Question

我尝试使用convolve2d函数在scipy中实现Sobel_X过滤器。

我将此函数的结果与之比较：

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

这里是Sobel_X内核和要比较的代码。

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()

您可以看到here的结果完全不同我不知道如何实现这些过滤器来获得相同的结果。我应该以某种方式更改过滤器功能，还是在numpy中执行一些特殊操作，好吗？我尝试像this和that示例中那样使scipy函数，但是结果相同或值得（我有黑色图像）。

Answer 1

您将获得略有不同的结果。设置阈值以删除所有小于0的数字。

convolved_np[convolved_np<0]=0

那会给你类似的东西，但还是不一样。一些工件appeared。我认为这些功能不同，这就是为什么我得到一些不同的结果的原因。也许有一些错误，所以如果您可以在此答案中添加一些内容，我将不胜感激。

Sobel滤波器在Scipy中的实现

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