Update app.py

app.py

@@ -44,13 +44,16 @@ def detect_license_plate(image):
     results = full_plate_model(img_bgr)
 
     detected_image = img_bgr.copy()
+    cropped_plates = []
     for result in results:
         if hasattr(result, 'boxes') and result.boxes is not None:
             for box in result.boxes.xyxy:
                 x1, y1, x2, y2 = map(int, box)
                 cv2.rectangle(detected_image, (x1, y1), (x2, y2), (255, 0, 0), 2)  # Draw bounding box
+                cropped_plate = img_bgr[y1:y2, x1:x2]
+                cropped_plates.append(cropped_plate)
 
-    return detected_image, img_bgr
+    return detected_image, img_bgr, cropped_plates
 
 # Function to detect characters
 def detect_characters(image):
@@ -94,22 +97,44 @@ if uploaded_file is not None:
 
     # Detect license plates
     with st.spinner("Processing image..."):
-        detected_image, original_image = detect_license_plate(image)
+        detected_image, original_image, cropped_plates = detect_license_plate(image)
 
         # Show the detected license plate image with bounding boxes
         st.image(cv2.cvtColor(detected_image, cv2.COLOR_BGR2RGB), caption="Detected License Plates", use_container_width=True)
 
-        # Detect characters on the original image
-        character_detected_image, character_crops = detect_characters(original_image)
+        # Initialize variables for comparison
+        max_characters = []
+        best_recognition_method = ""
 
-        # Show the detected characters with bounding boxes
-        st.image(cv2.cvtColor(character_detected_image, cv2.COLOR_BGR2RGB), caption="Detected Characters", use_container_width=True)
+        # Process original image for character detection
+        char_detected_img, character_crops_original = detect_characters(original_image)
 
-        # Recognize characters from the detected character crops
-        if character_crops:
-            recognized_characters = recognize_characters(character_crops)
-            st.write("Recognized Characters:", "".join(recognized_characters))
+        # Recognize characters from original image
+        recognized_original = recognize_characters(character_crops_original)
+
+        if recognized_original:
+            if len(recognized_original) > len(max_characters):
+                max_characters = recognized_original
+                best_recognition_method = "Original Image"
+
+        # Process cropped plates for character detection
+        for idx, cropped_plate in enumerate(cropped_plates, 1):
+            st.write(f"Processing Cropped Plate {idx}:")
+            cropped_char_detected_img, character_crops_cropped = detect_characters(cropped_plate)
+            recognized_cropped = recognize_characters(character_crops_cropped)
+
+            if recognized_cropped:
+                if len(recognized_cropped) > len(max_characters):
+                    max_characters = recognized_cropped
+                    best_recognition_method = f"Cropped Plate {idx}"
+
+        # Show the result
+        st.image(cv2.cvtColor(char_detected_img, cv2.COLOR_BGR2RGB), caption="Detected Characters from Original Image", use_container_width=True)
+
+        if best_recognition_method:
+            st.write(f"Best Recognition Method: {best_recognition_method}")
+            st.write("Recognized Characters:", "".join(max_characters))
         else:
-            st.write("No characters detected in the image.")
+            st.write("No characters detected in any method.")
 
     st.success("Processing complete!")