如何正确保留图像尺寸

Question

我目前正在进行一个项目，我必须训练一个网络，从输入中重建损坏的图像。每个输入图像的大小都不一样，并且已经裁剪出其中的一部分，我的网络应该“恢复”

所以我训练了自动编码器，测试恢复时间到了。我将每个测试图像加载到模型中并保存输出：

to_tensor = transforms.ToTensor()
new_test = []

for i in test_data['images']:
    i = to_tensor(i)
    new_test.append(i)

test_loader = torch.utils.data.DataLoader(dataset=new_test, batch_size=1)

def reconstruct(net, loader):
    for i,data in enumerate(loader):
        data = data.to(device)
        output = net(data)
        #save_image(data, f'test_outs/damaged_input_{i}.jpg')
        save_image(output, f'test_outs/recon_output_{i}.jpg')

net.to(device)
reconstruct(net, test_loader)

然后使用这些重建裁剪出恢复的零件：

from glob import glob
from pathlib import Path

def cropper(image_array, crop_size, crop_center):
    if type(image_array) != np.ndarray:
        raise ValueError('not a numpy array')
    if type(crop_size) != tuple or type(crop_center) != tuple:
        raise ValueError('use tuple as input for crop_size and crop_center')
    if len(crop_size) != 2 or len(crop_center) != 2:
        raise ValueError('invalid amount of values')
    if crop_size[0] % 2 == 0 or crop_size[1] % 2 == 0:
        raise ValueError('crop_size values should be odd!')

    img = image_array.copy()
    crop_array = np.zeros_like(image_array)

    size_x =  round(crop_center[0] - (crop_size[0]-1)/2)
    center_x = round(crop_center[0] + (crop_size[0]+1)/2)
    size_y = round(crop_center[1] - (crop_size[1]-1)/2)
    center_y = round(crop_center[1] + (crop_size[1]+1)/2)

    border_right = size_y
    border_left = size_x
    border_up = image_array.shape[0] - center_y
    border_down = image_array.shape[1] - center_x

    if border_right < 20 or border_left < 20:
        raise ValueError('minimal distance between crop and border is less than 20')
    if border_up < 20 or border_down < 20:
        raise ValueError('minimal distance between crop and border is less than 20')

    target_array = image_array[int(size_x):int(center_x), int(size_y):int(center_y)]
    pad = np.negative(target_array)
    ones_pad = np.ones_like(target_array)
    img[int(size_x):int(center_x), int(size_y):int(center_y)] += pad
    crop_array[int(size_x):int(center_x), int(size_y):int(center_y)] += ones_pad
    image_array = img

    return (image_array, crop_array, target_array)

def rgb2gray(rgb_array: np.ndarray, r=0.2989, g=0.5870, b=0.1140):
    grayscale_array = (rgb_array[..., 0] * r +
                       rgb_array[..., 1] * g +
                       rgb_array[..., 2] * b)
    grayscale_array = np.round(grayscale_array)
    grayscale_array = np.asarray(grayscale_array, dtype=np.uint8)
    return grayscale_array

def load_recons(root = 'test_outs'):
    recons = []
    path = Path(root).glob('**/*.jpg')
    for enum, f in enumerate(path):
        img = Image.open(f)
        arr = np.asarray(img)
        arr = rgb2gray(arr)
        recons.append(arr)
    return recons

recs = load_recons()

def crop_tests(recons, crop_centrs = test_data['crop_centers'], crop_szs = test_data['crop_sizes']):
    for enum, (i,j,arr) in enumerate(zip(crop_centrs, crop_szs, recons)):
            image, crop, target = cropper(arr, j, i)
            Image.fromarray(target).save(f'test_final/{enum}_target.jpg')

crop_tests(recs)

但问题是，当我调用crop_tests时，我得到了一个异常：

---------------------------------------------------------------------------

ValueError                                Traceback (most recent call last)

<ipython-input-27-f927403b1f35> in <module>
      6             Image.fromarray(target).save(f'test_final/{enum}_target.jpg')
      7 
----> 8 crop_tests(recs)
      9 

<ipython-input-27-f927403b1f35> in crop_tests(recons, crop_centrs, crop_szs)
      3 def crop_tests(recons, crop_centrs = test_data['crop_centers'], crop_szs = test_data['crop_sizes']):
      4     for enum, (i,j,arr) in enumerate(zip(crop_centrs, crop_szs, recons)):
----> 5             image, crop, target = cropper(arr, j, i)
      6             Image.fromarray(target).save(f'test_final/{enum}_target.jpg')
      7 

~\proj_git\folder_preparator.py in cropper(image_array, crop_size, crop_center)
     52                 raise ValueError('minimal distance between crop and border is less than 20')
     53         if border_up < 20 or border_down < 20:
---> 54                 raise ValueError('minimal distance between crop and border is less than 20')
     55 
     56 

ValueError: minimal distance between crop and border is less than 20

这意味着，要裁剪的正方形非常接近重建图像的边界（小于20像素）。然而，这并不是损坏图像的问题

因此，重建图像的形状如下所示：

(70, 93)
(70, 93)
(70, 93)
(70, 93)
(70, 93)
(93, 70)
(70, 93)
(93, 70)
(93, 70)
(70, 93)
(93, 70)
(70, 93)
(70, 93)
(70, 93)
(70, 93)

但是，原始图像的形状顺序稍有不同：

(70, 93)
(70, 93)
(70, 93)
(70, 93)
(70, 93)
(70, 93)
(70, 93)
(70, 93)
(93, 70)
(93, 70)
(70, 93)
(70, 93)
(70, 93)
(100, 70)

所以我需要我的重构与输入的形状完全相同，但我自己看不到代码中的问题。你能帮我查一下吗？请随时提出其他问题