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import streamlit as st
import matplotlib.pyplot as plt
import cv2
import easyocr
import numpy as np
from PIL import Image as PILImage
from io import BytesIO
import pdf2image
# Function to handle file upload and OCR processing
def process_image(image, language):
reader = easyocr.Reader([language]) # Initialize OCR reader with chosen language
# Convert the image to OpenCV format
open_cv_image = np.array(image)
open_cv_image = open_cv_image[:, :, ::-1].copy() # Convert RGB to BGR for OpenCV
# Apply the scan effect
processed_image = scan_effect(open_cv_image)
# Perform OCR
output = reader.readtext(processed_image)
return processed_image, output
# Function for image scan effect
def scan_effect(img):
blackPoint = 66
whitePoint = 130
image = highPassFilter(img, kSize=51)
image_white = whitePointSelect(image, whitePoint)
img_black = blackPointSelect(image_white, blackPoint)
image = blackPointSelect(img, blackPoint)
white = whitePointSelect(image, whitePoint)
img_black = blackAndWhite(white)
return img_black
# Helper functions for scan effect
def map(x, in_min, in_max, out_min, out_max):
return (x - in_min) * (out_max - out_min) / (in_max - in_min) + out_min
def highPassFilter(img, kSize):
if not kSize % 2:
kSize += 1
kernel = np.ones((kSize, kSize), np.float32) / (kSize * kSize)
filtered = cv2.filter2D(img, -1, kernel)
filtered = img.astype('float32') - filtered.astype('float32')
filtered = filtered + 127 * np.ones(img.shape, np.uint8)
filtered = filtered.astype('uint8')
return filtered
def blackPointSelect(img, blackPoint):
img = img.astype('int32')
img = map(img, blackPoint, 255, 0, 255)
_, img = cv2.threshold(img, 0, 255, cv2.THRESH_TOZERO)
img = img.astype('uint8')
return img
def whitePointSelect(img, whitePoint):
_, img = cv2.threshold(img, whitePoint, 255, cv2.THRESH_TRUNC)
img = img.astype('int32')
img = map(img, 0, whitePoint, 0, 255)
img = img.astype('uint8')
return img
def blackAndWhite(img):
lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
(l, a, b) = cv2.split(lab)
img = cv2.add(cv2.subtract(l, b), cv2.subtract(l, a))
return img
# Streamlit app layout
st.title("OCR and Image Processing App")
# Sidebar for file upload and language selection
st.sidebar.title("Upload and Settings")
uploaded_file = st.sidebar.file_uploader("Upload PDF or Image", type=["pdf", "png", "jpg", "jpeg"])
language_option = st.sidebar.radio("Select OCR Language", ('ne', 'en')) # 'ne' for Nepali, 'en' for English
if uploaded_file:
file_type = uploaded_file.type
if file_type == "application/pdf":
# Convert PDF to images
images = pdf2image.convert_from_bytes(uploaded_file.read())
st.image(images, caption='Uploaded PDF as Images', use_column_width=True)
if st.button("Process PDF"):
# Process the first page of the PDF
st.write("Processing PDF...")
for page_image in images:
with st.spinner('Processing...'):
processed_image, ocr_output = process_image(page_image, language_option)
st.image(processed_image, caption="Processed Image", use_column_width=True)
# Display OCR output
st.write("Extracted Text:")
for item in ocr_output:
st.write(item[1])
# Allow download of processed image
img = PILImage.fromarray(processed_image)
buf = BytesIO()
img.save(buf, format="PNG")
byte_im = buf.getvalue()
st.download_button(label="Download Processed Image", data=byte_im, file_name="processed_image.png", mime="image/png")
else:
# Handle image files
image = PILImage.open(uploaded_file)
st.image(image, caption="Uploaded Image", use_column_width=True)
if st.button("Process Image"):
# Process the uploaded image
with st.spinner('Processing...'):
processed_image, ocr_output = process_image(image, language_option)
st.image(processed_image, caption="Processed Image", use_column_width=True)
# Display OCR output
st.write("Extracted Text:")
for item in ocr_output:
st.write(item[1])
# Allow download of processed image
img = PILImage.fromarray(processed_image)
buf = BytesIO()
img.save(buf, format="PNG")
byte_im = buf.getvalue()
st.download_button(label="Download Processed Image", data=byte_im, file_name="processed_image.png", mime="image/png")
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