mirror of
https://github.com/opencv/opencv.git
synced 2024-12-04 08:49:14 +08:00
607 lines
19 KiB
C++
607 lines
19 KiB
C++
#include "pyhelpers.h"
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#include <iostream>
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#include <sstream>
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int PySwigObject_Check(PyObject *op);
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/* Py_ssize_t for old Pythons */
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#if PY_VERSION_HEX < 0x02050000
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typedef int Py_ssize_t;
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#endif
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PyObject * PyTuple_FromIntArray(int * arr, int len){
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PyObject * obj = PyTuple_New(len);
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for(int i=0; i<len; i++){
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PyTuple_SetItem(obj, i, PyLong_FromLong( arr[i] ) );
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}
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return obj;
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}
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PyObject * SWIG_SetResult(PyObject * result, PyObject * obj){
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if(result){
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Py_DECREF(result);
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}
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result = PyTuple_New(1);
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PyTuple_SetItem(result, 0, obj);
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return result;
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}
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PyObject * SWIG_AppendResult(PyObject * result, PyObject ** to_add, int num){
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if ((!result) || (result == Py_None)) {
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/* no other results, so just add our values */
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/* if only one object, return that */
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if(num==1){
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return to_add[0];
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}
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/* create a new tuple to put in our new pointer python objects */
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result = PyTuple_New (num);
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/* put in our new pointer python objects */
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for(int i=0; i<num; i++){
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PyTuple_SetItem (result, i, to_add[i]);
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}
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}
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else {
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/* we have other results, so add it to the end */
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if (!PyTuple_Check (result)) {
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/* previous result is not a tuple, so create one and put
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previous result and current pointer in it */
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/* first, save previous result */
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PyObject *obj_save = result;
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/* then, create the tuple */
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result = PyTuple_New (1);
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/* finaly, put the saved value in the tuple */
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PyTuple_SetItem (result, 0, obj_save);
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}
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/* create a new tuple to put in our new pointer python object */
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PyObject *my_obj = PyTuple_New (num);
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/* put in our new pointer python object */
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for( int i=0; i<num ; i++ ){
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PyTuple_SetItem (my_obj, i, to_add[i]);
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}
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/* save the previous result */
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PyObject *obj_save = result;
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/* concat previous and our new result */
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result = PySequence_Concat (obj_save, my_obj);
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/* decrement the usage of no more used objects */
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Py_DECREF (obj_save);
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Py_DECREF (my_obj);
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}
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return result;
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}
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template <typename T>
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void cv_arr_write(FILE * f, const char * fmt, T * data, size_t rows, size_t nch, size_t step){
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size_t i,j,k;
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char * cdata = (char *) data;
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const char * chdelim1="", * chdelim2="";
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// only output channel parens if > 1
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if(nch>1){
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chdelim1="(";
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chdelim2=")";
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}
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fputs("[",f);
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for(i=0; i<rows; i++){
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fputs("[",f);
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// first element
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// out<<chdelim1;
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fputs(chdelim1, f);
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fprintf(f, fmt, ((T*)(cdata+i*step))[0]);
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for(k=1; k<nch; k++){
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fputs(", ", f);
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fprintf(f, fmt, ((T*)(cdata+i*step))[k]);
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}
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fputs(chdelim2,f);
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// remaining elements
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for(j=nch*sizeof(T); j<step; j+=(nch*sizeof(T))){
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fprintf(f, ",%s", chdelim1);
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fprintf(f, fmt, ((T*)(cdata+i*step+j))[0]);
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for(k=1; k<nch; k++){
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fputs(", ", f);
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fprintf(f, fmt, ((T*)(cdata+i*step+j))[k]);
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}
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fputs(chdelim2, f);
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}
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fputs( "]\n", f );
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}
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fputs( "]", f );
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}
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void cvArrPrint(CvArr * arr){
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CvMat * mat;
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CvMat stub;
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mat = cvGetMat(arr, &stub);
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int cn = CV_MAT_CN(mat->type);
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int depth = CV_MAT_DEPTH(mat->type);
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int step = MAX(mat->step, cn*mat->cols*CV_ELEM_SIZE(depth));
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switch(depth){
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case CV_8U:
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cv_arr_write(stdout, "%u", (uchar *)mat->data.ptr, mat->rows, cn, step);
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break;
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case CV_8S:
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cv_arr_write(stdout, "%d", (char *)mat->data.ptr, mat->rows, cn, step);
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break;
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case CV_16U:
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cv_arr_write(stdout, "%u", (ushort *)mat->data.ptr, mat->rows, cn, step);
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break;
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case CV_16S:
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cv_arr_write(stdout, "%d", (short *)mat->data.ptr, mat->rows, cn, step);
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break;
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case CV_32S:
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cv_arr_write(stdout, "%d", (int *)mat->data.ptr, mat->rows, cn, step);
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break;
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case CV_32F:
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cv_arr_write(stdout, "%f", (float *)mat->data.ptr, mat->rows, cn, step);
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break;
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case CV_64F:
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cv_arr_write(stdout, "%g", (double *)mat->data.ptr, mat->rows, cn, step);
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break;
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default:
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CV_Error( CV_StsError, "Unknown element type");
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break;
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}
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}
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// deal with negative array indices
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int PyLong_AsIndex( PyObject * idx_object, int len ){
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int idx = PyLong_AsLong( idx_object );
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if(idx<0) return len+idx;
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return idx;
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}
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CvRect PySlice_to_CvRect(CvArr * src, PyObject * idx_object){
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CvSize sz = cvGetSize(src);
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//printf("Size %dx%d\n", sz.height, sz.width);
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int lower[2], upper[2];
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Py_ssize_t len, start, stop, step, slicelength;
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if(PyInt_Check(idx_object) || PyLong_Check(idx_object)){
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// if array is a row vector, assume index into columns
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if(sz.height>1){
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lower[0] = PyLong_AsIndex( idx_object, sz.height );
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upper[0] = lower[0] + 1;
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lower[1] = 0;
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upper[1] = sz.width;
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}
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else{
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lower[0] = 0;
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upper[0] = sz.height;
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lower[1] = PyLong_AsIndex( idx_object, sz.width );
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upper[1] = lower[1]+1;
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}
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}
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// 1. Slice
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else if(PySlice_Check(idx_object)){
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len = sz.height;
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if(PySlice_GetIndicesEx( (PySliceObject*)idx_object, len, &start, &stop, &step, &slicelength )!=0){
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printf("Error in PySlice_GetIndicesEx: returning NULL");
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PyErr_SetString(PyExc_Exception, "Error");
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return cvRect(0,0,0,0);
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}
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// if array is a row vector, assume index bounds are into columns
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if(sz.height>1){
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lower[0] = (int) start; // use c convention of start index = 0
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upper[0] = (int) stop; // use c convention
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lower[1] = 0;
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upper[1] = sz.width;
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}
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else{
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lower[1] = (int) start; // use c convention of start index = 0
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upper[1] = (int) stop; // use c convention
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lower[0] = 0;
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upper[0] = sz.height;
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}
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}
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// 2. Tuple
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else if(PyTuple_Check(idx_object)){
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//printf("PyTuple{\n");
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if(PyObject_Length(idx_object)!=2){
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//printf("Expected a sequence of length 2: returning NULL");
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PyErr_SetString(PyExc_ValueError, "Expected a sequence with 2 elements");
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return cvRect(0,0,0,0);
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}
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for(int i=0; i<2; i++){
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PyObject *o = PyTuple_GetItem(idx_object, i);
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// 2a. Slice -- same as above
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if(PySlice_Check(o)){
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//printf("PySlice\n");
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len = (i==0 ? sz.height : sz.width);
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if(PySlice_GetIndicesEx( (PySliceObject*)o, len, &start, &stop, &step, &slicelength )!=0){
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PyErr_SetString(PyExc_Exception, "Error");
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printf("Error in PySlice_GetIndicesEx: returning NULL");
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return cvRect(0,0,0,0);
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}
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//printf("PySlice_GetIndecesEx(%d, %d, %d, %d, %d)\n", len, start, stop, step, slicelength);
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lower[i] = start;
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upper[i] = stop;
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}
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// 2b. Integer
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else if(PyInt_Check(o) || PyLong_Check(o)){
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//printf("PyInt\n");
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lower[i] = PyLong_AsIndex(o, i==0 ? sz.height : sz.width);
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upper[i] = lower[i]+1;
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}
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else {
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PyErr_SetString(PyExc_TypeError, "Expected a sequence of slices or integers");
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printf("Expected a slice or int as sequence item: returning NULL");
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return cvRect(0,0,0,0);
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}
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}
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}
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else {
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PyErr_SetString( PyExc_TypeError, "Expected a slice or sequence");
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printf("Expected a slice or sequence: returning NULL");
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return cvRect(0,0,0,0);
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}
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//lower[0] = MAX(0, lower[0]);
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//lower[1] = MAX(0, lower[1]);
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//upper[0] = MIN(sz.height, upper[0]);
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//upper[1] = MIN(sz.width, upper[1]);
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//printf("Slice=%d %d %d %d\n", lower[0], upper[0], lower[1], upper[1]);
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return cvRect(lower[1],lower[0], upper[1]-lower[1], upper[0]-lower[0]);
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}
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int CheckSliceBounds(CvRect * rect, int w, int h){
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//printf("__setitem__ slice(%d:%d, %d:%d) array(%d,%d)", rect.x, rect.y, rect.x+rect.width, rect.y+rect.height, w, h);
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if(rect->width<=0 || rect->height<=0 ||
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rect->width>w || rect->height>h ||
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rect->x<0 || rect->y<0 ||
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rect->x>= w || rect->y >=h){
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char errstr[256];
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// previous function already set error string
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if(rect->width==0 && rect->height==0 && rect->x==0 && rect->y==0) return -1;
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sprintf(errstr, "Requested slice [ %d:%d %d:%d ] oversteps array sized [ %d %d ]",
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rect->x, rect->y, rect->x+rect->width, rect->y+rect->height, w, h);
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PyErr_SetString(PyExc_IndexError, errstr);
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//PyErr_SetString(PyExc_ValueError, errstr);
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return 0;
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}
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return 1;
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}
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double PyObject_AsDouble(PyObject * obj){
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if(PyNumber_Check(obj)){
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if(PyFloat_Check(obj)){
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return PyFloat_AsDouble(obj);
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}
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else if(PyInt_Check(obj) || PyLong_Check(obj)){
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return (double) PyLong_AsLong(obj);
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}
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}
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PyErr_SetString( PyExc_TypeError, "Could not convert python object to Double");
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return -1;
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}
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long PyObject_AsLong(PyObject * obj){
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if(PyNumber_Check(obj)){
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if(PyFloat_Check(obj)){
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return (long) PyFloat_AsDouble(obj);
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}
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else if(PyInt_Check(obj) || PyLong_Check(obj)){
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return PyLong_AsLong(obj);
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}
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}
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PyErr_SetString( PyExc_TypeError, "Could not convert python object to Long");
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return -1;
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}
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CvArr * PyArray_to_CvArr (PyObject * obj)
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{
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// let's try to create a temporary CvMat header that points to the
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// data owned by obj and reflects its memory layout
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CvArr * cvarr = NULL;
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void * raw_data = 0;
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long rows;
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long cols;
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long channels;
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long step;
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long mat_type = 7;
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long element_size = 1;
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// infer layout from array interface
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PyObject * interface = PyObject_GetAttrString (obj, "__array_interface__");
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// the array interface should be a dict
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if (PyMapping_Check (interface))
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{
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if (PyMapping_HasKeyString (interface, (char*)"version") &&
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PyMapping_HasKeyString (interface, (char*)"shape") &&
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PyMapping_HasKeyString (interface, (char*)"typestr") &&
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PyMapping_HasKeyString (interface, (char*)"data"))
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{
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PyObject * version = PyMapping_GetItemString (interface, (char*)"version");
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PyObject * shape = PyMapping_GetItemString (interface, (char*)"shape");
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PyObject * typestr = PyMapping_GetItemString (interface, (char*)"typestr");
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PyObject * data = PyMapping_GetItemString (interface, (char*)"data");
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if (!PyInt_Check (version) || PyInt_AsLong (version) != 3)
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PyErr_SetString(PyExc_TypeError, "OpenCV understands version 3 of the __array_interface__ only");
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else
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{
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if (!PyTuple_Check (shape) || PyTuple_Size (shape) < 2 || PyTuple_Size (shape) > 3)
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PyErr_SetString(PyExc_TypeError, "arrays must have a shape with 2 or 3 dimensions");
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else
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{
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rows = PyInt_AsLong (PyTuple_GetItem (shape, 0));
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cols = PyInt_AsLong (PyTuple_GetItem (shape, 1));
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channels = PyTuple_Size (shape) < 3 ? 1 : PyInt_AsLong (PyTuple_GetItem (shape, 2));
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if (rows < 1 || cols < 1 || channels < 1 || channels > 4)
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PyErr_SetString(PyExc_TypeError, "rows and columns must be positive, channels from 1 to 4");
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else
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{
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// fprintf (stderr, "rows: %ld, cols: %ld, channels %ld\n", rows, cols, channels); fflush (stderr);
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if (! PyTuple_Check (data) || PyTuple_Size (data) != 2 ||
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!(PyInt_Check (PyTuple_GetItem (data,0)) || PyLong_Check (PyTuple_GetItem (data,0))) ||
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!(PyBool_Check (PyTuple_GetItem (data,1)) && !PyInt_AsLong (PyTuple_GetItem (data,1))))
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PyErr_SetString (PyExc_TypeError, "arrays must have a pointer to writeable data");
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else
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{
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raw_data = PyLong_AsVoidPtr (PyTuple_GetItem (data,0));
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// fprintf(stderr, "raw_data: %p\n", raw_data); fflush (stderr);
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char * format_str = NULL;
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Py_ssize_t len = 0;
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if (!PyString_Check (typestr) || PyString_AsStringAndSize (typestr, & format_str, &len) == -1 || len !=3)
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PyErr_SetString(PyExc_TypeError, "there is something wrong with the format string");
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else
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{
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// fprintf(stderr, "format: %c %c\n", format_str[1], format_str[2]); fflush (stderr);
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if (format_str[1] == 'u' && format_str[2] == '1')
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{
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element_size = 1;
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mat_type = CV_MAKETYPE(CV_8U, channels);
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}
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else if (format_str[1] == 'i' && format_str[2] == '1')
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{
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element_size = 1;
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mat_type = CV_MAKETYPE(CV_8S, channels);
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}
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else if (format_str[1] == 'u' && format_str[2] == '2')
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{
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element_size = 2;
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mat_type = CV_MAKETYPE(CV_16U, channels);
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}
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else if (format_str[1] == 'i' && format_str[2] == '2')
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{
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element_size = 2;
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mat_type = CV_MAKETYPE(CV_16S, channels);
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}
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else if (format_str[1] == 'i' && format_str[2] == '4')
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{
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element_size = 4;
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mat_type = CV_MAKETYPE(CV_32S, channels);
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}
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else if (format_str[1] == 'f' && format_str[2] == '4')
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{
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element_size = 4;
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mat_type = CV_MAKETYPE(CV_32F, channels);
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}
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else if (format_str[1] == 'f' && format_str[2] == '8')
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{
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element_size = 8;
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mat_type = CV_MAKETYPE(CV_64F, channels);
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}
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else
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{
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PyErr_SetString(PyExc_TypeError, "unknown or unhandled element format");
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mat_type = CV_USRTYPE1;
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}
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// handle strides if given
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// TODO: implement stride handling
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step = cols * channels * element_size;
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if (PyMapping_HasKeyString (interface, (char*)"strides"))
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{
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PyObject * strides = PyMapping_GetItemString (interface, (char*)"strides");
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if (strides != Py_None)
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{
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fprintf(stderr, "we have strides ... not handled!\n"); fflush (stderr);
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PyErr_SetString(PyExc_TypeError, "arrays with strides not handled yet");
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mat_type = CV_USRTYPE1; // use this to denote, we've got an error
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}
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Py_DECREF (strides);
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}
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// create matrix header if everything is okay
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if (mat_type != CV_USRTYPE1)
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{
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CvMat * temp_matrix = cvCreateMatHeader (rows, cols, mat_type);
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cvSetData (temp_matrix, raw_data, step);
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cvarr = temp_matrix;
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// fprintf(stderr, "step_size: %ld, type: %ld\n", step, mat_type); fflush (stderr);
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}
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}
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}
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}
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}
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}
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Py_DECREF (data);
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Py_DECREF (typestr);
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Py_DECREF (shape);
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Py_DECREF (version);
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}
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}
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Py_DECREF (interface);
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return cvarr;
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}
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// Convert Python lists to CvMat *
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CvArr * PySequence_to_CvArr (PyObject * obj)
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{
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int dims [CV_MAX_DIM] = { 1, 1, 1};
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PyObject * container[CV_MAX_DIM+1] = {NULL, NULL, NULL, NULL};
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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;
|
|
}
|