debug

app.py

@@ -4,69 +4,17 @@ import tensorflow_hub as hub
 import matplotlib.pyplot as plt
 import numpy as np
 
-hub_model = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2')
-
-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 PIL.Image.fromarray(tensor)
-
-def load_img(path_to_img):
-    max_dim = 512
-    img = tf.io.read_file(path_to_img)
-    img = tf.image.decode_image(img, channels=3)
-    img = tf.image.convert_image_dtype(img, tf.float32)
-
-    shape = tf.cast(tf.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
-  
+hub_model = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2') 
 
 
 def stylize(content_image, style_image):
     content_image = content_image.astype(np.float32) / 255.
     style_image = style_image.astype(np.float32) / 255.
     
-    
-    style_image = tf.image.resize(style_image, (256, 256))
+    #style_image = tf.image.resize(style_image, (256, 256))
 
-    #content_image = load_img(content_path)
-    #style_image = load_img(style_path)
-    #stylized_image = hub_model(tf.constant(content_image), tf.constant(style_image))[0]
     return style_image
     
-    #content_image = plt.imread(content_image_path)
-    #style_image = plt.imread(style_image_path)
-    #content_image = content_image.astype(np.float32)[np.newaxis, ...] / 255.
-    #style_image = style_image.astype(np.float32)[np.newaxis, ...] / 255.
-    #style_image = tf.image.resize(style_image, (256, 256))
-    
-    #stylized_image = hub_model(tf.constant(content_image), tf.constant(style_image))[0]
-    #return tensor_to_image(stylized_image)
-    
-    
-# Load content and style images (see example in the attached colab).
-#content_image = plt.imread(content_image_path)
-#style_image = plt.imread(style_image_path)
-
-# Convert to float32 numpy array, add batch dimension, and normalize to range [0, 1]. Example using numpy:
-#content_image = content_image.astype(np.float32)[np.newaxis, ...] / 255.
-#style_image = style_image.astype(np.float32)[np.newaxis, ...] / 255.
-
-# Optionally resize the images. It is recommended that the style image is about
-# 256 pixels (this size was used when training the style transfer network).
-# The content image can be any size.
-# style_image = tf.image.resize(style_image, (256, 256))
-
 
 iface = gr.Interface(fn=stylize, inputs=["image", "image"], outputs="image")
 iface.launch()