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balanceBoxLabels

Balance bounding box labels for object detection

Since R2020a

Description

example

locationSet = balanceBoxLabels(boxLabels,blockedImages,blockSize,numObservations) balances bounding box labels, boxLabels, by oversampling blocks of images containing less frequent classes, contained in the collection of blocked image objects blockedImages. numObservations is the required number of block locations, and blockSize specifies the block size.

locationSet = balanceBoxLabels(boxLabels,blockedImages,blockSize,numObservations,Name=Value) specifies options using one or more name-value arguments in addition to any combination of arguments from previous syntaxes. For example, OverlapThreshold=0.5 specifies the overlap threshold between a bounding box and a cropping window to before boxes are clipped or discarded.

Examples

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Load box labels data that contains boxes and labels for one image. The height and width of each box is 20-by-20 pixels.

d = load("balanceBoxLabelsData.mat");
boxLabels = d.BoxLabels;

Create a blocked image of size 500-by-500 pixels.

blockedImages = blockedImage(zeros([500 500]));

Choose the images size of each observation.

blockSize = [50 50];

Visualize using a histogram to identify any class imbalance in the box labels.

blds = boxLabelDatastore(boxLabels);
datasetCount = countEachLabel(blds);
figure
unbalancedLabels = datasetCount.Label;
unbalancedCount  = datasetCount.Count;
h1 = histogram(Categories=unbalancedLabels,BinCounts=unbalancedCount);
title("Unbalanced Class Labels")

Figure contains an axes object. The axes object with title Unbalanced Class Labels contains an object of type categoricalhistogram.

Measure the distribution of box labels. If the coefficient of variation is more than 1, then there is class imbalance.

cvBefore = std(datasetCount.Count)/mean(datasetCount.Count)
cvBefore = 1.5746

Choose a heuristic value for number of observations by finding the mean of the counts of each class, multiplied by the number of classes.

numClasses = height(datasetCount);
numObservations = mean(datasetCount.Count) * numClasses;

Control the amount a box can be cut using OverlapThreshold. Using a lower threshold value will cut objects more at the border of a block. Increase this value to reduce the amount an object can be clipped at the border, at the expense of a less balanced box labels.

ThresholdValue = 0.5;

Balance boxLabels using the balanceBoxLabels function.

locationSet = balanceBoxLabels(boxLabels,blockedImages,blockSize, ...
        numObservations,OverlapThreshold=ThresholdValue);
[==================================================] 100%
Elaps[==================================================] 100%
Elapsed time: 00:00:00
Estimated time remaining: 00:00:00
Balancing box labels complete.

Count the labels that are contained within the image blocks.

bldsBalanced = boxLabelDatastore(boxLabels,locationSet);
balancedDatasetCount = countEachLabel(bldsBalanced);

Overlay another histogram against the original label count to see if the box labels are balanced. If the labels appear to be not balanced by looking at the histograms, increase the value for numObservations.

hold on
balancedLabels = balancedDatasetCount.Label;
balancedCount  = balancedDatasetCount.Count;
h2 = histogram(Categories=balancedLabels,BinCounts=balancedCount);
title(h2.Parent,"Balanced Class Labels (OverlapThreshold: " + ThresholdValue + ")" )
legend(h2.Parent,["Before" "After"])

Figure contains an axes object. The axes object with title Balanced Class Labels (OverlapThreshold: 0.5) contains 2 objects of type categoricalhistogram. These objects represent Before, After.

Measure the distribution of the new balanced box labels.

cvAfter = std(balancedCount)/mean(balancedCount)
cvAfter = 0.4588

Input Arguments

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Labeled bounding box data, specified as a table with two columns.

  • The first column contains either all rectangle or all rotated rectangle 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.

The table describes the format of the bounding boxes:

Bounding BoxDescription
rectangle

Defined in spatial coordinates as an M-by-4 numeric matrix with rows of the form [x y w h], where:

  • M is the number of axis-aligned rectangles.

  • x and y specify the upper-left corner of the rectangle.

  • w specifies the width of the rectangle, which is its length along the x-axis.

  • h specifies the height of the rectangle, which is its length along the y-axis.

rotated-rectangle

Defined in spatial coordinates as an M-by-5 numeric matrix with rows of the form [xctr yctr w h yaw], where:

  • M is the number of rotated rectangles.

  • xctr and yctr specify the center of the rectangle.

  • w specifies the width of the rectangle, which is its length along the x-axis before rotation.

  • h specifies the height of the rectangle, which is its length along the y-axis before rotation.

  • yaw specifies the rotation angle in degrees. The rotation is clockwise-positive around the center of the bounding box.

Square rectangle rotated by -30 degrees.

To create a box label table from ground truth data,

  1. Use the Image Labeler or Video Labeler app to label your ground truth. Export the labeled ground truth data to your workspace.

  2. Create a bounding box label datastore using the objectDetectorTrainingData function.

  3. You can obtain the boxLabels from the LabelData property of the box label datastore returned by objectDetectorTrainingData, ( blds.LabelData).

Labeled blocked images, specified as an array of blockedImage objects containing pixel label images.

Block size of read data, specified as a two-element row vector of positive integers, [numrows,numcols]. The first element specifies the number of rows in the block. The second element specifies the number of columns.

Number of block locations to return, specified as a positive integer.

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Before R2021a, use commas to separate each name and value, and enclose Name in quotes.

Example: (OverlapThreshold=0.5) specifies the overlap threshold between a bounding box and a cropping window to before boxes are clipped or discarded.

Resolution level of each image in the array of blockedImage objects, specified as a positive integer scalar or a B-by-1 vector of positive integers, where B is the length of the array of blockedImage objects.

Overlap threshold, specified as a positive scalar in the range [0,1]. When the overlap between a bounding box and a cropping window is greater than the threshold, boxes in the boxLabels input are clipped to the image block window border. When the overlap is less than the threshold, the boxes are discarded. When you lower the threshold, part of an object can get discarded. To reduce the amount an object can be clipped at the border, increase the threshold. Increasing the threshold can also cause less-balanced box labels.

The amount of overlap between the bounding box and a cropping window is defined as.

area(bboxAwindow)/area(bboxA)

Display progress information, specified as a numeric or logical 1 (true) or 0 (false). Set this property to true to display information.

Output Arguments

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Balanced box labels, returned as a blockLocationSet object. The object contains numObservations number of locations of balanced blocks, each of size blockSize.

Algorithms

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Balancing Box Labels

To balance box labels, the function over samples classes that are less represented in the blocked image or big image. The box labels are counted across the dataset and sorted based on each class count. Each image size is split into several quadrants, based on the blockSize input value. The algorithm randomly picks several blocks within each quadrant with less-represented classes. The blocks without any objects are discarded. The balancing stops once the specified number of blocks are selected.

Checking for Balance

You can check the success of balancing by comparing the histograms of label count before and after balancing. You can also check the coefficient of variation value. For best results, the value should be less than the original value. For more information, see the National Institute of Standards and Technology (NIST) website, see Coefficient of Variation for more information.

Version History

Introduced in R2020a

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