Like the previous chapter, this chapter has dealt with classification tasks, as well as interfaces among OpenCV, a source of images, and a GUI. This time, our classification labels have more objective meanings (a species or an individual's identity), so the classifier's success or failure is more obvious. To meet the challenge, we used much bigger sets of training images, we preprocessed the training images for greater consistency, and we applied two tried-and-true classification techniques in the sequence (either Haar cascades or LBP cascades for detection and then LBPH for recognition).
The methodology presented in this chapter, as well as in the entire Interactive Recognizer app and some of the other code, generalizes well with other original works in detection and recognition. With the right training images, you can detect and recognize many more animals in many poses. You can even detect an object such as a car and recognize the Batmobile!
For our next project, we turn our attention...
