ndarray.image

Image NDArray API of MXNet.

Functions

adjust_lighting([data, alpha, out, name])	Adjust the lighting level of the input.
crop([data, x, y, width, height, out, name])	Crop an image NDArray of shape (H x W x C) or (N x H x W x C) to the given size.
flip_left_right([data, out, name])	Defined in src/operator/image/image_random.cc:L195
flip_top_bottom([data, out, name])	Defined in src/operator/image/image_random.cc:L205
normalize([data, mean, std, out, name])	Normalize an tensor of shape (C x H x W) or (N x C x H x W) with mean and standard deviation.
random_brightness([data, min_factor, …])	Defined in src/operator/image/image_random.cc:L215
random_color_jitter([data, brightness, …])	Defined in src/operator/image/image_random.cc:L246
random_contrast([data, min_factor, …])	Defined in src/operator/image/image_random.cc:L222
random_flip_left_right([data, out, name])	Defined in src/operator/image/image_random.cc:L200
random_flip_top_bottom([data, out, name])	Defined in src/operator/image/image_random.cc:L210
random_hue([data, min_factor, max_factor, …])	Defined in src/operator/image/image_random.cc:L238
random_lighting([data, alpha_std, out, name])	Randomly add PCA noise.
random_saturation([data, min_factor, …])	Defined in src/operator/image/image_random.cc:L230
resize([data, size, keep_ratio, interp, …])	Resize an image NDArray of shape (H x W x C) or (N x H x W x C) to the given size ..
to_tensor([data, out, name])	Converts an image NDArray of shape (H x W x C) or (N x H x W x C)

mxnet.ndarray.image.adjust_lighting(data=None, alpha=_Null, out=None, name=None, **kwargs)

Adjust the lighting level of the input. Follow the AlexNet style.

Defined in src/operator/image/image_random.cc:L254

Parameters

• data (NDArray) – The input.

• alpha (tuple of <float>, required) – The lighting alphas for the R, G, B channels.

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays

mxnet.ndarray.image.crop(data=None, x=_Null, y=_Null, width=_Null, height=_Null, out=None, name=None, **kwargs)

Crop an image NDArray of shape (H x W x C) or (N x H x W x C) to the given size. .. rubric:: Example

Defined in src/operator/image/crop.cc:L65

Parameters

• data (NDArray) – 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.

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays

mxnet.ndarray.image.flip_left_right(data=None, out=None, name=None, **kwargs)

Defined in src/operator/image/image_random.cc:L195

Parameters

• data (NDArray) – The input.

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays

mxnet.ndarray.image.flip_top_bottom(data=None, out=None, name=None, **kwargs)

Defined in src/operator/image/image_random.cc:L205

Parameters

• data (NDArray) – The input.

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays

mxnet.ndarray.image.normalize(data=None, mean=_Null, std=_Null, out=None, name=None, **kwargs)

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</sub>)` for n channels, this transform normalizes each channel of the input tensor with:

\[ \begin{align}\begin{aligned} output[i] = (input[i] - m\ :sub:`i`\ ) / s\ :sub:`i`\\If mean or std is scalar, the same value will be applied to all channels.\\Default value for mean is 0.0 and stand deviation is 1.0.\end{aligned}\end{align} \]

Example

Defined in src/operator/image/image_random.cc:L167

Parameters

• data (NDArray) – 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.

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays

mxnet.ndarray.image.random_brightness(data=None, min_factor=_Null, max_factor=_Null, out=None, name=None, **kwargs)

Defined in src/operator/image/image_random.cc:L215

Parameters

• data (NDArray) – The input.

• min_factor (float, required) – Minimum factor.

• max_factor (float, required) – Maximum factor.

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays

mxnet.ndarray.image.random_color_jitter(data=None, brightness=_Null, contrast=_Null, saturation=_Null, hue=_Null, out=None, name=None, **kwargs)

Defined in src/operator/image/image_random.cc:L246

Parameters

• data (NDArray) – 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.

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays

mxnet.ndarray.image.random_contrast(data=None, min_factor=_Null, max_factor=_Null, out=None, name=None, **kwargs)

Defined in src/operator/image/image_random.cc:L222

Parameters

• data (NDArray) – The input.

• min_factor (float, required) – Minimum factor.

• max_factor (float, required) – Maximum factor.

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays

mxnet.ndarray.image.random_flip_left_right(data=None, out=None, name=None, **kwargs)

Defined in src/operator/image/image_random.cc:L200

Parameters

• data (NDArray) – The input.

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays

mxnet.ndarray.image.random_flip_top_bottom(data=None, out=None, name=None, **kwargs)

Defined in src/operator/image/image_random.cc:L210

Parameters

• data (NDArray) – The input.

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays

mxnet.ndarray.image.random_hue(data=None, min_factor=_Null, max_factor=_Null, out=None, name=None, **kwargs)

Defined in src/operator/image/image_random.cc:L238

Parameters

• data (NDArray) – The input.

• min_factor (float, required) – Minimum factor.

• max_factor (float, required) – Maximum factor.

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays

mxnet.ndarray.image.random_lighting(data=None, alpha_std=_Null, out=None, name=None, **kwargs)

Randomly add PCA noise. Follow the AlexNet style.

Defined in src/operator/image/image_random.cc:L262

Parameters

• data (NDArray) – The input.

• alpha_std (float, optional, default=0.0500000007) – Level of the lighting noise.

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays

mxnet.ndarray.image.random_saturation(data=None, min_factor=_Null, max_factor=_Null, out=None, name=None, **kwargs)

Defined in src/operator/image/image_random.cc:L230

Parameters

• data (NDArray) – The input.

• min_factor (float, required) – Minimum factor.

• max_factor (float, required) – Maximum factor.

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays

mxnet.ndarray.image.resize(data=None, size=_Null, keep_ratio=_Null, interp=_Null, out=None, name=None, **kwargs)

Resize an image NDArray of shape (H x W x C) or (N x H x W x C) to the given size .. rubric:: Example

Defined in src/operator/image/resize.cc:L70

Parameters

• data (NDArray) – 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)

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays

mxnet.ndarray.image.to_tensor(data=None, out=None, name=None, **kwargs)

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]

Example

Defined in src/operator/image/image_random.cc:L92

Parameters

• data (NDArray) – Input ndarray

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays

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