Update
Browse files- .pre-commit-config.yaml +59 -34
- .style.yapf +0 -5
- .vscode/settings.json +30 -0
- README.md +1 -1
- app.py +37 -38
- requirements.txt +3 -3
.pre-commit-config.yaml
CHANGED
@@ -1,35 +1,60 @@
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repos:
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- repo: https://github.com/pre-commit/pre-commit-hooks
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- repo: https://github.com/pre-commit/mirrors-mypy
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repos:
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- repo: https://github.com/pre-commit/pre-commit-hooks
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rev: v4.6.0
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hooks:
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- id: check-executables-have-shebangs
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- id: check-json
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- id: check-merge-conflict
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- id: check-shebang-scripts-are-executable
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- id: check-toml
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- id: check-yaml
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- id: end-of-file-fixer
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- id: mixed-line-ending
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args: ["--fix=lf"]
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- id: requirements-txt-fixer
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- id: trailing-whitespace
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- repo: https://github.com/myint/docformatter
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rev: v1.7.5
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hooks:
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- id: docformatter
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args: ["--in-place"]
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- repo: https://github.com/pycqa/isort
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rev: 5.13.2
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hooks:
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- id: isort
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args: ["--profile", "black"]
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- repo: https://github.com/pre-commit/mirrors-mypy
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rev: v1.10.0
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hooks:
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- id: mypy
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args: ["--ignore-missing-imports"]
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additional_dependencies:
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[
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"types-python-slugify",
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"types-requests",
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"types-PyYAML",
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"types-pytz",
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]
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- repo: https://github.com/psf/black
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rev: 24.4.2
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hooks:
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- id: black
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language_version: python3.10
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args: ["--line-length", "119"]
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- repo: https://github.com/kynan/nbstripout
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rev: 0.7.1
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hooks:
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- id: nbstripout
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args:
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[
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"--extra-keys",
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"metadata.interpreter metadata.kernelspec cell.metadata.pycharm",
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]
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- repo: https://github.com/nbQA-dev/nbQA
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rev: 1.8.5
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hooks:
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- id: nbqa-black
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- id: nbqa-pyupgrade
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args: ["--py37-plus"]
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- id: nbqa-isort
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args: ["--float-to-top"]
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.style.yapf
DELETED
@@ -1,5 +0,0 @@
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[style]
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based_on_style = pep8
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blank_line_before_nested_class_or_def = false
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spaces_before_comment = 2
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split_before_logical_operator = true
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.vscode/settings.json
ADDED
@@ -0,0 +1,30 @@
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{
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"editor.formatOnSave": true,
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"files.insertFinalNewline": false,
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"[python]": {
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"editor.defaultFormatter": "ms-python.black-formatter",
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"editor.formatOnType": true,
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"editor.codeActionsOnSave": {
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"source.organizeImports": "explicit"
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}
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},
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"[jupyter]": {
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"files.insertFinalNewline": false
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},
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"black-formatter.args": [
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"--line-length=119"
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],
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"isort.args": ["--profile", "black"],
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"flake8.args": [
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"--max-line-length=119"
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],
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"ruff.lint.args": [
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"--line-length=119"
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],
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"notebook.output.scrolling": true,
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"notebook.formatOnCellExecution": true,
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"notebook.formatOnSave.enabled": true,
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"notebook.codeActionsOnSave": {
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"source.organizeImports": "explicit"
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}
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}
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README.md
CHANGED
@@ -4,7 +4,7 @@ emoji: π
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colorFrom: blue
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colorTo: gray
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sdk: gradio
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sdk_version:
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app_file: app.py
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pinned: false
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---
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colorFrom: blue
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colorTo: gray
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sdk: gradio
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sdk_version: 4.33.0
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app_file: app.py
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pinned: false
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---
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app.py
CHANGED
@@ -12,26 +12,32 @@ mp_drawing = mp.solutions.drawing_utils
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mp_drawing_styles = mp.solutions.drawing_styles
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mp_pose = mp.solutions.pose
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TITLE =
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DESCRIPTION =
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def run(
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with mp_pose.Pose(
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results = pose.process(image)
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res = image[:, :, ::-1].copy()
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if enable_segmentation:
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if background_color ==
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bg_color = 255
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elif background_color ==
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bg_color = 0
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elif background_color ==
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bg_color = (0, 255, 0) # type: ignore
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else:
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raise ValueError
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@@ -41,43 +47,36 @@ def run(image: np.ndarray, model_complexity: int, enable_segmentation: bool,
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else:
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res[:] = bg_color
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mp_drawing.draw_landmarks(
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return res[:, :, ::-1]
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model_complexities = list(range(3))
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background_colors = [
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image_paths = sorted(pathlib.Path(
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examples = [[path, model_complexities[1], True, 0.5, background_colors[0]]
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for path in image_paths]
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gr.Interface(
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fn=run,
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inputs=[
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gr.Image(label=
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gr.Radio(label=
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gr.Checkbox(label='Enable Segmentation', value=True),
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gr.Slider(label='Minimum Detection Confidence',
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minimum=0,
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maximum=1,
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step=0.05,
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value=0.5),
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gr.Radio(label='Background Color',
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choices=background_colors,
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type='value',
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value=background_colors[0]),
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],
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outputs=gr.Image(label=
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examples=examples,
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title=TITLE,
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description=DESCRIPTION,
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)
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mp_drawing_styles = mp.solutions.drawing_styles
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mp_pose = mp.solutions.pose
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TITLE = "MediaPipe Human Pose Estimation"
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DESCRIPTION = "https://google.github.io/mediapipe/"
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def run(
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image: np.ndarray,
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model_complexity: int,
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enable_segmentation: bool,
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min_detection_confidence: float,
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background_color: str,
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) -> np.ndarray:
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with mp_pose.Pose(
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static_image_mode=True,
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model_complexity=model_complexity,
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enable_segmentation=enable_segmentation,
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min_detection_confidence=min_detection_confidence,
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) as pose:
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results = pose.process(image)
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res = image[:, :, ::-1].copy()
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if enable_segmentation:
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if background_color == "white":
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bg_color = 255
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elif background_color == "black":
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bg_color = 0
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elif background_color == "green":
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bg_color = (0, 255, 0) # type: ignore
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else:
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raise ValueError
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else:
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res[:] = bg_color
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mp_drawing.draw_landmarks(
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res,
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results.pose_landmarks,
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mp_pose.POSE_CONNECTIONS,
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landmark_drawing_spec=mp_drawing_styles.get_default_pose_landmarks_style(),
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)
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return res[:, :, ::-1]
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model_complexities = list(range(3))
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background_colors = ["white", "black", "green"]
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image_paths = sorted(pathlib.Path("images").rglob("*.jpg"))
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examples = [[path, model_complexities[1], True, 0.5, background_colors[0]] for path in image_paths]
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demo = gr.Interface(
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fn=run,
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inputs=[
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gr.Image(label="Input", type="numpy"),
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gr.Radio(label="Model Complexity", choices=model_complexities, type="index", value=model_complexities[1]),
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gr.Checkbox(label="Enable Segmentation", value=True),
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gr.Slider(label="Minimum Detection Confidence", minimum=0, maximum=1, step=0.05, value=0.5),
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gr.Radio(label="Background Color", choices=background_colors, type="value", value=background_colors[0]),
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],
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outputs=gr.Image(label="Output"),
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examples=examples,
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title=TITLE,
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description=DESCRIPTION,
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)
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if __name__ == "__main__":
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demo.queue().launch()
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requirements.txt
CHANGED
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mediapipe==0.10.
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numpy==1.
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opencv-python-headless==4.
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mediapipe==0.10.14
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numpy==1.26.4
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opencv-python-headless==4.10.0.82
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