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Project - Noise removal from images

This would be a generalization of multilabel classification where each label can be multiclass (i.e., it can have more than two possible values).

Here we'll build a system that removes noise from images. It will take as input a noisy digit image, and it will output a clean digit image, represented as an array of pixel intensities, just like the MNIST images.

Notice that the classifier’s output is multilabel (one label per pixel) and each label can have multiple values (pixel intensity ranges from 0 to 255). It is thus an example of a multioutput classification system.

Objectives:

For this example we'll be adding noise to our MNIST dataset. We will be generating random integer using randint() and adding to original image. To do the following you can use this code:

  1. Scikit-Learn provides many helper functions to download popular datasets. MNIST is one of them. The following code fetches the dataset.

    def sort_by_target(mnist):
        reorder_train = np.array(sorted([(target, i) for i, target in enumerate(mnist.target[:60000])]))[:, 1]
        reorder_test = np.array(sorted([(target, i) for i, target in enumerate(mnist.target[60000:])]))[:, 1]
        mnist.data[:60000] = mnist.data[reorder_train]
        mnist.target[:60000] = mnist.target[reorder_train]
        mnist.data[60000:] = mnist.data[reorder_test + 60000]
        mnist.target[60000:] = mnist.target[reorder_test + 60000]
    
    from sklearn.datasets import fetch_openml
    import numpy as np
    
    mnist = fetch_openml('mnist_784', version=1, cache=True)
    
    mnist.target = mnist.target.astype(np.int8)
    sort_by_target(mnist)
    
    X, y = mnist["data"], mnist["target"]
    X.shape
    y.shape
    
  2. To view the image of a single digit,all we need to do is grab an instance’s feature vector, reshape it to a 28×28 array, and display it using Matplotlib’s imshow() function.

    %matplotlib inline
    import matplotlib
    import matplotlib.pyplot as plt
    
    some_digit = X[36000]   # Selecting the 36,000th image.
    some_digit_image = some_digit.reshape(28, 28) # Reshaping it to get the 28x28 pixels
    plt.imshow(some_digit_image, cmap = matplotlib.cm.binary, interpolation="nearest")
    plt.axis("off")
    plt.show()
    
    plt.imshow(255-some_digit_image, cmap = matplotlib.cm.binary, interpolation="nearest")
    
    some_digit_image.shape
    X[36000].shape
    y[36000]
    y.shape
    
  3. We need to split the data into test and train data. The MNIST dataset is actually already split into a training set (the first 60,000 images) and a test set (the last 10,000 images)

    X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]
    
    print(X_train.shape)
    print(y_train.shape)
    print(X_test.shape)
    print(y_test.shape)
    
  4. Also we need to shuffle our training data so that it ensures that we don't miss out any digit in a cross validation fold.

    np.random.seed(42)
    shuffle_index = np.random.permutation(60000)
    X_train, y_train = X_train[shuffle_index], y_train[shuffle_index]
    
  5. Since KNN take a lot of time, we are trimming it here

    X_train = X_train[:30000]
    y_train = y_train[:30000]
    
    X_test = X_test[:5000]
    y_test = y_test[:5000]
    
    
    import numpy.random as rnd
    
    noise_train = rnd.randint(0, 100, (len(X_train), 784))
    X_train_mod = X_train + noise_train
    noise_test = rnd.randint(0, 100, (len(X_test), 784))
    X_test_mod = X_test + noise_test
    y_train_mod = X_train
    y_test_mod = X_test
    
  6. Let's view the noisy image

    def plot_digit(array):
        array_image = array.reshape(28, 28)
        plt.imshow(array_image, cmap = matplotlib.cm.binary, interpolation="nearest")
        plt.axis("off")
        plt.show()
    
    plot_digit(X_test_mod[4000])
    
    plot_digit(y_test_mod[4000])
    
  7. Now you need to clean the image using KNN classifier. It is an example of Multioutput classification. A single label is Multilabel as it has 784 classes and each of the 784 pixel can have values from 0 to 255, hence it is a Multioutput classification example.


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