__author__ = 'tsungyi'

from . import _mask

# Interface for manipulating masks stored in RLE format.
#
# RLE is a simple yet efficient format for storing binary masks. RLE
# first divides a vector (or vectorized image) into a series of piecewise
# constant regions and then for each piece simply stores the length of
# that piece. For example, given M=[0 0 1 1 1 0 1] the RLE counts would
# be [2 3 1 1], or for M=[1 1 1 1 1 1 0] the counts would be [0 6 1]
# (note that the odd counts are always the numbers of zeros). Instead of
# storing the counts directly, additional compression is achieved with a
# variable bitrate representation based on a common scheme called LEB128.
#
# Compression is greatest given large piecewise constant regions.
# Specifically, the size of the RLE is proportional to the number of
# *boundaries* in M (or for an image the number of boundaries in the y
# direction). Assuming fairly simple shapes, the RLE representation is
# O(sqrt(n)) where n is number of pixels in the object. Hence space usage
# is substantially lower, especially for large simple objects (large n).
#
# Many common operations on masks can be computed directly using the RLE
# (without need for decoding). This includes computations such as area,
# union, intersection, etc. All of these operations are linear in the
# size of the RLE, in other words they are O(sqrt(n)) where n is the area
# of the object. Computing these operations on the original mask is O(n).
# Thus, using the RLE can result in substantial computational savings.
#
# The following API functions are defined:
#  encode         - Encode binary masks using RLE.
#  decode         - Decode binary masks encoded via RLE.
#  merge          - Compute union or intersection of encoded masks.
#  iou            - Compute intersection over union between masks.
#  area           - Compute area of encoded masks.
#  toBbox         - Get bounding boxes surrounding encoded masks.
#  frPyObjects    - Convert polygon, bbox, and uncompressed RLE to encoded RLE mask.
#
# Usage:
#  Rs     = encode( masks )
#  masks  = decode( Rs )
#  R      = merge( Rs, intersect=false )
#  o      = iou( dt, gt, iscrowd )
#  a      = area( Rs )
#  bbs    = toBbox( Rs )
#  Rs     = frPyObjects( [pyObjects], h, w )
#
# In the API the following formats are used:
#  Rs      - [dict] Run-length encoding of binary masks
#  R       - dict Run-length encoding of binary mask
#  masks   - [hxwxn] Binary mask(s) (must have type np.ndarray(dtype=uint8) in column-major order)
#  iscrowd - [nx1] list of np.ndarray. 1 indicates corresponding gt image has crowd region to ignore
#  bbs     - [nx4] Bounding box(es) stored as [x y w h]
#  poly    - Polygon stored as [[x1 y1 x2 y2...],[x1 y1 ...],...] (2D list)
#  dt,gt   - May be either bounding boxes or encoded masks
# Both poly and bbs are 0-indexed (bbox=[0 0 1 1] encloses first pixel).
#
# Finally, a note about the intersection over union (iou) computation.
# The standard iou of a ground truth (gt) and detected (dt) object is
#  iou(gt,dt) = area(intersect(gt,dt)) / area(union(gt,dt))
# For "crowd" regions, we use a modified criteria. If a gt object is
# marked as "iscrowd", we allow a dt to match any subregion of the gt.
# Choosing gt' in the crowd gt that best matches the dt can be done using
# gt'=intersect(dt,gt). Since by definition union(gt',dt)=dt, computing
#  iou(gt,dt,iscrowd) = iou(gt',dt) = area(intersect(gt,dt)) / area(dt)
# For crowd gt regions we use this modified criteria above for the iou.
#
# To compile run "python setup.py build_ext --inplace"
# Please do not contact us for help with compiling.
#
# Microsoft COCO Toolbox.      version 2.0
# Data, paper, and tutorials available at:  http://mscoco.org/
# Code written by Piotr Dollar and Tsung-Yi Lin, 2015.
# Licensed under the Simplified BSD License [see coco/license.txt]

encode      = _mask.encode
decode      = _mask.decode
iou         = _mask.iou
merge       = _mask.merge
area        = _mask.area
toBbox      = _mask.toBbox
frPyObjects = _mask.frPyObjects