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Merge pull request #12364 from dkurt:dnn_change_blob_from_image

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Vadim Pisarevsky
2018-09-17 12:04:41 +00:00
parent 29bee6f07e 58ac3e09da
commit 2113818f19
5 changed files with 22 additions and 41 deletions
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modules/dnn/include/opencv2/dnn/dnn.hpp
+6 -6
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@@ -46,9 +46,9 @@
#include <opencv2/core.hpp>
#if !defined CV_DOXYGEN && !defined CV_DNN_DONT_ADD_EXPERIMENTAL_NS
#define CV__DNN_EXPERIMENTAL_NS_BEGIN namespace experimental_dnn_34_v8 {
#define CV__DNN_EXPERIMENTAL_NS_BEGIN namespace experimental_dnn_34_v9 {
#define CV__DNN_EXPERIMENTAL_NS_END }
namespace cv { namespace dnn { namespace experimental_dnn_34_v8 { } using namespace experimental_dnn_34_v8; }}
namespace cv { namespace dnn { namespace experimental_dnn_34_v9 { } using namespace experimental_dnn_34_v9; }}
#else
#define CV__DNN_EXPERIMENTAL_NS_BEGIN
#define CV__DNN_EXPERIMENTAL_NS_END
@@ -843,7 +843,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
* @returns 4-dimensional Mat with NCHW dimensions order.
*/
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,
const Scalar& mean = Scalar(), bool swapRB=false, bool crop=false,
int ddepth=CV_32F);
/** @brief Creates 4-dimensional blob from image.
@@ -852,7 +852,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
*/
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, int ddepth=CV_32F);
bool swapRB=false, bool crop=false, int ddepth=CV_32F);
/** @brief Creates 4-dimensional blob from series of images. Optionally resizes and
@@ -873,7 +873,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
* @returns 4-dimensional Mat with NCHW dimensions order.
*/
CV_EXPORTS_W Mat blobFromImages(InputArrayOfArrays images, double scalefactor=1.0,
Size size = Size(), const Scalar& mean = Scalar(), bool swapRB=true, bool crop=true,
Size size = Size(), const Scalar& mean = Scalar(), bool swapRB=false, bool crop=false,
int ddepth=CV_32F);
/** @brief Creates 4-dimensional blob from series of images.
@@ -882,7 +882,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
*/
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,
const Scalar& mean = Scalar(), bool swapRB=false, bool crop=false,
int ddepth=CV_32F);
/** @brief Parse a 4D blob and output the images it contains as 2D arrays through a simpler data structure
modules/dnn/test/test_caffe_importer.cpp
+2 -2
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@@ -307,7 +307,7 @@ TEST_P(Reproducibility_SqueezeNet_v1_1, Accuracy)
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableTarget(targetId);
Mat input = blobFromImage(imread(_tf("googlenet_0.png")), 1.0f, Size(227,227), Scalar(), false);
Mat input = blobFromImage(imread(_tf("googlenet_0.png")), 1.0f, Size(227,227), Scalar(), false, true);
ASSERT_TRUE(!input.empty());
Mat out;
@@ -403,7 +403,7 @@ TEST_P(Test_Caffe_nets, DenseNet_121)
const string model = findDataFile("dnn/DenseNet_121.caffemodel", false);
Mat inp = imread(_tf("dog416.png"));
inp = blobFromImage(inp, 1.0 / 255, Size(224, 224));
inp = blobFromImage(inp, 1.0 / 255, Size(224, 224), Scalar(), true, true);
Mat ref = blobFromNPY(_tf("densenet_121_output.npy"));
Net net = readNetFromCaffe(proto, model);
modules/dnn/test/test_tf_importer.cpp
+1 -2
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@@ -62,8 +62,7 @@ TEST(Test_TensorFlow, inception_accuracy)
Mat sample = imread(_tf("grace_hopper_227.png"));
ASSERT_TRUE(!sample.empty());
resize(sample, sample, Size(224, 224));
Mat inputBlob = blobFromImage(sample);
Mat inputBlob = blobFromImage(sample, 1.0, Size(224, 224), Scalar(), /*swapRB*/true);
net.setInput(inputBlob, "input");
Mat out = net.forward("softmax2");
modules/dnn/test/test_torch_importer.cpp
+2 -2
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@@ -278,7 +278,7 @@ TEST_P(Test_Torch_nets, OpenFace_accuracy)
sampleF32 /= 255;
resize(sampleF32, sampleF32, Size(96, 96), 0, 0, INTER_NEAREST);
Mat inputBlob = blobFromImage(sampleF32);
Mat inputBlob = blobFromImage(sampleF32, 1.0, Size(), Scalar(), /*swapRB*/true);
net.setInput(inputBlob);
Mat out = net.forward();
@@ -305,7 +305,7 @@ TEST_P(Test_Torch_nets, ENet_accuracy)
net.setPreferableTarget(target);
Mat sample = imread(_tf("street.png", false));
Mat inputBlob = blobFromImage(sample, 1./255);
Mat inputBlob = blobFromImage(sample, 1./255, Size(), Scalar(), /*swapRB*/true);
net.setInput(inputBlob, "");
Mat out = net.forward();