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import cv2
import cv2 as cv
import numpy as np
import streamlit as st
from PIL import Image
import pytesseract
def enhance(img):
# Apply GaussianBlur to reduce noise
blurred = cv2.GaussianBlur(img, (5, 5), 0)
# Apply adaptive thresholding to enhance text
thresh = cv2.adaptiveThreshold(
blurred,
255,
cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY,
11,
2
)
# Perform morphological operations to remove small noise and connect text components
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
morph = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
return morph
act = [(150,240), (610,260)]
act2 = [(130,440), (590,460)]
acts=[act,act2]
def align_images(ref_gray, input_gray, enh= False):
"""
Aligns the input image to the reference image using homography.
Parameters:
reference_image (numpy.ndarray): The reference image.
input_image (numpy.ndarray): The input image to be aligned.
Returns:
numpy.ndarray: The aligned version of the input image.
"""
# # Convert images to grayscale
# ref_gray = cv2.cvtColor(reference_image, cv2.COLOR_BGR2GRAY)
# input_gray = cv2.cvtColor(input_image, cv2.COLOR_BGR2GRAY)
if enh:
ref_gray = enhance(ref_gray)
input_gray = enhance(input_gray)
st.image(ref_gray)
st.image(input_gray)
# Detect ORB keypoints and descriptors
orb = cv2.ORB_create(nfeatures=3000)
keypoints1, descriptors1 = orb.detectAndCompute(ref_gray, None)
keypoints2, descriptors2 = orb.detectAndCompute(input_gray, None)
# Match descriptors using BFMatcher
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
matches = bf.match(descriptors1, descriptors2)
matches = sorted(matches, key=lambda x: x.distance)
# Extract location of good matches
ref_points = np.float32([keypoints1[m.queryIdx].pt for m in matches]).reshape(-1, 1, 2)
input_points = np.float32([keypoints2[m.trainIdx].pt for m in matches]).reshape(-1, 1, 2)
# Compute homography matrix
H, mask = cv2.findHomography(input_points, ref_points, cv2.RANSAC, 5.0)
# Warp input image to align with reference image
height, width = ref_gray.shape
aligned_image = cv2.warpPerspective(input_gray, H, (width, height))
return aligned_image
def ocr_with_crop(aligned_image):
# Open the image
# img = Image.open(image_path)
# img = cv2.imread(image_path,0)
# img = enhance(img)
# st.image(img)
# st.write(type(img))
# enh = enhance(np.array(img))
# st.image(enh)
# Define the coordinates for cropping
def ocr(act):
crop_coordinates = act
# Convert to rectangular bounds (x1, y1, x2, y2)
x1, y1 = crop_coordinates[0]
x2, y2 = crop_coordinates[1]
# Crop the image using the defined coordinates
# cropped_img = img.crop((x1, y1, x2, y2))
cropped_img = aligned_image[y1:y2,x1:x2]
st.image(cropped_img)
# Perform OCR on the cropped image
text = pytesseract.image_to_string(cropped_img)
# Print the extracted text
st.write(text)
for cor in acts:
ocr(cor)
if __name__== "__main__":
ref = cv.imread("r.png",0)
if inp:= st.file_uploader("upload your form in image format", type=['png']):
image = Image.open(inp)
gray_image_pil = image.convert('L')
image_array = np.array(gray_image_pil)
st.image(image_array)
align_image = align_images(ref,image_array)
ocr_with_crop(align_image)
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