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python - 与水平内核卷积会产生奇怪的输出

问题描述

所以我正在尝试使用看起来像这样的 3x3 水平内核来执行 2D 图像卷积，

horizontal = np.array([
                      [0, 0, 0], 
                      [-1, 0, 1], 
                      [0, 0, 0]]),

所以我使用下面的卷积函数，我在图像中循环，首先忽略前几个像素（内核的大小）并相乘和相加，

def perform_convolution(k_h, k_w, img_h, img_w, kernel, picture):
    # k_w = kernel width, k_h = kernel_height
    # img_h = image height, img_w = image width

    conv = np.zeros(picture.shape)

    for i in range(k_h, img_h - (k_h)):
        for j in range(k_w, img_w - (k_w)):
            tot = 0
            for m in range(k_h):
                for n in range(k_w):
                    tot = tot + kernel[m][n] * picture[i - (k_h) + m][j - (k_w) + n]
            conv[i][j] = tot
    return conv

但是我得到的输出是完全奇怪的，如下所示

在此处输入图像描述

或者，通过使用 PIL 的内核，我得到了这样一个适当的模糊图像，

在此处输入图像描述

那么谁能帮我弄清楚我哪里出错了？

我已经用盒子内核尝试了相同的功能，它工作得很好，但我无法弄清楚为什么它的输出如此奇怪。

我也尝试过分离 RGB 波段并分别对它们进行卷积，但没有结果。

原图是这个，

在此处输入图像描述

标签： pythonimage-processingpython-imaging-library

解决方案

Firstly the filter you are using is a gradient filter, which in this case is giving values in the range of [-255,255]. Here the main point to consider is that the value you are getting is not just magnitude, you are also pocking the direction of the edge. So, to account for this you can store the phase information i.e 0 degrees for positive value, 180 degrees for a negative value in another image. Finally while visualizing the gradient image, you can simply have a look at the magnitude of the image and know that a phase image exists that determines the direction of the gradient.

Secondly, the slightly smoothened image you are showing in the question can not be created with this kernel.

Also as a rule of thumb, when you want to apply smooth filter always make sure to have the sum of all elements in the kernel as 1.

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