基于python实现经典去雾算法

#!/usr/bin/env python # -*- coding: utf-8 -*- from PIL import Image def filter2d(input_img, filter, frame): """filter of the 2-dimension picture""" size = len(input_img), len(input_img[0]) output = [] for i in xrange(size[0]): temp = [] for j in xrange(size[1]): temp.append(filter(input_img, (i, j), frame)) output.append(temp) return output def minimizeFilter(input_img, point, size): """minimize filter for the input image""" begin = (point[0] - size[0] / 2, point[0] + size[0] / 2 + 1) end = (point[1] - size[1] / 2, point[1] + size[1] / 2 + 1) l = [] for i in xrange(*begin): for j in xrange(*end): if (i >= 0 and i < len(input_img)) and (j >= 0 and j < len(input_img[0])): l.append(input_img[i][j]) return min(l) def convertImageToMatrix(image): size = image.size out = [] for x in xrange(size[1]): temp = [] for y in xrange(size[0]): temp.append(image.getpixel((y, x))) out.append(temp) return out def boxFilter(im, radius): """box filter for the image of the radius""" height, width = len(im), len(im[0]) imDst = [] imCum = [] for x in xrange(height): imDst.append([0.0] * width) imCum.append([0.0] * width) #cumulative sum over Y axis for i in xrange(width): for j in xrange(height): if j == 0: imCum[j][i] = im[j][i] else: imCum[j][i] = im[j][i] + imCum[j - 1][i] #difference over Y axis for j in xrange(radius + 1): for i in xrange(width): imDst[j][i] = imCum[j + radius][i] for j in xrange(radius + 1, height - radius): for i in xrange(width): imDst[j][i] = imCum[j + radius][i] - imCum[j - radius - 1][i] for j in xrange(height - radius, height): for i in xrange(width): imDst[j][i] = imCum[height - 1][i] - imCum[j - radius - 1][i] #cumulative sum over X axis for j in xrange(height): for i in xrange(width): if i == 0: imCum[j][i] = imDst[j][i] else: imCum[j][i] = imDst[j][i] + imCum[j][i - 1] #difference over X axis for j in xrange(height): for i in xrange(radius + 1): imDst[j][i] = imCum[j][i + radius] for j in xrange(height): for i in xrange(radius + 1, width - radius): imDst[j][i] = imCum[j][i + radius] - imCum[j][i - radius - 1] for j in xrange(height): for i in xrange(width - radius, width): imDst[j][i] = imCum[j][width - 1] - imCum[j][i - radius - 1] return imDst def dot(matrix1, matrix2, operation): """dot operation for the matrix1 and matrix2""" out = [] size = len(matrix1), len(matrix1[0]) for x in xrange(size[0]): temp = [] for y in xrange(size[1]): temp.append(operation(matrix1[x][y], matrix2[x][y])) out.append(temp) return out def guidedFilter(srcImage, guidedImage, radius, epsilon): """guided filter for the image src image must be gray image guided image must be gray image """ size = srcImage.size src = convertImageToMatrix(srcImage) guided = convertImageToMatrix(guidedImage) one = [] for x in xrange(size[1]): one.append([1.0] * size[0]) n = boxFilter(one, radius) plus = lambda x, y: x + y minus = lambda x, y: x - y multiple = lambda x, y: x * y divide = lambda x, y: x / y meanI = dot(boxFilter(src, radius), n, divide) meanP = dot(boxFilter(guided, radius), n, divide) meanIP = dot(boxFilter(dot(src, guided, multiple), radius), n, divide) covIP = dot(meanIP, dot(meanI, meanP, multiple), minus) meanII = dot(boxFilter(dot(src, src, multiple), radius), n, divide) varI = dot(meanII, dot(meanI, meanI, multiple), minus) epsilonMatrix = [] for x in xrange(size[1]): epsilonMatrix.append([epsilon] * size[0]) a = dot(covIP, dot(varI, epsilonMatrix, plus), divide) b = dot(meanP, dot(a, meanI, multiple), minus) meanA = dot(boxFilter(a, radius), n, divide) meanB = dot(boxFilter(b, radius), n, divide) return dot(dot(meanA, src, multiple), meanB, plus)