import cv2
import easyocr
import requests
import os
import time
import threading
import numpy as np
import re
from flask import Flask, Response
from flask_cors import CORS
from ultralytics import YOLO

# Configuration
BACKEND_URL = os.environ.get('BACKEND_URL', 'http://localhost:3000')
CAMERA_ID = 0 
PROCESS_INTERVAL = 0.5 # Faster processing with YOLO (it's efficient)
CONFIDENCE_THRESHOLD = 0.4
MODEL_PATH = 'best.pt' # Expecting the model here

app = Flask(__name__)
CORS(app)

# Global variables
outputFrame = None
lock = threading.Lock()
# Store latest detections for visualization
latest_detections = [] 

def send_plate(plate_number):
    try:
        url = f"{BACKEND_URL}/api/detect"
        payload = {'plate_number': plate_number}
        print(f"Sending plate: {plate_number} to {url}")
        requests.post(url, json=payload, timeout=2)
    except Exception as e:
        print(f"Error sending plate: {e}")

def alpr_loop():
    global outputFrame, lock, latest_detections
    
    print("Initializing EasyOCR...")
    reader = easyocr.Reader(['en'], gpu=False)
    print("EasyOCR initialized.")
    
    # Load YOLO Model
    print(f"Loading YOLO model from {MODEL_PATH}...")
    try:
        model = YOLO(MODEL_PATH)
        print("YOLO model loaded successfully!")
    except Exception as e:
        print(f"Error loading YOLO model: {e}")
        print("CRITICAL: Please place the 'best.pt' file in the alpr-service directory.")
        return

    cap = cv2.VideoCapture(CAMERA_ID)
    time.sleep(2.0) 

    if not cap.isOpened():
        print("Error: Could not open video device.")
        return

    last_process_time = 0
    
    while True:
        ret, frame = cap.read()
        if not ret:
            print("Failed to grab frame")
            time.sleep(1)
            continue
        
        # Resize for performance 
        frame = cv2.resize(frame, (640, 480))
        
        current_time = time.time()
        
        # Detection Processing
        if current_time - last_process_time > PROCESS_INTERVAL:
            last_process_time = current_time
            
            # Run YOLO Inference
            results = model(frame, verbose=False)
            
            detections = []
            
            for r in results:
                boxes = r.boxes
                for box in boxes:
                    # Bounding Box
                    x1, y1, x2, y2 = box.xyxy[0]
                    x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
                    conf = float(box.conf[0])
                    
                    if conf > 0.5: # Valid plate detection
                         # Visualization data
                        detections.append((x1, y1, x2, y2, conf))
                        
                        # Crop Plate
                        plate_img = frame[y1:y2, x1:x2]
                        
                        # Run OCR on Crop
                        try:
                            ocr_results = reader.readtext(plate_img)
                            for (_, text, prob) in ocr_results:
                                if prob > CONFIDENCE_THRESHOLD:
                                    clean_text = ''.join(e for e in text if e.isalnum()).upper()
                                    validate_and_send(clean_text)
                        except Exception as e:
                             print(f"OCR Error on crop: {e}")

            with lock:
                latest_detections = detections

        # Draw Detections on Frame for Stream
        display_frame = frame.copy()
        with lock:
            for (x1, y1, x2, y2, conf) in latest_detections:
                cv2.rectangle(display_frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
                cv2.putText(display_frame, f"Plate {conf:.2f}", (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
            
            outputFrame = display_frame
            
        time.sleep(0.01)

def validate_and_send(text):
    # Chilean Plate Regex Patterns
    is_valid = False
    if re.match(r'^[A-Z]{4}\d{2}$', text): # BBBB11
        is_valid = True
    elif re.match(r'^[A-Z]{2}\d{4}$', text): # BB1111
            is_valid = True
    
    if is_valid:
        print(f"Detected Valid Plate: {text}")
        send_plate(text)

def generate():
    global outputFrame, lock
    while True:
        with lock:
            if outputFrame is None:
                continue
            (flag, encodedImage) = cv2.imencode(".jpg", outputFrame)
            if not flag:
                continue
        
        yield(b'--frame\r\n' b'Content-Type: image/jpeg\r\n\r\n' + 
              bytearray(encodedImage) + b'\r\n')

@app.route("/video_feed")
def video_feed():
    return Response(generate(), mimetype = "multipart/x-mixed-replace; boundary=frame")

if __name__ == "__main__":
    t = threading.Thread(target=alpr_loop)
    t.daemon = True
    t.start()
    
    print("Starting Video Stream on port 5001...")
    app.run(host="0.0.0.0", port=5001, debug=False, threaded=True, use_reloader=False)