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import streamlit as st |
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import matplotlib as plt |
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import cv2 |
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import numpy as np |
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from PIL import Image |
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harcascade = "haarcascade_russian_plate_number.xml" |
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min_area = 500 |
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count = 0 |
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import easyocr |
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st.image("anpr.jpg",use_column_width="always") |
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st.markdown("<h1 style='text-align: center; color: green;'>SnapText 😎</h1>", unsafe_allow_html=True) |
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st.subheader('Welcome to :blue[SnapText ] !! ') |
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st.subheader("Upload license plate photos of vehicles, and let us extract and rewrite the text for you. ") |
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st.divider() |
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uploaded_file = st.file_uploader("Upload your file here...", type=['jpg']) |
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if uploaded_file is not None: |
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with st.spinner("Extracting Text and License PLate"): |
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file_bytes = np.asarray(bytearray(uploaded_file.read()),dtype=np.uint8) |
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img=cv2.imdecode(file_bytes,1) |
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st.image(img,caption="Uploaded Raw Image") |
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img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) |
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plate_cascade = cv2.CascadeClassifier(harcascade) |
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plates = plate_cascade.detectMultiScale(img_gray, 1.1, 4) |
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for (x, y, w, h) in plates: |
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area = w * h |
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if area > min_area: |
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cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2) |
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cv2.putText(img, "Number Plate", (x, y - 5), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (255, 0, 255), 2) |
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img_roi = img[y: y + h, x: x + w] |
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cv2.imwrite("plate_0.jpg", img_roi) |
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count += 1 |
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image = Image.open('plate_0.jpg') |
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st.image(image, caption='Extracted License Plate') |
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reader = easyocr.Reader(['en']) |
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img = cv2.imread('plate_0.jpg') |
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result = reader.readtext(img) |
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st.success("Extracted Text: "+ result[0][-2].upper()) |
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