空域滤波学习

空域计算——2-D卷积（滤波）
直接2-D卷积方法：

1. 图像矩阵与卷积核
2. 乘积运算：对于图像的每一像素点，计算它的领域像素和卷积核的对应元素的乘积
3. 加法运算：将每项乘积相加，作为该像素位置的值。

空域滤波器

高斯滤波
采用高斯模板的空间卷积

掩膜
ROI(region of interest)感兴趣区域

import cv2
import numpy as np
def roi_mask(img,vertices):
mask = np.zeros_like(img)
if len(img.shape) > 2:
channel_count = img.shape[2]
mask_color = (255,)*channel_count
else:
mask_color = 256
cv2.fillPoly(mask,verticles,mask_color)
masked_img = cv2.bitwise_and(img,mask)
return masked_img
def showing(name,img):
cv2.imshow(name,img)
cv2.waitKey(0)
cv2.destroyAllWindows()
img = cv2.imread('imagelane.jpg')
# 灰度图像 img = cv2.cvtColor(img,cv2.COLOR_BGR2_GRAY)
> 这里是引用
h = image.shape[0]
w = image.shape[1]
roi_vtx = np.array([[(0,h),(260,325),(520,325),(w,h)]])
img_mask = roi_mask(img,roi_vtx)
showing('orgimg',img)
showing('newimg',img_mask)

空域滤波

import cv2
import matplotlib.pyplot as plt
import numpy as np
import math
def myfilter(img,kernel):
h = img.shape[0]
w = img.shape[1]
img1 = np.zeros((h,w),np.int8)
for i in range(1,h-1):
for j in range(1,w-1):
sum = 0
for k in range(-1,2):
for l in range(-1,2):
sum += img[i+k,j+l]*kernel[k+1,l+1]
img[i,j] = sum
return img1
img = np.array([[1,0,0,0,0,1],
[0,1,0,0,1,0],
[0,0,1,1,0,0],
[1,0,0,0,1,0],
[0,1,0,0,1,0],
[0,0,1,0,1,0]])
kernel = np.array([[1,-1, -1],
[-1,1, -1],
[-1,-1, 1]])
filtered_img = myfilter(img,kernel)
print(filtered_img)

图像空域滤波

import cv2
import matplotlib.pyplot as plt
import numpy as np
import math
def myfilter(img,kernel):
h = img.shape[0]
w = img.shape[1]
img1 = np.zeros((h,w),np.uint8)
for i in range(1,h-1):
for j in range(1,w-1):
sum = 0
for k in range(-1,2):
for l in range(-1,2):
sum += img[i+k][j+l]*kernel[i+1][j+1]
img1[i][j] = sum
return img1
def show(img):
cv2.imshow('img',img)
cv2.waitKey(0)
cv2.destroyAllWindows()

高斯函数中
sigma越小，图像越尖锐
sigma越大，图像越平缓

最后

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