Python 图像 pyramid .难以创建所需的合成图像

APPROACH 1: Custom Alpha Blending (shorter)

我不会太担心使用高斯 pyramid 或拉普拉斯 pyramid 来实现这一点.相反，您可以使用所提供的平滑版本的面膜执行Alpha混合(这确保了平滑的边界)，以达到所需的输出.以下是我解决您问题的方法:

import cv2
import numpy as np

# Read the input images and the mask
mask = cv2.imread("mask.jpeg", cv2.IMREAD_GRAYSCALE)
image1 = cv2.imread("image-1.jpeg")
image2 = cv2.imread("image-2.jpeg")

# Resize images to match mask dimensions
height, width = mask.shape[:2]
image1 = cv2.resize(image1, (width, height))
image2 = cv2.resize(image2, (width, height))

# Smooth out the mask and normalize to range [0, 1]
transparency_gradient = cv2.blur(mask, (25, 25))
transparency_gradient = cv2.cvtColor(transparency_gradient, cv2.COLOR_GRAY2BGR)
transparency_gradient = transparency_gradient / 255.0  # Normalize to range [0, 1]

# Perform manual alpha blending with transparency gradient
composite_image = image1 * transparency_gradient + image2 * (1 - transparency_gradient)

# Save the result
cv2.imwrite("composite_image.png", composite_image)

Which yields this final image:

APPROACH 2: Blending through Gaussian and Laplacian Pyramids

如果目标是通过 pyramid 实现这种混合，那么下面是一个可以做到这一点的代码:

import cv2
import numpy as np

# Read the input images and the mask
mask = cv2.imread("mask.jpeg", cv2.IMREAD_GRAYSCALE)
image1 = cv2.imread("image-1.jpeg")
image2 = cv2.imread("image-2.jpeg")

# Set the level of the pyramids (tweak it for better accuracy)
levels=6

# Resize images to match mask dimensions
height, width = image1.shape[:2]
mask = cv2.resize(mask, (width, height), interpolation=cv2.INTER_LINEAR)

# Duplicate the mask to match the number of channels in the images
mask = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)

# Generate Gaussian pyramids for both images and the mask
gaussian_pyramid1 = [image1.astype(np.float32)]
gaussian_pyramid2 = [image2.astype(np.float32)]
mask_pyramid = [mask.astype(np.float32) / 255.0]

for _ in range(levels - 1):
    image1 = cv2.pyrDown(image1)
    image2 = cv2.pyrDown(image2)
    mask = cv2.pyrDown(mask)

    gaussian_pyramid1.append(image1.astype(np.float32))
    gaussian_pyramid2.append(image2.astype(np.float32))
    mask_pyramid.append(mask.astype(np.float32) / 255.0)

# Generate Laplacian pyramids for both images
laplacian_pyramid1 = [gaussian_pyramid1[levels - 1]]
laplacian_pyramid2 = [gaussian_pyramid2[levels - 1]]
for i in range(levels - 2, -1, -1):
    expanded1 = cv2.pyrUp(gaussian_pyramid1[i + 1], dstsize=(gaussian_pyramid1[i].shape[1], gaussian_pyramid1[i].shape[0]))
    expanded2 = cv2.pyrUp(gaussian_pyramid2[i + 1], dstsize=(gaussian_pyramid2[i].shape[1], gaussian_pyramid2[i].shape[0]))
    laplacian1 = cv2.subtract(gaussian_pyramid1[i], expanded1)
    laplacian2 = cv2.subtract(gaussian_pyramid2[i], expanded2)
    laplacian_pyramid1.append(laplacian1)
    laplacian_pyramid2.append(laplacian2)

# Combine the corresponding levels of Laplacian pyramids using the mask
composite_pyramid = []
for laplacian1, laplacian2, mask in zip(laplacian_pyramid1, laplacian_pyramid2, mask_pyramid):
    mask_resized = cv2.resize(mask, (laplacian1.shape[1], laplacian1.shape[0]), interpolation=cv2.INTER_LINEAR)
    composite_level = laplacian1 * mask_resized + laplacian2 * (1.0 - mask_resized)
    composite_pyramid.append(composite_level)

# Reconstruct the final blended image
composite_image = composite_pyramid[0]
for i in range(1, levels):
    composite_image = cv2.pyrUp(composite_image, dstsize=(composite_pyramid[i].shape[1], composite_pyramid[i].shape[0]))
    composite_image += composite_pyramid[i]

# Ensure pixel values are within valid range
composite_image = np.clip(composite_image, 0, 255).astype(np.uint8)

# Save Image
cv2.imwrite("composite_image.png", composite_image)

Which produces:

现在是你的 Select 了.祝你好运！愿密码与你同在……