def main(): tarpath = "cifar-10-python.tar.gz" data_path = extract(tarpath) images, labels = load_batch(data_path[0]) labels_strings = load_meta() fig = plt.figure() N = 16 for i, img, label_i in zip(range(N), images[:N], labels[:N]): img = np.reshape(img, [32, 32, 3], order="F") img = np.rot90(img, k=3) ax = fig.add_subplot(4, 4, i + 1) ax.imshow(img) ax.set_xticklabels([]) ax.set_yticklabels([]) ax.axis('off') ax.set_title(labels_strings[label_i]) plt.show()
def compute_hog_features(images): features = [] hog_images = [] for image in images: fd, hog_image = hog(image, visualize=True) features.append(fd) hog_images.append(hog_image) return np.array(features), hog_images if __name__ == '__main__': # Import data X_train, y_train = load_batch() X_test, y_test = load_batch(test=True) # Subset data to 1/10th n_train = X_train.shape[0] // 2 X_train = X_train[0:n_train] y_train = y_train[0:n_train] print("Number of training images:", n_train) # Number of training images: 5000 n_test = X_test.shape[0] // 10 X_test = X_test[0:n_test] y_test = y_test[0:n_test] print("Number of testing images:", n_test) # Number of testing images: 1000
from load_cifar import load_batch import numpy as np import matplotlib.pyplot as plt X_train, y_train = load_batch() n = 0 fig = plt.figure() for c in range(0, 10): # Find images in class c idxs = np.where(y_train == c)[0][0:10] # Add them to the plot for i in idxs: a = fig.add_subplot(10, 10, n + 1) plt.imshow(X_train[i, :, :, :]) plt.axis("off") n += 1 fig.set_size_inches(12, 12) plt.savefig('cifar10test.png')
from load_cifar import load_batch import numpy as np images, _ = load_batch() images = np.reshape(images, (10000, 3, 32, 32)) from scipy.misc import imsave imsave('cat.jpg', images[26]) # image no #