Upload app.py

app.py

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+import tensorflow as tf
+import tensorflow_hub as hub
+import matplotlib.pyplot as plt
+import os
+from PIL import Image
+import numpy as np
+import math
+import functools
+from matplotlib import gridspec
+import gradio 
+import pickle
+
+def load_img(img):
+#     max_dim = 256
+#     img = tf.image.convert_image_dtype(img, tf.float32)
+
+#     shape = tf.cast(np.shape(img)[:-1], tf.float32)
+#     long_dim = max(shape)
+#     scale = max_dim / long_dim
+#     new_shape = tf.cast(shape * scale, tf.int32)
+#     img=tf.convert_to_tensor(img)
+#     img = tf.image.convert_image_dtype(img, tf.float32)
+#     img = tf.image.resize(img, (256,256))
+#     img = img[tf.newaxis, :]
+    max_dim = 256
+    img = tf.image.convert_image_dtype(img, tf.float32)
+
+    shape = tf.cast(np.shape(img)[:-1], tf.float32)
+    long_dim = max(shape)
+    scale = max_dim / long_dim
+
+    new_shape = tf.cast(shape * scale, tf.int32)
+
+    img = tf.image.resize(img, new_shape)
+    img = img[tf.newaxis, :]
+    return img
+
+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
+
+@functools.lru_cache(maxsize=None)
+def load_image(img, 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 = tf.io.decode_image(
+#         tf.io.read_file(image_url),
+#         channels=3, dtype=tf.float32)[tf.newaxis, ...]
+    max_dim = 256
+    img = tf.image.convert_image_dtype(img, tf.float32)
+
+    shape = tf.cast(np.shape(img)[:-1], tf.float32)
+    long_dim = max(shape)
+    scale = max_dim / long_dim
+    new_shape = tf.cast(shape * scale, tf.int32)
+
+    #img = crop_center(img)
+    img = tf.image.resize(img, new_shape, preserve_aspect_ratio=True)
+    img = img[tf.newaxis, :]
+    return img
+
+def show_n(images, titles=('',)):
+    n = len(images)
+    image_sizes = [image.shape[1] for image in images]
+    w = (image_sizes[0] * 6) // 320
+    plt.figure(figsize=(w * n, w))
+    gs = gridspec.GridSpec(1, n, width_ratios=image_sizes)
+    for i in range(n):
+        plt.subplot(gs[i])
+        plt.imshow(images[i][0], aspect='equal')
+        plt.axis('off')
+        plt.title(titles[i] if len(titles) > i else '')
+    plt.show()
+
+
+
+def load_content_style_img(style_image,content_image):
+    style_image=np.array(style_image)
+    content_image=np.array(content_image)
+    width,height=content_image.shape[1],content_image.shape[0]
+    content_image = load_img(content_image)
+    style_image = load_img(style_image)
+    #content_image = crop_center(content_image)
+    content_image = tf.image.resize(content_image, (width,height), preserve_aspect_ratio=True)
+    style_image = crop_center(style_image)
+    style_image = tf.image.resize(style_image, (256,256), preserve_aspect_ratio=True)
+    style_image = tf.nn.avg_pool(style_image, ksize=[3,3], strides=[1,1], padding='SAME')
+    return style_image,content_image
+
+# style_image,content_image=load_content_style_img(style,content)
+
+# display([content_image, style_image])
+#show_n([content_image, style_image], ['Content image', 'Style image'])
+
+hub_handle = 'https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2'
+hub_module = hub.load(hub_handle)
+
+def tensor_to_image(tensor):
+    tensor = tensor*255
+    tensor = np.array(tensor, dtype=np.uint8)
+    if np.ndim(tensor)>3:
+        assert tensor.shape[0] == 1
+        tensor = tensor[0]
+    return Image.fromarray(tensor)
+
+stylized_image=0
+def train(style,content):
+    style_image,content_image=load_content_style_img(style,content)
+    outputs = hub_module(tf.constant(content_image), tf.constant(style_image))
+    stylized_image = outputs[0]
+    stylized_image=tensor_to_image(stylized_image)
+    return stylized_image
+
+gr=gradio.Interface(fn=train, inputs=['image','image'], outputs='image')
+gr.launch(share=True)