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

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This commit is contained in:
Alexander Alekhin
2019-10-05 15:45:31 +00:00
parent c54753c185 98fc098216
commit 626bfbf309
23 changed files with 222 additions and 98 deletions
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modules/core/include/opencv2/core/cv_cpu_dispatch.h
+10 -4
View File
@@ -113,12 +113,18 @@
# define CV_AVX_512IFMA 1
# define CV_AVX_512VBMI 1
#endif
#ifdef CV_CPU_COMPILE_AVX512_CEL
# define CV_AVX512_CEL 1
#ifdef CV_CPU_COMPILE_AVX512_CLX
# define CV_AVX512_CLX 1
# define CV_AVX_512VNNI 1
#endif
#ifdef CV_CPU_COMPILE_AVX512_ICL
# define CV_AVX512_ICL 1
# undef CV_AVX_512IFMA
# define CV_AVX_512IFMA 1
# undef CV_AVX_512VBMI
# define CV_AVX_512VBMI 1
# undef CV_AVX_512VNNI
# define CV_AVX_512VNNI 1
# define CV_AVX_512VBMI2 1
# define CV_AVX_512BITALG 1
# define CV_AVX_512VPOPCNTDQ 1
@@ -311,8 +317,8 @@ struct VZeroUpperGuard {
#ifndef CV_AVX512_CNL
# define CV_AVX512_CNL 0
#endif
#ifndef CV_AVX512_CEL
# define CV_AVX512_CEL 0
#ifndef CV_AVX512_CLX
# define CV_AVX512_CLX 0
#endif
#ifndef CV_AVX512_ICL
# define CV_AVX512_ICL 0
modules/core/include/opencv2/core/cv_cpu_helper.h
+18 -18
View File
@@ -357,26 +357,26 @@
#endif
#define __CV_CPU_DISPATCH_CHAIN_AVX512_CNL(fn, args, mode, ...) CV_CPU_CALL_AVX512_CNL(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX512_CEL
# define CV_TRY_AVX512_CEL 1
# define CV_CPU_FORCE_AVX512_CEL 1
# define CV_CPU_HAS_SUPPORT_AVX512_CEL 1
# define CV_CPU_CALL_AVX512_CEL(fn, args) return (cpu_baseline::fn args)
# define CV_CPU_CALL_AVX512_CEL_(fn, args) return (opt_AVX512_CEL::fn args)
#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX512_CEL
# define CV_TRY_AVX512_CEL 1
# define CV_CPU_FORCE_AVX512_CEL 0
# define CV_CPU_HAS_SUPPORT_AVX512_CEL (cv::checkHardwareSupport(CV_CPU_AVX512_CEL))
# define CV_CPU_CALL_AVX512_CEL(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_CEL) return (opt_AVX512_CEL::fn args)
# define CV_CPU_CALL_AVX512_CEL_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_CEL) return (opt_AVX512_CEL::fn args)
#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX512_CLX
# define CV_TRY_AVX512_CLX 1
# define CV_CPU_FORCE_AVX512_CLX 1
# define CV_CPU_HAS_SUPPORT_AVX512_CLX 1
# define CV_CPU_CALL_AVX512_CLX(fn, args) return (cpu_baseline::fn args)
# define CV_CPU_CALL_AVX512_CLX_(fn, args) return (opt_AVX512_CLX::fn args)
#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX512_CLX
# define CV_TRY_AVX512_CLX 1
# define CV_CPU_FORCE_AVX512_CLX 0
# define CV_CPU_HAS_SUPPORT_AVX512_CLX (cv::checkHardwareSupport(CV_CPU_AVX512_CLX))
# define CV_CPU_CALL_AVX512_CLX(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_CLX) return (opt_AVX512_CLX::fn args)
# define CV_CPU_CALL_AVX512_CLX_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_CLX) return (opt_AVX512_CLX::fn args)
#else
# define CV_TRY_AVX512_CEL 0
# define CV_CPU_FORCE_AVX512_CEL 0
# define CV_CPU_HAS_SUPPORT_AVX512_CEL 0
# define CV_CPU_CALL_AVX512_CEL(fn, args)
# define CV_CPU_CALL_AVX512_CEL_(fn, args)
# define CV_TRY_AVX512_CLX 0
# define CV_CPU_FORCE_AVX512_CLX 0
# define CV_CPU_HAS_SUPPORT_AVX512_CLX 0
# define CV_CPU_CALL_AVX512_CLX(fn, args)
# define CV_CPU_CALL_AVX512_CLX_(fn, args)
#endif
#define __CV_CPU_DISPATCH_CHAIN_AVX512_CEL(fn, args, mode, ...) CV_CPU_CALL_AVX512_CEL(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
#define __CV_CPU_DISPATCH_CHAIN_AVX512_CLX(fn, args, mode, ...) CV_CPU_CALL_AVX512_CLX(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX512_ICL
# define CV_TRY_AVX512_ICL 1
modules/core/include/opencv2/core/cvdef.h
+2 -2
View File
@@ -255,7 +255,7 @@ namespace cv { namespace debug_build_guard { } using namespace debug_build_guard
#define CV_CPU_AVX512_KNL 258
#define CV_CPU_AVX512_KNM 259
#define CV_CPU_AVX512_CNL 260
#define CV_CPU_AVX512_CEL 261
#define CV_CPU_AVX512_CLX 261
#define CV_CPU_AVX512_ICL 262
// when adding to this list remember to update the following enum
@@ -306,7 +306,7 @@ enum CpuFeatures {
CPU_AVX512_KNL = 258, //!< Knights Landing with AVX-512F/CD/ER/PF
CPU_AVX512_KNM = 259, //!< Knights Mill with AVX-512F/CD/ER/PF/4FMAPS/4VNNIW/VPOPCNTDQ
CPU_AVX512_CNL = 260, //!< Cannon Lake with AVX-512F/CD/BW/DQ/VL/IFMA/VBMI
CPU_AVX512_CEL = 261, //!< Cascade Lake with AVX-512F/CD/BW/DQ/VL/IFMA/VBMI/VNNI
CPU_AVX512_CLX = 261, //!< Cascade Lake with AVX-512F/CD/BW/DQ/VL/VNNI
CPU_AVX512_ICL = 262, //!< Ice Lake with AVX-512F/CD/BW/DQ/VL/IFMA/VBMI/VNNI/VBMI2/BITALG/VPOPCNTDQ
CPU_MAX_FEATURE = 512 // see CV_HARDWARE_MAX_FEATURE
modules/core/src/softfloat.cpp
+9 -5
View File
@@ -2065,13 +2065,17 @@ static int_fast64_t f64_to_i64(float64_t a, uint_fast8_t roundingMode, bool exac
if (exp) sig |= UINT64_C(0x0010000000000000);
shiftDist = 0x433 - exp;
if (shiftDist <= 0) {
uint_fast64_t z = sig << -shiftDist;
if ((shiftDist < -11) || (z & UINT64_C(0x8000000000000000)))
bool isValid = shiftDist >= -11;
if (isValid)
{
raiseFlags(flag_invalid);
return sign ? i64_fromNegOverflow : i64_fromPosOverflow;
uint_fast64_t z = sig << -shiftDist;
if (0 == (z & UINT64_C(0x8000000000000000)))
{
return sign ? -(int_fast64_t)z : (int_fast64_t)z;
}
}
return sign ? -(int_fast64_t)z : (int_fast64_t)z;
raiseFlags(flag_invalid);
return sign ? i64_fromNegOverflow : i64_fromPosOverflow;
}
else {
if (shiftDist < 64)
modules/core/src/system.cpp
+19 -8
View File
@@ -370,11 +370,12 @@ struct HWFeatures
g_hwFeatureNames[CPU_MSA] = "CPU_MSA";
g_hwFeatureNames[CPU_AVX512_COMMON] = "AVX512-COMMON";
g_hwFeatureNames[CPU_AVX512_SKX] = "AVX512-SKX";
g_hwFeatureNames[CPU_AVX512_KNL] = "AVX512-KNL";
g_hwFeatureNames[CPU_AVX512_KNM] = "AVX512-KNM";
g_hwFeatureNames[CPU_AVX512_CNL] = "AVX512-CNL";
g_hwFeatureNames[CPU_AVX512_CEL] = "AVX512-CEL";
g_hwFeatureNames[CPU_AVX512_CLX] = "AVX512-CLX";
g_hwFeatureNames[CPU_AVX512_ICL] = "AVX512-ICL";
}
@@ -485,9 +486,11 @@ struct HWFeatures
have[CV_CPU_AVX_5124VNNIW] && have[CV_CPU_AVX_512VPOPCNTDQ];
have[CV_CPU_AVX512_SKX] = have[CV_CPU_AVX_512BW] && have[CV_CPU_AVX_512DQ] && have[CV_CPU_AVX_512VL];
have[CV_CPU_AVX512_CNL] = have[CV_CPU_AVX512_SKX] && have[CV_CPU_AVX_512IFMA] && have[CV_CPU_AVX_512VBMI];
have[CV_CPU_AVX512_CEL] = have[CV_CPU_AVX512_CNL] && have[CV_CPU_AVX_512VNNI];
have[CV_CPU_AVX512_ICL] = have[CV_CPU_AVX512_CEL] && have[CV_CPU_AVX_512VBMI2] &&
have[CV_CPU_AVX_512BITALG] && have[CV_CPU_AVX_512VPOPCNTDQ];
have[CV_CPU_AVX512_CLX] = have[CV_CPU_AVX512_SKX] && have[CV_CPU_AVX_512VNNI];
have[CV_CPU_AVX512_ICL] = have[CV_CPU_AVX512_SKX] &&
have[CV_CPU_AVX_512IFMA] && have[CV_CPU_AVX_512VBMI] &&
have[CV_CPU_AVX_512VNNI] &&
have[CV_CPU_AVX_512VBMI2] && have[CV_CPU_AVX_512BITALG] && have[CV_CPU_AVX_512VPOPCNTDQ];
}
else
{
@@ -495,7 +498,7 @@ struct HWFeatures
have[CV_CPU_AVX512_KNM] = false;
have[CV_CPU_AVX512_SKX] = false;
have[CV_CPU_AVX512_CNL] = false;
have[CV_CPU_AVX512_CEL] = false;
have[CV_CPU_AVX512_CLX] = false;
have[CV_CPU_AVX512_ICL] = false;
}
}
@@ -572,8 +575,16 @@ struct HWFeatures
have[CV_CPU_VSX3] = (CV_VSX3);
#endif
bool skip_baseline_check = false;
#ifndef NO_GETENV
if (getenv("OPENCV_SKIP_CPU_BASELINE_CHECK"))
{
skip_baseline_check = true;
}
#endif
int baseline_features[] = { CV_CPU_BASELINE_FEATURES };
if (!checkFeatures(baseline_features, sizeof(baseline_features) / sizeof(baseline_features[0])))
if (!checkFeatures(baseline_features, sizeof(baseline_features) / sizeof(baseline_features[0]))
&& !skip_baseline_check)
{
fprintf(stderr, "\n"
"******************************************************************\n"
@@ -600,12 +611,12 @@ struct HWFeatures
{
if (have[feature])
{
if (dump) fprintf(stderr, "%s - OK\n", getHWFeatureNameSafe(feature));
if (dump) fprintf(stderr, " ID=%3d (%s) - OK\n", feature, getHWFeatureNameSafe(feature));
}
else
{
result = false;
if (dump) fprintf(stderr, "%s - NOT AVAILABLE\n", getHWFeatureNameSafe(feature));
if (dump) fprintf(stderr, " ID=%3d (%s) - NOT AVAILABLE\n", feature, getHWFeatureNameSafe(feature));
}
}
}
modules/dnn/misc/python/test/test_dnn.py
+9
View File
@@ -64,11 +64,19 @@ def printParams(backend, target):
testdata_required = bool(os.environ.get('OPENCV_DNN_TEST_REQUIRE_TESTDATA', False))
g_dnnBackendsAndTargets = None
class dnn_test(NewOpenCVTests):
def setUp(self):
super(dnn_test, self).setUp()
global g_dnnBackendsAndTargets
if g_dnnBackendsAndTargets is None:
g_dnnBackendsAndTargets = self.initBackendsAndTargets()
self.dnnBackendsAndTargets = g_dnnBackendsAndTargets
def initBackendsAndTargets(self):
self.dnnBackendsAndTargets = [
[cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_CPU],
]
@@ -86,6 +94,7 @@ class dnn_test(NewOpenCVTests):
self.dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_OPENCL])
if self.checkIETarget(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_OPENCL_FP16):
self.dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_OPENCL_FP16])
return self.dnnBackendsAndTargets
def find_dnn_file(self, filename, required=True):
if not required:
modules/dnn/src/layers/pooling_layer.cpp
+2
View File
@@ -163,6 +163,8 @@ public:
{
if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
{
if (computeMaxIdx)
return false;
#ifdef HAVE_INF_ENGINE
if (kernel_size.size() == 3)
return preferableTarget == DNN_TARGET_CPU;
modules/dnn/src/op_inf_engine.hpp
+3 -2
View File
@@ -22,10 +22,11 @@
#define INF_ENGINE_RELEASE_2018R5 2018050000
#define INF_ENGINE_RELEASE_2019R1 2019010000
#define INF_ENGINE_RELEASE_2019R2 2019020000
#define INF_ENGINE_RELEASE_2019R3 2019030000
#ifndef INF_ENGINE_RELEASE
#warning("IE version have not been provided via command-line. Using 2019R2 by default")
#define INF_ENGINE_RELEASE INF_ENGINE_RELEASE_2019R2
#warning("IE version have not been provided via command-line. Using 2019R3 by default")
#define INF_ENGINE_RELEASE INF_ENGINE_RELEASE_2019R3
#endif
#define INF_ENGINE_VER_MAJOR_GT(ver) (((INF_ENGINE_RELEASE) / 10000) > ((ver) / 10000))
modules/dnn/test/test_common.hpp
+1
View File
@@ -19,6 +19,7 @@
#define CV_TEST_TAG_DNN_SKIP_IE_2019R1 "dnn_skip_ie_2019r1"
#define CV_TEST_TAG_DNN_SKIP_IE_2019R1_1 "dnn_skip_ie_2019r1_1"
#define CV_TEST_TAG_DNN_SKIP_IE_2019R2 "dnn_skip_ie_2019r2"
#define CV_TEST_TAG_DNN_SKIP_IE_2019R3 "dnn_skip_ie_2019r3"
#define CV_TEST_TAG_DNN_SKIP_IE_OPENCL "dnn_skip_ie_ocl"
#define CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16 "dnn_skip_ie_ocl_fp16"
#define CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_2 "dnn_skip_ie_myriad2"
modules/dnn/test/test_common.impl.hpp
+2
View File
@@ -324,6 +324,8 @@ void initDNNTests()
# endif
#elif INF_ENGINE_VER_MAJOR_EQ(2019020000)
CV_TEST_TAG_DNN_SKIP_IE_2019R2,
#elif INF_ENGINE_VER_MAJOR_EQ(2019030000)
CV_TEST_TAG_DNN_SKIP_IE_2019R3,
#endif
CV_TEST_TAG_DNN_SKIP_IE
);
modules/dnn/test/test_halide_layers.cpp
+2 -2
View File
@@ -554,9 +554,9 @@ TEST_P(ReLU, Accuracy)
Backend backendId = get<0>(get<1>(GetParam()));
Target targetId = get<1>(get<1>(GetParam()));
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019020000)
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019020000)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_MYRIAD && negativeSlope < 0)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE, CV_TEST_TAG_DNN_SKIP_IE_2019R2);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_2019R3, CV_TEST_TAG_DNN_SKIP_IE_2019R2, CV_TEST_TAG_DNN_SKIP_IE);
#endif
LayerParams lp;
modules/dnn/test/test_onnx_importer.cpp
+13 -4
View File
@@ -86,8 +86,8 @@ TEST_P(Test_ONNX_layers, InstanceNorm)
TEST_P(Test_ONNX_layers, MaxPooling)
{
testONNXModels("maxpooling");
testONNXModels("two_maxpooling");
testONNXModels("maxpooling", npy, 0, 0, false, false);
testONNXModels("two_maxpooling", npy, 0, 0, false, false);
}
TEST_P(Test_ONNX_layers, Convolution)
@@ -212,7 +212,7 @@ TEST_P(Test_ONNX_layers, MaxPooling3D)
#endif
if (target != DNN_TARGET_CPU)
throw SkipTestException("Only CPU is supported");
testONNXModels("max_pool3d");
testONNXModels("max_pool3d", npy, 0, 0, false, false);
}
TEST_P(Test_ONNX_layers, AvePooling3D)
@@ -422,13 +422,22 @@ TEST_P(Test_ONNX_nets, Googlenet)
TEST_P(Test_ONNX_nets, CaffeNet)
{
applyTestTag(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019030000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_2019R3);
#endif
testONNXModels("caffenet", pb);
}
TEST_P(Test_ONNX_nets, RCNN_ILSVRC13)
{
applyTestTag(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019030000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_2019R3);
#endif
// Reference output values are in range [-4.992, -1.161]
testONNXModels("rcnn_ilsvrc13", pb, 0.0045);
}
modules/dnn/test/test_tf_importer.cpp
+3 -3
View File
@@ -146,13 +146,13 @@ TEST_P(Test_TensorFlow_layers, padding)
runTensorFlowNet("padding_valid");
runTensorFlowNet("spatial_padding");
runTensorFlowNet("mirror_pad");
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019020000)
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019020000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
{
if (target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_2019R2);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_2019R3, CV_TEST_TAG_DNN_SKIP_IE_2019R2);
if (target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_2019R2);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_2019R3, CV_TEST_TAG_DNN_SKIP_IE_2019R2);
}
#endif
runTensorFlowNet("keras_pad_concat");
modules/dnn/test/test_torch_importer.cpp
+9 -3
View File
@@ -337,9 +337,15 @@ TEST_P(Test_Torch_nets, ENet_accuracy)
{
applyTestTag(target == DNN_TARGET_CPU ? "" : CV_TEST_TAG_MEMORY_512MB);
checkBackend();
if (backend == DNN_BACKEND_INFERENCE_ENGINE ||
(backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
throw SkipTestException("");
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU)
{
if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL);
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
throw SkipTestException("");
}
Net net;
{
modules/imgproc/misc/java/test/ImgprocTest.java
+1 -1
View File
@@ -807,7 +807,7 @@ public class ImgprocTest extends OpenCVTestCase {
points.put(0, 0, 0, 0, 2, 3, 3, 4, 5, 8);
Mat linePoints = new Mat(4, 1, CvType.CV_32FC1);
linePoints.put(0, 0, 0.53196341, 0.84676737, 2.496531, 3.7467217);
linePoints.put(0, 0, 0.53198653, 0.84675282, 2.5, 3.75);
Imgproc.fitLine(points, dst, Imgproc.CV_DIST_L12, 0, 0.01, 0.01);
modules/imgproc/src/linefit.cpp
+15 -4
View File
@@ -408,8 +408,14 @@ static void fitLine2D( const Point2f * points, int count, int dist,
}
/* calculate distances */
err = calcDist2D( points, count, _line, r );
if( err < EPS )
break;
if (err < min_err)
{
min_err = err;
memcpy(line, _line, 4 * sizeof(line[0]));
if (err < EPS)
break;
}
/* calculate weights */
if( calc_weights )
@@ -550,8 +556,13 @@ static void fitLine3D( Point3f * points, int count, int dist,
}
/* calculate distances */
err = calcDist3D( points, count, _line, r );
//if( err < FLT_EPSILON*count )
// break;
if (err < min_err)
{
min_err = err;
memcpy(line, _line, 6 * sizeof(line[0]));
if (err < EPS)
break;
}
/* calculate weights */
if( calc_weights )
modules/imgproc/test/test_convhull.cpp
+35
View File
@@ -1609,6 +1609,8 @@ int CV_FitLineTest::validate_test_results( int test_case_idx )
int k, max_k = 0;
double vec_diff = 0, t;
//std::cout << dims << " " << Mat(1, dims*2, CV_32FC1, line.data()) << " " << Mat(1, dims, CV_32FC1, line0.data()) << std::endl;
for( k = 0; k < dims*2; k++ )
{
if( cvIsNaN(line[k]) || cvIsInf(line[k]) )
@@ -2038,5 +2040,38 @@ INSTANTIATE_TEST_CASE_P(Imgproc, ConvexityDefects_regression_5908,
testing::Values(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
));
TEST(Imgproc_FitLine, regression_15083)
{
int points2i_[] = {
432, 654,
370, 656,
390, 656,
410, 656,
348, 658
};
Mat points(5, 1, CV_32SC2, points2i_);
Vec4f lineParam;
fitLine(points, lineParam, DIST_L1, 0, 0.01, 0.01);
EXPECT_GE(fabs(lineParam[0]), fabs(lineParam[1]) * 4) << lineParam;
}
TEST(Imgproc_FitLine, regression_4903)
{
float points2f_[] = {
1224.0, 576.0,
1234.0, 683.0,
1215.0, 471.0,
1184.0, 137.0,
1079.0, 377.0,
1239.0, 788.0,
};
Mat points(6, 1, CV_32FC2, points2f_);
Vec4f lineParam;
fitLine(points, lineParam, DIST_WELSCH, 0, 0.01, 0.01);
EXPECT_GE(fabs(lineParam[1]), fabs(lineParam[0]) * 4) << lineParam;
}
}} // namespace
/* End of file. */
modules/objdetect/src/qrcode.cpp
+18 -14
View File
@@ -359,22 +359,26 @@ bool QRDetect::localization()
bool suare_flag = false, local_points_flag = false;
double triangle_sides[3];
triangle_sides[0] = norm(localization_points[0] - localization_points[1]);
triangle_sides[1] = norm(localization_points[1] - localization_points[2]);
triangle_sides[2] = norm(localization_points[2] - localization_points[0]);
double triangle_perim = (triangle_sides[0] + triangle_sides[1] + triangle_sides[2]) / 2;
double square_area = sqrt((triangle_perim * (triangle_perim - triangle_sides[0])
* (triangle_perim - triangle_sides[1])
* (triangle_perim - triangle_sides[2]))) * 2;
double img_square_area = bin_barcode.cols * bin_barcode.rows;
if (square_area > (img_square_area * 0.2))
double triangle_perim, square_area, img_square_area;
if (localization_points.size() == 3)
{
suare_flag = true;
triangle_sides[0] = norm(localization_points[0] - localization_points[1]);
triangle_sides[1] = norm(localization_points[1] - localization_points[2]);
triangle_sides[2] = norm(localization_points[2] - localization_points[0]);
triangle_perim = (triangle_sides[0] + triangle_sides[1] + triangle_sides[2]) / 2;
square_area = sqrt((triangle_perim * (triangle_perim - triangle_sides[0])
* (triangle_perim - triangle_sides[1])
* (triangle_perim - triangle_sides[2]))) * 2;
img_square_area = bin_barcode.cols * bin_barcode.rows;
if (square_area > (img_square_area * 0.2))
{
suare_flag = true;
}
}
if (localization_points.size() != 3)
else
{
local_points_flag = true;
}