Create helper.py

helper.py

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+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