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Update
Browse files- .pre-commit-config.yaml +59 -34
- .style.yapf +0 -5
- .vscode/settings.json +30 -0
- README.md +2 -2
- app.py +16 -24
- 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
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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
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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
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@@ -4,9 +4,9 @@ emoji: 📚
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colorFrom: yellow
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colorTo: blue
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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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Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
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colorFrom: yellow
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colorTo: blue
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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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Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
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app.py
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from __future__ import annotations
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import os
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import pathlib
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import gradio as gr
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import huggingface_hub
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import mediapipe as mp
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import numpy as np
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mp_face_detection = mp.solutions.face_detection
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mp_drawing = mp.solutions.drawing_utils
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TITLE =
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DESCRIPTION =
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def run(image: np.ndarray, model_selection: int,
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min_detection_confidence: float) -> np.ndarray:
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with mp_face_detection.FaceDetection(
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min_detection_confidence=min_detection_confidence
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) as face_detection:
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results = face_detection.process(image)
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model_types = [
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]
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image_paths = sorted(pathlib.Path(
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examples = [[path, model_types[0], 0.5] 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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type='index',
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value=model_types[0]),
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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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],
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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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from __future__ import annotations
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import pathlib
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import gradio as gr
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import mediapipe as mp
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import numpy as np
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mp_face_detection = mp.solutions.face_detection
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mp_drawing = mp.solutions.drawing_utils
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TITLE = "MediaPipe Face Detection"
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DESCRIPTION = "https://google.github.io/mediapipe/"
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def run(image: np.ndarray, model_selection: int, min_detection_confidence: float) -> np.ndarray:
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with mp_face_detection.FaceDetection(
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model_selection=model_selection, min_detection_confidence=min_detection_confidence
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) as face_detection:
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results = face_detection.process(image)
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model_types = [
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"Short-range model (best for faces within 2 meters)",
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"Full-range model (best for faces within 5 meters)",
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]
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image_paths = sorted(pathlib.Path("images").rglob("*.jpg"))
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examples = [[path, model_types[0], 0.5] 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", choices=model_types, type="index", value=model_types[0]),
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gr.Slider(label="Minimum Detection Confidence", minimum=0, maximum=1, step=0.05, value=0.5),
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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
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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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