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dnn::blobFromImage with OutputArray

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This commit is contained in:
Dmitry Kurtaev 2018-01-13 18:17:56 +03:00
parent 0c00652f6b
commit 6a395d88ff
3 changed files with 65 additions and 23 deletions
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21
modules/dnn/include/opencv2/dnn/dnn.hpp
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@ -695,6 +695,16 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
*/
CV_EXPORTS_W Mat blobFromImage(InputArray image, double scalefactor=1.0, const Size& size = Size(),
const Scalar& mean = Scalar(), bool swapRB=true, bool crop=true);
/** @brief Creates 4-dimensional blob from image.
* @details This is an overloaded member function, provided for convenience.
* It differs from the above function only in what argument(s) it accepts.
*/
CV_EXPORTS void blobFromImage(InputArray image, OutputArray blob, double scalefactor=1.0,
const Size& size = Size(), const Scalar& mean = Scalar(),
bool swapRB=true, bool crop=true);
/** @brief Creates 4-dimensional blob from series of images. Optionally resizes and
* crops @p images from center, subtract @p mean values, scales values by @p scalefactor,
* swap Blue and Red channels.
@ -711,9 +721,18 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
* If @p crop is false, direct resize without cropping and preserving aspect ratio is performed.
* @returns 4-dimansional Mat with NCHW dimensions order.
*/
CV_EXPORTS_W Mat blobFromImages(const std::vector<Mat>& images, double scalefactor=1.0,
CV_EXPORTS_W Mat blobFromImages(InputArrayOfArrays images, double scalefactor=1.0,
Size size = Size(), const Scalar& mean = Scalar(), bool swapRB=true, bool crop=true);
/** @brief Creates 4-dimensional blob from series of images.
* @details This is an overloaded member function, provided for convenience.
* It differs from the above function only in what argument(s) it accepts.
*/
CV_EXPORTS void blobFromImages(InputArrayOfArrays images, OutputArray blob,
double scalefactor=1.0, Size size = Size(),
const Scalar& mean = Scalar(), bool swapRB=true, bool crop=true);
/** @brief Convert all weights of Caffe network to half precision floating point.
* @param src Path to origin model from Caffe framework contains single
* precision floating point weights (usually has `.caffemodel` extension).

57
modules/dnn/src/dnn.cpp
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@ -81,27 +81,39 @@ namespace
};
}
template<typename T>
static String toString(const T &v)
{
std::ostringstream ss;
ss << v;
return ss.str();
}
Mat blobFromImage(InputArray image, double scalefactor, const Size& size,
const Scalar& mean, bool swapRB, bool crop)
{
CV_TRACE_FUNCTION();
std::vector<Mat> images(1, image.getMat());
return blobFromImages(images, scalefactor, size, mean, swapRB, crop);
Mat blob;
blobFromImage(image, blob, scalefactor, size, mean, swapRB, crop);
return blob;
}
Mat blobFromImages(const std::vector<Mat>& images_, double scalefactor, Size size,
const Scalar& mean_, bool swapRB, bool crop)
void blobFromImage(InputArray image, OutputArray blob, double scalefactor,
const Size& size, const Scalar& mean, bool swapRB, bool crop)
{
CV_TRACE_FUNCTION();
std::vector<Mat> images = images_;
std::vector<Mat> images(1, image.getMat());
blobFromImages(images, blob, scalefactor, size, mean, swapRB, crop);
}
Mat blobFromImages(InputArrayOfArrays images, double scalefactor, Size size,
const Scalar& mean, bool swapRB, bool crop)
{
CV_TRACE_FUNCTION();
Mat blob;
blobFromImages(images, blob, scalefactor, size, mean, swapRB, crop);
return blob;
}
void blobFromImages(InputArrayOfArrays images_, OutputArray blob_, double scalefactor,
Size size, const Scalar& mean_, bool swapRB, bool crop)
{
CV_TRACE_FUNCTION();
std::vector<Mat> images;
images_.getMatVector(images);
CV_Assert(!images.empty());
for (int i = 0; i < images.size(); i++)
{
Size imgSize = images[i].size();
@ -133,16 +145,15 @@ Mat blobFromImages(const std::vector<Mat>& images_, double scalefactor, Size siz
}
size_t i, nimages = images.size();
if(nimages == 0)
return Mat();
Mat image0 = images[0];
int nch = image0.channels();
CV_Assert(image0.dims == 2);
Mat blob, image;
Mat image;
if (nch == 3 || nch == 4)
{
int sz[] = { (int)nimages, nch, image0.rows, image0.cols };
blob = Mat(4, sz, CV_32F);
blob_.create(4, sz, CV_32F);
Mat blob = blob_.getMat();
Mat ch[4];
for( i = 0; i < nimages; i++ )
@ -164,7 +175,8 @@ Mat blobFromImages(const std::vector<Mat>& images_, double scalefactor, Size siz
{
CV_Assert(nch == 1);
int sz[] = { (int)nimages, 1, image0.rows, image0.cols };
blob = Mat(4, sz, CV_32F);
blob_.create(4, sz, CV_32F);
Mat blob = blob_.getMat();
for( i = 0; i < nimages; i++ )
{
@ -177,7 +189,6 @@ Mat blobFromImages(const std::vector<Mat>& images_, double scalefactor, Size siz
image.copyTo(Mat(image.rows, image.cols, CV_32F, blob.ptr((int)i, 0)));
}
}
return blob;
}
class OpenCLBackendWrapper : public BackendWrapper
@ -886,7 +897,8 @@ struct Net::Impl
{
LayerPin storedFrom = ld.inputBlobsId[inNum];
if (storedFrom.valid() && !storedFrom.equal(from))
CV_Error(Error::StsError, "Input #" + toString(inNum) + "of layer \"" + ld.name + "\" already was connected");
CV_Error(Error::StsError, format("Input #%d of layer \"%s\" already was connected",
inNum, ld.name.c_str()));
}
ld.inputBlobsId[inNum] = from;
@ -1665,8 +1677,9 @@ struct Net::Impl
LayerData &ld = layers[pin.lid];
if ((size_t)pin.oid >= ld.outputBlobs.size())
{
CV_Error(Error::StsOutOfRange, "Layer \"" + ld.name + "\" produce only " + toString(ld.outputBlobs.size()) +
" outputs, the #" + toString(pin.oid) + " was requsted");
CV_Error(Error::StsOutOfRange, format("Layer \"%s\" produce only %d outputs, "
"the #%d was requsted", ld.name.c_str(),
ld.outputBlobs.size(), pin.oid));
}
if (preferableTarget != DNN_TARGET_CPU)
{

10
modules/dnn/test/test_misc.cpp
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@ -27,4 +27,14 @@ TEST(blobFromImage_4ch, Regression)
}
}
TEST(blobFromImage, allocated)
{
int size[] = {1, 3, 4, 5};
Mat img(size[2], size[3], CV_32FC(size[1]));
Mat blob(4, size, CV_32F);
void* blobData = blob.data;
dnn::blobFromImage(img, blob, 1.0 / 255, Size(), Scalar(), false, false);
ASSERT_EQ(blobData, blob.data);
}
}