Image ops

Original Link : https://keras.io/api/ops/image/
Last Checked at : 2024-11-25

`affine_transform` function

keras.ops.image.affine_transform(
    images,
    transform,
    interpolation="bilinear",
    fill_mode="constant",
    fill_value=0,
    data_format=None,
)

Applies the given transform(s) to the image(s).

Arguments

images: Input image or batch of images. Must be 3D or 4D.
transform: Projective transform matrix/matrices. A vector of length 8 or tensor of size N x 8. If one row of transform is [a0, a1, a2, b0, b1, b2, c0, c1], then it maps the output point (x, y) to a transformed input point (x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k), where k = c0 x + c1 y + 1. The transform is inverted compared to the transform mapping input points to output points. Note that gradients are not backpropagated into transformation parameters. Note that c0 and c1 are only effective when using TensorFlow backend and will be considered as 0 when using other backends.
interpolation: Interpolation method. Available methods are "nearest", and "bilinear". Defaults to "bilinear".
fill_mode: Points outside the boundaries of the input are filled according to the given mode. Available methods are "constant", "nearest", "wrap" and "reflect". Defaults to "constant".
- "reflect": (d c b a | a b c d | d c b a) The input is extended by reflecting about the edge of the last pixel.
- "constant": (k k k k | a b c d | k k k k) The input is extended by filling all values beyond the edge with the same constant value k specified by fill_value.
- "wrap": (a b c d | a b c d | a b c d) The input is extended by wrapping around to the opposite edge.
- "nearest": (a a a a | a b c d | d d d d) The input is extended by the nearest pixel.
fill_value: Value used for points outside the boundaries of the input if fill_mode="constant". Defaults to 0.
data_format: A string specifying the data format of the input tensor. It can be either "channels_last" or "channels_first". "channels_last" corresponds to inputs with shape (batch, height, width, channels), while "channels_first" corresponds to inputs with shape (batch, channels, height, width). If not specified, the value will default to keras.config.image_data_format.

Returns

Applied affine transform image or batch of images.

Examples

>>> x = np.random.random((2, 64, 80, 3)) # batch of 2 RGB images
>>> transform = np.array(
...     [
...         [1.5, 0, -20, 0, 1.5, -16, 0, 0],  # zoom
...         [1, 0, -20, 0, 1, -16, 0, 0],  # translation
...     ]
... )
>>> y = keras.ops.image.affine_transform(x, transform)
>>> y.shape
(2, 64, 80, 3)

>>> x = np.random.random((64, 80, 3)) # single RGB image
>>> transform = np.array([1.0, 0.5, -20, 0.5, 1.0, -16, 0, 0])  # shear
>>> y = keras.ops.image.affine_transform(x, transform)
>>> y.shape
(64, 80, 3)

>>> x = np.random.random((2, 3, 64, 80)) # batch of 2 RGB images
>>> transform = np.array(
...     [
...         [1.5, 0, -20, 0, 1.5, -16, 0, 0],  # zoom
...         [1, 0, -20, 0, 1, -16, 0, 0],  # translation
...     ]
... )
>>> y = keras.ops.image.affine_transform(x, transform,
...     data_format="channels_first")
>>> y.shape
(2, 3, 64, 80)

source

`crop_images` function

keras.ops.image.crop_images(
    images,
    top_cropping=None,
    left_cropping=None,
    bottom_cropping=None,
    right_cropping=None,
    target_height=None,
    target_width=None,
    data_format=None,
)

Crop images to a specified height and width.

Arguments

images: Input image or batch of images. Must be 3D or 4D.
top_cropping: Number of columns to crop from the top.
left_cropping: Number of columns to crop from the left.
bottom_cropping: Number of columns to crop from the bottom.
right_cropping: Number of columns to crop from the right.
target_height: Height of the output images.
target_width: Width of the output images.
data_format: A string specifying the data format of the input tensor. It can be either "channels_last" or "channels_first". "channels_last" corresponds to inputs with shape (batch, height, width, channels), while "channels_first" corresponds to inputs with shape (batch, channels, height, width). If not specified, the value will default to keras.config.image_data_format.

Returns

Cropped image or batch of images.

Example

>>> images = np.reshape(np.arange(1, 28, dtype="float32"), [3, 3, 3])
>>> images[:,:,0] # print the first channel of the images
array([[ 1.,  4.,  7.],
       [10., 13., 16.],
       [19., 22., 25.]], dtype=float32)
>>> cropped_images = keras.image.crop_images(images, 0, 0, 2, 2)
>>> cropped_images[:,:,0] # print the first channel of the cropped images
array([[ 1.,  4.],
       [10., 13.]], dtype=float32)

source

`extract_patches` function

keras.ops.image.extract_patches(
    images, size, strides=None, dilation_rate=1, padding="valid", data_format=None
)

Extracts patches from the image(s).

Arguments

images: Input image or batch of images. Must be 3D or 4D.
size: Patch size int or tuple (patch_height, patch_width)
strides: strides along height and width. If not specified, or if None, it defaults to the same value as size.
dilation_rate: This is the input stride, specifying how far two consecutive patch samples are in the input. For value other than 1, strides must be 1. NOTE: strides > 1 is not supported in conjunction with dilation_rate > 1
padding: The type of padding algorithm to use: "same" or "valid".
data_format: A string specifying the data format of the input tensor. It can be either "channels_last" or "channels_first". "channels_last" corresponds to inputs with shape (batch, height, width, channels), while "channels_first" corresponds to inputs with shape (batch, channels, height, width). If not specified, the value will default to keras.config.image_data_format.

Returns

Extracted patches 3D (if not batched) or 4D (if batched)

Examples

>>> image = np.random.random(
...     (2, 20, 20, 3)
... ).astype("float32") # batch of 2 RGB images
>>> patches = keras.ops.image.extract_patches(image, (5, 5))
>>> patches.shape
(2, 4, 4, 75)
>>> image = np.random.random((20, 20, 3)).astype("float32") # 1 RGB image
>>> patches = keras.ops.image.extract_patches(image, (3, 3), (1, 1))
>>> patches.shape
(18, 18, 27)

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`hsv_to_rgb` function

keras.ops.image.hsv_to_rgb(images, data_format=None)

Convert HSV images to RGB.

images must be of float dtype, and the output is only well defined if the values in images are in [0, 1].

Arguments

images: Input image or batch of images. Must be 3D or 4D.
data_format: A string specifying the data format of the input tensor. It can be either "channels_last" or "channels_first". "channels_last" corresponds to inputs with shape (batch, height, width, channels), while "channels_first" corresponds to inputs with shape (batch, channels, height, width). If not specified, the value will default to keras.config.image_data_format.

Returns

RGB image or batch of RGB images.

Examples

>>> import numpy as np
>>> from keras import ops
>>> x = np.random.random((2, 4, 4, 3))
>>> y = ops.image.hsv_to_rgb(x)
>>> y.shape
(2, 4, 4, 3)

>>> x = np.random.random((4, 4, 3)) # Single HSV image
>>> y = ops.image.hsv_to_rgb(x)
>>> y.shape
(4, 4, 3)

>>> x = np.random.random((2, 3, 4, 4))
>>> y = ops.image.hsv_to_rgb(x, data_format="channels_first")
>>> y.shape
(2, 3, 4, 4)

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`map_coordinates` function

keras.ops.image.map_coordinates(
    inputs, coordinates, order, fill_mode="constant", fill_value=0
)

Map the input array to new coordinates by interpolation.

Note that interpolation near boundaries differs from the scipy function, because we fixed an outstanding bug scipy/issues/2640.

Arguments

inputs: The input array.
coordinates: The coordinates at which inputs is evaluated.
order: The order of the spline interpolation. The order must be 0 or 1. 0 indicates the nearest neighbor and 1 indicates the linear interpolation.
fill_mode: Points outside the boundaries of the inputs are filled according to the given mode. Available methods are "constant", "nearest", "wrap" and "mirror" and "reflect". Defaults to "constant".
- "constant": (k k k k | a b c d | k k k k) The inputs is extended by filling all values beyond the edge with the same constant value k specified by fill_value.
- "nearest": (a a a a | a b c d | d d d d) The inputs is extended by the nearest pixel.
- "wrap": (a b c d | a b c d | a b c d) The inputs is extended by wrapping around to the opposite edge.
- "mirror": (c d c b | a b c d | c b a b) The inputs is extended by mirroring about the edge.
- "reflect": (d c b a | a b c d | d c b a) The inputs is extended by reflecting about the edge of the last pixel.
fill_value: Value used for points outside the boundaries of the inputs if fill_mode="constant". Defaults to 0.

Returns

Output input or batch of inputs.

source

`pad_images` function

keras.ops.image.pad_images(
    images,
    top_padding=None,
    left_padding=None,
    bottom_padding=None,
    right_padding=None,
    target_height=None,
    target_width=None,
    data_format=None,
)

Pad images with zeros to the specified height and width.

Arguments

images: Input image or batch of images. Must be 3D or 4D.
top_padding: Number of rows of zeros to add on top.
left_padding: Number of columns of zeros to add on the left.
bottom_padding: Number of rows of zeros to add at the bottom.
right_padding: Number of columns of zeros to add on the right.
target_height: Height of output images.
target_width: Width of output images.
data_format: A string specifying the data format of the input tensor. It can be either "channels_last" or "channels_first". "channels_last" corresponds to inputs with shape (batch, height, width, channels), while "channels_first" corresponds to inputs with shape (batch, channels, height, width). If not specified, the value will default to keras.config.image_data_format.

Returns

Padded image or batch of images.

Example

>>> images = np.random.random((15, 25, 3))
>>> padded_images = keras.ops.image.pad_images(
...     images, 2, 3, target_height=20, target_width=30
... )
>>> padded_images.shape
(20, 30, 3)

>>> batch_images = np.random.random((2, 15, 25, 3))
>>> padded_batch = keras.ops.image.pad_images(
...     batch_images, 2, 3, target_height=20, target_width=30
... )
>>> padded_batch.shape
(2, 20, 30, 3)

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`resize` function

keras.ops.image.resize(
    images,
    size,
    interpolation="bilinear",
    antialias=False,
    crop_to_aspect_ratio=False,
    pad_to_aspect_ratio=False,
    fill_mode="constant",
    fill_value=0.0,
    data_format=None,
)

Resize images to size using the specified interpolation method.

Arguments

images: Input image or batch of images. Must be 3D or 4D.
size: Size of output image in (height, width) format.
interpolation: Interpolation method. Available methods are "nearest", "bilinear", and "bicubic". Defaults to "bilinear".
antialias: Whether to use an antialiasing filter when downsampling an image. Defaults to False.
crop_to_aspect_ratio: If True, resize the images without aspect ratio distortion. When the original aspect ratio differs from the target aspect ratio, the output image will be cropped so as to return the largest possible window in the image (of size (height, width)) that matches the target aspect ratio. By default (crop_to_aspect_ratio=False), aspect ratio may not be preserved.
pad_to_aspect_ratio: If True, pad the images without aspect ratio distortion. When the original aspect ratio differs from the target aspect ratio, the output image will be evenly padded on the short side.
fill_mode: When using pad_to_aspect_ratio=True, padded areas are filled according to the given mode. Only "constant" is supported at this time (fill with constant value, equal to fill_value).
fill_value: Float. Padding value to use when pad_to_aspect_ratio=True.
data_format: A string specifying the data format of the input tensor. It can be either "channels_last" or "channels_first". "channels_last" corresponds to inputs with shape (batch, height, width, channels), while "channels_first" corresponds to inputs with shape (batch, channels, height, width). If not specified, the value will default to keras.config.image_data_format.

Returns

Resized image or batch of images.

Examples

>>> x = np.random.random((2, 4, 4, 3)) # batch of 2 RGB images
>>> y = keras.ops.image.resize(x, (2, 2))
>>> y.shape
(2, 2, 2, 3)

>>> x = np.random.random((4, 4, 3)) # single RGB image
>>> y = keras.ops.image.resize(x, (2, 2))
>>> y.shape
(2, 2, 3)

>>> x = np.random.random((2, 3, 4, 4)) # batch of 2 RGB images
>>> y = keras.ops.image.resize(x, (2, 2),
...     data_format="channels_first")
>>> y.shape
(2, 3, 2, 2)

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`rgb_to_hsv` function

keras.ops.image.rgb_to_hsv(images, data_format=None)

Convert RGB images to HSV.

images must be of float dtype, and the output is only well defined if the values in images are in [0, 1].

All HSV values are in [0, 1]. A hue of 0 corresponds to pure red, 1/3 is pure green, and 2/3 is pure blue.

Arguments

images: Input image or batch of images. Must be 3D or 4D.
data_format: A string specifying the data format of the input tensor. It can be either "channels_last" or "channels_first". "channels_last" corresponds to inputs with shape (batch, height, width, channels), while "channels_first" corresponds to inputs with shape (batch, channels, height, width). If not specified, the value will default to keras.config.image_data_format.

Returns

HSV image or batch of HSV images.

Examples

>>> import numpy as np
>>> from keras import ops
>>> x = np.random.random((2, 4, 4, 3))
>>> y = ops.image.rgb_to_hsv(x)
>>> y.shape
(2, 4, 4, 3)

>>> x = np.random.random((4, 4, 3)) # Single RGB image
>>> y = ops.image.rgb_to_hsv(x)
>>> y.shape
(4, 4, 3)

>>> x = np.random.random((2, 3, 4, 4))
>>> y = ops.image.rgb_to_hsv(x, data_format="channels_first")
>>> y.shape
(2, 3, 4, 4)

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`rgb_to_grayscale` function

keras.ops.image.rgb_to_grayscale(images, data_format=None)

Convert RGB images to grayscale.

This function converts RGB images to grayscale images. It supports both 3D and 4D tensors.

Arguments

images: Input image or batch of images. Must be 3D or 4D.
data_format: A string specifying the data format of the input tensor. It can be either "channels_last" or "channels_first". "channels_last" corresponds to inputs with shape (batch, height, width, channels), while "channels_first" corresponds to inputs with shape (batch, channels, height, width). If not specified, the value will default to keras.config.image_data_format.

Returns

Grayscale image or batch of grayscale images.

Examples

>>> import numpy as np
>>> from keras import ops
>>> x = np.random.random((2, 4, 4, 3))
>>> y = ops.image.rgb_to_grayscale(x)
>>> y.shape
(2, 4, 4, 1)

>>> x = np.random.random((4, 4, 3)) # Single RGB image
>>> y = ops.image.rgb_to_grayscale(x)
>>> y.shape
(4, 4, 1)

>>> x = np.random.random((2, 3, 4, 4))
>>> y = ops.image.rgb_to_grayscale(x, data_format="channels_first")
>>> y.shape
(2, 1, 4, 4)

Image ops

affine_transform function

crop_images function

extract_patches function

hsv_to_rgb function

map_coordinates function

pad_images function

resize function

rgb_to_hsv function

rgb_to_grayscale function

`affine_transform` function

`crop_images` function

`extract_patches` function

`hsv_to_rgb` function

`map_coordinates` function

`pad_images` function

`resize` function

`rgb_to_hsv` function

`rgb_to_grayscale` function