自己根据角度改写的方法 感觉还可以
作者:互联网
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)
dy=cv.filter2D(img,-1,gradient_x)
print('dy.shape=',dy.shape)
plt.imshow(dy,cmap='gray')
plt.title('dy')
# 求方向
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) # 求角度
# 生成高斯滤波器
"""
要生成一个 (2k+1)x(2k+1) 的高斯滤波器,滤波器的各个元素计算公式如下:
H[i, j] = (1/(2*pi*sigma**2))*exp(-1/2*sigma**2((i-k-1)**2 + (j-k-1)**2))
"""
sigma1 = 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)))/(2*math.pi*sigma1*sigma2)
gau_sum = gau_sum + gaussian[i, j]
gaussian = gaussian / gau_sum # 归一化处理
print('guss=',gaussian.shape)
W, H = img.shape # 原始图片大小 灰度图
_,g=gaussian.shape
new_gray = np.zeros([W-5, H-5])
new_magnitude = np.zeros([W-5, H-5])
for i in range(W-5):
for j in range(H-5):
'''
滤波处理 我自创一种方法尝试,根据角度,来重新配置权重
我先降低梯度较大的幅度,对梯度方向进行抑制,其它方向进行加强
'''
guss=gaussian
g_=int((g-1)/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()
标签:gray,plt,img,kk,gaussian,改写,角度,感觉,np 来源: https://www.cnblogs.com/tangjunjun/p/12514660.html