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| import cv2 | |
| import ultralytics | |
| import gradio as gr | |
| import torch | |
| # Load YOLOv8 model | |
| model = ultralytics.YOLO('yolov8n.pt') | |
| # Set the stream URL (optional, can be commented out if using only camera) | |
| stream_url = "https://edge01.london.nginx.hdontap.com/hosb5/ng_showcase-coke_bottle-street_fixed.stream/chunklist_w464099566.m3u8" | |
| # Low-resolution for inference | |
| LOW_RES = (320, 180) | |
| def detect_and_draw(frame): | |
| # Resize frame to low resolution for faster inference | |
| low_res_frame = cv2.resize(frame, LOW_RES) | |
| # Perform YOLOv8 inference | |
| results = model(low_res_frame) | |
| # Scale bounding boxes | |
| scale_x = frame.shape[1] / LOW_RES[0] | |
| scale_y = frame.shape[0] / LOW_RES[1] | |
| # Draw bounding boxes on high-res frame | |
| for detection in results[0].boxes.data: | |
| x1, y1, x2, y2, conf, cls = detection | |
| x1, y1, x2, y2 = int(x1*scale_x), int(y1*scale_y), int(x2*scale_x), int(y2*scale_y) | |
| label = f"{results[0].names[int(cls)]} {conf:.2f}" | |
| cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2) | |
| cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2) | |
| return frame | |
| def process_stream(stream_source): | |
| if stream_source == "camera": | |
| cap = cv2.VideoCapture(0) # Use 0 for the default camera | |
| else: | |
| cap = cv2.VideoCapture(stream_url) | |
| frame_count = 0 | |
| while cap.isOpened(): | |
| ret, frame = cap.read() | |
| if not ret: | |
| break | |
| frame_count += 3 | |
| if frame_count % 30 == 0: # Process every 30th frame | |
| result = detect_and_draw(frame) | |
| result_rgb = cv2.cvtColor(result, cv2.COLOR_BGR2RGB) | |
| yield result_rgb | |
| cap.release() | |
| # Gradio interface for live video stream | |
| iface = gr.Interface( | |
| fn=process_stream, | |
| inputs=gr.Dropdown(["camera", "stream"], label="Video Source"), | |
| outputs="image", | |
| live=True, | |
| title="YOLOv8 Real-Time Object Detection", | |
| description="Live stream processed with YOLOv8 for real-time object detection.") | |
| if __name__ == "__main__": | |
| if torch.cuda.is_available(): | |
| model.to('cuda') | |
| iface.queue() | |
| iface.launch() |