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import streamlit as st | |
import pytesseract | |
from PIL import Image | |
import cv2 | |
import numpy as np | |
def ocr_image(image): | |
"""Perform OCR on an image and classify logo.""" | |
# Convert image to grayscale | |
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) | |
# Perform OCR on the grayscale image | |
text = pytesseract.image_to_string(gray) | |
# Classify logo based on presence of space in 'amazonbasics' | |
if 'amazon basics' in text.lower(): | |
return 'New Logo' | |
else: | |
return 'Old Logo' | |
# Streamlit interface | |
st.title('Logo Classifier') | |
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"]) | |
if uploaded_file is not None: | |
image = Image.open(uploaded_file) | |
st.image(image, caption='Uploaded Image.', use_column_width=True) | |
st.write("") | |
st.write("Classifying...") | |
# Convert the image to an OpenCV array (numpy array) | |
open_cv_image = np.array(image) | |
# Convert RGB to BGR | |
open_cv_image = open_cv_image[:, :, ::-1].copy() | |
# Perform OCR and classify logo | |
logo_type = ocr_image(open_cv_image) | |
# Display classification result | |
st.write(f"Logo Type: {logo_type}") |