[
  {
    "path": ".github/workflows/publish_on_release_update.yml",
    "content": "# This is a basic workflow to help you get started with Actions\n\nname: Publish distribution 📦 to PyPI 🐍 on new release\n\n# Controls when the action will run. \non:\n  # Triggers the workflow on push or pull request events but only for the master branch\n    release: \n      types: [created]\n    \n# A workflow run is made up of one or more jobs that can run sequentially or in parallel\njobs:\n  # This workflow contains a single job called \"build\"\n  build-n-publish:\n    name: Build and publish Python 🐍 distribution 📦 to PyPI\n    # The type of runner that the job will run on\n    runs-on: ubuntu-latest\n    strategy:\n      matrix:\n        python-version: [3.6]   #, 3.7, 3.8, 3.9] # These are redundant and cause errors on upload since it's a version-independent build\n\n    # Steps represent a sequence of tasks that will be executed as part of the job\n    steps:\n      # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it\n      - uses: actions/checkout@v2\n\n      - name: Set up Python ${{ matrix.python-version }}\n        uses: actions/setup-python@v4\n        with:\n          python-version: ${{ matrix.python-version }}\n       # You can test your matrix by printing the current Python version\n      - name: Display Python version\n        run: python -c \"import sys; print(sys.version)\"  \n\n      - name: Install dependencies\n        run: |\n          python -m pip install --upgrade pip\n          pip install flake8 pytest setuptools wheel twine\n# Don't think I actually need to install dependencies, and opencv would be quite large anyway\n#          if [ -f requirements.txt ]; then pip install -r requirements.txt; fi\n      - name: Lint with flake8\n        run: |\n          # stop the build if there are Python syntax errors or undefined names\n          flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics\n          # exit-zero treats all errors as warnings.\n          flake8 . --count --exit-zero --statistics\n          \n# Included for the day when tests are added to the project\n#       - name: Test with pytest\n#         run: |\n#           pytest\n\n      - name: Install pypa/build\n        run: >-\n          python -m\n          pip install\n          build\n          --user\n           \n      - name: Build a binary wheel and a source tarball\n        run: >-\n          python -m\n          build\n          --sdist\n          --wheel\n          --outdir dist/\n\n      - name: Publish distribution 📦 to PyPI\n        if: startsWith(github.ref, 'refs/tags')\n        uses: pypa/gh-action-pypi-publish@master\n        with:\n          password: ${{ secrets.PROD_PYPI_API_TOKEN }}\n"
  },
  {
    "path": ".github/workflows/publish_to_testpypi_on_setup_change.yml",
    "content": "# This is a basic workflow to help you get started with Actions\n\nname: Publish distribution 📦 to TestPyPI 🐍\n\n# Controls when the action will run. \non:\n  push:\n    branches:\n     - master \n    paths:\n      - 'setup.py'\n\n# A workflow run is made up of one or more jobs that can run sequentially or in parallel\njobs:\n  # This workflow contains a single job called \"build\"\n  build-n-publish:\n    name: Build and publish Python 🐍 distribution 📦 to TestPyPI\n    # The type of runner that the job will run on\n    runs-on: ubuntu-latest\n    strategy:\n      matrix:\n        python-version: [3.6]   #, 3.7, 3.8, 3.9] # These are redundant and cause errors on upload since it's a version-independent build\n\n    # Steps represent a sequence of tasks that will be executed as part of the job\n    steps:\n      # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it\n      - uses: actions/checkout@v2\n\n      - name: Set up Python ${{ matrix.python-version }}\n        uses: actions/setup-python@v4\n        with:\n          python-version: ${{ matrix.python-version }}\n       # You can test your matrix by printing the current Python version\n      - name: Display Python version\n        run: python -c \"import sys; print(sys.version)\"  \n\n      - name: Install dependencies\n        run: |\n          python -m pip install --upgrade pip\n          pip install flake8 pytest setuptools wheel twine\n# Don't think I actually need to install dependencies, and opencv would be quite large anyway\n#          if [ -f requirements.txt ]; then pip install -r requirements.txt; fi\n      - name: Lint with flake8\n        run: |\n          # stop the build if there are Python syntax errors or undefined names\n          flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics\n          # exit-zero treats all errors as warnings.\n          flake8 . --count --exit-zero --statistics\n          \n# Included for the day when tests are added to the project\n#       - name: Test with pytest\n#         run: |\n#           pytest\n\n      - name: Install pypa/build\n        run: >-\n          python -m\n          pip install\n          build\n          --user\n           \n      - name: Build a binary wheel and a source tarball\n        run: >-\n          python -m\n          build\n          --sdist\n          --wheel\n          --outdir dist/\n      \n      - name: Publish distribution 📦 to Test PyPI\n        uses: pypa/gh-action-pypi-publish@master\n        with:\n          password: ${{ secrets.TEST_PYPI_API_TOKEN }}\n          repository_url: https://test.pypi.org/legacy/\n      \n"
  },
  {
    "path": ".gitignore",
    "content": "# These are some examples of commonly ignored file patterns.\n# You should customize this list as applicable to your project.\n# Learn more about .gitignore:\n#     https://www.atlassian.com/git/tutorials/saving-changes/gitignore\n\n# Project specific\n/saved_snapshots/*\n!/saved_snapshots/note.txt\n*.png\n\n# Pycache\n*__pycache__\n\n# Node artifact files\nnode_modules/\ndist/\n\n# Compiled Java class files\n*.class\n\n# Compiled Python bytecode\n*.py[cod]\n\n# Log files\n*.log\n\n# Package files\n*.jar\n\n# Maven\ntarget/\ndist/\n\n# JetBrains IDE\n.idea/\n\n# Unit test reports\nTEST*.xml\n\n# Generated by MacOS\n.DS_Store\n\n# Generated by Windows\nThumbs.db\n\n# Applications\n*.app\n*.exe\n*.war\n\n# Large media files\n*.mp4\n*.tiff\n*.avi\n*.flv\n*.mov\n*.wmv\n\n.vscode/\nbuild/\npithermalcam.egg-info"
  },
  {
    "path": "LICENSE",
    "content": "                    GNU AFFERO GENERAL PUBLIC LICENSE\n                       Version 3, 19 November 2007\n\n Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>\n Everyone is permitted to copy and distribute verbatim copies\n of this license document, but changing it is not allowed.\n\n                            Preamble\n\n  The GNU Affero General Public License is a free, copyleft license for\nsoftware and other kinds of works, specifically designed to ensure\ncooperation with the community in the case of network server software.\n\n  The licenses for most software and other practical works are designed\nto take away your freedom to share and change the works.  By contrast,\nour General Public Licenses are intended to guarantee your freedom to\nshare and change all versions of a program--to make sure it remains free\nsoftware for all its users.\n\n  When we speak of free software, we are referring to freedom, not\nprice.  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But this requirement does not apply\nif neither you nor any third party retains the ability to install\nmodified object code on the User Product (for example, the work has\nbeen installed in ROM).\n\n  The requirement to provide Installation Information does not include a\nrequirement to continue to provide support service, warranty, or updates\nfor a work that has been modified or installed by the recipient, or for\nthe User Product in which it has been modified or installed.  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If additional permissions\napply only to part of the Program, that part may be used separately\nunder those permissions, but the entire Program remains governed by\nthis License without regard to the additional permissions.\n\n  When you convey a copy of a covered work, you may at your option\nremove any additional permissions from that copy, or from any part of\nit.  (Additional permissions may be written to require their own\nremoval in certain cases when you modify the work.)  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Termination.\n\n  You may not propagate or modify a covered work except as expressly\nprovided under this License.  Any attempt otherwise to propagate or\nmodify it is void, and will automatically terminate your rights under\nthis License (including any patent licenses granted under the third\nparagraph of section 11).\n\n  However, if you cease all violation of this License, then your\nlicense from a particular copyright holder is reinstated (a)\nprovisionally, unless and until the copyright holder explicitly and\nfinally terminates your license, and (b) permanently, if the copyright\nholder fails to notify you of the violation by some reasonable means\nprior to 60 days after the cessation.\n\n  Moreover, your license from a particular copyright holder is\nreinstated permanently if the copyright holder notifies you of the\nviolation by some reasonable means, this is the first time you have\nreceived notice of violation of this License (for any work) from that\ncopyright holder, and you cure the violation prior to 30 days after\nyour receipt of the notice.\n\n  Termination of your rights under this section does not terminate the\nlicenses of parties who have received copies or rights from you under\nthis License.  If your rights have been terminated and not permanently\nreinstated, you do not qualify to receive new licenses for the same\nmaterial under section 10.\n\n  9. Acceptance Not Required for Having Copies.\n\n  You are not required to accept this License in order to receive or\nrun a copy of the Program.  Ancillary propagation of a covered work\noccurring solely as a consequence of using peer-to-peer transmission\nto receive a copy likewise does not require acceptance.  However,\nnothing other than this License grants you permission to propagate or\nmodify any covered work.  These actions infringe copyright if you do\nnot accept this License.  Therefore, by modifying or propagating a\ncovered work, you indicate your acceptance of this License to do so.\n\n  10. 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You may not convey a covered\nwork if you are a party to an arrangement with a third party that is\nin the business of distributing software, under which you make payment\nto the third party based on the extent of your activity of conveying\nthe work, and under which the third party grants, to any of the\nparties who would receive the covered work from you, a discriminatory\npatent license (a) in connection with copies of the covered work\nconveyed by you (or copies made from those copies), or (b) primarily\nfor and in connection with specific products or compilations that\ncontain the covered work, unless you entered into that arrangement,\nor that patent license was granted, prior to 28 March 2007.\n\n  Nothing in this License shall be construed as excluding or limiting\nany implied license or other defenses to infringement that may\notherwise be available to you under applicable patent law.\n\n  12. 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The terms of this\nLicense will continue to apply to the part which is the covered work,\nbut the work with which it is combined will remain governed by version\n3 of the GNU General Public License.\n\n  14. Revised Versions of this License.\n\n  The Free Software Foundation may publish revised and/or new versions of\nthe GNU Affero General Public License from time to time.  Such new versions\nwill be similar in spirit to the present version, but may differ in detail to\naddress new problems or concerns.\n\n  Each version is given a distinguishing version number.  If the\nProgram specifies that a certain numbered version of the GNU Affero General\nPublic License \"or any later version\" applies to it, you have the\noption of following the terms and conditions either of that numbered\nversion or of any later version published by the Free Software\nFoundation.  If the Program does not specify a version number of the\nGNU Affero General Public License, you may choose any version ever published\nby the Free Software Foundation.\n\n  If the Program specifies that a proxy can decide which future\nversions of the GNU Affero General Public License can be used, that proxy's\npublic statement of acceptance of a version permanently authorizes you\nto choose that version for the Program.\n\n  Later license versions may give you additional or different\npermissions.  However, no additional obligations are imposed on any\nauthor or copyright holder as a result of your choosing to follow a\nlater version.\n\n  15. Disclaimer of Warranty.\n\n  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY\nAPPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT\nHOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM \"AS IS\" WITHOUT WARRANTY\nOF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,\nTHE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR\nPURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM\nIS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF\nALL NECESSARY SERVICING, REPAIR OR CORRECTION.\n\n  16. 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Interpretation of Sections 15 and 16.\n\n  If the disclaimer of warranty and limitation of liability provided\nabove cannot be given local legal effect according to their terms,\nreviewing courts shall apply local law that most closely approximates\nan absolute waiver of all civil liability in connection with the\nProgram, unless a warranty or assumption of liability accompanies a\ncopy of the Program in return for a fee.\n\n                     END OF TERMS AND CONDITIONS\n\n            How to Apply These Terms to Your New Programs\n\n  If you develop a new program, and you want it to be of the greatest\npossible use to the public, the best way to achieve this is to make it\nfree software which everyone can redistribute and change under these terms.\n\n  To do so, attach the following notices to the program.  It is safest\nto attach them to the start of each source file to most effectively\nstate the exclusion of warranty; and each file should have at least\nthe \"copyright\" line and a pointer to where the full notice is found.\n\n    <one line to give the program's name and a brief idea of what it does.>\n    Copyright (C) <year>  <name of author>\n\n    This program is free software: you can redistribute it and/or modify\n    it under the terms of the GNU Affero General Public License as published\n    by the Free Software Foundation, either version 3 of the License, or\n    (at your option) any later version.\n\n    This program is distributed in the hope that it will be useful,\n    but WITHOUT ANY WARRANTY; without even the implied warranty of\n    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n    GNU Affero General Public License for more details.\n\n    You should have received a copy of the GNU Affero General Public License\n    along with this program.  If not, see <https://www.gnu.org/licenses/>.\n\nAlso add information on how to contact you by electronic and paper mail.\n\n  If your software can interact with users remotely through a computer\nnetwork, you should also make sure that it provides a way for users to\nget its source.  For example, if your program is a web application, its\ninterface could display a \"Source\" link that leads users to an archive\nof the code.  There are many ways you could offer source, and different\nsolutions will be better for different programs; see section 13 for the\nspecific requirements.\n\n  You should also get your employer (if you work as a programmer) or school,\nif any, to sign a \"copyright disclaimer\" for the program, if necessary.\nFor more information on this, and how to apply and follow the GNU AGPL, see\n<https://www.gnu.org/licenses/>.\n"
  },
  {
    "path": "MANIFEST.in",
    "content": "recursive-include pithermalcam/templates *\r\nrecursive-exclude lib/pithermalcam/templates *\r\n*.ini"
  },
  {
    "path": "README.md",
    "content": "# README #\n\nDocumentation of the pithermalcam project and accompanying [PyPI package](https://pypi.org/project/pithermalcam/), which connects an MLX90640 thermal camera up to a Raspberry Pi. (Built on a Pi 4)\n\nSetup based primarily off the articles [by Joshua Hrisko at MakerPortal](https://makersportal.com/blog/2020/6/8/high-resolution-thermal-camera-with-raspberry-pi-and-mlx90640) and by [Валерий Курышев’s under the name Walker2000 at Habr](https://habr.com/en/post/441050/) and flask pieces based on the work of [Adrian Rosebrock at pyimagesearch](https://www.pyimagesearch.com/2019/09/02/opencv-stream-video-to-web-browser-html-page/). Many thanks these people for their great work.\n\nFull details for this project are available at https://tomshaffner.github.io/PiThermalCam/, including comprehensive hardware/software setup, install, usage instructions, and examples of potential results. A cursory overview for development purposes only is included here.\n\n### Manual Install/Setup ###\n\nThis section discusses software setup only, and assumes you have hardware set up, the MLX90640 correctly wired up, I2C turned on, and the I2C baudrate increased to 400k. Refer to the above full details link for detailed instructions on both the hardware and software installs.\n\nThe below install is for manual operation of the library. For the Pip install from PyPi skip the below and simply execute `pip3 install pithermalcam`.\n\n1. Install, using apt-get, the following items:\nlibatlas-base-dev\npython-smbus\ni2c-tools\n\n2. Install remaining requirements using either:\na. \n    pip3 install the requirements.txt\nor\nb. \n    pip3 install the requirements_without_opencv.txt\n\n    Download, build, and install OpenCV locally (painstaking process, but results in more optimized code.).\n\n    Install cmapy using --no-deps pip3 flag to avoid installing OpenCV via pip3.\n\n\n### Usage ###\n\n#### Pip Library Install ####\n\nIf you install the library via Pip you can follow the usage shown in the examples folder to see usage instructions.\n\n#### Clone library locally ####\nIf you wish to clone the library, execute this clone command:\n\n`git clone -b master --single-branch https://github.com/tomshaffner/PiThermalCam.git`\n\nThis clones the code without cloning the pictures for the accompanying article (which take up excessive space).\n\nTo operate from here:\n\n1. Copy the icons to your desktop and make executable.\n\nor\n\n2. Run the files directly in python3:\n\nRun pithermalcam/web_server.py to set up a flask server and stream live video over the local network.\n\nRun pithermalcam/pi_therm_cam.py to display the video feed onscreen.\n\nCheck sequential_versions folder for sequential running approaches that are easier to track/follow (i.e. sequential running rather than object-oriented classes). These are less robust, but can be easier to understand/track/edit, particularly for those coming from a scientific background. Again, refer to the link at top for a detailed discussion.\n"
  },
  {
    "path": "examples/cam_test.py",
    "content": "import pithermalcam as ptc\n\n# Run a camera test, getting the mean temperature back\nptc.test_camera()\n"
  },
  {
    "path": "examples/live_video.py",
    "content": "import pithermalcam as ptc\n\n# Run this file locally to display the video feed live on screen\nptc.display_camera_live()\n"
  },
  {
    "path": "examples/web_server.py",
    "content": "import pithermalcam as ptc\n\n# Run this file to start a flask server streaming the video to the local network\n# IP Address and port of the video feed will be displayed as the server is started\nptc.stream_camera_online()\n"
  },
  {
    "path": "icons/MatPlotLib Thermal Camera",
    "content": "[Desktop Entry]\nVersion=1.0\nName=MatPlotLib Thermal Camera\nIcon=applets-screenshooter\nPath=/home/pi/PiThermalCam/sequential_versions/\nExec=/usr/bin/python3 /home/pi/PiThermalCam/sequential_versions/matplotlib_therm_cam.py\nTerminal=true\nType=Application\nStartUpNotify=true\nName[en_US]=Matplotlib Thermal Camera\n"
  },
  {
    "path": "icons/Run Flask Thermal Camera",
    "content": "[Desktop Entry]\nVersion=1.0\nName=Flask Thermal Camera\nIcon=screensaver\nPath=/home/pi/PiThermalCam/\nExec=/usr/bin/python3 /home/pi/PiThermalCam/pithermcam/web_server.py\nTerminal=true\nType=Application\nStartUpNotify=true\nName[en_US]=Flask Thermal Camera\n"
  },
  {
    "path": "icons/Run OpenCV Thermal Camera",
    "content": "[Desktop Entry]\nVersion=1.0\nName=OpenCV Thermal Camera\nIcon=xfce4-ui\nPath=/home/pi/PiThermalCam/sequential_versions/\nExec=/usr/bin/python3 /home/pi/PiThermalCam/sequential_versions/opencv_therm_cam.py\nTerminal=true\nType=Application\nStartUpNotify=true\nName[en_US]=OpenCV Thermal Camera\n"
  },
  {
    "path": "pithermalcam/__init__.py",
    "content": "\n# Imports here to enable access to these functions from the library import without having to import those files.\n# Effectively using this init in the same manner as a C header file. If there's a more pythonic way to do this, it should change to that.\nfrom pithermalcam.pi_therm_cam import pithermalcam\nfrom pithermalcam import web_server\n\n\ndef test_camera():\n    \"\"\"Check for an average temperature value to ensure the camera is connected and working.\"\"\"\n    try:\n        thermcam = pithermalcam()  # Instantiate class\n        temp_c = None\n        temp_f = None\n        temp_c, temp_f = thermcam.get_mean_temp()\n\n        print(\"Camera seems to be connected and returning a value:\")\n        print('Average MLX90640 Temperature: {0:2.1f}C ({1:2.1f}F)'.format(temp_c,temp_f))\n        print('To verify it\\'s working, change the average temperature')\n        print('(e.g. by hold your hand over the camera) and run again to verify that the average temperature has changed.')\n    except ValueError as e:\n        if str(e) == 'No I2C device at address: 0x33':\n            print(\"ERROR: Camera not found. There seems to be no device connected to I2C address 0x33.\")\n    except Exception as e:\n        print(\"Camera didn't seem to work properly. Returned the following error:\")\n        raise(e)\n\n\ndef display_camera_live(output_folder:str = '/home/pi/pithermalcam/saved_snapshots/'):\n    \"\"\"Display the camera live onscreen\"\"\"\n    thermcam = pithermalcam(output_folder=output_folder)  # Instantiate class\n    thermcam.display_camera_onscreen()\n\n\ndef stream_camera_online(output_folder:str = '/home/pi/pithermalcam/saved_snapshots/'):\n    \"\"\"Start a flask server streaming the camera live\"\"\"\n    # This is a clunky way to do this, the better approach would likely to be restructuring web_server.py with the Flask Blueprint approach\n    # If the code were restructure for this, the code would be much more complex and opaque for running directly though\n    web_server.start_server(output_folder=output_folder)\n\n# Add attributes to existing pithermalcam object\nsetattr(pithermalcam, 'stream_camera_online', stream_camera_online)\n"
  },
  {
    "path": "pithermalcam/pi_therm_cam.py",
    "content": "# -*- coding: utf-8 -*-\n#!/usr/bin/python3\n##################################\n# MLX90640 Thermal Camera w Raspberry Pi\n##################################\nimport time,board,busio, traceback\nimport numpy as np\nimport adafruit_mlx90640\nimport datetime as dt\nimport cv2\nimport logging\nimport cmapy\nfrom scipy import ndimage\n\n# Set up logging\nlogging.basicConfig(filename='pithermcam.log',filemode='a',\n                    format='%(asctime)s %(levelname)-8s [%(filename)s:%(name)s:%(lineno)d] %(message)s',\n                    level=logging.WARNING,datefmt='%d-%b-%y %H:%M:%S')\nlogger = logging.getLogger(__name__)\n\n\nclass pithermalcam:\n    # See https://gitlab.com/cvejarano-oss/cmapy/-/blob/master/docs/colorize_all_examples.md to for options that can be put in this list\n    _colormap_list=['jet','bwr','seismic','coolwarm','PiYG_r','tab10','tab20','gnuplot2','brg']\n    _interpolation_list =[cv2.INTER_NEAREST,cv2.INTER_LINEAR,cv2.INTER_AREA,cv2.INTER_CUBIC,cv2.INTER_LANCZOS4,5,6]\n    _interpolation_list_name = ['Nearest','Inter Linear','Inter Area','Inter Cubic','Inter Lanczos4','Pure Scipy', 'Scipy/CV2 Mixed']\n    _current_frame_processed=False  # Tracks if the current processed image matches the current raw image\n    i2c=None\n    mlx=None\n    _temp_min=None\n    _temp_max=None\n    _raw_image=None\n    _image=None\n    _file_saved_notification_start=None\n    _displaying_onscreen=False\n    _exit_requested=False\n\n    def __init__(self,use_f:bool = True, filter_image:bool = False, image_width:int=1200, \n                image_height:int=900, output_folder:str = '/home/pi/pithermalcam/saved_snapshots/'):\n        self.use_f=use_f\n        self.filter_image=filter_image\n        self.image_width=image_width\n        self.image_height=image_height\n        self.output_folder=output_folder\n\n        self._colormap_index = 0\n        self._interpolation_index = 3\n        self._setup_therm_cam()\n        self._t0 = time.time()\n        self.update_image_frame()\n\n    def __del__(self):\n        logger.debug(\"ThermalCam Object deleted.\")\n\n    def _setup_therm_cam(self):\n        \"\"\"Initialize the thermal camera\"\"\"\n        # Setup camera\n        self.i2c = busio.I2C(board.SCL, board.SDA, frequency=800000)  # setup I2C\n        self.mlx = adafruit_mlx90640.MLX90640(self.i2c)  # begin MLX90640 with I2C comm\n        self.mlx.refresh_rate = adafruit_mlx90640.RefreshRate.REFRESH_8_HZ  # set refresh rate\n        time.sleep(0.1)\n\n    def _c_to_f(self,temp:float):\n        \"\"\" Convert temperature from C to F \"\"\"\n        return ((9.0/5.0)*temp+32.0)\n\n    def get_mean_temp(self):\n        \"\"\"\n        Get mean temp of entire field of view. Return both temp C and temp F.\n        \"\"\"\n        frame = np.zeros((24*32,))  # setup array for storing all 768 temperatures\n        while True:\n            try:\n                self.mlx.getFrame(frame)  # read MLX temperatures into frame var\n                break\n            except ValueError:\n                continue  # if error, just read again\n\n        temp_c = np.mean(frame)\n        temp_f=self._c_to_f(temp_c)\n        return temp_c, temp_f\n\n    def _pull_raw_image(self):\n        \"\"\"Get one pull of the raw image data, converting temp units if necessary\"\"\"\n        # Get image\n        self._raw_image = np.zeros((24*32,))\n        try:\n            self.mlx.getFrame(self._raw_image)  # read mlx90640\n            self._temp_min = np.min(self._raw_image)\n            self._temp_max = np.max(self._raw_image)\n            self._raw_image=self._temps_to_rescaled_uints(self._raw_image,self._temp_min,self._temp_max)\n            self._current_frame_processed=False  # Note that the newly updated raw frame has not been processed\n        except ValueError:\n            print(\"Math error; continuing...\")\n            self._raw_image = np.zeros((24*32,))  # If something went wrong, make sure the raw image has numbers\n            logger.info(traceback.format_exc())\n        except OSError:\n            print(\"IO Error; continuing...\")\n            self._raw_image = np.zeros((24*32,))  # If something went wrong, make sure the raw image has numbers\n            logger.info(traceback.format_exc())\n\n    def _process_raw_image(self):\n        \"\"\"Process the raw temp data to a colored image. Filter if necessary\"\"\"\n        # Image processing\n        # Can't apply colormap before ndimage, so reversed in first two options, even though it seems slower\n        if self._interpolation_index==5:  # Scale via scipy only - slowest but seems higher quality\n            self._image = ndimage.zoom(self._raw_image,25)  # interpolate with scipy\n            self._image = cv2.applyColorMap(self._image, cmapy.cmap(self._colormap_list[self._colormap_index]))\n        elif self._interpolation_index==6:  # Scale partially via scipy and partially via cv2 - mix of speed and quality\n            self._image = ndimage.zoom(self._raw_image,10)  # interpolate with scipy\n            self._image = cv2.applyColorMap(self._image, cmapy.cmap(self._colormap_list[self._colormap_index]))\n            self._image = cv2.resize(self._image, (800,600), interpolation=cv2.INTER_CUBIC)\n        else:\n            self._image = cv2.applyColorMap(self._raw_image, cmapy.cmap(self._colormap_list[self._colormap_index]))\n            self._image = cv2.resize(self._image, (800,600), interpolation=self._interpolation_list[self._interpolation_index])\n        self._image = cv2.flip(self._image, 1)\n        if self.filter_image:\n            self._image=cv2.bilateralFilter(self._image,15,80,80)\n\n    def _add_image_text(self):\n        \"\"\"Set image text content\"\"\"\n        if self.use_f:\n            temp_min=self._c_to_f(self._temp_min)\n            temp_max=self._c_to_f(self._temp_max)\n            text = f'Tmin={temp_min:+.1f}F - Tmax={temp_max:+.1f}F - FPS={1/(time.time() - self._t0):.1f} - Interpolation: {self._interpolation_list_name[self._interpolation_index]} - Colormap: {self._colormap_list[self._colormap_index]} - Filtered: {self.filter_image}'\n        else:\n            text = f'Tmin={self._temp_min:+.1f}C - Tmax={self._temp_max:+.1f}C - FPS={1/(time.time() - self._t0):.1f} - Interpolation: {self._interpolation_list_name[self._interpolation_index]} - Colormap: {self._colormap_list[self._colormap_index]} - Filtered: {self.filter_image}'\n        cv2.putText(self._image, text, (30, 18), cv2.FONT_HERSHEY_SIMPLEX, .4, (255, 255, 255), 1)\n        self._t0 = time.time()  # Update time to this pull\n\n        # For a brief period after saving, display saved notification\n        if self._file_saved_notification_start is not None and (time.monotonic()-self._file_saved_notification_start)<1:\n            cv2.putText(self._image, 'Snapshot Saved!', (300,300),cv2.FONT_HERSHEY_SIMPLEX, .8, (255, 255, 255), 2)\n\n    def add_customized_text(self,text):\n        \"\"\"Add custom text to the center of the image, used mostly to notify user that server is off.\"\"\"\n        cv2.putText(self._image, text, (300,300),cv2.FONT_HERSHEY_SIMPLEX, .8, (255, 255, 255), 2)\n        time.sleep(0.1)\n\n    def _show_processed_image(self):\n        \"\"\"Resize image window and display it\"\"\"\n        cv2.namedWindow('Thermal Image', cv2.WINDOW_NORMAL)\n        cv2.resizeWindow('Thermal Image', self.image_width,self.image_height)\n        cv2.imshow('Thermal Image', self._image)\n\n    def _set_click_keyboard_events(self):\n        \"\"\"Add click and keyboard actions to image\"\"\"\n        # Set mouse click events\n        cv2.setMouseCallback('Thermal Image',self._mouse_click)\n\n        # Set keyboard events\n        # if 's' is pressed - saving of picture\n        key = cv2.waitKey(1) & 0xFF\n        if key == ord(\"s\"):  # If s is chosen, save an image to filec\n            self.save_image()\n        elif key == ord(\"c\"):  # If c is chosen cycle the colormap used\n            self.change_colormap()\n        elif key == ord(\"x\"):  # If c is chosen cycle the colormap used\n            self.change_colormap(forward=False)\n        elif key == ord(\"f\"):  # If f is chosen cycle the image filtering\n            self.filter_image = not self.filter_image\n        elif key == ord(\"t\"):  # If t is chosen cycle the units used for Temperature\n            self.use_f = not self.use_f\n        elif key == ord(\"u\"):  # If t is chosen cycle the units used for temperature\n            self.change_interpolation()\n        elif key == ord(\"i\"):  # If i is chosen cycle interpolation algorithm\n            self.change_interpolation(forward=False)\n        elif key==27:  # Exit nicely if escape key is used\n            cv2.destroyAllWindows()\n            self._displaying_onscreen = False\n            print(\"Code Stopped by User\")\n            self._exit_requested=True\n\n    def _mouse_click(self,event,x,y,flags,param):\n        \"\"\"Used to save an image on double-click\"\"\"\n        global mouseX,mouseY\n        if event == cv2.EVENT_LBUTTONDBLCLK:\n            self.save_image()\n\n    def _print_shortcuts_keys(self):\n        \"\"\"Print out a summary of the shortcut keys available during video runtime.\"\"\"\n        print(\"The following keys are shortcuts for controlling the video during a run:\")\n        print(\"Esc - Exit and Close.\")\n        print(\"S - Save a Snapshot of the Current Frame\")\n        print(\"X - Cycle the Colormap Backwards\")\n        print(\"C - Cycle the Colormap forward\")\n        print(\"F - Toggle Filtering On/Off\")\n        print(\"T - Toggle Temperature Units between C/F\")\n        print(\"U - Go back to the previous Interpolation Algorithm\")\n        print(\"I - Change the Interpolation Algorithm Used\")\n        print(\"Double-click with Mouse - Save a Snapshot of the Current Frame\")\n\n    def display_next_frame_onscreen(self):\n        \"\"\"Display the camera live to the display\"\"\"\n        # Display shortcuts reminder to user on first run\n        if not self._displaying_onscreen:\n            self._print_shortcuts_keys()\n            self._displaying_onscreen = True\n        self.update_image_frame()\n        self._show_processed_image()\n        self._set_click_keyboard_events()\n\n    def change_colormap(self, forward:bool = True):\n        \"\"\"Cycle colormap. Forward by default, backwards if param set to false.\"\"\"\n        if forward:\n            self._colormap_index+=1\n            if self._colormap_index==len(self._colormap_list):\n                self._colormap_index=0\n        else:\n            self._colormap_index-=1\n            if self._colormap_index<0:\n                self._colormap_index=len(self._colormap_list)-1\n\n    def change_interpolation(self, forward:bool = True):\n        \"\"\"Cycle interpolation. Forward by default, backwards if param set to false.\"\"\"\n        if forward:\n            self._interpolation_index+=1\n            if self._interpolation_index==len(self._interpolation_list):\n                self._interpolation_index=0\n        else:\n            self._interpolation_index-=1\n            if self._interpolation_index<0:\n                self._interpolation_index=len(self._interpolation_list)-1\n\n    def update_image_frame(self):\n        \"\"\"Pull raw temperature data, process it to an image, and update image text\"\"\"\n        self._pull_raw_image()\n        self._process_raw_image()\n        self._add_image_text()\n        self._current_frame_processed=True\n        return self._image\n\n    def update_raw_image_only(self):\n        \"\"\"Update only raw data without any further image processing or text updating\"\"\"\n        self._pull_raw_image()\n\n    def get_current_raw_image_frame(self):\n        \"\"\"Return the current raw image\"\"\"\n        self._pull_raw_image()\n        return self._raw_image\n\n    def get_current_image_frame(self):\n        \"\"\"Get the processed image\"\"\"\n        # If the current raw image hasn't been procssed, process and return it\n        if not self._current_frame_processed:\n            self._process_raw_image()\n            self._add_image_text()\n            self._current_frame_processed=True\n        return self._image\n\n    def save_image(self):\n        \"\"\"Save the current frame as a snapshot to the output folder.\"\"\"\n        fname = self.output_folder + 'pic_' + dt.datetime.now().strftime('%Y-%m-%d_%H-%M-%S') + '.jpg'\n        cv2.imwrite(fname, self._image)\n        self._file_saved_notification_start = time.monotonic()\n        print('Thermal Image ', fname)\n\n    def _temps_to_rescaled_uints(self,f,Tmin,Tmax):\n        \"\"\"Function to convert temperatures to pixels on image\"\"\"\n        f=np.nan_to_num(f)\n        norm = np.uint8((f - Tmin)*255/(Tmax-Tmin))\n        norm.shape = (24,32)\n        return norm\n\n    def display_camera_onscreen(self):\n        # Loop to display frames unless/until user requests exit\n        while not self._exit_requested:\n            try:\n                self.display_next_frame_onscreen()\n            # Catch a common I2C Error. If you get this too often consider checking/adjusting your I2C Baudrate\n            except RuntimeError as e:\n                if e.message == 'Too many retries':\n                    print(\"Too many retries error caught, potential I2C baudrate issue: continuing...\")\n                    continue\n                raise\n\nif __name__ == \"__main__\":\n    # If class is run as main, read ini and set up a live feed displayed to screen\n    output_folder = '/home/pi/PiThermalCam/saved_snapshots/'\n\n    thermcam = pithermalcam(output_folder=output_folder)  # Instantiate class\n    thermcam.display_camera_onscreen()\n"
  },
  {
    "path": "pithermalcam/templates/index.html",
    "content": "<html>\n\n<head>\n  <title>Pi Thermal Video</title>\n  <script src=\"//ajax.googleapis.com/ajax/libs/jquery/1.9.1/jquery.min.js\"></script>\n  <script type=text/javascript>\n        $(function() {\n          $('a#save').on('click', function(e) {\n            e.preventDefault()\n            $.getJSON('/save',\n                function(data) {\n              //do nothing\n            });\n            return false;\n          });\n        });\n</script>\n  <script type=text/javascript>\n  $(function() {\n    $('a#units').on('click', function(e) {\n      e.preventDefault()\n      $.getJSON('/units',\n          function(data) {\n        //do nothing\n      });\n      return false;\n    });\n  });\n</script>\n  <script type=text/javascript>\n        $(function() {\n          $('a#colormap').on('click', function(e) {\n            e.preventDefault()\n            $.getJSON('/colormap',\n                function(data) {\n              //do nothing\n            });\n            return false;\n          });\n        });\n</script>\n  <script type=text/javascript>\n        $(function() {\n          $('a#colormapback').on('click', function(e) {\n            e.preventDefault()\n            $.getJSON('/colormapback',\n                function(data) {\n              //do nothing\n            });\n            return false;\n          });\n        });\n</script>\n  <script type=text/javascript>\n        $(function() {\n          $('a#filter').on('click', function(e) {\n            e.preventDefault()\n            $.getJSON('/filter',\n                function(data) {\n              //do nothing\n            });\n            return false;\n          });\n        });\n</script>\n  <script type=text/javascript>\n      $(function() {\n        $('a#interpolation').on('click', function(e) {\n          e.preventDefault()\n          $.getJSON('/interpolation',\n              function(data) {\n            //do nothing\n          });\n          return false;\n        });\n      });\n      </script>\n  <script type=text/javascript>\n      $(function() {\n        $('a#interpolationback').on('click', function(e) {\n          e.preventDefault()\n          $.getJSON('/interpolationback',\n              function(data) {\n            //do nothing\n          });\n          return false;\n        });\n      });\n</script>\n  <script type=text/javascript>\n  $(function() {\n    $('a#exit').on('click', function(e) {\n      e.preventDefault()\n      $.getJSON('/exit',\n          function(data) {\n        //do nothing\n      });\n      return false;\n    });\n  });\n</script>\n</head>\n\n<!-- Center align things and add margins -->\n<style>\n  h1 {\n    text-align: center;\n  }\n\n  .container {\n    max-width: 800px;\n    margin: auto;\n  }\n\n  .container2 {\n    max-width: 50px;\n    margin: auto;\n  }\n\n  .form {\n    max-width: 800px;\n    margin: auto;\n  }\n\n  .form-inline {\n    margin: 5px;\n    margin-left: -10px;\n    margin-right: -10px;\n  }\n\n  .btn {\n    margin-left: 3px;\n  }\n</style>\n\n<body>\n  <div class='container'>\n    <h1>Pi Thermal Video</h1>\n    <img src=\"{{ url_for('video_feed') }}\" class='video'>\n  </div>\n  <div class='container'>\n    <form class=\"form-inline\">\n      <a href=# id=save><button class='btn btn-default'>Save</button></a>\n      <a href=# id=units><button class='btn btn-default'>Change Temp Units</button></a>\n      <a href=# id=colormapback><button class='btn btn-default'>Previous Colormap</button></a>\n      <a href=# id=colormap><button class='btn btn-default'>Next Colormap</button></a>\n      <a href=# id=interpolationback><button class='btn btn-default'>Previous Interpolation</button></a>\n      <a href=# id=interpolation><button class='btn btn-default'>Next Interpolation</button></a>\n      <a href=# id=filter><button class='btn btn-default'>Toggle Filter</button></a>\n    </form>\n  </div>\n  &nbsp;\n  <div class='container2'><a href=# id=exit><button class='btn btn-default'>Exit</button></a></div>\n  &nbsp;\n\n</body>\n\n</html>"
  },
  {
    "path": "pithermalcam/web_server.py",
    "content": "# -*- coding: utf-8 -*-\n#!/usr/bin/python3\n##################################\n# Flask web server for MLX90640 Thermal Camera w Raspberry Pi\n# If running directly, run from root folder, not pithermalcam folder\n##################################\ntry:  # If called as an imported module\n\tfrom pithermalcam import pithermalcam\nexcept:  # If run directly\n\tfrom pi_therm_cam import pithermalcam\nfrom flask import Response, request\nfrom flask import Flask\nfrom flask import render_template\nimport threading\nimport time, socket, logging, traceback\nimport cv2\n\n# Set up Logger\nlogging.basicConfig(filename='pithermcam.log',filemode='a',\n\t\t\t\t\tformat='%(asctime)s %(levelname)-8s [%(filename)s:%(name)s:%(lineno)d] %(message)s',\n\t\t\t\t\tlevel=logging.WARNING,datefmt='%d-%b-%y %H:%M:%S')\nlogger = logging.getLogger(__name__)\n\n# initialize the output frame and a lock used to ensure thread-safe exchanges of the output frames (useful when multiple browsers/tabs are viewing the stream)\noutputFrame = None\nthermcam = None\nlock = threading.Lock()\n\n# initialize a flask object\napp = Flask(__name__)\n\n@app.route(\"/\")\ndef index():\n\t# return the rendered template\n\treturn render_template(\"index.html\")\n\n#background processes happen without any refreshing (for button clicks)\n@app.route('/save')\ndef save_image():\n\tthermcam.save_image()\n\treturn (\"Snapshot Saved\")\n\n@app.route('/units')\ndef change_units():\n\tthermcam.use_f = not thermcam.use_f\n\treturn (\"Units changed\")\n\n@app.route('/colormap')\ndef increment_colormap():\n\tthermcam.change_colormap()\n\treturn (\"Colormap changed\")\n\n@app.route('/colormapback')\ndef decrement_colormap():\n\tthermcam.change_colormap(forward=False)\n\treturn (\"Colormap changed back\")\n\n@app.route('/filter')\ndef toggle_filter():\n\tthermcam.filter_image=not thermcam.filter_image\n\treturn (\"Filtering Toggled\")\n\n@app.route('/interpolation')\ndef increment_interpolation():\n\tthermcam.change_interpolation()\n\treturn (\"Interpolation Changed\")\n\n@app.route('/interpolationback')\ndef decrement_interpolation():\n\tthermcam.change_interpolation(forward=False)\n\treturn (\"Interpolation Changed Back\")\n\n@app.route('/exit')\ndef appexit():\n\tglobal thermcam\n\tfunc = request.environ.get('werkzeug.server.shutdown')\n\tif func is None:\n\t\traise RuntimeError('Not running with the Werkzeug Server')\n\tfunc()\n\tthermcam = None\n\treturn 'Server shutting down...'\n\n@app.route(\"/video_feed\")\ndef video_feed():\n\t# return the response generated along with the specific media\n\t# type (mime type)\n\treturn Response(generate(), mimetype=\"multipart/x-mixed-replace; boundary=frame\")\n\ndef get_ip_address():\n\t\"\"\"Find the current IP address of the device\"\"\"\n\ts = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n\ts.connect((\"8.8.8.8\", 80))\n\tip_address=s.getsockname()[0]\n\ts.close()\n\treturn ip_address\n\ndef pull_images():\n\tglobal thermcam, outputFrame\n\t# loop over frames from the video stream\n\twhile thermcam is not None:\n\t\tcurrent_frame=None\n\t\ttry:\n\t\t\tcurrent_frame = thermcam.update_image_frame()\n\t\texcept Exception:\n\t\t\tprint(\"Too many retries error caught; continuing...\")\n\t\t\tlogger.info(traceback.format_exc())\n\n\t\t# If we have a frame, acquire the lock, set the output frame, and release the lock\n\t\tif current_frame is not None:\n\t\t\twith lock:\n\t\t\t\toutputFrame = current_frame.copy()\n\ndef generate():\n\t# grab global references to the output frame and lock variables\n\tglobal outputFrame, lock\n\t# loop over frames from the output stream\n\twhile True:\n\t\t# wait until the lock is acquired\n\t\twith lock:\n\t\t\t# check if the output frame is available, otherwise skip the iteration of the loop\n\t\t\tif outputFrame is None:\n\t\t\t\tcontinue\n\t\t\t# encode the frame in JPEG format\n\t\t\t(flag, encodedImage) = cv2.imencode(\".jpg\", outputFrame)\n\t\t\t# ensure the frame was successfully encoded\n\t\t\tif not flag:\n\t\t\t\tcontinue\n\t\t# yield the output frame in the byte format\n\t\tyield(b'--frame\\r\\n' b'Content-Type: image/jpeg\\r\\n\\r\\n' + bytearray(encodedImage) + b'\\r\\n')\n\ndef start_server(output_folder:str = '/home/pi/pithermalcam/saved_snapshots/'):\n\tglobal thermcam\n\t# initialize the video stream and allow the camera sensor to warmup\n\tthermcam = pithermalcam(output_folder=output_folder)\n\ttime.sleep(0.1)\n\n\t# start a thread that will perform motion detection\n\tt = threading.Thread(target=pull_images)\n\tt.daemon = True\n\tt.start()\n\n\tip=get_ip_address()\n\tport=8000\n\n\tprint(f'Server can be found at {ip}:{port}')\n\n\t# start the flask app\n\tapp.run(host=ip, port=port, debug=False,threaded=True, use_reloader=False)\n\n\n# If this is the main thread, simply start the server\nif __name__ == '__main__':\n\tstart_server()\n"
  },
  {
    "path": "requirements.txt",
    "content": "numpy>=1.16.5\nmatplotlib\nscipy>=1.6.0\nRPI.GPIO\nAdafruit-Blinka\nadafruit-circuitpython-mlx90640\nflask\nopencv-python\ncmapy\n"
  },
  {
    "path": "requirements_without_opencv.txt",
    "content": "numpy>=1.16.5\nmatplotlib\nscipy>=1.6.0\nRPI.GPIO\nAdafruit-Blinka\nadafruit-circuitpython-mlx90640\nflask\n"
  },
  {
    "path": "saved_snapshots/note.txt",
    "content": "This folder should be excluded from the git, apart from this file to keep it in existance."
  },
  {
    "path": "sequential_versions/Method Comparison.ipynb",
    "content": "{\n \"metadata\": {\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.3-final\"\n  },\n  \"orig_nbformat\": 2,\n  \"kernelspec\": {\n   \"name\": \"python3\",\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2,\n \"cells\": [\n  {\n   \"source\": [\n    \"# Matplotlib v. OpenCV Method Comparison\\n\",\n    \"\\n\",\n    \"This notebook is used to look at the details of the matplotlib and opencv methods in a comparative way. It's useful for debugging. It's based off the code in the examples folder.\\n\",\n    \"\\n\",\n    \"The first cell does the imports and performs a single image data pull from the query.\\n\",\n    \"\\n\",\n    \"The second cell performs the matplotlib code to transform, scale, and present this result.\\n\",\n    \"\\n\",\n    \"The third cell performs the same via the opencv approach.\\n\",\n    \"\\n\",\n    \"This enables the two to be compared easily on the same input data.\\n\",\n    \"\\n\",\n    \"Of note; in video mode the OpenCV method seems to run MUCH faster, but the Matplotlib color schemes are more useful (and are thus applied to the OpenCV version using the cmapy library).\"\n   ],\n   \"cell_type\": \"markdown\",\n   \"metadata\": {}\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import time,board,busio\\n\",\n    \"import numpy as np\\n\",\n    \"import adafruit_mlx90640\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from matplotlib import colors\\n\",\n    \"import logging, configparser\\n\",\n    \"from scipy import ndimage\\n\",\n    \"from util_functions import *\\n\",\n    \"import datetime as dt\\n\",\n    \"import cv2\\n\",\n    \"import cmapy\\n\",\n    \"\\n\",\n    \"# Setup camera\\n\",\n    \"i2c = busio.I2C(board.SCL, board.SDA, frequency=800000) # setup I2C\\n\",\n    \"mlx = adafruit_mlx90640.MLX90640(i2c) # begin MLX90640 with I2C comm\\n\",\n    \"mlx.refresh_rate = adafruit_mlx90640.RefreshRate.REFRESH_2_HZ  # set refresh rate\\n\",\n    \"frame = np.zeros(mlx_shape[0]*mlx_shape[1]) # 768 pts\\n\",\n    \"mlx.getFrame(frame) # read mlx90640\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"output_type\": \"display_data\",\n     \"data\": {\n      \"text/plain\": \"<Figure size 648x360 with 2 Axes>\",\n      \"image/svg+xml\": \"<?xml version=\\\"1.0\\\" 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\\n\"\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     }\n    }\n   ],\n   \"source\": [\n    \"mlx_shape = (24,32)\\n\",\n    \"\\n\",\n    \"# Set up figure\\n\",\n    \"fig = plt.figure(figsize=(9,5)) # start figure\\n\",\n    \"ax = fig.add_subplot(111) # add subplot\\n\",\n    \"fig.subplots_adjust(0.05,0.05,0.95,0.95) # get rid of unnecessary padding\\n\",\n    \"\\n\",\n    \"# Interpolate\\n\",\n    \"mlx_interp_val = 10 # interpolate # on each dimension\\n\",\n    \"mlx_interp_shape = (mlx_shape[0]*mlx_interp_val,\\n\",\n    \"                    mlx_shape[1]*mlx_interp_val) # new shape\\n\",\n    \"therm1 = ax.imshow(np.zeros(mlx_interp_shape),interpolation='none', cmap=plt.cm.bwr,vmin=25,vmax=45) # preemptive image\\n\",\n    \"\\n\",\n    \"# Add colorbar\\n\",\n    \"cbar = fig.colorbar(therm1) # setup colorbar\\n\",\n    \"cbar.set_label('Temperature [$^{\\\\circ}$C]',fontsize=14) # colorbar label\\n\",\n    \"\\n\",\n    \"# Raw Figure\\n\",\n    \"fig.canvas.draw() # draw figure to copy background\\n\",\n    \"fig.show() # show the figure before blitting\\n\",\n    \"\\n\",\n    \"# fig.canvas.restore_region(ax_background) # restore background\\n\",\n    \"data_array = np.fliplr(np.reshape(frame,mlx_shape)) # reshape, flip data\\n\",\n    \"data_array = ndimage.zoom(data_array,mlx_interp_val) # interpolate\\n\",\n    \"\\n\",\n    \"therm1.set_array(data_array) # set data\\n\",\n    \"therm1.set_clim(vmin=np.min(data_array),vmax=np.max(data_array)) # set bounds\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"output_type\": \"execute_result\",\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.image.AxesImage at 0x9a73e6b0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"execution_count\": 9\n    },\n    {\n     \"output_type\": \"display_data\",\n     \"data\": {\n      \"text/plain\": \"<Figure size 648x360 with 1 Axes>\",\n  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\\n\"\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     }\n    }\n   ],\n   \"source\": [\n    \"colormap_list=['jet','bwr','seismic','coolwarm','PiYG_r','tab10','tab20','gnuplot2','brg']\\n\",\n    \"colormap=colormap_list[1] # Use index to pick colormap from the above line's list.\\n\",\n    \"\\n\",\n    \"# function to convert temperatures to pixels on image\\n\",\n    \"def temps_to_rescaled_uints(f,Tmin,Tmax):\\n\",\n    \"    norm = np.uint8((f - Tmin)*255/(Tmax-Tmin))\\n\",\n    \"    norm.shape = (24,32)\\n\",\n    \"    return norm\\n\",\n    \"\\n\",\n    \"image=frame\\n\",\n    \"temp_min = np.min(image)\\n\",\n    \"temp_max = np.max(image)\\n\",\n    \"img=temps_to_rescaled_uints(image,temp_min,temp_max)     \\n\",\n    \"\\n\",\n    \"# Image processing\\n\",\n    \"img = cv2.applyColorMap(img, cmapy.cmap(colormap))\\n\",\n    \"img = cv2.resize(img, (800,600), interpolation = cv2.INTER_CUBIC) #INTER_LANCZOS4) #INTER_LINEAR)\\n\",\n    \"img = cv2.flip(img, 1)\\n\",\n    \"text = f'Tmin={temp_min:+.1f}C Tmax={temp_max:+.1f}C Colormap: {colormap}'\\n\",\n    \"cv2.putText(img, text, (10, 18), cv2.FONT_HERSHEY_SIMPLEX, .6, (255, 255, 255), 2)\\n\",\n    \"# cv2.namedWindow('Thermal Image', cv2.WINDOW_NORMAL)\\n\",\n    \"# cv2.resizeWindow('Thermal Image', 1200,900)\\n\",\n    \"# cv2.imshow('Thermal Image', img)\\n\",\n    \"\\n\",\n    \"# Note: The above lines had to be replaced with the below to show this in Jupyter.\\n\",\n    \"# Note: CV2 uses bgr color formatting, so to display it accurately here via matplotlib it must be converted to rgb\\n\",\n    \"# When run normally using the above cv2 lines the output will look the same as the below does\\n\",\n    \"img=cv2.cvtColor(img, cv2.COLOR_BGR2RGB) \\n\",\n    \"fig = plt.figure(figsize=(9,5))\\n\",\n    \"ax = fig.add_subplot(111) # add subplot\\n\",\n    \"ax.imshow(img)\"\n   ]\n  }\n ]\n}"
  },
  {
    "path": "sequential_versions/matplotlib_therm_cam.py",
    "content": "# -*- coding: utf-8 -*-\n#!/usr/bin/python3\n##################################\n# MLX90640 Thermal Camera w Raspberry Pi\n##################################\n\nimport time, board, busio\nimport numpy as np\nimport adafruit_mlx90640\nimport matplotlib.pyplot as plt\nimport logging, configparser\nfrom scipy import ndimage\n\nprofiling = False  # Flag to turn profiling on\nif profiling:\n    import cProfile, pstats\n\n# Manual Params\nDEBUG_MODE = False\n\n# Set up Logger\nif DEBUG_MODE:\n    logging.basicConfig(format='%(asctime)s %(levelname)-8s [%(filename)s:%(name)s:%(lineno)d] %(message)s',level=logging.DEBUG)\n    logging.getLogger('matplotlib.font_manager').disabled = True  # Disable warnings from matplotlib font manager when in debug mode\nelse:\n    logging.basicConfig(filename='pithermcam.log',filemode='a',\n                        format='%(asctime)s %(levelname)-8s [%(filename)s:%(name)s:%(lineno)d] %(message)s',\n                        level=logging.WARNING,datefmt='%d-%b-%y %H:%M:%S')\nlogger = logging.getLogger(__name__)\n\n# Parse Config file\nconfig = configparser.ConfigParser(inline_comment_prefixes='#')\nconfig.read('sequential_config.ini')\nlogger.debug(f'Config file sections found: {config.sections()}')\n\n# Read Global variables from config file\n# Note: Raw parsing used to avoid having to escape % characters in time strings\nlogger.debug(\"Reading config file and initializing variables...\")\noutput_folder = config.get(section='FILEPATHS',option='output_folder',raw=True)\n\n# Setup camera\ni2c = busio.I2C(board.SCL, board.SDA, frequency=800000)  # setup I2C\nmlx = adafruit_mlx90640.MLX90640(i2c)  # begin MLX90640 with I2C comm\n\n\ndef c_to_f(temp:float):\n    \"\"\" Convert temperature from C to F \"\"\"\n    return ((9.0/5.0)*temp+32.0)\n\n\n# print out the average temperature from the MLX90640\ndef print_mean_temp():\n    \"\"\"\n    Get mean temp of entire field of view. Return both temp C and temp F.\n    \"\"\"\n    frame = np.zeros((24*32,))  # setup array for storing all 768 temperatures\n    while True:\n        try:\n            mlx.getFrame(frame)  # read MLX temperatures into frame var\n            break\n        except ValueError:\n            continue  # if error, just read again\n\n    temp_c = np.mean(frame)\n    temp_f=c_to_f(temp_c)\n    print('Average MLX90640 Temperature: {0:2.1f}C ({1:2.1f}F)'.format(temp_c,temp_f))\n    return temp_c, temp_f\n\n\ndef simple_pic():\n    \"\"\"# -- 2Hz Sampling with Simple Routine\"\"\"\n    mlx.refresh_rate = adafruit_mlx90640.RefreshRate.REFRESH_2_HZ  # set refresh rate\n    mlx_shape = (24,32)\n\n    # setup the figure for plotting\n    # plt.ion() # enables interactive plotting\n    fig,ax = plt.subplots(figsize=(12,7))\n    therm1 = ax.imshow(np.zeros(mlx_shape),vmin=0,vmax=60)  # start plot with zeros\n    cbar = fig.colorbar(therm1)  # setup colorbar for temps\n    cbar.set_label('Temperature [$^{\\circ}$C]',fontsize=14)  # colorbar label\n\n    frame = np.zeros((24*32,))  # setup array for storing all 768 temperatures\n    mlx.getFrame(frame)  # read MLX temperatures into frame var\n    data_array = (np.reshape(frame,mlx_shape))  # reshape to 24x32\n    therm1.set_data(np.fliplr(data_array))  # flip left to right\n    therm1.set_clim(vmin=np.min(data_array),vmax=np.max(data_array))  # set bounds\n    cbar.on_mappable_changed(therm1)  # update colorbar range\n    plt.pause(0.001)  # required\n    fig.savefig(output_folder + 'simple_pic.png',dpi=300,facecolor='#FCFCFC', bbox_inches='tight')\n\n\ndef simple_camera_read():\n    \"\"\"# -- Sampling with Simple Routine\"\"\"\n    mlx.refresh_rate = adafruit_mlx90640.RefreshRate.REFRESH_8_HZ  # set refresh rate\n    mlx_shape = (24,32)\n\n    # setup the figure for plotting\n    plt.ion()  # enables interactive plotting\n    fig,ax = plt.subplots(figsize=(12,7))\n    therm1 = ax.imshow(np.zeros(mlx_shape),vmin=0,vmax=60)  # start plot with zeros\n    cbar = fig.colorbar(therm1)  # setup colorbar for temps\n    cbar.set_label('Temperature [$^{\\circ}$C]',fontsize=14)  # colorbar label\n\n    frame = np.zeros((24*32,))  # setup array for storing all 768 temperatures\n    t_array = []\n    while True:\n        t1 = time.monotonic()\n        try:\n            mlx.getFrame(frame)  # read MLX temperatures into frame var\n            data_array = (np.reshape(frame,mlx_shape))  # reshape to 24x32\n            therm1.set_data(np.fliplr(data_array))  # flip left to right\n            therm1.set_clim(vmin=np.min(data_array),vmax=np.max(data_array))  # set bounds\n            cbar.on_mappable_changed(therm1)  # update colorbar range\n            plt.pause(0.001)  # required\n            # fig.savefig(output_folder + 'mlx90640_test_fliplr.png',dpi=300,facecolor='#FCFCFC', bbox_inches='tight') # comment out to speed up\n            t_array.append(time.monotonic()-t1)\n            print('Sample Rate: {0:2.1f}fps'.format(len(t_array)/np.sum(t_array)))\n        except ValueError:\n            continue  # if error, just read again\n\n\ndef interpolated_pic():\n    \"\"\"# -- 2fps with Interpolation and Blitting\"\"\"\n    mlx.refresh_rate = adafruit_mlx90640.RefreshRate.REFRESH_4_HZ  # set refresh rate\n\n    mlx_shape = (24,32)\n\n    mlx_interp_val = 10  # interpolate # on each dimension\n    mlx_interp_shape = (mlx_shape[0]*mlx_interp_val,\n                        mlx_shape[1]*mlx_interp_val)  # new shape\n\n    logger.debug(\"Setting up plot\")\n    fig = plt.figure(figsize=(12,9))  # start figure\n    ax = fig.add_subplot(111)  # add subplot\n    fig.subplots_adjust(0.05,0.05,0.95,0.95)  # get rid of unnecessary padding\n    therm1 = ax.imshow(np.zeros(mlx_interp_shape),interpolation='none', cmap=plt.cm.bwr,vmin=25,vmax=45)  # preemptive image\n    cbar = fig.colorbar(therm1)  # setup colorbar\n    cbar.set_label('Temperature [$^{\\circ}$C]',fontsize=14)  # colorbar label\n\n    fig.canvas.draw()  # draw figure to copy background\n    ax_background = fig.canvas.copy_from_bbox(ax.bbox)  # copy background\n    fig.show()  # show the figure before blitting\n\n    frame = np.zeros(mlx_shape[0]*mlx_shape[1])  # 768 pts\n\n    def plot_update():\n        logger.debug(\"Updating plot\")\n        fig.canvas.restore_region(ax_background)  # restore background\n        mlx.getFrame(frame)  # read mlx90640\n        data_array = np.fliplr(np.reshape(frame,mlx_shape))  # reshape, flip data\n        data_array = ndimage.zoom(data_array,mlx_interp_val)  # interpolate\n        therm1.set_array(data_array)  # set data\n        therm1.set_clim(vmin=np.min(data_array),vmax=np.max(data_array))  # set bounds\n        cbar.on_mappable_changed(therm1)  # update colorbar range\n\n        ax.draw_artist(therm1)  # draw new thermal image\n        fig.canvas.blit(ax.bbox)  # draw background\n        fig.canvas.flush_events()  # show the new image\n        return\n\n    plot_update()\n    fig.savefig(output_folder + 'interp_pic.png',dpi=300,facecolor='#FCFCFC', bbox_inches='tight')\n\n\ndef interpolated_camera_read():\n    \"\"\"# -- Higher fps with Interpolation and Blitting\"\"\"\n    colorbar_update_interval=5  # Seconds\n    mlx.refresh_rate = adafruit_mlx90640.RefreshRate.REFRESH_8_HZ  # set refresh rate\n\n    if profiling:\n        pr=cProfile.Profile()\n        pr.enable()\n\n    mlx_shape = (24,32)\n\n    mlx_interp_val = 10  # interpolate # on each dimension\n    mlx_interp_shape = (mlx_shape[0]*mlx_interp_val,\n                        mlx_shape[1]*mlx_interp_val)  # new shape\n\n    fig = plt.figure(figsize=(9,5))  # start figure\n    ax = fig.add_subplot(111)  # add subplot\n    fig.subplots_adjust(0.05,0.05,0.95,0.95)  # get rid of unnecessary padding\n    therm1 = ax.imshow(np.zeros(mlx_interp_shape),interpolation='none', cmap=plt.cm.bwr,vmin=25,vmax=45)  # preemptive image\n    cbar = fig.colorbar(therm1)  # setup colorbar\n    cbar.set_label('Temperature [$^{\\circ}$C]',fontsize=14)  # colorbar label\n\n    fig.canvas.draw()  # draw figure to copy background\n    ax_background = fig.canvas.copy_from_bbox(ax.bbox)  # copy background\n    fig.show()  # show the figure before blitting\n\n    frame = np.zeros(mlx_shape[0]*mlx_shape[1])  # 768 pts\n\n    def plot_update(full_update: bool):\n        fig.canvas.restore_region(ax_background)  # restore background\n        mlx.getFrame(frame)  # read mlx90640\n        data_array = np.fliplr(np.reshape(frame,mlx_shape))  # reshape, flip data\n        data_array = ndimage.zoom(data_array,mlx_interp_val)  # interpolate\n        if full_update:\n            therm1.set_array(data_array)  # set data\n            therm1.set_clim(vmin=np.min(data_array),vmax=np.max(data_array))  # set bounds\n            cbar.on_mappable_changed(therm1)  # update colorbar range\n\n        ax.draw_artist(therm1)  # draw new thermal image\n        fig.canvas.blit(ax.bbox)  # draw background\n        fig.canvas.flush_events()  # show the new image\n        return\n\n    t_array = []\n    last_update=-100\n    count=0\n    while True:\n        t1 = time.monotonic()  # for determining frame rate\n        try:\n            if t1-last_update>colorbar_update_interval:\n                last_update=t1\n                full_update=True\n            else:\n                full_update=False\n            plot_update(full_update)  # update plot\n        except:\n            continue\n        # approximating frame rate\n        t_array.append(time.monotonic()-t1)\n        if len(t_array)>10:\n            t_array = t_array[1:]  # recent times for frame rate approx\n        if full_update:\n            print('Frame Rate: {0:2.1f}fps'.format(len(t_array)/np.sum(t_array)))\n            count+=1\n            if profiling & (count>=20):\n                logger.info(\"Printing and dumping profiling stats\")\n                pr.disable()\n                pr.dump_stats(output_folder+'profiling_stats.prof')\n                ps = pstats.Stats(output_folder+'profiling_stats.prof')\n                ps.sort_stats(pstats.SortKey.CUMULATIVE)\n                ps.print_stats(50)\n                break\n\nif __name__ == \"__main__\":\n    # Pick the mode to run in. Mode corresponds to functions in elif below.\n    mode=1\n\n    if mode==1:\n        print_mean_temp()\n    elif mode==2:\n        simple_pic()\n    elif mode==3:\n        simple_camera_read()\n    elif mode==4:\n        interpolated_pic()\n    elif mode==5:\n        interpolated_camera_read()\n    else:\n        print(\"Incorrect or missing mode.\")\n"
  },
  {
    "path": "sequential_versions/opencv_therm_cam.py",
    "content": "# -*- coding: utf-8 -*-\n#!/usr/bin/python3\n##################################\n# MLX90640 Thermal Camera w Raspberry Pi\n##################################\nimport time,board,busio\nimport numpy as np\nimport adafruit_mlx90640\nimport datetime as dt\nimport cv2\nimport logging, configparser\nimport cmapy\nimport traceback\nfrom scipy import ndimage\n\n# Manual Params\nDEBUG_MODE=False\n\n# Set up Logger\nif DEBUG_MODE:\n    logging.basicConfig(format='%(asctime)s %(levelname)-8s [%(filename)s:%(name)s:%(lineno)d] %(message)s',level=logging.DEBUG)\n    logging.getLogger('matplotlib.font_manager').disabled = True  # Disable warnings from matplotlib font manager when in debug mode\nelse:\n    logging.basicConfig(filename='pithermcam.log',filemode='a',\n                        format='%(asctime)s %(levelname)-8s [%(filename)s:%(name)s:%(lineno)d] %(message)s',\n                        level=logging.WARNING,datefmt='%d-%b-%y %H:%M:%S')\nlogger = logging.getLogger(__name__)\n\n# Parse Config file\nconfig = configparser.ConfigParser(inline_comment_prefixes='#')\nconfig.read('sequential_config.ini')\nlogger.debug(f'Config file sections found: {config.sections()}')\n\n# Read Global variables from config file\n# Note: Raw parsing used to avoid having to escape % characters in time strings\nlogger.debug(\"Reading config file and initializing variables...\")\noutput_folder = config.get(section='FILEPATHS',option='output_folder',raw=True)\n\n# Setup camera\ni2c = busio.I2C(board.SCL, board.SDA, frequency=800000)  # setup I2C\nmlx = adafruit_mlx90640.MLX90640(i2c)  # begin MLX90640 with I2C comm\nmlx.refresh_rate = adafruit_mlx90640.RefreshRate.REFRESH_8_HZ  # set refresh rate\ntime.sleep(0.1)\n\n\ndef c_to_f(temp:float):\n    \"\"\" Convert temperature from C to F \"\"\"\n    return ((9.0/5.0)*temp+32.0)\n\n\n# function to convert temperatures to pixels on image\ndef temps_to_rescaled_uints(f,Tmin,Tmax):\n    norm = np.uint8((f - Tmin)*255/(Tmax-Tmin))\n    norm.shape = (24,32)\n    return norm\n\n\ndef take_pic(use_f=True):\n    \"\"\"Take single pic\"\"\"\n    image = np.zeros((24*32,))\n\n    # Get image\n    mlx.getFrame(image)  # read mlx90640\n    temp_min = np.min(image)\n    temp_max = np.max(image)\n    image=temps_to_rescaled_uints(image,temp_min,temp_max)\n\n    # Image processing\n    img = cv2.applyColorMap(image, cmapy.cmap('bwr'))\n    img = cv2.resize(img, (320,240), interpolation=cv2.INTER_CUBIC)\n    img = cv2.flip(img, 1)\n\n    if use_f:\n        temp_min=c_to_f(temp_min)\n        temp_max=c_to_f(temp_max)\n        text = f'Tmin={temp_min:+.1f}F Tmax={temp_max:+.1f}F'\n    else:\n        text = f'Tmin={temp_min:+.1f}C Tmax={temp_max:+.1f}C'\n\n    text = 'Tmin = {:+.1f} Tmax = {:+.1f} '.format(temp_min/100, temp_max/100)\n    cv2.putText(img, text, (5, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 0), 1)\n    cv2.imshow('Output', img)\n\n    fname = output_folder + 'pic_' + dt.datetime.now().strftime('%Y-%m-%d_%H-%M-%S') + '.jpg'\n    cv2.imwrite(fname, img)\n    print('Saving image ', fname)\n\n\ndef save_snapshot(event,x,y,flags,param):\n    \"\"\"Used to save an image on double-click\"\"\"\n    img=param[0]\n    global mouseX,mouseY\n    if event == cv2.EVENT_LBUTTONDBLCLK:\n        fname = output_folder + 'pic_' + dt.datetime.now().strftime('%Y-%m-%d_%H-%M-%S') + '.jpg'\n        cv2.imwrite(fname, img)\n        print('Thermal Image ', fname)\n\n\ndef camera_read(use_f:bool = True, filter_image:bool = False):\n    image = np.zeros((24*32,))\n    t0 = time.time()\n    colormap_index = 0\n    interpolation_index = 3\n    file_saved_notification_start = None\n    # See https://gitlab.com/cvejarano-oss/cmapy/-/blob/master/docs/colorize_all_examples.md to develop list\n    colormap_list=['jet','bwr','seismic','coolwarm','PiYG_r','tab10','tab20','gnuplot2','brg']\n    interpolation_list =[cv2.INTER_NEAREST,cv2.INTER_LINEAR,cv2.INTER_AREA,cv2.INTER_CUBIC,cv2.INTER_LANCZOS4,5,6]\n    interpolation_list_name = ['Nearest','Inter Linear','Inter Area','Inter Cubic','Inter Lanczos4','Scipy', 'Scipy/CV2 Mixed']\n\n    print_shortcuts_keys()\n\n    try:\n        while True:\n            # Get image\n            try:\n                mlx.getFrame(image)  # read mlx90640\n            except RuntimeError as e:\n                if e.message == 'Too many retries':\n                    print(\"Too many retries error caught, potential I2C baudrate issue: continuing...\")\n                    logger.info(traceback.format_exc())\n                    continue\n                raise\n            temp_min = np.min(image)\n            temp_max = np.max(image)\n            img=temps_to_rescaled_uints(image,temp_min,temp_max)\n\n            # Image processing\n            # Can't apply colormap before ndimage, so reversed in first two options, even though it seems slower\n            if interpolation_index==5:  # Scale via scipy only - slowest but seems higher quality\n                img = ndimage.zoom(img,25)  # interpolate with scipy\n                img = cv2.applyColorMap(img, cmapy.cmap(colormap_list[colormap_index]))\n            elif interpolation_index==6:  # Scale partially via scipy and partially via cv2 - mix of speed and quality\n                img = ndimage.zoom(img,10)  # interpolate with scipy\n                img = cv2.applyColorMap(img, cmapy.cmap(colormap_list[colormap_index]))\n                img = cv2.resize(img, (800,600), interpolation=cv2.INTER_CUBIC)\n            else:\n                img = cv2.applyColorMap(img, cmapy.cmap(colormap_list[colormap_index]))\n                img = cv2.resize(img, (800,600), interpolation=interpolation_list[interpolation_index])\n            img = cv2.flip(img, 1)\n            if filter_image:\n                img=cv2.bilateralFilter(img,15,80,80)\n\n                # Alternative filtering possibilities tested and currently removed as inferior to the above\n                # img=cv2.fastNlMeansDenoisingColored(img,None,10,10,7,21)\n                # img = cv2.erode(img, None, iterations=2)\n                # img = cv2.dilate(img, None, iterations=2)\n            if use_f:\n                temp_min=c_to_f(temp_min)\n                temp_max=c_to_f(temp_max)\n                text = f'Tmin={temp_min:+.1f}F - Tmax={temp_max:+.1f}F - FPS={1/(time.time() - t0):.1f} - Interpolation: {interpolation_list_name[interpolation_index]} - Colormap: {colormap_list[colormap_index]} - Filtered: {filter_image}'\n            else:\n                text = f'Tmin={temp_min:+.1f}C - Tmax={temp_max:+.1f}C - FPS={1/(time.time() - t0):.1f} - Interpolation: {interpolation_list_name[interpolation_index]} - Colormap: {colormap_list[colormap_index]} - Filtered: {filter_image}'\n            cv2.putText(img, text, (30, 18), cv2.FONT_HERSHEY_SIMPLEX, .4, (255, 255, 255), 1)\n\n            # For a brief period after saving, display saved notification\n            if file_saved_notification_start is not None and (time.monotonic()-file_saved_notification_start)<1:\n                cv2.putText(img, 'Snapshot Saved!', (300,300),cv2.FONT_HERSHEY_SIMPLEX, .8, (255, 255, 255), 2)\n\n            # Display image on screen\n            cv2.namedWindow('Thermal Image', cv2.WINDOW_NORMAL)\n            cv2.resizeWindow('Thermal Image', 1200,900)\n            cv2.imshow('Thermal Image', img)\n\n            # Set mouse click events\n            cv2.setMouseCallback('Thermal Image',save_snapshot,param=[img])\n\n            # if 's' is pressed - saving of picture\n            key = cv2.waitKey(1) & 0xFF\n            if key == ord(\"s\"):  # If s is chosen, save an image to filec\n                fname = output_folder + 'pic_' + dt.datetime.now().strftime('%Y-%m-%d_%H-%M-%S') + '.jpg'\n                cv2.imwrite(fname, img)\n                file_saved_notification_start = time.monotonic()\n                print('Thermal Image ', fname)\n            elif key == ord(\"c\"):  # If c is chosen cycle the colormap used\n                colormap_index+=1\n                if colormap_index==len(colormap_list):\n                    colormap_index=0\n            elif key == ord(\"x\"):  # If c is chosen cycle the colormap used back\n                colormap_index-=1\n                if colormap_index<0:\n                    colormap_index=len(colormap_list)-1\n            elif key == ord(\"f\"):  # If f is chosen cycle the image filtering\n                filter_image = not filter_image\n                print(f\"Filter On: {filter_image}\")\n            elif key == ord(\"t\"):  # If t is chosen cycle the units used for Temperature\n                use_f = not use_f\n                print(f\"Using Fahrenheit: {use_f}\")\n            elif key == ord(\"u\"):  # If u is chosen cycle interpolation algorithm back\n                interpolation_index-=1\n                if interpolation_index<0:\n                    interpolation_index=len(interpolation_list)-1\n            elif key == ord(\"i\"):  # If i is chosen cycle interpolation algorithm\n                interpolation_index+=1\n                if interpolation_index==len(interpolation_list):\n                    interpolation_index=0\n            elif key==27:  # Break if escape key is used\n                raise KeyboardInterrupt\n\n            t0 = time.time()\n    except KeyboardInterrupt:\n        cv2.destroyAllWindows()\n        print(\"Code Stopped by User\")\n    except Exception:\n        logger.info(traceback.format_exc())\n        pass\n\n    cv2.destroyAllWindows()\n\n\ndef print_shortcuts_keys():\n    \"\"\"Print out a summary of the shortcut keys available during video runtime.\"\"\"\n    print(\"The following keys are shortcuts for controlling the video during a run:\")\n    print(\"Esc - Exit and Close.\")\n    print(\"S - Save a Snapshot of the Current Frame\")\n    print(\"X - Cycle the Colormap Backwards\")\n    print(\"C - Cycle the Colormap forward\")\n    print(\"F - Toggle Filtering On/Off\")\n    print(\"T - Toggle Temperature Units between C/F\")\n    print(\"U - Go back to the previous Interpolation Algorithm\")\n    print(\"I - Change the Interpolation Algorithm Used\")\n\nif __name__ == \"__main__\":\n    # Pick the mode to run in. Mode corresponds to functions in elif below.\n    mode=2\n\n    if mode==1:\n        take_pic()\n    elif mode==2:\n        camera_read()\n    else:\n        print(\"Incorrect or missing mode.\")\n"
  },
  {
    "path": "sequential_versions/sequential_config.ini",
    "content": "# Note: Inline comment prefixes must be enabled in config parser for this file to work\n[FILEPATHS]\noutput_folder = /home/pi/PiThermalCam/saved_snapshots/"
  },
  {
    "path": "setup.cfg",
    "content": "[flake8]\nignore = D203, E265, E401, E231, E226, E225, E252, E305, W191, W605\nmax-line-length = 160\nexclude =\n    # No need to traverse these directories\n    .git,.github.vscode\n    # There's no value in checking cache directories\n    __pycache__,\n    # The conf file is mostly autogenerated, ignore it\n    docs/source/conf.py,\n    # This contains our built documentation\n    build,\n    # This contains builds of flake8 that we don't want to check\n    dist\nmax-complexity = 10\nper-file-ignores = pithermalcam/web_server.py:E302"
  },
  {
    "path": "setup.py",
    "content": "import setuptools\n\nwith open(\"README.md\", \"r\", encoding=\"utf-8\") as fh:\n    long_description = fh.read()\n\nsetuptools.setup(\n    name=\"pithermalcam\",\n    version=\"1.0.0\",\n    author=\"Tom Shaffner\",\n    description=\"A package which connects an MLX90640 Thermal IR Camera to a Raspberry Pi for viewing or web streaming.\",\n    long_description=long_description,\n    long_description_content_type=\"text/markdown\",\n    url=\"https://tomshaffner.github.io/PiThermalCam/\",\n    packages=['pithermalcam'],\n    install_requires=[\n        'numpy>=2.1.1',\n        'matplotlib',\n        'scipy>=1.6.0',\n        'RPI.GPIO',\n        'Adafruit-Blinka',\n        'adafruit-circuitpython-mlx90640',\n        'flask',\n        'opencv-python',\n        'cmapy',\n    ],\n    classifiers=[\n        # Full list: https://pypi.org/classifiers/ or https://pypi.python.org/pypi?%3Aaction=list_classifiers\n        \"Programming Language :: Python\",\n        \"Programming Language :: Python :: 3\",\n        \"License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)\",\n        \"Operating System :: POSIX :: Linux\",\n    ],\n    keywords=\"raspberry pi mlx90640 thermal camera ir flir\",\n    python_requires='>=3.6',\n    setup_requires=[\n        \"flake8\"\n    ],\n    include_package_data=True,\n    # What does your project relate to?\n)\n"
  }
]