def postprocess(self, y):
if self.labeled_segments:
y, coordinates = y
else:
coordinates = []
if self.type == "auto":
if isinstance(y, np.ndarray):
dtype = "numpy"
elif isinstance(y, PIL.Image.Image):
dtype = "pil"
elif isinstance(y, str):
dtype = "file"
elif isinstance(y, ModuleType):
dtype = "plot"
else:
raise ValueError("Unknown type. Please choose from: 'numpy', 'pil', 'file', 'plot'.")
else:
dtype = self.type
if dtype in ["numpy", "pil"]:
if dtype == "pil":
y = np.array(y)
out_y = processing_utils.encode_array_to_base64(y)
elif dtype == "file":
out_y = processing_utils.encode_file_to_base64(y)
elif dtype == "plot":
out_y = processing_utils.encode_plot_to_base64(y)
else:
raise ValueError("Unknown type: " + dtype + ". Please choose from: 'numpy', 'pil', 'file', 'plot'.")
return out_y, coordinates
def postprocess(self, y):
"""
Parameters:
y (Union[numpy.array, PIL.Image, str, matplotlib.pyplot, Tuple[Union[numpy.array, PIL.Image, str], List[Tuple[str, float, float, float, float]]]]): image in specified format
Returns:
(str): base64 url data
"""
if self.type == "auto":
if isinstance(y, np.ndarray):
dtype = "numpy"
elif isinstance(y, PIL.Image.Image):
dtype = "pil"
elif isinstance(y, str):
dtype = "file"
elif isinstance(y, ModuleType):
dtype = "plot"
else:
raise ValueError(
"Unknown type. Please choose from: 'numpy', 'pil', 'file', 'plot'."
)
else:
dtype = self.type
if dtype in ["numpy", "pil"]:
if dtype == "pil":
y = np.array(y)
out_y = processing_utils.encode_array_to_base64(y)
elif dtype == "file":
out_y = processing_utils.encode_url_or_file_to_base64(y)
elif dtype == "plot":
out_y = processing_utils.encode_plot_to_base64(y)
else:
raise ValueError(
"Unknown type: " + dtype +
". Please choose from: 'numpy', 'pil', 'file', 'plot'.")
return out_y
def test_default_image(self):
max_pixel_value = lambda img: img.max()
img_interface = Interface(max_pixel_value, "image", "number", interpretation="default")
array = np.zeros((100,100))
array[0, 0] = 1
img = encode_array_to_base64(array)
interpretation = img_interface.interpret([img])[0]
self.assertGreater(interpretation[0][0], 0) # Checks to see if the top-left has >0 score.
def postprocess(self, y):
if self.type in ["numpy", "pil"]:
if self.type == "pil":
y = np.array(y)
return processing_utils.encode_array_to_base64(y)
elif self.type == "file":
return processing_utils.encode_file_to_base64(y)
elif self.type == "plot":
return processing_utils.encode_plot_to_base64(y)
else:
raise ValueError("Unknown type: " + self.type + ". Please choose from: 'numpy', 'pil', 'file', 'plot'.")
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
