Update app.py

app.py

@@ -1,9 +1,9 @@
 import streamlit as st
-import cv2
 import numpy as np
 import torch
 from ultralytics import YOLO
 import easyocr
+from PIL import Image
 
 # Title of the app
 st.title("License Plate Recognition 🚗")
@@ -28,9 +28,12 @@ ocr_reader = load_easyocr_reader()
 
 # Function to process the uploaded image
 def process_image(image, confidence_threshold=0.5):
+    # Convert the PIL image to a numpy array
+    image_np = np.array(image)
+    
     # Perform license plate detection
-    results = yolo_model(image, conf=confidence_threshold)
-    annotated_image = cv2.cvtColor(results[0].plot(), cv2.COLOR_BGR2RGB)
+    results = yolo_model(image_np, conf=confidence_threshold)
+    annotated_image = Image.fromarray(results[0].plot())
     st.image(annotated_image, caption="Detected License Plate(s)", use_container_width=True)
     
     # Loop through detections
@@ -41,12 +44,12 @@ def process_image(image, confidence_threshold=0.5):
             return
         for i, box in enumerate(boxes):
             x1, y1, x2, y2 = box
-            cropped_image = image[y1:y2, x1:x2]
-            cropped_image_rgb = cv2.cvtColor(cropped_image, cv2.COLOR_BGR2RGB)
-            st.image(cropped_image_rgb, caption=f"Cropped License Plate {i+1}", use_container_width=True)
+            cropped_image = image_np[y1:y2, x1:x2]
+            cropped_image_pil = Image.fromarray(cropped_image)
+            st.image(cropped_image_pil, 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)
+            text_results = ocr_reader.readtext(np.array(cropped_image_pil), detail=0)
             detected_text = " ".join(text_results)
             st.write(f"**Extracted Text (Plate {i+1}):** {detected_text}")
 
@@ -55,8 +58,8 @@ confidence_threshold = st.sidebar.slider("Confidence Threshold", 0.0, 1.0, 0.5,
 uploaded_file = st.file_uploader("Upload an Image", type=["jpg", "jpeg", "png"])
 
 if uploaded_file is not None:
-    # Read the uploaded image
-    image = cv2.imdecode(np.frombuffer(uploaded_file.read(), np.uint8), 1)
+    # Open the uploaded file as a PIL image
+    image = Image.open(uploaded_file)
     process_image(image, confidence_threshold)
 
 st.markdown("---")