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| #!/usr/bin/env python | |
| from __future__ import annotations | |
| import os | |
| import pathlib | |
| import shlex | |
| import subprocess | |
| import tarfile | |
| if os.environ.get('SYSTEM') == 'spaces': | |
| subprocess.call(shlex.split('pip uninstall -y opencv-python')) | |
| subprocess.call(shlex.split('pip uninstall -y opencv-python-headless')) | |
| subprocess.call( | |
| shlex.split('pip install opencv-python-headless==4.5.5.64')) | |
| import gradio as gr | |
| import huggingface_hub | |
| import mediapipe as mp | |
| import numpy as np | |
| mp_drawing = mp.solutions.drawing_utils | |
| mp_drawing_styles = mp.solutions.drawing_styles | |
| mp_face_mesh = mp.solutions.face_mesh | |
| TITLE = 'MediaPipe Face Mesh' | |
| DESCRIPTION = 'https://google.github.io/mediapipe/' | |
| HF_TOKEN = os.getenv('HF_TOKEN') | |
| def load_sample_images() -> list[pathlib.Path]: | |
| image_dir = pathlib.Path('images') | |
| if not image_dir.exists(): | |
| image_dir.mkdir() | |
| dataset_repo = 'hysts/input-images' | |
| filenames = ['001.tar', '005.tar'] | |
| for name in filenames: | |
| path = huggingface_hub.hf_hub_download(dataset_repo, | |
| name, | |
| repo_type='dataset', | |
| use_auth_token=HF_TOKEN) | |
| with tarfile.open(path) as f: | |
| f.extractall(image_dir.as_posix()) | |
| return sorted(image_dir.rglob('*.jpg')) | |
| def run( | |
| image: np.ndarray, | |
| max_num_faces: int, | |
| min_detection_confidence: float, | |
| show_tesselation: bool, | |
| show_contours: bool, | |
| show_irises: bool, | |
| ) -> np.ndarray: | |
| with mp_face_mesh.FaceMesh( | |
| static_image_mode=True, | |
| max_num_faces=max_num_faces, | |
| refine_landmarks=True, | |
| min_detection_confidence=min_detection_confidence) as face_mesh: | |
| results = face_mesh.process(image) | |
| res = image[:, :, ::-1].copy() | |
| if results.multi_face_landmarks is not None: | |
| for face_landmarks in results.multi_face_landmarks: | |
| if show_tesselation: | |
| mp_drawing.draw_landmarks( | |
| image=res, | |
| landmark_list=face_landmarks, | |
| connections=mp_face_mesh.FACEMESH_TESSELATION, | |
| landmark_drawing_spec=None, | |
| connection_drawing_spec=mp_drawing_styles. | |
| get_default_face_mesh_tesselation_style()) | |
| if show_contours: | |
| mp_drawing.draw_landmarks( | |
| image=res, | |
| landmark_list=face_landmarks, | |
| connections=mp_face_mesh.FACEMESH_CONTOURS, | |
| landmark_drawing_spec=None, | |
| connection_drawing_spec=mp_drawing_styles. | |
| get_default_face_mesh_contours_style()) | |
| if show_irises: | |
| mp_drawing.draw_landmarks( | |
| image=res, | |
| landmark_list=face_landmarks, | |
| connections=mp_face_mesh.FACEMESH_IRISES, | |
| landmark_drawing_spec=None, | |
| connection_drawing_spec=mp_drawing_styles. | |
| get_default_face_mesh_iris_connections_style()) | |
| return res[:, :, ::-1] | |
| image_paths = load_sample_images() | |
| examples = [[path.as_posix(), 5, 0.5, True, True, True] | |
| for path in image_paths] | |
| gr.Interface( | |
| fn=run, | |
| inputs=[ | |
| gr.Image(label='Input', type='numpy'), | |
| gr.Slider(label='Max Number of Faces', | |
| minimum=0, | |
| maximum=10, | |
| step=1, | |
| value=5), | |
| gr.Slider(label='Minimum Detection Confidence', | |
| minimum=0, | |
| maximum=1, | |
| step=0.05, | |
| value=0.5), | |
| gr.Checkbox(label='Show Tesselation', value=True), | |
| gr.Checkbox(label='Show Contours', value=True), | |
| gr.Checkbox(label='Show Irises', value=True), | |
| ], | |
| outputs=gr.Image(label='Output', type='numpy'), | |
| examples=examples, | |
| title=TITLE, | |
| description=DESCRIPTION, | |
| ).launch(show_api=False) | |