强化学习例分享，应用强化学习分析

clear all %经典的Reinhard颜色迁移算法 close all clc input = im2double(imread('p5.jpg')); target = im2double(imread('p8.jpg')); input_temp = zeros(3,1); %创建一个三行一列的零矩阵 target_temp = zeros(3,1); thresh = -10; % rgb2lab = makecform('srgb2lab'); % lab2rgb = makecform('lab2srgb'); a = 1/sqrt(3); b = 1/sqrt(6); c = 1/sqrt(2); %transformation matrices 变换矩阵 rgb2lms_ratio = [0.3811 0.5783 0.0402; 0.1967 0.7244 0.0782; 0.0241 0.1288 0.8444]; lms2rgb_ratio = [4.4679 -3.5873 0.1193; -1.2186 2.3809 -0.1624; 0.0497 -0.2439 1.2045]; lms2lab_Ratio = [a 0 0; 0 b 0; 0 0 c]*[1 1 1; 1 1 -2; 1 -1 0]; lab2lms_Ratio = [1 1 1; 1 1 -1; 1 -2 0]*[a 0 0; 0 b 0; 0 0 c]; %converting RGB color space to uncorelated lab color space for the source 将RGB转化为lab [m n k] = size(input); in_lab = zeros(m,n,k); % in_lab = applycform(input,rgb2lab); for i = 1 : 1 : m for j = 1 : 1 : n input_temp(1,1) = double(input(i,j,1)); input_temp(2,1) = double(input(i,j,2)); input_temp(3,1) = double(input(i,j,3)); input_temp = rgb2lms_ratio*input_temp; input_temp = log10(input_temp); input_temp = lms2lab_Ratio*input_temp; ind1 = find(isnan(input_temp)); ind2 = find(input_temp < thresh); input_temp(ind1)= thresh; input_temp(ind2)= thresh; in_lab(i,j,:) = input_temp; end end %calculating mean and standard deviation for the source separately for each %channel 为每个通道分别计算源的平均值和标准偏差 mean_l_in = mean(mean(in_lab(:,:,1))); mean_a_in = mean(mean(in_lab(:,:,2))); mean_b_in = mean(mean(in_lab(:,:,3))); sig_l_in = sqrt(var(double(reshape(in_lab(:,:,1),m*n,1)))); sig_a_in = sqrt(var(double(reshape(in_lab(:,:,2),m*n,1)))); sig_b_in = sqrt(var(double(reshape(in_lab(:,:,3),m*n,1)))); %converting RGB color space to uncorelated lab color space for the target [m n k] = size(target); tar_lab = zeros(m,n,k); % tar_lab = applycform(target,rgb2lab); for i = 1 : 1 : m for j = 1 : 1 : n target_temp(1,1) = double(target(i,j,1)); target_temp(2,1) = double(target(i,j,2)); target_temp(3,1) = double(target(i,j,3)); target_temp = rgb2lms_ratio*target_temp; target_temp = log10(target_temp); target_temp = lms2lab_Ratio*target_temp; ind1 = find(isnan(target_temp)); ind2 = find(target_temp < thresh); target_temp(ind1)= thresh; target_temp(ind2)= thresh; tar_lab(i,j,:) = target_temp; end end %calculating mean and standard deviation for the target separately for each %channel mean_l_tar = mean(mean(tar_lab(:,:,1))); mean_a_tar = mean(mean(tar_lab(:,:,2))); mean_b_tar = mean(mean(tar_lab(:,:,3))); sig_l_tar = sqrt(var(double(reshape(tar_lab(:,:,1),m*n,1)))); sig_a_tar = sqrt(var(double(reshape(tar_lab(:,:,2),m*n,1)))); sig_b_tar = sqrt(var(double(reshape(tar_lab(:,:,3),m*n,1)))); %perfomring color transfer as color process in lab color space [m n k] = size(input); % lab_res = zeros(m,n,k); lab_res = in_lab;%结构图象lab空间的值赋给lab_res lab_res(:,:,1) = in_lab(:,:,1) - mean_l_in; lab_res(:,:,2) = in_lab(:,:,2) - mean_a_in; lab_res(:,:,3) = in_lab(:,:,3) - mean_b_in; lab_res(:,:,1) = (sig_l_tar/sig_l_in).*lab_res(:,:,1); lab_res(:,:,2) = (sig_a_tar/sig_a_in).*lab_res(:,:,2); lab_res(:,:,3) = (sig_b_tar/sig_b_in).*lab_res(:,:,3); lab_res(:,:,1) = lab_res(:,:,1) + mean_l_tar; lab_res(:,:,2) = lab_res(:,:,2) + mean_a_tar; lab_res(:,:,3) = lab_res(:,:,3) + mean_b_tar; for i = 1:1:m for j = 1:1:n for l = 1:1:k if isnan(lab_res(i,j,l)) lab_res(i,j,l) = in_lab(i,j,l); end end end end %converting the result back to RGB color space to display 将结果转换回RGB颜色空间以显示 [m n k] = size(input); fin_res = zeros(m,n,k); % fin_res = applycform(lab_res,lab2rgb); for i = 1 : 1 : m for j = 1 : 1 : n input_temp(1,1) = double(lab_res(i,j,1)); input_temp(2,1) = double(lab_res(i,j,2)); input_temp(3,1) = double(lab_res(i,j,3)); input_temp = lab2lms_Ratio*input_temp; input_temp = 10.^input_temp; fin_res(i,j,:) = lms2rgb_ratio*input_temp; end end figure subplot(1,3,1); imshow(input); subplot(1,3,2); imshow(target); subplot(1,3,3); imshow(uint8(fin_res));