111 lines
4 KiB
Python
Executable file
111 lines
4 KiB
Python
Executable file
#!/usr/bin/env python3
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import cv2
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import torch
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import yt_dlp
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import subprocess
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import time
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import os
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import numpy as np
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from flask import Flask, Response
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import warnings
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warnings.simplefilter("ignore", category=FutureWarning)
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app = Flask(__name__)
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YOUTUBE_URL = "https://www.youtube.com/watch?v=t45_gP7I82I" # stream URL
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CONFIDENCE_THRESHOLD = 0.3 # Confidence threshold for object detection
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MODEL = "yolov5s" # YOLO model version (yolov5s, yolov5m, etc.)
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# Load YOLOv5 model
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print("🔄 Loading YOLOv5 model...")
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model = torch.hub.load("ultralytics/yolov5", "custom", path=MODEL, force_reload=True)
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print("✅ YOLOv5 loaded successfully!")
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# Load object list from file
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OBJECT_LIST_FILE = "objectlist.txt"
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if os.path.exists(OBJECT_LIST_FILE):
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with open(OBJECT_LIST_FILE, "r") as f:
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OBJECT_LIST = set(line.strip().lower() for line in f if line.strip())
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else:
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OBJECT_LIST = set()
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def get_stream_url():
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"""Fetch fresh 720p YouTube stream URL using yt-dlp."""
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ydl_opts = {'quiet': True, 'format': 'bestvideo[height=720]'}
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with yt_dlp.YoutubeDL(ydl_opts) as ydl:
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info_dict = ydl.extract_info(YOUTUBE_URL, download=False)
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return info_dict.get("url", None)
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def generate_frames():
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"""Capture video frames, apply object detection, and stream as MJPEG."""
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stream_url = get_stream_url()
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if not stream_url:
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print(" Failed to fetch stream URL!")
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return
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print("🎥 Starting FFmpeg stream...")
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ffmpeg_process = subprocess.Popen([
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"ffmpeg", "-re", "-i", stream_url,
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"-f", "rawvideo", "-pix_fmt", "bgr24", "pipe:1"
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], stdout=subprocess.PIPE, stderr=subprocess.DEVNULL, bufsize=10**8)
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frame_width, frame_height = 1280, 720 # Set video frame size
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while True:
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raw_frame = ffmpeg_process.stdout.read(frame_width * frame_height * 3) # Read raw BGR frame
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if not raw_frame:
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print("No frame received!")
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break
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frame = np.frombuffer(raw_frame, np.uint8).reshape((frame_height, frame_width, 3)) # Convert to NumPy array
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# Run YOLO object detection
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results = model(frame)
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detections = results.pandas().xyxy[0] # Convert detections to Pandas DataFrame
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if detections.empty:
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print("No objects detected in this frame.")
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frame = frame.copy() # Make a writable copy
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frame[:] = 0 # Make entire frame black
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else:
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print(f"Detected {len(detections)} objects!")
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print(detections[["name", "confidence"]]) # Print detected object names and confidence
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mask = np.zeros_like(frame) # Create black mask
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for _, row in detections.iterrows():
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if row["confidence"] > CONFIDENCE_THRESHOLD and row["name"].lower() in OBJECT_LIST:
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x1, y1, x2, y2 = int(row["xmin"]), int(row["ymin"]), int(row["xmax"]), int(row["ymax"])
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# Expand bounding box by 75px
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x1, y1 = max(0, x1 - 75), max(0, y1 - 75)
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x2, y2 = min(frame_width, x2 + 75), min(frame_height, y2 + 75)
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# Copy detected object to mask
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mask[y1:y2, x1:x2] = frame[y1:y2, x1:x2]
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#dont draw bounding box
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#cv2.rectangle(mask, (x1, y1), (x2, y2), (0, 255, 0), 2)
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#label = f"{row['name']} ({row['confidence']:.2f})"
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#cv2.putText(mask, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
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frame = mask # Replace original frame with masked frame
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# Encode and yield the frame as JPEG
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_, buffer = cv2.imencode('.jpg', frame)
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yield (b'--frame\r\n'
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b'Content-Type: image/jpeg\r\n\r\n' + buffer.tobytes() + b'\r\n')
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@app.route('/video')
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def video_feed():
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"""Stream processed video frames."""
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return Response(generate_frames(), mimetype='multipart/x-mixed-replace; boundary=frame')
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if __name__ == '__main__':
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print("Running at http://localhost:5000/video") #port
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app.run(host='0.0.0.0', port=5000, debug=True, threaded=True)
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