visualize_data.py

"""
EECS 445 - Introduction to Machine Learning
Fall 2018 - Project 2
Visualize Dogs
    This will open up a window displaying randomly selected training
    images. The label of the image is shown. Click on the figure to
    refresh with a set of new images. You can save the images using
    the save button. Close the window to break out of the loop.

    The success of this script is a good indication that the data flow
    part of this project is running smoothly.

    Usage: python visualize_data.py
"""

import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd

from dataset import resize, ImageStandardizer, DogsDataset
from scipy.misc import imread
from utils import config, denormalize_image

training_set = DogsDataset('train', config('autoencoder.num_classes'))
training_set.X = resize(training_set.X)
standardizer = ImageStandardizer()
standardizer.fit(training_set.X)

metadata = pd.read_csv(config('csv_file'))
print('I will display some images. Click on the figure to refresh. Close the figure to exit.')

N = 4
fig, axes = plt.subplots(nrows=2, ncols=N, figsize=(2*N,2*2))

pad = 3
axes[0,0].annotate('Original', xy=(0, 0.5), xytext=(-axes[0,0].yaxis.labelpad - pad, 0),
    xycoords=axes[0,0].yaxis.label, textcoords='offset points',
    size='large', ha='right', va='center', rotation='vertical')
axes[1,0].annotate('Preprocessed', xy=(0, 0.5), xytext=(-axes[1,0].yaxis.labelpad - pad, 0),
    xycoords=axes[1,0].yaxis.label, textcoords='offset points',
    size='large', ha='right', va='center', rotation='vertical')

for ax in axes.flatten():
    ax.set_xticks([])
    ax.set_yticks([])

while True:
    rand_idx = np.random.choice(np.arange(len(metadata)), size=N, replace=False)
    X, y = [], []
    for idx in rand_idx:
        filename = os.path.join(
            config('image_path'), metadata.loc[idx, 'filename'])
        X.append(imread(filename))
        y.append(metadata.loc[idx, 'semantic_label'])

    for i, (xi, yi) in enumerate(zip(X, y)):
        axes[0,i].imshow(xi)
        axes[0,i].set_title(yi)

    X_ = resize(np.array(X))
    X_ = standardizer.transform(X_)

    for i, (xi, yi) in enumerate(zip(X_, y)):
        axes[1,i].imshow(denormalize_image(xi), interpolation='bicubic')

    plt.draw()
    if plt.waitforbuttonpress(0) == None:
        break

print('OK, bye!')