Create app.py

app.py

@@ -0,0 +1,66 @@
+from ultralytics import YOLO
+import cv2
+import gradio as gr
+import torch
+
+# Load YOLOv8 model
+model = YOLO('yolov8n.pt')
+
+# Set the stream URL
+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():
+    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=None,
+    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()