Add files

.gitignore

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+images

.gitmodules

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+[submodule "face_detection"]
+	path = face_detection
+	url = https://github.com/ibug-group/face_detection
+[submodule "face_parsing"]
+	path = face_parsing
+	url = https://github.com/hhj1897/face_parsing
+[submodule "roi_tanh_warping"]
+	path = roi_tanh_warping
+	url = https://github.com/ibug-group/roi_tanh_warping

app.py

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+#!/usr/bin/env python
+
+from __future__ import annotations
+
+import argparse
+import functools
+import os
+import pathlib
+import sys
+import tarfile
+
+import gradio as gr
+import huggingface_hub
+import numpy as np
+import torch
+
+sys.path.insert(0, 'face_detection')
+sys.path.insert(0, 'face_parsing')
+sys.path.insert(0, 'roi_tanh_warping')
+
+from ibug.face_detection import RetinaFacePredictor
+from ibug.face_parsing.parser import WEIGHT, FaceParser
+from ibug.face_parsing.utils import label_colormap
+
+REPO_URL = 'https://github.com/hhj1897/face_parsing'
+TITLE = 'hhj1897/face_parsing'
+DESCRIPTION = f'This is a demo for {REPO_URL}.'
+ARTICLE = None
+
+TOKEN = os.environ['TOKEN']
+
+
+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 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 = ['000.tar', '001.tar']
+        for name in filenames:
+            path = huggingface_hub.hf_hub_download(dataset_repo,
+                                                   name,
+                                                   repo_type='dataset',
+                                                   use_auth_token=TOKEN)
+            with tarfile.open(path) as f:
+                f.extractall(image_dir.as_posix())
+    return sorted(image_dir.rglob('*.jpg'))
+
+
+def load_detector(device: torch.device) -> RetinaFacePredictor:
+    model = RetinaFacePredictor(
+        threshold=0.8,
+        device=device,
+        model=RetinaFacePredictor.get_model('mobilenet0.25'))
+    return model
+
+
+def load_model(model_name: str, device: torch.device) -> FaceParser:
+    encoder, decoder, num_classes = model_name.split('-')
+    num_classes = int(num_classes)
+    model = FaceParser(device=device,
+                       encoder=encoder,
+                       decoder=decoder,
+                       num_classes=num_classes)
+    model.num_classes = num_classes
+    return model
+
+
+def predict(image: np.ndarray, model_name: str, max_num_faces: int,
+            detector: RetinaFacePredictor,
+            models: dict[str, FaceParser]) -> np.ndarray:
+    model = models[model_name]
+    colormap = label_colormap(model.num_classes)
+
+    # RGB -> BGR
+    image = image[:, :, ::-1]
+
+    faces = detector(image, rgb=False)
+    if len(faces) == 0:
+        raise RuntimeError('No face was found.')
+    faces = sorted(list(faces), key=lambda x: -x[4])[:max_num_faces][::-1]
+    masks = model.predict_img(image, faces, rgb=False)
+
+    mask_image = np.zeros_like(image)
+    for mask in masks:
+        temp = colormap[mask]
+        mask_image[temp > 0] = temp[temp > 0]
+
+    res = image.astype(float) * 0.5 + mask_image[:, :, ::-1] * 0.5
+    res = np.clip(np.round(res), 0, 255).astype(np.uint8)
+    return res[:, :, ::-1]
+
+
+def main():
+    gr.close_all()
+
+    args = parse_args()
+    device = torch.device(args.device)
+
+    detector = load_detector(device)
+
+    model_names = list(WEIGHT.keys())
+    models = {name: load_model(name, device=device) for name in model_names}
+
+    func = functools.partial(predict, detector=detector, models=models)
+    func = functools.update_wrapper(func, predict)
+
+    image_paths = load_sample_images()
+    examples = [[path.as_posix(), model_names[1], 10] for path in image_paths]
+
+    gr.Interface(
+        func,
+        [
+            gr.inputs.Image(type='numpy', label='Input'),
+            gr.inputs.Radio(model_names,
+                            type='value',
+                            default=model_names[1],
+                            label='Model'),
+            gr.inputs.Slider(
+                1, 20, step=1, default=10, label='Max Number of Faces'),
+        ],
+        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

face_parsing

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+Subproject commit 8ce84123d0433e6ed389b33e5d3dc2a6a1609d70

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

roi_tanh_warping

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+Subproject commit f9cb77ed9d4ce4e40f026b2425d62efe517691c9