如何修改patchify制作的补丁？

Question

我正在使用 patchify 库来修补大图像：

img = cv2.imread("resized.jpg")
patches_img = patchify(img, (224,224,3), step=224)
print(patches_img.shape)

然后我保存补丁：

for i in range(patches_img.shape[0]):
    for j in range(patches_img.shape[1]):
        single_patch_img = patches_img[i, j, 0, :, :, :]
        if not cv2.imwrite('patches/images/' + 'image_' + '_'+ str(i)+str(j)+'.jpg', single_patch_img):
            raise Exception("Could not write the image")

然后，我想对这些补丁中的任何一个进行一些修改，例如绘制边界框，所以当我使用 unpatchify 将补丁合并在一起时，边界框将显示在重建图像上。

进行修改后，我运行以下代码将补丁重新合并在一起：

reconstructed_image = unpatchify(patches_img, img.shape)
cv2.imwrite("unpatched.jpg", reconstructed_image)

但是生成的重建图像与原始图像相同，看不到任何变化。 我认为这是因为 unpatchify 读取了变量 patches_img，该变量仍然存储了原始的、未修改的补丁。

我尝试了以下方法：

patches = 'patches/images/*.jpg'
reconstructed_image = unpatchify(patches, img.shape)
cv2.imwrite("unpatched.jpg", reconstructed_image)

但我得到 AttributeError: 'str' object has no attribute 'shape'

谢谢！

Answer 1

为了重建图像，我们必须一张一张地读取图像，并将每张图像放置在原始补丁位置。

文件命名存在错误，例如：
i = 1 和 j = 11 与 i = 11 和 j = 1 ('image__111.jpg') 同名。
更好的文件命名：

cv2.imwrite('patches/images/' + 'image_' + '_'+ str(i).zfill(2) + '_' + str(j).zfill(2) + '.png', single_patch_img)

注意：

• 我将图像文件格式从 JPEG 更改为 PNG，以保持原始图像质量。
  JPEG 是一种无损图像格式，因此每次存储和加载都会降低一些质量。

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建议的重构解决方案：

• 阅读test.jpg 只是为了了解形状（img）

    img = cv2.imread("test.jpg")
    img = np.zeros_like(img)  # Fill with zeros for the example (start from an empty image).

• 使用 patchify 只是为了获取形状（patches）

    patches = patchify(img, (224,224,3), step=224)  # We could have also used: patches = np.zeros((14, 18, 1, 224, 224, 3), np.uint8)

• 读取图像并将它们放置在patches中的原始位置：

    for i in range(patches.shape[0]):
        for j in range(patches.shape[1]):
            single_patch_img = cv2.imread('patches/images/' + 'image_' + '_'+ str(i).zfill(2) + '_' + str(j).zfill(2) + '.png')  # Read a patch image.
            if single_patch_img is None:
                raise Exception("Could not read the image") 
            patches[i, j, 0, :, :, :] = single_patch_img.copy()  # Copy single path image to patches

• 取消补丁

    reconstructed_image = unpatchify(patches, img.shape)

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这是一个完整的代码示例，用于修补、保存修补程序、加载修补程序和取消修补：

import cv2
import numpy as np
from patchify import patchify, unpatchify


img = cv2.imread("test.jpg")
patches_img = patchify(img, (224,224,3), step=224)  # patches_img.shape = (14, 18, 1, 224, 224, 3)

for i in range(patches_img.shape[0]):
    for j in range(patches_img.shape[1]):
        single_patch_img = patches_img[i, j, 0, :, :, :]
        cv2.rectangle(single_patch_img, (30, 30), (224-30, 224-30), (0, 255, 0), 3)  # Draw something (for testing).
        if not cv2.imwrite('patches/images/' + 'image_' + '_'+ str(i).zfill(2) + '_' + str(j).zfill(2) + '.png', single_patch_img):  # Save as PNG, not JPEG for keeping the quality.
            raise Exception("Could not write the image") 

# Store an unpatchified reference for testing
cv2.imwrite("unpatched_ref.jpg", unpatchify(patches_img, img.shape))

# Unpatchify
################################################################################

# Allocate sapces for storing the patches
img = cv2.imread("test.jpg")  # Read test.jpg just for getting the shape
img = np.zeros_like(img)  # Fill with zeros for the example (start from an empty image).

# Use patchify just for getting the size. shape = (14, 18, 1, 224, 224, 3)
# We could have also used: patches = np.zeros((14, 18, 1, 224, 224, 3), np.uint8)
patches = patchify(img, (224,224,3), step=224)

for i in range(patches.shape[0]):
    for j in range(patches.shape[1]):
        single_patch_img = cv2.imread('patches/images/' + 'image_' + '_'+ str(i).zfill(2) + '_' + str(j).zfill(2) + '.png')  # Read a patch image.
        if single_patch_img is None:
            raise Exception("Could not read the image") 
        patches[i, j, 0, :, :, :] = single_patch_img.copy()  # Copy single path image to patches

reconstructed_image = unpatchify(patches, img.shape)

cv2.imwrite("unpatched.jpg", reconstructed_image)

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样本输出（缩小尺寸）：