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

pull/19087/head
Alexander Alekhin 4 years ago
commit dd1494eebf
  1. 5
      3rdparty/protobuf/CMakeLists.txt
  2. 154
      cmake/OpenCVFindMKL.cmake
  3. 1
      modules/calib3d/include/opencv2/calib3d.hpp
  4. 111
      modules/calib3d/src/calibinit.cpp
  5. 6
      modules/calib3d/src/circlesgrid.cpp
  6. 54
      modules/calib3d/test/test_chesscorners.cpp
  7. 29
      modules/dnn/src/layers/pooling_layer.cpp
  8. 50
      modules/dnn/src/layers/resize_layer.cpp
  9. 18
      modules/dnn/test/test_backends.cpp
  10. 13
      modules/dnn/test/test_tf_importer.cpp
  11. 13
      modules/features2d/src/orb.cpp
  12. 21
      modules/features2d/test/test_orb.cpp
  13. 4
      modules/imgproc/misc/java/test/ImgprocTest.java
  14. 2
      modules/imgproc/src/rotcalipers.cpp
  15. 128
      modules/imgproc/src/smooth.simd.hpp
  16. 78
      modules/imgproc/test/test_convhull.cpp
  17. 2
      modules/python/test/test_legacy.py
  18. 5
      modules/videoio/src/cap_gstreamer.cpp
  19. 3
      samples/dnn/.gitignore
  20. 32
      samples/dnn/README.md
  21. 364
      samples/dnn/download_models.py
  22. 74
      samples/dnn/face_detector/download_weights.py
  23. 4
      samples/dnn/face_detector/weights.meta4
  24. 40
      samples/dnn/models.yml

@ -153,6 +153,11 @@ set_target_properties(libprotobuf
ARCHIVE_OUTPUT_DIRECTORY ${3P_LIBRARY_OUTPUT_PATH}
)
if(ANDROID)
# https://github.com/opencv/opencv/issues/17282
target_link_libraries(libprotobuf INTERFACE "-landroid" "-llog")
endif()
get_protobuf_version(Protobuf_VERSION "${PROTOBUF_ROOT}/src")
set(Protobuf_VERSION ${Protobuf_VERSION} CACHE INTERNAL "" FORCE)

@ -3,7 +3,14 @@
# installation/package
#
# Parameters:
# MKL_WITH_TBB
# MKL_ROOT_DIR / ENV{MKLROOT}
# MKL_INCLUDE_DIR
# MKL_LIBRARIES
# MKL_USE_SINGLE_DYNAMIC_LIBRARY - use single dynamic library mkl_rt.lib / libmkl_rt.so
# MKL_WITH_TBB / MKL_WITH_OPENMP
#
# Extra:
# MKL_LIB_FIND_PATHS
#
# On return this will define:
#
@ -13,12 +20,6 @@
# MKL_LIBRARIES - MKL libraries that are used by OpenCV
#
macro (mkl_find_lib VAR NAME DIRS)
find_path(${VAR} ${NAME} ${DIRS} NO_DEFAULT_PATH)
set(${VAR} ${${VAR}}/${NAME})
unset(${VAR} CACHE)
endmacro()
macro(mkl_fail)
set(HAVE_MKL OFF)
set(MKL_ROOT_DIR "${MKL_ROOT_DIR}" CACHE PATH "Path to MKL directory")
@ -39,43 +40,50 @@ macro(get_mkl_version VERSION_FILE)
set(MKL_VERSION_STR "${MKL_VERSION_MAJOR}.${MKL_VERSION_MINOR}.${MKL_VERSION_UPDATE}" CACHE STRING "MKL version" FORCE)
endmacro()
OCV_OPTION(MKL_USE_SINGLE_DYNAMIC_LIBRARY "Use MKL Single Dynamic Library thorugh mkl_rt.lib / libmkl_rt.so" OFF)
OCV_OPTION(MKL_WITH_TBB "Use MKL with TBB multithreading" OFF)#ON IF WITH_TBB)
OCV_OPTION(MKL_WITH_OPENMP "Use MKL with OpenMP multithreading" OFF)#ON IF WITH_OPENMP)
if(NOT DEFINED MKL_USE_MULTITHREAD)
OCV_OPTION(MKL_WITH_TBB "Use MKL with TBB multithreading" OFF)#ON IF WITH_TBB)
OCV_OPTION(MKL_WITH_OPENMP "Use MKL with OpenMP multithreading" OFF)#ON IF WITH_OPENMP)
if(NOT MKL_ROOT_DIR AND DEFINED MKL_INCLUDE_DIR AND EXISTS "${MKL_INCLUDE_DIR}/mkl.h")
file(TO_CMAKE_PATH "${MKL_INCLUDE_DIR}" MKL_INCLUDE_DIR)
get_filename_component(MKL_ROOT_DIR "${MKL_INCLUDE_DIR}/.." ABSOLUTE)
endif()
#check current MKL_ROOT_DIR
if(NOT MKL_ROOT_DIR OR NOT EXISTS "${MKL_ROOT_DIR}/include/mkl.h")
set(mkl_root_paths "${MKL_ROOT_DIR}")
if(DEFINED ENV{MKLROOT})
list(APPEND mkl_root_paths "$ENV{MKLROOT}")
if(NOT MKL_ROOT_DIR)
file(TO_CMAKE_PATH "${MKL_ROOT_DIR}" mkl_root_paths)
if(DEFINED ENV{MKLROOT})
file(TO_CMAKE_PATH "$ENV{MKLROOT}" path)
list(APPEND mkl_root_paths "${path}")
endif()
if(WITH_MKL AND NOT mkl_root_paths)
if(WIN32)
set(ProgramFilesx86 "ProgramFiles(x86)")
file(TO_CMAKE_PATH "$ENV{${ProgramFilesx86}}" path)
list(APPEND mkl_root_paths ${path}/IntelSWTools/compilers_and_libraries/windows/mkl)
endif()
if(WITH_MKL AND NOT mkl_root_paths)
if(WIN32)
set(ProgramFilesx86 "ProgramFiles(x86)")
list(APPEND mkl_root_paths $ENV{${ProgramFilesx86}}/IntelSWTools/compilers_and_libraries/windows/mkl)
endif()
if(UNIX)
list(APPEND mkl_root_paths "/opt/intel/mkl")
endif()
if(UNIX)
list(APPEND mkl_root_paths "/opt/intel/mkl")
endif()
endif()
find_path(MKL_ROOT_DIR include/mkl.h PATHS ${mkl_root_paths})
find_path(MKL_ROOT_DIR include/mkl.h PATHS ${mkl_root_paths})
endif()
set(MKL_INCLUDE_DIRS "${MKL_ROOT_DIR}/include" CACHE PATH "Path to MKL include directory")
if(NOT MKL_ROOT_DIR OR NOT EXISTS "${MKL_ROOT_DIR}/include/mkl.h")
mkl_fail()
endif()
set(MKL_INCLUDE_DIR "${MKL_ROOT_DIR}/include" CACHE PATH "Path to MKL include directory")
if(NOT MKL_ROOT_DIR
OR NOT EXISTS "${MKL_ROOT_DIR}"
OR NOT EXISTS "${MKL_INCLUDE_DIRS}"
OR NOT EXISTS "${MKL_INCLUDE_DIRS}/mkl_version.h"
OR NOT EXISTS "${MKL_INCLUDE_DIR}"
OR NOT EXISTS "${MKL_INCLUDE_DIR}/mkl_version.h"
)
mkl_fail()
mkl_fail()
endif()
get_mkl_version(${MKL_INCLUDE_DIRS}/mkl_version.h)
get_mkl_version(${MKL_INCLUDE_DIR}/mkl_version.h)
#determine arch
if(CMAKE_CXX_SIZEOF_DATA_PTR EQUAL 8)
@ -95,52 +103,66 @@ else()
set(MKL_ARCH_SUFFIX "c")
endif()
if(MKL_VERSION_STR VERSION_GREATER "11.3.0" OR MKL_VERSION_STR VERSION_EQUAL "11.3.0")
set(mkl_lib_find_paths
${MKL_ROOT_DIR}/lib)
foreach(MKL_ARCH ${MKL_ARCH_LIST})
list(APPEND mkl_lib_find_paths
${MKL_ROOT_DIR}/lib/${MKL_ARCH}
${MKL_ROOT_DIR}/../tbb/lib/${MKL_ARCH}
${MKL_ROOT_DIR}/${MKL_ARCH})
endforeach()
set(mkl_lib_list "mkl_intel_${MKL_ARCH_SUFFIX}")
if(MKL_WITH_TBB)
list(APPEND mkl_lib_list mkl_tbb_thread tbb)
elseif(MKL_WITH_OPENMP)
if(MSVC)
list(APPEND mkl_lib_list mkl_intel_thread libiomp5md)
else()
list(APPEND mkl_lib_list mkl_gnu_thread)
endif()
set(mkl_lib_find_paths ${MKL_LIB_FIND_PATHS} ${MKL_ROOT_DIR}/lib)
foreach(MKL_ARCH ${MKL_ARCH_LIST})
list(APPEND mkl_lib_find_paths
${MKL_ROOT_DIR}/lib/${MKL_ARCH}
${MKL_ROOT_DIR}/${MKL_ARCH}
)
endforeach()
if(MKL_USE_SINGLE_DYNAMIC_LIBRARY AND NOT (MKL_VERSION_STR VERSION_LESS "10.3.0"))
# https://software.intel.com/content/www/us/en/develop/articles/a-new-linking-model-single-dynamic-library-mkl_rt-since-intel-mkl-103.html
set(mkl_lib_list "mkl_rt")
elseif(NOT (MKL_VERSION_STR VERSION_LESS "11.3.0"))
foreach(MKL_ARCH ${MKL_ARCH_LIST})
list(APPEND mkl_lib_find_paths
${MKL_ROOT_DIR}/../tbb/lib/${MKL_ARCH}
)
endforeach()
set(mkl_lib_list "mkl_intel_${MKL_ARCH_SUFFIX}")
if(MKL_WITH_TBB)
list(APPEND mkl_lib_list mkl_tbb_thread tbb)
elseif(MKL_WITH_OPENMP)
if(MSVC)
list(APPEND mkl_lib_list mkl_intel_thread libiomp5md)
else()
list(APPEND mkl_lib_list mkl_sequential)
list(APPEND mkl_lib_list mkl_gnu_thread)
endif()
else()
list(APPEND mkl_lib_list mkl_sequential)
endif()
list(APPEND mkl_lib_list mkl_core)
list(APPEND mkl_lib_list mkl_core)
else()
message(STATUS "MKL version ${MKL_VERSION_STR} is not supported")
mkl_fail()
message(STATUS "MKL version ${MKL_VERSION_STR} is not supported")
mkl_fail()
endif()
set(MKL_LIBRARIES "")
foreach(lib ${mkl_lib_list})
find_library(${lib} NAMES ${lib} ${lib}_dll HINTS ${mkl_lib_find_paths})
mark_as_advanced(${lib})
if(NOT ${lib})
mkl_fail()
if(NOT MKL_LIBRARIES)
set(MKL_LIBRARIES "")
foreach(lib ${mkl_lib_list})
set(lib_var_name MKL_LIBRARY_${lib})
find_library(${lib_var_name} NAMES ${lib} ${lib}_dll HINTS ${mkl_lib_find_paths})
mark_as_advanced(${lib_var_name})
if(NOT ${lib_var_name})
mkl_fail()
endif()
list(APPEND MKL_LIBRARIES ${${lib}})
endforeach()
list(APPEND MKL_LIBRARIES ${${lib_var_name}})
endforeach()
endif()
message(STATUS "Found MKL ${MKL_VERSION_STR} at: ${MKL_ROOT_DIR}")
set(HAVE_MKL ON)
set(MKL_ROOT_DIR "${MKL_ROOT_DIR}" CACHE PATH "Path to MKL directory")
set(MKL_INCLUDE_DIRS "${MKL_INCLUDE_DIRS}" CACHE PATH "Path to MKL include directory")
set(MKL_LIBRARIES "${MKL_LIBRARIES}" CACHE STRING "MKL libraries")
if(UNIX AND NOT MKL_LIBRARIES_DONT_HACK)
set(MKL_INCLUDE_DIRS "${MKL_INCLUDE_DIR}")
set(MKL_LIBRARIES "${MKL_LIBRARIES}")
if(UNIX AND NOT MKL_USE_SINGLE_DYNAMIC_LIBRARY AND NOT MKL_LIBRARIES_DONT_HACK)
#it's ugly but helps to avoid cyclic lib problem
set(MKL_LIBRARIES ${MKL_LIBRARIES} ${MKL_LIBRARIES} ${MKL_LIBRARIES} "-lpthread" "-lm" "-ldl")
endif()

@ -1670,6 +1670,7 @@ typedef CirclesGridFinderParameters CirclesGridFinderParameters2;
- **CALIB_CB_CLUSTERING** uses a special algorithm for grid detection. It is more robust to
perspective distortions but much more sensitive to background clutter.
@param blobDetector feature detector that finds blobs like dark circles on light background.
If `blobDetector` is NULL then `image` represents Point2f array of candidates.
@param parameters struct for finding circles in a grid pattern.
The function attempts to determine whether the input image contains a grid of circles. If it is, the

@ -2178,13 +2178,6 @@ void drawChessboardCorners( InputOutputArray image, Size patternSize,
}
}
static int quiet_error(int /*status*/, const char* /*func_name*/,
const char* /*err_msg*/, const char* /*file_name*/,
int /*line*/, void* /*userdata*/)
{
return 0;
}
bool findCirclesGrid( InputArray _image, Size patternSize,
OutputArray _centers, int flags, const Ptr<FeatureDetector> &blobDetector,
const CirclesGridFinderParameters& parameters_)
@ -2197,15 +2190,22 @@ bool findCirclesGrid( InputArray _image, Size patternSize,
bool isSymmetricGrid = (flags & CALIB_CB_SYMMETRIC_GRID ) ? true : false;
CV_Assert(isAsymmetricGrid ^ isSymmetricGrid);
Mat image = _image.getMat();
std::vector<Point2f> centers;
std::vector<KeyPoint> keypoints;
blobDetector->detect(image, keypoints);
std::vector<Point2f> points;
for (size_t i = 0; i < keypoints.size(); i++)
if (blobDetector)
{
std::vector<KeyPoint> keypoints;
blobDetector->detect(_image, keypoints);
for (size_t i = 0; i < keypoints.size(); i++)
{
points.push_back(keypoints[i].pt);
}
}
else
{
points.push_back (keypoints[i].pt);
CV_CheckTypeEQ(_image.type(), CV_32FC2, "blobDetector must be provided or image must contains Point2f array (std::vector<Point2f>) with candidates");
_image.copyTo(points);
}
if(flags & CALIB_CB_ASYMMETRIC_GRID)
@ -2221,64 +2221,59 @@ bool findCirclesGrid( InputArray _image, Size patternSize,
return !centers.empty();
}
bool isValid = false;
const int attempts = 2;
const size_t minHomographyPoints = 4;
Mat H;
for (int i = 0; i < attempts; i++)
{
centers.clear();
CirclesGridFinder boxFinder(patternSize, points, parameters);
bool isFound = false;
#define BE_QUIET 1
#if BE_QUIET
void* oldCbkData;
ErrorCallback oldCbk = redirectError(quiet_error, 0, &oldCbkData); // FIXIT not thread safe
#endif
try
{
isFound = boxFinder.findHoles();
}
catch (const cv::Exception &)
{
}
#if BE_QUIET
redirectError(oldCbk, oldCbkData);
#endif
if (isFound)
{
switch(parameters.gridType)
centers.clear();
CirclesGridFinder boxFinder(patternSize, points, parameters);
try
{
case CirclesGridFinderParameters::SYMMETRIC_GRID:
boxFinder.getHoles(centers);
break;
case CirclesGridFinderParameters::ASYMMETRIC_GRID:
boxFinder.getAsymmetricHoles(centers);
break;
default:
CV_Error(Error::StsBadArg, "Unknown pattern type");
bool isFound = boxFinder.findHoles();
if (isFound)
{
switch(parameters.gridType)
{
case CirclesGridFinderParameters::SYMMETRIC_GRID:
boxFinder.getHoles(centers);
break;
case CirclesGridFinderParameters::ASYMMETRIC_GRID:
boxFinder.getAsymmetricHoles(centers);
break;
default:
CV_Error(Error::StsBadArg, "Unknown pattern type");
}
isValid = true;
break; // done, return result
}
}
catch (const cv::Exception& e)
{
CV_UNUSED(e);
CV_LOG_DEBUG(NULL, "findCirclesGrid2: attempt=" << i << ": " << e.what());
// nothing, next attempt
}
if (i != 0)
boxFinder.getHoles(centers);
if (i != attempts - 1)
{
Mat orgPointsMat;
transform(centers, orgPointsMat, H.inv());
convertPointsFromHomogeneous(orgPointsMat, centers);
if (centers.size() < minHomographyPoints)
break;
H = CirclesGridFinder::rectifyGrid(boxFinder.getDetectedGridSize(), centers, points, points);
}
Mat(centers).copyTo(_centers);
return true;
}
boxFinder.getHoles(centers);
if (i != attempts - 1)
{
if (centers.size() < minHomographyPoints)
break;
H = CirclesGridFinder::rectifyGrid(boxFinder.getDetectedGridSize(), centers, points, points);
}
}
if (!H.empty()) // undone rectification
{
Mat orgPointsMat;
transform(centers, orgPointsMat, H.inv());
convertPointsFromHomogeneous(orgPointsMat, centers);
}
Mat(centers).copyTo(_centers);
return false;
return isValid;
}
bool findCirclesGrid(InputArray _image, Size patternSize,

@ -1614,7 +1614,7 @@ size_t CirclesGridFinder::getFirstCorner(std::vector<Point> &largeCornerIndices,
int cornerIdx = 0;
bool waitOutsider = true;
for(;;)
for (size_t i = 0; i < cornersCount * 2; ++i)
{
if (waitOutsider)
{
@ -1624,11 +1624,11 @@ size_t CirclesGridFinder::getFirstCorner(std::vector<Point> &largeCornerIndices,
else
{
if (isInsider[(cornerIdx + 1) % cornersCount])
break;
return cornerIdx;
}
cornerIdx = (cornerIdx + 1) % cornersCount;
}
return cornerIdx;
CV_Error(Error::StsNoConv, "isInsider array has the same values");
}

@ -656,5 +656,59 @@ TEST(Calib3d_CirclesPatternDetectorWithClustering, accuracy)
ASSERT_LE(error, precise_success_error_level);
}
TEST(Calib3d_AsymmetricCirclesPatternDetector, regression_18713)
{
float pts_[][2] = {
{ 166.5, 107 }, { 146, 236 }, { 147, 92 }, { 184, 162 }, { 150, 185.5 },
{ 215, 105 }, { 270.5, 186 }, { 159, 142 }, { 6, 205.5 }, { 32, 148.5 },
{ 126, 163.5 }, { 181, 208.5 }, { 240.5, 62 }, { 84.5, 76.5 }, { 190, 120.5 },
{ 10, 189 }, { 266, 104 }, { 307.5, 207.5 }, { 97, 184 }, { 116.5, 210 },
{ 114, 139 }, { 84.5, 233 }, { 269.5, 139 }, { 136, 126.5 }, { 120, 107.5 },
{ 129.5, 65.5 }, { 212.5, 140.5 }, { 204.5, 60.5 }, { 207.5, 241 }, { 61.5, 94.5 },
{ 186.5, 61.5 }, { 220, 63 }, { 239, 120.5 }, { 212, 186 }, { 284, 87.5 },
{ 62, 114.5 }, { 283, 61.5 }, { 238.5, 88.5 }, { 243, 159 }, { 245, 208 },
{ 298.5, 158.5 }, { 57, 129 }, { 156.5, 63.5 }, { 192, 90.5 }, { 281, 235.5 },
{ 172, 62.5 }, { 291.5, 119.5 }, { 90, 127 }, { 68.5, 166.5 }, { 108.5, 83.5 },
{ 22, 176 }
};
Mat candidates(51, 1, CV_32FC2, (void*)pts_);
Size patternSize(4, 9);
std::vector< Point2f > result;
bool res = false;
// issue reports about hangs
EXPECT_NO_THROW(res = findCirclesGrid(candidates, patternSize, result, CALIB_CB_ASYMMETRIC_GRID, Ptr<FeatureDetector>()/*blobDetector=NULL*/));
EXPECT_FALSE(res);
if (cvtest::debugLevel > 0)
{
std::cout << Mat(candidates) << std::endl;
std::cout << Mat(result) << std::endl;
Mat img(Size(400, 300), CV_8UC3, Scalar::all(0));
std::vector< Point2f > centers;
candidates.copyTo(centers);
for (size_t i = 0; i < centers.size(); i++)
{
const Point2f& pt = centers[i];
//printf("{ %g, %g }, \n", pt.x, pt.y);
circle(img, pt, 5, Scalar(0, 255, 0));
}
for (size_t i = 0; i < result.size(); i++)
{
const Point2f& pt = result[i];
circle(img, pt, 10, Scalar(0, 0, 255));
}
imwrite("test_18713.png", img);
if (cvtest::debugLevel >= 10)
{
imshow("result", img);
waitKey();
}
}
}
}} // namespace
/* End of file. */

@ -71,6 +71,14 @@ using std::min;
using namespace cv::dnn::ocl4dnn;
#endif
#ifdef HAVE_HALIDE
#if 0 // size_t is not well supported in Halide operations
typedef size_t HALIDE_DIFF_T;
#else
typedef int HALIDE_DIFF_T;
#endif
#endif
#ifdef HAVE_CUDA
#include "../cuda4dnn/primitives/pooling.hpp"
#include "../cuda4dnn/primitives/roi_pooling.hpp"
@ -78,6 +86,7 @@ using namespace cv::dnn::ocl4dnn;
using namespace cv::dnn::cuda4dnn;
#endif
namespace cv
{
namespace dnn
@ -1097,12 +1106,12 @@ public:
Halide::Buffer<float> inputBuffer = halideBuffer(inputs[0]);
const int inWidth = inputBuffer.width();
const int inHeight = inputBuffer.height();
const size_t kernelHeight = kernel_size[0];
const size_t kernelWidth = kernel_size[1];
const size_t strideHeight = strides[0];
const size_t strideWidth = strides[1];
const size_t paddingTop = pads_begin[0];
const size_t paddingLeft = pads_begin[1];
const HALIDE_DIFF_T kernelHeight = (HALIDE_DIFF_T)kernel_size[0];
const HALIDE_DIFF_T kernelWidth = (HALIDE_DIFF_T)kernel_size[1];
const HALIDE_DIFF_T strideHeight = (HALIDE_DIFF_T)strides[0];
const HALIDE_DIFF_T strideWidth = (HALIDE_DIFF_T)strides[1];
const HALIDE_DIFF_T paddingTop = (HALIDE_DIFF_T)pads_begin[0];
const HALIDE_DIFF_T paddingLeft = (HALIDE_DIFF_T)pads_begin[1];
Halide::Var x("x"), y("y"), c("c"), n("n");
Halide::Func top = (name.empty() ? Halide::Func() : Halide::Func(name));
@ -1148,10 +1157,10 @@ public:
Halide::Buffer<float> inputBuffer = halideBuffer(inputs[0]);
const int inW = inputBuffer.width(), inH = inputBuffer.height();
const size_t kernelHeight = kernel_size[0];
const size_t kernelWidth = kernel_size[1];
const size_t strideHeight = strides[0];
const size_t strideWidth = strides[1];
const HALIDE_DIFF_T kernelHeight = (HALIDE_DIFF_T)kernel_size[0];
const HALIDE_DIFF_T kernelWidth = (HALIDE_DIFF_T)kernel_size[1];
const HALIDE_DIFF_T strideHeight = (HALIDE_DIFF_T)strides[0];
const HALIDE_DIFF_T strideWidth = (HALIDE_DIFF_T)strides[1];
if ((inW - kernelWidth) % strideWidth || (inH - kernelHeight) % strideHeight)
{
CV_Error(cv::Error::StsNotImplemented,

@ -124,7 +124,7 @@ public:
Mat& inp = inputs[0];
Mat& out = outputs[0];
if (interpolation == "nearest" || interpolation == "opencv_linear" || (interpolation == "bilinear" && halfPixelCenters))
if ((interpolation == "nearest" && !alignCorners && !halfPixelCenters) || interpolation == "opencv_linear" || (interpolation == "bilinear" && halfPixelCenters))
{
InterpolationFlags mode = interpolation == "nearest" ? INTER_NEAREST : INTER_LINEAR;
for (size_t n = 0; n < inputs[0].size[0]; ++n)
@ -136,6 +136,54 @@ public:
}
}
}
else if (interpolation == "nearest")
{
const int inpHeight = inp.size[2];
const int inpWidth = inp.size[3];
const int inpSpatialSize = inpHeight * inpWidth;
const int outSpatialSize = outHeight * outWidth;
const int numPlanes = inp.size[0] * inp.size[1];
CV_Assert_N(inp.isContinuous(), out.isContinuous());
Mat inpPlanes = inp.reshape(1, numPlanes * inpHeight);
Mat outPlanes = out.reshape(1, numPlanes * outHeight);
float heightOffset = 0.0f;
float widthOffset = 0.0f;
if (halfPixelCenters)
{
heightOffset = 0.5f * scaleHeight;
widthOffset = 0.5f * scaleWidth;
}
for (int y = 0; y < outHeight; ++y)
{
float input_y = y * scaleHeight + heightOffset;
int y0 = halfPixelCenters ? std::floor(input_y) : lroundf(input_y);
y0 = std::min(y0, inpHeight - 1);
const float* inpData_row = inpPlanes.ptr<float>(y0);
for (int x = 0; x < outWidth; ++x)
{
float input_x = x * scaleWidth + widthOffset;
int x0 = halfPixelCenters ? std::floor(input_x) : lroundf(input_x);
x0 = std::min(x0, inpWidth - 1);
float* outData = outPlanes.ptr<float>(y, x);
const float* inpData_row_c = inpData_row;
for (int c = 0; c < numPlanes; ++c)
{
*outData = inpData_row_c[x0];
inpData_row_c += inpSpatialSize;
outData += outSpatialSize;
}
}
}
}
else if (interpolation == "bilinear")
{
const int inpHeight = inp.size[2];

@ -101,6 +101,9 @@ public:
TEST_P(DNNTestNetwork, AlexNet)
{
applyTestTag(CV_TEST_TAG_MEMORY_1GB);
if (backend == DNN_BACKEND_HALIDE) // Realization contains wrong number of Images (1) for realizing pipeline with 2 outputs
applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
processNet("dnn/bvlc_alexnet.caffemodel", "dnn/bvlc_alexnet.prototxt",
Size(227, 227), "prob",
target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_alexnet.yml" :
@ -115,6 +118,9 @@ TEST_P(DNNTestNetwork, ResNet_50)
(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB),
CV_TEST_TAG_DEBUG_LONG
);
if (backend == DNN_BACKEND_HALIDE) // Realization contains wrong number of Images (1) for realizing pipeline with 2 outputs
applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
processNet("dnn/ResNet-50-model.caffemodel", "dnn/ResNet-50-deploy.prototxt",
Size(224, 224), "prob",
target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_resnet_50.yml" :
@ -125,6 +131,9 @@ TEST_P(DNNTestNetwork, ResNet_50)
TEST_P(DNNTestNetwork, SqueezeNet_v1_1)
{
if (backend == DNN_BACKEND_HALIDE) // Realization contains wrong number of Images (1) for realizing pipeline with 2 outputs
applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
processNet("dnn/squeezenet_v1.1.caffemodel", "dnn/squeezenet_v1.1.prototxt",
Size(227, 227), "prob",
target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_squeezenet_v1_1.yml" :
@ -136,6 +145,9 @@ TEST_P(DNNTestNetwork, SqueezeNet_v1_1)
TEST_P(DNNTestNetwork, GoogLeNet)
{
applyTestTag(target == DNN_TARGET_CPU ? "" : CV_TEST_TAG_MEMORY_512MB);
if (backend == DNN_BACKEND_HALIDE) // Realization contains wrong number of Images (1) for realizing pipeline with 2 outputs
applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
processNet("dnn/bvlc_googlenet.caffemodel", "dnn/bvlc_googlenet.prototxt",
Size(224, 224), "prob");
expectNoFallbacksFromIE(net);
@ -145,6 +157,9 @@ TEST_P(DNNTestNetwork, GoogLeNet)
TEST_P(DNNTestNetwork, Inception_5h)
{
applyTestTag(CV_TEST_TAG_MEMORY_512MB);
if (backend == DNN_BACKEND_HALIDE) // Realization contains wrong number of Images (1) for realizing pipeline with 2 outputs
applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
double l1 = default_l1, lInf = default_lInf;
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && (target == DNN_TARGET_CPU || target == DNN_TARGET_OPENCL))
{
@ -162,6 +177,9 @@ TEST_P(DNNTestNetwork, Inception_5h)
TEST_P(DNNTestNetwork, ENet)
{
applyTestTag(target == DNN_TARGET_CPU ? "" : CV_TEST_TAG_MEMORY_512MB);
if (backend == DNN_BACKEND_HALIDE) // Realization contains wrong number of Images (1) for realizing pipeline with 2 outputs
applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)

@ -1001,6 +1001,19 @@ TEST_P(Test_TensorFlow_layers, resize_nearest_neighbor)
runTensorFlowNet("keras_upsampling2d");
}
TEST_P(Test_TensorFlow_layers, resize_nearest_neighbor_align_corners)
{
runTensorFlowNet("resize_nearest_neighbor", false, 0.0, 0.0, false, "_align_corners");
}
TEST_P(Test_TensorFlow_layers, resize_nearest_neighbor_half_pixel)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
runTensorFlowNet("resize_nearest_neighbor", false, 0.0, 0.0, false, "_half_pixel");
}
TEST_P(Test_TensorFlow_layers, fused_resize_conv)
{
runTensorFlowNet("fused_resize_conv");

@ -1025,15 +1025,20 @@ void ORB_Impl::detectAndCompute( InputArray _image, InputArray _mask,
Mat imagePyramid, maskPyramid;
UMat uimagePyramid, ulayerInfo;
int level_dy = image.rows + border*2;
Point level_ofs(0,0);
Size bufSize((cvRound(image.cols/getScale(0, firstLevel, scaleFactor)) + border*2 + 15) & -16, 0);
float level0_inv_scale = 1.0f / getScale(0, firstLevel, scaleFactor);
size_t level0_width = (size_t)cvRound(image.cols * level0_inv_scale);
size_t level0_height = (size_t)cvRound(image.rows * level0_inv_scale);
Size bufSize((int)alignSize(level0_width + border*2, 16), 0); // TODO change alignment to 64
int level_dy = (int)level0_height + border*2;
Point level_ofs(0, 0);
for( level = 0; level < nLevels; level++ )
{
float scale = getScale(level, firstLevel, scaleFactor);
layerScale[level] = scale;
Size sz(cvRound(image.cols/scale), cvRound(image.rows/scale));
float inv_scale = 1.0f / scale;
Size sz(cvRound(image.cols * inv_scale), cvRound(image.rows * inv_scale));
Size wholeSize(sz.width + border*2, sz.height + border*2);
if( level_ofs.x + wholeSize.width > bufSize.width )
{

@ -90,7 +90,7 @@ TEST(Features2D_ORB, _1996)
ASSERT_EQ(0, roiViolations);
}
TEST(Features2D_ORB, crash)
TEST(Features2D_ORB, crash_5031)
{
cv::Mat image = cv::Mat::zeros(cv::Size(1920, 1080), CV_8UC3);
@ -123,4 +123,23 @@ TEST(Features2D_ORB, crash)
ASSERT_NO_THROW(orb->compute(image, keypoints, descriptors));
}
TEST(Features2D_ORB, regression_16197)
{
Mat img(Size(72, 72), CV_8UC1, Scalar::all(0));
Ptr<ORB> orbPtr = ORB::create();
orbPtr->setNLevels(5);
orbPtr->setFirstLevel(3);
orbPtr->setScaleFactor(1.8);
orbPtr->setPatchSize(8);
orbPtr->setEdgeThreshold(8);
std::vector<KeyPoint> kps;
Mat fv;
// exception in debug mode, crash in release
ASSERT_NO_THROW(orbPtr->detectAndCompute(img, noArray(), kps, fv));
}
}} // namespace

@ -1344,8 +1344,8 @@ public class ImgprocTest extends OpenCVTestCase {
RotatedRect rrect = Imgproc.minAreaRect(points);
assertEquals(new Size(2, 5), rrect.size);
assertEquals(-90., rrect.angle);
assertEquals(new Size(5, 2), rrect.size);
assertEquals(0., rrect.angle);
assertEquals(new Point(3.5, 2), rrect.center);
}

@ -352,7 +352,7 @@ cv::RotatedRect cv::minAreaRect( InputArray _points )
Point2f out[3];
RotatedRect box;
convexHull(_points, hull, true, true);
convexHull(_points, hull, false, true);
if( hull.depth() != CV_32F )
{

@ -1197,6 +1197,78 @@ void hlineSmoothONa_yzy_a<uint8_t, ufixedpoint16>(const uint8_t* src, int cn, co
}
}
}
template <>
void hlineSmoothONa_yzy_a<uint16_t, ufixedpoint32>(const uint16_t* src, int cn, const ufixedpoint32* m, int n, ufixedpoint32* dst, int len, int borderType)
{
int pre_shift = n / 2;
int post_shift = n - pre_shift;
int i = 0;
for (; i < min(pre_shift, len); i++, dst += cn) // Points that fall left from border
{
for (int k = 0; k < cn; k++)
dst[k] = m[pre_shift - i] * src[k];
if (borderType != BORDER_CONSTANT)// If BORDER_CONSTANT out of border values are equal to zero and could be skipped
for (int j = i - pre_shift, mid = 0; j < 0; j++, mid++)
{
int src_idx = borderInterpolate(j, len, borderType);
for (int k = 0; k < cn; k++)
dst[k] = dst[k] + m[mid] * src[src_idx*cn + k];
}
int j, mid;
for (j = 1, mid = pre_shift - i + 1; j < min(i + post_shift, len); j++, mid++)
for (int k = 0; k < cn; k++)
dst[k] = dst[k] + m[mid] * src[j*cn + k];
if (borderType != BORDER_CONSTANT)
for (; j < i + post_shift; j++, mid++)
{
int src_idx = borderInterpolate(j, len, borderType);
for (int k = 0; k < cn; k++)
dst[k] = dst[k] + m[mid] * src[src_idx*cn + k];
}
}
i *= cn;
int lencn = (len - post_shift + 1)*cn;
#if CV_SIMD
const int VECSZ = v_uint32::nlanes;
for (; i <= lencn - VECSZ * 2; i += VECSZ * 2, src += VECSZ * 2, dst += VECSZ * 2)
{
v_uint32 v_res0, v_res1;
v_mul_expand(vx_load(src + pre_shift * cn), vx_setall_u16((uint16_t) *((uint32_t*)(m + pre_shift))), v_res0, v_res1);
for (int j = 0; j < pre_shift; j ++)
{
v_uint32 v_add0, v_add1;
v_mul_expand(vx_load(src + j * cn) + vx_load(src + (n - 1 - j)*cn), vx_setall_u16((uint16_t) *((uint32_t*)(m + j))), v_add0, v_add1);
v_res0 += v_add0;
v_res1 += v_add1;
}
v_store((uint32_t*)dst, v_res0);
v_store((uint32_t*)dst + VECSZ, v_res1);
}
#endif
for (; i < lencn; i++, src++, dst++)
{
*dst = m[pre_shift] * src[pre_shift*cn];
for (int j = 0; j < pre_shift; j++)
*dst = *dst + m[j] * src[j*cn] + m[j] * src[(n - 1 - j)*cn];
}
i /= cn;
for (i -= pre_shift; i < len - pre_shift; i++, src += cn, dst += cn) // Points that fall right from border
{
for (int k = 0; k < cn; k++)
dst[k] = m[0] * src[k];
int j = 1;
for (; j < len - i; j++)
for (int k = 0; k < cn; k++)
dst[k] = dst[k] + m[j] * src[j*cn + k];
if (borderType != BORDER_CONSTANT)// If BORDER_CONSTANT out of border values are equal to zero and could be skipped
for (; j < n; j++)
{
int src_idx = borderInterpolate(i + j, len, borderType) - i;
for (int k = 0; k < cn; k++)
dst[k] = dst[k] + m[j] * src[src_idx*cn + k];
}
}
}
template <typename ET, typename FT>
void vlineSmooth1N(const FT* const * src, const FT* m, int, ET* dst, int len)
{
@ -1788,6 +1860,62 @@ void vlineSmoothONa_yzy_a<uint8_t, ufixedpoint16>(const ufixedpoint16* const * s
dst[i] = val;
}
}
template <>
void vlineSmoothONa_yzy_a<uint16_t, ufixedpoint32>(const ufixedpoint32* const * src, const ufixedpoint32* m, int n, uint16_t* dst, int len)
{
int i = 0;
#if CV_SIMD
int pre_shift = n / 2;
const int VECSZ = v_uint32::nlanes;
for (; i <= len - 2*VECSZ; i += 2*VECSZ)
{
v_uint32 v_src00, v_src10, v_src01, v_src11;
v_uint64 v_res0, v_res1, v_res2, v_res3;
v_uint64 v_tmp0, v_tmp1, v_tmp2, v_tmp3, v_tmp4, v_tmp5, v_tmp6, v_tmp7;
v_uint32 v_mul = vx_setall_u32(*((uint32_t*)(m + pre_shift)));
const uint32_t* srcp = (const uint32_t*)src[pre_shift] + i;
v_src00 = vx_load(srcp);
v_src10 = vx_load(srcp + VECSZ);
v_mul_expand(v_src00, v_mul, v_res0, v_res1);
v_mul_expand(v_src10, v_mul, v_res2, v_res3);
int j = 0;
for (; j < pre_shift; j++)
{
v_mul = vx_setall_u32(*((uint32_t*)(m + j)));
const uint32_t* srcj0 = (const uint32_t*)src[j] + i;
const uint32_t* srcj1 = (const uint32_t*)src[n - 1 - j] + i;
v_src00 = vx_load(srcj0);
v_src01 = vx_load(srcj1);
v_mul_expand(v_src00, v_mul, v_tmp0, v_tmp1);
v_mul_expand(v_src01, v_mul, v_tmp2, v_tmp3);
v_res0 += v_tmp0 + v_tmp2;
v_res1 += v_tmp1 + v_tmp3;
v_src10 = vx_load(srcj0 + VECSZ);
v_src11 = vx_load(srcj1 + VECSZ);
v_mul_expand(v_src10, v_mul, v_tmp4, v_tmp5);
v_mul_expand(v_src11, v_mul, v_tmp6, v_tmp7);
v_res2 += v_tmp4 + v_tmp6;
v_res3 += v_tmp5 + v_tmp7;
}
v_store(dst + i, v_pack(v_rshr_pack<32>(v_res0, v_res1),
v_rshr_pack<32>(v_res2, v_res3)));
}
#endif
for (; i < len; i++)
{
ufixedpoint64 val = m[0] * src[0][i];
for (int j = 1; j < n; j++)
{
val = val + m[j] * src[j][i];
}
dst[i] = (uint16_t)val;
}
}
template <typename ET, typename FT>
class fixedSmoothInvoker : public ParallelLoopBody
{

@ -2306,5 +2306,83 @@ TEST(Imgproc_ConvexHull, overflow)
ASSERT_EQ(hull, hullf);
}
static
bool checkMinAreaRect(const RotatedRect& rr, const Mat& c, double eps = 0.5f)
{
int N = c.rows;
Mat rr_pts;
boxPoints(rr, rr_pts);
double maxError = 0.0;
int nfailed = 0;
for (int i = 0; i < N; i++)
{
double d = pointPolygonTest(rr_pts, c.at<Point2f>(i), true);
maxError = std::max(-d, maxError);
if (d < -eps)
nfailed++;
}
if (nfailed)
std::cout << "nfailed=" << nfailed << " (total=" << N << ") maxError=" << maxError << std::endl;
return nfailed == 0;
}
TEST(Imgproc_minAreaRect, reproducer_18157)
{
const int N = 168;
float pts_[N][2] = {
{ 1903, 266 }, { 1897, 267 }, { 1893, 268 }, { 1890, 269 },
{ 1878, 275 }, { 1875, 277 }, { 1872, 279 }, { 1868, 282 },
{ 1862, 287 }, { 1750, 400 }, { 1748, 402 }, { 1742, 407 },
{ 1742, 408 }, { 1740, 410 }, { 1738, 412 }, { 1593, 558 },
{ 1590, 560 }, { 1588, 562 }, { 1586, 564 }, { 1580, 570 },
{ 1443, 709 }, { 1437, 714 }, { 1435, 716 }, { 1304, 848 },
{ 1302, 850 }, { 1292, 860 }, { 1175, 979 }, { 1172, 981 },
{ 1049, 1105 }, { 936, 1220 }, { 933, 1222 }, { 931, 1224 },
{ 830, 1326 }, { 774, 1383 }, { 769, 1389 }, { 766, 1393 },
{ 764, 1396 }, { 762, 1399 }, { 760, 1402 }, { 757, 1408 },
{ 757, 1410 }, { 755, 1413 }, { 754, 1416 }, { 753, 1420 },
{ 752, 1424 }, { 752, 1442 }, { 753, 1447 }, { 754, 1451 },
{ 755, 1454 }, { 757, 1457 }, { 757, 1459 }, { 761, 1467 },
{ 763, 1470 }, { 765, 1473 }, { 767, 1476 }, { 771, 1481 },
{ 779, 1490 }, { 798, 1510 }, { 843, 1556 }, { 847, 1560 },
{ 851, 1564 }, { 863, 1575 }, { 907, 1620 }, { 909, 1622 },
{ 913, 1626 }, { 1154, 1866 }, { 1156, 1868 }, { 1158, 1870 },
{ 1207, 1918 }, { 1238, 1948 }, { 1252, 1961 }, { 1260, 1968 },
{ 1264, 1971 }, { 1268, 1974 }, { 1271, 1975 }, { 1273, 1977 },
{ 1283, 1982 }, { 1286, 1983 }, { 1289, 1984 }, { 1294, 1985 },
{ 1300, 1986 }, { 1310, 1986 }, { 1316, 1985 }, { 1320, 1984 },
{ 1323, 1983 }, { 1326, 1982 }, { 1338, 1976 }, { 1341, 1974 },
{ 1344, 1972 }, { 1349, 1968 }, { 1358, 1960 }, { 1406, 1911 },
{ 1421, 1897 }, { 1624, 1693 }, { 1788, 1528 }, { 1790, 1526 },
{ 1792, 1524 }, { 1794, 1522 }, { 1796, 1520 }, { 1798, 1518 },
{ 1800, 1516 }, { 1919, 1396 }, { 1921, 1394 }, { 2038, 1275 },
{ 2047, 1267 }, { 2048, 1265 }, { 2145, 1168 }, { 2148, 1165 },
{ 2260, 1052 }, { 2359, 952 }, { 2434, 876 }, { 2446, 863 },
{ 2450, 858 }, { 2453, 854 }, { 2455, 851 }, { 2457, 846 },
{ 2459, 844 }, { 2460, 842 }, { 2460, 840 }, { 2462, 837 },
{ 2463, 834 }, { 2464, 830 }, { 2465, 825 }, { 2465, 809 },
{ 2464, 804 }, { 2463, 800 }, { 2462, 797 }, { 2461, 794 },
{ 2456, 784 }, { 2454, 781 }, { 2452, 778 }, { 2450, 775 },
{ 2446, 770 }, { 2437, 760 }, { 2412, 734 }, { 2410, 732 },
{ 2408, 730 }, { 2382, 704 }, { 2380, 702 }, { 2378, 700 },
{ 2376, 698 }, { 2372, 694 }, { 2370, 692 }, { 2368, 690 },
{ 2366, 688 }, { 2362, 684 }, { 2360, 682 }, { 2252, 576 },
{ 2250, 573 }, { 2168, 492 }, { 2166, 490 }, { 2085, 410 },
{ 2026, 352 }, { 1988, 315 }, { 1968, 296 }, { 1958, 287 },
{ 1953, 283 }, { 1949, 280 }, { 1946, 278 }, { 1943, 276 },
{ 1940, 274 }, { 1936, 272 }, { 1934, 272 }, { 1931, 270 },
{ 1928, 269 }, { 1925, 268 }, { 1921, 267 }, { 1915, 266 }
};
Mat contour(N, 1, CV_32FC2, (void*)pts_);
RotatedRect rr = cv::minAreaRect(contour);
EXPECT_TRUE(checkMinAreaRect(rr, contour)) << rr.center << " " << rr.size << " " << rr.angle;
}
}} // namespace
/* End of file. */

@ -76,7 +76,7 @@ class Hackathon244Tests(NewOpenCVTests):
mc, mr = cv.minEnclosingCircle(a)
be0 = ((150.2511749267578, 150.77322387695312), (158.024658203125, 197.57696533203125), 37.57804489135742)
br0 = ((161.2974090576172, 154.41793823242188), (199.2301483154297, 207.7177734375), -9.164555549621582)
br0 = ((161.2974090576172, 154.41793823242188), (207.7177734375, 199.2301483154297), 80.83544921875)
mc0, mr0 = (160.41790771484375, 144.55152893066406), 136.713500977
self.check_close_boxes(be, be0, 5, 15)

@ -870,6 +870,11 @@ bool GStreamerCapture::open(const String &filename_)
gst_app_sink_set_max_buffers(GST_APP_SINK(sink.get()), 1);
}
if (!manualpipeline)
{
gst_base_sink_set_sync(GST_BASE_SINK(sink.get()), FALSE);
}
//do not emit signals: all calls will be synchronous and blocking
gst_app_sink_set_emit_signals (GST_APP_SINK(sink.get()), FALSE);

@ -0,0 +1,3 @@
*.caffemodel
*.pb
*.weights

@ -19,6 +19,36 @@ Check `-h` option to know which values are used by default:
python object_detection.py opencv_fd -h
```
### Sample models
You can download sample models using ```download_models.py```. For example, the following command will download network weights for OpenCV Face Detector model and store them in FaceDetector folder:
```bash
python download_models.py --save_dir FaceDetector opencv_fd
```
You can use default configuration files adopted for OpenCV from [here](https://github.com/opencv/opencv_extra/tree/master/testdata/dnn).
You also can use the script to download necessary files from your code. Assume you have the following code inside ```your_script.py```:
```python
from download_models import downloadFile
filepath1 = downloadFile("https://drive.google.com/uc?export=download&id=0B3gersZ2cHIxRm5PMWRoTkdHdHc", None, filename="MobileNetSSD_deploy.caffemodel", save_dir="save_dir_1")
filepath2 = downloadFile("https://drive.google.com/uc?export=download&id=0B3gersZ2cHIxRm5PMWRoTkdHdHc", "994d30a8afaa9e754d17d2373b2d62a7dfbaaf7a", filename="MobileNetSSD_deploy.caffemodel")
print(filepath1)
print(filepath2)
# Your code
```
By running the following commands, you will get **MobileNetSSD_deploy.caffemodel** file:
```bash
export OPENCV_DOWNLOAD_DATA_PATH=download_folder
python your_script.py
```
**Note** that you can provide a directory using **save_dir** parameter or via **OPENCV_SAVE_DIR** environment variable.
#### Face detection
[An origin model](https://github.com/opencv/opencv/tree/master/samples/dnn/face_detector)
with single precision floating point weights has been quantized using [TensorFlow framework](https://www.tensorflow.org/).
@ -48,7 +78,7 @@ AR @[ IoU=0.50:0.95 | area= large | maxDets=100 ] | 0.528 | 0.528 |
```
## References
* [Models downloading script](https://github.com/opencv/opencv_extra/blob/master/testdata/dnn/download_models.py)
* [Models downloading script](https://github.com/opencv/opencv/samples/dnn/download_models.py)
* [Configuration files adopted for OpenCV](https://github.com/opencv/opencv_extra/tree/master/testdata/dnn)
* [How to import models from TensorFlow Object Detection API](https://github.com/opencv/opencv/wiki/TensorFlow-Object-Detection-API)
* [Names of classes from different datasets](https://github.com/opencv/opencv/tree/master/samples/data/dnn)

@ -0,0 +1,364 @@
'''
Helper module to download extra data from Internet
'''
from __future__ import print_function
import os
import cv2
import sys
import yaml
import argparse
import tarfile
import platform
import tempfile
import hashlib
import requests
import shutil
from pathlib import Path
from datetime import datetime
if sys.version_info[0] < 3:
from urllib2 import urlopen
else:
from urllib.request import urlopen
import xml.etree.ElementTree as ET
__all__ = ["downloadFile"]
class HashMismatchException(Exception):
def __init__(self, expected, actual):
Exception.__init__(self)
self.expected = expected
self.actual = actual
def __str__(self):
return 'Hash mismatch: expected {} vs actual of {}'.format(self.expected, self.actual)
def getHashsumFromFile(filepath):
sha = hashlib.sha1()
if os.path.exists(filepath):
print(' there is already a file with the same name')
with open(filepath, 'rb') as f:
while True:
buf = f.read(10*1024*1024)
if not buf:
break
sha.update(buf)
hashsum = sha.hexdigest()
return hashsum
def checkHashsum(expected_sha, filepath, silent=True):
print(' expected SHA1: {}'.format(expected_sha))
actual_sha = getHashsumFromFile(filepath)
print(' actual SHA1:{}'.format(actual_sha))
hashes_matched = expected_sha == actual_sha
if not hashes_matched and not silent:
raise HashMismatchException(expected_sha, actual_sha)
return hashes_matched
def isArchive(filepath):
return tarfile.is_tarfile(filepath)
class DownloadInstance:
def __init__(self, **kwargs):
self.name = kwargs.pop('name')
self.filename = kwargs.pop('filename')
self.loader = kwargs.pop('loader', None)
self.save_dir = kwargs.pop('save_dir')
self.sha = kwargs.pop('sha', None)
def __str__(self):
return 'DownloadInstance <{}>'.format(self.name)
def get(self):
print(" Working on " + self.name)
print(" Getting file " + self.filename)
if self.sha is None:
print(' No expected hashsum provided, loading file')
else:
filepath = os.path.join(self.save_dir, self.sha, self.filename)
if checkHashsum(self.sha, filepath):
print(' hash match - file already exists, skipping')
return filepath
else:
print(' hash didn\'t match, loading file')
if not os.path.exists(self.save_dir):
print(' creating directory: ' + self.save_dir)
os.makedirs(self.save_dir)
print(' hash check failed - loading')
assert self.loader
try:
self.loader.load(self.filename, self.sha, self.save_dir)
print(' done')
print(' file {}'.format(self.filename))
if self.sha is None:
download_path = os.path.join(self.save_dir, self.filename)
self.sha = getHashsumFromFile(download_path)
new_dir = os.path.join(self.save_dir, self.sha)
if not os.path.exists(new_dir):
os.makedirs(new_dir)
filepath = os.path.join(new_dir, self.filename)
if not (os.path.exists(filepath)):
shutil.move(download_path, new_dir)
print(' No expected hashsum provided, actual SHA is {}'.format(self.sha))
else:
checkHashsum(self.sha, filepath, silent=False)
except Exception as e:
print(" There was some problem with loading file {} for {}".format(self.filename, self.name))
print(" Exception: {}".format(e))
return
print(" Finished " + self.name)
return filepath
class Loader(object):
MB = 1024*1024
BUFSIZE = 10*MB
def __init__(self, download_name, download_sha, archive_member = None):
self.download_name = download_name
self.download_sha = download_sha
self.archive_member = archive_member
def load(self, requested_file, sha, save_dir):
if self.download_sha is None:
download_dir = save_dir
else:
# create a new folder in save_dir to avoid possible name conflicts
download_dir = os.path.join(save_dir, self.download_sha)
if not os.path.exists(download_dir):
os.makedirs(download_dir)
download_path = os.path.join(download_dir, self.download_name)
print(" Preparing to download file " + self.download_name)
if checkHashsum(self.download_sha, download_path):
print(' hash match - file already exists, no need to download')
else:
filesize = self.download(download_path)
print(' Downloaded {} with size {} Mb'.format(self.download_name, filesize/self.MB))
if self.download_sha is not None:
checkHashsum(self.download_sha, download_path, silent=False)
if self.download_name == requested_file:
return
else:
if isArchive(download_path):
if sha is not None:
extract_dir = os.path.join(save_dir, sha)
else:
extract_dir = save_dir
if not os.path.exists(extract_dir):
os.makedirs(extract_dir)
self.extract(requested_file, download_path, extract_dir)
else:
raise Exception("Downloaded file has different name")
def download(self, filepath):
print("Warning: download is not implemented, this is a base class")
return 0
def extract(self, requested_file, archive_path, save_dir):
filepath = os.path.join(save_dir, requested_file)
try:
with tarfile.open(archive_path) as f:
if self.archive_member is None:
pathDict = dict((os.path.split(elem)[1], os.path.split(elem)[0]) for elem in f.getnames())
self.archive_member = pathDict[requested_file]
assert self.archive_member in f.getnames()
self.save(filepath, f.extractfile(self.archive_member))
except Exception as e:
print(' catch {}'.format(e))
def save(self, filepath, r):
with open(filepath, 'wb') as f:
print(' progress ', end="")
sys.stdout.flush()
while True:
buf = r.read(self.BUFSIZE)
if not buf:
break
f.write(buf)
print('>', end="")
sys.stdout.flush()
class URLLoader(Loader):
def __init__(self, download_name, download_sha, url, archive_member = None):
super(URLLoader, self).__init__(download_name, download_sha, archive_member)
self.download_name = download_name
self.download_sha = download_sha
self.url = url
def download(self, filepath):
r = urlopen(self.url, timeout=60)
self.printRequest(r)
self.save(filepath, r)
return os.path.getsize(filepath)
def printRequest(self, r):
def getMB(r):
d = dict(r.info())
for c in ['content-length', 'Content-Length']:
if c in d:
return int(d[c]) / self.MB
return '<unknown>'
print(' {} {} [{} Mb]'.format(r.getcode(), r.msg, getMB(r)))
class GDriveLoader(Loader):
BUFSIZE = 1024 * 1024
PROGRESS_SIZE = 10 * 1024 * 1024
def __init__(self, download_name, download_sha, gid, archive_member = None):
super(GDriveLoader, self).__init__(download_name, download_sha, archive_member)
self.download_name = download_name
self.download_sha = download_sha
self.gid = gid
def download(self, filepath):
session = requests.Session() # re-use cookies
URL = "https://docs.google.com/uc?export=download"
response = session.get(URL, params = { 'id' : self.gid }, stream = True)
def get_confirm_token(response): # in case of large files
for key, value in response.cookies.items():
if key.startswith('download_warning'):
return value
return None
token = get_confirm_token(response)
if token:
params = { 'id' : self.gid, 'confirm' : token }
response = session.get(URL, params = params, stream = True)
sz = 0
progress_sz = self.PROGRESS_SIZE
with open(filepath, "wb") as f:
for chunk in response.iter_content(self.BUFSIZE):
if not chunk:
continue # keep-alive
f.write(chunk)
sz += len(chunk)
if sz >= progress_sz:
progress_sz += self.PROGRESS_SIZE
print('>', end='')
sys.stdout.flush()
print('')
return sz
def produceDownloadInstance(instance_name, filename, sha, url, save_dir, download_name=None, download_sha=None, archive_member=None):
spec_param = url
loader = URLLoader
if download_name is None:
download_name = filename
if download_sha is None:
download_sha = sha
if "drive.google.com" in url:
token = ""
token_part = url.rsplit('/', 1)[-1]
if "&id=" not in token_part:
token_part = url.rsplit('/', 1)[-2]
for param in token_part.split("&"):
if param.startswith("id="):
token = param[3:]
if token:
loader = GDriveLoader
spec_param = token
else:
print("Warning: possibly wrong Google Drive link")
return DownloadInstance(
name=instance_name,
filename=filename,
sha=sha,
save_dir=save_dir,
loader=loader(download_name, download_sha, spec_param, archive_member)
)
def getSaveDir():
env_path = os.environ.get("OPENCV_DOWNLOAD_DATA_PATH", None)
if env_path:
save_dir = env_path
else:
# TODO reuse binding function cv2.utils.fs.getCacheDirectory when issue #19011 is fixed
if platform.system() == "Darwin":
#On Apple devices
temp_env = os.environ.get("TMPDIR", None)
if temp_env is None or not os.path.isdir(temp_env):
temp_dir = Path("/tmp")
print("Using world accessible cache directory. This may be not secure: ", temp_dir)
else:
temp_dir = temp_env
elif platform.system() == "Windows":
temp_dir = tempfile.gettempdir()
else:
xdg_cache_env = os.environ.get("XDG_CACHE_HOME", None)
if (xdg_cache_env and xdg_cache_env[0] and os.path.isdir(xdg_cache_env)):
temp_dir = xdg_cache_env
else:
home_env = os.environ.get("HOME", None)
if (home_env and home_env[0] and os.path.isdir(home_env)):
home_path = os.path.join(home_env, ".cache/")
if os.path.isdir(home_path):
temp_dir = home_path
else:
temp_dir = tempfile.gettempdir()
print("Using world accessible cache directory. This may be not secure: ", temp_dir)
save_dir = os.path.join(temp_dir, "downloads")
if not os.path.exists(save_dir):
os.makedirs(save_dir)
return save_dir
def downloadFile(url, sha=None, save_dir=None, filename=None):
if save_dir is None:
save_dir = getSaveDir()
if filename is None:
filename = "download_" + datetime.now().__str__()
name = filename
return produceDownloadInstance(name, filename, sha, url, save_dir).get()
def parseMetalinkFile(metalink_filepath, save_dir):
NS = {'ml': 'urn:ietf:params:xml:ns:metalink'}
models = []
for file_elem in ET.parse(metalink_filepath).getroot().findall('ml:file', NS):
url = file_elem.find('ml:url', NS).text
fname = file_elem.attrib['name']
name = file_elem.find('ml:identity', NS).text
hash_sum = file_elem.find('ml:hash', NS).text
models.append(produceDownloadInstance(name, fname, hash_sum, url, save_dir))
return models
def parseYAMLFile(yaml_filepath, save_dir):
models = []
with open(yaml_filepath, 'r') as stream:
data_loaded = yaml.safe_load(stream)
for name, params in data_loaded.items():
load_info = params.get("load_info", None)
if load_info:
fname = os.path.basename(params.get("model"))
hash_sum = load_info.get("sha1")
url = load_info.get("url")
download_sha = load_info.get("download_sha")
download_name = load_info.get("download_name")
archive_member = load_info.get("member")
models.append(produceDownloadInstance(name, fname, hash_sum, url, save_dir,
download_name=download_name, download_sha=download_sha, archive_member=archive_member))
return models
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='This is a utility script for downloading DNN models for samples.')
parser.add_argument('--save_dir', action="store", default=os.getcwd(),
help='Path to the directory to store downloaded files')
parser.add_argument('model_name', type=str, default="", nargs='?', action="store",
help='name of the model to download')
args = parser.parse_args()
models = []
save_dir = args.save_dir
selected_model_name = args.model_name
models.extend(parseMetalinkFile('face_detector/weights.meta4', save_dir))
models.extend(parseYAMLFile('models.yml', save_dir))
for m in models:
print(m)
if selected_model_name and not m.name.startswith(selected_model_name):
continue
print('Model: ' + selected_model_name)
m.get()

@ -1,74 +0,0 @@
#!/usr/bin/env python
from __future__ import print_function
import hashlib
import time
import sys
import xml.etree.ElementTree as ET
if sys.version_info[0] < 3:
from urllib2 import urlopen
else:
from urllib.request import urlopen
class HashMismatchException(Exception):
def __init__(self, expected, actual):
Exception.__init__(self)
self.expected = expected
self.actual = actual
def __str__(self):
return 'Hash mismatch: {} vs {}'.format(self.expected, self.actual)
class MetalinkDownloader(object):
BUFSIZE = 10*1024*1024
NS = {'ml': 'urn:ietf:params:xml:ns:metalink'}
tick = 0
def download(self, metalink_file):
status = True
for file_elem in ET.parse(metalink_file).getroot().findall('ml:file', self.NS):
url = file_elem.find('ml:url', self.NS).text
fname = file_elem.attrib['name']
hash_sum = file_elem.find('ml:hash', self.NS).text
print('*** {}'.format(fname))
try:
self.verify(hash_sum, fname)
except Exception as ex:
print(' {}'.format(ex))
try:
print(' {}'.format(url))
with open(fname, 'wb') as file_stream:
self.buffered_read(urlopen(url), file_stream.write)
self.verify(hash_sum, fname)
except Exception as ex:
print(' {}'.format(ex))
print(' FAILURE')
status = False
continue
print(' SUCCESS')
return status
def print_progress(self, msg, timeout = 0):
if time.time() - self.tick > timeout:
print(msg, end='')
sys.stdout.flush()
self.tick = time.time()
def buffered_read(self, in_stream, processing):
self.print_progress(' >')
while True:
buf = in_stream.read(self.BUFSIZE)
if not buf:
break
processing(buf)
self.print_progress('>', 5)
print(' done')
def verify(self, hash_sum, fname):
sha = hashlib.sha1()
with open(fname, 'rb') as file_stream:
self.buffered_read(file_stream, sha.update)
if hash_sum != sha.hexdigest():
raise HashMismatchException(hash_sum, sha.hexdigest())
if __name__ == '__main__':
sys.exit(0 if MetalinkDownloader().download('weights.meta4') else 1)

@ -1,12 +1,12 @@
<?xml version="1.0" encoding="UTF-8"?>
<metalink xmlns="urn:ietf:params:xml:ns:metalink">
<file name="res10_300x300_ssd_iter_140000_fp16.caffemodel">
<identity>OpenCV face detector FP16 weights</identity>
<identity>opencv_face_detector_fp16</identity>
<hash type="sha-1">31fc22bfdd907567a04bb45b7cfad29966caddc1</hash>
<url>https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodel</url>
</file>
<file name="opencv_face_detector_uint8.pb">
<identity>OpenCV face detector UINT8 weights</identity>
<identity>opencv_face_detector_uint8</identity>
<hash type="sha-1">4f2fdf6f231d759d7bbdb94353c5a68690f3d2ae</hash>
<url>https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180220_uint8/opencv_face_detector_uint8.pb</url>
</file>

@ -1,11 +1,14 @@
%YAML:1.0
%YAML 1.0
---
################################################################################
# Object detection models.
################################################################################
# OpenCV's face detection network
opencv_fd:
load_info:
url: "https://github.com/opencv/opencv_3rdparty/raw/dnn_samples_face_detector_20170830/res10_300x300_ssd_iter_140000.caffemodel"
sha1: "15aa726b4d46d9f023526d85537db81cbc8dd566"
model: "opencv_face_detector.caffemodel"
config: "opencv_face_detector.prototxt"
mean: [104, 177, 123]
@ -19,6 +22,9 @@ opencv_fd:
# YOLO object detection family from Darknet (https://pjreddie.com/darknet/yolo/)
# Might be used for all YOLOv2, TinyYolov2, YOLOv3, YOLOv4 and TinyYolov4
yolo:
load_info:
url: "https://pjreddie.com/media/files/yolov3.weights"
sha1: "520878f12e97cf820529daea502acca380f1cb8e"
model: "yolov3.weights"
config: "yolov3.cfg"
mean: [0, 0, 0]
@ -30,6 +36,9 @@ yolo:
sample: "object_detection"
tiny-yolo-voc:
load_info:
url: "https://pjreddie.com/media/files/yolov2-tiny-voc.weights"
sha1: "24b4bd049fc4fa5f5e95f684a8967e65c625dff9"
model: "tiny-yolo-voc.weights"
config: "tiny-yolo-voc.cfg"
mean: [0, 0, 0]
@ -42,6 +51,9 @@ tiny-yolo-voc:
# Caffe implementation of SSD model from https://github.com/chuanqi305/MobileNet-SSD
ssd_caffe:
load_info:
url: "https://drive.google.com/uc?export=download&id=0B3gersZ2cHIxRm5PMWRoTkdHdHc"
sha1: "994d30a8afaa9e754d17d2373b2d62a7dfbaaf7a"
model: "MobileNetSSD_deploy.caffemodel"
config: "MobileNetSSD_deploy.prototxt"
mean: [127.5, 127.5, 127.5]
@ -54,6 +66,12 @@ ssd_caffe:
# TensorFlow implementation of SSD model from https://github.com/tensorflow/models/tree/master/research/object_detection
ssd_tf:
load_info:
url: "http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_2017_11_17.tar.gz"
sha1: "9e4bcdd98f4c6572747679e4ce570de4f03a70e2"
download_sha: "6157ddb6da55db2da89dd561eceb7f944928e317"
download_name: "ssd_mobilenet_v1_coco_2017_11_17.tar.gz"
member: "ssd_mobilenet_v1_coco_2017_11_17/frozen_inference_graph.pb"
model: "ssd_mobilenet_v1_coco_2017_11_17.pb"
config: "ssd_mobilenet_v1_coco_2017_11_17.pbtxt"
mean: [0, 0, 0]
@ -66,6 +84,12 @@ ssd_tf:
# TensorFlow implementation of Faster-RCNN model from https://github.com/tensorflow/models/tree/master/research/object_detection
faster_rcnn_tf:
load_info:
url: "http://download.tensorflow.org/models/object_detection/faster_rcnn_inception_v2_coco_2018_01_28.tar.gz"
sha1: "f2e4bf386b9bb3e25ddfcbbd382c20f417e444f3"
download_sha: "c710f25e5c6a3ce85fe793d5bf266d581ab1c230"
download_name: "faster_rcnn_inception_v2_coco_2018_01_28.tar.gz"
member: "faster_rcnn_inception_v2_coco_2018_01_28/frozen_inference_graph.pb"
model: "faster_rcnn_inception_v2_coco_2018_01_28.pb"
config: "faster_rcnn_inception_v2_coco_2018_01_28.pbtxt"
mean: [0, 0, 0]
@ -81,6 +105,9 @@ faster_rcnn_tf:
# SqueezeNet v1.1 from https://github.com/DeepScale/SqueezeNet
squeezenet:
load_info:
url: "https://raw.githubusercontent.com/DeepScale/SqueezeNet/b5c3f1a23713c8b3fd7b801d229f6b04c64374a5/SqueezeNet_v1.1/squeezenet_v1.1.caffemodel"
sha1: "3397f026368a45ae236403ccc81cfcbe8ebe1bd0"
model: "squeezenet_v1.1.caffemodel"
config: "squeezenet_v1.1.prototxt"
mean: [0, 0, 0]
@ -93,6 +120,9 @@ squeezenet:
# Googlenet from https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet
googlenet:
load_info:
url: "http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel"
sha1: "405fc5acd08a3bb12de8ee5e23a96bec22f08204"
model: "bvlc_googlenet.caffemodel"
config: "bvlc_googlenet.prototxt"
mean: [104, 117, 123]
@ -110,6 +140,9 @@ googlenet:
# ENet road scene segmentation network from https://github.com/e-lab/ENet-training
# Works fine for different input sizes.
enet:
load_info:
url: "https://www.dropbox.com/s/tdde0mawbi5dugq/Enet-model-best.net?dl=1"
sha1: "b4123a73bf464b9ebe9cfc4ab9c2d5c72b161315"
model: "Enet-model-best.net"
mean: [0, 0, 0]
scale: 0.00392
@ -120,6 +153,9 @@ enet:
sample: "segmentation"
fcn8s:
load_info:
url: "http://dl.caffe.berkeleyvision.org/fcn8s-heavy-pascal.caffemodel"
sha1: "c449ea74dd7d83751d1357d6a8c323fcf4038962"
model: "fcn8s-heavy-pascal.caffemodel"
config: "fcn8s-heavy-pascal.prototxt"
mean: [0, 0, 0]

Loading…
Cancel
Save