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