ex2_2_1.py

# exercise 2.2.1

import importlib_resources
import matplotlib.pyplot as plt
import numpy as np
from scipy.io import loadmat

from dtuimldmtools import similarity

filename = importlib_resources.files("dtuimldmtools").joinpath("data/digits.mat")
# Image to use as query
i = 1

# Similarity: 'SMC', 'Jaccard', 'ExtendedJaccard', 'Cosine', 'Correlation'
similarity_measure = "SMC"

# Load the digits
# Load Matlab data file to python dict structure
X = loadmat(filename)["X"]
# You can also try the CBCL faces dataset (remember to change 'transpose')
# X = loadmat('../Data/wildfaces_grayscale.mat')['X']
N, M = X.shape
transpose = False  # should the plotted images be transposed?


# Search the face database for similar faces
# Index of all other images than i
noti = list(range(0, i)) + list(range(i + 1, N))
# Compute similarity between image i and all others
sim = similarity(X[i, :], X[noti, :], similarity_measure)
sim = sim.tolist()[0]
# Tuples of sorted similarities and their indices
sim_to_index = sorted(zip(sim, noti))


# Visualize query image and 5 most/least similar images
plt.figure(figsize=(12, 8))
plt.subplot(3, 1, 1)

img_hw = int(np.sqrt(len(X[0])))
img = np.reshape(X[i], (img_hw, img_hw))
if transpose:
    img = img.T
plt.imshow(img, cmap=plt.cm.gray)
plt.xticks([])
plt.yticks([])
plt.title("Query image")
plt.ylabel("image #{0}".format(i))


for ms in range(5):

    # 5 most similar images found
    plt.subplot(3, 5, 6 + ms)
    im_id = sim_to_index[-ms - 1][1]
    im_sim = sim_to_index[-ms - 1][0]
    img = np.reshape(X[im_id], (img_hw, img_hw))
    if transpose:
        img = img.T
    plt.imshow(img, cmap=plt.cm.gray)
    plt.xlabel("sim={0:.3f}".format(im_sim))
    plt.ylabel("image #{0}".format(im_id))
    plt.xticks([])
    plt.yticks([])
    if ms == 2:
        plt.title("Most similar images")

    # 5 least similar images found
    plt.subplot(3, 5, 11 + ms)
    im_id = sim_to_index[ms][1]
    im_sim = sim_to_index[ms][0]
    img = np.reshape(X[im_id], (img_hw, img_hw))
    if transpose:
        img = img.T
    plt.imshow(img, cmap=plt.cm.gray)
    plt.xlabel("sim={0:.3f}".format(im_sim))
    plt.ylabel("image #{0}".format(im_id))
    plt.xticks([])
    plt.yticks([])
    if ms == 2:
        plt.title("Least similar images")

plt.show()

print("Ran Exercise 2.2.1")