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# distutils: language = c
# distutils: sources = ../common/maskApi.c
# Microsoft COCO Toolbox. version 2.0
# Data, paper, and tutorials available at:
# Code written by Piotr Dollar and Tsung-Yi Lin, 2015.
# Licensed under the Simplified BSD License [see coco/license.txt]
__author__ = 'tsungyi'
import sys
PYTHON_VERSION = sys.version_info[0]
# import both Python-level and C-level symbols of Numpy
# the API uses Numpy to interface C and Python
import numpy as np
cimport numpy as np
from libc.stdlib cimport malloc, free
# intialized Numpy. must do.
# import numpy C function
# we use PyArray_ENABLEFLAGS to make Numpy ndarray responsible to memoery management
cdef extern from "numpy/arrayobject.h":
void PyArray_ENABLEFLAGS(np.ndarray arr, int flags)
# Declare the prototype of the C functions in MaskApi.h
cdef extern from "maskApi.h":
ctypedef unsigned int uint
ctypedef unsigned long siz
ctypedef unsigned char byte
ctypedef double* BB
ctypedef struct RLE:
siz h,
siz w,
siz m,
uint* cnts,
void rlesInit( RLE **R, siz n )
void rleEncode( RLE *R, const byte *M, siz h, siz w, siz n )
void rleDecode( const RLE *R, byte *mask, siz n )
void rleMerge( const RLE *R, RLE *M, siz n, int intersect )
void rleArea( const RLE *R, siz n, uint *a )
void rleIou( RLE *dt, RLE *gt, siz m, siz n, byte *iscrowd, double *o )
void bbIou( BB dt, BB gt, siz m, siz n, byte *iscrowd, double *o )
void rleToBbox( const RLE *R, BB bb, siz n )
void rleFrBbox( RLE *R, const BB bb, siz h, siz w, siz n )
void rleFrPoly( RLE *R, const double *xy, siz k, siz h, siz w )
char* rleToString( const RLE *R )
void rleFrString( RLE *R, char *s, siz h, siz w )
# python class to wrap RLE array in C
# the class handles the memory allocation and deallocation
cdef class RLEs:
cdef RLE *_R
cdef siz _n
def __cinit__(self, siz n =0):
rlesInit(&self._R, n)
self._n = n
# free the RLE array here
def __dealloc__(self):
if self._R is not NULL:
for i in range(self._n):
def __getattr__(self, key):
if key == 'n':
return self._n
raise AttributeError(key)
# python class to wrap Mask array in C
# the class handles the memory allocation and deallocation
cdef class Masks:
cdef byte *_mask
cdef siz _h
cdef siz _w
cdef siz _n
def __cinit__(self, h, w, n):
self._mask = <byte*> malloc(h*w*n* sizeof(byte))
self._h = h
self._w = w
self._n = n
# def __dealloc__(self):
# the memory management of _mask has been passed to np.ndarray
# it doesn't need to be freed here
# called when passing into np.array() and return an np.ndarray in column-major order
def __array__(self):
cdef np.npy_intp shape[1]
shape[0] = <np.npy_intp> self._h*self._w*self._n
# Create a 1D array, and reshape it to fortran/Matlab column-major array
ndarray = np.PyArray_SimpleNewFromData(1, shape, np.NPY_UINT8, self._mask).reshape((self._h, self._w, self._n), order='F')
# The _mask allocated by Masks is now handled by ndarray
return ndarray
# internal conversion from Python RLEs object to compressed RLE format
def _toString(RLEs Rs):
cdef siz n = Rs.n
cdef bytes py_string
cdef char* c_string
objs = []
for i in range(n):
c_string = rleToString( <RLE*> &Rs._R[i] )
py_string = c_string
'size': [Rs._R[i].h, Rs._R[i].w],
'counts': py_string
return objs
# internal conversion from compressed RLE format to Python RLEs object
def _frString(rleObjs):
cdef siz n = len(rleObjs)
Rs = RLEs(n)
cdef bytes py_string
cdef char* c_string
for i, obj in enumerate(rleObjs):
py_string = str(obj['counts']).encode('utf8')
py_string = str.encode(obj['counts']) if type(obj['counts']) == str else obj['counts']
raise Exception('Python version must be 2 or 3')
c_string = py_string
rleFrString( <RLE*> &Rs._R[i], <char*> c_string, obj['size'][0], obj['size'][1] )
return Rs
# encode mask to RLEs objects
# list of RLE string can be generated by RLEs member function
def encode(np.ndarray[np.uint8_t, ndim=3, mode='fortran'] mask):
h, w, n = mask.shape[0], mask.shape[1], mask.shape[2]
cdef RLEs Rs = RLEs(n)
objs = _toString(Rs)
return objs
# decode mask from compressed list of RLE string or RLEs object
def decode(rleObjs):
cdef RLEs Rs = _frString(rleObjs)
h, w, n = Rs._R[0].h, Rs._R[0].w, Rs._n
masks = Masks(h, w, n)
rleDecode(<RLE*>Rs._R, masks._mask, n);
return np.array(masks)
def merge(rleObjs, intersect=0):
cdef RLEs Rs = _frString(rleObjs)
cdef RLEs R = RLEs(1)
rleMerge(<RLE*>Rs._R, <RLE*> R._R, <siz> Rs._n, intersect)
obj = _toString(R)[0]
return obj
def area(rleObjs):
cdef RLEs Rs = _frString(rleObjs)
cdef uint* _a = <uint*> malloc(Rs._n* sizeof(uint))
rleArea(Rs._R, Rs._n, _a)
cdef np.npy_intp shape[1]
shape[0] = <np.npy_intp> Rs._n
a = np.array((Rs._n, ), dtype=np.uint8)
a = np.PyArray_SimpleNewFromData(1, shape, np.NPY_UINT32, _a)
return a
# iou computation. support function overload (RLEs-RLEs and bbox-bbox).
def iou( dt, gt, pyiscrowd ):
def _preproc(objs):
if len(objs) == 0:
return objs
if type(objs) == np.ndarray:
if len(objs.shape) == 1:
objs = objs.reshape((objs[0], 1))
# check if it's Nx4 bbox
if not len(objs.shape) == 2 or not objs.shape[1] == 4:
raise Exception('numpy ndarray input is only for *bounding boxes* and should have Nx4 dimension')
objs = objs.astype(np.double)
elif type(objs) == list:
# check if list is in box format and convert it to np.ndarray
isbox = np.all(np.array([(len(obj)==4) and ((type(obj)==list) or (type(obj)==np.ndarray)) for obj in objs]))
isrle = np.all(np.array([type(obj) == dict for obj in objs]))
if isbox:
objs = np.array(objs, dtype=np.double)
if len(objs.shape) == 1:
objs = objs.reshape((1,objs.shape[0]))
elif isrle:
objs = _frString(objs)
raise Exception('list input can be bounding box (Nx4) or RLEs ([RLE])')
raise Exception('unrecognized type. The following type: RLEs (rle), np.ndarray (box), and list (box) are supported.')
return objs
def _rleIou(RLEs dt, RLEs gt, np.ndarray[np.uint8_t, ndim=1] iscrowd, siz m, siz n, np.ndarray[np.double_t, ndim=1] _iou):
rleIou( <RLE*> dt._R, <RLE*> gt._R, m, n, <byte*>, <double*> )
def _bbIou(np.ndarray[np.double_t, ndim=2] dt, np.ndarray[np.double_t, ndim=2] gt, np.ndarray[np.uint8_t, ndim=1] iscrowd, siz m, siz n, np.ndarray[np.double_t, ndim=1] _iou):
bbIou( <BB>, <BB>, m, n, <byte*>, <double*> )
def _len(obj):
cdef siz N = 0
if type(obj) == RLEs:
N = obj.n
elif len(obj)==0:
elif type(obj) == np.ndarray:
N = obj.shape[0]
return N
# convert iscrowd to numpy array
cdef np.ndarray[np.uint8_t, ndim=1] iscrowd = np.array(pyiscrowd, dtype=np.uint8)
# simple type checking
cdef siz m, n
dt = _preproc(dt)
gt = _preproc(gt)
m = _len(dt)
n = _len(gt)
if m == 0 or n == 0:
return []
if not type(dt) == type(gt):
raise Exception('The dt and gt should have the same data type, either RLEs, list or np.ndarray')
# define local variables
cdef double* _iou = <double*> 0
cdef np.npy_intp shape[1]
# check type and assign iou function
if type(dt) == RLEs:
_iouFun = _rleIou
elif type(dt) == np.ndarray:
_iouFun = _bbIou
raise Exception('input data type not allowed.')
_iou = <double*> malloc(m*n* sizeof(double))
iou = np.zeros((m*n, ), dtype=np.double)
shape[0] = <np.npy_intp> m*n
iou = np.PyArray_SimpleNewFromData(1, shape, np.NPY_DOUBLE, _iou)
_iouFun(dt, gt, iscrowd, m, n, iou)
return iou.reshape((m,n), order='F')
def toBbox( rleObjs ):
cdef RLEs Rs = _frString(rleObjs)
cdef siz n = Rs.n
cdef BB _bb = <BB> malloc(4*n* sizeof(double))
rleToBbox( <const RLE*> Rs._R, _bb, n )
cdef np.npy_intp shape[1]
shape[0] = <np.npy_intp> 4*n
bb = np.array((1,4*n), dtype=np.double)
bb = np.PyArray_SimpleNewFromData(1, shape, np.NPY_DOUBLE, _bb).reshape((n, 4))
return bb
def frBbox(np.ndarray[np.double_t, ndim=2] bb, siz h, siz w ):
cdef siz n = bb.shape[0]
Rs = RLEs(n)
rleFrBbox( <RLE*> Rs._R, <const BB>, h, w, n )
objs = _toString(Rs)
return objs
def frPoly( poly, siz h, siz w ):
cdef np.ndarray[np.double_t, ndim=1] np_poly
n = len(poly)
Rs = RLEs(n)
for i, p in enumerate(poly):
np_poly = np.array(p, dtype=np.double, order='F')
rleFrPoly( <RLE*>&Rs._R[i], <const double*>, int(len(p)/2), h, w )
objs = _toString(Rs)
return objs
def frUncompressedRLE(ucRles, siz h, siz w):
cdef np.ndarray[np.uint32_t, ndim=1] cnts
cdef RLE R
cdef uint *data
n = len(ucRles)
objs = []
for i in range(n):
Rs = RLEs(1)
cnts = np.array(ucRles[i]['counts'], dtype=np.uint32)
# time for malloc can be saved here but it's fine
data = <uint*> malloc(len(cnts)* sizeof(uint))
for j in range(len(cnts)):
data[j] = <uint> cnts[j]
R = RLE(ucRles[i]['size'][0], ucRles[i]['size'][1], len(cnts), <uint*> data)
Rs._R[0] = R
return objs
def frPyObjects(pyobj, h, w):
# encode rle from a list of python objects
if type(pyobj) == np.ndarray:
objs = frBbox(pyobj, h, w)
elif type(pyobj) == list and len(pyobj[0]) == 4:
objs = frBbox(pyobj, h, w)
elif type(pyobj) == list and len(pyobj[0]) > 4:
objs = frPoly(pyobj, h, w)
elif type(pyobj) == list and type(pyobj[0]) == dict \
and 'counts' in pyobj[0] and 'size' in pyobj[0]:
objs = frUncompressedRLE(pyobj, h, w)
# encode rle from single python object
elif type(pyobj) == list and len(pyobj) == 4:
objs = frBbox([pyobj], h, w)[0]
elif type(pyobj) == list and len(pyobj) > 4:
objs = frPoly([pyobj], h, w)[0]
elif type(pyobj) == dict and 'counts' in pyobj and 'size' in pyobj:
objs = frUncompressedRLE([pyobj], h, w)[0]
raise Exception('input type is not supported.')
return objs