Документация

ans = 

  12x1 Layer array with layers:

     1   'imageinput'    Image Input                   64x32x3 images with 'zerocenter' normalization
     2   'conv_1'        Convolution                   20 5x5x3 convolutions with stride [1  1] and padding [0  0  0  0]
     3   'relu_1'        ReLU                          ReLU
     4   'maxpool_1'     Max Pooling                   2x2 max pooling with stride [2  2] and padding [0  0  0  0]
     5   'crossnorm'     Cross Channel Normalization   cross channel normalization with 5 channels per element
     6   'conv_2'        Convolution                   20 5x5x20 convolutions with stride [1  1] and padding [0  0  0  0]
     7   'relu_2'        ReLU                          ReLU
     8   'maxpool_2'     Max Pooling                   2x2 max pooling with stride [2  2] and padding [0  0  0  0]
     9   'fc_1'          Fully Connected               512 fully connected layer
    10   'fc_2'          Fully Connected               2 fully connected layer
    11   'softmax'       Softmax                       softmax
    12   'classoutput'   Classification Output         crossentropyex with classes 'NonPed' and 'Ped'

function selectedBbox = pedDetect_predict(img)
%#codegen

% Copyright 2017 The MathWorks, Inc.

% function for predicting the pedestrians
% A persistent object pednet is used to load the series network object.
% At the first call to this function, the persistent object is constructed and
% setup. When the function is called subsequent times, the same object is reused 
% to call predict on inputs, thus avoiding reconstructing and reloading the
% network object.

coder.gpu.kernelfun;

persistent pednet;
if isempty(pednet) 
    pednet = coder.loadDeepLearningNetwork(coder.const('PedNet.mat'),'Pedestrian_Detection');
end

[imgHt , imgWd , ~] = size(img);
VrHt = [imgHt - 30 , imgHt]; % Two bands of vertical heights are considered

% patchHt and patchWd are obtained from heat maps (heat map here refers to
% pedestrians data represented in the form of a map with different
% colors. Different colors indicate presence of pedestrians at various
% scales).
patchHt = 300; 
patchWd = patchHt/3;

% PatchCount is used to estimate number of patches per image
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
    

Code generation successful: To view the report, open('codegen/mex/pedDetect_predict/html/report.mldatx').