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# under the License.
# File content is auto-generated. Do not modify.
# pylint: skip-file
from ._internal import SymbolBase
from ..base import _Null
[docs]
def adjust_lighting(data=None, alpha=_Null, name=None, attr=None, out=None, **kwargs):
r"""Adjust the lighting level of the input. Follow the AlexNet style.
Defined in /home/smola/mxnet/src/operator/image/image_random.cc:L259
Parameters
----------
data : Symbol
The input.
alpha : tuple of <float>, required
The lighting alphas for the R, G, B channels.
name : string, optional.
Name of the resulting symbol.
Returns
-------
Symbol
The result symbol.
"""
return (0,)
[docs]
def crop(data=None, x=_Null, y=_Null, width=_Null, height=_Null, name=None, attr=None, out=None, **kwargs):
r"""Crop an image NDArray of shape (H x W x C) or (N x H x W x C)
to the given size. Example:
.. code-block:: python
image = mx.nd.random.uniform(0, 255, (4, 2, 3)).astype(dtype=np.uint8)
mx.nd.image.crop(image, 1, 1, 2, 2).shape # (2, 2, 3)
image = mx.nd.random.uniform(0, 255, (2, 4, 2, 3)).astype(dtype=np.uint8)
mx.nd.image.crop(image, 1, 1, 2, 2) # (2, 2, 2, 3)
Defined in /home/smola/mxnet/src/operator/image/crop.cc:L49
Parameters
----------
data : Symbol
The input.
x : int, required
Left boundary of the cropping area.
y : int, required
Top boundary of the cropping area.
width : int, required
Width of the cropping area.
height : int, required
Height of the cropping area.
name : string, optional.
Name of the resulting symbol.
Returns
-------
Symbol
The result symbol.
"""
return (0,)
[docs]
def flip_left_right(data=None, name=None, attr=None, out=None, **kwargs):
r"""
Defined in /home/smola/mxnet/src/operator/image/image_random.cc:L199
Parameters
----------
data : Symbol
The input.
name : string, optional.
Name of the resulting symbol.
Returns
-------
Symbol
The result symbol.
"""
return (0,)
[docs]
def flip_top_bottom(data=None, name=None, attr=None, out=None, **kwargs):
r"""
Defined in /home/smola/mxnet/src/operator/image/image_random.cc:L211
Parameters
----------
data : Symbol
The input.
name : string, optional.
Name of the resulting symbol.
Returns
-------
Symbol
The result symbol.
"""
return (0,)
[docs]
def normalize(data=None, mean=_Null, std=_Null, name=None, attr=None, out=None, **kwargs):
r"""Normalize an tensor of shape (C x H x W) or (N x C x H x W) with mean and
standard deviation.
Given mean `(m1, ..., mn)` and std `(s\ :sub:`1`\ , ..., s\ :sub:`n`)` for `n` channels,
this transform normalizes each channel of the input tensor with:
.. math::
output[i] = (input[i] - m\ :sub:`i`\ ) / s\ :sub:`i`
If mean or std is scalar, the same value will be applied to all channels.
Default value for mean is 0.0 and stand deviation is 1.0.
Example:
.. code-block:: python
image = mx.nd.random.uniform(0, 1, (3, 4, 2))
normalize(image, mean=(0, 1, 2), std=(3, 2, 1))
[[[ 0.18293785 0.19761486]
[ 0.23839645 0.28142193]
[ 0.20092112 0.28598186]
[ 0.18162774 0.28241724]]
[[-0.2881726 -0.18821815]
[-0.17705294 -0.30780914]
[-0.2812064 -0.3512327 ]
[-0.05411351 -0.4716435 ]]
[[-1.0363373 -1.7273437 ]
[-1.6165586 -1.5223348 ]
[-1.208275 -1.1878313 ]
[-1.4711051 -1.5200229 ]]]
<NDArray 3x4x2 @cpu(0)>
image = mx.nd.random.uniform(0, 1, (2, 3, 4, 2))
normalize(image, mean=(0, 1, 2), std=(3, 2, 1))
[[[[ 0.18934818 0.13092826]
[ 0.3085322 0.27869293]
[ 0.02367868 0.11246539]
[ 0.0290431 0.2160573 ]]
[[-0.4898908 -0.31587923]
[-0.08369008 -0.02142242]
[-0.11092162 -0.42982462]
[-0.06499392 -0.06495637]]
[[-1.0213816 -1.526392 ]
[-1.2008414 -1.1990893 ]
[-1.5385206 -1.4795225 ]
[-1.2194707 -1.3211205 ]]]
[[[ 0.03942481 0.24021089]
[ 0.21330701 0.1940066 ]
[ 0.04778443 0.17912441]
[ 0.31488964 0.25287187]]
[[-0.23907584 -0.4470462 ]
[-0.29266903 -0.2631998 ]
[-0.3677222 -0.40683383]
[-0.11288315 -0.13154092]]
[[-1.5438497 -1.7834496 ]
[-1.431566 -1.8647819 ]
[-1.9812102 -1.675859 ]
[-1.3823645 -1.8503251 ]]]]
<NDArray 2x3x4x2 @cpu(0)>
Defined in /home/smola/mxnet/src/operator/image/image_random.cc:L171
Parameters
----------
data : Symbol
Input ndarray
mean : tuple of <float>, optional, default=[0,0,0,0]
Sequence of means for each channel. Default value is 0.
std : tuple of <float>, optional, default=[1,1,1,1]
Sequence of standard deviations for each channel. Default value is 1.
name : string, optional.
Name of the resulting symbol.
Returns
-------
Symbol
The result symbol.
"""
return (0,)
[docs]
def random_brightness(data=None, min_factor=_Null, max_factor=_Null, name=None, attr=None, out=None, **kwargs):
r"""
Defined in /home/smola/mxnet/src/operator/image/image_random.cc:L223
Parameters
----------
data : Symbol
The input.
min_factor : float, required
Minimum factor.
max_factor : float, required
Maximum factor.
name : string, optional.
Name of the resulting symbol.
Returns
-------
Symbol
The result symbol.
"""
return (0,)
[docs]
def random_color_jitter(data=None, brightness=_Null, contrast=_Null, saturation=_Null, hue=_Null, name=None, attr=None, out=None, **kwargs):
r"""
Defined in /home/smola/mxnet/src/operator/image/image_random.cc:L251
Parameters
----------
data : Symbol
The input.
brightness : float, required
How much to jitter brightness.
contrast : float, required
How much to jitter contrast.
saturation : float, required
How much to jitter saturation.
hue : float, required
How much to jitter hue.
name : string, optional.
Name of the resulting symbol.
Returns
-------
Symbol
The result symbol.
"""
return (0,)
[docs]
def random_contrast(data=None, min_factor=_Null, max_factor=_Null, name=None, attr=None, out=None, **kwargs):
r"""
Defined in /home/smola/mxnet/src/operator/image/image_random.cc:L230
Parameters
----------
data : Symbol
The input.
min_factor : float, required
Minimum factor.
max_factor : float, required
Maximum factor.
name : string, optional.
Name of the resulting symbol.
Returns
-------
Symbol
The result symbol.
"""
return (0,)
[docs]
def random_crop(data=None, xrange=_Null, yrange=_Null, width=_Null, height=_Null, interp=_Null, name=None, attr=None, out=None, **kwargs):
r"""Randomly crop an image NDArray of shape (H x W x C) or (N x H x W x C)
to the given size. Upsample result if `src` is smaller than `size`. Example:
.. code-block:: python
im = mx.nd.array(cv2.imread("flower.jpg"))
cropped_im, rect = mx.nd.image.random_crop(im, (100, 100))
Defined in /home/smola/mxnet/src/operator/image/crop.cc:L77
Parameters
----------
data : Symbol
The input.
xrange : tuple of <float>, optional, default=[0,1]
Left boundaries of the cropping area.
yrange : tuple of <float>, optional, default=[0,1]
Top boundaries of the cropping area.
width : int, required
The target image width
height : int, required
The target image height.
interp : int, optional, default='1'
Interpolation method for resizing. By default uses bilinear interpolationOptions are INTER_NEAREST - a nearest-neighbor interpolationINTER_LINEAR - a bilinear interpolationINTER_AREA - resampling using pixel area relationINTER_CUBIC - a bicubic interpolation over 4x4 pixel neighborhoodINTER_LANCZOS4 - a Lanczos interpolation over 8x8 pixel neighborhoodNote that the GPU version only support bilinear interpolation(1)
name : string, optional.
Name of the resulting symbol.
Returns
-------
Symbol
The result symbol.
"""
return (0,)
[docs]
def random_flip_left_right(data=None, p=_Null, name=None, attr=None, out=None, **kwargs):
r"""
Defined in /home/smola/mxnet/src/operator/image/image_random.cc:L205
Parameters
----------
data : Symbol
The input.
p : float, optional, default=0.5
The probablity of flipping the image.
name : string, optional.
Name of the resulting symbol.
Returns
-------
Symbol
The result symbol.
"""
return (0,)
[docs]
def random_flip_top_bottom(data=None, p=_Null, name=None, attr=None, out=None, **kwargs):
r"""
Defined in /home/smola/mxnet/src/operator/image/image_random.cc:L217
Parameters
----------
data : Symbol
The input.
p : float, optional, default=0.5
The probablity of flipping the image.
name : string, optional.
Name of the resulting symbol.
Returns
-------
Symbol
The result symbol.
"""
return (0,)
[docs]
def random_hue(data=None, min_factor=_Null, max_factor=_Null, name=None, attr=None, out=None, **kwargs):
r"""
Defined in /home/smola/mxnet/src/operator/image/image_random.cc:L244
Parameters
----------
data : Symbol
The input.
min_factor : float, required
Minimum factor.
max_factor : float, required
Maximum factor.
name : string, optional.
Name of the resulting symbol.
Returns
-------
Symbol
The result symbol.
"""
return (0,)
[docs]
def random_lighting(data=None, alpha_std=_Null, name=None, attr=None, out=None, **kwargs):
r"""Randomly add PCA noise. Follow the AlexNet style.
Defined in /home/smola/mxnet/src/operator/image/image_random.cc:L266
Parameters
----------
data : Symbol
The input.
alpha_std : float, optional, default=0.0500000007
Level of the lighting noise.
name : string, optional.
Name of the resulting symbol.
Returns
-------
Symbol
The result symbol.
"""
return (0,)
[docs]
def random_resized_crop(data=None, width=_Null, height=_Null, area=_Null, ratio=_Null, interp=_Null, max_trial=_Null, name=None, attr=None, out=None, **kwargs):
r"""Randomly crop an image NDArray of shape (H x W x C) or (N x H x W x C)
to the given size. Randomize area and aspect ratio. Upsample result if `src` is smaller than `size`.
Example:
.. code-block:: python
im = mx.nd.array(cv2.imread("flower.jpg"))
cropped_im, rect = mx.nd.image.random_resized_crop(im, (100, 100))
Defined in /home/smola/mxnet/src/operator/image/crop.cc:L114
Parameters
----------
data : Symbol
The input.
width : int, required
The target image width
height : int, required
The target image height.
area : tuple of <float>, optional, default=[0.08,1]
Range of cropping area percentage.
ratio : tuple of <float>, optional, default=[0.75,1.33333]
Range of aspect ratio of the randomly cropped area.
interp : int, optional, default='1'
Interpolation method for resizing. By default uses bilinear interpolationOptions are INTER_NEAREST - a nearest-neighbor interpolationINTER_LINEAR - a bilinear interpolationINTER_AREA - resampling using pixel area relationINTER_CUBIC - a bicubic interpolation over 4x4 pixel neighborhoodINTER_LANCZOS4 - a Lanczos interpolation over 8x8 pixel neighborhoodNote that the GPU version only support bilinear interpolation(1)
max_trial : int, optional, default='10'
Max trial before fallback to center crop.
name : string, optional.
Name of the resulting symbol.
Returns
-------
Symbol
The result symbol.
"""
return (0,)
[docs]
def random_saturation(data=None, min_factor=_Null, max_factor=_Null, name=None, attr=None, out=None, **kwargs):
r"""
Defined in /home/smola/mxnet/src/operator/image/image_random.cc:L237
Parameters
----------
data : Symbol
The input.
min_factor : float, required
Minimum factor.
max_factor : float, required
Maximum factor.
name : string, optional.
Name of the resulting symbol.
Returns
-------
Symbol
The result symbol.
"""
return (0,)
[docs]
def resize(data=None, size=_Null, keep_ratio=_Null, interp=_Null, name=None, attr=None, out=None, **kwargs):
r"""Resize an image NDArray of shape (H x W x C) or (N x H x W x C)
to the given size. Example:
.. code-block:: python
image = mx.nd.random.uniform(0, 255, (4, 2, 3)).astype(dtype=np.uint8)
mx.nd.image.resize(image, (3, 3))
[[[124 111 197]
[158 80 155]
[193 50 112]]
[[110 100 113]
[134 165 148]
[157 231 182]]
[[202 176 134]
[174 191 149]
[147 207 164]]]
<NDArray 3x3x3 @cpu(0)>
image = mx.nd.random.uniform(0, 255, (2, 4, 2, 3)).astype(dtype=np.uint8)
mx.nd.image.resize(image, (2, 2))
[[[[ 59 133 80]
[187 114 153]]
[[ 38 142 39]
[207 131 124]]]
[[[117 125 136]
[191 166 150]]
[[129 63 113]
[182 109 48]]]]
<NDArray 2x2x2x3 @cpu(0)>
Defined in /home/smola/mxnet/src/operator/image/resize.cc:L73
Parameters
----------
data : Symbol
The input.
size : Shape(tuple), optional, default=[]
Size of new image. Could be (width, height) or (size)
keep_ratio : boolean, optional, default=0
Whether to resize the short edge or both edges to `size`, if size is give as an integer.
interp : int, optional, default='1'
Interpolation method for resizing. By default uses bilinear interpolationOptions are INTER_NEAREST - a nearest-neighbor interpolationINTER_LINEAR - a bilinear interpolationINTER_AREA - resampling using pixel area relationINTER_CUBIC - a bicubic interpolation over 4x4 pixel neighborhoodINTER_LANCZOS4 - a Lanczos interpolation over 8x8 pixel neighborhoodNote that the GPU version only support bilinear interpolation(1)
name : string, optional.
Name of the resulting symbol.
Returns
-------
Symbol
The result symbol.
"""
return (0,)
[docs]
def to_tensor(data=None, name=None, attr=None, out=None, **kwargs):
r"""Converts an image NDArray of shape (H x W x C) or (N x H x W x C)
with values in the range [0, 255] to a tensor NDArray of shape (C x H x W) or (N x C x H x W)
with values in the range [0, 1].
Examples
--------
>>> image = mx.nd.random.uniform(0, 255, (4, 2, 3)).astype(dtype=np.uint8)
>>> to_tensor(image)
[[[ 0.85490197 0.72156864]
[ 0.09019608 0.74117649]
[ 0.61960787 0.92941177]
[ 0.96470588 0.1882353 ]]
[[ 0.6156863 0.73725492]
[ 0.46666667 0.98039216]
[ 0.44705883 0.45490196]
[ 0.01960784 0.8509804 ]]
[[ 0.39607844 0.03137255]
[ 0.72156864 0.52941179]
[ 0.16470589 0.7647059 ]
[ 0.05490196 0.70588237]]]
<NDArray 3x4x2 @cpu(0)>
>>> image = mx.nd.random.uniform(0, 255, (2, 4, 2, 3)).astype(dtype=np.uint8)
>>> to_tensor(image)
[[[[0.11764706 0.5803922 ]
[0.9411765 0.10588235]
[0.2627451 0.73333335]
[0.5647059 0.32156864]]
[[0.7176471 0.14117648]
[0.75686276 0.4117647 ]
[0.18431373 0.45490196]
[0.13333334 0.6156863 ]]
[[0.6392157 0.5372549 ]
[0.52156866 0.47058824]
[0.77254903 0.21568628]
[0.01568628 0.14901961]]]
[[[0.6117647 0.38431373]
[0.6784314 0.6117647 ]
[0.69411767 0.96862745]
[0.67058825 0.35686275]]
[[0.21960784 0.9411765 ]
[0.44705883 0.43529412]
[0.09803922 0.6666667 ]
[0.16862746 0.1254902 ]]
[[0.6156863 0.9019608 ]
[0.35686275 0.9019608 ]
[0.05882353 0.6509804 ]
[0.20784314 0.7490196 ]]]]
<NDArray 2x3x4x2 @cpu(0)>
Defined in /home/smola/mxnet/src/operator/image/image_random.cc:L94
Parameters
----------
data : Symbol
Input ndarray
name : string, optional.
Name of the resulting symbol.
Returns
-------
Symbol
The result symbol.
"""
return (0,)
__all__ = ['adjust_lighting', 'crop', 'flip_left_right', 'flip_top_bottom', 'normalize', 'random_brightness', 'random_color_jitter', 'random_contrast', 'random_crop', 'random_flip_left_right', 'random_flip_top_bottom', 'random_hue', 'random_lighting', 'random_resized_crop', 'random_saturation', 'resize', 'to_tensor']