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import cv2
import numpy as np
def generate_blurred_images(image, blur_strength, steps, focus_spread=1):
blurred_images = []
for step in range(1, steps + 1):
# Adjust the curve based on the curve_weight
blur_factor = (step / steps) ** focus_spread * blur_strength
blur_size = max(1, int(blur_factor))
blur_size = blur_size if blur_size % 2 == 1 else blur_size + 1 # Ensure blur_size is odd
# Apply Gaussian Blur
blurred_image = cv2.GaussianBlur(image, (blur_size, blur_size), 0)
blurred_images.append(blurred_image)
return blurred_images
def apply_blurred_images(image, blurred_images, mask):
steps = len(blurred_images) # Calculate the number of steps based on the blurred images provided
final_image = np.zeros_like(image)
step_size = 1.0 / steps
for i, blurred_image in enumerate(blurred_images):
# Calculate the mask for the current step
current_mask = np.clip((mask - i * step_size) * steps, 0, 1)
next_mask = np.clip((mask - (i + 1) * step_size) * steps, 0, 1)
blend_mask = current_mask - next_mask
# Apply the blend mask
final_image += blend_mask[:, :, np.newaxis] * blurred_image
# Ensure no division by zero; add the original image for areas without blurring
final_image += (1 - np.clip(mask * steps, 0, 1))[:, :, np.newaxis] * image
return final_image |