快速引导滤波python

快速引导滤波python"/>

快速引导滤波python

# -*- coding: utf-8 -*-
import cv2
import numpy as np
import sysdef integral(image):rows,cols = image.shape#行积分运算inteImageC = np.zeros((rows,cols),np.float32)for r in range(rows):for c in range(cols):if c == 0:inteImageC[r][c] = image[r][c]else:inteImageC[r][c] = inteImageC[r][c-1] + image[r][c]#列积分运算inteImage = np.zeros(image.shape,np.float32)for c in range(cols):for r in range(rows):if r == 0:inteImage[r][c] = inteImageC[r][c]else:inteImage[r][c] = inteImage[r-1][c] + inteImageC[r][c]#为了在快速均值平滑使用中省去判断边界的问题#上边和左边进行补零inteImage_0 = np.zeros((rows+1,cols+1),np.float32)inteImage_0[1:rows+1,1:cols+1] = inteImagereturn inteImage_0
#快速均值平滑：返回数组的数据类型是浮点型，winSize = ( 高，宽 )
def fastMeanBlur(image,winSize,borderType = cv2.BORDER_DEFAULT):halfH = (winSize[0]-1)//8halfW = (winSize[1]-1)//8ratio = 1.0/(winSize[0]*winSize[1])#边缘扩充paddImage = cv2.copyMakeBorder(image,halfH,halfH,halfW,halfW,borderType)#图像积分paddIntegral = integral(paddImage)#图像的宽高rows,cols = image.shape#均值滤波后的结果meanBlurImage = np.zeros(image.shape,np.float32)r,c = 0,0for h in range(halfH,halfH+rows,1):for w in range(halfW,halfW+cols,1):meanBlurImage[r][c] = (paddIntegral[h+halfH+1][w+halfW+1] + paddIntegral[h-halfH][w-halfW]-paddIntegral[h+halfH+1][w-halfW]-paddIntegral[h-halfH][w+halfW+1])*ratioc+=1r+=1c=0return meanBlurImage
#导向滤波
def guidedFilter(I,p,winSize,eps):#输入图像的宽高rows,cols = I.shape# I 的均值平滑mean_I = fastMeanBlur(I,winSize,cv2.BORDER_DEFAULT)# p 的均值平滑mean_p = fastMeanBlur(p,winSize,cv2.BORDER_DEFAULT)# I.*p 的均值平滑Ip = I*pmean_Ip = fastMeanBlur(Ip,winSize,cv2.BORDER_DEFAULT)#协方差cov_Ip = mean_Ip - mean_I*mean_pmean_II = fastMeanBlur(I*I,winSize,cv2.BORDER_DEFAULT)#方差var_I = mean_II - mean_I*mean_Ia = cov_Ip/(var_I+eps)b = mean_p - a*mean_I# 对 a 和 b进行均值平滑mean_a = fastMeanBlur(a,winSize,cv2.BORDER_DEFAULT)mean_b = fastMeanBlur(b,winSize,cv2.BORDER_DEFAULT)q = mean_a*I + mean_breturn q
#主函数
if __name__ =="__main__":# if len(sys.argv) > 1:#     I = cv2.imread(sys.argv[1],cv2.CV_LOAD_IMAGE_COLOR)#     p = cv2.imread(sys.argv[2],cv2.CV_LOAD_IMAGE_GRAYSCALE)# else:#     print "Usge:python guidedFilter.py imageFile"I = cv2.imread("fog1.jpg")p = I#将图像归一化image_0_1 = I/255.0p = p/255.0#显示原图cv2.imshow("image_0_1",image_0_1)#导向滤波result = np.zeros(I.shape)result[:,:,0] = guidedFilter(image_0_1[:,:,0],image_0_1[:,:,0],(17,17),pow(0.2,2.0))result[:,:,1] = guidedFilter(image_0_1[:,:,1],image_0_1[:,:,1],(17,17),pow(0.2,2.0))result[:,:,2] = guidedFilter(image_0_1[:,:,2],image_0_1[:,:,2],(17,17),pow(0.2,2.0))cv2.imshow("guidedFilter",result)#保存导向滤波的结果result = result*255result[result>255] = 255result = np.round(result)result = result.astype(np.uint8)cv2.imwrite("guidedFilter.jpg",result)cv2.waitKey(0)cv2.destroyAllWindows()