Step_1_Rotation.py

import numpy as np
import cv2
import os  # Import os to create the directory if it doesn't exist

from skimage.transform import radon

# Create a directory for saving intermediate images
output_directory = "Step_1_Rotation_Result"
os.makedirs(output_directory, exist_ok=True)

filename = './Input Image/Image2.png'
# Load file, converting to grayscale
img = cv2.imread(filename)
I = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
h, w = I.shape
# If the resolution is high, resize the image to reduce processing time.
if (w > 640):
    I = cv2.resize(I, (640, int((h / w) * 640)))
I = I - np.mean(I)  # Demean; make the brightness extend above and below zero
# Save the intermediate image after demean
cv2.imwrite(os.path.join(output_directory, 'step_demean.jpg'), I)

# Do the radon transform
sinogram = radon(I)
# Save the intermediate sinogram image
cv2.imwrite(os.path.join(output_directory, 'step_sinogram.jpg'), sinogram)

# Find the RMS value of each row and find "busiest" rotation,
# where the transform is lined up perfectly with the alternating dark
# text and white lines
r = np.array([np.sqrt(np.mean(np.abs(line) ** 2)) for line in sinogram.transpose()])
rotation = np.argmax(r)
print('Rotation: {:.2f} degrees'.format(90 - rotation))

# Rotate and save with the original resolution
M = cv2.getRotationMatrix2D((w/2, h/2), 90 - rotation, 1)
dst = cv2.warpAffine(img, M, (w, h))
cv2.imwrite(os.path.join(output_directory, 'step_rotated.jpg'), dst)

# Convert the image to grayscale
gray = cv2.cvtColor(dst, cv2.COLOR_BGR2GRAY)
# Save the final grayscale image
cv2.imwrite(os.path.join(output_directory, 'final_gray.jpg'), gray)

cv2.imshow("rotated.jpg", dst)
cv2.waitKey(0)