utils.py

import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
plt.switch_backend('agg')

def denormalize(img):
    img = (img + 1) * 127.5
    return img.astype(np.uint8)

def normalize(img):
    return (img - 127.5) / 127.5

def visualize_rgb(img):
    """
    Visualize a rgb image
    :param img: RGB image
    """
    fig = plt.figure()
    ax = fig.add_subplot(1, 1, 1)
    ax.imshow(img)
    ax.axis("off")
    ax.set_title("Image")
    plt.show()

def save_rgb_img(img, path):
    """
    Save a rgb image
    """
    fig, ax = plt.subplots(1,1)
    ax.imshow(img)
    ax.axis("off")
    ax.set_title("RGB Image")

    plt.savefig(path)
    plt.close()

def write_log(callback, name, loss, batch_no):
    """
    Write training summary to TensorBoard
    """
    # for name, value in zip(names, logs):
    summary = tf.Summary()
    summary_value = summary.value.add()
    summary_value.simple_value = loss
    summary_value.tag = name
    callback.writer.add_summary(summary, batch_no)
    callback.writer.flush()