sharpedge.seam_carving

Functions

`_energy`(img)	Computes the energy map of an image.
`_find_vertical_seam`(energy)	Find the vertical seam of lowest total energy in the image.
`_find_horizontal_seam`(energy)	Find the horizontal seam of lowest total energy
`_remove_vertical_seam`(img, seam)	Remove a vertical seam from an image.
`_remove_horizontal_seam`(img, seam)	Remove a horizontal seam from an image.
`seam_carve`(img, target_height, target_width)	Seam carve an image to resize it to the target dimensions.

Module Contents

sharpedge.seam_carving._energy(img)[source]

Computes the energy map of an image.

This function calculates the energy of each pixel. The energy map highlights areas with high contrast, which are less likely to be removed during seam carving. Private function to be invoked by seam_carve().

Parameters:: img (numpy.ndarray) – A color image represented as a 3D numpy array of shape (height, width, 3).
Returns:: A 2D array of shape (height, width) containing the energy values for each pixel in the original image.
Return type:: numpy.ndarray
Raises:: ValueError – If the input image is not a 3D numpy array with 3 channels.

Examples

>>> img = np.random.rand(8, 5, 3)
>>> e = _energy(img)
>>> print(e.shape)
(8, 5)

sharpedge.seam_carving._find_vertical_seam(energy)[source]

Find the vertical seam of lowest total energy in the image. Private function to be invoked by seam_carve().

Parameters:: energy (numpy.ndarray) – A 2D array representing the energy of each pixel.
Returns:: A list indicating the seam of column indices.
Return type:: list
Raises:: ValueError – If the energy map is not a 2D numpy array.

Examples

>>> e = np.array([[0.6625, 0.3939], [1.0069, 0.7383]])
>>> seam = _find_vertical_seam(e)
>>> print(seam)
[1, 1]

sharpedge.seam_carving._find_horizontal_seam(energy)[source]

Find the horizontal seam of lowest total energy in the image by transposing the energy map. Private function to be invoked by seam_carve().

Parameters:: energy (numpy.ndarray) – A 2D array representing the energy of each pixel.
Returns:: A list indicating the seam of row indices.
Return type:: list
Raises:: ValueError – If the energy map is not a 2D numpy array.

Examples

>>> e = np.array([[0.6625, 0.3939], [1.0069, 0.7383]])
>>> seam = _find_horizontal_seam(e)
>>> print(seam)
[0, 0]

sharpedge.seam_carving._remove_vertical_seam(img, seam)[source]

Remove a vertical seam from an image. Private function to be invoked by seam_carve().

Parameters:

img (numpy.ndarray) – A 3D array representing the original image (height, width, 3).
seam (numpy.ndarray) – A 1D array (or list) of column indices indicating which pixel to remove in each row.

Returns:

A new image with one less column, of shape (height, width - 1, 3).

Return type:

numpy.ndarray

Raises:

ValueError –

If the input img is not a 3D numpy array with 3 channels.
If the input seam is not a 1D array or a list.
If the length of the seam does not match the height of the image.

Examples

>>> img = np.random.rand(8, 5, 3)
>>> seam = [2, 1, 3, 2, 0, 1, 4, 3]
>>> new_img = _remove_vertical_seam(img, seam)
>>> print(new_img.shape)
(8, 4, 3)

sharpedge.seam_carving._remove_horizontal_seam(img, seam)[source]

Remove a horizontal seam from an image. Private function to be invoked by seam_carve().

Parameters:

img (numpy.ndarray) – A 3D array representing the original image (height, width, 3).
seam (numpy.ndarray) – A 1D array (or list) of row indices indicating which pixel to remove in each column.

Returns:

A new image with one less row, of shape (height - 1, width, 3).

Return type:

numpy.ndarray

Raises:

ValueError –

If the input img is not a 3D numpy array with 3 channels.
If the input seam is not a 1D array or a list.
If the length of the seam does not match the width of the image.

Examples

>>> img = np.random.rand(5, 8, 3)
>>> seam = [2, 1, 3, 2, 0, 1, 4, 3]
>>> new_img = _remove_horizontal_seam(img, seam)
>>> print(new_img.shape)
(4, 8, 3)

sharpedge.seam_carving.seam_carve(img, target_height, target_width)[source]

Seam carve an image to resize it to the target dimensions.

Parameters:

img (numpy.ndarray) – A 3D array representing the original image (height, width, 3).
target_height (int) – The desired height of the resized image.
target_width (int) – The desired width of the resized image.

Returns:

The resized image with dimensions (target_height, target_width, 3).

Return type:

numpy.ndarray

Raises:

ValueError –

If the input img is not a 3D numpy array with 3 channels.
If target_height or target_width is not an integer.
If target_height is greater than the original height or less than 1.
If target_width is greater than the original width or less than 1.

Warning

UserWarning

If the target size is the same as the original size (no resizing needed).
If only one dimension is resized (height or width remains the same).
If the original image or target size is reduced to a single pixel.
If the resizing is significant (difference of 200+ pixels), which may cause long processing times.

Examples

>>> img = np.random.rand(5, 5, 3)
>>> resized_img = seam_carve(img, 3, 3)
>>> print(resized_img.shape)
(3, 3, 3)