Update app.py

app.py

@@ -1,3 +1,106 @@
+# import streamlit as st
+# import cv2
+# import numpy as np
+# import easyocr
+# from PIL import Image
+# from tensorflow.keras.models import load_model
+# from tensorflow.keras.preprocessing import image as keras_image
+
+# # Load model and OCR tools
+# model = load_model("Vehicle_number_plate_Detection.keras")
+# plate_detector = cv2.CascadeClassifier("haarcascade_russian_plate_number.xml")
+# reader = easyocr.Reader(['en'])
+
+# # Plate Detection Function
+# def detect_and_predict(img_input):
+#     img = np.array(img_input.convert("RGB"))
+#     frame = img.copy()
+#     gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
+#     plates = plate_detector.detectMultiScale(gray, 1.1, 4)
+
+#     plate_text = "Not Detected"
+#     confidence = None
+
+#     for x, y, w, h in plates:
+#         roi = frame[y:y+h, x:x+w]
+#         try:
+#             test_img = cv2.resize(roi, (200, 200))
+#             test_img = keras_image.img_to_array(test_img) / 255.0
+#             test_img = np.expand_dims(test_img, axis=0)
+#             pred = model.predict(test_img)[0][0]
+#         except Exception as e:
+#             print(f"Prediction error: {e}")
+#             continue
+
+#         if pred < 0.5:
+#             result = reader.readtext(roi)
+#             if result:
+#                 plate_text = result[0][1]
+#                 confidence = result[0][2]
+#                 label = f"Plate: {plate_text}"
+#             else:
+#                 label = "Plate Detected (No text)"
+#         else:
+#             label = "Plate Not Detected"
+
+#         # Draw detection
+#         cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
+#         cv2.putText(frame, label, (x, y - 10),
+#                     cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
+
+#     return frame, confidence, plate_text
+
+# # Streamlit App UI
+# st.set_page_config(page_title="License Plate Detection", layout="wide")
+# st.title("🚘 License Plate Detection App")
+
+# tab1, tab2 = st.tabs(["📁 Upload Image", "📷 Webcam Capture"])
+
+# # Tab 1 - Upload Image
+# with tab1:
+#     col1, col2 = st.columns([1, 2])
+#     uploaded_file = st.file_uploader("Upload Image", type=["jpg", "jpeg", "png"])
+#     if uploaded_file:
+#         image_input = Image.open(uploaded_file)
+#         with col1:
+#             st.image(image_input, caption="Uploaded Image", use_container_width=True)
+
+#             if st.button("🔍 Detect from Upload"):
+#                 with st.spinner("Processing..."):
+#                     result_img, confidence, label = detect_and_predict(image_input)
+#             with col2:
+#                 st.image(result_img, caption="Detection Result", channels="RGB", use_container_width=True)
+#                 if confidence:
+#                     st.metric("Confidence", f"{confidence * 100:.2f}%")
+#                     st.success(f"Detected Text: {label}")
+#                 else:
+#                     st.warning("No plate text detected.")
+
+# # Tab 2 - Webcam Input (camera snapshot)
+# with tab2:
+#     col1, col2 = st.columns([1, 2])
+#     with col1:
+#         camera_image = st.camera_input("📷 Take a picture using your webcam")
+#     if camera_image:
+#         try:
+#             image_input = Image.open(camera_image)
+#             # with col2:
+#             #     st.image(image_input, caption="Webcam Snapshot", use_container_width=True)
+
+#             with st.spinner("Analyzing..."):
+#                 result_img, confidence, label = detect_and_predict(image_input)
+#             with col2:
+#                 st.image(result_img, caption="Detection Result", channels="RGB", use_container_width=True)
+#                 if confidence is not None:
+#                     st.metric("Confidence", f"{confidence*100:.2f}%")
+#                     st.success(f"Detected Text: {label}")
+#                 else:
+#                     st.warning("Plate detected but no readable text found.")
+
+#         except Exception as e:
+#             st.error(f"❌ Error: {str(e)}")
+
+
 import streamlit as st
 import cv2
 import numpy as np
@@ -48,7 +151,9 @@ def detect_and_predict(img_input):
         cv2.putText(frame, label, (x, y - 10),
                     cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
 
-    return frame, confidence, plate_text
+    # Ensure output image is same size as input
+    result_img = Image.fromarray(frame)
+    return result_img, confidence, plate_text
 
 # Streamlit App UI
 st.set_page_config(page_title="License Plate Detection", layout="wide")
@@ -65,11 +170,12 @@ with tab1:
         with col1:
             st.image(image_input, caption="Uploaded Image", use_container_width=True)
 
-            if st.button("🔍 Detect from Upload"):
-                with st.spinner("Processing..."):
-                    result_img, confidence, label = detect_and_predict(image_input)
+        if st.button("🔍 Detect from Upload"):
+            with st.spinner("Processing..."):
+                result_img, confidence, label = detect_and_predict(image_input)
+
             with col2:
-                st.image(result_img, caption="Detection Result", channels="RGB", use_container_width=True)
+                st.image(result_img, caption="Detection Result", use_container_width=True)
                 if confidence:
                     st.metric("Confidence", f"{confidence * 100:.2f}%")
                     st.success(f"Detected Text: {label}")
@@ -84,18 +190,14 @@ with tab2:
     if camera_image:
         try:
             image_input = Image.open(camera_image)
-            # with col2:
-            #     st.image(image_input, caption="Webcam Snapshot", use_container_width=True)
-
             with st.spinner("Analyzing..."):
                 result_img, confidence, label = detect_and_predict(image_input)
             with col2:
-                st.image(result_img, caption="Detection Result", channels="RGB", use_container_width=True)
+                st.image(result_img, caption="Detection Result", use_container_width=True)
                 if confidence is not None:
                     st.metric("Confidence", f"{confidence*100:.2f}%")
                     st.success(f"Detected Text: {label}")
                 else:
                     st.warning("Plate detected but no readable text found.")
-
         except Exception as e:
             st.error(f"❌ Error: {str(e)}")