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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 tools
model = load_model("Vehicle Number Plates.keras")
plate_detector = cv2.CascadeClassifier("haarcascade_russian_plate_number.xml")
reader = easyocr.Reader(['en'])
# Detect and predict 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"
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
# App Config
st.set_page_config(page_title="Vehicle Plate Identifier", layout="centered")
# # Sidebar style
# with st.sidebar:
# st.markdown("## 🚗 License Plate Scanner")
# st.markdown("Upload an image or take a photo to detect and read vehicle number plates using AI and OCR.")
# st.markdown("---")
# st.info("📌 Tip: Use clear images with visible plates for best results.")
# Title
st.markdown("<h3 style='text-align: center; color: navy;'>🔍 AI-Powered Vehicle Plate Detection & OCR</h3>", unsafe_allow_html=True)
# Tabs
tab1, tab2 = st.tabs([
"🖼️ **:blue[Upload Vehicle Image]**",
"📷 **:green[Use Live Webcam]**"
])
# Tab 1 - Upload
with tab1:
st.markdown("#### :blue[Upload an image to detect number plate]", unsafe_allow_html=True)
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", width=250)
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", width=250)
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
with tab2:
st.markdown("#### :green[Capture from your webcam to scan a vehicle plate]", unsafe_allow_html=True)
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", width=250)
with st.spinner("Analyzing..."):
result_img, confidence, label = detect_and_predict(image_input)
st.image(result_img, caption="Detection Result", channels="RGB", width=250)
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)}")
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