Disclaimer: I don't own this code, I just have used the code in this book (Please buy from pyimagesearch.com) for learning purposes, and added to it few pieces to build my image classifier (For learning purposes).
Deep Learning for Computer Vision with Python – Starter Bundle written by Dr. Adrian Rosebrock.
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class SimpleDatasetLoader: Loads the images, labels into a lists and returns a numpy array for both the images and labels. Uses CV2.
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class SimplePreprocessor: This is for re-sizing the images, as we all know before training the models, we need to be cognizant of our dataset size before even starting to work with image classification algorithms.
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Finally, the image classification, using as arguments 1) the path to input dataset" 2)number of nearest neighbor classification 3) # of jobs for KNN distance (-1 uses all avaialble cores)
- and 2. Can be used independently of the training model, which makes this code reausable.
We use sklearn library for image classification, and as ML classifier we use k-Nearest Neighbors (k-NN) classifier.
Images inside the Animals dataset belong to three distinct classes: dogs, cats, and pandas, with 1,000 example images per class. The dog and cat images were sampled from the Kaggle Dogs vs. Cats challenge (http://pyimg.co/ogx37). . Best of all, a deep learning model can quickly be trained on this dataset on either a CPU or GPU. Regardless of your hardware setup, you can use this dataset to learn the basics of machine learning and deep learning.
Note: Our feature matrix only consumes 9MB of memory for 3,000 images, each of size 32×32×3 – this dataset can easily be stored in memory on modern machines without a problem.
Our goal in this chapter is to leverage the k-NN classifier to attempt to recognize each of these species in an image using only the raw pixel intensities (i.e., no feature extraction is taking place). As we’ll see, raw pixel intensities do not lend themselves well to the k-NN algorithm. Nonetheless, this is an important benchmark experiment to run so we can appreciate why Convolutional Neural Networks are able to obtain such high accuracy on raw pixel intensities while traditional machine learning algorithms fail to do so.
Evaluating our classifier, we see that we obtained 52% accuracy – this accuracy isn’t bad for a classifier that doesn’t do any true “learning” at all, given that the probability of randomly guessing the correct answer is 1/3. However, it is interesting to inspect the accuracy for each of the class labels. The “panda” class was correctly classified 79% of the time, likely due to the fact that pandas are largely black and white and thus these images lie closer together in our 3,072-dim space. Dogs and cats obtain substantially lower classification accuracy at 39% and 36%, respectively. These results can be attributed to the fact that dogs and cats can have very similar shades of fur coats and the color of their coats cannot be used to discriminate between them. Background noise (such as grass in a backyard, the color of a couch an animal is resting on, etc.) can also “confuse” the k-NN algorithm as its unable to learn any discriminating patterns between these species. This confusion is one of the primary drawbacks of the k-NN algorithm: while it’s simple, it is also unable to learn from the data.
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