visualize_faces.py

import matplotlib
matplotlib.use("TkAgg")
#from keras.preprocessing.image import load_img, img_to_array
from keras.utils import load_img, img_to_array
import os
import numpy as np
from matplotlib import pyplot as plt

faces_dir = 'att_faces/'

X_train, Y_train = [], []
X_test, Y_test = [], []

subfolders = sorted([file.path for file in os.scandir(faces_dir) if file.is_dir()])
for idx, folder in enumerate(subfolders):
    for file in sorted(os.listdir(folder)):
        img = load_img(folder+"/"+file, color_mode='grayscale')
        img = img_to_array(img).astype('float32')/255
        img = img.reshape(img.shape[0], img.shape[1],1)
        if idx < 35:
            X_train.append(img)
            Y_train.append(idx)
        else:
            X_test.append(img)
            Y_test.append(idx-35)

X_train = np.array(X_train)
X_test = np.array(X_test)
Y_train = np.array(Y_train)
Y_test = np.array(Y_test)

subject_idx = 4
fig, ((ax1,ax2,ax3),(ax4,ax5,ax6),(ax7,ax8,ax9)) = plt.subplots(3,3,figsize=(10,10))
subject_img_idx = np.where(Y_train==subject_idx)[0].tolist()
for i, ax in enumerate([ax1,ax2,ax3,ax4,ax5,ax6,ax7,ax8,ax9]):
    img = X_train[subject_img_idx[i]]
    img = img.reshape(img.shape[0], img.shape[1])
    ax.imshow(img, cmap='gray')
    ax.grid(False)
    ax.set_xticks([])
    ax.set_yticks([])
plt.tight_layout()
plt.show()

subjects = range(10)
fig, ((ax1,ax2,ax3),(ax4,ax5,ax6),(ax7,ax8,ax9)) = plt.subplots(3,3,
figsize=(10,12))
subject_img_idx = [np.where(Y_train==i)[0].tolist()[0] for i in subjects]
for i, ax in enumerate([ax1,ax2,ax3,ax4,ax5,ax6,ax7,ax8,ax9]):
    img = X_train[subject_img_idx[i]]
    img = img.reshape(img.shape[0], img.shape[1])
    ax.imshow(img, cmap='gray')
    ax.grid(False)
    ax.set_xticks([])
    ax.set_yticks([])
    ax.set_title("Subject {}".format(i))
plt.tight_layout()
plt.show()