OpenCV
Once you have a *.xml file that you created from training the classifier then you can test the performance of that classifier cascade against your prositive test or you could create a validation test set to test performance against. OpenCV comes with a built in performance utility C:\OpenCV2.2\bin>opencv_performance.exe -data “C:\My Documents\HaarClassifier\haarcascade\haarcascade.xml” -info “C:\My Documents\Positive Test […]
Once your training is done there will be a number of directories created in your haarcascade location you specified when your training first began. The directories will be labelled from 0…N where N is the total number of stages the trainer completed. In each directory there will be one AdaBoostCARTHaarClassisifer.txt file and each of the […]
Continue reading about AdaBoostCARTHaarClassifier Text Files
When going through the training stages you might find that your training has come to a halt where its progress isn’t getting any further and or it has exited out improperly. If this happens you can still turn the stages it did successfully complete into a Classifier Cascade XML. OpenCV comes with a built in […]
Continue reading about Converting an intermediate cascade set
Once you have a *.vec file that you created from your positive images you are ready to train your classifier. OpenCV comes with a built in training utility opencv_haartraining.exe -data “C:\My Documents\HaarClassifier\haarcascade” -vec “C:\My Documents\Positive Test Set\positives.vec” -bg “C:\My Documents\Negative Test Set\negatives.txt” -npos 1134 -nneg 625 -nstages 20 Explanation of the code above: opencv_haartraining.exe is […]
Once you have a file that lists your positive images with the corresponding positive locations within each image you can then create a training sample based off of this file that will then create a vector of samples and translations to be used to train your classifier. opencv_createsamples.exe -info “C:\My Documents\Positive Test Set\positives.txt” -vec “C:\My […]
Continue reading about Create samples from positive markups to a vector file
How do we tell our program what we want to look for? I created a directory full of positive images (images that contain the object(s) that I want to identify) and I created a directory full of images that I know do not contain any of the objects that I want to identify. My positive […]
Machine Learning is a rapidly growing Haar Classifier is a supervised classifier, it has mainly been used for facial detection but it can also be trained to detect other objects. Computer vision is such a growing field that there are so many resources available for your own personal use. OpenCV provides a lot of functionality […]
OpenCV is an image processing library that I use in conjunction with C++ in Visual Studio. This library has proven to be very useful so far. I have experienced a number of crashes, as well as using some of their supplied binaries. Some of these I was able to get around by actually loading the […]