add yolov8 test

object_detection/yolo8showobjects.py

@@ -0,0 +1,80 @@
+#!/usr/bin/env python3
+
+import cv2
+import yt_dlp
+import subprocess
+import numpy as np
+from flask import Flask, Response
+from ultralytics import YOLO #changed
+
+
+app = Flask(__name__)
+
+YOUTUBE_URL = "https://www.youtube.com/watch?v=i3w7qZVSAsY"
+CONFIDENCE_THRESHOLD = 0.25
+
+# Load YOLOv8 model
+print("Loading YOLOv8 model...")
+model = YOLO("yolov8s.pt")  # Using YOLOv8 small model
+print("YOLOv8 loaded successfully!")
+
+def get_stream_url():
+    """Fetch fresh 720p YouTube stream URL using yt-dlp."""
+    ydl_opts = {'quiet': True, 'format': 'bestvideo[height=720]'}
+    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
+        info_dict = ydl.extract_info(YOUTUBE_URL, download=False)
+        return info_dict.get("url", None)
+
+def generate_frames():
+    """Capture video frames, apply object detection, and stream as MJPEG."""
+    stream_url = get_stream_url()
+    if not stream_url:
+        print("Failed to fetch stream URL!")
+        return
+
+    print("Starting FFmpeg stream...")
+    ffmpeg_process = subprocess.Popen([
+        "ffmpeg", "-re", "-i", stream_url, "-r", "10",  # Frame rate to 10 FPS
+        "-fflags", "nobuffer", "-flags", "low_delay",
+        "-f", "rawvideo", "-pix_fmt", "bgr24", "pipe:1"
+    ], stdout=subprocess.PIPE, stderr=subprocess.DEVNULL, bufsize=10**8)
+
+    while True:
+        raw_frame = ffmpeg_process.stdout.read(1280 * 720 * 3)
+        if not raw_frame:
+            print("No frame received!")
+            break
+
+        frame = np.frombuffer(raw_frame, np.uint8).reshape((720, 1280, 3))
+
+        # Run YOLOv8 object detection
+        results = model(frame, stream=True)
+
+        for result in results:
+            boxes = result.boxes.xyxy.cpu().numpy()
+            scores = result.boxes.conf.cpu().numpy()
+            class_ids = result.boxes.cls.cpu().numpy().astype(int)
+            names = result.names
+
+            for i in range(len(boxes)):
+                if scores[i] > CONFIDENCE_THRESHOLD:
+                    x1, y1, x2, y2 = map(int, boxes[i])
+                    label = f"{names[class_ids[i]]} ({scores[i]:.2f})"
+
+                    cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 255), 2)
+                    cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+
+        # Encode and yield the frame as JPEG
+        _, buffer = cv2.imencode('.jpg', frame)
+        yield (b'--frame\r\n'
+               b'Content-Type: image/jpeg\r\n\r\n' + buffer.tobytes() + b'\r\n')
+
+@app.route('/video')
+def video_feed():
+    """Stream processed video frames."""
+    return Response(generate_frames(), mimetype='multipart/x-mixed-replace; boundary=frame')
+
+if __name__ == '__main__':
+    print("Running at http://localhost:5000/video")
+    app.run(host='0.0.0.0', port=5000, debug=True, threaded=True)
+