#!/usr/bin/env python3

import cv2
import torch
import yt_dlp
import subprocess
import time
import os
import numpy as np
from flask import Flask, Response
import warnings


warnings.simplefilter("ignore", category=FutureWarning)

app = Flask(__name__)

YOUTUBE_URL = "https://www.youtube.com/watch?v=t45_gP7I82I"  # stream URL
CONFIDENCE_THRESHOLD = 0.3  # Confidence threshold for object detection
MODEL = "yolov5s"  # YOLO model version (yolov5s, yolov5m, etc.)

# Load YOLOv5 model
print("🔄 Loading YOLOv5 model...")
model = torch.hub.load("ultralytics/yolov5", "custom", path=MODEL, force_reload=True)
print("✅ YOLOv5 loaded successfully!")

# Load object list from file
OBJECT_LIST_FILE = "objectlist.txt"
if os.path.exists(OBJECT_LIST_FILE):
    with open(OBJECT_LIST_FILE, "r") as f:
        OBJECT_LIST = set(line.strip().lower() for line in f if line.strip())
else:
    OBJECT_LIST = set()

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,
        "-f", "rawvideo", "-pix_fmt", "bgr24", "pipe:1"
    ], stdout=subprocess.PIPE, stderr=subprocess.DEVNULL, bufsize=10**8)

    frame_width, frame_height = 1280, 720  # Set video frame size

    while True:
        raw_frame = ffmpeg_process.stdout.read(frame_width * frame_height * 3)  # Read raw BGR frame
        if not raw_frame:
            print("No frame received!")
            break

        frame = np.frombuffer(raw_frame, np.uint8).reshape((frame_height, frame_width, 3))  # Convert to NumPy array

        # Run YOLO object detection
        results = model(frame)
        detections = results.pandas().xyxy[0]  # Convert detections to Pandas DataFrame

        if detections.empty:
            print("No objects detected in this frame.")
            frame = frame.copy()  # Make a writable copy
            frame[:] = 0  # Make entire frame black

        else:
            print(f"Detected {len(detections)} objects!")
            print(detections[["name", "confidence"]])  # Print detected object names and confidence

            mask = np.zeros_like(frame)  # Create black mask

            for _, row in detections.iterrows():
                if row["confidence"] > CONFIDENCE_THRESHOLD and row["name"].lower() in OBJECT_LIST:
                    x1, y1, x2, y2 = int(row["xmin"]), int(row["ymin"]), int(row["xmax"]), int(row["ymax"])

                    # Expand bounding box by 75px
                    x1, y1 = max(0, x1 - 75), max(0, y1 - 75)
                    x2, y2 = min(frame_width, x2 + 75), min(frame_height, y2 + 75)

                    # Copy detected object to mask
                    mask[y1:y2, x1:x2] = frame[y1:y2, x1:x2]

                    #dont draw bounding box
                    #cv2.rectangle(mask, (x1, y1), (x2, y2), (0, 255, 0), 2)
                    #label = f"{row['name']} ({row['confidence']:.2f})"
                    #cv2.putText(mask, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

            frame = mask  # Replace original frame with masked frame

        # 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")  #port
    app.run(host='0.0.0.0', port=5000, debug=True, threaded=True)