import cv2
import numpy as np
fn = "/home/kirk/Pictures/cat.jpg"
myimg = cv2.imread(fn)
img = cv2.cvtColor(myimg, cv2.COLOR_BGR2GRAY)
# img = myimg
# 加上椒盐噪声
# 灰阶范围
w = img.shape[1]
h = img.shape[0]
newimg = np.array(img)
# 噪声点数量
noisecount = 10000
for k in range(noisecount):
xi = int(np.random.uniform(0, newimg.shape[1]))
xj = int(np.random.uniform(0, newimg.shape[0]))
newimg[xj, xi] = 255
def get_mid(nump):
l = nump.flatten()
arg = sorted(enumerate(l), key=lambda x:x[1])
return l[arg[int(len(arg)/2)][0]]
def get_mid_bgr(nump):
gray = cv2.cvtColor(nump, cv2.COLOR_BGR2GRAY)
l = gray.flatten()
arg = sorted(enumerate(l), key=lambda x:x[1])
bgrl = nump.flatten()
bgrl = np.reshape(bgrl, [-1, 3])
return bgrl[arg[int(len(arg)/2)+1][0]]
def medianBlur(img, window_size):
if window_size <= 0:
return -1
img_copy = np.copy(img)
step = int(window_size / 2)
img_copy = np.pad(img_copy, ((step, step), (step, step)), mode='edge')
for i in range(step, img_copy.shape[0] - step):
for j in range(step, img_copy.shape[1] - step):
# img_copy[i][j] = get_mid_bgr(img[i-step:i+step+1, j-step:j+step+1])
img_copy[i][j] = get_mid(img[i - step:i + step + 1, j - step:j + step + 1])
return img_copy[step:-step, step:-step]
# 滤波去噪
cvimg = cv2.medianBlur(newimg, 3)
primg = medianBlur(newimg, 3)
cv2.imshow('src', newimg)
cv2.imshow('cvdst', cvimg)
cv2.imshow('prdst', primg)
cv2.imshow('org', myimg)
cv2.waitKey()
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