The method can also feel their own angle rewritten



import cv2 as cv
import numpy as np
import matplotlib.pyplot as plt
import math

img = cv.imread(r'C:\Users\51102\Desktop\tradition\1.jpg',0)
img = cv.cvtColor(img, cv.COLOR_BGR2RGB)
img = np.dot(img[..., :3], [0.299, 0.587, 0.114])
plt.subplot(331)
plt.imshow(img,cmap='gray')
plt.title('original_img')
print('original.shape=',img.shape)

# 构建梯度
gradient_x=np.array([[-1,0,1],[-2,0,2],[-1,0,1]])
gradient_y=np.array([[-1,-2,-1],[0,0,0],[1,2,1]])

plt.subplot(332)

dx=cv.filter2D(img,ddepth=-1,kernel=gradient_x)
print('dx.shape=',dx.shape)
plt.imshow(dx,cmap='gray')
plt.title('dx')
plt.subplot(333)
cv.filter2D = Dy (IMG, -1, gradient_x) 
Print ( 'dy.shape =', dy.shape) 
plt.imshow (Dy, CMap = 'Gray') 
plt.title ( 'Dy') 




# Directional 


plt .subplot (334) 
Magnitude = (np.multiply (Dy, Dy) + np.multiply (DX, DX)) 
plt.imshow (Magnitude, CMap = 'Gray') 
plt.title ( 'Magnitude') 



CITA = CV. phase (dy, dx, angleInDegrees = True) # find the angle 


# generated Gaussian filter 
"" " 
Gaussian filter to generate a (2k + 1) x (2k + 1) , the individual elements of the filter is calculated as follows: 
H [i, j] = (1 / (2 * pi * sigma ** 2)) * exp (-1 / 2 * sigma ** 2 ((ik-1) ** 2 + (jk-1) ** 2 )) 
"" " 
Sigma- 1 = Sigma2 = 0.6 
gau_sum = 0 
Gaussian np.zeros = ([. 5,. 5]) 
for I in Range (. 5): 
    for J in Range (. 5):
        gaussian [i, j] = math.exp ((- 1 / (2 * sigma1 * sigma2)) * (np.square (i-2-1) + np.square (j-2-1))) / ( Math.PI Sigma- 1 * * * 2 Sigma2) 
        gau_sum = gau_sum Gaussian + [I, J] 
Gaussian = Gaussian / gau_sum # normalized 

Print ( '= Guss', gaussian.shape) 

W is, H = original img.shape # grayscale image size 
_, G = gaussian.shape 
new_gray np.zeros = ([W is-. 5,. 5-H]) 
new_magnitude np.zeros = ([W is-. 5,. 5-H]) 

for I in Range (W is -5): 
    for J in Range (. 5-H): 
        '' ' 
        filtering my own attempt a method, according to the angle to reconfigure the weights 
        I to reduce the amplitude of the larger gradient of the gradient direction can be suppressed, other strengthening direction 
        '' ' 
        Guss = Gaussian 
        G_ = int ((. 1-G) / 2) 

        IF 0 <CITA [I, J] <90 or 180 [<CITA [I, J] <270 or -180 <CITA [I , j] <- 90 or -360 <cita [i,j]<-270:
            for k in range(g_):
                for kk in range(g_):
                    guss[g_+1+k,g_+1+kk]=gaussian[g_+1+k,g_+1+kk]/4
                    guss[g_-1 - k, g_ - 1 - kk] = gaussian[g_ - 1 - k, g_ - 1 - kk] / 4
            if 90 < cita[i, j] < 180 or 270 < cita[i, j] < 360 or -90 < cita[i, j] < 0 or -180 < cita[i, j] < -270:
                for k in range(g_):
                    for kk in range(g_):
                        guss[g_ + 1 + k, g_ - 1 - kk] = gaussian[g_ + 1 + k, g_ - 1 - kk] / 4
                        guss[g_ - 1 - k, g_ + 1 + kk] = gaussian[g_ - 1 - k, g_ + 1 + kk] / 4
            new_gray[i, j] = np.sum(img[i:i+5, j:j+5] * guss)
            new_magnitude[i, j] = np.sum(magnitude[i:i + 5, j:j + 5] * guss)








plt.subplot(335)
plt.imshow(new_gray,cmap='gray')
plt.title('new_gray')

plt.subplot(336)
plt.imshow(new_magnitude,cmap='gray')
plt.title('new_magnitude')




plt.show()
The method can also feel their own angle rewritten

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