boxLabelDatastore

Datastore for bounding box label data

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Description

The boxLabelDatastore object creates a datastore for bounding box label data. Use this object to read labeled bounding box data for object detection.

To read bounding box label data from a boxLabelDatastore object, use the read function. This object function returns a cell array with either two or three columns. You can create a datastore that combines the boxLabelDatastore object with an ImageDatastore object using the combine object function. Use the combined datastore to train object detectors using the training functions such as trainYOLOv2ObjectDetector and trainFasterRCNNObjectDetector. You can access and manage data in the datastore using object functions. To modify the ReadSize property, you can use dot notation.

Creation

Syntax

blds = boxLabelDatastore(tbl1,...,tbln)
blds = boxLabelDatastore(tbl1,...,tbln,bSet)

Description

example

blds = boxLabelDatastore(tbl1,...,tbln) creates a boxLabelDatastore object from one or more tables containing labeled bounding box data.

blds = boxLabelDatastore(tbl1,...,tbln,bSet) creates a boxLabelDatastore object from a bigimage data by using the resolution level, block size, and block positions specified by the block locations in bSet.

Input Arguments

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Labeled bounding box data, specified as a table with one or more columns. Bounding boxes can be axis-aligned rectangles, rotated rectangles, or cuboids.

Bounding BoxFormatDescription
Axis-aligned rectangle[xmin,ymin,width,height]Defined in pixel coordinates as an M-by-4 matrix representing M bounding boxes
Rotated rectangle[xcenter,ycenter,width,height,yaw]Defined in spatial coordinates as an M-by-5 matrix representing M bounding boxes. The [xcenter,ycenter] coordinates represent the center of the bounding box. The [width,height] elements represent the length of the box along the x and y axes, respectively. The yaw represents the rotation angle in degrees. The amount of rotation about the center of the bounding box is measured in the clockwise direction.
Cuboid[xcenter,ycenter,zcenter,width,height,depth,rx,ry,rz]Defined in spatial coordinates as an M-by-9 matrix representing M bounding boxes. The [xcenter,ycenter,ycenter] coordinates represent the center of the bounding box. The [width,height] elements represent the length of the box along the x and y axes, respectively. [rx,ry,rz] are rotation angles, in degrees about the cuboid center. The rotation is positive in the clockwise direction, with respect to the positive direction of the axis.

Rotation matrices are computed assuming ZYX order Euler angles [rx.ry,rz].

The training table can be in one of these two formats.

  • A table with one or more columns:

    All columns contain bounding boxes. Each column must be a cell vector containing M-by-N matrices. M is the number of images and N represents a single object class, such as stopSign, carRear, or carFront.

  • A table with two columns.

    The first column contains bounding boxes. The second column must be a cell vector that contains the label names corresponding to each bounding box. Each element in the cell vector must be an M-by-1 categorical or string vector, where M represents the number of labels.

To create a ground truth table, use the Image Labeler or Video Labeler app. To create a table of training data from the generated ground truth, use the objectDetectorTrainingData function.

Data Types: table

Block locations, specified as a blockLocationSet object. You can create this object by using the balanceBoxLabels function.

Properties

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This property is read-only.

Labeled bounding box data, specified as an N-by-2 cell matrix of N images. The first column must be a cell vector that contains bounding boxes. Each element in the cell contains a vector representing either an axis-aligned rectangle, rotated rectangle, or a cuboid. The second column must be a cell vector that contains the label names corresponding to each bounding box. An M-by-1 categorical vector represents each label name.

Bounding Box Descriptions

Bounding BoxCell VectorFormat
Axis-aligned rectangleM-by-4 for M bounding boxes[x,y,width,height]
Rotated rectangleM-by-5 for M bounding boxes[xcenter,ycenter,width,height,yaw]
CuboidM-by-9 for M bounding boxes[xcenter,ycenter,zcenter,width,height,depth,rx,ry,rz]

Maximum number of rows of label data to read in each call to the read function, specified as a positive integer.

Object Functions

combineCombine data from multiple datastores
countEachLabelCount occurrence of pixel or box labels
hasdataDetermine if data is available to read from datastore
numpartitionsNumber of partitions for a datastore
partitionPartition a label datastore
previewRead first row of data in datastore
progressPercentage of data read from a datastore
readRead data from a datastore
readallRead all data in datastore
resetReset datastore to initial state
shuffleReturn shuffled version of datastore
subsetCreate subset of datastore or file-set
transformTransform datastore
isPartitionableDetermine whether datastore is partitionable
isShuffleableDetermine whether datastore is shuffleable

Examples

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This example uses:

Open Live Script

This example shows how to estimate anchor boxes using a table containing the training data. The first column contains the training images and the remaining columns contain the labeled bounding boxes.

data = load('vehicleTrainingData.mat');
trainingData = data.vehicleTrainingData;

Create a boxLabelDatastore object using the labeled bounding boxes from the training data.

blds = boxLabelDatastore(trainingData(:,2:end));

Estimate the anchor boxes using the boxLabelDatastore object.

numAnchors = 5;
anchorBoxes = estimateAnchorBoxes(blds,numAnchors);

Specify the image size.

inputImageSize = [128,228,3];

Specify the number of classes to detect.

numClasses = 1;

Use a pretrained ResNet-50 network as a base network for the YOLO v2 network.

network = resnet50();

Specify the network layer to use for feature extraction. You can use the analyzeNetwork function to see all the layer names in a network.

featureLayer = 'activation_49_relu';

Create the YOLO v2 object detection network.

lgraph = yolov2Layers(inputImageSize,numClasses,anchorBoxes,network, featureLayer)
lgraph = 
  LayerGraph with properties:

         Layers: [182×1 nnet.cnn.layer.Layer]
    Connections: [197×2 table]
     InputNames: {'input_1'}
    OutputNames: {'yolov2OutputLayer'}

Visualize the network using the network analyzer.

analyzeNetwork(lgraph)
Open Live Script

Load a table of vehicle class training data that contains bounding boxes with labels.

data = load('vehicleTrainingData.mat');
trainingData = data.vehicleTrainingData;

Add the fullpath to the local vehicle data folder.

dataDir = fullfile(toolboxdir('vision'),'visiondata');
trainingData.imageFilename = fullfile(dataDir,trainingData.imageFilename);

Create an imageDatastore object using the file names in the table.

imds = imageDatastore(trainingData.imageFilename);

Create a boxLabelDatastore object using the table with label data.

blds = boxLabelDatastore(trainingData(:,2:end));

Combine the imageDatastore and boxLabelDatastore objects.

cds = combine(imds,blds);

Read the data for training. Use the read object function to return images, bounding boxes, and labels.

read(cds)
ans=1×3 cell array
    {128x228x3 uint8}    {1x4 double}    {[vehicle]}
Open Live Script

Load a table of vehicle class training data that contains bounding boxes with labels.

load('vehicleTrainingData.mat');

Load a table of stop signs and cars class training data that contains bounding boxes with labels.

load('stopSignsAndCars.mat');

Create ground truth tables from the training data.

vehiclesTbl  = vehicleTrainingData(:,2:end);
stopSignsTbl = stopSignsAndCars(:,2:end);

Create a boxLabelDatastore object using two tables: one with vehicle label data and the other with the stop signs and cars label data.

blds = boxLabelDatastore(vehiclesTbl,stopSignsTbl);

Create an imageDatastore object using the file names in the training data tables.

dataDir = fullfile(toolboxdir('vision'),'visiondata');
vehicleFiles = fullfile(dataDir,vehicleTrainingData.imageFilename);
stopSignFiles = fullfile(dataDir,stopSignsAndCars.imageFilename);
imds = imageDatastore([vehicleFiles;stopSignFiles]);

Combine the imageDatastore and boxLabelDatastore objects. 

cds = combine(imds,blds);

Read the data for training. Use the read object function to return images, bounding boxes, and labels.

read(cds)
ans=1×3 cell array
    {128x228x3 uint8}    {1x4 double}    {[vehicle]}

See Also

Apps

Functions

Objects

Topics

Introduced in R2019b

Computer Vision Toolbox Documentation
Support