This library is an extended and improved version of the COCO API's pycocotools.mask
module (which was originally written by Piotr Dollár and Tsung-Yi Lin).
It offers the following additional features:
rlemasklib.connected_components
) rle_mask = rlemasklib.encode(binary_mask)
: Encode a binary mask as RLEbinary_mask = rlemasklib.decode(rle_mask)
: Decode an RLE mask to a binary maskrle_mask = rlemasklib.compress(rle_mask)
: Compress an RLE mask using LEB128 (and optionally gzip)rle_mask = rlemasklib.decompress(rle_mask)
: Decompress an RLE mask from LEB128 or gzip to an array of integers (run-lengths)rle_mask = rlemasklib.empty(imshape)
: Create an empty RLE mask of given sizerle_mask = rlemasklib.full(imshape)
: Create a full RLE mask of given sizerle_mask = rlemasklib.intersection(rle_masks)
: Compute the intersection of multiple RLE masks.rle_mask = rlemasklib.union(rle_masks)
: Compute the union of multiple RLE masks.rle_mask = rlemasklib.complement(rle_mask)
: Compute the complement of an RLE mask.rle_mask = rlemasklib.difference(rle_mask1, rle_mask2)
: Compute the difference of two RLE masks.rle_mask = rlemasklib.symmetric_difference(rle_mask1, rle_mask2)
: Compute the symmetric difference of two RLE masks.area = rlemasklib.area(rle_mask)
: Compute the area of an RLE maskcentroid = rlemasklib.centroid(rle_mask)
: Compute the centroid of an RLE mask (or multiple masks). Returns [x, y] coordinates. The centroid is the average position of the foreground pixels. iou = rlemasklib.iou(rle_masks)
: Compute the intersection-over-union of multiple (typically two) RLE masks.rle_mask = rlemasklib.crop(rle_mask, bbox)
: Crop an RLE mask to a given bounding box, yielding a mask with smaller height and/or width.rle_mask = rlemasklib.pad(rle_mask, paddings, value=0)
: Pad an RLE mask with given amount of [left, right, top, bottom] pixels with given value (0 or 1).rle_mask = rlemasklib.shift(rle_mask, offset, border_value=0)
: Shift an RLE mask by a given pixel offset [dx, dy], filling the border with a given value.rle_masks = rlemasklib.connected_components(rle_mask, connectivity=4, min_size=1)
: Extract the connected components of the foreground from an RLE mask. Connectivity can be 4 or 8. Minimum size can be set to filter out small components.rle_mask = rlemasklib.largest_connected_component(rle_mask, connectivity=4)
: Returns the largest connected component of the foreground from an RLE mask. Returns None if there is no foreground.rle_mask = rlemasklib.remove_small_components(rle_mask, connectivity, min_size)
: Remove small connected components from the foreground of an RLE mask.rle_mask = rlemasklib.fill_small_holes(rle_mask, connectivity, min_size)
: Fill small holes (connected components of the background) in an RLE mask.[x_start, y_start, width, height] = rlemasklib.to_bbox(rle_mask)
: Convert an RLE mask to a bounding box.rle_mask = rlemasklib.from_bbox([x_start, y_start, width, height], imshape)
: Convert a bounding box to an RLE mask inside a given image size.rle_mask = rlemasklib.from_polygon(polygon, imshape)
: Convert a polygon to an RLE mask inside a given image size.