Python：中心阵列图像
Python: Center array image
使用 skimage 库，我必须将这些图像居中。我发现 SimilarityTransform 应该接受偏移量，但缺少文档。我将如何检测此类图像的角，然后确定如何将其居中？
阵列总是存在于 28x28 图像之外。有些图像中有噪点，例如此处的第二张图像代表四。我不小心抓到了两张居中的图片，但它们可以在数组中无处不在。
在底部，您还可以看到 4 的更多视觉表示。
[[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11 203 229 32 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 26 47 47 30 95 254 215 13 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 45 154 185 185 223 253 253 133 175 255 188 19 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 110 253 253 253 246 161 228 253 253 254 92 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 128 245 253 158 137 21 0 48 233 253 233 8 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 139 254 223 25 0 0 36 170 254 244 106 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 55 212 253 161 11 26 178 253 236 113 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 7 155 253 228 80 223 253 253 109 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 141 253 253 253 254 253 154 29 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 110 253 253 253 254 179 38 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 3 171 254 254 254 179 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 171 253 253 253 253 178 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 26 123 254 253 203 156 253 200 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 93 253 254 121 13 93 253 158 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 64 239 253 76 8 32 219 253 126 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 133 254 191 0 5 108 234 254 106 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 132 253 190 5 85 253 236 154 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 153 253 169 192 253 253 77 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 112 253 253 254 236 129 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 17 118 243 191 113 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]
[[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 42 164 252 63 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 23 34 0 244 254 112 0 0 0 0 85 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 4 190 225 0 255 185 13 0 0 0 0 95 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 6 170 254 197 64 254 59 0 0 0 0 0 95 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 9 132 254 204 23 112 254 28 0 0 0 0 0 77 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 6 167 254 216 58 24 242 225 16 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 76 254 254 162 85 138 254 188 0 0 0 48 85 25 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 3 159 254 254 254 254 254 228 151 151 214 250 254 75 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 7 79 131 158 254 254 226 225 225 225 190 148 39 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 127 254 148 0 0 0 0 0 0 0 71 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 23 248 201 0 0 0 0 0 0 0 0 36 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 85 254 118 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 12 189 227 22 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 114 254 103 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 44 226 175 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 148 203 59 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 26 242 140 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 131 169 22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 19 233 65 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 174 109 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]
解决方案代码：
@anmol_uppal.
提供的代码略有改动
def pad_image(img, pad_t, pad_r, pad_b, pad_l):
"""Add padding of zeroes to an image.
Add padding to an array image.
:param img:
:param pad_t:
:param pad_r:
:param pad_b:
:param pad_l:
"""
height, width = img.shape
# Adding padding to the left side.
pad_left = np.zeros((height, pad_l), dtype = np.int)
img = np.concatenate((pad_left, img), axis = 1)
# Adding padding to the top.
pad_up = np.zeros((pad_t, pad_l + width))
img = np.concatenate((pad_up, img), axis = 0)
# Adding padding to the right.
pad_right = np.zeros((height + pad_t, pad_r))
img = np.concatenate((img, pad_right), axis = 1)
# Adding padding to the bottom
pad_bottom = np.zeros((pad_b, pad_l + width + pad_r))
img = np.concatenate((img, pad_bottom), axis = 0)
return img
def center_image(img):
"""Return a centered image.
:param img:
"""
col_sum = np.where(np.sum(img, axis=0) > 0)
row_sum = np.where(np.sum(img, axis=1) > 0)
y1, y2 = row_sum[0][0], row_sum[0][-1]
x1, x2 = col_sum[0][0], col_sum[0][-1]
cropped_image = img[y1:y2, x1:x2]
zero_axis_fill = (images[0].shape[0] - cropped_image.shape[0])
one_axis_fill = (images[0].shape[1] - cropped_image.shape[1])
top = zero_axis_fill / 2
bottom = zero_axis_fill - top
left = one_axis_fill / 2
right = one_axis_fill - left
padded_image = pad_image(cropped_image, top, left, bottom, right)
return padded_image
# Usage is in a for loop that iterates through 60.000 images.
for item in permuList:
shuImages.append(center_image(images[item]).reshape((28 * 28)))
img = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 11, 203, 229, 32, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 26, 47, 47, 30, 95, 254, 215, 13, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 45, 154, 185, 185, 223, 253, 253, 133, 175, 255, 188, 19, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 110, 253, 253, 253, 246, 161, 228, 253, 253, 254, 92, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 128, 245, 253, 158, 137, 21, 0, 48, 233, 253, 233, 8, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 139, 254, 223, 25, 0, 0, 36, 170, 254, 244, 106, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 212, 253, 161, 11, 26, 178, 253, 236, 113, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 155, 253, 228, 80, 223, 253, 253, 109, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 141, 253, 253, 253, 254, 253, 154, 29, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 110, 253, 253, 253, 254, 179, 38, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 171, 254, 254, 254, 179, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 171, 253, 253, 253, 253, 178, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 26, 123, 254, 253, 203, 156, 253, 200, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 93, 253, 254, 121, 13, 93, 253, 158, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 64, 239, 253, 76, 8, 32, 219, 253, 126, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 133, 254, 191, 0, 5, 108, 234, 254, 106, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 132, 253, 190, 5, 85, 253, 236, 154, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 153, 253, 169, 192, 253, 253, 77, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 112, 253, 253, 254, 236, 129, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 17, 118, 243, 191, 113, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
所以问题的第一部分是找到包含图像的边界框，第二部分涉及将图像居中，对于第二部分，我们将为边界框的每个顶点添加填充。
第 1 部分：
要绘制边界框，方法是我们可以遍历每一行并找到具有 1 个或多个非零元素的行，从顶部和底部执行此过程我们将得到 [=13 的限制=] 和我们通过遍历列对 X-axis 做的一样，Numpy 提供了一些很棒的函数，例如 sum()，我们将使用这个函数来检查 row/column 是否有任何非零字符，在那种情况下 sum > 0:
设(x1, y1)为左上坐标，(x2, y2)为右下坐标，这两点足以定义一个边界框。
col_sum = np.where(np.sum(a, axis = 0)>0)
row_sum = np.where(np.sum(a, axis = 1)>0)
y1, y2 = row_sum[0][0], row_sum[0][-1]
x1, x2 = col_sum[0][0], col_sum[0][-1]
print x1, y1
print x2, y2
第 2 部分：
现在我们必须将图像居中，而不是计算每边所需的填充，我们只需按照边界框的形状裁剪图像，然后对每边应用相等的填充。
def add_padding(img, pad_l, pad_t, pad_r, pad_b):
height, width = img.shape
#Adding padding to the left side.
pad_left = np.zeros((height, pad_l), dtype = np.int)
img = np.concatenate((pad_left, img), axis = 1)
#Adding padding to the top.
pad_up = np.zeros((pad_t, pad_l + width))
img = np.concatenate((pad_up, img), axis = 0)
#Adding padding to the right.
pad_right = np.zeros((height + pad_t, pad_r))
img = np.concatenate((img, pad_right), axis = 1)
#Adding padding to the bottom
pad_bottom = np.zeros((pad_b, pad_l + width + pad_r))
img = np.concatenate((img, pad_bottom), axis = 0)
return img
cropped_image = img[y1:y2, x1:x2]
padded_image = add_padding(cropped_image, 20, 20, 20, 20)
使用 skimage 库，我必须将这些图像居中。我发现 SimilarityTransform 应该接受偏移量，但缺少文档。我将如何检测此类图像的角，然后确定如何将其居中？
阵列总是存在于 28x28 图像之外。有些图像中有噪点，例如此处的第二张图像代表四。我不小心抓到了两张居中的图片，但它们可以在数组中无处不在。
在底部，您还可以看到 4 的更多视觉表示。
[[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11 203 229 32 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 26 47 47 30 95 254 215 13 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 45 154 185 185 223 253 253 133 175 255 188 19 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 110 253 253 253 246 161 228 253 253 254 92 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 128 245 253 158 137 21 0 48 233 253 233 8 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 139 254 223 25 0 0 36 170 254 244 106 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 55 212 253 161 11 26 178 253 236 113 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 7 155 253 228 80 223 253 253 109 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 141 253 253 253 254 253 154 29 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 110 253 253 253 254 179 38 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 3 171 254 254 254 179 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 171 253 253 253 253 178 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 26 123 254 253 203 156 253 200 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 93 253 254 121 13 93 253 158 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 64 239 253 76 8 32 219 253 126 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 133 254 191 0 5 108 234 254 106 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 132 253 190 5 85 253 236 154 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 153 253 169 192 253 253 77 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 112 253 253 254 236 129 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 17 118 243 191 113 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]
[[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 42 164 252 63 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 23 34 0 244 254 112 0 0 0 0 85 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 4 190 225 0 255 185 13 0 0 0 0 95 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 6 170 254 197 64 254 59 0 0 0 0 0 95 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 9 132 254 204 23 112 254 28 0 0 0 0 0 77 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 6 167 254 216 58 24 242 225 16 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 76 254 254 162 85 138 254 188 0 0 0 48 85 25 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 3 159 254 254 254 254 254 228 151 151 214 250 254 75 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 7 79 131 158 254 254 226 225 225 225 190 148 39 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 127 254 148 0 0 0 0 0 0 0 71 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 23 248 201 0 0 0 0 0 0 0 0 36 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 85 254 118 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 12 189 227 22 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 114 254 103 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 44 226 175 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 148 203 59 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 26 242 140 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 131 169 22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 19 233 65 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 174 109 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]
解决方案代码： @anmol_uppal.
提供的代码略有改动def pad_image(img, pad_t, pad_r, pad_b, pad_l):
"""Add padding of zeroes to an image.
Add padding to an array image.
:param img:
:param pad_t:
:param pad_r:
:param pad_b:
:param pad_l:
"""
height, width = img.shape
# Adding padding to the left side.
pad_left = np.zeros((height, pad_l), dtype = np.int)
img = np.concatenate((pad_left, img), axis = 1)
# Adding padding to the top.
pad_up = np.zeros((pad_t, pad_l + width))
img = np.concatenate((pad_up, img), axis = 0)
# Adding padding to the right.
pad_right = np.zeros((height + pad_t, pad_r))
img = np.concatenate((img, pad_right), axis = 1)
# Adding padding to the bottom
pad_bottom = np.zeros((pad_b, pad_l + width + pad_r))
img = np.concatenate((img, pad_bottom), axis = 0)
return img
def center_image(img):
"""Return a centered image.
:param img:
"""
col_sum = np.where(np.sum(img, axis=0) > 0)
row_sum = np.where(np.sum(img, axis=1) > 0)
y1, y2 = row_sum[0][0], row_sum[0][-1]
x1, x2 = col_sum[0][0], col_sum[0][-1]
cropped_image = img[y1:y2, x1:x2]
zero_axis_fill = (images[0].shape[0] - cropped_image.shape[0])
one_axis_fill = (images[0].shape[1] - cropped_image.shape[1])
top = zero_axis_fill / 2
bottom = zero_axis_fill - top
left = one_axis_fill / 2
right = one_axis_fill - left
padded_image = pad_image(cropped_image, top, left, bottom, right)
return padded_image
# Usage is in a for loop that iterates through 60.000 images.
for item in permuList:
shuImages.append(center_image(images[item]).reshape((28 * 28)))
img = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 11, 203, 229, 32, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 26, 47, 47, 30, 95, 254, 215, 13, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 45, 154, 185, 185, 223, 253, 253, 133, 175, 255, 188, 19, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 110, 253, 253, 253, 246, 161, 228, 253, 253, 254, 92, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 128, 245, 253, 158, 137, 21, 0, 48, 233, 253, 233, 8, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 139, 254, 223, 25, 0, 0, 36, 170, 254, 244, 106, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 212, 253, 161, 11, 26, 178, 253, 236, 113, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 155, 253, 228, 80, 223, 253, 253, 109, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 141, 253, 253, 253, 254, 253, 154, 29, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 110, 253, 253, 253, 254, 179, 38, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 171, 254, 254, 254, 179, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 171, 253, 253, 253, 253, 178, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 26, 123, 254, 253, 203, 156, 253, 200, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 93, 253, 254, 121, 13, 93, 253, 158, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 64, 239, 253, 76, 8, 32, 219, 253, 126, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 133, 254, 191, 0, 5, 108, 234, 254, 106, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 132, 253, 190, 5, 85, 253, 236, 154, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 153, 253, 169, 192, 253, 253, 77, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 112, 253, 253, 254, 236, 129, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 17, 118, 243, 191, 113, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
所以问题的第一部分是找到包含图像的边界框，第二部分涉及将图像居中，对于第二部分，我们将为边界框的每个顶点添加填充。
第 1 部分：
要绘制边界框，方法是我们可以遍历每一行并找到具有 1 个或多个非零元素的行，从顶部和底部执行此过程我们将得到 [=13 的限制=] 和我们通过遍历列对 X-axis 做的一样，Numpy 提供了一些很棒的函数，例如 sum()，我们将使用这个函数来检查 row/column 是否有任何非零字符，在那种情况下 sum > 0:
设(x1, y1)为左上坐标，(x2, y2)为右下坐标，这两点足以定义一个边界框。
col_sum = np.where(np.sum(a, axis = 0)>0)
row_sum = np.where(np.sum(a, axis = 1)>0)
y1, y2 = row_sum[0][0], row_sum[0][-1]
x1, x2 = col_sum[0][0], col_sum[0][-1]
print x1, y1
print x2, y2
第 2 部分：
现在我们必须将图像居中，而不是计算每边所需的填充，我们只需按照边界框的形状裁剪图像，然后对每边应用相等的填充。
def add_padding(img, pad_l, pad_t, pad_r, pad_b):
height, width = img.shape
#Adding padding to the left side.
pad_left = np.zeros((height, pad_l), dtype = np.int)
img = np.concatenate((pad_left, img), axis = 1)
#Adding padding to the top.
pad_up = np.zeros((pad_t, pad_l + width))
img = np.concatenate((pad_up, img), axis = 0)
#Adding padding to the right.
pad_right = np.zeros((height + pad_t, pad_r))
img = np.concatenate((img, pad_right), axis = 1)
#Adding padding to the bottom
pad_bottom = np.zeros((pad_b, pad_l + width + pad_r))
img = np.concatenate((img, pad_bottom), axis = 0)
return img
cropped_image = img[y1:y2, x1:x2]
padded_image = add_padding(cropped_image, 20, 20, 20, 20)