Sobel滤波器在Sobel中的实现

Question

我尝试用Sobel_X函数实现convolve2d过滤器。

我将此函数的结果进行了比较：

from scipy.signal import convolve2d 
from scipy import misc
from skimage.exposure import rescale_intensity
import cv2
import numpy as np
#https://www.pyimagesearch.com/2016/07/25/convolutions-with-opencv-and-python/ 


def convolve(image, kernel):
    # grab the spatial dimensions of the image, along with
    # the spatial dimensions of the kernel
    (iH, iW) = image.shape[:2]
    (kH, kW) = kernel.shape[:2]
#         print("Kh,Kw", kernel.shape[:2])

    # allocate memory for the output image, taking care to
    # "pad" the borders of the input image so the spatial
    # size (i.e., width and height) are not reduced
    pad = (kW - 1) // 2
#         print("pad", pad)
    image = cv2.copyMakeBorder(image, pad, pad, pad, pad,
        cv2.BORDER_REPLICATE)
#         self.imshow(image, "padded image")
    output = np.zeros((iH, iW), dtype="float32")
    # loop over the input image, "sliding" the kernel across
    # each (x, y)-coordinate from left-to-right and top to
    # bottom
    for y in np.arange(pad, iH + pad):
        for x in np.arange(pad, iW + pad):
            # extract the ROI of the image by extracting the
            # *center* region of the current (x, y)-coordinates
            # dimensions
            roi = image[y - pad:y + pad + 1, x - pad:x + pad + 1]

            # perform the actual convolution by taking the
            # element-wise multiplicate between the ROI and
            # the kernel, then summing the matrix
            k = (roi * kernel).sum()

            # store the convolved value in the output (x,y)-
            # coordinate of the output image
            output[y - pad, x - pad] = k
#             self.imshow(output, "padded image")
    # rescale the output image to be in the range [0, 255]
    output = rescale_intensity(output, in_range=(0, 255))
    output = (output * 255).astype("uint8")

    # return the output image
    return output

下面是要比较的Sobel_X内核和代码。

sobelX = np.array((
        [-1, 0, 1],
        [-2, 0, 2],
        [-1, 0, 1]), dtype="int")]

testim=misc.face(gray=True)
convolved_func=convolve(testim, sobelX)
convolved_np=convolve2d(testim, sobelX, boundary='symm', mode='same')

cv2.imshow("Face", np.hstack((convolved_func,np.array(convolved_np, dtype="uint8"))))
cv2.waitKey(0)
cv2.destroyAllWindows()

正如您可以看到的，这里的结果是完全不同的，我无法了解如何实现这些过滤器来获得相同的结果。赖特，我应该以某种方式改变过滤器函数，还是在numpy中有一些特殊的东西来实现它呢？我试图像在这和那示例中那样，为scipy创建函数，但是结果是相同的或值的(我有黑色的图像)。

Answer 1

你会得到稍微不同的结果。进行阈值处理以删除所有小于0的数字。

convolved_np[convolved_np<0]=0 

这会给你一些相似的东西，但还是不一样。一些人工制品出现了。我认为这些函数不同，这就是为什么我得到了一些不同的结果。也许有一些错误，所以如果你能在这个答案中加上一些，我会很感激的。