Patch locations/sampling¶

dplaptools provides a set of classes for calculating patch locations on the inside and outside of annotated regions (represented by polygons) or whole images. Patch sampling methods include: random patches, grid patches and Poisson disk patches. Additionally, using externally computed patch locations is also possible by utilizing classes dedicated to custom patches.

Objects created from classes introduced below can be fed directly into Patch Extraction classes for patch extraction, or be processed independently using embedded properties.

Other class features:

Built-in counters with calculated patch information.
Generating preview images with calculated patches.

Additional features for annotated WSIs:

Configurable handling of embedded polygons.
Built-in checks and warnings for overlapping polygons.
Filtering polygons based on arbitrary label values.
Ability to apply different processing parameters to polygons with different labels.

Random patches on whole images¶

class dplabtools.slides.patches.WholeImageRandomPatches(...)¶: Class for calculating patch locations on whole images using random sampling.

Basic usage¶

from dplabtools.slides.patches import WholeImageRandomPatches

wsi_file = "/tmp/wsi1.svs"
mask_file = "/tmp/wsi1_mask.png"

random_patches = WholeImageRandomPatches(
    wsi_file=wsi_file,
    mask_data=mask_file,
    patch_size=500,
    num_patches=20,
)
random_patches.save_preview_image("patches_image_random.jpg")

Output (patches_image_random.jpg):

Class details¶

Parameters specific to WholeImageRandomPatches:

class dplabtoolshiddenclass_a30d0ab7b68a4008aa6d17819ed56d63

Parameters:

num_patches (int) – Number of patch locations to calculate.
weak_label (str, optional) – Label assigned to all calculated patches.

See also

Parameters common in all patch classes

Poisson disk patches on whole images¶

class dplabtools.slides.patches.WholeImagePoissonDiskPatches(...)¶: Class for calculating patch locations on whole images using Poisson disk sampling.

Basic usage¶

from dplabtools.slides.patches import WholeImagePoissonDiskPatches

wsi_file = "/tmp/wsi1.svs"
mask_file = "/tmp/wsi1_mask.png"

poisson_patches = WholeImagePoissonDiskPatches(
    wsi_file=wsi_file,
    mask_data=mask_file,
    patch_size=500,
    poisson_spacing=60,
)
poisson_patches.save_preview_image("patches_image_poisson.jpg")

Output (patches_image_poisson.jpg)

Class details¶

Parameters specific to WholeImagePoissonDiskPatches:

class dplabtoolshiddenclass_b3af069d1840447fb9e5d456dd9cb9a9

Parameters:

poisson_spacing (int, default=50) – Spacing between calculated patches, the optimal value should be determined experimentally.
weak_label (str, optional) – Label assigned to all calculated patches.

See also

Parameters common in all patch classes

Grid based patches on whole images¶

class dplabtools.slides.patches.WholeImageGridPatches(...)¶: Class for calculating patch locations on whole images using grid based sampling.

Basic usage¶

from dplabtools.slides.patches import WholeImageGridPatches

wsi_file = "/tmp/wsi1.svs"
mask_file = "/tmp/wsi1_mask.png"

grid_patches = WholeImageGridPatches(
    wsi_file=wsi_file,
    mask_data=mask_file,
    patch_size=500,
    patch_stride=1,
)
grid_patches.save_preview_image("patches_image_grid.jpg")

Output (patches_image_grid.jpg):

Class details¶

Parameters specific to WholeImageGridPatches:

class dplabtoolshiddenclass_b8efff2164324456a6ad2d3786f019dc

Parameters:

patch_stride (int or float, default=1) – A measurement of overlap between grid patches.
weak_label (str, optional) – Label assigned to all calculated patches.

See also

Parameters common in all patch classes

Inverted random patches on whole images¶

class dplabtools.slides.patches.WholeImageInvertedRandomPatches(...)¶: Class for calculating patch locations on whole images with excluded polygon regions using random sampling.

Basic usage¶

from dplabtools.slides.patches import WholeImageInvertedRandomPatches
from dplabtools.slides.utils import AnnotationPolygon

wsi_file = "/tmp/wsi1.svs"
mask_file = "/tmp/wsi1_mask.png"

poly1 = AnnotationPolygon(
    points=[(10000, 22000), (10000, 25000), (13000, 25000), (13000, 22000)], label=""
)
poly2 = AnnotationPolygon(
    points=[(6000, 20000), (6000, 23000), (9000, 23000), (9000, 20000)], label=""
)
polygon_data = [poly1, poly2]

random_patches = WholeImageInvertedRandomPatches(
    wsi_file=wsi_file,
    mask_data=mask_file,
    patch_size=500,
    num_patches=50,
    polygon_data=polygon_data,
)
random_patches.save_preview_image("patches_image_inverted_random.jpg")

Output (patches_image_inverted_random.jpg):

Class details¶

Parameters specific to WholeImageInvertedRandomPatches:

class dplabtoolshiddenclass_c0ee57a5d4184863a1c9398083874e06

Parameters:

num_patches (int) – Number of patch locations to calculate.
polygon_data (list of AnnotationPolygon objects or JSON file/string with serialized AnnotationPolygon objects) – Polygons representing excluded regions.
weak_label (str, optional) – Label assigned to all calculated patches.
included_labels (list of str, optional) – Polygon labels included in calculations, all other labels will be ignored.
excluded_labels (list of str, optional) – Polygon labels excluded from calculations.

See also

Parameters common in all patch classes

Inverted Poisson disk patches on whole images¶

class dplabtools.slides.patches.WholeImageInvertedPoissonDiskPatches(...)¶: Class for calculating patch locations on whole images with excluded polygon regions using Poisson disk sampling.

Basic usage¶

from dplabtools.slides.patches import WholeImageInvertedPoissonDiskPatches
from dplabtools.slides.utils import AnnotationPolygon

wsi_file = "/tmp/wsi1.svs"
mask_file = "/tmp/wsi1_mask.png"

poly1 = AnnotationPolygon(
    points=[(10000, 22000), (10000, 25000), (13000, 25000), (13000, 22000)], label=""
)
poly2 = AnnotationPolygon(
    points=[(6000, 20000), (6000, 23000), (9000, 23000), (9000, 20000)], label=""
)
polygon_data = [poly1, poly2]

poisson_patches = WholeImageInvertedPoissonDiskPatches(
    wsi_file=wsi_file,
    mask_data=mask_file,
    patch_size=500,
    poisson_spacing=60,
    polygon_data=polygon_data,
)
poisson_patches.save_preview_image("patches_image_inverted_poisson.jpg")

Output (patches_image_inverted_poisson.jpg):

Class details¶

Parameters specific to WholeImageInvertedPoissonDiskPatches:

class dplabtoolshiddenclass_00161225c17044e9b575df99b9c8adae

Parameters:

poisson_spacing (int, default=50) – Spacing between calculated patches, the optimal value should be determined experimentally.
polygon_data (list of AnnotationPolygon objects or JSON file/string with serialized AnnotationPolygon objects) – Polygons representing excluded regions.
weak_label (str, optional) – Label assigned to all calculated patches.
included_labels (list of str, optional) – Polygon labels included in calculations, all other labels will be ignored.
excluded_labels (list of str, optional) – Polygon labels excluded from calculations.

See also

Parameters common in all patch classes

Inverted grid based patches on whole images¶

class dplabtools.slides.patches.WholeImageInvertedGridPatches(...)¶: Class for calculating patch locations on whole images with excluded polygon regions using grid based sampling.

Basic usage¶

from dplabtools.slides.patches import WholeImageInvertedGridPatches
from dplabtools.slides.utils import AnnotationPolygon

wsi_file = "/tmp/wsi1.svs"
mask_file = "/tmp/wsi1_mask.png"

poly1 = AnnotationPolygon(
    points=[(10000, 22000), (10000, 25000), (13000, 25000), (13000, 22000)], label=""
)
poly2 = AnnotationPolygon(
    points=[(6000, 20000), (6000, 23000), (9000, 23000), (9000, 20000)], label=""
)
polygon_data = [poly1, poly2]

grid_patches = WholeImageInvertedGridPatches(
    wsi_file=wsi_file,
    mask_data=mask_file,
    patch_size=500,
    patch_stride=1,
    polygon_data=polygon_data,
)
grid_patches.save_preview_image("patches_image_inverted_grid.jpg")

Output (patches_image_inverted_grid.jpg):

Class details¶

Parameters specific to WholeImageInvertedGridPatches:

class dplabtoolshiddenclass_24406c89fa2b4476a460d0358b7b26bc

Parameters:

patch_stride (int or float, default=1) – A measurement of overlap between grid patches.
polygon_data (list of AnnotationPolygon objects or JSON file/string with serialized AnnotationPolygon objects) – Polygons representing excluded regions.
weak_label (str, optional) – Label assigned to all calculated patches.
included_labels (list of str, optional) – Polygon labels included in calculations, all other labels will be ignored.
excluded_labels (list of str, optional) – Polygon labels excluded from calculations.

See also

Parameters common in all patch classes

Random patches on polygon regions¶

class dplabtools.slides.patches.PolygonRegionRandomPatches(...)¶: Class for calculating patch locations from polygon regions using random sampling.

Basic usage¶

from dplabtools.slides.patches import PolygonRegionRandomPatches
from dplabtools.slides.utils import AnnotationPolygon

wsi_file = "/tmp/wsi1.svs"
mask_file = "/tmp/wsi1_mask.png"

poly1 = AnnotationPolygon(
    points=[(10000, 22000), (10000, 25000), (13000, 25000), (13000, 22000)], label=""
)
poly2 = AnnotationPolygon(
    points=[(6000, 20000), (6000, 23000), (9000, 23000), (9000, 20000)], label=""
)
polygon_data = [poly1, poly2]

random_patches = PolygonRegionRandomPatches(
    wsi_file=wsi_file,
    mask_data=mask_file,
    patch_size=500,
    num_patches=10,
    polygon_data=polygon_data,
)
random_patches.save_preview_image("patches_polygons_random.jpg")

Output (patches_polygons_random.jpg):

Class details¶

Parameters specific to PolygonRegionRandomPatches:

class dplabtoolshiddenclass_e7862fb1600e46baaddaf01291c6fbe7

Parameters:

num_patches (int) – Number of patch locations to calculate.
polygon_data (list of AnnotationPolygon objects or JSON file/string with serialized AnnotationPolygon objects) – Polygons representing regions of interest.
check_polygons (bool, default=True) – Enables checking for overlapping polygons and polygons contained in other polygons.
included_labels (list of str, optional) – Polygon labels included in calculations, all other labels will be ignored.
excluded_labels (list of str, optional) – Polygon labels excluded from calculations.

Note

In this class, parameter num_patches denotes the number of patches per one polygon region. This behavior can be changed using Expandable parameters.

See also

“check_polygons” explained

See also

Parameters common in all patch classes

See also

Expandable parameters

Poisson disk patches on polygon regions¶

class dplabtools.slides.patches.PolygonRegionPoissonDiskPatches(...)¶: Class for calculating patch locations from polygon regions using Poisson disk sampling.

Basic usage¶

from dplabtools.slides.patches import PolygonRegionPoissonDiskPatches
from dplabtools.slides.utils import AnnotationPolygon

wsi_file = "/tmp/wsi1.svs"
mask_file = "/tmp/wsi1_mask.png"

poly1 = AnnotationPolygon(
    points=[(10000, 22000), (10000, 25000), (13000, 25000), (13000, 22000)], label=""
)
poly2 = AnnotationPolygon(
    points=[(6000, 20000), (6000, 23000), (9000, 23000), (9000, 20000)], label=""
)
polygon_data = [poly1, poly2]

poisson_patches = PolygonRegionPoissonDiskPatches(
    wsi_file=wsi_file,
    mask_data=mask_file,
    patch_size=500,
    poisson_spacing=40,
    polygon_data=polygon_data,
)
poisson_patches.save_preview_image("patches_polygons_poisson.jpg")

Output (patches_polygons_poisson.jpg):

Class details¶

Parameters specific to PolygonRegionPoissonDiskPatches:

class dplabtoolshiddenclass_3e6d3dd56317427fbfabb178de04846a

Parameters:

poisson_spacing (int, default=50) – Spacing between calculated patches, the optimal value should be determined experimentally.
polygon_data (list of AnnotationPolygon objects or JSON file/string with serialized AnnotationPolygon objects) – Polygons representing regions of interest.
check_polygons (bool, default=True) – Enables checking for overlapping polygons and polygons contained in other polygons.
included_labels (list of str, optional) – Polygon labels included in calculations, all other labels will be ignored.
excluded_labels (list of str, optional) – Polygon labels excluded from calculations.

See also

“check_polygons” explained

See also

Parameters common in all patch classes

See also

Expandable parameters

Grid based patches on polygon regions¶

class dplabtools.slides.patches.PolygonRegionGridPatches(...)¶: Class for calculating patch locations from polygon regions using grid based sampling.

Basic usage¶

from dplabtools.slides.patches import PolygonRegionGridPatches
from dplabtools.slides.utils import AnnotationPolygon

wsi_file = "/tmp/wsi1.svs"
mask_file = "/tmp/wsi1_mask.png"

poly1 = AnnotationPolygon(
    points=[(10000, 22000), (10000, 25000), (13000, 25000), (13000, 22000)], label=""
)
poly2 = AnnotationPolygon(
    points=[(6000, 20000), (6000, 23000), (9000, 23000), (9000, 20000)], label=""
)
polygon_data = [poly1, poly2]

grid_patches = PolygonRegionGridPatches(
    wsi_file=wsi_file,
    mask_data=mask_file,
    patch_size=500,
    patch_stride=1,
    polygon_data=polygon_data,
)
grid_patches.save_preview_image("patches_polygons_grid.jpg")

Output (patches_polygons_grid.jpg):

Class details¶

Parameters specific to PolygonRegionGridPatches:

class dplabtoolshiddenclass_2739a5035da744578432914c31df5a6e

Parameters:

patch_stride (int or float, default=1) – A measurement of overlap between grid patches.
polygon_data (list of AnnotationPolygon objects or JSON file/string with serialized AnnotationPolygon objects) – Polygons representing regions of interest.
check_polygons (bool, default=True) – Enables checking for overlapping polygons and polygons contained in other polygons.
included_labels (list of str, optional) – Polygon labels included in calculations, all other labels will be ignored.
excluded_labels (list of str, optional) – Polygon labels excluded from calculations.

See also

“check_polygons” explained

See also

Parameters common in all patch classes

See also

Expandable parameters

Custom patches¶

The following classes allow the user to feed and process externally computed patch locations:

class dplabtools.slides.patches.WholeImageCustomPatches(...)¶

Class for calculating patch locations on whole images using custom points.
class dplabtools.slides.patches.WholeImageInvertedCustomPatches(...)¶

Class for calculating patch locations on whole images with excluded polygon regions using custom points.
class dplabtools.slides.patches.PolygonRegionCustomPatches(...)¶

Class for calculating patch locations from polygon regions using custom points.

Example¶

from dplabtools.slides.patches import WholeImageCustomPatches

wsi_file = "/tmp/wsi1.svs"
mask_file = "/tmp/wsi1_mask.png"
points = [(3000, 3000), (4000, 4000), (5000, 5000), (6000, 6000)]

custom_patches = WholeImageCustomPatches(
    wsi_file=wsi_file,
    mask_data=mask_file,
    patch_size=500,
    points=points,
)

Class details¶

Parameters specific to all custom patches classes:

class dplabtoolshiddenclass_b425e1835ddc4313a6f4bb13f9ae61a7

Parameters:: points (list of tuples) – List of (x,y) int values representing points that are calculated using WSI level 0 coordinates. Points should represent the top-left corner of patches.

Note

It is worth noting that points provided by the user represent candidate patches, not the final patches. Each point must satisfy other relevant criteria (foreground_ratio, overlap_ratio, etc.) to be considered a valid (final) patch.

See also

Parameters common in all patch classes

Parameters common in all patch classes¶

class dplabtoolshiddenclass_b2faaf63a99f44ed94b05d9d5e356322

Parameters:

wsi_file (str) – WSI file name or path.
mask_data (str or object) – Mask file name or path, NumPy array object, or Pillow image object.
patch_size (int, default=256) – Size of calculated patches.
level_or_mpp (int or float, default=0) – WSI level or MPP value of calculated patches.
foreground_ratio (float, default=0.95) – Minimum percentage of tissue in each calculated patch.
overlap_ratio (float, default=0.95) – Minimum percentage of overlap between each calculated patch and the image or polygon region.
polygon_buffer (int, default=0) – Additional buffer to increase or decrease the image or polygon region.

See also

level_or_mpp

See also

Parameters details

See also

Expandable parameters

Methods and properties common to all patch classes¶

Common methods and properties are derived from the base class.

class dplabtools.slides.patches.locations.base.BasePatches(...)¶

Base class for whole image or polygon based patch locations computing.

save_preview_image(image_file, patch_color='red', bbox_color='lime', polygon_color='blue', holes_color='teal', buffer_color='black', thickness=2, patch_markers=False, level_or_minsize=None, drawn_labels=[], drawn_patches_labels=[])¶

Save preview image presenting calculated patches and image/polygon boundaries.

Draw and save patch/polygon preview image based on mask size (default) or an arbitrary WSI level.

Parameters:

image_file (str) – Image file name or path.
patch_color (str, default="red") – Color of drawn patches and patch markers.
bbox_color (str, default="lime") – Color of drawn polygon bounding boxes.
polygon_color (str, default="blue") – Color of drawn polygons.
holes_color (str, default="teal") – Color of drawn polygon holes.
buffer_color (str, default="black") – Color of drawn polygons after applying the buffer.
thickness (int, default=2) – Thickness of the drawing line.
patch_markers (bool, default=False) – Whether to draw patch markers or not.
level_or_minsize (int, optional) – Determines physical dimensions of the saved image. It’s either a WSI level or a minimal desired size in pixels.
drawn_labels (list of str, optional) – List of labels for drawing polygons, all other polygons will be ignored.
drawn_patches_labels (list of str, optional) – List of labels for drawing patches, all other patches will be ignored.

property class_name¶: Return the name of the patches object class.

property level_or_mpp¶: Return the level or MPP value used in patch calculations.

property mask_shape¶: Return the shape of the used mask.

property param_info¶: Return the parameter information used in patch calculations.

property patch_count¶: Return the number of calculated patches.

property patch_data¶: Return the patch data (location, size, label) for all calculated patches at level 0.

property patch_data_mask_level¶: Return the patch data (location, size, label) for all calculated patches at the mask level.

property patch_info¶: Return the patch information (counts and labels) for all calculated patches.

property patch_labels¶: Return the distinct polygon labels used in patch calculations.

property patch_size¶: Return the provided patch size.

property polygon_labels¶: Return all polygon labels used in patch calculations.

property shapely_polygons¶: Return the Shapely polygon objects representing polygons in patch calculations.

property wsi_file¶: Return the provided WSI file name.

property wsi_slide¶: Return the WSI slide object created during patch calculations.

See also

level_or_minsize

See also

Preview image

“check_polygons” explained¶

Parameter check_polygons controls how overlapping and contained (embedded) polygons are handled.

Case 1 (check_polygons=False)

In this mode each polygon is treated as an independent entity, which will cause certain regions to be sampled more than once:

Case 2 (check_polygons=True - default value)

In this mode mutual relationships between polygons are taken into account and polygon transformations are applied to prevent embedded regions from being sampled more than once:

Additionally, in this mode a warning message will be logged in case of partially overlapping polygons found:

wsi1.svs: Polygon 1 overlaps with polygon 2, overlapping area: 33.428571 patches

Parameters details¶

mask_data

WSI mask represented by an image file, numpy array file, or a numpy memory object. The dimensions of the mask should match one of the WSI levels.

patch_size

Patches are calculated as squares with dimensions patch_size x patch_size.

level_or_mpp

Provided int number will be interpreted as a WSI level, while float value will be interpreted as an MPP value.

foreground_ratio

Minimum percentage of foreground tissue to be present in calculated patches (based on the mask_data). The higher the foreground_ratio value, the more tissue-only area will be covered by calculated patches. Comparison of three different foreground_ratio values (0.5, 0.8, 0.95):

overlap_ratio

Minimum percentage of overlap between calculated patches and the image or polygon region. The higher the overlap_ratio value, the more “contained” calculated patches will become. Comparison of three different overlap_ratio values (0.1, 0.5, 0.95):

polygon_buffer

Non-zero value will either increase (positive polygon_buffer value) or decrease (negative polygon_buffer value) the area of the image or polygon region, thus changing the number of calculated patches. Comparison of three different polygon_buffer values (0, 30, -30):

Expandable parameters¶

The following list defines so called “expandable parameters”, which means they can accept either a single value or a list of values:

num_patches
patch_stride
poisson_spacing
foreground_ratio
overlap_ratio
polygon_buffer

This behavior is possible when using one of the polygon based classes:

PolygonRegionRandomPatches
PolygonRegionPoissonDiskPatches
PolygonRegionGridPatches

The size (len) of the expanded list should match the number of polygons present. Expanded parameters will be applied to corresponding polygons based on their position in the list.

Example 1 (num_patches=5 i.e. parameter is not expanded):

from dplabtools.slides.patches import PolygonRegionRandomPatches
from dplabtools.slides.utils import AnnotationPolygon

wsi_file = "/tmp/wsi1.svs"
mask_file = "/tmp/wsi1_mask.png"

poly1 = AnnotationPolygon(
    points=[(10000, 22000), (10000, 25000), (13000, 25000), (13000, 22000)], label="poly"
)
poly2 = AnnotationPolygon(
    points=[(6000, 20000), (6000, 23000), (9000, 23000), (9000, 20000)], label="poly"
)
poly3 = AnnotationPolygon(
    points=[(13000, 18000), (13000, 21000), (16000, 21000), (16000, 18000)], label="poly"
)
polygon_data = [poly1, poly2, poly3]

random_patches = PolygonRegionRandomPatches(
    wsi_file=wsi_file,
    mask_data=mask_file,
    patch_size=500,
    num_patches=5,
    polygon_data=polygon_data,
)
random_patches.save_preview_image("patches_polygons_not_expanded.jpg")

Output (patches_polygons_not_expanded.jpg):

Example 2 (num_patches=[1, 2, 3] i.e. parameter is expanded):

from dplabtools.slides.patches import PolygonRegionRandomPatches
from dplabtools.slides.utils import AnnotationPolygon

wsi_file = "/tmp/wsi1.svs"
mask_file = "/tmp/wsi1_mask.png"

poly1 = AnnotationPolygon(
    points=[(10000, 22000), (10000, 25000), (13000, 25000), (13000, 22000)], label="poly"
)
poly2 = AnnotationPolygon(
    points=[(6000, 20000), (6000, 23000), (9000, 23000), (9000, 20000)], label="poly"
)
poly3 = AnnotationPolygon(
    points=[(13000, 18000), (13000, 21000), (16000, 21000), (16000, 18000)], label="poly"
)
polygon_data = [poly1, poly2, poly3]

random_patches = PolygonRegionRandomPatches(
    wsi_file=wsi_file,
    mask_data=mask_file,
    patch_size=500,
    num_patches=[1, 2, 3],
    polygon_data=polygon_data,
)
random_patches.save_preview_image("patches_polygons_expanded.jpg")

Output (patches_polygons_expanded.jpg):

The same principle applies to all expandable parameters. Using a simple utility function, it is possible to automatically differentiate between different polygon labels, such as in the example below.

Example 3 (different patch_stride values applied to different polygon labels):

from dplabtools.slides.patches import PolygonRegionGridPatches
from dplabtools.slides.utils import AnnotationPolygon

wsi_file = "/tmp/wsi1.svs"
mask_file = "/tmp/wsi1_mask.png"

poly1 = AnnotationPolygon(
    points=[(10000, 22000), (10000, 25000), (13000, 25000), (13000, 22000)], label="rect"
)
poly2 = AnnotationPolygon(
    points=[(6000, 20000), (6000, 23000), (9000, 23000), (9000, 20000)], label="rect"
)
poly3 = AnnotationPolygon(
    points=[(13000, 18000), (13000, 21000), (16000, 21000)], label="triangle"
)
poly4 = AnnotationPolygon(
    points=[(6000, 24000), (6000, 27000), (9000, 27000)], label="triangle"
)
polygon_data = [poly1, poly2, poly3, poly4]

def get_stride_list(polygons, rect_stride=0.5, triangle_stride=1):
    return [rect_stride if poly.label == "rect" else triangle_stride for poly in polygons]

grid_patches = PolygonRegionGridPatches(
    wsi_file=wsi_file,
    mask_data=mask_file,
    patch_size=500,
    patch_stride=get_stride_list(polygon_data),
    polygon_data=polygon_data,
)
grid_patches.save_preview_image("patches_polygons_expanded_stride.jpg")

Output (patches_polygons_expanded_stride.jpg):

Preview image¶

By calling save_preview_image, calculated patches can be visualized and saved as a WSI overlay. The following elements are included in the preview images, the order they are listed in indicates the drawing order, as some elements may overlap and not be seen in each image:

Calculated patches and optionally patch markers - red.
Bounding boxes for polygons or polygon buffers - lime.
Polygons or one polygon representing the whole image - blue.
Polygon holes (used in inverted patch classes) - teal.
Polygon buffers - black.

Additional notes:

Patch markers are represented by small red circles placed in the top left corner of each calculated patch.
Each drawn element can be turned off individually by setting its color value to None.
Both drawn_labels and drawn_patches_labels provide label level filtering of the drawn content, however the former parameter takes precedence over the latter.
level_or_minsize controls the dimensions of the saved image, if not specified then the image dimensions will match the dimensions of the provided mask.

Patch locations/sampling¶

Random patches on whole images¶

Basic usage¶

Class details¶

Poisson disk patches on whole images¶

Basic usage¶

Class details¶

Grid based patches on whole images¶

Basic usage¶

Class details¶

Inverted random patches on whole images¶

Basic usage¶

Class details¶

Inverted Poisson disk patches on whole images¶

Basic usage¶

Class details¶

Inverted grid based patches on whole images¶

Basic usage¶

Class details¶

Random patches on polygon regions¶

Basic usage¶

Class details¶

Poisson disk patches on polygon regions¶

Basic usage¶

Class details¶

Grid based patches on polygon regions¶

Basic usage¶

Class details¶

Custom patches¶

Example¶

Class details¶

Parameters common in all patch classes¶

Methods and properties common to all patch classes¶

“check_polygons” explained¶

Parameters details¶

Expandable parameters¶

Preview image¶

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