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return z;
}
static INLINE __pyx_t_double_complex __pyx_t_double_complex_mul(__pyx_t_double_complex a, __pyx_t_double_complex b) {
__pyx_t_double_complex z;
z.real = a.real * b.real - a.imag * b.imag;
z.imag = a.real * b.imag + a.imag * b.real;
return z;
}
static INLINE __pyx_t_double_complex __pyx_t_double_complex_div(__pyx_t_double_complex a, __pyx_t_double_complex b) {
__pyx_t_double_complex z;
double denom = b.real*b.real + b.imag*b.imag;
z.real = (a.real * b.real + a.imag * b.imag) / denom;
z.imag = (a.imag * b.real - a.real * b.imag) / denom;
return z;
}
static INLINE __pyx_t_double_complex __pyx_t_double_complex_neg(__pyx_t_double_complex a) {
__pyx_t_double_complex z;
z.real = -a.real;
z.imag = -a.imag;
return z;
}
#endif
#if __PYX_USE_C99_COMPLEX
typedef long double _Complex __pyx_t_long__double_complex;
static INLINE __pyx_t_long__double_complex __pyx_t_long__double_complex_from_parts(long double x, long double y) {
return x + y*(__pyx_t_long__double_complex)_Complex_I;
}
#define __pyx_t_long__double_complex_is_zero(a) ((a) == 0)
#define __pyx_t_long__double_complex_eq(a, b) ((a) == (b))
#define __pyx_t_long__double_complex_add(a, b) ((a)+(b))
#define __pyx_t_long__double_complex_sub(a, b) ((a)-(b))
#define __pyx_t_long__double_complex_mul(a, b) ((a)*(b))
#define __pyx_t_long__double_complex_div(a, b) ((a)/(b))
#define __pyx_t_long__double_complex_neg(a) (-(a))
#else
typedef struct { long double real, imag; } __pyx_t_long__double_complex;
static INLINE __pyx_t_long__double_complex __pyx_t_long__double_complex_from_parts(long double x, long double y) {
__pyx_t_long__double_complex c; c.real = x; c.imag = y; return c;
}
static INLINE int __pyx_t_long__double_complex_is_zero(__pyx_t_long__double_complex a) {
return (a.real == 0) & (a.imag == 0);
}
static INLINE int __pyx_t_long__double_complex_eq(__pyx_t_long__double_complex a, __pyx_t_long__double_complex b) {
return (a.real == b.real) & (a.imag == b.imag);
}
static INLINE __pyx_t_long__double_complex __pyx_t_long__double_complex_add(__pyx_t_long__double_complex a, __pyx_t_long__double_complex b) {
__pyx_t_long__double_complex z;
z.real = a.real + b.real;
z.imag = a.imag + b.imag;
return z;
}
static INLINE __pyx_t_long__double_complex __pyx_t_long__double_complex_sub(__pyx_t_long__double_complex a, __pyx_t_long__double_complex b) {
__pyx_t_long__double_complex z;
z.real = a.real - b.real;
z.imag = a.imag - b.imag;
return z;
}
static INLINE __pyx_t_long__double_complex __pyx_t_long__double_complex_mul(__pyx_t_long__double_complex a, __pyx_t_long__double_complex b) {
__pyx_t_long__double_complex z;
z.real = a.real * b.real - a.imag * b.imag;
z.imag = a.real * b.imag + a.imag * b.real;
return z;
}
static INLINE __pyx_t_long__double_complex __pyx_t_long__double_complex_div(__pyx_t_long__double_complex a, __pyx_t_long__double_complex b) {
__pyx_t_long__double_complex z;
long double denom = b.real*b.real + b.imag*b.imag;
z.real = (a.real * b.real + a.imag * b.imag) / denom;
z.imag = (a.imag * b.real - a.real * b.imag) / denom;
return z;
}
static INLINE __pyx_t_long__double_complex __pyx_t_long__double_complex_neg(__pyx_t_long__double_complex a) {
__pyx_t_long__double_complex z;
z.real = -a.real;
z.imag = -a.imag;
return z;
}
#endif
static PyObject *__Pyx_UnpackItem(PyObject *, Py_ssize_t index); /*proto*/
static int __Pyx_EndUnpack(PyObject *); /*proto*/
static INLINE unsigned char __Pyx_PyInt_AsUnsignedChar(PyObject *);
static INLINE unsigned short __Pyx_PyInt_AsUnsignedShort(PyObject *);
static INLINE unsigned int __Pyx_PyInt_AsUnsignedInt(PyObject *);
static INLINE char __Pyx_PyInt_AsChar(PyObject *);
static INLINE short __Pyx_PyInt_AsShort(PyObject *);
static INLINE int __Pyx_PyInt_AsInt(PyObject *);
static INLINE signed char __Pyx_PyInt_AsSignedChar(PyObject *);
static INLINE signed short __Pyx_PyInt_AsSignedShort(PyObject *);
static INLINE signed int __Pyx_PyInt_AsSignedInt(PyObject *);
static INLINE unsigned long __Pyx_PyInt_AsUnsignedLong(PyObject *);
static INLINE unsigned PY_LONG_LONG __Pyx_PyInt_AsUnsignedLongLong(PyObject *);
static INLINE long __Pyx_PyInt_AsLong(PyObject *);
static INLINE PY_LONG_LONG __Pyx_PyInt_AsLongLong(PyObject *);
static INLINE signed long __Pyx_PyInt_AsSignedLong(PyObject *);
static INLINE signed PY_LONG_LONG __Pyx_PyInt_AsSignedLongLong(PyObject *);
static INLINE int __Pyx_StrEq(const char *, const char *); /*proto*/
static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, long size); /*proto*/
static PyObject *__Pyx_ImportModule(const char *name); /*proto*/
static int __Pyx_ImportFunction(PyObject *module, const char *funcname, void (**f)(void), const char *sig); /*proto*/
static void __Pyx_AddTraceback(const char *funcname); /*proto*/
static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); /*proto*/
/* Type declarations */
typedef npy_int8 __pyx_t_5numpy_int8_t;
typedef npy_int16 __pyx_t_5numpy_int16_t;
typedef npy_int32 __pyx_t_5numpy_int32_t;
typedef npy_int64 __pyx_t_5numpy_int64_t;
typedef npy_uint8 __pyx_t_5numpy_uint8_t;
typedef npy_uint16 __pyx_t_5numpy_uint16_t;
typedef npy_uint32 __pyx_t_5numpy_uint32_t;
typedef npy_uint64 __pyx_t_5numpy_uint64_t;
typedef npy_float32 __pyx_t_5numpy_float32_t;
typedef npy_float64 __pyx_t_5numpy_float64_t;
typedef __pyx_t_float_complex __pyx_t_5numpy_complex64_t;
typedef __pyx_t_double_complex __pyx_t_5numpy_complex128_t;
typedef npy_long __pyx_t_5numpy_int_t;
typedef npy_longlong __pyx_t_5numpy_long_t;
typedef npy_ulong __pyx_t_5numpy_uint_t;
typedef npy_ulonglong __pyx_t_5numpy_ulong_t;
typedef npy_double __pyx_t_5numpy_float_t;
typedef npy_double __pyx_t_5numpy_double_t;
typedef npy_longdouble __pyx_t_5numpy_longdouble_t;
typedef npy_cfloat __pyx_t_5numpy_cfloat_t;
typedef npy_cdouble __pyx_t_5numpy_cdouble_t;
typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t;
typedef npy_cdouble __pyx_t_5numpy_complex_t;
/* "/home/brucewayne/scikits_image/scikits.image/scikits/image/opencv/opencv_type.pxd":5
* # available at build time.
*
* cdef struct _IplImage: # <<<<<<<<<<<<<<
* int nSize # sizeof(_IplImage)
* int ID # must be 0
*/
struct __pyx_t_7scikits_5image_6opencv_11opencv_type__IplImage {
int nSize;
int ID;
int nChannels;
int alphaChannel;
int depth;
char colorModel[4];
char channelSeq[4];
int dataOrder;
int origin;
int align;
int width;
int height;
void *roi;
void *maskROI;
void *imageId;
void *tileInfo;
int imageSize;
char *imageData;
int widthStep;
int BorderMode[4];
int BorderConst[4];
char *imageDataOrigin;
};
typedef struct __pyx_t_7scikits_5image_6opencv_11opencv_type__IplImage __pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage;
/* "/home/brucewayne/scikits_image/scikits.image/scikits/image/opencv/opencv_type.pxd":38
*
* # you will never directly populate a CvMat.
* cdef union CvMat_uProxy: # <<<<<<<<<<<<<<
* unsigned char* ptr
* short* s
*/
union __pyx_t_7scikits_5image_6opencv_11opencv_type_CvMat_uProxy {
unsigned char *ptr;
short *s;
int *i;
float *fl;
double *db;
};
/* "/home/brucewayne/scikits_image/scikits.image/scikits/image/opencv/opencv_type.pxd":45
* double* db
*
* cdef struct CvMat: # <<<<<<<<<<<<<<
* int type
* int step
*/
struct __pyx_t_7scikits_5image_6opencv_11opencv_type_CvMat {
int type;
int step;
int *refcount;
int hdr_refcount;
union __pyx_t_7scikits_5image_6opencv_11opencv_type_CvMat_uProxy data;
int rows;
int cols;
};
/* "/home/brucewayne/scikits_image/scikits.image/scikits/image/opencv/opencv_type.pxd":54
* int cols
*
* cdef struct CvPoint: # <<<<<<<<<<<<<<
* int x
* int y
*/
struct __pyx_t_7scikits_5image_6opencv_11opencv_type_CvPoint {
int x;
int y;
};
/* "/home/brucewayne/scikits_image/scikits.image/scikits/image/opencv/opencv_type.pxd":58
* int y
*
* cdef struct CvPoint2D32f: # <<<<<<<<<<<<<<
* float x
* float y
*/
struct __pyx_t_7scikits_5image_6opencv_11opencv_type_CvPoint2D32f {
float x;
float y;
};
/* "/home/brucewayne/scikits_image/scikits.image/scikits/image/opencv/opencv_type.pxd":62
* float y
*
* cdef struct CvSize: # <<<<<<<<<<<<<<
* int width
* int height
*/
struct __pyx_t_7scikits_5image_6opencv_11opencv_type_CvSize {
int width;
int height;
};
/* "/home/brucewayne/scikits_image/scikits.image/scikits/image/opencv/opencv_type.pxd":66
* int height
*
* cdef struct CvTermCriteria: # <<<<<<<<<<<<<<
* int type
* int max_iter
*/
struct __pyx_t_7scikits_5image_6opencv_11opencv_type_CvTermCriteria {
int type;
int max_iter;
double epsilon;
};
/* "/home/brucewayne/scikits_image/scikits.image/scikits/image/opencv/opencv_type.pxd":71
* double epsilon
*
* cdef struct CvScalar: # <<<<<<<<<<<<<<
* double val[4]
*
*/
struct __pyx_t_7scikits_5image_6opencv_11opencv_type_CvScalar {
double val[4];
};
/* "/home/brucewayne/scikits_image/scikits.image/scikits/image/opencv/opencv_type.pxd":74
* double val[4]
*
* cdef struct _IplConvKernel: # <<<<<<<<<<<<<<
* int nCols
* int nRows
*/
struct __pyx_t_7scikits_5image_6opencv_11opencv_type__IplConvKernel {
int nCols;
int nRows;
int anchorX;
int anchorY;
int *values;
int nShiftR;
};
typedef struct __pyx_t_7scikits_5image_6opencv_11opencv_type__IplConvKernel __pyx_t_7scikits_5image_6opencv_11opencv_type_IplConvKernel;
typedef __pyx_t_5numpy_uint8_t __pyx_t_7scikits_5image_6opencv_14opencv_backend_UINT8_t;
typedef __pyx_t_5numpy_int8_t __pyx_t_7scikits_5image_6opencv_14opencv_backend_INT8_t;
typedef __pyx_t_5numpy_int16_t __pyx_t_7scikits_5image_6opencv_14opencv_backend_INT16_t;
typedef __pyx_t_5numpy_int32_t __pyx_t_7scikits_5image_6opencv_14opencv_backend_INT32_t;
typedef __pyx_t_5numpy_float32_t __pyx_t_7scikits_5image_6opencv_14opencv_backend_FLOAT32_t;
typedef __pyx_t_5numpy_float64_t __pyx_t_7scikits_5image_6opencv_14opencv_backend_FLOAT64_t;
typedef void (*__pyx_t_7scikits_5image_6opencv_9opencv_cv_cvSobelPtr)(__pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, __pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, int, int, int);
typedef void (*__pyx_t_7scikits_5image_6opencv_9opencv_cv_cvLaplacePtr)(__pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, __pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, int);
typedef void (*__pyx_t_7scikits_5image_6opencv_9opencv_cv_cvCannyPtr)(__pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, __pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, double, double, int);
typedef void (*__pyx_t_7scikits_5image_6opencv_9opencv_cv_cvPreCorneDetectPtr)(__pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, __pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, int);
typedef void (*__pyx_t_7scikits_5image_6opencv_9opencv_cv_cvCornerEigenValsAndVecsPtr)(__pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, __pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, int, int);
typedef void (*__pyx_t_7scikits_5image_6opencv_9opencv_cv_cvCornerMinEigenValPtr)(__pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, __pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, int, int);
typedef void (*__pyx_t_7scikits_5image_6opencv_9opencv_cv_cvCornerHarrisPtr)(__pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, __pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, int, int, double);
typedef void (*__pyx_t_7scikits_5image_6opencv_9opencv_cv_cvFindCornerSubPixPtr)(__pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, struct __pyx_t_7scikits_5image_6opencv_11opencv_type_CvPoint2D32f *, int, struct __pyx_t_7scikits_5image_6opencv_11opencv_type_CvSize, struct __pyx_t_7scikits_5image_6opencv_11opencv_type_CvSize, struct __pyx_t_7scikits_5image_6opencv_11opencv_type_CvTermCriteria);
typedef void (*__pyx_t_7scikits_5image_6opencv_9opencv_cv_cvGoodFeaturesToTrackPtr)(__pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, __pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, __pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, struct __pyx_t_7scikits_5image_6opencv_11opencv_type_CvPoint2D32f *, int *, double, double, __pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, int, int, double);
typedef void (*__pyx_t_7scikits_5image_6opencv_9opencv_cv_cvGetRectSubPixPtr)(__pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, __pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, struct __pyx_t_7scikits_5image_6opencv_11opencv_type_CvPoint2D32f);
typedef void (*__pyx_t_7scikits_5image_6opencv_9opencv_cv_cvGetQuadrangleSubPixPtr)(__pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, __pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, struct __pyx_t_7scikits_5image_6opencv_11opencv_type_CvMat *);
typedef void (*__pyx_t_7scikits_5image_6opencv_9opencv_cv_cvResizePtr)(__pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, __pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, int);
typedef void (*__pyx_t_7scikits_5image_6opencv_9opencv_cv_cvWarpAffinePtr)(__pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, __pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, struct __pyx_t_7scikits_5image_6opencv_11opencv_type_CvMat *, int, struct __pyx_t_7scikits_5image_6opencv_11opencv_type_CvScalar);
typedef void (*__pyx_t_7scikits_5image_6opencv_9opencv_cv_cvWarpPerspectivePtr)(__pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, __pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, struct __pyx_t_7scikits_5image_6opencv_11opencv_type_CvMat *, int, struct __pyx_t_7scikits_5image_6opencv_11opencv_type_CvScalar);
typedef void (*__pyx_t_7scikits_5image_6opencv_9opencv_cv_cvLogPolarPtr)(__pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, __pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, struct __pyx_t_7scikits_5image_6opencv_11opencv_type_CvPoint2D32f, double, int);
typedef void (*__pyx_t_7scikits_5image_6opencv_9opencv_cv_cvErodePtr)(__pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, __pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, __pyx_t_7scikits_5image_6opencv_11opencv_type_IplConvKernel *, int);
typedef void (*__pyx_t_7scikits_5image_6opencv_9opencv_cv_cvDilatePtr)(__pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, __pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, __pyx_t_7scikits_5image_6opencv_11opencv_type_IplConvKernel *, int);
typedef void (*__pyx_t_7scikits_5image_6opencv_9opencv_cv_cvMorphologyExPtr)(__pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, __pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, __pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, __pyx_t_7scikits_5image_6opencv_11opencv_type_IplConvKernel *, int, int);
typedef void (*__pyx_t_7scikits_5image_6opencv_9opencv_cv_cvSmoothPtr)(__pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, __pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, int, int, int, double, double);
typedef void (*__pyx_t_7scikits_5image_6opencv_9opencv_cv_cvFilter2DPtr)(__pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, __pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, struct __pyx_t_7scikits_5image_6opencv_11opencv_type_CvMat *, struct __pyx_t_7scikits_5image_6opencv_11opencv_type_CvPoint);
typedef void (*__pyx_t_7scikits_5image_6opencv_9opencv_cv_cvIntegralPtr)(__pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, __pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, __pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, __pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *);
typedef void (*__pyx_t_7scikits_5image_6opencv_9opencv_cv_cvCvtColorPtr)(__pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, __pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, int);
typedef double (*__pyx_t_7scikits_5image_6opencv_9opencv_cv_cvThresholdPtr)(__pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, __pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, double, double, int);
typedef void (*__pyx_t_7scikits_5image_6opencv_9opencv_cv_cvAdaptiveThresholdPtr)(__pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, __pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, double, int, int, int, double);
typedef void (*__pyx_t_7scikits_5image_6opencv_9opencv_cv_cvPyrDownPtr)(__pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, __pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, int);
typedef void (*__pyx_t_7scikits_5image_6opencv_9opencv_cv_cvPyrUpPtr)(__pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, __pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, int);
typedef void (*__pyx_t_7scikits_5image_6opencv_9opencv_cv_cvCalibrateCamera2Ptr)(struct __pyx_t_7scikits_5image_6opencv_11opencv_type_CvMat *, struct __pyx_t_7scikits_5image_6opencv_11opencv_type_CvMat *, struct __pyx_t_7scikits_5image_6opencv_11opencv_type_CvMat *, struct __pyx_t_7scikits_5image_6opencv_11opencv_type_CvSize, struct __pyx_t_7scikits_5image_6opencv_11opencv_type_CvMat *, struct __pyx_t_7scikits_5image_6opencv_11opencv_type_CvMat *, struct __pyx_t_7scikits_5image_6opencv_11opencv_type_CvMat *, struct __pyx_t_7scikits_5image_6opencv_11opencv_type_CvMat *, int);
typedef void (*__pyx_t_7scikits_5image_6opencv_9opencv_cv_cvFindChessboardCornersPtr)(__pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, struct __pyx_t_7scikits_5image_6opencv_11opencv_type_CvSize, struct __pyx_t_7scikits_5image_6opencv_11opencv_type_CvPoint2D32f *, int *, int);
typedef void (*__pyx_t_7scikits_5image_6opencv_9opencv_cv_cvDrawChessboardCornersPtr)(__pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *, struct __pyx_t_7scikits_5image_6opencv_11opencv_type_CvSize, struct __pyx_t_7scikits_5image_6opencv_11opencv_type_CvPoint2D32f *, int, int);
/* Module declarations from python_buffer */
/* Module declarations from stdlib */
/* Module declarations from stdio */
/* Module declarations from numpy */
/* Module declarations from numpy */
static PyTypeObject *__pyx_ptype_5numpy_dtype = 0;
static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0;
static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0;
static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0;
static INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *, char *, char *, int *); /*proto*/
/* Module declarations from python_version */
/* Module declarations from python_ref */
/* Module declarations from python_exc */
/* Module declarations from python_module */
/* Module declarations from python_mem */
/* Module declarations from python_tuple */
/* Module declarations from python_list */
/* Module declarations from python_object */
/* Module declarations from python_sequence */
/* Module declarations from python_mapping */
/* Module declarations from python_iterator */
/* Module declarations from python_type */
/* Module declarations from python_number */
/* Module declarations from python_int */
/* Module declarations from python_bool */
/* Module declarations from python_long */
/* Module declarations from python_float */
/* Module declarations from python_complex */
/* Module declarations from python_string */
/* Module declarations from python_unicode */
/* Module declarations from python_dict */
/* Module declarations from python_instance */
/* Module declarations from python_function */
/* Module declarations from python_method */
/* Module declarations from python_set */
/* Module declarations from python */
/* Module declarations from scikits.image.opencv.opencv_type */
/* Module declarations from scikits.image.opencv.opencv_backend */
static void (*__pyx_f_7scikits_5image_6opencv_14opencv_backend_populate_iplimage)(PyArrayObject *, __pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *); /*proto*/
static struct __pyx_t_7scikits_5image_6opencv_11opencv_type_CvMat *(*__pyx_f_7scikits_5image_6opencv_14opencv_backend_cvmat_ptr_from_iplimage)(__pyx_t_7scikits_5image_6opencv_11opencv_type_IplImage *); /*proto*/
static int (*__pyx_f_7scikits_5image_6opencv_14opencv_backend_validate_array)(PyArrayObject *); /*proto*/
static int (*__pyx_f_7scikits_5image_6opencv_14opencv_backend_assert_dtype)(PyArrayObject *, PyObject *); /*proto*/
static int (*__pyx_f_7scikits_5image_6opencv_14opencv_backend_assert_ndims)(PyArrayObject *, PyObject *); /*proto*/
static int (*__pyx_f_7scikits_5image_6opencv_14opencv_backend_assert_nchannels)(PyArrayObject *, PyObject *); /*proto*/
static int (*__pyx_f_7scikits_5image_6opencv_14opencv_backend_assert_same_dtype)(PyArrayObject *, PyArrayObject *); /*proto*/
static int (*__pyx_f_7scikits_5image_6opencv_14opencv_backend_assert_same_shape)(PyArrayObject *, PyArrayObject *); /*proto*/
static int (*__pyx_f_7scikits_5image_6opencv_14opencv_backend_assert_same_width_and_height)(PyArrayObject *, PyArrayObject *); /*proto*/
static int (*__pyx_f_7scikits_5image_6opencv_14opencv_backend_assert_like)(PyArrayObject *, PyArrayObject *); /*proto*/
static int (*__pyx_f_7scikits_5image_6opencv_14opencv_backend_assert_not_sharing_data)(PyArrayObject *, PyArrayObject *); /*proto*/
static PyArrayObject *(*__pyx_f_7scikits_5image_6opencv_14opencv_backend_new_array)(int, npy_intp *, PyObject *); /*proto*/
static PyArrayObject *(*__pyx_f_7scikits_5image_6opencv_14opencv_backend_new_array_like)(PyArrayObject *); /*proto*/
static PyArrayObject *(*__pyx_f_7scikits_5image_6opencv_14opencv_backend_new_array_like_diff_dtype)(PyArrayObject *, PyObject *); /*proto*/
static npy_intp (*__pyx_f_7scikits_5image_6opencv_14opencv_backend_get_array_nbytes)(PyArrayObject *); /*proto*/
static npy_intp *(*__pyx_f_7scikits_5image_6opencv_14opencv_backend_clone_array_shape)(PyArrayObject *); /*proto*/
static struct __pyx_t_7scikits_5image_6opencv_11opencv_type_CvPoint2D32f *(*__pyx_f_7scikits_5image_6opencv_14opencv_backend_array_as_cvPoint2D32f_ptr)(PyArrayObject *); /*proto*/
static struct __pyx_t_7scikits_5image_6opencv_11opencv_type_CvTermCriteria (*__pyx_f_7scikits_5image_6opencv_14opencv_backend_get_cvTermCriteria)(int, double); /*proto*/
static __pyx_t_7scikits_5image_6opencv_11opencv_type_IplConvKernel *(*__pyx_f_7scikits_5image_6opencv_14opencv_backend_get_IplConvKernel_ptr_from_array)(PyArrayObject *, PyObject *); /*proto*/
static void (*__pyx_f_7scikits_5image_6opencv_14opencv_backend_free_IplConvKernel)(__pyx_t_7scikits_5image_6opencv_11opencv_type_IplConvKernel *); /*proto*/
/* Module declarations from scikits.image.opencv.opencv_cv */
static __pyx_t_7scikits_5image_6opencv_9opencv_cv_cvSobelPtr __pyx_v_7scikits_5image_6opencv_9opencv_cv_c_cvSobel;
static __pyx_t_7scikits_5image_6opencv_9opencv_cv_cvLaplacePtr __pyx_v_7scikits_5image_6opencv_9opencv_cv_c_cvLaplace;
static __pyx_t_7scikits_5image_6opencv_9opencv_cv_cvCannyPtr __pyx_v_7scikits_5image_6opencv_9opencv_cv_c_cvCanny;
static __pyx_t_7scikits_5image_6opencv_9opencv_cv_cvPreCorneDetectPtr __pyx_v_7scikits_5image_6opencv_9opencv_cv_c_cvPreCornerDetect;
static __pyx_t_7scikits_5image_6opencv_9opencv_cv_cvCornerEigenValsAndVecsPtr __pyx_v_7scikits_5image_6opencv_9opencv_cv_c_cvCornerEigenValsAndVecs;
static __pyx_t_7scikits_5image_6opencv_9opencv_cv_cvCornerMinEigenValPtr __pyx_v_7scikits_5image_6opencv_9opencv_cv_c_cvCornerMinEigenVal;
static __pyx_t_7scikits_5image_6opencv_9opencv_cv_cvCornerHarrisPtr __pyx_v_7scikits_5image_6opencv_9opencv_cv_c_cvCornerHarris;
static __pyx_t_7scikits_5image_6opencv_9opencv_cv_cvFindCornerSubPixPtr __pyx_v_7scikits_5image_6opencv_9opencv_cv_c_cvFindCornerSubPix;
static __pyx_t_7scikits_5image_6opencv_9opencv_cv_cvGoodFeaturesToTrackPtr __pyx_v_7scikits_5image_6opencv_9opencv_cv_c_cvGoodFeaturesToTrack;
static __pyx_t_7scikits_5image_6opencv_9opencv_cv_cvGetRectSubPixPtr __pyx_v_7scikits_5image_6opencv_9opencv_cv_c_cvGetRectSubPix;
static __pyx_t_7scikits_5image_6opencv_9opencv_cv_cvGetQuadrangleSubPixPtr __pyx_v_7scikits_5image_6opencv_9opencv_cv_c_cvGetQuadrangleSubPix;
static __pyx_t_7scikits_5image_6opencv_9opencv_cv_cvResizePtr __pyx_v_7scikits_5image_6opencv_9opencv_cv_c_cvResize;
static __pyx_t_7scikits_5image_6opencv_9opencv_cv_cvWarpAffinePtr __pyx_v_7scikits_5image_6opencv_9opencv_cv_c_cvWarpAffine;
static __pyx_t_7scikits_5image_6opencv_9opencv_cv_cvWarpPerspectivePtr __pyx_v_7scikits_5image_6opencv_9opencv_cv_c_cvWarpPerspective;
static __pyx_t_7scikits_5image_6opencv_9opencv_cv_cvLogPolarPtr __pyx_v_7scikits_5image_6opencv_9opencv_cv_c_cvLogPolar;
static __pyx_t_7scikits_5image_6opencv_9opencv_cv_cvErodePtr __pyx_v_7scikits_5image_6opencv_9opencv_cv_c_cvErode;
static __pyx_t_7scikits_5image_6opencv_9opencv_cv_cvDilatePtr __pyx_v_7scikits_5image_6opencv_9opencv_cv_c_cvDilate;
static __pyx_t_7scikits_5image_6opencv_9opencv_cv_cvMorphologyExPtr __pyx_v_7scikits_5image_6opencv_9opencv_cv_c_cvMorphologyEx;
static __pyx_t_7scikits_5image_6opencv_9opencv_cv_cvSmoothPtr __pyx_v_7scikits_5image_6opencv_9opencv_cv_c_cvSmooth;
static __pyx_t_7scikits_5image_6opencv_9opencv_cv_cvFilter2DPtr __pyx_v_7scikits_5image_6opencv_9opencv_cv_c_cvFilter2D;
static __pyx_t_7scikits_5image_6opencv_9opencv_cv_cvIntegralPtr __pyx_v_7scikits_5image_6opencv_9opencv_cv_c_cvIntegral;
static __pyx_t_7scikits_5image_6opencv_9opencv_cv_cvCvtColorPtr __pyx_v_7scikits_5image_6opencv_9opencv_cv_c_cvCvtColor;
static __pyx_t_7scikits_5image_6opencv_9opencv_cv_cvThresholdPtr __pyx_v_7scikits_5image_6opencv_9opencv_cv_c_cvThreshold;
static __pyx_t_7scikits_5image_6opencv_9opencv_cv_cvAdaptiveThresholdPtr __pyx_v_7scikits_5image_6opencv_9opencv_cv_c_cvAdaptiveThreshold;
static __pyx_t_7scikits_5image_6opencv_9opencv_cv_cvPyrDownPtr __pyx_v_7scikits_5image_6opencv_9opencv_cv_c_cvPyrDown;
static __pyx_t_7scikits_5image_6opencv_9opencv_cv_cvPyrUpPtr __pyx_v_7scikits_5image_6opencv_9opencv_cv_c_cvPyrUp;
static __pyx_t_7scikits_5image_6opencv_9opencv_cv_cvCalibrateCamera2Ptr __pyx_v_7scikits_5image_6opencv_9opencv_cv_c_cvCalibrateCamera2;
static __pyx_t_7scikits_5image_6opencv_9opencv_cv_cvFindChessboardCornersPtr __pyx_v_7scikits_5image_6opencv_9opencv_cv_c_cvFindChessboardCorners;
static __pyx_t_7scikits_5image_6opencv_9opencv_cv_cvDrawChessboardCornersPtr __pyx_v_7scikits_5image_6opencv_9opencv_cv_c_cvDrawChessboardCorners;
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#define __Pyx_MODULE_NAME "scikits.image.opencv.opencv_cv"
int __pyx_module_is_main_scikits__image__opencv__opencv_cv = 0;
/* Implementation of scikits.image.opencv.opencv_cv */
static char __pyx_k_38[] = "Could not load libcv";
static char __pyx_k_123[] = "cvSobel(src, xorder=1, yorder=0, aperture_size=3)\n\nApply the Sobel operator to the input image.\n\nParameters\n----------\nsrc : ndarray, 2D, dtype=[uint8, int8, float32]\n The source image.\nxorder : integer\n The x order of the Sobel operator.\nyorder : integer\n The y order of the Sobel operator.\naperture_size : integer=[3, 5, 7]\n The size of the Sobel kernel.\n\nReturns\n-------\nout : ndarray\n A new which is the result of applying the Sobel\n operator to src.";
static char __pyx_k_126[] = "cvLaplace(src, aperture_size=3)\n\nApply the Laplace operator to the input image.\n\nParameters\n----------\nsrc : ndarray, 2D, dtype=[uint8, int8, float32]\n The source image.\naperture_size : integer=[3, 5, 7]\n The size of the Sobel kernel.\n\nReturns\n-------\nout : ndarray\n A new which is the result of applying the Laplace\n operator to src.";
static char __pyx_k_129[] = "cvCanny(src, threshold1=10, threshold2=50, aperture_size=3)\n\nApply Canny edge detection to the input image.\n\nParameters\n----------\nsrc : ndarray, 2D, dtype=[uint8]\n The source image.\nthreshold1 : float\n The lower threshold used for edge linking.\nthreshold2 : float\n The upper threshold used to find strong edges.\naperture_size : integer=[3, 5, 7]\n The size of the Sobel kernel.\n\nReturns\n-------\nout : ndarray\n A new which is the result of applying Canny\n edge detection to src.";
static char __pyx_k_132[] = "cvPreCornerDetect(src, aperture_size=3)\n\nCalculate the feature map for corner detection.\n\nParameters\n----------\nsrc : ndarray, 2D, dtype=[uint8, float32]\n The source image.\naperture_size : integer=[3, 5, 7]\n The size of the Sobel kernel.\n\nReturns\n-------\nout : ndarray\n A new array of the corner candidates.";
static char __pyx_k_135[] = "cvCornerEigenValsAndVecs(src, block_size=3, aperture_size=3)\n\nCalculates the eigenvalues and eigenvectors of image\nblocks for corner detection.\n\nParameters\n----------\nsrc : ndarray, 2D, dtype=[uint8, float32]\n The source image.\nblock_size : integer\n The size of the neighborhood in which to calculate\n the eigenvalues and eigenvectors.\naperture_size : integer=[3, 5, 7]\n The size of the Sobel kernel.\n\nReturns\n-------\nout : ndarray\n A new array of the eigenvalues and eigenvectors.\n The shape of this array is (height, width, 6),\n Where height and width are the same as that\n of src.";
static char __pyx_k_138[] = "cvCornerMinEigenVal(src, block_size=3, aperture_size=3)\n\nCalculates the minimum eigenvalues of gradient matrices\nfor corner detection.\n\nParameters\n----------\nsrc : ndarray, 2D, dtype=[uint8, float32]\n The source image.\nblock_size : integer\n The size of the neighborhood in which to calculate\n the eigenvalues.\naperture_size : integer=[3, 5, 7]\n The size of the Sobel kernel.\n\nReturns\n-------\nout : ndarray\n A new array of the eigenvalues.";
static char __pyx_k_141[] = "cvCornerHarris(src, block_size=3, aperture_size=3, k=0.04)\n\nApplies the Harris edge detector to the input image.\n\nParameters\n----------\nsrc : ndarray, 2D, dtype=[uint8, float32]\n The source image.\nblock_size : integer\n The size of the neighborhood in which to apply the detector.\naperture_size : integer=[3, 5, 7]\n The size of the Sobel kernel.\nk : float\n Harris detector free parameter. See Notes.\n\nReturns\n-------\nout : ndarray\n A new array of the Harris corners.\n\nNotes\n-----\nThe function cvCornerHarris() runs the Harris edge\ndetector on the image. Similarly to cvCornerMinEigenVal()\nand cvCornerEigenValsAndVecs(), for each pixel it calculates\na gradient covariation matrix M over a block_size X block_size\nneighborhood. Then, it stores det(M) - k * trace(M)**2\nto the output image. Corners in the image can be found as the\nlocal maxima of the output image.";
static char __pyx_k_145[] = "cvFindCornerSubPix(src, corners, win, zero_zone=(-1, -1), iterations=0, epsilon=1e-5)\n\nRefines corner locations to sub-pixel accuracy.\n\nParameters\n----------\nsrc : ndarray, 2D, dtype=[uint8]\n The source image.\ncorners : ndarray, shape=(N x 2)\n An initial approximation of the corners in the image.\n The corners will be refined in-place in this array.\nwin : tuple, (height, width)\n The window within which the function iterates until it\n converges on the real corner. The actual window is twice\n the size of what is declared here. (an OpenCV peculiarity).\nzero_zone : Half of the size of the dead region in the middle\n of the search zone over which the calculations are not\n performed. It is used sometimes to avoid possible\n singularities of the autocorrelation matrix.\n The value of (-1,-1) indicates that there is no such size.\niterations : integer\n The maximum number of iterations to perform. If 0,\n the function iterates until the error is less than epsilon.\nepsilon : float\n The epsilon error, below which the function terminates.\n Can be used in combination with iterations.\n\nReturns\n-------\nNone. The array 'corners' is modified in place.";
static char __pyx_k_148[] = "cvGoodFeaturesToTrack(src, corner_count, quality_level, min_distance, block_size=3, use_harris=0, k=0.04)\n\nDetermines strong corners in an image.\n\nParameters\n----------\nsrc : ndarray, 2D, dtype=[uint8, float32]\n The source image.\ncorner_count : int\n The maximum number of corners to find.\n Only found corners are returned.\nquality_level : float\n Multiplier for the max/min eigenvalue;\n specifies the minimal accepted quality of\n image corners.\nmin_distance : float\n Limit, specifying the minimum possible\n distance between the returned corners;\n Euclidian distance is used.\nblock_size : integer\n The size of the neighborhood in which to apply the detector.\nuse_harris : integer\n If nonzero, Harris operator (cvCornerHarris())\n is used instead of default cvCornerMinEigenVal()\nk : float\n Harris detector free parameter.\n Used only if use_harris != 0.\n\nReturns\n-------\nout : ndarray\n The locations of the found corners in the image.\n\nNotes\n-----\nThis function finds distinct and strong corners\nin an image which can be used as features in a tracking\nalgorithm. It also insures that features are distanced\nfrom one another by at least min_distance.";
static char __pyx_k_151[] = "cvGetRectSubPix(src, size, center)\n\nRetrieves the pixel rectangle from an image with\nsub-pixel accuracy.\n\nParameters\n----------\nsrc : ndarray\n The source image.\nsize : two tuple, integers, (height, width)\n The size of the rectangle to extract.\ncenter : two tuple, floats, (x, y)\n The center location of the rectangle.\n The center must lie within the image, but the\n rectangle may extend beyond the bounds of the image.\n\nReturns\n-------\nout : ndarray\n The extracted rectangle of the image.\n\nNotes\n-----\nThe center of the specified rectangle must\nlie within the image, but the bounds of the rectangle\nmay extend beyond the image. Border replication is used\nto fill in missing pixels.";
static char __pyx_k_155[] = "cvGetQuadrangleSubPix(src, warpmat, float_out=False)\n\nRetrieves the pixel quandrangle from an image with\nsub-pixel accuracy. In english: apply an affine transform to an image.\n\nParameters\n----------\nsrc : ndarray\n The source image.\nwarpmat : ndarray, 2x3\n The affine transformation to apply to the src image.\nfloat_out : bool\n If True, the return array will have dtype np.float32.\n Otherwise, the return array will have the same dtype\n as the src array.\n If True, the src array MUST have dtype np.uint8\n\nReturns\n-------\nout : ndarray\n Warped image of same size as src.\n\nNotes\n-----\nThe values of pixels at non-integer coordinates are retrieved\nusing bilinear interpolation. When the function needs pixels\noutside of the image, it uses replication border mode to\nreconstruct the values. Every channel of multiple-channel\nimages is processed independently.\n\nThis function has less overhead than cvWarpAffine\nand should be used unless specific feature of that\nfunction are required.";
static char __pyx_k_159[] = "cvResize(src, size, method=CV_INTER_LINEAR)\n\nResize an to the given size.\n\nParameters\n----------\nsrc : ndarray\n The source image.\nsize : tuple, (height, width)\n The target resize size.\nmethod : integer\n The interpolation method used for resizing.\n Supported methods are:\n CV_INTER_NN\n CV_INTER_LINEAR\n CV_INTER_AREA\n CV_INTER_CUBIC\n\nReturns\n-------\nout : ndarray\n The resized image.";
static char __pyx_k_165[] = "cvWarpAffine(src, warpmat, flag=CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS, fillval=(0., 0., 0., 0.))\n\nApplies an affine transformation to the image.\n\nParameters\n----------\nsrc : ndarray\n The source image.\nwarpmat : ndarray, 2x3\n The affine transformation to apply to the src image.\nflag : integer\n A combination of interpolation and method flags.\n Supported flags are: (see notes)\n Interpolation:\n CV_INTER_NN\n CV_INTER_LINEAR\n CV_INTER_AREA\n CV_INTER_CUBIC\n Method:\n CV_WARP_FILL_OUTLIERS\n CV_WARP_INVERSE_MAP\nfillval : 4-tuple, (R, G, B, A)\n The color to fill in missing pixels. Defaults to black.\n For < 4 channel images, use 0.'s for the value.\n\nReturns\n-------\nout : ndarray\n The warped image of same size and dtype as src.\n\nNotes\n-----\nCV_WARP_FILL_OUTLIERS - fills all of the destination image pixels;\n if some of them correspond to outliers in the source image,\n they are set to fillval.\nCV_WARP_INVERSE_MAP - indicates that warpmat is inversely transformed\n from the destination image to the source and, thus, can be used\n directly for pixel interpolation. Otherwise, the function finds\n the inverse transform from warpmat.\n\nThis function has a larger overhead than cvGetQuadrangleSubPix,\nand that function should be used instead, unless specific\nfeatures of this function are needed.";
static char __pyx_k_170[] = "cvWarpPerspective(src, warpmat, flag=CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS, fillval=(0., 0., 0., 0.))\n\nApplies a perspective transformation to an image.\n\nParameters\n----------\nsrc : ndarray\n The source image.\nwarpmat : ndarray, 3x3\n The affine transformation to apply to the src image.\nflag : integer\n A combination of interpolation and method flags.\n Supported flags are: (see notes)\n Interpolation:\n CV_INTER_NN\n CV_INTER_LINEAR\n CV_INTER_AREA\n CV_INTER_CUBIC\n Method:\n CV_WARP_FILL_OUTLIERS\n CV_WARP_INVERSE_MAP\nfillval : 4-tuple, (R, G, B, A)\n The color to fill in missing pixels. Defaults to black.\n For < 4 channel images, use 0.'s for the value.\n\nReturns\n-------\nout : ndarray\n The warped image of same size and dtype as src.\n\nNotes\n-----\nCV_WARP_FILL_OUTLIERS - fills all of the destination image pixels;\n if some of them correspond to outliers in the source image,\n they are set to fillval.\nCV_WARP_INVERSE_MAP - indicates that warpmat is inversely transformed\n from the destination image to the source and, thus, can be used\n directly for pixel interpolation. Otherwise, the function finds\n the inverse transform from warpmat.";
static char __pyx_k_174[] = "cvLogPolar(src, center, M, flag=CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS)\n\nRemaps and image to Log-Polar space.\n\nParameters\n----------\nsrc : ndarray\n The source image.\ncenter : tuple, (x, y)\n The keypoint for the log polar transform.\nM : float\n The scale factor for the transform.\n (40 is a good starting point for a 256x256 image)\nflag : integer\n A combination of interpolation and method flags.\n Supported flags are: (see notes)\n Interpolation:\n CV_INTER_NN\n CV_INTER_LINEAR\n CV_INTER_AREA\n CV_INTER_CUBIC\n Method:\n CV_WARP_FILL_OUTLIERS\n CV_WARP_INVERSE_MAP\n\nReturns\n-------\nout : ndarray\n A transformed image the same size and dtype as src.\n\nNotes\n-----\nCV_WARP_FILL_OUTLIERS - fills all of the destination image pixels;\n if some of them correspond to outliers in the source image,\n they are set to zero.\nCV_WARP_INVERSE_MAP - assume that the source image is already\n in Log-Polar space, and transform back to cartesian space.\n\nThe function emulates the human \342\200\234foveal\342\200\235 vision and can be used\nfor fast scale and rotation-invariant template matching,\nfor object tracking and so forth.";
static char __pyx_k_178[] = "cvErode(src, element=None, iterations=1, anchor=None, in_place=False)\n\nErode the source image with the given element.\n\nParameters\n----------\nsrc : ndarray\n The source image.\nelement : ndarray, 2D\n The structuring element. Must be 2D. Non-zero elements\n indicate which pixels of the underlying image to include\n in the operation as the element is slid over the image.\n If None, a 3x3 block element is used.\niterations : integer\n The number of times to perform the operation.\nanchor: 2-tuple, (x, y)\n The anchor of the structuring element. Must be\n FULLY inside the element. If None, the center of the\n element is used.\nin_place: bool\n If True, perform the operation in place.\n Otherwise, store the results in a new image.\n\nReturns\n-------\nout/None : ndarray or None\n An new array is returned only if in_place=False.\n Otherwise, this function returns None.";
static char __pyx_k_182[] = "cvDilate(src, element=None, iterations=1, anchor=None, in_place=False)\n\nDilate the source image with the given element.\n\nParameters\n----------\nsrc : ndarray\n The source image.\nelement : ndarray, 2D\n The structuring element. Must be 2D. Non-zero elements\n indicate which pixels of the underlying image to include\n in the operation as the element is slid over the image.\n If None, a 3x3 block element is used.\niterations : integer\n The number of times to perform the operation.\nanchor: 2-tuple, (x, y)\n The anchor of the structuring element. Must be\n FULLY inside the element. If None, the center of the\n element is used.\nin_place: bool\n If True, perform the operation in place.\n Otherwise, store the results in a new image.\n\nReturns\n-------\nout/None : ndarray or None\n An new array is returned only if in_place=False.\n Otherwise, this function returns None.";
static char __pyx_k_186[] = "cvMorphologyEx(src, element, operation, iterations=1, anchor=None, in_place=False)\n\nApply a morphological operation to the image.\n\nParameters\n----------\nsrc : ndarray\n The source image.\nelement : ndarray, 2D\n The structuring element. Must be 2D. Non-zero elements\n indicate which pixels of the underlying image to include\n in the operation as the element is slid over the image.\n Cannot be None.\noperation : flag\n The morphology operation to perform. Must be one of:\n CV_MOP_OPEN\n CV_MOP_CLOSE\n CV_MOP_GRADIENT\n CV_MOP_TOPHAT\n CV_MOP_BLACKHAT\niterations : integer\n The number of times to perform the operation.\nanchor: 2-tuple, (x, y)\n The anchor of the structuring element. Must be\n FULLY inside the element. If None, the center of the\n element is used.\nin_place: bool\n If True, perform the operation in place.\n Otherwise, store the results in a new image.\n\nReturns\n-------\nout/None : ndarray or None\n An new array is returned only if in_place=False.\n Otherwise, this function returns None.";
static char __pyx_k_191[] = "cvSmooth(src, smoothtype=CV_GAUSSIAN, param1=3, param2=0, param3=0., param4=0., in_place=False)\n\nSmooth an image with the specified filter.\n\nParameters\n----------\nsrc : ndarray\n The source image.\nsmoothtype : integer\n The flag representing which smoothing operation to perfom.\n See notes on restrictions.\n Must be one of:\n CV_BLUR_NO_SCALE\n CV_BLUR\n CV_GAUSSIAN\n CV_MEDIAN\n CV_BILATERAL\nparam1 : integer\n See notes.\nparam2 : integer\n See notes.\nparam3 : float\n See notes.\nparam4 : float\n See notes.\nin_place : bool\n If True, perform the operation in place.\n This is not supported for every combination of arguments.\n See notes.\n\nReturns\n-------\nout/None : ndarray or None\n If in_place == True the function operates in place and returns None.\n Otherwise, the operation returns a new array that is\n the result of the smoothing operation.\n\nNotes\n-----\nThe following details the restrictions and argument interpretaions\nfor each of the smoothing operations.\n\nCV_BLUR_NO_SCALE:\n Source image must be 2D and have dtype uint8, int8, or float32.\n param1 x param2 define the neighborhood over which the pixels\n are summed. If param2 is zero it is set equal to param1.\n param3 and param4 are ignored.\n in_place operation is not supported.\nCV_BLUR:\n Source image must have dtype uint8, int8, or float32.\n param1 x param2 define the neighborhood over which the pixels\n are summed. If param2 is zero it is set equal to param1.\n param3 and param4 are ignored.\nCV_GAUSSIAN:\n Source image must have dtype uint8, int8, or float32.\n param1 x param2 defines the size of the gaussian kernel.\n If param2 is zero it is set equal to param1.\n param3 is the standard deviation of the kernel.\n If param3 is zero, an optimum stddev is calculated based\n on the kernel size. If both param1 and param2 or zero,\n then an optimum kernel size is calculated based on\n param3.\n in_place operation is supported.\nCV_MEDIAN:\n Source image must have dtype uint8, or int8.\n param1 x param1 define the neigborhood over which\n to find the median.\n param2, param3, and param4 are ignored.\n in_place operation is not supported.\nCV_BILATERAL:\n Source image must have dtype uint8, or int8.\n param1 x param2 define the neighborhood.\n param3 defines the color stddev.\n param4 defines the space stddev.\n in_place operation is not supported.\n\nUsing standard sigma for small kernels (3x3 to 7x7)\ngives better speed.";
static char __pyx_k_195[] = "cvFilter2D(src, kernel, anchor=None, in_place=False)\n\nConvolve an image with the given kernel.\n\nParameters\n----------\nsrc : ndarray\n The source image.\nkernel : ndarray, 2D, dtype=float32\n The kernel with which to convolve the image.\nanchor : 2-tuple, (x, y)\n The kernel anchor.\nin_place : bool\n If True, perform the operation in_place.\n\nReturns\n-------\nout/None : ndarray or None\n If in_place is True, returns None.\n Otherwise a new array is returned which is the result\n of the convolution.\n\nNotes\n-----\nThis is a high performance function. OpenCV automatically\ndetermines, based on the size of the image and the kernel,\nwhether it will faster to do the convolution in the spatial\nor the frequency domain, and behaves accordingly.";
static char __pyx_k_200[] = "cvIntegral(src, square_sum=False, titled_sum=False)\n\nCalculate the integral of an image.\n\nParameters\n----------\nsrc : ndarray, dtyp=[uint8, float32, float64]\n The source image.\nsquare_sum : bool\n If True, also returns the square sum.\ntilted_sum : bool\n If True, also returns the titled sum (45 degree tilt)\n\nReturns\n-------\n[out1, out2, out3] : list of ndarray's\n Returns a list consisting at least of:\n out1: the integral image, and optionally:\n out2: the square sum image\n out3: the titled sum image,\n or any combination of these two.";
static char __pyx_k_203[] = "cvCvtColor(src, code)\n\nConvert an image to another color space.\n\nParameters\n----------\nsrc : ndarray, dtype=[uint8, uint16, float32]\n The source image.\ncode : integer\n A flag representing which color conversion to perform.\n Valid flags are the following:\n CV_BGR2BGRA, CV_RGB2RGBA, CV_BGRA2BGR, CV_RGBA2RGB,\n CV_BGR2RGBA, CV_RGB2BGRA, CV_RGBA2BGR, CV_BGRA2RGB,\n CV_BGR2RGB, CV_RGB2BGR, CV_BGRA2RGBA, CV_RGBA2BGRA,\n CV_BGR2GRAY, CV_RGB2GRAY, CV_GRAY2BGR, CV_GRAY2RGB,\n CV_GRAY2BGRA, CV_GRAY2RGBA, CV_BGRA2GRAY, CV_RGBA2GRAY,\n CV_BGR2BGR565, CV_RGB2BGR565, CV_BGR5652BGR, CV_BGR5652RGB,\n CV_BGRA2BGR565, CV_RGBA2BGR565, CV_BGR5652BGRA, CV_BGR5652RGBA,\n CV_GRAY2BGR565, CV_BGR5652GRAY, CV_BGR2BGR555, CV_RGB2BGR555,\n CV_BGR5552BGR, CV_BGR5552RGB, CV_BGRA2BGR555, CV_RGBA2BGR555,\n CV_BGR5552BGRA, CV_BGR5552RGBA, CV_GRAY2BGR555, CV_BGR5552GRAY,\n CV_BGR2XYZ, CV_RGB2XYZ, CV_XYZ2BGR, CV_XYZ2RGB,\n CV_BGR2YCrCb, CV_RGB2YCrCb, CV_YCrCb2BGR, CV_YCrCb2RGB,\n CV_BGR2HSV, CV_RGB2HSV, CV_BGR2Lab, CV_RGB2Lab,\n CV_BayerBG2BGR, CV_BayerGB2BGR, CV_BayerRG2BGR, CV_BayerGR2BGR,\n CV_BayerBG2RGB, CV_BayerGB2RGB, CV_BayerRG2RGB, CV_BayerGR2RGB,\n CV_BGR2Luv, CV_RGB2Luv, CV_BGR2HLS, CV_RGB2HLS,\n CV_HSV2BGR, CV_HSV2RGB, CV_Lab2BGR, CV_Lab2RGB,\n CV_Luv2BGR, CV_Luv2RGB, CV_HLS2BGR, CV_HLS2RGB\n\nReturns\n-------\nout : ndarray\n A new image in the requested color-space, with\n an appropriate dtype.\n\nNotes\n-----\nNot all conversion types support all dtypes.\nAn exception will be raise if the dtype is not supported.\nSee the OpenCV documentation for more details\nabout the specific color conversions.";
static char __pyx_k_208[] = "cvThreshold(src, threshold, max_value=255, threshold_type=CV_THRESH_BINARY, use_otsu=False)\n\nThreshold an image.\n\nParameters\n----------\nsrc : ndarray, 2D, dtype=[uint8, float32]\nthreshold : float\n The threshold value. (decision value)\nmax_value : float\n The maximum value.\nthreshold_type : integer\n The flag representing which type of thresholding to apply.\n Valid flags are:\n CV_THRESH_BINARY (max_value if src(x,y) > threshold else 0)\n CV_THRESH_BINARY_INV (0 if src(x,y) > threshold else max_value)\n CV_THRESH_TRUNC (threshold if src(x,y) > threshold else src(x,y))\n CV_THRESH_TOZERO (src(x,y) if src(x,y) > threshold else 0)\n CV_THRESH_TOZERO_INV (0 if src(x,y) > threshold else src(x,y))\nuse_otsu : bool\n If true, the optimum threshold is automatically computed\n and the passed in threshold value is ignored.\n Only implemented for uint8 source images.\n\nReturns\n-------\nout/(out, threshold) : ndarray or (ndarray, float)\n If use_otsu is True, then the computed threshold value is\n returned in addition to the thresholded image. Otherwise\n just the thresholded image is returned.";
static char __pyx_k_214[] = "cvAdaptiveThreshold(src, max_value, adaptive_method=CV_ADAPTIVE_THRESH_MEAN_C, threshold_type=CV_THRESH_BINARY, block_size=3, param1=5)\n\nApply an adaptive threshold to an image.\n\nParameters\n----------\nsrc : ndarray, 2D, dtype=uint8\nmax_value : float\n The maximum value.\nadaptive_method : integer\n The flag representing the adaptive method.\n Valid flags are:\n CV_ADAPTIVE_THRESH_MEAN_C (uses mean of the neighborhood)\n CV_ADAPTIVE_THRESH_GAUSSIAN_C (uses gaussian of the neighborhood)\nthreshold_type : integer\n The flag representing which type of thresholding to apply.\n Valid flags are:\n CV_THRESH_BINARY (max_value if src(x,y) > threshold else 0)\n CV_THRESH_BINARY_INV (0 if src(x,y) > threshold else max_value)\nblock_size : integer\n Defines a block_size x block_size neighborhood\nparam1 : float\n The weight to be subtracted from the neighborhood computation.\n\nReturns\n-------\nout : ndarray\n The thresholded image.";
static char __pyx_k_217[] = "cvPyrDown(src)\n\nDownsample an image.\n\nParameters\n----------\nsrc : ndarray, dtype=[uint8, uint16, float32, float64]\n\nReturns\n-------\nout : ndarray\n Downsampled image half the size of the original\n in each dimension.";
static char __pyx_k_220[] = "cvPyrUp(src)\n\nUpsample an image.\n\nParameters\n----------\nsrc : ndarray, dtype=[uint8, uint16, float32, float64]\n\nReturns\n-------\nout : ndarray\n Upsampled image twice the size of the original\n in each dimension.";
static char __pyx_k_223[] = "cvCalibrateCamera2(object_points, image_points, point_counts, image_size)\n\nFinds the intrinsic and extrinsic camera parameters\nusing a calibration pattern.\n\nParameters\n----------\nobject_points : ndarray, Nx3\n An array representing the (X, Y, Z) known coordinates of the\n calibration object.\nimage_points : ndarry, Nx2\n An array representing the pixel image coordinate of the\n points in object_points.\npoint_counts : ndarry, 1D, dtype=int32\n Vector containing the number of points in each particular view.\nimage_size : 2-tuple, (height, width)\n The height and width of the images used.\n\nReturns\n-------\n(intrinsics, distortion) : ndarray 3x3, ndarray 5-vector\n Intrinsics is the 3x3 camera instrinsics matrix.\n Distortion is the 5-vector of distortion coefficients.";
static char __pyx_k_228[] = "cvFindChessboardCorners(src, pattern_size, flag=CV_CALIB_CB_ADAPTIVE_THRESH)\n\nFinds the position of the internal corners of a chessboard.\n\nParameters\n----------\nsrc : ndarray, dtype=uint8\n Image to search for chessboard corners.\npattern_size : 2-tuple of inner corners (h,w)\nflag : integer\n CV_CALIB_CB_ADAPTIVE_THRESH - use adaptive thresholding\n to convert the image to black and white,\n rather than a fixed threshold level\n (computed from the average image brightness).\n CV_CALIB_CB_NORMALIZE_IMAGE - normalize the image using\n cvNormalizeHist() before applying fixed or adaptive\n thresholding.\n CV_CALIB_CB_FILTER_QUADS - use additional criteria\n (like contour area, perimeter, square-like shape) to\n filter out false quads that are extracted at the contour\n retrieval stage.\n\nReturns\n-------\nout : ndarray Nx2\n An nx2 array of the corners found.";
static char __pyx_k_232[] = "cvDrawChessboardCorners(src, pattern_size, corners, in_place=False)\n\nRenders found chessboard corners into an image.\n\nParameters\n----------\nsrc : ndarray, dim 3, dtype: uint8\n Image to draw into.\npattern_size : 2-tuple, (h, w)\n Number of inner corners (h,w)\ncorners : ndarray, nx2, dtype=float32\n Corners found in the image. See cvFindChessboardCorners and\n cvFindCornerSubPix\nin_place: bool\n If true, perform the drawing on the submitted\n image. If false, a copy of the image will be made and drawn to.\n\nReturns\n-------\nout/None : ndarray or none\n If in_place is True, the function returns None.\n Otherwise, the function returns a new image with\n the corners drawn into it.";
static PyObject *__pyx_int_3;
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static PyObject *__pyx_kp_block_size;
static char __pyx_k_k[] = "k";
static PyObject *__pyx_kp_k;
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static PyObject *__pyx_kp_corners;
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static PyObject *__pyx_kp_corner_count;
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static PyObject *__pyx_kp_quality_level;
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__Pyx_RaiseArgtupleInvalid("cvGoodFeaturesToTrack", 0, 4, 7, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 756; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
__pyx_L3_error:;
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/* "/home/brucewayne/scikits_image/scikits.image/scikits/image/opencv/opencv_cv.pyx":760
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/* "/home/brucewayne/scikits_image/scikits.image/scikits/image/opencv/opencv_cv.pyx":761
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/* "/home/brucewayne/scikits_image/scikits.image/scikits/image/opencv/opencv_cv.pyx":1041
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/* "/home/brucewayne/scikits_image/scikits.image/scikits/image/opencv/opencv_cv.pyx":1055
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/* "/home/brucewayne/scikits_image/scikits.image/scikits/image/opencv/opencv_cv.pyx":1068
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/* "/home/brucewayne/scikits_image/scikits.image/scikits/image/opencv/opencv_cv.pyx":1348
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/* "/home/brucewayne/scikits_image/scikits.image/scikits/image/opencv/opencv_cv.pyx":1411
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/* "/home/brucewayne/scikits_image/scikits.image/scikits/image/opencv/opencv_cv.pyx":1417
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/* "/home/brucewayne/scikits_image/scikits.image/scikits/image/opencv/opencv_cv.pyx":1420
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/* "/home/brucewayne/scikits_image/scikits.image/scikits/image/opencv/opencv_cv.pyx":1712
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/* "/home/brucewayne/scikits_image/scikits.image/scikits/image/opencv/opencv_cv.pyx":2114
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/* "/home/brucewayne/scikits_image/scikits.image/scikits/image/opencv/opencv_cv.pyx":2118
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/* "/home/brucewayne/scikits_image/scikits.image/scikits/image/opencv/opencv_cv.pyx":2123
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/* "/home/brucewayne/scikits_image/scikits.image/scikits/image/opencv/opencv_cv.pyx":2128
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/* "/home/brucewayne/scikits_image/scikits.image/scikits/image/opencv/opencv_cv.pyx":2129
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/* "/home/brucewayne/scikits_image/scikits.image/scikits/image/opencv/opencv_cv.pyx":2130
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/* "/home/brucewayne/scikits_image/scikits.image/scikits/image/opencv/opencv_cv.pyx":2132
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/* "/home/brucewayne/scikits_image/scikits.image/scikits/image/opencv/opencv_cv.pyx":2133
*
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/* "/home/brucewayne/scikits_image/scikits.image/scikits/image/opencv/opencv_cv.pyx":2134
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/* "/home/brucewayne/scikits_image/scikits.image/scikits/image/opencv/opencv_cv.pyx":2138
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/* "/usr/local/lib/python2.6/dist-packages/Cython-0.11.3-py2.6-linux-x86_64.egg/Cython/Includes/numpy.pxd":217
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/* "/usr/local/lib/python2.6/dist-packages/Cython-0.11.3-py2.6-linux-x86_64.egg/Cython/Includes/numpy.pxd":223
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/* "/usr/local/lib/python2.6/dist-packages/Cython-0.11.3-py2.6-linux-x86_64.egg/Cython/Includes/numpy.pxd":224
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/* "/usr/local/lib/python2.6/dist-packages/Cython-0.11.3-py2.6-linux-x86_64.egg/Cython/Includes/numpy.pxd":226
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{__Pyx_NAMESTR("cvSmooth"), (PyCFunction)__pyx_pf_7scikits_5image_6opencv_9opencv_cv_cvSmooth, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(0)},
{__Pyx_NAMESTR("cvFilter2D"), (PyCFunction)__pyx_pf_7scikits_5image_6opencv_9opencv_cv_cvFilter2D, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(0)},
{__Pyx_NAMESTR("cvIntegral"), (PyCFunction)__pyx_pf_7scikits_5image_6opencv_9opencv_cv_cvIntegral, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(0)},
{__Pyx_NAMESTR("cvCvtColor"), (PyCFunction)__pyx_pf_7scikits_5image_6opencv_9opencv_cv_cvCvtColor, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(0)},
{__Pyx_NAMESTR("cvThreshold"), (PyCFunction)__pyx_pf_7scikits_5image_6opencv_9opencv_cv_cvThreshold, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(0)},
{__Pyx_NAMESTR("cvAdaptiveThreshold"), (PyCFunction)__pyx_pf_7scikits_5image_6opencv_9opencv_cv_cvAdaptiveThreshold, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(0)},
{__Pyx_NAMESTR("cvPyrDown"), (PyCFunction)__pyx_pf_7scikits_5image_6opencv_9opencv_cv_cvPyrDown, METH_O, __Pyx_DOCSTR(0)},
{__Pyx_NAMESTR("cvPyrUp"), (PyCFunction)__pyx_pf_7scikits_5image_6opencv_9opencv_cv_cvPyrUp, METH_O, __Pyx_DOCSTR(0)},
{__Pyx_NAMESTR("cvCalibrateCamera2"), (PyCFunction)__pyx_pf_7scikits_5image_6opencv_9opencv_cv_cvCalibrateCamera2, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(0)},
{__Pyx_NAMESTR("cvFindChessboardCorners"), (PyCFunction)__pyx_pf_7scikits_5image_6opencv_9opencv_cv_cvFindChessboardCorners, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(0)},
{__Pyx_NAMESTR("cvDrawChessboardCorners"), (PyCFunction)__pyx_pf_7scikits_5image_6opencv_9opencv_cv_cvDrawChessboardCorners, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(0)},
{0, 0, 0, 0}
};
static void __pyx_init_filenames(void); /*proto*/
char* __pyx_import_star_type_names[] = {
"cvErodePtr",
"IplImage",
"cvFindCornerSubPixPtr",
"va_list",
"cvCornerEigenValsAndVecsPtr",
"_IplConvKernel",
"cvCannyPtr",
"cvResizePtr",
"cvPyrDownPtr",
"cvCvtColorPtr",
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"cvAdaptiveThresholdPtr",
"Py_complex",
"_IplImage",
"cvGetRectSubPixPtr",
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"FILE",
"cvFilter2DPtr",
"cvGoodFeaturesToTrackPtr",
"cvCalibrateCamera2Ptr",
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"_inittab",
"UINT8_t",
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"cvCornerHarrisPtr",
"cvDilatePtr",
"CvScalar",
"cvSobelPtr",
"INT16_t",
"FLOAT64_t",
"CvMat_uProxy",
"FLOAT32_t",
"IplConvKernel",
"INT32_t",
"PyTypeObject",
"cvLogPolarPtr",
"CvTermCriteria",
"CvMat",
"PyObject",
"cvMorphologyExPtr",
"cvLaplacePtr",
"cvFindChessboardCornersPtr",
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"cvWarpPerspectivePtr",
"cvGetQuadrangleSubPixPtr",
"CvPoint2D32f",
"CvSize",
"cvPreCorneDetectPtr",
"PY_LONG_LONG",
"CvPoint",
"cvSmoothPtr",
"Py_UNICODE",
0
};
static int __pyx_import_star_set(PyObject *o, PyObject* py_name, char *name) {
char** type_name = __pyx_import_star_type_names;
while (*type_name) {
if (__Pyx_StrEq(name, *type_name)) {
PyErr_Format(PyExc_TypeError, "Cannot overwrite C type %s", name);
goto bad;
}
type_name++;
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if (0);
else if (__Pyx_StrEq(name, "c_cvSmooth")) {
PyErr_Format(PyExc_TypeError, "Cannot convert Python object c_cvSmooth to scikits.image.opencv.opencv_cv.cvSmoothPtr");
{__pyx_filename = __pyx_f[0]; __pyx_lineno = 222; __pyx_clineno = __LINE__; goto __pyx_L2_error;}
}
else if (__Pyx_StrEq(name, "c_cvCanny")) {
PyErr_Format(PyExc_TypeError, "Cannot convert Python object c_cvCanny to scikits.image.opencv.opencv_cv.cvCannyPtr");
{__pyx_filename = __pyx_f[0]; __pyx_lineno = 125; __pyx_clineno = __LINE__; goto __pyx_L2_error;}
}
else if (__Pyx_StrEq(name, "c_cvCvtColor")) {
PyErr_Format(PyExc_TypeError, "Cannot convert Python object c_cvCvtColor to scikits.image.opencv.opencv_cv.cvCvtColorPtr");
{__pyx_filename = __pyx_f[0]; __pyx_lineno = 237; __pyx_clineno = __LINE__; goto __pyx_L2_error;}
}
else if (__Pyx_StrEq(name, "c_cvCornerMinEigenVal")) {
PyErr_Format(PyExc_TypeError, "Cannot convert Python object c_cvCornerMinEigenVal to scikits.image.opencv.opencv_cv.cvCornerMinEigenValPtr");
{__pyx_filename = __pyx_f[0]; __pyx_lineno = 142; __pyx_clineno = __LINE__; goto __pyx_L2_error;}
}
else if (__Pyx_StrEq(name, "c_cvErode")) {
PyErr_Format(PyExc_TypeError, "Cannot convert Python object c_cvErode to scikits.image.opencv.opencv_cv.cvErodePtr");
{__pyx_filename = __pyx_f[0]; __pyx_lineno = 204; __pyx_clineno = __LINE__; goto __pyx_L2_error;}
}
else if (__Pyx_StrEq(name, "c_cvFilter2D")) {
PyErr_Format(PyExc_TypeError, "Cannot convert Python object c_cvFilter2D to scikits.image.opencv.opencv_cv.cvFilter2DPtr");
{__pyx_filename = __pyx_f[0]; __pyx_lineno = 227; __pyx_clineno = __LINE__; goto __pyx_L2_error;}
}
else if (__Pyx_StrEq(name, "c_cvFindChessboardCorners")) {
PyErr_Format(PyExc_TypeError, "Cannot convert Python object c_cvFindChessboardCorners to scikits.image.opencv.opencv_cv.cvFindChessboardCornersPtr");
{__pyx_filename = __pyx_f[0]; __pyx_lineno = 272; __pyx_clineno = __LINE__; goto __pyx_L2_error;}
}
else if (__Pyx_StrEq(name, "c_cvGetRectSubPix")) {
PyErr_Format(PyExc_TypeError, "Cannot convert Python object c_cvGetRectSubPix to scikits.image.opencv.opencv_cv.cvGetRectSubPixPtr");
{__pyx_filename = __pyx_f[0]; __pyx_lineno = 169; __pyx_clineno = __LINE__; goto __pyx_L2_error;}
}
else if (__Pyx_StrEq(name, "c_cvResize")) {
PyErr_Format(PyExc_TypeError, "Cannot convert Python object c_cvResize to scikits.image.opencv.opencv_cv.cvResizePtr");
{__pyx_filename = __pyx_f[0]; __pyx_lineno = 181; __pyx_clineno = __LINE__; goto __pyx_L2_error;}
}
else if (__Pyx_StrEq(name, "c_cvGoodFeaturesToTrack")) {
PyErr_Format(PyExc_TypeError, "Cannot convert Python object c_cvGoodFeaturesToTrack to scikits.image.opencv.opencv_cv.cvGoodFeaturesToTrackPtr");
{__pyx_filename = __pyx_f[0]; __pyx_lineno = 163; __pyx_clineno = __LINE__; goto __pyx_L2_error;}
}
else if (__Pyx_StrEq(name, "c_cvCornerEigenValsAndVecs")) {
PyErr_Format(PyExc_TypeError, "Cannot convert Python object c_cvCornerEigenValsAndVecs to scikits.image.opencv.opencv_cv.cvCornerEigenValsAndVecsPtr");
{__pyx_filename = __pyx_f[0]; __pyx_lineno = 136; __pyx_clineno = __LINE__; goto __pyx_L2_error;}
}
else if (__Pyx_StrEq(name, "c_cvPreCornerDetect")) {
PyErr_Format(PyExc_TypeError, "Cannot convert Python object c_cvPreCornerDetect to scikits.image.opencv.opencv_cv.cvPreCorneDetectPtr");
{__pyx_filename = __pyx_f[0]; __pyx_lineno = 130; __pyx_clineno = __LINE__; goto __pyx_L2_error;}
}
else if (__Pyx_StrEq(name, "c_cvPyrDown")) {
PyErr_Format(PyExc_TypeError, "Cannot convert Python object c_cvPyrDown to scikits.image.opencv.opencv_cv.cvPyrDownPtr");
{__pyx_filename = __pyx_f[0]; __pyx_lineno = 254; __pyx_clineno = __LINE__; goto __pyx_L2_error;}
}
else if (__Pyx_StrEq(name, "c_cvGetQuadrangleSubPix")) {
PyErr_Format(PyExc_TypeError, "Cannot convert Python object c_cvGetQuadrangleSubPix to scikits.image.opencv.opencv_cv.cvGetQuadrangleSubPixPtr");
{__pyx_filename = __pyx_f[0]; __pyx_lineno = 175; __pyx_clineno = __LINE__; goto __pyx_L2_error;}
}
else if (__Pyx_StrEq(name, "c_cvLogPolar")) {
PyErr_Format(PyExc_TypeError, "Cannot convert Python object c_cvLogPolar to scikits.image.opencv.opencv_cv.cvLogPolarPtr");
{__pyx_filename = __pyx_f[0]; __pyx_lineno = 199; __pyx_clineno = __LINE__; goto __pyx_L2_error;}
}
else if (__Pyx_StrEq(name, "c_cvThreshold")) {
PyErr_Format(PyExc_TypeError, "Cannot convert Python object c_cvThreshold to scikits.image.opencv.opencv_cv.cvThresholdPtr");
{__pyx_filename = __pyx_f[0]; __pyx_lineno = 242; __pyx_clineno = __LINE__; goto __pyx_L2_error;}
}
else if (__Pyx_StrEq(name, "c_cvLaplace")) {
PyErr_Format(PyExc_TypeError, "Cannot convert Python object c_cvLaplace to scikits.image.opencv.opencv_cv.cvLaplacePtr");
{__pyx_filename = __pyx_f[0]; __pyx_lineno = 120; __pyx_clineno = __LINE__; goto __pyx_L2_error;}
}
else if (__Pyx_StrEq(name, "c_cvMorphologyEx")) {
PyErr_Format(PyExc_TypeError, "Cannot convert Python object c_cvMorphologyEx to scikits.image.opencv.opencv_cv.cvMorphologyExPtr");
{__pyx_filename = __pyx_f[0]; __pyx_lineno = 215; __pyx_clineno = __LINE__; goto __pyx_L2_error;}
}
else if (__Pyx_StrEq(name, "c_cvCornerHarris")) {
PyErr_Format(PyExc_TypeError, "Cannot convert Python object c_cvCornerHarris to scikits.image.opencv.opencv_cv.cvCornerHarrisPtr");
{__pyx_filename = __pyx_f[0]; __pyx_lineno = 148; __pyx_clineno = __LINE__; goto __pyx_L2_error;}
}
else if (__Pyx_StrEq(name, "c_cvWarpAffine")) {
PyErr_Format(PyExc_TypeError, "Cannot convert Python object c_cvWarpAffine to scikits.image.opencv.opencv_cv.cvWarpAffinePtr");
{__pyx_filename = __pyx_f[0]; __pyx_lineno = 186; __pyx_clineno = __LINE__; goto __pyx_L2_error;}
}
else if (__Pyx_StrEq(name, "c_cvCalibrateCamera2")) {
PyErr_Format(PyExc_TypeError, "Cannot convert Python object c_cvCalibrateCamera2 to scikits.image.opencv.opencv_cv.cvCalibrateCamera2Ptr");
{__pyx_filename = __pyx_f[0]; __pyx_lineno = 265; __pyx_clineno = __LINE__; goto __pyx_L2_error;}
}
else if (__Pyx_StrEq(name, "c_cvSobel")) {
PyErr_Format(PyExc_TypeError, "Cannot convert Python object c_cvSobel to scikits.image.opencv.opencv_cv.cvSobelPtr");
{__pyx_filename = __pyx_f[0]; __pyx_lineno = 115; __pyx_clineno = __LINE__; goto __pyx_L2_error;}
}
else if (__Pyx_StrEq(name, "c_cvPyrUp")) {
PyErr_Format(PyExc_TypeError, "Cannot convert Python object c_cvPyrUp to scikits.image.opencv.opencv_cv.cvPyrUpPtr");
{__pyx_filename = __pyx_f[0]; __pyx_lineno = 259; __pyx_clineno = __LINE__; goto __pyx_L2_error;}
}
else if (__Pyx_StrEq(name, "c_cvDrawChessboardCorners")) {
PyErr_Format(PyExc_TypeError, "Cannot convert Python object c_cvDrawChessboardCorners to scikits.image.opencv.opencv_cv.cvDrawChessboardCornersPtr");
{__pyx_filename = __pyx_f[0]; __pyx_lineno = 279; __pyx_clineno = __LINE__; goto __pyx_L2_error;}
}
else if (__Pyx_StrEq(name, "c_cvDilate")) {
PyErr_Format(PyExc_TypeError, "Cannot convert Python object c_cvDilate to scikits.image.opencv.opencv_cv.cvDilatePtr");
{__pyx_filename = __pyx_f[0]; __pyx_lineno = 209; __pyx_clineno = __LINE__; goto __pyx_L2_error;}
}
else if (__Pyx_StrEq(name, "c_cvWarpPerspective")) {
PyErr_Format(PyExc_TypeError, "Cannot convert Python object c_cvWarpPerspective to scikits.image.opencv.opencv_cv.cvWarpPerspectivePtr");
{__pyx_filename = __pyx_f[0]; __pyx_lineno = 193; __pyx_clineno = __LINE__; goto __pyx_L2_error;}
}
else if (__Pyx_StrEq(name, "c_cvFindCornerSubPix")) {
PyErr_Format(PyExc_TypeError, "Cannot convert Python object c_cvFindCornerSubPix to scikits.image.opencv.opencv_cv.cvFindCornerSubPixPtr");
{__pyx_filename = __pyx_f[0]; __pyx_lineno = 155; __pyx_clineno = __LINE__; goto __pyx_L2_error;}
}
else if (__Pyx_StrEq(name, "c_cvIntegral")) {
PyErr_Format(PyExc_TypeError, "Cannot convert Python object c_cvIntegral to scikits.image.opencv.opencv_cv.cvIntegralPtr");
{__pyx_filename = __pyx_f[0]; __pyx_lineno = 232; __pyx_clineno = __LINE__; goto __pyx_L2_error;}
}
else if (__Pyx_StrEq(name, "c_cvAdaptiveThreshold")) {
PyErr_Format(PyExc_TypeError, "Cannot convert Python object c_cvAdaptiveThreshold to scikits.image.opencv.opencv_cv.cvAdaptiveThresholdPtr");
{__pyx_filename = __pyx_f[0]; __pyx_lineno = 248; __pyx_clineno = __LINE__; goto __pyx_L2_error;}
}
else {
if (PyObject_SetAttr(__pyx_m, py_name, o) < 0) goto bad;
}
return 0;
__pyx_L2_error:;
__Pyx_AddTraceback("scikits.image.opencv.opencv_cv");
bad:
Py_DECREF(o);
return -1;
}
/* import_all_from is an unexposed function from ceval.c */
static int
__Pyx_import_all_from(PyObject *locals, PyObject *v)
{
PyObject *all = __Pyx_GetAttrString(v, "__all__");
PyObject *dict, *name, *value;
int skip_leading_underscores = 0;
int pos, err;
if (all == NULL) {
if (!PyErr_ExceptionMatches(PyExc_AttributeError))
return -1; /* Unexpected error */
PyErr_Clear();
dict = __Pyx_GetAttrString(v, "__dict__");
if (dict == NULL) {
if (!PyErr_ExceptionMatches(PyExc_AttributeError))
return -1;
PyErr_SetString(PyExc_ImportError,
"from-import-* object has no __dict__ and no __all__");
return -1;
}
all = PyMapping_Keys(dict);
Py_DECREF(dict);
if (all == NULL)
return -1;
skip_leading_underscores = 1;
}
for (pos = 0, err = 0; ; pos++) {
name = PySequence_GetItem(all, pos);
if (name == NULL) {
if (!PyErr_ExceptionMatches(PyExc_IndexError))
err = -1;
else
PyErr_Clear();
break;
}
if (skip_leading_underscores &&
#if PY_MAJOR_VERSION < 3
PyString_Check(name) &&
PyString_AS_STRING(name)[0] == '_')
#else
PyUnicode_Check(name) &&
PyUnicode_AS_UNICODE(name)[0] == '_')
#endif
{
Py_DECREF(name);
continue;
}
value = PyObject_GetAttr(v, name);
if (value == NULL)
err = -1;
else if (PyDict_CheckExact(locals))
err = PyDict_SetItem(locals, name, value);
else
err = PyObject_SetItem(locals, name, value);
Py_DECREF(name);
Py_XDECREF(value);
if (err != 0)
break;
}
Py_DECREF(all);
return err;
}
static int __pyx_import_star(PyObject* m) {
int i;
int ret = -1;
char* s;
PyObject *locals = 0;
PyObject *list = 0;
PyObject *name;
PyObject *item;
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* from stdlib cimport *
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/* "/home/brucewayne/scikits_image/scikits.image/scikits/image/opencv/opencv_cv.pyx":22
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/* "/home/brucewayne/scikits_image/scikits.image/scikits/image/opencv/opencv_cv.pyx":2224
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/* "/usr/local/lib/python2.6/dist-packages/Cython-0.11.3-py2.6-linux-x86_64.egg/Cython/Includes/python_mem.pxd":1
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if (__pyx_m) {
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PyErr_Format(PyExc_TypeError,
#if PY_MAJOR_VERSION >= 3
"%s() got multiple values for keyword argument '%U'", func_name, kw_name);
#else
"%s() got multiple values for keyword argument '%s'", func_name,
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#if PY_VERSION_HEX < 0x02050000
"%s() takes %s %d positional argument%s (%d given)",
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"%s() takes %s %zd positional argument%s (%zd given)",
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func_name, more_or_less, num_expected, number, num_found);
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static int __Pyx_ParseOptionalKeywords(
PyObject *kwds,
PyObject **argnames[],
PyObject *kwds2,
PyObject *values[],
Py_ssize_t num_pos_args,
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{
PyObject *key = 0, *value = 0;
Py_ssize_t pos = 0;
PyObject*** name;
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#if PY_MAJOR_VERSION < 3
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if (unlikely(!PyUnicode_CheckExact(key)) && unlikely(!PyUnicode_Check(key))) {
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goto invalid_keyword_type;
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#if PY_MAJOR_VERSION >= 3
if (PyUnicode_GET_SIZE(**name) == PyUnicode_GET_SIZE(key) &&
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#if PY_MAJOR_VERSION >= 3
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arg_passed_twice:
__Pyx_RaiseDoubleKeywordsError(function_name, **name);
goto bad;
invalid_keyword_type:
PyErr_Format(PyExc_TypeError,
"%s() keywords must be strings", function_name);
goto bad;
invalid_keyword:
PyErr_Format(PyExc_TypeError,
#if PY_MAJOR_VERSION < 3
"%s() got an unexpected keyword argument '%s'",
function_name, PyString_AsString(key));
#else
"%s() got an unexpected keyword argument '%U'",
function_name, key);
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bad:
return -1;
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PyErr_NormalizeException(type, value, tb);
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goto bad;
Py_INCREF(*type);
Py_INCREF(*value);
Py_INCREF(*tb);
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Py_XDECREF(tmp_type);
Py_XDECREF(tmp_value);
Py_XDECREF(tmp_tb);
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Py_XDECREF(*value);
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PyErr_Format(PyExc_TypeError, "Cannot convert %s to %s",
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static INLINE void __Pyx_RaiseNoneNotIterableError(void) {
PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable");
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static INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) {
PyErr_Format(PyExc_ValueError,
#if PY_VERSION_HEX < 0x02050000
"need more than %d value%s to unpack", (int)index,
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"need more than %zd value%s to unpack", index,
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(index == 1) ? "" : "s");
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static INLINE void __Pyx_RaiseTooManyValuesError(void) {
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static void __Pyx_UnpackTupleError(PyObject *t, Py_ssize_t index) {
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__Pyx_RaiseTooManyValuesError();
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static int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed,
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else {
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static INLINE void __Pyx_ExceptionSave(PyObject **type, PyObject **value, PyObject **tb) {
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list = empty_list;
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empty_dict = PyDict_New();
if (!empty_dict)
goto bad;
module = PyObject_CallFunctionObjArgs(__import__,
name, global_dict, empty_dict, list, NULL);
bad:
Py_XDECREF(empty_list);
Py_XDECREF(__import__);
Py_XDECREF(empty_dict);
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#if PY_VERSION_HEX < 0x02050000
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Py_INCREF(type);
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type = 0;
PyErr_SetString(PyExc_TypeError,
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#else
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Py_INCREF(type);
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Py_XDECREF(type);
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PyErr_NormalizeException(&type, &value, &tb);
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Py_XDECREF(tmp_tb);
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PyLong_FromLongLong((PY_LONG_LONG)val) :
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Py_DECREF(item);
__Pyx_RaiseTooManyValuesError();
return -1;
}
else if (!PyErr_Occurred())
return 0;
else
return -1;
}
static INLINE unsigned char __Pyx_PyInt_AsUnsignedChar(PyObject* x) {
if (sizeof(unsigned char) < sizeof(long)) {
long val = __Pyx_PyInt_AsLong(x);
if (unlikely(val != (long)(unsigned char)val)) {
if (!unlikely(val == -1 && PyErr_Occurred())) {
PyErr_SetString(PyExc_OverflowError,
(((unsigned char)-1) > ((unsigned char)0) && unlikely(val < 0)) ?
"can't convert negative value to unsigned char" :
"value too large to convert to unsigned char");
}
return (unsigned char)-1;
}
return (unsigned char)val;
}
return (unsigned char)__Pyx_PyInt_AsUnsignedLong(x);
}
static INLINE unsigned short __Pyx_PyInt_AsUnsignedShort(PyObject* x) {
if (sizeof(unsigned short) < sizeof(long)) {
long val = __Pyx_PyInt_AsLong(x);
if (unlikely(val != (long)(unsigned short)val)) {
if (!unlikely(val == -1 && PyErr_Occurred())) {
PyErr_SetString(PyExc_OverflowError,
(((unsigned short)-1) > ((unsigned short)0) && unlikely(val < 0)) ?
"can't convert negative value to unsigned short" :
"value too large to convert to unsigned short");
}
return (unsigned short)-1;
}
return (unsigned short)val;
}
return (unsigned short)__Pyx_PyInt_AsUnsignedLong(x);
}
static INLINE unsigned int __Pyx_PyInt_AsUnsignedInt(PyObject* x) {
if (sizeof(unsigned int) < sizeof(long)) {
long val = __Pyx_PyInt_AsLong(x);
if (unlikely(val != (long)(unsigned int)val)) {
if (!unlikely(val == -1 && PyErr_Occurred())) {
PyErr_SetString(PyExc_OverflowError,
(((unsigned int)-1) > ((unsigned int)0) && unlikely(val < 0)) ?
"can't convert negative value to unsigned int" :
"value too large to convert to unsigned int");
}
return (unsigned int)-1;
}
return (unsigned int)val;
}
return (unsigned int)__Pyx_PyInt_AsUnsignedLong(x);
}
static INLINE char __Pyx_PyInt_AsChar(PyObject* x) {
if (sizeof(char) < sizeof(long)) {
long val = __Pyx_PyInt_AsLong(x);
if (unlikely(val != (long)(char)val)) {
if (!unlikely(val == -1 && PyErr_Occurred())) {
PyErr_SetString(PyExc_OverflowError,
(((char)-1) > ((char)0) && unlikely(val < 0)) ?
"can't convert negative value to char" :
"value too large to convert to char");
}
return (char)-1;
}
return (char)val;
}
return (char)__Pyx_PyInt_AsLong(x);
}
static INLINE short __Pyx_PyInt_AsShort(PyObject* x) {
if (sizeof(short) < sizeof(long)) {
long val = __Pyx_PyInt_AsLong(x);
if (unlikely(val != (long)(short)val)) {
if (!unlikely(val == -1 && PyErr_Occurred())) {
PyErr_SetString(PyExc_OverflowError,
(((short)-1) > ((short)0) && unlikely(val < 0)) ?
"can't convert negative value to short" :
"value too large to convert to short");
}
return (short)-1;
}
return (short)val;
}
return (short)__Pyx_PyInt_AsLong(x);
}
static INLINE int __Pyx_PyInt_AsInt(PyObject* x) {
if (sizeof(int) < sizeof(long)) {
long val = __Pyx_PyInt_AsLong(x);
if (unlikely(val != (long)(int)val)) {
if (!unlikely(val == -1 && PyErr_Occurred())) {
PyErr_SetString(PyExc_OverflowError,
(((int)-1) > ((int)0) && unlikely(val < 0)) ?
"can't convert negative value to int" :
"value too large to convert to int");
}
return (int)-1;
}
return (int)val;
}
return (int)__Pyx_PyInt_AsLong(x);
}
static INLINE signed char __Pyx_PyInt_AsSignedChar(PyObject* x) {
if (sizeof(signed char) < sizeof(long)) {
long val = __Pyx_PyInt_AsLong(x);
if (unlikely(val != (long)(signed char)val)) {
if (!unlikely(val == -1 && PyErr_Occurred())) {
PyErr_SetString(PyExc_OverflowError,
(((signed char)-1) > ((signed char)0) && unlikely(val < 0)) ?
"can't convert negative value to signed char" :
"value too large to convert to signed char");
}
return (signed char)-1;
}
return (signed char)val;
}
return (signed char)__Pyx_PyInt_AsSignedLong(x);
}
static INLINE signed short __Pyx_PyInt_AsSignedShort(PyObject* x) {
if (sizeof(signed short) < sizeof(long)) {
long val = __Pyx_PyInt_AsLong(x);
if (unlikely(val != (long)(signed short)val)) {
if (!unlikely(val == -1 && PyErr_Occurred())) {
PyErr_SetString(PyExc_OverflowError,
(((signed short)-1) > ((signed short)0) && unlikely(val < 0)) ?
"can't convert negative value to signed short" :
"value too large to convert to signed short");
}
return (signed short)-1;
}
return (signed short)val;
}
return (signed short)__Pyx_PyInt_AsSignedLong(x);
}
static INLINE signed int __Pyx_PyInt_AsSignedInt(PyObject* x) {
if (sizeof(signed int) < sizeof(long)) {
long val = __Pyx_PyInt_AsLong(x);
if (unlikely(val != (long)(signed int)val)) {
if (!unlikely(val == -1 && PyErr_Occurred())) {
PyErr_SetString(PyExc_OverflowError,
(((signed int)-1) > ((signed int)0) && unlikely(val < 0)) ?
"can't convert negative value to signed int" :
"value too large to convert to signed int");
}
return (signed int)-1;
}
return (signed int)val;
}
return (signed int)__Pyx_PyInt_AsSignedLong(x);
}
static INLINE unsigned long __Pyx_PyInt_AsUnsignedLong(PyObject* x) {
#if PY_VERSION_HEX < 0x03000000
if (likely(PyInt_Check(x))) {
long val = PyInt_AS_LONG(x);
if (((unsigned long)-1) > ((unsigned long)0) && unlikely(val < 0)) {
PyErr_SetString(PyExc_OverflowError,
"can't convert negative value to unsigned long");
return (unsigned long)-1;
}
return (unsigned long)val;
} else
#endif
if (likely(PyLong_Check(x))) {
if (((unsigned long)-1) > ((unsigned long)0) && unlikely(Py_SIZE(x) < 0)) {
PyErr_SetString(PyExc_OverflowError,
"can't convert negative value to unsigned long");
return (unsigned long)-1;
}
return (((unsigned long)-1) < ((unsigned long)0)) ?
PyLong_AsLong(x) :
PyLong_AsUnsignedLong(x);
} else {
unsigned long val;
PyObject *tmp = __Pyx_PyNumber_Int(x);
if (!tmp) return (unsigned long)-1;
val = __Pyx_PyInt_AsUnsignedLong(tmp);
Py_DECREF(tmp);
return val;
}
}
static INLINE unsigned PY_LONG_LONG __Pyx_PyInt_AsUnsignedLongLong(PyObject* x) {
#if PY_VERSION_HEX < 0x03000000
if (likely(PyInt_Check(x))) {
long val = PyInt_AS_LONG(x);
if (((unsigned PY_LONG_LONG)-1) > ((unsigned PY_LONG_LONG)0) && unlikely(val < 0)) {
PyErr_SetString(PyExc_OverflowError,
"can't convert negative value to unsigned PY_LONG_LONG");
return (unsigned PY_LONG_LONG)-1;
}
return (unsigned PY_LONG_LONG)val;
} else
#endif
if (likely(PyLong_Check(x))) {
if (((unsigned PY_LONG_LONG)-1) > ((unsigned PY_LONG_LONG)0) && unlikely(Py_SIZE(x) < 0)) {
PyErr_SetString(PyExc_OverflowError,
"can't convert negative value to unsigned PY_LONG_LONG");
return (unsigned PY_LONG_LONG)-1;
}
return (((unsigned PY_LONG_LONG)-1) < ((unsigned PY_LONG_LONG)0)) ?
PyLong_AsLongLong(x) :
PyLong_AsUnsignedLongLong(x);
} else {
unsigned PY_LONG_LONG val;
PyObject *tmp = __Pyx_PyNumber_Int(x);
if (!tmp) return (unsigned PY_LONG_LONG)-1;
val = __Pyx_PyInt_AsUnsignedLongLong(tmp);
Py_DECREF(tmp);
return val;
}
}
static INLINE long __Pyx_PyInt_AsLong(PyObject* x) {
#if PY_VERSION_HEX < 0x03000000
if (likely(PyInt_Check(x))) {
long val = PyInt_AS_LONG(x);
if (((long)-1) > ((long)0) && unlikely(val < 0)) {
PyErr_SetString(PyExc_OverflowError,
"can't convert negative value to long");
return (long)-1;
}
return (long)val;
} else
#endif
if (likely(PyLong_Check(x))) {
if (((long)-1) > ((long)0) && unlikely(Py_SIZE(x) < 0)) {
PyErr_SetString(PyExc_OverflowError,
"can't convert negative value to long");
return (long)-1;
}
return (((long)-1) < ((long)0)) ?
PyLong_AsLong(x) :
PyLong_AsUnsignedLong(x);
} else {
long val;
PyObject *tmp = __Pyx_PyNumber_Int(x);
if (!tmp) return (long)-1;
val = __Pyx_PyInt_AsLong(tmp);
Py_DECREF(tmp);
return val;
}
}
static INLINE PY_LONG_LONG __Pyx_PyInt_AsLongLong(PyObject* x) {
#if PY_VERSION_HEX < 0x03000000
if (likely(PyInt_Check(x))) {
long val = PyInt_AS_LONG(x);
if (((PY_LONG_LONG)-1) > ((PY_LONG_LONG)0) && unlikely(val < 0)) {
PyErr_SetString(PyExc_OverflowError,
"can't convert negative value to PY_LONG_LONG");
return (PY_LONG_LONG)-1;
}
return (PY_LONG_LONG)val;
} else
#endif
if (likely(PyLong_Check(x))) {
if (((PY_LONG_LONG)-1) > ((PY_LONG_LONG)0) && unlikely(Py_SIZE(x) < 0)) {
PyErr_SetString(PyExc_OverflowError,
"can't convert negative value to PY_LONG_LONG");
return (PY_LONG_LONG)-1;
}
return (((PY_LONG_LONG)-1) < ((PY_LONG_LONG)0)) ?
PyLong_AsLongLong(x) :
PyLong_AsUnsignedLongLong(x);
} else {
PY_LONG_LONG val;
PyObject *tmp = __Pyx_PyNumber_Int(x);
if (!tmp) return (PY_LONG_LONG)-1;
val = __Pyx_PyInt_AsLongLong(tmp);
Py_DECREF(tmp);
return val;
}
}
static INLINE signed long __Pyx_PyInt_AsSignedLong(PyObject* x) {
#if PY_VERSION_HEX < 0x03000000
if (likely(PyInt_Check(x))) {
long val = PyInt_AS_LONG(x);
if (((signed long)-1) > ((signed long)0) && unlikely(val < 0)) {
PyErr_SetString(PyExc_OverflowError,
"can't convert negative value to signed long");
return (signed long)-1;
}
return (signed long)val;
} else
#endif
if (likely(PyLong_Check(x))) {
if (((signed long)-1) > ((signed long)0) && unlikely(Py_SIZE(x) < 0)) {
PyErr_SetString(PyExc_OverflowError,
"can't convert negative value to signed long");
return (signed long)-1;
}
return (((signed long)-1) < ((signed long)0)) ?
PyLong_AsLong(x) :
PyLong_AsUnsignedLong(x);
} else {
signed long val;
PyObject *tmp = __Pyx_PyNumber_Int(x);
if (!tmp) return (signed long)-1;
val = __Pyx_PyInt_AsSignedLong(tmp);
Py_DECREF(tmp);
return val;
}
}
static INLINE signed PY_LONG_LONG __Pyx_PyInt_AsSignedLongLong(PyObject* x) {
#if PY_VERSION_HEX < 0x03000000
if (likely(PyInt_Check(x))) {
long val = PyInt_AS_LONG(x);
if (((signed PY_LONG_LONG)-1) > ((signed PY_LONG_LONG)0) && unlikely(val < 0)) {
PyErr_SetString(PyExc_OverflowError,
"can't convert negative value to signed PY_LONG_LONG");
return (signed PY_LONG_LONG)-1;
}
return (signed PY_LONG_LONG)val;
} else
#endif
if (likely(PyLong_Check(x))) {
if (((signed PY_LONG_LONG)-1) > ((signed PY_LONG_LONG)0) && unlikely(Py_SIZE(x) < 0)) {
PyErr_SetString(PyExc_OverflowError,
"can't convert negative value to signed PY_LONG_LONG");
return (signed PY_LONG_LONG)-1;
}
return (((signed PY_LONG_LONG)-1) < ((signed PY_LONG_LONG)0)) ?
PyLong_AsLongLong(x) :
PyLong_AsUnsignedLongLong(x);
} else {
signed PY_LONG_LONG val;
PyObject *tmp = __Pyx_PyNumber_Int(x);
if (!tmp) return (signed PY_LONG_LONG)-1;
val = __Pyx_PyInt_AsSignedLongLong(tmp);
Py_DECREF(tmp);
return val;
}
}
static INLINE int __Pyx_StrEq(const char *s1, const char *s2) {
while (*s1 != '\0' && *s1 == *s2) { s1++; s2++; }
return *s1 == *s2;
}
#ifndef __PYX_HAVE_RT_ImportType
#define __PYX_HAVE_RT_ImportType
static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name,
long size)
{
PyObject *py_module = 0;
PyObject *result = 0;
PyObject *py_name = 0;
py_module = __Pyx_ImportModule(module_name);
if (!py_module)
goto bad;
#if PY_MAJOR_VERSION < 3
py_name = PyString_FromString(class_name);
#else
py_name = PyUnicode_FromString(class_name);
#endif
if (!py_name)
goto bad;
result = PyObject_GetAttr(py_module, py_name);
Py_DECREF(py_name);
py_name = 0;
Py_DECREF(py_module);
py_module = 0;
if (!result)
goto bad;
if (!PyType_Check(result)) {
PyErr_Format(PyExc_TypeError,
"%s.%s is not a type object",
module_name, class_name);
goto bad;
}
if (((PyTypeObject *)result)->tp_basicsize != size) {
PyErr_Format(PyExc_ValueError,
"%s.%s does not appear to be the correct type object",
module_name, class_name);
goto bad;
}
return (PyTypeObject *)result;
bad:
Py_XDECREF(py_module);
Py_XDECREF(result);
return 0;
}
#endif
#ifndef __PYX_HAVE_RT_ImportModule
#define __PYX_HAVE_RT_ImportModule
static PyObject *__Pyx_ImportModule(const char *name) {
PyObject *py_name = 0;
PyObject *py_module = 0;
#if PY_MAJOR_VERSION < 3
py_name = PyString_FromString(name);
#else
py_name = PyUnicode_FromString(name);
#endif
if (!py_name)
goto bad;
py_module = PyImport_Import(py_name);
Py_DECREF(py_name);
return py_module;
bad:
Py_XDECREF(py_name);
return 0;
}
#endif
#ifndef __PYX_HAVE_RT_ImportFunction
#define __PYX_HAVE_RT_ImportFunction
static int __Pyx_ImportFunction(PyObject *module, const char *funcname, void (**f)(void), const char *sig) {
#if PY_VERSION_HEX < 0x02050000
char *api = (char *)"__pyx_capi__";
#else
const char *api = "__pyx_capi__";
#endif
PyObject *d = 0;
PyObject *cobj = 0;
const char *desc;
const char *s1, *s2;
union {
void (*fp)(void);
void *p;
} tmp;
d = PyObject_GetAttrString(module, api);
if (!d)
goto bad;
cobj = PyDict_GetItemString(d, funcname);
if (!cobj) {
PyErr_Format(PyExc_ImportError,
"%s does not export expected C function %s",
PyModule_GetName(module), funcname);
goto bad;
}
desc = (const char *)PyCObject_GetDesc(cobj);
if (!desc)
goto bad;
s1 = desc; s2 = sig;
while (*s1 != '\0' && *s1 == *s2) { s1++; s2++; }
if (*s1 != *s2) {
PyErr_Format(PyExc_TypeError,
"C function %s.%s has wrong signature (expected %s, got %s)",
PyModule_GetName(module), funcname, sig, desc);
goto bad;
}
tmp.p = PyCObject_AsVoidPtr(cobj);
*f = tmp.fp;
Py_DECREF(d);
return 0;
bad:
Py_XDECREF(d);
return -1;
}
#endif
#include "compile.h"
#include "frameobject.h"
#include "traceback.h"
static void __Pyx_AddTraceback(const char *funcname) {
PyObject *py_srcfile = 0;
PyObject *py_funcname = 0;
PyObject *py_globals = 0;
PyCodeObject *py_code = 0;
PyFrameObject *py_frame = 0;
#if PY_MAJOR_VERSION < 3
py_srcfile = PyString_FromString(__pyx_filename);
#else
py_srcfile = PyUnicode_FromString(__pyx_filename);
#endif
if (!py_srcfile) goto bad;
if (__pyx_clineno) {
#if PY_MAJOR_VERSION < 3
py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, __pyx_clineno);
#else
py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, __pyx_clineno);
#endif
}
else {
#if PY_MAJOR_VERSION < 3
py_funcname = PyString_FromString(funcname);
#else
py_funcname = PyUnicode_FromString(funcname);
#endif
}
if (!py_funcname) goto bad;
py_globals = PyModule_GetDict(__pyx_m);
if (!py_globals) goto bad;
py_code = PyCode_New(
0, /*int argcount,*/
#if PY_MAJOR_VERSION >= 3
0, /*int kwonlyargcount,*/
#endif
0, /*int nlocals,*/
0, /*int stacksize,*/
0, /*int flags,*/
__pyx_empty_bytes, /*PyObject *code,*/
__pyx_empty_tuple, /*PyObject *consts,*/
__pyx_empty_tuple, /*PyObject *names,*/
__pyx_empty_tuple, /*PyObject *varnames,*/
__pyx_empty_tuple, /*PyObject *freevars,*/
__pyx_empty_tuple, /*PyObject *cellvars,*/
py_srcfile, /*PyObject *filename,*/
py_funcname, /*PyObject *name,*/
__pyx_lineno, /*int firstlineno,*/
__pyx_empty_bytes /*PyObject *lnotab*/
);
if (!py_code) goto bad;
py_frame = PyFrame_New(
PyThreadState_GET(), /*PyThreadState *tstate,*/
py_code, /*PyCodeObject *code,*/
py_globals, /*PyObject *globals,*/
0 /*PyObject *locals*/
);
if (!py_frame) goto bad;
py_frame->f_lineno = __pyx_lineno;
PyTraceBack_Here(py_frame);
bad:
Py_XDECREF(py_srcfile);
Py_XDECREF(py_funcname);
Py_XDECREF(py_code);
Py_XDECREF(py_frame);
}
static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
while (t->p) {
#if PY_MAJOR_VERSION < 3
if (t->is_unicode && (!t->is_identifier)) {
*t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL);
} else if (t->intern) {
*t->p = PyString_InternFromString(t->s);
} else {
*t->p = PyString_FromStringAndSize(t->s, t->n - 1);
}
#else /* Python 3+ has unicode identifiers */
if (t->is_identifier || (t->is_unicode && t->intern)) {
*t->p = PyUnicode_InternFromString(t->s);
} else if (t->is_unicode) {
*t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1);
} else {
*t->p = PyBytes_FromStringAndSize(t->s, t->n - 1);
}
#endif
if (!*t->p)
return -1;
++t;
}
return 0;
}
/* Type Conversion Functions */
static INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {
if (x == Py_True) return 1;
else if ((x == Py_False) | (x == Py_None)) return 0;
else return PyObject_IsTrue(x);
}
static INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x) {
PyNumberMethods *m;
const char *name = NULL;
PyObject *res = NULL;
#if PY_VERSION_HEX < 0x03000000
if (PyInt_Check(x) || PyLong_Check(x))
#else
if (PyLong_Check(x))
#endif
return Py_INCREF(x), x;
m = Py_TYPE(x)->tp_as_number;
#if PY_VERSION_HEX < 0x03000000
if (m && m->nb_int) {
name = "int";
res = PyNumber_Int(x);
}
else if (m && m->nb_long) {
name = "long";
res = PyNumber_Long(x);
}
#else
if (m && m->nb_int) {
name = "int";
res = PyNumber_Long(x);
}
#endif
if (res) {
#if PY_VERSION_HEX < 0x03000000
if (!PyInt_Check(res) && !PyLong_Check(res)) {
#else
if (!PyLong_Check(res)) {
#endif
PyErr_Format(PyExc_TypeError,
"__%s__ returned non-%s (type %.200s)",
name, name, Py_TYPE(res)->tp_name);
Py_DECREF(res);
return NULL;
}
}
else if (!PyErr_Occurred()) {
PyErr_SetString(PyExc_TypeError,
"an integer is required");
}
return res;
}
static INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {
Py_ssize_t ival;
PyObject* x = PyNumber_Index(b);
if (!x) return -1;
ival = PyInt_AsSsize_t(x);
Py_DECREF(x);
return ival;
}
static INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {
#if PY_VERSION_HEX < 0x02050000
if (ival <= LONG_MAX)
return PyInt_FromLong((long)ival);
else {
unsigned char *bytes = (unsigned char *) &ival;
int one = 1; int little = (int)*(unsigned char*)&one;
return _PyLong_FromByteArray(bytes, sizeof(size_t), little, 0);
}
#else
return PyInt_FromSize_t(ival);
#endif
}
static INLINE size_t __Pyx_PyInt_AsSize_t(PyObject* x) {
unsigned PY_LONG_LONG val = __Pyx_PyInt_AsUnsignedLongLong(x);
if (unlikely(val == (unsigned PY_LONG_LONG)-1 && PyErr_Occurred())) {
return (size_t)-1;
} else if (unlikely(val != (unsigned PY_LONG_LONG)(size_t)val)) {
PyErr_SetString(PyExc_OverflowError,
"value too large to convert to size_t");
return (size_t)-1;
}
return (size_t)val;
}
#endif /* Py_PYTHON_H */
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