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from __future__ import annotations |
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import argparse |
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import functools |
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import os |
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import pathlib |
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import subprocess |
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if os.environ.get('SYSTEM') == 'spaces': |
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subprocess.call('pip install insightface==0.6.2'.split()) |
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import cv2 |
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import gradio as gr |
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import huggingface_hub |
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import insightface |
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import numpy as np |
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import onnxruntime as ort |
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TITLE = 'insightface Person Detection' |
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DESCRIPTION = 'This is an unofficial demo for https://github.com/deepinsight/insightface/tree/master/examples/person_detection.' |
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ARTICLE = '<center><img src="https://visitor-badge.glitch.me/badge?page_id=hysts.insightface-person-detection" alt="visitor badge"/></center>' |
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TOKEN = os.environ['TOKEN'] |
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def parse_args() -> argparse.Namespace: |
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parser = argparse.ArgumentParser() |
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parser.add_argument('--theme', type=str) |
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parser.add_argument('--live', action='store_true') |
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parser.add_argument('--share', action='store_true') |
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parser.add_argument('--port', type=int) |
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parser.add_argument('--disable-queue', |
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dest='enable_queue', |
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action='store_false') |
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parser.add_argument('--allow-flagging', type=str, default='never') |
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return parser.parse_args() |
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def load_model(): |
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path = huggingface_hub.hf_hub_download('hysts/insightface', |
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'models/scrfd_person_2.5g.onnx', |
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use_auth_token=TOKEN) |
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options = ort.SessionOptions() |
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options.intra_op_num_threads = 8 |
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options.inter_op_num_threads = 8 |
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session = ort.InferenceSession(path, |
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sess_options=options, |
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providers=['CPUExecutionProvider']) |
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model = insightface.model_zoo.retinaface.RetinaFace(model_file=path, |
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session=session) |
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return model |
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def detect_person( |
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img: np.ndarray, detector: insightface.model_zoo.retinaface.RetinaFace |
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) -> tuple[np.ndarray, np.ndarray]: |
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bboxes, kpss = detector.detect(img) |
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bboxes = np.round(bboxes[:, :4]).astype(np.int) |
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kpss = np.round(kpss).astype(np.int) |
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kpss[:, :, 0] = np.clip(kpss[:, :, 0], 0, img.shape[1]) |
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kpss[:, :, 1] = np.clip(kpss[:, :, 1], 0, img.shape[0]) |
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vbboxes = bboxes.copy() |
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vbboxes[:, 0] = kpss[:, 0, 0] |
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vbboxes[:, 1] = kpss[:, 0, 1] |
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vbboxes[:, 2] = kpss[:, 4, 0] |
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vbboxes[:, 3] = kpss[:, 4, 1] |
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return bboxes, vbboxes |
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def visualize(image: np.ndarray, bboxes: np.ndarray, |
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vbboxes: np.ndarray) -> np.ndarray: |
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res = image.copy() |
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for i in range(bboxes.shape[0]): |
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bbox = bboxes[i] |
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vbbox = vbboxes[i] |
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x1, y1, x2, y2 = bbox |
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vx1, vy1, vx2, vy2 = vbbox |
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cv2.rectangle(res, (x1, y1), (x2, y2), (0, 255, 0), 1) |
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alpha = 0.8 |
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color = (255, 0, 0) |
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for c in range(3): |
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res[vy1:vy2, vx1:vx2, |
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c] = res[vy1:vy2, vx1:vx2, |
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c] * alpha + color[c] * (1.0 - alpha) |
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cv2.circle(res, (vx1, vy1), 1, color, 2) |
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cv2.circle(res, (vx1, vy2), 1, color, 2) |
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cv2.circle(res, (vx2, vy1), 1, color, 2) |
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cv2.circle(res, (vx2, vy2), 1, color, 2) |
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return res |
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def detect(image: np.ndarray, detector) -> np.ndarray: |
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image = image[:, :, ::-1] |
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bboxes, vbboxes = detect_person(image, detector) |
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res = visualize(image, bboxes, vbboxes) |
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return res[:, :, ::-1] |
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def main(): |
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args = parse_args() |
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detector = load_model() |
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detector.prepare(-1, nms_thresh=0.5, input_size=(640, 640)) |
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func = functools.partial(detect, detector=detector) |
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func = functools.update_wrapper(func, detect) |
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image_dir = pathlib.Path('images') |
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examples = [[path.as_posix()] for path in sorted(image_dir.glob('*.jpg'))] |
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gr.Interface( |
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func, |
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gr.inputs.Image(type='numpy', label='Input'), |
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gr.outputs.Image(type='numpy', label='Output'), |
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examples=examples, |
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examples_per_page=30, |
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title=TITLE, |
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description=DESCRIPTION, |
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article=ARTICLE, |
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theme=args.theme, |
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allow_flagging=args.allow_flagging, |
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live=args.live, |
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).launch( |
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enable_queue=args.enable_queue, |
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server_port=args.port, |
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share=args.share, |
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) |
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if __name__ == '__main__': |
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main() |
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