Add files

app.py

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+#!/usr/bin/env python
+
+from __future__ import annotations
+
+import argparse
+import functools
+import pathlib
+import subprocess
+
+subprocess.call('pip uninstall -y opencv-python'.split())
+subprocess.call('pip uninstall -y opencv-python-headless'.split())
+subprocess.call('pip install opencv-python-headless==4.5.5.62'.split())
+
+import cv2
+import face_alignment
+import gradio as gr
+import numpy as np
+import torch
+
+ORIGINAL_REPO_URL = 'https://github.com/1adrianb/face-alignment'
+TITLE = '1adrianb/face-alignment'
+DESCRIPTION = f'A demo for {ORIGINAL_REPO_URL}'
+ARTICLE = ''
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--device', type=str, default='cpu')
+    parser.add_argument('--theme', type=str)
+    parser.add_argument('--live', action='store_true')
+    parser.add_argument('--share', action='store_true')
+    parser.add_argument('--port', type=int)
+    parser.add_argument('--disable-queue',
+                        dest='enable_queue',
+                        action='store_false')
+    parser.add_argument('--allow-flagging', type=str, default='never')
+    parser.add_argument('--allow-screenshot', action='store_true')
+    return parser.parse_args()
+
+
+def detect(
+    image: np.ndarray,
+    detector,
+    device: torch.device,
+) -> np.ndarray:
+    preds = detector.get_landmarks(image)
+    if len(preds) == 0:
+        raise RuntimeError('No face was found')
+
+    res = image.copy()
+    for pts in preds:
+        for pt in np.round(pts).astype(int):
+            cv2.circle(res, tuple(pt), 2, (0, 255, 0), cv2.FILLED)
+    return res
+
+
+def main():
+    gr.close_all()
+
+    args = parse_args()
+    device = torch.device(args.device)
+
+    detector = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D,
+                                            device=device.type)
+
+    func = functools.partial(detect, detector=detector, device=device)
+    func = functools.update_wrapper(func, detect)
+
+    image_paths = sorted(pathlib.Path('images').glob('*.jpg'))
+    examples = [[path.as_posix()] for path in image_paths]
+
+    gr.Interface(
+        func,
+        gr.inputs.Image(type='numpy', label='Input'),
+        gr.outputs.Image(type='numpy', label='Output'),
+        examples=examples,
+        theme=args.theme,
+        title=TITLE,
+        description=DESCRIPTION,
+        article=ARTICLE,
+        allow_screenshot=args.allow_screenshot,
+        allow_flagging=args.allow_flagging,
+        live=args.live,
+    ).launch(
+        enable_queue=args.enable_queue,
+        server_port=args.port,
+        share=args.share,
+    )
+
+
+if __name__ == '__main__':
+    main()

requirements.txt

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+face-alignment==1.3.5
+numba==0.55.1
+numpy==1.21.5
+opencv-python-headless==4.5.5.62
+Pillow==9.0.1
+scikit-image==0.19.2
+scipy==1.8.0
+torch==1.11.0
+torchvision==0.12.0