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| #!/usr/bin/env python | |
| from __future__ import annotations | |
| import argparse | |
| import functools | |
| import os | |
| import pathlib | |
| import subprocess | |
| import tarfile | |
| if os.environ.get('SYSTEM') == 'spaces': | |
| import mim | |
| mim.uninstall('mmcv-full', confirm_yes=True) | |
| mim.install('mmcv-full==1.3.16', is_yes=True) | |
| subprocess.call('pip uninstall -y opencv-python'.split()) | |
| subprocess.call('pip uninstall -y opencv-python-headless'.split()) | |
| subprocess.call('pip install opencv-python-headless'.split()) | |
| import anime_face_detector | |
| import cv2 | |
| import gradio as gr | |
| import huggingface_hub | |
| import numpy as np | |
| import torch | |
| TITLE = 'hysts/anime-face-detector' | |
| DESCRIPTION = 'This is a demo for https://github.com/hysts/anime-face-detector.' | |
| ARTICLE = '<center><img src="https://visitor-badge.glitch.me/badge?page_id=hysts.anime-face-detector" alt="visitor badge"/></center>' | |
| 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_image_paths() -> list[pathlib.Path]: | |
| image_dir = pathlib.Path('images') | |
| if not image_dir.exists(): | |
| dataset_repo = 'hysts/sample-images-TADNE' | |
| path = huggingface_hub.hf_hub_download(dataset_repo, | |
| 'images.tar.gz', | |
| repo_type='dataset', | |
| use_auth_token=TOKEN) | |
| with tarfile.open(path) as f: | |
| f.extractall() | |
| return sorted(image_dir.glob('*')) | |
| def detect( | |
| image: np.ndarray, detector_name: str, face_score_threshold: float, | |
| landmark_score_threshold: float, | |
| detectors: dict[str, | |
| anime_face_detector.LandmarkDetector]) -> np.ndarray: | |
| detector = detectors[detector_name] | |
| # RGB -> BGR | |
| image = image[:, :, ::-1] | |
| preds = detector(image) | |
| res = image.copy() | |
| for pred in preds: | |
| box = pred['bbox'] | |
| box, score = box[:4], box[4] | |
| if score < face_score_threshold: | |
| continue | |
| box = np.round(box).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) | |
| pred_pts = pred['keypoints'] | |
| for *pt, score in pred_pts: | |
| if score < landmark_score_threshold: | |
| color = (0, 255, 255) | |
| else: | |
| color = (0, 0, 255) | |
| pt = np.round(pt).astype(int) | |
| cv2.circle(res, tuple(pt), line_width, color, cv2.FILLED) | |
| return res[:, :, ::-1] | |
| def main(): | |
| args = parse_args() | |
| device = torch.device(args.device) | |
| detector_names = ['faster-rcnn', 'yolov3'] | |
| detectors = { | |
| detector_name: anime_face_detector.create_detector(detector_name, | |
| device=device) | |
| for detector_name in detector_names | |
| } | |
| func = functools.partial(detect, detectors=detectors) | |
| func = functools.update_wrapper(func, detect) | |
| image_paths = load_sample_image_paths() | |
| examples = [[path.as_posix(), 'yolov3', 0.5, 0.3] for path in image_paths] | |
| gr.Interface( | |
| func, | |
| [ | |
| gr.inputs.Image(type='numpy', label='Input'), | |
| gr.inputs.Radio(detector_names, | |
| type='value', | |
| default='yolov3', | |
| label='Detector'), | |
| gr.inputs.Slider( | |
| 0, 1, step=0.05, default=0.5, label='Face Score Threshold'), | |
| gr.inputs.Slider( | |
| 0, 1, step=0.05, default=0.3, | |
| label='Landmark 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() | |