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import gradio as gr | |
import cv2 | |
import torch | |
def detect_objects(frame): | |
results = model(frame) | |
for *box, conf, cls in results.xyxy[0]: | |
label = f'{model.names[int(cls)]} {conf:.2f}' | |
frame = cv2.rectangle(frame, (int(box[0]), int(box[1])), (int(box[2]), int(box[3])), (255, 0, 0), 2) | |
frame = cv2.putText(frame, label, (int(box[0]), int(box[1]) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 0, 0), 2) | |
return cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) | |
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True) | |
video = gr.inputs.Video(type='opencv', label="č¾å „č§é¢") | |
output = gr.outputs.Image(type='pil',label="ę£ęµē»ę") | |
iface = gr.Interface(fn=detect_objects, inputs=video, outputs=output, title="ē®ę ę£ęµ") | |
iface.launch(share=True) | |