Update app.py

app.py

@@ -8,6 +8,95 @@ import numpy as np
 import requests
 from io import BytesIO
 
+def load_image(img_path, max_size=400, shape=None):
+    ''' Load in and transform an image, making sure the image
+       is <= 400 pixels in the x-y dims.'''
+    if "http" in img_path:
+        response = requests.get(img_path)
+        image = Image.open(BytesIO(response.content)).convert('RGB')
+    else:
+        image = Image.open(img_path).convert('RGB')
+    
+    # large images will slow down processing
+    if max(image.size) > max_size:
+        size = max_size
+    else:
+        size = max(image.size)
+    
+    if shape is not None:
+        size = shape
+        
+    in_transform = transforms.Compose([
+                        transforms.Resize(size),
+                        transforms.ToTensor(),
+                        transforms.Normalize((0.485, 0.456, 0.406), 
+                                             (0.229, 0.224, 0.225))])
+
+    # discard the transparent, alpha channel (that's the :3) and add the batch dimension
+    image = in_transform(image)[:3,:,:].unsqueeze(0)
+    
+    return image
+
+# helper function for un-normalizing an image 
+# and converting it from a Tensor image to a NumPy image for display
+def im_convert(tensor):
+    """ Display a tensor as an image. """
+    
+    image = tensor.to("cpu").clone().detach()
+    image = image.numpy().squeeze()
+    image = image.transpose(1,2,0)
+    image = image * np.array((0.229, 0.224, 0.225)) + np.array((0.485, 0.456, 0.406))
+    image = image.clip(0, 1)
+
+    return image
+
+def get_features(image, model, layers=None):
+    """ Run an image forward through a model and get the features for 
+        a set of layers. Default layers are for VGGNet matching Gatys et al (2016)
+    """
+    
+    ## TODO: Complete mapping layer names of PyTorch's VGGNet to names from the paper
+    ## Need the layers for the content and style representations of an image
+    if layers is None:
+        layers = {'0': 'conv1_1',
+                  '5': 'conv2_1', 
+                  '10': 'conv3_1', 
+                  '19': 'conv4_1',
+                  '21': 'conv4_2',  ## content representation
+                  '28': 'conv5_1'}
+        
+        
+    ## -- do not need to change the code below this line -- ##
+    features = {}
+    x = image
+    # model._modules is a dictionary holding each module in the model
+    for name, layer in model._modules.items():
+        x = layer(x)
+        if name in layers:
+            features[layers[name]] = x
+            
+    return features
+
+
+def gram_matrix(tensor):
+    """ Calculate the Gram Matrix of a given tensor 
+        Gram Matrix: https://en.wikipedia.org/wiki/Gramian_matrix
+    """
+    
+    ## get the batch_size, depth, height, and width of the Tensor
+    ## reshape it, so we're multiplying the features for each channel
+    ## calculate the gram matrix
+   # get the batch_size, depth, height, and width of the Tensor
+    b, d, h, w = tensor.size()
+    
+    # reshape so we're multiplying the features for each channel
+    tensor = tensor.view(b * d, h * w)
+    
+    # calculate the gram matrix
+    gram = torch.mm(tensor, tensor.t())
+    
+    return gram
+
 #Load VGG19 model
 vgg = models.vgg19(pretrained=True).features
 for param in vgg.parameters():