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 sequence (either Haar cascades or LBP cascades for detection and then LBPH for recognition).
The methodology presented in this chapter, as well as the entire Interactive Recognizer app and some of the other code, generalizes well to other original work in detection and recognition. With the right training images, you could detect and recognize many more animals in many...
