--- /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