Let's start by loading the olivetti face dataset from scikit-learn; it contains 400 face images, each with a dimension of 64 x 64 pixels.
The following code block shows a few random faces from the dataset:
from sklearn.datasets import fetch_olivetti_faces faces = fetch_olivetti_faces().dataprint(faces.shape) # there are 400 faces each of them is of 64x64=4096 pixelsfig = plt.figure(figsize=(5,5)) fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05) # plot 25 random facesj = 1np.random.seed(0)for i in np.random.choice(range(faces.shape[0]), 25): ax = fig.add_subplot(5, 5, j, xticks=[], yticks=[]) ax.imshow(np.reshape(faces[i,:],(64,64)), cmap=plt.cm.bone, interpolation='nearest') j += 1plt.show() ...
