Update app.py

app.py

@@ -3,11 +3,21 @@ import cv2
 import numpy as np
 import easyocr
 from ultralytics import YOLO
-from PIL import Image
 
 # Title of the app
 st.title("License Plate Recognition System🚗")
 
+# Add background image using custom CSS
+st.markdown("""
+    <style>
+        .stApp {
+            background-image: url("https://i.postimg.cc/zBX9cDZy/bmw-german-luxury-cars-brands-black-shiny-fast-car-in-a-misty-fog-01-11-2024-1730444676-hd-wallpaper.jpg");
+            background-size: cover;
+            background-position: center center;
+        }
+    </style>
+""", unsafe_allow_html=True)
+
 # Load the YOLO model for license plate detection
 @st.cache_resource
 def load_yolo_model():
@@ -30,69 +40,47 @@ def process_image(image, confidence_threshold=0.5):
     results = yolo_model(image, conf=confidence_threshold)
     annotated_image = cv2.cvtColor(results[0].plot(), cv2.COLOR_BGR2RGB)
     
-    # Prepare the cropped images and detected text for each plate
-    license_plate_text = []
-    cropped_images = []
+    # Create columns to display the uploaded image, cropped image, and extracted text side by side
+    c1, c2 = st.columns(2)
     
+    with c1:
+        st.image(annotated_image, caption="Detected License Plate(s)", use_container_width=True)
+
     # Loop through detections and perform OCR
     for result in results:
         boxes = result.boxes.xyxy.cpu().numpy().astype(int)
-        confidences = result.boxes.conf.cpu().numpy()
-        
         if len(boxes) == 0:
             st.warning("No license plate detected!")
-            return [], [], None
+            return
         
-        for i, (box, conf) in enumerate(zip(boxes, confidences)):
+        for i, box in enumerate(boxes):
             x1, y1, x2, y2 = box
-            # Draw bounding box on the annotated image
-            cv2.rectangle(annotated_image, (x1, y1), (x2, y2), (255, 0, 0), 2)
-            cv2.putText(annotated_image, f"{conf:.2f}", (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
-
-            # Crop the license plate from the image
             cropped_image = image[y1:y2, x1:x2]
             cropped_image_rgb = cv2.cvtColor(cropped_image, cv2.COLOR_BGR2RGB)
-            cropped_images.append(cropped_image_rgb)
+
+            with c2:
+                st.image(cropped_image_rgb, caption=f"Cropped License Plate {i+1}", use_container_width=True)
 
             # Perform OCR on the cropped image
             text_results = ocr_reader.readtext(cropped_image_rgb, detail=0)
             detected_text = " ".join(text_results)
-            license_plate_text.append(detected_text)
-    
-    return license_plate_text, cropped_images, annotated_image
+            
+            with c2:
+                st.write(f"**Extracted Text (Plate {i+1}):** {detected_text}")
+                st.write(f"**Confidence Score:** {result.boxes.conf.cpu().numpy()[i]:.2f}")
 
 # Sidebar input for file upload
 uploaded_file = st.file_uploader("Upload an Image or Video", type=["mp4", "avi", "mov", "jpg", "jpeg", "png"])
 
 if uploaded_file is not None:
-    # Read and process the image
-    image = np.array(Image.open(uploaded_file))
-    confidence_threshold = st.sidebar.slider("Confidence Threshold", 0.0, 1.0, 0.5, 0.01)
-
-    # Create three columns to display the images side by side
-    c1, c2, c3 = st.columns(3)
-
-    with c1:
-        # Display uploaded image with bounding boxes
-        license_plate_text, cropped_images, annotated_image = process_image(image, confidence_threshold)
-        if annotated_image is not None:
-            st.image(annotated_image, caption='Uploaded Image with Bounding Boxes', use_container_width=True)
-
-    with c2:
-        # Display cropped license plates
-        if cropped_images:
-            for i, cropped_image in enumerate(cropped_images):
-                st.image(cropped_image, caption=f'Cropped License Plate {i+1}', use_container_width=True)
-        else:
-            st.write('No License Plate Detected')
+    # Check if it's an image or video
+    file_type = uploaded_file.type
 
-    with c3:
-        # Display the extracted text for license plates
-        if license_plate_text:
-            st.success(', '.join(license_plate_text))
-            st.write('License Plate Text')
-        else:
-            st.write('No text detected')
+    if file_type.startswith("image"):
+        # Read and process the image
+        image = cv2.imdecode(np.frombuffer(uploaded_file.read(), np.uint8), 1)
+        confidence_threshold = st.sidebar.slider("Confidence Threshold", 0.0, 1.0, 0.5, 0.01)
+        process_image(image, confidence_threshold)
 
 st.markdown("---")
 st.info("This application uses Fine Tuned YOLOv8 for detection and EasyOCR for text recognition.")