将索引选定的 numpy 数组添加到另一个具有重叠索引的 numpy 数组 [英] Add a index selected numpy array to another numpy array with overlapping indices

问题描述

我有两个 numpy 数组 image 和 warped_image 和索引数组 ix,iy.我需要将 image 添加到 warped_image 以便将 image[i,j] 添加到 warped_image[iy[i,j],ix[i,j]].如果 (iy[i,j], ix[i,j]) 对对于所有 i,j 都是唯一的，则以下代码有效.但是当它们不是唯一的时，即当需要将 image 中的 2 个元素添加到 warped_image 中的同一元素时，只会添加其中一个.如何将 image 中的两个元素添加到 warped_image 中的同一个元素?

I have two numpy arrays image and warped_image and indices arrays ix,iy. I need to add image to warped_image such that image[i,j] is added to warped_image[iy[i,j],ix[i,j]]. The below code works if the pairs (iy[i,j], ix[i,j]) are unique for all i,j. But when they are not unique i.e. when 2 elements from image need to be added to the same element in warped_image, only one of them gets added. How can I add both elements from image to the same element in warped_image?

请注意，我不想使用任何 for 循环.我想保持这个矢量化.我计划将来将代码转换为 TensorFlow 或 PyTorch，以便为此使用 GPU 功能.那是因为，我有数百张这样的图像，每张图像都是全高清分辨率.

Note that, I don't want to use any for loops. I want to keep this vectorized. I'm planning to convert the code to TensorFlow or PyTorch in the future to use GPU capabilities for this. That's because, I have hundreds of such images and each image is of full HD resolution.

import numpy
image = numpy.array([[246,  50, 101], [116,   1, 113], [187, 110,  64]])
iy = numpy.array([[1, 0, 2], [1, 1, 0], [2, 0, 2]])
ix = numpy.array([[0, 2, 1], [1, 2, 0], [0, 1, 2]])
warped_image = numpy.zeros(shape=image.shape)
warped_image[iy, ix] += image

>> warped_image
Out[31]: 
array([[  113., 110.,  50.],
       [246., 116.,   1.],
       [187., 101.,  64.]])
   

对于上述情况，索引是唯一的，因此输出符合预期.

For the above case, indices are unique and hence the output is as expected.

import numpy
image = numpy.array([[246,  50, 101], [116,   1, 113], [187, 110,  64]])
iy = numpy.array([[1, 0, 2], [1, 0, 2], [2, 2, 2]])
ix = numpy.array([[0, 2, 1], [1, 2, 0], [0, 1, 2]])
warped_image = numpy.zeros(shape=image.shape)
warped_image[iy, ix] += image

>> warped_image
Out[32]: 
array([[  0.,   0.,   1.],
       [246., 116.,   0.],
       [187., 110.,  64.]])
   

预期输出:

array([[  0.,   0.,   51.],
       [246., 116.,   0.],
       [300., 211.,  64.]])
       

在这种情况下，有 3 对索引重叠，因此失败.例如.image[0,1] 和 image[1,1] 应该将 gt 添加到 warped_image[0,2] 以给出值 51.然而，只有其中一个 (image[1,1]) 被添加到值 1.

In this case, there are 3 pairs of indices which overlap and hence it fails. E.g. image[0,1] and image[1,1] should gt added to warped_image[0,2] to give a value 51. However only one of them (image[1,1]) gets added to give a value 1.

上下文:
我正在尝试将图像从 view1 扭曲到 view2.我已经计算出哪个像素必须去哪里.在重叠像素的情况下，我需要对它们进行加权平均.所以，我需要实现上述目标.更多详情此处

推荐答案

使用 numpy.add.at:

import numpy
image = numpy.array([[246,  50, 101], [116,   1, 113], [187, 110,  64]])
iy = numpy.array([[1, 0, 2], [1, 0, 2], [2, 2, 2]])
ix = numpy.array([[0, 2, 1], [1, 2, 0], [0, 1, 2]])
warped_image = numpy.zeros(shape=image.shape)

np.add.at(warped_image, (iy, ix), image)

print(warped_image)

输出

[[  0.   0.  51.]
 [246. 116.   0.]
 [300. 211.  64.]]

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