function [bboxes,scores,labelsIndex] = yolov3Detect(matFile, im，…networkInputSize, networkOutputs, confidenceThreshold，…yolov3Detect函数检测图像中的边界框、分数和% labelsIndex。此示例将所有预处理转换应用到训练期间应用的数据集%，数据增强除外。因为示例%使用预先训练的YOLO v3网络，所以输入数据必须是原始数据的代表性%，并且不进行修改，以便进行无偏评估。具体来说，以下预处理操作应用于输入数据。% 1。将图像调整为网络输入大小，因为图像比networkInputSize大%。2.在%[0 1]范围内缩放图像像素。 3. Convert the resized and rescaled image to a dlarray object. im = dlarray(preprocessData(im, networkInputSize), "SSCB"); imageSize = size(im,[1,2]); %% Define Anchor Boxes % Specify the anchor boxes estimated on the basis of the preprocessed % training data used when training the YOLO v3 network. These anchor box % values are same as mentioned in "Object Detection Using YOLO v3 Deep % Learning" example. For details on estimating anchor boxes, see "Anchor % Boxes for Object Detection". anchors = [ 41 34; 163 130; 98 93; 144 125; 33 24; 69 66]; % Specify anchorBoxMasks to select anchor boxes to use in both the % detection heads of the YOLO v3 network. anchorBoxMasks is a cell array of % size M-by-1, where M denotes the number of detection heads. Each % detection head consists of a 1-by-N array of row index of anchors in % anchorBoxes, where N is the number of anchor boxes to use. Select anchor % boxes for each detection head based on size-use larger anchor boxes at % lower scale and smaller anchor boxes at higher scale. To do so, sort the % anchor boxes with the larger anchor boxes first and assign the first % three to the first detection head and the next three to the second % detection head. area = anchors(:, 1).*anchors(:, 2); [~, idx] = sort(area, 'descend'); anchors = anchors(idx, :); anchorBoxMasks = {[1,2,3],[4,5,6]}; %% Predict on Yolov3 % Predict and filter the detections based on confidence threshold. predictions = yolov3Predict(matFile,im,networkOutputs,anchorBoxMasks); %% Generate Detections % indices corresponding to x,y,w,h predictions for bounding boxes anchorIndex = 2:5; tiledAnchors = generateTiledAnchors(predictions,anchors,anchorBoxMasks,... anchorIndex); predictions = applyAnchorBoxOffsets(tiledAnchors, predictions,... networkInputSize, anchorIndex); [bboxes,scores,labelsIndex] = generateYOLOv3DetectionsForCodegen(predictions,... confidenceThreshold, overlapThreshold, imageSize, classes); end function YPredCell = yolov3Predict(matFile,im,networkOutputs,anchorBoxMask) % Predict the output of network and extract the confidence, x, y, % width, height, and class. % load the deep learning network for prediction persistent net; if isempty(net) net = coder.loadDeepLearningNetwork(matFile); end YPredictions = cell(coder.const(networkOutputs), 1); [YPredictions{:}] = predict(net, im); YPredCell = extractPredictions(YPredictions, anchorBoxMask); % Apply activation to the predicted cell array. YPredCell = applyActivations(YPredCell); end

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function YPredCell = applyactivation (YPredCell) %#codegen %版权所有The MathWorks, Inc. numCells = size(YPredCell, 1);YPredCell{iCell,idx} = sigmoidActivation(YPredCell{iCell,idx});YPredCell{iCell, idx} = exp(YPredCell{iCell, idx});1:numCells YPredCell{iCell, 6} = sigmoidActivation(YPredCell{iCell, 6});end end function out = sigmoidActivation(x) out = 1./(1+exp(-x));结束

The MathWorks, Inc. numPredictionHeads = size(yforecasts, 1);forecasts = cell(numPredictionHeads,6);获取所需的特征大小信息。numChannelsPred = size(yprediction {ii}，3);numAnchors = size(anchorBoxMask{ii}，2);numpredelemsperanchor = numChannelsPred/numAnchors;allIds = (1:numChannelsPred);stride = numpredelemsperanchor;endIdx = numChannelsPred;YPredictionsData = extractdata(yforecasts {ii}); % X positions. startIdx = 1; predictions{ii,2} = YPredictionsData(:,:,startIdx:stride:endIdx,:); xIds = startIdx:stride:endIdx; % Y positions. startIdx = 2; predictions{ii,3} = YPredictionsData(:,:,startIdx:stride:endIdx,:); yIds = startIdx:stride:endIdx; % Width. startIdx = 3; predictions{ii,4} = YPredictionsData(:,:,startIdx:stride:endIdx,:); wIds = startIdx:stride:endIdx; % Height. startIdx = 4; predictions{ii,5} = YPredictionsData(:,:,startIdx:stride:endIdx,:); hIds = startIdx:stride:endIdx; % Confidence scores. startIdx = 5; predictions{ii,1} = YPredictionsData(:,:,startIdx:stride:endIdx,:); confIds = startIdx:stride:endIdx; % Accumulate all the non-class indexes nonClassIds = [xIds yIds wIds hIds confIds]; % Class probabilities. % Get the indexes which do not belong to the nonClassIds classIdx = setdiff(allIds, nonClassIds, 'stable'); predictions{ii,6} = YPredictionsData(:,:,classIdx,:); end end

函数tiledAnchors = generateTiledAnchors(YPredCell,anchorBoxes，…生成平铺锚偏移，用于将预测从YOLO % v3网格单元格坐标转换为边界框坐标%#codegen %版权所有the MathWorks, Inc. numPredictionHeads = size(YPredCell,1);tiledAnchors = cell(numPredictionHeads, size(anchorIndex, 2));for i = 1:numPredictionHeads锚= anchorBoxes(anchorBoxMask{i}，:);[h,w，~，n] = size(YPredCell{i,1});[tiledAnchors{2},我tiledAnchors{1}我]= ndgrid (h - 0: 0: w1,…1:尺寸(锚,1),1:n);[~, ~, tiledAnchors{我3}]= ndgrid (h - 0: 0: w1,锚(:,2),1:n);[~, ~, tiledAnchors{我4}]= ndgrid (h - 0: 0: w1,锚(:1),1:n);结束结束

函数YPredCell = applyAnchorBoxOffsets(tiledAnchors,YPredCell，…the MathWorks, Inc. for i = 1:size(YPredCell,1) [h,w，~，~] = size(YPredCell{i,1});YPredCell{i,anchorIndex(1)} = (tiledAnchors{i,1}+…YPredCell{我anchorIndex(1)})。/ w;YPredCell{i,anchorIndex(2)} = (tiledAnchors{i,2}+…YPredCell{我anchorIndex (2)}) / h;YPredCell{i,anchorIndex(3)} = (tiledAnchors{i,3}.*…YPredCell{我anchorIndex(3)})。/ inputImageSize (2);YPredCell{i,anchorIndex(4)} = (tiledAnchors{i,4}.*…YPredCell{我anchorIndex(4)})。/ inputImageSize (1); end end

调整图像的大小，并将像素缩放到0到1之间。版权所有The MathWorks, Inc. imgSize = size(图片);将单通道输入图像转换为3通道。if numel(imgSize) < 1 image = repmat(image,1,1,3);结束图像= im2single(rescale(image));image = iLetterBoxImage(image,code .const(targetSize(1:2)));函数Inew = iLetterBoxImage(I,targetSize) % LetterBoxImage通过保留输入图像I的宽高比%返回调整后的图像。'targetSize'是一个1 × 2的向量，包含目标维度的%。% % Input I可以是uint8, uint16, int16, double, single或logical，并且%必须是实的和非稀疏的。[Irow,Icol,Ichannels] = size(I); % Compute aspect Ratio. arI = Irow./Icol; % Preserve the maximum dimension based on the aspect ratio. if arI<1 IcolFin = targetSize(1,2); IrowFin = floor(IcolFin.*arI); else IrowFin = targetSize(1,1); IcolFin = floor(IrowFin./arI); end % Resize the input image. Itmp = imresize(I,[IrowFin,IcolFin]); % Initialize Inew with gray values. Inew = ones([targetSize,Ichannels],'like',I).*0.5; % Compute the offset. if arI<1 buff = targetSize(1,1)-IrowFin; else buff = targetSize(1,2)-IcolFin; end % Place the resized image on the canvas image. if (buff==0) Inew = Itmp; else buffVal = floor(buff/2); if arI<1 Inew(buffVal:buffVal+IrowFin-1,:,:) = Itmp; else Inew(:,buffVal:buffVal+IcolFin-1,:) = Itmp; end end end