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classifyRegions

(Not recommended) Classify objects in image regions using R-CNN object detector

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The classifyRegions function and R-CNN object detectors are not recommended. Use a different type of object detector instead. For more information, see Version History.

Syntax

[labels,scores] = classifyRegions(detector,I,rois)
[labels,scores,allScores] = classifyRegions(detector,I,rois)
[___] = classifyRegions(___Name=Value)

Description

[labels,scores] = classifyRegions(detector,I,rois) classifies objects within the regions of interest of image I, using an R-CNN (regions with convolutional neural networks) object detector. For each region, classifyRegions returns the class label with the corresponding highest classification score.

When using this function, use of a CUDA® enabled NVIDIA® GPU is highly recommended. The GPU reduces computation time significantly. Usage of the GPU requires Parallel Computing Toolbox™. For information about the supported compute capabilities, see GPU Computing Requirements (Parallel Computing Toolbox).

example

[labels,scores,allScores] = classifyRegions(detector,I,rois) also returns all the classification scores of each region. The scores are returned in an M-by-N matrix of M regions and N class labels.

[___] = classifyRegions(___Name=Value) specifies options using one or more name-value arguments in addition to any combination of arguments from previous syntaxes. For example, ExecutionEnvironment="cpu" classifies objects within image regions using only the CPU hardware.

Examples

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Open Live Script

Load a pretrained detector.

load('rcnnStopSigns.mat','rcnn')

Read the test image. 

img = imread('stopSignTest.jpg');

Specify multiple regions to classify within the test image.

rois = [416   143    33    27
        347   168    36    54];

Classify regions.

[labels,scores] = classifyRegions(rcnn,img,rois);
detectedImg = insertObjectAnnotation(img,'rectangle',rois,cellstr(labels));
figure
imshow(detectedImg)

Figure contains an axes object. The hidden axes object contains an object of type image.

Input Arguments

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R-CNN object detector, specified as an rcnnObjectDetector object. To create this object, call the trainRCNNObjectDetector function with training data as input.

Input image, specified as a real, nonsparse, grayscale or RGB image.

The detector is sensitive to the range of the input image. Therefore, ensure that the input image range is similar to the range of the images used to train the detector. For example, if the detector was trained on uint8 images, rescale this input image to the range [0, 255] by using the im2uint8 or rescale function. The size of this input image should be comparable to the sizes of the images used in training. If these sizes are very different, the detector has difficulty detecting objects because the scale of the objects in the input image differs from the scale of the objects the detector was trained to identify. Consider whether you used the SmallestImageDimension property during training to modify the size of training images.

Data Types: uint8 | uint16 | int16 | double | single | logical

Regions of interest within the image, specified as an M-by-4 matrix defining M rectangular regions. Each row contains a four-element vector of the form [x y width height]. This vector specifies the upper left corner and size of a region in pixels.

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: ExecutionEnvironment="cpu" classifies objects within image regions using only the CPU hardware.

Size of smaller batches for R-CNN data processing, specified as an integer. Larger batch sizes lead to faster processing but take up more memory.

Hardware resource used to classify image regions, specified as "auto", "gpu", or "cpu".

  • "auto" — Use a GPU if it is available. Otherwise, use the CPU.

  • "gpu" — Use the GPU. To use a GPU, you must have Parallel Computing Toolbox and a CUDA enabled NVIDIA GPU. If a suitable GPU is not available, the function returns an error. For information about the supported compute capabilities, see GPU Computing Requirements (Parallel Computing Toolbox).

  • "cpu" — Use the CPU.

Output Arguments

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Classification labels of regions, returned as an M-by-1 categorical array. M is the number of regions of interest in rois. Each class name in labels corresponds to a classification score in scores and a region of interest in rois. classifyRegions obtains the class names from the input detector.

Highest classification score per region, returned as an M-by-1 vector of values in the range [0, 1]. M is the number of regions of interest in rois. Each classification score in scores corresponds to a class name in labels and a region of interest in rois. A higher score indicates higher confidence in the classification.

All classification scores per region, returned as an M-by-N matrix of values in the range [0, 1]. M is the number of regions in rois. N is the number of class names stored in the input detector. Each row of classification scores in allscores corresponds to a region of interest in rois. A higher score indicates higher confidence in the classification.

Version History

Introduced in R2016b

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R2024b: Not Recommended

Starting in R2024b, R-CNN object detectors are no longer recommended. Instead, use a different type of object detector, such as a yoloxObjectDetector or yolov4ObjectDetector object. These object detectors are faster than R-CNN object detectors. For more information, see Choose an Object Detector.

See Also

Apps

Functions

Objects