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#!/usr/bin/env python
from __future__ import annotations
import cv2
import gradio as gr
import huggingface_hub
import insightface
import numpy as np
import onnxruntime as ort
TITLE = "insightface Person Detection"
DESCRIPTION = "https://github.com/deepinsight/insightface/tree/master/examples/person_detection"
def load_model():
path = huggingface_hub.hf_hub_download("public-data/insightface", "models/scrfd_person_2.5g.onnx")
options = ort.SessionOptions()
options.intra_op_num_threads = 8
options.inter_op_num_threads = 8
session = ort.InferenceSession(
path, sess_options=options, providers=["CPUExecutionProvider", "CUDAExecutionProvider"]
)
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(int)
kpss = np.round(kpss).astype(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) -> list[np.ndarray]:
person_images = []
for i in range(bboxes.shape[0]):
bbox = bboxes[i]
x1, y1, x2, y2 = bbox
person_img = image[y1:y2, x1:x2]
# Append the cropped person image
person_images.append(person_img)
return person_images
detector = load_model()
detector.prepare(-1, nms_thresh=0.5, input_size=(640, 640))
def detect(image: np.ndarray) -> list[np.ndarray]:
if image is None:
return []
image = image[:, :, ::-1] # RGB -> BGR
bboxes, vbboxes = detect_person(image, detector)
person_images = visualize(image, bboxes, vbboxes)
return [img[:, :, ::-1] for img in person_images] # BGR -> RGB
demo = gr.Interface(
fn=detect,
inputs=gr.Image(label="Input", type="numpy"),
outputs=gr.Gallery(label="Detected Persons"),
title=TITLE,
description=DESCRIPTION,
)
if __name__ == "__main__":
demo.queue(max_size=10).launch()
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