函数[bboxes,分数,labelsIndex] = yolov3Detect (matFile, im,…networkInputSize、networkOutputs confidenceThreshold,……overlapThreshold类)% yolov3Detect函数检测边界框,分数,和% labelsIndex在一个图像。% # codegen % 2020 - 2021版权MathWorks, Inc . % % %预处理数据这个例子中所有的预处理变换适用于数据集%应用培训期间,除了数据增大。因为示例%使用pretrained YOLO v3意思网络,输入数据必须代表%的原始数据和修改的公正的评价。%专门应用于%以下预处理操作输入数据。% 1。调整网络输入的图像大小,图像%比networkInputSize。2。规模的图像像素在% [0 1]。 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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函数YPredCell = applyActivations (YPredCell) % # codegen % 2020 - 2021版权MathWorks, inc . numCells =大小(YPredCell, 1);iCell = 1: numCells idx = 1:3 YPredCell {iCell, idx} = sigmoidActivation (YPredCell {iCell, idx});结束结束iCell = 1: numCells idx = 4:5 YPredCell {iCell, idx} = exp (YPredCell {iCell, idx});结束结束iCell = 1: numCells YPredCell {iCell 6} = sigmoidActivation (YPredCell {iCell 6});结束结束函数= sigmoidActivation (x) = 1. / (1 + exp (- x));结束

函数预测= extractPredictions (YPredictions anchorBoxMask) % # codegen % 2020 - 2021版权MathWorks, inc . numPredictionHeads =大小(YPredictions, 1);预测=细胞(numPredictionHeads 6);2 = 1:numPredictionHeads %在特征尺寸得到所需的信息。numChannelsPred =大小(YPredictions {2}, 3);numAnchors =大小(anchorBoxMask {2}, 2);numPredElemsPerAnchors = numChannelsPred / numAnchors;allIds = (1: numChannelsPred);步= numPredElemsPerAnchors;endIdx = numChannelsPred;YPredictionsData = extractdata (YPredictions {2}); % 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,…anchorBoxMask anchorIndex) %为转换生成瓦锚抵消YOLO %意思v3的预测网格单元坐标边界框坐标% # codegen % 2020 - 2021版权MathWorks, inc . numPredictionHeads =大小(YPredCell, 1);tiledAnchors =细胞(numPredictionHeads、大小(anchorIndex 2));i = 1: numPredictionHeads锚= anchorBoxes (anchorBoxMask我}{:);[h, w ~ n] =大小(YPredCell{我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,…inputImageSize anchorIndex) % # codegen %转换的预测YOLO v3意思网格单元坐标%边界框坐标% 2020 - 2021版权MathWorks, inc . i = 1:尺寸(YPredCell, 1) [h, w, ~, ~] =大小(YPredCell{我1});YPredCell{我anchorIndex (1)} = (tiledAnchors{1}我+……YPredCell{我anchorIndex (1)})。/ w;YPredCell{我anchorIndex (2)} = (tiledAnchors{我2}+……YPredCell{我anchorIndex (2)}) / h;YPredCell{我anchorIndex (3)} = (tiledAnchors{3}我。*……YPredCell{我anchorIndex (3)})。/ inputImageSize (2);YPredCell{我anchorIndex (4)} = (tiledAnchors{4}我。*……YPredCell{我anchorIndex (4)})。/ inputImageSize (1); end end

函数图像= preprocessData(形象,targetSize) %改变图像的大小和规模在0和1之间的像素。% # codegen % 2020 - 2021版权,MathWorks公司imgSize =大小(图片);%与单通道输入图像转换为3频道。如果元素个数(imgSize) < 1 = repmat图像(图像,1,1,3);终端形象= im2single(重新调节(图片);形象= iLetterBoxImage(形象,coder.const (targetSize (1:2)));结束函数Inew = iLetterBoxImage(我targetSize) % LetterBoxImage返回一个缩放图像保存输入图像的宽度和高度%纵横比。“targetSize”是一个1×2向量组成%目标维度。% %输入我可以uint8、uint16 int16,翻倍,单身,或逻辑,%必须真实和non-sparse。[Irow, Icol Ichannels] =大小(我); % 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