Create app.py

app.py

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+
+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)