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 […]
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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 […]
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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 […]
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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 […]
This is specifically for Windows using the command prompt. When doing image processing and classifcations you’ll find yourself with directories full of images. And in some cases if you choose to use a learner you’ll have to load each image in your postives and/or negatives directory. So you can just list your image names in […]
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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 […]
Solar Cavities are said (in theory) to be precursors to CMEs (Coronal Mass Ejections). These prominences are therefor clues to the state of the corona prior to emitting a CME, which in turn could be the signature of stored magnetic energy that is then capable of creating and driving the CME.