无法重建分解的图像

Question

我试图重建我分解成补丁的图像。我尝试使用 sklearn.feature_extraction.image.reconstruct_from_patches_2d 或自定义函数，但结果相同

这是我的尝试：

image_path=r'C:\Users\User\Desktop\Thesis-Diabetic Retinopathy\Datasets\DIARETDB1\SeeFar Project\Images\Processed images\image001.png'

os.mkdir(r'C:\Users\User\Desktop\Patched Image')

def create_patches_from_lesions(image_path,patch_widht,patch_height):
    im1=cv2.imread(image_path)
    im2 = cv2.cvtColor(im1, cv2.COLOR_BGR2RGB) 
    x1,y1=im2.shape[0]//50,im2.shape[1]//50
    x2,y2=x1*50,y1*50
    im3 = cv2.resize(im2, (x2,y2))
    print(im3.shape)
    patches=[]

    for row in range(0,im3.shape[0],50):
        for column in range(0,im3.shape[1],50):
            patch=im3[row:(row+patch_widht),column:(column+patch_height)]
            patches.append(patch)


    for i in tqdm(range(len(patches))): #### keep only the patches with non zero elememts

        cv2.imwrite(os.path.join(r'C:\Users\User\Desktop\Patched Image','{}.png'.format(i)),patches[i])

    return patches,x2,y2



patches,x2,y2=create_patches_from_lesions(image_path,50,50)


def reconstruct_image(patches,image_height,image_width):
    patches=np.asarray(patches)
    print(patches.shape)
    reshaped_patches=np.reshape(patches,(image_height,image_width,3))
    print([reshaped_patches.shape])
    cv2.imwrite(r'C:\Users\User\Desktop\Reconstructed Image.png',reshaped_patches)
reconstruct_image(patches,x2,y2)

Answer 1

sklearn.feature_extraction.image.extract_patches_2d reshapes a 2D image into a collection of overlapping patches. To decompose your image into non-overlapping blocks you could use skimage.util.view_as_blocks.

以下代码段是您的自定义函数的可能实现。请注意，如果图像大小不是补丁大小的整数倍，create_patches 会去除图像的底部和右侧边框。我采用此解决方案是为了避免调整图像大小（一种可能会引入伪影的操作）。可以从补丁文件（未实现）或直接从 create_patches.

返回的 4D 数组重建原始图像

import os
import numpy as np
from skimage import io, util

def create_patches(img, folder, patch_width, patch_height):
    # Trim right and bottom borders if image size is not 
    # an integer multiple of patch size
    nrows, ncols = patch_height, patch_width   
    trimmed = img[:img.shape[0]//nrows*nrows, :img.shape[1]//ncols*ncols]   
    
    # Create folder to store results if necessary
    patch_dir = os.path.join(folder, 'Patched Image')
    if not os.path.isdir(patch_dir):
        os.mkdir(patch_dir)

    # Generate patches and save them to disk           
    patches = util.view_as_blocks(trimmed, (nrows, ncols))
    for i in range(patches.shape[0]):
        for j in range(patches.shape[1]):
            patch = patches[i, j, :, :]
            patch_name = f'patch_{i:02}_{j:02}.png'
            io.imsave(os.path.join(patch_dir, patch_name), patch)

    return patches


def reconstruct_image(patches):
    img_height = patches.shape[0]*patches.shape[2]
    img_width = patches.shape[1]*patches.shape[3]
    return patches.transpose(0, 2, 1, 3).reshape(img_height, img_width)

演示

In [134]: import matplotlib.pyplot as plt
     ...: 
     ...: folder = r'C:\Users\User\path-to-your-folder'
     ...: filename = 'sample_image.png'
     ...: 
     ...: original = io.imread(os.path.join(folder, filename))
     ...: patches = create_patches(original, folder, 50, 50)
     ...: reconstructed = reconstruct_image(patches)
     ...: 
     ...: fig, (ax0, ax1) = plt.subplots(1, 2)
     ...: ax0.imshow(original, cmap='gray')
     ...: ax0.set_title('Original')
     ...: ax1.imshow(reconstructed, cmap='gray')
     ...: ax1.set_title('Reconstructed')
     ...: plt.show(fig)
     ...: 
     ...: for patch in os.listdir(os.path.join(folder, 'Patched Image')):
     ...:     print(patch)
     ...:     
patch_00_00.png
patch_00_01.png
patch_00_02.png
patch_00_03.png
...
patch_07_05.png
patch_07_06.png
patch_07_07.png
patch_07_08.png
patch_07_09.png

上例中使用的图片可以从here

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无法重建分解的图像

Cannot reconstruct decomposed image

python

opencv

image-processing

scikit-learn

scikit-image

演示