repeat(pts4D[:, 3], 3).reshape(-1, 3)
# plot with matplotlib
Ys = pts3D[:, 0]
Zs = pts3D[:, 1]
Xs = pts3D[:, 2]
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(Xs, Ys, Zs, c='r', marker='o')
ax.set_xlabel('Y')
ax.set_ylabel('Z')
ax.set_zlabel('X')
plt.title('3D point cloud: Use pan axes button below to inspect')
plt.show()
def _extract_keypoints(self, feat_mode):
"""Extracts keypoints
This method extracts keypoints for feature matching based on
a specified mode:
- "surf": use rich SURF descriptor
- "flow": use optic flow
:param feat_mode: keypoint extraction mode ("surf" or "flow")
"""
# extract features
if feat_mode.lower() == "surf":
# feature matching via SURF and BFMatcher
self._extract_keypoints_surf()
else:
if feat_mode.lower() == "flow":
# feature matching via optic flow
self._extract_keypoints_flow()
else:
sys.exit("Unknown feat_mode " + feat_mode +
". Use 'SURF' or 'FLOW'")
def _extract_keypoints_surf(self):
"""Extracts keypoints via SURF descriptors"""
# extract keypoints and descriptors from both images
detector = cv2.SURF(250)
first_key_points, first_desc = detector.detectAndCompute(self.img1,
None)
second_key_points, second_desc = detector.detectAndCompute(self.img2,
None)
# match descriptors
matcher = cv2.BFMatcher(cv2.NORM_L1, True)
matches = matcher.match(first_desc, second_desc)
# generate lists of point correspondences
first_match_points = np.zeros((len(matches), 2), dtype=np.float32)
second_match_points = np.zeros_like(first_match_points)
for i in range(len(matches)):
first_match_points[i] = first_key_points[matches[i].queryIdx].pt
second_match_points[i] = second_key_points[matches[i].trainIdx].pt
self.match_pts1 = first_match_points
self.match_pts2 = second_match_points
def _extract_keypoints_flow(self):
"""Extracts keypoints via optic flow"""
# find FAST features
fast = cv2.FastFeatureDetector()
first_key_points = fast.detect(self.img1, None)
first_key_list = [i.pt for i in first_key_points]
first_key_arr = np.array(first_key_list).astype(np.float32)
second_key_arr, status, err = cv2.calcOpticalFlowPyrLK(self.img1,
self.img2,
first_key_arr)
# filter out the points with high error
# keep only entries with status=1 and small error
condition = (status == 1) * (err < 5.)
concat = np.concatenate((condition, condition), axis=1)
first_match_points = first_key_arr[concat].reshape(-1, 2)
second_match_points = second_key_arr[concat].reshape(-1, 2)
self.match_pts1 = first_match_points
self.match_pts2 = second_match_points
def _find_fundamental_matrix(self):
"""Estimates fundamental matrix """
self.F, self.Fmask = cv2.findFundamentalMat(self.match_pts1,
self.match_pts2,
cv2.FM_RANSAC, 0.1, 0.99)
def _find_essential_matrix(self):
"""Estimates essential matrix based on fundamental matrix """
self.E = self.K.T.dot(self.F).dot(self.K)
def _find_camera_matrices_rt(self):
"""Finds the [R|t] camera matrix"""
# decompose essential matrix into R, t (See Hartley and Zisserman 9.13)
U, S, Vt = np.linalg.svd(self.E)
W = np.array([0.0, -1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0,
1.0]).reshape(3, 3)
# iterate over all point correspondences used in the estimation of the
# fundamental matrix
first_inliers = []
second_inliers = []
for i in range(len(self.Fmask)):
if self.Fmask[i]:
# normalize and homogenize the image coordinates
first_inliers.append(self.K_inv.dot([self.match_pts1[i][0],
self.match_pts1[i][1], 1.0]))
second_inliers.append(self.K_inv.dot([self.match_pts2[i][0],
self.match_pts2[i][1], 1.0]))
# Determine the correct choice of second camera matrix
# only in one of the four configurations will all the points be in
# front of both cameras
# First choice: R = U * Wt * Vt, T = +u_3 (See Hartley Zisserman 9.19)
R = U.dot(W).dot(Vt)
T = U[:, 2]
if not self._in_front_of_both_cameras(first_inliers, second_inliers,
R, T):
# Second choice: R = U * W * Vt, T = -u_3
T = - U[:, 2]
if not self._in_front_of_both_cameras(first_inliers, second_inliers,
R, T):
# Third choice: R = U * Wt * Vt, T = u_3
R = U.dot(W.T).dot(Vt)
T = U[:, 2]
if not self._in_front_of_both_cameras(first_inliers,
second_inliers, R, T):
# Fourth choice: R = U * Wt * Vt, T = -u_3
T = - U[:, 2]
self.match_inliers1 = first_inliers
self.match_inliers2 = second_inliers
self.Rt1 = np.hstack((np.eye(3), np.zeros((3, 1))))
self.Rt2 = np.hstack((R, T.reshape(3, 1)))
def _draw_epipolar_lines_helper(self, img1, img2, lines, pts1, pts2):
"""Helper method to draw epipolar lines and features """
if img1.shape[2] == 1:
img1 = cv2.cvtColor(img1, cv2.COLOR_GRAY2BGR)
if img2.shape[2] == 1:
img2 = cv2.cvtColor(img2, cv2.COLOR_GRAY2BGR)
c = img1.shape[1]
for r, pt1, pt2 in zip(lines, pts1, pts2):
color = tuple(np.random.randint(0, 255, 3).tolist())
x0, y0 = map(int, [0, -r[2]/r[1]])
x1, y1 = map(int, [c, -(r[2] + r[0]*c) / r[1]])
cv2.line(img1, (x0, y0), (x1, y1), color, 1)
cv2.circle(img1, tuple(pt1), 5, color, -1)
cv2.circle(img2, tuple(pt2), 5, color, -1)
return img1, img2
def _in_front_of_both_cameras(self, first_points, second_points, rot,
trans):
"""Determines whether point correspondences are in front of both
images"""
rot_inv = rot
for first, second in zip(first_points, second_points):
first_z = np.dot(rot[0, :] - second[0]*rot[2, :],
trans) / np.dot(rot[0, :] - second[0]*rot[2, :],
second)
first_3d_point = np.array([first[0] * first_z,
second[0] * first_z, first_z])
second_3d_point = np.dot(rot.T, first_3d_point) - np.dot(rot.T,
trans)
if first_3d_point[2] < 0 or second_3d_point[2] < 0:
return False
return True
def _linear_ls_triangulation(self, u1, P1, u2, P2):
"""Triangulation via Linear-LS method"""
# build A matrix for homogeneous equation system Ax=0
# assume X = (x,y,z,1) for Linear-LS method
# which turns it into AX=B system, where A is 4x3, X is 3x1 & B is 4x1
A = np.array([u1[0]*P1[2, 0] - P1[0, 0], u1[0]*P1[2, 1] - P1[0, 1],
u1[0]*P1[2, 2] - P1[0, 2], u1[1]*P1[2, 0] - P1[1, 0],
u1[1]*P1[2, 1] - P1[1, 1], u1[1]*P1[2, 2] - P1[1, 2],
u2[0]*P2[2, 0] - P2[0, 0], u2[0]*P2[2, 1] - P2[0, 1],
u2[0]*P2[2, 2] - P2[0, 2], u2[1]*P2[2, 0] - P2[1, 0],
u2[1]*P2[2, 1] - P2[1, 1],
u2[1]*P2[2, 2] - P2[1, 2]]).reshape(4, 3)
B = np.array([-(u1[0]*P1[2, 3] - P1[0, 3]),
-(u1[1]*P1[2, 3] - P1[1, 3]),
-(u2[0]*P2[2, 3] - P2[0, 3]),
-(u2[1]*P2[2, 3] - P2[1, 3])]).reshape(4, 1)
ret, X = cv2.solve(A, B, flags=cv2.DECOMP_SVD)
return X.reshape(1, 3)
================================================
FILE: chapter5/chapter5.py
================================================
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""OpenCV with Python Blueprints
Chapter 5: Tracking Visually Salient Objects
An app to track multiple visually salient objects in a video sequence.
"""
import cv2
import numpy as np
from os import path
from saliency import Saliency
from tracking import MultipleObjectsTracker
def main(video_file='soccer.avi', roi=((140, 100), (500, 600))):
# open video file
if path.isfile(video_file):
video = cv2.VideoCapture(video_file)
else:
print 'File "' + video_file + '" does not exist.'
raise SystemExit
# initialize tracker
mot = MultipleObjectsTracker()
while True:
# grab next frame
success, img = video.read()
if success:
if roi:
# original video is too big: grab some meaningful ROI
img = img[roi[0][0]:roi[1][0], roi[0][1]:roi[1][1]]
# generate saliency map
sal = Saliency(img, use_numpy_fft=False, gauss_kernel=(3, 3))
cv2.imshow('original', img)
cv2.imshow('saliency', sal.get_saliency_map())
cv2.imshow('objects', sal.get_proto_objects_map(use_otsu=False))
cv2.imshow('tracker', mot.advance_frame(img,
sal.get_proto_objects_map(use_otsu=False)))
if cv2.waitKey(100) & 0xFF == ord('q'):
break
else:
break
if __name__ == '__main__':
main()
================================================
FILE: chapter5/saliency.py
================================================
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""A module to generate a saliency map from an RGB image
This code is based on the approach described in:
[1] X. Hou and L. Zhang (2007). Saliency Detection: A Spectral Residual
Approach. IEEE Transactions on Computer Vision and Pattern Recognition
(CVPR), p.1-8. doi: 10.1109/CVPR.2007.383267
"""
import cv2
import numpy as np
from matplotlib import pyplot as plt
class Saliency:
"""Generate saliency map from RGB images with the spectral residual method
This class implements an algorithm that is based on the spectral
residual approach (Hou & Zhang, 2007).
"""
def __init__(self, img, use_numpy_fft=True, gauss_kernel=(5, 5)):
"""Constructor
This method initializes the saliency algorithm.
:param img: an RGB input image
:param use_numpy_fft: flag whether to use NumPy's FFT (True) or
OpenCV's FFT (False)
:param gauss_kernel: Kernel size for Gaussian blur
"""
self.use_numpy_fft = use_numpy_fft
self.gauss_kernel = gauss_kernel
self.frame_orig = img
# downsample image for processing
self.small_shape = (64, 64)
self.frame_small = cv2.resize(img, self.small_shape[1::-1])
# whether we need to do the math (True) or it has already
# been done (False)
self.need_saliency_map = True
def get_saliency_map(self):
"""Returns a saliency map
This method generates a saliency map for the image that was
passed to the class constructor.
:returns: grayscale saliency map
"""
if self.need_saliency_map:
# haven't calculated saliency map for this image yet
num_channels = 1
if len(self.frame_orig.shape) == 2:
# single channel
sal = self._get_channel_sal_magn(self.frame_small)
else:
# multiple channels: consider each channel independently
sal = np.zeros_like(self.frame_small).astype(np.float32)
for c in xrange(self.frame_small.shape[2]):
small = self.frame_small[:, :, c]
sal[:, :, c] = self._get_channel_sal_magn(small)
# overall saliency: channel mean
sal = np.mean(sal, 2)
# postprocess: blur, square, and normalize
if self.gauss_kernel is not None:
sal = cv2.GaussianBlur(sal, self.gauss_kernel, sigmaX=8,
sigmaY=0)
sal = sal**2
sal = np.float32(sal)/np.max(sal)
# scale up
sal = cv2.resize(sal, self.frame_orig.shape[1::-1])
# store a copy so we do the work only once per frame
self.saliencyMap = sal
self.need_saliency_map = False
return self.saliencyMap
def _get_channel_sal_magn(self, channel):
"""Returns the log-magnitude of the Fourier spectrum
This method calculates the log-magnitude of the Fourier spectrum
of a single-channel image. This image could be a regular grayscale
image, or a single color channel of an RGB image.
:param channel: single-channel input image
:returns: log-magnitude of Fourier spectrum
"""
# do FFT and get log-spectrum
if self.use_numpy_fft:
img_dft = np.fft.fft2(channel)
magnitude, angle = cv2.cartToPolar(np.real(img_dft),
np.imag(img_dft))
else:
img_dft = cv2.dft(np.float32(channel),
flags=cv2.DFT_COMPLEX_OUTPUT)
magnitude, angle = cv2.cartToPolar(img_dft[:, :, 0],
img_dft[:, :, 1])
# get log amplitude
log_ampl = np.log10(magnitude.clip(min=1e-9))
# blur log amplitude with avg filter
log_ampl_blur = cv2.blur(log_ampl, (3, 3))
# residual
residual = np.exp(log_ampl - log_ampl_blur)
# back to cartesian frequency domain
if self.use_numpy_fft:
real_part, imag_part = cv2.polarToCart(residual, angle)
img_combined = np.fft.ifft2(real_part + 1j*imag_part)
magnitude, _ = cv2.cartToPolar(np.real(img_combined),
np.imag(img_combined))
else:
img_dft[:, :, 0], img_dft[:, :, 1] = cv2.polarToCart(residual,
angle)
img_combined = cv2.idft(img_dft)
magnitude, _ = cv2.cartToPolar(img_combined[:, :, 0],
img_combined[:, :, 1])
return magnitude
def calc_magnitude_spectrum(self):
"""Plots the magnitude spectrum
This method calculates the magnitude spectrum of the image passed
to the class constructor.
:returns: magnitude spectrum
"""
# convert the frame to grayscale if necessary
if len(self.frame_orig.shape) > 2:
frame = cv2.cvtColor(self.frame_orig, cv2.COLOR_BGR2GRAY)
else:
frame = self.frame_orig
# expand the image to an optimal size for FFT
rows, cols = self.frame_orig.shape[:2]
nrows = cv2.getOptimalDFTSize(rows)
ncols = cv2.getOptimalDFTSize(cols)
frame = cv2.copyMakeBorder(frame, 0, ncols-cols, 0, nrows-rows,
cv2.BORDER_CONSTANT, value=0)
# do FFT and get log-spectrum
img_dft = np.fft.fft2(frame)
spectrum = np.log10(np.abs(np.fft.fftshift(img_dft)))
# return for plotting
return 255*spectrum/np.max(spectrum)
def plot_power_spectrum(self):
"""Plots the power spectrum
This method plots the power spectrum of the image passed to
the class constructor.
:returns: power spectrum
"""
# convert the frame to grayscale if necessary
if len(self.frame_orig.shape) > 2:
frame = cv2.cvtColor(self.frame_orig, cv2.COLOR_BGR2GRAY)
else:
frame = self.frame_orig
# expand the image to an optimal size for FFT
rows, cols = self.frame_orig.shape[:2]
nrows = cv2.getOptimalDFTSize(rows)
ncols = cv2.getOptimalDFTSize(cols)
frame = cv2.copyMakeBorder(frame, 0, ncols - cols, 0, nrows - rows,
cv2.BORDER_CONSTANT, value=0)
# do FFT and get log-spectrum
if self.use_numpy_fft:
img_dft = np.fft.fft2(frame)
spectrum = np.log10(np.real(np.abs(img_dft))**2)
else:
img_dft = cv2.dft(np.float32(frame), flags=cv2.DFT_COMPLEX_OUTPUT)
spectrum = np.log10(img_dft[:, :, 0]**2+img_dft[:, :, 1]**2)
# radial average
L = max(frame.shape)
freqs = np.fft.fftfreq(L)[:L/2]
dists = np.sqrt(np.fft.fftfreq(frame.shape[0])[:, np.newaxis]**2 +
np.fft.fftfreq(frame.shape[1])**2)
dcount = np.histogram(dists.ravel(), bins=freqs)[0]
histo, bins = np.histogram(dists.ravel(), bins=freqs,
weights=spectrum.ravel())
centers = (bins[:-1] + bins[1:]) / 2
plt.plot(centers, histo/dcount)
plt.xlabel('frequency')
plt.ylabel('log-spectrum')
plt.show()
def get_proto_objects_map(self, use_otsu=True):
"""Returns the proto-objects map of an RGB image
This method generates a proto-objects map of an RGB image.
Proto-objects are saliency hot spots, generated by thresholding
the saliency map.
:param use_otsu: flag whether to use Otsu thresholding (True) or
a hardcoded threshold value (False)
:returns: proto-objects map
"""
saliency = self.get_saliency_map()
if use_otsu:
_, img_objects = cv2.threshold(np.uint8(saliency*255), 0, 255,
cv2.THRESH_BINARY + cv2.THRESH_OTSU)
else:
thresh = np.mean(saliency)*255*3
_, img_objects = cv2.threshold(np.uint8(saliency*255), thresh, 255,
cv2.THRESH_BINARY)
return img_objects
================================================
FILE: chapter5/tracking.py
================================================
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""A module that contains an algorithm for multiple-objects tracking"""
import cv2
import numpy as np
import copy
class MultipleObjectsTracker:
"""Multiple-objects tracker
This class implements an algorithm for tracking multiple objects in
a video sequence.
The algorithm combines a saliency map for object detection and
mean-shift tracking for object tracking.
"""
def __init__(self, min_area=400, min_shift2=5):
"""Constructor
This method initializes the multiple-objects tracking algorithm.
:param min_area: Minimum area for a proto-object contour to be
considered a real object
:param min_shift2: Minimum distance for a proto-object to drift
from frame to frame ot be considered a real
object
"""
self.object_roi = []
self.object_box = []
self.min_cnt_area = min_area
self.min_shift2 = min_shift2
# Setup the termination criteria, either 100 iteration or move by at
# least 1 pt
self.term_crit = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT,
100, 1)
def advance_frame(self, frame, proto_objects_map):
"""Advances the algorithm by a single frame
This method tracks all objects via the following steps:
- adds all bounding boxes from saliency map as potential
targets
- finds bounding boxes from previous frame in current frame
via mean-shift tracking
- combines the two lists by removing duplicates
certain targets are discarded:
- targets that are too small
- targets that don't move
:param frame: New input RGB frame
:param proto_objects_map: corresponding proto-objects map of the
frame
:returns: frame annotated with bounding boxes around all objects
that are being tracked
"""
self.tracker = copy.deepcopy(frame)
# build a list of all bounding boxes
box_all = []
# append to the list all bounding boxes found from the
# current proto-objects map
box_all = self._append_boxes_from_saliency(proto_objects_map, box_all)
# find all bounding boxes extrapolated from last frame
# via mean-shift tracking
box_all = self._append_boxes_from_meanshift(frame, box_all)
# only keep those that are both salient and in mean shift
if len(self.object_roi) == 0:
group_thresh = 0 # no previous frame: keep all form saliency
else:
group_thresh = 1 # previous frame + saliency
box_grouped, _ = cv2.groupRectangles(box_all, group_thresh, 0.1)
# update mean-shift bookkeeping for remaining boxes
self._update_mean_shift_bookkeeping(frame, box_grouped)
# draw remaining boxes
for (x, y, w, h) in box_grouped:
cv2.rectangle(self.tracker, (x, y), (x + w, y + h),
(0, 255, 0), 2)
return self.tracker
def _append_boxes_from_saliency(self, proto_objects_map, box_all):
"""Adds to the list all bounding boxes found with the saliency map
A saliency map is used to find objects worth tracking in each
frame. This information is combined with a mean-shift tracker
to find objects of relevance that move, and to discard everything
else.
:param proto_objects_map: proto-objects map of the current frame
:param box_all: append bounding boxes from saliency to this list
:returns: new list of all collected bounding boxes
"""
# find all bounding boxes in new saliency map
box_sal = []
cnt_sal, _ = cv2.findContours(proto_objects_map, 1, 2)
for cnt in cnt_sal:
# discard small contours
if cv2.contourArea(cnt) < self.min_cnt_area:
continue
# otherwise add to list of boxes found from saliency map
box = cv2.boundingRect(cnt)
box_all.append(box)
return box_all
def _append_boxes_from_meanshift(self, frame, box_all):
"""Adds to the list all bounding boxes found with mean-shift tracking
Mean-shift tracking is used to track objects from frame to frame.
This information is combined with a saliency map to discard
false-positives and focus only on relevant objects that move.
:param frame: current RGB image frame
:box_all: append bounding boxes from tracking to this list
:returns: new list of all collected bounding boxes
"""
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
for i in xrange(len(self.object_roi)):
roi_hist = copy.deepcopy(self.object_roi[i])
box_old = copy.deepcopy(self.object_box[i])
dst = cv2.calcBackProject([hsv], [0], roi_hist, [0, 180], 1)
ret, box_new = cv2.meanShift(dst, tuple(box_old), self.term_crit)
self.object_box[i] = copy.deepcopy(box_new)
# discard boxes that don't move
(xo, yo, wo, ho) = box_old
(xn, yn, wn, hn) = box_new
co = [xo + wo/2, yo + ho/2]
cn = [xn + wn/2, yn + hn/2]
if (co[0]-cn[0])**2 + (co[1]-cn[1])**2 >= self.min_shift2:
box_all.append(box_new)
return box_all
def _update_mean_shift_bookkeeping(self, frame, box_grouped):
"""Preprocess all valid bounding boxes for mean-shift tracking
This method preprocesses all relevant bounding boxes (those that
have been detected by both mean-shift tracking and saliency) for
the next mean-shift step.
:param frame: current RGB input frame
:param box_grouped: list of bounding boxes
"""
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
self.object_roi = []
self.object_box = []
for box in box_grouped:
(x, y, w, h) = box
hsv_roi = hsv[y:y + h, x:x + w]
mask = cv2.inRange(hsv_roi, np.array((0., 60., 32.)),
np.array((180., 255., 255.)))
roi_hist = cv2.calcHist([hsv_roi], [0], mask, [180], [0, 180])
cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX)
self.object_roi.append(roi_hist)
self.object_box.append(box)
================================================
FILE: chapter6/chapter6.py
================================================
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""OpenCV with Python Blueprints
Chapter 6: Learning to Recognize Traffic Signs
Traffic sign recognition using support vector machines (SVMs).
SVMs are extended for multi-class classification using the "one-vs-one"
and "one-vs-all" strategies.
"""
import numpy as np
import matplotlib.pyplot as plt
from datasets import gtsrb
from classifiers import MultiClassSVM
__author__ = "Michael Beyeler"
__license__ = "GNU GPL 3.0 or later"
def main():
strategies = ['one-vs-one', 'one-vs-all']
features = [None, 'gray', 'rgb', 'hsv', 'surf', 'hog']
accuracy = np.zeros((2, len(features)))
precision = np.zeros((2, len(features)))
recall = np.zeros((2, len(features)))
for f in xrange(len(features)):
print "feature", features[f]
(X_train, y_train), (X_test, y_test) = gtsrb.load_data(
"datasets/gtsrb_training",
feature=features[f],
test_split=0.2,
seed=42)
# convert to numpy
X_train = np.squeeze(np.array(X_train)).astype(np.float32)
y_train = np.array(y_train)
X_test = np.squeeze(np.array(X_test)).astype(np.float32)
y_test = np.array(y_test)
# find all class labels
labels = np.unique(np.hstack((y_train, y_test)))
for s in xrange(len(strategies)):
print " - strategy", strategies[s]
# set up SVMs
MCS = MultiClassSVM(len(labels), strategies[s])
# training phase
print " - train"
MCS.fit(X_train, y_train)
# test phase
print " - test"
acc, prec, rec = MCS.evaluate(X_test, y_test)
accuracy[s, f] = acc
precision[s, f] = np.mean(prec)
recall[s, f] = np.mean(rec)
print " - accuracy: ", acc
print " - mean precision: ", np.mean(prec)
print " - mean recall: ", np.mean(rec)
# plot results as stacked bar plot
f, ax = plt.subplots(2)
for s in xrange(len(strategies)):
x = np.arange(len(features))
ax[s].bar(x - 0.2, accuracy[s, :], width=0.2, color='b',
hatch='/', align='center')
ax[s].bar(x, precision[s, :], width=0.2, color='r', hatch='\\',
align='center')
ax[s].bar(x + 0.2, recall[s, :], width=0.2, color='g', hatch='x',
align='center')
ax[s].axis([-0.5, len(features) + 0.5, 0, 1.5])
ax[s].legend(('Accuracy', 'Precision', 'Recall'), loc=2, ncol=3,
mode='expand')
ax[s].set_xticks(np.arange(len(features)))
ax[s].set_xticklabels(features)
ax[s].set_title(strategies[s])
plt.show()
if __name__ == '__main__':
main()
================================================
FILE: chapter6/classifiers.py
================================================
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""A module that contains various classifiers"""
import cv2
import numpy as np
from abc import ABCMeta, abstractmethod
from matplotlib import pyplot as plt
__author__ = "Michael Beyeler"
__license__ = "GNU GPL 3.0 or later"
class Classifier:
"""
Abstract base class for all classifiers
A classifier needs to implement at least two methods:
- fit: A method to train the classifier by fitting the model to
the data.
- evaluate: A method to test the classifier by predicting labels of
some test data based on the trained model.
A classifier also needs to specify a classification strategy via
setting self.mode to either "one-vs-all" or "one-vs-one".
The one-vs-all strategy involves training a single classifier per
class, with the samples of that class as positive samples and all
other samples as negatives.
The one-vs-one strategy involves training a single classifier per
class pair, with the samples of the first class as positive samples
and the samples of the second class as negative samples.
This class also provides method to calculate accuracy, precision,
recall, and the confusion matrix.
"""
__metaclass__ = ABCMeta
@abstractmethod
def fit(self, X_train, y_train):
pass
@abstractmethod
def evaluate(self, X_test, y_test, visualize=False):
pass
def _accuracy(self, y_test, Y_vote):
"""Calculates accuracy
This method calculates the accuracy based on a vector of
ground-truth labels (y_test) and a 2D voting matrix (Y_vote) of
size (len(y_test), num_classes).
:param y_test: vector of ground-truth labels
:param Y_vote: 2D voting matrix (rows=samples, cols=class votes)
:returns: accuracy e[0,1]
"""
# predicted classes
y_hat = np.argmax(Y_vote, axis=1)
# all cases where predicted class was correct
mask = y_hat == y_test
return np.float32(np.count_nonzero(mask)) / len(y_test)
def _precision(self, y_test, Y_vote):
"""Calculates precision
This method calculates precision extended to multi-class
classification by help of a confusion matrix.
:param y_test: vector of ground-truth labels
:param Y_vote: 2D voting matrix (rows=samples, cols=class votes)
:returns: precision e[0,1]
"""
# predicted classes
y_hat = np.argmax(Y_vote, axis=1)
if self.mode == "one-vs-one":
# need confusion matrix
conf = self._confusion(y_test, Y_vote)
# consider each class separately
prec = np.zeros(self.num_classes)
for c in xrange(self.num_classes):
# true positives: label is c, classifier predicted c
tp = conf[c, c]
# false positives: label is c, classifier predicted not c
fp = np.sum(conf[:, c]) - conf[c, c]
if tp + fp != 0:
prec[c] = tp * 1. / (tp + fp)
elif self.mode == "one-vs-all":
# consider each class separately
prec = np.zeros(self.num_classes)
for c in xrange(self.num_classes):
# true positives: label is c, classifier predicted c
tp = np.count_nonzero((y_test == c) * (y_hat == c))
# false positives: label is c, classifier predicted not c
fp = np.count_nonzero((y_test == c) * (y_hat != c))
if tp + fp != 0:
prec[c] = tp * 1. / (tp + fp)
return prec
def _recall(self, y_test, Y_vote):
"""Calculates recall
This method calculates recall extended to multi-class
classification by help of a confusion matrix.
:param y_test: vector of ground-truth labels
:param Y_vote: 2D voting matrix (rows=samples, cols=class votes)
:returns: recall e[0,1]
"""
# predicted classes
y_hat = np.argmax(Y_vote, axis=1)
if self.mode == "one-vs-one":
# need confusion matrix
conf = self._confusion(y_test, Y_vote)
# consider each class separately
recall = np.zeros(self.num_classes)
for c in xrange(self.num_classes):
# true positives: label is c, classifier predicted c
tp = conf[c, c]
# false negatives: label is not c, classifier predicted c
fn = np.sum(conf[c, :]) - conf[c, c]
if tp + fn != 0:
recall[c] = tp * 1. / (tp + fn)
elif self.mode == "one-vs-all":
# consider each class separately
recall = np.zeros(self.num_classes)
for c in xrange(self.num_classes):
# true positives: label is c, classifier predicted c
tp = np.count_nonzero((y_test == c) * (y_hat == c))
# false negatives: label is not c, classifier predicted c
fn = np.count_nonzero((y_test != c) * (y_hat == c))
if tp + fn != 0:
recall[c] = tp * 1. / (tp + fn)
return recall
def _confusion(self, y_test, Y_vote):
"""Calculates confusion matrix
This method calculates the confusion matrix based on a vector of
ground-truth labels (y-test) and a 2D voting matrix (Y_vote) of
size (len(y_test), num_classes).
Matrix element conf[r,c] will contain the number of samples that
were predicted to have label r but have ground-truth label c.
:param y_test: vector of ground-truth labels
:param Y_vote: 2D voting matrix (rows=samples, cols=class votes)
:returns: confusion matrix
"""
y_hat = np.argmax(Y_vote, axis=1)
conf = np.zeros((self.num_classes, self.num_classes)).astype(np.int32)
for c_true in xrange(self.num_classes):
# looking at all samples of a given class, c_true
# how many were classified as c_true? how many as others?
for c_pred in xrange(self.num_classes):
y_this = np.where((y_test == c_true) * (y_hat == c_pred))
conf[c_pred, c_true] = np.count_nonzero(y_this)
return conf
class MultiClassSVM(Classifier):
"""
Multi-class classification using Support Vector Machines (SVMs)
This class implements an SVM for multi-class classification. Whereas
some classifiers naturally permit the use of more than two classes
(such as neural networks), SVMs are binary in nature.
However, we can turn SVMs into multinomial classifiers using at least
two different strategies:
* one-vs-all: A single classifier is trained per class, with the
samples of that class as positives (label 1) and all
others as negatives (label 0).
* one-vs-one: For k classes, k*(k-1)/2 classifiers are trained for each
pair of classes, with the samples of the one class as
positives (label 1) and samples of the other class as
negatives (label 0).
Each classifier then votes for a particular class label, and the final
decision (classification) is based on a majority vote.
"""
def __init__(self, num_classes, mode="one-vs-all", params=None):
"""
The constructor makes sure the correct number of classifiers is
initialized, depending on the mode ("one-vs-all" or "one-vs-one").
:param num_classes: The number of classes in the data.
:param mode: Which classification mode to use.
"one-vs-all": single classifier per class
"one-vs-one": single classifier per class pair
Default: "one-vs-all"
:param params: SVM training parameters.
For now, default values are used for all SVMs.
Hyperparameter exploration can be achieved by
embedding the MultiClassSVM process flow in a
for-loop that classifies the data with
different parameter values, then pick the
values that yield the best accuracy.
Default: None
"""
self.num_classes = num_classes
self.mode = mode
self.params = params or dict()
# initialize correct number of classifiers
self.classifiers = []
if mode == "one-vs-one":
# k classes: need k*(k-1)/2 classifiers
for _ in xrange(num_classes*(num_classes - 1) / 2):
self.classifiers.append(cv2.SVM())
elif mode == "one-vs-all":
# k classes: need k classifiers
for _ in xrange(num_classes):
self.classifiers.append(cv2.SVM())
else:
print "Unknown mode ", mode
def fit(self, X_train, y_train, params=None):
"""Fits the model to training data
This method trains the classifier on data (X_train) using either
the "one-vs-one" or "one-vs-all" strategy.
:param X_train: input data (rows=samples, cols=features)
:param y_train: vector of class labels
:param params: dict to specify training options for cv2.SVM.train
leave blank to use the parameters passed to the
constructor
"""
if params is None:
params = self.params
if self.mode == "one-vs-one":
svm_id = 0
for c1 in xrange(self.num_classes):
for c2 in xrange(c1 + 1, self.num_classes):
# indices where class labels are either `c1` or `c2`
data_id = np.where((y_train == c1) + (y_train == c2))[0]
# set class label to 1 where class is `c1`, else 0
y_train_bin = np.where(y_train[data_id] == c1, 1,
0).flatten()
self.classifiers[svm_id].train(X_train[data_id, :],
y_train_bin,
params=self.params)
svm_id += 1
elif self.mode == "one-vs-all":
for c in xrange(self.num_classes):
# train c-th SVM on class c vs. all other classes
# set class label to 1 where class==c, else 0
y_train_bin = np.where(y_train == c, 1, 0).flatten()
# train SVM
self.classifiers[c].train(X_train, y_train_bin,
params=self.params)
def evaluate(self, X_test, y_test, visualize=False):
"""Evaluates the model on test data
This method evaluates the classifier's performance on test data
(X_test) using either the "one-vs-one" or "one-vs-all" strategy.
:param X_test: input data (rows=samples, cols=features)
:param y_test: vector of class labels
:param visualize: flag whether to plot the results (True) or not
(False)
:returns: accuracy, precision, recall
"""
# prepare Y_vote: for each sample, count how many times we voted
# for each class
Y_vote = np.zeros((len(y_test), self.num_classes))
if self.mode == "one-vs-one":
svm_id = 0
for c1 in xrange(self.num_classes):
for c2 in xrange(c1 + 1, self.num_classes):
data_id = np.where((y_test == c1) + (y_test == c2))[0]
X_test_id = X_test[data_id, :]
y_test_id = y_test[data_id]
# set class label to 1 where class==c1, else 0
# y_test_bin = np.where(y_test_id==c1,1,0).reshape(-1,1)
# predict labels
y_hat = self.classifiers[svm_id].predict_all(X_test_id)
for i in xrange(len(y_hat)):
if y_hat[i] == 1:
Y_vote[data_id[i], c1] += 1
elif y_hat[i] == 0:
Y_vote[data_id[i], c2] += 1
else:
print "y_hat[", i, "] = ", y_hat[i]
# we vote for c1 where y_hat is 1, and for c2 where y_hat
# is 0 np.where serves as the inner index into the data_id
# array, which in turn serves as index into the results
# array
# Y_vote[data_id[np.where(y_hat == 1)[0]], c1] += 1
# Y_vote[data_id[np.where(y_hat == 0)[0]], c2] += 1
svm_id += 1
elif self.mode == "one-vs-all":
for c in xrange(self.num_classes):
# set class label to 1 where class==c, else 0
# predict class labels
# y_test_bin = np.where(y_test==c,1,0).reshape(-1,1)
# predict labels
y_hat = self.classifiers[c].predict_all(X_test)
# we vote for c where y_hat is 1
if np.any(y_hat):
Y_vote[np.where(y_hat == 1)[0], c] += 1
# with this voting scheme it's possible to end up with samples
# that have no label at all...in this case, pick a class at
# random...
no_label = np.where(np.sum(Y_vote, axis=1) == 0)[0]
Y_vote[no_label, np.random.randint(self.num_classes,
size=len(no_label))] = 1
accuracy = self._accuracy(y_test, Y_vote)
precision = self._precision(y_test, Y_vote)
recall = self._recall(y_test, Y_vote)
return accuracy, precision, recall
================================================
FILE: chapter6/datasets/__init__.py
================================================
================================================
FILE: chapter6/datasets/gtsrb.py
================================================
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""A module to load the German Traffic Sign Recognition Benchmark (GTSRB)
The dataset contains more than 50,000 images of traffic signs belonging
to more than 40 classes. The dataset can be freely obtained from:
http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset.
"""
import cv2
import numpy as np
import csv
from matplotlib import cm
from matplotlib import pyplot as plt
__author__ = "Michael Beyeler"
__license__ = "GNU GPL 3.0 or later"
def load_data(rootpath="datasets/gtsrb_training", feature=None, cut_roi=True,
test_split=0.2, plot_samples=False, seed=113):
"""Loads the GTSRB dataset
This function loads the German Traffic Sign Recognition Benchmark
(GTSRB), performs feature extraction, and partitions the data into
mutually exclusive training and test sets.
:param rootpath: root directory of data files, should contain
subdirectories "00000" for all samples of class
0, "00004" for all samples of class 4, etc.
:param feature: which feature to extract: None, "gray", "rgb",
"hsv", surf", or "hog"
:param cut_roi: flag whether to remove regions surrounding the
actual traffic sign (True) or not (False)
:param test_split: fraction of samples to reserve for the test set
:param plot_samples: flag whether to plot samples (True) or not
(False)
:param seed: which random seed to use
:returns: (X_train, y_train), (X_test, y_test)
"""
# hardcode available class labels
classes = np.arange(0, 42, 2)
# read all training samples and corresponding class labels
X = [] # data
labels = [] # corresponding labels
for c in xrange(len(classes)):
# subdirectory for class
prefix = rootpath + '/' + format(classes[c], '05d') + '/'
# annotations file
gt_file = open(prefix + 'GT-' + format(classes[c], '05d') + '.csv')
# csv parser for annotations file
gt_reader = csv.reader(gt_file, delimiter=';')
gt_reader.next() # skip header
# loop over all images in current annotations file
for row in gt_reader:
# first column is filename
im = cv2.imread(prefix + row[0])
# remove regions surrounding the actual traffic sign
if cut_roi:
im = im[np.int(row[4]):np.int(row[6]),
np.int(row[3]):np.int(row[5]), :]
X.append(im)
labels.append(c)
gt_file.close()
# perform feature extraction
X = _extract_feature(X, feature)
np.random.seed(seed)
np.random.shuffle(X)
np.random.seed(seed)
np.random.shuffle(labels)
if plot_samples:
num_samples = 15
sample_idx = np.random.randint(len(X), size=num_samples)
sp = 1
for r in xrange(3):
for c in xrange(5):
ax = plt.subplot(3, 5, sp)
sample = X[sample_idx[sp - 1]]
ax.imshow(sample.reshape((32, 32)), cmap=cm.Greys_r)
ax.axis('off')
sp += 1
plt.show()
X_train = X[:int(len(X)*(1-test_split))]
y_train = labels[:int(len(X)*(1-test_split))]
X_test = X[int(len(X)*(1-test_split)):]
y_test = labels[int(len(X)*(1-test_split)):]
return (X_train, y_train), (X_test, y_test)
def _extract_feature(X, feature):
"""Performs feature extraction
:param X: data (rows=images, cols=pixels)
:param feature: which feature to extract
- None: no feature is extracted
- "gray": grayscale features
- "rgb": RGB features
- "hsv": HSV features
- "surf": SURF features
- "hog": HOG features
:returns: X (rows=samples, cols=features)
"""
# transform color space
if feature == 'gray' or feature == 'surf':
X = [cv2.cvtColor(x, cv2.COLOR_BGR2GRAY) for x in X]
elif feature == 'hsv':
X = [cv2.cvtColor(x, cv2.COLOR_BGR2HSV) for x in X]
# operate on smaller image
small_size = (32, 32)
X = [cv2.resize(x, small_size) for x in X]
# extract features
if feature == 'surf':
surf = cv2.SURF(400)
surf.upright = True
surf.extended = True
num_surf_features = 36
# create dense grid of keypoints
dense = cv2.FeatureDetector_create("Dense")
kp = dense.detect(np.zeros(small_size).astype(np.uint8))
# compute keypoints and descriptors
kp_des = [surf.compute(x, kp) for x in X]
# the second element is descriptor: choose first num_surf_features
# elements
X = [d[1][:num_surf_features, :] for d in kp_des]
elif feature == 'hog':
# histogram of gradients
block_size = (small_size[0] / 2, small_size[1] / 2)
block_stride = (small_size[0] / 4, small_size[1] / 4)
cell_size = block_stride
num_bins = 9
hog = cv2.HOGDescriptor(small_size, block_size, block_stride,
cell_size, num_bins)
X = [hog.compute(x) for x in X]
elif feature is not None:
# normalize all intensities to be between 0 and 1
X = np.array(X).astype(np.float32) / 255
# subtract mean
X = [x - np.mean(x) for x in X]
X = [x.flatten() for x in X]
return X
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gitextract__p8st6ul/ ├── .gitignore ├── CONTRIBUTING.md ├── LICENSE ├── README.md ├── chapter1/ │ ├── chapter1.py │ ├── filters.py │ └── gui.py ├── chapter2/ │ ├── chapter2.py │ ├── gestures.py │ └── gui.py ├── chapter3/ │ ├── chapter3.py │ ├── feature_matching.py │ └── gui.py ├── chapter4/ │ ├── calibrate.py │ ├── chapter4.py │ ├── gui.py │ └── scene3D.py ├── chapter5/ │ ├── chapter5.py │ ├── saliency.py │ └── tracking.py ├── chapter6/ │ ├── chapter6.py │ ├── classifiers.py │ └── datasets/ │ ├── __init__.py │ ├── gtsrb.py │ └── gtsrb_training/ │ ├── 00000/ │ │ ├── 00000_00000.ppm │ │ ├── 00000_00001.ppm │ │ ├── 00000_00002.ppm │ │ ├── 00000_00003.ppm │ │ ├── 00000_00004.ppm │ │ ├── 00000_00005.ppm │ │ ├── 00000_00006.ppm │ │ ├── 00000_00007.ppm │ │ ├── 00000_00008.ppm │ │ ├── 00000_00009.ppm │ │ ├── 00000_00010.ppm │ │ ├── 00000_00011.ppm │ │ ├── 00000_00012.ppm │ │ ├── 00000_00013.ppm │ │ ├── 00000_00014.ppm │ │ ├── 00000_00015.ppm │ │ ├── 00000_00016.ppm │ │ ├── 00000_00017.ppm │ │ ├── 00000_00018.ppm │ │ ├── 00000_00019.ppm │ │ ├── 00000_00020.ppm │ │ ├── 00000_00021.ppm │ │ ├── 00000_00022.ppm │ │ ├── 00000_00023.ppm │ │ ├── 00000_00024.ppm │ │ ├── 00000_00025.ppm │ │ ├── 00000_00026.ppm │ │ ├── 00000_00027.ppm │ │ ├── 00000_00028.ppm │ │ ├── 00000_00029.ppm │ │ ├── 00001_00000.ppm │ │ ├── 00001_00001.ppm │ │ ├── 00001_00002.ppm │ │ ├── 00001_00003.ppm │ │ ├── 00001_00004.ppm │ │ ├── 00001_00005.ppm │ │ ├── 00001_00006.ppm │ │ ├── 00001_00007.ppm │ │ ├── 00001_00008.ppm │ │ ├── 00001_00009.ppm │ │ ├── 00001_00010.ppm │ │ ├── 00001_00011.ppm │ │ ├── 00001_00012.ppm │ │ ├── 00001_00013.ppm │ │ ├── 00001_00014.ppm │ │ ├── 00001_00015.ppm │ │ ├── 00001_00016.ppm │ │ ├── 00001_00017.ppm │ │ ├── 00001_00018.ppm │ │ ├── 00001_00019.ppm │ │ ├── 00001_00020.ppm │ │ ├── 00001_00021.ppm │ │ ├── 00001_00022.ppm │ │ ├── 00001_00023.ppm │ │ ├── 00001_00024.ppm │ │ ├── 00001_00025.ppm │ │ ├── 00001_00026.ppm │ │ ├── 00001_00027.ppm │ │ ├── 00001_00028.ppm │ │ ├── 00001_00029.ppm │ │ ├── 00002_00000.ppm │ │ ├── 00002_00001.ppm │ │ ├── 00002_00002.ppm │ │ ├── 00002_00003.ppm │ │ ├── 00002_00004.ppm │ │ ├── 00002_00005.ppm │ │ ├── 00002_00006.ppm │ │ ├── 00002_00007.ppm │ │ ├── 00002_00008.ppm │ │ ├── 00002_00009.ppm │ │ ├── 00002_00010.ppm │ │ ├── 00002_00011.ppm │ │ ├── 00002_00012.ppm │ │ ├── 00002_00013.ppm │ │ ├── 00002_00014.ppm │ │ ├── 00002_00015.ppm │ │ ├── 00002_00016.ppm │ │ ├── 00002_00017.ppm │ │ ├── 00002_00018.ppm │ │ ├── 00002_00019.ppm │ │ ├── 00002_00020.ppm │ │ ├── 00002_00021.ppm │ │ ├── 00002_00022.ppm │ │ ├── 00002_00023.ppm │ │ ├── 00002_00024.ppm │ │ ├── 00002_00025.ppm │ │ ├── 00002_00026.ppm │ │ ├── 00002_00027.ppm │ │ ├── 00002_00028.ppm │ │ ├── 00002_00029.ppm │ │ ├── 00003_00000.ppm │ │ ├── 00003_00001.ppm │ │ ├── 00003_00002.ppm │ │ ├── 00003_00003.ppm │ │ ├── 00003_00004.ppm │ │ ├── 00003_00005.ppm │ │ ├── 00003_00006.ppm │ │ ├── 00003_00007.ppm │ │ ├── 00003_00008.ppm │ │ ├── 00003_00009.ppm │ │ ├── 00003_00010.ppm │ │ ├── 00003_00011.ppm │ │ ├── 00003_00012.ppm │ │ ├── 00003_00013.ppm │ │ ├── 00003_00014.ppm │ │ ├── 00003_00015.ppm │ │ ├── 00003_00016.ppm │ │ ├── 00003_00017.ppm │ │ ├── 00003_00018.ppm │ │ ├── 00003_00019.ppm │ │ ├── 00003_00020.ppm │ │ ├── 00003_00021.ppm │ │ ├── 00003_00022.ppm │ │ ├── 00003_00023.ppm │ │ ├── 00003_00024.ppm │ │ ├── 00003_00025.ppm │ │ ├── 00003_00026.ppm │ │ ├── 00003_00027.ppm │ │ ├── 00003_00028.ppm │ │ ├── 00003_00029.ppm │ │ ├── 00004_00000.ppm │ │ ├── 00004_00001.ppm │ │ ├── 00004_00002.ppm │ │ ├── 00004_00003.ppm │ │ ├── 00004_00004.ppm │ │ ├── 00004_00005.ppm │ │ ├── 00004_00006.ppm │ │ ├── 00004_00007.ppm │ │ ├── 00004_00008.ppm │ │ ├── 00004_00009.ppm │ │ ├── 00004_00010.ppm │ │ ├── 00004_00011.ppm │ │ ├── 00004_00012.ppm │ │ ├── 00004_00013.ppm │ │ ├── 00004_00014.ppm │ │ ├── 00004_00015.ppm │ │ ├── 00004_00016.ppm │ │ ├── 00004_00017.ppm │ │ ├── 00004_00018.ppm │ │ ├── 00004_00019.ppm │ │ ├── 00004_00020.ppm │ │ ├── 00004_00021.ppm │ │ ├── 00004_00022.ppm │ │ ├── 00004_00023.ppm │ │ ├── 00004_00024.ppm │ │ ├── 00004_00025.ppm │ │ ├── 00004_00026.ppm │ │ ├── 00004_00027.ppm │ │ ├── 00004_00028.ppm │ │ ├── 00004_00029.ppm │ │ ├── 00005_00000.ppm │ │ ├── 00005_00001.ppm │ │ ├── 00005_00002.ppm │ │ ├── 00005_00003.ppm │ │ ├── 00005_00004.ppm │ │ ├── 00005_00005.ppm │ │ ├── 00005_00006.ppm │ │ ├── 00005_00007.ppm │ │ ├── 00005_00008.ppm │ │ ├── 00005_00009.ppm │ │ ├── 00005_00010.ppm │ │ ├── 00005_00011.ppm │ │ ├── 00005_00012.ppm │ │ ├── 00005_00013.ppm │ │ ├── 00005_00014.ppm │ │ ├── 00005_00015.ppm │ │ ├── 00005_00016.ppm │ │ ├── 00005_00017.ppm │ │ ├── 00005_00018.ppm │ │ ├── 00005_00019.ppm │ │ ├── 00005_00020.ppm │ │ ├── 00005_00021.ppm │ │ ├── 00005_00022.ppm │ │ ├── 00005_00023.ppm │ │ ├── 00005_00024.ppm │ │ ├── 00005_00025.ppm │ │ ├── 00005_00026.ppm │ │ ├── 00005_00027.ppm │ │ ├── 00005_00028.ppm │ │ ├── 00005_00029.ppm │ │ ├── 00006_00000.ppm │ │ ├── 00006_00001.ppm │ │ ├── 00006_00002.ppm │ │ ├── 00006_00003.ppm │ │ ├── 00006_00004.ppm │ │ ├── 00006_00005.ppm │ │ ├── 00006_00006.ppm │ │ ├── 00006_00007.ppm │ │ ├── 00006_00008.ppm │ │ ├── 00006_00009.ppm │ │ ├── 00006_00010.ppm │ │ ├── 00006_00011.ppm │ │ ├── 00006_00012.ppm │ │ ├── 00006_00013.ppm │ │ ├── 00006_00014.ppm │ │ ├── 00006_00015.ppm │ │ ├── 00006_00016.ppm │ │ ├── 00006_00017.ppm │ │ ├── 00006_00018.ppm │ │ ├── 00006_00019.ppm │ │ ├── 00006_00020.ppm │ │ ├── 00006_00021.ppm │ │ ├── 00006_00022.ppm │ │ ├── 00006_00023.ppm │ │ ├── 00006_00024.ppm │ │ ├── 00006_00025.ppm │ │ ├── 00006_00026.ppm │ │ ├── 00006_00027.ppm │ │ ├── 00006_00028.ppm │ │ ├── 00006_00029.ppm │ │ └── GT-00000.csv │ ├── 00002/ │ │ ├── 00000_00000.ppm │ │ ├── 00000_00001.ppm │ │ ├── 00000_00002.ppm │ │ ├── 00000_00003.ppm │ │ ├── 00000_00004.ppm │ │ ├── 00000_00005.ppm │ │ ├── 00000_00006.ppm │ │ ├── 00000_00007.ppm │ │ ├── 00000_00008.ppm │ │ ├── 00000_00009.ppm │ │ ├── 00000_00010.ppm │ │ ├── 00000_00011.ppm │ │ ├── 00000_00012.ppm │ │ ├── 00000_00013.ppm │ │ ├── 00000_00014.ppm │ │ ├── 00000_00015.ppm │ │ ├── 00000_00016.ppm │ │ ├── 00000_00017.ppm │ │ ├── 00000_00018.ppm │ │ ├── 00000_00019.ppm │ │ ├── 00000_00020.ppm │ │ ├── 00000_00021.ppm │ │ ├── 00000_00022.ppm │ │ ├── 00000_00023.ppm │ │ ├── 00000_00024.ppm │ │ ├── 00000_00025.ppm │ │ ├── 00000_00026.ppm │ │ ├── 00000_00027.ppm │ │ ├── 00000_00028.ppm │ │ ├── 00000_00029.ppm │ │ ├── 00001_00000.ppm │ │ ├── 00001_00001.ppm │ │ ├── 00001_00002.ppm │ │ ├── 00001_00003.ppm │ │ ├── 00001_00004.ppm │ │ ├── 00001_00005.ppm │ │ ├── 00001_00006.ppm │ │ ├── 00001_00007.ppm │ │ ├── 00001_00008.ppm │ │ ├── 00001_00009.ppm │ │ ├── 00001_00010.ppm │ │ ├── 00001_00011.ppm │ │ ├── 00001_00012.ppm │ │ ├── 00001_00013.ppm │ │ ├── 00001_00014.ppm │ │ ├── 00001_00015.ppm │ │ ├── 00001_00016.ppm │ │ ├── 00001_00017.ppm │ │ ├── 00001_00018.ppm │ │ ├── 00001_00019.ppm │ │ ├── 00001_00020.ppm │ │ ├── 00001_00021.ppm │ │ ├── 00001_00022.ppm │ │ ├── 00001_00023.ppm │ │ ├── 00001_00024.ppm │ │ ├── 00001_00025.ppm │ │ ├── 00001_00026.ppm │ │ ├── 00001_00027.ppm │ │ ├── 00001_00028.ppm │ │ ├── 00001_00029.ppm │ │ ├── 00002_00000.ppm │ │ ├── 00002_00001.ppm │ │ ├── 00002_00002.ppm │ │ ├── 00002_00003.ppm │ │ ├── 00002_00004.ppm │ │ ├── 00002_00005.ppm │ │ ├── 00002_00006.ppm │ │ ├── 00002_00007.ppm │ │ ├── 00002_00008.ppm │ │ ├── 00002_00009.ppm │ │ ├── 00002_00010.ppm │ │ ├── 00002_00011.ppm │ │ ├── 00002_00012.ppm │ │ ├── 00002_00013.ppm │ │ ├── 00002_00014.ppm │ │ ├── 00002_00015.ppm │ │ ├── 00002_00016.ppm │ │ ├── 00002_00017.ppm │ │ ├── 00002_00018.ppm │ │ ├── 00002_00019.ppm │ │ ├── 00002_00020.ppm │ │ ├── 00002_00021.ppm │ │ ├── 00002_00022.ppm │ │ ├── 00002_00023.ppm │ │ ├── 00002_00024.ppm │ │ ├── 00002_00025.ppm │ │ ├── 00002_00026.ppm │ │ ├── 00002_00027.ppm │ │ ├── 00002_00028.ppm │ │ ├── 00002_00029.ppm │ │ ├── 00003_00000.ppm │ │ ├── 00003_00001.ppm │ │ ├── 00003_00002.ppm │ │ ├── 00003_00003.ppm │ │ ├── 00003_00004.ppm │ │ ├── 00003_00005.ppm │ │ ├── 00003_00006.ppm │ │ ├── 00003_00007.ppm │ │ ├── 00003_00008.ppm │ │ ├── 00003_00009.ppm │ │ ├── 00003_00010.ppm │ │ ├── 00003_00011.ppm │ │ ├── 00003_00012.ppm │ │ ├── 00003_00013.ppm │ │ ├── 00003_00014.ppm │ │ ├── 00003_00015.ppm │ │ ├── 00003_00016.ppm │ │ ├── 00003_00017.ppm │ │ ├── 00003_00018.ppm │ │ ├── 00003_00019.ppm │ │ ├── 00003_00020.ppm │ │ ├── 00003_00021.ppm │ │ ├── 00003_00022.ppm │ │ ├── 00003_00023.ppm │ │ ├── 00003_00024.ppm │ │ ├── 00003_00025.ppm │ │ ├── 00003_00026.ppm │ │ ├── 00003_00027.ppm │ │ ├── 00003_00028.ppm │ │ ├── 00003_00029.ppm │ │ ├── 00004_00000.ppm │ │ ├── 00004_00001.ppm │ │ ├── 00004_00002.ppm │ │ ├── 00004_00003.ppm │ │ ├── 00004_00004.ppm │ │ ├── 00004_00005.ppm │ │ ├── 00004_00006.ppm │ │ ├── 00004_00007.ppm │ │ ├── 00004_00008.ppm │ │ ├── 00004_00009.ppm │ │ ├── 00004_00010.ppm │ │ ├── 00004_00011.ppm │ │ ├── 00004_00012.ppm │ │ ├── 00004_00013.ppm │ │ ├── 00004_00014.ppm │ │ ├── 00004_00015.ppm │ │ ├── 00004_00016.ppm │ │ ├── 00004_00017.ppm │ │ ├── 00004_00018.ppm │ │ ├── 00004_00019.ppm │ │ ├── 00004_00020.ppm │ │ ├── 00004_00021.ppm │ │ ├── 00004_00022.ppm │ │ ├── 00004_00023.ppm │ │ ├── 00004_00024.ppm │ │ ├── 00004_00025.ppm │ │ ├── 00004_00026.ppm │ │ ├── 00004_00027.ppm │ │ ├── 00004_00028.ppm │ │ ├── 00004_00029.ppm │ │ ├── 00005_00000.ppm │ │ ├── 00005_00001.ppm │ │ ├── 00005_00002.ppm │ │ ├── 00005_00003.ppm │ │ ├── 00005_00004.ppm │ │ ├── 00005_00005.ppm │ │ ├── 00005_00006.ppm │ │ ├── 00005_00007.ppm │ │ ├── 00005_00008.ppm │ │ ├── 00005_00009.ppm │ │ ├── 00005_00010.ppm │ │ ├── 00005_00011.ppm │ │ ├── 00005_00012.ppm │ │ ├── 00005_00013.ppm │ │ ├── 00005_00014.ppm │ │ ├── 00005_00015.ppm │ │ ├── 00005_00016.ppm │ │ ├── 00005_00017.ppm │ │ ├── 00005_00018.ppm │ │ ├── 00005_00019.ppm │ │ ├── 00005_00020.ppm │ │ ├── 00005_00021.ppm │ │ ├── 00005_00022.ppm │ │ ├── 00005_00023.ppm │ │ ├── 00005_00024.ppm │ │ ├── 00005_00025.ppm │ │ ├── 00005_00026.ppm │ │ ├── 00005_00027.ppm │ │ ├── 00005_00028.ppm │ │ ├── 00005_00029.ppm │ │ ├── 00006_00000.ppm │ │ ├── 00006_00001.ppm │ │ ├── 00006_00002.ppm │ │ ├── 00006_00003.ppm │ │ ├── 00006_00004.ppm │ │ ├── 00006_00005.ppm │ │ ├── 00006_00006.ppm │ │ ├── 00006_00007.ppm │ │ ├── 00006_00008.ppm │ │ ├── 00006_00009.ppm │ │ ├── 00006_00010.ppm │ │ ├── 00006_00011.ppm │ │ ├── 00006_00012.ppm │ │ ├── 00006_00013.ppm │ │ ├── 00006_00014.ppm │ │ ├── 00006_00015.ppm │ │ ├── 00006_00016.ppm │ │ ├── 00006_00017.ppm │ │ ├── 00006_00018.ppm │ │ ├── 00006_00019.ppm │ │ ├── 00006_00020.ppm │ │ ├── 00006_00021.ppm │ │ ├── 00006_00022.ppm │ │ ├── 00006_00023.ppm │ │ ├── 00006_00024.ppm │ │ ├── 00006_00025.ppm │ │ ├── 00006_00026.ppm │ │ ├── 00006_00027.ppm │ │ ├── 00006_00028.ppm │ │ ├── 00006_00029.ppm │ │ ├── 00007_00000.ppm │ │ ├── 00007_00001.ppm │ │ ├── 00007_00002.ppm │ │ ├── 00007_00003.ppm │ │ ├── 00007_00004.ppm │ │ ├── 00007_00005.ppm │ │ ├── 00007_00006.ppm │ │ ├── 00007_00007.ppm │ │ ├── 00007_00008.ppm │ │ ├── 00007_00009.ppm │ │ ├── 00007_00010.ppm │ │ ├── 00007_00011.ppm │ │ ├── 00007_00012.ppm │ │ ├── 00007_00013.ppm │ │ ├── 00007_00014.ppm │ │ ├── 00007_00015.ppm │ │ ├── 00007_00016.ppm │ │ ├── 00007_00017.ppm │ │ ├── 00007_00018.ppm │ │ ├── 00007_00019.ppm │ │ ├── 00007_00020.ppm │ │ ├── 00007_00021.ppm │ │ ├── 00007_00022.ppm │ │ ├── 00007_00023.ppm │ │ ├── 00007_00024.ppm │ │ ├── 00007_00025.ppm │ │ ├── 00007_00026.ppm │ │ ├── 00007_00027.ppm │ │ ├── 00007_00028.ppm │ │ ├── 00007_00029.ppm │ │ ├── 00008_00000.ppm │ │ ├── 00008_00001.ppm │ │ ├── 00008_00002.ppm │ │ ├── 00008_00003.ppm │ │ ├── 00008_00004.ppm │ │ ├── 00008_00005.ppm │ │ ├── 00008_00006.ppm │ │ ├── 00008_00007.ppm │ │ ├── 00008_00008.ppm │ │ ├── 00008_00009.ppm │ │ ├── 00008_00010.ppm │ │ ├── 00008_00011.ppm │ │ ├── 00008_00012.ppm │ │ ├── 00008_00013.ppm │ │ ├── 00008_00014.ppm │ │ ├── 00008_00015.ppm │ │ ├── 00008_00016.ppm │ │ ├── 00008_00017.ppm │ │ ├── 00008_00018.ppm │ │ ├── 00008_00019.ppm │ │ ├── 00008_00020.ppm │ │ ├── 00008_00021.ppm │ │ ├── 00008_00022.ppm │ │ ├── 00008_00023.ppm │ │ ├── 00008_00024.ppm │ │ ├── 00008_00025.ppm │ │ ├── 00008_00026.ppm │ │ ├── 00008_00027.ppm │ │ ├── 00008_00028.ppm │ │ ├── 00008_00029.ppm │ │ ├── 00009_00000.ppm │ │ ├── 00009_00001.ppm │ │ ├── 00009_00002.ppm │ │ ├── 00009_00003.ppm │ │ ├── 00009_00004.ppm │ │ ├── 00009_00005.ppm │ │ ├── 00009_00006.ppm │ │ ├── 00009_00007.ppm │ │ ├── 00009_00008.ppm │ │ ├── 00009_00009.ppm │ │ ├── 00009_00010.ppm │ │ ├── 00009_00011.ppm │ │ ├── 00009_00012.ppm │ │ ├── 00009_00013.ppm │ │ ├── 00009_00014.ppm │ │ ├── 00009_00015.ppm │ │ ├── 00009_00016.ppm │ │ ├── 00009_00017.ppm │ │ ├── 00009_00018.ppm │ │ ├── 00009_00019.ppm │ │ ├── 00009_00020.ppm │ │ ├── 00009_00021.ppm │ │ ├── 00009_00022.ppm │ │ ├── 00009_00023.ppm │ │ ├── 00009_00024.ppm │ │ ├── 00009_00025.ppm │ │ ├── 00009_00026.ppm │ │ ├── 00009_00027.ppm │ │ ├── 00009_00028.ppm │ │ ├── 00009_00029.ppm │ │ ├── 00010_00000.ppm │ │ ├── 00010_00001.ppm │ │ ├── 00010_00002.ppm │ │ ├── 00010_00003.ppm │ │ ├── 00010_00004.ppm │ │ ├── 00010_00005.ppm │ │ ├── 00010_00006.ppm │ │ ├── 00010_00007.ppm │ │ ├── 00010_00008.ppm │ │ ├── 00010_00009.ppm │ │ ├── 00010_00010.ppm │ │ ├── 00010_00011.ppm │ │ ├── 00010_00012.ppm │ │ ├── 00010_00013.ppm │ │ ├── 00010_00014.ppm │ │ ├── 00010_00015.ppm │ │ ├── 00010_00016.ppm │ │ ├── 00010_00017.ppm │ │ ├── 00010_00018.ppm │ │ ├── 00010_00019.ppm │ │ ├── 00010_00020.ppm │ │ ├── 00010_00021.ppm │ │ ├── 00010_00022.ppm │ │ ├── 00010_00023.ppm │ │ ├── 00010_00024.ppm │ │ ├── 00010_00025.ppm │ │ ├── 00010_00026.ppm │ │ ├── 00010_00027.ppm │ │ ├── 00010_00028.ppm │ │ ├── 00010_00029.ppm │ │ ├── 00011_00000.ppm │ │ ├── 00011_00001.ppm │ │ ├── 00011_00002.ppm │ │ ├── 00011_00003.ppm │ │ ├── 00011_00004.ppm │ │ ├── 00011_00005.ppm │ │ ├── 00011_00006.ppm │ │ ├── 00011_00007.ppm │ │ ├── 00011_00008.ppm │ │ ├── 00011_00009.ppm │ │ ├── 00011_00010.ppm │ │ ├── 00011_00011.ppm │ │ ├── 00011_00012.ppm │ │ ├── 00011_00013.ppm │ │ ├── 00011_00014.ppm │ │ ├── 00011_00015.ppm │ │ ├── 00011_00016.ppm │ │ ├── 00011_00017.ppm │ │ ├── 00011_00018.ppm │ │ ├── 00011_00019.ppm │ │ ├── 00011_00020.ppm │ │ ├── 00011_00021.ppm │ │ ├── 00011_00022.ppm │ │ ├── 00011_00023.ppm │ │ ├── 00011_00024.ppm │ │ ├── 00011_00025.ppm │ │ ├── 00011_00026.ppm │ │ ├── 00011_00027.ppm │ │ ├── 00011_00028.ppm │ │ ├── 00011_00029.ppm │ │ ├── 00012_00000.ppm │ │ ├── 00012_00001.ppm │ │ ├── 00012_00002.ppm │ │ ├── 00012_00003.ppm │ │ ├── 00012_00004.ppm │ │ ├── 00012_00005.ppm │ │ ├── 00012_00006.ppm │ │ ├── 00012_00007.ppm │ │ ├── 00012_00008.ppm │ │ ├── 00012_00009.ppm │ │ ├── 00012_00010.ppm │ │ ├── 00012_00011.ppm │ │ ├── 00012_00012.ppm │ │ ├── 00012_00013.ppm │ │ ├── 00012_00014.ppm │ │ ├── 00012_00015.ppm │ │ ├── 00012_00016.ppm │ │ ├── 00012_00017.ppm │ │ ├── 00012_00018.ppm │ │ ├── 00012_00019.ppm │ │ ├── 00012_00020.ppm │ │ ├── 00012_00021.ppm │ │ ├── 00012_00022.ppm │ │ ├── 00012_00023.ppm │ │ ├── 00012_00024.ppm │ │ ├── 00012_00025.ppm │ │ ├── 00012_00026.ppm │ │ ├── 00012_00027.ppm │ │ ├── 00012_00028.ppm │ │ ├── 00012_00029.ppm │ │ ├── 00013_00000.ppm │ │ ├── 00013_00001.ppm │ │ ├── 00013_00002.ppm │ │ ├── 00013_00003.ppm │ │ ├── 00013_00004.ppm │ │ ├── 00013_00005.ppm │ │ ├── 00013_00006.ppm │ │ ├── 00013_00007.ppm │ │ ├── 00013_00008.ppm │ │ ├── 00013_00009.ppm │ │ ├── 00013_00010.ppm │ │ ├── 00013_00011.ppm │ │ ├── 00013_00012.ppm │ │ ├── 00013_00013.ppm │ │ ├── 00013_00014.ppm │ │ ├── 00013_00015.ppm │ │ ├── 00013_00016.ppm │ │ ├── 00013_00017.ppm │ │ ├── 00013_00018.ppm │ │ ├── 00013_00019.ppm │ │ ├── 00013_00020.ppm │ │ ├── 00013_00021.ppm │ │ ├── 00013_00022.ppm │ │ ├── 00013_00023.ppm │ │ ├── 00013_00024.ppm │ │ ├── 00013_00025.ppm │ │ ├── 00013_00026.ppm │ │ ├── 00013_00027.ppm │ │ ├── 00013_00028.ppm │ │ ├── 00013_00029.ppm │ │ ├── 00014_00000.ppm │ │ ├── 00014_00001.ppm │ │ ├── 00014_00002.ppm │ │ ├── 00014_00003.ppm │ │ ├── 00014_00004.ppm │ │ ├── 00014_00005.ppm │ │ ├── 00014_00006.ppm │ │ ├── 00014_00007.ppm │ │ ├── 00014_00008.ppm │ │ ├── 00014_00009.ppm │ │ ├── 00014_00010.ppm │ │ ├── 00014_00011.ppm │ │ ├── 00014_00012.ppm │ │ ├── 00014_00013.ppm │ │ ├── 00014_00014.ppm │ │ ├── 00014_00015.ppm │ │ ├── 00014_00016.ppm │ │ ├── 00014_00017.ppm │ │ ├── 00014_00018.ppm │ │ ├── 00014_00019.ppm │ │ ├── 00014_00020.ppm │ │ ├── 00014_00021.ppm │ │ ├── 00014_00022.ppm │ │ ├── 00014_00023.ppm │ │ ├── 00014_00024.ppm │ │ ├── 00014_00025.ppm │ │ ├── 00014_00026.ppm │ │ ├── 00014_00027.ppm │ │ ├── 00014_00028.ppm │ │ ├── 00014_00029.ppm │ │ ├── 00015_00000.ppm │ │ ├── 00015_00001.ppm │ │ ├── 00015_00002.ppm │ │ ├── 00015_00003.ppm │ │ ├── 00015_00004.ppm │ │ ├── 00015_00005.ppm │ │ ├── 00015_00006.ppm │ │ ├── 00015_00007.ppm │ │ ├── 00015_00008.ppm │ │ ├── 00015_00009.ppm │ │ ├── 00015_00010.ppm │ │ ├── 00015_00011.ppm │ │ ├── 00015_00012.ppm │ │ ├── 00015_00013.ppm │ │ ├── 00015_00014.ppm │ │ ├── 00015_00015.ppm │ │ ├── 00015_00016.ppm │ │ ├── 00015_00017.ppm │ │ ├── 00015_00018.ppm │ │ ├── 00015_00019.ppm │ │ ├── 00015_00020.ppm │ │ ├── 00015_00021.ppm │ │ ├── 00015_00022.ppm │ │ ├── 00015_00023.ppm │ │ ├── 00015_00024.ppm │ │ ├── 00015_00025.ppm │ │ ├── 00015_00026.ppm │ │ ├── 00015_00027.ppm │ │ ├── 00015_00028.ppm │ │ ├── 00015_00029.ppm │ │ ├── 00016_00000.ppm │ │ ├── 00016_00001.ppm │ │ ├── 00016_00002.ppm │ │ ├── 00016_00003.ppm │ │ ├── 00016_00004.ppm │ │ ├── 00016_00005.ppm │ │ ├── 00016_00006.ppm │ │ ├── 00016_00007.ppm │ │ ├── 00016_00008.ppm │ │ ├── 00016_00009.ppm │ │ ├── 00016_00010.ppm │ │ ├── 00016_00011.ppm │ │ ├── 00016_00012.ppm │ │ ├── 00016_00013.ppm │ │ ├── 00016_00014.ppm │ │ ├── 00016_00015.ppm │ │ ├── 00016_00016.ppm │ │ ├── 00016_00017.ppm │ │ ├── 00016_00018.ppm │ │ ├── 00016_00019.ppm │ │ ├── 00016_00020.ppm │ │ ├── 00016_00021.ppm │ │ ├── 00016_00022.ppm │ │ ├── 00016_00023.ppm │ │ ├── 00016_00024.ppm │ │ ├── 00016_00025.ppm │ │ ├── 00016_00026.ppm │ │ ├── 00016_00027.ppm │ │ ├── 00016_00028.ppm │ │ ├── 00016_00029.ppm │ │ ├── 00017_00000.ppm │ │ ├── 00017_00001.ppm │ │ ├── 00017_00002.ppm │ │ ├── 00017_00003.ppm │ │ ├── 00017_00004.ppm │ │ ├── 00017_00005.ppm │ │ ├── 00017_00006.ppm │ │ ├── 00017_00007.ppm │ │ ├── 00017_00008.ppm │ │ ├── 00017_00009.ppm │ │ ├── 00017_00010.ppm │ │ ├── 00017_00011.ppm │ │ ├── 00017_00012.ppm │ │ ├── 00017_00013.ppm │ │ ├── 00017_00014.ppm │ │ ├── 00017_00015.ppm │ │ ├── 00017_00016.ppm │ │ ├── 00017_00017.ppm │ │ ├── 00017_00018.ppm │ │ ├── 00017_00019.ppm │ │ ├── 00017_00020.ppm │ │ ├── 00017_00021.ppm │ │ ├── 00017_00022.ppm │ │ ├── 00017_00023.ppm │ │ ├── 00017_00024.ppm │ │ ├── 00017_00025.ppm │ │ ├── 00017_00026.ppm │ │ ├── 00017_00027.ppm │ │ ├── 00017_00028.ppm │ │ ├── 00017_00029.ppm │ │ ├── 00018_00000.ppm │ │ ├── 00018_00001.ppm │ │ ├── 00018_00002.ppm │ │ ├── 00018_00003.ppm │ │ ├── 00018_00004.ppm │ │ ├── 00018_00005.ppm │ │ ├── 00018_00006.ppm │ │ ├── 00018_00007.ppm │ │ ├── 00018_00008.ppm │ │ ├── 00018_00009.ppm │ │ ├── 00018_00010.ppm │ │ ├── 00018_00011.ppm │ │ ├── 00018_00012.ppm │ │ ├── 00018_00013.ppm │ │ ├── 00018_00014.ppm │ │ ├── 00018_00015.ppm │ │ ├── 00018_00016.ppm │ │ ├── 00018_00017.ppm │ │ ├── 00018_00018.ppm │ │ ├── 00018_00019.ppm │ │ ├── 00018_00020.ppm │ │ ├── 00018_00021.ppm │ │ ├── 00018_00022.ppm │ │ ├── 00018_00023.ppm │ │ ├── 00018_00024.ppm │ │ ├── 00018_00025.ppm │ │ ├── 00018_00026.ppm │ │ ├── 00018_00027.ppm │ │ ├── 00018_00028.ppm │ │ ├── 00018_00029.ppm │ │ ├── 00019_00000.ppm │ │ ├── 00019_00001.ppm │ │ ├── 00019_00002.ppm │ │ ├── 00019_00003.ppm │ │ ├── 00019_00004.ppm │ │ ├── 00019_00005.ppm │ │ ├── 00019_00006.ppm │ │ ├── 00019_00007.ppm │ │ ├── 00019_00008.ppm │ │ ├── 00019_00009.ppm │ │ ├── 00019_00010.ppm │ │ ├── 00019_00011.ppm │ │ ├── 00019_00012.ppm │ │ ├── 00019_00013.ppm │ │ ├── 00019_00014.ppm │ │ ├── 00019_00015.ppm │ │ ├── 00019_00016.ppm │ │ ├── 00019_00017.ppm │ │ ├── 00019_00018.ppm │ │ ├── 00019_00019.ppm │ │ ├── 00019_00020.ppm │ │ ├── 00019_00021.ppm │ │ ├── 00019_00022.ppm │ │ ├── 00019_00023.ppm │ │ ├── 00019_00024.ppm │ │ ├── 00019_00025.ppm │ │ ├── 00019_00026.ppm │ │ ├── 00019_00027.ppm │ │ ├── 00019_00028.ppm │ │ ├── 00019_00029.ppm │ │ ├── 00020_00000.ppm │ │ ├── 00020_00001.ppm │ │ ├── 00020_00002.ppm │ │ ├── 00020_00003.ppm │ │ ├── 00020_00004.ppm │ │ ├── 00020_00005.ppm │ │ ├── 00020_00006.ppm │ │ ├── 00020_00007.ppm │ │ ├── 00020_00008.ppm │ │ ├── 00020_00009.ppm │ │ ├── 00020_00010.ppm │ │ ├── 00020_00011.ppm │ │ ├── 00020_00012.ppm │ │ ├── 00020_00013.ppm │ │ ├── 00020_00014.ppm │ │ ├── 00020_00015.ppm │ │ ├── 00020_00016.ppm │ │ ├── 00020_00017.ppm │ │ ├── 00020_00018.ppm │ │ ├── 00020_00019.ppm │ │ ├── 00020_00020.ppm │ │ ├── 00020_00021.ppm │ │ ├── 00020_00022.ppm │ │ ├── 00020_00023.ppm │ │ ├── 00020_00024.ppm │ │ ├── 00020_00025.ppm │ │ ├── 00020_00026.ppm │ │ ├── 00020_00027.ppm │ │ ├── 00020_00028.ppm │ │ ├── 00020_00029.ppm │ │ ├── 00021_00000.ppm │ │ ├── 00021_00001.ppm │ │ ├── 00021_00002.ppm │ │ ├── 00021_00003.ppm │ │ ├── 00021_00004.ppm │ │ ├── 00021_00005.ppm │ │ ├── 00021_00006.ppm │ │ ├── 00021_00007.ppm │ │ ├── 00021_00008.ppm │ │ ├── 00021_00009.ppm │ │ ├── 00021_00010.ppm │ │ ├── 00021_00011.ppm │ │ ├── 00021_00012.ppm │ │ ├── 00021_00013.ppm │ │ ├── 00021_00014.ppm │ │ ├── 00021_00015.ppm │ │ ├── 00021_00016.ppm │ │ ├── 00021_00017.ppm │ │ ├── 00021_00018.ppm │ │ ├── 00021_00019.ppm │ │ ├── 00021_00020.ppm │ │ ├── 00021_00021.ppm │ │ ├── 00021_00022.ppm │ │ ├── 00021_00023.ppm │ │ ├── 00021_00024.ppm │ │ ├── 00021_00025.ppm │ │ ├── 00021_00026.ppm │ │ ├── 00021_00027.ppm │ │ ├── 00021_00028.ppm │ │ ├── 00021_00029.ppm │ │ ├── 00022_00000.ppm │ │ ├── 00022_00001.ppm │ │ ├── 00022_00002.ppm │ │ ├── 00022_00003.ppm │ │ ├── 00022_00004.ppm │ │ ├── 00022_00005.ppm │ │ ├── 00022_00006.ppm │ │ ├── 00022_00007.ppm │ │ ├── 00022_00008.ppm │ │ ├── 00022_00009.ppm │ │ ├── 00022_00010.ppm │ │ ├── 00022_00011.ppm │ │ ├── 00022_00012.ppm │ │ ├── 00022_00013.ppm │ │ ├── 00022_00014.ppm │ │ ├── 00022_00015.ppm │ │ ├── 00022_00016.ppm │ │ ├── 00022_00017.ppm │ │ ├── 00022_00018.ppm │ │ ├── 00022_00019.ppm │ │ ├── 00022_00020.ppm │ │ ├── 00022_00021.ppm │ │ ├── 00022_00022.ppm │ │ ├── 00022_00023.ppm │ │ ├── 00022_00024.ppm │ │ ├── 00022_00025.ppm │ │ ├── 00022_00026.ppm │ │ ├── 00022_00027.ppm │ │ ├── 00022_00028.ppm │ │ ├── 00022_00029.ppm │ │ ├── 00023_00000.ppm │ │ ├── 00023_00001.ppm │ │ ├── 00023_00002.ppm │ │ ├── 00023_00003.ppm │ │ ├── 00023_00004.ppm │ │ ├── 00023_00005.ppm │ │ ├── 00023_00006.ppm │ │ ├── 00023_00007.ppm │ │ ├── 00023_00008.ppm │ │ ├── 00023_00009.ppm │ │ ├── 00023_00010.ppm │ │ ├── 00023_00011.ppm │ │ ├── 00023_00012.ppm │ │ ├── 00023_00013.ppm │ │ ├── 00023_00014.ppm │ │ ├── 00023_00015.ppm │ │ ├── 00023_00016.ppm │ │ ├── 00023_00017.ppm │ │ ├── 00023_00018.ppm │ │ ├── 00023_00019.ppm │ │ ├── 00023_00020.ppm │ │ ├── 00023_00021.ppm │ │ ├── 00023_00022.ppm │ │ ├── 00023_00023.ppm │ │ ├── 00023_00024.ppm │ │ ├── 00023_00025.ppm │ │ ├── 00023_00026.ppm │ │ ├── 00023_00027.ppm │ │ ├── 00023_00028.ppm │ │ ├── 00023_00029.ppm │ │ ├── 00024_00000.ppm │ │ ├── 00024_00001.ppm │ │ ├── 00024_00002.ppm │ │ ├── 00024_00003.ppm │ │ ├── 00024_00004.ppm │ │ ├── 00024_00005.ppm │ │ ├── 00024_00006.ppm │ │ ├── 00024_00007.ppm │ │ ├── 00024_00008.ppm │ │ ├── 00024_00009.ppm │ │ ├── 00024_00010.ppm │ │ ├── 00024_00011.ppm │ │ ├── 00024_00012.ppm │ │ ├── 00024_00013.ppm │ │ ├── 00024_00014.ppm │ │ ├── 00024_00015.ppm │ │ ├── 00024_00016.ppm │ │ ├── 00024_00017.ppm │ │ ├── 00024_00018.ppm │ │ ├── 00024_00019.ppm │ │ ├── 00024_00020.ppm │ │ ├── 00024_00021.ppm │ │ ├── 00024_00022.ppm │ │ ├── 00024_00023.ppm │ │ ├── 00024_00024.ppm │ │ ├── 00024_00025.ppm │ │ ├── 00024_00026.ppm │ │ ├── 00024_00027.ppm │ │ ├── 00024_00028.ppm │ │ ├── 00024_00029.ppm │ │ ├── 00025_00000.ppm │ │ ├── 00025_00001.ppm │ │ ├── 00025_00002.ppm │ │ ├── 00025_00003.ppm │ │ ├── 00025_00004.ppm │ │ ├── 00025_00005.ppm │ │ ├── 00025_00006.ppm │ │ ├── 00025_00007.ppm │ │ ├── 00025_00008.ppm │ │ ├── 00025_00009.ppm │ │ ├── 00025_00010.ppm │ │ ├── 00025_00011.ppm │ │ ├── 00025_00012.ppm │ │ ├── 00025_00013.ppm │ │ ├── 00025_00014.ppm │ │ ├── 00025_00015.ppm │ │ ├── 00025_00016.ppm │ │ ├── 00025_00017.ppm │ │ ├── 00025_00018.ppm │ │ ├── 00025_00019.ppm │ │ ├── 00025_00020.ppm │ │ ├── 00025_00021.ppm │ │ ├── 00025_00022.ppm │ │ ├── 00025_00023.ppm │ │ ├── 00025_00024.ppm │ │ ├── 00025_00025.ppm │ │ ├── 00025_00026.ppm │ │ ├── 00025_00027.ppm │ │ ├── 00025_00028.ppm │ │ ├── 00025_00029.ppm │ │ ├── 00026_00000.ppm │ │ ├── 00026_00001.ppm │ │ ├── 00026_00002.ppm │ │ ├── 00026_00003.ppm │ │ ├── 00026_00004.ppm │ │ ├── 00026_00005.ppm │ │ ├── 00026_00006.ppm │ │ ├── 00026_00007.ppm │ │ ├── 00026_00008.ppm │ │ ├── 00026_00009.ppm │ │ ├── 00026_00010.ppm │ │ ├── 00026_00011.ppm │ │ ├── 00026_00012.ppm │ │ ├── 00026_00013.ppm │ │ ├── 00026_00014.ppm │ │ ├── 00026_00015.ppm │ │ ├── 00026_00016.ppm │ │ ├── 00026_00017.ppm │ │ ├── 00026_00018.ppm │ │ ├── 00026_00019.ppm │ │ ├── 00026_00020.ppm │ │ ├── 00026_00021.ppm │ │ ├── 00026_00022.ppm │ │ ├── 00026_00023.ppm │ │ ├── 00026_00024.ppm │ │ ├── 00026_00025.ppm │ │ ├── 00026_00026.ppm │ │ ├── 00026_00027.ppm │ │ ├── 00026_00028.ppm │ │ ├── 00026_00029.ppm │ │ ├── 00027_00000.ppm │ │ ├── 00027_00001.ppm │ │ ├── 00027_00002.ppm │ │ ├── 00027_00003.ppm │ │ ├── 00027_00004.ppm │ │ ├── 00027_00005.ppm │ │ ├── 00027_00006.ppm │ │ ├── 00027_00007.ppm │ │ ├── 00027_00008.ppm │ │ ├── 00027_00009.ppm │ │ ├── 00027_00010.ppm │ │ ├── 00027_00011.ppm │ │ ├── 00027_00012.ppm │ │ ├── 00027_00013.ppm │ │ ├── 00027_00014.ppm │ │ ├── 00027_00015.ppm │ │ ├── 00027_00016.ppm │ │ ├── 00027_00017.ppm │ │ ├── 00027_00018.ppm │ │ ├── 00027_00019.ppm │ │ ├── 00027_00020.ppm │ │ ├── 00027_00021.ppm │ │ ├── 00027_00022.ppm │ │ ├── 00027_00023.ppm │ │ ├── 00027_00024.ppm │ │ ├── 00027_00025.ppm │ │ ├── 00027_00026.ppm │ │ ├── 00027_00027.ppm │ │ ├── 00027_00028.ppm │ │ ├── 00027_00029.ppm │ │ ├── 00028_00000.ppm │ │ ├── 00028_00001.ppm │ │ ├── 00028_00002.ppm │ │ ├── 00028_00003.ppm │ │ ├── 00028_00004.ppm │ │ ├── 00028_00005.ppm │ │ ├── 00028_00006.ppm │ │ ├── 00028_00007.ppm │ │ ├── 00028_00008.ppm │ │ ├── 00028_00009.ppm │ │ ├── 00028_00010.ppm │ │ ├── 00028_00011.ppm │ │ ├── 00028_00012.ppm │ │ ├── 00028_00013.ppm │ │ ├── 00028_00014.ppm │ │ ├── 00028_00015.ppm │ │ ├── 00028_00016.ppm │ │ ├── 00028_00017.ppm │ │ ├── 00028_00018.ppm │ │ ├── 00028_00019.ppm │ │ ├── 00028_00020.ppm │ │ ├── 00028_00021.ppm │ │ ├── 00028_00022.ppm │ │ ├── 00028_00023.ppm │ │ ├── 00028_00024.ppm │ │ ├── 00028_00025.ppm │ │ ├── 00028_00026.ppm │ │ ├── 00028_00027.ppm │ │ ├── 00028_00028.ppm │ │ ├── 00028_00029.ppm │ │ ├── 00029_00000.ppm │ │ ├── 00029_00001.ppm │ │ ├── 00029_00002.ppm │ │ ├── 00029_00003.ppm │ │ ├── 00029_00004.ppm │ │ ├── 00029_00005.ppm │ │ ├── 00029_00006.ppm │ │ ├── 00029_00007.ppm │ │ ├── 00029_00008.ppm │ │ ├── 00029_00009.ppm │ │ ├── 00029_00010.ppm │ │ ├── 00029_00011.ppm │ │ ├── 00029_00012.ppm │ │ ├── 00029_00013.ppm │ │ ├── 00029_00014.ppm │ │ ├── 00029_00015.ppm │ │ ├── 00029_00016.ppm │ │ ├── 00029_00017.ppm │ │ ├── 00029_00018.ppm │ │ ├── 00029_00019.ppm │ │ ├── 00029_00020.ppm │ │ ├── 00029_00021.ppm │ │ ├── 00029_00022.ppm │ │ ├── 00029_00023.ppm │ │ ├── 00029_00024.ppm │ │ ├── 00029_00025.ppm │ │ ├── 00029_00026.ppm │ │ ├── 00029_00027.ppm │ │ ├── 00029_00028.ppm │ │ ├── 00029_00029.ppm │ │ ├── 00030_00000.ppm │ │ ├── 00030_00001.ppm │ │ ├── 00030_00002.ppm │ │ ├── 00030_00003.ppm │ │ ├── 00030_00004.ppm │ │ ├── 00030_00005.ppm │ │ ├── 00030_00006.ppm │ │ ├── 00030_00007.ppm │ │ ├── 00030_00008.ppm │ │ ├── 00030_00009.ppm │ │ ├── 00030_00010.ppm │ │ ├── 00030_00011.ppm │ │ ├── 00030_00012.ppm │ │ ├── 00030_00013.ppm │ │ ├── 00030_00014.ppm │ │ ├── 00030_00015.ppm │ │ ├── 00030_00016.ppm │ │ ├── 00030_00017.ppm │ │ ├── 00030_00018.ppm │ │ ├── 00030_00019.ppm │ │ ├── 00030_00020.ppm │ │ ├── 00030_00021.ppm │ │ ├── 00030_00022.ppm │ │ ├── 00030_00023.ppm │ │ ├── 00030_00024.ppm │ │ ├── 00030_00025.ppm │ │ ├── 00030_00026.ppm │ │ ├── 00030_00027.ppm │ │ ├── 00030_00028.ppm │ │ ├── 00030_00029.ppm │ │ ├── 00031_00000.ppm │ │ ├── 00031_00001.ppm │ │ ├── 00031_00002.ppm │ │ ├── 00031_00003.ppm │ │ ├── 00031_00004.ppm │ │ ├── 00031_00005.ppm │ │ ├── 00031_00006.ppm │ │ ├── 00031_00007.ppm │ │ ├── 00031_00008.ppm │ │ ├── 00031_00009.ppm │ │ ├── 00031_00010.ppm │ │ ├── 00031_00011.ppm │ │ ├── 00031_00012.ppm │ │ ├── 00031_00013.ppm │ │ ├── 00031_00014.ppm │ │ ├── 00031_00015.ppm │ │ ├── 00031_00016.ppm │ │ ├── 00031_00017.ppm │ │ ├── 00031_00018.ppm │ │ ├── 00031_00019.ppm │ │ ├── 00031_00020.ppm │ │ ├── 00031_00021.ppm │ │ ├── 00031_00022.ppm │ │ ├── 00031_00023.ppm │ │ ├── 00031_00024.ppm │ │ ├── 00031_00025.ppm │ │ ├── 00031_00026.ppm │ │ ├── 00031_00027.ppm │ │ ├── 00031_00028.ppm │ │ ├── 00031_00029.ppm │ │ ├── 00032_00000.ppm │ │ ├── 00032_00001.ppm │ │ ├── 00032_00002.ppm │ │ ├── 00032_00003.ppm │ │ ├── 00032_00004.ppm │ │ ├── 00032_00005.ppm │ │ ├── 00032_00006.ppm │ │ ├── 00032_00007.ppm │ │ ├── 00032_00008.ppm │ │ ├── 00032_00009.ppm │ │ ├── 00032_00010.ppm │ │ ├── 00032_00011.ppm │ │ ├── 00032_00012.ppm │ │ ├── 00032_00013.ppm │ │ ├── 00032_00014.ppm │ │ ├── 00032_00015.ppm │ │ ├── 00032_00016.ppm │ │ ├── 00032_00017.ppm │ │ ├── 00032_00018.ppm │ │ ├── 00032_00019.ppm │ │ ├── 00032_00020.ppm │ │ ├── 00032_00021.ppm │ │ ├── 00032_00022.ppm │ │ ├── 00032_00023.ppm │ │ ├── 00032_00024.ppm │ │ ├── 00032_00025.ppm │ │ ├── 00032_00026.ppm │ │ ├── 00032_00027.ppm │ │ ├── 00032_00028.ppm │ │ ├── 00032_00029.ppm │ │ ├── 00033_00000.ppm │ │ ├── 00033_00001.ppm │ │ ├── 00033_00002.ppm │ │ ├── 00033_00003.ppm │ │ ├── 00033_00004.ppm │ │ ├── 00033_00005.ppm │ │ ├── 00033_00006.ppm │ │ ├── 00033_00007.ppm │ │ ├── 00033_00008.ppm │ │ ├── 00033_00009.ppm │ │ ├── 00033_00010.ppm │ │ ├── 00033_00011.ppm │ │ ├── 00033_00012.ppm │ │ ├── 00033_00013.ppm │ │ ├── 00033_00014.ppm │ │ ├── 00033_00015.ppm │ │ ├── 00033_00016.ppm │ │ ├── 00033_00017.ppm │ │ ├── 00033_00018.ppm │ │ ├── 00033_00019.ppm │ │ ├── 00033_00020.ppm │ │ ├── 00033_00021.ppm │ │ ├── 00033_00022.ppm │ │ ├── 00033_00023.ppm │ │ ├── 00033_00024.ppm │ │ ├── 00033_00025.ppm │ │ ├── 00033_00026.ppm │ │ ├── 00033_00027.ppm │ │ ├── 00033_00028.ppm │ │ ├── 00033_00029.ppm │ │ ├── 00034_00000.ppm │ │ ├── 00034_00001.ppm │ │ ├── 00034_00002.ppm │ │ ├── 00034_00003.ppm │ │ ├── 00034_00004.ppm │ │ ├── 00034_00005.ppm │ │ ├── 00034_00006.ppm │ │ ├── 00034_00007.ppm │ │ ├── 00034_00008.ppm │ │ ├── 00034_00009.ppm │ │ ├── 00034_00010.ppm │ │ ├── 00034_00011.ppm │ │ ├── 00034_00012.ppm │ │ ├── 00034_00013.ppm │ │ ├── 00034_00014.ppm │ │ ├── 00034_00015.ppm │ │ ├── 00034_00016.ppm │ │ ├── 00034_00017.ppm │ │ ├── 00034_00018.ppm │ │ ├── 00034_00019.ppm │ │ ├── 00034_00020.ppm │ │ ├── 00034_00021.ppm │ │ ├── 00034_00022.ppm │ │ ├── 00034_00023.ppm │ │ ├── 00034_00024.ppm │ │ ├── 00034_00025.ppm │ │ ├── 00034_00026.ppm │ │ ├── 00034_00027.ppm │ │ ├── 00034_00028.ppm │ │ ├── 00034_00029.ppm │ │ ├── 00035_00000.ppm │ │ ├── 00035_00001.ppm │ │ ├── 00035_00002.ppm │ │ ├── 00035_00003.ppm │ │ ├── 00035_00004.ppm │ │ ├── 00035_00005.ppm │ │ ├── 00035_00006.ppm │ │ ├── 00035_00007.ppm │ │ ├── 00035_00008.ppm │ │ ├── 00035_00009.ppm │ │ ├── 00035_00010.ppm │ │ ├── 00035_00011.ppm │ │ ├── 00035_00012.ppm │ │ ├── 00035_00013.ppm │ │ ├── 00035_00014.ppm │ │ ├── 00035_00015.ppm │ │ ├── 00035_00016.ppm │ │ ├── 00035_00017.ppm │ │ ├── 00035_00018.ppm │ │ ├── 00035_00019.ppm │ │ ├── 00035_00020.ppm │ │ ├── 00035_00021.ppm │ │ ├── 00035_00022.ppm │ │ ├── 00035_00023.ppm │ │ ├── 00035_00024.ppm │ │ ├── 00035_00025.ppm │ │ ├── 00035_00026.ppm │ │ ├── 00035_00027.ppm │ │ ├── 00035_00028.ppm │ │ ├── 00035_00029.ppm │ │ ├── 00036_00000.ppm │ │ ├── 00036_00001.ppm │ │ ├── 00036_00002.ppm │ │ ├── 00036_00003.ppm │ │ ├── 00036_00004.ppm │ │ ├── 00036_00005.ppm │ │ ├── 00036_00006.ppm │ │ ├── 00036_00007.ppm │ │ ├── 00036_00008.ppm │ │ ├── 00036_00009.ppm │ │ ├── 00036_00010.ppm │ │ ├── 00036_00011.ppm │ │ ├── 00036_00012.ppm │ │ ├── 00036_00013.ppm │ │ ├── 00036_00014.ppm │ │ ├── 00036_00015.ppm │ │ ├── 00036_00016.ppm │ │ ├── 00036_00017.ppm │ │ ├── 00036_00018.ppm │ │ ├── 00036_00019.ppm │ │ ├── 00036_00020.ppm │ │ ├── 00036_00021.ppm │ │ ├── 00036_00022.ppm │ │ ├── 00036_00023.ppm │ │ ├── 00036_00024.ppm │ │ ├── 00036_00025.ppm │ │ ├── 00036_00026.ppm │ │ ├── 00036_00027.ppm │ │ ├── 00036_00028.ppm │ │ ├── 00036_00029.ppm │ │ ├── 00037_00000.ppm │ │ ├── 00037_00001.ppm │ │ ├── 00037_00002.ppm │ │ ├── 00037_00003.ppm │ │ ├── 00037_00004.ppm │ │ ├── 00037_00005.ppm │ │ ├── 00037_00006.ppm │ │ ├── 00037_00007.ppm │ │ ├── 00037_00008.ppm │ │ ├── 00037_00009.ppm │ │ ├── 00037_00010.ppm │ │ ├── 00037_00011.ppm │ │ ├── 00037_00012.ppm │ │ ├── 00037_00013.ppm │ │ ├── 00037_00014.ppm │ │ ├── 00037_00015.ppm │ │ ├── 00037_00016.ppm │ │ ├── 00037_00017.ppm │ │ ├── 00037_00018.ppm │ │ ├── 00037_00019.ppm │ │ ├── 00037_00020.ppm │ │ ├── 00037_00021.ppm │ │ ├── 00037_00022.ppm │ │ ├── 00037_00023.ppm │ │ ├── 00037_00024.ppm │ │ ├── 00037_00025.ppm │ │ ├── 00037_00026.ppm │ │ ├── 00037_00027.ppm │ │ ├── 00037_00028.ppm │ │ ├── 00037_00029.ppm │ │ ├── 00038_00000.ppm │ │ ├── 00038_00001.ppm │ │ ├── 00038_00002.ppm │ │ ├── 00038_00003.ppm │ │ ├── 00038_00004.ppm │ │ ├── 00038_00005.ppm │ │ ├── 00038_00006.ppm │ │ ├── 00038_00007.ppm │ │ ├── 00038_00008.ppm │ │ ├── 00038_00009.ppm │ │ ├── 00038_00010.ppm │ │ ├── 00038_00011.ppm │ │ ├── 00038_00012.ppm │ │ ├── 00038_00013.ppm │ │ ├── 00038_00014.ppm │ │ ├── 00038_00015.ppm │ │ ├── 00038_00016.ppm │ │ ├── 00038_00017.ppm │ │ ├── 00038_00018.ppm │ │ ├── 00038_00019.ppm │ │ ├── 00038_00020.ppm │ │ ├── 00038_00021.ppm │ │ ├── 00038_00022.ppm │ │ ├── 00038_00023.ppm │ │ ├── 00038_00024.ppm │ │ ├── 00038_00025.ppm │ │ ├── 00038_00026.ppm │ │ ├── 00038_00027.ppm │ │ ├── 00038_00028.ppm │ │ ├── 00038_00029.ppm │ │ ├── 00039_00000.ppm │ │ ├── 00039_00001.ppm │ │ ├── 00039_00002.ppm │ │ ├── 00039_00003.ppm │ │ ├── 00039_00004.ppm │ │ ├── 00039_00005.ppm │ │ ├── 00039_00006.ppm │ │ ├── 00039_00007.ppm │ │ ├── 00039_00008.ppm │ │ ├── 00039_00009.ppm │ │ ├── 00039_00010.ppm │ │ ├── 00039_00011.ppm │ │ ├── 00039_00012.ppm │ │ ├── 00039_00013.ppm │ │ ├── 00039_00014.ppm │ │ ├── 00039_00015.ppm │ │ ├── 00039_00016.ppm │ │ ├── 00039_00017.ppm │ │ ├── 00039_00018.ppm │ │ ├── 00039_00019.ppm │ │ ├── 00039_00020.ppm │ │ ├── 00039_00021.ppm │ │ ├── 00039_00022.ppm │ │ ├── 00039_00023.ppm │ │ ├── 00039_00024.ppm │ │ ├── 00039_00025.ppm │ │ ├── 00039_00026.ppm │ │ ├── 00039_00027.ppm │ │ ├── 00039_00028.ppm │ │ ├── 00039_00029.ppm │ │ ├── 00040_00000.ppm │ │ ├── 00040_00001.ppm │ │ ├── 00040_00002.ppm │ │ ├── 00040_00003.ppm │ │ ├── 00040_00004.ppm │ │ ├── 00040_00005.ppm │ │ ├── 00040_00006.ppm │ │ ├── 00040_00007.ppm │ │ ├── 00040_00008.ppm │ │ ├── 00040_00009.ppm │ │ ├── 00040_00010.ppm │ │ ├── 00040_00011.ppm │ │ ├── 00040_00012.ppm │ │ ├── 00040_00013.ppm │ │ ├── 00040_00014.ppm │ │ ├── 00040_00015.ppm │ │ ├── 00040_00016.ppm │ │ ├── 00040_00017.ppm │ │ ├── 00040_00018.ppm │ │ ├── 00040_00019.ppm │ │ ├── 00040_00020.ppm │ │ ├── 00040_00021.ppm │ │ ├── 00040_00022.ppm │ │ ├── 00040_00023.ppm │ │ ├── 00040_00024.ppm │ │ ├── 00040_00025.ppm │ │ ├── 00040_00026.ppm │ │ ├── 00040_00027.ppm │ │ ├── 00040_00028.ppm │ │ ├── 00040_00029.ppm │ │ ├── 00041_00000.ppm │ │ ├── 00041_00001.ppm │ │ ├── 00041_00002.ppm │ │ ├── 00041_00003.ppm │ │ ├── 00041_00004.ppm │ │ ├── 00041_00005.ppm │ │ ├── 00041_00006.ppm │ │ ├── 00041_00007.ppm │ │ ├── 00041_00008.ppm │ │ ├── 00041_00009.ppm │ │ ├── 00041_00010.ppm │ │ ├── 00041_00011.ppm │ │ ├── 00041_00012.ppm │ │ ├── 00041_00013.ppm │ │ ├── 00041_00014.ppm │ │ ├── 00041_00015.ppm │ │ ├── 00041_00016.ppm │ │ ├── 00041_00017.ppm │ │ ├── 00041_00018.ppm │ │ ├── 00041_00019.ppm │ │ ├── 00041_00020.ppm │ │ ├── 00041_00021.ppm │ │ ├── 00041_00022.ppm │ │ ├── 00041_00023.ppm │ │ ├── 00041_00024.ppm │ │ ├── 00041_00025.ppm │ │ ├── 00041_00026.ppm │ │ ├── 00041_00027.ppm │ │ ├── 00041_00028.ppm │ │ ├── 00041_00029.ppm │ │ ├── 00042_00000.ppm │ │ ├── 00042_00001.ppm │ │ ├── 00042_00002.ppm │ │ ├── 00042_00003.ppm │ │ ├── 00042_00004.ppm │ │ ├── 00042_00005.ppm │ │ ├── 00042_00006.ppm │ │ ├── 00042_00007.ppm │ │ ├── 00042_00008.ppm │ │ ├── 00042_00009.ppm │ │ ├── 00042_00010.ppm │ │ ├── 00042_00011.ppm │ │ ├── 00042_00012.ppm │ │ ├── 00042_00013.ppm │ │ ├── 00042_00014.ppm │ │ ├── 00042_00015.ppm │ │ ├── 00042_00016.ppm │ │ ├── 00042_00017.ppm │ │ ├── 00042_00018.ppm │ │ ├── 00042_00019.ppm │ │ ├── 00042_00020.ppm │ │ ├── 00042_00021.ppm │ │ ├── 00042_00022.ppm │ │ ├── 00042_00023.ppm │ │ ├── 00042_00024.ppm │ │ ├── 00042_00025.ppm │ │ ├── 00042_00026.ppm │ │ ├── 00042_00027.ppm │ │ ├── 00042_00028.ppm │ │ ├── 00042_00029.ppm │ │ ├── 00043_00000.ppm │ │ ├── 00043_00001.ppm │ │ ├── 00043_00002.ppm │ │ ├── 00043_00003.ppm │ │ ├── 00043_00004.ppm │ │ ├── 00043_00005.ppm │ │ ├── 00043_00006.ppm │ │ ├── 00043_00007.ppm │ │ ├── 00043_00008.ppm │ │ ├── 00043_00009.ppm │ │ ├── 00043_00010.ppm │ │ ├── 00043_00011.ppm │ │ ├── 00043_00012.ppm │ │ ├── 00043_00013.ppm │ │ ├── 00043_00014.ppm │ │ ├── 00043_00015.ppm │ │ ├── 00043_00016.ppm │ │ ├── 00043_00017.ppm │ │ ├── 00043_00018.ppm │ │ ├── 00043_00019.ppm │ │ ├── 00043_00020.ppm │ │ ├── 00043_00021.ppm │ │ ├── 00043_00022.ppm │ │ ├── 00043_00023.ppm │ │ ├── 00043_00024.ppm │ │ ├── 00043_00025.ppm │ │ ├── 00043_00026.ppm │ │ ├── 00043_00027.ppm │ │ ├── 00043_00028.ppm │ │ ├── 00043_00029.ppm │ │ ├── 00044_00000.ppm │ │ ├── 00044_00001.ppm │ │ ├── 00044_00002.ppm │ │ ├── 00044_00003.ppm │ │ ├── 00044_00004.ppm │ │ ├── 00044_00005.ppm │ │ ├── 00044_00006.ppm │ │ ├── 00044_00007.ppm │ │ ├── 00044_00008.ppm │ │ ├── 00044_00009.ppm │ │ ├── 00044_00010.ppm │ │ ├── 00044_00011.ppm │ │ ├── 00044_00012.ppm │ │ ├── 00044_00013.ppm │ │ ├── 00044_00014.ppm │ │ ├── 00044_00015.ppm │ │ ├── 00044_00016.ppm │ │ ├── 00044_00017.ppm │ │ ├── 00044_00018.ppm │ │ ├── 00044_00019.ppm │ │ ├── 00044_00020.ppm │ │ ├── 00044_00021.ppm │ │ ├── 00044_00022.ppm │ │ ├── 00044_00023.ppm │ │ ├── 00044_00024.ppm │ │ ├── 00044_00025.ppm │ │ ├── 00044_00026.ppm │ │ ├── 00044_00027.ppm │ │ ├── 00044_00028.ppm │ │ ├── 00044_00029.ppm │ │ ├── 00045_00000.ppm │ │ ├── 00045_00001.ppm │ │ ├── 00045_00002.ppm │ │ ├── 00045_00003.ppm │ │ ├── 00045_00004.ppm │ │ ├── 00045_00005.ppm │ │ ├── 00045_00006.ppm │ │ ├── 00045_00007.ppm │ │ ├── 00045_00008.ppm │ │ ├── 00045_00009.ppm │ │ ├── 00045_00010.ppm │ │ ├── 00045_00011.ppm │ │ ├── 00045_00012.ppm │ │ ├── 00045_00013.ppm │ │ ├── 00045_00014.ppm │ │ ├── 00045_00015.ppm │ │ ├── 00045_00016.ppm │ │ ├── 00045_00017.ppm │ │ ├── 00045_00018.ppm │ │ ├── 00045_00019.ppm │ │ ├── 00045_00020.ppm │ │ ├── 00045_00021.ppm │ │ ├── 00045_00022.ppm │ │ ├── 00045_00023.ppm │ │ ├── 00045_00024.ppm │ │ ├── 00045_00025.ppm │ │ ├── 00045_00026.ppm │ │ ├── 00045_00027.ppm │ │ ├── 00045_00028.ppm │ │ ├── 00045_00029.ppm │ │ ├── 00046_00000.ppm │ │ ├── 00046_00001.ppm │ │ ├── 00046_00002.ppm │ │ ├── 00046_00003.ppm │ │ ├── 00046_00004.ppm │ │ ├── 00046_00005.ppm │ │ ├── 00046_00006.ppm │ │ ├── 00046_00007.ppm │ │ ├── 00046_00008.ppm │ │ ├── 00046_00009.ppm │ │ ├── 00046_00010.ppm │ │ ├── 00046_00011.ppm │ │ ├── 00046_00012.ppm │ │ ├── 00046_00013.ppm │ │ ├── 00046_00014.ppm │ │ ├── 00046_00015.ppm │ │ ├── 00046_00016.ppm │ │ ├── 00046_00017.ppm │ │ ├── 00046_00018.ppm │ │ ├── 00046_00019.ppm │ │ ├── 00046_00020.ppm │ │ ├── 00046_00021.ppm │ │ ├── 00046_00022.ppm │ │ ├── 00046_00023.ppm │ │ ├── 00046_00024.ppm │ │ ├── 00046_00025.ppm │ │ ├── 00046_00026.ppm │ │ ├── 00046_00027.ppm │ │ ├── 00046_00028.ppm │ │ ├── 00046_00029.ppm │ │ ├── 00047_00000.ppm │ │ ├── 00047_00001.ppm │ │ ├── 00047_00002.ppm │ │ ├── 00047_00003.ppm │ │ ├── 00047_00004.ppm │ │ ├── 00047_00005.ppm │ │ ├── 00047_00006.ppm │ │ ├── 00047_00007.ppm │ │ ├── 00047_00008.ppm │ │ ├── 00047_00009.ppm │ │ ├── 00047_00010.ppm │ │ ├── 00047_00011.ppm │ │ ├── 00047_00012.ppm │ │ ├── 00047_00013.ppm │ │ ├── 00047_00014.ppm │ │ ├── 00047_00015.ppm │ │ ├── 00047_00016.ppm │ │ ├── 00047_00017.ppm │ │ ├── 00047_00018.ppm │ │ ├── 00047_00019.ppm │ │ ├── 00047_00020.ppm │ │ ├── 00047_00021.ppm │ │ ├── 00047_00022.ppm │ │ ├── 00047_00023.ppm │ │ ├── 00047_00024.ppm │ │ ├── 00047_00025.ppm │ │ ├── 00047_00026.ppm │ │ ├── 00047_00027.ppm │ │ ├── 00047_00028.ppm │ │ ├── 00047_00029.ppm │ │ ├── 00048_00000.ppm │ │ ├── 00048_00001.ppm │ │ ├── 00048_00002.ppm │ │ ├── 00048_00003.ppm │ │ ├── 00048_00004.ppm │ │ ├── 00048_00005.ppm │ │ ├── 00048_00006.ppm │ │ ├── 00048_00007.ppm │ │ ├── 00048_00008.ppm │ │ ├── 00048_00009.ppm │ │ ├── 00048_00010.ppm │ │ ├── 00048_00011.ppm │ │ ├── 00048_00012.ppm │ │ ├── 00048_00013.ppm │ │ ├── 00048_00014.ppm │ │ ├── 00048_00015.ppm │ │ ├── 00048_00016.ppm │ │ ├── 00048_00017.ppm │ │ ├── 00048_00018.ppm │ │ ├── 00048_00019.ppm │ │ ├── 00048_00020.ppm │ │ ├── 00048_00021.ppm │ │ ├── 00048_00022.ppm │ │ ├── 00048_00023.ppm │ │ ├── 00048_00024.ppm │ │ ├── 00048_00025.ppm │ │ ├── 00048_00026.ppm │ │ ├── 00048_00027.ppm │ │ ├── 00048_00028.ppm │ │ ├── 00048_00029.ppm │ │ ├── 00049_00000.ppm │ │ ├── 00049_00001.ppm │ │ ├── 00049_00002.ppm │ │ ├── 00049_00003.ppm │ │ ├── 00049_00004.ppm │ │ ├── 00049_00005.ppm │ │ ├── 00049_00006.ppm │ │ ├── 00049_00007.ppm │ │ ├── 00049_00008.ppm │ │ ├── 00049_00009.ppm │ │ ├── 00049_00010.ppm │ │ ├── 00049_00011.ppm │ │ ├── 00049_00012.ppm │ │ ├── 00049_00013.ppm │ │ ├── 00049_00014.ppm │ │ ├── 00049_00015.ppm │ │ ├── 00049_00016.ppm │ │ ├── 00049_00017.ppm │ │ ├── 00049_00018.ppm │ │ ├── 00049_00019.ppm │ │ ├── 00049_00020.ppm │ │ ├── 00049_00021.ppm │ │ ├── 00049_00022.ppm │ │ ├── 00049_00023.ppm │ │ ├── 00049_00024.ppm │ │ ├── 00049_00025.ppm │ │ ├── 00049_00026.ppm │ │ ├── 00049_00027.ppm │ │ ├── 00049_00028.ppm │ │ ├── 00049_00029.ppm │ │ ├── 00050_00000.ppm │ │ ├── 00050_00001.ppm │ │ ├── 00050_00002.ppm │ │ ├── 00050_00003.ppm │ │ ├── 00050_00004.ppm │ │ ├── 00050_00005.ppm │ │ ├── 00050_00006.ppm │ │ ├── 00050_00007.ppm │ │ ├── 00050_00008.ppm │ │ ├── 00050_00009.ppm │ │ ├── 00050_00010.ppm │ │ ├── 00050_00011.ppm │ │ ├── 00050_00012.ppm │ │ ├── 00050_00013.ppm │ │ ├── 00050_00014.ppm │ │ ├── 00050_00015.ppm │ │ ├── 00050_00016.ppm │ │ ├── 00050_00017.ppm │ │ ├── 00050_00018.ppm │ │ ├── 00050_00019.ppm │ │ ├── 00050_00020.ppm │ │ ├── 00050_00021.ppm │ │ ├── 00050_00022.ppm │ │ ├── 00050_00023.ppm │ │ ├── 00050_00024.ppm │ │ ├── 00050_00025.ppm │ │ ├── 00050_00026.ppm │ │ ├── 00050_00027.ppm │ │ ├── 00050_00028.ppm │ │ ├── 00050_00029.ppm │ │ ├── 00051_00000.ppm │ │ ├── 00051_00001.ppm │ │ ├── 00051_00002.ppm │ │ ├── 00051_00003.ppm │ │ ├── 00051_00004.ppm │ │ ├── 00051_00005.ppm │ │ ├── 00051_00006.ppm │ │ ├── 00051_00007.ppm │ │ ├── 00051_00008.ppm │ │ ├── 00051_00009.ppm │ │ ├── 00051_00010.ppm │ │ ├── 00051_00011.ppm │ │ ├── 00051_00012.ppm │ │ ├── 00051_00013.ppm │ │ ├── 00051_00014.ppm │ │ ├── 00051_00015.ppm │ │ ├── 00051_00016.ppm │ │ ├── 00051_00017.ppm │ │ ├── 00051_00018.ppm │ │ ├── 00051_00019.ppm │ │ ├── 00051_00020.ppm │ │ ├── 00051_00021.ppm │ │ ├── 00051_00022.ppm │ │ ├── 00051_00023.ppm │ │ ├── 00051_00024.ppm │ │ ├── 00051_00025.ppm │ │ ├── 00051_00026.ppm │ │ ├── 00051_00027.ppm │ │ ├── 00051_00028.ppm │ │ ├── 00051_00029.ppm │ │ ├── 00052_00000.ppm │ │ ├── 00052_00001.ppm │ │ ├── 00052_00002.ppm │ │ ├── 00052_00003.ppm │ │ ├── 00052_00004.ppm │ │ ├── 00052_00005.ppm │ │ ├── 00052_00006.ppm │ │ ├── 00052_00007.ppm │ │ ├── 00052_00008.ppm │ │ ├── 00052_00009.ppm │ │ ├── 00052_00010.ppm │ │ ├── 00052_00011.ppm │ │ ├── 00052_00012.ppm │ │ ├── 00052_00013.ppm │ │ ├── 00052_00014.ppm │ │ ├── 00052_00015.ppm │ │ ├── 00052_00016.ppm │ │ ├── 00052_00017.ppm │ │ ├── 00052_00018.ppm │ │ ├── 00052_00019.ppm │ │ ├── 00052_00020.ppm │ │ ├── 00052_00021.ppm │ │ ├── 00052_00022.ppm │ │ ├── 00052_00023.ppm │ │ ├── 00052_00024.ppm │ │ ├── 00052_00025.ppm │ │ ├── 00052_00026.ppm │ │ ├── 00052_00027.ppm │ │ ├── 00052_00028.ppm │ │ ├── 00052_00029.ppm │ │ ├── 00053_00000.ppm │ │ ├── 00053_00001.ppm │ │ ├── 00053_00002.ppm │ │ ├── 00053_00003.ppm │ │ ├── 00053_00004.ppm │ │ ├── 00053_00005.ppm │ │ ├── 00053_00006.ppm │ │ ├── 00053_00007.ppm │ │ ├── 00053_00008.ppm │ │ ├── 00053_00009.ppm │ │ ├── 00053_00010.ppm │ │ ├── 00053_00011.ppm │ │ ├── 00053_00012.ppm │ │ ├── 00053_00013.ppm │ │ ├── 00053_00014.ppm │ │ ├── 00053_00015.ppm │ │ ├── 00053_00016.ppm │ │ ├── 00053_00017.ppm │ │ ├── 00053_00018.ppm │ │ ├── 00053_00019.ppm │ │ ├── 00053_00020.ppm │ │ ├── 00053_00021.ppm │ │ ├── 00053_00022.ppm │ │ ├── 00053_00023.ppm │ │ ├── 00053_00024.ppm │ │ ├── 00053_00025.ppm │ │ ├── 00053_00026.ppm │ │ ├── 00053_00027.ppm │ │ ├── 00053_00028.ppm │ │ ├── 00053_00029.ppm │ │ ├── 00054_00000.ppm │ │ ├── 00054_00001.ppm │ │ ├── 00054_00002.ppm │ │ ├── 00054_00003.ppm │ │ ├── 00054_00004.ppm │ │ ├── 00054_00005.ppm │ │ ├── 00054_00006.ppm │ │ ├── 00054_00007.ppm │ │ ├── 00054_00008.ppm │ │ ├── 00054_00009.ppm │ │ ├── 00054_00010.ppm │ │ ├── 00054_00011.ppm │ │ ├── 00054_00012.ppm │ │ ├── 00054_00013.ppm │ │ ├── 00054_00014.ppm │ │ ├── 00054_00015.ppm │ │ ├── 00054_00016.ppm │ │ ├── 00054_00017.ppm │ │ ├── 00054_00018.ppm │ │ ├── 00054_00019.ppm │ │ ├── 00054_00020.ppm │ │ ├── 00054_00021.ppm │ │ ├── 00054_00022.ppm │ │ ├── 00054_00023.ppm │ │ ├── 00054_00024.ppm │ │ ├── 00054_00025.ppm │ │ ├── 00054_00026.ppm │ │ ├── 00054_00027.ppm │ │ ├── 00054_00028.ppm │ │ ├── 00054_00029.ppm │ │ ├── 00055_00000.ppm │ │ ├── 00055_00001.ppm │ │ ├── 00055_00002.ppm │ │ ├── 00055_00003.ppm │ │ ├── 00055_00004.ppm │ │ ├── 00055_00005.ppm │ │ ├── 00055_00006.ppm │ │ ├── 00055_00007.ppm │ │ ├── 00055_00008.ppm │ │ ├── 00055_00009.ppm │ │ ├── 00055_00010.ppm │ │ ├── 00055_00011.ppm │ │ ├── 00055_00012.ppm │ │ ├── 00055_00013.ppm │ │ ├── 00055_00014.ppm │ │ ├── 00055_00015.ppm │ │ ├── 00055_00016.ppm │ │ ├── 00055_00017.ppm │ │ ├── 00055_00018.ppm │ │ ├── 00055_00019.ppm │ │ ├── 00055_00020.ppm │ │ ├── 00055_00021.ppm │ │ ├── 00055_00022.ppm │ │ ├── 00055_00023.ppm │ │ ├── 00055_00024.ppm │ │ ├── 00055_00025.ppm │ │ ├── 00055_00026.ppm │ │ ├── 00055_00027.ppm │ │ ├── 00055_00028.ppm │ │ ├── 00055_00029.ppm │ │ ├── 00056_00000.ppm │ │ ├── 00056_00001.ppm │ │ ├── 00056_00002.ppm │ │ ├── 00056_00003.ppm │ │ ├── 00056_00004.ppm │ │ ├── 00056_00005.ppm │ │ ├── 00056_00006.ppm │ │ ├── 00056_00007.ppm │ │ ├── 00056_00008.ppm │ │ ├── 00056_00009.ppm │ │ ├── 00056_00010.ppm │ │ ├── 00056_00011.ppm │ │ ├── 00056_00012.ppm │ │ ├── 00056_00013.ppm │ │ ├── 00056_00014.ppm │ │ ├── 00056_00015.ppm │ │ ├── 00056_00016.ppm │ │ ├── 00056_00017.ppm │ │ ├── 00056_00018.ppm │ │ ├── 00056_00019.ppm │ │ ├── 00056_00020.ppm │ │ ├── 00056_00021.ppm │ │ ├── 00056_00022.ppm │ │ ├── 00056_00023.ppm │ │ ├── 00056_00024.ppm │ │ ├── 00056_00025.ppm │ │ ├── 00056_00026.ppm │ │ ├── 00056_00027.ppm │ │ ├── 00056_00028.ppm │ │ ├── 00056_00029.ppm │ │ ├── 00057_00000.ppm │ │ ├── 00057_00001.ppm │ │ ├── 00057_00002.ppm │ │ ├── 00057_00003.ppm │ │ ├── 00057_00004.ppm │ │ ├── 00057_00005.ppm │ │ ├── 00057_00006.ppm │ │ ├── 00057_00007.ppm │ │ ├── 00057_00008.ppm │ │ ├── 00057_00009.ppm │ │ ├── 00057_00010.ppm │ │ ├── 00057_00011.ppm │ │ ├── 00057_00012.ppm │ │ ├── 00057_00013.ppm │ │ ├── 00057_00014.ppm │ │ ├── 00057_00015.ppm │ │ ├── 00057_00016.ppm │ │ ├── 00057_00017.ppm │ │ ├── 00057_00018.ppm │ │ ├── 00057_00019.ppm │ │ ├── 00057_00020.ppm │ │ ├── 00057_00021.ppm │ │ ├── 00057_00022.ppm │ │ ├── 00057_00023.ppm │ │ ├── 00057_00024.ppm │ │ ├── 00057_00025.ppm │ │ ├── 00057_00026.ppm │ │ ├── 00057_00027.ppm │ │ ├── 00057_00028.ppm │ │ ├── 00057_00029.ppm │ │ ├── 00058_00000.ppm │ │ ├── 00058_00001.ppm │ │ ├── 00058_00002.ppm │ │ ├── 00058_00003.ppm │ │ ├── 00058_00004.ppm │ │ ├── 00058_00005.ppm │ │ ├── 00058_00006.ppm │ │ ├── 00058_00007.ppm │ │ ├── 00058_00008.ppm │ │ ├── 00058_00009.ppm │ │ ├── 00058_00010.ppm │ │ ├── 00058_00011.ppm │ │ ├── 00058_00012.ppm │ │ ├── 00058_00013.ppm │ │ ├── 00058_00014.ppm │ │ ├── 00058_00015.ppm │ │ ├── 00058_00016.ppm │ │ ├── 00058_00017.ppm │ │ ├── 00058_00018.ppm │ │ ├── 00058_00019.ppm │ │ ├── 00058_00020.ppm │ │ ├── 00058_00021.ppm │ │ ├── 00058_00022.ppm │ │ ├── 00058_00023.ppm │ │ ├── 00058_00024.ppm │ │ ├── 00058_00025.ppm │ │ ├── 00058_00026.ppm │ │ ├── 00058_00027.ppm │ │ ├── 00058_00028.ppm │ │ ├── 00058_00029.ppm │ │ ├── 00059_00000.ppm │ │ ├── 00059_00001.ppm │ │ ├── 00059_00002.ppm │ │ ├── 00059_00003.ppm │ │ ├── 00059_00004.ppm │ │ ├── 00059_00005.ppm │ │ ├── 00059_00006.ppm │ │ ├── 00059_00007.ppm │ │ ├── 00059_00008.ppm │ │ ├── 00059_00009.ppm │ │ ├── 00059_00010.ppm │ │ ├── 00059_00011.ppm │ │ ├── 00059_00012.ppm │ │ ├── 00059_00013.ppm │ │ ├── 00059_00014.ppm │ │ ├── 00059_00015.ppm │ │ ├── 00059_00016.ppm │ │ ├── 00059_00017.ppm │ │ ├── 00059_00018.ppm │ │ ├── 00059_00019.ppm │ │ ├── 00059_00020.ppm │ │ ├── 00059_00021.ppm │ │ ├── 00059_00022.ppm │ │ ├── 00059_00023.ppm │ │ ├── 00059_00024.ppm │ │ ├── 00059_00025.ppm │ │ ├── 00059_00026.ppm │ │ ├── 00059_00027.ppm │ │ ├── 00059_00028.ppm │ │ ├── 00059_00029.ppm │ │ ├── 00060_00000.ppm │ │ ├── 00060_00001.ppm │ │ ├── 00060_00002.ppm │ │ ├── 00060_00003.ppm │ │ ├── 00060_00004.ppm │ │ ├── 00060_00005.ppm │ │ ├── 00060_00006.ppm │ │ ├── 00060_00007.ppm │ │ ├── 00060_00008.ppm │ │ ├── 00060_00009.ppm │ │ ├── 00060_00010.ppm │ │ ├── 00060_00011.ppm │ │ ├── 00060_00012.ppm │ │ ├── 00060_00013.ppm │ │ ├── 00060_00014.ppm │ │ ├── 00060_00015.ppm │ │ ├── 00060_00016.ppm │ │ ├── 00060_00017.ppm │ │ ├── 00060_00018.ppm │ │ ├── 00060_00019.ppm │ │ ├── 00060_00020.ppm │ │ ├── 00060_00021.ppm │ │ ├── 00060_00022.ppm │ │ ├── 00060_00023.ppm │ │ ├── 00060_00024.ppm │ │ ├── 00060_00025.ppm │ │ ├── 00060_00026.ppm │ │ ├── 00060_00027.ppm │ │ ├── 00060_00028.ppm │ │ ├── 00060_00029.ppm │ │ ├── 00061_00000.ppm │ │ ├── 00061_00001.ppm │ │ ├── 00061_00002.ppm │ │ ├── 00061_00003.ppm │ │ ├── 00061_00004.ppm │ │ ├── 00061_00005.ppm │ │ ├── 00061_00006.ppm │ │ ├── 00061_00007.ppm │ │ ├── 00061_00008.ppm │ │ ├── 00061_00009.ppm │ │ ├── 00061_00010.ppm │ │ ├── 00061_00011.ppm │ │ ├── 00061_00012.ppm │ │ ├── 00061_00013.ppm │ │ ├── 00061_00014.ppm │ │ ├── 00061_00015.ppm │ │ ├── 00061_00016.ppm │ │ ├── 00061_00017.ppm │ │ ├── 00061_00018.ppm │ │ ├── 00061_00019.ppm │ │ ├── 00061_00020.ppm │ │ ├── 00061_00021.ppm │ │ ├── 00061_00022.ppm │ │ ├── 00061_00023.ppm │ │ ├── 00061_00024.ppm │ │ ├── 00061_00025.ppm │ │ ├── 00061_00026.ppm │ │ ├── 00061_00027.ppm │ │ ├── 00061_00028.ppm │ │ ├── 00061_00029.ppm │ │ ├── 00062_00000.ppm │ │ ├── 00062_00001.ppm │ │ ├── 00062_00002.ppm │ │ ├── 00062_00003.ppm │ │ ├── 00062_00004.ppm │ │ ├── 00062_00005.ppm │ │ ├── 00062_00006.ppm │ │ ├── 00062_00007.ppm │ │ ├── 00062_00008.ppm │ │ ├── 00062_00009.ppm │ │ ├── 00062_00010.ppm │ │ ├── 00062_00011.ppm │ │ ├── 00062_00012.ppm │ │ ├── 00062_00013.ppm │ │ ├── 00062_00014.ppm │ │ ├── 00062_00015.ppm │ │ ├── 00062_00016.ppm │ │ ├── 00062_00017.ppm │ │ ├── 00062_00018.ppm │ │ ├── 00062_00019.ppm │ │ ├── 00062_00020.ppm │ │ ├── 00062_00021.ppm │ │ ├── 00062_00022.ppm │ │ ├── 00062_00023.ppm │ │ ├── 00062_00024.ppm │ │ ├── 00062_00025.ppm │ │ ├── 00062_00026.ppm │ │ ├── 00062_00027.ppm │ │ ├── 00062_00028.ppm │ │ ├── 00062_00029.ppm │ │ ├── 00063_00000.ppm │ │ ├── 00063_00001.ppm │ │ ├── 00063_00002.ppm │ │ ├── 00063_00003.ppm │ │ ├── 00063_00004.ppm │ │ ├── 00063_00005.ppm │ │ ├── 00063_00006.ppm │ │ ├── 00063_00007.ppm │ │ ├── 00063_00008.ppm │ │ ├── 00063_00009.ppm │ │ ├── 00063_00010.ppm │ │ ├── 00063_00011.ppm │ │ ├── 00063_00012.ppm │ │ ├── 00063_00013.ppm │ │ ├── 00063_00014.ppm │ │ ├── 00063_00015.ppm │ │ ├── 00063_00016.ppm │ │ ├── 00063_00017.ppm │ │ ├── 00063_00018.ppm │ │ ├── 00063_00019.ppm │ │ ├── 00063_00020.ppm │ │ ├── 00063_00021.ppm │ │ ├── 00063_00022.ppm │ │ ├── 00063_00023.ppm │ │ ├── 00063_00024.ppm │ │ ├── 00063_00025.ppm │ │ ├── 00063_00026.ppm │ │ ├── 00063_00027.ppm │ │ ├── 00063_00028.ppm │ │ ├── 00063_00029.ppm │ │ ├── 00064_00000.ppm │ │ ├── 00064_00001.ppm │ │ ├── 00064_00002.ppm │ │ ├── 00064_00003.ppm │ │ ├── 00064_00004.ppm │ │ ├── 00064_00005.ppm │ │ ├── 00064_00006.ppm │ │ ├── 00064_00007.ppm │ │ ├── 00064_00008.ppm │ │ ├── 00064_00009.ppm │ │ ├── 00064_00010.ppm │ │ ├── 00064_00011.ppm │ │ ├── 00064_00012.ppm │ │ ├── 00064_00013.ppm │ │ ├── 00064_00014.ppm │ │ ├── 00064_00015.ppm │ │ ├── 00064_00016.ppm │ │ ├── 00064_00017.ppm │ │ ├── 00064_00018.ppm │ │ ├── 00064_00019.ppm │ │ ├── 00064_00020.ppm │ │ ├── 00064_00021.ppm │ │ ├── 00064_00022.ppm │ │ ├── 00064_00023.ppm │ │ ├── 00064_00024.ppm │ │ ├── 00064_00025.ppm │ │ ├── 00064_00026.ppm │ │ ├── 00064_00027.ppm │ │ ├── 00064_00028.ppm │ │ ├── 00064_00029.ppm │ │ ├── 00065_00000.ppm │ │ ├── 00065_00001.ppm │ │ ├── 00065_00002.ppm │ │ ├── 00065_00003.ppm │ │ ├── 00065_00004.ppm │ │ ├── 00065_00005.ppm │ │ ├── 00065_00006.ppm │ │ ├── 00065_00007.ppm │ │ ├── 00065_00008.ppm │ │ ├── 00065_00009.ppm │ │ ├── 00065_00010.ppm │ │ ├── 00065_00011.ppm │ │ ├── 00065_00012.ppm │ │ ├── 00065_00013.ppm │ │ ├── 00065_00014.ppm │ │ ├── 00065_00015.ppm │ │ ├── 00065_00016.ppm │ │ ├── 00065_00017.ppm │ │ ├── 00065_00018.ppm │ │ ├── 00065_00019.ppm │ │ ├── 00065_00020.ppm │ │ ├── 00065_00021.ppm │ │ ├── 00065_00022.ppm │ │ ├── 00065_00023.ppm │ │ ├── 00065_00024.ppm │ │ ├── 00065_00025.ppm │ │ ├── 00065_00026.ppm │ │ ├── 00065_00027.ppm │ │ ├── 00065_00028.ppm │ │ ├── 00065_00029.ppm │ │ ├── 00066_00000.ppm │ │ ├── 00066_00001.ppm │ │ ├── 00066_00002.ppm │ │ ├── 00066_00003.ppm │ │ ├── 00066_00004.ppm │ │ ├── 00066_00005.ppm │ │ ├── 00066_00006.ppm │ │ ├── 00066_00007.ppm │ │ ├── 00066_00008.ppm │ │ ├── 00066_00009.ppm │ │ ├── 00066_00010.ppm │ │ ├── 00066_00011.ppm │ │ ├── 00066_00012.ppm │ │ ├── 00066_00013.ppm │ │ ├── 00066_00014.ppm │ │ ├── 00066_00015.ppm │ │ ├── 00066_00016.ppm │ │ ├── 00066_00017.ppm │ │ ├── 00066_00018.ppm │ │ ├── 00066_00019.ppm │ │ ├── 00066_00020.ppm │ │ ├── 00066_00021.ppm │ │ ├── 00066_00022.ppm │ │ ├── 00066_00023.ppm │ │ ├── 00066_00024.ppm │ │ ├── 00066_00025.ppm │ │ ├── 00066_00026.ppm │ │ ├── 00066_00027.ppm │ │ ├── 00066_00028.ppm │ │ ├── 00066_00029.ppm │ │ ├── 00067_00000.ppm │ │ ├── 00067_00001.ppm │ │ ├── 00067_00002.ppm │ │ ├── 00067_00003.ppm │ │ ├── 00067_00004.ppm │ │ ├── 00067_00005.ppm │ │ ├── 00067_00006.ppm │ │ ├── 00067_00007.ppm │ │ ├── 00067_00008.ppm │ │ ├── 00067_00009.ppm │ │ ├── 00067_00010.ppm │ │ ├── 00067_00011.ppm │ │ ├── 00067_00012.ppm │ │ ├── 00067_00013.ppm │ │ ├── 00067_00014.ppm │ │ ├── 00067_00015.ppm │ │ ├── 00067_00016.ppm │ │ ├── 00067_00017.ppm │ │ ├── 00067_00018.ppm │ │ ├── 00067_00019.ppm │ │ ├── 00067_00020.ppm │ │ ├── 00067_00021.ppm │ │ ├── 00067_00022.ppm │ │ ├── 00067_00023.ppm │ │ ├── 00067_00024.ppm │ │ ├── 00067_00025.ppm │ │ ├── 00067_00026.ppm │ │ ├── 00067_00027.ppm │ │ ├── 00067_00028.ppm │ │ ├── 00067_00029.ppm │ │ ├── 00068_00000.ppm │ │ ├── 00068_00001.ppm │ │ ├── 00068_00002.ppm │ │ ├── 00068_00003.ppm │ │ ├── 00068_00004.ppm │ │ ├── 00068_00005.ppm │ │ ├── 00068_00006.ppm │ │ ├── 00068_00007.ppm │ │ ├── 00068_00008.ppm │ │ ├── 00068_00009.ppm │ │ ├── 00068_00010.ppm │ │ ├── 00068_00011.ppm │ │ ├── 00068_00012.ppm │ │ ├── 00068_00013.ppm │ │ ├── 00068_00014.ppm │ │ ├── 00068_00015.ppm │ │ ├── 00068_00016.ppm │ │ ├── 00068_00017.ppm │ │ ├── 00068_00018.ppm │ │ ├── 00068_00019.ppm │ │ ├── 00068_00020.ppm │ │ ├── 00068_00021.ppm │ │ ├── 00068_00022.ppm │ │ ├── 00068_00023.ppm │ │ ├── 00068_00024.ppm │ │ ├── 00068_00025.ppm │ │ ├── 00068_00026.ppm │ │ ├── 00068_00027.ppm │ │ ├── 00068_00028.ppm │ │ ├── 00068_00029.ppm │ │ ├── 00069_00000.ppm │ │ ├── 00069_00001.ppm │ │ ├── 00069_00002.ppm │ │ ├── 00069_00003.ppm │ │ ├── 00069_00004.ppm │ │ ├── 00069_00005.ppm │ │ ├── 00069_00006.ppm │ │ ├── 00069_00007.ppm │ │ ├── 00069_00008.ppm │ │ ├── 00069_00009.ppm │ │ ├── 00069_00010.ppm │ │ ├── 00069_00011.ppm │ │ ├── 00069_00012.ppm │ │ ├── 00069_00013.ppm │ │ ├── 00069_00014.ppm │ │ ├── 00069_00015.ppm │ │ ├── 00069_00016.ppm │ │ ├── 00069_00017.ppm │ │ ├── 00069_00018.ppm │ │ ├── 00069_00019.ppm │ │ ├── 00069_00020.ppm │ │ ├── 00069_00021.ppm │ │ ├── 00069_00022.ppm │ │ ├── 00069_00023.ppm │ │ ├── 00069_00024.ppm │ │ ├── 00069_00025.ppm │ │ ├── 00069_00026.ppm │ │ ├── 00069_00027.ppm │ │ ├── 00069_00028.ppm │ │ ├── 00069_00029.ppm │ │ ├── 00070_00000.ppm │ │ ├── 00070_00001.ppm │ │ ├── 00070_00002.ppm │ │ ├── 00070_00003.ppm │ │ ├── 00070_00004.ppm │ │ ├── 00070_00005.ppm │ │ ├── 00070_00006.ppm │ │ ├── 00070_00007.ppm │ │ ├── 00070_00008.ppm │ │ ├── 00070_00009.ppm │ │ ├── 00070_00010.ppm │ │ ├── 00070_00011.ppm │ │ ├── 00070_00012.ppm │ │ ├── 00070_00013.ppm │ │ ├── 00070_00014.ppm │ │ ├── 00070_00015.ppm │ │ ├── 00070_00016.ppm │ │ ├── 00070_00017.ppm │ │ ├── 00070_00018.ppm │ │ ├── 00070_00019.ppm │ │ ├── 00070_00020.ppm │ │ ├── 00070_00021.ppm │ │ ├── 00070_00022.ppm │ │ ├── 00070_00023.ppm │ │ ├── 00070_00024.ppm │ │ ├── 00070_00025.ppm │ │ ├── 00070_00026.ppm │ │ ├── 00070_00027.ppm │ │ ├── 00070_00028.ppm │ │ ├── 00070_00029.ppm │ │ ├── 00071_00000.ppm │ │ ├── 00071_00001.ppm │ │ ├── 00071_00002.ppm │ │ ├── 00071_00003.ppm │ │ ├── 00071_00004.ppm │ │ ├── 00071_00005.ppm │ │ ├── 00071_00006.ppm │ │ ├── 00071_00007.ppm │ │ ├── 00071_00008.ppm │ │ ├── 00071_00009.ppm │ │ ├── 00071_00010.ppm │ │ ├── 00071_00011.ppm │ │ ├── 00071_00012.ppm │ │ ├── 00071_00013.ppm │ │ ├── 00071_00014.ppm │ │ ├── 00071_00015.ppm │ │ ├── 00071_00016.ppm │ │ ├── 00071_00017.ppm │ │ ├── 00071_00018.ppm │ │ ├── 00071_00019.ppm │ │ ├── 00071_00020.ppm │ │ ├── 00071_00021.ppm │ │ ├── 00071_00022.ppm │ │ ├── 00071_00023.ppm │ │ ├── 00071_00024.ppm │ │ ├── 00071_00025.ppm │ │ ├── 00071_00026.ppm │ │ ├── 00071_00027.ppm │ │ ├── 00071_00028.ppm │ │ ├── 00071_00029.ppm │ │ ├── 00072_00000.ppm │ │ ├── 00072_00001.ppm │ │ ├── 00072_00002.ppm │ │ ├── 00072_00003.ppm │ │ ├── 00072_00004.ppm │ │ ├── 00072_00005.ppm │ │ ├── 00072_00006.ppm │ │ ├── 00072_00007.ppm │ │ ├── 00072_00008.ppm │ │ ├── 00072_00009.ppm │ │ ├── 00072_00010.ppm │ │ ├── 00072_00011.ppm │ │ ├── 00072_00012.ppm │ │ ├── 00072_00013.ppm │ │ ├── 00072_00014.ppm │ │ ├── 00072_00015.ppm │ │ ├── 00072_00016.ppm │ │ ├── 00072_00017.ppm │ │ ├── 00072_00018.ppm │ │ ├── 00072_00019.ppm │ │ ├── 00072_00020.ppm │ │ ├── 00072_00021.ppm │ │ ├── 00072_00022.ppm │ │ ├── 00072_00023.ppm │ │ ├── 00072_00024.ppm │ │ ├── 00072_00025.ppm │ │ ├── 00072_00026.ppm │ │ ├── 00072_00027.ppm │ │ ├── 00072_00028.ppm │ │ ├── 00072_00029.ppm │ │ ├── 00073_00000.ppm │ │ ├── 00073_00001.ppm │ │ ├── 00073_00002.ppm │ │ ├── 00073_00003.ppm │ │ ├── 00073_00004.ppm │ │ ├── 00073_00005.ppm │ │ ├── 00073_00006.ppm │ │ ├── 00073_00007.ppm │ │ ├── 00073_00008.ppm │ │ ├── 00073_00009.ppm │ │ ├── 00073_00010.ppm │ │ ├── 00073_00011.ppm │ │ ├── 00073_00012.ppm │ │ ├── 00073_00013.ppm │ │ ├── 00073_00014.ppm │ │ ├── 00073_00015.ppm │ │ ├── 00073_00016.ppm │ │ ├── 00073_00017.ppm │ │ ├── 00073_00018.ppm │ │ ├── 00073_00019.ppm │ │ ├── 00073_00020.ppm │ │ ├── 00073_00021.ppm │ │ ├── 00073_00022.ppm │ │ ├── 00073_00023.ppm │ │ ├── 00073_00024.ppm │ │ ├── 00073_00025.ppm │ │ ├── 00073_00026.ppm │ │ ├── 00073_00027.ppm │ │ ├── 00073_00028.ppm │ │ ├── 00073_00029.ppm │ │ ├── 00074_00000.ppm │ │ ├── 00074_00001.ppm │ │ ├── 00074_00002.ppm │ │ ├── 00074_00003.ppm │ │ ├── 00074_00004.ppm │ │ ├── 00074_00005.ppm │ │ ├── 00074_00006.ppm │ │ ├── 00074_00007.ppm │ │ ├── 00074_00008.ppm │ │ ├── 00074_00009.ppm │ │ ├── 00074_00010.ppm │ │ ├── 00074_00011.ppm │ │ ├── 00074_00012.ppm │ │ ├── 00074_00013.ppm │ │ ├── 00074_00014.ppm │ │ ├── 00074_00015.ppm │ │ ├── 00074_00016.ppm │ │ ├── 00074_00017.ppm │ │ ├── 00074_00018.ppm │ │ ├── 00074_00019.ppm │ │ ├── 00074_00020.ppm │ │ ├── 00074_00021.ppm │ │ ├── 00074_00022.ppm │ │ ├── 00074_00023.ppm │ │ ├── 00074_00024.ppm │ │ ├── 00074_00025.ppm │ │ ├── 00074_00026.ppm │ │ ├── 00074_00027.ppm │ │ ├── 00074_00028.ppm │ │ ├── 00074_00029.ppm │ │ └── GT-00002.csv │ ├── 00004/ │ │ ├── 00000_00000.ppm │ │ ├── 00000_00001.ppm │ │ ├── 00000_00002.ppm │ │ ├── 00000_00003.ppm │ │ ├── 00000_00004.ppm │ │ ├── 00000_00005.ppm │ │ ├── 00000_00006.ppm │ │ ├── 00000_00007.ppm │ │ ├── 00000_00008.ppm │ │ ├── 00000_00009.ppm │ │ ├── 00000_00010.ppm │ │ ├── 00000_00011.ppm │ │ ├── 00000_00012.ppm │ │ ├── 00000_00013.ppm │ │ ├── 00000_00014.ppm │ │ ├── 00000_00015.ppm │ │ ├── 00000_00016.ppm │ │ ├── 00000_00017.ppm │ │ ├── 00000_00018.ppm │ │ ├── 00000_00019.ppm │ │ ├── 00000_00020.ppm │ │ ├── 00000_00021.ppm │ │ ├── 00000_00022.ppm │ │ ├── 00000_00023.ppm │ │ ├── 00000_00024.ppm │ │ ├── 00000_00025.ppm │ │ ├── 00000_00026.ppm │ │ ├── 00000_00027.ppm │ │ ├── 00000_00028.ppm │ │ ├── 00000_00029.ppm │ │ ├── 00001_00000.ppm │ │ ├── 00001_00001.ppm │ │ ├── 00001_00002.ppm │ │ ├── 00001_00003.ppm │ │ ├── 00001_00004.ppm │ │ ├── 00001_00005.ppm │ │ ├── 00001_00006.ppm │ │ ├── 00001_00007.ppm │ │ ├── 00001_00008.ppm │ │ ├── 00001_00009.ppm │ │ ├── 00001_00010.ppm │ │ ├── 00001_00011.ppm │ │ ├── 00001_00012.ppm │ │ ├── 00001_00013.ppm │ │ ├── 00001_00014.ppm │ │ ├── 00001_00015.ppm │ │ ├── 00001_00016.ppm │ │ ├── 00001_00017.ppm │ │ ├── 00001_00018.ppm │ │ ├── 00001_00019.ppm │ │ ├── 00001_00020.ppm │ │ ├── 00001_00021.ppm │ │ ├── 00001_00022.ppm │ │ ├── 00001_00023.ppm │ │ ├── 00001_00024.ppm │ │ ├── 00001_00025.ppm │ │ ├── 00001_00026.ppm │ │ ├── 00001_00027.ppm │ │ ├── 00001_00028.ppm │ │ ├── 00001_00029.ppm │ │ ├── 00002_00000.ppm │ │ ├── 00002_00001.ppm │ │ ├── 00002_00002.ppm │ │ ├── 00002_00003.ppm │ │ ├── 00002_00004.ppm │ │ ├── 00002_00005.ppm │ │ ├── 00002_00006.ppm │ │ ├── 00002_00007.ppm │ │ ├── 00002_00008.ppm │ │ ├── 00002_00009.ppm │ │ ├── 00002_00010.ppm │ │ ├── 00002_00011.ppm │ │ ├── 00002_00012.ppm │ │ ├── 00002_00013.ppm │ │ ├── 00002_00014.ppm │ │ ├── 00002_00015.ppm │ │ ├── 00002_00016.ppm │ │ ├── 00002_00017.ppm │ │ ├── 00002_00018.ppm │ │ ├── 00002_00019.ppm │ │ ├── 00002_00020.ppm │ │ ├── 00002_00021.ppm │ │ ├── 00002_00022.ppm │ │ ├── 00002_00023.ppm │ │ ├── 00002_00024.ppm │ │ ├── 00002_00025.ppm │ │ ├── 00002_00026.ppm │ │ ├── 00002_00027.ppm │ │ ├── 00002_00028.ppm │ │ ├── 00002_00029.ppm │ │ ├── 00003_00000.ppm │ │ ├── 00003_00001.ppm │ │ ├── 00003_00002.ppm │ │ ├── 00003_00003.ppm │ │ ├── 00003_00004.ppm │ │ ├── 00003_00005.ppm │ │ ├── 00003_00006.ppm │ │ ├── 00003_00007.ppm │ │ ├── 00003_00008.ppm │ │ ├── 00003_00009.ppm │ │ ├── 00003_00010.ppm │ │ ├── 00003_00011.ppm │ │ ├── 00003_00012.ppm │ │ ├── 00003_00013.ppm │ │ ├── 00003_00014.ppm │ │ ├── 00003_00015.ppm │ │ ├── 00003_00016.ppm │ │ ├── 00003_00017.ppm │ │ ├── 00003_00018.ppm │ │ ├── 00003_00019.ppm │ │ ├── 00003_00020.ppm │ │ ├── 00003_00021.ppm │ │ ├── 00003_00022.ppm │ │ ├── 00003_00023.ppm │ │ ├── 00003_00024.ppm │ │ ├── 00003_00025.ppm │ │ ├── 00003_00026.ppm │ │ ├── 00003_00027.ppm │ │ ├── 00003_00028.ppm │ │ ├── 00003_00029.ppm │ │ ├── 00004_00000.ppm │ │ ├── 00004_00001.ppm │ │ ├── 00004_00002.ppm │ │ ├── 00004_00003.ppm │ │ ├── 00004_00004.ppm │ │ ├── 00004_00005.ppm │ │ ├── 00004_00006.ppm │ │ ├── 00004_00007.ppm │ │ ├── 00004_00008.ppm │ │ ├── 00004_00009.ppm │ │ ├── 00004_00010.ppm │ │ ├── 00004_00011.ppm │ │ ├── 00004_00012.ppm │ │ ├── 00004_00013.ppm │ │ ├── 00004_00014.ppm │ │ ├── 00004_00015.ppm │ │ ├── 00004_00016.ppm │ │ ├── 00004_00017.ppm │ │ ├── 00004_00018.ppm │ │ ├── 00004_00019.ppm │ │ ├── 00004_00020.ppm │ │ ├── 00004_00021.ppm │ │ ├── 00004_00022.ppm │ │ ├── 00004_00023.ppm │ │ ├── 00004_00024.ppm │ │ ├── 00004_00025.ppm │ │ ├── 00004_00026.ppm │ │ ├── 00004_00027.ppm │ │ ├── 00004_00028.ppm │ │ ├── 00004_00029.ppm │ │ ├── 00005_00000.ppm │ │ ├── 00005_00001.ppm │ │ ├── 00005_00002.ppm │ │ ├── 00005_00003.ppm │ │ ├── 00005_00004.ppm │ │ ├── 00005_00005.ppm │ │ ├── 00005_00006.ppm │ │ ├── 00005_00007.ppm │ │ ├── 00005_00008.ppm │ │ ├── 00005_00009.ppm │ │ ├── 00005_00010.ppm │ │ ├── 00005_00011.ppm │ │ ├── 00005_00012.ppm │ │ ├── 00005_00013.ppm │ │ ├── 00005_00014.ppm │ │ ├── 00005_00015.ppm │ │ ├── 00005_00016.ppm │ │ ├── 00005_00017.ppm │ │ ├── 00005_00018.ppm │ │ ├── 00005_00019.ppm │ │ ├── 00005_00020.ppm │ │ ├── 00005_00021.ppm │ │ ├── 00005_00022.ppm │ │ ├── 00005_00023.ppm │ │ ├── 00005_00024.ppm │ │ ├── 00005_00025.ppm │ │ ├── 00005_00026.ppm │ │ ├── 00005_00027.ppm │ │ ├── 00005_00028.ppm │ │ ├── 00005_00029.ppm │ │ ├── 00006_00000.ppm │ │ ├── 00006_00001.ppm │ │ ├── 00006_00002.ppm │ │ ├── 00006_00003.ppm │ │ ├── 00006_00004.ppm │ │ ├── 00006_00005.ppm │ │ ├── 00006_00006.ppm │ │ ├── 00006_00007.ppm │ │ ├── 00006_00008.ppm │ │ ├── 00006_00009.ppm │ │ ├── 00006_00010.ppm │ │ ├── 00006_00011.ppm │ │ ├── 00006_00012.ppm │ │ ├── 00006_00013.ppm │ │ ├── 00006_00014.ppm │ │ ├── 00006_00015.ppm │ │ ├── 00006_00016.ppm │ │ ├── 00006_00017.ppm │ │ ├── 00006_00018.ppm │ │ ├── 00006_00019.ppm │ │ ├── 00006_00020.ppm │ │ ├── 00006_00021.ppm │ │ ├── 00006_00022.ppm │ │ ├── 00006_00023.ppm │ │ ├── 00006_00024.ppm │ │ ├── 00006_00025.ppm │ │ ├── 00006_00026.ppm │ │ ├── 00006_00027.ppm │ │ ├── 00006_00028.ppm │ │ ├── 00006_00029.ppm │ │ ├── 00007_00000.ppm │ │ ├── 00007_00001.ppm │ │ ├── 00007_00002.ppm │ │ ├── 00007_00003.ppm │ │ ├── 00007_00004.ppm │ │ ├── 00007_00005.ppm │ │ ├── 00007_00006.ppm │ │ ├── 00007_00007.ppm │ │ ├── 00007_00008.ppm │ │ ├── 00007_00009.ppm │ │ ├── 00007_00010.ppm │ │ ├── 00007_00011.ppm │ │ ├── 00007_00012.ppm │ │ ├── 00007_00013.ppm │ │ ├── 00007_00014.ppm │ │ ├── 00007_00015.ppm │ │ ├── 00007_00016.ppm │ │ ├── 00007_00017.ppm │ │ ├── 00007_00018.ppm │ │ ├── 00007_00019.ppm │ │ ├── 00007_00020.ppm │ │ ├── 00007_00021.ppm │ │ ├── 00007_00022.ppm │ │ ├── 00007_00023.ppm │ │ ├── 00007_00024.ppm │ │ ├── 00007_00025.ppm │ │ ├── 00007_00026.ppm │ │ ├── 00007_00027.ppm │ │ ├── 00007_00028.ppm │ │ ├── 00007_00029.ppm │ │ ├── 00008_00000.ppm │ │ ├── 00008_00001.ppm │ │ ├── 00008_00002.ppm │ │ ├── 00008_00003.ppm │ │ ├── 00008_00004.ppm │ │ ├── 00008_00005.ppm │ │ ├── 00008_00006.ppm │ │ ├── 00008_00007.ppm │ │ ├── 00008_00008.ppm │ │ ├── 00008_00009.ppm │ │ ├── 00008_00010.ppm │ │ ├── 00008_00011.ppm │ │ ├── 00008_00012.ppm │ │ ├── 00008_00013.ppm │ │ ├── 00008_00014.ppm │ │ ├── 00008_00015.ppm │ │ ├── 00008_00016.ppm │ │ ├── 00008_00017.ppm │ │ ├── 00008_00018.ppm │ │ ├── 00008_00019.ppm │ │ ├── 00008_00020.ppm │ │ ├── 00008_00021.ppm │ │ ├── 00008_00022.ppm │ │ ├── 00008_00023.ppm │ │ ├── 00008_00024.ppm │ │ ├── 00008_00025.ppm │ │ ├── 00008_00026.ppm │ │ ├── 00008_00027.ppm │ │ ├── 00008_00028.ppm │ │ ├── 00008_00029.ppm │ │ ├── 00009_00000.ppm │ │ ├── 00009_00001.ppm │ │ ├── 00009_00002.ppm │ │ ├── 00009_00003.ppm │ │ ├── 00009_00004.ppm │ │ ├── 00009_00005.ppm │ │ ├── 00009_00006.ppm │ │ ├── 00009_00007.ppm │ │ ├── 00009_00008.ppm │ │ ├── 00009_00009.ppm │ │ ├── 00009_00010.ppm │ │ ├── 00009_00011.ppm │ │ ├── 00009_00012.ppm │ │ ├── 00009_00013.ppm │ │ ├── 00009_00014.ppm │ │ ├── 00009_00015.ppm │ │ ├── 00009_00016.ppm │ │ ├── 00009_00017.ppm │ │ ├── 00009_00018.ppm │ │ ├── 00009_00019.ppm │ │ ├── 00009_00020.ppm │ │ ├── 00009_00021.ppm │ │ ├── 00009_00022.ppm │ │ ├── 00009_00023.ppm │ │ ├── 00009_00024.ppm │ │ ├── 00009_00025.ppm │ │ ├── 00009_00026.ppm │ │ ├── 00009_00027.ppm │ │ ├── 00009_00028.ppm │ │ ├── 00009_00029.ppm │ │ ├── 00010_00000.ppm │ │ ├── 00010_00001.ppm │ │ ├── 00010_00002.ppm │ │ ├── 00010_00003.ppm │ │ ├── 00010_00004.ppm │ │ ├── 00010_00005.ppm │ │ ├── 00010_00006.ppm │ │ ├── 00010_00007.ppm │ │ ├── 00010_00008.ppm │ │ ├── 00010_00009.ppm │ │ ├── 00010_00010.ppm │ │ ├── 00010_00011.ppm │ │ ├── 00010_00012.ppm │ │ ├── 00010_00013.ppm │ │ ├── 00010_00014.ppm │ │ ├── 00010_00015.ppm │ │ ├── 00010_00016.ppm │ │ ├── 00010_00017.ppm │ │ ├── 00010_00018.ppm │ │ ├── 00010_00019.ppm │ │ ├── 00010_00020.ppm │ │ ├── 00010_00021.ppm │ │ ├── 00010_00022.ppm │ │ ├── 00010_00023.ppm │ │ ├── 00010_00024.ppm │ │ ├── 00010_00025.ppm │ │ ├── 00010_00026.ppm │ │ ├── 00010_00027.ppm │ │ ├── 00010_00028.ppm │ │ ├── 00010_00029.ppm │ │ ├── 00011_00000.ppm │ │ ├── 00011_00001.ppm │ │ ├── 00011_00002.ppm │ │ ├── 00011_00003.ppm │ │ ├── 00011_00004.ppm │ │ ├── 00011_00005.ppm │ │ ├── 00011_00006.ppm │ │ ├── 00011_00007.ppm │ │ ├── 00011_00008.ppm │ │ ├── 00011_00009.ppm │ │ ├── 00011_00010.ppm │ │ ├── 00011_00011.ppm │ │ ├── 00011_00012.ppm │ │ ├── 00011_00013.ppm │ │ ├── 00011_00014.ppm │ │ ├── 00011_00015.ppm │ │ ├── 00011_00016.ppm │ │ ├── 00011_00017.ppm │ │ ├── 00011_00018.ppm │ │ ├── 00011_00019.ppm │ │ ├── 00011_00020.ppm │ │ ├── 00011_00021.ppm │ │ ├── 00011_00022.ppm │ │ ├── 00011_00023.ppm │ │ ├── 00011_00024.ppm │ │ ├── 00011_00025.ppm │ │ ├── 00011_00026.ppm │ │ ├── 00011_00027.ppm │ │ ├── 00011_00028.ppm │ │ ├── 00011_00029.ppm │ │ ├── 00012_00000.ppm │ │ ├── 00012_00001.ppm │ │ ├── 00012_00002.ppm │ │ ├── 00012_00003.ppm │ │ ├── 00012_00004.ppm │ │ ├── 00012_00005.ppm │ │ ├── 00012_00006.ppm │ │ ├── 00012_00007.ppm │ │ ├── 00012_00008.ppm │ │ ├── 00012_00009.ppm │ │ ├── 00012_00010.ppm │ │ ├── 00012_00011.ppm │ │ ├── 00012_00012.ppm │ │ ├── 00012_00013.ppm │ │ ├── 00012_00014.ppm │ │ ├── 00012_00015.ppm │ │ ├── 00012_00016.ppm │ │ ├── 00012_00017.ppm │ │ ├── 00012_00018.ppm │ │ ├── 00012_00019.ppm │ │ ├── 00012_00020.ppm │ │ ├── 00012_00021.ppm │ │ ├── 00012_00022.ppm │ │ ├── 00012_00023.ppm │ │ ├── 00012_00024.ppm │ │ ├── 00012_00025.ppm │ │ ├── 00012_00026.ppm │ │ ├── 00012_00027.ppm │ │ ├── 00012_00028.ppm │ │ ├── 00012_00029.ppm │ │ ├── 00013_00000.ppm │ │ ├── 00013_00001.ppm │ │ ├── 00013_00002.ppm │ │ ├── 00013_00003.ppm │ │ ├── 00013_00004.ppm │ │ ├── 00013_00005.ppm │ │ ├── 00013_00006.ppm │ │ ├── 00013_00007.ppm │ │ ├── 00013_00008.ppm │ │ ├── 00013_00009.ppm │ │ ├── 00013_00010.ppm │ │ ├── 00013_00011.ppm │ │ ├── 00013_00012.ppm │ │ ├── 00013_00013.ppm │ │ ├── 00013_00014.ppm │ │ ├── 00013_00015.ppm │ │ ├── 00013_00016.ppm │ │ ├── 00013_00017.ppm │ │ ├── 00013_00018.ppm │ │ ├── 00013_00019.ppm │ │ ├── 00013_00020.ppm │ │ ├── 00013_00021.ppm │ │ ├── 00013_00022.ppm │ │ ├── 00013_00023.ppm │ │ ├── 00013_00024.ppm │ │ ├── 00013_00025.ppm │ │ ├── 00013_00026.ppm │ │ ├── 00013_00027.ppm │ │ ├── 00013_00028.ppm │ │ ├── 00013_00029.ppm │ │ ├── 00014_00000.ppm │ │ ├── 00014_00001.ppm │ │ ├── 00014_00002.ppm │ │ ├── 00014_00003.ppm │ │ ├── 00014_00004.ppm │ │ ├── 00014_00005.ppm │ │ ├── 00014_00006.ppm │ │ ├── 00014_00007.ppm │ │ ├── 00014_00008.ppm │ │ ├── 00014_00009.ppm │ │ ├── 00014_00010.ppm │ │ ├── 00014_00011.ppm │ │ ├── 00014_00012.ppm │ │ ├── 00014_00013.ppm │ │ ├── 00014_00014.ppm │ │ ├── 00014_00015.ppm │ │ ├── 00014_00016.ppm │ │ ├── 00014_00017.ppm │ │ ├── 00014_00018.ppm │ │ ├── 00014_00019.ppm │ │ ├── 00014_00020.ppm │ │ ├── 00014_00021.ppm │ │ ├── 00014_00022.ppm │ │ ├── 00014_00023.ppm │ │ ├── 00014_00024.ppm │ │ ├── 00014_00025.ppm │ │ ├── 00014_00026.ppm │ │ ├── 00014_00027.ppm │ │ ├── 00014_00028.ppm │ │ ├── 00014_00029.ppm │ │ ├── 00015_00000.ppm │ │ ├── 00015_00001.ppm │ │ ├── 00015_00002.ppm │ │ ├── 00015_00003.ppm │ │ ├── 00015_00004.ppm │ │ ├── 00015_00005.ppm │ │ ├── 00015_00006.ppm │ │ ├── 00015_00007.ppm │ │ ├── 00015_00008.ppm │ │ ├── 00015_00009.ppm │ │ ├── 00015_00010.ppm │ │ ├── 00015_00011.ppm │ │ ├── 00015_00012.ppm │ │ ├── 00015_00013.ppm │ │ ├── 00015_00014.ppm │ │ ├── 00015_00015.ppm │ │ ├── 00015_00016.ppm │ │ ├── 00015_00017.ppm │ │ ├── 00015_00018.ppm │ │ ├── 00015_00019.ppm │ │ ├── 00015_00020.ppm │ │ ├── 00015_00021.ppm │ │ ├── 00015_00022.ppm │ │ ├── 00015_00023.ppm │ │ ├── 00015_00024.ppm │ │ ├── 00015_00025.ppm │ │ ├── 00015_00026.ppm │ │ ├── 00015_00027.ppm │ │ ├── 00015_00028.ppm │ │ ├── 00015_00029.ppm │ │ ├── 00016_00000.ppm │ │ ├── 00016_00001.ppm │ │ ├── 00016_00002.ppm │ │ ├── 00016_00003.ppm │ │ ├── 00016_00004.ppm │ │ ├── 00016_00005.ppm │ │ ├── 00016_00006.ppm │ │ ├── 00016_00007.ppm │ │ ├── 00016_00008.ppm │ │ ├── 00016_00009.ppm │ │ ├── 00016_00010.ppm │ │ ├── 00016_00011.ppm │ │ ├── 00016_00012.ppm │ │ ├── 00016_00013.ppm │ │ ├── 00016_00014.ppm │ │ ├── 00016_00015.ppm │ │ ├── 00016_00016.ppm │ │ ├── 00016_00017.ppm │ │ ├── 00016_00018.ppm │ │ ├── 00016_00019.ppm │ │ ├── 00016_00020.ppm │ │ ├── 00016_00021.ppm │ │ ├── 00016_00022.ppm │ │ ├── 00016_00023.ppm │ │ ├── 00016_00024.ppm │ │ ├── 00016_00025.ppm │ │ ├── 00016_00026.ppm │ │ ├── 00016_00027.ppm │ │ ├── 00016_00028.ppm │ │ ├── 00016_00029.ppm │ │ ├── 00017_00000.ppm │ │ ├── 00017_00001.ppm │ │ ├── 00017_00002.ppm │ │ ├── 00017_00003.ppm │ │ ├── 00017_00004.ppm │ │ ├── 00017_00005.ppm │ │ ├── 00017_00006.ppm │ │ ├── 00017_00007.ppm │ │ ├── 00017_00008.ppm │ │ ├── 00017_00009.ppm │ │ ├── 00017_00010.ppm │ │ ├── 00017_00011.ppm │ │ ├── 00017_00012.ppm │ │ ├── 00017_00013.ppm │ │ ├── 00017_00014.ppm │ │ ├── 00017_00015.ppm │ │ ├── 00017_00016.ppm │ │ ├── 00017_00017.ppm │ │ ├── 00017_00018.ppm │ │ ├── 00017_00019.ppm │ │ ├── 00017_00020.ppm │ │ ├── 00017_00021.ppm │ │ ├── 00017_00022.ppm │ │ ├── 00017_00023.ppm │ │ ├── 00017_00024.ppm │ │ ├── 00017_00025.ppm │ │ ├── 00017_00026.ppm │ │ ├── 00017_00027.ppm │ │ ├── 00017_00028.ppm │ │ ├── 00017_00029.ppm │ │ ├── 00018_00000.ppm │ │ ├── 00018_00001.ppm │ │ ├── 00018_00002.ppm │ │ ├── 00018_00003.ppm │ │ ├── 00018_00004.ppm │ │ ├── 00018_00005.ppm │ │ ├── 00018_00006.ppm │ │ ├── 00018_00007.ppm │ │ ├── 00018_00008.ppm │ │ ├── 00018_00009.ppm │ │ ├── 00018_00010.ppm │ │ ├── 00018_00011.ppm │ │ ├── 00018_00012.ppm │ │ ├── 00018_00013.ppm │ │ ├── 00018_00014.ppm │ │ ├── 00018_00015.ppm │ │ ├── 00018_00016.ppm │ │ ├── 00018_00017.ppm │ │ ├── 00018_00018.ppm │ │ ├── 00018_00019.ppm │ │ ├── 00018_00020.ppm │ │ ├── 00018_00021.ppm │ │ ├── 00018_00022.ppm │ │ ├── 00018_00023.ppm │ │ ├── 00018_00024.ppm │ │ ├── 00018_00025.ppm │ │ ├── 00018_00026.ppm │ │ ├── 00018_00027.ppm │ │ ├── 00018_00028.ppm │ │ ├── 00018_00029.ppm │ │ ├── 00019_00000.ppm │ │ ├── 00019_00001.ppm │ │ ├── 00019_00002.ppm │ │ ├── 00019_00003.ppm │ │ ├── 00019_00004.ppm │ │ ├── 00019_00005.ppm │ │ ├── 00019_00006.ppm │ │ ├── 00019_00007.ppm │ │ ├── 00019_00008.ppm │ │ ├── 00019_00009.ppm │ │ ├── 00019_00010.ppm │ │ ├── 00019_00011.ppm │ │ ├── 00019_00012.ppm │ │ ├── 00019_00013.ppm │ │ ├── 00019_00014.ppm │ │ ├── 00019_00015.ppm │ │ ├── 00019_00016.ppm │ │ ├── 00019_00017.ppm │ │ ├── 00019_00018.ppm │ │ ├── 00019_00019.ppm │ │ ├── 00019_00020.ppm │ │ ├── 00019_00021.ppm │ │ ├── 00019_00022.ppm │ │ ├── 00019_00023.ppm │ │ ├── 00019_00024.ppm │ │ ├── 00019_00025.ppm │ │ ├── 00019_00026.ppm │ │ ├── 00019_00027.ppm │ │ ├── 00019_00028.ppm │ │ ├── 00019_00029.ppm │ │ ├── 00020_00000.ppm │ │ ├── 00020_00001.ppm │ │ ├── 00020_00002.ppm │ │ ├── 00020_00003.ppm │ │ ├── 00020_00004.ppm │ │ ├── 00020_00005.ppm │ │ ├── 00020_00006.ppm │ │ ├── 00020_00007.ppm │ │ ├── 00020_00008.ppm │ │ ├── 00020_00009.ppm │ │ ├── 00020_00010.ppm │ │ ├── 00020_00011.ppm │ │ ├── 00020_00012.ppm │ │ ├── 00020_00013.ppm │ │ ├── 00020_00014.ppm │ │ ├── 00020_00015.ppm │ │ ├── 00020_00016.ppm │ │ ├── 00020_00017.ppm │ │ ├── 00020_00018.ppm │ │ ├── 00020_00019.ppm │ │ ├── 00020_00020.ppm │ │ ├── 00020_00021.ppm │ │ ├── 00020_00022.ppm │ │ ├── 00020_00023.ppm │ │ ├── 00020_00024.ppm │ │ ├── 00020_00025.ppm │ │ ├── 00020_00026.ppm │ │ ├── 00020_00027.ppm │ │ ├── 00020_00028.ppm │ │ ├── 00020_00029.ppm │ │ ├── 00021_00000.ppm │ │ ├── 00021_00001.ppm │ │ ├── 00021_00002.ppm │ │ ├── 00021_00003.ppm │ │ ├── 00021_00004.ppm │ │ ├── 00021_00005.ppm │ │ ├── 00021_00006.ppm │ │ ├── 00021_00007.ppm │ │ ├── 00021_00008.ppm │ │ ├── 00021_00009.ppm │ │ ├── 00021_00010.ppm │ │ ├── 00021_00011.ppm │ │ ├── 00021_00012.ppm │ │ ├── 00021_00013.ppm │ │ ├── 00021_00014.ppm │ │ ├── 00021_00015.ppm │ │ ├── 00021_00016.ppm │ │ ├── 00021_00017.ppm │ │ ├── 00021_00018.ppm │ │ ├── 00021_00019.ppm │ │ ├── 00021_00020.ppm │ │ ├── 00021_00021.ppm │ │ ├── 00021_00022.ppm │ │ ├── 00021_00023.ppm │ │ ├── 00021_00024.ppm │ │ ├── 00021_00025.ppm │ │ ├── 00021_00026.ppm │ │ ├── 00021_00027.ppm │ │ ├── 00021_00028.ppm │ │ ├── 00021_00029.ppm │ │ ├── 00022_00000.ppm │ │ ├── 00022_00001.ppm │ │ ├── 00022_00002.ppm │ │ ├── 00022_00003.ppm │ │ ├── 00022_00004.ppm │ │ ├── 00022_00005.ppm │ │ ├── 00022_00006.ppm │ │ ├── 00022_00007.ppm │ │ ├── 00022_00008.ppm │ │ ├── 00022_00009.ppm │ │ ├── 00022_00010.ppm │ │ ├── 00022_00011.ppm │ │ ├── 00022_00012.ppm │ │ ├── 00022_00013.ppm │ │ ├── 00022_00014.ppm │ │ ├── 00022_00015.ppm │ │ ├── 00022_00016.ppm │ │ ├── 00022_00017.ppm │ │ ├── 00022_00018.ppm │ │ ├── 00022_00019.ppm │ │ ├── 00022_00020.ppm │ │ ├── 00022_00021.ppm │ │ ├── 00022_00022.ppm │ │ ├── 00022_00023.ppm │ │ ├── 00022_00024.ppm │ │ ├── 00022_00025.ppm │ │ ├── 00022_00026.ppm │ │ ├── 00022_00027.ppm │ │ ├── 00022_00028.ppm │ │ ├── 00022_00029.ppm │ │ ├── 00023_00000.ppm │ │ ├── 00023_00001.ppm │ │ ├── 00023_00002.ppm │ │ ├── 00023_00003.ppm │ │ ├── 00023_00004.ppm │ │ ├── 00023_00005.ppm │ │ ├── 00023_00006.ppm │ │ ├── 00023_00007.ppm │ │ ├── 00023_00008.ppm │ │ ├── 00023_00009.ppm │ │ ├── 00023_00010.ppm │ │ ├── 00023_00011.ppm │ │ ├── 00023_00012.ppm │ │ ├── 00023_00013.ppm │ │ ├── 00023_00014.ppm │ │ ├── 00023_00015.ppm │ │ ├── 00023_00016.ppm │ │ ├── 00023_00017.ppm │ │ ├── 00023_00018.ppm │ │ ├── 00023_00019.ppm │ │ ├── 00023_00020.ppm │ │ ├── 00023_00021.ppm │ │ ├── 00023_00022.ppm │ │ ├── 00023_00023.ppm │ │ ├── 00023_00024.ppm │ │ ├── 00023_00025.ppm │ │ ├── 00023_00026.ppm │ │ ├── 00023_00027.ppm │ │ ├── 00023_00028.ppm │ │ ├── 00023_00029.ppm │ │ ├── 00024_00000.ppm │ │ ├── 00024_00001.ppm │ │ ├── 00024_00002.ppm │ │ ├── 00024_00003.ppm │ │ ├── 00024_00004.ppm │ │ ├── 00024_00005.ppm │ │ ├── 00024_00006.ppm │ │ ├── 00024_00007.ppm │ │ ├── 00024_00008.ppm │ │ ├── 00024_00009.ppm │ │ ├── 00024_00010.ppm │ │ ├── 00024_00011.ppm │ │ ├── 00024_00012.ppm │ │ ├── 00024_00013.ppm │ │ ├── 00024_00014.ppm │ │ ├── 00024_00015.ppm │ │ ├── 00024_00016.ppm │ │ ├── 00024_00017.ppm │ │ ├── 00024_00018.ppm │ │ ├── 00024_00019.ppm │ │ ├── 00024_00020.ppm │ │ ├── 00024_00021.ppm │ │ ├── 00024_00022.ppm │ │ ├── 00024_00023.ppm │ │ ├── 00024_00024.ppm │ │ ├── 00024_00025.ppm │ │ ├── 00024_00026.ppm │ │ ├── 00024_00027.ppm │ │ ├── 00024_00028.ppm │ │ ├── 00024_00029.ppm │ │ ├── 00025_00000.ppm │ │ ├── 00025_00001.ppm │ │ ├── 00025_00002.ppm │ │ ├── 00025_00003.ppm │ │ ├── 00025_00004.ppm │ │ ├── 00025_00005.ppm │ │ ├── 00025_00006.ppm │ │ ├── 00025_00007.ppm │ │ ├── 00025_00008.ppm │ │ ├── 00025_00009.ppm │ │ ├── 00025_00010.ppm │ │ ├── 00025_00011.ppm │ │ ├── 00025_00012.ppm │ │ ├── 00025_00013.ppm │ │ ├── 00025_00014.ppm │ │ ├── 00025_00015.ppm │ │ ├── 00025_00016.ppm │ │ ├── 00025_00017.ppm │ │ ├── 00025_00018.ppm │ │ ├── 00025_00019.ppm │ │ ├── 00025_00020.ppm │ │ ├── 00025_00021.ppm │ │ ├── 00025_00022.ppm │ │ ├── 00025_00023.ppm │ │ ├── 00025_00024.ppm │ │ ├── 00025_00025.ppm │ │ ├── 00025_00026.ppm │ │ ├── 00025_00027.ppm │ │ ├── 00025_00028.ppm │ │ ├── 00025_00029.ppm │ │ ├── 00026_00000.ppm │ │ ├── 00026_00001.ppm │ │ ├── 00026_00002.ppm │ │ ├── 00026_00003.ppm │ │ ├── 00026_00004.ppm │ │ ├── 00026_00005.ppm │ │ ├── 00026_00006.ppm │ │ ├── 00026_00007.ppm │ │ ├── 00026_00008.ppm │ │ ├── 00026_00009.ppm │ │ ├── 00026_00010.ppm │ │ ├── 00026_00011.ppm │ │ ├── 00026_00012.ppm │ │ ├── 00026_00013.ppm │ │ ├── 00026_00014.ppm │ │ ├── 00026_00015.ppm │ │ ├── 00026_00016.ppm │ │ ├── 00026_00017.ppm │ │ ├── 00026_00018.ppm │ │ ├── 00026_00019.ppm │ │ ├── 00026_00020.ppm │ │ ├── 00026_00021.ppm │ │ ├── 00026_00022.ppm │ │ ├── 00026_00023.ppm │ │ ├── 00026_00024.ppm │ │ ├── 00026_00025.ppm │ │ ├── 00026_00026.ppm │ │ ├── 00026_00027.ppm │ │ ├── 00026_00028.ppm │ │ ├── 00026_00029.ppm │ │ ├── 00027_00000.ppm │ │ ├── 00027_00001.ppm │ │ ├── 00027_00002.ppm │ │ ├── 00027_00003.ppm │ │ ├── 00027_00004.ppm │ │ ├── 00027_00005.ppm │ │ ├── 00027_00006.ppm │ │ ├── 00027_00007.ppm │ │ ├── 00027_00008.ppm │ │ ├── 00027_00009.ppm │ │ ├── 00027_00010.ppm │ │ ├── 00027_00011.ppm │ │ ├── 00027_00012.ppm │ │ ├── 00027_00013.ppm │ │ ├── 00027_00014.ppm │ │ ├── 00027_00015.ppm │ │ ├── 00027_00016.ppm │ │ ├── 00027_00017.ppm │ │ ├── 00027_00018.ppm │ │ ├── 00027_00019.ppm │ │ ├── 00027_00020.ppm │ │ ├── 00027_00021.ppm │ │ ├── 00027_00022.ppm │ │ ├── 00027_00023.ppm │ │ ├── 00027_00024.ppm │ │ ├── 00027_00025.ppm │ │ ├── 00027_00026.ppm │ │ ├── 00027_00027.ppm │ │ ├── 00027_00028.ppm │ │ ├── 00027_00029.ppm │ │ ├── 00028_00000.ppm │ │ ├── 00028_00001.ppm │ │ ├── 00028_00002.ppm │ │ ├── 00028_00003.ppm │ │ ├── 00028_00004.ppm │ │ ├── 00028_00005.ppm │ │ ├── 00028_00006.ppm │ │ ├── 00028_00007.ppm │ │ ├── 00028_00008.ppm │ │ ├── 00028_00009.ppm │ │ ├── 00028_00010.ppm │ │ ├── 00028_00011.ppm │ │ ├── 00028_00012.ppm │ │ ├── 00028_00013.ppm │ │ ├── 00028_00014.ppm │ │ ├── 00028_00015.ppm │ │ ├── 00028_00016.ppm │ │ ├── 00028_00017.ppm │ │ ├── 00028_00018.ppm │ │ ├── 00028_00019.ppm │ │ ├── 00028_00020.ppm │ │ ├── 00028_00021.ppm │ │ ├── 00028_00022.ppm │ │ ├── 00028_00023.ppm │ │ ├── 00028_00024.ppm │ │ ├── 00028_00025.ppm │ │ ├── 00028_00026.ppm │ │ ├── 00028_00027.ppm │ │ ├── 00028_00028.ppm │ │ ├── 00028_00029.ppm │ │ ├── 00029_00000.ppm │ │ ├── 00029_00001.ppm │ │ ├── 00029_00002.ppm │ │ ├── 00029_00003.ppm │ │ ├── 00029_00004.ppm │ │ ├── 00029_00005.ppm │ │ ├── 00029_00006.ppm │ │ ├── 00029_00007.ppm │ │ ├── 00029_00008.ppm │ │ ├── 00029_00009.ppm │ │ ├── 00029_00010.ppm │ │ ├── 00029_00011.ppm │ │ ├── 00029_00012.ppm │ │ ├── 00029_00013.ppm │ │ ├── 00029_00014.ppm │ │ ├── 00029_00015.ppm │ │ ├── 00029_00016.ppm │ │ ├── 00029_00017.ppm │ │ ├── 00029_00018.ppm │ │ ├── 00029_00019.ppm │ │ ├── 00029_00020.ppm │ │ ├── 00029_00021.ppm │ │ ├── 00029_00022.ppm │ │ ├── 00029_00023.ppm │ │ ├── 00029_00024.ppm │ │ ├── 00029_00025.ppm │ │ ├── 00029_00026.ppm │ │ ├── 00029_00027.ppm │ │ ├── 00029_00028.ppm │ │ ├── 00029_00029.ppm │ │ ├── 00030_00000.ppm │ │ ├── 00030_00001.ppm │ │ ├── 00030_00002.ppm │ │ ├── 00030_00003.ppm │ │ ├── 00030_00004.ppm │ │ ├── 00030_00005.ppm │ │ ├── 00030_00006.ppm │ │ ├── 00030_00007.ppm │ │ ├── 00030_00008.ppm │ │ ├── 00030_00009.ppm │ │ ├── 00030_00010.ppm │ │ ├── 00030_00011.ppm │ │ ├── 00030_00012.ppm │ │ ├── 00030_00013.ppm │ │ ├── 00030_00014.ppm │ │ ├── 00030_00015.ppm │ │ ├── 00030_00016.ppm │ │ ├── 00030_00017.ppm │ │ ├── 00030_00018.ppm │ │ ├── 00030_00019.ppm │ │ ├── 00030_00020.ppm │ │ ├── 00030_00021.ppm │ │ ├── 00030_00022.ppm │ │ ├── 00030_00023.ppm │ │ ├── 00030_00024.ppm │ │ ├── 00030_00025.ppm │ │ ├── 00030_00026.ppm │ │ ├── 00030_00027.ppm │ │ ├── 00030_00028.ppm │ │ ├── 00030_00029.ppm │ │ ├── 00031_00000.ppm │ │ ├── 00031_00001.ppm │ │ ├── 00031_00002.ppm │ │ ├── 00031_00003.ppm │ │ ├── 00031_00004.ppm │ │ ├── 00031_00005.ppm │ │ ├── 00031_00006.ppm │ │ ├── 00031_00007.ppm │ │ ├── 00031_00008.ppm │ │ ├── 00031_00009.ppm │ │ ├── 00031_00010.ppm │ │ ├── 00031_00011.ppm │ │ ├── 00031_00012.ppm │ │ ├── 00031_00013.ppm │ │ ├── 00031_00014.ppm │ │ ├── 00031_00015.ppm │ │ ├── 00031_00016.ppm │ │ ├── 00031_00017.ppm │ │ ├── 00031_00018.ppm │ │ ├── 00031_00019.ppm │ │ ├── 00031_00020.ppm │ │ ├── 00031_00021.ppm │ │ ├── 00031_00022.ppm │ │ ├── 00031_00023.ppm │ │ ├── 00031_00024.ppm │ │ ├── 00031_00025.ppm │ │ ├── 00031_00026.ppm │ │ ├── 00031_00027.ppm │ │ ├── 00031_00028.ppm │ │ ├── 00031_00029.ppm │ │ ├── 00032_00000.ppm │ │ ├── 00032_00001.ppm │ │ ├── 00032_00002.ppm │ │ ├── 00032_00003.ppm │ │ ├── 00032_00004.ppm │ │ ├── 00032_00005.ppm │ │ ├── 00032_00006.ppm │ │ ├── 00032_00007.ppm │ │ ├── 00032_00008.ppm │ │ ├── 00032_00009.ppm │ │ ├── 00032_00010.ppm │ │ ├── 00032_00011.ppm │ │ ├── 00032_00012.ppm │ │ ├── 00032_00013.ppm │ │ ├── 00032_00014.ppm │ │ ├── 00032_00015.ppm │ │ ├── 00032_00016.ppm │ │ ├── 00032_00017.ppm │ │ ├── 00032_00018.ppm │ │ ├── 00032_00019.ppm │ │ ├── 00032_00020.ppm │ │ ├── 00032_00021.ppm │ │ ├── 00032_00022.ppm │ │ ├── 00032_00023.ppm │ │ ├── 00032_00024.ppm │ │ ├── 00032_00025.ppm │ │ ├── 00032_00026.ppm │ │ ├── 00032_00027.ppm │ │ ├── 00032_00028.ppm │ │ ├── 00032_00029.ppm │ │ ├── 00033_00000.ppm │ │ ├── 00033_00001.ppm │ │ ├── 00033_00002.ppm │ │ ├── 00033_00003.ppm │ │ ├── 00033_00004.ppm │ │ ├── 00033_00005.ppm │ │ ├── 00033_00006.ppm │ │ ├── 00033_00007.ppm │ │ ├── 00033_00008.ppm │ │ ├── 00033_00009.ppm │ │ ├── 00033_00010.ppm │ │ ├── 00033_00011.ppm │ │ ├── 00033_00012.ppm │ │ ├── 00033_00013.ppm │ │ ├── 00033_00014.ppm │ │ ├── 00033_00015.ppm │ │ ├── 00033_00016.ppm │ │ ├── 00033_00017.ppm │ │ ├── 00033_00018.ppm │ │ ├── 00033_00019.ppm │ │ ├── 00033_00020.ppm │ │ ├── 00033_00021.ppm │ │ ├── 00033_00022.ppm │ │ ├── 00033_00023.ppm │ │ ├── 00033_00024.ppm │ │ ├── 00033_00025.ppm │ │ ├── 00033_00026.ppm │ │ ├── 00033_00027.ppm │ │ ├── 00033_00028.ppm │ │ ├── 00033_00029.ppm │ │ ├── 00034_00000.ppm │ │ ├── 00034_00001.ppm │ │ ├── 00034_00002.ppm │ │ ├── 00034_00003.ppm │ │ ├── 00034_00004.ppm │ │ ├── 00034_00005.ppm │ │ ├── 00034_00006.ppm │ │ ├── 00034_00007.ppm │ │ ├── 00034_00008.ppm │ │ ├── 00034_00009.ppm │ │ ├── 00034_00010.ppm │ │ ├── 00034_00011.ppm │ │ ├── 00034_00012.ppm │ │ ├── 00034_00013.ppm │ │ ├── 00034_00014.ppm │ │ ├── 00034_00015.ppm │ │ ├── 00034_00016.ppm │ │ ├── 00034_00017.ppm │ │ ├── 00034_00018.ppm │ │ ├── 00034_00019.ppm │ │ ├── 00034_00020.ppm │ │ ├── 00034_00021.ppm │ │ ├── 00034_00022.ppm │ │ ├── 00034_00023.ppm │ │ ├── 00034_00024.ppm │ │ ├── 00034_00025.ppm │ │ ├── 00034_00026.ppm │ │ ├── 00034_00027.ppm │ │ ├── 00034_00028.ppm │ │ ├── 00034_00029.ppm │ │ ├── 00035_00000.ppm │ │ ├── 00035_00001.ppm │ │ ├── 00035_00002.ppm │ │ ├── 00035_00003.ppm │ │ ├── 00035_00004.ppm │ │ ├── 00035_00005.ppm │ │ ├── 00035_00006.ppm │ │ ├── 00035_00007.ppm │ │ ├── 00035_00008.ppm │ │ ├── 00035_00009.ppm │ │ ├── 00035_00010.ppm │ │ ├── 00035_00011.ppm │ │ ├── 00035_00012.ppm │ │ ├── 00035_00013.ppm │ │ ├── 00035_00014.ppm │ │ ├── 00035_00015.ppm │ │ ├── 00035_00016.ppm │ │ ├── 00035_00017.ppm │ │ ├── 00035_00018.ppm │ │ ├── 00035_00019.ppm │ │ ├── 00035_00020.ppm │ │ ├── 00035_00021.ppm │ │ ├── 00035_00022.ppm │ │ ├── 00035_00023.ppm │ │ ├── 00035_00024.ppm │ │ ├── 00035_00025.ppm │ │ ├── 00035_00026.ppm │ │ ├── 00035_00027.ppm │ │ ├── 00035_00028.ppm │ │ ├── 00035_00029.ppm │ │ ├── 00036_00000.ppm │ │ ├── 00036_00001.ppm │ │ ├── 00036_00002.ppm │ │ ├── 00036_00003.ppm │ │ ├── 00036_00004.ppm │ │ ├── 00036_00005.ppm │ │ ├── 00036_00006.ppm │ │ ├── 00036_00007.ppm │ │ ├── 00036_00008.ppm │ │ ├── 00036_00009.ppm │ │ ├── 00036_00010.ppm │ │ ├── 00036_00011.ppm │ │ ├── 00036_00012.ppm │ │ ├── 00036_00013.ppm │ │ ├── 00036_00014.ppm │ │ ├── 00036_00015.ppm │ │ ├── 00036_00016.ppm │ │ ├── 00036_00017.ppm │ │ ├── 00036_00018.ppm │ │ ├── 00036_00019.ppm │ │ ├── 00036_00020.ppm │ │ ├── 00036_00021.ppm │ │ ├── 00036_00022.ppm │ │ ├── 00036_00023.ppm │ │ ├── 00036_00024.ppm │ │ ├── 00036_00025.ppm │ │ ├── 00036_00026.ppm │ │ ├── 00036_00027.ppm │ │ ├── 00036_00028.ppm │ │ ├── 00036_00029.ppm │ │ ├── 00037_00000.ppm │ │ ├── 00037_00001.ppm │ │ ├── 00037_00002.ppm │ │ ├── 00037_00003.ppm │ │ ├── 00037_00004.ppm │ │ ├── 00037_00005.ppm │ │ ├── 00037_00006.ppm │ │ ├── 00037_00007.ppm │ │ ├── 00037_00008.ppm │ │ ├── 00037_00009.ppm │ │ ├── 00037_00010.ppm │ │ ├── 00037_00011.ppm │ │ ├── 00037_00012.ppm │ │ ├── 00037_00013.ppm │ │ ├── 00037_00014.ppm │ │ ├── 00037_00015.ppm │ │ ├── 00037_00016.ppm │ │ ├── 00037_00017.ppm │ │ ├── 00037_00018.ppm │ │ ├── 00037_00019.ppm │ │ ├── 00037_00020.ppm │ │ ├── 00037_00021.ppm │ │ ├── 00037_00022.ppm │ │ ├── 00037_00023.ppm │ │ ├── 00037_00024.ppm │ │ ├── 00037_00025.ppm │ │ ├── 00037_00026.ppm │ │ ├── 00037_00027.ppm │ │ ├── 00037_00028.ppm │ │ ├── 00037_00029.ppm │ │ ├── 00038_00000.ppm │ │ ├── 00038_00001.ppm │ │ ├── 00038_00002.ppm │ │ ├── 00038_00003.ppm │ │ ├── 00038_00004.ppm │ │ ├── 00038_00005.ppm │ │ ├── 00038_00006.ppm │ │ ├── 00038_00007.ppm │ │ ├── 00038_00008.ppm │ │ ├── 00038_00009.ppm │ │ ├── 00038_00010.ppm │ │ ├── 00038_00011.ppm │ │ ├── 00038_00012.ppm │ │ ├── 00038_00013.ppm │ │ ├── 00038_00014.ppm │ │ ├── 00038_00015.ppm │ │ ├── 00038_00016.ppm │ │ ├── 00038_00017.ppm │ │ ├── 00038_00018.ppm │ │ ├── 00038_00019.ppm │ │ ├── 00038_00020.ppm │ │ ├── 00038_00021.ppm │ │ ├── 00038_00022.ppm │ │ ├── 00038_00023.ppm │ │ ├── 00038_00024.ppm │ │ ├── 00038_00025.ppm │ │ ├── 00038_00026.ppm │ │ ├── 00038_00027.ppm │ │ ├── 00038_00028.ppm │ │ ├── 00038_00029.ppm │ │ ├── 00039_00000.ppm │ │ ├── 00039_00001.ppm │ │ ├── 00039_00002.ppm │ │ ├── 00039_00003.ppm │ │ ├── 00039_00004.ppm │ │ ├── 00039_00005.ppm │ │ ├── 00039_00006.ppm │ │ ├── 00039_00007.ppm │ │ ├── 00039_00008.ppm │ │ ├── 00039_00009.ppm │ │ ├── 00039_00010.ppm │ │ ├── 00039_00011.ppm │ │ ├── 00039_00012.ppm │ │ ├── 00039_00013.ppm │ │ ├── 00039_00014.ppm │ │ ├── 00039_00015.ppm │ │ ├── 00039_00016.ppm │ │ ├── 00039_00017.ppm │ │ ├── 00039_00018.ppm │ │ ├── 00039_00019.ppm │ │ ├── 00039_00020.ppm │ │ ├── 00039_00021.ppm │ │ ├── 00039_00022.ppm │ │ ├── 00039_00023.ppm │ │ ├── 00039_00024.ppm │ │ ├── 00039_00025.ppm │ │ ├── 00039_00026.ppm │ │ ├── 00039_00027.ppm │ │ ├── 00039_00028.ppm │ │ ├── 00039_00029.ppm │ │ ├── 00040_00000.ppm │ │ ├── 00040_00001.ppm │ │ ├── 00040_00002.ppm │ │ ├── 00040_00003.ppm │ │ ├── 00040_00004.ppm │ │ ├── 00040_00005.ppm │ │ ├── 00040_00006.ppm │ │ ├── 00040_00007.ppm │ │ ├── 00040_00008.ppm │ │ ├── 00040_00009.ppm │ │ ├── 00040_00010.ppm │ │ ├── 00040_00011.ppm │ │ ├── 00040_00012.ppm │ │ ├── 00040_00013.ppm │ │ ├── 00040_00014.ppm │ │ ├── 00040_00015.ppm │ │ ├── 00040_00016.ppm │ │ ├── 00040_00017.ppm │ │ ├── 00040_00018.ppm │ │ ├── 00040_00019.ppm │ │ ├── 00040_00020.ppm │ │ ├── 00040_00021.ppm │ │ ├── 00040_00022.ppm │ │ ├── 00040_00023.ppm │ │ ├── 00040_00024.ppm │ │ ├── 00040_00025.ppm │ │ ├── 00040_00026.ppm │ │ ├── 00040_00027.ppm │ │ ├── 00040_00028.ppm │ │ ├── 00040_00029.ppm │ │ ├── 00041_00000.ppm │ │ ├── 00041_00001.ppm │ │ ├── 00041_00002.ppm │ │ ├── 00041_00003.ppm │ │ ├── 00041_00004.ppm │ │ ├── 00041_00005.ppm │ │ ├── 00041_00006.ppm │ │ ├── 00041_00007.ppm │ │ ├── 00041_00008.ppm │ │ ├── 00041_00009.ppm │ │ ├── 00041_00010.ppm │ │ ├── 00041_00011.ppm │ │ ├── 00041_00012.ppm │ │ ├── 00041_00013.ppm │ │ ├── 00041_00014.ppm │ │ ├── 00041_00015.ppm │ │ ├── 00041_00016.ppm │ │ ├── 00041_00017.ppm │ │ ├── 00041_00018.ppm │ │ ├── 00041_00019.ppm │ │ ├── 00041_00020.ppm │ │ ├── 00041_00021.ppm │ │ ├── 00041_00022.ppm │ │ ├── 00041_00023.ppm │ │ ├── 00041_00024.ppm │ │ ├── 00041_00025.ppm │ │ ├── 00041_00026.ppm │ │ ├── 00041_00027.ppm │ │ ├── 00041_00028.ppm │ │ ├── 00041_00029.ppm │ │ ├── 00042_00000.ppm │ │ ├── 00042_00001.ppm │ │ ├── 00042_00002.ppm │ │ ├── 00042_00003.ppm │ │ ├── 00042_00004.ppm │ │ ├── 00042_00005.ppm │ │ ├── 00042_00006.ppm │ │ ├── 00042_00007.ppm │ │ ├── 00042_00008.ppm │ │ ├── 00042_00009.ppm │ │ ├── 00042_00010.ppm │ │ ├── 00042_00011.ppm │ │ ├── 00042_00012.ppm │ │ ├── 00042_00013.ppm │ │ ├── 00042_00014.ppm │ │ ├── 00042_00015.ppm │ │ ├── 00042_00016.ppm │ │ ├── 00042_00017.ppm │ │ ├── 00042_00018.ppm │ │ ├── 00042_00019.ppm │ │ ├── 00042_00020.ppm │ │ ├── 00042_00021.ppm │ │ ├── 00042_00022.ppm │ │ ├── 00042_00023.ppm │ │ ├── 00042_00024.ppm │ │ ├── 00042_00025.ppm │ │ ├── 00042_00026.ppm │ │ ├── 00042_00027.ppm │ │ ├── 00042_00028.ppm │ │ ├── 00042_00029.ppm │ │ ├── 00043_00000.ppm │ │ ├── 00043_00001.ppm │ │ ├── 00043_00002.ppm │ │ ├── 00043_00003.ppm │ │ ├── 00043_00004.ppm │ │ ├── 00043_00005.ppm │ │ ├── 00043_00006.ppm │ │ ├── 00043_00007.ppm │ │ ├── 00043_00008.ppm │ │ ├── 00043_00009.ppm │ │ ├── 00043_00010.ppm │ │ ├── 00043_00011.ppm │ │ ├── 00043_00012.ppm │ │ ├── 00043_00013.ppm │ │ ├── 00043_00014.ppm │ │ ├── 00043_00015.ppm │ │ ├── 00043_00016.ppm │ │ ├── 00043_00017.ppm │ │ ├── 00043_00018.ppm │ │ ├── 00043_00019.ppm │ │ ├── 00043_00020.ppm │ │ ├── 00043_00021.ppm │ │ ├── 00043_00022.ppm │ │ ├── 00043_00023.ppm │ │ ├── 00043_00024.ppm │ │ ├── 00043_00025.ppm │ │ ├── 00043_00026.ppm │ │ ├── 00043_00027.ppm │ │ ├── 00043_00028.ppm │ │ ├── 00043_00029.ppm │ │ ├── 00044_00000.ppm │ │ ├── 00044_00001.ppm │ │ ├── 00044_00002.ppm │ │ ├── 00044_00003.ppm │ │ ├── 00044_00004.ppm │ │ ├── 00044_00005.ppm │ │ ├── 00044_00006.ppm │ │ ├── 00044_00007.ppm │ │ ├── 00044_00008.ppm │ │ ├── 00044_00009.ppm │ │ ├── 00044_00010.ppm │ │ ├── 00044_00011.ppm │ │ ├── 00044_00012.ppm │ │ ├── 00044_00013.ppm │ │ ├── 00044_00014.ppm │ │ ├── 00044_00015.ppm │ │ ├── 00044_00016.ppm │ │ ├── 00044_00017.ppm │ │ ├── 00044_00018.ppm │ │ ├── 00044_00019.ppm │ │ ├── 00044_00020.ppm │ │ ├── 00044_00021.ppm │ │ ├── 00044_00022.ppm │ │ ├── 00044_00023.ppm │ │ ├── 00044_00024.ppm │ │ ├── 00044_00025.ppm │ │ ├── 00044_00026.ppm │ │ ├── 00044_00027.ppm │ │ ├── 00044_00028.ppm │ │ ├── 00044_00029.ppm │ │ ├── 00045_00000.ppm │ │ ├── 00045_00001.ppm │ │ ├── 00045_00002.ppm │ │ ├── 00045_00003.ppm │ │ ├── 00045_00004.ppm │ │ ├── 00045_00005.ppm │ │ ├── 00045_00006.ppm │ │ ├── 00045_00007.ppm │ │ ├── 00045_00008.ppm │ │ ├── 00045_00009.ppm │ │ ├── 00045_00010.ppm │ │ ├── 00045_00011.ppm │ │ ├── 00045_00012.ppm │ │ ├── 00045_00013.ppm │ │ ├── 00045_00014.ppm │ │ ├── 00045_00015.ppm │ │ ├── 00045_00016.ppm │ │ ├── 00045_00017.ppm │ │ ├── 00045_00018.ppm │ │ ├── 00045_00019.ppm │ │ ├── 00045_00020.ppm │ │ ├── 00045_00021.ppm │ │ ├── 00045_00022.ppm │ │ ├── 00045_00023.ppm │ │ ├── 00045_00024.ppm │ │ ├── 00045_00025.ppm │ │ ├── 00045_00026.ppm │ │ ├── 00045_00027.ppm │ │ ├── 00045_00028.ppm │ │ ├── 00045_00029.ppm │ │ ├── 00046_00000.ppm │ │ ├── 00046_00001.ppm │ │ ├── 00046_00002.ppm │ │ ├── 00046_00003.ppm │ │ ├── 00046_00004.ppm │ │ ├── 00046_00005.ppm │ │ ├── 00046_00006.ppm │ │ ├── 00046_00007.ppm │ │ ├── 00046_00008.ppm │ │ ├── 00046_00009.ppm │ │ ├── 00046_00010.ppm │ │ ├── 00046_00011.ppm │ │ ├── 00046_00012.ppm │ │ ├── 00046_00013.ppm │ │ ├── 00046_00014.ppm │ │ ├── 00046_00015.ppm │ │ ├── 00046_00016.ppm │ │ ├── 00046_00017.ppm │ │ ├── 00046_00018.ppm │ │ ├── 00046_00019.ppm │ │ ├── 00046_00020.ppm │ │ ├── 00046_00021.ppm │ │ ├── 00046_00022.ppm │ │ ├── 00046_00023.ppm │ │ ├── 00046_00024.ppm │ │ ├── 00046_00025.ppm │ │ ├── 00046_00026.ppm │ │ ├── 00046_00027.ppm │ │ ├── 00046_00028.ppm │ │ ├── 00046_00029.ppm │ │ ├── 00047_00000.ppm │ │ ├── 00047_00001.ppm │ │ ├── 00047_00002.ppm │ │ ├── 00047_00003.ppm │ │ ├── 00047_00004.ppm │ │ ├── 00047_00005.ppm │ │ ├── 00047_00006.ppm │ │ ├── 00047_00007.ppm │ │ ├── 00047_00008.ppm │ │ ├── 00047_00009.ppm │ │ ├── 00047_00010.ppm │ │ ├── 00047_00011.ppm │ │ ├── 00047_00012.ppm │ │ ├── 00047_00013.ppm │ │ ├── 00047_00014.ppm │ │ ├── 00047_00015.ppm │ │ ├── 00047_00016.ppm │ │ ├── 00047_00017.ppm │ │ ├── 00047_00018.ppm │ │ ├── 00047_00019.ppm │ │ ├── 00047_00020.ppm │ │ ├── 00047_00021.ppm │ │ ├── 00047_00022.ppm │ │ ├── 00047_00023.ppm │ │ ├── 00047_00024.ppm │ │ ├── 00047_00025.ppm │ │ ├── 00047_00026.ppm │ │ ├── 00047_00027.ppm │ │ ├── 00047_00028.ppm │ │ ├── 00047_00029.ppm │ │ ├── 00048_00000.ppm │ │ ├── 00048_00001.ppm │ │ ├── 00048_00002.ppm │ │ ├── 00048_00003.ppm │ │ ├── 00048_00004.ppm │ │ ├── 00048_00005.ppm │ │ ├── 00048_00006.ppm │ │ ├── 00048_00007.ppm │ │ ├── 00048_00008.ppm │ │ ├── 00048_00009.ppm │ │ ├── 00048_00010.ppm │ │ ├── 00048_00011.ppm │ │ ├── 00048_00012.ppm │ │ ├── 00048_00013.ppm │ │ ├── 00048_00014.ppm │ │ ├── 00048_00015.ppm │ │ ├── 00048_00016.ppm │ │ ├── 00048_00017.ppm │ │ ├── 00048_00018.ppm │ │ ├── 00048_00019.ppm │ │ ├── 00048_00020.ppm │ │ ├── 00048_00021.ppm │ │ ├── 00048_00022.ppm │ │ ├── 00048_00023.ppm │ │ ├── 00048_00024.ppm │ │ ├── 00048_00025.ppm │ │ ├── 00048_00026.ppm │ │ ├── 00048_00027.ppm │ │ ├── 00048_00028.ppm │ │ ├── 00048_00029.ppm │ │ ├── 00049_00000.ppm │ │ ├── 00049_00001.ppm │ │ ├── 00049_00002.ppm │ │ ├── 00049_00003.ppm │ │ ├── 00049_00004.ppm │ │ ├── 00049_00005.ppm │ │ ├── 00049_00006.ppm │ │ ├── 00049_00007.ppm │ │ ├── 00049_00008.ppm │ │ ├── 00049_00009.ppm │ │ ├── 00049_00010.ppm │ │ ├── 00049_00011.ppm │ │ ├── 00049_00012.ppm │ │ ├── 00049_00013.ppm │ │ ├── 00049_00014.ppm │ │ ├── 00049_00015.ppm │ │ ├── 00049_00016.ppm │ │ ├── 00049_00017.ppm │ │ ├── 00049_00018.ppm │ │ ├── 00049_00019.ppm │ │ ├── 00049_00020.ppm │ │ ├── 00049_00021.ppm │ │ ├── 00049_00022.ppm │ │ ├── 00049_00023.ppm │ │ ├── 00049_00024.ppm │ │ ├── 00049_00025.ppm │ │ ├── 00049_00026.ppm │ │ ├── 00049_00027.ppm │ │ ├── 00049_00028.ppm │ │ ├── 00049_00029.ppm │ │ ├── 00050_00000.ppm │ │ ├── 00050_00001.ppm │ │ ├── 00050_00002.ppm │ │ ├── 00050_00003.ppm │ │ ├── 00050_00004.ppm │ │ ├── 00050_00005.ppm │ │ ├── 00050_00006.ppm │ │ ├── 00050_00007.ppm │ │ ├── 00050_00008.ppm │ │ ├── 00050_00009.ppm │ │ ├── 00050_00010.ppm │ │ ├── 00050_00011.ppm │ │ ├── 00050_00012.ppm │ │ ├── 00050_00013.ppm │ │ ├── 00050_00014.ppm │ │ ├── 00050_00015.ppm │ │ ├── 00050_00016.ppm │ │ ├── 00050_00017.ppm │ │ ├── 00050_00018.ppm │ │ ├── 00050_00019.ppm │ │ ├── 00050_00020.ppm │ │ ├── 00050_00021.ppm │ │ ├── 00050_00022.ppm │ │ ├── 00050_00023.ppm │ │ ├── 00050_00024.ppm │ │ ├── 00050_00025.ppm │ │ ├── 00050_00026.ppm │ │ ├── 00050_00027.ppm │ │ ├── 00050_00028.ppm │ │ ├── 00050_00029.ppm │ │ ├── 00051_00000.ppm │ │ ├── 00051_00001.ppm │ │ ├── 00051_00002.ppm │ │ ├── 00051_00003.ppm │ │ ├── 00051_00004.ppm │ │ ├── 00051_00005.ppm │ │ ├── 00051_00006.ppm │ │ ├── 00051_00007.ppm │ │ ├── 00051_00008.ppm │ │ ├── 00051_00009.ppm │ │ ├── 00051_00010.ppm │ │ ├── 00051_00011.ppm │ │ ├── 00051_00012.ppm │ │ ├── 00051_00013.ppm │ │ ├── 00051_00014.ppm │ │ ├── 00051_00015.ppm │ │ ├── 00051_00016.ppm │ │ ├── 00051_00017.ppm │ │ ├── 00051_00018.ppm │ │ ├── 00051_00019.ppm │ │ ├── 00051_00020.ppm │ │ ├── 00051_00021.ppm │ │ ├── 00051_00022.ppm │ │ ├── 00051_00023.ppm │ │ ├── 00051_00024.ppm │ │ ├── 00051_00025.ppm │ │ ├── 00051_00026.ppm │ │ ├── 00051_00027.ppm │ │ ├── 00051_00028.ppm │ │ ├── 00051_00029.ppm │ │ ├── 00052_00000.ppm │ │ ├── 00052_00001.ppm │ │ ├── 00052_00002.ppm │ │ ├── 00052_00003.ppm │ │ ├── 00052_00004.ppm │ │ ├── 00052_00005.ppm │ │ ├── 00052_00006.ppm │ │ ├── 00052_00007.ppm │ │ ├── 00052_00008.ppm │ │ ├── 00052_00009.ppm │ │ ├── 00052_00010.ppm │ │ ├── 00052_00011.ppm │ │ ├── 00052_00012.ppm │ │ ├── 00052_00013.ppm │ │ ├── 00052_00014.ppm │ │ ├── 00052_00015.ppm │ │ ├── 00052_00016.ppm │ │ ├── 00052_00017.ppm │ │ ├── 00052_00018.ppm │ │ ├── 00052_00019.ppm │ │ ├── 00052_00020.ppm │ │ ├── 00052_00021.ppm │ │ ├── 00052_00022.ppm │ │ ├── 00052_00023.ppm │ │ ├── 00052_00024.ppm │ │ ├── 00052_00025.ppm │ │ ├── 00052_00026.ppm │ │ ├── 00052_00027.ppm │ │ ├── 00052_00028.ppm │ │ ├── 00052_00029.ppm │ │ ├── 00053_00000.ppm │ │ ├── 00053_00001.ppm │ │ ├── 00053_00002.ppm │ │ ├── 00053_00003.ppm │ │ ├── 00053_00004.ppm │ │ ├── 00053_00005.ppm │ │ ├── 00053_00006.ppm │ │ ├── 00053_00007.ppm │ │ ├── 00053_00008.ppm │ │ ├── 00053_00009.ppm │ │ ├── 00053_00010.ppm │ │ ├── 00053_00011.ppm │ │ ├── 00053_00012.ppm │ │ ├── 00053_00013.ppm │ │ ├── 00053_00014.ppm │ │ ├── 00053_00015.ppm │ │ ├── 00053_00016.ppm │ │ ├── 00053_00017.ppm │ │ ├── 00053_00018.ppm │ │ ├── 00053_00019.ppm │ │ ├── 00053_00020.ppm │ │ ├── 00053_00021.ppm │ │ ├── 00053_00022.ppm │ │ ├── 00053_00023.ppm │ │ ├── 00053_00024.ppm │ │ ├── 00053_00025.ppm │ │ ├── 00053_00026.ppm │ │ ├── 00053_00027.ppm │ │ ├── 00053_00028.ppm │ │ ├── 00053_00029.ppm │ │ ├── 00054_00000.ppm │ │ ├── 00054_00001.ppm │ │ ├── 00054_00002.ppm │ │ ├── 00054_00003.ppm │ │ ├── 00054_00004.ppm │ │ ├── 00054_00005.ppm │ │ ├── 00054_00006.ppm │ │ ├── 00054_00007.ppm │ │ ├── 00054_00008.ppm │ │ ├── 00054_00009.ppm │ │ ├── 00054_00010.ppm │ │ ├── 00054_00011.ppm │ │ ├── 00054_00012.ppm │ │ ├── 00054_00013.ppm │ │ ├── 00054_00014.ppm │ │ ├── 00054_00015.ppm │ │ ├── 00054_00016.ppm │ │ ├── 00054_00017.ppm │ │ ├── 00054_00018.ppm │ │ ├── 00054_00019.ppm │ │ ├── 00054_00020.ppm │ │ ├── 00054_00021.ppm │ │ ├── 00054_00022.ppm │ │ ├── 00054_00023.ppm │ │ ├── 00054_00024.ppm │ │ ├── 00054_00025.ppm │ │ ├── 00054_00026.ppm │ │ ├── 00054_00027.ppm │ │ ├── 00054_00028.ppm │ │ ├── 00054_00029.ppm │ │ ├── 00055_00000.ppm │ │ ├── 00055_00001.ppm │ │ ├── 00055_00002.ppm │ │ ├── 00055_00003.ppm │ │ ├── 00055_00004.ppm │ │ ├── 00055_00005.ppm │ │ ├── 00055_00006.ppm │ │ ├── 00055_00007.ppm │ │ ├── 00055_00008.ppm │ │ ├── 00055_00009.ppm │ │ ├── 00055_00010.ppm │ │ ├── 00055_00011.ppm │ │ ├── 00055_00012.ppm │ │ ├── 00055_00013.ppm │ │ ├── 00055_00014.ppm │ │ ├── 00055_00015.ppm │ │ ├── 00055_00016.ppm │ │ ├── 00055_00017.ppm │ │ ├── 00055_00018.ppm │ │ ├── 00055_00019.ppm │ │ ├── 00055_00020.ppm │ │ ├── 00055_00021.ppm │ │ ├── 00055_00022.ppm │ │ ├── 00055_00023.ppm │ │ ├── 00055_00024.ppm │ │ ├── 00055_00025.ppm │ │ ├── 00055_00026.ppm │ │ ├── 00055_00027.ppm │ │ ├── 00055_00028.ppm │ │ ├── 00055_00029.ppm │ │ ├── 00056_00000.ppm │ │ ├── 00056_00001.ppm │ │ ├── 00056_00002.ppm │ │ ├── 00056_00003.ppm │ │ ├── 00056_00004.ppm │ │ ├── 00056_00005.ppm │ │ ├── 00056_00006.ppm │ │ ├── 00056_00007.ppm │ │ ├── 00056_00008.ppm │ │ ├── 00056_00009.ppm │ │ ├── 00056_00010.ppm │ │ ├── 00056_00011.ppm │ │ ├── 00056_00012.ppm │ │ ├── 00056_00013.ppm │ │ ├── 00056_00014.ppm │ │ ├── 00056_00015.ppm │ │ ├── 00056_00016.ppm │ │ ├── 00056_00017.ppm │ │ ├── 00056_00018.ppm │ │ ├── 00056_00019.ppm │ │ ├── 00056_00020.ppm │ │ ├── 00056_00021.ppm │ │ ├── 00056_00022.ppm │ │ ├── 00056_00023.ppm │ │ ├── 00056_00024.ppm │ │ ├── 00056_00025.ppm │ │ ├── 00056_00026.ppm │ │ ├── 00056_00027.ppm │ │ ├── 00056_00028.ppm │ │ ├── 00056_00029.ppm │ │ ├── 00057_00000.ppm │ │ ├── 00057_00001.ppm │ │ ├── 00057_00002.ppm │ │ ├── 00057_00003.ppm │ │ ├── 00057_00004.ppm │ │ ├── 00057_00005.ppm │ │ ├── 00057_00006.ppm │ │ ├── 00057_00007.ppm │ │ ├── 00057_00008.ppm │ │ ├── 00057_00009.ppm │ │ ├── 00057_00010.ppm │ │ ├── 00057_00011.ppm │ │ ├── 00057_00012.ppm │ │ ├── 00057_00013.ppm │ │ ├── 00057_00014.ppm │ │ ├── 00057_00015.ppm │ │ ├── 00057_00016.ppm │ │ ├── 00057_00017.ppm │ │ ├── 00057_00018.ppm │ │ ├── 00057_00019.ppm │ │ ├── 00057_00020.ppm │ │ ├── 00057_00021.ppm │ │ ├── 00057_00022.ppm │ │ ├── 00057_00023.ppm │ │ ├── 00057_00024.ppm │ │ ├── 00057_00025.ppm │ │ ├── 00057_00026.ppm │ │ ├── 00057_00027.ppm │ │ ├── 00057_00028.ppm │ │ ├── 00057_00029.ppm │ │ ├── 00058_00000.ppm │ │ ├── 00058_00001.ppm │ │ ├── 00058_00002.ppm │ │ ├── 00058_00003.ppm │ │ ├── 00058_00004.ppm │ │ ├── 00058_00005.ppm │ │ ├── 00058_00006.ppm │ │ ├── 00058_00007.ppm │ │ ├── 00058_00008.ppm │ │ ├── 00058_00009.ppm │ │ ├── 00058_00010.ppm │ │ ├── 00058_00011.ppm │ │ ├── 00058_00012.ppm │ │ ├── 00058_00013.ppm │ │ ├── 00058_00014.ppm │ │ ├── 00058_00015.ppm │ │ ├── 00058_00016.ppm │ │ ├── 00058_00017.ppm │ │ ├── 00058_00018.ppm │ │ ├── 00058_00019.ppm │ │ ├── 00058_00020.ppm │ │ ├── 00058_00021.ppm │ │ ├── 00058_00022.ppm │ │ ├── 00058_00023.ppm │ │ ├── 00058_00024.ppm │ │ ├── 00058_00025.ppm │ │ ├── 00058_00026.ppm │ │ ├── 00058_00027.ppm │ │ ├── 00058_00028.ppm │ │ ├── 00058_00029.ppm │ │ ├── 00059_00000.ppm │ │ ├── 00059_00001.ppm │ │ ├── 00059_00002.ppm │ │ ├── 00059_00003.ppm │ │ ├── 00059_00004.ppm │ │ ├── 00059_00005.ppm │ │ ├── 00059_00006.ppm │ │ ├── 00059_00007.ppm │ │ ├── 00059_00008.ppm │ │ ├── 00059_00009.ppm │ │ ├── 00059_00010.ppm │ │ ├── 00059_00011.ppm │ │ ├── 00059_00012.ppm │ │ ├── 00059_00013.ppm │ │ ├── 00059_00014.ppm │ │ ├── 00059_00015.ppm │ │ ├── 00059_00016.ppm │ │ ├── 00059_00017.ppm │ │ ├── 00059_00018.ppm │ │ ├── 00059_00019.ppm │ │ ├── 00059_00020.ppm │ │ ├── 00059_00021.ppm │ │ ├── 00059_00022.ppm │ │ ├── 00059_00023.ppm │ │ ├── 00059_00024.ppm │ │ ├── 00059_00025.ppm │ │ ├── 00059_00026.ppm │ │ ├── 00059_00027.ppm │ │ ├── 00059_00028.ppm │ │ ├── 00059_00029.ppm │ │ ├── 00060_00000.ppm │ │ ├── 00060_00001.ppm │ │ ├── 00060_00002.ppm │ │ ├── 00060_00003.ppm │ │ ├── 00060_00004.ppm │ │ ├── 00060_00005.ppm │ │ ├── 00060_00006.ppm │ │ ├── 00060_00007.ppm │ │ ├── 00060_00008.ppm │ │ ├── 00060_00009.ppm │ │ ├── 00060_00010.ppm │ │ ├── 00060_00011.ppm │ │ ├── 00060_00012.ppm │ │ ├── 00060_00013.ppm │ │ ├── 00060_00014.ppm │ │ ├── 00060_00015.ppm │ │ ├── 00060_00016.ppm │ │ ├── 00060_00017.ppm │ │ ├── 00060_00018.ppm │ │ ├── 00060_00019.ppm │ │ ├── 00060_00020.ppm │ │ ├── 00060_00021.ppm │ │ ├── 00060_00022.ppm │ │ ├── 00060_00023.ppm │ │ ├── 00060_00024.ppm │ │ ├── 00060_00025.ppm │ │ ├── 00060_00026.ppm │ │ ├── 00060_00027.ppm │ │ ├── 00060_00028.ppm │ │ ├── 00060_00029.ppm │ │ ├── 00061_00000.ppm │ │ ├── 00061_00001.ppm │ │ ├── 00061_00002.ppm │ │ ├── 00061_00003.ppm │ │ ├── 00061_00004.ppm │ │ ├── 00061_00005.ppm │ │ ├── 00061_00006.ppm │ │ ├── 00061_00007.ppm │ │ ├── 00061_00008.ppm │ │ ├── 00061_00009.ppm │ │ ├── 00061_00010.ppm │ │ ├── 00061_00011.ppm │ │ ├── 00061_00012.ppm │ │ ├── 00061_00013.ppm │ │ ├── 00061_00014.ppm │ │ ├── 00061_00015.ppm │ │ ├── 00061_00016.ppm │ │ ├── 00061_00017.ppm │ │ ├── 00061_00018.ppm │ │ ├── 00061_00019.ppm │ │ ├── 00061_00020.ppm │ │ ├── 00061_00021.ppm │ │ ├── 00061_00022.ppm │ │ ├── 00061_00023.ppm │ │ ├── 00061_00024.ppm │ │ ├── 00061_00025.ppm │ │ ├── 00061_00026.ppm │ │ ├── 00061_00027.ppm │ │ ├── 00061_00028.ppm │ │ ├── 00061_00029.ppm │ │ ├── 00062_00000.ppm │ │ ├── 00062_00001.ppm │ │ ├── 00062_00002.ppm │ │ ├── 00062_00003.ppm │ │ ├── 00062_00004.ppm │ │ ├── 00062_00005.ppm │ │ ├── 00062_00006.ppm │ │ ├── 00062_00007.ppm │ │ ├── 00062_00008.ppm │ │ ├── 00062_00009.ppm │ │ ├── 00062_00010.ppm │ │ ├── 00062_00011.ppm │ │ ├── 00062_00012.ppm │ │ ├── 00062_00013.ppm │ │ ├── 00062_00014.ppm │ │ ├── 00062_00015.ppm │ │ ├── 00062_00016.ppm │ │ ├── 00062_00017.ppm │ │ ├── 00062_00018.ppm │ │ ├── 00062_00019.ppm │ │ ├── 00062_00020.ppm │ │ ├── 00062_00021.ppm │ │ ├── 00062_00022.ppm │ │ ├── 00062_00023.ppm │ │ ├── 00062_00024.ppm │ │ ├── 00062_00025.ppm │ │ ├── 00062_00026.ppm │ │ ├── 00062_00027.ppm │ │ ├── 00062_00028.ppm │ │ ├── 00062_00029.ppm │ │ ├── 00063_00000.ppm │ │ ├── 00063_00001.ppm │ │ ├── 00063_00002.ppm │ │ ├── 00063_00003.ppm │ │ ├── 00063_00004.ppm │ │ ├── 00063_00005.ppm │ │ ├── 00063_00006.ppm │ │ ├── 00063_00007.ppm │ │ ├── 00063_00008.ppm │ │ ├── 00063_00009.ppm │ │ ├── 00063_00010.ppm │ │ ├── 00063_00011.ppm │ │ ├── 00063_00012.ppm │ │ ├── 00063_00013.ppm │ │ ├── 00063_00014.ppm │ │ ├── 00063_00015.ppm │ │ ├── 00063_00016.ppm │ │ ├── 00063_00017.ppm │ │ ├── 00063_00018.ppm │ │ ├── 00063_00019.ppm │ │ ├── 00063_00020.ppm │ │ ├── 00063_00021.ppm │ │ ├── 00063_00022.ppm │ │ ├── 00063_00023.ppm │ │ ├── 00063_00024.ppm │ │ ├── 00063_00025.ppm │ │ ├── 00063_00026.ppm │ │ ├── 00063_00027.ppm │ │ ├── 00063_00028.ppm │ │ ├── 00063_00029.ppm │ │ ├── 00064_00000.ppm │ │ ├── 00064_00001.ppm │ │ ├── 00064_00002.ppm │ │ ├── 00064_00003.ppm │ │ ├── 00064_00004.ppm │ │ ├── 00064_00005.ppm │ │ ├── 00064_00006.ppm │ │ ├── 00064_00007.ppm │ │ ├── 00064_00008.ppm │ │ ├── 00064_00009.ppm │ │ ├── 00064_00010.ppm │ │ ├── 00064_00011.ppm │ │ ├── 00064_00012.ppm │ │ ├── 00064_00013.ppm │ │ ├── 00064_00014.ppm │ │ ├── 00064_00015.ppm │ │ ├── 00064_00016.ppm │ │ ├── 00064_00017.ppm │ │ ├── 00064_00018.ppm │ │ ├── 00064_00019.ppm │ │ ├── 00064_00020.ppm │ │ ├── 00064_00021.ppm │ │ ├── 00064_00022.ppm │ │ ├── 00064_00023.ppm │ │ ├── 00064_00024.ppm │ │ ├── 00064_00025.ppm │ │ ├── 00064_00026.ppm │ │ ├── 00064_00027.ppm │ │ ├── 00064_00028.ppm │ │ ├── 00064_00029.ppm │ │ ├── 00065_00000.ppm │ │ ├── 00065_00001.ppm │ │ ├── 00065_00002.ppm │ │ ├── 00065_00003.ppm │ │ ├── 00065_00004.ppm │ │ ├── 00065_00005.ppm │ │ ├── 00065_00006.ppm │ │ ├── 00065_00007.ppm │ │ ├── 00065_00008.ppm │ │ ├── 00065_00009.ppm │ │ ├── 00065_00010.ppm │ │ ├── 00065_00011.ppm │ │ ├── 00065_00012.ppm │ │ ├── 00065_00013.ppm │ │ ├── 00065_00014.ppm │ │ ├── 00065_00015.ppm │ │ ├── 00065_00016.ppm │ │ ├── 00065_00017.ppm │ │ ├── 00065_00018.ppm │ │ ├── 00065_00019.ppm │ │ ├── 00065_00020.ppm │ │ ├── 00065_00021.ppm │ │ ├── 00065_00022.ppm │ │ ├── 00065_00023.ppm │ │ ├── 00065_00024.ppm │ │ ├── 00065_00025.ppm │ │ ├── 00065_00026.ppm │ │ ├── 00065_00027.ppm │ │ ├── 00065_00028.ppm │ │ ├── 00065_00029.ppm │ │ └── GT-00004.csv │ ├── 00006/ │ │ ├── 00000_00000.ppm │ │ ├── 00000_00001.ppm │ │ ├── 00000_00002.ppm │ │ ├── 00000_00003.ppm │ │ ├── 00000_00004.ppm │ │ ├── 00000_00005.ppm │ │ ├── 00000_00006.ppm │ │ ├── 00000_00007.ppm │ │ ├── 00000_00008.ppm │ │ ├── 00000_00009.ppm │ │ ├── 00000_00010.ppm │ │ ├── 00000_00011.ppm │ │ ├── 00000_00012.ppm │ │ ├── 00000_00013.ppm │ │ ├── 00000_00014.ppm │ │ ├── 00000_00015.ppm │ │ ├── 00000_00016.ppm │ │ ├── 00000_00017.ppm │ │ ├── 00000_00018.ppm │ │ ├── 00000_00019.ppm │ │ ├── 00000_00020.ppm │ │ ├── 00000_00021.ppm │ │ ├── 00000_00022.ppm │ │ ├── 00000_00023.ppm │ │ ├── 00000_00024.ppm │ │ ├── 00000_00025.ppm │ │ ├── 00000_00026.ppm │ │ ├── 00000_00027.ppm │ │ ├── 00000_00028.ppm │ │ ├── 00000_00029.ppm │ │ ├── 00001_00000.ppm │ │ ├── 00001_00001.ppm │ │ ├── 00001_00002.ppm │ │ ├── 00001_00003.ppm │ │ ├── 00001_00004.ppm │ │ ├── 00001_00005.ppm │ │ ├── 00001_00006.ppm │ │ ├── 00001_00007.ppm │ │ ├── 00001_00008.ppm │ │ ├── 00001_00009.ppm │ │ ├── 00001_00010.ppm │ │ ├── 00001_00011.ppm │ │ ├── 00001_00012.ppm │ │ ├── 00001_00013.ppm │ │ ├── 00001_00014.ppm │ │ ├── 00001_00015.ppm │ │ ├── 00001_00016.ppm │ │ ├── 00001_00017.ppm │ │ ├── 00001_00018.ppm │ │ ├── 00001_00019.ppm │ │ ├── 00001_00020.ppm │ │ ├── 00001_00021.ppm │ │ ├── 00001_00022.ppm │ │ ├── 00001_00023.ppm │ │ ├── 00001_00024.ppm │ │ ├── 00001_00025.ppm │ │ ├── 00001_00026.ppm │ │ ├── 00001_00027.ppm │ │ ├── 00001_00028.ppm │ │ ├── 00001_00029.ppm │ │ ├── 00002_00000.ppm │ │ ├── 00002_00001.ppm │ │ ├── 00002_00002.ppm │ │ ├── 00002_00003.ppm │ │ ├── 00002_00004.ppm │ │ ├── 00002_00005.ppm │ │ ├── 00002_00006.ppm │ │ ├── 00002_00007.ppm │ │ ├── 00002_00008.ppm │ │ ├── 00002_00009.ppm │ │ ├── 00002_00010.ppm │ │ ├── 00002_00011.ppm │ │ ├── 00002_00012.ppm │ │ ├── 00002_00013.ppm │ │ ├── 00002_00014.ppm │ │ ├── 00002_00015.ppm │ │ ├── 00002_00016.ppm │ │ ├── 00002_00017.ppm │ │ ├── 00002_00018.ppm │ │ ├── 00002_00019.ppm │ │ ├── 00002_00020.ppm │ │ ├── 00002_00021.ppm │ │ ├── 00002_00022.ppm │ │ ├── 00002_00023.ppm │ │ ├── 00002_00024.ppm │ │ ├── 00002_00025.ppm │ │ ├── 00002_00026.ppm │ │ ├── 00002_00027.ppm │ │ ├── 00002_00028.ppm │ │ ├── 00002_00029.ppm │ │ ├── 00003_00000.ppm │ │ ├── 00003_00001.ppm │ │ ├── 00003_00002.ppm │ │ ├── 00003_00003.ppm │ │ ├── 00003_00004.ppm │ │ ├── 00003_00005.ppm │ │ ├── 00003_00006.ppm │ │ ├── 00003_00007.ppm │ │ ├── 00003_00008.ppm │ │ ├── 00003_00009.ppm │ │ ├── 00003_00010.ppm │ │ ├── 00003_00011.ppm │ │ ├── 00003_00012.ppm │ │ ├── 00003_00013.ppm │ │ ├── 00003_00014.ppm │ │ ├── 00003_00015.ppm │ │ ├── 00003_00016.ppm │ │ ├── 00003_00017.ppm │ │ ├── 00003_00018.ppm │ │ ├── 00003_00019.ppm │ │ ├── 00003_00020.ppm │ │ ├── 00003_00021.ppm │ │ ├── 00003_00022.ppm │ │ ├── 00003_00023.ppm │ │ ├── 00003_00024.ppm │ │ ├── 00003_00025.ppm │ │ ├── 00003_00026.ppm │ │ ├── 00003_00027.ppm │ │ ├── 00003_00028.ppm │ │ ├── 00003_00029.ppm │ │ ├── 00004_00000.ppm │ │ ├── 00004_00001.ppm │ │ ├── 00004_00002.ppm │ │ ├── 00004_00003.ppm │ │ ├── 00004_00004.ppm │ │ ├── 00004_00005.ppm │ │ ├── 00004_00006.ppm │ │ ├── 00004_00007.ppm │ │ ├── 00004_00008.ppm │ │ ├── 00004_00009.ppm │ │ ├── 00004_00010.ppm │ │ ├── 00004_00011.ppm │ │ ├── 00004_00012.ppm │ │ ├── 00004_00013.ppm │ │ ├── 00004_00014.ppm │ │ ├── 00004_00015.ppm │ │ ├── 00004_00016.ppm │ │ ├── 00004_00017.ppm │ │ ├── 00004_00018.ppm │ │ ├── 00004_00019.ppm │ │ ├── 00004_00020.ppm │ │ ├── 00004_00021.ppm │ │ ├── 00004_00022.ppm │ │ ├── 00004_00023.ppm │ │ ├── 00004_00024.ppm │ │ ├── 00004_00025.ppm │ │ ├── 00004_00026.ppm │ │ ├── 00004_00027.ppm │ │ ├── 00004_00028.ppm │ │ ├── 00004_00029.ppm │ │ ├── 00005_00000.ppm │ │ ├── 00005_00001.ppm │ │ ├── 00005_00002.ppm │ │ ├── 00005_00003.ppm │ │ ├── 00005_00004.ppm │ │ ├── 00005_00005.ppm │ │ ├── 00005_00006.ppm │ │ ├── 00005_00007.ppm │ │ ├── 00005_00008.ppm │ │ ├── 00005_00009.ppm │ │ ├── 00005_00010.ppm │ │ ├── 00005_00011.ppm │ │ ├── 00005_00012.ppm │ │ ├── 00005_00013.ppm │ │ ├── 00005_00014.ppm │ │ ├── 00005_00015.ppm │ │ ├── 00005_00016.ppm │ │ ├── 00005_00017.ppm │ │ ├── 00005_00018.ppm │ │ ├── 00005_00019.ppm │ │ ├── 00005_00020.ppm │ │ ├── 00005_00021.ppm │ │ ├── 00005_00022.ppm │ │ ├── 00005_00023.ppm │ │ ├── 00005_00024.ppm │ │ ├── 00005_00025.ppm │ │ ├── 00005_00026.ppm │ │ ├── 00005_00027.ppm │ │ ├── 00005_00028.ppm │ │ ├── 00005_00029.ppm │ │ ├── 00006_00000.ppm │ │ ├── 00006_00001.ppm │ │ ├── 00006_00002.ppm │ │ ├── 00006_00003.ppm │ │ ├── 00006_00004.ppm │ │ ├── 00006_00005.ppm │ │ ├── 00006_00006.ppm │ │ ├── 00006_00007.ppm │ │ ├── 00006_00008.ppm │ │ ├── 00006_00009.ppm │ │ ├── 00006_00010.ppm │ │ ├── 00006_00011.ppm │ │ ├── 00006_00012.ppm │ │ ├── 00006_00013.ppm │ │ ├── 00006_00014.ppm │ │ ├── 00006_00015.ppm │ │ ├── 00006_00016.ppm │ │ ├── 00006_00017.ppm │ │ ├── 00006_00018.ppm │ │ ├── 00006_00019.ppm │ │ ├── 00006_00020.ppm │ │ ├── 00006_00021.ppm │ │ ├── 00006_00022.ppm │ │ ├── 00006_00023.ppm │ │ ├── 00006_00024.ppm │ │ ├── 00006_00025.ppm │ │ ├── 00006_00026.ppm │ │ ├── 00006_00027.ppm │ │ ├── 00006_00028.ppm │ │ ├── 00006_00029.ppm │ │ ├── 00007_00000.ppm │ │ ├── 00007_00001.ppm │ │ ├── 00007_00002.ppm │ │ ├── 00007_00003.ppm │ │ ├── 00007_00004.ppm │ │ ├── 00007_00005.ppm │ │ ├── 00007_00006.ppm │ │ ├── 00007_00007.ppm │ │ ├── 00007_00008.ppm │ │ ├── 00007_00009.ppm │ │ ├── 00007_00010.ppm │ │ ├── 00007_00011.ppm │ │ ├── 00007_00012.ppm │ │ ├── 00007_00013.ppm │ │ ├── 00007_00014.ppm │ │ ├── 00007_00015.ppm │ │ ├── 00007_00016.ppm │ │ ├── 00007_00017.ppm │ │ ├── 00007_00018.ppm │ │ ├── 00007_00019.ppm │ │ ├── 00007_00020.ppm │ │ ├── 00007_00021.ppm │ │ ├── 00007_00022.ppm │ │ ├── 00007_00023.ppm │ │ ├── 00007_00024.ppm │ │ ├── 00007_00025.ppm │ │ ├── 00007_00026.ppm │ │ ├── 00007_00027.ppm │ │ ├── 00007_00028.ppm │ │ ├── 00007_00029.ppm │ │ ├── 00008_00000.ppm │ │ ├── 00008_00001.ppm │ │ ├── 00008_00002.ppm │ │ ├── 00008_00003.ppm │ │ ├── 00008_00004.ppm │ │ ├── 00008_00005.ppm │ │ ├── 00008_00006.ppm │ │ ├── 00008_00007.ppm │ │ ├── 00008_00008.ppm │ │ ├── 00008_00009.ppm │ │ ├── 00008_00010.ppm │ │ ├── 00008_00011.ppm │ │ ├── 00008_00012.ppm │ │ ├── 00008_00013.ppm │ │ ├── 00008_00014.ppm │ │ ├── 00008_00015.ppm │ │ ├── 00008_00016.ppm │ │ ├── 00008_00017.ppm │ │ ├── 00008_00018.ppm │ │ ├── 00008_00019.ppm │ │ ├── 00008_00020.ppm │ │ ├── 00008_00021.ppm │ │ ├── 00008_00022.ppm │ │ ├── 00008_00023.ppm │ │ ├── 00008_00024.ppm │ │ ├── 00008_00025.ppm │ │ ├── 00008_00026.ppm │ │ ├── 00008_00027.ppm │ │ ├── 00008_00028.ppm │ │ ├── 00008_00029.ppm │ │ ├── 00009_00000.ppm │ │ ├── 00009_00001.ppm │ │ ├── 00009_00002.ppm │ │ ├── 00009_00003.ppm │ │ ├── 00009_00004.ppm │ │ ├── 00009_00005.ppm │ │ ├── 00009_00006.ppm │ │ ├── 00009_00007.ppm │ │ ├── 00009_00008.ppm │ │ ├── 00009_00009.ppm │ │ ├── 00009_00010.ppm │ │ ├── 00009_00011.ppm │ │ ├── 00009_00012.ppm │ │ ├── 00009_00013.ppm │ │ ├── 00009_00014.ppm │ │ ├── 00009_00015.ppm │ │ ├── 00009_00016.ppm │ │ ├── 00009_00017.ppm │ │ ├── 00009_00018.ppm │ │ ├── 00009_00019.ppm │ │ ├── 00009_00020.ppm │ │ ├── 00009_00021.ppm │ │ ├── 00009_00022.ppm │ │ ├── 00009_00023.ppm │ │ ├── 00009_00024.ppm │ │ ├── 00009_00025.ppm │ │ ├── 00009_00026.ppm │ │ ├── 00009_00027.ppm │ │ ├── 00009_00028.ppm │ │ ├── 00009_00029.ppm │ │ ├── 00010_00000.ppm │ │ ├── 00010_00001.ppm │ │ ├── 00010_00002.ppm │ │ ├── 00010_00003.ppm │ │ ├── 00010_00004.ppm │ │ ├── 00010_00005.ppm │ │ ├── 00010_00006.ppm │ │ ├── 00010_00007.ppm │ │ ├── 00010_00008.ppm │ │ ├── 00010_00009.ppm │ │ ├── 00010_00010.ppm │ │ ├── 00010_00011.ppm │ │ ├── 00010_00012.ppm │ │ ├── 00010_00013.ppm │ │ ├── 00010_00014.ppm │ │ ├── 00010_00015.ppm │ │ ├── 00010_00016.ppm │ │ ├── 00010_00017.ppm │ │ ├── 00010_00018.ppm │ │ ├── 00010_00019.ppm │ │ ├── 00010_00020.ppm │ │ ├── 00010_00021.ppm │ │ ├── 00010_00022.ppm │ │ ├── 00010_00023.ppm │ │ ├── 00010_00024.ppm │ │ ├── 00010_00025.ppm │ │ ├── 00010_00026.ppm │ │ ├── 00010_00027.ppm │ │ ├── 00010_00028.ppm │ │ ├── 00010_00029.ppm │ │ ├── 00011_00000.ppm │ │ ├── 00011_00001.ppm │ │ ├── 00011_00002.ppm │ │ ├── 00011_00003.ppm │ │ ├── 00011_00004.ppm │ │ ├── 00011_00005.ppm │ │ ├── 00011_00006.ppm │ │ ├── 00011_00007.ppm │ │ ├── 00011_00008.ppm │ │ ├── 00011_00009.ppm │ │ ├── 00011_00010.ppm │ │ ├── 00011_00011.ppm │ │ ├── 00011_00012.ppm │ │ ├── 00011_00013.ppm │ │ ├── 00011_00014.ppm │ │ ├── 00011_00015.ppm │ │ ├── 00011_00016.ppm │ │ ├── 00011_00017.ppm │ │ ├── 00011_00018.ppm │ │ ├── 00011_00019.ppm │ │ ├── 00011_00020.ppm │ │ ├── 00011_00021.ppm │ │ ├── 00011_00022.ppm │ │ ├── 00011_00023.ppm │ │ ├── 00011_00024.ppm │ │ ├── 00011_00025.ppm │ │ ├── 00011_00026.ppm │ │ ├── 00011_00027.ppm │ │ ├── 00011_00028.ppm │ │ ├── 00011_00029.ppm │ │ ├── 00012_00000.ppm │ │ ├── 00012_00001.ppm │ │ ├── 00012_00002.ppm │ │ ├── 00012_00003.ppm │ │ ├── 00012_00004.ppm │ │ ├── 00012_00005.ppm │ │ ├── 00012_00006.ppm │ │ ├── 00012_00007.ppm │ │ ├── 00012_00008.ppm │ │ ├── 00012_00009.ppm │ │ ├── 00012_00010.ppm │ │ ├── 00012_00011.ppm │ │ ├── 00012_00012.ppm │ │ ├── 00012_00013.ppm │ │ ├── 00012_00014.ppm │ │ ├── 00012_00015.ppm │ │ ├── 00012_00016.ppm │ │ ├── 00012_00017.ppm │ │ ├── 00012_00018.ppm │ │ ├── 00012_00019.ppm │ │ ├── 00012_00020.ppm │ │ ├── 00012_00021.ppm │ │ ├── 00012_00022.ppm │ │ ├── 00012_00023.ppm │ │ ├── 00012_00024.ppm │ │ ├── 00012_00025.ppm │ │ ├── 00012_00026.ppm │ │ ├── 00012_00027.ppm │ │ ├── 00012_00028.ppm │ │ ├── 00012_00029.ppm │ │ ├── 00013_00000.ppm │ │ ├── 00013_00001.ppm │ │ ├── 00013_00002.ppm │ │ ├── 00013_00003.ppm │ │ ├── 00013_00004.ppm │ │ ├── 00013_00005.ppm │ │ ├── 00013_00006.ppm │ │ ├── 00013_00007.ppm │ │ ├── 00013_00008.ppm │ │ ├── 00013_00009.ppm │ │ ├── 00013_00010.ppm │ │ ├── 00013_00011.ppm │ │ ├── 00013_00012.ppm │ │ ├── 00013_00013.ppm │ │ ├── 00013_00014.ppm │ │ ├── 00013_00015.ppm │ │ ├── 00013_00016.ppm │ │ ├── 00013_00017.ppm │ │ ├── 00013_00018.ppm │ │ ├── 00013_00019.ppm │ │ ├── 00013_00020.ppm │ │ ├── 00013_00021.ppm │ │ ├── 00013_00022.ppm │ │ ├── 00013_00023.ppm │ │ ├── 00013_00024.ppm │ │ ├── 00013_00025.ppm │ │ ├── 00013_00026.ppm │ │ ├── 00013_00027.ppm │ │ ├── 00013_00028.ppm │ │ ├── 00013_00029.ppm │ │ └── GT-00006.csv │ ├── 00008/ │ │ ├── 00000_00000.ppm │ │ ├── 00000_00001.ppm │ │ ├── 00000_00002.ppm │ │ ├── 00000_00003.ppm │ │ ├── 00000_00004.ppm │ │ ├── 00000_00005.ppm │ │ ├── 00000_00006.ppm │ │ ├── 00000_00007.ppm │ │ ├── 00000_00008.ppm │ │ ├── 00000_00009.ppm │ │ ├── 00000_00010.ppm │ │ ├── 00000_00011.ppm │ │ ├── 00000_00012.ppm │ │ ├── 00000_00013.ppm │ │ ├── 00000_00014.ppm │ │ ├── 00000_00015.ppm │ │ ├── 00000_00016.ppm │ │ ├── 00000_00017.ppm │ │ ├── 00000_00018.ppm │ │ ├── 00000_00019.ppm │ │ ├── 00000_00020.ppm │ │ ├── 00000_00021.ppm │ │ ├── 00000_00022.ppm │ │ ├── 00000_00023.ppm │ │ ├── 00000_00024.ppm │ │ ├── 00000_00025.ppm │ │ ├── 00000_00026.ppm │ │ ├── 00000_00027.ppm │ │ ├── 00000_00028.ppm │ │ ├── 00000_00029.ppm │ │ ├── 00001_00000.ppm │ │ ├── 00001_00001.ppm │ │ ├── 00001_00002.ppm │ │ ├── 00001_00003.ppm │ │ ├── 00001_00004.ppm │ │ ├── 00001_00005.ppm │ │ ├── 00001_00006.ppm │ │ ├── 00001_00007.ppm │ │ ├── 00001_00008.ppm │ │ ├── 00001_00009.ppm │ │ ├── 00001_00010.ppm │ │ ├── 00001_00011.ppm │ │ ├── 00001_00012.ppm │ │ ├── 00001_00013.ppm │ │ ├── 00001_00014.ppm │ │ ├── 00001_00015.ppm │ │ ├── 00001_00016.ppm │ │ ├── 00001_00017.ppm │ │ ├── 00001_00018.ppm │ │ ├── 00001_00019.ppm │ │ ├── 00001_00020.ppm │ │ ├── 00001_00021.ppm │ │ ├── 00001_00022.ppm │ │ ├── 00001_00023.ppm │ │ ├── 00001_00024.ppm │ │ ├── 00001_00025.ppm │ │ ├── 00001_00026.ppm │ │ ├── 00001_00027.ppm │ │ ├── 00001_00028.ppm │ │ ├── 00001_00029.ppm │ │ ├── 00002_00000.ppm │ │ ├── 00002_00001.ppm │ │ ├── 00002_00002.ppm │ │ ├── 00002_00003.ppm │ │ ├── 00002_00004.ppm │ │ ├── 00002_00005.ppm │ │ ├── 00002_00006.ppm │ │ ├── 00002_00007.ppm │ │ ├── 00002_00008.ppm │ │ ├── 00002_00009.ppm │ │ ├── 00002_00010.ppm │ │ ├── 00002_00011.ppm │ │ ├── 00002_00012.ppm │ │ ├── 00002_00013.ppm │ │ ├── 00002_00014.ppm │ │ ├── 00002_00015.ppm │ │ ├── 00002_00016.ppm │ │ ├── 00002_00017.ppm │ │ ├── 00002_00018.ppm │ │ ├── 00002_00019.ppm │ │ ├── 00002_00020.ppm │ │ ├── 00002_00021.ppm │ │ ├── 00002_00022.ppm │ │ ├── 00002_00023.ppm │ │ ├── 00002_00024.ppm │ │ ├── 00002_00025.ppm │ │ ├── 00002_00026.ppm │ │ ├── 00002_00027.ppm │ │ ├── 00002_00028.ppm │ │ ├── 00002_00029.ppm │ │ ├── 00003_00000.ppm │ │ ├── 00003_00001.ppm │ │ ├── 00003_00002.ppm │ │ ├── 00003_00003.ppm │ │ ├── 00003_00004.ppm │ │ ├── 00003_00005.ppm │ │ ├── 00003_00006.ppm │ │ ├── 00003_00007.ppm │ │ ├── 00003_00008.ppm │ │ ├── 00003_00009.ppm │ │ ├── 00003_00010.ppm │ │ ├── 00003_00011.ppm │ │ ├── 00003_00012.ppm │ │ ├── 00003_00013.ppm │ │ ├── 00003_00014.ppm │ │ ├── 00003_00015.ppm │ │ ├── 00003_00016.ppm │ │ ├── 00003_00017.ppm │ │ ├── 00003_00018.ppm │ │ ├── 00003_00019.ppm │ │ ├── 00003_00020.ppm │ │ ├── 00003_00021.ppm │ │ ├── 00003_00022.ppm │ │ ├── 00003_00023.ppm │ │ ├── 00003_00024.ppm │ │ ├── 00003_00025.ppm │ │ ├── 00003_00026.ppm │ │ ├── 00003_00027.ppm │ │ ├── 00003_00028.ppm │ │ ├── 00003_00029.ppm │ │ ├── 00004_00000.ppm │ │ ├── 00004_00001.ppm │ │ ├── 00004_00002.ppm │ │ ├── 00004_00003.ppm │ │ ├── 00004_00004.ppm │ │ ├── 00004_00005.ppm │ │ ├── 00004_00006.ppm │ │ ├── 00004_00007.ppm │ │ ├── 00004_00008.ppm │ │ ├── 00004_00009.ppm │ │ ├── 00004_00010.ppm │ │ ├── 00004_00011.ppm │ │ ├── 00004_00012.ppm │ │ ├── 00004_00013.ppm │ │ ├── 00004_00014.ppm │ │ ├── 00004_00015.ppm │ │ ├── 00004_00016.ppm │ │ ├── 00004_00017.ppm │ │ ├── 00004_00018.ppm │ │ ├── 00004_00019.ppm │ │ ├── 00004_00020.ppm │ │ ├── 00004_00021.ppm │ │ ├── 00004_00022.ppm │ │ ├── 00004_00023.ppm │ │ ├── 00004_00024.ppm │ │ ├── 00004_00025.ppm │ │ ├── 00004_00026.ppm │ │ ├── 00004_00027.ppm │ │ ├── 00004_00028.ppm │ │ ├── 00004_00029.ppm │ │ ├── 00005_00000.ppm │ │ ├── 00005_00001.ppm │ │ ├── 00005_00002.ppm │ │ ├── 00005_00003.ppm │ │ ├── 00005_00004.ppm │ │ ├── 00005_00005.ppm │ │ ├── 00005_00006.ppm │ │ ├── 00005_00007.ppm │ │ ├── 00005_00008.ppm │ │ ├── 00005_00009.ppm │ │ ├── 00005_00010.ppm │ │ ├── 00005_00011.ppm │ │ ├── 00005_00012.ppm │ │ ├── 00005_00013.ppm │ │ ├── 00005_00014.ppm │ │ ├── 00005_00015.ppm │ │ ├── 00005_00016.ppm │ │ ├── 00005_00017.ppm │ │ ├── 00005_00018.ppm │ │ ├── 00005_00019.ppm │ │ ├── 00005_00020.ppm │ │ ├── 00005_00021.ppm │ │ ├── 00005_00022.ppm │ │ ├── 00005_00023.ppm │ │ ├── 00005_00024.ppm │ │ ├── 00005_00025.ppm │ │ ├── 00005_00026.ppm │ │ ├── 00005_00027.ppm │ │ ├── 00005_00028.ppm │ │ ├── 00005_00029.ppm │ │ ├── 00006_00000.ppm │ │ ├── 00006_00001.ppm │ │ ├── 00006_00002.ppm │ │ ├── 00006_00003.ppm │ │ ├── 00006_00004.ppm │ │ ├── 00006_00005.ppm │ │ ├── 00006_00006.ppm │ │ ├── 00006_00007.ppm │ │ ├── 00006_00008.ppm │ │ ├── 00006_00009.ppm │ │ ├── 00006_00010.ppm │ │ ├── 00006_00011.ppm │ │ ├── 00006_00012.ppm │ │ ├── 00006_00013.ppm │ │ ├── 00006_00014.ppm │ │ ├── 00006_00015.ppm │ │ ├── 00006_00016.ppm │ │ ├── 00006_00017.ppm │ │ ├── 00006_00018.ppm │ │ ├── 00006_00019.ppm │ │ ├── 00006_00020.ppm │ │ ├── 00006_00021.ppm │ │ ├── 00006_00022.ppm │ │ ├── 00006_00023.ppm │ │ ├── 00006_00024.ppm │ │ ├── 00006_00025.ppm │ │ ├── 00006_00026.ppm │ │ ├── 00006_00027.ppm │ │ ├── 00006_00028.ppm │ │ ├── 00006_00029.ppm │ │ ├── 00007_00000.ppm │ │ ├── 00007_00001.ppm │ │ ├── 00007_00002.ppm │ │ ├── 00007_00003.ppm │ │ ├── 00007_00004.ppm │ │ ├── 00007_00005.ppm │ │ ├── 00007_00006.ppm │ │ ├── 00007_00007.ppm │ │ ├── 00007_00008.ppm │ │ ├── 00007_00009.ppm │ │ ├── 00007_00010.ppm │ │ ├── 00007_00011.ppm │ │ ├── 00007_00012.ppm │ │ ├── 00007_00013.ppm │ │ ├── 00007_00014.ppm │ │ ├── 00007_00015.ppm │ │ ├── 00007_00016.ppm │ │ ├── 00007_00017.ppm │ │ ├── 00007_00018.ppm │ │ ├── 00007_00019.ppm │ │ ├── 00007_00020.ppm │ │ ├── 00007_00021.ppm │ │ ├── 00007_00022.ppm │ │ ├── 00007_00023.ppm │ │ ├── 00007_00024.ppm │ │ ├── 00007_00025.ppm │ │ ├── 00007_00026.ppm │ │ ├── 00007_00027.ppm │ │ ├── 00007_00028.ppm │ │ ├── 00007_00029.ppm │ │ ├── 00008_00000.ppm │ │ ├── 00008_00001.ppm │ │ ├── 00008_00002.ppm │ │ ├── 00008_00003.ppm │ │ ├── 00008_00004.ppm │ │ ├── 00008_00005.ppm │ │ ├── 00008_00006.ppm │ │ ├── 00008_00007.ppm │ │ ├── 00008_00008.ppm │ │ ├── 00008_00009.ppm │ │ ├── 00008_00010.ppm │ │ ├── 00008_00011.ppm │ │ ├── 00008_00012.ppm │ │ ├── 00008_00013.ppm │ │ ├── 00008_00014.ppm │ │ ├── 00008_00015.ppm │ │ ├── 00008_00016.ppm │ │ ├── 00008_00017.ppm │ │ ├── 00008_00018.ppm │ │ ├── 00008_00019.ppm │ │ ├── 00008_00020.ppm │ │ ├── 00008_00021.ppm │ │ ├── 00008_00022.ppm │ │ ├── 00008_00023.ppm │ │ ├── 00008_00024.ppm │ │ ├── 00008_00025.ppm │ │ ├── 00008_00026.ppm │ │ ├── 00008_00027.ppm │ │ ├── 00008_00028.ppm │ │ ├── 00008_00029.ppm │ │ ├── 00009_00000.ppm │ │ ├── 00009_00001.ppm │ │ ├── 00009_00002.ppm │ │ ├── 00009_00003.ppm │ │ ├── 00009_00004.ppm │ │ ├── 00009_00005.ppm │ │ ├── 00009_00006.ppm │ │ ├── 00009_00007.ppm │ │ ├── 00009_00008.ppm │ │ ├── 00009_00009.ppm │ │ ├── 00009_00010.ppm │ │ ├── 00009_00011.ppm │ │ ├── 00009_00012.ppm │ │ ├── 00009_00013.ppm │ │ ├── 00009_00014.ppm │ │ ├── 00009_00015.ppm │ │ ├── 00009_00016.ppm │ │ ├── 00009_00017.ppm │ │ ├── 00009_00018.ppm │ │ ├── 00009_00019.ppm │ │ ├── 00009_00020.ppm │ │ ├── 00009_00021.ppm │ │ ├── 00009_00022.ppm │ │ ├── 00009_00023.ppm │ │ ├── 00009_00024.ppm │ │ ├── 00009_00025.ppm │ │ ├── 00009_00026.ppm │ │ ├── 00009_00027.ppm │ │ ├── 00009_00028.ppm │ │ ├── 00009_00029.ppm │ │ ├── 00010_00000.ppm │ │ ├── 00010_00001.ppm │ │ ├── 00010_00002.ppm │ │ ├── 00010_00003.ppm │ │ ├── 00010_00004.ppm │ │ ├── 00010_00005.ppm │ │ ├── 00010_00006.ppm │ │ ├── 00010_00007.ppm │ │ ├── 00010_00008.ppm │ │ ├── 00010_00009.ppm │ │ ├── 00010_00010.ppm │ │ ├── 00010_00011.ppm │ │ ├── 00010_00012.ppm │ │ ├── 00010_00013.ppm │ │ ├── 00010_00014.ppm │ │ ├── 00010_00015.ppm │ │ ├── 00010_00016.ppm │ │ ├── 00010_00017.ppm │ │ ├── 00010_00018.ppm │ │ ├── 00010_00019.ppm │ │ ├── 00010_00020.ppm │ │ ├── 00010_00021.ppm │ │ ├── 00010_00022.ppm │ │ ├── 00010_00023.ppm │ │ ├── 00010_00024.ppm │ │ ├── 00010_00025.ppm │ │ ├── 00010_00026.ppm │ │ ├── 00010_00027.ppm │ │ ├── 00010_00028.ppm │ │ ├── 00010_00029.ppm │ │ ├── 00011_00000.ppm │ │ ├── 00011_00001.ppm │ │ ├── 00011_00002.ppm │ │ ├── 00011_00003.ppm │ │ ├── 00011_00004.ppm │ │ ├── 00011_00005.ppm │ │ ├── 00011_00006.ppm │ │ ├── 00011_00007.ppm │ │ ├── 00011_00008.ppm │ │ ├── 00011_00009.ppm │ │ ├── 00011_00010.ppm │ │ ├── 00011_00011.ppm │ │ ├── 00011_00012.ppm │ │ ├── 00011_00013.ppm │ │ ├── 00011_00014.ppm │ │ ├── 00011_00015.ppm │ │ ├── 00011_00016.ppm │ │ ├── 00011_00017.ppm │ │ ├── 00011_00018.ppm │ │ ├── 00011_00019.ppm │ │ ├── 00011_00020.ppm │ │ ├── 00011_00021.ppm │ │ ├── 00011_00022.ppm │ │ ├── 00011_00023.ppm │ │ ├── 00011_00024.ppm │ │ ├── 00011_00025.ppm │ │ ├── 00011_00026.ppm │ │ ├── 00011_00027.ppm │ │ ├── 00011_00028.ppm │ │ ├── 00011_00029.ppm │ │ ├── 00012_00000.ppm │ │ ├── 00012_00001.ppm │ │ ├── 00012_00002.ppm │ │ ├── 00012_00003.ppm │ │ ├── 00012_00004.ppm │ │ ├── 00012_00005.ppm │ │ ├── 00012_00006.ppm │ │ ├── 00012_00007.ppm │ │ ├── 00012_00008.ppm │ │ ├── 00012_00009.ppm │ │ ├── 00012_00010.ppm │ │ ├── 00012_00011.ppm │ │ ├── 00012_00012.ppm │ │ ├── 00012_00013.ppm │ │ ├── 00012_00014.ppm │ │ ├── 00012_00015.ppm │ │ ├── 00012_00016.ppm │ │ ├── 00012_00017.ppm │ │ ├── 00012_00018.ppm │ │ ├── 00012_00019.ppm │ │ ├── 00012_00020.ppm │ │ ├── 00012_00021.ppm │ │ ├── 00012_00022.ppm │ │ ├── 00012_00023.ppm │ │ ├── 00012_00024.ppm │ │ ├── 00012_00025.ppm │ │ ├── 00012_00026.ppm │ │ ├── 00012_00027.ppm │ │ ├── 00012_00028.ppm │ │ ├── 00012_00029.ppm │ │ ├── 00013_00000.ppm │ │ ├── 00013_00001.ppm │ │ ├── 00013_00002.ppm │ │ ├── 00013_00003.ppm │ │ ├── 00013_00004.ppm │ │ ├── 00013_00005.ppm │ │ ├── 00013_00006.ppm │ │ ├── 00013_00007.ppm │ │ ├── 00013_00008.ppm │ │ ├── 00013_00009.ppm │ │ ├── 00013_00010.ppm │ │ ├── 00013_00011.ppm │ │ ├── 00013_00012.ppm │ │ ├── 00013_00013.ppm │ │ ├── 00013_00014.ppm │ │ ├── 00013_00015.ppm │ │ ├── 00013_00016.ppm │ │ ├── 00013_00017.ppm │ │ ├── 00013_00018.ppm │ │ ├── 00013_00019.ppm │ │ ├── 00013_00020.ppm │ │ ├── 00013_00021.ppm │ │ ├── 00013_00022.ppm │ │ ├── 00013_00023.ppm │ │ ├── 00013_00024.ppm │ │ ├── 00013_00025.ppm │ │ ├── 00013_00026.ppm │ │ ├── 00013_00027.ppm │ │ ├── 00013_00028.ppm │ │ ├── 00013_00029.ppm │ │ ├── 00014_00000.ppm │ │ ├── 00014_00001.ppm │ │ ├── 00014_00002.ppm │ │ ├── 00014_00003.ppm │ │ ├── 00014_00004.ppm │ │ ├── 00014_00005.ppm │ │ ├── 00014_00006.ppm │ │ ├── 00014_00007.ppm │ │ ├── 00014_00008.ppm │ │ ├── 00014_00009.ppm │ │ ├── 00014_00010.ppm │ │ ├── 00014_00011.ppm │ │ ├── 00014_00012.ppm │ │ ├── 00014_00013.ppm │ │ ├── 00014_00014.ppm │ │ ├── 00014_00015.ppm │ │ ├── 00014_00016.ppm │ │ ├── 00014_00017.ppm │ │ ├── 00014_00018.ppm │ │ ├── 00014_00019.ppm │ │ ├── 00014_00020.ppm │ │ ├── 00014_00021.ppm │ │ ├── 00014_00022.ppm │ │ ├── 00014_00023.ppm │ │ ├── 00014_00024.ppm │ │ ├── 00014_00025.ppm │ │ ├── 00014_00026.ppm │ │ ├── 00014_00027.ppm │ │ ├── 00014_00028.ppm │ │ ├── 00014_00029.ppm │ │ ├── 00015_00000.ppm │ │ ├── 00015_00001.ppm │ │ ├── 00015_00002.ppm │ │ ├── 00015_00003.ppm │ │ ├── 00015_00004.ppm │ │ ├── 00015_00005.ppm │ │ ├── 00015_00006.ppm │ │ ├── 00015_00007.ppm │ │ ├── 00015_00008.ppm │ │ ├── 00015_00009.ppm │ │ ├── 00015_00010.ppm │ │ ├── 00015_00011.ppm │ │ ├── 00015_00012.ppm │ │ ├── 00015_00013.ppm │ │ ├── 00015_00014.ppm │ │ ├── 00015_00015.ppm │ │ ├── 00015_00016.ppm │ │ ├── 00015_00017.ppm │ │ ├── 00015_00018.ppm │ │ ├── 00015_00019.ppm │ │ ├── 00015_00020.ppm │ │ ├── 00015_00021.ppm │ │ ├── 00015_00022.ppm │ │ ├── 00015_00023.ppm │ │ ├── 00015_00024.ppm │ │ ├── 00015_00025.ppm │ │ ├── 00015_00026.ppm │ │ ├── 00015_00027.ppm │ │ ├── 00015_00028.ppm │ │ ├── 00015_00029.ppm │ │ ├── 00016_00000.ppm │ │ ├── 00016_00001.ppm │ │ ├── 00016_00002.ppm │ │ ├── 00016_00003.ppm │ │ ├── 00016_00004.ppm │ │ ├── 00016_00005.ppm │ │ ├── 00016_00006.ppm │ │ ├── 00016_00007.ppm │ │ ├── 00016_00008.ppm │ │ ├── 00016_00009.ppm │ │ ├── 00016_00010.ppm │ │ ├── 00016_00011.ppm │ │ ├── 00016_00012.ppm │ │ ├── 00016_00013.ppm │ │ ├── 00016_00014.ppm │ │ ├── 00016_00015.ppm │ │ ├── 00016_00016.ppm │ │ ├── 00016_00017.ppm │ │ ├── 00016_00018.ppm │ │ ├── 00016_00019.ppm │ │ ├── 00016_00020.ppm │ │ ├── 00016_00021.ppm │ │ ├── 00016_00022.ppm │ │ ├── 00016_00023.ppm │ │ ├── 00016_00024.ppm │ │ ├── 00016_00025.ppm │ │ ├── 00016_00026.ppm │ │ ├── 00016_00027.ppm │ │ ├── 00016_00028.ppm │ │ ├── 00016_00029.ppm │ │ ├── 00017_00000.ppm │ │ ├── 00017_00001.ppm │ │ ├── 00017_00002.ppm │ │ ├── 00017_00003.ppm │ │ ├── 00017_00004.ppm │ │ ├── 00017_00005.ppm │ │ ├── 00017_00006.ppm │ │ ├── 00017_00007.ppm │ │ ├── 00017_00008.ppm │ │ ├── 00017_00009.ppm │ │ ├── 00017_00010.ppm │ │ ├── 00017_00011.ppm │ │ ├── 00017_00012.ppm │ │ ├── 00017_00013.ppm │ │ ├── 00017_00014.ppm │ │ ├── 00017_00015.ppm │ │ ├── 00017_00016.ppm │ │ ├── 00017_00017.ppm │ │ ├── 00017_00018.ppm │ │ ├── 00017_00019.ppm │ │ ├── 00017_00020.ppm │ │ ├── 00017_00021.ppm │ │ ├── 00017_00022.ppm │ │ ├── 00017_00023.ppm │ │ ├── 00017_00024.ppm │ │ ├── 00017_00025.ppm │ │ ├── 00017_00026.ppm │ │ ├── 00017_00027.ppm │ │ ├── 00017_00028.ppm │ │ ├── 00017_00029.ppm │ │ ├── 00018_00000.ppm │ │ ├── 00018_00001.ppm │ │ ├── 00018_00002.ppm │ │ ├── 00018_00003.ppm │ │ ├── 00018_00004.ppm │ │ ├── 00018_00005.ppm │ │ ├── 00018_00006.ppm │ │ ├── 00018_00007.ppm │ │ ├── 00018_00008.ppm │ │ ├── 00018_00009.ppm │ │ ├── 00018_00010.ppm │ │ ├── 00018_00011.ppm │ │ ├── 00018_00012.ppm │ │ ├── 00018_00013.ppm │ │ ├── 00018_00014.ppm │ │ ├── 00018_00015.ppm │ │ ├── 00018_00016.ppm │ │ ├── 00018_00017.ppm │ │ ├── 00018_00018.ppm │ │ ├── 00018_00019.ppm │ │ ├── 00018_00020.ppm │ │ ├── 00018_00021.ppm │ │ ├── 00018_00022.ppm │ │ ├── 00018_00023.ppm │ │ ├── 00018_00024.ppm │ │ ├── 00018_00025.ppm │ │ ├── 00018_00026.ppm │ │ ├── 00018_00027.ppm │ │ ├── 00018_00028.ppm │ │ ├── 00018_00029.ppm │ │ ├── 00019_00000.ppm │ │ ├── 00019_00001.ppm │ │ ├── 00019_00002.ppm │ │ ├── 00019_00003.ppm │ │ ├── 00019_00004.ppm │ │ ├── 00019_00005.ppm │ │ ├── 00019_00006.ppm │ │ ├── 00019_00007.ppm │ │ ├── 00019_00008.ppm │ │ ├── 00019_00009.ppm │ │ ├── 00019_00010.ppm │ │ ├── 00019_00011.ppm │ │ ├── 00019_00012.ppm │ │ ├── 00019_00013.ppm │ │ ├── 00019_00014.ppm │ │ ├── 00019_00015.ppm │ │ ├── 00019_00016.ppm │ │ ├── 00019_00017.ppm │ │ ├── 00019_00018.ppm │ │ ├── 00019_00019.ppm │ │ ├── 00019_00020.ppm │ │ ├── 00019_00021.ppm │ │ ├── 00019_00022.ppm │ │ ├── 00019_00023.ppm │ │ ├── 00019_00024.ppm │ │ ├── 00019_00025.ppm │ │ ├── 00019_00026.ppm │ │ ├── 00019_00027.ppm │ │ ├── 00019_00028.ppm │ │ ├── 00019_00029.ppm │ │ ├── 00020_00000.ppm │ │ ├── 00020_00001.ppm │ │ ├── 00020_00002.ppm │ │ ├── 00020_00003.ppm │ │ ├── 00020_00004.ppm │ │ ├── 00020_00005.ppm │ │ ├── 00020_00006.ppm │ │ ├── 00020_00007.ppm │ │ ├── 00020_00008.ppm │ │ ├── 00020_00009.ppm │ │ ├── 00020_00010.ppm │ │ ├── 00020_00011.ppm │ │ ├── 00020_00012.ppm │ │ ├── 00020_00013.ppm │ │ ├── 00020_00014.ppm │ │ ├── 00020_00015.ppm │ │ ├── 00020_00016.ppm │ │ ├── 00020_00017.ppm │ │ ├── 00020_00018.ppm │ │ ├── 00020_00019.ppm │ │ ├── 00020_00020.ppm │ │ ├── 00020_00021.ppm │ │ ├── 00020_00022.ppm │ │ ├── 00020_00023.ppm │ │ ├── 00020_00024.ppm │ │ ├── 00020_00025.ppm │ │ ├── 00020_00026.ppm │ │ ├── 00020_00027.ppm │ │ ├── 00020_00028.ppm │ │ ├── 00020_00029.ppm │ │ ├── 00021_00000.ppm │ │ ├── 00021_00001.ppm │ │ ├── 00021_00002.ppm │ │ ├── 00021_00003.ppm │ │ ├── 00021_00004.ppm │ │ ├── 00021_00005.ppm │ │ ├── 00021_00006.ppm │ │ ├── 00021_00007.ppm │ │ ├── 00021_00008.ppm │ │ ├── 00021_00009.ppm │ │ ├── 00021_00010.ppm │ │ ├── 00021_00011.ppm │ │ ├── 00021_00012.ppm │ │ ├── 00021_00013.ppm │ │ ├── 00021_00014.ppm │ │ ├── 00021_00015.ppm │ │ ├── 00021_00016.ppm │ │ ├── 00021_00017.ppm │ │ ├── 00021_00018.ppm │ │ ├── 00021_00019.ppm │ │ ├── 00021_00020.ppm │ │ ├── 00021_00021.ppm │ │ ├── 00021_00022.ppm │ │ ├── 00021_00023.ppm │ │ ├── 00021_00024.ppm │ │ ├── 00021_00025.ppm │ │ ├── 00021_00026.ppm │ │ ├── 00021_00027.ppm │ │ ├── 00021_00028.ppm │ │ ├── 00021_00029.ppm │ │ ├── 00022_00000.ppm │ │ ├── 00022_00001.ppm │ │ ├── 00022_00002.ppm │ │ ├── 00022_00003.ppm │ │ ├── 00022_00004.ppm │ │ ├── 00022_00005.ppm │ │ ├── 00022_00006.ppm │ │ ├── 00022_00007.ppm │ │ ├── 00022_00008.ppm │ │ ├── 00022_00009.ppm │ │ ├── 00022_00010.ppm │ │ ├── 00022_000
SYMBOL INDEX (142 symbols across 19 files)
FILE: chapter1/chapter1.py
class FilterLayout (line 27) | class FilterLayout(BaseLayout):
method _init_custom_layout (line 38) | def _init_custom_layout(self):
method _create_custom_layout (line 45) | def _create_custom_layout(self):
method _process_frame (line 67) | def _process_frame(self, frame_rgb):
function main (line 85) | def main():
FILE: chapter1/filters.py
class PencilSketch (line 20) | class PencilSketch:
method __init__ (line 28) | def __init__(self, (width, height), bg_gray='pencilsketch_bg.jpg'):
method render (line 43) | def render(self, img_rgb):
class WarmingFilter (line 60) | class WarmingFilter:
method __init__ (line 69) | def __init__(self):
method render (line 77) | def render(self, img_rgb):
method _create_LUT_8UC1 (line 95) | def _create_LUT_8UC1(self, x, y):
class CoolingFilter (line 101) | class CoolingFilter:
method __init__ (line 110) | def __init__(self):
method render (line 118) | def render(self, img_rgb):
method _create_LUT_8UC1 (line 135) | def _create_LUT_8UC1(self, x, y):
class Cartoonizer (line 141) | class Cartoonizer:
method __init__ (line 149) | def __init__(self):
method render (line 152) | def render(self, img_rgb):
FILE: chapter1/gui.py
class Meta1 (line 17) | class Meta1(wx.Frame):
class BaseLayout (line 22) | class BaseLayout(Meta1):
method __init__ (line 45) | def __init__(self, capture, title=None, parent=None, id=-1, fps=10):
method _init_base_layout (line 78) | def _init_base_layout(self):
method _create_base_layout (line 95) | def _create_base_layout(self):
method _on_next_frame (line 124) | def _on_next_frame(self, event):
method _on_paint (line 140) | def _on_paint(self, event):
method _acquire_frame (line 151) | def _acquire_frame(self):
method _init_custom_layout (line 160) | def _init_custom_layout(self):
method _create_custom_layout (line 169) | def _create_custom_layout(self):
method _process_frame (line 181) | def _process_frame(self, frame_rgb):
FILE: chapter2/chapter2.py
class KinectLayout (line 24) | class KinectLayout(BaseLayout):
method _init_custom_layout (line 32) | def _init_custom_layout(self):
method _create_custom_layout (line 36) | def _create_custom_layout(self):
method _acquire_frame (line 40) | def _acquire_frame(self):
method _process_frame (line 49) | def _process_frame(self, frame):
function main (line 72) | def main():
FILE: chapter2/gestures.py
class HandGestureRecognition (line 13) | class HandGestureRecognition:
method __init__ (line 26) | def __init__(self):
method recognize (line 38) | def recognize(self, img_gray):
method _segment_arm (line 64) | def _segment_arm(self, frame):
method _find_hull_defects (line 104) | def _find_hull_defects(self, segment):
method _detect_num_fingers (line 128) | def _detect_num_fingers(self, contours, defects, img_draw):
function angle_rad (line 185) | def angle_rad(v1, v2):
function deg2rad (line 195) | def deg2rad(angle_deg):
FILE: chapter2/gui.py
class Meta1 (line 17) | class Meta1(wx.Frame):
class BaseLayout (line 22) | class BaseLayout(Meta1):
method __init__ (line 45) | def __init__(self, capture, title=None, parent=None, id=-1, fps=10):
method _init_base_layout (line 78) | def _init_base_layout(self):
method _create_base_layout (line 95) | def _create_base_layout(self):
method _on_next_frame (line 124) | def _on_next_frame(self, event):
method _on_paint (line 140) | def _on_paint(self, event):
method _acquire_frame (line 151) | def _acquire_frame(self):
method _init_custom_layout (line 160) | def _init_custom_layout(self):
method _create_custom_layout (line 169) | def _create_custom_layout(self):
method _process_frame (line 181) | def _process_frame(self, frame_rgb):
FILE: chapter3/chapter3.py
class FeatureMatchingLayout (line 19) | class FeatureMatchingLayout(BaseLayout):
method _init_custom_layout (line 27) | def _init_custom_layout(self):
method _create_custom_layout (line 31) | def _create_custom_layout(self):
method _process_frame (line 35) | def _process_frame(self, frame):
function main (line 45) | def main():
FILE: chapter3/feature_matching.py
class FeatureMatching (line 13) | class FeatureMatching:
method __init__ (line 30) | def __init__(self, train_image="salinger.jpg"):
method match (line 66) | def match(self, frame):
method _extract_features (line 165) | def _extract_features(self, frame):
method _match_features (line 173) | def _match_features(self, desc_frame):
method _detect_corner_points (line 195) | def _detect_corner_points(self, key_frame, good_matches):
method _warp_keypoints (line 224) | def _warp_keypoints(self, good_matches, key_frame, sh_frame):
function draw_good_matches (line 261) | def draw_good_matches(img1, kp1, img2, kp2, matches):
FILE: chapter3/gui.py
class Meta1 (line 17) | class Meta1(wx.Frame):
class BaseLayout (line 22) | class BaseLayout(Meta1):
method __init__ (line 45) | def __init__(self, capture, title=None, parent=None, id=-1, fps=10):
method _init_base_layout (line 78) | def _init_base_layout(self):
method _create_base_layout (line 95) | def _create_base_layout(self):
method _on_next_frame (line 124) | def _on_next_frame(self, event):
method _on_paint (line 140) | def _on_paint(self, event):
method _acquire_frame (line 151) | def _acquire_frame(self):
method _init_custom_layout (line 160) | def _init_custom_layout(self):
method _create_custom_layout (line 169) | def _create_custom_layout(self):
method _process_frame (line 181) | def _process_frame(self, frame_rgb):
FILE: chapter4/calibrate.py
class CameraCalibration (line 13) | class CameraCalibration(BaseLayout):
method _init_custom_layout (line 21) | def _init_custom_layout(self):
method _create_custom_layout (line 37) | def _create_custom_layout(self):
method _process_frame (line 49) | def _process_frame(self, frame):
method _on_button_calibrate (line 122) | def _on_button_calibrate(self, event):
method _reset_recording (line 128) | def _reset_recording(self):
function main (line 135) | def main():
FILE: chapter4/chapter4.py
function main (line 21) | def main():
FILE: chapter4/gui.py
class Meta1 (line 17) | class Meta1(wx.Frame):
class BaseLayout (line 22) | class BaseLayout(Meta1):
method __init__ (line 45) | def __init__(self, capture, title=None, parent=None, id=-1, fps=10):
method _init_base_layout (line 78) | def _init_base_layout(self):
method _create_base_layout (line 95) | def _create_base_layout(self):
method _on_next_frame (line 124) | def _on_next_frame(self, event):
method _on_paint (line 140) | def _on_paint(self, event):
method _acquire_frame (line 151) | def _acquire_frame(self):
method _init_custom_layout (line 160) | def _init_custom_layout(self):
method _create_custom_layout (line 169) | def _create_custom_layout(self):
method _process_frame (line 181) | def _process_frame(self, frame_rgb):
FILE: chapter4/scene3D.py
class SceneReconstruction3D (line 14) | class SceneReconstruction3D:
method __init__ (line 29) | def __init__(self, K, dist):
method load_image_pair (line 41) | def load_image_pair(self, img_path1, img_path2, use_pyr_down=True):
method plot_optic_flow (line 78) | def plot_optic_flow(self):
method draw_epipolar_lines (line 104) | def draw_epipolar_lines(self, feat_mode="SURF"):
method plot_rectified_images (line 137) | def plot_rectified_images(self, feat_mode="SURF"):
method plot_point_cloud (line 181) | def plot_point_cloud(self, feat_mode="SURF"):
method _extract_keypoints (line 218) | def _extract_keypoints(self, feat_mode):
method _extract_keypoints_surf (line 240) | def _extract_keypoints_surf(self):
method _extract_keypoints_flow (line 263) | def _extract_keypoints_flow(self):
method _find_fundamental_matrix (line 286) | def _find_fundamental_matrix(self):
method _find_essential_matrix (line 292) | def _find_essential_matrix(self):
method _find_camera_matrices_rt (line 296) | def _find_camera_matrices_rt(self):
method _draw_epipolar_lines_helper (line 342) | def _draw_epipolar_lines_helper(self, img1, img2, lines, pts1, pts2):
method _in_front_of_both_cameras (line 359) | def _in_front_of_both_cameras(self, first_points, second_points, rot,
method _linear_ls_triangulation (line 378) | def _linear_ls_triangulation(self, u1, P1, u2, P2):
FILE: chapter5/chapter5.py
function main (line 18) | def main(video_file='soccer.avi', roi=((140, 100), (500, 600))):
FILE: chapter5/saliency.py
class Saliency (line 17) | class Saliency:
method __init__ (line 23) | def __init__(self, img, use_numpy_fft=True, gauss_kernel=(5, 5)):
method get_saliency_map (line 45) | def get_saliency_map(self):
method _get_channel_sal_magn (line 85) | def _get_channel_sal_magn(self, channel):
method calc_magnitude_spectrum (line 130) | def calc_magnitude_spectrum(self):
method plot_power_spectrum (line 158) | def plot_power_spectrum(self):
method get_proto_objects_map (line 202) | def get_proto_objects_map(self, use_otsu=True):
FILE: chapter5/tracking.py
class MultipleObjectsTracker (line 11) | class MultipleObjectsTracker:
method __init__ (line 19) | def __init__(self, min_area=400, min_shift2=5):
method advance_frame (line 41) | def advance_frame(self, frame, proto_objects_map):
method _append_boxes_from_saliency (line 91) | def _append_boxes_from_saliency(self, proto_objects_map, box_all):
method _append_boxes_from_meanshift (line 117) | def _append_boxes_from_meanshift(self, frame, box_all):
method _update_mean_shift_bookkeeping (line 149) | def _update_mean_shift_bookkeeping(self, frame, box_grouped):
FILE: chapter6/chapter6.py
function main (line 22) | def main():
FILE: chapter6/classifiers.py
class Classifier (line 16) | class Classifier:
method fit (line 41) | def fit(self, X_train, y_train):
method evaluate (line 45) | def evaluate(self, X_test, y_test, visualize=False):
method _accuracy (line 48) | def _accuracy(self, y_test, Y_vote):
method _precision (line 66) | def _precision(self, y_test, Y_vote):
method _recall (line 108) | def _recall(self, y_test, Y_vote):
method _confusion (line 148) | def _confusion(self, y_test, Y_vote):
class MultiClassSVM (line 172) | class MultiClassSVM(Classifier):
method __init__ (line 194) | def __init__(self, num_classes, mode="one-vs-all", params=None):
method fit (line 230) | def fit(self, X_train, y_train, params=None):
method evaluate (line 270) | def evaluate(self, X_test, y_test, visualize=False):
FILE: chapter6/datasets/gtsrb.py
function load_data (line 22) | def load_data(rootpath="datasets/gtsrb_training", feature=None, cut_roi=...
function _extract_feature (line 104) | def _extract_feature(X, feature):
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Condensed preview — 19168 files, each showing path, character count, and a content snippet. Download the .json file for the full structured content (271,007K chars).
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// ... and 18968 more files (download for full content)
About this extraction
This page contains the full source code of the mbeyeler/opencv-python-blueprints GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 19168 files (106.1 MB), approximately 27.2M tokens, and a symbol index with 142 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.