import os
from matplotlib import gridspec
import matplotlib.pylab as plt
import numpy as np
import tensorflow as tf


def crop_center(image):
    """Returns a cropped square image."""
    shape = image.shape
    new_shape = min(shape[1], shape[2])
    offset_y = max(shape[1] - shape[2], 0) // 2
    offset_x = max(shape[2] - shape[1], 0) // 2
    image = tf.image.crop_to_bounding_box(
      image, offset_y, offset_x, new_shape, new_shape)
    return image

def load_image(image_url, image_size=(256, 256), preserve_aspect_ratio=True):
    """Loads and preprocesses images."""
    # Cache image file locally.
    image_path = tf.keras.utils.get_file(os.path.basename(image_url)[-128:], image_url)
    # Load and convert to float32 numpy array, add batch dimension, and normalize to range [0, 1].
    img = plt.imread(image_path).astype(np.float32)[np.newaxis, ...]
    if img.max() > 1.0:
        img = img / 255.
    if len(img.shape) == 3:
        img = tf.stack([img, img, img], axis=-1)
    img = crop_center(img)
    img = tf.image.resize(img, image_size, preserve_aspect_ratio=True)
    return img