麻点产品 [英] Numpy dot product

问题描述

我有一个用Numpy数组表示的图像，即每个像素都是一个数组[r，g，b].现在，我想使用矩阵乘法并尝试不使用循环将其转换为YUV.

I have an image represented with a Numpy array, i.e. each pixel is an array [r,g,b]. Now, I want to convert it in YUV using matrix multiplication and trying not to use loops.

self.yuv=self.rgb
self.yuv=dot([[   0.299,  0.587,    0.114  ],
              [-0.14713, -0.28886,  0.436  ],
              [   0.615, -0.51499, -0.10001]], 
             self.yuv[:,:])

我得到了错误-对象未对齐.我猜这是因为self.yuv [i，j]不是垂直向量.转置无济于事.

I get the error - objects not aligned. I guess that's because self.yuv[i,j] is not a vertical vector. transpose doesn't help.

有什么想法吗?

推荐答案

您的矩阵的形状为(3，3)，而您的图像的形状为(rows，cols，3)和 np.dot 进行在a的最后一个轴和b的倒数第二个轴上的和积".

Your matrix has shape (3, 3) while your image has shape (rows, cols, 3) and np.dot does "a sum product over the last axis of a and the second-to-last of b."

最简单的解决方案是反转 np.dot 中操作数的顺序并转置转换矩阵:

The simplest solution is to reverse the order of the operands inside np.dot and transpose your conversion matrix:

rgb2yuv = np.array([[0.299, 0.587, 0.114],
                    [-0.14713, -0.28886, 0.436],
                    [0.615, -0.51499, -0.10001]])
self.yuv = np.dot(self.rgb, rgb2yuv.T)

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