utility_functions.py
def process_images(image, input_size):
    ''' 
    Scales, crops, and normalizes a PIL image for a PyTorch model,
    returns an Numpy array.
    
    Parameters:
     image        - PIL image 
     input_size   - input size for the image required by the model
    
    Returns:
     np_image     - Numpy array of image 
    
    '''
    
    # Define image transofrmations (resize, crop, tensor conversion, normalize)
    transform_image = transforms.Compose([transforms.Resize(256),
                                          transforms.CenterCrop(input_size),
                                          transforms.ToTensor(),
                                          transforms.Normalize([0.485, 0.456, 0.406], 
                                                               [0.229, 0.224, 0.225])])
    # Apply transformations to image
    np_image = transform_image(image)
    
    # Return Numpy array of image
    return np_image
 
    Scales, crops, and normalizes a PIL image for a PyTorch model,
    returns an Numpy array.
    
    Parameters:
     image        - PIL image 
     input_size   - input size for the image required by the model
    
    Returns:
     np_image     - Numpy array of image 
    
    
Scales, crops, and normalizes a PIL image for a PyTorch model,
returns an Numpy array.

image        - PIL image
input_size   - input size for the image required by the model

np_image     - Numpy array of image
Scales crops and normalizes a PIL image for a PyTorch model returns an Numpy array . image - PIL image input_size - input size for the image required by the model np_image - Numpy array of image
def process_images(image, input_size):
    transform_image = transforms.Compose([transforms.Resize(256),
                                          transforms.CenterCrop(input_size),
                                          transforms.ToTensor(),
                                          transforms.Normalize([0.485, 0.456, 0.406], 
                                                               [0.229, 0.224, 0.225])])
    np_image = transform_image(image)
    return np_image
def process_images ( image , input_size ) : transform_image = transforms . Compose ( [ transforms . Resize ( 256 ) , transforms . CenterCrop ( input_size ) , transforms . ToTensor ( ) , transforms . Normalize ( [ 0.485 , 0.456 , 0.406 ] , [ 0.229 , 0.224 , 0.225 ] ) ] ) np_image = transform_image ( image ) return np_image
Scales, crops, and normalizes a PIL image for a PyTorch model,
returns an Numpy array.
Scales crops and normalizes a PIL image for a PyTorch model returns an Numpy array .
''' 
    Scales, crops, and normalizes a PIL image for a PyTorch model,
    returns an Numpy array.
    
    Parameters:
     image        - PIL image 
     input_size   - input size for the image required by the model
    
    Returns:
     np_image     - Numpy array of image 
    
    ''' # Define image transofrmations (resize, crop, tensor conversion, normalize) # Apply transformations to image # Return Numpy array of image
image
input_size