def preprocess(self, x):
"""
Default preprocessing method for is to convert the picture to black and white and resize to be 48x48
"""
im = processing_utils.decode_base64_to_image(x)
im = im.convert('RGB')
im = processing_utils.resize_and_crop(
im, (self.image_width, self.image_height))
return np.array(im)
def embed(self, x):
shape = (100, 100) if self.shape is None else self.shape
if self.type == "pil":
im = x
elif self.type == "numpy":
im = PIL.Image.fromarray(x)
elif self.type == "file":
im = PIL.Image.open(x)
else:
raise ValueError("Unknown type: " + str(self.type) + ". Please choose from: 'numpy', 'pil', 'file'.")
im = processing_utils.resize_and_crop(im, (shape[0], shape[1]))
return np.asarray(im).flatten()
def preprocess(self, x):
im = processing_utils.decode_base64_to_image(x)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
im = im.convert(self.image_mode)
if self.shape is not None:
im = processing_utils.resize_and_crop(
im, (self.shape[0], self.shape[1]))
if self.type == "pil":
return im
elif self.type == "numpy":
return np.array(im)
elif self.type == "file":
file_obj = tempfile.NamedTemporaryFile()
im.save(file_obj.name)
return file_obj
def get_interpretation_neighbors(self, x):
x = processing_utils.decode_base64_to_image(x)
if self.shape is not None:
x = processing_utils.resize_and_crop(x, self.shape)
image = np.array(x)
segments_slic = slic(image, self.interpretation_segments, compactness=10, sigma=1)
leave_one_out_tokens, masks = [], []
replace_color = np.mean(image, axis=(0, 1))
for (i, segVal) in enumerate(np.unique(segments_slic)):
mask = segments_slic == segVal
white_screen = np.copy(image)
white_screen[segments_slic == segVal] = replace_color
leave_one_out_tokens.append(
processing_utils.encode_array_to_base64(white_screen))
masks.append(mask)
return leave_one_out_tokens, {"masks": masks}, True
def get_interpretation_scores(self, x, neighbors, scores, masks):
"""
Returns:
(List[List[float]]): A 2D array representing the interpretation score of each pixel of the image.
"""
x = processing_utils.decode_base64_to_image(x)
if self.shape is not None:
x = processing_utils.resize_and_crop(x, self.shape)
x = np.array(x)
output_scores = np.zeros((x.shape[0], x.shape[1]))
for score, mask in zip(scores, masks):
output_scores += score * mask
max_val, min_val = np.max(output_scores), np.min(output_scores)
if max_val > 0:
output_scores = (output_scores - min_val) / (max_val - min_val)
return output_scores.tolist()
def preprocess(self, x):
im = processing_utils.decode_base64_to_image(x)
fmt = im.format
with warnings.catch_warnings():
warnings.simplefilter("ignore")
im = im.convert(self.image_mode)
if self.shape is not None:
im = processing_utils.resize_and_crop(im, self.shape)
if self.invert_colors:
im = PIL.ImageOps.invert(im)
if self.type == "pil":
return im
elif self.type == "numpy":
return np.array(im)
elif self.type == "file":
file_obj = tempfile.NamedTemporaryFile(suffix=("."+fmt.lower() if fmt is not None else ".png"))
im.save(file_obj.name)
return file_obj
else:
raise ValueError("Unknown type: " + str(self.type) + ". Please choose from: 'numpy', 'pil', 'file'.")
