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‎CAMheatmap_squeezenet.m‎

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function HeatMap = CAMheatmap_squeezenet(image,act,scores,Weights)
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coder.gpu.kernelfun
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imageActivations = act;
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% check the classified category from scores
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if scores(1) > scores(2)
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classIndex = 1;
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else
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classIndex = 2;
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end
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% get classified category's Weight data at the fully conntected layer
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weightVector = Weights(classIndex,:);
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% calculate Classification Activation Map
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weightVectorSize = size(weightVector);
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weightVector = reshape(weightVector,[1 weightVectorSize]);
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dotProduct = bsxfun(@times,imageActivations,weightVector);
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classActivationMap = sum(dotProduct,3);
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originalSize = size(image);
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% resize
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classActivationMap = imresize(classActivationMap,originalSize(1:2));
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imgmap = double(classActivationMap);
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range = [min(imgmap(:)) max(imgmap(:))];
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heatmap_gray = imgmap - range(1);
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heatmap_gray = heatmap_gray/(range(2)-range(1));
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heatmap_x = round(heatmap_gray * 255);
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%persistent tbl;
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%if isempty(tbl)
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tbl = coder.const(jet(256));
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%end
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% Make sure A is in the range from 1 to size(cm,1)
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a = max(1,min(heatmap_x,size(tbl,1)));
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useMap = true;
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if ~useMap
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b = uint8(a);
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bw = imbinarize(b);
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sz = size(bw);
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flag = (sz(1)*sz(2)/2) > sum(bw(:));
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%
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if flag
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pad = b;
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pad(~bw) = 0;
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else
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pad = 255 - b;
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pad(bw) = 0;
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end
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pad = pad + 1;
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else
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pad = a;
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end
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% Extract r,g,b components
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r = tbl(pad,1);
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r = reshape(r,[227 227]);
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g = tbl(pad,2);
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g = reshape(g,[227 227]);
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b = tbl(pad,3);
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b = reshape(b,[227 227]);
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tmp = im2double(image)*0.3 + (cat(3, r, g, b) * 0.5);
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HeatMap = uint8(tmp*255);
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end
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%% Copyright 2020 The MathWorks, Inc.
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%% Reference
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% Learning Deep Features for Discriminative Localization
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% Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, Antonio Torralba Computer Science and Arti?cial Intelligence Laboratory, MIT
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% http://cnnlocalization.csail.mit.edu/

‎computeModelAccuracy.m‎

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function accuracy = computeModelAccuracy(predictionScores, net, dataStore)
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%% Computes model-level accuracy statistics
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% Load ground truth
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tmp = readall(dataStore);
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groundTruth = tmp.response;
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% Compare with predicted label with actual ground truth
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predictionError = {};
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for idx=1:numel(groundTruth)
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[~, idy] = max(predictionScores(idx,:));
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yActual = net.Layers(end).Classes(idy);
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predictionError{end+1} = (yActual == groundTruth(idx)); %#ok
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end
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% Sum all prediction errors.
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predictionError = [predictionError{:}];
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accuracy = sum(predictionError)/numel(predictionError);
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end
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%% Copyright 2020 The MathWorks, Inc.

‎gpu_predict.m‎

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function [out, act] = gpu_predict(in)
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%#codegen
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persistent mynet;
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if isempty(mynet)
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% Load the mySqueezenet.mat into the persistent mynet for code
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% generation
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mynet = coder.loadDeepLearningNetwork('mySqueezeNet.mat','convnet');
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end
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out = mynet.predict(in);
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act = mynet.activations(in, 'relu_conv10','OutputAs','channels');
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end
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%% Copyright 2020 The MathWorks, Inc.

‎main_nutsCam.cpp‎

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/* Copyright 2020 The MathWorks, Inc. */
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/* Headers */
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#include <stdio.h>
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#include <cuda.h>
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#include <sys/timeb.h>
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/* Opencv includes*/
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#include "opencv2/opencv.hpp"
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using namespace cv;
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/* targetFunction source headers*/
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#include "targetFunction.h"
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int main(int argc, const char * const argv[])
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{
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unsigned int wi = 320;
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unsigned int he = 240;
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//unsigned int ch = 3;
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//unsigned int size = wi*he*ch;
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int sw;
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sw = atoi(argv[1]);
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/* Input arguments */
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VideoCapture cap;
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if ( sw < 3 ) {
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if ( sw < 2 ) {
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//VideoCapture cap(atoi(argv[2])); //use device number for camera.
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cap.open(atoi(argv[2])); //use device number for camera.
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if (!cap.isOpened()) {
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printf("Could not open the video capture device.\n");
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return -1;
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}
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cap.set(CAP_PROP_FRAME_WIDTH, wi);
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cap.set(CAP_PROP_FRAME_HEIGHT, he);
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//cap.set(CAP_PROP_AUTOFOCUS, 0);
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//cap.set(28, 70);
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} else {
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//VideoCapture cap(argv[2]);
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cap.open(argv[2]);
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if (!cap.isOpened()) {
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printf("Could not open the video capture device.\n");
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return -1;
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}
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}
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}
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namedWindow("Defect Detection Demo",CV_WINDOW_NORMAL);
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resizeWindow("Defect Detection Demo", 300,300);
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cudaEvent_t start, stop;
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float fps=0;
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Mat inImg;
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//Mat im;
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Mat outImg;
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cudaEventCreate(&start);
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cudaEventCreate(&stop);
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unsigned char output[230400];
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/* Start reading frames */
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for(;;)
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{
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//Mat inImg;
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cudaEventRecord(start);
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if ( sw < 3 ) {
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cap >> inImg;
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} else {
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inImg = imread(argv[2], 1);
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}
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if (inImg.empty()) {
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if ( sw == 2 ) {
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cap.set(CAP_PROP_POS_FRAMES, 0);
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cap >> inImg;
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} else {
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printf("Could not open the video capture device.\n");
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break;
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}
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}
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//Size size(wi,he);
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//resize(inImg,im,size);
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targetFunction(inImg.data, output);
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cudaEventRecord(stop);
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cudaEventSynchronize(stop);
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Mat outImg(inImg.size(),inImg.type(),output);
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char strbuf[50];
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float milliseconds = -1.0;
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cudaEventElapsedTime(&milliseconds, start, stop);
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fps = fps*.9+1000.0/milliseconds*.1;
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if( waitKey(50)%256 == 27 ) break; // stop capturing by pressing ESC
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if ( sw == 3 ) {
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imwrite("output.png", outImg);
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break;
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}
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sprintf (strbuf, "%.2f FPS", fps);
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putText(outImg, strbuf, cvPoint(120,30), CV_FONT_HERSHEY_DUPLEX, 0.5, CV_RGB(0,0,0), 1);
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imshow("Defect Detection Demo", outImg);
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}
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destroyWindow("Defect Detection Demo");
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return 0;
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}
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‎mat2ocv.m‎

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function out = mat2ocv(img)
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% Convert image data format to OpenCV compatible format
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sz = size(img);
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Iocv = zeros([prod(sz), 1], 'uint8');
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% permute data
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Ir = permute(img, [2 1 3]);
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Iocv(1:3:end) = Ir(:,:,3);
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Iocv(2:3:end) = Ir(:,:,2);
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Iocv(3:3:end) = Ir(:,:,1);
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% reshape
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out = reshape(Iocv, sz);
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end
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%% Copyright 2020 The MathWorks, Inc.

‎myNDNet_Postprocess.m‎

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function [imgPacked] = myNDNet_Postprocess(Iori, num, bbox, scores, wi, he, ch, HeatMap)
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%% Parameters of input image
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% wi = 640;
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% he = 480;
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% ch = 3;
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coder.gpu.kernelfun
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labeltbl = {'Defective';'Good'};
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colortbl = [255 255 102; 73 6 248];
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%% Insert annotation as an post-processing
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img2 = Iori;
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for i = 1:num
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idx = (scores(1, i) < scores(2, i)) + 1;
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bbox = round(bbox);
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img2 = insertObjectAnnotation(img2, 'rectangle', bbox(i,:),...
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labeltbl{idx}, 'FontSize', 16, 'Color', colortbl(idx,:));
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tmp = imresize(HeatMap(:,:,:,i), [bbox(i,4)+1 bbox(i,3)+1]);
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img2(bbox(i,2):bbox(i,2)+bbox(i,4),bbox(i,1):bbox(i,1)+bbox(i,3),:) = tmp;
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end
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%% Convert image data format to OpenCV compatible image format
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imgPacked = coder.nullcopy(zeros([1,he*wi*ch], 'uint8'));
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for i = 1:he
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for j = 1:wi
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imgPacked((i-1)*wi*ch + (j-1)*ch + 3) = img2(i,j,1);
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imgPacked((i-1)*wi*ch + (j-1)*ch + 2) = img2(i,j,2);
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imgPacked((i-1)*wi*ch + (j-1)*ch + 1) = img2(i,j,3);
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end
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end
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end
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%% Copyright 2020 The MathWorks, Inc.

‎myNDNet_Preprocess.m‎

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function [Iori, nuts, num1, bbox] = myNDNet_Preprocess(inImg)
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%#codegen
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coder.gpu.kernelfun
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%% Parameters of input image
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sz = size(inImg);
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wi = sz(2);
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he = sz(1);
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ch = sz(3);
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ratio = wi*he*ch / 921600;
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imgSz = 227;
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%% Convert image data format to MATLAB compatible image format
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Iori = coder.nullcopy(zeros([he,wi,ch], 'uint8'));
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%Iori = inImg;
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% r = reshape(inImg(3:3:end), [he,wi]);
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% g = reshape(inImg(2:3:end), [he,wi]);
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% b = reshape(inImg(1:3:end), [he,wi]);
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% Iori = cat(3, r, g, b);
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for i = 1:he
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for j = 1:wi
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Iori(i,j,3) = inImg((i-1)*wi*ch + (j-1)*ch+1);
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Iori(i,j,2) = inImg((i-1)*wi*ch + (j-1)*ch+2);
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Iori(i,j,1) = inImg((i-1)*wi*ch + (j-1)*ch+3);
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end
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end
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%% Extract where the nuts are, as an ROI
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hsv = rgb2hsv(Iori); %RGB to HSV color space conversion
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th = otsuthresh(imhist(hsv(:,:,2))); %Binarize image
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bw = hsv(:,:,2) < th;
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bw = bwareaopen(bw, 20000*ratio); %Remove noise
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%% Measure boundingbox of roi instead of using regionprops
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bwl = bwlabel(bw);
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num = max(bwl(:));
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gray = rgb2gray(Iori);
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gray = imadjust(gray);
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bbox = coder.nullcopy(zeros(4));
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nuts = coder.nullcopy(zeros([imgSz,imgSz,4], 'uint8'));
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k=0;
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if num > 0 && num < 5
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for i = 1:num
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[r, c] = find(bwl == i);
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rmin = min(r);
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rmax = max(r);
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cmin = min(c);
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cmax = max(c);
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%Crop the image if measured area is in a specified range
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if (cmax-cmin) * (rmax-rmin) < 20000 && rmin >0 && cmin >0
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k=k+1;
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bbox(k, :) = [cmin, rmin, cmax-cmin, rmax-rmin];
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tmp = imcrop(gray, bbox(k,:));
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nuts(:,:,k) = imresize(tmp, [imgSz imgSz]);
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end
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end
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num1 = k;
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else
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num1 = 0;
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end
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end
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%% Copyright 2020 The MathWorks, Inc.

‎ocv2mat.m‎

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function out = ocv2mat(img, sz)
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% Convert image data format to MATLAB compatible image format
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sec = sz(1)*sz(2);
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Im = zeros([sz(2), sz(1), sz(3)], 'uint8');
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Im(1:sec) = img(3:3:end);
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Im(sec+1:sec*2) = img(2:3:end);
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Im(sec*2+1:sec*3) = img(1:3:end);
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out = permute(Im, [2 1 3]);
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end
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%% Copyright 2020 The MathWorks, Inc.

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