How to create dataset from images in matlab. I have 100 images i want to load in mat file for further model

2 visualizzazioni (ultimi 30 giorni)
Naresh khuriwal
Naresh khuriwal il 25 Dic 2017
Commentato: Fadi Alsuhimat il 14 Nov 2018
i have load my all images in mat file using DIR function. but when i use this data set and train model the following error occurs.
breast_cancer
trainingData =
76×6 table
name folder date bytes isdir datenum
____________ _______________________ ______________________ __________ _____ __________
'mdb001.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:47:56' 1.0486e+06 false 7.3179e+05
'mdb002.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:47:56' 1.0486e+06 false 7.3179e+05
'mdb003.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:47:56' 1.0486e+06 false 7.3179e+05
'mdb004.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:47:56' 1.0486e+06 false 7.3179e+05
'mdb005.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:47:56' 1.0486e+06 false 7.3179e+05
'mdb006.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:47:56' 1.0486e+06 false 7.3179e+05
'mdb007.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:47:56' 1.0486e+06 false 7.3179e+05
'mdb008.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:47:56' 1.0486e+06 false 7.3179e+05
'mdb009.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:47:56' 1.0486e+06 false 7.3179e+05
'mdb010.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:47:56' 1.0486e+06 false 7.3179e+05
'mdb011.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:47:56' 1.0486e+06 false 7.3179e+05
'mdb012.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:47:56' 1.0486e+06 false 7.3179e+05
'mdb013.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:47:56' 1.0486e+06 false 7.3179e+05
'mdb014.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:47:56' 1.0486e+06 false 7.3179e+05
'mdb015.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:47:56' 1.0486e+06 false 7.3179e+05
'mdb016.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:47:56' 1.0486e+06 false 7.3179e+05
'mdb017.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:47:56' 1.0486e+06 false 7.3179e+05
'mdb018.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:47:58' 1.0486e+06 false 7.3179e+05
'mdb019.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:47:58' 1.0486e+06 false 7.3179e+05
'mdb020.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:47:58' 1.0486e+06 false 7.3179e+05
'mdb021.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:47:58' 1.0486e+06 false 7.3179e+05
'mdb022.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:47:58' 1.0486e+06 false 7.3179e+05
'mdb023.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:47:58' 1.0486e+06 false 7.3179e+05
'mdb024.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:47:58' 1.0486e+06 false 7.3179e+05
'mdb025.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:47:58' 1.0486e+06 false 7.3179e+05
'mdb026.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:47:58' 1.0486e+06 false 7.3179e+05
'mdb027.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:47:58' 1.0486e+06 false 7.3179e+05
'mdb028.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:47:58' 1.0486e+06 false 7.3179e+05
'mdb029.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:47:58' 1.0486e+06 false 7.3179e+05
'mdb030.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:47:58' 1.0486e+06 false 7.3179e+05
'mdb031.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:47:58' 1.0486e+06 false 7.3179e+05
'mdb032.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:47:58' 1.0486e+06 false 7.3179e+05
'mdb033.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:47:58' 1.0486e+06 false 7.3179e+05
'mdb034.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:47:58' 1.0486e+06 false 7.3179e+05
'mdb035.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:47:58' 1.0486e+06 false 7.3179e+05
'mdb036.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:47:58' 1.0486e+06 false 7.3179e+05
'mdb037.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:47:58' 1.0486e+06 false 7.3179e+05
'mdb038.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:47:58' 1.0486e+06 false 7.3179e+05
'mdb039.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:47:58' 1.0486e+06 false 7.3179e+05
'mdb040.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:00' 1.0486e+06 false 7.3179e+05
'mdb041.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:00' 1.0486e+06 false 7.3179e+05
'mdb042.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:00' 1.0486e+06 false 7.3179e+05
'mdb043.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:00' 1.0486e+06 false 7.3179e+05
'mdb044.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:00' 1.0486e+06 false 7.3179e+05
'mdb045.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:00' 1.0486e+06 false 7.3179e+05
'mdb046.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:00' 1.0486e+06 false 7.3179e+05
'mdb047.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:00' 1.0486e+06 false 7.3179e+05
'mdb048.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:00' 1.0486e+06 false 7.3179e+05
'mdb049.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:00' 1.0486e+06 false 7.3179e+05
'mdb050.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:00' 1.0486e+06 false 7.3179e+05
'mdb051.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:00' 1.0486e+06 false 7.3179e+05
'mdb052.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:00' 1.0486e+06 false 7.3179e+05
'mdb053.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:00' 1.0486e+06 false 7.3179e+05
'mdb054.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:00' 1.0486e+06 false 7.3179e+05
'mdb055.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:00' 1.0486e+06 false 7.3179e+05
'mdb056.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:00' 1.0486e+06 false 7.3179e+05
'mdb057.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:00' 1.0486e+06 false 7.3179e+05
'mdb058.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:00' 1.0486e+06 false 7.3179e+05
'mdb059.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:00' 1.0486e+06 false 7.3179e+05
'mdb060.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:00' 1.0486e+06 false 7.3179e+05
'mdb061.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:02' 1.0486e+06 false 7.3179e+05
'mdb062.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:02' 1.0486e+06 false 7.3179e+05
'mdb063.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:06' 1.0486e+06 false 7.3179e+05
'mdb064.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:06' 1.0486e+06 false 7.3179e+05
'mdb065.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:06' 1.0486e+06 false 7.3179e+05
'mdb066.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:06' 1.0486e+06 false 7.3179e+05
'mdb067.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:06' 1.0486e+06 false 7.3179e+05
'mdb068.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:06' 1.0486e+06 false 7.3179e+05
'mdb069.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:06' 1.0486e+06 false 7.3179e+05
'mdb070.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:06' 1.0486e+06 false 7.3179e+05
'mdb071.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:06' 1.0486e+06 false 7.3179e+05
'mdb072.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:06' 1.0486e+06 false 7.3179e+05
'mdb073.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:06' 1.0486e+06 false 7.3179e+05
'mdb074.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:06' 1.0486e+06 false 7.3179e+05
'mdb075.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:06' 1.0486e+06 false 7.3179e+05
'mdb076.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:08' 1.0486e+06 false 7.3179e+05
testData =
19×6 table
name folder date bytes isdir datenum
____________ _______________________ ______________________ __________ _____ __________
'mdb077.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:08' 1.0486e+06 false 7.3179e+05
'mdb078.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:08' 1.0486e+06 false 7.3179e+05
'mdb079.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:08' 1.0486e+06 false 7.3179e+05
'mdb080.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:08' 1.0486e+06 false 7.3179e+05
'mdb081.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:08' 1.0486e+06 false 7.3179e+05
'mdb082.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:08' 1.0486e+06 false 7.3179e+05
'mdb083.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:08' 1.0486e+06 false 7.3179e+05
'mdb084.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:08' 1.0486e+06 false 7.3179e+05
'mdb085.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:08' 1.0486e+06 false 7.3179e+05
'mdb086.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:08' 1.0486e+06 false 7.3179e+05
'mdb087.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:08' 1.0486e+06 false 7.3179e+05
'mdb088.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:08' 1.0486e+06 false 7.3179e+05
'mdb089.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:08' 1.0486e+06 false 7.3179e+05
'mdb090.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:08' 1.0486e+06 false 7.3179e+05
'mdb091.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:08' 1.0486e+06 false 7.3179e+05
'mdb092.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:08' 1.0486e+06 false 7.3179e+05
'mdb093.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:08' 1.0486e+06 false 7.3179e+05
'mdb094.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:08' 1.0486e+06 false 7.3179e+05
'mdb095.pgm' 'E:\Warehouse\all-mias' '30-Jul-2003 14:48:10' 7.9667e+05 false 7.3179e+05
layers =
11x1 Layer array with layers:
1 '' Image Input 1024x1024x1 images with 'zerocenter' normalization
2 '' Convolution 32 3x3 convolutions with stride [1 1] and padding [1 1]
3 '' ReLU ReLU
4 '' Convolution 32 3x3 convolutions with stride [1 1] and padding [1 1]
5 '' ReLU ReLU
6 '' Max Pooling 3x3 max pooling with stride [2 2] and padding [0 0]
7 '' Fully Connected 6 fully connected layer
8 '' ReLU ReLU
9 '' Fully Connected 6 fully connected layer
10 '' Softmax softmax
11 '' Classification Output crossentropyex
Error using vision.internal.cnn.parseInputsFasterRCNN>iAllGroundTruthBoxes (line 306)
Cannot concatenate the table variables 'bytes' and 'folder', because their types are double and cell.
Error in vision.internal.cnn.parseInputsFasterRCNN (line 172) allBoxes = iAllGroundTruthBoxes(trainingData);
Error in trainFasterRCNNObjectDetector (line 239) vision.internal.cnn.parseInputsFasterRCNN(...
Error in breast_cancer (line 80) detector = trainFasterRCNNObjectDetector(trainingData, layers, options)
these are the my code
%% Train Faster R-CNN Vehicle Detector
%% % Load training data. load('BCI.mat');
nrows = size(vehicleDataset,1); r80 = round(0.80 * nrows); trainingData = vehicleDataset(1:r80,:,:); testData = vehicleDataset(r80+1:end,:,:);
%[trainingData,testData] = splitEachLabel(vehicleDataset,0.3,'randomize');
trainingData = struct2table(trainingData) testData = struct2table(testData)
objectClasses = size(trainingData,1);
numClassesPlusBackground = objectClasses + 1;
%% % Configure training options.
% Create image input layer. inputLayer = imageInputLayer([1024 1024]);
% Define the convolutional layer parameters. filterSize = [3 3]; numFilters = 32;
% Create the middle layers. middleLayers = [
convolution2dLayer(filterSize, numFilters, 'Padding', 1)
reluLayer()
convolution2dLayer(filterSize, numFilters, 'Padding', 1)
reluLayer()
maxPooling2dLayer(3, 'Stride',2)
];
finalLayers = [
% Add a fully connected layer with 64 output neurons. The output size
% of this layer will be an array with a length of 64.
fullyConnectedLayer(6)
% Add a ReLU non-linearity.
reluLayer()
% Add the last fully connected layer. At this point, the network must
% produce outputs that can be used to measure whether the input image
% belongs to one of the object classes or background. This measurement
% is made using the subsequent loss layers.
fullyConnectedLayer(6)
% Add the softmax loss layer and classification layer.
softmaxLayer()
classificationLayer()
];
layers = [ inputLayer middleLayers finalLayers ]
options = trainingOptions('sgdm', ... 'MiniBatchSize', 32, ... 'InitialLearnRate', 1e-6, ... 'MaxEpochs', 10);
%% % Train detector. Training will take a few minutes. detector = trainFasterRCNNObjectDetector(trainingData, layers, options)
%% % Test the Fast R-CNN detector on a test image. img = imread('E:\Warehouse\all-mias/mdb001.pgm');
%% % Run detector. [bbox, score, label] = detect(detector, img);
%% % Display detection results. detectedImg = insertShape(img, 'Rectangle', bbox); figure imshow(detectedImg)

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