在此实现中的立体声差距块的%%修改算法匹配％，而不是查找移位图像，索引是％映射以节省内存和一些处理。RGBA专栏主要填充数据用作与CUDA内在兼容性的输入。使用可分离滤波器（水平和％垂直）来执行％卷积。函数[out_disp] =立体探测器（IMG0，IMG1）％＃codegen％copyright 2017-2019 MathWorks，Inc.％GPU代码生成Pragma Coder.gpu.kernelfun;%%立体声差距参数％| Win_rad |是待操作窗的半径。| min_disparity |％是搜索继续的最低差异级别。| max_disparity |PAURION继续的最大差异级别是％的。 WIN_RAD = 8; min_disparity = -16; max_disparity = 0; %% Image Dimensions for Loop Control % The number of channels packed are 4 (RGBA) so as nChannels are 4. [imgHeight,imgWidth]=size(img0); nChannels = 4; imgHeight = imgHeight/nChannels; %% Store the Raw Differences diff_img = zeros([imgHeight+2*WIN_RAD,imgWidth+2*WIN_RAD],'int32'); % Store the minimum cost min_cost = zeros([imgHeight,imgWidth],'int32'); min_cost(:,:) = 99999999; % Store the final disparity out_disp = zeros([imgHeight,imgWidth],'int16'); %% Filters for Aggregating the Differences % |filter_h| is the horizontal filter used in separable convolution. % |filter_v| is the vertical filter used in separable convolution which % operates on the output of the row convolution. filt_h = ones([1 17],'int32'); filt_v = ones([17 1],'int32'); % Main Loop that runs for all the disparity levels. This loop is % expected to run on CPU. for d=min_disparity:max_disparity % Find the difference matrix for the current disparity level. Expect % this to generate a Kernel function. coder.gpu.kernel; for colIdx=1:imgWidth+2*WIN_RAD coder.gpu.kernel; for rowIdx=1:imgHeight+2*WIN_RAD % Row index calculation. ind_h = rowIdx - WIN_RAD; % Column indices calculation for left image. ind_w1 = colIdx - WIN_RAD; % Row indices calculation for right image. ind_w2 = colIdx + d - WIN_RAD; % Border clamping for row Indices. if ind_h <= 0 ind_h = 1; end if ind_h > imgHeight ind_h = imgHeight; end % Border clamping for column indices for left image. if ind_w1 <= 0 ind_w1 = 1; end if ind_w1 > imgWidth ind_w1 = imgWidth; end % Border clamping for column indices for right image. if ind_w2 <= 0 ind_w2 = 1; end if ind_w2 > imgWidth ind_w2 = imgWidth; end % In this step, Sum of absolute Differences is performed % across tour channels. tDiff = int32(0); for chIdx = 1:nChannels tDiff = tDiff + abs(int32(img0((ind_h-1)*(nChannels)+chIdx,ind_w1))-int32(img1((ind_h-1)*(nChannels)+chIdx,ind_w2))); end % Store the SAD cost into a matrix. diff_img(rowIdx,colIdx) = tDiff; end end % Aggregating the differences using separable convolution. Expect this % to generate two kernels using shared memory.The first kernel is the % convolution with the horizontal kernel and second kernel operates on % its output the column wise convolution. cost_v = conv2(diff_img,filt_h,'valid'); cost = conv2(cost_v,filt_v,'valid'); % This part updates the min_cost matrix with by comparing the values % with current disparity level. for ll=1:imgWidth for kk=1:imgHeight % load the cost temp_cost = int32(cost(kk,ll)); % Compare against the minimum cost available and store the % disparity value. if min_cost(kk,ll) > temp_cost min_cost(kk,ll) = temp_cost; out_disp(kk,ll) = abs(d) + 8; end end end end end

代码生成成功：要查看报告，请打开（'codegen / mex / stereodisodisparity / html / export.mldatx'）。

代码生成成功：要查看报告，请打开（'codegen / mex / stereodisparityhalfprecision / html / eport.mldatx'）。