# Util¶

Parameters
• img (PIL.Image.Image) – Input image

Returns

Return type

PIL.Image.Image

lazyproperty(f)[source]

Decorator like @property, but evaluated only on first access.

Like @property, this can only be used to decorate methods having only a self parameter, and is accessed like an attribute on an instance, i.e. trailing parentheses are not used. Unlike @property, the decorated method is only evaluated on first access; the resulting value is cached and that same value returned on second and later access without re-evaluation of the method.

Like @property, this class produces a data descriptor object, which is stored in the __dict__ of the class under the name of the decorated method (‘fget’ nominally). The cached value is stored in the __dict__ of the instance under that same name.

Because it is a data descriptor (as opposed to a non-data descriptor), its __get__() method is executed on each access of the decorated attribute; the __dict__ item of the same name is “shadowed” by the descriptor.

While this may represent a performance improvement over a property, its greater benefit may be its other characteristics. One common use is to construct collaborator objects, removing that “real work” from the constructor, while still only executing once. It also de-couples client code from any sequencing considerations; if it’s accessed from more than one location, it’s assured it will be ready whenever needed.

A lazyproperty is read-only. There is no counterpart to the optional “setter” (or deleter) behavior of an @property. This is critically important to maintaining its immutability and idempotence guarantees. Attempting to assign to a lazyproperty raises AttributeError unconditionally. The parameter names in the methods below correspond to this usage example:

class Obj(object):

@lazyproperty
def fget(self):
return 'some result'

obj = Obj()


Not suitable for wrapping a function (as opposed to a method) because it is not callable.

Parameters

f (Callable[[...], Any]) –

method_dispatch(func)[source]

Decorator like @singledispatch to dispatch on the second argument of a method.

It relies on @singledispatch to return a wrapper function that selects which registered function to call based on the type of the second argument.

This is implementation is required in order to be compatible with Python versions older than 3.8. In the future we could use functools.singledispatchmethod.

Parameters

func (Callable[..., Any]) – Method to dispatch

Returns

Selected method

Return type

Callable[…, Any]

np_to_pil(np_img)[source]

Convert a NumPy array to a PIL Image.

Parameters

np_img (np.ndarray) – The image represented as a NumPy array.

Returns

The image represented as PIL Image

Return type

PIL.Image.Image

Return a random pair of indices (column, row) where the binary_mask is True.

Parameters

Returns

Random pair of indices (column, row) where the binary_mask is True.

Return type

Tuple[int, int]

Return a binary mask with True inside of rectangle vertices and False outside.

The returned mask has shape dims.

Parameters
• dims (Tuple[int, int]) – (rows, columns) of the binary mask

• vertices (CoordinatePair) – CoordinatePair representing the upper left and bottom right vertices of the rectangle

Returns

Binary mask with True inside of the rectangle, False outside.

Return type

np.ndarray

region_coordinates(region)[source]

Extract bbox coordinates from the region.

Parameters

region (Region) – Region from which to extract the coordinates of the bbox

Returns

Coordinates of the bbox

Return type

CoordinatePair

Calculate regions properties from a binary mask.

Parameters

Returns

Properties for all the regions present in the binary mask

Return type

List[Region]

Create a binary mask given a list of regions.

For each region r, the areas within r.coords are filled with True, False outside.

Parameters
• regions (List[Region]) – The regions to create the binary mask.

• dims (Tuple[int, int]) – Dimensions of the resulting binary mask.

Returns

Binary mask from the regions coordinates.

Return type

np.ndarray

scale_coordinates(reference_coords, reference_size, target_size)[source]

Compute the coordinates corresponding to a scaled version of the image.

Parameters
• reference_coords (CoordinatePair) – Coordinates referring to the upper left and lower right corners respectively.

• reference_size (tuple of int) – Reference (width, height) size to which input coordinates refer to

• target_size (tuple of int) – Target (width, height) size of the resulting scaled image

Returns

coords – Coordinates in the scaled image

Return type

CoordinatesPair

Mask image with pixel according to the threshold value.

Parameters
• img (PIL.Image.Image) – Input image

• threshold (float) – The threshold value to exceed.

• relate (callable operator) – Comparison operator between img pixel values and threshold

Returns

Boolean NumPy array representing a mask where a pixel has a value True if the corresponding input array pixel exceeds the threshold value. if the corresponding input array pixel exceeds the threshold value.

Return type

np.ndarray