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| import gradio as gr | |
| import cv2 | |
| import torch | |
| import numpy as np | |
| # Load YOLOv5 model | |
| model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True) | |
| def object_detection(video_data): | |
| # Convert video data to numpy array | |
| video_bytes = video_data.read() | |
| video_np = np.frombuffer(video_bytes, np.uint8) | |
| cap = cv2.VideoCapture(video_np) | |
| while True: | |
| ret, frame = cap.read() | |
| if not ret: | |
| break | |
| # Object detection | |
| results = model(frame) | |
| # Render results | |
| results.render() | |
| yield results.imgs[0] | |
| if cv2.waitKey(1) & 0xFF == ord('q'): | |
| break | |
| cap.release() | |
| iface = gr.Interface(fn=object_detection, inputs=gr.Video(label="Upload Video"), outputs=gr.Image(label="Object Detection")) | |
| iface.launch() |