# Image ops

[source]

### `affine_transform` function

``````keras.ops.image.affine_transform(
image,
transform,
interpolation="bilinear",
fill_mode="constant",
fill_value=0,
data_format="channels_last",
)
``````

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

Arguments

• image: 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: string, either `"channels_last"` or `"channels_first"`. The ordering of the dimensions in the inputs. `"channels_last"` corresponds to inputs with shape `(batch, height, width, channels)` while `"channels_first"` corresponds to inputs with shape `(batch, channels, height, weight)`. It defaults to the `image_data_format` value found in your Keras config file at `~/.keras/keras.json`. If you never set it, then it will be `"channels_last"`.

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)
``````

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### `crop_images` function

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

Crop `images` to a specified `height` and `width`.

Arguments

• images: 4-D batch of images of shape `(batch, height, width, channels)` or 3-D single image of shape `(height, width, channels)`.
• top_cropping: Number of columns to crop from the top.
• bottom_cropping: Number of columns to crop from the bottom.
• left_cropping: Number of columns to crop from the left.
• right_cropping: Number of columns to crop from the right.
• target_height: Height of the output images.
• target_width: Width of the output images.

Returns

If `images` were 4D, a 4D float Tensor of shape `(batch, target_height, target_width, channels)` If `images` were 3D, a 3D float Tensor of shape `(target_height, target_width, channels)`

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)
``````

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### `extract_patches` function

``````keras.ops.image.extract_patches(
image,
size,
strides=None,
dilation_rate=1,
data_format="channels_last",
)
``````

Extracts patches from the image(s).

Arguments

• image: Input image or batch of images. Must be 3D or 4D.
• size: Patch size int or tuple (patch_height, patch_widht)
• 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: string, either `"channels_last"` or `"channels_first"`. The ordering of the dimensions in the inputs. `"channels_last"` corresponds to inputs with shape `(batch, height, width, channels)` while `"channels_first"` corresponds to inputs with shape `(batch, channels, height, weight)`. It defaults to the `image_data_format` value found in your Keras config file at `~/.keras/keras.json`. If you never set it, then it will be `"channels_last"`.

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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### `map_coordinates` function

``````keras.ops.image.map_coordinates(
input, 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

• input: The input array.
• coordinates: The coordinates at which input 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 input 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 input 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 input is extended by the nearest pixel.
• `"wrap"`: `(a b c d | a b c d | a b c d)` The input is extended by wrapping around to the opposite edge.
• `"mirror"`: `(c d c b | a b c d | c b a b)` The input is extended by mirroring about the edge.
• `"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.
• fill_value: Value used for points outside the boundaries of the input if `fill_mode="constant"`. Defaults to `0`.

Returns

Output image or batch of images.

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### `pad_images` function

``````keras.ops.image.pad_images(
images,
target_height=None,
target_width=None,
)
``````

Pad `images` with zeros to the specified `height` and `width`.

Arguments

• images: 4D Tensor of shape `(batch, height, width, channels)` or 3D Tensor of shape `(height, width, channels)`.
• bottom_padding: Number of rows of zeros to add at the bottom.
• left_padding: Number of columns of zeros to add on the left.
• right_padding: Number of columns of zeros to add on the right.
• target_height: Height of output images.
• target_width: Width of output images.

Returns

If `images` were 4D, a 4D float Tensor of shape `(batch, target_height, target_width, channels)` If `images` were 3D, a 3D float Tensor of shape `(target_height, target_width, channels)`

Example

``````>>> images = np.random.random((15, 25, 3))
...     images, 2, 3, target_height=20, target_width=30
... )
(20, 30, 3)
``````
``````>>> batch_images = np.random.random((2, 15, 25, 3))
...     batch_images, 2, 3, target_height=20, target_width=30
... )
(2, 20, 30, 3)
``````

[source]

### `resize` function

``````keras.ops.image.resize(
image,
size,
interpolation="bilinear",
antialias=False,
crop_to_aspect_ratio=False,
fill_mode="constant",
fill_value=0.0,
data_format="channels_last",
)
``````

Resize images to size using the specified interpolation method.

Arguments

• image: 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: string, either `"channels_last"` or `"channels_first"`. The ordering of the dimensions in the inputs. `"channels_last"` corresponds to inputs with shape `(batch, height, width, channels)` while `"channels_first"` corresponds to inputs with shape `(batch, channels, height, weight)`. It defaults to the `image_data_format` value found in your Keras config file at `~/.keras/keras.json`. If you never set it, then it will be `"channels_last"`.

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)
``````