I tried to implement the Sobel_X filter in scipy with convolve2d function.
I compared with the results from this function:
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
Here are the Sobel_X Kernel and code to compare.
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()
As you can see here the results are entirely different I can't get how to implement these filters to get the same results. Should I somehow change the filter function or maybe there some special things in numpy to implement it, wright? I tried to make the function for scipy as in this and that examples, but the results the same or worth (I've got black image).
You will get results slightly different. Do thresholding to remove all numbers which are less than 0.
convolved_np[convolved_np<0]=0
That will give you something similar, still not the same. Some artifacts appeared. I think these functions differ, that's why I have got a bit different results. Maybe there are some mistakes, so if you can add some to this answer, I will appreciate it.
