Merge branch 4.x

Author: asmorkalov

.github/PULL_REQUEST_TEMPLATE.md

@@ -2,8 +2,8 @@
 
 See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
 
-- [ ] I agree to contribute to the project under Apache 2 License.
-- [ ] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
+- [x] I agree to contribute to the project under Apache 2 License.
+- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
 - [ ] The PR is proposed to the proper branch
 - [ ] There is a reference to the original bug report and related work
 - [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable

modules/bgsegm/include/opencv2/bgsegm.hpp

@@ -99,6 +99,19 @@ CV_EXPORTS_W Ptr<BackgroundSubtractorMOG>
 class CV_EXPORTS_W BackgroundSubtractorGMG : public BackgroundSubtractor
 {
 public:
+    // BackgroundSubtractor interface
+    /** @brief Computes a foreground mask.
+
+    @param image Next video frame of type CV_8UC(n),CV_8SC(n),CV_16UC(n),CV_16SC(n),CV_32SC(n),CV_32FC(n),CV_64FC(n), where n is 1,2,3,4.
+    @param fgmask The output foreground mask as an 8-bit binary image.
+    @param learningRate The value between 0 and 1 that indicates how fast the background model is
+    learnt. Negative parameter value makes the algorithm to use some automatically chosen learning
+    rate. 0 means that the background model is not updated at all, 1 means that the background model
+    is completely reinitialized from the last frame.
+     */
+    CV_WRAP virtual void apply(InputArray image, OutputArray fgmask, double learningRate=-1) CV_OVERRIDE = 0;
+    CV_WRAP virtual void getBackgroundImage(OutputArray backgroundImage) const CV_OVERRIDE = 0;
+
     /** @brief Returns total number of distinct colors to maintain in histogram.
     */
     CV_WRAP virtual int getMaxFeatures() const = 0;

modules/bgsegm/src/bgfg_gmg.cpp

@@ -426,8 +426,13 @@ void BackgroundSubtractorGMGImpl::apply(InputArray _frame, OutputArray _fgmask,
 {
     Mat frame = _frame.getMat();
 
-    CV_Assert(frame.depth() == CV_8U || frame.depth() == CV_16U || frame.depth() == CV_32F);
-    CV_Assert(frame.channels() == 1 || frame.channels() == 3 || frame.channels() == 4);
+    const int depth = frame.depth();
+    CV_CheckDepth(depth, (depth == CV_8U)  || (depth == CV_8S)  ||
+                         (depth == CV_16U) || (depth == CV_16S) ||
+                                              (depth == CV_32S) ||
+                         (depth == CV_32F) || (depth == CV_64F), "Unsupported depth");
+    CV_CheckGE(frame.channels(), 1, "Unsupported channels");
+    CV_CheckLE(frame.channels(), 4, "Unsupported channels");
 
     if (newLearningRate != -1.0)
     {
@@ -441,8 +446,12 @@ void BackgroundSubtractorGMGImpl::apply(InputArray _frame, OutputArray _fgmask,
         double maxval = maxVal_;
         if( minVal_ == 0 && maxVal_ == 0 )
         {
-            minval = 0;
-            maxval = frame.depth() == CV_8U ? 255.0 : frame.depth() == CV_16U ? std::numeric_limits<ushort>::max() : 1.0;
+            if( depth == CV_8U )        { minval = std::numeric_limits<uint8_t>::min(); maxval = std::numeric_limits<uint8_t>::max(); }
+            else if( depth == CV_8S )   { minval = std::numeric_limits<int8_t>::min();  maxval = std::numeric_limits<int8_t>::max();  }
+            else if( depth == CV_16U )  { minval = std::numeric_limits<uint16_t>::min();maxval = std::numeric_limits<uint16_t>::max();}
+            else if( depth == CV_16S )  { minval = std::numeric_limits<int16_t>::min(); maxval = std::numeric_limits<int16_t>::max(); }
+            else if( depth == CV_32S )  { minval = std::numeric_limits<int32_t>::min(); maxval = std::numeric_limits<int32_t>::max(); }
+            else /* CV_32F or CV_64F */ { minval = 0.0; maxval = 1.0; }
         }
         initialize(frame.size(), minval, maxval);
     }

modules/bgsegm/test/test_backgroundsubtractor_gbh.cpp

@@ -7,41 +7,24 @@
 
 namespace opencv_test { namespace {
 
-class CV_BackgroundSubtractorTest : public cvtest::BaseTest
-{
-public:
-    CV_BackgroundSubtractorTest();
-protected:
-    void run(int);
-};
-
-CV_BackgroundSubtractorTest::CV_BackgroundSubtractorTest()
-{
-}
-
 /**
  * This test checks the following:
  * (i) BackgroundSubtractorGMG can operate with matrices of various types and sizes
  * (ii) Training mode returns empty fgmask
  * (iii) End of training mode, and anomalous frame yields every pixel detected as FG
  */
-void CV_BackgroundSubtractorTest::run(int)
+typedef testing::TestWithParam<std::tuple<perf::MatDepth,int>> bgsubgmg_allTypes;
+TEST_P(bgsubgmg_allTypes, accuracy)
 {
-    int code = cvtest::TS::OK;
-    RNG& rng = ts->get_rng();
-    int type = ((unsigned int)rng) % 3;
-    type = (type == 0) ? CV_8U : (type == 1) ? CV_16U : CV_32F; // 8U, 16U, 32F
-    int channels = ((unsigned int)rng)%3;
-    channels = (channels == 2) ? 4 : channels; // 1, 3, 4
-    int channelsAndType = CV_MAKETYPE(type,channels);
-    int width = 2 + ((unsigned int)rng)%98; //!< Mat will be 2 to 100 in width and height
-    int height = 2 + ((unsigned int)rng)%98;
+    const int depth = get<0>(GetParam());
+    const int ncn   = get<1>(GetParam());
+    const int mtype = CV_MAKETYPE(depth, ncn);
+    const int width  = 64;
+    const int height = 64;
+    RNG& rng = TS::ptr()->get_rng();
 
     Ptr<BackgroundSubtractorGMG> fgbg = createBackgroundSubtractorGMG();
-    Mat fgmask;
-
-    if (!fgbg)
-        CV_Error(Error::StsError,"Failed to create Algorithm\n");
+    ASSERT_TRUE(fgbg != nullptr) << "Failed to call createBackgroundSubtractorGMG()";
 
     /**
      * Set a few parameters
@@ -59,49 +42,51 @@ void CV_BackgroundSubtractorTest::run(int)
      * Max value for simulated images picked randomly in upper half of type range
      * Min value for simulated images picked randomly in lower half of type range
      */
-    if (type == CV_8U)
+    if (depth == CV_8U)
     {
         uchar half = UCHAR_MAX/2;
         maxd = (unsigned char)rng.uniform(half+32, UCHAR_MAX);
         mind = (unsigned char)rng.uniform(0, half-32);
     }
-    else if (type == CV_8S)
+    else if (depth == CV_8S)
     {
         maxd = (char)rng.uniform(32, CHAR_MAX);
         mind = (char)rng.uniform(CHAR_MIN, -32);
     }
-    else if (type == CV_16U)
+    else if (depth == CV_16U)
     {
         ushort half = USHRT_MAX/2;
         maxd = (unsigned int)rng.uniform(half+32, USHRT_MAX);
         mind = (unsigned int)rng.uniform(0, half-32);
     }
-    else if (type == CV_16S)
+    else if (depth == CV_16S)
     {
         maxd = rng.uniform(32, SHRT_MAX);
         mind = rng.uniform(SHRT_MIN, -32);
     }
-    else if (type == CV_32S)
+    else if (depth == CV_32S)
     {
         maxd = rng.uniform(32, INT_MAX);
         mind = rng.uniform(INT_MIN, -32);
     }
-    else if (type == CV_32F)
+    else
     {
-        maxd = rng.uniform(32.0f, FLT_MAX);
-        mind = rng.uniform(-FLT_MAX, -32.0f);
-    }
-    else if (type == CV_64F)
-    {
-        maxd = rng.uniform(32.0, DBL_MAX);
-        mind = rng.uniform(-DBL_MAX, -32.0);
+        ASSERT_TRUE( (depth == CV_32F)||(depth == CV_64F) ) << "Unsupported depth";
+        const double harf = 0.5;
+        const double bias = 0.125; // = 32/256 (Like CV_8U)
+        maxd = rng.uniform(harf + bias, 1.0);
+        mind = rng.uniform(0.0, harf - bias );
     }
 
     fgbg->setMinVal(mind);
     fgbg->setMaxVal(maxd);
 
-    Mat simImage = Mat::zeros(height, width, channelsAndType);
-    int numLearningFrames = 120;
+    Mat simImage(height, width, mtype);
+    Mat fgmask;
+
+    const Mat fullbg(height, width, CV_8UC1, cv::Scalar(0)); // all background.
+
+    const int numLearningFrames = 120;
     for (int i = 0; i < numLearningFrames; ++i)
     {
         /**
@@ -113,27 +98,21 @@ void CV_BackgroundSubtractorTest::run(int)
          * Feed simulated images into background subtractor
          */
         fgbg->apply(simImage,fgmask);
-        Mat fullbg = Mat::zeros(simImage.rows, simImage.cols, CV_8U);
 
-        //! fgmask should be entirely background during training
-        code = cvtest::cmpEps2( ts, fgmask, fullbg, 0, false, "The training foreground mask" );
-        if (code < 0)
-            ts->set_failed_test_info( code );
+        EXPECT_EQ(cv::norm(fgmask, fullbg, NORM_INF), 0) << "foreground mask should be entirely background during training";
     }
     //! generate last image, distinct from training images
     rng.fill(simImage, RNG::UNIFORM, mind, maxd);
-
     fgbg->apply(simImage,fgmask);
-    //! now fgmask should be entirely foreground
-    Mat fullfg = 255*Mat::ones(simImage.rows, simImage.cols, CV_8U);
-    code = cvtest::cmpEps2( ts, fgmask, fullfg, 255, false, "The final foreground mask" );
-    if (code < 0)
-    {
-        ts->set_failed_test_info( code );
-    }
 
+    const Mat fullfg(height, width, CV_8UC1, cv::Scalar(255)); // all foreground.
+    EXPECT_EQ(cv::norm(fgmask, fullfg, NORM_INF), 0) << "foreground mask should be entirely foreground finally";
 }
 
-TEST(VIDEO_BGSUBGMG, accuracy) { CV_BackgroundSubtractorTest test; test.safe_run(); }
+INSTANTIATE_TEST_CASE_P(/**/,
+                        bgsubgmg_allTypes,
+                        testing::Combine(
+                            testing::Values(CV_8U, CV_8S, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F),
+                            testing::Values(1,2,3,4)));
 
 }} // namespace