utils.py

"""
Most codes from https://github.com/carpedm20/DCGAN-tensorflow
"""
from __future__ import division
import math
import random
import gzip
import numpy as np
from time import gmtime, strftime
from six.moves import xrange
import matplotlib.pyplot as plt
import os, gzip
from six.moves import cPickle as pickle
import os
import platform
from subprocess import check_output
from tensorflow.keras.datasets import cifar10

classes = ('plane', 'car', 'bird', 'cat',
           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')

import tensorflow as tf

def load_cifar10(dataset_name):
    (X_train, y_train), (X_test, y_test) = cifar10.load_data()
    x_train = X_train.astype('float32')
    x_test = X_test.astype('float32')
    X = np.concatenate((x_train, x_test), axis=0)
    y = np.concatenate((y_train, y_test), axis=0).astype(np.int)
    seed = 333
    np.random.seed(seed)
    np.random.shuffle(X)
    np.random.seed(seed)
    np.random.shuffle(y)

    X, X_test = np.split(X,[55000])
    y, y_test= np.split(y,[55000])

    y_vec = np.zeros((len(y), 10), dtype=np.float)
    for i, label in enumerate(y):
        y_vec[i, y[i]] = 1.0

    y_vec_test = np.zeros((len(y), 10), dtype=np.float)
    for i, label in enumerate(y_test):
        y_vec_test[i, y_test[i]] = 1.0
    return X / 255., X_test / 255., y_vec, y_vec_test



def load_mnist(dataset_name):
    data_dir = os.path.join("./data", dataset_name)

    def extract_data(filename, num_data, head_size, data_size):
        with gzip.open(filename) as bytestream:
            bytestream.read(head_size)
            buf = bytestream.read(data_size * num_data)
            data = np.frombuffer(buf, dtype=np.uint8).astype(np.float)
        return data

    data = extract_data(data_dir + '/train-images-idx3-ubyte.gz', 60000, 16, 28 * 28)
    trX = data.reshape((60000, 28, 28, 1))

    data = extract_data(data_dir + '/train-labels-idx1-ubyte.gz', 60000, 8, 1)
    trY = data.reshape((60000))

    data = extract_data(data_dir + '/t10k-images-idx3-ubyte.gz', 10000, 16, 28 * 28)
    teX = data.reshape((10000, 28, 28, 1))

    data = extract_data(data_dir + '/t10k-labels-idx1-ubyte.gz', 10000, 8, 1)
    teY = data.reshape((10000))

    trY = np.asarray(trY)
    teY = np.asarray(teY)

    X = np.concatenate((trX, teX), axis=0)
    y = np.concatenate((trY, teY), axis=0).astype(np.int)

    seed = 547
    np.random.seed(seed)
    np.random.shuffle(X)
    np.random.seed(seed)
    np.random.shuffle(y)

    X, X_test = np.split(X,[69000])
    y, y_test= np.split(y,[69000])

    y_vec = np.zeros((len(y), 10), dtype=np.float)
    for i, label in enumerate(y):
        y_vec[i, y[i]] = 1.0
    y_vec_test = np.zeros((len(y_test), 10), dtype=np.float)
    for i, label in enumerate(y_test):
        y_vec_test[i, y_test[i]] = 1.0

    return X / 255., X_test / 255., y_vec, y_vec_test

def check_folder(log_dir):
    if not os.path.exists(log_dir):
        os.makedirs(log_dir)
    return log_dir


def get_image(image_path, input_height, input_width, resize_height=64, resize_width=64, crop=True, grayscale=False):
    image = imread(image_path, grayscale)
    return transform(image, input_height, input_width, resize_height, resize_width, crop)

def save_images(images, size, image_path):
    return imsave(images, size, image_path)

def imread(path, grayscale = False):
    if (grayscale):
        return scipy.misc.imread(path, flatten = True).astype(np.float)
    else:
        return scipy.misc.imread(path).astype(np.float)

def merge_images(images, size):
    return inverse_transform(images)

def merge(images, size):
    h, w = images.shape[1], images.shape[2]
    if (images.shape[3] in (3,4)):
        c = images.shape[3]
        img = np.zeros((h * size[0], w * size[1], c))
        for idx, image in enumerate(images):
            i = idx % size[1]
            j = idx // size[1]
            img[j * h:j * h + h, i * w:i * w + w, :] = image
        return img
    elif images.shape[3]==1:
        img = np.zeros((h * size[0], w * size[1]))
        for idx, image in enumerate(images):
            i = idx % size[1]
            j = idx // size[1]
            img[j * h:j * h + h, i * w:i * w + w] = image[:,:,0]
        return img
    else:
        raise ValueError('in merge(images,size) images parameter ''must have dimensions: HxW or HxWx3 or HxWx4')

def imsave(images, size, path):
    image = np.squeeze(merge(images, size))
    return imageio.imwrite(path, image)

def center_crop(x, crop_h, crop_w, resize_h=64, resize_w=64):
    if crop_w is None:
        crop_w = crop_h
    h, w = x.shape[:2]
    j = int(round((h - crop_h)/2.))
    i = int(round((w - crop_w)/2.))
    return scipy.misc.imresize(x[j:j+crop_h, i:i+crop_w], [resize_h, resize_w])

def transform(image, input_height, input_width, resize_height=64, resize_width=64, crop=True):
    if crop:
        cropped_image = center_crop(image, input_height, input_width, resize_height, resize_width)
    else:
        cropped_image = scipy.misc.imresize(image, [resize_height, resize_width])
    return np.array(cropped_image)/127.5 - 1.


""" Drawing Tools """
# borrowed from https://github.com/ykwon0407/variational_autoencoder/blob/master/variational_bayes.ipynb
def save_scattered_image(z, id, z_range_x, z_range_y, name='scattered_image.jpg'):
    N = 10
    plt.figure(figsize=(8, 6))
    plt.scatter(z[:, 0], z[:, 1], c=np.argmax(id, 1), marker='o', edgecolor='none', cmap=discrete_cmap(N, 'jet'))
    plt.colorbar(ticks=range(N))
    axes = plt.gca()
    axes.set_xlim([-z_range_x, z_range_x])
    axes.set_ylim([-z_range_y, z_range_y])
    plt.grid(True)
    plt.savefig(name)

# borrowed from https://gist.github.com/jakevdp/91077b0cae40f8f8244a
def discrete_cmap(N, base_cmap=None):
    """Create an N-bin discrete colormap from the specified input map"""

    # Note that if base_cmap is a string or None, you can simply do
    #    return plt.cm.get_cmap(base_cmap, N)
    # The following works for string, None, or a colormap instance:

    base = plt.cm.get_cmap(base_cmap)
    color_list = base(np.linspace(0, 1, N))
    cmap_name = base.name + str(N)
    return base.from_list(cmap_name, color_list, N)