英文:
I can't seem to understand these lines of code. Can someone please explain it to me?
问题
我遇到了这段代码，但似乎无法理解它。
label_seg = np.zeros(img.shape, dtype=np.uint8)
for i in range(mask_labels.shape[0]):
label_seg[np.all(img == list(mask_labels.iloc[i, [1,2,3]]), axis=-1)] = i
label_seg = label_seg[:,:,0]
我尝试分解它，但仍然没有得出很好的理解。
英文:
I came accros this piece of code and i cant seem to understand it.
label_seg = np.zeros(img.shape,dtype=np.uint8)
for i in range(mask_labels.shape[0]):
label_seg[np.all(img == list(mask_labels.iloc[i, [1,2,3]]), axis=-1)] = i
label_seg = label_seg[:,:,0]
i tried breaking it down but then ive still not come up with any good understanding
答案1
得分: 1
这段代码的作用是比较一个三通道图像（例如RGB图像）与一个名为mask_labels的DataFrame中的已知颜色，然后将颜色的索引赋值给一个新的数组label_seg，以识别匹配的颜色。
代码中可能存在一些错误：
- 最后一行不应该在for循环内部。
- DataFrame的第一行/颜色映射到
0，这也是输入的默认值。
使用示例：
np.random.seed(0)
img = np.random.randint(0, 255, (5, 5, 3))
mask_labels = pd.DataFrame([[0, 58, 193, 230],
[0, 127, 32, 31],
[0, 193, 9, 185],
])
# 生成与输入图像相同形状的输出
label_seg = np.zeros(img.shape, dtype=np.uint8)
# 对于DataFrame中的每一行
for i in range(mask_labels.shape[0]):
# 取前3列（即列1,2,3）
# 如果img数组中的所有3个值匹配（相同的x/y位置，所有3个通道）
# 在输出数组中相同的x/y位置上赋值为行索引
label_seg[np.all(img == list(mask_labels.iloc[i, [1,2,3]]), axis=-1)] = i
label_seg = label_seg[:,:,0]
输出 label_seg:
array([[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 2, 1, 0],
[0, 0, 0, 0, 0]], dtype=uint8)
img（作为图像，每个单元格是一个像素）：
mask_labels：
0 1 2 3
0 0 58 193 230
1 0 127 32 31
2 0 193 9 185
作为颜色：
英文:
My (wild) guess, since your didn't provide an example of input.
This code takes a 3 channels image img (e.g. RGB) and compares it against known colors in a DataFrame mask_labels, then assigns the index of the color in a new array label_seg to identify the matches.
I believe there are some mistakes in the code:
- the last line shouldn't be part of the for loop
- the first row/color of the DataFrame is mapped to
0, which is also the default value in the input.
Example of use:
np.random.seed(0)
img = np.random.randint(0, 255, (5, 5, 3))
mask_labels = pd.DataFrame([[0, 58, 193, 230],
[0, 127, 32, 31],
[0, 193, 9, 185],
])
# generate an output of the same shape as the input image
label_seg = np.zeros(img.shape, dtype=np.uint8)
# for each row in the DataFrame
for i in range(mask_labels.shape[0]):
# take the columns 1,2,3
# if all 3 values match in the img array (same x/y position, all 3 channels)
# assign the row index in the output array at the same x/y position
label_seg[np.all(img == list(mask_labels.iloc[i, [1,2,3]]), axis=-1)] = i
label_seg = label_seg[:,:,0]
Output label_seg:
array([[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 2, 1, 0],
[0, 0, 0, 0, 0]], dtype=uint8)
img (as image, each cell is a pixel):
masked_labels:
0 1 2 3
0 0 58 193 230
1 0 127 32 31
2 0 193 9 185
as colors:
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