utility.py
def process_image(image):
    ''' Scales, crops, and normalizes a PIL image for a PyTorch model,
        returns an Numpy array
    '''
    
    # Resize image       
    ratio = image.size[1] / image.size[0]
    image = image.resize((256, int(ratio * 256)))
    half_width = image.size[0] / 2
    half_height = image.size[1] / 2
    
    # Crop image
    cropped_image = image.crop(
        (
        half_width - 112,
        half_height - 112,
        half_width + 112,
        half_height + 112
        )
                        )

    np_image = np.array(cropped_image) / 255.
    mean = np.array([0.485, 0.456, 0.406])
    std = np.array([0.229, 0.224, 0.225])     
    np_image = (np_image - mean) / std
    np_image = np_image.transpose((2, 0, 1))
    
    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
def process_image(image):
    ratio = image.size[1] / image.size[0]
    image = image.resize((256, int(ratio * 256)))
    half_width = image.size[0] / 2
    half_height = image.size[1] / 2
    cropped_image = image.crop(
        (
        half_width - 112,
        half_height - 112,
        half_width + 112,
        half_height + 112
        )
                        )
    np_image = np.array(cropped_image) / 255.
    mean = np.array([0.485, 0.456, 0.406])
    std = np.array([0.229, 0.224, 0.225])     
    np_image = (np_image - mean) / std
    np_image = np_image.transpose((2, 0, 1))
    return np_image
def process_image ( image ) : ratio = image . size [ 1 ] / image . size [ 0 ] image = image . resize ( ( 256 , int ( ratio * 256 ) ) ) half_width = image . size [ 0 ] / 2 half_height = image . size [ 1 ] / 2 cropped_image = image . crop ( ( half_width - 112 , half_height - 112 , half_width + 112 , half_height + 112 ) ) np_image = np . array ( cropped_image ) / 255. mean = np . array ( [ 0.485 , 0.456 , 0.406 ] ) std = np . array ( [ 0.229 , 0.224 , 0.225 ] ) np_image = ( np_image - mean ) / std np_image = np_image . transpose ( ( 2 , 0 , 1 ) ) 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
    ''' # Resize image        # Crop image
image