Add files

.gitmodules

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+[submodule "face_detection"]
+	path = face_detection
+	url = https://github.com/ibug-group/face_detection

app.py

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+#!/usr/bin/env python
+
+from __future__ import annotations
+
+import argparse
+import functools
+import pathlib
+import sys
+import urllib.request
+from typing import Union
+
+import cv2
+import gradio as gr
+import numpy as np
+import torch
+import torch.nn as nn
+
+sys.path.insert(0, 'face_detection')
+
+from ibug.face_detection import RetinaFacePredictor, S3FDPredictor
+
+REPO_URL = 'https://github.com/ibug-group/face_detection'
+TITLE = 'ibug-group/face_detection'
+DESCRIPTION = f'This is a demo for {REPO_URL}.'
+ARTICLE = None
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--face-score-slider-step', type=float, default=0.05)
+    parser.add_argument('--face-score-threshold', type=float, default=0.8)
+    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 load_model(
+        model_name: str, threshold: float,
+        device: torch.device) -> Union[RetinaFacePredictor, S3FDPredictor]:
+    if model_name == 's3fd':
+        model = S3FDPredictor(threshold=threshold, device=device)
+    else:
+        model_name = model_name.replace('retinaface_', '')
+        model = RetinaFacePredictor(
+            threshold=threshold,
+            device=device,
+            model=RetinaFacePredictor.get_model(model_name))
+    return model
+
+
+def detect(image: np.ndarray, model_name: str, face_score_threshold: float,
+           detectors: dict[str, nn.Module]) -> np.ndarray:
+    model = detectors[model_name]
+    model.threshold = face_score_threshold
+
+    # RGB -> BGR
+    image = image[:, :, ::-1]
+    preds = model(image, rgb=False)
+
+    res = image.copy()
+    for pred in preds:
+        box = np.round(pred[:4]).astype(int)
+
+        line_width = max(2, int(3 * (box[2:] - box[:2]).max() / 256))
+        cv2.rectangle(res, tuple(box[:2]), tuple(box[2:]), (0, 255, 0),
+                      line_width)
+
+        if len(pred) == 15:
+            pts = pred[5:].reshape(-1, 2)
+            for pt in np.round(pts).astype(int):
+                cv2.circle(res, tuple(pt), line_width, (0, 255, 0), cv2.FILLED)
+
+    return res[:, :, ::-1]
+
+
+def main():
+    gr.close_all()
+
+    args = parse_args()
+    device = torch.device(args.device)
+
+    model_names = [
+        'retinaface_mobilenet0.25',
+        'retinaface_resnet50',
+        's3fd',
+    ]
+    detectors = {
+        name: load_model(name,
+                         threshold=args.face_score_threshold,
+                         device=device)
+        for name in model_names
+    }
+
+    func = functools.partial(detect, detectors=detectors)
+    func = functools.update_wrapper(func, detect)
+
+    image_path = pathlib.Path('selfie.jpg')
+    if not image_path.exists():
+        url = 'https://raw.githubusercontent.com/peiyunh/tiny/master/data/demo/selfie.jpg'
+        urllib.request.urlretrieve(url, image_path)
+    examples = [[image_path.as_posix(), model_names[1], 0.8]]
+
+    gr.Interface(
+        func,
+        [
+            gr.inputs.Image(type='numpy', label='Input'),
+            gr.inputs.Radio(model_names,
+                            type='value',
+                            default='retinaface_resnet50',
+                            label='Model'),
+            gr.inputs.Slider(0,
+                             1,
+                             step=args.face_score_slider_step,
+                             default=args.face_score_threshold,
+                             label='Face Score Threshold'),
+        ],
+        gr.outputs.Image(type='numpy', label='Output'),
+        examples=examples,
+        title=TITLE,
+        description=DESCRIPTION,
+        article=ARTICLE,
+        theme=args.theme,
+        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()

face_detection

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+Subproject commit bc1e392b11d731fa20b1397c8ff3faed5e7fc76e

requirements.txt

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+numpy==1.22.3
+opencv-python-headless==4.5.5.64
+torch==1.11.0
+torchvision==0.12.0