trainCascadeObjectDetector
Train cascade object detector model
Syntax
Description
trainCascadeObjectDetector(
writes a trained cascade detector XML file named,
outputXMLFilename,positiveInstances,negativeImages)outputXMLFilename. The file name must include an XML
extension. For a more detailed explanation on how this function works, refer to
Get Started with Cascade Object Detector.
trainCascadeObjectDetector( resumes
an interrupted training session. The outputXMLFilename,'resume')outputXMLFilename input
must match the output file name from the interrupted session. All
arguments saved from the earlier session are reused automatically.
trainCascadeObjectDetector(___,
specifies options using one or more name-value arguments in addition to any
combination of arguments from previous syntaxes. For example,
Name=Value)ObjectTrainingSize=[100,100] sets the height and width of
objects during training.
Examples
Load the positive samples data from a MAT file. The file contains the ground truth, specified as table of bounding boxes for several object categories. The ground truth was labeled and exported from the Image Labeler app.
load("stopSignsAndCars.mat");Prefix the fullpath to the stop sign images.
stopSigns = fullfile(toolboxdir("vision"),"visiondata",stopSignsAndCars{:,1});
Create datastores to load the ground truth data for stop signs.
imds = imageDatastore(stopSigns); blds = boxLabelDatastore(stopSignsAndCars(:,2));
Combine the image and box label datastores.
positiveInstances = combine(imds,blds);
Add the image folder path to the MATLAB® path.
imDir = fullfile(matlabroot,"toolbox","vision","visiondata","stopSignImages"); addpath(imDir);
Specify a folder for negative images.
negativeFolder = fullfile(matlabroot,"toolbox","vision","visiondata","nonStopSigns");
Create an imageDatastore object containing negative images.
negativeImages = imageDatastore(negativeFolder);
Train a cascade object detector called "stopSignDetector.xml" using HOG features. NOTE: The command can take a few minutes to run.
trainCascadeObjectDetector("stopSignDetector.xml",positiveInstances,negativeFolder,FalseAlarmRate=0.01,NumCascadeStages=3);Automatically setting ObjectTrainingSize to [35, 32] Using at most 42 of 42 positive samples per stage Using at most 84 negative samples per stage --cascadeParams-- Training stage 1 of 3 [........................................................................] Used 42 positive and 84 negative samples Time to train stage 1: 0 seconds Training stage 2 of 3 [........................................................................] Used 42 positive and 84 negative samples Time to train stage 2: 0 seconds Training stage 3 of 3 [........................................................................] Used 42 positive and 84 negative samples Time to train stage 3: 2 seconds Training complete
Use the newly trained classifier to detect a stop sign in an image.
detector = vision.CascadeObjectDetector("stopSignDetector.xml");Read the test image.
img = imread("stopSignTest.jpg");Detect a stop sign in the test image.
bbox = step(detector,img);
Insert bounding box rectangles and return the marked image.
detectedImg = insertObjectAnnotation(img,"rectangle",bbox,"stop sign");
Display the detected stop sign.
figure imshow(detectedImg)
Remove the image folder from the path.
rmpath(imDir);
Input Arguments
Positive samples, specified as a datastore or a two-column table.
If you use a datastore, your data must be set up so that calling the datastore with the
readandreadallfunctions returns a cell array or table with at least two columns. The table describes the data contained in the columns:Images boxes labels (optional) Cell vector of grayscale or RGB images.
M-by-4 matrices of bounding boxes of the form [x, y, width, height], where [x,y] represent the top-left coordinates of the bounding box.
Cell array that contains an M-element categorical vector containing object class names. All categorical data returned by the datastore must contain the same categories.
When you provide this data, the function uses the class label to fill the
ClassificationModelproperty of the trained detector, specified as avision.CascadeObjectDetectorobject. Otherwise, the class labels are not required for training because the cascade object detector is a single class detector.If you use a table, the table must have two or more columns. The first column of the table must contain image file names with paths. The images must be grayscale or truecolor (RGB) and they can be in any format supported by
imread. Each of the remaining columns must be a cell vector that contains M-by-4 matrices that represent a single object class, such as vehicle, flower, or stop sign. The columns contain 4-element double arrays of M bounding boxes in the format [x,y,width,height]. The format specifies the upper-left corner location and size of the bounding box in the corresponding image. To create a ground truth table, you can use the Image Labeler app or Video Labeler app. To create a table of training data from the generated ground truth, use theobjectDetectorTrainingDatafunction.
Negative images, specified as an ImageDatastore object, a path
to a folder containing images, or as a cell array of image file names.
Because the images are used to generate negative samples, they must not
contain any objects of interest. Instead, they should contain backgrounds
associated with the object.
Trained cascade detector file name, specified as a character vector or a string scalar with an
XML extension. For example,
'stopSignDetector.xml'.
Data Types: char
Name-Value Arguments
Tips
Training a good detector requires thousands of training samples. Processing time for a large amount of data varies, but it is likely to take hours or even days. During training, the function displays the time it took to train each stage in the MATLAB® command window.
The OpenCV HOG parameters used in this function are:
Numbins:
9CellSize =
[8 8]BlockSize =
[4 4]BlockOverlap =
[2 2]UseSignedOrientation =
false
References
[1] Viola, P., and M. Jones. “Rapid Object Detection using a
Boosted Cascade of Simple Features.” Proceedings of the 2001 IEEE
Computer Society Conference. CVPR 2001, 1:I-511-I–518. Kauai, HI, USA:
IEEE Comput. Soc, 2001.
[2] Ojala, T., M. Pietikainen, and T. Maenpaa. “Multiresolution Gray-scale and Rotation Invariant Texture Classification With Local Binary Patterns.” In IEEE Transactions on Pattern Analysis and Machine Intelligence, no. 7: 971–87, 2002. DOI.org (Crossref), https://doi.org/10.1109/TPAMI.2002.1017623.
[3] Dalal, N., and B. Triggs. “Histograms of Oriented Gradients for Human Detection.” In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05), 1:886–93. San Diego, CA, USA: IEEE, 2005. DOI.org (Crossref), https://doi.org/10.1109/CVPR.2005.177.
[4] Lienhart, R., Kuranov, A., Pisarevsky, V.. “Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection” DAGM 2003. Lecture Notes in Computer Science. 2781:297-304. Springer, 2003. DOI.org (Crossref), https://doi.org/10.1007/978-3-540-45243-0_39.
