#!/usr/bin/env python3

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

# Warning! saves objects in objectlist.txt to file /detections/$object

warnings.simplefilter("ignore", category=FutureWarning)  #ignores torch warning

app = Flask(__name__)

YOUTUBE_URL = "https://www.youtube.com/watch?v=t45_gP7I82I"  # Stream URL
CONFIDENCE_THRESHOLD = 0.4  # Confidence threshold
MODEL = "yolov5s"  # YOLO model version

# Load YOLOv5 model everytime, this keeps it updated, make force_reload = False if not.
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_NAMES = set(line.strip().lower() for line in f if line.strip())
    print(f"📜 Loaded {len(OBJECT_NAMES)} objects from {OBJECT_LIST_FILE}")
else:
    print(f"⚠️ {OBJECT_LIST_FILE} not found! No filtering will be applied.")
    OBJECT_NAMES = set()  # Empty set (detects all objects)

# Ensure directories exist
os.makedirs("detections", exist_ok=True)

def get_stream_url():
    """Fetch 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 save_cropped_object(frame, bbox, label):
    """Save cropped object with 50px padding in its respective folder."""
    x1, y1, x2, y2 = bbox
    h, w, _ = frame.shape

    # Add 50px padding while ensuring within image bounds
    x1, y1 = max(0, x1 - 50), max(0, y1 - 50)
    x2, y2 = min(w, x2 + 50), min(h, y2 + 50)

    cropped = frame[y1:y2, x1:x2]  # Crop the bounding box

    # Create folder for the object if it doesn't exist
    save_dir = os.path.join("detections", label)
    os.makedirs(save_dir, exist_ok=True)

    # Save with timestamp
    filename = f"{datetime.now().strftime('%Y%m%d_%H%M%S_%f')}.jpg"
    cv2.imwrite(os.path.join(save_dir, filename), cropped)
    print(f"📸 Saved: {os.path.join(save_dir, filename)}")

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  # Expecting 720p stream

    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.")
        else:
            print(f"✅ Detected {len(detections)} objects!")

            for _, row in detections.iterrows():
                label = row["name"].lower()
                confidence = row["confidence"]

                if confidence > CONFIDENCE_THRESHOLD and (not OBJECT_NAMES or label in OBJECT_NAMES):
                    x1, y1, x2, y2 = int(row["xmin"]), int(row["ymin"]), int(row["xmax"]), int(row["ymax"])
                    save_cropped_object(frame, (x1, y1, x2, y2), label)

                    # Draw bounding boxes
                    cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)  # Green box
                    cv2.putText(frame, f"{label} ({confidence:.2f})", (x1, y1 - 10),
                                cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

        # Encode and present 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)