我试图用convolve2d函数在scipy中实现Sobel_X过滤器。
我比较了这个函数的结果：
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
以下是要比较的内核和代码Sobel_X。
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()
正如你在这里看到的，结果完全不同 我无法了解如何实现这些过滤器以获得相同的结果。 我应该以某种方式更改过滤器功能，或者 numpy 中有一些特殊的东西来实现它，赖特？ 我试图像这个和那个例子一样为 scipy 制作函数，但结果相同或值得(我有黑色图像(。
你会得到略有不同的结果。 执行阈值以删除所有小于 0 的数字。
convolved_np[convolved_np<0]=0
那会给你一些类似的东西，但仍然不一样。出现了一些文物。 我认为这些功能不同，这就是为什么我得到的结果有点不同。也许有一些错误，所以如果你能在这个答案中添加一些，我将不胜感激。