5 int PySwigObject_Check(PyObject *op);
7 /* Py_ssize_t for old Pythons */
8 #if PY_VERSION_HEX < 0x02050000
9 typedef int Py_ssize_t;
12 PyObject * PyTuple_FromIntArray(int * arr, int len){
13 PyObject * obj = PyTuple_New(len);
14 for(int i=0; i<len; i++){
15 PyTuple_SetItem(obj, i, PyLong_FromLong( arr[i] ) );
20 PyObject * SWIG_SetResult(PyObject * result, PyObject * obj){
24 result = PyTuple_New(1);
25 PyTuple_SetItem(result, 0, obj);
29 PyObject * SWIG_AppendResult(PyObject * result, PyObject ** to_add, int num){
30 if ((!result) || (result == Py_None)) {
31 /* no other results, so just add our values */
33 /* if only one object, return that */
38 /* create a new tuple to put in our new pointer python objects */
39 result = PyTuple_New (num);
41 /* put in our new pointer python objects */
42 for(int i=0; i<num; i++){
43 PyTuple_SetItem (result, i, to_add[i]);
47 /* we have other results, so add it to the end */
49 if (!PyTuple_Check (result)) {
50 /* previous result is not a tuple, so create one and put
51 previous result and current pointer in it */
53 /* first, save previous result */
54 PyObject *obj_save = result;
56 /* then, create the tuple */
57 result = PyTuple_New (1);
59 /* finaly, put the saved value in the tuple */
60 PyTuple_SetItem (result, 0, obj_save);
63 /* create a new tuple to put in our new pointer python object */
64 PyObject *my_obj = PyTuple_New (num);
66 /* put in our new pointer python object */
67 for( int i=0; i<num ; i++ ){
68 PyTuple_SetItem (my_obj, i, to_add[i]);
71 /* save the previous result */
72 PyObject *obj_save = result;
74 /* concat previous and our new result */
75 result = PySequence_Concat (obj_save, my_obj);
77 /* decrement the usage of no more used objects */
85 void cv_arr_write(FILE * f, const char * fmt, T * data, size_t rows, size_t nch, size_t step){
87 char * cdata = (char *) data;
88 const char * chdelim1="", * chdelim2="";
90 // only output channel parens if > 1
97 for(i=0; i<rows; i++){
103 fprintf(f, fmt, ((T*)(cdata+i*step))[0]);
104 for(k=1; k<nch; k++){
106 fprintf(f, fmt, ((T*)(cdata+i*step))[k]);
110 // remaining elements
111 for(j=nch*sizeof(T); j<step; j+=(nch*sizeof(T))){
112 fprintf(f, ",%s", chdelim1);
113 fprintf(f, fmt, ((T*)(cdata+i*step+j))[0]);
114 for(k=1; k<nch; k++){
116 fprintf(f, fmt, ((T*)(cdata+i*step+j))[k]);
125 void cvArrPrint(CvArr * arr){
126 CV_FUNCNAME( "cvArrPrint" );
132 mat = cvGetMat(arr, &stub);
134 int cn = CV_MAT_CN(mat->type);
135 int depth = CV_MAT_DEPTH(mat->type);
136 int step = MAX(mat->step, cn*mat->cols*CV_ELEM_SIZE(depth));
141 cv_arr_write(stdout, "%u", (uchar *)mat->data.ptr, mat->rows, cn, step);
144 cv_arr_write(stdout, "%d", (char *)mat->data.ptr, mat->rows, cn, step);
147 cv_arr_write(stdout, "%u", (ushort *)mat->data.ptr, mat->rows, cn, step);
150 cv_arr_write(stdout, "%d", (short *)mat->data.ptr, mat->rows, cn, step);
153 cv_arr_write(stdout, "%d", (int *)mat->data.ptr, mat->rows, cn, step);
156 cv_arr_write(stdout, "%f", (float *)mat->data.ptr, mat->rows, cn, step);
159 cv_arr_write(stdout, "%g", (double *)mat->data.ptr, mat->rows, cn, step);
162 CV_ERROR( CV_StsError, "Unknown element type");
169 // deal with negative array indices
170 int PyLong_AsIndex( PyObject * idx_object, int len ){
171 int idx = PyLong_AsLong( idx_object );
172 if(idx<0) return len+idx;
176 CvRect PySlice_to_CvRect(CvArr * src, PyObject * idx_object){
177 CvSize sz = cvGetSize(src);
178 //printf("Size %dx%d\n", sz.height, sz.width);
179 int lower[2], upper[2];
180 Py_ssize_t len, start, stop, step, slicelength;
182 if(PyInt_Check(idx_object) || PyLong_Check(idx_object)){
183 // if array is a row vector, assume index into columns
185 lower[0] = PyLong_AsIndex( idx_object, sz.height );
186 upper[0] = lower[0] + 1;
192 upper[0] = sz.height;
193 lower[1] = PyLong_AsIndex( idx_object, sz.width );
194 upper[1] = lower[1]+1;
199 else if(PySlice_Check(idx_object)){
201 if(PySlice_GetIndicesEx( (PySliceObject*)idx_object, len, &start, &stop, &step, &slicelength )!=0){
202 printf("Error in PySlice_GetIndicesEx: returning NULL");
203 PyErr_SetString(PyExc_Exception, "Error");
204 return cvRect(0,0,0,0);
206 // if array is a row vector, assume index bounds are into columns
208 lower[0] = (int) start; // use c convention of start index = 0
209 upper[0] = (int) stop; // use c convention
214 lower[1] = (int) start; // use c convention of start index = 0
215 upper[1] = (int) stop; // use c convention
217 upper[0] = sz.height;
222 else if(PyTuple_Check(idx_object)){
223 //printf("PyTuple{\n");
224 if(PyObject_Length(idx_object)!=2){
225 //printf("Expected a sequence of length 2: returning NULL");
226 PyErr_SetString(PyExc_ValueError, "Expected a sequence with 2 elements");
227 return cvRect(0,0,0,0);
229 for(int i=0; i<2; i++){
230 PyObject *o = PyTuple_GetItem(idx_object, i);
232 // 2a. Slice -- same as above
233 if(PySlice_Check(o)){
234 //printf("PySlice\n");
235 len = (i==0 ? sz.height : sz.width);
236 if(PySlice_GetIndicesEx( (PySliceObject*)o, len, &start, &stop, &step, &slicelength )!=0){
237 PyErr_SetString(PyExc_Exception, "Error");
238 printf("Error in PySlice_GetIndicesEx: returning NULL");
239 return cvRect(0,0,0,0);
241 //printf("PySlice_GetIndecesEx(%d, %d, %d, %d, %d)\n", len, start, stop, step, slicelength);
248 else if(PyInt_Check(o) || PyLong_Check(o)){
250 lower[i] = PyLong_AsIndex(o, i==0 ? sz.height : sz.width);
251 upper[i] = lower[i]+1;
255 PyErr_SetString(PyExc_TypeError, "Expected a sequence of slices or integers");
256 printf("Expected a slice or int as sequence item: returning NULL");
257 return cvRect(0,0,0,0);
263 PyErr_SetString( PyExc_TypeError, "Expected a slice or sequence");
264 printf("Expected a slice or sequence: returning NULL");
265 return cvRect(0,0,0,0);
268 //lower[0] = MAX(0, lower[0]);
269 //lower[1] = MAX(0, lower[1]);
270 //upper[0] = MIN(sz.height, upper[0]);
271 //upper[1] = MIN(sz.width, upper[1]);
272 //printf("Slice=%d %d %d %d\n", lower[0], upper[0], lower[1], upper[1]);
273 return cvRect(lower[1],lower[0], upper[1]-lower[1], upper[0]-lower[0]);
276 int CheckSliceBounds(CvRect * rect, int w, int h){
277 //printf("__setitem__ slice(%d:%d, %d:%d) array(%d,%d)", rect.x, rect.y, rect.x+rect.width, rect.y+rect.height, w, h);
278 if(rect->width<=0 || rect->height<=0 ||
279 rect->width>w || rect->height>h ||
280 rect->x<0 || rect->y<0 ||
281 rect->x>= w || rect->y >=h){
284 // previous function already set error string
285 if(rect->width==0 && rect->height==0 && rect->x==0 && rect->y==0) return -1;
287 sprintf(errstr, "Requested slice [ %d:%d %d:%d ] oversteps array sized [ %d %d ]",
288 rect->x, rect->y, rect->x+rect->width, rect->y+rect->height, w, h);
289 PyErr_SetString(PyExc_IndexError, errstr);
290 //PyErr_SetString(PyExc_ValueError, errstr);
296 double PyObject_AsDouble(PyObject * obj){
297 if(PyNumber_Check(obj)){
298 if(PyFloat_Check(obj)){
299 return PyFloat_AsDouble(obj);
301 else if(PyInt_Check(obj) || PyLong_Check(obj)){
302 return (double) PyLong_AsLong(obj);
305 PyErr_SetString( PyExc_TypeError, "Could not convert python object to Double");
309 long PyObject_AsLong(PyObject * obj){
310 if(PyNumber_Check(obj)){
311 if(PyFloat_Check(obj)){
312 return (long) PyFloat_AsDouble(obj);
314 else if(PyInt_Check(obj) || PyLong_Check(obj)){
315 return PyLong_AsLong(obj);
318 PyErr_SetString( PyExc_TypeError, "Could not convert python object to Long");
322 CvArr * PyArray_to_CvArr (PyObject * obj)
324 // let's try to create a temporary CvMat header that points to the
325 // data owned by obj and reflects its memory layout
327 CvArr * cvarr = NULL;
335 long element_size = 1;
337 // infer layout from array interface
338 PyObject * interface = PyObject_GetAttrString (obj, "__array_interface__");
341 // the array interface should be a dict
342 if (PyMapping_Check (interface))
344 if (PyMapping_HasKeyString (interface, (char*)"version") &&
345 PyMapping_HasKeyString (interface, (char*)"shape") &&
346 PyMapping_HasKeyString (interface, (char*)"typestr") &&
347 PyMapping_HasKeyString (interface, (char*)"data"))
349 PyObject * version = PyMapping_GetItemString (interface, (char*)"version");
350 PyObject * shape = PyMapping_GetItemString (interface, (char*)"shape");
351 PyObject * typestr = PyMapping_GetItemString (interface, (char*)"typestr");
352 PyObject * data = PyMapping_GetItemString (interface, (char*)"data");
354 if (!PyInt_Check (version) || PyInt_AsLong (version) != 3)
355 PyErr_SetString(PyExc_TypeError, "OpenCV understands version 3 of the __array_interface__ only");
358 if (!PyTuple_Check (shape) || PyTuple_Size (shape) < 2 || PyTuple_Size (shape) > 3)
359 PyErr_SetString(PyExc_TypeError, "arrays must have a shape with 2 or 3 dimensions");
362 rows = PyInt_AsLong (PyTuple_GetItem (shape, 0));
363 cols = PyInt_AsLong (PyTuple_GetItem (shape, 1));
364 channels = PyTuple_Size (shape) < 3 ? 1 : PyInt_AsLong (PyTuple_GetItem (shape, 2));
366 if (rows < 1 || cols < 1 || channels < 1 || channels > 4)
367 PyErr_SetString(PyExc_TypeError, "rows and columns must be positive, channels from 1 to 4");
370 // fprintf (stderr, "rows: %ld, cols: %ld, channels %ld\n", rows, cols, channels); fflush (stderr);
372 if (! PyTuple_Check (data) || PyTuple_Size (data) != 2 ||
373 !(PyInt_Check (PyTuple_GetItem (data,0)) || PyLong_Check (PyTuple_GetItem (data,0))) ||
374 !(PyBool_Check (PyTuple_GetItem (data,1)) && !PyInt_AsLong (PyTuple_GetItem (data,1))))
375 PyErr_SetString (PyExc_TypeError, "arrays must have a pointer to writeable data");
378 raw_data = PyLong_AsVoidPtr (PyTuple_GetItem (data,0));
379 // fprintf(stderr, "raw_data: %p\n", raw_data); fflush (stderr);
381 char * format_str = NULL;
384 if (!PyString_Check (typestr) || PyString_AsStringAndSize (typestr, & format_str, &len) == -1 || len !=3)
385 PyErr_SetString(PyExc_TypeError, "there is something wrong with the format string");
388 // fprintf(stderr, "format: %c %c\n", format_str[1], format_str[2]); fflush (stderr);
390 if (format_str[1] == 'u' && format_str[2] == '1')
393 mat_type = CV_MAKETYPE(CV_8U, channels);
395 else if (format_str[1] == 'i' && format_str[2] == '1')
398 mat_type = CV_MAKETYPE(CV_8S, channels);
400 else if (format_str[1] == 'u' && format_str[2] == '2')
403 mat_type = CV_MAKETYPE(CV_16U, channels);
405 else if (format_str[1] == 'i' && format_str[2] == '2')
408 mat_type = CV_MAKETYPE(CV_16S, channels);
410 else if (format_str[1] == 'i' && format_str[2] == '4')
413 mat_type = CV_MAKETYPE(CV_32S, channels);
415 else if (format_str[1] == 'f' && format_str[2] == '4')
418 mat_type = CV_MAKETYPE(CV_32F, channels);
420 else if (format_str[1] == 'f' && format_str[2] == '8')
423 mat_type = CV_MAKETYPE(CV_64F, channels);
427 PyErr_SetString(PyExc_TypeError, "unknown or unhandled element format");
428 mat_type = CV_USRTYPE1;
431 // handle strides if given
432 // TODO: implement stride handling
433 step = cols * channels * element_size;
434 if (PyMapping_HasKeyString (interface, (char*)"strides"))
436 PyObject * strides = PyMapping_GetItemString (interface, (char*)"strides");
438 if (strides != Py_None)
440 fprintf(stderr, "we have strides ... not handled!\n"); fflush (stderr);
441 PyErr_SetString(PyExc_TypeError, "arrays with strides not handled yet");
442 mat_type = CV_USRTYPE1; // use this to denote, we've got an error
448 // create matrix header if everything is okay
449 if (mat_type != CV_USRTYPE1)
451 CvMat * temp_matrix = cvCreateMatHeader (rows, cols, mat_type);
452 cvSetData (temp_matrix, raw_data, step);
455 // fprintf(stderr, "step_size: %ld, type: %ld\n", step, mat_type); fflush (stderr);
471 Py_DECREF (interface);
477 // Convert Python lists to CvMat *
478 CvArr * PySequence_to_CvArr (PyObject * obj)
480 int dims [CV_MAX_DIM] = { 1, 1, 1};
481 PyObject * container[CV_MAX_DIM+1] = {NULL, NULL, NULL, NULL};
483 PyObject * item = Py_None;
485 // TODO: implement type detection - currently we create CV_64F only
486 // scan full array to
487 // - figure out dimensions
488 // - check consistency of dimensions
489 // - find appropriate data-type and signedness
490 // enum NEEDED_DATATYPE { NEEDS_CHAR, NEEDS_INTEGER, NEEDS_FLOAT, NEEDS_DOUBLE };
491 // NEEDED_DATATYPE needed_datatype = NEEDS_CHAR;
492 // bool needs_sign = false;
494 // scan first entries to find out dimensions
495 for (item = obj, ndim = 0; PySequence_Check (item) && ndim <= CV_MAX_DIM; ndim++)
497 dims [ndim] = PySequence_Size (item);
498 container [ndim] = PySequence_GetItem (item, 0);
499 item = container[ndim];
502 // in contrast to PyTuple_GetItem, PySequence_GetItame returns a NEW reference
505 Py_DECREF (container[0]);
509 Py_DECREF (container[1]);
513 Py_DECREF (container[2]);
517 Py_DECREF (container[3]);
520 // it only makes sense to support 2 and 3 dimensional data at this time
521 if (ndim < 2 || ndim > 3)
523 PyErr_SetString (PyExc_TypeError, "Nested sequences should have 2 or 3 dimensions");
527 // also, the number of channels should match what's typical for OpenCV
528 if (ndim == 3 && (dims[2] < 1 || dims[2] > 4))
530 PyErr_SetString (PyExc_TypeError, "Currently, the third dimension of CvMat only supports 1 to 4 channels");
535 CvMat * matrix = cvCreateMat (dims[0], dims[1], CV_MAKETYPE (CV_64F, dims[2]));
537 for (int y = 0; y < dims[0]; y++)
539 PyObject * rowobj = PySequence_GetItem (obj, y);
542 if (PySequence_Check (rowobj) && PySequence_Size (rowobj) == dims[1])
544 for (int x = 0; x < dims[1]; x++)
546 PyObject * colobj = PySequence_GetItem (rowobj, x);
550 if (PySequence_Check (colobj) && PySequence_Size (colobj) == dims[2])
552 PyObject * tuple = PySequence_Tuple (colobj);
555 if (PyArg_ParseTuple (colobj, "d|d|d|d", &a, &b, &c, &d))
557 cvSet2D (matrix, y, x, cvScalar (a, b, c, d));
561 PyErr_SetString (PyExc_TypeError, "OpenCV only accepts numbers that can be converted to float");
562 cvReleaseMat (& matrix);
573 PyErr_SetString (PyExc_TypeError, "All sub-sequences must have the same number of entries");
574 cvReleaseMat (& matrix);
582 if (PyFloat_Check (colobj) || PyInt_Check (colobj))
584 cvmSet (matrix, y, x, PyFloat_AsDouble (colobj));
588 PyErr_SetString (PyExc_TypeError, "OpenCV only accepts numbers that can be converted to float");
589 cvReleaseMat (& matrix);
601 PyErr_SetString (PyExc_TypeError, "All sub-sequences must have the same number of entries");
602 cvReleaseMat (& matrix);