John Richard Chipps-Harding
6b7d5203ba
* feat: Add exponential_base parameter to radial blur and depth map blur This commit adds an "exponential_base" parameter to the "ProPostRadialBlur" and "ProPostDepthMapBlur" classes. This parameter allows for more control over the blurring effect by applying an exponential function to calculate the blur size. The default value is 1, and it can be adjusted between 1.0 and 8.0 with a step of 0.1. The "generate_blurred_images" function in the "processing.py" file has been updated to use this new parameter when generating blurred images. * more * Docs tweaks
32 lines
1.4 KiB
Python
32 lines
1.4 KiB
Python
import cv2
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import numpy as np
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def generate_blurred_images(image, blur_strength, steps, focus_spread=1):
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blurred_images = []
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for step in range(1, steps + 1):
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# Adjust the curve based on the curve_weight
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blur_factor = (step / steps) ** focus_spread * blur_strength
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blur_size = max(1, int(blur_factor))
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blur_size = blur_size if blur_size % 2 == 1 else blur_size + 1 # Ensure blur_size is odd
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# Apply Gaussian Blur
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blurred_image = cv2.GaussianBlur(image, (blur_size, blur_size), 0)
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blurred_images.append(blurred_image)
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return blurred_images
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def apply_blurred_images(image, blurred_images, mask):
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steps = len(blurred_images) # Calculate the number of steps based on the blurred images provided
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final_image = np.zeros_like(image)
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step_size = 1.0 / steps
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for i, blurred_image in enumerate(blurred_images):
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# Calculate the mask for the current step
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current_mask = np.clip((mask - i * step_size) * steps, 0, 1)
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next_mask = np.clip((mask - (i + 1) * step_size) * steps, 0, 1)
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blend_mask = current_mask - next_mask
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# Apply the blend mask
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final_image += blend_mask[:, :, np.newaxis] * blurred_image
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# Ensure no division by zero; add the original image for areas without blurring
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final_image += (1 - np.clip(mask * steps, 0, 1))[:, :, np.newaxis] * image
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return final_image |