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930647e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | import os
import numpy as np
import cv2
# Function to apply the blending step
def render(img_rgb):
img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)
img_blur = cv2.GaussianBlur(img_gray, (21, 21), 0, 0) # Gaussian Blur for smoothening
img_blend = cv2.divide(img_gray, img_blur, scale=256) # Blend grayscale with blurred version
return img_blend
# Function to apply gamma correction
def adjust_gamma(image, gamma=1.0):
invGamma = 1.0 / gamma
table = np.array([((i / 255.0) ** invGamma) * 255 for i in np.arange(0, 256)]).astype("uint8")
return cv2.LUT(image, table)
# Paths for the input and output folders
input_folder = "input_images" # Replace with your input folder path
output_folder = "output_images" # Replace with your output folder path
# Create the output folder if it doesn't exist
os.makedirs(output_folder, exist_ok=True)
# Loop through all images in the input folder
for filename in os.listdir(input_folder):
if filename.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp', '.tiff', '.tif')): # Check for valid image extensions
input_image_path = os.path.join(input_folder, filename)
output_image_path = os.path.join(output_folder, f"processed_{filename}")
# Read the input image
image_array = cv2.imread(input_image_path)
if image_array is not None: # Check if the image was read correctly
# Apply the CAT process
img_blend = render(image_array)
img_gamma = adjust_gamma(img_blend, gamma=.001) # Adjust gamma for better contrast
# Save the processed image
cv2.imwrite(output_image_path, img_gamma)
print(f"Processed image saved: {output_image_path}")
else:
print(f"Failed to process image: {input_image_path}")
print("Batch processing complete!")
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