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insightface-person-detection
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#!/usr/bin/env python

from __future__ import annotations

import argparse
import functools
import os
import pathlib
import subprocess

if os.environ.get('SYSTEM') == 'spaces':
    subprocess.call('pip install insightface==0.6.2'.split())

import cv2
import gradio as gr
import huggingface_hub
import insightface
import numpy as np
import onnxruntime as ort

REPO_URL = 'https://github.com/deepinsight/insightface/tree/master/examples/person_detection'
TITLE = 'insightface Person Detection'
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('--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():
    path = huggingface_hub.hf_hub_download('hysts/insightface',
                                           'models/scrfd_person_2.5g.onnx',
                                           use_auth_token=TOKEN)
    options = ort.SessionOptions()
    options.intra_op_num_threads = 8
    options.inter_op_num_threads = 8
    session = ort.InferenceSession(path,
                                   sess_options=options,
                                   providers=['CPUExecutionProvider'])
    model = insightface.model_zoo.retinaface.RetinaFace(model_file=path,
                                                        session=session)
    return model


def detect_person(
    img: np.ndarray, detector: insightface.model_zoo.retinaface.RetinaFace
) -> tuple[np.ndarray, np.ndarray]:
    bboxes, kpss = detector.detect(img)
    bboxes = np.round(bboxes[:, :4]).astype(np.int)
    kpss = np.round(kpss).astype(np.int)
    kpss[:, :, 0] = np.clip(kpss[:, :, 0], 0, img.shape[1])
    kpss[:, :, 1] = np.clip(kpss[:, :, 1], 0, img.shape[0])
    vbboxes = bboxes.copy()
    vbboxes[:, 0] = kpss[:, 0, 0]
    vbboxes[:, 1] = kpss[:, 0, 1]
    vbboxes[:, 2] = kpss[:, 4, 0]
    vbboxes[:, 3] = kpss[:, 4, 1]
    return bboxes, vbboxes


def visualize(image: np.ndarray, bboxes: np.ndarray,
              vbboxes: np.ndarray) -> np.ndarray:
    res = image.copy()
    for i in range(bboxes.shape[0]):
        bbox = bboxes[i]
        vbbox = vbboxes[i]
        x1, y1, x2, y2 = bbox
        vx1, vy1, vx2, vy2 = vbbox
        cv2.rectangle(res, (x1, y1), (x2, y2), (0, 255, 0), 1)
        alpha = 0.8
        color = (255, 0, 0)
        for c in range(3):
            res[vy1:vy2, vx1:vx2,
                c] = res[vy1:vy2, vx1:vx2,
                         c] * alpha + color[c] * (1.0 - alpha)
        cv2.circle(res, (vx1, vy1), 1, color, 2)
        cv2.circle(res, (vx1, vy2), 1, color, 2)
        cv2.circle(res, (vx2, vy1), 1, color, 2)
        cv2.circle(res, (vx2, vy2), 1, color, 2)
    return res


def detect(image: np.ndarray, detector) -> np.ndarray:
    image = image[:, :, ::-1]  # RGB -> BGR
    bboxes, vbboxes = detect_person(image, detector)
    res = visualize(image, bboxes, vbboxes)
    return res[:, :, ::-1]  # BGR -> RGB


def main():
    gr.close_all()

    args = parse_args()

    detector = load_model()
    detector.prepare(-1, nms_thresh=0.5, input_size=(640, 640))

    func = functools.partial(detect, detector=detector)
    func = functools.update_wrapper(func, detect)

    image_dir = pathlib.Path('images')
    examples = [[path.as_posix()] for path in sorted(image_dir.glob('*.jpg'))]

    gr.Interface(
        func,
        gr.inputs.Image(type='numpy', label='Input'),
        gr.outputs.Image(type='numpy', label='Output'),
        examples=examples,
        examples_per_page=30,
        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()