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image.py
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image.py
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#
# Copyright (c) 2019, Neptune Labs Sp. z o.o.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import io
import os
import numpy
import six
from PIL import Image
from neptune.exceptions import FileNotFound, InvalidChannelValue
def get_image_content(image):
if isinstance(image, six.string_types):
if not os.path.exists(image):
raise FileNotFound(image)
with open(image, "rb") as image_file:
return image_file.read()
elif isinstance(image, numpy.ndarray):
return _get_numpy_as_image(image)
elif isinstance(image, Image.Image):
return _get_pil_image_data(image)
else:
try:
from matplotlib import figure
if isinstance(image, figure.Figure):
return _get_figure_as_image(image)
except ImportError:
pass
try:
from torch import Tensor as TorchTensor
if isinstance(image, TorchTensor):
return _get_numpy_as_image(image.detach().numpy())
except ImportError:
pass
try:
from tensorflow import Tensor as TensorflowTensor
if isinstance(image, TensorflowTensor):
return _get_numpy_as_image(image.numpy())
except ImportError:
pass
raise InvalidChannelValue(expected_type="image", actual_type=type(image).__name__)
def _get_figure_as_image(figure):
with io.BytesIO() as image_buffer:
figure.savefig(image_buffer, format="png", bbox_inches="tight")
return image_buffer.getvalue()
def _get_pil_image_data(image):
with io.BytesIO() as image_buffer:
image.save(image_buffer, format="PNG")
return image_buffer.getvalue()
def _get_numpy_as_image(array):
array = array.copy() # prevent original array from modifying
array *= 255
shape = array.shape
if len(shape) == 2:
return _get_pil_image_data(Image.fromarray(array.astype(numpy.uint8)))
if len(shape) == 3:
if shape[2] == 1:
array2d = numpy.array([[col[0] for col in row] for row in array])
return _get_pil_image_data(Image.fromarray(array2d.astype(numpy.uint8)))
if shape[2] in (3, 4):
return _get_pil_image_data(Image.fromarray(array.astype(numpy.uint8)))
raise ValueError(
"Incorrect size of numpy.ndarray. Should be 2-dimensional or"
" 3-dimensional with 3rd dimension of size 1, 3 or 4."
)