Update app.py

app.py

@@ -6,7 +6,6 @@ import numpy as np
 import gradio as gr
 import os
 import logging
-import time
 
 # Set up logging
 logging.basicConfig(level=logging.INFO)
@@ -16,13 +15,13 @@ logger = logging.getLogger(__name__)
 os.makedirs(os.getenv('EASYOCR_MODULE_PATH', '/app/.EasyOCR'), exist_ok=True)
 os.makedirs(os.getenv('YOLO_CONFIG_DIR', '/app/.config/Ultralytics'), exist_ok=True)
 
-# Download pretrained ANPR model
+# Download pretrained model
 ANPR_WEIGHTS = "anpr_yolov8.pt"
 if not os.path.exists(ANPR_WEIGHTS):
     logger.info(f"Downloading model weights to {ANPR_WEIGHTS}")
     os.system(f"wget -O {ANPR_WEIGHTS} https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt")
 
-# Load YOLO ANPR model with error handling
+# Load YOLO model
 try:
     model = YOLO(ANPR_WEIGHTS)
     logger.info(f"Successfully loaded YOLO model from {ANPR_WEIGHTS}")
@@ -30,7 +29,7 @@ except Exception as e:
     logger.error(f"Error loading YOLO model from {ANPR_WEIGHTS}: {str(e)}")
     raise
 
-# Load OCR reader with specified model storage directory
+# Load OCR reader
 try:
     reader = easyocr.Reader(['en'], model_storage_directory=os.getenv('EASYOCR_MODULE_PATH', '/app/.EasyOCR'))
     logger.info("Successfully initialized EasyOCR reader")
@@ -39,74 +38,45 @@ except Exception as e:
     raise
 
 def detect_and_read_plate(image):
-    start_time = time.time()
     logger.info("Starting image processing for license plate detection")
-    
     try:
-        # Resize image to reduce processing time (optional, adjust as needed)
-        max_size = 640
-        h, w = image.shape[:2]
-        if max(h, w) > max_size:
-            scale = max_size / max(h, w)
-            image = cv2.resize(image, (int(w * scale), int(h * scale)))
-            logger.info(f"Resized image to {image.shape[:2]}")
-
         detected_texts = []
-        results = model(image, conf=0.25)  # Lower confidence threshold for detection
+        results = model(image)  # Use default confidence threshold
         logger.info(f"YOLO model returned {len(results)} results")
         
         for result in results:
-            boxes = result.boxes.xyxy.cpu().numpy()  # [x1, y1, x2, y2]
-            confidences = result.boxes.conf.cpu().numpy()
+            boxes = result.boxes.xyxy.cpu().numpy()
             logger.info(f"Detected {len(boxes)} bounding boxes")
-            
-            for box, conf in zip(boxes, confidences):
+            for box in boxes:
                 x1, y1, x2, y2 = map(int, box)
-                # Skip small or invalid boxes
-                if (x2 - x1) < 20 or (y2 - y1) < 10:
-                    logger.warning(f"Skipping small box: ({x1}, {y1}, {x2}, {y2})")
-                    continue
-                
                 # Crop the detected license plate
                 plate_img = image[y1:y2, x1:x2]
                 if plate_img.size == 0:
                     logger.warning("Empty cropped image, skipping")
                     continue
-                
-                # Run OCR with timeout
-                try:
-                    logger.info("Running EasyOCR on cropped plate")
-                    ocr_result = reader.readtext(plate_img, timeout=5)  # 5-second timeout
-                    if ocr_result:
-                        text = " ".join([res[1] for res in ocr_result if res[2] > 0.3])  # Filter low-confidence OCR
-                        if text.strip():
-                            detected_texts.append(text)
-                            logger.info(f"Detected Plate: {text} (confidence: {conf:.2f})")
-                        else:
-                            logger.info("OCR returned empty or low-confidence text")
-                    else:
-                        logger.info("No text detected by EasyOCR")
-                    
+                # Run OCR
+                logger.info("Running EasyOCR on cropped plate")
+                ocr_result = reader.readtext(plate_img)
+                if ocr_result:
+                    text = " ".join([res[1] for res in ocr_result])
+                    detected_texts.append(text)
+                    logger.info(f"Detected Plate: {text}")
                     # Draw bounding box
                     cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
-                except Exception as e:
-                    logger.warning(f"OCR processing failed: {str(e)}")
-                    continue
+                else:
+                    logger.info("No text detected by EasyOCR")
         
-        # Prepare output
         output_text = "\n".join(detected_texts) if detected_texts else "No license plate detected"
-        processing_time = time.time() - start_time
-        logger.info(f"Processing completed in {processing_time:.2f} seconds. Output text: {output_text}")
-        
+        logger.info(f"Output text: {output_text}")
         return image, output_text
     except Exception as e:
         logger.error(f"Error during detection: {str(e)}")
         return image, f"Error processing image: {str(e)}"
 
-# Create Gradio interface with progress feedback
+# Create Gradio interface
 with gr.Blocks() as demo:
     gr.Markdown("# Automatic Number Plate Recognition (ANPR)")
-    gr.Markdown("Upload an image of a car to detect and read its license plate. Results may take a few seconds.")
+    gr.Markdown("Upload an image of a car to detect and read its license plate.")
     
     with gr.Row():
         image_input = gr.Image(type="numpy", label="Upload an image of a car")