#include "pyhelpers.h"
#include <iostream>
#include <sstream>
int PySwigObject_Check(PyObject *op);
/* Py_ssize_t for old Pythons */
#if PY_VERSION_HEX < 0x02050000
typedef int Py_ssize_t;
#endif
PyObject * PyTuple_FromIntArray(int * arr, int len){
PyObject * obj = PyTuple_New(len);
for(int i=0; i<len; i++){
PyTuple_SetItem(obj, i, PyLong_FromLong( arr[i] ) );
}
return obj;
}
PyObject * SWIG_SetResult(PyObject * result, PyObject * obj){
if(result){
Py_DECREF(result);
}
result = PyTuple_New(1);
PyTuple_SetItem(result, 0, obj);
return result;
}
PyObject * SWIG_AppendResult(PyObject * result, PyObject ** to_add, int num){
if ((!result) || (result == Py_None)) {
/* no other results, so just add our values */
/* if only one object, return that */
if(num==1){
return to_add[0];
}
/* create a new tuple to put in our new pointer python objects */
result = PyTuple_New (num);
/* put in our new pointer python objects */
for(int i=0; i<num; i++){
PyTuple_SetItem (result, i, to_add[i]);
}
}
else {
/* we have other results, so add it to the end */
if (!PyTuple_Check (result)) {
/* previous result is not a tuple, so create one and put
previous result and current pointer in it */
/* first, save previous result */
PyObject *obj_save = result;
/* then, create the tuple */
result = PyTuple_New (1);
/* finaly, put the saved value in the tuple */
PyTuple_SetItem (result, 0, obj_save);
}
/* create a new tuple to put in our new pointer python object */
PyObject *my_obj = PyTuple_New (num);
/* put in our new pointer python object */
for( int i=0; i<num ; i++ ){
PyTuple_SetItem (my_obj, i, to_add[i]);
}
/* save the previous result */
PyObject *obj_save = result;
/* concat previous and our new result */
result = PySequence_Concat (obj_save, my_obj);
/* decrement the usage of no more used objects */
Py_DECREF (obj_save);
Py_DECREF (my_obj);
}
return result;
}
template <typename T>
void cv_arr_write(FILE * f, const char * fmt, T * data, size_t rows, size_t nch, size_t step){
size_t i,j,k;
char * cdata = (char *) data;
const char * chdelim1="", * chdelim2="";
// only output channel parens if > 1
if(nch>1){
chdelim1="(";
chdelim2=")";
}
fputs("[",f);
for(i=0; i<rows; i++){
fputs("[",f);
// first element
// out<<chdelim1;
fputs(chdelim1, f);
fprintf(f, fmt, ((T*)(cdata+i*step))[0]);
for(k=1; k<nch; k++){
fputs(", ", f);
fprintf(f, fmt, ((T*)(cdata+i*step))[k]);
}
fputs(chdelim2,f);
// remaining elements
for(j=nch*sizeof(T); j<step; j+=(nch*sizeof(T))){
fprintf(f, ",%s", chdelim1);
fprintf(f, fmt, ((T*)(cdata+i*step+j))[0]);
for(k=1; k<nch; k++){
fputs(", ", f);
fprintf(f, fmt, ((T*)(cdata+i*step+j))[k]);
}
fputs(chdelim2, f);
}
fputs( "]\n", f );
}
fputs( "]", f );
}
void cvArrPrint(CvArr * arr){
CvMat * mat;
CvMat stub;
mat = cvGetMat(arr, &stub);
int cn = CV_MAT_CN(mat->type);
int depth = CV_MAT_DEPTH(mat->type);
int step = MAX(mat->step, cn*mat->cols*CV_ELEM_SIZE(depth));
switch(depth){
case CV_8U:
cv_arr_write(stdout, "%u", (uchar *)mat->data.ptr, mat->rows, cn, step);
break;
case CV_8S:
cv_arr_write(stdout, "%d", (char *)mat->data.ptr, mat->rows, cn, step);
break;
case CV_16U:
cv_arr_write(stdout, "%u", (ushort *)mat->data.ptr, mat->rows, cn, step);
break;
case CV_16S:
cv_arr_write(stdout, "%d", (short *)mat->data.ptr, mat->rows, cn, step);
break;
case CV_32S:
cv_arr_write(stdout, "%d", (int *)mat->data.ptr, mat->rows, cn, step);
break;
case CV_32F:
cv_arr_write(stdout, "%f", (float *)mat->data.ptr, mat->rows, cn, step);
break;
case CV_64F:
cv_arr_write(stdout, "%g", (double *)mat->data.ptr, mat->rows, cn, step);
break;
default:
CV_Error( CV_StsError, "Unknown element type");
break;
}
}
// deal with negative array indices
int PyLong_AsIndex( PyObject * idx_object, int len ){
int idx = PyLong_AsLong( idx_object );
if(idx<0) return len+idx;
return idx;
}
CvRect PySlice_to_CvRect(CvArr * src, PyObject * idx_object){
CvSize sz = cvGetSize(src);
//printf("Size %dx%d\n", sz.height, sz.width);
int lower[2], upper[2];
Py_ssize_t len, start, stop, step, slicelength;
if(PyInt_Check(idx_object) || PyLong_Check(idx_object)){
// if array is a row vector, assume index into columns
if(sz.height>1){
lower[0] = PyLong_AsIndex( idx_object, sz.height );
upper[0] = lower[0] + 1;
lower[1] = 0;
upper[1] = sz.width;
}
else{
lower[0] = 0;
upper[0] = sz.height;
lower[1] = PyLong_AsIndex( idx_object, sz.width );
upper[1] = lower[1]+1;
}
}
// 1. Slice
else if(PySlice_Check(idx_object)){
len = sz.height;
if(PySlice_GetIndicesEx( (PySliceObject*)idx_object, len, &start, &stop, &step, &slicelength )!=0){
printf("Error in PySlice_GetIndicesEx: returning NULL");
PyErr_SetString(PyExc_Exception, "Error");
return cvRect(0,0,0,0);
}
// if array is a row vector, assume index bounds are into columns
if(sz.height>1){
lower[0] = (int) start; // use c convention of start index = 0
upper[0] = (int) stop; // use c convention
lower[1] = 0;
upper[1] = sz.width;
}
else{
lower[1] = (int) start; // use c convention of start index = 0
upper[1] = (int) stop; // use c convention
lower[0] = 0;
upper[0] = sz.height;
}
}
// 2. Tuple
else if(PyTuple_Check(idx_object)){
//printf("PyTuple{\n");
if(PyObject_Length(idx_object)!=2){
//printf("Expected a sequence of length 2: returning NULL");
PyErr_SetString(PyExc_ValueError, "Expected a sequence with 2 elements");
return cvRect(0,0,0,0);
}
for(int i=0; i<2; i++){
PyObject *o = PyTuple_GetItem(idx_object, i);
// 2a. Slice -- same as above
if(PySlice_Check(o)){
//printf("PySlice\n");
len = (i==0 ? sz.height : sz.width);
if(PySlice_GetIndicesEx( (PySliceObject*)o, len, &start, &stop, &step, &slicelength )!=0){
PyErr_SetString(PyExc_Exception, "Error");
printf("Error in PySlice_GetIndicesEx: returning NULL");
return cvRect(0,0,0,0);
}
//printf("PySlice_GetIndecesEx(%d, %d, %d, %d, %d)\n", len, start, stop, step, slicelength);
lower[i] = start;
upper[i] = stop;
}
// 2b. Integer
else if(PyInt_Check(o) || PyLong_Check(o)){
//printf("PyInt\n");
lower[i] = PyLong_AsIndex(o, i==0 ? sz.height : sz.width);
upper[i] = lower[i]+1;
}
else {
PyErr_SetString(PyExc_TypeError, "Expected a sequence of slices or integers");
printf("Expected a slice or int as sequence item: returning NULL");
return cvRect(0,0,0,0);
}
}
}
else {
PyErr_SetString( PyExc_TypeError, "Expected a slice or sequence");
printf("Expected a slice or sequence: returning NULL");
return cvRect(0,0,0,0);
}
//lower[0] = MAX(0, lower[0]);
//lower[1] = MAX(0, lower[1]);
//upper[0] = MIN(sz.height, upper[0]);
//upper[1] = MIN(sz.width, upper[1]);
//printf("Slice=%d %d %d %d\n", lower[0], upper[0], lower[1], upper[1]);
return cvRect(lower[1],lower[0], upper[1]-lower[1], upper[0]-lower[0]);
}
int CheckSliceBounds(CvRect * rect, int w, int h){
//printf("__setitem__ slice(%d:%d, %d:%d) array(%d,%d)", rect.x, rect.y, rect.x+rect.width, rect.y+rect.height, w, h);
if(rect->width<=0 || rect->height<=0 ||
rect->width>w || rect->height>h ||
rect->x<0 || rect->y<0 ||
rect->x>= w || rect->y >=h){
char errstr[256];
// previous function already set error string
if(rect->width==0 && rect->height==0 && rect->x==0 && rect->y==0) return -1;
sprintf(errstr, "Requested slice [ %d:%d %d:%d ] oversteps array sized [ %d %d ]",
rect->x, rect->y, rect->x+rect->width, rect->y+rect->height, w, h);
PyErr_SetString(PyExc_IndexError, errstr);
//PyErr_SetString(PyExc_ValueError, errstr);
return 0;
}
return 1;
}
double PyObject_AsDouble(PyObject * obj){
if(PyNumber_Check(obj)){
if(PyFloat_Check(obj)){
return PyFloat_AsDouble(obj);
}
else if(PyInt_Check(obj) || PyLong_Check(obj)){
return (double) PyLong_AsLong(obj);
}
}
PyErr_SetString( PyExc_TypeError, "Could not convert python object to Double");
return -1;
}
long PyObject_AsLong(PyObject * obj){
if(PyNumber_Check(obj)){
if(PyFloat_Check(obj)){
return (long) PyFloat_AsDouble(obj);
}
else if(PyInt_Check(obj) || PyLong_Check(obj)){
return PyLong_AsLong(obj);
}
}
PyErr_SetString( PyExc_TypeError, "Could not convert python object to Long");
return -1;
}
CvArr * PyArray_to_CvArr (PyObject * obj)
{
// let's try to create a temporary CvMat header that points to the
// data owned by obj and reflects its memory layout
CvArr * cvarr = NULL;
void * raw_data = 0;
long rows;
long cols;
long channels;
long step;
long mat_type = 7;
long element_size = 1;
// infer layout from array interface
PyObject * interface = PyObject_GetAttrString (obj, "__array_interface__");
// the array interface should be a dict
if (PyMapping_Check (interface))
{
if (PyMapping_HasKeyString (interface, (char*)"version") &&
PyMapping_HasKeyString (interface, (char*)"shape") &&
PyMapping_HasKeyString (interface, (char*)"typestr") &&
PyMapping_HasKeyString (interface, (char*)"data"))
{
PyObject * version = PyMapping_GetItemString (interface, (char*)"version");
PyObject * shape = PyMapping_GetItemString (interface, (char*)"shape");
PyObject * typestr = PyMapping_GetItemString (interface, (char*)"typestr");
PyObject * data = PyMapping_GetItemString (interface, (char*)"data");
if (!PyInt_Check (version) || PyInt_AsLong (version) != 3)
PyErr_SetString(PyExc_TypeError, "OpenCV understands version 3 of the __array_interface__ only");
else
{
if (!PyTuple_Check (shape) || PyTuple_Size (shape) < 2 || PyTuple_Size (shape) > 3)
PyErr_SetString(PyExc_TypeError, "arrays must have a shape with 2 or 3 dimensions");
else
{
rows = PyInt_AsLong (PyTuple_GetItem (shape, 0));
cols = PyInt_AsLong (PyTuple_GetItem (shape, 1));
channels = PyTuple_Size (shape) < 3 ? 1 : PyInt_AsLong (PyTuple_GetItem (shape, 2));
if (rows < 1 || cols < 1 || channels < 1 || channels > 4)
PyErr_SetString(PyExc_TypeError, "rows and columns must be positive, channels from 1 to 4");
else
{
// fprintf (stderr, "rows: %ld, cols: %ld, channels %ld\n", rows, cols, channels); fflush (stderr);
if (! PyTuple_Check (data) || PyTuple_Size (data) != 2 ||
!(PyInt_Check (PyTuple_GetItem (data,0)) || PyLong_Check (PyTuple_GetItem (data,0))) ||
!(PyBool_Check (PyTuple_GetItem (data,1)) && !PyInt_AsLong (PyTuple_GetItem (data,1))))
PyErr_SetString (PyExc_TypeError, "arrays must have a pointer to writeable data");
else
{
raw_data = PyLong_AsVoidPtr (PyTuple_GetItem (data,0));
// fprintf(stderr, "raw_data: %p\n", raw_data); fflush (stderr);
char * format_str = NULL;
Py_ssize_t len = 0;
if (!PyString_Check (typestr) || PyString_AsStringAndSize (typestr, & format_str, &len) == -1 || len !=3)
PyErr_SetString(PyExc_TypeError, "there is something wrong with the format string");
else
{
// fprintf(stderr, "format: %c %c\n", format_str[1], format_str[2]); fflush (stderr);
if (format_str[1] == 'u' && format_str[2] == '1')
{
element_size = 1;
mat_type = CV_MAKETYPE(CV_8U, channels);
}
else if (format_str[1] == 'i' && format_str[2] == '1')
{
element_size = 1;
mat_type = CV_MAKETYPE(CV_8S, channels);
}
else if (format_str[1] == 'u' && format_str[2] == '2')
{
element_size = 2;
mat_type = CV_MAKETYPE(CV_16U, channels);
}
else if (format_str[1] == 'i' && format_str[2] == '2')
{
element_size = 2;
mat_type = CV_MAKETYPE(CV_16S, channels);
}
else if (format_str[1] == 'i' && format_str[2] == '4')
{
element_size = 4;
mat_type = CV_MAKETYPE(CV_32S, channels);
}
else if (format_str[1] == 'f' && format_str[2] == '4')
{
element_size = 4;
mat_type = CV_MAKETYPE(CV_32F, channels);
}
else if (format_str[1] == 'f' && format_str[2] == '8')
{
element_size = 8;
mat_type = CV_MAKETYPE(CV_64F, channels);
}
else
{
PyErr_SetString(PyExc_TypeError, "unknown or unhandled element format");
mat_type = CV_USRTYPE1;
}
// handle strides if given
// TODO: implement stride handling
step = cols * channels * element_size;
if (PyMapping_HasKeyString (interface, (char*)"strides"))
{
PyObject * strides = PyMapping_GetItemString (interface, (char*)"strides");
if (strides != Py_None)
{
fprintf(stderr, "we have strides ... not handled!\n"); fflush (stderr);
PyErr_SetString(PyExc_TypeError, "arrays with strides not handled yet");
mat_type = CV_USRTYPE1; // use this to denote, we've got an error
}
Py_DECREF (strides);
}
// create matrix header if everything is okay
if (mat_type != CV_USRTYPE1)
{
CvMat * temp_matrix = cvCreateMatHeader (rows, cols, mat_type);
cvSetData (temp_matrix, raw_data, step);
cvarr = temp_matrix;
// fprintf(stderr, "step_size: %ld, type: %ld\n", step, mat_type); fflush (stderr);
}
}
}
}
}
}
Py_DECREF (data);
Py_DECREF (typestr);
Py_DECREF (shape);
Py_DECREF (version);
}
}
Py_DECREF (interface);
return cvarr;
}
// Convert Python lists to CvMat *
CvArr * PySequence_to_CvArr (PyObject * obj)
{
int dims [CV_MAX_DIM] = { 1, 1, 1};
PyObject * container[CV_MAX_DIM+1] = {NULL, NULL, NULL, NULL};
int ndim = 0;
PyObject * item = Py_None;
// TODO: implement type detection - currently we create CV_64F only
// scan full array to
// - figure out dimensions
// - check consistency of dimensions
// - find appropriate data-type and signedness
// enum NEEDED_DATATYPE { NEEDS_CHAR, NEEDS_INTEGER, NEEDS_FLOAT, NEEDS_DOUBLE };
// NEEDED_DATATYPE needed_datatype = NEEDS_CHAR;
// bool needs_sign = false;
// scan first entries to find out dimensions
for (item = obj, ndim = 0; PySequence_Check (item) && ndim <= CV_MAX_DIM; ndim++)
{
dims [ndim] = PySequence_Size (item);
container [ndim] = PySequence_GetItem (item, 0);
item = container[ndim];
}
// in contrast to PyTuple_GetItem, PySequence_GetItame returns a NEW reference
if (container[0])
{
Py_DECREF (container[0]);
}
if (container[1])
{
Py_DECREF (container[1]);
}
if (container[2])
{
Py_DECREF (container[2]);
}
if (container[3])
{
Py_DECREF (container[3]);
}
// it only makes sense to support 2 and 3 dimensional data at this time
if (ndim < 2 || ndim > 3)
{
PyErr_SetString (PyExc_TypeError, "Nested sequences should have 2 or 3 dimensions");
return NULL;
}
// also, the number of channels should match what's typical for OpenCV
if (ndim == 3 && (dims[2] < 1 || dims[2] > 4))
{
PyErr_SetString (PyExc_TypeError, "Currently, the third dimension of CvMat only supports 1 to 4 channels");
return NULL;
}
// CvMat
CvMat * matrix = cvCreateMat (dims[0], dims[1], CV_MAKETYPE (CV_64F, dims[2]));
for (int y = 0; y < dims[0]; y++)
{
PyObject * rowobj = PySequence_GetItem (obj, y);
// double check size
if (PySequence_Check (rowobj) && PySequence_Size (rowobj) == dims[1])
{
for (int x = 0; x < dims[1]; x++)
{
PyObject * colobj = PySequence_GetItem (rowobj, x);
if (dims [2] > 1)
{
if (PySequence_Check (colobj) && PySequence_Size (colobj) == dims[2])
{
PyObject * tuple = PySequence_Tuple (colobj);
double a, b, c, d;
if (PyArg_ParseTuple (colobj, "d|d|d|d", &a, &b, &c, &d))
{
cvSet2D (matrix, y, x, cvScalar (a, b, c, d));
}
else
{
PyErr_SetString (PyExc_TypeError, "OpenCV only accepts numbers that can be converted to float");
cvReleaseMat (& matrix);
Py_DECREF (tuple);
Py_DECREF (colobj);
Py_DECREF (rowobj);
return NULL;
}
Py_DECREF (tuple);
}
else
{
PyErr_SetString (PyExc_TypeError, "All sub-sequences must have the same number of entries");
cvReleaseMat (& matrix);
Py_DECREF (colobj);
Py_DECREF (rowobj);
return NULL;
}
}
else
{
if (PyFloat_Check (colobj) || PyInt_Check (colobj))
{
cvmSet (matrix, y, x, PyFloat_AsDouble (colobj));
}
else
{
PyErr_SetString (PyExc_TypeError, "OpenCV only accepts numbers that can be converted to float");
cvReleaseMat (& matrix);
Py_DECREF (colobj);
Py_DECREF (rowobj);
return NULL;
}
}
Py_DECREF (colobj);
}
}
else
{
PyErr_SetString (PyExc_TypeError, "All sub-sequences must have the same number of entries");
cvReleaseMat (& matrix);
Py_DECREF (rowobj);
return NULL;
}
Py_DECREF (rowobj);
}
return matrix;
}