(self, raw_inputs)
return imagenet_utils.preprocess_input(all_raw_inputs)
Args:
            raw_inputs (list of Images): a list of PIL Image objects
        Returns:
            array (float32): num images * height * width * num channels
Args:
            raw_inputs (list of Images): a list of PIL Image objects
        Returns:
            array (float32): num images * height * width * num channels
Args : raw_inputs ( list of Images ) : a list of PIL Image objects Returns : array ( float32 ) : num images * height * width * num channels
def preprocess(self, raw_inputs):
        """
        Args:
            raw_inputs (list of Images): a list of PIL Image objects
        Returns:
            array (float32): num images * height * width * num channels
        """
        image_arrays = []
        for raw_im in raw_inputs:
            im = raw_im.resize(VGG16_DIM[:2], Image.ANTIALIAS)
            im = im.convert('RGB')
            arr = np.array(im).astype('float32')
            image_arrays.append(arr)

        all_raw_inputs = np.array(image_arrays)
        return imagenet_utils.preprocess_input(all_raw_inputs)
def preprocess ( self , raw_inputs ) : image_arrays = [ ] for raw_im in raw_inputs : im = raw_im . resize ( VGG16_DIM [ : 2 ] , Image . ANTIALIAS ) im = im . convert ( 'RGB' ) arr = np . array ( im ) . astype ( 'float32' ) image_arrays . append ( arr ) all_raw_inputs = np . array ( image_arrays ) return imagenet_utils . preprocess_input ( all_raw_inputs )