％Copyright 2020 MathWorks，Inc。函数out = Reset_predict（in）％＃codegen％持久对象mynet用于加载系列网络对象。％在第一个调用此函数时，构建持久对象并％设置。当函数被称为后续时间时，重复使用相同的对象以呼叫在输入上的预测，避免重建和重新加载％网络对象。持久性yornet;如果isEmpty（mynet）％呼叫reset50返回resnet-50模型的DAG网络％。MyNet = Coder.LoadDeePlearningnetwork（'resnet50'，'resnet'）;输入Out = mynet.predict（in）;

代码生成成功：要查看报告，请打开（'codegen \ mex \ resnet_predict \ html \ thine.mldatx'）。

％Copyright 2020 MathWorks，Inc。函数Reset_predict_exe（InputVideo，Batchsize）％#codegen％持久对象mynet用于加载系列网络对象。％在第一个调用此函数时，构建持久对象并％设置。当函数被称为后续时间时，重复使用相同的对象以呼叫在输入上的预测，避免重建和重新加载％网络对象。持久性yornet;如果isEmpty（mynet）％呼叫reset50返回resnet-50模型的DAG网络％。MyNet = Coder.LoadDeePlearningnetwork（'resnet50'，'resnet'）;结束％创建视频读者和视频播放器对象％videoreader = videoreader（inputvideo）;depvideoplayer = Vision.DeployableVideplayer;％读取分类标签名称％synsetut = ReadimageClassLabels（'synsetwords.txt'）;我= 1; % Read frames until end of video file % while ~(i+batchSize > (videoReader.NumFrames+1)) % Read and resize batch of frames as specified by input argument% reSizedImagesBatch = readImageInputBatch(videoReader,batchSize,i); % run predict on resized input images % predict_scores = mynet.predict(reSizedImagesBatch); % overlay the prediction scores on images and display % overlayResultsOnImages(predict_scores,synsetOut,reSizedImagesBatch,batchSize,depVideoPlayer) i = i+ batchSize; end release(depVideoPlayer); end function synsetOut = readImageClassLabels(classLabelsFile) % Read the classification label names from the file % % Inputs : % classLabelsFile - supplied by user % % Outputs : % synsetOut - cell array filled with 1000 image class labels synsetOut = cell(1000,1); fid = fopen(classLabelsFile); for i = 1:1000 synsetOut{i} = fgetl(fid); end fclose(fid); end function reSizedImagesBatch = readImageInputBatch(videoReader,batchSize,i) % Read and resize batch of frames as specified by input argument% % % Inputs : % videoReader - Object used for reading the images from video file % batchSize - Number of images in batch to process. Supplied by user % i - index to track frames read from video file % % Outputs : % reSizedImagesBatch - Batch of images resized to 224x224x3xbatchsize img = read(videoReader,[i (i+batchSize-1)]); reSizedImagesBatch = coder.nullcopy(ones(224,224,3,batchSize,'like',img)); resizeTo = coder.const([224,224]); reSizedImagesBatch(:,:,:,:) = imresize(img,resizeTo); end function overlayResultsOnImages(predict_scores,synsetOut,reSizedImagesBatch,batchSize,depVideoPlayer) % Read and resize batch of frames as specified by input argument% % % Inputs : % predict_scores - classification results for given network % synsetOut - cell array filled with 1000 image class labels % reSizedImagesBatch - Batch of images resized to 224x224x3xbatchsize % batchSize - Number of images in batch to process. Supplied by user % depVideoPlayer - Object for displaying results % % Outputs : % Predicted results overlayed on input images % sort the predicted scores % [val,indx] = sort(transpose(predict_scores), 'descend'); for j = 1:batchSize scores = val(1:5,j)*100; outputImage = zeros(224,400,3, 'uint8'); for k = 1:3 outputImage(:,177:end,k) = reSizedImagesBatch(:,:,k,j); end % Overlay the results on image % scol = 1; srow = 1; outputImage = insertText(outputImage, [scol, srow], 'Classification with ResNet-50', 'TextColor', [255 255 255],'FontSize',20, 'BoxColor', [0 0 0]); srow = srow + 30; for k = 1:5 scoreStr = sprintf('%2.2f',scores(k)); outputImage = insertText(outputImage, [scol, srow], [synsetOut{indx(k,j)},' ',scoreStr,'%'], 'TextColor', [255 255 255],'FontSize',15, 'BoxColor', [0 0 0]); srow = srow + 25; end depVideoPlayer(outputImage); end end