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from __future__ import annotations |
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import pathlib |
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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 = "https://github.com/deepinsight/insightface/tree/master/examples/person_detection" |
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def load_model(): |
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path = huggingface_hub.hf_hub_download("public-data/insightface", "models/scrfd_person_2.5g.onnx") |
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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( |
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path, sess_options=options, providers=["CPUExecutionProvider", "CUDAExecutionProvider"] |
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) |
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model = insightface.model_zoo.retinaface.RetinaFace(model_file=path, 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(int) |
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kpss = np.round(kpss).astype(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, 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, c] = res[vy1:vy2, vx1:vx2, 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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detector = load_model() |
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detector.prepare(-1, nms_thresh=0.5, input_size=(640, 640)) |
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def detect(image: np.ndarray) -> 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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examples = sorted(pathlib.Path("images").glob("*.jpg")) |
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demo = gr.Interface( |
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fn=detect, |
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inputs=gr.Image(label="Input", type="numpy"), |
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outputs=gr.Image(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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) |
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if __name__ == "__main__": |
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demo.queue(max_size=10).launch() |
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