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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Vehicle[[:space:]]Number[[:space:]]Plates.keras filter=lfs diff=lfs merge=lfs -text

Vehicle Number Plates.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:52b09e2c1037e0e08da476491191e918d2b3965262b46649aa172c765a151811
+size 410173

app.py

@@ -0,0 +1,96 @@
+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 Plates.keras")
+plate_detector = cv2.CascadeClassifier("haarcascade_license_plate_rus_16stages.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:
+    uploaded_file = st.file_uploader("Upload Image", type=["jpg", "jpeg", "png"])
+    if uploaded_file:
+        image_input = Image.open(uploaded_file)
+        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)
+
+            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:
+    camera_image = st.camera_input("📷 Take a picture using your webcam")
+    if camera_image:
+        try:
+            image_input = Image.open(camera_image)
+            st.image(image_input, caption="Webcam Snapshot", use_container_width=True)
+
+            with st.spinner("Analyzing..."):
+                result_img, confidence, label = detect_and_predict(image_input)
+
+            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)}")

haarcascade_license_plate_rus_16stages.xml

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+<?xml version="1.0"?>
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+</opencv_storage>

requirements.txt

@@ -0,0 +1,5 @@
+streamlit
+opencv-python-headless
+Pillow
+tensorflow
+easyocr