ans = 12x1 Layer array with layers:64 x32x3 imageinput的图像输入图像2”zerocenter“正常化”conv_1卷积20 5 x5x3旋转步[1]和填充[0 0 0 0]3‘relu_1 ReLU ReLU 4“maxpool_1”马克斯池2 x2马克斯池步(2 - 2)和填充[0 0 0 0]5 crossnorm的横通道正常化横通道规范化5频道每个元素6‘conv_2卷积20 5 x5x20旋转步[1]和填充[0 0 0 0]7‘relu_2 ReLU ReLU 8“maxpool_2”马克斯池2 x2 Max池步(2 - 2)和填充[0 0 0 0]9“fc_1”完全连接512完全连接层10“fc_2”完全连接2完全连接层11的softmax softmax softmax 12“classoutput”具有类'NonPed'和'Ped'的输出交联

版权所有2017-2019 The MathWorks, Inc. codefun;持久pednet;if isempty(pednet) pednet = coder.loadDeepLearningNetwork(coder.const(' pednet .mat')，'行人检测');end [imgHt, imgWd， ~] = size(img);VrHt = [imgHt - 30, imgHt];% patchHt和patchWd是从热图中得到的(这里的热图是指%行人数据，以不同的%颜色的地图形式表示。不同的颜色表示在不同的百分比范围内存在行人)。patchHt = 300;patchWd = patchHt / 3;% PatchCount用于估计每个图像的补丁数PatchCount = ((imgWd - patchWd)/20) + 2; maxPatchCount = PatchCount * 2; Itmp = zeros(64 , 32 , 3 , maxPatchCount); ltMin = zeros(maxPatchCount); lttop = zeros(maxPatchCount); idx = 1; % To count number of image patches obtained from sliding window cnt = 1; % To count number of patches predicted as pedestrians bbox = zeros(maxPatchCount , 4); value = zeros(maxPatchCount , 1); %% Region proposal for two bands for VrStride = 1 : 2 for HrStride = 1 : 20 : (imgWd - 60) % Obtain horizontal patches with stride 20. ltMin(idx) = HrStride + 1; rtMax = min(ltMin(idx) + patchWd , imgWd); lttop(idx) = (VrHt(VrStride) - patchHt); It = img(lttop(idx): VrHt(VrStride) , ltMin(idx) : rtMax , :); Itmp(:,:,:,idx) = imresize(It,[64,32]); idx = idx + 1; end end for j = 1 : size (Itmp,4) score = pednet.predict(Itmp(:,:,:,j)); % Classify ROI % accuracy of detected box should be greater than 0.90 if (score(1,2) > 0.80) bbox(cnt,:) = [ltMin(j),lttop(j), patchWd , patchHt]; value(cnt,:) = score(1,2); cnt = cnt + 1; end end %% NMS to merge similar boxes if ~isempty(bbox) [selectedBbox,~] = selectStrongestBbox(bbox(1:cnt-1,:),... value(1:cnt-1,:),'OverlapThreshold',0.002); end

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