block out persons, currently cpu only

object_detection/yolo8hidepersons.py

@@ -0,0 +1,79 @@
+import cv2
+import numpy as np
+import subprocess
+import yt_dlp
+from ultralytics import YOLO
+from flask import Flask, Response
+
+app = Flask(__name__)
+
+# currently cuda not working with torch version, use cpu
+
+YOUTUBE_URL = "https://www.youtube.com/watch?v=i3w7qZVSAsY"  # Example stream
+CONFIDENCE_THRESHOLD = 0.25
+MODEL_PATH = "yolov8n.pt"  # Using YOLOv8 nano for speed
+
+# Load YOLOv8 model
+print("Loading YOLOv8 model...")
+model = YOLO(MODEL_PATH)
+model.to("cpu")  # set for cpu
+print("YOLOv8 loaded successfully!")
+
+def get_stream_url():
+    """Fetch fresh 720p YouTube stream URL."""
+    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 anonymize persons."""
+    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",  # Set 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)  # Read raw BGR frame
+        if not raw_frame:
+            print("No frame received!")
+            break
+
+        frame = np.frombuffer(raw_frame, np.uint8).reshape((720, 1280, 3)).copy()  # Make writable
+
+
+        # Run YOLOv8 object detection
+        results = model(frame)[0]  # Get first result
+
+        for box in results.boxes:
+            x1, y1, x2, y2 = map(int, box.xyxy[0])
+            confidence = box.conf[0].item()
+            class_id = int(box.cls[0].item())
+            label = model.names[class_id]
+
+            if confidence > CONFIDENCE_THRESHOLD and label == "person":
+                roi = frame[y1:y2, x1:x2]
+                avg_color = np.mean(roi, axis=(0, 1), dtype=int)  # Compute avg color
+                frame[y1:y2, x1:x2] = avg_color  # Fill with avg color
+
+        # 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)
+