Integracion de modelo/ funcional

alpr-service/main.py

@@ -5,21 +5,26 @@ 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 = 2.0 # OCR every 2 seconds
+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 (using simple globals for PoC)
+# Global variables
 outputFrame = None
 lock = threading.Lock()
+# Store latest detections for visualization
+latest_detections = [] 
 
 def send_plate(plate_number):
     try:
@@ -31,14 +36,24 @@ def send_plate(plate_number):
         print(f"Error sending plate: {e}")
 
 def alpr_loop():
-    global outputFrame, lock
+    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) # Warmup
+    time.sleep(2.0) 
 
     if not cap.isOpened():
         print("Error: Could not open video device.")
@@ -53,62 +68,70 @@ def alpr_loop():
             time.sleep(1)
             continue
         
-        # Resize for performance and streaming bandwidth
+        # Resize for performance 
         frame = cv2.resize(frame, (640, 480))
         
         current_time = time.time()
         
-        # OCR Processing
+        # Detection Processing
         if current_time - last_process_time > PROCESS_INTERVAL:
             last_process_time = current_time
-            threading.Thread(target=perform_ocr, args=(reader, frame.copy())).start()
+            
+            # 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}")
 
-        # Update output frame
+            with lock:
+                latest_detections = detections
+
+        # Draw Detections on Frame for Stream
+        display_frame = frame.copy()
         with lock:
-            outputFrame = frame.copy()
+            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)
 
-import re
-
-# ... (imports)
-
-def perform_ocr(reader, img):
-    try:
-        results = reader.readtext(img)
-        for (bbox, text, prob) in results:
-            if prob > CONFIDENCE_THRESHOLD:
-                # Clean text: keep alphanumerics
-                clean_text = ''.join(e for e in text if e.isalnum()).upper()
-                
-                # Chilean Plate Regex Patterns
-                # 1. New format: 4 letters + 2 numbers (e.g., BB-BB-10) -> ^[A-Z]{4}\d{2}$
-                # 2. Old format: 2 letters + 4 numbers (e.g., AA-1000) -> ^[A-Z]{2}\d{4}$
-                # 3. Moto format (common): 3 letters + 2 numbers or 3 numbers
-                
-                # General robust filter for Chile:
-                # - Length 6 (standard)
-                # - Must match specific patterns
-                
-                is_valid = False
-                
-                # Check Pattern 1: BBBB11 (4 Letters, 2 Digits)
-                if re.match(r'^[A-Z]{4}\d{2}$', clean_text):
-                    is_valid = True
-                # Check Pattern 2: BB1111 (2 Letters, 4 Digits)
-                elif re.match(r'^[A-Z]{2}\d{4}$', clean_text):
-                     is_valid = True
-                
-                if is_valid:
-                    print(f"Detected Valid Plate: {clean_text}")
-                    send_plate(clean_text)
-                else:
-                    # Optional logging for debugging ignored text
-                    # print(f"Ignored: {clean_text} (Format mismatch)")
-                    pass
-                    
-    except Exception as e:
-        print(f"OCR Error: {e}")
+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
@@ -128,12 +151,9 @@ def video_feed():
     return Response(generate(), mimetype = "multipart/x-mixed-replace; boundary=frame")
 
 if __name__ == "__main__":
-    # Start capturing thread
     t = threading.Thread(target=alpr_loop)
     t.daemon = True
     t.start()
     
-    # Start Flask Server
     print("Starting Video Stream on port 5001...")
-    # Using 0.0.0.0 to allow access if needed, though mostly used locally
     app.run(host="0.0.0.0", port=5001, debug=False, threaded=True, use_reloader=False)

alpr-service/requirements.txt

@@ -3,3 +3,5 @@ easyocr
 requests
 numpy
 flask
+flask-cors
+ultralytics