OpenCV+Tensorflow 人工智能图像处理（三十八）——高斯均值滤波、中值滤波

#高斯均值
#用滤波核和图像进行卷积运算
import cv2
import numpy as np
img = cv2.imread('car.jpg', 1)   #读取图片
#dst = cv2.GaussianBlur(img, (5, 5), 1, 5)  #直接高斯滤波
cv2.imshow('src', img)
imgInfo = img.shape  #维度信息
height = imgInfo[0]
width = imgInfo[1]
dst = np.zeros((height, width, 3), np.uint8)
for i in range(3, height-3):     #模板6*6
    for j in range(3, width-3):
        (b, g, r) = img[i, j]
        sum_b = int(0)
        sum_g = int(0)
        sum_r = int(0)
        for m in range(-3, 3):
            for n in range(-3, 3):
                (b, g, r) = img[i+m, j+n]
                sum_b = sum_b + int(b)
                sum_g = sum_g + int(g)
                sum_r = sum_r + int(r)
        b = np.uint8(sum_b/36)   #36个像素除以36
        g = np.uint8(sum_g / 36)
        r = np.uint8(sum_r / 36)
        dst[i, j] = (b, g, r)
cv2.imshow('dst', dst)
cv2.waitKey(0)

#中值
#中值滤波：中间值代替原来像素值
import cv2
import numpy as np
img = cv2.imread('car.jpg', 1)   #读取图片
imgInfo = img.shape  #维度信息
height = imgInfo[0]
width = imgInfo[1]
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv2.imshow('src', img)
dst = np.zeros((height, width, 3), np.uint8)
collect = np.zeros(9, np.uint8)
for i in range(1, height-1):   #防止越界
    for j in range(1, width-1):
        k = 0
        for m in range(-1, 2):   #模板3*3
            for n in range(-1, 2):
                gray = img[i+m, j+n]
                collect[k] = gray
                k = k + 1
        for k in range(0, 9):
            p1 = collect[k]
            for t in range(k+1, 9):   #排序
                if p1 < collect[t]:
                    mid = collect[t]
                    collect[t] = p1
                    p1 = mid
        dst[i, j] = collect[4]
cv2.imshow('dst', dst)
cv2.waitKey(0)

 

 

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