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fix for gpu::StereoBeliefPropogation:

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use continuous memory for internal buffers
This commit is contained in:
Vladislav Vinogradov 2015-09-08 16:04:28 +03:00
parent 76235cbb70
commit e2a9df408f
1 changed files with 10 additions and 10 deletions
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20
modules/gpu/src/stereobp.cpp
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@ -158,7 +158,7 @@ namespace
init(stream);
datas[0].create(rows * rthis.ndisp, cols, rthis.msg_type);
createContinuous(rows * rthis.ndisp, cols, rthis.msg_type, datas[0]);
comp_data_callers[rthis.msg_type == CV_32F][left.channels()](left, right, datas[0], StreamAccessor::getStream(stream));
@ -187,10 +187,10 @@ namespace
private:
void init(Stream& stream)
{
u.create(rows * rthis.ndisp, cols, rthis.msg_type);
d.create(rows * rthis.ndisp, cols, rthis.msg_type);
l.create(rows * rthis.ndisp, cols, rthis.msg_type);
r.create(rows * rthis.ndisp, cols, rthis.msg_type);
createContinuous(rows * rthis.ndisp, cols, rthis.msg_type, u);
createContinuous(rows * rthis.ndisp, cols, rthis.msg_type, d);
createContinuous(rows * rthis.ndisp, cols, rthis.msg_type, l);
createContinuous(rows * rthis.ndisp, cols, rthis.msg_type, r);
if (rthis.levels & 1)
{
@ -216,10 +216,10 @@ namespace
int less_rows = rows / 2;
int less_cols = cols / 2;
u2.create(less_rows * rthis.ndisp, less_cols, rthis.msg_type);
d2.create(less_rows * rthis.ndisp, less_cols, rthis.msg_type);
l2.create(less_rows * rthis.ndisp, less_cols, rthis.msg_type);
r2.create(less_rows * rthis.ndisp, less_cols, rthis.msg_type);
createContinuous(less_rows * rthis.ndisp, less_cols, rthis.msg_type, u2);
createContinuous(less_rows * rthis.ndisp, less_cols, rthis.msg_type, d2);
createContinuous(less_rows * rthis.ndisp, less_cols, rthis.msg_type, l2);
createContinuous(less_rows * rthis.ndisp, less_cols, rthis.msg_type, r2);
if ((rthis.levels & 1) == 0)
{
@ -286,7 +286,7 @@ namespace
cols_all[i] = cols_all[i-1] / 2;
rows_all[i] = rows_all[i-1] / 2;
datas[i].create(rows_all[i] * rthis.ndisp, cols_all[i], rthis.msg_type);
createContinuous(rows_all[i] * rthis.ndisp, cols_all[i], rthis.msg_type, datas[i]);
data_step_down_callers[funcIdx](cols_all[i], rows_all[i], rows_all[i-1], datas[i-1], datas[i], cudaStream);
}