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Repository: wkentaro/labelme
Branch: main
Commit: 89a615e5d777
Files: 162
Total size: 1.1 MB
Directory structure:
gitextract_ghzsofy7/
├── .claude/
│ └── CLAUDE.md
├── .git-blame-ignore-revs
├── .github/
│ ├── ISSUE_TEMPLATE/
│ │ ├── 1.bug_report.yml
│ │ └── config.yml
│ └── workflows/
│ ├── ci.yml
│ ├── cla.yml
│ └── discord-notify.yml
├── .gitignore
├── .gitmodules
├── .python-version
├── CITATION.cff
├── CLA.md
├── LICENSE
├── Makefile
├── README.md
├── examples/
│ ├── bbox_detection/
│ │ ├── README.md
│ │ ├── data_annotated/
│ │ │ ├── 2011_000003.json
│ │ │ ├── 2011_000006.json
│ │ │ └── 2011_000025.json
│ │ ├── data_dataset_voc/
│ │ │ ├── Annotations/
│ │ │ │ ├── 2011_000003.xml
│ │ │ │ ├── 2011_000006.xml
│ │ │ │ └── 2011_000025.xml
│ │ │ └── class_names.txt
│ │ ├── labelme2voc.py
│ │ └── labels.txt
│ ├── classification/
│ │ ├── README.md
│ │ ├── data_annotated/
│ │ │ ├── 0001.json
│ │ │ └── 0002.json
│ │ └── flags.txt
│ ├── instance_segmentation/
│ │ ├── README.md
│ │ ├── data_annotated/
│ │ │ ├── 2011_000003.json
│ │ │ ├── 2011_000006.json
│ │ │ └── 2011_000025.json
│ │ ├── data_dataset_coco/
│ │ │ └── annotations.json
│ │ ├── data_dataset_voc/
│ │ │ ├── SegmentationClassNpy/
│ │ │ │ ├── 2011_000003.npy
│ │ │ │ ├── 2011_000006.npy
│ │ │ │ └── 2011_000025.npy
│ │ │ ├── SegmentationObjectNpy/
│ │ │ │ ├── 2011_000003.npy
│ │ │ │ ├── 2011_000006.npy
│ │ │ │ └── 2011_000025.npy
│ │ │ └── class_names.txt
│ │ ├── labelme2coco.py
│ │ ├── labelme2voc.py
│ │ └── labels.txt
│ ├── primitives/
│ │ └── primitives.json
│ ├── semantic_segmentation/
│ │ ├── README.md
│ │ ├── data_annotated/
│ │ │ ├── 2011_000003.json
│ │ │ ├── 2011_000006.json
│ │ │ └── 2011_000025.json
│ │ ├── data_dataset_voc/
│ │ │ ├── SegmentationClassNpy/
│ │ │ │ ├── 2011_000003.npy
│ │ │ │ ├── 2011_000006.npy
│ │ │ │ └── 2011_000025.npy
│ │ │ └── class_names.txt
│ │ └── labels.txt
│ ├── tutorial/
│ │ ├── README.md
│ │ ├── apc2016_obj3/
│ │ │ └── label_names.txt
│ │ ├── apc2016_obj3.json
│ │ ├── draw_json.py
│ │ ├── draw_label_png.py
│ │ ├── export_json.py
│ │ └── load_label_png.py
│ └── video_annotation/
│ ├── README.md
│ ├── data_annotated/
│ │ ├── 00000100.json
│ │ ├── 00000101.json
│ │ ├── 00000102.json
│ │ ├── 00000103.json
│ │ └── 00000104.json
│ ├── data_dataset_voc/
│ │ ├── SegmentationClass/
│ │ │ ├── 00000100.npy
│ │ │ ├── 00000101.npy
│ │ │ ├── 00000102.npy
│ │ │ ├── 00000103.npy
│ │ │ └── 00000104.npy
│ │ └── class_names.txt
│ └── labels.txt
├── labelme/
│ ├── __init__.py
│ ├── __main__.py
│ ├── _automation/
│ │ ├── __init__.py
│ │ ├── _osam_session.py
│ │ ├── bbox_from_text.py
│ │ └── polygon_from_mask.py
│ ├── _label_file.py
│ ├── app.py
│ ├── config/
│ │ ├── __init__.py
│ │ └── default_config.yaml
│ ├── shape.py
│ ├── testing.py
│ ├── translate/
│ │ ├── de_DE.qm
│ │ ├── de_DE.ts
│ │ ├── es_ES.qm
│ │ ├── es_ES.ts
│ │ ├── fa_IR.qm
│ │ ├── fa_IR.ts
│ │ ├── fr_FR.qm
│ │ ├── fr_FR.ts
│ │ ├── hu_HU.qm
│ │ ├── hu_HU.ts
│ │ ├── it_IT.qm
│ │ ├── it_IT.ts
│ │ ├── ja_JP.qm
│ │ ├── ja_JP.ts
│ │ ├── ko_KR.qm
│ │ ├── ko_KR.ts
│ │ ├── nl_NL.qm
│ │ ├── nl_NL.ts
│ │ ├── pl_PL.qm
│ │ ├── pl_PL.ts
│ │ ├── pt_BR.qm
│ │ ├── pt_BR.ts
│ │ ├── th_TH.qm
│ │ ├── th_TH.ts
│ │ ├── tr_TR.qm
│ │ ├── tr_TR.ts
│ │ ├── vi_VN.qm
│ │ ├── vi_VN.ts
│ │ ├── zh_CN.qm
│ │ ├── zh_CN.ts
│ │ ├── zh_TW.qm
│ │ └── zh_TW.ts
│ ├── utils/
│ │ ├── __init__.py
│ │ ├── _io.py
│ │ ├── image.py
│ │ ├── qt.py
│ │ └── shape.py
│ └── widgets/
│ ├── __init__.py
│ ├── _ai_assisted_annotation_widget.py
│ ├── _ai_text_to_annotation_widget.py
│ ├── _info_button.py
│ ├── _status.py
│ ├── brightness_contrast_dialog.py
│ ├── canvas.py
│ ├── download.py
│ ├── file_dialog_preview.py
│ ├── label_dialog.py
│ ├── label_list_widget.py
│ ├── tool_bar.py
│ ├── unique_label_qlist_widget.py
│ └── zoom_widget.py
├── pyproject.toml
├── tests/
│ ├── conftest.py
│ ├── e2e/
│ │ ├── __init__.py
│ │ ├── annotation_test.py
│ │ ├── config_test.py
│ │ ├── conftest.py
│ │ ├── file_loading_test.py
│ │ ├── navigation_test.py
│ │ └── smoke_test.py
│ └── unit/
│ ├── __init__.py
│ ├── _label_file_test.py
│ ├── config_test.py
│ ├── load_image_file_test.py
│ ├── shape_contains_point_test.py
│ ├── shape_test.py
│ ├── utils/
│ │ ├── __init__.py
│ │ ├── image_test.py
│ │ ├── qt_test.py
│ │ ├── shape_test.py
│ │ └── util.py
│ └── widgets/
│ ├── __init__.py
│ ├── canvas_test.py
│ ├── label_dialog_test.py
│ └── label_list_widget_test.py
└── tools/
└── update_translate.py
================================================
FILE CONTENTS
================================================
================================================
FILE: .claude/CLAUDE.md
================================================
# Project Rules
- use `uv` to run Python commands (e.g., `uv run python`, `uv run pytest`).
- use `make test` to run tests.
- use `make update_translate` to update translation files (`.ts` and `.qm`).
================================================
FILE: .git-blame-ignore-revs
================================================
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# Mypy check # type: ignore
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================================================
FILE: .github/ISSUE_TEMPLATE/1.bug_report.yml
================================================
name: Bug Report
description: Create a bug report
labels: 'bug'
body:
- type: markdown
attributes:
value: Thanks for taking the time to file a bug report! Please fill out this form as completely as possible.
- type: markdown
attributes:
value: If you leave out sections there is a high likelihood it will be moved to the GitHub Discussions ["Q&A / Help" section](https://github.com/wkentaro/labelme/discussions/categories/q-a-help).
- type: textarea
attributes:
label: Provide environment information
description: Please run `which python; python --version; python -m pip list | grep labelme` in the root directory of your project and paste the results.
validations:
required: true
- type: input
attributes:
label: What OS are you using?
description: 'Please specify the exact version. For example: macOS 12.4, Ubuntu 20.04.4'
validations:
required: true
- type: textarea
attributes:
label: Describe the Bug
description: A clear and concise description of what the bug is.
validations:
required: true
- type: textarea
attributes:
label: Expected Behavior
description: A clear and concise description of what you expected to happen.
- type: textarea
attributes:
label: To Reproduce
description: Steps to reproduce the behavior, please provide a clear description of how to reproduce the issue, based on the linked minimal reproduction. Screenshots can be provided in the issue body below. If using code blocks, make sure that [syntax highlighting is correct](https://docs.github.com/en/get-started/writing-on-github/working-with-advanced-formatting/creating-and-highlighting-code-blocks#syntax-highlighting) and double check that the rendered preview is not broken.
- type: markdown
attributes:
value: Before posting the issue go through the steps you've written down to make sure the steps provided are detailed and clear.
- type: markdown
attributes:
value: Contributors should be able to follow the steps provided in order to reproduce the bug.
- type: markdown
attributes:
value: These steps are used to add integration tests to ensure the same issue does not happen again. Thanks in advance!
================================================
FILE: .github/ISSUE_TEMPLATE/config.yml
================================================
contact_links:
- name: Ideas / Feature request
url: https://github.com/wkentaro/labelme/discussions/categories/ideas-feature-requests
about: Share ideas for new features
- name: Q&A / Help
url: https://github.com/wkentaro/labelme/discussions/categories/q-a-help
about: Ask the community for help
- name: Show and tell
url: https://github.com/wkentaro/labelme/discussions/categories/show-and-tell
about: Show off something you've made
================================================
FILE: .github/workflows/ci.yml
================================================
name: ci
on:
push:
branches:
- main
pull_request:
jobs:
check:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: astral-sh/setup-uv@v5
with:
python-version: "3.10"
- uses: awalsh128/cache-apt-pkgs-action@v1
with:
packages: qttools5-dev
version: 1.0
- run: make setup
- run: |
make check
build:
runs-on: ${{ matrix.os }}
strategy:
matrix:
os: [windows-latest, macos-latest, ubuntu-latest]
steps:
- uses: actions/checkout@v4
- uses: astral-sh/setup-uv@v5
with:
python-version: "3.10"
- shell: bash
run: |
make setup
- uses: awalsh128/cache-apt-pkgs-action@v1
if: matrix.os == 'ubuntu-latest'
with:
packages: xvfb libqt5widgets5
version: 1.0
- name: Test
shell: bash
if: matrix.os == 'ubuntu-latest'
env:
MPLBACKEND: 'agg'
run: |
Xvfb :99 -screen 0 1024x768x24 > /dev/null 2>&1 &
export DISPLAY=:99
make test
- name: Run examples
shell: bash
if: matrix.os != 'windows-latest'
env:
MPLBACKEND: agg
run: |
labelme --help
labelme --version
(cd examples/primitives && ../tutorial/export_json.py primitives.json && rm -rf primitives)
(cd examples/tutorial && rm -rf apc2016_obj3_json && ./export_json.py apc2016_obj3.json && python load_label_png.py && git checkout -- .)
(cd examples/semantic_segmentation && rm -rf data_dataset_voc && ./labelme2voc.py data_annotated data_dataset_voc --labels labels.txt && git checkout -- .)
(cd examples/instance_segmentation && rm -rf data_dataset_voc && ./labelme2voc.py data_annotated data_dataset_voc --labels labels.txt && git checkout -- .)
(cd examples/video_annotation && rm -rf data_dataset_voc && ./labelme2voc.py data_annotated data_dataset_voc --labels labels.txt && git checkout -- .)
uv pip install 'lxml<5.0.0' # for bbox_detection/labelme2voc.py
(cd examples/bbox_detection && rm -rf data_dataset_voc && ./labelme2voc.py data_annotated data_dataset_voc --labels labels.txt && git checkout -- .)
uv pip install cython && uv pip install pycocotools # for instance_segmentation/labelme2coco.py
(cd examples/instance_segmentation && rm -rf data_dataset_coco && ./labelme2coco.py data_annotated data_dataset_coco --labels labels.txt && git checkout -- .)
- name: Build wheel and install from it
shell: bash
run: |
uv build
uv pip install dist/labelme-*.whl
================================================
FILE: .github/workflows/cla.yml
================================================
name: CLA Assistant
on:
pull_request_target:
types: [opened, synchronize]
issue_comment:
types: [created]
jobs:
cla:
runs-on: ubuntu-latest
steps:
- name: CLA Assistant
uses: contributor-assistant/github-action@v2.6.1
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
PERSONAL_ACCESS_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN }}
with:
path-to-signatures: "signatures/version1/cla.json"
path-to-document: "https://github.com/wkentaro/labelme/blob/main/CLA.md"
branch: "cla-signatures"
allowlist: wkentaro,bot*
================================================
FILE: .github/workflows/discord-notify.yml
================================================
name: discord-notify
on:
pull_request:
types: [closed]
branches: [main]
jobs:
notify:
if: |
github.event.pull_request.merged == true &&
contains(github.event.pull_request.labels.*.name, 'feature')
runs-on: ubuntu-latest
steps:
- name: Send to Discord
env:
DISCORD_WEBHOOK: ${{ secrets.DISCORD_WEBHOOK }}
PR_TITLE: ${{ github.event.pull_request.title }}
PR_BODY: ${{ github.event.pull_request.body }}
run: |
if [[ -z "$DISCORD_WEBHOOK" ]]; then
echo "Error: DISCORD_WEBHOOK secret not configured"
exit 1
fi
TYPE="🎉 **New feature merged!** Try it in the next release"
COLOR=3066993
MILESTONE="${{ github.event.pull_request.milestone.title }}"
if [[ -z "$MILESTONE" ]]; then
MILESTONE="None"
fi
TITLE_ESCAPED=$(echo "$PR_TITLE" | jq -Rs '.[:-1]')
BODY_ESCAPED=$(echo "$PR_BODY" | jq -Rs '.[:-1] | if . == "" then "No description provided" elif length > 2000 then .[0:1997] + "..." else . end')
curl -fsS -X POST "$DISCORD_WEBHOOK" \
-H "Content-Type: application/json" \
-d "{
\"content\": \"$TYPE\",
\"embeds\": [{
\"title\": $TITLE_ESCAPED,
\"url\": \"${{ github.event.pull_request.html_url }}\",
\"color\": $COLOR,
\"fields\": [
{
\"name\": \"Pull request\",
\"value\": \"[#${{ github.event.pull_request.number }}](${{ github.event.pull_request.html_url }})\",
\"inline\": true
},
{
\"name\": \"Milestone\",
\"value\": \"$MILESTONE\",
\"inline\": true
},
{
\"name\": \"💬 Feedback\",
\"value\": \"[Share your thoughts](https://github.com/wkentaro/labelme/discussions)\",
\"inline\": true
}
],
\"footer\": {
\"text\": $BODY_ESCAPED
}
}]
}"
================================================
FILE: .gitignore
================================================
/.cache/
/.pytest_cache/
/build/
/dist/
/*.egg-info/
*.py[cdo]
.DS_Store
.idea/
================================================
FILE: .gitmodules
================================================
================================================
FILE: .python-version
================================================
3.10
================================================
FILE: CITATION.cff
================================================
cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Wada"
given-names: "Kentaro"
orcid: "https://orcid.org/0000-0002-6347-5156"
title: "Labelme: Image Polygonal Annotation with Python"
doi: 10.5281/zenodo.5711226
url: "https://github.com/wkentaro/labelme"
license: GPL-3
================================================
FILE: CLA.md
================================================
# Contributor License Agreement (CLA)
*Version 1.0 — March 2026*
By submitting a pull request or otherwise contributing to this repository,
you agree to the following terms:
1. **License Grant**: You grant Kentaro Wada a perpetual, worldwide,
non-exclusive, irrevocable, royalty-free license to reproduce, prepare
derivative works of, publicly display, publicly perform, sublicense,
and distribute your contribution under any license terms Kentaro Wada
chooses, including terms different from those of this project's
open-source license.
2. **Representations**: You represent that (a) you are legally entitled
to grant the above license, and (b) your contribution is your original
work or you have the right to submit it under these terms.
**How to sign:** When you open a pull request, the CLA Assistant bot will
ask you to sign by commenting `I have read the CLA Document and I hereby
sign the CLA`. Signing is required before your PR can be merged.
================================================
FILE: LICENSE
================================================
GNU GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
Preamble
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The licenses for most software and other practical works are designed
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================================================
FILE: Makefile
================================================
ifneq ($(OS),Windows_NT)
# On Unix-based systems, use ANSI codes
BLUE = \033[36m
BOLD_BLUE = \033[1;36m
BOLD_GREEN = \033[1;32m
RED = \033[31m
YELLOW = \033[33m
BOLD = \033[1m
NC = \033[0m
endif
escape = $(subst $$,\$$,$(subst ",\",$(subst ',\',$(1))))
define exec
@echo "$(BOLD_BLUE)$(call escape,$(1))$(NC)"
@$(1)
endef
help:
@echo "$(BOLD_GREEN)Available targets:$(NC)"
@grep -E '^[a-zA-Z_-].+:.*?# .*$$' $(MAKEFILE_LIST) | \
awk 'BEGIN {FS = ":.*?# "}; \
{printf " $(BOLD_BLUE)%-20s$(NC) %s\n", $$1, $$2}'
PACKAGE_NAME:=labelme
setup: # Setup the development environment
$(call exec,uv sync)
format: # Format code
$(call exec,uv run ruff format)
$(call exec,uv run ruff check --fix)
lint:
$(call exec,uv run ruff format --check)
$(call exec,uv run ruff check)
$(call exec,uv run ty check --no-progress)
check_translate: update_translate
$(call exec,git diff --exit-code labelme/translate)
@if grep -r 'type="unfinished"' labelme/translate/*.ts; then \
echo "$(RED)Error: unfinished translations found$(NC)"; \
exit 1; \
fi
check: lint check_translate # Run checks
test: # Run tests
$(call exec,uv run pytest -v tests/)
update_translate:
$(call exec,uv run --no-sync tools/update_translate.py)
================================================
FILE: README.md
================================================
<h1 align="center">
<img src="labelme/icons/icon-256.png" width="200" height="200"><br/>labelme
</h1>
<h4 align="center">
Image annotation with Python.
</h4>
<div align="center">
<a href="https://pypi.python.org/pypi/labelme"><img src="https://img.shields.io/pypi/v/labelme.svg"></a>
<!-- <a href="https://pypi.org/project/labelme"><img src="https://img.shields.io/pypi/pyversions/labelme.svg"></a> -->
<a href="https://github.com/wkentaro/labelme/actions"><img src="https://github.com/wkentaro/labelme/actions/workflows/ci.yml/badge.svg?branch=main&event=push"></a>
<a href="https://discord.com/invite/uAjxGcJm83"><img src="https://dcbadge.limes.pink/api/server/uAjxGcJm83?style=flat"></a>
</div>
<div align="center">
<a href="#installation"><b>Installation</b></a>
| <a href="#usage"><b>Usage</b></a>
| <a href="#examples"><b>Examples</b></a>
| <a href="https://labelme.io"><b>labelme.io ↗</b></a>
<!-- | <a href="https://github.com/wkentaro/labelme/discussions"><b>Community</b></a> -->
<!-- | <a href="https://www.youtube.com/playlist?list=PLI6LvFw0iflh3o33YYnVIfOpaO0hc5Dzw"><b>Youtube FAQ</b></a> -->
</div>
<br/>
<div align="center">
<img src="examples/instance_segmentation/.readme/annotation.jpg" width="70%">
</div>
## Description
Labelme is a graphical image annotation tool inspired by <http://labelme.csail.mit.edu>.
It is written in Python and uses Qt for its graphical interface.
> Looking for a simple install without Python or Qt? Get the standalone app at **[labelme.io](https://labelme.io)**.
<img src="examples/instance_segmentation/data_dataset_voc/JPEGImages/2011_000006.jpg" width="19%" /> <img src="examples/instance_segmentation/data_dataset_voc/SegmentationClass/2011_000006.png" width="19%" /> <img src="examples/instance_segmentation/data_dataset_voc/SegmentationClassVisualization/2011_000006.jpg" width="19%" /> <img src="examples/instance_segmentation/data_dataset_voc/SegmentationObject/2011_000006.png" width="19%" /> <img src="examples/instance_segmentation/data_dataset_voc/SegmentationObjectVisualization/2011_000006.jpg" width="19%" />
<i>VOC dataset example of instance segmentation.</i>
<img src="examples/semantic_segmentation/.readme/annotation.jpg" width="30%" /> <img src="examples/bbox_detection/.readme/annotation.jpg" width="30%" /> <img src="examples/classification/.readme/annotation_cat.jpg" width="35%" />
<i>Other examples (semantic segmentation, bbox detection, and classification).</i>
<img src="https://user-images.githubusercontent.com/4310419/47907116-85667800-de82-11e8-83d0-b9f4eb33268f.gif" width="30%" /> <img src="https://user-images.githubusercontent.com/4310419/47922172-57972880-deae-11e8-84f8-e4324a7c856a.gif" width="30%" /> <img src="https://user-images.githubusercontent.com/14256482/46932075-92145f00-d080-11e8-8d09-2162070ae57c.png" width="32%" />
<i>Various primitives (polygon, rectangle, circle, line, and point).</i>
<img src="https://github.com/user-attachments/assets/53bf09db-b097-48b7-9f32-ab490da5ac53" width="32%" />
<p><i>Multi-language support (English, 中文, 日本語, 한국어, Deutsch, Français, and more).</i></p>
## Features
- [x] Image annotation for polygon, rectangle, circle, line and point ([tutorial](examples/tutorial))
- [x] Image flag annotation for classification and cleaning ([#166](https://github.com/wkentaro/labelme/pull/166))
- [x] Video annotation ([video annotation](examples/video_annotation))
- [x] GUI customization (predefined labels / flags, auto-saving, label validation, etc) ([#144](https://github.com/wkentaro/labelme/pull/144))
- [x] Exporting VOC-format dataset for [semantic segmentation](examples/semantic_segmentation), [instance segmentation](examples/instance_segmentation)
- [x] Exporting COCO-format dataset for [instance segmentation](examples/instance_segmentation)
- [x] AI-assisted point-to-polygon/mask annotation by SAM, EfficientSAM models
- [x] AI text-to-annotation by YOLO-world, SAM3 models
**🌏 Available in 16 languages** - English · 日本語 · 한국어 · 简体中文 · 繁體中文 · Deutsch · Français · Español · Italiano · Português · Nederlands · Magyar · Tiếng Việt · Türkçe · Polski · فارسی (`LANG=ja_JP.UTF-8 labelme`)
## Installation
There are 3 options to install labelme:
### Option 1: Using pip
For more detail, check ["Install Labelme using Terminal"](https://www.labelme.io/docs/install-labelme-terminal)
```bash
pip install labelme
# To install the latest version from GitHub:
# pip install git+https://github.com/wkentaro/labelme.git
```
### Option 2: Using standalone executable (Easiest)
If you're willing to invest in the convenience of simple installation without any dependencies (Python, Qt),
you can download the standalone executable from ["Install Labelme as App"](https://www.labelme.io/docs/install-labelme-app).
It's a one-time payment for lifetime access, and it helps us to maintain this project.
### Option 3: Using a package manager in each Linux distribution
In some Linux distributions, you can install labelme via their package managers (e.g., apt, pacman). The following systems are currently available:
[](https://repology.org/project/labelme/versions)
## Usage
Run `labelme --help` for detail.
The annotations are saved as a [JSON](http://www.json.org/) file.
```bash
labelme # just open gui
# tutorial (single image example)
cd examples/tutorial
labelme apc2016_obj3.jpg # specify image file
labelme apc2016_obj3.jpg --output annotations/ # save annotation JSON files to a directory
labelme apc2016_obj3.jpg --with-image-data # include image data in JSON file
labelme apc2016_obj3.jpg \
--labels highland_6539_self_stick_notes,mead_index_cards,kong_air_dog_squeakair_tennis_ball # specify label list
# semantic segmentation example
cd examples/semantic_segmentation
labelme data_annotated/ # Open directory to annotate all images in it
labelme data_annotated/ --labels labels.txt # specify label list with a file
```
### Command Line Arguments
- `--output` specifies the location that annotations will be written to. If the location ends with .json, a single annotation will be written to this file. Only one image can be annotated if a location is specified with .json. If the location does not end with .json, the program will assume it is a directory. Annotations will be stored in this directory with a name that corresponds to the image that the annotation was made on.
- The first time you run labelme, it will create a config file at `~/.labelmerc`. Add only the settings you want to override. For all available options and their defaults, see [`default_config.yaml`](labelme/config/default_config.yaml). If you would prefer to use a config file from another location, you can specify this file with the `--config` flag.
- Without the `--nosortlabels` flag, the program will list labels in alphabetical order. When the program is run with this flag, it will display labels in the order that they are provided.
- Flags are assigned to an entire image. [Example](examples/classification)
- Labels are assigned to a single polygon. [Example](examples/bbox_detection)
### FAQ
- **How to convert JSON file to numpy array?** See [examples/tutorial](examples/tutorial#convert-to-dataset).
- **How to load label PNG file?** See [examples/tutorial](examples/tutorial#how-to-load-label-png-file).
- **How to get annotations for semantic segmentation?** See [examples/semantic_segmentation](examples/semantic_segmentation).
- **How to get annotations for instance segmentation?** See [examples/instance_segmentation](examples/instance_segmentation).
## Examples
* [Image Classification](examples/classification)
* [Bounding Box Detection](examples/bbox_detection)
* [Semantic Segmentation](examples/semantic_segmentation)
* [Instance Segmentation](examples/instance_segmentation)
* [Video Annotation](examples/video_annotation)
## How to build standalone executable
```bash
LABELME_PATH=./labelme
OSAM_PATH=$(python -c 'import os, osam; print(os.path.dirname(osam.__file__))')
pip install 'numpy<2.0' # numpy>=2.0 causes build errors (see #1532)
pyinstaller labelme/labelme/__main__.py \
--name=Labelme \
--windowed \
--noconfirm \
--specpath=build \
--add-data=$(OSAM_PATH)/_models/yoloworld/clip/bpe_simple_vocab_16e6.txt.gz:osam/_models/yoloworld/clip \
--add-data=$(LABELME_PATH)/config/default_config.yaml:labelme/config \
--add-data=$(LABELME_PATH)/icons/*:labelme/icons \
--add-data=$(LABELME_PATH)/translate/*:translate \
--icon=$(LABELME_PATH)/icons/icon-256.png \
--onedir
```
## Acknowledgement
This repo is the fork of [mpitid/pylabelme](https://github.com/mpitid/pylabelme).
================================================
FILE: examples/bbox_detection/README.md
================================================
# Bounding Box Detection Example
## Usage
```bash
labelme data_annotated --labels labels.txt
```
## Convert to VOC-format Dataset
```bash
# It generates:
# - data_dataset_voc/JPEGImages
# - data_dataset_voc/Annotations
# - data_dataset_voc/AnnotationsVisualization
./labelme2voc.py data_annotated data_dataset_voc --labels labels.txt
```
<img src="data_dataset_voc/JPEGImages/2011_000003.jpg" width="33%" /> <img src="data_dataset_voc/AnnotationsVisualization/2011_000003.jpg" width="33%" />
<i>Fig1. JPEG image (left), Bounding box annotation visualization (right).</i>
================================================
FILE: examples/bbox_detection/data_annotated/2011_000003.json
================================================
{
"version": "4.0.0",
"flags": {},
"shapes": [
{
"label": "person",
"points": [
[
191.0,
107.36900369003689
],
[
313.0,
329.36900369003695
]
],
"group_id": null,
"shape_type": "rectangle",
"flags": {}
},
{
"label": "person",
"points": [
[
365.0,
83.0
],
[
500.0,
333.0
]
],
"group_id": null,
"shape_type": "rectangle",
"flags": {}
}
],
"imagePath": "2011_000003.jpg",
"imageData": null,
"imageHeight": 338,
"imageWidth": 500
}
================================================
FILE: examples/bbox_detection/data_annotated/2011_000006.json
================================================
{
"version": "4.0.0",
"flags": {},
"shapes": [
{
"label": "person",
"points": [
[
91.0,
107.0
],
[
240.0,
330.0
]
],
"group_id": null,
"shape_type": "rectangle",
"flags": {}
},
{
"label": "person",
"points": [
[
178.0,
110.0
],
[
298.0,
282.0
]
],
"group_id": null,
"shape_type": "rectangle",
"flags": {}
},
{
"label": "person",
"points": [
[
254.38461538461536,
115.38461538461539
],
[
369.38461538461536,
292.38461538461536
]
],
"group_id": null,
"shape_type": "rectangle",
"flags": {}
},
{
"label": "person",
"points": [
[
395.0,
81.0
],
[
447.0,
117.0
]
],
"group_id": null,
"shape_type": "rectangle",
"flags": {}
}
],
"imagePath": "2011_000006.jpg",
"imageData": null,
"imageHeight": 375,
"imageWidth": 500
}
================================================
FILE: examples/bbox_detection/data_annotated/2011_000025.json
================================================
{
"version": "4.0.0",
"flags": {},
"shapes": [
{
"label": "bus",
"points": [
[
84.0,
20.384615384615387
],
[
435.0,
373.38461538461536
]
],
"group_id": null,
"shape_type": "rectangle",
"flags": {}
},
{
"label": "bus",
"points": [
[
1.0,
99.0
],
[
107.0,
282.0
]
],
"group_id": null,
"shape_type": "rectangle",
"flags": {}
},
{
"label": "car",
"points": [
[
409.0,
167.0
],
[
500.0,
266.0
]
],
"group_id": null,
"shape_type": "rectangle",
"flags": {}
}
],
"imagePath": "2011_000025.jpg",
"imageData": null,
"imageHeight": 375,
"imageWidth": 500
}
================================================
FILE: examples/bbox_detection/data_dataset_voc/Annotations/2011_000003.xml
================================================
<annotation>
<folder/>
<filename>2011_000003.jpg</filename>
<database/>
<annotation/>
<image/>
<size>
<height>338</height>
<width>500</width>
<depth>3</depth>
</size>
<segmented/>
<object>
<name>person</name>
<pose/>
<truncated/>
<difficult/>
<bndbox>
<xmin>191.0</xmin>
<ymin>107.36900369003689</ymin>
<xmax>313.0</xmax>
<ymax>329.36900369003695</ymax>
</bndbox>
</object>
<object>
<name>person</name>
<pose/>
<truncated/>
<difficult/>
<bndbox>
<xmin>365.0</xmin>
<ymin>83.0</ymin>
<xmax>500.0</xmax>
<ymax>333.0</ymax>
</bndbox>
</object>
</annotation>
================================================
FILE: examples/bbox_detection/data_dataset_voc/Annotations/2011_000006.xml
================================================
<annotation>
<folder/>
<filename>2011_000006.jpg</filename>
<database/>
<annotation/>
<image/>
<size>
<height>375</height>
<width>500</width>
<depth>3</depth>
</size>
<segmented/>
<object>
<name>person</name>
<pose/>
<truncated/>
<difficult/>
<bndbox>
<xmin>91.0</xmin>
<ymin>107.0</ymin>
<xmax>240.0</xmax>
<ymax>330.0</ymax>
</bndbox>
</object>
<object>
<name>person</name>
<pose/>
<truncated/>
<difficult/>
<bndbox>
<xmin>178.0</xmin>
<ymin>110.0</ymin>
<xmax>298.0</xmax>
<ymax>282.0</ymax>
</bndbox>
</object>
<object>
<name>person</name>
<pose/>
<truncated/>
<difficult/>
<bndbox>
<xmin>254.38461538461536</xmin>
<ymin>115.38461538461539</ymin>
<xmax>369.38461538461536</xmax>
<ymax>292.38461538461536</ymax>
</bndbox>
</object>
<object>
<name>person</name>
<pose/>
<truncated/>
<difficult/>
<bndbox>
<xmin>395.0</xmin>
<ymin>81.0</ymin>
<xmax>447.0</xmax>
<ymax>117.0</ymax>
</bndbox>
</object>
</annotation>
================================================
FILE: examples/bbox_detection/data_dataset_voc/Annotations/2011_000025.xml
================================================
<annotation>
<folder/>
<filename>2011_000025.jpg</filename>
<database/>
<annotation/>
<image/>
<size>
<height>375</height>
<width>500</width>
<depth>3</depth>
</size>
<segmented/>
<object>
<name>bus</name>
<pose/>
<truncated/>
<difficult/>
<bndbox>
<xmin>84.0</xmin>
<ymin>20.384615384615387</ymin>
<xmax>435.0</xmax>
<ymax>373.38461538461536</ymax>
</bndbox>
</object>
<object>
<name>bus</name>
<pose/>
<truncated/>
<difficult/>
<bndbox>
<xmin>1.0</xmin>
<ymin>99.0</ymin>
<xmax>107.0</xmax>
<ymax>282.0</ymax>
</bndbox>
</object>
<object>
<name>car</name>
<pose/>
<truncated/>
<difficult/>
<bndbox>
<xmin>409.0</xmin>
<ymin>167.0</ymin>
<xmax>500.0</xmax>
<ymax>266.0</ymax>
</bndbox>
</object>
</annotation>
================================================
FILE: examples/bbox_detection/data_dataset_voc/class_names.txt
================================================
_background_
aeroplane
bicycle
bird
boat
bottle
bus
car
cat
chair
cow
diningtable
dog
horse
motorbike
person
potted plant
sheep
sofa
train
tv/monitor
================================================
FILE: examples/bbox_detection/labelme2voc.py
================================================
#!/usr/bin/env python
import argparse
import glob
import os
import os.path as osp
import sys
import imgviz
import labelme
try:
import lxml.builder # type: ignore
import lxml.etree # type: ignore
except ImportError:
print("Please install lxml:\n\n pip install lxml\n")
sys.exit(1)
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument("input_dir", help="input annotated directory")
parser.add_argument("output_dir", help="output dataset directory")
parser.add_argument("--labels", help="labels file", required=True)
parser.add_argument("--noviz", help="no visualization", action="store_true")
args = parser.parse_args()
if osp.exists(args.output_dir):
print("Output directory already exists:", args.output_dir)
sys.exit(1)
os.makedirs(args.output_dir)
os.makedirs(osp.join(args.output_dir, "JPEGImages"))
os.makedirs(osp.join(args.output_dir, "Annotations"))
if not args.noviz:
os.makedirs(osp.join(args.output_dir, "AnnotationsVisualization"))
print("Creating dataset:", args.output_dir)
class_names = []
class_name_to_id = {}
for i, line in enumerate(open(args.labels).readlines()):
class_id = i - 1 # starts with -1
class_name = line.strip()
class_name_to_id[class_name] = class_id
if class_id == -1:
assert class_name == "__ignore__"
continue
elif class_id == 0:
assert class_name == "_background_"
class_names.append(class_name)
class_names = tuple(class_names)
print("class_names:", class_names)
out_class_names_file = osp.join(args.output_dir, "class_names.txt")
with open(out_class_names_file, "w") as f:
f.writelines("\n".join(class_names))
print("Saved class_names:", out_class_names_file)
for filename in glob.glob(osp.join(args.input_dir, "*.json")):
print("Generating dataset from:", filename)
label_file = labelme.LabelFile(filename=filename)
base = osp.splitext(osp.basename(filename))[0]
out_img_file = osp.join(args.output_dir, "JPEGImages", f"{base}.jpg")
out_xml_file = osp.join(args.output_dir, "Annotations", f"{base}.xml")
if not args.noviz:
out_viz_file = osp.join(
args.output_dir, "AnnotationsVisualization", f"{base}.jpg"
)
img = labelme.utils.img_data_to_arr(label_file.imageData)
imgviz.io.imsave(out_img_file, img)
maker = lxml.builder.ElementMaker()
xml = maker.annotation(
maker.folder(),
maker.filename(f"{base}.jpg"),
maker.database(), # e.g., The VOC2007 Database
maker.annotation(), # e.g., Pascal VOC2007
maker.image(), # e.g., flickr
maker.size(
maker.height(str(img.shape[0])),
maker.width(str(img.shape[1])),
maker.depth(str(img.shape[2])),
),
maker.segmented(),
)
bboxes: list[list[float]] = []
labels = []
for shape in label_file.shapes:
if shape["shape_type"] != "rectangle":
print(
"Skipping shape: label={label}, shape_type={shape_type}".format(
**shape
)
)
continue
class_name = shape["label"]
class_id = class_names.index(class_name)
(xmin, ymin), (xmax, ymax) = shape["points"]
# swap if min is larger than max.
xmin, xmax = sorted([xmin, xmax])
ymin, ymax = sorted([ymin, ymax])
bboxes.append([ymin, xmin, ymax, xmax])
labels.append(class_id)
xml.append(
maker.object(
maker.name(shape["label"]),
maker.pose(),
maker.truncated(),
maker.difficult(),
maker.bndbox(
maker.xmin(str(xmin)),
maker.ymin(str(ymin)),
maker.xmax(str(xmax)),
maker.ymax(str(ymax)),
),
)
)
if not args.noviz:
captions: list[str] = [class_names[label] for label in labels]
viz = imgviz.instances2rgb(
image=img,
labels=labels,
bboxes=bboxes,
captions=captions,
font_size=15,
)
imgviz.io.imsave(out_viz_file, viz)
with open(out_xml_file, "wb") as f:
f.write(lxml.etree.tostring(xml, pretty_print=True))
if __name__ == "__main__":
main()
================================================
FILE: examples/bbox_detection/labels.txt
================================================
__ignore__
_background_
aeroplane
bicycle
bird
boat
bottle
bus
car
cat
chair
cow
diningtable
dog
horse
motorbike
person
potted plant
sheep
sofa
train
tv/monitor
================================================
FILE: examples/classification/README.md
================================================
# Classification Example
## Usage
```bash
labelme data_annotated --flags flags.txt
```
<img src=".readme/annotation_cat.jpg" width="100%" />
<img src=".readme/annotation_dog.jpg" width="100%" />
================================================
FILE: examples/classification/data_annotated/0001.json
================================================
{
"version": "4.0.0",
"flags": {
"__ignore__": false,
"cat": true,
"dog": false
},
"shapes": [],
"imagePath": "0001.jpg",
"imageData": null,
"imageHeight": 480,
"imageWidth": 640
}
================================================
FILE: examples/classification/data_annotated/0002.json
================================================
{
"version": "4.0.0",
"flags": {
"__ignore__": false,
"cat": false,
"dog": true
},
"shapes": [],
"imagePath": "0002.jpg",
"imageData": null,
"imageHeight": 480,
"imageWidth": 640
}
================================================
FILE: examples/classification/flags.txt
================================================
__ignore__
cat
dog
================================================
FILE: examples/instance_segmentation/README.md
================================================
# Instance Segmentation Example
## Annotation
```bash
labelme data_annotated --labels labels.txt --validatelabel exact --config '{shift_auto_shape_color: -2}'
labelme data_annotated --labels labels.txt --labelflags '{.*: [occluded, truncated], person: [male]}'
```
## Convert to VOC-format Dataset
```bash
# It generates:
# - data_dataset_voc/JPEGImages
# - data_dataset_voc/SegmentationClass
# - data_dataset_voc/SegmentationClassNpy
# - data_dataset_voc/SegmentationClassVisualization
# - data_dataset_voc/SegmentationObject
# - data_dataset_voc/SegmentationObjectNpy
# - data_dataset_voc/SegmentationObjectVisualization
./labelme2voc.py data_annotated data_dataset_voc --labels labels.txt
```
<img src="data_dataset_voc/JPEGImages/2011_000003.jpg" width="33%" /> <img src="data_dataset_voc/SegmentationClassVisualization/2011_000003.jpg" width="33%" /> <img src="data_dataset_voc/SegmentationObjectVisualization/2011_000003.jpg" width="33%" />
Fig 1. JPEG image (left), JPEG class label visualization (center), JPEG instance label visualization (right)
Note that the label file contains only very low label values (ex. `0, 4, 14`), and
`255` indicates the `__ignore__` label value (`-1` in the npy file).
You can see the label PNG file by following.
```bash
../tutorial/draw_label_png.py data_dataset_voc/SegmentationClass/2011_000003.png # left
../tutorial/draw_label_png.py data_dataset_voc/SegmentationObject/2011_000003.png # right
```
<img src=".readme/draw_label_png_class.jpg" width="33%" /> <img src=".readme/draw_label_png_object.jpg" width="33%" />
## Convert to COCO-format Dataset
```bash
# It generates:
# - data_dataset_coco/JPEGImages
# - data_dataset_coco/annotations.json
./labelme2coco.py data_annotated data_dataset_coco --labels labels.txt
```
================================================
FILE: examples/instance_segmentation/data_annotated/2011_000003.json
================================================
{
"version": "4.0.0",
"flags": {},
"shapes": [
{
"label": "person",
"points": [
[
250.8142292490119,
107.33596837944665
],
[
229.8142292490119,
119.33596837944665
],
[
221.8142292490119,
135.33596837944665
],
[
223.8142292490119,
148.33596837944665
],
[
217.8142292490119,
161.33596837944665
],
[
202.8142292490119,
168.33596837944665
],
[
192.8142292490119,
200.33596837944665
],
[
194.8142292490119,
222.33596837944665
],
[
199.8142292490119,
227.33596837944665
],
[
191.8142292490119,
234.33596837944665
],
[
197.8142292490119,
264.3359683794467
],
[
213.8142292490119,
295.3359683794467
],
[
214.8142292490119,
320.3359683794467
],
[
221.8142292490119,
327.3359683794467
],
[
235.8142292490119,
326.3359683794467
],
[
240.8142292490119,
323.3359683794467
],
[
235.8142292490119,
298.3359683794467
],
[
238.8142292490119,
287.3359683794467
],
[
234.8142292490119,
268.3359683794467
],
[
257.81422924901193,
258.3359683794467
],
[
264.81422924901193,
264.3359683794467
],
[
256.81422924901193,
273.3359683794467
],
[
259.81422924901193,
282.3359683794467
],
[
284.81422924901193,
288.3359683794467
],
[
297.81422924901193,
278.3359683794467
],
[
288.81422924901193,
270.3359683794467
],
[
281.81422924901193,
270.3359683794467
],
[
283.81422924901193,
264.3359683794467
],
[
292.81422924901193,
261.3359683794467
],
[
308.81422924901193,
236.33596837944665
],
[
313.81422924901193,
217.33596837944665
],
[
309.81422924901193,
208.33596837944665
],
[
312.81422924901193,
202.33596837944665
],
[
308.81422924901193,
185.33596837944665
],
[
291.81422924901193,
173.33596837944665
],
[
269.81422924901193,
159.33596837944665
],
[
261.81422924901193,
154.33596837944665
],
[
264.81422924901193,
142.33596837944665
],
[
273.81422924901193,
137.33596837944665
],
[
278.81422924901193,
130.33596837944665
],
[
270.81422924901193,
121.33596837944665
]
],
"group_id": null,
"shape_type": "polygon",
"flags": {}
},
{
"label": "person",
"points": [
[
482.81422924901193,
87.18098682963114
],
[
468.81422924901193,
92.18098682963114
],
[
460.81422924901193,
112.18098682963114
],
[
460.81422924901193,
129.18098682963114
],
[
444.81422924901193,
139.18098682963114
],
[
419.81422924901193,
155.18098682963114
],
[
410.81422924901193,
165.18098682963114
],
[
403.81422924901193,
170.18098682963114
],
[
394.81422924901193,
172.18098682963114
],
[
386.81422924901193,
170.18098682963114
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"image_id": 1, "category_id": 6, "segmentation": [[88.93617021276599, 115.56382978723406, 0.9361702127659877, 96.56382978723406, 0.0, 251.968085106388, 0.9361702127659877, 265.56382978723406, 27.936170212765987, 265.56382978723406, 29.936170212765987, 283.56382978723406, 63.93617021276599, 281.56382978723406, 89.93617021276599, 252.56382978723406, 100.93617021276599, 183.56382978723406, 108.93617021276599, 145.56382978723406, 81.93617021276599, 151.56382978723406]], "area": 15781.0, "bbox": [0.0, 96.0, 109.0, 188.0], "iscrowd": 0}, {"id": 5, "image_id": 1, "category_id": 7, "segmentation": [[413.936170212766, 168.94844517184944, 497.936170212766, 168.94844517184944, 497.936170212766, 256.94844517184936, 431.936170212766, 258.94844517184936, 430.936170212766, 236.94844517184944, 408.936170212766, 218.94844517184944]], "area": 7256.0, "bbox": [408.0, 168.0, 90.0, 91.0], "iscrowd": 0}, {"id": 6, "image_id": 2, "category_id": 15, "segmentation": [[204.936170212766, 108.56382978723406, 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{"id": 7, "image_id": 2, "category_id": 15, "segmentation": [[271.936170212766, 109.56382978723406, 249.936170212766, 110.56382978723406, 244.936170212766, 150.56382978723406, 215.936170212766, 219.56382978723406, 208.936170212766, 245.56382978723406, 214.936170212766, 220.56382978723406, 188.936170212766, 227.56382978723406, 170.936170212766, 246.56382978723406, 170.936170212766, 275.56382978723406, 221.936170212766, 278.56382978723406, 233.936170212766, 259.56382978723406, 246.936170212766, 253.56382978723406, 245.936170212766, 256.56382978723406, 242.936170212766, 251.56382978723406, 262.936170212766, 256.56382978723406, 304.936170212766, 226.56382978723406, 297.936170212766, 199.56382978723406, 308.936170212766, 164.56382978723406, 296.936170212766, 148.56382978723406]], "area": 11735.0, "bbox": [170.0, 109.0, 139.0, 170.0], "iscrowd": 0}, {"id": 8, "image_id": 2, "category_id": 15, "segmentation": [[308.936170212766, 115.56382978723406, 298.936170212766, 145.56382978723406, 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373.9484451718494, 498.7054009819968, 336.9484451718494, 498.7054009819968, 202.94844517184941, 454.7054009819968, 193.94844517184941, 435.7054009819968, 212.94844517184941, 368.7054009819968, 224.94844517184941, 351.7054009819968, 241.94844517184941]], "area": 44532.0, "bbox": [149.0, 193.0, 350.0, 182.0], "iscrowd": 0}, {"id": 10, "image_id": 2, "category_id": 15, "segmentation": [[425.936170212766, 82.56382978723406, 404.936170212766, 109.56382978723406, 400.936170212766, 114.56382978723406, 437.936170212766, 114.56382978723406, 448.936170212766, 102.56382978723406, 446.936170212766, 91.56382978723406]], "area": 996.0, "bbox": [400.0, 82.0, 49.0, 33.0], "iscrowd": 0}, {"id": 11, "image_id": 2, "category_id": 18, "segmentation": [[183.936170212766, 140.56382978723406, 125.93617021276599, 140.56382978723406, 110.93617021276599, 187.56382978723406, 22.936170212765987, 199.56382978723406, 18.936170212765987, 218.56382978723406, 22.936170212765987, 234.56382978723406, 93.93617021276599, 239.56382978723406, 91.93617021276599, 229.56382978723406, 110.93617021276599, 203.56382978723406], [103.93617021276599, 290.56382978723406, 58.93617021276599, 303.56382978723406, 97.93617021276599, 311.56382978723406], [348.936170212766, 146.56382978723406, 472.936170212766, 149.56382978723406, 477.936170212766, 162.56382978723406, 471.936170212766, 196.56382978723406, 453.936170212766, 192.56382978723406, 434.936170212766, 213.56382978723406, 368.936170212766, 226.56382978723406, 375.936170212766, 187.56382978723406, 353.936170212766, 164.56382978723406], [246.936170212766, 252.56382978723406, 219.936170212766, 277.56382978723406, 254.936170212766, 287.56382978723406, 261.936170212766, 256.56382978723406]], "area": 14001.0, "bbox": [18.0, 140.0, 460.0, 172.0], "iscrowd": 0}], "categories": [{"supercategory": null, "id": 0, "name": "_background_"}, {"supercategory": null, "id": 1, "name": "aeroplane"}, {"supercategory": null, "id": 2, "name": "bicycle"}, {"supercategory": null, "id": 3, "name": "bird"}, {"supercategory": null, "id": 4, "name": "boat"}, {"supercategory": null, "id": 5, "name": "bottle"}, {"supercategory": null, "id": 6, "name": "bus"}, {"supercategory": null, "id": 7, "name": "car"}, {"supercategory": null, "id": 8, "name": "cat"}, {"supercategory": null, "id": 9, "name": "chair"}, {"supercategory": null, "id": 10, "name": "cow"}, {"supercategory": null, "id": 11, "name": "diningtable"}, {"supercategory": null, "id": 12, "name": "dog"}, {"supercategory": null, "id": 13, "name": "horse"}, {"supercategory": null, "id": 14, "name": "motorbike"}, {"supercategory": null, "id": 15, "name": "person"}, {"supercategory": null, "id": 16, "name": "potted plant"}, {"supercategory": null, "id": 17, "name": "sheep"}, {"supercategory": null, "id": 18, "name": "sofa"}, {"supercategory": null, "id": 19, "name": "train"}, {"supercategory": null, "id": 20, "name": "tv/monitor"}]}
================================================
FILE: examples/instance_segmentation/data_dataset_voc/class_names.txt
================================================
_background_
aeroplane
bicycle
bird
boat
bottle
bus
car
cat
chair
cow
diningtable
dog
horse
motorbike
person
potted plant
sheep
sofa
train
tv/monitor
================================================
FILE: examples/instance_segmentation/labelme2coco.py
================================================
#!/usr/bin/env python
import argparse
import collections
import datetime
import glob
import json
import os
import os.path as osp
import sys
import uuid
import imgviz
import numpy as np
import labelme
try:
import pycocotools.mask # type: ignore
except ImportError:
print("Please install pycocotools:\n\n pip install pycocotools\n")
sys.exit(1)
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument("input_dir", help="input annotated directory")
parser.add_argument("output_dir", help="output dataset directory")
parser.add_argument("--labels", help="labels file", required=True)
parser.add_argument("--noviz", help="no visualization", action="store_true")
args = parser.parse_args()
if osp.exists(args.output_dir):
print("Output directory already exists:", args.output_dir)
sys.exit(1)
os.makedirs(args.output_dir)
os.makedirs(osp.join(args.output_dir, "JPEGImages"))
if not args.noviz:
os.makedirs(osp.join(args.output_dir, "Visualization"))
print("Creating dataset:", args.output_dir)
now = datetime.datetime.now()
data = dict(
info=dict(
description=None,
url=None,
version=None,
year=now.year,
contributor=None,
date_created=now.strftime("%Y-%m-%d %H:%M:%S.%f"),
),
licenses=[
dict(
url=None,
id=0,
name=None,
)
],
type="instances",
)
data["images"] = [] # license, url, file_name, height, width, date_captured, id
data["categories"] = [] # supercategory, id, name
data[
"annotations"
] = [] # segmentation, area, iscrowd, image_id, bbox, category_id, id
class_name_to_id = {}
for i, line in enumerate(open(args.labels).readlines()):
class_id = i - 1 # starts with -1
class_name = line.strip()
if class_id == -1:
assert class_name == "__ignore__"
continue
class_name_to_id[class_name] = class_id
data["categories"].append(
dict(
supercategory=None,
id=class_id,
name=class_name,
)
)
out_ann_file = osp.join(args.output_dir, "annotations.json")
label_files = glob.glob(osp.join(args.input_dir, "*.json"))
for image_id, filename in enumerate(label_files):
print("Generating dataset from:", filename)
label_file = labelme.LabelFile(filename=filename)
base = osp.splitext(osp.basename(filename))[0]
out_img_file = osp.join(args.output_dir, "JPEGImages", f"{base}.jpg")
img = labelme.utils.img_data_to_arr(label_file.imageData)
if img.ndim == 3 and img.shape[2] == 4:
img = imgviz.rgba2rgb(img)
imgviz.io.imsave(out_img_file, img)
data["images"].append(
dict(
license=0,
url=None,
file_name=osp.relpath(out_img_file, osp.dirname(out_ann_file)),
height=img.shape[0],
width=img.shape[1],
date_captured=None,
id=image_id,
)
)
masks = {} # for area
segmentations = collections.defaultdict(list) # for segmentation
for shape in label_file.shapes:
points: list[list[int | float]] = shape["points"]
label = shape["label"]
group_id = shape.get("group_id")
shape_type = shape.get("shape_type", "polygon")
mask = labelme.utils.shape_to_mask(img.shape[:2], points, shape_type)
if group_id is None:
group_id = uuid.uuid1()
instance = (label, group_id)
if instance in masks:
masks[instance] = masks[instance] | mask
else:
masks[instance] = mask
points_coco: list[int | float]
if shape_type == "rectangle":
(x1, y1), (x2, y2) = points
x1, x2 = sorted([x1, x2])
y1, y2 = sorted([y1, y2])
points_coco = [x1, y1, x2, y1, x2, y2, x1, y2]
if shape_type == "circle":
(x1, y1), (x2, y2) = points
r = np.linalg.norm([x2 - x1, y2 - y1])
# r(1-cos(a/2))<x, a=2*pi/N => N>pi/arccos(1-x/r)
# x: tolerance of the gap between the arc and the line segment
n_points_circle = max(int(np.pi / np.arccos(1 - 1 / r)), 12)
i = np.arange(n_points_circle)
x = x1 + r * np.sin(2 * np.pi / n_points_circle * i)
y = y1 + r * np.cos(2 * np.pi / n_points_circle * i)
points_coco = np.stack((x, y), axis=1).flatten().tolist()
else:
points_coco = np.asarray(points).flatten().tolist()
segmentations[instance].append(points_coco)
segmentations = dict(segmentations)
for instance, mask in masks.items():
cls_name, group_id = instance
if cls_name not in class_name_to_id:
continue
cls_id = class_name_to_id[cls_name]
mask = np.asfortranarray(mask.astype(np.uint8))
mask = pycocotools.mask.encode(mask)
area = float(pycocotools.mask.area(mask))
bbox = pycocotools.mask.toBbox(mask).flatten().tolist()
data["annotations"].append(
dict(
id=len(data["annotations"]),
image_id=image_id,
category_id=cls_id,
segmentation=segmentations[instance],
area=area,
bbox=bbox,
iscrowd=0,
)
)
if not args.noviz:
viz = img
if masks:
labels, captions, masks = zip(
*[
(class_name_to_id[cnm], cnm, msk)
for (cnm, gid), msk in masks.items()
if cnm in class_name_to_id
]
)
viz = imgviz.instances2rgb(
image=img,
labels=labels,
masks=masks,
captions=captions,
font_size=15,
line_width=2,
)
out_viz_file = osp.join(args.output_dir, "Visualization", f"{base}.jpg")
imgviz.io.imsave(out_viz_file, viz)
with open(out_ann_file, "w") as f:
json.dump(data, f)
if __name__ == "__main__":
main()
================================================
FILE: examples/instance_segmentation/labelme2voc.py
================================================
#!/usr/bin/env python
import argparse
import glob
import os
import os.path as osp
import sys
import imgviz
import numpy as np
import labelme
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument("input_dir", help="Input annotated directory")
parser.add_argument("output_dir", help="Output dataset directory")
parser.add_argument(
"--labels", help="Labels file or comma separated text", required=True
)
parser.add_argument(
"--noobject", help="Flag not to generate object label", action="store_true"
)
parser.add_argument(
"--nonpy", help="Flag not to generate .npy files", action="store_true"
)
parser.add_argument(
"--noviz", help="Flag to disable visualization", action="store_true"
)
args = parser.parse_args()
if osp.exists(args.output_dir):
print("Output directory already exists:", args.output_dir)
sys.exit(1)
os.makedirs(args.output_dir)
os.makedirs(osp.join(args.output_dir, "JPEGImages"))
os.makedirs(osp.join(args.output_dir, "SegmentationClass"))
if not args.nonpy:
os.makedirs(osp.join(args.output_dir, "SegmentationClassNpy"))
if not args.noviz:
os.makedirs(osp.join(args.output_dir, "SegmentationClassVisualization"))
if not args.noobject:
os.makedirs(osp.join(args.output_dir, "SegmentationObject"))
if not args.nonpy:
os.makedirs(osp.join(args.output_dir, "SegmentationObjectNpy"))
if not args.noviz:
os.makedirs(osp.join(args.output_dir, "SegmentationObjectVisualization"))
print("Creating dataset:", args.output_dir)
if osp.exists(args.labels):
with open(args.labels) as f:
labels = [label.strip() for label in f if label]
else:
labels = [label.strip() for label in args.labels.split(",")]
class_names: list[str] = []
class_name_to_id = {}
for i, label in enumerate(labels):
class_id = i - 1 # starts with -1
class_name = label.strip()
class_name_to_id[class_name] = class_id
if class_id == -1:
assert class_name == "__ignore__"
continue
elif class_id == 0:
assert class_name == "_background_"
class_names.append(class_name)
print("class_names:", class_names)
out_class_names_file = osp.join(args.output_dir, "class_names.txt")
with open(out_class_names_file, "w") as f:
f.writelines("\n".join(class_names))
print("Saved class_names:", out_class_names_file)
for filename in sorted(glob.glob(osp.join(args.input_dir, "*.json"))):
print("Generating dataset from:", filename)
label_file = labelme.LabelFile(filename=filename)
base = osp.splitext(osp.basename(filename))[0]
out_img_file = osp.join(args.output_dir, "JPEGImages", f"{base}.jpg")
out_clsp_file = osp.join(args.output_dir, "SegmentationClass", f"{base}.png")
if not args.nonpy:
out_cls_file = osp.join(
args.output_dir, "SegmentationClassNpy", f"{base}.npy"
)
if not args.noviz:
out_clsv_file = osp.join(
args.output_dir,
"SegmentationClassVisualization",
f"{base}.jpg",
)
if not args.noobject:
out_insp_file = osp.join(
args.output_dir, "SegmentationObject", f"{base}.png"
)
if not args.nonpy:
out_ins_file = osp.join(
args.output_dir, "SegmentationObjectNpy", f"{base}.npy"
)
if not args.noviz:
out_insv_file = osp.join(
args.output_dir,
"SegmentationObjectVisualization",
f"{base}.jpg",
)
img = labelme.utils.img_data_to_arr(label_file.imageData)
imgviz.io.imsave(out_img_file, img)
cls, ins = labelme.utils.shapes_to_label(
img_shape=img.shape,
shapes=label_file.shapes,
label_name_to_value=class_name_to_id,
)
ins[cls == -1] = 0 # ignore it.
# class label
labelme.utils.lblsave(out_clsp_file, cls)
if not args.nonpy:
np.save(out_cls_file, cls)
if not args.noviz:
clsv = imgviz.label2rgb(
cls,
imgviz.rgb2gray(img),
label_names=class_names,
font_size=15,
loc="rb",
)
imgviz.io.imsave(out_clsv_file, clsv)
if not args.noobject:
# instance label
labelme.utils.lblsave(out_insp_file, ins)
if not args.nonpy:
np.save(out_ins_file, ins)
if not args.noviz:
instance_ids = np.unique(ins)
instance_names = [str(i) for i in range(max(instance_ids) + 1)]
insv = imgviz.label2rgb(
ins,
imgviz.rgb2gray(img),
label_names=instance_names,
font_size=15,
loc="rb",
)
imgviz.io.imsave(out_insv_file, insv)
if __name__ == "__main__":
main()
================================================
FILE: examples/instance_segmentation/labels.txt
================================================
__ignore__
_background_
aeroplane
bicycle
bird
boat
bottle
bus
car
cat
chair
cow
diningtable
dog
horse
motorbike
person
potted plant
sheep
sofa
train
tv/monitor
================================================
FILE: examples/primitives/primitives.json
================================================
{
"version": "5.7.0",
"flags": {},
"shapes": [
{
"label": "rectangle",
"points": [
[
32.0,
35.0
],
[
132.0,
135.0
]
],
"group_id": null,
"description": null,
"shape_type": "rectangle",
"flags": {},
"mask": null
},
{
"label": "circle",
"points": [
[
195.0,
84.0
],
[
225.0,
125.0
]
],
"group_id": null,
"description": null,
"shape_type": "circle",
"flags": {},
"mask": null
},
{
"label": "rectangle",
"points": [
[
391.0,
33.0
],
[
542.0,
135.0
]
],
"group_id": null,
"description": null,
"shape_type": "rectangle",
"flags": {},
"mask": null
},
{
"label": "polygon",
"points": [
[
69.0,
318.0
],
[
45.0,
403.0
],
[
173.0,
406.0
],
[
198.0,
321.0
]
],
"group_id": null,
"description": null,
"shape_type": "polygon",
"flags": {},
"mask": null
},
{
"label": "line",
"points": [
[
188.0,
178.0
],
[
160.0,
224.0
]
],
"group_id": null,
"description": null,
"shape_type": "line",
"flags": {},
"mask": null
},
{
"label": "point",
"points": [
[
345.0,
174.0
]
],
"group_id": null,
"description": null,
"shape_type": "point",
"flags": {},
"mask": null
},
{
"label": "line_strip",
"points": [
[
440.53703703703707,
181.46296296296293
],
[
402.53703703703707,
274.46296296296293
],
[
544.5370370370371,
275.46296296296293
]
],
"group_id": null,
"description": null,
"shape_type": "linestrip",
"flags": {},
"mask": null
},
{
"label": "octagon",
"points": [
[
417.39759036144574,
305.0
],
[
516.3975903614457,
404.0
]
],
"group_id": null,
"description": "",
"shape_type": "mask",
"flags": {},
"mask": "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"
}
],
"imagePath": "primitives.jpg",
"imageData": null,
"imageHeight": 450,
"imageWidth": 560
}
================================================
FILE: examples/semantic_segmentation/README.md
================================================
# Semantic Segmentation Example
## Annotation
```bash
labelme data_annotated --labels labels.txt --validatelabel exact --config '{shift_auto_shape_color: -2}'
```
## Convert to VOC-format Dataset
```bash
# It generates:
# - data_dataset_voc/JPEGImages
# - data_dataset_voc/SegmentationClass
# - data_dataset_voc/SegmentationClassNpy
# - data_dataset_voc/SegmentationClassVisualization
./labelme2voc.py data_annotated data_dataset_voc --labels labels.txt --noobject
```
<img src="data_dataset_voc/JPEGImages/2011_000003.jpg" width="33%" /> <img src="data_dataset_voc/SegmentationClass/2011_000003.png" width="33%" /> <img src="data_dataset_voc/SegmentationClassVisualization/2011_000003.jpg" width="33%" />
Fig 1. JPEG image (left), PNG label (center), JPEG label visualization (right)
Note that the label file contains only very low label values (ex. `0, 4, 14`), and
`255` indicates the `__ignore__` label value (`-1` in the npy file).
You can see the label PNG file by following.
```bash
../tutorial/draw_label_png.py data_dataset_voc/SegmentationClass/2011_000003.png
```
<img src=".readme/draw_label_png.jpg" width="33%" />
================================================
FILE: examples/semantic_segmentation/data_annotated/2011_000003.json
================================================
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================================================
FILE: examples/semantic_segmentation/data_annotated/2011_000006.json
================================================
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================================================
FILE: examples/semantic_segmentation/data_annotated/2011_000025.json
================================================
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================================================
FILE: examples/semantic_segmentation/data_dataset_voc/class_names.txt
================================================
_background_
aeroplane
bicycle
bird
boat
bottle
bus
car
cat
chair
cow
diningtable
dog
horse
motorbike
person
potted plant
sheep
sofa
train
tv/monitor
================================================
FILE: examples/semantic_segmentation/labels.txt
================================================
__ignore__
_background_
aeroplane
bicycle
bird
boat
bottle
bus
car
cat
chair
cow
diningtable
dog
horse
motorbike
person
potted plant
sheep
sofa
train
tv/monitor
================================================
FILE: examples/tutorial/README.md
================================================
# Tutorial (Single Image Example)
## Annotation
```bash
labelme apc2016_obj3.jpg
```
## Visualization
To view the json file quickly, you can use utility script:
```bash
./draw_json.py apc2016_obj3.json
```
<img src=".readme/draw_json.jpg" width="70%" />
## Convert to Dataset
To convert the json to set of image and label, you can run following:
```bash
./export_json.py apc2016_obj3.json
```
It generates standard files from the JSON file.
- [img.png](apc2016_obj3/img.png): Image file.
- [label.png](apc2016_obj3/label.png): uint8 label file.
- [label_viz.png](apc2016_obj3/label_viz.png): Visualization of `label.png`.
- [label_names.txt](apc2016_obj3/label_names.txt): Label names for values in `label.png`.
## How to load label PNG file?
Note that loading `label.png` is a bit difficult
(`scipy.misc.imread`, `skimage.io.imread` may not work correctly),
and please use `PIL.Image.open` to avoid unexpected behavior:
```python
# see load_label_png.py also.
>>> import numpy as np
>>> import PIL.Image
>>> label_png = 'apc2016_obj3/label.png'
>>> lbl = np.asarray(PIL.Image.open(label_png))
>>> print(lbl.dtype)
dtype('uint8')
>>> np.unique(lbl)
array([0, 1, 2, 3], dtype=uint8)
>>> lbl.shape
(907, 1210)
```
Also, you can see the label PNG file by:
```bash
./draw_label_png.py apc2016_obj3/label.png
```
<img src=".readme/draw_label_png.jpg" width="35%" />
================================================
FILE: examples/tutorial/apc2016_obj3/label_names.txt
================================================
_background_
highland_6539_self_stick_notes
kong_air_dog_squeakair_tennis_ball
mead_index_cards
shelf
================================================
FILE: examples/tutorial/apc2016_obj3.json
================================================
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9O+0kEEGqGD3NKTxRYC99pAbIqQXY2cms0EinAnHWnYDRa5GzJNQ/aghGKqYyKADmiwXL32njOetM+1HtVYnim5pcoGgt1gdacLrFZmWBqQNmiwGgbv0NM+01QZ8GnLyKdguXkuB1zSi4Jzg1RBOcDmpduKLDuTeec0oueOtVyMdetJjB460WAvicletJ5nrVb7oFOD8UrAWPN245py3GQeaqEk9elKDg4pWAt+ax5zSGX3qtvPalDbutFhljzcjvThLgVB0oFFhlgSZOam8w7elUs4NShjtosBL5mDmmtKetB5FNbG2iwyZZOKeT8oNVgefapC/y4IpWAd5vPFTeedtVuOtPB9qLDJBKwzzSxXXzkVEfm4pyRqDmgCyJSwqTdxUAwop5fOBQBIMsKjyc4zSh8D1qMsS1AiygxTyM0xBxk09SFPNMA2vjnpSYOKsFwUqBgSaAHo2BzUuM81WHUnPNSROzL04FAEpHFADAZzSHJo3HHJpgKznFNDDGTS9qYT1FMQ9WBPAqYP0AqsCF6Unm47UxWLueKjNQiUk+1O3k0xEydOKXGKiBxzmq8skhb5TimIvgikLdqqpM23nrUwPGTQIDSxknNMLZqVOVFMB6dDmnBwpxTCcVHuAbk0CLJnzwKlD/ACVVHFPRiRjFACmQlSB1pkRKtk1JtA5xQBzz3oACS3amfNnHSrAAA6cVBNMkQLE4wM0AUvEGrpo2hzXEjYIXjmvmu48bXD3Mrbjy5P3V9frXX/Ffxe11cHTrWQ7Rw2O1eUYPqP8AvmjRDR6yT7UmT6U8SKOvel8yOsDq5RvNNJx2qTehOQaUshoDlI+Nue9IOtP3Ie4pfk7EUBYbkDjijj0p2EPBIo2rnrQLlG7VznAoIUnGKeAvc0u1PUUBykR2+lJgegqbYp7ikwoPUUBykDYxwKMAHpU7Ih5pCgx1FAcpCMA9KXcvXFSeWuOtJsT1FMLDMrjNN8xR1FSbB6il8lW70gsReajdjQSmfepDbqBgUnkD0JNAWIyynFG5RUggGaX7LnvQFiHKmm/LxU/2bNBtDj1oDlIABng0uAVxTjbMelKbdgKYuUgdcDIJ/OkHTJNTNCxGBzTPJfpzigXKRlsEcmgMx6E0/wAhqPJYdqA5RAz4+8350efKOfMbp60bGPamtG2KLi5RDd3CjiaTP+8ai/tC8zj7S+PrTzA2PTNRm1bOcincXKOXUr0HP2hyfrThrN8Bj7Q/p1qE2rHvTTZue9Fw5S0uu6iox55/OpR4g1FeRcNVEWr9M4pGtpAcZBoFymoPEeoY/wBdzUw8T36oPn5HrWP5D8cUv2dz3xRcOU1v+Eqv8dQaUeLL4dQuayTbnbweahMMgJp3DlN1vF17/s/lSjxhe4zsX86wGikH8NJ5b46GgOU6MeNbnp5Q/E08eN5VGGirlzC/XFNETHsaAsdWPGrMxzD+tOXxsAc+U30rk/JbuMUogb3o0CzOwHjZWA/dEUHxvABzGa45oWA4HNMWB92CtArHcp45g4BQ1IPGto3OCDXBmFuuKTYV7c0Bqegf8JpaFgoJB9cVKvjGzOMy154F6nFNYc9KVhHpJ8W2JGDKPzo/4SmxAGJBz0rzUoSwJFDLkcU7DPTF8T2hH3wPqaU+I7VhxIK8yKnGO1J83qc0rAelf25bueJAKeusW+B+8X868xHmDualCnH3jRYD0k6vbMT+8XP1pTqUOMiRffmvND5gP32/OnF3H8bg9OtFgPS11GIEnev50h1BGJIYfnXmgkmX/lo/50n2i5HSV/xOaLBc9M+2A9WFOS7U9SMV5qt5dD/lu+frTzfXYORO/wCdFguekm7Q9CKa9wCMCvOl1C8B/wBe1SDUrwZ/fE0WC53wuVAp32pa4D+1b3I/edPanrq15jmTmiwXO8E6EnmnCcEVwY1i7B+8D9akGuXfIJGKLCudq0oHSlSUMOa4sa7dDjA/GlGu3Jz8oxj1osB2jSp0FIHVq47+3psY2Dn3qVPEEoGDHk/WgDq9yqOtRtIGNc3/AG+/dP1pP7cbOdmKAOnz8tIGUVz39v5X7h4pV1xCTlDTA6JZF9KRpVNYP9uRj+E0x9Zj3A4IoEbzMMZpoYHvWIdbhwRhqYNYiU45pgb+9cdaXcuOtc//AG1DnuPwpx1q2xyx/KgDd3KeM08kVgprNqvV6c+uW5A2vx70AbhYDvTlZcdawBrFueTJS/2xADxKOaYjeYgLmo92frWQur2+3mUVKNWtcf61R+NAGoD3oz6Gsr+1bbP+uH50q6lCW5lApWGafB5qQEbetZp1KDGBKtH9oQ4H71efegDVUgCn7s1li9iK485fzpRfRDgSr+dMVzVIzQq1mC/TPEgP41LFfITgSLn60h3Lrk5FPHSqBulI++v505L0c4YH8aALvagckGqQvQRwRTzeKB1H50rDLhwaEXmqX2wdQR+dSreAdcUh3Lh5pBVRrwKfalF8pXI5oC5cVcmpduKopeDPapjdLnrTC5ZbIFNJHU1Et2GODTTKjnGaB3LS7cZqTjrmqayqoxml+0jFAXLo6cY5pQMDpVJbpeuanW6XaOaQyTv0p/pUH2lOpYUn2uMtgMDSAtFuM0B+BVdZlPU08OueooAmzzTh1GajWRfWnb1I4amBbDjbTTksMGq6vtPXNKs/z0AX1I6UHHrVdX289zTfMJzk4oAlztJyaeSQg21WJD9DmpQ4AAzSAmD/ACikfkCoSN3Q1IgzwTTAlT7o5pcAnFN6cUBsnNMQ4qOwpjJu5pfNOelKGDcUwBYwBmlXrxSkcAZxTANrdaYiXHHvUbKRzT85FNJHQmgkFUDrUhYMOKjY8Uxm2jg0wJQeamQ1QEp3e1TJMVJ5piJZJQGIqMfOcmombe+TT0OKBE4Yg47VYSUKtUHc5HNSKxYA5pgXDJkYFISQOaaGCpk9arXFwFUMTgCgC55mEJboK5Dxdrv2XT5jH/dxkVbvtSkdNqkhfX1rzXx5qe2z8oNktnIrOU9bI0jDS7PMNRuWv9QeVskk9TQLXI4xUUKF5BjqT2rXxjjH/jtAjpxOxNH2kgnNZX9tWIOBJ9KjfWrU5+boe1ZXOm5sm596abkgferGGr2pH3z69KYdVtj/AMtAPY8Urhc21uuevNOF98uM1gnUYDx5inv1oXUYMf6wZ+tFwub4vAM88UG+PrWD/aEHaQUC/h5zIPc5ouFze+356tSDUD/erD+2Q/31+uaU3cIOPMB/GncVzcGoE85o+388n9awvtMfUOCfQGl+1Jjh1z9RQFzc/tD1Jpv9oc5zWL9pjORvXP1o+0J3YfgaAubY1DgjNMW+fccHisfzl/vDn3oEygj5ue/NMVzb/tAY5NH9oY/irDMy5+8M/WgygfxfSkF2bo1JgMg4pw1R/wAKwfNB707zD2NFwubo1I9DThqeP8a58yEHrS+dx1ouO50X9qgelKNWxwcYrm/NY96USt0NAXOi/tVAf/rUDVgc5Iwa51pOM+tM80+tMXMdINUT2/GgalHgZxXMmYnqaTzWAyD2oDmOqXUFHUCnDUIu4rkxcPzzjml+1MDjmgOY6wXsJ5ApRewk8jj2rlPtj+tAu2P3j+NA+Y6r7VCW9BSG5twcg5rm3lmSEOchT0NRfbX9aBcx1f2m39cfWnia3J5PFch9tYHq350ovCcnNAcx2KvZNwWANNK2x5VwRXI/bWHIJ/OnC+cfxUaj5l2OsCwHo4zSMkf94fnXKjUmBADGnDUyp6t+dAcyOp8hCM7qDbp13VzH9qsMHPT3pf7WYj7xo1C8Tpvsqno9Bsv9oVza6u2PvGnjV2AJ3E/U0ahzROgNnx1FBsDjIYVgjWSOr046056t09DRZhzRNo2LkfeWm/ZXXuKyBqvfzDTxq7ZGGp6ivE0hbSbvu80otZTztzVAaw396nLrTgH5/wBKWoe6XhayjOY6RrVsfMgqn/bD45cYpw1ctwSM09ROxOLYkcLTPsuOqUwapjnI/Cgahnqcn6UC0JDBx/qyR9KYbdMf6s/lT11IYwMGnjUAeMClcqyIPJTuhppgT+7+VWDeJnnFKbqM8baLi5UVfKQY4pDGo6VZM8R52jik82ItwKLhyorGJetAiQ8GrJaHPQUZhHYUXDlKwgWjyV9asfu6blPWi4rEBt1z1pPI96nzH60vyZPNO4rFbyCO9KYSanO3P3qNo9RTuKxX8noBQImxxVjaufvCl2Ds1FxWKhRs9KChz71aEeOppGX0oFYrlGxmkCE1Y20FfbmmFivtNO2njFS7acF46UCIRnvTug96eFHelwMYoAhAPpR8wNTbAaQrjpQBGc0ZJqQKDQFFAiM9aOc1KABS4BoAgI46UbamKA0bF7mgCArzTdoqw0YA60CNfXmgCvjtTTxxVkxCmmEU7iK+OOnFJjjrU5XAxTTGcdKLgViSKbk1a8tSORUZjHvxTAiDNnr0pwkPcmlVVDUrhW7GgBRKezU4SP8A3jUWAAOKQnHegRMbplPDn86UXbZzvbP1qmVyaXYQR70xl0Xkmf8AWN+dPW8lGQJGx7GqCg56VIODmgRd+2Sj/low/Gl+2Sg581vzqg/J64FN2tQBp/b5f+ezfnQL2YZ/fN+dZjZxxQhYA5NIDWGoTr/y2bH1py6lPkEymscMc4yaVC2T6CgDaOqz9RKc0o1e56+a3FYbOR0NIZmxgZ5FAzd/tm5UZErE+9H9t3R/5atWF5jHA7U7efQ0Aby61eE/NKSKk/tq7GcSdawN5BwM04TMDzQBujXbxOjjH0p416+2/fH4isFpvSl884oC5tjxBdj7xB/ClXxDdrkqFz24rC87vjmnLMCeaVgudCniW7HUAU9fE10rZxmsAnilDgDrTsO7OhbxVdD+AHNKPFdyvRQRXOb9wpC1FguzqV8WXR5KLTh4vmXBaMH6GuVD7hmkMmOp4osF2dgvjZ5OBEakHi9yOY64xCoJxTzIAKLILs7OPxbs4K9e9PHi05BKVxHm57U/fxRZBzM7pPGQU/czn1pf+E1iU4Kn8q4YMemaYTgmiyHzM7v/AITiAHGx+fanjxpbAfNuHua4HPrTTytFg5j0OPxlZnnLAn1qRfGFnjliPrXm+QDycUbgef60rBzHpo8ZWR/5ag4qYeLbE/xDivLEPzVcUqVHtQFz0QeKYCxJcbe3NObxHbHGZBmvPPOAx3pwuBn2oA9KTxBbbBulUe+aP7at3/5aDH1rznzd1SRh24ycUxHoX9rWzEBJAfxqx/aEG0YcZrg7f5OMnNWlk5+9yKYjsf7SiH8Q+tSC/jIyHH51xwkJ70olbGQx/Oi4WOwN9GWxuqeC+jVfmYDHvXGLM+PvGpllfuxouOx01zq38MXPvVNp2kOXYmspJsd6cspJ5OKiUmaRii1qDgWT4YA44rxbxbcvcX3lNJnn9K9J8QXaw2bEvxj1rx+7nNzfSPndg4yaiC1uXOWlhLKD94Mdu+a1vLP92q9hH8hJ6+taPP8AkVoYnJY4phXj/GpthAOaYQR2rA6CIrzyMj6UnOfUU9ulNJznigQnb1zSg9qBQKAE596AD/8ArpcHPFHbv+VABj3xQWPrQOnWlcggcYPegQzJ55/Cky2Dg/hRySMUowCaYBljgZP0p+5sfepn+frS5oAXfJ03H86TzJM8sTR160f496AF811OQxz60vnSbfvt+dMI9KT3oAk8+QdHb86DPLn/AFhPsTmmYJ7Udh1oAl+1Tg58xs/Wj7XP2lb86iJyMdeaSgCx9vue0h/CkF/c9DKSDxVc9KUDnNAFhb64A+/+FO/tC4APzfjiqvQ80Ec9MmmItf2hP14/KmjUZx1qseenT60cc/WgC2NSm74znrTjqkhOdozVID2pv0oA0P7Vfui0DVGH/LMYxWceR16UfWgDUOtSFFQr8o/hph1QH+E/nWcaTtjFMRpf2oM52cfWl/tSP+4wrKx3pT17UAa39qw8ZDUf2nCTwCKxyTjFH1oA2BqMRGSTmg6hCeCxA9cVj+vSkNMRtC+g6b+aX7Zb/wDPTFYZz0zTeuP5UDN37ZDnAlUk9gaBdRn/AJaj86weKKYjfFzGT/rV/Ol+0xg/6xfzrnj70hJx1oA6H7SD/wAtBnp1pVuPRwfxrnsnOc0BjnrQI6JrliThx+dILh+fnODWKrsO9Lvb1NAG4LpwPvnNON1Kf4qwRI4/iNOEj/3jRcLG+t7Kcc1MLuQc5rnRPKvRjTjdTE8vzSuOx0iXbBgeo96trOePm/WuT+2T7cFj0608X1wD97PtRcLHWed/tUouCBwa5T+0LjgbunpSjU7hSSCPypAdT9obsaBdMK5U6pcEH5gR7Cnf2tOP7v5UAdR9qb+9SG9I9a5f+15gRwppP7Xl7gUDOp/tAg9eKBqH5Vy41Y948/Q0/wDtc9PL/WnYR0p1EAdKRdQ3Nk5Fc2NVU9YzSDU14wCKAOp+2qOc046gp4rll1QKeQfpTzqcbH5sj6CgDpPto7E4p6369MnNc0NSiB43Ypf7Rh7kg0AdN9vUE89Kb/aq5+6frXOnUoT/ABjrS/b4M4L4piOi/tNMcZzR/aae/wCVYC3sGM+YDTvt0HaQUAdCuoQuvDgfXipPtsY6uK5U3UZPDCn/AGiPAHmDPTrQFjpTfRgj5hzTTfR44auaM6g/LIPzo+04P+sH50BY6cXw7daeLwEVyf2t+SHGPrSi8cEfMc/WmKx1n2lTzuGKTz1PeuXF6wGS/wCtKNQcdZOKQWOqWdQOopPtKZ7YrmBftk4anfbnz1oCx05uEyOaTz1zXOC6JGd3J604XMjDhulAWOhM64oE64rA89wclqX7T780Csb3nrn/AAp3nx4rB+1nrmgXR654oCxvCWI+1O8xKwPtRzwaX7U+PvUAbvmRZzQGiY1zz6g6+/NMOpOpoCx0h8g8AjJpcRY6iuYOqOOcdaT+1JMYwPwpgdNtgPGRQUhwQGBrlzqcmQaQ6pJ69qBHT7IsjpTtsakfdrlv7TfuSKd/akm4AnP40wOoKwtyAufWmmCMnsa5v+1JD3IHYUo1OX1oA6LyYs9iaBCg6Yrnv7VOcfNntSnVXzwT+VAG/wDZ0JzimeQoPNYn9rvjqTS/2uSOKANkwpnmjy481jHVm4o/tfkUBY2PIQ/w0nkR9AOazBrBB74pf7W47ZouBp/Zo/f86aYEzxWcdWGcipP7TGee9FwsXjbjsTS/ZgepNUP7WA4xkU8aspA460XCxdFqv980fZR2PFUf7VUDnn6Uq6qOuO3rRcLF37MAeW5xQLfp81VP7UU9uvegakoI4zzii4F0W7HjdmlNuy/xc/Sqq6mo9M+9OGpIeTgUXCxOIWxyRTvJbb94VX/tBD2p4vkzRcLD/LcKMEUeQ5IORTRfR5Ao+2Jmi4WHeW46YNKEf2pBeRnrQLxMckUXCw4RP04pQjjjH6037WnrTvtkR6Hmi4DsP6U3Dgk4pftUYHUUv2mPH3uadwsIAx4wRTDFJkjHFSi5iIA3daQ3EZHXNFwsV2ikNBV1XFT+fHj71NMiE/ezSuBHEWLcfrVgrJtyKjV40bgipPtEZHLCgB6RuwxUqW8jDtxUAu4sZB6U77ao/j47UAWkDpwRwKsJKRjis9dQ+XOQR71It8meQDTEaIuWHOKct0fSqP22Nugp6XUTHBBFAF03R5qSO5IAGTVIyR8c1IJEBHNMDQF0oHXmpUvMnA6VnZRjTipA+U0h3NYzgAGopbtgpIPIrNF15fEg6UyW7UjINKw7mN4n1NjbMHPrxXC2qsz/AFbqK1/Elw01zsB4J5+lVLCI5BxyOtCQ27mlbpsjA71Nn6fnQgGOlLnHcUyTFeIFxnnPpVeaAdVFa0kO1iuc81DsBGQK4lI7XExZIipPHHWomU46VqzxdcL+VVzFngCrUiGilyD/AIUYyKlMRBNDKRxincmxGB0zjIpCOOoqQg9vrTSMUwGAUo579aXAFIevrQITHejA6UvbFIefxoAQ+9Hft1o6DPSl9qYWEOKPpTsEjim7cmgAI+tJ1peOOPzoxznqaADHtSUvb2oHIxQAnGcGkxzS+gpaQDQKUYzigD1o6en1oATP50UflR78nimAh6cUnalP1pD3piF4OT1pDQORxR7YpAGKbx2p3NJ/KmAhptOIwKMZHSgBvQ0H9KMUhNMQnvRS9aTt2oAQ0UUmaBCGk7UpOaaaYBSGl/Cg0ANooPWimAUUUUASqflp34U1PuCnUgEpwpMf5xSgUDHAc0fWgUtIBRzTsY703606kAHFBo7e1IaAEJpppxpretMBppKCcZpO9Ahc5pc0lHNACk8UlFAoAXtRmjqKPegBc0UlGaAHDijFJS80AJ0pSaT+tH5UwDPrS5pKB1pDHBjQCR3P50naimAu4+tG4g9aSj3oAduYn7xpdxHemUuO1IB4dhzmnCWQDrUY/KkoAsC4kXGGxR9pmA++fpUFHagCwL24H8f6UhvZiOWz+FQdKQ0wsWTfTdOKQX03TIqsP0ozRcVi2NQm9RS/2hOeSapj9aWi4WLJvZGPzc0n2xs8iq340Ci4WLQuiTjFL9pPT+tVaXtRcLFkXHJyKU3AP8NVxR0ouFi0LkbeaPPT3qr3oouHKWxcKvr+dSfbI/7pGao0Yo5g5S19oDZP8qPtACniq1NkOIm+lHMHKTm8Bxmg3SHGD2qgOwxR+dO4rF9bmPoWI96DOhORJms/3pM8UXCxqLcRBeTzTfPUfxYrO59aOfwouFjS89c/eqRbgHo1ZOaUEj1zSuFjW+0DP3hQbjphqyeTz6UuTjrRcLGsLhf72fxpRP0GayMnrnn1p4Y4wKLhY1vP5+8fzo8/phvyrJ3nnBo3sepNFx2NkTcA7qUTkdG/KscO3PJoEjY+9xSuFjcFxwMt+tO+0Hn5u1YYkcNjefrSiWTn5zii47G2LjtuH50onO3rwPSsQTPjAbIpftD5+8aLhY3PPb+91oMzf3qxFuHxyaUXUq55xRcLG2LhsnJ5py3DD/GsQ3sp789qX7bIOwHpii4cpufaWpftL9d1YYu5cdacL2TA4460rhym0Lljzk/SnCcjnNYn29x0HfilW+cjoKdw5TaFyTyOtKLhgT/Ksb7cewpwvWIzjjFHMHKbH2hsdTQJz+FY/wBvOMbelH2/plT+FFw5TY8/3NJ5w9efpWT9u6jbSi+GcEGjmDlNXz+eOn1qVbhuuax1vlxgg077aMEA0cwcpsi8I6n9alTUCCcfWsH7apJB4pVu0OMGnzC5DoRfk85PtirUN+McnPpzXNR3IOeTmrUcw3ZqkyXE6iO8xjNW47vnk4rnoJgRjOMdKtCQ7hzxTuLlNeeeN06jPrWVNclAQCaRpCQTn9ax9VuTFFuVvm7YphYy7x2mvW3fw9K0LKMbckVnJIbllYgZzW3CgVAKAJMCjb7fpS9eh/Kl20xBqlobTU5YvfIz71QdCOldX4ttgl4k4XiRetcwVweQcfSvNi7noMqshycgVB5QyfrWiy44xUBjB59+pqrk2KrWwIyvJqo0ZLH8v8/lWps29/xqJoQXOM1SZLRmmIjvTDGw5rQ8rA56ZzUTKD261SZNikyY5I5qM5q26DGBxURjz9aq4rFcZoI5p7DB4FIfX0oEN5pQOc8Y69aT+VOHTFMQvGM03v0pxHpSdTjrQA0A46fnQRS4A4FBwTkUDGnI4oz/ADpcUhHPWgQd6Qfype44o+lACgUjGjr/APqoPSgBM/pTTmnH600j9aYBRj+dAHpRQA2l6dqWk7/WgQh5z3o+lGKKAE60dqT2o70wDI60Z4ANLSHGKAG008innpTetMQh96QnuaWkoAQjim/hTjTeaYgpDS0hoASijNJmmAtGaKTPNAEkfSpPrUcZ61JSAPrTsU0UvekMcKUUmeKWgB340ZpvNFIB3pSH3oyBikoAUnvTCaDSHpTAQ0n9KWkoEApaM0fSgA7Ue9H4UdKBi0evakFLQAUd6KKADNL9KT8qO1AC0mcUZooAB+lLSUZoAWlpKWmAc0d6KKQB+FLSUtAB2ozRRmgBaP60gIFLQAh6UGijjmgBD70f55oxS49qAEx60d6Un3pKAClFJS/WgAHqKcPak7UooAUcCij8aT8KQC/Wjt2o96B60DFFKelJxml+tAB3qOY/IB6mpB+tRS8kUCIqMUtJTATFB+vFL0//AFUlMAooIo696QB/Kil+lFABS0UCgA7c07+Gkoz/AJxQAdD7U7pTQcd/1pe1IYvWik/Gl7UALxTifakH+TR70AA4pBmj2FPA5oGJ7UvGMfzozR04xSAOKXPAoP8Ak0Dr60gFByOlB6/zoA49KXGT9KBiAfnTj0BHSmjnHT8aXvyaAAYAzninHoc/nTeT3pe2ecD0oGNHp/Kn59uvNN708Dnpz70CFxgc4pP8O1PwcAGkAJIx09KQwwMbqTnIBp3bAFBXvigYh6YBOKUEnBz1NHtjrntSDIx/hQIkV/LbAJq5G/AOfy7VRzkf/XpVch1Q9CeuKuLJkjetpRxzWrCN9c/ZPiQDGPaugtQOCD1qyFqx8yskR65rmdTDFgu4muzuY/MtiRj1rjNQBFyQexx9KcWOcbDtNj3EE44FbQXC9faqWnRhYwfSr/A5zVGYnue9GD/tfkaXp2pcD0FMR1moqt/4bDjmSPHXtXGnoAwwfbtXbabC8tlc2xxjH5VxtwhS4dSDgEivKhoekytKelMBBX09asEAjmo9o/GtCCPY2Dg8Z9aYEJBPSrAGBgU0jCmi4WKzrlcjiqrLgnA6VolQByPoagIGSaaJaKDL61HsHPWrrJuB6fyquwHQ9atMlortCWP8qaY8DFTsPz6UnGO1O4rFRkHXimkcGrDICcZppTacCncViHOaT1HepGTBAppT/wCvTEM/Ec0pzk0EHJowe/amAD1xRS4/Sk4Pf8qAGngA80cEU5sc96aBQIQUlPAy2Kaww3bigBOpHrRijHr0o7UAB4PFJ/8AWpT1zSYoACOKQjjP8qUdTzRjimIQ9qT1p2P1pD06UAMxmgUoHpSgUwEIxjFNPSlY03PFAhCeKQ0vammmAd6TNL70hoAaeaSlPSkpiCkNH60lABmkpaSmAUUhpaAHJ1NTVCn36m/pSAP6Uvek5FLmgYv50uaaDR9BSAUH8aXOBTc88UZoAXPPWgnB60maTPFACk0nSj+VJQIXPPNJQKKYBS0lFIBe9A9qKKACiiigApf1pKO1AC5pOKDRQAtFJRQMXpRQD+nvRQAooHpSA0tMAz0pQaT1ozzSAWj2zSfhRQAtFBo+tAAOlLSUtAB9KKP50h70AHU0dqKKACg0UY+uKAD/ADxS4opR6UAFKPbtRgUUDDvQPxpfWjGaQC4ooxiloAQUo796KMetAC9aglPz/SrA+lVZM7ufWhCYnSk69KWkpgHrSEZNH6UY9/rQAUfWjqaMZ6UwAUpzSDP0pfr+tIBaXrTcUuTzQAvpSgUnrS9jigYc/SijvR6UAA9/50ucYpKX05pAKPrinA84600A5p2MdTQADg049ufxpMd+KOcc4pDFPrR3/GkJzzSnrQAehpDz0pfakzSAdnp7Upz1zSKR0z39aU/exg5FAw5zilwc4H6Uds8UoOKAD+Lv+dGMdehHNHcilPTpxQMTGO1KBgnjpRwV/oKdjA4//VSAUnkDr609FG0nHbio/wCL+dLng46UDHDHqSfrQegpCBn2/OnHp+tIBncn+VKOvY0Ac+vtS4yNwH4UwEwMA07uPagH8fpT48FxnvQnqJ7FmyLLIOuTXTWI3KoJrnYUJlXHY9zXR6evI6VszKO5sRJmFh1yK4q+G/VWQfNg4ruCfs1s0j9Aua4e3DT3kkufvNnp2zRE1q7I0oECIPepT/nmhOFH0p2K0OYQDPNLxRS/NQB2OnSGLVVU5CyDH41h6/ZNaanLkfI/zLWxKShjlUYZGB6UniiMzRW1yoJDDr+FeSj02cfJgAYqE5ycCpZUIc5xTdvI9a0IGAc5/SjuR60pbacCk5PPOaAGMCB/hULIQeByas7s8mmsM0xFZlwSMcGq8kY3HOauSYb6CoG689+KaE0VmUbaZ5eR0689KtbOBkUxlI6jNVcmxXVMfe7c0bATjmpQoGOOtIVC4PNO4iuUB9QKhZD1xmrhXIOBn6VG4PTH507iKhXPOKQrU+w5z6daYwGadxEWMCkJPWpAMcd6RlzkYpiIyKTqKdtJpMZoAAOfamkAnrTz+dR9e1MQY4yKTIx1NPAwKQgfiaAG0n8X40pxzSUAHek7Uo5NKR8tACHpTSD1607H5UEZpgN+lKRjofxoHQnNICD3piGMcE00U88/WmYxQIQ9KDSmm9T/APXpgIfpzRR2oPSgBD1pDS9TSHpTENpKU9KSmAUlBo7+1ABRRRQAq8MKm6moV+8KmIxSAUf5NFA6c0flSAKDSGjvQAUZpD+NH86YC0vXr+tJ+lFABR0o60HNACc0UfSigBRj0opB1pc9qACijrRQMKWkpaQhKWkxxQKAFoo/GjtQAUUGigYUCiigBfxoz70n1pe1AC/WkzR1o60ALRRzRigBelFFFAB/WgdKOfwpaACkpetJQAUUvek7UAKBmkFFLQAfypelJnmlFIBcfn70DrRS0DClApBTvwoAO/AooooAM5PXrS0lLQAHOKrv1FTn2qGXl8e1CEMpKDR/KmAUcfhR2pM80AB9M/WlznHvSCl70AFHaijFAB1HpTgM0g680vf60AHVqd0z60g5wcUoHakMQjBzilo70vagBBzTu9JS4NAxfTpS/WkH0pe/Xn+dIBKXkClPU4pOi/1oAMevIpenPagHIz2o65H6UAH60mKUevB+lFIYAYOf6U7OeT1pDwcCnAHHGaADoeaXHUYpBg5ycijpSGOHTJoUA9RSD2HGBTh6fpQAoGaMc9qUdKXGfz/OgBuPmHHWlA9qcB9BS/Q96QxoHOTRj+dKRgEn8qAcjJoAXHPvTgTk/nik5pwUk4x+tAxpA4PH50sa5YEHmnlSVJxTlU56mi4M1LRQQDitizcbxjqKzdNXdECfxrVtYyDvA4FbLUw2ZL4jvvK0oQj78nFYenxBIweM07Xbn7XqaQrysYHT1qzAoWMAcelXHQUnck/Cl7+9IAfalzgdqogRjim7z71DcXG0ED9KoGdc/eP50AeoXsQ3HB4YZFJcJ9t8Njkjyj/KrFyoktyw4IGRUGkhporm3b7h5U/WvIPTOLuItjkk8VAyjucfStDUUKTMCOQSKzXA9fxrREjT8xPHPqKMcH2piEg89Kcz9MUxC8dKjYZ5zRu9qVipUnmgCHZnJHbimlCWHfNTYwOO/rSEAcA/hTERMpGABSqgPBpxbjpzQowOcY+lMRUZNrEe/FJt3Aj9anmQHkVCOvPOadxWEdQOMdRULDLk9M1cYDkY5x3qJkwTxTFYrMpzwKiZNx6VbwAOR0HFRshzj+VO4miptI575o28/Q4q00WcHPH0qNlBc8Yp3EVzGM498UnlnGcVMUIboKTGc07isQMnNMEePzqwUIxwORTdvy9KLisQlR6cCmE9ulTEYJzTGXI6c0wIuM0GnlcmkK4GKYho46UtIP8AOKcPTPNADSuT9aVQOnrTm+Ug96azcnFMBpx0plKc/SmnpQIQ8mkHX2pTTc81QgP9abxinHrSAEmgBDSUp/rSfzoASkpx6dqbTASm+9ONN7UCEooopgFH8qKO1ACjrU5FVx1q2PujvSYEeOvWlpSKSkAUlLSUAH4UlLRQAdKKKT9aYC59KSjrRQAdqT1paSgBaKKKACl4pKKAFopM0fjQAp96KP1ooAWjrSUUgFoz3pKO1MAooooAWjtSZ5paQBmlpKWgAH5UoxSGlH+RQMWijORRQAUvakpaAA0lKaTrQAe9FAo+lABSikpaAFHSl5pKBQA4cUUUe9IYuORmlpO/SlApABo79KCKWgAFB6CjpSd+KYAeRUDkb2+pqf8AlVdutAMb3o/CiimISiij60ALR/WgDFKPpxQAYyKTFL6etFAB1GKWkHXrSjNAxwHAFKOmab707vQAUvt7Ug+uaU47UgA/hRx1/Gj1ox2oGOHU0uec8UgHNL046+tIAI4pOcnvS9aPxNACHI7+9KTnP5/SkODRjHX/APVQA4DPApQueM8UgHr607pzSGIQM4oyeuevejij8aAH4wAT26jNABx3NAyafjj6UhjAuegFKFpyrz0p4Q8e1AxmMc+9KB8wz608AAAEcCk27SO5pAKvpSlcDvTwoz7UpGB60rjI9vBJHvSqvBHYU/APXv60DrzzRcYwjHUfrTkUMfWpNuR6c0KNp60rhYXA6YwPWjvx1o75wRzTgAPei4GnpUg3GMkLW/Ky21m8p4VVzXIITE4YZ/CtG91nztL8jOJG4I9RWtN30Mpq2pUsx59zJKT945z3NbCjAx6VR09NsYrQ7H0rcxYDjFRTPtSpMcVWuOlMRn3Em1GY1k+c/r/48avX5wgAPHWs3cPQ/lQB7hYOLvS4pf76YP1qrp0xttWEXIEny4rP8HX3m2stqSTsOQK0L791dCUfKQwIIryJKzPTWxl+I7cR6jIQMKxyOKwJEP512viSETRxygHJTj3rkHzjDDkGnFiZRcbTUQyTkVZbG8nPNIq8mrJIsEDpSqmRz+tPwASMUDOQCeOtMBGTC8VCykHFWiM85xUTAZHrRcBipuOT0pQu0etOGRgZxRjHB5piI2j4J7Gq4XkZqzICVxnioMEjihAIy7h7UwjKng1MiYY56UvGSMdeKdxFcruAPXmmMuDjv7VZCheOajlBNO4rERX171EYyXX+lTbScY5zU3lhV3L19aYrFJ0xwKjA+b2qxIu5yw6E1EeeP5imIhYH600D0qdQXIHtzzTSPmIoFYgaP9TUbDBB/WrB5FRlAR1pgQlevSkIzzjv1qUqBwfXmk2jvVE2K5GDS7NqZI69BU4jDjgjjmmsuRg07isVyc01upqVkI4qMjjNMBmaG5GPWlxmg5z1oAZ3ppGD709qbgUxCEcgUZ6UrZ4FJTEIRzSEUtJ3NADT0NIaU0UwGYpDTjTaBCUUUUwCiiigAq4nMYNU6uwp+6BHr3pMYmKaePrUu3gU0gVIEVIRzT8fWm0xCfnRRilFADTR6cU6kpgJRxzRSdqAFzzSUUUAGfWj+dFHWgAooooAKX+dJQP50ALS5/Cko70ALRRRQAUGjNIaAFpKTNKKAFooFFAC0opBSikMKUY70lHegB1FIKWgAozzRQKACilpBQACilo4oADQKKWgYfWlo6d6KBCilApO3TPNOUUhhil6UUf1pAFKKTjFLQAh6c0nenH60nGaBiHpmq1WGOUNQGmhDTRRRTEJ1oFHfFLQAd6XvSUv1oAOvSigUoBoAMUvej6UHgUDAdaM4ozk0vbNIBe30pfbBpKUZ60AHT8KX09fSk7H0peoIpDHDqOelH1zR/jSf4UAKeue2aMfpQfeg/TrQAHpyM0YBP60d/8A61OFABg4yaXGQDQBk8c08DP0pDG7eR9aNuPWnhRil6YpDEAyMdDUwjyc59uKYByByfWrAxg+tK40RIOSMfhT+Saco5waNncflSuOxHjsM/SlIx0H1qUDn601gCfcUrgLGCRk07b68/WlXBBweg70p74zQUMxkikAHTAOfXin4A5wc9MUhx264pAKehz1owxP6jilK/8A6qcOeOvvQAzHqPzpVXJwM/lT15xnJ4qUqB0APNAERX5cEVEF826C+h7VYkwF4POKNNQPMX/pWtJamVV6GvbJsjAA4xip/wCdIoxxS9q6TnEOcH9KqSg9+BVzGRUMqZGRnNAGHeoG4PbmsvBHXH/fVdBcxnGcc/Ws4wnP3T/3yaAOu8J3P2fV1RiNsqlTXY6tDmLeByODXmlnN5F3FISflYZxXqMsgnsFccq6g5ryprqejEa++60RHIyyD+VcnqltsxIg4brXZ6YUNpJb5O5c5Hsa53VY8KYxwQxxUJ6lM5UnnuDTg4HXrViaHIbAwfWq2CW5PA961JDnP9RTlx/9agdBxSrz0oEI7bdvQcVEx3dqstgrhvrVZzhiP1pgN5x0p4YNznBpFGcmoj6CgCRyrJxzUKmnd8jOBSkDAwOaYhEyzdOB1pZF2twPfNPTK8+1NkyVGaAGZ3Bc+mKY65B9fepMjHao2cc+uKYiFJfLcMVDY7VIZjIegAPYVA6/N0qwkYCAj070xEbAZxwciq7J3xVtlO7PGQajCZkBJ+WgTKyqU5x36UmMSZ5xmp3PznAwAcCoivOce1O4hix5Yg9u9IwAGBUnA5NOIyD6ZpgViu5c96YBlsfhmpwhJ4pMcn1/pTEQH5flpjDmpSDhs8GmlcDFMRG4BHSoSvb9KsEAnnrRs5zTuIqFcfhSYqdl9KiK4xTuIiNNbrUuPemEdaaEN9KbTyvGaaB1piGmkNOIpp6+1MBuaQ9etONNNMBp60hp1NP1oEJR2oPWimAUCiloAVeKvWp/dEHtVIcCrNs2M4NSxoslajZeKm3ZxSOuRUjKxUZ4ppUelTbSPpTGHtTEMxmgrT/WmnmmAwikxTj1pP60CG02nmkIpgNopcfhSYoAKKKPzoAKKKKACgUd6OtAC/jSikzS9qACkJ5oooATNFFJmgBaM4pKWgBaWm0vagBaXNJS0ALRSDmlB596Qw6UooooAWlpO+KWgANFFA9aQBR1paUUwE56UCnEYooAKMDNGPWl6elIYUooxgelA9qQC/0pQMGgUY7UAH04oxxzS/zo6UANNBpR9aDwM8UARy8IBUBqaU81DVIQlHag+1HegBKWkIFL3oAKBRS/WgA780vFFFAB+NL1o7ClpDDBBHalxxyaQU7oKADPTBo9P6UoHSkIx34pDDHPXinCkFL+PNAC0YoA460pHtSGJj5qXH+cUoUZp5U8UANK7eD1pFGPwNSP8y5PX1pqjkZoAVfunjNOB4FHQHnpSgfQ0higEkCnFSAPXvSqByaVgenekMaBk5wfSpgTTQMLz1py465PFJjFA4PPelGM8Y9KD94jNByOo6e9IYo78fSmsu0gEfhSqcHrwPancbievtQAqjYopccE54prDK461LsIAxjrSGIRkdOnFJj2HFPK4bnihj8vA/8ArUDEbqppAR1HNIA2e9SbRgZz9KQCqQB0FPPI9TTMY579qcATyccc80wILlsKAeSenFaenQmOIcde9Zh/fXig84rehTEYAXHFdNNWRy1HdkmKf15poPPNOA7mtTMMc0pQGgU78aAIHgBqH7H7Kfw/+tV3HtRg+tAGBXpHhy5+2eHNpOWj+Xn9K8y86In5XHPvXb+A7wNLNZ4DBhkH0rzai0O+MtTptMby7wF+jLt59aydeAW/kHQZyMGtQ7oLx0K42tke9Vdeg3Sxy8YYY+tc/U1OWnT94QOhqlJEd2M/StqaD91nHINZc4wxU1smQysSQSucj3pYz8vB5qRsbenNRKvPBpiFLlk2/wAVRhS2d1KOHyRSjJ5zj3xQA0DYMVHkZyRkU4kZyeabkEUwF4I6HFA5XkY54pf4egIpCAAuOopgK3AzjpS5DKOORTQfX9KViFxjp0NAEEpCuAAMEUmw9T6U4oXPseeaGYk4x3piIyo6/rT0bC+3amyDjOBg0AZUHoaBDhhmPv0NRMCp2tnI6mpYW/eZI4PU02THmkj1piKzZJ5zkmkbhx6VISPzNMcYNAhp+lKU+XilGePf2pwHBBHJ/SmIhAwTkjmkZDnIPHpUgUZwwpGGOnSgCJlJHPJPWo2QEDnkVKcF+h4ppGTwOaaEQFcdKXoc1I6ADpUR4BzTEMZRTGUMBjr3qfG5cDrTDz2xTEViABTME884qbANJtBJx+tUIiYYXFN2gEVIw4IBpPr64poREVyOlMIwamYcH6VHgYOaYDCO9MqRhg0wimIYaSnGmkUxCUmKWkxTAKUUnHSlHFADv881PbnBIqvmpIT84pMC5k0ob1qPdRmpKJQQfpTWHPT9aYG+tO3Z96AEIxTSKeWpMjFAiPFJT+wphwKYDelIacabQITH60lLRTATrzSd6U9aSgAx7UUUUAFA+lFFAC0d6KKAA/jSUGigBKKKKACjvRRQAUtJmj/PFADh7U4U0fX8KUUALS0lFIBw/wA8UUg60tAxyD5xmpeMVGnXmpQKAEx2xShR9ailkOcKcYPJpz5zwcHsKAJNo9KXaMcCoSzEM2cBT09akMgCZHX0oAdtBo2LnOKYrMHKnk9aUSny9x5OeKQDtgpfLHvTVclScg/SnI+Yt5wKBiiIev4U4Q+9Nik3gkjHNBm2scLkA4JzQA7ygccml8ketIZx12nGcZqR32AcEk8DFIY3yfRqPI7Zz+FOWdducMOcYxzQsqsu7aeuMYosA37OexpkkJC53VZjdXXcvIplwcJ7mgDOfBY57VGakfqcE81H3qiRKPajvSjrQAmKPal7dKTv2oAOaUUg9qUfnQAv4UUCnYpDADFKBxQOtOHJpAHNKaCOtA6c0DEFKRnn3o5z0pwH4UAMxjnmnbc8U7b36+lAHNIA2joDQBgZpcc4pce9AwUY45p4GV5pB0Bpw9O9IYnJz6d6Bwacce/tS44HHHvQAn8/WpFFR455qZD8oOOfSkxhtxk9KUc0E9+SaMYXOM0hgAMdeacAeBjgcUwNgk08Me1IBcDOTzSlcn+ppBnnPWpANh5HPX60DGLhTg9/alHLduOMU5gAAe+cUIPXrSGIcYyD9easLjbnoe9REAP0qTcW7dDQMOGPrmlDDoPWkBGTx19OtIo5yecUgHcAZ4yf0pTztz1puSH5HHXNLzg5/DrQMOvH5059qxEnsPxpAeQRmo7lh5QXnLGnFXdiZOyH6dEXlMjA9cVuqOAAKo6fD5cYz1q907YrtSsjjbuxcfnTsU0dKcB7GmIWlx60g+vFLQA7NLk46Gk96OO3T6UAcT5Kk5xW74VvxpetQPn5C4DANjNYw4PpUi53BlxkEVxtXR0p2PcNQTM8cqfdccEd6ralEZ9Kyoy8bZ/CnaJc/wBqeHbWd+ZAMNznkVZhKm6e3ODlelcTVmdSehyuN6EH0rJvYtuG/Aity5U295LEQV2seKydQBMgx0IzVREzLkyRwcH2poUk4HWnT5U4FRIXEnetCQbOelWLeMSxlV+/1571G4yvaktmKThuwPNMCKSErncO+DUJIBIPrWxPHnkYINVbixJYuo/CmIqx/dxjI7U0H5jninrnGSORUTq2Qc5FADx1OKCBjpzUana4I7mncjHPGetAxeOnFO4x7U1ByQeaeB8rA4xmgRG3EnXI9KcF3HkY75p0cQBJ9KJQExt7UAV2UB8LwO1Dgc8HmnEAY+nFM5Zuv50xDDGCuc0iKplUPjB44qZeAeMgjFR7DtwM5pgRSJsb5en+f/rU3r9albOCO9NZCo+v50yRgGeDTpVxkjpSKvTpSS8kAH60AR4z2z+NR5A6Z47VaQDIIGKhMfz5xxQIjc9cZ54pojyOTk5qYpuJI4pMLg9j1piIGXbnmoiODnpUzJ8/UetNAHUimIr4wMGkwenpTyM8daAp4FUIYAc1FjBqccNUeDkUxMZjjJ9KYy85HFS8c5xTD0piIWXA+nFMx3qcjPeo8H0piIW/OmH8KmYYqIjmqAZig040mM0xCUUo6UmKACpIv9YKjFSRsFctgn6UgLXNNNRG6HZDS/aU64NKwXJKXpxUQuYyOh/Kl+0xdyfyosFyQ0nTjpTPPix96l86Mj79AXHZPrTT+lMMsfZhR5iH+KgBe/vSUIGmkWOP5mY4AHenTxS20rRTRtHIOoYYpgMNFJuHrSDHrQAp9qKTj2paAAmk70UZoAWik7U4KWIwKGAlFSmCQ9BSfZZ2/urSuhXIvzpKkNpKBwQfeke0lSPIJZs9KLoLjDSd6YPNUEsCFHqKRZSxwFP4UwuS0Ud8UUALRSUuaAFpc02nZ+uaBi80UmaWkAoPNO7UwfpTxQA+PFSfhUIODTt/OKBjmRWIyBS+V828MR6UzeaeJKAF8n34PJGKc0YYe/rTd59KPM9qAFCEZOfmNJ5X7sKSDzmgSe1L5nqKAAIwDHI3GhYm2KGbkelKJR6UvmdOlIYRxbVwzUnlPgrjgnOc08SADODSiUE5oAb5JZwAMLnJ5qWSIuyf3RnPNIJlPGCO1PEq46UARKjRlGKk4JzinYKxsWDAsc/LUomQeop3mp/e/SlcY23UpEoIwaivGAIHftU3nL6k1RuZBLNkdAMUAQt1yOaYTzSmkPrVEiUUUUABoo/nRmgAoBoHQinCgApSeaBzilwQaQxQOuad26UgHQ0o5PpSAce/XNAA60pFAHPrQMUDk0DjpQvXrxSk4INIYqjKgnpTSPmB6808cjjp3pQo64pAIeuaXb+NL34oHbAOaBjcc+uad0789TTtvTFIAc+1IBRyMYp3UetIOcUoJyB+dADU5bb6ipgMcDGKizk5xzmnljxgnNIY5sZFGckcCkxn1z605UHX86QxVUHnA6YFKFPII9qcmMA05gPxzQMaMbuOvtTslvxpMAfj61Iq/NSGRnJbBAP1p6jnt1xShcg54zTlXHWkMQpzgDPGKRSQSOfepME85oK9OPwoGJjPOOAKeoPPHtSL8oI9Kfzt4/KgAKq2ccVE4K8YxUo7DHXjFKVIUZGaQEI5I56+tRhfPvAv93tU7gLEW29BSaYhkkLnkk9a2pLW5jVdtDZiTYij2qXigDtil6/Wuk5gANLj2/Cjt1pcGmAopQMjFIKcASeOtAFO/vo7NOvzYzis0a4+Og/OquoQzPdyO6MOeuO1UcN/eP61DlLoaJRsSd6kTg80wcUox39KwND0P4f6oPKnsTkD74FdJezrbazBIeEbAP48V5v4PmMPiO2xJtV8qQD1H+cV3+vBh5WCcjJHtXLVVpHRB3RDrcXl6i0hOVcA1g3ijG4V1F8Rf6FFcqA0qj5uPzrmZhuixis0WzGm4OTTV557ip5/mTbgZ6VXHynB5rUgmPCZ7EVAXC881YZwYvaofKzzigANw4CrnIFW47sTEjaQwrPkUhxzxTs7cnNMRNcgKSQMZ9KrHkcCrsZ8+1JONwNVmQxsVZSPrTArY9B3qRRn5qXyzk56HpTQH65oAUf64cfe4qQ9SAear4J9cjmraSKI33dWAOaBj0CJGzZy+elVmGWyeDmpCMgn1pMgHrwKBDAvzZJqOZM8jsKmyCCBTD06e/HegCMbsYpyrweeQaUAjgjAzQRtzycGmBGEy27pTnUbAT1/nVhUz1HU1DJleM89OtMRBIoBFRtGQNw571Kw3EDFH0HBoEVwTgHPNJu5GeR3qQrnjuKicEjOPemIUnBpCBgUoUle+aaevGaBETDGfWoxx261Mw6560wDv2qhEIXn6Gmnjp1qXGM5qJjknvTExvFCj5gD3PWkXqM08jEhPQCmIh24Yjv/ACphXripmHOc/nTCO1O4rERH+FMx/k1KVJPemOMHGO9MRCwwOlRN1/Gp2Xr61E3IqhMiPAoxzSnt1pccdaYhp6U3FPpO3SgBKVeKKTvTESE0mPQUAUYHegAAHoKMDnilooAQKufuik2r6U6k60ANMa9hSeWtPpKBDUzFIrxsyMvIKnBH406eSS4k8yeWSV8Y3OxY/maSkoAZsHvSbPen9KKYDdnuaMH1NOopAJ83rSqrs2Bmgc+9aFpbHGSOTUylYCGC1dmzIeKvJCF4CgVYWI+lTJGVH161hKdxlXy8U/ys9M1ZEYHY08KOKzcgsUvKI7delL5J6+varyw7sUhiVetLmAoNb5BDDIqhJpxRiYWwT2rf2gdeaiaLI6cU41WhWOX+zyRy4dgp9SaexRWClgc9xW1d2STREEYPrWHd2a2oHzkk+1dMJqQbCnjrnmkDCmRz7yEfHoCKey7TitAuLvX1pwZeeRUdFAEgYetPyPUVDgGlxRYdyUYwCSKUEZzkVBShQe1FguTZGeopdw9RUOBRjvSsFybeM0/IHOcZqtsFLtFFguWNy4NG5epaoNgHrRtHvRYdywCM9RQSBxmq5QdjSbB03UWC5Yzz1pc1W257n86Nh/vGiwXLW/3pQR2qns9zSiM/3jSsFy9jPfrTl69qohGH8bD8aUIQfvt6daLDuXyKMHB9KpAP081/zpw34/1jfnSsHMWmbCnFVFPBOeaN5RhuJYU0HJosFxSfWkozR1pgJRQfpRQAd6PfFHWjGDQAo4NL3pKdQMVQcZ6mn44FIvXmnc1ICY6UoGPSlA5pe+DSGKB78e9N5/Cnjr14pCBgCgAH9KXgdaFHFOC856UDAZ6d6eBSAYGaCfUcUgAdyKcBxupnAHJzUmcKMfpSGNY/5NKen49aCMf4UtADlGB7Z/SlIx/9akycdfpT8ZPtSGR4I5607HQ44xTgvY8ZpByOtIB8Q3Z9qeP0x3pFG1dw/WlGM0FDuOMUZAJOePelVSGyDQRg4PX+dIYuMAnAHvUiDg5PX0piAbCMc1KBwT7UhiBecU8LyN2KaDgnjj6U8HjJ70DELDPUj+lNXaF68UuQVzjA/pUfUHjrSAfgZ6DHXNSAcYqNeABzUqDOD/WgYnXnPHalIYqCentRjB70EAKaAK1052BBkA9vWtPTogkQ456/WstVM9yqH7q+lb8CbUFddJWRx1JXZLxS84pBxS1qZhTsetIOaXoKAFxmnjg8HGKb3pwoAnZreWPEsCsai8mwHH2GL8RRwetG0mgDix69KUAfhSDgcfrTq5ToLFjObW7imB2lHBB9MV65qeLzTIbtOpAavHR644r1fw3djUfCSJgF41KEDsO1YVl1Nab6EukO01jdWbAcDcMd8/5FYTxp8y5x2rT0aYw6wEc/fUqeO4qjqcajUJgBj5z2rA2MCVTHI0bdulV26nmr1/G6upxwR1rPOea1RLFC78jPApxIxxUffANIQQOtMQrgsMg9OKWNQyNu/OmKSTtGMEUsWcsO2PzoAntpURipyB2IqS4QOdwPPoaqAdcrn2pwlcEZ5FMQx8g+o9xTuSBz1IqcqrjgHBqsMA7evpQBNLGsGD1B5qsWJPtU+3eoUk+2ag2npkCgCVW/cjnpTZPvYppGFA/lSdFJ9O1ACgbcH+lIwJGe/pSr82OtPA544oAQEk/MaGB25pDjIPTFBJKbcH2oAliIYEL1Ud6hugchs4zT1OyTOCG6VMyBgBjnHSmIomPJ3elBLDg5wTT2Qgt2+tIo3bhjpQBHtIPPHvUWwk5PAqyDkFSP0ppUAHBGe1Ait070gAyOBipWAH4UhX8gO1USRumTnHQVCwB9qsMCT17c1A2enegCJ1z0x+NRldx6fjVhQDnP5VGVAOf1qhEBjO4fypzj5GP0FSHoM/nSMvyp78mmSQ9Kaw4zUrLntgCo3HH0pgRdye9McA088UxuaYiJl+UVC/FWGxjHeoXHNUhEdJ0HFOx6Uh6VRI32oxRRQAnSkzig9RSCmIkHSlpB0oJA96B2FopuST2pe3ekFhaP6U3PvSjJ+lMQdOtJS4pKAEpKKDQITpR34oo70AFFIDnJxTlBYgDvSYFmzg8wliOB0rZhhxgkHNV7KPaoA6Ec1qxIAM1y1J3Y0MSL25+lSpHlsd6fjI4FPRGHbis7pLUrYbsC+9MKgVbEQODilMIJxio9qFyFeB/Wm+UHlOOwqfyih6cUxPkzk8k5q1KMgIfLGelI0fb27VbGDxQ0R9KzkrA0Z5i+Ug1Ru7MToUKg+9a7JweKhdOOetOM2mSYK2EcQxjLDvVe4gwM4GRW3JFVOaMMCK6YVGwMTvS1JKu1yOlR10piCloA5pKAFFLSCl70AOxRj8KBS0AGMmlxikzSg0DCiig0gD/9dHWil9aAEFLigenSloATFLtxS9qXtQA3IUcnFP8Al7MD9DRgEcikCqDkDmgYuKXFGQaXP+NAiCdTsyBUUL9jVqX7hBxVMfK+KALGPzo60A5FFIYn0opaTrQMOlLSUtAC9adimr3p4/WkMUDnPanjHcU1elO/GkA40BehoPJpR0xSGL9fWl9xRig9CPWkMB0pxHy0i8/hSk//AF6AAeg59qPek7+5p+3OMg0DGEY61IBwAcUjCnL0z70gAH1/GlXr1600Cn+9IYu3n+lSKuOnH1pq4759qkQ5z0xSAbIDn5R+VMwAO59qlxkjPejAORjn3oGCHI/xp45PNNVeeO/SnJ97HOMUikSjnn1pNobk/wAqAD705RhaQxVHHajpzmjGO/40HjgEjAoAOn0pHYnkDpxwKDyOtOwSDnmgYv3vvDJ+tM8r8BjvT/1px7kjikAzndgevSpVyBUa5J749qkH93NAxevTOcU1m2ZJ7A9qdgDDY5qvePhAmcluKcVd2Jm7Ik0yNmcyEZ5rbUD0qlYQ+VAufT0q91Fd0VZHE9xfr1ooxSimIUClA7ijrSgUAA607ApOhHPNLnvmgBcYNJkeg/Kl6UZ4oA43PJpR07U0YApc9xXKbjwc13/w4vDuvLRiSCN6r6etefDNdF4JvDaeI4FLMFlypAbrx6VE1eLLg9Trbpza6t54U/u3LVe1+0VkivY1G1wN1R63F5V3nHyuKvQSNfeHSpUFlUgYHpXGzpOQuwHhPqORWI+5gB37iuhkUeWST2rHdAJDVxZLRVRCrZ7Urrk4IwankycAEComwcKwz6GtBDVVQRk04pjkU4gdvwp2cLkdMflTEQqPmxkUMoAzn86UCiQcKScevvQA+3k3A+o4NR7cOWPJojKq/HTpUk2EXPAIoAULng/jVdxtcrzkE8/jT1nHO0HjtUZJJJ/mKABxhguBn+VMZcE96lkJKLjqOKazANkE+/NAEfYc8+1SqM556c1H1HGamVG2o3HPUUAN5z605jgnHanINzMARnGRUbfL26UANyxBOc1aEiqFyRkYqAYIBB7dKaVZjgcnpxQIlnG5gV5zjFQrEASTnB5q7DCwjDPjrxiq8pCDZnJPNMRABgg4yKjlOGxjoKmY/KMnrUTfOuKAGqMDceeKbIQIV29T1AqXGAQWwQOopjjJz260wI2I2fWq8gJIP6VYYZQA5puMsB70ySJAQAKjcdsfpVjqc/pUTfeJ7CmhEBX1p91lTHnsooIzx/KllIZR7YpiITz2+tMbOP8AGnA5TrTWJYHFMCFhhs9qjbpUx75FRSA561SEQtz1qNyKlI6f4VEcE+1UiWMNNNSEUz1qiRp44pOgNDHmmFuMUDsB60o5FNqW3KA5aPeewzTHy2Gj9KUZY4AyatLPanhrT8n6UgjOSUG1e2etILohEMmR8jflT/JPBY49qmMgQDDEnp1qFnJPWgVxpCp3FHXHIpmOaOnQ0xDiueabs56UGkx70CGspHvTTx14qTJoILA96BEJkGOFJ9KRiQ4z8p7irfmpHErBA2OGXuKr3EqTlTGpz0wRzQhEbSMrAL0/nV60USOMg1SaCYFSQK1bGNl5PFTN2QI17WMYwR9avDj5RyT3qvb4CZ749KkTLNnPFcT7miLEacf55qwEwPc1EoHWpg52CueTuIlCBlA7/Wjbg5x0pUHHU1JhQMnmswK5GcmgxZXpU+xWFLsHb0ovYZVKEDGOM80oOBVnaCpBHNVmBRjwSDWkJ9GNCOoPSq0qDHI4q0GBB4qOVSVyKqStqJoz5U/iBrNvHCfjWs6nBBrntRfDt+QrajqJK7KMzAvkCo8kkADJNMdiB3p1vcPAN21SCe9dyWmgT0LRtcY+cA+h70XMHlIHIPJqS7miu9MWUHEiONy56VTkuXkt1iJyFOQTRGMnqZ3ClpuSpUOCMjIzTh1qmUPGcUUgNGc96QwpaTpQOKAFBz1paBR25pAFAoo+lAC9TR3pR7UD1oAWjFFOB96ADt3o6iiigBaUU38aXNAA3SqUwwQatMeKgkGQRTAfGcr7U+oIWwCCeanxwMUmNCUd6Wk5zSGB6UuPWgClx0/nQADinjH4Ug69acvf0pDHCnfnQMD/APVQB06Uhh179qUdaQAjinKtIB3v3pG7elKeM96UDOOlIY1cgUuCMZpwUdRRjk9BigA4J96f1AwRx1puMdOlKo655pDFPzd6F6YHpSkdf/10KcL70AA+7nj86X8c0qAjocUpHNIYDBQnHPSnKxC0KpxjI5o5JAHrSGSdOvWjHP1o/hz/APqp3JTNIYgyHz78U9AN3PYZyaQcHPT+lA65OM0DJCR0Hp29KkTlM45qMMAecipQcL06+lIY0nAHH0pucDOMZpSCPu9KFBbOR07UDHrtODxwM0gJyT2zRwTyPpSov5e1IBM4XGOaUZYEe1IwxwDT48D29xQMYo+b6dqlboCO9MaPGWHXuKcGO0e1AChc9OlVYR518c544zVpm2QM+cHHU03SoW3GTue5raitbmFZ2VjWij2qKlHFIBjrnFLXUcwuPpQPfij+VRzzrDHk8k9qTdgSvsSPKsYyxwKoTasinEY3VTubhpOpPrVBu5FYyq9jdUu5fbVrhmyAv5Un9pXAxyPyqjgVKozyecGo9pIrkialtq4ZgsgwfWr/ANri/vD865vYBlucVGbog4yfzq1VfUh010IwetKOKb0PpTgcVIx2cVa024+zanbTngI4Y9qqg5FBHuRSaGnY9p1SH7XaLIOTtDKQPaqehTArcwA4ZhlQfpijwlfrqfhxFZizw/Ickk1BZMttqikqdu4oT+NcMlZ2OuLurmPMpGVbI5wazLmNVJHcciun8RW4t9RPljAcbj9a5y4GTu7d6IsGZx5znseOacTv7c0jcOwzjmlRgrHPetiBUxtJ6GkQkqTzSOwd8A4A4FIHZDx0piFTnjGeaSbGADTVbBOKGOVPOR2oAQDkZ7dakciRSO+OtNQjAyBn3709ACcDGCOBQBFtCR9MZpg4HJzWhJDHswSAMd+1VTayKpY4we470AIP9XxzUJUZBWpgDswc0xuCOe2KAEQHOeoFSJiSTac5PIpi8cYppYiQMox6UATquJQe470+T5nOB15qFZAzc8E9icVNIQm0k5JHrQIrcBgOgPFPVijcEZ7E9qRV3kkdqdjjAoGPjneMbjypPIqEqZnwMZbJHPem5OME45oUnOc9KBWEkz0IPFNI+UBe1PkZi/PIzSsgwCOhFAETjIznqaRiAoXH41NsYuMd+maWaJN+c5GOcetMRWbLIMepqMjHc4q0wAQBcdPyqrKpGVP6UxCYwT7VDgnIxxntUy5MeTjOacqFkyOtMRDGoVix6gGoByrD34qzNujjUkY3dKgj6E0xEW0gZ7Uzbx1qVm6ios+tUhETjB5FRHk96lkIznFRZNMRFKcZx+VRYwvpUs3ygtiqrSflVoVrisQBUZORgU/YTnsPekLIjfLyaofKRkcc00jNWUglmOei+tWYrSKMZYbm96AukUY4JJPuj5fWra2wCAFicfhVk4HA/KozIN2Np+tMhu4ixrGP3ceT65qOQStnKnntUwkA4xkntTi5HJBoEUtjDqhpCD/dNXc7hnBoxQBn/MTwDSbWHODWhTSKAKJOOtIuTwKulfUCkx6AflQIgEI6kmmSSKowOakuGKqAO9VetA0gBIyTjB4IPerOnIhkO9FwOQc1VAyMGlQlG46UA4mjduqt+6KE+56U+zlJIzz71nO26prSUxH2zWc1oHKdKDiLA71LGDkGqUUyyBcMCKvRt8wx61yVNEMshuOakJwvBzUeeQQakXp8w/KuZiHqHbnPFSgkcGgEjBHpTshsVAxwI9adu6VCODipACOR0pNAPPSmOAFNLnA/wprkAYzQhlbJDH60+U5TFNfkjmk5wa6FrEZRucxozVzF6S0gz9TXT3obyzn6Vyly+Lp+eBxXTQWg4EaQNKrEDkcDirZ0wiSOMKMMuST7VLpU6AyhwPl5q1aaik4kZgBsz37Vs5TWyMpu7M6+04WSBxzG3B9qnNmraZCYwPOHXA6g1El8l6jWsvGT8hqxLerbanGuQqiPB+vNNyqWS6ozJdZt4/IjQKPNhwCR3FYxXacGp7jUPN1AtuJixjPrUNw+JOCCOx9RThGSWpSEozSUd6soWlzSUUAL396AeaQGikAtKKbmlHpigBwpRTQaWgB2aKb3pc0AOzx2opMjFNPpnmgB5NGaTzpQpTKlfQgfzptAA2femHmnGkNCAiX5ZcHvVgGq8g5DDtUyNlaGNElJSg/L70lIYd6UUCjPNIBw+lPAJpi/5xTx1xSYxx9qUde1BP50UhjhxkilpBnHvTiOgpDE64p4746mmgcj0pecd6AH5OOtGB2FIDxSikMcRnjoKE5P1FJjjIpV69f1oGOPQmm4ycevanEY57Uqrg80gALTwpB96AOMg5p4+uD6UhjVGP8A69GKcynJx+NOUHJ5pDHDBOPQZpRxgDvQo4Ynv+tJ/FwOO3NADtnFIBt+lPzweOaa2SCR1oKDpjrUwwR+HeowBkr2HWnqpxz2FIBdvIBOaNuDnNA+9ljinYGeuaQxiDLZqQ+gFCKFAp4GDkigZFIDu6exxTo1wwxSMSc565zTVY7ucke1AEh+8efzoA5wD75oJ/ixxSqMvj1pAV7xjsSIDljWlYReXAuRWXGDLqAVuVXIrfiUKgHFdlONonHUldj+1LSfjTq1MxskgiiLE8DtWLJMZWLMT9PSr+oSgFYx261mOOwxg9a5qstbHTTjZXIpWBHTvjiomHXgU5x83cYoHOBzWRoLGpbtx71MF5wBQMhc9KBywAwf1pAQ3Z8uEkZHGTWJ9olPOwf99Vo6tckoI0HJ4qgLdQAD5me+DW0I6GM5WZ0GyMfwL+IoNvG3JQY9RxS54p43YyM4+lXYRWktgozGc47GoR9K0QMKWYcY4rPfAkPbuKloZ2Xw/vhDqclmzELOuVHuK6fVUCXEgGRu54FeceH79dP1m2nbIAcA4969U15Ve2WZOe+R6Vx1laVzopO6GajbjUdGW8z++WPPHc1xj/NFnpmux0md5dKuIRglM7R9RXKSApvjddrgng1kjVmVICrjuKjYYGR0qedSSTg5B7VCcZG0VsjNkAYq/se5qYRlunWophiQHpViOU+XtwDTEQhSrEZ6mhQCuR1pZODuPBzSqFwT2PNMBue/GBUZfBDg4549al2DcCaR1Rcdxn8aAFQv5nzMSOxz2q/A4kBVvvHtWeCCgK5BHGKnBwikdQeooAJrdovm6j2qqwIYfLnPQ1ZLsUZS+DnNRqwXgjPuaQDUj3zYzjPTNEiBOO/enx4eRcetRTOGncdgelMBhXGDT958rB5IPFI43e1NGdvJoAepweDnPWncbxmo84II7j1px3c5FABKgRlIHBPNJuG4HFOc5jVs49qiVipGe/agLj2AL5OMUMCVyD2HSh13RHB4zT0G1AB+tAiKIgtjpjp706Ujaox9aQAq+cd6eQGGD6c0AREjbnnOagkUiU5PFTsMMQOlRHleKaJGKNyEY49amiXAODz/AEpMAZAxjFMgm8lmJHABx70xEV2Qz4HIQYBqGMfepW5P60Lww+lUIgkHzE/pUWNo96mkwZMZ+tRspP07U0JkTYP0qLbg1JxTT0z71RIwOY5FYKpIOcN3rRbWbcxFJdMgbPUhQP6VmHliRxTW6e1UguTPcaczHNgyjsFc1JBe6PDkvp8jtnOWPT9aoMKilAqkJyZtf2vo7EZsplX2b/69B1LReD9nnA7/ADVz+KQiqsRc3jqGinpFce/zU37ZozdftC+2c/0rCwKTFFhG79o0YE4e4+uBTvtOk/8APaYfVBWBikPU0WGdB5mlkZF2w+qUuNOYZGoLn3SudxSkU7AdEILNiAt/Cc98GnnSjjK3MBH1I/pXOKoxmn4x3osFzdbSZ15DRN9HH9aYdNu8/LEW+jA1jb37O34GnG4nPWZz9WNKwrl24067K/8AHtICOny1msrKxVgQw6gjpU63l0n3Z3HtmnNqNyeWcMfVlBosUpIq0cGrH9oPn5oYm/4DSLcRv961T8CRRYfMiEdfepY+2Kd5lofvRSL9DmpFks+NsrA/7QqWmO6HozIwKsQavQ38in5gG/Gq62xcBomDjpkU8208fJQ49awnFPce5qJqkWPmyD9KuR3sDjiQfTOK57bIOSjfXFG7saxdJMOU6iOdDxu4NTiVR05FciGIIwSDTxPKowJXA+tZugHKdasybgf6VL5ileK5Fb26U8SnAHepF1O6Xup/CpdBiszqC6jnIo27hu9a5pdYmByUBJ96lGuMfvRn86XsZBZmzJtXPrSIMjd6VhtrCt/CwNSrrK4IJIz7VrGnJIepb1E5hIJ5rj7rH2mTt81bl1qkEnAY591NYd06SSllPXqK6KEWlqOOhWbIPyORkc0zlAcEgng+9PoxkV1JkuIwZUqyfeBzmlkkaViz/eJ604Ag0pBai5PKIr4UpgY+lPgjaeRUHSmAFWB7ip4ZPLDEDDHjNNvQlxYFSrEEdKSnxyKrlpFLg9eaJEQgvEeP7p7VJVhlLSoEbO59p9xxTmgdfQjHUGkFhnajNIaKYhaWkooAWlziko6UgFzS00Hml/HpQAtHvRRQAGkpc/WkoAG6U00GkpgIwJFEJ6ilpg+Sb2NAFoD60YoByP8A61KcVJQnc0DucUClByaQDlH5U8HBxTQMUo4IpMY8jt1pMZOKUcnoaccE9KQwQZ7+9SMcEdKavHT6U5j+lIYe+TS8ce1N3fL2p+CcYoAQAAcjIpexNO28be9NBOcY+tIY7b8vWlUYK/WlC9MUrY3UhisCxOPwpB1Ap6AdSaFHfsPakMUAgD+VL0boc0oPWlAPpigBcnPPTrTgACememaYvLVKgDNweRSGJjr/AFpQvAzwPWnAYGcZyc5oPXGM8YpDE4dcA8844pygD5s8du9KBhPwoPUY6jtQMVQCCR/+ugjBzRG2e3GPWnAc5PYUgDJwSfeheu4HHek+6AxP1pNoU8HrzQMezAHNCnBNR5zIF/XpUmew6Y60DEJyeox1NKgGM496aT3zwaeDyO1AD/QADFV538sEjpipi43A1UuTvZEBwTVQV2TN2Ra0qAklyRzWyowOtVrKIRwgYxxVrFdq2OJvUPw/SnJ1GaQDuBTwOnPNMRk35/0huKpO5X29Oa19Ut+ROo4xhqx5SC3TpXJUVpHXCV0REjqeeKI1JbkcUEDOM5p4+n41BQ4gAcH9aY8hiXcByRS52g84AqGO3l1CTESnGcdKqEbsiUrIqxQPe3a92PT2roRpNuAAQ2e/C/4Vd0/S47Fc/ec9TV/H+yv+fwrqUUczkcvvHccUocDoOD2zVzUdHuNLaPzCrxv9116fSqB9PTvWZqSyybwAMeuKrXK5jEijODg08elKBuBU9CKTApE8gnnnpXr+j3A1bwnbudu7y9rY5wQP514+3yuUPUV3Pw71PbLc6ZIx2yL5iD3GAa560bxua0nZnQaHP9m1BVY4WX5T9e1M8U2qwmK4G3czlTz7UuoKbe8ymVwd2RU3iKSObTIZRyHKsDXJ1Ok5OZBgg9azjlHwenrWjMcrnHNVJflk3c81rElkFwoYg5xTYX2ptxketTSKGBzUUAIB3DjPpVkEjgvGueDSkDaB3pedowOBQ2CoYCgBhJ+7TGBYnOfQfSpF+ZumCfaldCpz2PFAESR9eeBT14Bx+PvSqQPkHOeKjdzu+Ucd/rQAH5iePSmsxPvinOoZMA+lIq7cAUACgryBjHNLIAG56nFOVSc4J/Ch15BbO7jj2oAiJJGQOmKUAEYoRd2eaMHI54oAbkKwJFTsvynBGPSoQN0pGKmDZG0jpxmmBEDtTH5YpPLLEA9u9PCljjIpxO306UCCRWVVXH1NNXkdc4p25njAPXPFRMMYHvQA0Es2Pyp+CD6fWo1IaQDnj1qbHPB4ycUAMfO0njp1pmAYyR6VI4JG0GmDldo4piISDtP59aiJ5wanZcRk55HaomxuBzTEyFkwMikOVbPNSEY9qbIBtGeOaZJXlALFh1+tRkk9f/1U6Q/MT2NM6riqQiNxjPvUTHjHarDfN+VV2HNWiRAM0wjrUijGB0oKjB96YiAgCq0vLVbfgEkVTcfNVIljMcUnanU2rJEIpKceaTFADcUdqWigoAOKVVyc0YzT1GKYmA7Ud6ax9KTNBI/86TtTd1LnNMBcUmKUHmjFICNhUsZAXGKTGaQcGhiHSIGGKgZNrVODk5PWo3xuzUodi5p909ucDlfSuutXV41II5HauFVyp4roNI1FGURPww6Vz14Nq6GtDoDCmeUX8qieyt5DzEv4cYqVJAcc8+9LnBxjiuG8kO5SbSYGxgkVWk0ZsnZL+fFbHTvTd+DVKrJFczOffTrhD0B+lV2R1PKH8q6hwCBt/GozGGHKg/UVard0PmOWOeRjBppOa6SS3iOCY0/KoJraGQHfEv8AvLwf0rWM0x8yMAjNIxwMmrk1kyyHyjlewJ5FV3srvyyy20rKBkkITitlqUUZGyTTFXOcjimlvmIIIOehqVPmjwK02BlcnFKCakaMBSTUagk4qriHDkUtR5I7VKDxQAgzSiiigB1A5PWk5pf50gAoRUkc7KNpzimKStO+Ujgc0AKV38rUWSDzUoBQ5DEe1K+xl3HrQS0RClpRjbhf8mimQxOnNLSY5paAClpO/tS0gClpPaj3oAPakyaWkoAQ0lBo7UwDvTJBxkdafQeQaBEkJDL71J2HpVaBtrEVa6ipZSG4pQPrQelOHT/61IYo7jNPApuPSnjpUsoMYH0pc9+cUYz2pccUAL0GadjJHAzSdh/SnZ+b60hibc1IigKcUH/OaVT2NIY7FAGT2pe4PT6UvAP+NIYgx3FKc4wDSDqePrQcfiaAJVGEz+NNyFyfalUYXNHbFIYqdCad/CTjBoB+TjFKV470gEBzz0z1qUcMB3poX5cg0+JflBPXp9KBocR+mcUoU8nv1zSdQTj2p27070ikOxyQO3FJz1zx3po3ZOcc8cU/OBjPNIYKvWl3d+1KvTPWiUDAHQ0ARs3QHJHtR2xQeCOOKUtuA9KBjW+97/WnAFxxjNB7cfSpAuAOeDQAgTjGefakC5fjOKk7dKATjFIYbcdeT3qnbp596DnKr0q1K3lwls9BTtKh+Xeeproox6nNWfQ1o1woFPxQAAMUvSuk5wxzS45xQOp5pRQBDqMmywI7k45rn3GRmtPV5gXWMH7vOPestj3A4z1rlqv3jppq0RvINKrcn0qMsSOtKW8tM9hWdirjJg00qQoep+aursohbwqkaheO1YGj25muGnf6CunjXaBXXCNkc03djsUnNLS49x+dWQLqifbI41f7kWWzmuUcq0jFR8pPAruJLcHSb1zy3lMBj6VwkbDGD+NYs2QMMcg5FAPPFScY7800xtyQrYHOeaQyrdLtKyYPIwTU2jai+marb3aZPluCw9u9EmZYyhGT/Ws8g9COnByKTSejGnY9j1oq/lXEXMcg+Vgc0GM3nhkRquSgxk9RisPwvfjVPDEtozlp7Y5G88lexH8q2vD90xuHtmfEbpkfWvPkuV2OyLurnNmMGPjkmqVwh25xyK2763NrqE8IPyhsr9KoyLknI600xMyGkBI4NLH0P86b5eMjoc4qRCVGDWpADIBpOgA7U89x+VNHJH65oAaGwR65pz8hSPypq7VYnB980gO1Qc0wEB8ts9800lSSSevNMzkBj0pVQujcHHakA1c5wDnnpUxCqACec1GB5fA60vJOT1oAch2ucDrS3BKlcDnPWmfxBupFLKMkHHagAjwvb3yaGU457U5che1IWxwegpgMBKsCMZx3pxchuQMU1tpxnH1NNGST9M5oAkB4yODSnAAqJCckEUvVwQSMe9AiRQAwJNMcgg5PP8qJODimHg+tAAgw7egp4Py+uKbuUcHjinHvg0ABPJHrzULDa/H1p5bBBJAps52tt/ipkiMC3f8ACmMhQHPJpyEnJHpRIeOeppgQsPlPtUMnKjHNSup2HBqDI/IUxMjfr900w8HpUsq52noTUec+2TVEkbYIx3qAc1O464xUZU5qkJjdo65oOKfwO/FDAKuaZJUlPQelVW61Yc8moCOa0RDGUlOxQF9qokaFzSFeasJHx0604x5HBoGipikxUrLg9Kb1oKuIo7U48ClAA9Kax5pkjD9aSlpKAEopaTvTEKDzS5pKWgBQaUnFJjimk0hpCZpKX2pKRQUquyOGUkEelIBTgtDEbVjq/RJuPetyO7V1B3AjsRXGqlTRyyx/ccgexrmnRi3dE3OvaZSeG5A7Uol49a4w3DK3PJJzmp1v3BBEhXA4Gaz+rBc6zzKBLtGK5tNUnPWUH8BVlL6YOo3b1J54qXh2h3Nh5SRgVWklJzUbXKDqwH1qrLfRJxnJ9jSjB3C5JvxJnOB71E2sravmKYhh/cNZV3dtL8oPFUCprqhDuHMb/wDbtheHZqFmpyf9Yo+b6kjmo30yOXLadOk0ZGdrNhhWFilDMpBDEEdCK15RqbNGaCWIYkidfZhioVxycVLba7e2/DsJo+6SDORV0anpN8m25s/szn/lpDwB+H/1qXKylIyGQqMk9aAePpWu2li6jzY3cM49CdpH51Tm0u/tlLS2soUdWC5A/EUykVqBQDjrQDQULS0lLikAUA4OaOlKKAJ8qU54xzVaRxkhfxp0sgSIJ/H39qhTkimkS2TDpS0gpaDMKKXtSUAFHPFApaQBRR0oFAB2pEleJshVOOfmGc0de1IaAFknM7lmjRP9wYplHNHemAtHWkpaBDR8soPTNW1OR1qo4+XI7VLAxwDSZSJiuaeBx70n507FQUKBwKcBkUnFO4wKQwHSjPNKBzQRzQMUd6cOSKQLjJpR8q980gHkfT+lKoIJpB90k/ypy5xikULn3pQeTSEYFLgYOeaQDl4Ofwp2wHAPSmDrj+tSDkfypDAHApo4zzT1GG571IiDJ9fegCMZ6eh608Hjj6UhXB4pyevrmkMNoUetTAYiAFRgk9KcO3H5UhjmA3YyKACXHNPdQxGBmkH1560hidselPxuAP8AKgkbSfbHFPRRsBzzigYwHA60pOVDdu1ABySc+tIWJX0BoAQEHAppBGcc0lORSSTQA5F3YPQCpSoBximAgZzwBSs2Op/SkULkZODn0pR8zAUiDK5z+VP+XcOwoAp3kgYrCG5JyRWxZRCKIKCfbNZMCCe/L7cgVvIAF4FdtONkcNSV5Due9Lj3PNAH0NHGBzWhmLinKM4pBT04INAHO3533UmT0OKpHritnUtLmSQzxKWibk47VkPG4PzLz6VzSg2zpjJWGbRu9vao3LSyCGMZJParENldXb7I
gitextract_ghzsofy7/
├── .claude/
│ └── CLAUDE.md
├── .git-blame-ignore-revs
├── .github/
│ ├── ISSUE_TEMPLATE/
│ │ ├── 1.bug_report.yml
│ │ └── config.yml
│ └── workflows/
│ ├── ci.yml
│ ├── cla.yml
│ └── discord-notify.yml
├── .gitignore
├── .gitmodules
├── .python-version
├── CITATION.cff
├── CLA.md
├── LICENSE
├── Makefile
├── README.md
├── examples/
│ ├── bbox_detection/
│ │ ├── README.md
│ │ ├── data_annotated/
│ │ │ ├── 2011_000003.json
│ │ │ ├── 2011_000006.json
│ │ │ └── 2011_000025.json
│ │ ├── data_dataset_voc/
│ │ │ ├── Annotations/
│ │ │ │ ├── 2011_000003.xml
│ │ │ │ ├── 2011_000006.xml
│ │ │ │ └── 2011_000025.xml
│ │ │ └── class_names.txt
│ │ ├── labelme2voc.py
│ │ └── labels.txt
│ ├── classification/
│ │ ├── README.md
│ │ ├── data_annotated/
│ │ │ ├── 0001.json
│ │ │ └── 0002.json
│ │ └── flags.txt
│ ├── instance_segmentation/
│ │ ├── README.md
│ │ ├── data_annotated/
│ │ │ ├── 2011_000003.json
│ │ │ ├── 2011_000006.json
│ │ │ └── 2011_000025.json
│ │ ├── data_dataset_coco/
│ │ │ └── annotations.json
│ │ ├── data_dataset_voc/
│ │ │ ├── SegmentationClassNpy/
│ │ │ │ ├── 2011_000003.npy
│ │ │ │ ├── 2011_000006.npy
│ │ │ │ └── 2011_000025.npy
│ │ │ ├── SegmentationObjectNpy/
│ │ │ │ ├── 2011_000003.npy
│ │ │ │ ├── 2011_000006.npy
│ │ │ │ └── 2011_000025.npy
│ │ │ └── class_names.txt
│ │ ├── labelme2coco.py
│ │ ├── labelme2voc.py
│ │ └── labels.txt
│ ├── primitives/
│ │ └── primitives.json
│ ├── semantic_segmentation/
│ │ ├── README.md
│ │ ├── data_annotated/
│ │ │ ├── 2011_000003.json
│ │ │ ├── 2011_000006.json
│ │ │ └── 2011_000025.json
│ │ ├── data_dataset_voc/
│ │ │ ├── SegmentationClassNpy/
│ │ │ │ ├── 2011_000003.npy
│ │ │ │ ├── 2011_000006.npy
│ │ │ │ └── 2011_000025.npy
│ │ │ └── class_names.txt
│ │ └── labels.txt
│ ├── tutorial/
│ │ ├── README.md
│ │ ├── apc2016_obj3/
│ │ │ └── label_names.txt
│ │ ├── apc2016_obj3.json
│ │ ├── draw_json.py
│ │ ├── draw_label_png.py
│ │ ├── export_json.py
│ │ └── load_label_png.py
│ └── video_annotation/
│ ├── README.md
│ ├── data_annotated/
│ │ ├── 00000100.json
│ │ ├── 00000101.json
│ │ ├── 00000102.json
│ │ ├── 00000103.json
│ │ └── 00000104.json
│ ├── data_dataset_voc/
│ │ ├── SegmentationClass/
│ │ │ ├── 00000100.npy
│ │ │ ├── 00000101.npy
│ │ │ ├── 00000102.npy
│ │ │ ├── 00000103.npy
│ │ │ └── 00000104.npy
│ │ └── class_names.txt
│ └── labels.txt
├── labelme/
│ ├── __init__.py
│ ├── __main__.py
│ ├── _automation/
│ │ ├── __init__.py
│ │ ├── _osam_session.py
│ │ ├── bbox_from_text.py
│ │ └── polygon_from_mask.py
│ ├── _label_file.py
│ ├── app.py
│ ├── config/
│ │ ├── __init__.py
│ │ └── default_config.yaml
│ ├── shape.py
│ ├── testing.py
│ ├── translate/
│ │ ├── de_DE.qm
│ │ ├── de_DE.ts
│ │ ├── es_ES.qm
│ │ ├── es_ES.ts
│ │ ├── fa_IR.qm
│ │ ├── fa_IR.ts
│ │ ├── fr_FR.qm
│ │ ├── fr_FR.ts
│ │ ├── hu_HU.qm
│ │ ├── hu_HU.ts
│ │ ├── it_IT.qm
│ │ ├── it_IT.ts
│ │ ├── ja_JP.qm
│ │ ├── ja_JP.ts
│ │ ├── ko_KR.qm
│ │ ├── ko_KR.ts
│ │ ├── nl_NL.qm
│ │ ├── nl_NL.ts
│ │ ├── pl_PL.qm
│ │ ├── pl_PL.ts
│ │ ├── pt_BR.qm
│ │ ├── pt_BR.ts
│ │ ├── th_TH.qm
│ │ ├── th_TH.ts
│ │ ├── tr_TR.qm
│ │ ├── tr_TR.ts
│ │ ├── vi_VN.qm
│ │ ├── vi_VN.ts
│ │ ├── zh_CN.qm
│ │ ├── zh_CN.ts
│ │ ├── zh_TW.qm
│ │ └── zh_TW.ts
│ ├── utils/
│ │ ├── __init__.py
│ │ ├── _io.py
│ │ ├── image.py
│ │ ├── qt.py
│ │ └── shape.py
│ └── widgets/
│ ├── __init__.py
│ ├── _ai_assisted_annotation_widget.py
│ ├── _ai_text_to_annotation_widget.py
│ ├── _info_button.py
│ ├── _status.py
│ ├── brightness_contrast_dialog.py
│ ├── canvas.py
│ ├── download.py
│ ├── file_dialog_preview.py
│ ├── label_dialog.py
│ ├── label_list_widget.py
│ ├── tool_bar.py
│ ├── unique_label_qlist_widget.py
│ └── zoom_widget.py
├── pyproject.toml
├── tests/
│ ├── conftest.py
│ ├── e2e/
│ │ ├── __init__.py
│ │ ├── annotation_test.py
│ │ ├── config_test.py
│ │ ├── conftest.py
│ │ ├── file_loading_test.py
│ │ ├── navigation_test.py
│ │ └── smoke_test.py
│ └── unit/
│ ├── __init__.py
│ ├── _label_file_test.py
│ ├── config_test.py
│ ├── load_image_file_test.py
│ ├── shape_contains_point_test.py
│ ├── shape_test.py
│ ├── utils/
│ │ ├── __init__.py
│ │ ├── image_test.py
│ │ ├── qt_test.py
│ │ ├── shape_test.py
│ │ └── util.py
│ └── widgets/
│ ├── __init__.py
│ ├── canvas_test.py
│ ├── label_dialog_test.py
│ └── label_list_widget_test.py
└── tools/
└── update_translate.py
SYMBOL INDEX (428 symbols across 53 files)
FILE: examples/bbox_detection/labelme2voc.py
function main (line 22) | def main():
FILE: examples/instance_segmentation/labelme2coco.py
function main (line 25) | def main():
FILE: examples/instance_segmentation/labelme2voc.py
function main (line 16) | def main():
FILE: examples/tutorial/draw_json.py
function main (line 12) | def main():
FILE: examples/tutorial/draw_label_png.py
function main (line 12) | def main():
FILE: examples/tutorial/export_json.py
function main (line 17) | def main():
FILE: examples/tutorial/load_label_png.py
function main (line 12) | def main():
FILE: labelme/__main__.py
class _LoggerIO (line 25) | class _LoggerIO(io.StringIO):
method write (line 26) | def write(self, s: AnyStr) -> int:
method flush (line 32) | def flush(self) -> None:
method writable (line 35) | def writable(self) -> bool:
method readable (line 38) | def readable(self) -> bool:
method seekable (line 41) | def seekable(self) -> bool:
method closed (line 45) | def closed(self) -> bool:
function _setup_loguru (line 49) | def _setup_loguru(logger_level: str) -> None:
function _handle_exception (line 80) | def _handle_exception(exc_type, exc_value, exc_traceback):
function main (line 102) | def main():
FILE: labelme/_automation/_osam_session.py
class OsamSession (line 11) | class OsamSession:
method __init__ (line 16) | def __init__(
method model_name (line 28) | def model_name(self) -> str:
method run (line 31) | def run(
method _get_or_compute_embedding (line 75) | def _get_or_compute_embedding(
method _get_or_load_model (line 89) | def _get_or_load_model(self) -> osam.types.Model:
FILE: labelme/_automation/bbox_from_text.py
function get_bboxes_from_texts (line 17) | def get_bboxes_from_texts(
function nms_bboxes (line 65) | def nms_bboxes(
function get_shapes_from_bboxes (line 100) | def get_shapes_from_bboxes(
FILE: labelme/_automation/polygon_from_mask.py
function _get_contour_length (line 8) | def _get_contour_length(contour: NDArray[np.float32]) -> float:
function compute_polygon_from_mask (line 14) | def compute_polygon_from_mask(mask: NDArray[np.bool_]) -> NDArray[np.flo...
FILE: labelme/_label_file.py
function _open (line 25) | def _open(name, mode):
class ShapeDict (line 32) | class ShapeDict(TypedDict):
function _load_shape_json_obj (line 43) | def _load_shape_json_obj(shape_json_obj: dict) -> ShapeDict:
class LabelFileError (line 133) | class LabelFileError(Exception):
class LabelFile (line 137) | class LabelFile:
method __init__ (line 141) | def __init__(self, filename: str | None = None):
method load_image_file (line 150) | def load_image_file(filename):
method load (line 172) | def load(self, filename):
method _check_image_height_and_width (line 222) | def _check_image_height_and_width(imageData, imageHeight, imageWidth):
method save (line 239) | def save(
method is_label_file (line 279) | def is_label_file(filename):
function _imread (line 286) | def _imread(filename: str) -> PIL.Image.Image:
function _imread_tiff (line 299) | def _imread_tiff(filename: str) -> PIL.Image.Image:
function _normalize_to_uint8 (line 318) | def _normalize_to_uint8(arr: NDArray) -> NDArray[np.uint8]:
FILE: labelme/app.py
class _ZoomMode (line 67) | class _ZoomMode(enum.Enum):
class _StatusBarWidgets (line 83) | class _StatusBarWidgets(NamedTuple):
class _CanvasWidgets (line 88) | class _CanvasWidgets(NamedTuple):
class _DockWidgets (line 94) | class _DockWidgets(NamedTuple):
class _Actions (line 106) | class _Actions(NamedTuple):
class _Menus (line 159) | class _Menus(NamedTuple):
class MainWindow (line 168) | class MainWindow(QtWidgets.QMainWindow):
method __init__ (line 200) | def __init__(
method _setup_actions (line 283) | def _setup_actions(self) -> _Actions:
method _setup_menus (line 781) | def _setup_menus(self) -> _Menus:
method _setup_toolbars (line 891) | def _setup_toolbars(self) -> None:
method _setup_app_state (line 937) | def _setup_app_state(
method _setup_status_bar (line 995) | def _setup_status_bar(self) -> _StatusBarWidgets:
method _setup_canvas (line 1003) | def _setup_canvas(self) -> _CanvasWidgets:
method _setup_dock_widgets (line 1040) | def _setup_dock_widgets(self) -> _DockWidgets:
method _load_config (line 1123) | def _load_config(
method menu (line 1153) | def menu(self, title, actions=None):
method noShapes (line 1161) | def noShapes(self) -> bool:
method populateModeActions (line 1164) | def populateModeActions(self) -> None:
method _get_window_title (line 1177) | def _get_window_title(self, dirty: bool) -> str:
method setDirty (line 1191) | def setDirty(self) -> None:
method setClean (line 1207) | def setClean(self) -> None:
method toggleActions (line 1219) | def toggleActions(self, value: bool = True) -> None:
method queueEvent (line 1226) | def queueEvent(self, function: Callable[[], None]) -> None:
method show_status_message (line 1229) | def show_status_message(self, message: str, delay: int = 500) -> None:
method _submit_ai_prompt (line 1232) | def _submit_ai_prompt(self, _) -> None:
method resetState (line 1313) | def resetState(self) -> None:
method currentItem (line 1322) | def currentItem(self) -> LabelListWidgetItem | None:
method addRecentFile (line 1328) | def addRecentFile(self, filename: str) -> None:
method undoShapeEdit (line 1337) | def undoShapeEdit(self) -> None:
method tutorial (line 1343) | def tutorial(self):
method toggleDrawingSensitive (line 1347) | def toggleDrawingSensitive(self, drawing=True):
method _switch_canvas_mode (line 1357) | def _switch_canvas_mode(
method updateFileMenu (line 1377) | def updateFileMenu(self):
method popLabelListMenu (line 1394) | def popLabelListMenu(self, point: QtCore.QPoint) -> None:
method validateLabel (line 1397) | def validateLabel(self, label):
method _edit_label (line 1409) | def _edit_label(self, value=None):
method fileSearchChanged (line 1503) | def fileSearchChanged(self):
method fileSelectionChanged (line 1508) | def fileSelectionChanged(self) -> None:
method shapeSelectionChanged (line 1524) | def shapeSelectionChanged(self, selected_shapes: list[Shape]) -> None:
method addLabel (line 1546) | def addLabel(self, shape: Shape) -> None:
method _update_shape_color (line 1572) | def _update_shape_color(self, shape: Shape) -> None:
method _get_rgb_by_label (line 1584) | def _get_rgb_by_label(
method remLabels (line 1623) | def remLabels(self, shapes: list[Shape]) -> None:
method _load_shapes (line 1628) | def _load_shapes(self, shapes: list[Shape], replace: bool = True) -> N...
method _load_shape_dicts (line 1641) | def _load_shape_dicts(self, shape_dicts: list[ShapeDict]) -> None:
method _load_flags (line 1672) | def _load_flags(
method saveLabels (line 1686) | def saveLabels(self, filename):
method duplicateSelectedShape (line 1745) | def duplicateSelectedShape(self) -> None:
method pasteSelectedShape (line 1749) | def pasteSelectedShape(self) -> None:
method copySelectedShape (line 1754) | def copySelectedShape(self) -> None:
method _label_selection_changed (line 1760) | def _label_selection_changed(self) -> None:
method labelItemChanged (line 1770) | def labelItemChanged(self, item: LabelListWidgetItem) -> None:
method labelOrderChanged (line 1776) | def labelOrderChanged(self) -> None:
method newShape (line 1784) | def newShape(self) -> None:
method scrollRequest (line 1824) | def scrollRequest(self, delta: int, orientation: Qt.Orientation) -> None:
method setScroll (line 1830) | def setScroll(self, orientation: Qt.Orientation, value: float) -> None:
method _set_zoom (line 1835) | def _set_zoom(self, value: int, pos: QtCore.QPointF | None = None) -> ...
method _set_zoom_to_original (line 1869) | def _set_zoom_to_original(self):
method _add_zoom (line 1873) | def _add_zoom(self, increment: float, pos: QtCore.QPointF | None = Non...
method _zoom_requested (line 1884) | def _zoom_requested(self, delta: int, pos: QtCore.QPointF) -> None:
method setFitWindow (line 1887) | def setFitWindow(self, value=True):
method setFitWidth (line 1893) | def setFitWidth(self, value=True):
method enableKeepPrevScale (line 1899) | def enableKeepPrevScale(self, enabled):
method onNewBrightnessContrast (line 1903) | def onNewBrightnessContrast(self, qimage):
method brightnessContrast (line 1908) | def brightnessContrast(self, value: bool, is_initial_load: bool = False):
method toggleShapes (line 1958) | def toggleShapes(self, value):
method _load_file (line 1965) | def _load_file(self, filename=None):
method resizeEvent (line 2102) | def resizeEvent(self, a0: QtGui.QResizeEvent) -> None:
method _paint_canvas (line 2111) | def _paint_canvas(self) -> None:
method _adjust_scale (line 2121) | def _adjust_scale(self) -> None:
method scaleFitWindow (line 2124) | def scaleFitWindow(self) -> float:
method scaleFitWidth (line 2136) | def scaleFitWidth(self):
method enableSaveImageWithData (line 2141) | def enableSaveImageWithData(self, enabled):
method _reset_layout (line 2145) | def _reset_layout(self):
method closeEvent (line 2149) | def closeEvent(self, a0: QtGui.QCloseEvent) -> None:
method dragEnterEvent (line 2158) | def dragEnterEvent(self, a0: QtGui.QDragEnterEvent) -> None:
method dropEvent (line 2170) | def dropEvent(self, a0: QtGui.QDropEvent) -> None:
method loadRecent (line 2179) | def loadRecent(self, filename):
method _open_prev_image (line 2183) | def _open_prev_image(self, _value=False) -> None:
method _open_next_image (line 2193) | def _open_next_image(self, _value=False) -> None:
method _open_file_with_dialog (line 2203) | def _open_file_with_dialog(self, _value: bool = False) -> None:
method changeOutputDirDialog (line 2227) | def changeOutputDirDialog(self, _value=False):
method saveFile (line 2262) | def saveFile(self, _value: bool = False) -> None:
method saveFileAs (line 2269) | def saveFileAs(self, _value: bool = False) -> None:
method saveFileDialog (line 2273) | def saveFileDialog(self) -> str:
method _saveFile (line 2302) | def _saveFile(self, filename: str | None) -> None:
method closeFile (line 2307) | def closeFile(self, _value: bool = False) -> None:
method getLabelFile (line 2317) | def getLabelFile(self) -> str:
method deleteFile (line 2323) | def deleteFile(self) -> None:
method _open_config_file (line 2343) | def _open_config_file(self) -> None:
method hasLabels (line 2367) | def hasLabels(self) -> bool:
method hasLabelFile (line 2376) | def hasLabelFile(self) -> bool:
method _can_continue (line 2383) | def _can_continue(self) -> bool:
method errorMessage (line 2403) | def errorMessage(self, title: str, message: str) -> int:
method currentPath (line 2408) | def currentPath(self) -> str:
method toggleKeepPrevMode (line 2411) | def toggleKeepPrevMode(self) -> None:
method removeSelectedPoint (line 2414) | def removeSelectedPoint(self) -> None:
method deleteSelectedShape (line 2428) | def deleteSelectedShape(self) -> None:
method copyShape (line 2442) | def copyShape(self) -> None:
method moveShape (line 2449) | def moveShape(self) -> None:
method _open_dir_with_dialog (line 2453) | def _open_dir_with_dialog(self, _value: bool = False) -> None:
method imageList (line 2476) | def imageList(self) -> list[str]:
method importDroppedImageFiles (line 2484) | def importDroppedImageFiles(self, imageFiles):
method _import_images_from_dir (line 2512) | def _import_images_from_dir(
method _update_status_stats (line 2544) | def _update_status_stats(self, mouse_pos: QtCore.QPointF) -> None:
function _scan_image_files (line 2551) | def _scan_image_files(root_dir: str) -> list[str]:
FILE: labelme/config/__init__.py
function _update_dict (line 11) | def _update_dict(target_dict, new_dict, validate_item=None):
function _validate_config_item (line 23) | def _validate_config_item(key, value):
function _migrate_config_from_file (line 32) | def _migrate_config_from_file(config_from_yaml: dict) -> None:
function get_user_config_file (line 84) | def get_user_config_file(create_if_missing: bool = True) -> str:
function load_config (line 105) | def load_config(config_file: Path | None, config_overrides: dict) -> dict:
FILE: labelme/shape.py
class Shape (line 15) | class Shape:
method __init__ (line 42) | def __init__(
method _scale_point (line 81) | def _scale_point(self, point: QtCore.QPointF) -> QtCore.QPointF:
method setShapeRefined (line 84) | def setShapeRefined(self, shape_type, points, point_labels, mask=None):
method restoreShapeRaw (line 91) | def restoreShapeRaw(self):
method shape_type (line 98) | def shape_type(self):
method shape_type (line 102) | def shape_type(self, value):
method close (line 118) | def close(self):
method addPoint (line 121) | def addPoint(self, point, label=1):
method canAddPoint (line 128) | def canAddPoint(self):
method popPoint (line 131) | def popPoint(self):
method insertPoint (line 138) | def insertPoint(self, i, point, label=1):
method canRemovePoint (line 142) | def canRemovePoint(self) -> bool:
method removePoint (line 154) | def removePoint(self, i: int):
method isClosed (line 166) | def isClosed(self):
method setOpen (line 169) | def setOpen(self):
method paint (line 172) | def paint(self, painter):
method drawVertex (line 282) | def drawVertex(self, path, i):
method nearestVertex (line 300) | def nearestVertex(self, point: QtCore.QPointF, epsilon: float) -> int ...
method nearestEdge (line 312) | def nearestEdge(self, point: QtCore.QPointF, epsilon: float) -> int | ...
method containsPoint (line 326) | def containsPoint(self, point: QtCore.QPointF) -> bool:
method makePath (line 346) | def makePath(self):
method boundingRect (line 362) | def boundingRect(self):
method moveBy (line 365) | def moveBy(self, offset):
method moveVertex (line 368) | def moveVertex(self, i: int, pos: QtCore.QPointF) -> None:
method highlightVertex (line 371) | def highlightVertex(self, i: int, action: int) -> None:
method highlightClear (line 375) | def highlightClear(self) -> None:
method copy (line 378) | def copy(self):
method __len__ (line 381) | def __len__(self):
method __getitem__ (line 384) | def __getitem__(self, key):
method __setitem__ (line 387) | def __setitem__(self, key, value):
FILE: labelme/testing.py
function assert_labelfile_sanity (line 9) | def assert_labelfile_sanity(filename):
FILE: labelme/utils/_io.py
function lblsave (line 10) | def lblsave(filename, lbl):
FILE: labelme/utils/image.py
function img_data_to_pil (line 13) | def img_data_to_pil(img_data):
function img_data_to_arr (line 20) | def img_data_to_arr(img_data):
function img_b64_to_arr (line 26) | def img_b64_to_arr(img_b64):
function img_pil_to_data (line 32) | def img_pil_to_data(img_pil):
function img_arr_to_b64 (line 39) | def img_arr_to_b64(img_arr):
function img_arr_to_data (line 45) | def img_arr_to_data(img_arr):
function img_data_to_png_data (line 51) | def img_data_to_png_data(img_data):
function img_qt_to_arr (line 62) | def img_qt_to_arr(img_qt):
function apply_exif_orientation (line 69) | def apply_exif_orientation(image):
FILE: labelme/utils/qt.py
function newIcon (line 12) | def newIcon(icon_file_name: str) -> QtGui.QIcon:
function newButton (line 19) | def newButton(text, icon=None, slot=None):
function newAction (line 28) | def newAction(
function addActions (line 61) | def addActions(widget, actions):
function labelValidator (line 71) | def labelValidator():
function distance (line 75) | def distance(p):
function distancetoline (line 79) | def distancetoline(point, line):
function fmtShortcut (line 96) | def fmtShortcut(text):
FILE: labelme/utils/shape.py
function shape_to_mask (line 13) | def shape_to_mask(
function shapes_to_label (line 54) | def shapes_to_label(img_shape, shapes, label_name_to_value):
function masks_to_bboxes (line 90) | def masks_to_bboxes(masks):
FILE: labelme/widgets/_ai_assisted_annotation_widget.py
class AiAssistedAnnotationWidget (line 11) | class AiAssistedAnnotationWidget(QtWidgets.QWidget):
method __init__ (line 26) | def __init__(
method _init_ui (line 35) | def _init_ui(
method setEnabled (line 84) | def setEnabled(self, a0: bool) -> None:
method eventFilter (line 87) | def eventFilter(self, a0: QtCore.QObject, a1: QtCore.QEvent) -> bool:
FILE: labelme/widgets/_ai_text_to_annotation_widget.py
class AiTextToAnnotationWidget (line 10) | class AiTextToAnnotationWidget(QtWidgets.QWidget):
method __init__ (line 25) | def __init__(self, on_submit, parent: QtWidgets.QWidget | None = None):
method _init_ui (line 29) | def _init_ui(self, on_submit: Callable[[], None]) -> None:
method get_text_prompt (line 119) | def get_text_prompt(self) -> str:
method get_model_name (line 122) | def get_model_name(self) -> str:
method get_score_threshold (line 125) | def get_score_threshold(self) -> float:
method get_iou_threshold (line 128) | def get_iou_threshold(self) -> float:
method setEnabled (line 131) | def setEnabled(self, a0: bool) -> None:
method eventFilter (line 134) | def eventFilter(self, a0: QtCore.QObject, a1: QtCore.QEvent) -> bool:
FILE: labelme/widgets/_info_button.py
class InfoButton (line 8) | class InfoButton(QtWidgets.QToolButton):
method __init__ (line 9) | def __init__(self, tooltip: str, parent: QtWidgets.QWidget | None = No...
method enterEvent (line 28) | def enterEvent(self, a0: QtCore.QEvent) -> None:
FILE: labelme/widgets/_status.py
class StatusStats (line 5) | class StatusStats(QtWidgets.QLabel):
method __init__ (line 6) | def __init__(self):
FILE: labelme/widgets/brightness_contrast_dialog.py
class BrightnessContrastDialog (line 8) | class BrightnessContrastDialog(QtWidgets.QDialog):
method __init__ (line 13) | def __init__(self, img: PIL.Image.Image, callback, parent=None):
method onNewValue (line 64) | def onNewValue(self, _):
FILE: labelme/widgets/canvas.py
class CanvasMode (line 33) | class CanvasMode(enum.Enum):
class Canvas (line 38) | class Canvas(QtWidgets.QWidget):
method __init__ (line 83) | def __init__(self, *args, **kwargs):
method fillDrawing (line 135) | def fillDrawing(self):
method setFillDrawing (line 138) | def setFillDrawing(self, value):
method createMode (line 142) | def createMode(self):
method createMode (line 146) | def createMode(self, value):
method set_ai_model_name (line 160) | def set_ai_model_name(self, model_name: str) -> None:
method _get_osam_session (line 163) | def _get_osam_session(self) -> OsamSession:
method _update_shape_with_ai (line 171) | def _update_shape_with_ai(
method storeShapes (line 187) | def storeShapes(self):
method isShapeRestorable (line 196) | def isShapeRestorable(self):
method restoreShape (line 204) | def restoreShape(self):
method enterEvent (line 221) | def enterEvent(self, a0: QtCore.QEvent) -> None:
method leaveEvent (line 225) | def leaveEvent(self, a0: QtCore.QEvent) -> None:
method focusOutEvent (line 231) | def focusOutEvent(self, a0: QtGui.QFocusEvent) -> None:
method isVisible (line 235) | def isVisible(self, shape: Shape) -> bool: # type: ignore[override]
method drawing (line 238) | def drawing(self) -> bool:
method editing (line 241) | def editing(self) -> bool:
method setEditing (line 244) | def setEditing(self, value=True):
method _set_highlight (line 258) | def _set_highlight(
method selectedVertex (line 274) | def selectedVertex(self) -> bool:
method selectedEdge (line 277) | def selectedEdge(self) -> bool:
method _update_status (line 280) | def _update_status(self, extra_messages: list[str] | None = None) -> N...
method _get_create_mode_message (line 296) | def _get_create_mode_message(self) -> str:
method mouseMoveEvent (line 331) | def mouseMoveEvent(self, a0: QtGui.QMouseEvent) -> None:
method _highlight_hover_shape (line 448) | def _highlight_hover_shape(self, pos: QPointF, status_messages: list[s...
method addPointToEdge (line 493) | def addPointToEdge(self):
method removeSelectedPoint (line 506) | def removeSelectedPoint(self):
method mousePressEvent (line 517) | def mousePressEvent(self, a0: QtGui.QMouseEvent) -> None:
method mouseReleaseEvent (line 610) | def mouseReleaseEvent(self, a0: QtGui.QMouseEvent) -> None:
method endMove (line 641) | def endMove(self, copy):
method hideBackroundShapes (line 657) | def hideBackroundShapes(self, value):
method setHiding (line 665) | def setHiding(self, enable=True):
method canCloseShape (line 668) | def canCloseShape(self) -> bool:
method mouseDoubleClickEvent (line 679) | def mouseDoubleClickEvent(self, a0: QtGui.QMouseEvent) -> None:
method selectShapes (line 686) | def selectShapes(self, shapes):
method selectShapePoint (line 691) | def selectShapePoint(self, point, multiple_selection_mode):
method calculateOffsets (line 714) | def calculateOffsets(self, point: QPointF) -> None:
method boundedMoveVertex (line 736) | def boundedMoveVertex(self, pos: QPointF, is_shift_pressed: bool) -> N...
method boundedMoveShapes (line 760) | def boundedMoveShapes(self, shapes, pos):
method deSelectShape (line 785) | def deSelectShape(self) -> bool:
method deleteSelected (line 794) | def deleteSelected(self):
method deleteShape (line 805) | def deleteShape(self, shape):
method paintEvent (line 813) | def paintEvent(self, a0: QtGui.QPaintEvent) -> None:
method transformPos (line 898) | def transformPos(self, point: QPointF) -> QPointF:
method enableDragging (line 902) | def enableDragging(self, enabled: bool):
method offsetToCenter (line 905) | def offsetToCenter(self) -> QPointF:
method outOfPixmap (line 914) | def outOfPixmap(self, p: QPointF) -> bool:
method finalise (line 918) | def finalise(self):
method closeEnough (line 935) | def closeEnough(self, p1, p2):
method intersectionPoint (line 942) | def intersectionPoint(self, p1: QPointF, p2: QPointF) -> QPointF:
method intersectingEdges (line 968) | def intersectingEdges(self, point1, point2, points):
method sizeHint (line 999) | def sizeHint(self):
method minimumSizeHint (line 1002) | def minimumSizeHint(self):
method wheelEvent (line 1013) | def wheelEvent(self, a0: QtGui.QWheelEvent) -> None:
method moveByKeyboard (line 1026) | def moveByKeyboard(self, offset):
method keyPressEvent (line 1032) | def keyPressEvent(self, a0: QtGui.QKeyEvent) -> None:
method keyReleaseEvent (line 1058) | def keyReleaseEvent(self, a0: QtGui.QKeyEvent) -> None:
method setLastLabel (line 1076) | def setLastLabel(self, text, flags):
method undoLastLine (line 1084) | def undoLastLine(self):
method undoLastPoint (line 1097) | def undoLastPoint(self):
method loadPixmap (line 1108) | def loadPixmap(self, pixmap, clear_shapes=True):
method loadShapes (line 1117) | def loadShapes(self, shapes, replace=True):
method setShapeVisible (line 1129) | def setShapeVisible(self, shape, value):
method overrideCursor (line 1133) | def overrideCursor(self, cursor):
method restoreCursor (line 1140) | def restoreCursor(self):
method resetState (line 1144) | def resetState(self):
function _update_shape_with_ai_response (line 1163) | def _update_shape_with_ai_response(
function _snap_cursor_pos_for_square (line 1210) | def _snap_cursor_pos_for_square(pos: QPointF, opposite_vertex: QPointF) ...
FILE: labelme/widgets/download.py
class _AiModelDownloadSignals (line 16) | class _AiModelDownloadSignals(QObject):
class _AiModelDownloadWorker (line 21) | class _AiModelDownloadWorker(QRunnable):
method __init__ (line 22) | def __init__(self, model_type, signals: _AiModelDownloadSignals):
method run (line 27) | def run(self):
function download_ai_model (line 35) | def download_ai_model(model_name: str, parent: QtWidgets.QWidget) -> bool:
FILE: labelme/widgets/file_dialog_preview.py
class ScrollAreaPreview (line 9) | class ScrollAreaPreview(QtWidgets.QScrollArea):
method __init__ (line 10) | def __init__(self, *args, **kwargs):
method setText (line 25) | def setText(self, text):
method setPixmap (line 28) | def setPixmap(self, pixmap):
method clear (line 31) | def clear(self):
class FileDialogPreview (line 35) | class FileDialogPreview(QtWidgets.QFileDialog):
method __init__ (line 36) | def __init__(self, *args, **kwargs):
method onChange (line 54) | def onChange(self, path):
FILE: labelme/widgets/label_dialog.py
class LabelQLineEdit (line 15) | class LabelQLineEdit(QtWidgets.QLineEdit):
method setListWidget (line 16) | def setListWidget(self, list_widget):
method keyPressEvent (line 19) | def keyPressEvent(self, a0: QtGui.QKeyEvent) -> None:
class LabelDialog (line 26) | class LabelDialog(QtWidgets.QDialog):
method __init__ (line 27) | def __init__(
method addLabelHistory (line 115) | def addLabelHistory(self, label):
method labelSelected (line 122) | def labelSelected(self, item):
method validate (line 125) | def validate(self):
method _get_stripped_text (line 133) | def _get_stripped_text(self) -> str:
method labelDoubleClicked (line 141) | def labelDoubleClicked(self, item):
method postProcess (line 144) | def postProcess(self):
method updateFlags (line 147) | def updateFlags(self, label_new):
method deleteFlags (line 158) | def deleteFlags(self):
method resetFlags (line 164) | def resetFlags(self, label=""):
method setFlags (line 172) | def setFlags(self, flags):
method getFlags (line 180) | def getFlags(self):
method getGroupId (line 188) | def getGroupId(self):
method popUp (line 194) | def popUp(
FILE: labelme/widgets/label_list_widget.py
class HTMLDelegate (line 12) | class HTMLDelegate(QtWidgets.QStyledItemDelegate):
method __init__ (line 13) | def __init__(self, parent=None):
method paint (line 17) | def paint(self, painter, option, index):
method sizeHint (line 62) | def sizeHint(self, option, index):
class LabelListWidgetItem (line 70) | class LabelListWidgetItem(QtGui.QStandardItem):
method __init__ (line 71) | def __init__(self, text=None, shape=None):
method clone (line 81) | def clone(self):
method setShape (line 84) | def setShape(self, shape):
method shape (line 87) | def shape(self):
method __hash__ (line 90) | def __hash__(self):
method __repr__ (line 93) | def __repr__(self):
class _ItemModel (line 97) | class _ItemModel(QtGui.QStandardItemModel):
method removeRows (line 100) | def removeRows(self, *args, **kwargs):
method dropMimeData (line 105) | def dropMimeData(self, data, action, row: int, column: int, parent):
class LabelListWidget (line 122) | class LabelListWidget(QtWidgets.QListView):
method __init__ (line 126) | def __init__(self):
method __len__ (line 144) | def __len__(self):
method __getitem__ (line 147) | def __getitem__(self, i):
method __iter__ (line 150) | def __iter__(self):
method itemDropped (line 155) | def itemDropped(self):
method itemChanged (line 159) | def itemChanged(self):
method itemSelectionChangedEvent (line 162) | def itemSelectionChangedEvent(self, selected, deselected):
method itemDoubleClickedEvent (line 167) | def itemDoubleClickedEvent(self, index):
method selectedItems (line 170) | def selectedItems(self):
method scrollToItem (line 173) | def scrollToItem(self, item):
method addItem (line 176) | def addItem(self, item):
method removeItem (line 182) | def removeItem(self, item):
method selectItem (line 186) | def selectItem(self, item):
method findItemByShape (line 190) | def findItemByShape(self, shape):
method clear (line 198) | def clear(self):
FILE: labelme/widgets/tool_bar.py
class ToolBar (line 11) | class ToolBar(QtWidgets.QToolBar):
method __init__ (line 12) | def __init__(
method addAction (line 41) | def addAction(self, action): # type: ignore[override]
FILE: labelme/widgets/unique_label_qlist_widget.py
class _EscapableQListWidget (line 10) | class _EscapableQListWidget(QtWidgets.QListWidget):
method keyPressEvent (line 11) | def keyPressEvent(self, keyEvent: QtGui.QKeyEvent) -> None: # type: i...
class UniqueLabelQListWidget (line 17) | class UniqueLabelQListWidget(_EscapableQListWidget):
method __init__ (line 18) | def __init__(self, *args, **kwargs):
method mousePressEvent (line 22) | def mousePressEvent(self, mouseEvent: QtGui.QMouseEvent) -> None: # t...
method find_label_item (line 27) | def find_label_item(self, label: str) -> QtWidgets.QListWidgetItem | N...
method add_label_item (line 34) | def add_label_item(self, label: str, color: tuple[int, int, int]) -> N...
FILE: labelme/widgets/zoom_widget.py
class ZoomWidget (line 6) | class ZoomWidget(QtWidgets.QSpinBox):
method __init__ (line 7) | def __init__(self, value=100):
method minimumSizeHint (line 17) | def minimumSizeHint(self):
FILE: tests/conftest.py
function _create_annotated_nested (line 8) | def _create_annotated_nested(data_path: Path) -> None:
function data_path (line 28) | def data_path(tmp_path: Path) -> Path:
FILE: tests/e2e/annotation_test.py
function test_MainWindow_annotate_jpg (line 19) | def test_MainWindow_annotate_jpg(
FILE: tests/e2e/config_test.py
function test_MainWindow_config (line 20) | def test_MainWindow_config(
FILE: tests/e2e/conftest.py
function _isolated_qtsettings (line 14) | def _isolated_qtsettings(
function show_window_and_wait_for_imagedata (line 25) | def show_window_and_wait_for_imagedata(
FILE: tests/e2e/file_loading_test.py
function test_MainWindow_open_img (line 18) | def test_MainWindow_open_img(
function test_MainWindow_open_json (line 30) | def test_MainWindow_open_json(
function test_MainWindow_open_dir (line 50) | def test_MainWindow_open_dir(
FILE: tests/e2e/navigation_test.py
function test_image_navigation_while_selecting_shape (line 16) | def test_image_navigation_while_selecting_shape(
FILE: tests/e2e/smoke_test.py
function test_MainWindow_open (line 12) | def test_MainWindow_open(qtbot: QtBot) -> None:
function test_file_search_config_filters_on_startup (line 20) | def test_file_search_config_filters_on_startup(qtbot: QtBot, data_path: ...
FILE: tests/unit/_label_file_test.py
function test_LabelFile_load_windows_path (line 10) | def test_LabelFile_load_windows_path(data_path: Path, tmp_path: Path) ->...
FILE: tests/unit/config_test.py
function test_get_user_config_file_creates_sparse (line 11) | def test_get_user_config_file_creates_sparse(tmp_path, monkeypatch):
function test_get_user_config_file_does_not_overwrite (line 20) | def test_get_user_config_file_does_not_overwrite(tmp_path, monkeypatch):
function test_get_user_config_file_skip_creation (line 29) | def test_get_user_config_file_skip_creation(tmp_path, monkeypatch):
function test_migrate_store_data_to_with_image_data (line 36) | def test_migrate_store_data_to_with_image_data(tmp_path, old_value):
function test_migrate_ai_model_name (line 54) | def test_migrate_ai_model_name(input_name: str, expected_name: str) -> N...
function test_migrate_polygon_shortcut_to_shape (line 77) | def test_migrate_polygon_shortcut_to_shape(old_key, new_key):
function test_migrate_polygon_shortcuts_no_shortcuts_key (line 84) | def test_migrate_polygon_shortcuts_no_shortcuts_key():
function test_migrate_polygon_shortcut_skips_when_new_key_exists (line 90) | def test_migrate_polygon_shortcut_skips_when_new_key_exists():
FILE: tests/unit/load_image_file_test.py
function _make_image (line 13) | def _make_image(tmp_path: Path, filename: str, mode: str = "RGB", size=(...
function test_tiff_without_alpha_encoded_as_jpeg (line 21) | def test_tiff_without_alpha_encoded_as_jpeg(tmp_path):
function test_tiff_with_alpha_encoded_as_png (line 27) | def test_tiff_with_alpha_encoded_as_png(tmp_path):
function test_jpeg_returns_raw_bytes (line 33) | def test_jpeg_returns_raw_bytes(tmp_path):
function test_png_returns_raw_bytes (line 39) | def test_png_returns_raw_bytes(tmp_path):
function test_multispectral_tiff_float32 (line 45) | def test_multispectral_tiff_float32(tmp_path):
function test_grayscale_tiff_float32 (line 58) | def test_grayscale_tiff_float32(tmp_path):
function test_constant_value_tiff_returns_black (line 68) | def test_constant_value_tiff_returns_black(tmp_path):
function test_two_band_tiff_falls_back_to_first_band (line 79) | def test_two_band_tiff_falls_back_to_first_band(tmp_path):
FILE: tests/unit/shape_contains_point_test.py
function _make_point_shape (line 8) | def _make_point_shape(x: float, y: float) -> Shape:
function test_point_shape_contains_center (line 15) | def test_point_shape_contains_center():
function test_point_shape_contains_within_radius (line 21) | def test_point_shape_contains_within_radius():
function test_point_shape_at_exact_boundary (line 28) | def test_point_shape_at_exact_boundary():
function test_point_shape_outside_radius (line 35) | def test_point_shape_outside_radius():
function test_point_shape_empty_points (line 42) | def test_point_shape_empty_points():
FILE: tests/unit/shape_test.py
function _make_mask_shape (line 9) | def _make_mask_shape(
function test_mask_contains_point (line 30) | def test_mask_contains_point(point: tuple[int, int], expected: bool) -> ...
FILE: tests/unit/utils/image_test.py
function test_img_b64_to_arr (line 10) | def test_img_b64_to_arr():
function test_img_arr_to_b64 (line 16) | def test_img_arr_to_b64():
function test_img_data_to_png_data (line 24) | def test_img_data_to_png_data():
FILE: tests/unit/utils/qt_test.py
function test_distancetoline (line 6) | def test_distancetoline():
FILE: tests/unit/utils/shape_test.py
function test_shapes_to_label (line 8) | def test_shapes_to_label():
function test_shape_to_mask (line 21) | def test_shape_to_mask():
function test_shape_to_mask_rectangle_reversed_coords (line 29) | def test_shape_to_mask_rectangle_reversed_coords():
FILE: tests/unit/utils/util.py
function get_img_and_data (line 11) | def get_img_and_data():
function get_img_and_lbl (line 20) | def get_img_and_lbl():
FILE: tests/unit/widgets/canvas_test.py
function canvas (line 16) | def canvas(qtbot) -> Canvas:
function test_outOfPixmap (line 38) | def test_outOfPixmap(canvas: Canvas, point: QPointF, is_outside: bool):
function test_intersectionPoint (line 68) | def test_intersectionPoint(
FILE: tests/unit/widgets/label_dialog_test.py
function test_LabelQLineEdit (line 12) | def test_LabelQLineEdit(qtbot):
function test_LabelDialog_addLabelHistory (line 37) | def test_LabelDialog_addLabelHistory(qtbot):
function test_LabelDialog_popUp (line 52) | def test_LabelDialog_popUp(qtbot):
FILE: tests/unit/widgets/label_list_widget_test.py
function test_LabelListWidget (line 8) | def test_LabelListWidget(qtbot):
FILE: tools/update_translate.py
function main (line 11) | def main():
Condensed preview — 162 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (1,159K chars).
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"path": "examples/instance_segmentation/labelme2coco.py",
"chars": 6762,
"preview": "#!/usr/bin/env python\n\nimport argparse\nimport collections\nimport datetime\nimport glob\nimport json\nimport os\nimport os.pa"
},
{
"path": "examples/instance_segmentation/labelme2voc.py",
"chars": 5334,
"preview": "#!/usr/bin/env python\n\n\nimport argparse\nimport glob\nimport os\nimport os.path as osp\nimport sys\n\nimport imgviz\nimport num"
},
{
"path": "examples/instance_segmentation/labels.txt",
"chars": 160,
"preview": "__ignore__\n_background_\naeroplane\nbicycle\nbird\nboat\nbottle\nbus\ncar\ncat\nchair\ncow\ndiningtable\ndog\nhorse\nmotorbike\nperson\n"
},
{
"path": "examples/primitives/primitives.json",
"chars": 3272,
"preview": "{\n \"version\": \"5.7.0\",\n \"flags\": {},\n \"shapes\": [\n {\n \"label\": \"rectangle\",\n \"points\": [\n [\n "
},
{
"path": "examples/semantic_segmentation/README.md",
"chars": 1180,
"preview": "# Semantic Segmentation Example\n\n## Annotation\n\n```bash\nlabelme data_annotated --labels labels.txt --validatelabel exact"
},
{
"path": "examples/semantic_segmentation/data_annotated/2011_000003.json",
"chars": 9334,
"preview": "{\n \"version\": \"4.0.0\",\n \"flags\": {},\n \"shapes\": [\n {\n \"label\": \"person\",\n \"points\": [\n [\n "
},
{
"path": "examples/semantic_segmentation/data_annotated/2011_000006.json",
"chars": 10129,
"preview": "{\n \"version\": \"4.0.0\",\n \"flags\": {},\n \"shapes\": [\n {\n \"label\": \"person\",\n \"points\": [\n [\n "
},
{
"path": "examples/semantic_segmentation/data_annotated/2011_000025.json",
"chars": 3995,
"preview": "{\n \"version\": \"4.0.0\",\n \"flags\": {},\n \"shapes\": [\n {\n \"label\": \"bus\",\n \"points\": [\n [\n 2"
},
{
"path": "examples/semantic_segmentation/data_dataset_voc/class_names.txt",
"chars": 149,
"preview": "_background_\naeroplane\nbicycle\nbird\nboat\nbottle\nbus\ncar\ncat\nchair\ncow\ndiningtable\ndog\nhorse\nmotorbike\nperson\npotted plan"
},
{
"path": "examples/semantic_segmentation/labels.txt",
"chars": 160,
"preview": "__ignore__\n_background_\naeroplane\nbicycle\nbird\nboat\nbottle\nbus\ncar\ncat\nchair\ncow\ndiningtable\ndog\nhorse\nmotorbike\nperson\n"
},
{
"path": "examples/tutorial/README.md",
"chars": 1411,
"preview": "# Tutorial (Single Image Example)\n\n## Annotation\n\n```bash\nlabelme apc2016_obj3.jpg\n```\n\n\n\n\n##"
},
{
"path": "examples/tutorial/apc2016_obj3/label_names.txt",
"chars": 102,
"preview": "_background_\nhighland_6539_self_stick_notes\nkong_air_dog_squeakair_tennis_ball\nmead_index_cards\nshelf\n"
},
{
"path": "examples/tutorial/apc2016_obj3.json",
"chars": 146255,
"preview": "{\n \"version\": \"4.0.0\",\n \"flags\": {},\n \"shapes\": [\n {\n \"label\": \"shelf\",\n \"points\": [\n [\n "
},
{
"path": "examples/tutorial/draw_json.py",
"chars": 1297,
"preview": "#!/usr/bin/env python\n\nimport argparse\n\nimport imgviz\nimport matplotlib.pyplot as plt\n\nfrom labelme import utils\nfrom la"
},
{
"path": "examples/tutorial/draw_label_png.py",
"chars": 2234,
"preview": "#!/usr/bin/env python\n\nimport argparse\nimport os\n\nimport imgviz\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom "
},
{
"path": "examples/tutorial/export_json.py",
"chars": 1983,
"preview": "#!/usr/bin/env python\n\nimport argparse\nimport os\nimport os.path as osp\n\nimport imgviz\nimport numpy as np\nimport PIL.Imag"
},
{
"path": "examples/tutorial/load_label_png.py",
"chars": 850,
"preview": "#!/usr/bin/env python\n\n\nimport os.path as osp\n\nimport numpy as np\nimport PIL.Image\n\nhere = osp.dirname(osp.abspath(__fil"
},
{
"path": "examples/video_annotation/README.md",
"chars": 629,
"preview": "# Video Annotation Example\n\n\n## Annotation\n\n```bash\nlabelme data_annotated --labels labels.txt --keep-prev --config '{sh"
},
{
"path": "examples/video_annotation/data_annotated/00000100.json",
"chars": 2363,
"preview": "{\n \"version\": \"4.0.0\",\n \"flags\": {},\n \"shapes\": [\n {\n \"label\": \"track\",\n \"points\": [\n [\n "
},
{
"path": "examples/video_annotation/data_annotated/00000101.json",
"chars": 2517,
"preview": "{\n \"version\": \"4.0.0\",\n \"flags\": {},\n \"shapes\": [\n {\n \"label\": \"track\",\n \"points\": [\n [\n "
},
{
"path": "examples/video_annotation/data_annotated/00000102.json",
"chars": 2531,
"preview": "{\n \"version\": \"4.0.0\",\n \"flags\": {},\n \"shapes\": [\n {\n \"label\": \"track\",\n \"points\": [\n [\n "
},
{
"path": "examples/video_annotation/data_annotated/00000103.json",
"chars": 2363,
"preview": "{\n \"version\": \"4.0.0\",\n \"flags\": {},\n \"shapes\": [\n {\n \"label\": \"track\",\n \"points\": [\n [\n "
},
{
"path": "examples/video_annotation/data_annotated/00000104.json",
"chars": 2531,
"preview": "{\n \"version\": \"4.0.0\",\n \"flags\": {},\n \"shapes\": [\n {\n \"label\": \"track\",\n \"points\": [\n [\n "
},
{
"path": "examples/video_annotation/data_dataset_voc/class_names.txt",
"chars": 22,
"preview": "_background_\ncar\ntrack"
},
{
"path": "examples/video_annotation/labels.txt",
"chars": 34,
"preview": "__ignore__\n_background_\ncar\ntrack\n"
},
{
"path": "labelme/__init__.py",
"chars": 632,
"preview": "import importlib.metadata\n\n__appname__ = \"Labelme\"\n\n# Semantic Versioning 2.0.0: https://semver.org/\n# 1. MAJOR version "
},
{
"path": "labelme/__main__.py",
"chars": 9640,
"preview": "import argparse\nimport codecs\nimport contextlib\nimport io\nimport os\nimport os.path as osp\nimport sys\nimport traceback\nim"
},
{
"path": "labelme/_automation/__init__.py",
"chars": 39,
"preview": "from ._osam_session import OsamSession\n"
},
{
"path": "labelme/_automation/_osam_session.py",
"chars": 3197,
"preview": "from __future__ import annotations\n\nimport collections\n\nimport numpy as np\nimport osam\nfrom loguru import logger\nfrom nu"
},
{
"path": "labelme/_automation/bbox_from_text.py",
"chars": 5052,
"preview": "import json\nimport time\nfrom typing import Literal\n\nimport numpy as np\nimport osam\nfrom loguru import logger\nfrom numpy."
},
{
"path": "labelme/_automation/polygon_from_mask.py",
"chars": 1563,
"preview": "import imgviz\nimport numpy as np\nimport skimage\nfrom loguru import logger\nfrom numpy.typing import NDArray\n\n\ndef _get_co"
},
{
"path": "labelme/_label_file.py",
"chars": 10595,
"preview": "from __future__ import annotations\n\nimport base64\nimport contextlib\nimport io\nimport json\nimport os.path as osp\nimport t"
},
{
"path": "labelme/app.py",
"chars": 95338,
"preview": "from __future__ import annotations\n\nimport enum\nimport functools\nimport html\nimport math\nimport os\nimport os.path as osp"
},
{
"path": "labelme/config/__init__.py",
"chars": 5001,
"preview": "import os.path as osp\nimport re\nfrom pathlib import Path\n\nimport yaml\nfrom loguru import logger\n\nhere = osp.dirname(osp."
},
{
"path": "labelme/config/default_config.yaml",
"chars": 2298,
"preview": "auto_save: true\ndisplay_label_popup: true\nwith_image_data: false\nkeep_prev: false\nkeep_prev_scale: false\nkeep_prev_brigh"
},
{
"path": "labelme/shape.py",
"chars": 13620,
"preview": "from __future__ import annotations\n\nimport copy\n\nimport numpy as np\nimport numpy.typing as npt\nimport skimage.measure\nfr"
},
{
"path": "labelme/testing.py",
"chars": 850,
"preview": "import json\nimport os.path as osp\n\nimport imgviz\n\nimport labelme.utils\n\n\ndef assert_labelfile_sanity(filename):\n asse"
},
{
"path": "labelme/translate/de_DE.ts",
"chars": 33603,
"preview": "<?xml version=\"1.0\" encoding=\"utf-8\"?>\n<!DOCTYPE TS>\n<TS version=\"2.1\" language=\"de_DE\">\n<context>\n <name>AiAssistedA"
},
{
"path": "labelme/translate/es_ES.ts",
"chars": 33527,
"preview": "<?xml version=\"1.0\" encoding=\"utf-8\"?>\n<!DOCTYPE TS>\n<TS version=\"2.1\" language=\"es_ES\">\n<context>\n <name>AiAssistedA"
},
{
"path": "labelme/translate/fa_IR.ts",
"chars": 32614,
"preview": "<?xml version=\"1.0\" encoding=\"utf-8\"?>\n<!DOCTYPE TS>\n<TS version=\"2.1\" language=\"fa_IR\">\n<context>\n <name>AiAssistedA"
},
{
"path": "labelme/translate/fr_FR.ts",
"chars": 34100,
"preview": "<?xml version=\"1.0\" encoding=\"utf-8\"?>\n<!DOCTYPE TS>\n<TS version=\"2.1\" language=\"fr_FR\">\n<context>\n <name>AiAssistedA"
},
{
"path": "labelme/translate/hu_HU.ts",
"chars": 33547,
"preview": "<?xml version=\"1.0\" encoding=\"utf-8\"?>\n<!DOCTYPE TS>\n<TS version=\"2.1\" language=\"hu_HU\">\n<context>\n <name>AiAssistedA"
},
{
"path": "labelme/translate/it_IT.ts",
"chars": 33595,
"preview": "<?xml version=\"1.0\" encoding=\"utf-8\"?>\n<!DOCTYPE TS>\n<TS version=\"2.1\" language=\"it_IT\">\n<context>\n <name>AiAssistedA"
},
{
"path": "labelme/translate/ja_JP.ts",
"chars": 30753,
"preview": "<?xml version=\"1.0\" encoding=\"utf-8\"?>\n<!DOCTYPE TS>\n<TS version=\"2.1\" language=\"ja_JP\">\n<context>\n <name>AiAssistedA"
},
{
"path": "labelme/translate/ko_KR.ts",
"chars": 30693,
"preview": "<?xml version=\"1.0\" encoding=\"utf-8\"?>\n<!DOCTYPE TS>\n<TS version=\"2.1\" language=\"ko_KR\">\n<context>\n <name>AiAssistedA"
},
{
"path": "labelme/translate/nl_NL.ts",
"chars": 33291,
"preview": "<?xml version=\"1.0\" encoding=\"utf-8\"?>\n<!DOCTYPE TS>\n<TS version=\"2.1\" language=\"nl_NL\">\n<context>\n <name>AiAssistedA"
},
{
"path": "labelme/translate/pl_PL.ts",
"chars": 33021,
"preview": "<?xml version=\"1.0\" encoding=\"utf-8\"?>\n<!DOCTYPE TS>\n<TS version=\"2.1\" language=\"pl_PL\">\n<context>\n <name>AiAssistedA"
},
{
"path": "labelme/translate/pt_BR.ts",
"chars": 33386,
"preview": "<?xml version=\"1.0\" encoding=\"utf-8\"?>\n<!DOCTYPE TS>\n<TS version=\"2.1\" language=\"pt_BR\">\n<context>\n <name>AiAssistedA"
},
{
"path": "labelme/translate/th_TH.ts",
"chars": 32234,
"preview": "<?xml version=\"1.0\" encoding=\"utf-8\"?>\n<!DOCTYPE TS>\n<TS version=\"2.1\" language=\"th_TH\">\n<context>\n <name>AiAssistedA"
},
{
"path": "labelme/translate/tr_TR.ts",
"chars": 33217,
"preview": "<?xml version=\"1.0\" encoding=\"utf-8\"?>\n<!DOCTYPE TS>\n<TS version=\"2.1\" language=\"tr_TR\">\n<context>\n <name>AiAssistedA"
},
{
"path": "labelme/translate/vi_VN.ts",
"chars": 32736,
"preview": "<?xml version=\"1.0\" encoding=\"utf-8\"?>\n<!DOCTYPE TS>\n<TS version=\"2.1\" language=\"vi_VN\">\n<context>\n <name>AiAssistedA"
},
{
"path": "labelme/translate/zh_CN.ts",
"chars": 30080,
"preview": "<?xml version=\"1.0\" encoding=\"utf-8\"?>\n<!DOCTYPE TS>\n<TS version=\"2.1\" language=\"zh_CN\">\n<context>\n <name>AiAssistedA"
},
{
"path": "labelme/translate/zh_TW.ts",
"chars": 30081,
"preview": "<?xml version=\"1.0\" encoding=\"utf-8\"?>\n<!DOCTYPE TS>\n<TS version=\"2.1\" language=\"zh_TW\">\n<context>\n <name>AiAssistedA"
},
{
"path": "labelme/utils/__init__.py",
"chars": 669,
"preview": "from ._io import lblsave\nfrom .image import apply_exif_orientation\nfrom .image import img_arr_to_b64\nfrom .image import "
},
{
"path": "labelme/utils/_io.py",
"chars": 717,
"preview": "# MIT License\n# Copyright (c) Kentaro Wada\n\nimport os.path as osp\n\nimport numpy as np\nimport PIL.Image\n\n\ndef lblsave(fil"
},
{
"path": "labelme/utils/image.py",
"chars": 2598,
"preview": "# MIT License\n# Copyright (c) Kentaro Wada\n\nimport base64\nimport io\n\nimport numpy as np\nimport PIL.ExifTags\nimport PIL.I"
},
{
"path": "labelme/utils/qt.py",
"chars": 2487,
"preview": "import os.path as osp\nfrom math import sqrt\n\nimport numpy as np\nfrom PyQt5 import QtCore\nfrom PyQt5 import QtGui\nfrom Py"
},
{
"path": "labelme/utils/shape.py",
"chars": 3534,
"preview": "# MIT License\n# Copyright (c) Kentaro Wada\n\nimport math\nimport uuid\n\nimport numpy as np\nimport PIL.Image\nimport PIL.Imag"
},
{
"path": "labelme/widgets/__init__.py",
"chars": 660,
"preview": "from ._ai_assisted_annotation_widget import AiAssistedAnnotationWidget\nfrom ._ai_text_to_annotation_widget import AiText"
},
{
"path": "labelme/widgets/_ai_assisted_annotation_widget.py",
"chars": 3421,
"preview": "from collections.abc import Callable\n\nfrom loguru import logger\nfrom PyQt5 import QtCore\nfrom PyQt5 import QtGui\nfrom Py"
},
{
"path": "labelme/widgets/_ai_text_to_annotation_widget.py",
"chars": 5159,
"preview": "from collections.abc import Callable\n\nfrom PyQt5 import QtCore\nfrom PyQt5 import QtGui\nfrom PyQt5 import QtWidgets\n\nfrom"
},
{
"path": "labelme/widgets/_info_button.py",
"chars": 931,
"preview": "from PyQt5 import QtCore\nfrom PyQt5 import QtGui\nfrom PyQt5 import QtWidgets\n\nfrom labelme.utils.qt import newIcon\n\n\ncla"
},
{
"path": "labelme/widgets/_status.py",
"chars": 286,
"preview": "from PyQt5 import QtGui\nfrom PyQt5 import QtWidgets\n\n\nclass StatusStats(QtWidgets.QLabel):\n def __init__(self):\n "
},
{
"path": "labelme/widgets/brightness_contrast_dialog.py",
"chars": 2834,
"preview": "import PIL.Image\nimport PIL.ImageEnhance\nfrom PyQt5 import QtWidgets\nfrom PyQt5.QtCore import Qt\nfrom PyQt5.QtGui import"
},
{
"path": "labelme/widgets/canvas.py",
"chars": 45262,
"preview": "from __future__ import annotations\n\nimport enum\nfrom typing import Literal\n\nimport imgviz\nimport numpy as np\nimport osam"
},
{
"path": "labelme/widgets/download.py",
"chars": 2152,
"preview": "from __future__ import annotations\n\nimport types\n\nimport osam\nfrom loguru import logger\nfrom PyQt5 import QtWidgets\nfrom"
},
{
"path": "labelme/widgets/file_dialog_preview.py",
"chars": 2449,
"preview": "import json\nfrom typing import cast\n\nfrom PyQt5 import QtCore\nfrom PyQt5 import QtGui\nfrom PyQt5 import QtWidgets\n\n\nclas"
},
{
"path": "labelme/widgets/label_dialog.py",
"chars": 8593,
"preview": "import re\nfrom typing import cast\n\nfrom loguru import logger\nfrom PyQt5 import QtCore\nfrom PyQt5 import QtGui\nfrom PyQt5"
},
{
"path": "labelme/widgets/label_list_widget.py",
"chars": 6411,
"preview": "from typing import cast\n\nfrom PyQt5 import QtCore\nfrom PyQt5 import QtGui\nfrom PyQt5 import QtWidgets\nfrom PyQt5.QtCore "
},
{
"path": "labelme/widgets/tool_bar.py",
"chars": 1720,
"preview": "from __future__ import annotations\n\nfrom PyQt5 import QtCore\nfrom PyQt5 import QtGui\nfrom PyQt5 import QtWidgets\nfrom Py"
},
{
"path": "labelme/widgets/unique_label_qlist_widget.py",
"chars": 1544,
"preview": "import html\n\nfrom PyQt5 import QtGui\nfrom PyQt5 import QtWidgets\nfrom PyQt5.QtCore import Qt\n\nfrom .label_list_widget im"
},
{
"path": "labelme/widgets/zoom_widget.py",
"chars": 683,
"preview": "from PyQt5 import QtCore\nfrom PyQt5 import QtGui\nfrom PyQt5 import QtWidgets\n\n\nclass ZoomWidget(QtWidgets.QSpinBox):\n "
},
{
"path": "pyproject.toml",
"chars": 1984,
"preview": "[build-system]\nrequires = [\"hatchling>=1.20.0\", \"hatch-vcs\", \"hatch-fancy-pypi-readme\"]\nbuild-backend = \"hatchling.build"
},
{
"path": "tests/conftest.py",
"chars": 1076,
"preview": "import json\nimport shutil\nfrom pathlib import Path\n\nimport pytest\n\n\ndef _create_annotated_nested(data_path: Path) -> Non"
},
{
"path": "tests/e2e/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "tests/e2e/annotation_test.py",
"chars": 2483,
"preview": "from __future__ import annotations\n\nfrom pathlib import Path\n\nimport pytest\nfrom PyQt5.QtCore import QPoint\nfrom PyQt5.Q"
},
{
"path": "tests/e2e/config_test.py",
"chars": 1490,
"preview": "from __future__ import annotations\n\nfrom pathlib import Path\n\nimport pytest\nfrom PyQt5 import QtWidgets\nfrom pytestqt.qt"
},
{
"path": "tests/e2e/conftest.py",
"chars": 856,
"preview": "from __future__ import annotations\n\nfrom collections.abc import Generator\nfrom pathlib import Path\n\nimport pytest\nfrom P"
},
{
"path": "tests/e2e/file_loading_test.py",
"chars": 2810,
"preview": "from __future__ import annotations\n\nfrom pathlib import Path\nfrom typing import Literal\n\nimport pytest\nfrom PyQt5 import"
},
{
"path": "tests/e2e/navigation_test.py",
"chars": 1070,
"preview": "from __future__ import annotations\n\nfrom pathlib import Path\n\nimport pytest\nfrom PyQt5.QtCore import QPoint\nfrom PyQt5.Q"
},
{
"path": "tests/e2e/smoke_test.py",
"chars": 816,
"preview": "from __future__ import annotations\n\nfrom pathlib import Path\n\nimport pytest\nfrom pytestqt.qtbot import QtBot\n\nimport lab"
},
{
"path": "tests/unit/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "tests/unit/_label_file_test.py",
"chars": 997,
"preview": "from __future__ import annotations\n\nimport json\nimport shutil\nfrom pathlib import Path\n\nfrom labelme._label_file import "
},
{
"path": "tests/unit/config_test.py",
"chars": 3190,
"preview": "from pathlib import Path\n\nimport pytest\nimport yaml\n\nfrom labelme.config import _migrate_config_from_file\nfrom labelme.c"
},
{
"path": "tests/unit/load_image_file_test.py",
"chars": 2538,
"preview": "from __future__ import annotations\n\nimport io\nfrom pathlib import Path\n\nimport numpy as np\nimport PIL.Image\nimport tifff"
},
{
"path": "tests/unit/shape_contains_point_test.py",
"chars": 1636,
"preview": "from __future__ import annotations\n\nfrom PyQt5 import QtCore\n\nfrom labelme.shape import Shape\n\n\ndef _make_point_shape(x:"
},
{
"path": "tests/unit/shape_test.py",
"chars": 1008,
"preview": "import numpy as np\nimport pytest\nfrom numpy.typing import NDArray\nfrom PyQt5 import QtCore\n\nfrom labelme.shape import Sh"
},
{
"path": "tests/unit/utils/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "tests/unit/utils/image_test.py",
"chars": 841,
"preview": "import numpy as np\nimport PIL.Image\n\nfrom labelme.utils import image as image_module\n\nfrom .util import data_dir\nfrom .u"
},
{
"path": "tests/unit/utils/qt_test.py",
"chars": 413,
"preview": "from PyQt5.QtCore import QPointF\n\nfrom labelme.utils.qt import distancetoline\n\n\ndef test_distancetoline():\n line = (Q"
},
{
"path": "tests/unit/utils/shape_test.py",
"chars": 1470,
"preview": "import numpy as np\n\nfrom labelme.utils import shape as shape_module\n\nfrom .util import get_img_and_data\n\n\ndef test_shape"
},
{
"path": "tests/unit/utils/util.py",
"chars": 1072,
"preview": "import json\nfrom pathlib import Path\n\nfrom labelme.utils import image as image_module\nfrom labelme.utils import shape as"
},
{
"path": "tests/unit/widgets/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "tests/unit/widgets/canvas_test.py",
"chars": 1926,
"preview": "from __future__ import annotations\n\nfrom typing import Final\n\nimport pytest\nfrom PyQt5 import QtGui\nfrom PyQt5.QtCore im"
},
{
"path": "tests/unit/widgets/label_dialog_test.py",
"chars": 3057,
"preview": "from __future__ import annotations\n\nimport pytest\nfrom PyQt5 import QtCore\nfrom PyQt5 import QtWidgets\n\nfrom labelme.wid"
},
{
"path": "tests/unit/widgets/label_list_widget_test.py",
"chars": 460,
"preview": "import pytest\n\nfrom labelme.widgets import LabelListWidget\nfrom labelme.widgets import LabelListWidgetItem\n\n\n@pytest.mar"
},
{
"path": "tools/update_translate.py",
"chars": 2465,
"preview": "import re\nimport subprocess\nimport sys\nfrom pathlib import Path\n\nfrom loguru import logger\n\nhere: Path = Path(__file__)."
}
]
// ... and 30 more files (download for full content)
About this extraction
This page contains the full source code of the wkentaro/labelme GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 162 files (1.1 MB), approximately 342.9k tokens, and a symbol index with 428 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.