+++ /dev/null
-# distutils: language = c
-# distutils: sources = ../common/maskApi.c
-
-#**************************************************************************
-# 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]
-#**************************************************************************
-
-__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.
-np.import_array()
-
-# 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):
- free(self._R[i].cnts)
- free(self._R)
- 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
- PyArray_ENABLEFLAGS(ndarray, np.NPY_OWNDATA)
- 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
- objs.append({
- 'size': [Rs._R[i].h, Rs._R[i].w],
- 'counts': py_string
- })
- free(c_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):
- if PYTHON_VERSION == 2:
- py_string = str(obj['counts']).encode('utf8')
- elif PYTHON_VERSION == 3:
- py_string = str.encode(obj['counts']) if type(obj['counts']) == str else obj['counts']
- else:
- 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)
- rleEncode(Rs._R,<byte*>mask.data,h,w,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)
- PyArray_ENABLEFLAGS(a, np.NPY_OWNDATA)
- 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)
- else:
- raise Exception('list input can be bounding box (Nx4) or RLEs ([RLE])')
- else:
- 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*> iscrowd.data, <double*> _iou.data )
- 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> dt.data, <BB> gt.data, m, n, <byte*> iscrowd.data, <double*>_iou.data )
- def _len(obj):
- cdef siz N = 0
- if type(obj) == RLEs:
- N = obj.n
- elif len(obj)==0:
- pass
- 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
- else:
- 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)
- PyArray_ENABLEFLAGS(iou, np.NPY_OWNDATA)
- _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))
- PyArray_ENABLEFLAGS(bb, np.NPY_OWNDATA)
- 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> bb.data, 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*> np_poly.data, 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
- objs.append(_toString(Rs)[0])
- 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]
- else:
- raise Exception('input type is not supported.')
- return objs
Code Review / ealt-edge.git / blobdiff