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

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pull/21051/head
Alexander Alekhin 3 years ago
parent b48eb4e88b 8041ab8a61
commit 394e640909
9 changed files with 139 additions and 50 deletions
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  1. 18
      modules/core/src/system.cpp
  2. 25
      modules/dnn/src/opencl/ocl4dnn_lrn.cl
  3. 77
      modules/dnn/src/tensorflow/tf_importer.cpp
  4. 2
      modules/dnn/test/test_caffe_importer.cpp
  5. 13
      modules/dnn/test/test_tf_importer.cpp
  6. 10
      modules/imgproc/include/opencv2/imgproc.hpp
  7. 4
      modules/imgproc/src/hough.cpp
  8. 30
      modules/imgproc/test/test_houghlines.cpp
  9. 10
      samples/cpp/falsecolor.cpp

18
modules/core/src/system.cpp
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@ -157,6 +157,9 @@ void* allocSingletonNewBuffer(size_t size) { return malloc(size); }
# ifndef PPC_FEATURE2_ARCH_3_00
# define PPC_FEATURE2_ARCH_3_00 0x00800000
# endif
# ifndef PPC_FEATURE_HAS_VSX
# define PPC_FEATURE_HAS_VSX 0x00000080
# endif
#endif
#if defined _WIN32 || defined WINCE
@ -616,7 +619,7 @@ struct HWFeatures
have[CV_CPU_MSA] = true;
#endif
#if (defined __ppc64__ || defined __PPC64__) && defined __unix__
#if (defined __ppc64__ || defined __PPC64__) && defined __linux__
unsigned int hwcap = getauxval(AT_HWCAP);
if (hwcap & PPC_FEATURE_HAS_VSX) {
hwcap = getauxval(AT_HWCAP2);
@ -626,8 +629,19 @@ struct HWFeatures
have[CV_CPU_VSX] = (hwcap & PPC_FEATURE2_ARCH_2_07) != 0;
}
}
#elif (defined __ppc64__ || defined __PPC64__) && defined __FreeBSD__
unsigned int hwcap = 0;
elf_aux_info(AT_HWCAP, &hwcap, sizeof(hwcap));
if (hwcap & PPC_FEATURE_HAS_VSX) {
elf_aux_info(AT_HWCAP2, &hwcap, sizeof(hwcap));
if (hwcap & PPC_FEATURE2_ARCH_3_00) {
have[CV_CPU_VSX] = have[CV_CPU_VSX3] = true;
} else {
have[CV_CPU_VSX] = (hwcap & PPC_FEATURE2_ARCH_2_07) != 0;
}
}
#else
// TODO: AIX, FreeBSD
// TODO: AIX, OpenBSD
#if CV_VSX || defined _ARCH_PWR8 || defined __POWER9_VECTOR__
have[CV_CPU_VSX] = true;
#endif

25
modules/dnn/src/opencl/ocl4dnn_lrn.cl
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@ -64,36 +64,37 @@ __kernel void TEMPLATE(lrn_full_no_scale,Dtype)(const int nthreads, __global con
const int step = height * width;
__global const Dtype* in_off = in + offset;
__global Dtype* out_off = out + offset;
KERNEL_ARG_DTYPE scale_val;
int head = 0;
const int pre_pad = (size - 1) / 2;
const int post_pad = size - pre_pad - 1;
KERNEL_ARG_DTYPE accum_scale = 0;
float accum_scale = 0;
// fill the scale at [n, :, h, w]
// accumulate values
while (head < post_pad && head < channels) {
accum_scale += in_off[head * step] * in_off[head * step];
float v = in_off[head * step];
accum_scale += v * v;
++head;
}
// both add and subtract
while (head < channels) {
accum_scale += in_off[head * step] * in_off[head * step];
float v = in_off[head * step];
accum_scale += v * v;
if (head - size >= 0) {
accum_scale -= in_off[(head - size) * step]
* in_off[(head - size) * step];
v = in_off[(head - size) * step];
accum_scale -= v * v;
}
scale_val = k + accum_scale * alpha_over_size;
out_off[(head - post_pad) * step] = in_off[(head - post_pad) * step] * (Dtype)native_powr(scale_val, negative_beta);
float scale_val = k + accum_scale * alpha_over_size;
out_off[(head - post_pad) * step] = (Dtype)((float)in_off[(head - post_pad) * step] * native_powr(scale_val, negative_beta));
++head;
}
// subtract only
while (head < channels + post_pad) {
if (head - size >= 0) {
accum_scale -= in_off[(head - size) * step]
* in_off[(head - size) * step];
float v = in_off[(head - size) * step];
accum_scale -= v * v;
}
scale_val = k + accum_scale * alpha_over_size;
out_off[(head - post_pad) * step] = in_off[(head - post_pad) * step] * (Dtype)native_powr(scale_val, negative_beta);
float scale_val = k + accum_scale * alpha_over_size;
out_off[(head - post_pad) * step] = (Dtype)((float)in_off[(head - post_pad) * step] * native_powr(scale_val, negative_beta));
++head;
}
}

77
modules/dnn/src/tensorflow/tf_importer.cpp
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@ -2289,6 +2289,7 @@ void TFImporter::parseMean(tensorflow::GraphDef& net, const tensorflow::NodeDef&
const std::string& type = layer.op();
const int num_inputs = layer.input_size();
std::string pool_type = cv::toLowerCase(type);
DataLayout layout = getDataLayout(name, data_layouts);
if (pool_type == "mean")
{
@ -2352,6 +2353,16 @@ void TFImporter::parseMean(tensorflow::GraphDef& net, const tensorflow::NodeDef&
if (!keepDims)
{
if (layout == DATA_LAYOUT_NHWC)
{
LayerParams permLP;
int order[] = {0, 2, 3, 1}; // From OpenCV's NCHW to NHWC.
std::string permName = name + "/nhwc";
Pin inpId = Pin(layerShapeName);
addPermuteLayer(order, permName, inpId);
layerShapeName = permName;
}
LayerParams squeezeLp;
std::string squeezeName = name + "/squeeze";
CV_Assert(layer_id.find(squeezeName) == layer_id.end());
@ -2374,22 +2385,30 @@ void TFImporter::parseMean(tensorflow::GraphDef& net, const tensorflow::NodeDef&
layerParams.set("pool", pool_type);
layerParams.set(axis == 2 ? "kernel_w" : "kernel_h", 1);
layerParams.set(axis == 2 ? "global_pooling_h" : "global_pooling_w", true);
int id = dstNet.addLayer(name, "Pooling", layerParams);
layer_id[name] = id;
connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);
if (!keepDims)
if (keepDims)
{
int id = dstNet.addLayer(name, "Pooling", layerParams);
layer_id[name] = id;
connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);
}
else
{
// To keep correct order after squeeze dims we first need to change layout from NCHW to NHWC
std::string poolingName = name + "/Pooling";
CV_Assert(layer_id.find(poolingName) == layer_id.end());
int id = dstNet.addLayer(poolingName, "Pooling", layerParams);
layer_id[poolingName] = id;
connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);
LayerParams permLP;
int order[] = {0, 2, 3, 1}; // From OpenCV's NCHW to NHWC.
std::string permName = name + "/nchw";
Pin inpId = Pin(name);
std::string permName = name + "/nhwc";
Pin inpId = Pin(poolingName);
addPermuteLayer(order, permName, inpId);
LayerParams squeezeLp;
std::string squeezeName = name + "/squeeze";
CV_Assert(layer_id.find(squeezeName) == layer_id.end());
const std::string& squeezeName = name;
squeezeLp.set("axis", indices.at<int>(0));
squeezeLp.set("end_axis", indices.at<int>(0) + 1);
int squeezeId = dstNet.addLayer(squeezeName, "Flatten", squeezeLp);
@ -2401,32 +2420,34 @@ void TFImporter::parseMean(tensorflow::GraphDef& net, const tensorflow::NodeDef&
{
int order[] = {0, 2, 3, 1}; // From OpenCV's NCHW to NHWC.
Pin inpId = parsePin(layer.input(0));
addPermuteLayer(order, name + "/nhwc", inpId);
std::string permName = name + "/nhwc";
addPermuteLayer(order, permName, inpId);
layerParams.set("pool", pool_type);
layerParams.set("kernel_h", 1);
layerParams.set("global_pooling_w", true);
int id = dstNet.addLayer(name, "Pooling", layerParams);
layer_id[name] = id;
connect(layer_id, dstNet, inpId, id, 0);
std::string poolingName = name + "/Pooling";
CV_Assert(layer_id.find(poolingName) == layer_id.end());
int id = dstNet.addLayer(poolingName, "Pooling", layerParams);
layer_id[poolingName] = id;
connect(layer_id, dstNet, Pin(permName), id, 0);
if (!keepDims)
{
LayerParams squeezeLp;
std::string squeezeName = name + "/squeeze";
CV_Assert(layer_id.find(squeezeName) == layer_id.end());
const std::string& squeezeName = name;
int channel_id = 3; // TF NHWC layout
squeezeLp.set("axis", channel_id - 1);
squeezeLp.set("end_axis", channel_id);
int squeezeId = dstNet.addLayer(squeezeName, "Flatten", squeezeLp);
layer_id[squeezeName] = squeezeId;
connect(layer_id, dstNet, Pin(name), squeezeId, 0);
connect(layer_id, dstNet, Pin(poolingName), squeezeId, 0);
}
else
{
int order[] = {0, 3, 1, 2}; // From NHWC to OpenCV's NCHW.
Pin inpId = parsePin(name);
addPermuteLayer(order, name + "/nchw", inpId);
Pin inpId = parsePin(poolingName);
addPermuteLayer(order, name, inpId);
}
}
} else {
@ -2435,18 +2456,26 @@ void TFImporter::parseMean(tensorflow::GraphDef& net, const tensorflow::NodeDef&
layerParams.set("pool", pool_type);
layerParams.set("global_pooling", true);
int id = dstNet.addLayer(name, "Pooling", layerParams);
layer_id[name] = id;
connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);
if (!keepDims)
if (keepDims)
{
int id = dstNet.addLayer(name, "Pooling", layerParams);
layer_id[name] = id;
connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);
}
else
{
std::string poolingName = name + "/Pooling";
CV_Assert(layer_id.find(poolingName) == layer_id.end());
int id = dstNet.addLayer(poolingName, "Pooling", layerParams);
layer_id[poolingName] = id;
connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);
LayerParams flattenLp;
std::string flattenName = name + "/flatten";
CV_Assert(layer_id.find(flattenName) == layer_id.end());
const std::string& flattenName = name;
int flattenId = dstNet.addLayer(flattenName, "Flatten", flattenLp);
layer_id[flattenName] = flattenId;
connect(layer_id, dstNet, Pin(name), flattenId, 0);
connect(layer_id, dstNet, Pin(poolingName), flattenId, 0);
data_layouts[name] = DATA_LAYOUT_PLANAR;
}
}
}

2
modules/dnn/test/test_caffe_importer.cpp
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@ -207,7 +207,7 @@ TEST_P(Reproducibility_AlexNet, Accuracy)
ASSERT_EQ(inLayerShapes[0][3], 227);
const float l1 = 1e-5;
const float lInf = (targetId == DNN_TARGET_OPENCL_FP16) ? 3e-3 : 1e-4;
const float lInf = (targetId == DNN_TARGET_OPENCL_FP16) ? 4e-3 : 1e-4;
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableTarget(targetId);

13
modules/dnn/test/test_tf_importer.cpp
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@ -423,6 +423,19 @@ TEST_P(Test_TensorFlow_layers, pooling_reduce_sum)
runTensorFlowNet("reduce_sum"); // a SUM pooling over all spatial dimensions.
}
TEST_P(Test_TensorFlow_layers, pooling_reduce_sum2)
{
int axises[] = {0, 1, 2, 3};
for (int keepdims = 0; keepdims <= 1; ++keepdims)
{
for (int i = 0; i < sizeof(axises)/sizeof(axises[0]); ++i)
{
runTensorFlowNet(cv::format("reduce_sum_%d_%s", axises[i], (keepdims ? "True" : "False")));
}
runTensorFlowNet(cv::format("reduce_sum_1_2_%s", keepdims ? "True" : "False"));
}
}
TEST_P(Test_TensorFlow_layers, max_pool_grad)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)

10
modules/imgproc/include/opencv2/imgproc.hpp
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@ -2157,12 +2157,12 @@ The function finds lines in a set of points using a modification of the Hough tr
@param point Input vector of points. Each vector must be encoded as a Point vector \f$(x,y)\f$. Type must be CV_32FC2 or CV_32SC2.
@param lines Output vector of found lines. Each vector is encoded as a vector<Vec3d> \f$(votes, rho, theta)\f$.
The larger the value of 'votes', the higher the reliability of the Hough line.
@param lines_max Max count of hough lines.
@param lines_max Max count of Hough lines.
@param threshold Accumulator threshold parameter. Only those lines are returned that get enough
votes ( \f$>\texttt{threshold}\f$ )
@param min_rho Minimum Distance value of the accumulator in pixels.
@param max_rho Maximum Distance value of the accumulator in pixels.
@param rho_step Distance resolution of the accumulator in pixels.
votes ( \f$>\texttt{threshold}\f$ ).
@param min_rho Minimum value for \f$\rho\f$ for the accumulator (Note: \f$\rho\f$ can be negative. The absolute value \f$|\rho|\f$ is the distance of a line to the origin.).
@param max_rho Maximum value for \f$\rho\f$ for the accumulator.
@param rho_step Distance resolution of the accumulator.
@param min_theta Minimum angle value of the accumulator in radians.
@param max_theta Maximum angle value of the accumulator in radians.
@param theta_step Angle resolution of the accumulator in radians.

4
modules/imgproc/src/hough.cpp
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@ -975,7 +975,9 @@ void HoughLinesPointSet( InputArray _point, OutputArray _lines, int lines_max, i
for(int n = 0; n < numangle; n++ )
{
int r = cvRound( point.at(i).x * tabCos[n] + point.at(i).y * tabSin[n] - irho_min);
accum[(n+1) * (numrho+2) + r+1]++;
if ( r >= 0 && r <= numrho) {
accum[(n+1) * (numrho+2) + r+1]++;
}
}
// stage 2. find local maximums

30
modules/imgproc/test/test_houghlines.cpp
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@ -299,6 +299,36 @@ TEST_P(HoughLinesPointSetTest, regression)
run_test();
}
TEST(HoughLinesPointSet, regression_21029)
{
std::vector<Point2f> points;
points.push_back(Point2f(100, 100));
points.push_back(Point2f(1000, 1000));
points.push_back(Point2f(10000, 10000));
points.push_back(Point2f(100000, 100000));
double rhoMin = 0;
double rhoMax = 10;
double rhoStep = 0.1;
double thetaMin = 85 * CV_PI / 180.0;
double thetaMax = 95 * CV_PI / 180.0;
double thetaStep = 1 * CV_PI / 180.0;
int lines_max = 5;
int threshold = 100;
Mat lines;
HoughLinesPointSet(points, lines,
lines_max, threshold,
rhoMin, rhoMax, rhoStep,
thetaMin, thetaMax, thetaStep
);
EXPECT_TRUE(lines.empty());
}
INSTANTIATE_TEST_CASE_P( ImgProc, StandartHoughLinesTest, testing::Combine(testing::Values( "shared/pic5.png", "../stitching/a1.png" ),
testing::Values( 1, 10 ),
testing::Values( 0.05, 0.1 ),

10
samples/cpp/falsecolor.cpp
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@ -16,14 +16,14 @@ struct ParamColorMap {
String winName="False color";
static const String ColorMaps[] = { "Autumn", "Bone", "Jet", "Winter", "Rainbow", "Ocean", "Summer", "Spring",
"Cool", "HSV", "Pink", "Hot", "Parula", "Magma", "Inferno", "Plasma", "Viridis",
"Cividis", "Twilight", "Twilight Shifted", "Turbo", "User defined (random)" };
"Cividis", "Twilight", "Twilight Shifted", "Turbo", "Deep Green", "User defined (random)" };
static void TrackColorMap(int x, void *r)
{
ParamColorMap *p = (ParamColorMap*)r;
Mat dst;
p->iColormap= x;
if (x == COLORMAP_TURBO + 1)
if (x == COLORMAP_DEEPGREEN + 1)
{
Mat lutRND(256, 1, CV_8UC3);
randu(lutRND, Scalar(0, 0, 0), Scalar(255, 255, 255));
@ -97,10 +97,10 @@ int main(int argc, char** argv)
imshow("Gray image",img);
namedWindow(winName);
createTrackbar("colormap", winName,&p.iColormap,1,TrackColorMap,(void*)&p);
createTrackbar("colormap", winName, NULL, COLORMAP_DEEPGREEN + 1, TrackColorMap, (void*)&p);
setTrackbarMin("colormap", winName, COLORMAP_AUTUMN);
setTrackbarMax("colormap", winName, COLORMAP_TURBO+1);
setTrackbarPos("colormap", winName, -1);
setTrackbarMax("colormap", winName, COLORMAP_DEEPGREEN + 1);
setTrackbarPos("colormap", winName, COLORMAP_AUTUMN);
TrackColorMap(0, (void*)&p);

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