如何重现按不同维度合并多个图像

Question

我之前问过一个将每 4 张大小为 64x64 的图像合并为 128x128 的问题，我将答案编辑如下：

# Initializing counters
i = 0  # Old image number
j = 0  # New image number

# Pre-allocate new images array
pred_128 = np.zeros((32, 128, 128, 1))

# Loop over new images
while j < 32:
    pred_128 [j, :64, :64, 0] = pred_64[0+i, :, :, 0]  # Upper left
    pred_128 [j, 64:, :64, 0] = pred_64[2+i, :, :, 0]  # Lower left
    pred_128 [j, :64, 64:, 0] = pred_64[1+i, :, :, 0]  # Upper right
    pred_128 [j, 64:, 64:, 0] = pred_64[3+i, :, :, 0]  # Lower right

    # Add to counters
    i += 4
    j += 1

我现在想重新使用此代码从不同的图像大小生成 (32, 128, 128, 1) 并且： 1- (512, 32, 32, 1) 2- (2048, 16, 16, 1)

对于第一种情况(512, 32, 32, 1)，我使用了下面的代码，它returns错误：

# Initializing counters
i = 0  # Old image number
j = 0  # New image number

# Pre-allocate new images array
pred_128 = np.zeros((32, 128, 128, 1))

# Loop over new images
while j < 32:
    pred_128 [j, :32, :32, 0] = pred_32[0+i, :, :, 0]  # Upper left
    pred_128 [j, 32:, :32, 0] = pred_32[2+i, :, :, 0]  # Lower left
    pred_128 [j, :32, 32:, 0] = pred_32[1+i, :, :, 0]  # Upper right
    pred_128 [j, 32:, 32:, 0] = pred_32[3+i, :, :, 0]  # Lower right

    # Add to counters
    i += 8
    j += 1



ValueError                                Traceback (most recent call last)
<ipython-input-48-b4a45801c652> in <module>()
      9 while j < 32:
     10     pred_128 [j, :32, :32, 0] = pred_32[0+i, :, :, 0]  # Upper left
---> 11     pred_128 [j, 32:, :32, 0] = pred_32[2+i, :, :, 0]  # Lower left
     12     pred_128 [j, :32, 32:, 0] = pred_32[1+i, :, :, 0]  # Upper right
     13     pred_128 [j, 32:, 32:, 0] = pred_32[3+i, :, :, 0]  # Lower right

ValueError: could not broadcast input array from shape (32,32) into shape (96,32)

任何人都可以帮助复制代码并解决两种不同情况下的问题： 1- (512, 32, 32, 1) #merging every 16 images

2- (2048, 16, 16, 1) #merging every 64 images

使用建议的代码后出现错误：

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-79-b71bf1e0ef80> in <module>()
     12 # Loop over new images
     13 for i in range(0, out_shape[0]):
---> 14     for x in range(0, out_shape[1]/dx):
     15         for y in range(0, out_shape[2]/dy):
     16             pred_128[i, 0+dx*x:dx*(x+1), 0+dy*y:dy*(y+1), 0] = pred_32[input_im_no, :, :, 0]

TypeError: 'float' object cannot be interpreted as an integer

-原始数据中每16张32x32图像的顺序

Answer 1

您需要添加更多图块才能获得完整图像。对于 pred_32 情况，您将需要 16 个输入图像来生成 1 个输出图像，而对于 pred_16 情况，您需要 64 个输入图像来生成 1 个输出图像。在这种情况下，编写一个 'shifts' 遍历所需输出图像并一次输入一个图像的循环可能更容易。假设您的图像从左到右填充更大的图像，我认为以下代码可能会帮助您：

# Pre-allocate new images array
out_shape = (32, 128, 128, 1))
pred_128 = np.zeros(out_shape)

# Input sizes
dx = 32  # 16 for the pred_16
dy = 32  # 16 for the pred_16

# Input images counter
input_im_no = 0

# Loop over new images
for i in range(0, out_shape[0]):
    for y in range(0, int(out_shape[1]/dy)):
        for x in range(0, int(out_shape[2]/dx)):
            pred_128[i, 0+dx*x:dx*(x+1), 0+dy*y:dy*(y+1), 0] = pred_32[input_im_no, :, :, 0]

            # Select next image
            input_im_no += 1

编辑：问题更新后的 x 和 y 顺序。

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