SF.net SVN: matplotlib:[8966] trunk/matplotlib/src/_image.cpp

Revision: 8966
 http://matplotlib.svn.sourceforge.net/matplotlib/?rev=8966&view=rev
Author: efiring
Date: 2011年02月09日 07:45:18 +0000 (2011年2月09日)
Log Message:
-----------
_image.cpp: speed up image_frombyte; patch by C. Gohlke
Non-contiguous arrays, such as those resulting from pan and zoom,
are handled efficiently without additional copying. This
noticeably speeds up zoom and pan on large images.
Modified Paths:
--------------
 trunk/matplotlib/src/_image.cpp
Modified: trunk/matplotlib/src/_image.cpp
===================================================================
--- trunk/matplotlib/src/_image.cpp	2011年02月09日 04:16:08 UTC (rev 8965)
+++ trunk/matplotlib/src/_image.cpp	2011年02月09日 07:45:18 UTC (rev 8966)
@@ -1085,7 +1085,7 @@
 Py::Object x = args[0];
 int isoutput = Py::Int(args[1]);
 
- PyArrayObject *A = (PyArrayObject *) PyArray_ContiguousFromObject(x.ptr(), PyArray_UBYTE, 3, 3);
+ PyArrayObject *A = (PyArrayObject *) PyArray_FromObject(x.ptr(), PyArray_UBYTE, 3, 3);
 if (A == NULL)
 {
 throw Py::ValueError("Array must have 3 dimensions");
@@ -1104,35 +1104,86 @@
 
 agg::int8u *arrbuf;
 agg::int8u *buffer;
+ agg::int8u *dstbuf;
 
 arrbuf = reinterpret_cast<agg::int8u *>(A->data);
 
 size_t NUMBYTES(imo->colsIn * imo->rowsIn * imo->BPP);
- buffer = new agg::int8u[NUMBYTES];
+ buffer = dstbuf = new agg::int8u[NUMBYTES];
 
 if (buffer == NULL) //todo: also handle allocation throw
 {
 throw Py::MemoryError("_image_module::frombyte could not allocate memory");
 }
 
- const size_t N = imo->rowsIn * imo->colsIn * imo->BPP;
- size_t i = 0;
- if (A->dimensions[2] == 4)
+ if PyArray_ISCONTIGUOUS(A)
 {
- memmove(buffer, arrbuf, N);
+ if (A->dimensions[2] == 4)
+ {
+ memmove(dstbuf, arrbuf, imo->rowsIn * imo->colsIn * 4);
+ }
+ else
+ {
+ size_t i = imo->rowsIn * imo->colsIn;
+ while (i--)
+ {
+ *dstbuf++ = *arrbuf++;
+ *dstbuf++ = *arrbuf++;
+ *dstbuf++ = *arrbuf++;
+ *dstbuf++ = 255;
+ }
+ }
 }
+ else if ((A->strides[1] == 4) && (A->strides[2] == 1))
+ {
+ const size_t N = imo->colsIn * 4;
+ const size_t stride = A->strides[0];
+ for (size_t rownum = 0; rownum < imo->rowsIn; rownum++)
+ {
+ memmove(dstbuf, arrbuf, N);
+ arrbuf += stride;
+ dstbuf += N;
+ }
+ }
+ else if ((A->strides[1] == 3) && (A->strides[2] == 1))
+ {
+ const size_t stride = A->strides[0] - imo->colsIn * 3;
+ for (size_t rownum = 0; rownum < imo->rowsIn; rownum++)
+ {
+ for (size_t colnum = 0; colnum < imo->colsIn; colnum++)
+ {
+ *dstbuf++ = *arrbuf++;
+ *dstbuf++ = *arrbuf++;
+ *dstbuf++ = *arrbuf++;
+ *dstbuf++ = 255;
+ }
+ arrbuf += stride;
+ }
+ }
 else
 {
- while (i < N)
+ PyArrayIterObject *iter;
+ iter = (PyArrayIterObject *)PyArray_IterNew((PyObject *)A);
+ if (A->dimensions[2] == 4)
 {
- memmove(buffer, arrbuf, 3);
- buffer += 3;
- arrbuf += 3;
- *buffer++ = 255;
- i += 4;
+ while (iter->index < iter->size) {
+ *dstbuf++ = *((unsigned char *)iter->dataptr);
+ PyArray_ITER_NEXT(iter);
+ }
 }
- buffer -= N;
- arrbuf -= imo->rowsIn * imo->colsIn;
+ else
+ {
+ while (iter->index < iter->size) {
+ *dstbuf++ = *((unsigned char *)iter->dataptr);
+ PyArray_ITER_NEXT(iter);
+ *dstbuf++ = *((unsigned char *)iter->dataptr);
+ PyArray_ITER_NEXT(iter);
+ *dstbuf++ = *((unsigned char *)iter->dataptr);
+ PyArray_ITER_NEXT(iter);
+ *dstbuf++ = 255;
+ }
+ }
+ Py_DECREF(iter);
 }
 
 if (isoutput)
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