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Author: anshika-MW

test/tTextDetection.m

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+classdef(SharedTestFixtures = {DownloadPretrainedCRAFTFixture}) tTextDetection < matlab.unittest.TestCase
+    % Test for TextDetection
+    
+    % Copyright 2021 The MathWorks, Inc.
+    
+    % The shared test fixture downloads the model. Here we check the
+    % inference on the pretrained model.
+    properties        
+        RepoRoot = getRepoRoot;        
+    end
+    
+    methods(Test)
+        function exerciseDetection(test)            
+            model = load(fullfile(test.RepoRoot,'model','craftNet.mat'));
+            inpImage = imread('businessCard.png');
+            outputNames = {'Conv_118','Relu_109'};
+            out = cell(size(outputNames'));
+
+            expectedBBoxes = [161.3333, 61.3333,542.6667, 61.3333,542.6667,145.3333,161.3333,145.3333;
+                169.3333,312.0000,192.0000,312.0000,192.0000,340.0000,169.3333,340.0000;
+                170.6667,272.0000,218.6667,272.0000,218.6667,304.0000,170.6667,304.0000;
+                170.6667,348.0000,260.0000,348.0000,260.0000,384.0000,170.6667,384.0000;
+                170.6667,386.6667,228.0000,386.6667,228.0000,418.6667,170.6667,418.6667;
+                170.6667,424.0000,416.0000,424.0000,416.0000,454.6667,170.6667,454.6667;
+                196.0000,310.6667,270.6667,310.6667,270.6667,349.3333,196.0000,349.3333;
+                217.6480,269.3688,363.4014,272.5033,362.6325,308.2573,216.8791,305.1228;
+                261.3333,349.3333,308.0000,349.3333,308.0000,380.0000,261.3333,380.0000;
+                270.6667,310.6667,314.6667,310.6667,314.6667,342.6667,270.6667,342.6667;
+                309.3333,349.3333,465.3333,349.3333,465.3333,381.3333,309.3333,381.3333;
+                314.6667,312.0000,381.3333,312.0000,381.3333,344.0000,314.6667,344.0000;
+                364.0000,276.0000,408.0000,276.0000,408.0000,304.0000,364.0000,304.0000];
+            %Pre process
+            [image, imageScale] = helper.preprocess(inpImage);
+           
+            %Inferance
+            [out{:}] = predict(model.craftNet,dlarray(image,'SSCB'),'Outputs',outputNames);
+
+            %Post process to get the boundingBoxes
+            boundingBoxes = helper.postprocess(out,imageScale);
+
+            test.verifyEqual(boundingBoxes,expectedBBoxes,'AbsTol',double(1e-4));
+        end      
+    end
+end

test/tdownloadPretrainedCRAFT.m

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+classdef(SharedTestFixtures = {DownloadPretrainedCRAFTFixture}) tdownloadPretrainedCRAFT < matlab.unittest.TestCase
+    % Test for downloadPretrainedCRAFT
+    
+    % Copyright 2021 The MathWorks, Inc.
+    
+    % The shared test fixture DownloadPretrainedCRAFTFixture calls
+    % tdownloadPretrainedCRAFT. Here we check that the downloaded files
+    % exists in the appropriate location.
+    
+    properties        
+        DataDir = fullfile(getRepoRoot(),'model');
+    end
+    
+    methods(Test)
+        function verifyDownloadedFilesExist(test)
+            dataFileName = 'craftNet.mat';
+            test.verifyTrue(isequal(exist(fullfile(test.DataDir,dataFileName),'file'),2));
+        end
+    end
+end

test/tload.m

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+classdef(SharedTestFixtures = {DownloadPretrainedCRAFTFixture}) tload < matlab.unittest.TestCase
+    % Test for loading the downloaded model.
+    
+    % Copyright 2021 The MathWorks, Inc.
+    
+    % The shared test fixture DownloadPretrainedCRAFTFixture calls
+    % downloadPretrainedCRAFT. Here we check that the properties of
+    % downloaded model.
+    
+    properties        
+        DataDir = fullfile(getRepoRoot(),'model');        
+    end
+    
+    methods(Test)
+        function verifyModelAndFields(test)
+            % Test point to verify the fields of the downloaded models are
+            % as expected.
+                                    
+            loadedModel = load(fullfile(test.DataDir,'craftNet.mat'));
+            model = loadedModel.craftNet;
+            test.verifyClass(model,'dlnetwork');
+            test.verifyEqual(numel(model.Layers),63);
+            test.verifyEqual(size(model.Connections),[66 2])
+            test.verifyEqual(model.InputNames,{'Input_input'});
+            test.verifyEqual(model.OutputNames,{'Conv_118'});
+        end        
+    end
+end

test/tools/DownloadPretrainedCRAFTFixture.m

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+classdef DownloadPretrainedCRAFTFixture < matlab.unittest.fixtures.Fixture
+    % DownloadPretrainedCRAFTFixture   A fixture for calling 
+    % downloadDownloadPretrainedCRAFT if necessary. This is to 
+    % ensure that this function is only called once and only when tests 
+    % need it. It also provides a teardown to return the test environment
+    % to the expected state before testing.
+    
+    % Copyright 2021 The MathWorks, Inc
+    
+    properties(Constant)
+        CRAFTDataDir = fullfile(getRepoRoot(),'model')
+    end
+    
+    properties
+        CRAFTExist (1,1) logical        
+    end
+    
+    methods
+        function setup(this) 
+            import matlab.unittest.fixtures.CurrentFolderFixture;
+            this.applyFixture(CurrentFolderFixture ...
+                (getRepoRoot()));
+            
+            this.CRAFTExist = exist(fullfile(this.CRAFTDataDir,'craftNet.mat'),'file')==2;
+            
+            % Call this in eval to capture and drop any standard output
+            % that we don't want polluting the test logs.
+            if ~this.CRAFTExist
+            	evalc('helper.downloadPretrainedCRAFT();');
+            end       
+        end
+        
+        function teardown(this)
+            if this.CRAFTExist
+            	delete(fullfile(this.CRAFTDataDir,'craftNet.mat'));
+            end              
+        end
+    end
+end

test/tools/getRepoRoot.m

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+function path = getRepoRoot()
+% getRepoRoot   Return a path to the repository's root directory.
+
+% Copyright 2020 The MathWorks, Inc.
+
+thisFile = mfilename('fullpath');
+thisDir = fileparts(thisFile);
+
+% the root is up two directories (<root>/test/tools/getRepoRoot.m)
+path = fullfile(thisDir,'..','..');
+end