openCV HOG Trainer

Example linux program on how to train your custom HOG detecting vector for use with openCV hog.setSVMDetector(_descriptor)

Information

For the paper regarding Histograms of Oriented Gradients (HOG), @see http://lear.inrialpes.fr/pubs/2005/DT05/ You can populate the positive samples dir with files from the INRIA person detection dataset, @see http://pascal.inrialpes.fr/data/human/ This program uses SVMlight as machine learning algorithm (@see http://svmlight.joachims.org/), but is not restricted to it Tested in Ubuntu Linux 64bit 12.04 "Precise Pangolin" with openCV 2.3.1, SVMlight 6.02, g++ 4.6.3 and standard HOG settings, training images of size 64x128px.

What this program basically does:

Read positive and negative training sample image files from specified directories
Calculate their HOG features and keep track of their classes (pos, neg)
Save the feature map (vector of vectors/matrix) to file system
Read in and pass the features and their classes to a machine learning algorithm, e.g. SVMlight
Train the machine learning algorithm using the specified parameters
Use the calculated support vectors and SVM model to calculate a single detecting descriptor vector

Usage

See the tutorial at http://opencv.willowgarage.com/wiki/trainHOG for instructions on how to use.

Build by issuing: g++ pkg-config --cflags opencv -c -g -MMD -MP -MF main.o.d -o main.o main.cpp gcc -c -g pkg-config --cflags opencv -MMD -MP -MF svmlight/svm_learn.o.d -o svmlight/svm_learn.o svmlight/svm_learn.c gcc -c -g pkg-config --cflags opencv -MMD -MP -MF svmlight/svm_hideo.o.d -o svmlight/svm_hideo.o svmlight/svm_hideo.c gcc -c -g pkg-config --cflags opencv -MMD -MP -MF svmlight/svm_common.o.d -o svmlight/svm_common.o svmlight/svm_common.c g++ pkg-config --cflags opencv -o trainhog main.o svmlight/svm_learn.o svmlight/svm_hideo.o svmlight/svm_common.o pkg-config --libs opencv

Warning words

At least one of the functions (opendir) doing file system operations is unix/linux-only, for using the program in other operating systems a alternative API functions have to be used.
Be aware that the program may consume a considerable amount of main memory, hard disk memory and time, dependent on the amount of training samples.
Also be aware that (esp. for 32bit systems), there are limitations for the maximum file size which may take effect when writing the features file.

Contact

Jan Hendriks (dahoc3150 [at] yahoo.com)

Terms of use

This program is to be used as an example and is provided on an "as-is" basis without any warranties of any kind, either express or implied. Use at your own risk. For used third-party software, refer to their respective terms of use and licensing

cyberdecker/trainHOG

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