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Merge remote-tracking branch 'upstream/3.4' into merge-3.4

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pull/18368/head
Alexander Alekhin 4 years ago
parent 95fd61c9b4 5e90802b1a
commit f52a2cf5e1
14 changed files with 181 additions and 40 deletions
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  1. 1
      CMakeLists.txt
  2. 4
      cmake/OpenCVModule.cmake
  3. 2
      doc/py_tutorials/py_imgproc/py_contours/py_contour_properties/py_contour_properties.markdown
  4. 14
      modules/core/src/matrix_wrap.cpp
  5. 37
      modules/core/src/ocl.cpp
  6. 13
      modules/dnn/src/ocl4dnn/src/math_functions.cpp
  7. 65
      modules/dnn/src/onnx/onnx_importer.cpp
  8. 2
      modules/dnn/src/opencl/prior_box.cl
  9. 17
      modules/dnn/test/test_onnx_importer.cpp
  10. 17
      modules/imgcodecs/src/loadsave.cpp
  11. 21
      modules/imgproc/src/connectedcomponents.cpp
  12. 14
      modules/imgproc/test/test_connectedcomponents.cpp
  13. 6
      modules/videoio/doc/videoio_overview.markdown
  14. 8
      samples/samples_utils.cmake

1
CMakeLists.txt
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@ -464,6 +464,7 @@ OCV_OPTION(BUILD_OBJC "Enable Objective-C support"
# OpenCV installation options
# ===================================================
OCV_OPTION(INSTALL_CREATE_DISTRIB "Change install rules to build the distribution package" OFF )
OCV_OPTION(INSTALL_BIN_EXAMPLES "Install prebuilt examples" WIN32 IF BUILD_EXAMPLES)
OCV_OPTION(INSTALL_C_EXAMPLES "Install C examples" OFF )
OCV_OPTION(INSTALL_PYTHON_EXAMPLES "Install Python examples" OFF )
OCV_OPTION(INSTALL_ANDROID_EXAMPLES "Install Android examples" OFF IF ANDROID )

4
cmake/OpenCVModule.cmake
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@ -1364,8 +1364,8 @@ function(ocv_add_samples)
add_dependencies(${the_target} opencv_videoio_plugins)
endif()
if(WIN32)
install(TARGETS ${the_target} RUNTIME DESTINATION "samples/${module_id}" COMPONENT samples)
if(INSTALL_BIN_EXAMPLES)
install(TARGETS ${the_target} RUNTIME DESTINATION "${OPENCV_SAMPLES_BIN_INSTALL_PATH}/${module_id}" COMPONENT samples)
endif()
endforeach()
endif()

2
doc/py_tutorials/py_imgproc/py_contours/py_contour_properties/py_contour_properties.markdown
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@ -78,7 +78,7 @@ pixelpoints = np.transpose(np.nonzero(mask))
Here, two methods, one using Numpy functions, next one using OpenCV function (last commented line)
are given to do the same. Results are also same, but with a slight difference. Numpy gives
coordinates in **(row, column)** format, while OpenCV gives coordinates in **(x,y)** format. So
basically the answers will be interchanged. Note that, **row = x** and **column = y**.
basically the answers will be interchanged. Note that, **row = y** and **column = x**.
7. Maximum Value, Minimum Value and their locations
---------------------------------------------------

14
modules/core/src/matrix_wrap.cpp
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@ -1248,6 +1248,7 @@ void _OutputArray::create(int d, const int* sizes, int mtype, int i,
{
CV_Assert( i < 0 );
Mat& m = *(Mat*)obj;
CV_Assert(!(m.empty() && fixedType() && fixedSize()) && "Can't reallocate empty Mat with locked layout (probably due to misused 'const' modifier)");
if (allowTransposed && !m.empty() &&
d == 2 && m.dims == 2 &&
m.type() == mtype && m.rows == sizes[1] && m.cols == sizes[0] &&
@ -1261,13 +1262,13 @@ void _OutputArray::create(int d, const int* sizes, int mtype, int i,
if(CV_MAT_CN(mtype) == m.channels() && ((1 << CV_MAT_TYPE(flags)) & fixedDepthMask) != 0 )
mtype = m.type();
else
CV_CheckTypeEQ(m.type(), CV_MAT_TYPE(mtype), "");
CV_CheckTypeEQ(m.type(), CV_MAT_TYPE(mtype), "Can't reallocate Mat with locked type (probably due to misused 'const' modifier)");
}
if(fixedSize())
{
CV_CheckEQ(m.dims, d, "");
CV_CheckEQ(m.dims, d, "Can't reallocate Mat with locked size (probably due to misused 'const' modifier)");
for(int j = 0; j < d; ++j)
CV_CheckEQ(m.size[j], sizes[j], "");
CV_CheckEQ(m.size[j], sizes[j], "Can't reallocate Mat with locked size (probably due to misused 'const' modifier)");
}
m.create(d, sizes, mtype);
return;
@ -1277,6 +1278,7 @@ void _OutputArray::create(int d, const int* sizes, int mtype, int i,
{
CV_Assert( i < 0 );
UMat& m = *(UMat*)obj;
CV_Assert(!(m.empty() && fixedType() && fixedSize()) && "Can't reallocate empty UMat with locked layout (probably due to misused 'const' modifier)");
if (allowTransposed && !m.empty() &&
d == 2 && m.dims == 2 &&
m.type() == mtype && m.rows == sizes[1] && m.cols == sizes[0] &&
@ -1290,13 +1292,13 @@ void _OutputArray::create(int d, const int* sizes, int mtype, int i,
if(CV_MAT_CN(mtype) == m.channels() && ((1 << CV_MAT_TYPE(flags)) & fixedDepthMask) != 0 )
mtype = m.type();
else
CV_CheckTypeEQ(m.type(), CV_MAT_TYPE(mtype), "");
CV_CheckTypeEQ(m.type(), CV_MAT_TYPE(mtype), "Can't reallocate UMat with locked type (probably due to misused 'const' modifier)");
}
if(fixedSize())
{
CV_CheckEQ(m.dims, d, "");
CV_CheckEQ(m.dims, d, "Can't reallocate UMat with locked size (probably due to misused 'const' modifier)");
for(int j = 0; j < d; ++j)
CV_CheckEQ(m.size[j], sizes[j], "");
CV_CheckEQ(m.size[j], sizes[j], "Can't reallocate UMat with locked size (probably due to misused 'const' modifier)");
}
m.create(d, sizes, mtype);
return;

37
modules/core/src/ocl.cpp
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@ -3311,7 +3311,7 @@ KernelArg KernelArg::Constant(const Mat& m)
struct Kernel::Impl
{
Impl(const char* kname, const Program& prog) :
refcount(1), handle(NULL), isInProgress(false), nu(0)
refcount(1), handle(NULL), isInProgress(false), isAsyncRun(false), nu(0)
{
cl_program ph = (cl_program)prog.ptr();
cl_int retval = 0;
@ -3388,6 +3388,7 @@ struct Kernel::Impl
enum { MAX_ARRS = 16 };
UMatData* u[MAX_ARRS];
bool isInProgress;
bool isAsyncRun; // true if kernel was scheduled in async mode
int nu;
std::list<Image2D> images;
bool haveTempDstUMats;
@ -3667,13 +3668,45 @@ bool Kernel::run(int dims, size_t _globalsize[], size_t _localsize[],
}
static bool isRaiseErrorOnReuseAsyncKernel()
{
static bool initialized = false;
static bool value = false;
if (!initialized)
{
value = cv::utils::getConfigurationParameterBool("OPENCV_OPENCL_RAISE_ERROR_REUSE_ASYNC_KERNEL", false);
initialized = true;
}
return value;
}
bool Kernel::Impl::run(int dims, size_t globalsize[], size_t localsize[],
bool sync, int64* timeNS, const Queue& q)
{
CV_INSTRUMENT_REGION_OPENCL_RUN(name.c_str());
if (!handle || isInProgress)
if (!handle)
{
CV_LOG_ERROR(NULL, "OpenCL kernel has zero handle: " << name);
return false;
}
if (isAsyncRun)
{
CV_LOG_ERROR(NULL, "OpenCL kernel can't be reused in async mode: " << name);
if (isRaiseErrorOnReuseAsyncKernel())
CV_Assert(0);
return false; // OpenCV 5.0: raise error
}
isAsyncRun = !sync;
if (isInProgress)
{
CV_LOG_ERROR(NULL, "Previous OpenCL kernel launch is not finished: " << name);
if (isRaiseErrorOnReuseAsyncKernel())
CV_Assert(0);
return false; // OpenCV 5.0: raise error
}
cl_command_queue qq = getQueue(q);
if (haveTempDstUMats)

13
modules/dnn/src/ocl4dnn/src/math_functions.cpp
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@ -46,6 +46,8 @@
#include <vector>
#include "opencl_kernels_dnn.hpp"
#include "opencv2/core/utils/logger.hpp"
namespace cv { namespace dnn { namespace ocl4dnn {
enum gemm_data_type_t
@ -238,10 +240,6 @@ static bool ocl4dnnFastImageGEMM(const CBLAS_TRANSPOSE TransA,
kernel_name += "_float";
}
ocl::Kernel oclk_gemm_float(kernel_name.c_str(), ocl::dnn::gemm_image_oclsrc, opts);
if (oclk_gemm_float.empty())
return false;
while (C_start_y < M)
{
blockC_width = std::min(static_cast<int>(N) - C_start_x, blocksize);
@ -348,6 +346,10 @@ static bool ocl4dnnFastImageGEMM(const CBLAS_TRANSPOSE TransA,
}
local[1] = 1;
ocl::Kernel oclk_gemm_float(kernel_name.c_str(), ocl::dnn::gemm_image_oclsrc, opts);
if (oclk_gemm_float.empty())
return false;
cl_uint arg_idx = 0;
if (is_image_a)
oclk_gemm_float.set(arg_idx++, ocl::KernelArg::PtrReadOnly(A));
@ -378,7 +380,10 @@ static bool ocl4dnnFastImageGEMM(const CBLAS_TRANSPOSE TransA,
oclk_gemm_float.set(arg_idx++, isFirstColBlock);
if (!oclk_gemm_float.run(2, global, local, false))
{
CV_LOG_WARNING(NULL, "OpenCL kernel enqueue failed: " << kernel_name);
return false;
}
if (TransA == CblasNoTrans)
A_start_x += blockA_width;

65
modules/dnn/src/onnx/onnx_importer.cpp
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@ -392,24 +392,21 @@ void ONNXImporter::populateNet(Net dstNet)
layerParams.set("ave_pool_padded_area", framework_name == "pytorch");
}
else if (layer_type == "GlobalAveragePool" || layer_type == "GlobalMaxPool" ||
layer_type == "ReduceMean" || layer_type == "ReduceSum")
layer_type == "ReduceMean" || layer_type == "ReduceSum" || layer_type == "ReduceMax")
{
CV_Assert(node_proto.input_size() == 1);
layerParams.type = "Pooling";
String pool;
if (layer_type == "GlobalMaxPool")
if (layer_type == "GlobalMaxPool" || layer_type == "ReduceMax")
pool = "MAX";
else if (layer_type == "ReduceSum")
pool = "SUM";
else
pool = "AVE";
layerParams.set("pool", pool);
layerParams.set("global_pooling", layer_type == "GlobalAveragePool" || layer_type == "GlobalMaxPool");
if (layer_type == "ReduceMean" || layer_type == "ReduceSum")
layerParams.set("global_pooling", !layerParams.has("axes"));
if (layerParams.has("axes") && (layer_type == "ReduceMean" || layer_type == "ReduceSum" || layer_type == "ReduceMax"))
{
if (!layerParams.has("axes"))
CV_Error(Error::StsNotImplemented, "Unsupported mode of " + layer_type + " operation.");
MatShape inpShape = outShapes[node_proto.input(0)];
DictValue axes = layerParams.get("axes");
bool keepdims = layerParams.get<int>("keepdims");
@ -487,6 +484,36 @@ void ONNXImporter::populateNet(Net dstNet)
layerParams.type = "Reshape";
layerParams.set("dim", DictValue::arrayInt(&targetShape[0], targetShape.size()));
node_proto.set_input(0, node_proto.output(0));
node_proto.set_output(0, layerParams.name);
}
else if (!layerParams.has("axes") && (layer_type == "ReduceMean" || layer_type == "ReduceSum" || layer_type == "ReduceMax"))
{
CV_CheckEQ(layerParams.get<int>("keepdims"), 0, (layer_type + " layer only supports keepdims = false").c_str());
LayerParams reshapeLp;
reshapeLp.name = layerParams.name + "/reshape";
reshapeLp.type = "Reshape";
CV_Assert(layer_id.find(reshapeLp.name) == layer_id.end());
int newShape[] = {1, 1, 1, -1};
reshapeLp.set("dim", DictValue::arrayInt(&newShape[0], 4));
opencv_onnx::NodeProto proto;
proto.add_input(node_proto.input(0));
proto.add_output(reshapeLp.name);
addLayer(dstNet, reshapeLp, proto, layer_id, outShapes);
LayerParams poolLp = layerParams;
poolLp.name = layerParams.name + "/pool";
CV_Assert(layer_id.find(poolLp.name) == layer_id.end());
node_proto.set_input(0, reshapeLp.name);
node_proto.set_output(0, poolLp.name);
addLayer(dstNet, poolLp, node_proto, layer_id, outShapes);
layerParams.type = "Reshape";
int targetShape[] = {1};
layerParams.set("dim", DictValue::arrayInt(&targetShape[0], 1));
node_proto.set_input(0, node_proto.output(0));
node_proto.set_output(0, layerParams.name);
}
@ -653,7 +680,7 @@ void ONNXImporter::populateNet(Net dstNet)
LayerParams constParams;
constParams.name = layerParams.name + "/const";
constParams.type = "Const";
constParams.blobs.push_back(blob);
constParams.blobs.push_back((isSub ? -1 : 1) * blob);
int id = dstNet.addLayer(constParams.name, constParams.type, constParams);
layer_id.insert(std::make_pair(constParams.name, LayerInfo(id, 0)));
outShapes[constParams.name] = shape(blob);
@ -1024,6 +1051,16 @@ void ONNXImporter::populateNet(Net dstNet)
}
else
{
// Scale layer allocate output with the first input shape
if (total(outShapes[node_proto.input(0)]) < total(outShapes[node_proto.input(1)]))
{
opencv_onnx::NodeProto proto;
proto.add_input(node_proto.input(1));
proto.add_input(node_proto.input(0));
proto.add_output(layerParams.name);
node_proto = proto;
}
if (isDiv)
{
LayerParams powerParams;
@ -1427,8 +1464,10 @@ void ONNXImporter::populateNet(Net dstNet)
case opencv_onnx::TensorProto_DataType_INT64: type = CV_32S; break;
default: type = blob.type();
}
blob.convertTo(blob, type);
addConstant(layerParams.name, blob, constBlobs, outShapes);
Mat dst;
blob.convertTo(dst, type);
dst.dims = blob.dims;
addConstant(layerParams.name, dst, constBlobs, outShapes);
continue;
}
else
@ -1477,6 +1516,8 @@ void ONNXImporter::populateNet(Net dstNet)
{
outShape.erase(outShape.begin() + axis);
out.reshape(0, outShape);
} else {
out.dims = 1;
}
addConstant(layerParams.name, out, constBlobs, outShapes);
continue;
@ -1557,7 +1598,9 @@ void ONNXImporter::populateNet(Net dstNet)
Mat shapes = getBlob(node_proto, constBlobs, node_proto.input_size() - 1);
CV_CheckEQ(shapes.size[0], 4, "");
CV_CheckEQ(shapes.size[1], 1, "");
CV_CheckTypeEQ(shapes.depth(), CV_32S, "");
CV_CheckDepth(shapes.depth(), shapes.depth() == CV_32S || shapes.depth() == CV_32F, "");
if (shapes.depth() == CV_32F)
shapes.convertTo(shapes, CV_32S);
int height = shapes.at<int>(2);
int width = shapes.at<int>(3);
if (node_proto.input_size() == 3)

2
modules/dnn/src/opencl/prior_box.cl
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@ -114,6 +114,6 @@ __kernel void clip(const int nthreads,
for (int index = get_global_id(0); index < nthreads; index += get_global_size(0))
{
Dtype4 vec = vload4(index, dst);
vstore4(clamp(vec, 0.0f, 1.0f), index, dst);
vstore4(clamp(vec, (Dtype)0.0f, (Dtype)1.0f), index, dst);
}
}

17
modules/dnn/test/test_onnx_importer.cpp
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@ -275,6 +275,18 @@ TEST_P(Test_ONNX_layers, ReduceSum)
testONNXModels("reduce_sum");
}
TEST_P(Test_ONNX_layers, ReduceMaxGlobal)
{
testONNXModels("reduce_max");
}
TEST_P(Test_ONNX_layers, Scale)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
testONNXModels("scale");
}
TEST_P(Test_ONNX_layers, ReduceMean3D)
{
if (backend == DNN_BACKEND_CUDA)
@ -664,6 +676,11 @@ TEST_P(Test_ONNX_layers, MatmulWithTwoInputs)
testONNXModels("matmul_with_two_inputs");
}
TEST_P(Test_ONNX_layers, ResizeOpset11_Torch1_6)
{
testONNXModels("resize_opset11_torch1.6");
}
INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_ONNX_layers, dnnBackendsAndTargets());
class Test_ONNX_nets : public Test_ONNX_layers

17
modules/imgcodecs/src/loadsave.cpp
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@ -197,7 +197,19 @@ struct ImageCodecInitializer
std::vector<ImageEncoder> encoders;
};
static ImageCodecInitializer codecs;
static
ImageCodecInitializer& getCodecs()
{
#ifdef CV_CXX11
static ImageCodecInitializer g_codecs;
return g_codecs;
#else
// C++98 doesn't guarantee correctness of multi-threaded initialization of static global variables
// (memory leak here is not critical, use C++11 to avoid that)
static ImageCodecInitializer* g_codecs = new ImageCodecInitializer();
return *g_codecs;
#endif
}
/**
* Find the decoders
@ -211,6 +223,7 @@ static ImageDecoder findDecoder( const String& filename ) {
size_t i, maxlen = 0;
/// iterate through list of registered codecs
ImageCodecInitializer& codecs = getCodecs();
for( i = 0; i < codecs.decoders.size(); i++ )
{
size_t len = codecs.decoders[i]->signatureLength();
@ -248,6 +261,7 @@ static ImageDecoder findDecoder( const Mat& buf )
if( buf.rows*buf.cols < 1 || !buf.isContinuous() )
return ImageDecoder();
ImageCodecInitializer& codecs = getCodecs();
for( i = 0; i < codecs.decoders.size(); i++ )
{
size_t len = codecs.decoders[i]->signatureLength();
@ -280,6 +294,7 @@ static ImageEncoder findEncoder( const String& _ext )
for( ext++; len < 128 && isalnum(ext[len]); len++ )
;
ImageCodecInitializer& codecs = getCodecs();
for( size_t i = 0; i < codecs.encoders.size(); i++ )
{
String description = codecs.encoders[i]->getDescription();

21
modules/imgproc/src/connectedcomponents.cpp
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@ -145,14 +145,21 @@ namespace cv{
void finish(){
for (int l = 0; l < statsv.rows; ++l){
int *row =& statsv.at<int>(l, 0);
row[CC_STAT_WIDTH] = row[CC_STAT_WIDTH] - row[CC_STAT_LEFT] + 1;
row[CC_STAT_HEIGHT] = row[CC_STAT_HEIGHT] - row[CC_STAT_TOP] + 1;
Point2ui64& integral = integrals[l];
double *centroid = &centroidsv.at<double>(l, 0);
double area = ((unsigned*)row)[CC_STAT_AREA];
centroid[0] = double(integral.x) / area;
centroid[1] = double(integral.y) / area;
double *centroid = &centroidsv.at<double>(l, 0);
if (area > 0){
row[CC_STAT_WIDTH] = row[CC_STAT_WIDTH] - row[CC_STAT_LEFT] + 1;
row[CC_STAT_HEIGHT] = row[CC_STAT_HEIGHT] - row[CC_STAT_TOP] + 1;
Point2ui64& integral = integrals[l];
centroid[0] = double(integral.x) / area;
centroid[1] = double(integral.y) / area;
} else {
row[CC_STAT_WIDTH] = 0;
row[CC_STAT_HEIGHT] = 0;
row[CC_STAT_LEFT] = -1;
centroid[0] = std::numeric_limits<double>::quiet_NaN();
centroid[1] = std::numeric_limits<double>::quiet_NaN();
}
}
}

14
modules/imgproc/test/test_connectedcomponents.cpp
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@ -225,5 +225,19 @@ TEST(Imgproc_ConnectedComponents, parallel_wu_labels)
EXPECT_EQ(nbPixels, area);
}
TEST(Imgproc_ConnectedComponents, missing_background_pixels)
{
cv::Mat m = Mat::ones(10, 10, CV_8U);
cv::Mat labels;
cv::Mat stats;
cv::Mat centroids;
EXPECT_NO_THROW(cv::connectedComponentsWithStats(m, labels, stats, centroids, 8, CV_32S, cv::CCL_WU) );
EXPECT_EQ(stats.at<int32_t>(0, cv::CC_STAT_WIDTH), 0);
EXPECT_EQ(stats.at<int32_t>(0, cv::CC_STAT_HEIGHT), 0);
EXPECT_EQ(stats.at<int32_t>(0, cv::CC_STAT_LEFT), -1);
EXPECT_TRUE(std::isnan(centroids.at<double>(0, 0)));
EXPECT_TRUE(std::isnan(centroids.at<double>(0, 1)));
}
}} // namespace

6
modules/videoio/doc/videoio_overview.markdown
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@ -80,7 +80,7 @@ for the operating system. Thus you can't use VideoCapture or VideoWriter with t
To get access to their devices, manufactures provide their own C++ API and library that you have to
include and link with your OpenCV application.
Is common case that this libraries read/write images from/to a memory buffer. If it so, it is
It is a common case that these libraries read/write images from/to a memory buffer. If it so, it is
possible to make a `Mat` header for memory buffer (user-allocated data) and process it
in-place using OpenCV functions. See cv::Mat::Mat() for more details.
@ -88,9 +88,9 @@ The FFmpeg library
------------------
OpenCV can use the FFmpeg library (http://ffmpeg.org/) as backend to record, convert and stream audio and video.
FFMpeg is a complete, cross-reference solution. If you enable FFmpeg while configuring OpenCV than
FFmpeg is a complete, cross-reference solution. If you enable FFmpeg while configuring OpenCV than
CMake will download and install the binaries in `OPENCV_SOURCE_CODE/3rdparty/ffmpeg/`. To use
FFMpeg at runtime, you must deploy the FFMepg binaries with your application.
FFmpeg at runtime, you must deploy the FFMepg binaries with your application.
@note FFmpeg is licensed under the GNU Lesser General Public License (LGPL) version 2.1 or later.
See `OPENCV_SOURCE_CODE/3rdparty/ffmpeg/readme.txt` and http://ffmpeg.org/legal.html for details and

8
samples/samples_utils.cmake
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@ -15,8 +15,12 @@ function(ocv_define_sample out_target source sub)
if(WIN32 AND MSVC AND NOT BUILD_SHARED_LIBS)
set_target_properties(${the_target} PROPERTIES LINK_FLAGS "/NODEFAULTLIB:atlthunk.lib /NODEFAULTLIB:atlsd.lib /DEBUG")
endif()
if(WIN32)
install(TARGETS ${the_target} RUNTIME DESTINATION "samples/${sub}" COMPONENT samples)
# Should be usable in stand-alone build scenario
if((NOT DEFINED INSTALL_BIN_EXAMPLES AND WIN32) OR INSTALL_BIN_EXAMPLES)
if(NOT DEFINED OPENCV_SAMPLES_BIN_INSTALL_PATH)
set(OPENCV_SAMPLES_BIN_INSTALL_PATH "samples")
endif()
install(TARGETS ${the_target} RUNTIME DESTINATION "${OPENCV_SAMPLES_BIN_INSTALL_PATH}/${sub}" COMPONENT samples)
endif()
# Add single target to build all samples in the group: 'make opencv_samples_cpp'
set(parent_target opencv_samples_${sub})

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