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Classify objects in image regions using Fast R-CNN object detector



[labels,scores] = classifyRegions(detector,I,rois) classifies objects within the regions of interest of image I, using a Fast 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 with a compute capability of 3.0 or higher is highly recommended. The GPU reduces computation time significantly. Usage of the GPU requires Parallel Computing Toolbox™.

[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(___,'ExecutionEnvironment',resource) specifies the hardware resource used to classify object within image regions: 'auto', 'cpu', or 'gpu'. You can use this syntax with either of the preceding syntaxes.


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Configure a Fast R-CNN object detector and use it to classify objects within multiple regions of an image.

Load a fastRCNNObjectDetector object that is pretrained to detect stop signs.

data = load('rcnnStopSigns.mat','fastRCNN');
fastRCNN = data.fastRCNN;

Read in a test image containing a stop sign.

I = imread('stopSignTest.jpg');

Specify regions of interest to classify within the test image.

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

Classify the image regions and inspect the output labels and classification scores. The labels come from the ClassNames property of the detector.

[labels,scores] = classifyRegions(fastRCNN,I,rois)
labels = 2x1 categorical

scores = 2x1 single column vector


The detector has high confidence in the classifications. Display the classified regions on the test image.

detectedI = insertObjectAnnotation(I,'rectangle',rois,cellstr(labels));

Input Arguments

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Fast R-CNN object detector, specified as a fastRCNNObjectDetector object. To create this object, call the trainFastRCNNObjectDetector function with training data as input.

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

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.

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 with a compute capability of 3.0 or higher. If a suitable GPU is not available, the function returns an error.

  • 'cpu' — Use the CPU.

Example: 'ExecutionEnvironment','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.

Introduced in R2017a