Merge upstream

README.md

@@ -13,7 +13,8 @@ provides production quality support for this module.
 
 ### How to build OpenCV with extra modules
 
-You can build OpenCV, so it will include the modules from this repository.
+You can build OpenCV, so it will include the modules from this repository. Contrib modules are under constant development and it is recommended to use them alongside the master branch or latest releases of OpenCV.
+
 Here is the CMake command for you:
 
 ```
@@ -46,6 +47,8 @@ If you prefer using the gui version of cmake (cmake-gui), then, you can add `ope
 
 7. build the `opencv` core with the method you chose (make and make install if you chose Unix makfile at step 6) 
 
+8. to run, linker flags to contrib modules will need to be added to use them in your code/IDE. For example to use the aruco module, "-lopencv_aruco" flag will be added.
+
 ### Update the repository documentation
 
 In order to keep a clean overview containing all contributed modules the following files need to be created/adapted.

modules/aruco/samples/calibrate_camera.cpp

@@ -58,7 +58,7 @@ const char* about =
 const char* keys  =
         "{w        |       | Number of squares in X direction }"
         "{h        |       | Number of squares in Y direction }"
-        "{l        |       | Marker side lenght (in meters) }"
+        "{l        |       | Marker side length (in meters) }"
         "{s        |       | Separation between two consecutive markers in the grid (in meters) }"
         "{d        |       | dictionary: DICT_4X4_50=0, DICT_4X4_100=1, DICT_4X4_250=2,"
         "DICT_4X4_1000=3, DICT_5X5_50=4, DICT_5X5_100=5, DICT_5X5_250=6, DICT_5X5_1000=7, "

modules/aruco/samples/calibrate_camera_charuco.cpp

@@ -57,8 +57,8 @@ const char* about =
 const char* keys  =
         "{w        |       | Number of squares in X direction }"
         "{h        |       | Number of squares in Y direction }"
-        "{sl       |       | Square side length (in pixels) }"
-        "{ml       |       | Marker side length (in pixels) }"
+        "{sl       |       | Square side length (in meters) }"
+        "{ml       |       | Marker side length (in meters) }"
         "{d        |       | dictionary: DICT_4X4_50=0, DICT_4X4_100=1, DICT_4X4_250=2,"
         "DICT_4X4_1000=3, DICT_5X5_50=4, DICT_5X5_100=5, DICT_5X5_250=6, DICT_5X5_1000=7, "
         "DICT_6X6_50=8, DICT_6X6_100=9, DICT_6X6_250=10, DICT_6X6_1000=11, DICT_7X7_50=12,"

modules/aruco/samples/create_board.cpp

@@ -48,7 +48,7 @@ const char* keys  =
         "{@outfile |<none> | Output image }"
         "{w        |       | Number of markers in X direction }"
         "{h        |       | Number of markers in Y direction }"
-        "{l        |       | Marker side lenght (in pixels) }"
+        "{l        |       | Marker side length (in pixels) }"
         "{s        |       | Separation between two consecutive markers in the grid (in pixels)}"
         "{d        |       | dictionary: DICT_4X4_50=0, DICT_4X4_100=1, DICT_4X4_250=2,"
         "DICT_4X4_1000=3, DICT_5X5_50=4, DICT_5X5_100=5, DICT_5X5_250=6, DICT_5X5_1000=7, "

modules/aruco/samples/create_board_charuco.cpp

@@ -48,8 +48,8 @@ const char* keys  =
         "{@outfile |<none> | Output image }"
         "{w        |       | Number of squares in X direction }"
         "{h        |       | Number of squares in Y direction }"
-        "{sl       |       | Square side lenght (in pixels) }"
-        "{ml       |       | Marker side lenght (in pixels) }"
+        "{sl       |       | Square side length (in pixels) }"
+        "{ml       |       | Marker side length (in pixels) }"
         "{d        |       | dictionary: DICT_4X4_50=0, DICT_4X4_100=1, DICT_4X4_250=2,"
         "DICT_4X4_1000=3, DICT_5X5_50=4, DICT_5X5_100=5, DICT_5X5_250=6, DICT_5X5_1000=7, "
         "DICT_6X6_50=8, DICT_6X6_100=9, DICT_6X6_250=10, DICT_6X6_1000=11, DICT_7X7_50=12,"

modules/aruco/samples/create_diamond.cpp

@@ -49,8 +49,8 @@ namespace {
 const char* about = "Create a ChArUco marker image";
 const char* keys  =
         "{@outfile |<none> | Output image }"
-        "{sl       |       | Square side lenght (in pixels) }"
-        "{ml       |       | Marker side lenght (in pixels) }"
+        "{sl       |       | Square side length (in pixels) }"
+        "{ml       |       | Marker side length (in pixels) }"
         "{d        |       | dictionary: DICT_4X4_50=0, DICT_4X4_100=1, DICT_4X4_250=2,"
         "DICT_4X4_1000=3, DICT_5X5_50=4, DICT_5X5_100=5, DICT_5X5_250=6, DICT_5X5_1000=7, "
         "DICT_6X6_50=8, DICT_6X6_100=9, DICT_6X6_250=10, DICT_6X6_1000=11, DICT_7X7_50=12,"

modules/aruco/samples/detect_board_charuco.cpp

@@ -51,8 +51,8 @@ const char* about = "Pose estimation using a ChArUco board";
 const char* keys  =
         "{w        |       | Number of squares in X direction }"
         "{h        |       | Number of squares in Y direction }"
-        "{sl       |       | Square side lenght (in pixels) }"
-        "{ml       |       | Marker side lenght (in pixels) }"
+        "{sl       |       | Square side length (in meters) }"
+        "{ml       |       | Marker side length (in meters) }"
         "{d        |       | dictionary: DICT_4X4_50=0, DICT_4X4_100=1, DICT_4X4_250=2,"
         "DICT_4X4_1000=3, DICT_5X5_50=4, DICT_5X5_100=5, DICT_5X5_250=6, DICT_5X5_1000=7, "
         "DICT_6X6_50=8, DICT_6X6_100=9, DICT_6X6_250=10, DICT_6X6_1000=11, DICT_7X7_50=12,"

modules/aruco/samples/detect_diamonds.cpp

@@ -49,8 +49,8 @@ using namespace cv;
 namespace {
 const char* about = "Detect ChArUco markers";
 const char* keys  =
-        "{sl       |       | Square side lenght (in pixels) }"
-        "{ml       |       | Marker side lenght (in pixels) }"
+        "{sl       |       | Square side length (in meters) }"
+        "{ml       |       | Marker side length (in meters) }"
         "{d        |       | dictionary: DICT_4X4_50=0, DICT_4X4_100=1, DICT_4X4_250=2,"
         "DICT_4X4_1000=3, DICT_5X5_50=4, DICT_5X5_100=5, DICT_5X5_250=6, DICT_5X5_1000=7, "
         "DICT_6X6_50=8, DICT_6X6_100=9, DICT_6X6_250=10, DICT_6X6_1000=11, DICT_7X7_50=12,"

modules/aruco/samples/detector_params.yml

 %YAML:1.0
 nmarkers: 1024
+adaptiveThreshWinSizeMin: 3
+adaptiveThreshWinSizeMax: 23
+adaptiveThreshWinSizeStep: 10
 adaptiveThreshWinSize: 21
 adaptiveThreshConstant: 7
 minMarkerPerimeterRate: 0.03
@@ -8,6 +11,8 @@ polygonalApproxAccuracyRate: 0.05
 minCornerDistance: 10.0
 minDistanceToBorder: 3
 minMarkerDistance: 10.0
+minMarkerDistanceRate: 0.05
+doCornerRefinement: false
 cornerRefinementWinSize: 5
 cornerRefinementMaxIterations: 30
 cornerRefinementMinAccuracy: 0.1
@@ -15,3 +20,5 @@ markerBorderBits: 1
 perspectiveRemovePixelPerCell: 8
 perspectiveRemoveIgnoredMarginPerCell: 0.13
 maxErroneousBitsInBorderRate: 0.04
+minOtsuStdDev: 5.0
+errorCorrectionRate: 0.6

modules/aruco/tutorials/aruco_board_detection/aruco_board_detection.markdown

@@ -155,6 +155,11 @@ The output image will be something like this:
 
 A full working example of board creation is included in the ```create_board.cpp``` inside the module samples folder.
 
+Note: The samples now take input via commandline via the [OpenCV Commandline Parser](http://docs.opencv.org/trunk/d0/d2e/classcv_1_1CommandLineParser.html#gsc.tab=0). For this file the example parameters will look like
+``` c++
+    "_output path_/aboard.png" -w=5 -h=7 -l=100 -s=10 -d=10
+```
+
 Finally, a full example of board detection:
 
 ``` c++
@@ -204,6 +209,12 @@ Sample video:
 
 A full working example is included in the ```detect_board.cpp``` inside the module samples folder.
 
+Note: The samples now take input via commandline via the [OpenCV Commandline Parser](http://docs.opencv.org/trunk/d0/d2e/classcv_1_1CommandLineParser.html#gsc.tab=0). For this file the example parameters will look like
+``` c++
+    -c="_path_"/calib.txt" "_path_/aboard.png" -w=5 -h=7 -l=100 -s=10 -d=10
+```
+
+
 
 Refine marker detection
 -----

modules/aruco/tutorials/aruco_calibration/aruco_calibration.markdown

@@ -61,6 +61,11 @@ Finally, the ```calibrationFlags``` parameter determines some of the options for
 
 A full working example is included in the ```calibrate_camera_charuco.cpp``` inside the module samples folder.
 
+Note: The samples now take input via commandline via the [OpenCV Commandline Parser](http://docs.opencv.org/trunk/d0/d2e/classcv_1_1CommandLineParser.html#gsc.tab=0). For this file the example parameters will look like
+``` c++
+    _output path_" -dp="_path_/detector_params.yml" -w=5 -h=7 -sl=0.04 -ml=0.02 -d=10
+```
+
 
 
 Calibration with ArUco Boards
@@ -100,3 +105,8 @@ The rest of parameters are the same than in ```calibrateCameraCharuco()```, exce
 any ```Board``` object.
 
 A full working example is included in the ```calibrate_camera.cpp``` inside the module samples folder.
+
+Note: The samples now take input via commandline via the [OpenCV Commandline Parser](http://docs.opencv.org/trunk/d0/d2e/classcv_1_1CommandLineParser.html#gsc.tab=0). For this file the example parameters will look like
+``` c++
+    "_path_/calib.txt" -w=5 -h=7 -l=100 -s=10 -d=10
+```

modules/aruco/tutorials/aruco_detection/aruco_detection.markdown

@@ -103,6 +103,10 @@ The generated image is:
 
 A full working example is included in the ```create_marker.cpp``` inside the module samples folder.
 
+Note: The samples now take input via commandline via the [OpenCV Commandline Parser](http://docs.opencv.org/trunk/d0/d2e/classcv_1_1CommandLineParser.html#gsc.tab=0). For this file the example parameters will look like
+``` c++
+    "/Users/Sarthak/Dropbox/OpenCV_GSoC/marker.png" -d=10 -id=1
+```
 
 Marker Detection
 ------
@@ -226,6 +230,11 @@ output vector of rejected candidates.
 
 A full working example is included in the ```detect_markers.cpp``` inside the module samples folder.
 
+Note: The samples now take input via commandline via the [OpenCV Commandline Parser](http://docs.opencv.org/trunk/d0/d2e/classcv_1_1CommandLineParser.html#gsc.tab=0). For this file the example parameters will look like
+``` c++
+    -c="_path_/calib.txt" -d=10
+```
+
 
 
 Pose Estimation
@@ -331,6 +340,12 @@ Sample video:
 
 A full working example is included in the ```detect_markers.cpp``` inside the module samples folder.
 
+Note: The samples now take input via commandline via the [OpenCV Commandline Parser](http://docs.opencv.org/trunk/d0/d2e/classcv_1_1CommandLineParser.html#gsc.tab=0). For this file the example parameters will look like
+``` c++
+    -c="_path_/calib.txt" -d=10
+```
+
+
 
 Selecting a dictionary
 ------

modules/aruco/tutorials/charuco_detection/charuco_detection.markdown

@@ -78,6 +78,11 @@ The output image will be something like this:
 
 A full working example is included in the ```create_board_charuco.cpp``` inside the module samples folder.
 
+Note: The samples now take input via commandline via the [OpenCV Commandline Parser](http://docs.opencv.org/trunk/d0/d2e/classcv_1_1CommandLineParser.html#gsc.tab=0). For this file the example parameters will look like
+``` c++
+    "_ output path_/chboard.png" -w=5 -h=7 -sl=200 -ml=120 -d=10
+```
+
 
 ChArUco Board Detection
 ------
@@ -240,6 +245,11 @@ Sample video:
 
 A full working example is included in the ```detect_board_charuco.cpp``` inside the module samples folder.
 
+Note: The samples now take input via commandline via the [OpenCV Commandline Parser](http://docs.opencv.org/trunk/d0/d2e/classcv_1_1CommandLineParser.html#gsc.tab=0). For this file the example parameters will look like
+``` c++
+    -c="_path_/calib.txt" -dp="_path_/detector_params.yml" -w=5 -h=7 -sl=0.04 -ml=0.02 -d=10
+```
+
 ChArUco Pose Estimation
 ------
 
@@ -312,3 +322,8 @@ A full example of ChArUco detection with pose estimation:
 ```
 
 A full working example is included in the ```detect_board_charuco.cpp``` inside the module samples folder.
+
+Note: The samples now take input via commandline via the [OpenCV Commandline Parser](http://docs.opencv.org/trunk/d0/d2e/classcv_1_1CommandLineParser.html#gsc.tab=0). For this file the example parameters will look like
+``` c++
+    "_path_/calib.txt" -dp="_path_/detector_params.yml" -w=5 -h=7 -sl=0.04 -ml=0.02 -d=10
+```

modules/aruco/tutorials/charuco_diamond_detection/charuco_diamond_detection.markdown

@@ -60,6 +60,10 @@ The image produced will be:
 
 A full working example is included in the ```create_diamond.cpp``` inside the module samples folder.
 
+Note: The samples now take input via commandline via the [OpenCV Commandline Parser](http://docs.opencv.org/trunk/d0/d2e/classcv_1_1CommandLineParser.html#gsc.tab=0). For this file the example parameters will look like
+``` c++
+    "_path_/mydiamond.png" -sl=200 -ml=120 -d=10 -ids=45,68,28,74
+```
 
 ChArUco Diamond Detection
 ------
@@ -116,6 +120,10 @@ The result is the same that the one produced by ```drawDetectedMarkers()```, but
 
 A full working example is included in the ```detect_diamonds.cpp``` inside the module samples folder.
 
+Note: The samples now take input via commandline via the [OpenCV Commandline Parser](http://docs.opencv.org/trunk/d0/d2e/classcv_1_1CommandLineParser.html#gsc.tab=0). For this file the example parameters will look like
+``` c++
+    -c="_path_/calib.txt" -dp="_path_/detector_params.yml" -sl=0.04 -ml=0.02 -d=10
+```
 
 ChArUco Diamond Pose Estimation
 ------
@@ -159,3 +167,8 @@ Sample video:
 @endhtmlonly
 
 A full working example is included in the ```detect_diamonds.cpp``` inside the module samples folder.
+
+Note: The samples now take input via commandline via the [OpenCV Commandline Parser](http://docs.opencv.org/trunk/d0/d2e/classcv_1_1CommandLineParser.html#gsc.tab=0). For this file the example parameters will look like
+``` c++
+    -c="_output path_/calib.txt" -dp="_path_/detector_params.yml" -sl=0.04 -ml=0.02 -d=10
+```

modules/optflow/doc/optflow.bib

@@ -44,3 +44,11 @@
   journal={arXiv preprint arXiv:1603.03590},
   year={2016}
 }
+
+@inproceedings{Brox2004,
+  title={High accuracy optical flow estimation based on a theory for warping},
+  author={Brox, Thomas and Bruhn, Andr{\'e}s and Papenberg, Nils and Weickert, Joachim},
+  booktitle={European Conference on Computer Vision (ECCV)},
+  pages={25--36},
+  year={2004}
+}

modules/optflow/include/opencv2/optflow.hpp

@@ -156,6 +156,64 @@ to the flow in the horizontal direction (u), second - vertical (v).
  */
 CV_EXPORTS_W bool writeOpticalFlow( const String& path, InputArray flow );
 
+/** @brief Variational optical flow refinement
+
+This class implements variational refinement of the input flow field, i.e.
+it uses input flow to initialize the minimization of the following functional:
+\f$E(U) = \int_{\Omega} \delta \Psi(E_I) + \gamma \Psi(E_G) + \alpha \Psi(E_S) \f$,
+where \f$E_I,E_G,E_S\f$ are color constancy, gradient constancy and smoothness terms
+respectively. \f$\Psi(s^2)=\sqrt{s^2+\epsilon^2}\f$ is a robust penalizer to limit the
+influence of outliers. A complete formulation and a description of the minimization
+procedure can be found in @cite Brox2004
+*/
+class CV_EXPORTS_W VariationalRefinement : public DenseOpticalFlow
+{
+public:
+    /** @brief calc function overload to handle separate horizontal (u) and vertical (v) flow components
+    (to avoid extra splits/merges) */
+    CV_WRAP virtual void calcUV(InputArray I0, InputArray I1, InputOutputArray flow_u, InputOutputArray flow_v) = 0;
+
+    /** @brief Number of outer (fixed-point) iterations in the minimization procedure.
+    @see setFixedPointIterations */
+    CV_WRAP virtual int getFixedPointIterations() const = 0;
+    /** @copybrief getFixedPointIterations @see getFixedPointIterations */
+    CV_WRAP virtual void setFixedPointIterations(int val) = 0;
+
+    /** @brief Number of inner successive over-relaxation (SOR) iterations
+        in the minimization procedure to solve the respective linear system.
+    @see setSorIterations */
+    CV_WRAP virtual int getSorIterations() const = 0;
+    /** @copybrief getSorIterations @see getSorIterations */
+    CV_WRAP virtual void setSorIterations(int val) = 0;
+
+    /** @brief Relaxation factor in SOR
+    @see setOmega */
+    CV_WRAP virtual float getOmega() const = 0;
+    /** @copybrief getOmega @see getOmega */
+    CV_WRAP virtual void setOmega(float val) = 0;
+
+    /** @brief Weight of the smoothness term
+    @see setAlpha */
+    CV_WRAP virtual float getAlpha() const = 0;
+    /** @copybrief getAlpha @see getAlpha */
+    CV_WRAP virtual void setAlpha(float val) = 0;
+
+    /** @brief Weight of the color constancy term
+    @see setDelta */
+    CV_WRAP virtual float getDelta() const = 0;
+    /** @copybrief getDelta @see getDelta */
+    CV_WRAP virtual void setDelta(float val) = 0;
+
+    /** @brief Weight of the gradient constancy term
+    @see setGamma */
+    CV_WRAP virtual float getGamma() const = 0;
+    /** @copybrief getGamma @see getGamma */
+    CV_WRAP virtual void setGamma(float val) = 0;
+};
+
+/** @brief Creates an instance of VariationalRefinement
+*/
+CV_EXPORTS_W Ptr<VariationalRefinement> createVariationalFlowRefinement();
 
 /** @brief DeepFlow optical flow algorithm implementation.
 
@@ -196,40 +254,80 @@ CV_EXPORTS_W Ptr<DenseOpticalFlow> createOptFlow_SparseToDense();
 /** @brief DIS optical flow algorithm.
 
 This class implements the Dense Inverse Search (DIS) optical flow algorithm. More
-details about the algorithm can be found at @cite Kroeger2016 .
+details about the algorithm can be found at @cite Kroeger2016 . Includes three presets with preselected
+parameters to provide reasonable trade-off between speed and quality. However, even the slowest preset is
+still relatively fast, use DeepFlow if you need better quality and don't care about speed.
 */
 class CV_EXPORTS_W DISOpticalFlow : public DenseOpticalFlow
 {
 public:
-    /** @brief Finest level of the gaussian pyramid on which the flow is computed (zero level
-        corresponds to the original image resolution).The final flow is obtained by bilinear upscaling.
+    enum
+    {
+        PRESET_ULTRAFAST = 0,
+        PRESET_FAST = 1,
+        PRESET_MEDIUM = 2
+    };
+
+    /** @brief Finest level of the Gaussian pyramid on which the flow is computed (zero level
+        corresponds to the original image resolution). The final flow is obtained by bilinear upscaling.
         @see setFinestScale */
-    virtual int getFinestScale() const = 0;
+    CV_WRAP virtual int getFinestScale() const = 0;
     /** @copybrief getFinestScale @see getFinestScale */
-    virtual void setFinestScale(int val) = 0;
+    CV_WRAP virtual void setFinestScale(int val) = 0;
 
-    /** @brief Size of an image patch for matching (in pixels)
+    /** @brief Size of an image patch for matching (in pixels). Normally, default 8x8 patches work well
+        enough in most cases.
         @see setPatchSize */
-    virtual int getPatchSize() const = 0;
+    CV_WRAP virtual int getPatchSize() const = 0;
     /** @copybrief getPatchSize @see getPatchSize */
-    virtual void setPatchSize(int val) = 0;
+    CV_WRAP virtual void setPatchSize(int val) = 0;
 
-    /** @brief Stride between neighbor patches. Must be less than patch size.
+    /** @brief Stride between neighbor patches. Must be less than patch size. Lower values correspond
+        to higher flow quality.
         @see setPatchStride */
-    virtual int getPatchStride() const = 0;
+    CV_WRAP virtual int getPatchStride() const = 0;
     /** @copybrief getPatchStride @see getPatchStride */
-    virtual void setPatchStride(int val) = 0;
+    CV_WRAP virtual void setPatchStride(int val) = 0;
 
-    /** @brief number of gradient descent iterations in the patch inverse search stage
+    /** @brief Maximum number of gradient descent iterations in the patch inverse search stage. Higher values
+        may improve quality in some cases.
         @see setGradientDescentIterations */
-    virtual int getGradientDescentIterations() const = 0;
+    CV_WRAP virtual int getGradientDescentIterations() const = 0;
     /** @copybrief getGradientDescentIterations @see getGradientDescentIterations */
-    virtual void setGradientDescentIterations(int val) = 0;
+    CV_WRAP virtual void setGradientDescentIterations(int val) = 0;
+
+    /** @brief Number of fixed point iterations of variational refinement per scale. Set to zero to
+        disable variational refinement completely. Higher values will typically result in more smooth and
+        high-quality flow.
+    @see setGradientDescentIterations */
+    CV_WRAP virtual int getVariationalRefinementIterations() const = 0;
+    /** @copybrief getGradientDescentIterations @see getGradientDescentIterations */
+    CV_WRAP virtual void setVariationalRefinementIterations(int val) = 0;
+
+    /** @brief Whether to use mean-normalization of patches when computing patch distance. It is turned on
+        by default as it typically provides a noticeable quality boost because of increased robustness to
+        illumanition variations. Turn it off if you are certain that your sequence does't contain any changes
+        in illumination.
+    @see setUseMeanNormalization */
+    CV_WRAP virtual bool getUseMeanNormalization() const = 0;
+    /** @copybrief getUseMeanNormalization @see getUseMeanNormalization */
+    CV_WRAP virtual void setUseMeanNormalization(bool val) = 0;
+
+    /** @brief Whether to use spatial propagation of good optical flow vectors. This option is turned on by
+        default, as it tends to work better on average and can sometimes help recover from major errors
+        introduced by the coarse-to-fine scheme employed by the DIS optical flow algorithm. Turning this
+        option off can make the output flow field a bit smoother, however.
+    @see setUseSpatialPropagation */
+    CV_WRAP virtual bool getUseSpatialPropagation() const = 0;
+    /** @copybrief getUseSpatialPropagation @see getUseSpatialPropagation */
+    CV_WRAP virtual void setUseSpatialPropagation(bool val) = 0;
 };
 
 /** @brief Creates an instance of DISOpticalFlow
+
+@param preset one of PRESET_ULTRAFAST, PRESET_FAST and PRESET_MEDIUM
 */
-CV_EXPORTS_W Ptr<DISOpticalFlow> createOptFlow_DIS();
+CV_EXPORTS_W Ptr<DISOpticalFlow> createOptFlow_DIS(int preset = DISOpticalFlow::PRESET_FAST);
 
 //! @}
 

modules/optflow/perf/perf_deepflow.cpp

+/*
+ *  By downloading, copying, installing or using the software you agree to this license.
+ *  If you do not agree to this license, do not download, install,
+ *  copy or use the software.
+ *
+ *
+ *  License Agreement
+ *  For Open Source Computer Vision Library
+ *  (3 - clause BSD License)
+ *
+ *  Redistribution and use in source and binary forms, with or without modification,
+ *  are permitted provided that the following conditions are met :
+ *
+ *  *Redistributions of source code must retain the above copyright notice,
+ *  this list of conditions and the following disclaimer.
+ *
+ *  * Redistributions in binary form must reproduce the above copyright notice,
+ *  this list of conditions and the following disclaimer in the documentation
+ *  and / or other materials provided with the distribution.
+ *
+ *  * Neither the names of the copyright holders nor the names of the contributors
+ *  may be used to endorse or promote products derived from this software
+ *  without specific prior written permission.
+ *
+ *  This software is provided by the copyright holders and contributors "as is" and
+ *  any express or implied warranties, including, but not limited to, the implied
+ *  warranties of merchantability and fitness for a particular purpose are disclaimed.
+ *  In no event shall copyright holders or contributors be liable for any direct,
+ *  indirect, incidental, special, exemplary, or consequential damages
+ *  (including, but not limited to, procurement of substitute goods or services;
+ *  loss of use, data, or profits; or business interruption) however caused
+ *  and on any theory of liability, whether in contract, strict liability,
+ *  or tort(including negligence or otherwise) arising in any way out of
+ *  the use of this software, even if advised of the possibility of such damage.
+ */
+
+#include "perf_precomp.hpp"
+
+using std::tr1::tuple;
+using std::tr1::get;
+using namespace perf;
+using namespace testing;
+using namespace cv;
+using namespace cv::optflow;
+
+typedef tuple<Size> DFParams;
+typedef TestBaseWithParam<DFParams> DenseOpticalFlow_DeepFlow;
+
+PERF_TEST_P(DenseOpticalFlow_DeepFlow, perf, Values(szVGA, sz720p))
+{
+    DFParams params = GetParam();
+    Size sz = get<0>(params);
+
+    Mat frame1(sz, CV_8U);
+    Mat frame2(sz, CV_8U);
+    Mat flow;
+
+    randu(frame1, 0, 255);
+    randu(frame2, 0, 255);
+
+    cv::setNumThreads(cv::getNumberOfCPUs());
+    TEST_CYCLE_N(1)
+    {
+        Ptr<DenseOpticalFlow> algo = createOptFlow_DeepFlow();
+        algo->calc(frame1, frame2, flow);
+    }
+
+    SANITY_CHECK_NOTHING();
+}

modules/optflow/perf/perf_disflow.cpp

+/*
+ *  By downloading, copying, installing or using the software you agree to this license.
+ *  If you do not agree to this license, do not download, install,
+ *  copy or use the software.
+ *
+ *
+ *  License Agreement
+ *  For Open Source Computer Vision Library
+ *  (3 - clause BSD License)
+ *
+ *  Redistribution and use in source and binary forms, with or without modification,
+ *  are permitted provided that the following conditions are met :
+ *
+ *  *Redistributions of source code must retain the above copyright notice,
+ *  this list of conditions and the following disclaimer.
+ *
+ *  * Redistributions in binary form must reproduce the above copyright notice,
+ *  this list of conditions and the following disclaimer in the documentation
+ *  and / or other materials provided with the distribution.
+ *
+ *  * Neither the names of the copyright holders nor the names of the contributors
+ *  may be used to endorse or promote products derived from this software
+ *  without specific prior written permission.
+ *
+ *  This software is provided by the copyright holders and contributors "as is" and
+ *  any express or implied warranties, including, but not limited to, the implied
+ *  warranties of merchantability and fitness for a particular purpose are disclaimed.
+ *  In no event shall copyright holders or contributors be liable for any direct,
+ *  indirect, incidental, special, exemplary, or consequential damages
+ *  (including, but not limited to, procurement of substitute goods or services;
+ *  loss of use, data, or profits; or business interruption) however caused
+ *  and on any theory of liability, whether in contract, strict liability,
+ *  or tort(including negligence or otherwise) arising in any way out of
+ *  the use of this software, even if advised of the possibility of such damage.
+ */
+
+#include "perf_precomp.hpp"
+
+using std::tr1::tuple;
+using std::tr1::get;
+using namespace perf;
+using namespace testing;
+using namespace cv;
+using namespace cv::optflow;
+
+void MakeArtificialExample(Mat &dst_frame1, Mat &dst_frame2);
+
+typedef tuple<String, Size> DISParams;
+typedef TestBaseWithParam<DISParams> DenseOpticalFlow_DIS;
+
+PERF_TEST_P(DenseOpticalFlow_DIS, perf,
+            Combine(Values("PRESET_ULTRAFAST", "PRESET_FAST", "PRESET_MEDIUM"), Values(szVGA, sz720p, sz1080p)))
+{
+    DISParams params = GetParam();
+
+    // use strings to print preset names in the perf test results:
+    String preset_string = get<0>(params);
+    int preset = DISOpticalFlow::PRESET_FAST;
+    if (preset_string == "PRESET_ULTRAFAST")
+        preset = DISOpticalFlow::PRESET_ULTRAFAST;
+    else if (preset_string == "PRESET_FAST")
+        preset = DISOpticalFlow::PRESET_FAST;
+    else if (preset_string == "PRESET_MEDIUM")
+        preset = DISOpticalFlow::PRESET_MEDIUM;
+    Size sz = get<1>(params);
+
+    Mat frame1(sz, CV_8U);
+    Mat frame2(sz, CV_8U);
+    Mat flow;
+
+    MakeArtificialExample(frame1, frame2);
+
+    cv::setNumThreads(cv::getNumberOfCPUs());
+    TEST_CYCLE_N(10)
+    {
+        Ptr<DenseOpticalFlow> algo = createOptFlow_DIS(preset);
+        algo->calc(frame1, frame2, flow);
+    }
+
+    SANITY_CHECK_NOTHING();
+}
+
+void MakeArtificialExample(Mat &dst_frame1, Mat &dst_frame2)
+{
+    int src_scale = 2;
+    int OF_scale = 6;
+    double sigma = dst_frame1.cols / 300;
+
+    Mat tmp(Size(dst_frame1.cols / (int)pow(2, src_scale), dst_frame1.rows / (int)pow(2, src_scale)), CV_8U);
+    randu(tmp, 0, 255);
+    resize(tmp, dst_frame1, dst_frame1.size(), 0.0, 0.0, INTER_LINEAR);
+    resize(tmp, dst_frame2, dst_frame2.size(), 0.0, 0.0, INTER_LINEAR);
+
+    Mat displacement_field(Size(dst_frame1.cols / (int)pow(2, OF_scale), dst_frame1.rows / (int)pow(2, OF_scale)),
+                           CV_32FC2);
+    randn(displacement_field, 0.0, sigma);
+    resize(displacement_field, displacement_field, dst_frame2.size(), 0.0, 0.0, INTER_CUBIC);
+    for (int i = 0; i < displacement_field.rows; i++)
+        for (int j = 0; j < displacement_field.cols; j++)
+            displacement_field.at<Vec2f>(i, j) += Vec2f((float)j, (float)i);
+
+    remap(dst_frame2, dst_frame2, displacement_field, Mat(), INTER_LINEAR, BORDER_REPLICATE);
+}

modules/optflow/perf/perf_main.cpp

+#include "perf_precomp.hpp"
+
+CV_PERF_TEST_MAIN(optflow)

modules/optflow/perf/perf_precomp.hpp

+#ifdef __GNUC__
+#  pragma GCC diagnostic ignored "-Wmissing-declarations"
+#  if defined __clang__ || defined __APPLE__
+#    pragma GCC diagnostic ignored "-Wmissing-prototypes"
+#    pragma GCC diagnostic ignored "-Wextra"
+#  endif
+#endif
+
+#ifndef __OPENCV_PERF_PRECOMP_HPP__
+#define __OPENCV_PERF_PRECOMP_HPP__
+
+#include "opencv2/ts.hpp"
+#include "opencv2/imgproc.hpp"
+#include "opencv2/optflow.hpp"
+#include "opencv2/highgui.hpp"
+
+#endif

modules/optflow/perf/perf_variational_refinement.cpp

+/*
+*  By downloading, copying, installing or using the software you agree to this license.
+*  If you do not agree to this license, do not download, install,
+*  copy or use the software.
+*
+*
+*  License Agreement
+*  For Open Source Computer Vision Library
+*  (3 - clause BSD License)
+*
+*  Redistribution and use in source and binary forms, with or without modification,
+*  are permitted provided that the following conditions are met :
+*
+*  *Redistributions of source code must retain the above copyright notice,
+*  this list of conditions and the following disclaimer.
+*
+*  * Redistributions in binary form must reproduce the above copyright notice,
+*  this list of conditions and the following disclaimer in the documentation
+*  and / or other materials provided with the distribution.
+*
+*  * Neither the names of the copyright holders nor the names of the contributors
+*  may be used to endorse or promote products derived from this software
+*  without specific prior written permission.
+*
+*  This software is provided by the copyright holders and contributors "as is" and
+*  any express or implied warranties, including, but not limited to, the implied
+*  warranties of merchantability and fitness for a particular purpose are disclaimed.
+*  In no event shall copyright holders or contributors be liable for any direct,
+*  indirect, incidental, special, exemplary, or consequential damages
+*  (including, but not limited to, procurement of substitute goods or services;
+*  loss of use, data, or profits; or business interruption) however caused
+*  and on any theory of liability, whether in contract, strict liability,
+*  or tort(including negligence or otherwise) arising in any way out of
+*  the use of this software, even if advised of the possibility of such damage.
+*/
+
+#include "perf_precomp.hpp"
+
+using std::tr1::tuple;
+using std::tr1::get;
+using namespace perf;
+using namespace testing;
+using namespace cv;
+using namespace cv::optflow;
+
+typedef tuple<Size, int, int> VarRefParams;
+typedef TestBaseWithParam<VarRefParams> DenseOpticalFlow_VariationalRefinement;
+
+PERF_TEST_P(DenseOpticalFlow_VariationalRefinement, perf, Combine(Values(szQVGA, szVGA), Values(5, 10), Values(5, 10)))
+{
+    VarRefParams params = GetParam();
+    Size sz = get<0>(params);
+    int sorIter = get<1>(params);
+    int fixedPointIter = get<2>(params);
+
+    Mat frame1(sz, CV_8U);
+    Mat frame2(sz, CV_8U);
+    Mat flow(sz, CV_32FC2);
+
+    randu(frame1, 0, 255);
+    randu(frame2, 0, 255);
+    flow.setTo(0.0f);
+
+    cv::setNumThreads(cv::getNumberOfCPUs());
+    TEST_CYCLE_N(10)
+    {
+        Ptr<VariationalRefinement> var = createVariationalFlowRefinement();
+        var->setAlpha(20.0f);
+        var->setGamma(10.0f);
+        var->setDelta(5.0f);
+        var->setSorIterations(sorIter);
+        var->setFixedPointIterations(fixedPointIter);
+        var->calc(frame1, frame2, flow);
+    }
+
+    SANITY_CHECK_NOTHING();
+}

modules/optflow/samples/optical_flow_evaluation.cpp

@@ -12,7 +12,7 @@ using namespace optflow;
 const String keys = "{help h usage ? |      | print this message   }"
         "{@image1        |      | image1               }"
         "{@image2        |      | image2               }"
-        "{@algorithm     |      | [farneback, simpleflow, tvl1, deepflow, sparsetodenseflow, pcaflow or DISflow] }"
+        "{@algorithm     |      | [farneback, simpleflow, tvl1, deepflow, sparsetodenseflow, pcaflow, DISflow_ultrafast, DISflow_fast, DISflow_medium] }"
         "{@groundtruth   |      | path to the .flo file  (optional), Middlebury format }"
         "{m measure      |endpoint| error measure - [endpoint or angular] }"
         "{r region       |all   | region to compute stats about [all, discontinuities, untextured] }"
@@ -236,7 +236,7 @@ int main( int argc, char** argv )
     if ( i2.depth() != CV_8U )
         i2.convertTo(i2, CV_8U);
 
-    if ( (method == "farneback" || method == "tvl1" || method == "deepflow" || method == "DISflow") && i1.channels() == 3 )
+    if ( (method == "farneback" || method == "tvl1" || method == "deepflow" || method == "DISflow_ultrafast" || method == "DISflow_fast" || method == "DISflow_medium") && i1.channels() == 3 )
     {   // 1-channel images are expected
         cvtColor(i1, i1, COLOR_BGR2GRAY);
         cvtColor(i2, i2, COLOR_BGR2GRAY);
@@ -268,8 +268,12 @@ int main( int argc, char** argv )
         else
             algorithm = createOptFlow_PCAFlow();
     }
-    else if ( method == "DISflow" )
-        algorithm = createOptFlow_DIS();
+    else if ( method == "DISflow_ultrafast" )
+        algorithm = createOptFlow_DIS(DISOpticalFlow::PRESET_ULTRAFAST);
+    else if (method == "DISflow_fast")
+        algorithm = createOptFlow_DIS(DISOpticalFlow::PRESET_FAST);
+    else if (method == "DISflow_medium")
+        algorithm = createOptFlow_DIS(DISOpticalFlow::PRESET_MEDIUM);
     else
     {
         printf("Wrong method!\n");

modules/optflow/test/test_simpleflow.cpp → modules/optflow/test/test_OF_accuracy.cpp

@@ -40,74 +40,39 @@
 //M*/
 
 #include "test_precomp.hpp"
-
-#include <string>
+#include <fstream>
 
 using namespace std;
+using namespace cv;
+using namespace cvtest;
+using namespace optflow;
 
-/* ///////////////////// simpleflow_test ///////////////////////// */
-
-class CV_SimpleFlowTest : public cvtest::BaseTest
-{
-public:
-    CV_SimpleFlowTest();
-protected:
-    void run(int);
-};
-
+static string getDataDir() { return TS::ptr()->get_data_path(); }
 
-CV_SimpleFlowTest::CV_SimpleFlowTest() {}
-
-static bool readOpticalFlowFromFile(FILE* file, cv::Mat& flow) {
-  char header[5];
-  if (fread(header, 1, 4, file) < 4 && (string)header != "PIEH") {
-    return false;
-  }
+static bool isFlowCorrect(float u) { return !cvIsNaN(u) && (fabs(u) < 1e9); }
 
-  int cols, rows;
-  if (fread(&cols, sizeof(int), 1, file) != 1||
-      fread(&rows, sizeof(int), 1, file) != 1) {
-    return false;
-  }
-
-  flow = cv::Mat::zeros(rows, cols, CV_32FC2);
-
-  for (int i = 0; i < rows; ++i) {
-    for (int j = 0; j < cols; ++j) {
-      cv::Vec2f flow_at_point;
-      if (fread(&(flow_at_point[0]), sizeof(float), 1, file) != 1 ||
-          fread(&(flow_at_point[1]), sizeof(float), 1, file) != 1) {
-        return false;
-      }
-      flow.at<cv::Vec2f>(i, j) = flow_at_point;
-    }
-  }
-
-  return true;
-}
-
-static bool isFlowCorrect(float u) {
-  return !cvIsNaN(u) && (fabs(u) < 1e9);
-}
-
-static float calc_rmse(cv::Mat flow1, cv::Mat flow2) {
+static float calcRMSE(Mat flow1, Mat flow2)
+{
     float sum = 0;
     int counter = 0;
     const int rows = flow1.rows;
     const int cols = flow1.cols;
 
-  for (int y = 0; y < rows; ++y) {
-    for (int x = 0; x < cols; ++x) {
-      cv::Vec2f flow1_at_point = flow1.at<cv::Vec2f>(y, x);
-      cv::Vec2f flow2_at_point = flow2.at<cv::Vec2f>(y, x);
+    for (int y = 0; y < rows; ++y)
+    {
+        for (int x = 0; x < cols; ++x)
+        {
+            Vec2f flow1_at_point = flow1.at<Vec2f>(y, x);
+            Vec2f flow2_at_point = flow2.at<Vec2f>(y, x);
 
             float u1 = flow1_at_point[0];
             float v1 = flow1_at_point[1];
             float u2 = flow2_at_point[0];
             float v2 = flow2_at_point[1];
 
-      if (isFlowCorrect(u1) && isFlowCorrect(u2) && isFlowCorrect(v1) && isFlowCorrect(v2)) {
-        sum += (u1-u2)*(u1-u2) + (v1-v2)*(v1-v2);
+            if (isFlowCorrect(u1) && isFlowCorrect(u2) && isFlowCorrect(v1) && isFlowCorrect(v2))
+            {
+                sum += (u1 - u2) * (u1 - u2) + (v1 - v2) * (v1 - v2);
                 counter++;
             }
         }
@@ -115,76 +80,111 @@ static float calc_rmse(cv::Mat flow1, cv::Mat flow2) {
     return (float)sqrt(sum / (1e-9 + counter));
 }
 
-void CV_SimpleFlowTest::run(int) {
-    const float MAX_RMSE = 0.6f;
-    const string frame1_path = ts->get_data_path() + "optflow/RubberWhale1.png";
-    const string frame2_path = ts->get_data_path() + "optflow/RubberWhale2.png";
-    const string gt_flow_path = ts->get_data_path() + "optflow/RubberWhale.flo";
-
-    cv::Mat frame1 = cv::imread(frame1_path);
-    cv::Mat frame2 = cv::imread(frame2_path);
-
-    if (frame1.empty()) {
-      ts->printf(cvtest::TS::LOG, "could not read image %s\n", frame2_path.c_str());
-      ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
-      return;
-    }
-
-    if (frame2.empty()) {
-      ts->printf(cvtest::TS::LOG, "could not read image %s\n", frame2_path.c_str());
-      ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
-      return;
-    }
-
-    if (frame1.rows != frame2.rows && frame1.cols != frame2.cols) {
-      ts->printf(cvtest::TS::LOG, "images should be of equal sizes (%s and %s)",
-                 frame1_path.c_str(), frame2_path.c_str());
-      ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
-      return;
-    }
-
-    if (frame1.type() != 16 || frame2.type() != 16) {
-      ts->printf(cvtest::TS::LOG, "images should be of equal type CV_8UC3 (%s and %s)",
-                 frame1_path.c_str(), frame2_path.c_str());
-      ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
-      return;
-    }
-
-    cv::Mat flow_gt;
+bool readRubberWhale(Mat &dst_frame_1, Mat &dst_frame_2, Mat &dst_GT)
+{
+    const string frame1_path = getDataDir() + "optflow/RubberWhale1.png";
+    const string frame2_path = getDataDir() + "optflow/RubberWhale2.png";
+    const string gt_flow_path = getDataDir() + "optflow/RubberWhale.flo";
 
-    FILE* gt_flow_file = fopen(gt_flow_path.c_str(), "rb");
-    if (gt_flow_file == NULL) {
-      ts->printf(cvtest::TS::LOG, "could not read ground-thuth flow from file %s",
-                 gt_flow_path.c_str());
-      ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
-      return;
-    }
+    dst_frame_1 = imread(frame1_path);
+    dst_frame_2 = imread(frame2_path);
+    dst_GT = readOpticalFlow(gt_flow_path);
 
-    if (!readOpticalFlowFromFile(gt_flow_file, flow_gt)) {
-      ts->printf(cvtest::TS::LOG, "error while reading flow data from file %s",
-                 gt_flow_path.c_str());
-      ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
-      return;
-    }
-    fclose(gt_flow_file);
+    if (dst_frame_1.empty() || dst_frame_2.empty() || dst_GT.empty())
+        return false;
+    else
+        return true;
+}
 
-    cv::Mat flow;
-    cv::optflow::calcOpticalFlowSF(frame1, frame2, flow, 3, 2, 4);
+TEST(DenseOpticalFlow_SimpleFlow, ReferenceAccuracy)
+{
+    Mat frame1, frame2, GT;
+    ASSERT_TRUE(readRubberWhale(frame1, frame2, GT));
+    float target_RMSE = 0.37f;
+
+    Mat flow;
+    Ptr<DenseOpticalFlow> algo;
+    algo = createOptFlow_SimpleFlow();
+    algo->calc(frame1, frame2, flow);
+    ASSERT_EQ(GT.rows, flow.rows);
+    ASSERT_EQ(GT.cols, flow.cols);
+    EXPECT_LE(calcRMSE(GT, flow), target_RMSE);
+}
 
-    float rmse = calc_rmse(flow_gt, flow);
+TEST(DenseOpticalFlow_DeepFlow, ReferenceAccuracy)
+{
+    Mat frame1, frame2, GT;
+    ASSERT_TRUE(readRubberWhale(frame1, frame2, GT));
+    float target_RMSE = 0.35f;
+    cvtColor(frame1, frame1, COLOR_BGR2GRAY);
+    cvtColor(frame2, frame2, COLOR_BGR2GRAY);
+
+    Mat flow;
+    Ptr<DenseOpticalFlow> algo;
+    algo = createOptFlow_DeepFlow();
+    algo->calc(frame1, frame2, flow);
+    ASSERT_EQ(GT.rows, flow.rows);
+    ASSERT_EQ(GT.cols, flow.cols);
+    EXPECT_LE(calcRMSE(GT, flow), target_RMSE);
+}
 
-    ts->printf(cvtest::TS::LOG, "Optical flow estimation RMSE for SimpleFlow algorithm : %lf\n",
-               rmse);
+TEST(DenseOpticalFlow_SparseToDenseFlow, ReferenceAccuracy)
+{
+    Mat frame1, frame2, GT;
+    ASSERT_TRUE(readRubberWhale(frame1, frame2, GT));
+    float target_RMSE = 0.52f;
+
+    Mat flow;
+    Ptr<DenseOpticalFlow> algo;
+    algo = createOptFlow_SparseToDense();
+    algo->calc(frame1, frame2, flow);
+    ASSERT_EQ(GT.rows, flow.rows);
+    ASSERT_EQ(GT.cols, flow.cols);
+    EXPECT_LE(calcRMSE(GT, flow), target_RMSE);
+}
 
-    if (rmse > MAX_RMSE) {
-      ts->printf( cvtest::TS::LOG,
-                 "Too big rmse error : %lf ( >= %lf )\n", rmse, MAX_RMSE);
-      ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
-      return;
+TEST(DenseOpticalFlow_DIS, ReferenceAccuracy)
+{
+    Mat frame1, frame2, GT;
+    ASSERT_TRUE(readRubberWhale(frame1, frame2, GT));
+    int presets[] = {DISOpticalFlow::PRESET_ULTRAFAST, DISOpticalFlow::PRESET_FAST, DISOpticalFlow::PRESET_MEDIUM};
+    float target_RMSE[] = {0.86f, 0.74f, 0.49f};
+    cvtColor(frame1, frame1, COLOR_BGR2GRAY);
+    cvtColor(frame2, frame2, COLOR_BGR2GRAY);
+
+    Ptr<DenseOpticalFlow> algo;
+
+    // iterate over presets:
+    for (int i = 0; i < 3; i++)
+    {
+        Mat flow;
+        algo = createOptFlow_DIS(presets[i]);
+        algo->calc(frame1, frame2, flow);
+        ASSERT_EQ(GT.rows, flow.rows);
+        ASSERT_EQ(GT.cols, flow.cols);
+        EXPECT_LE(calcRMSE(GT, flow), target_RMSE[i]);
     }
 }
 
-
-TEST(Video_OpticalFlowSimpleFlow, accuracy) { CV_SimpleFlowTest test; test.safe_run(); }
-
-/* End of file. */
+TEST(DenseOpticalFlow_VariationalRefinement, ReferenceAccuracy)
+{
+    Mat frame1, frame2, GT;
+    ASSERT_TRUE(readRubberWhale(frame1, frame2, GT));
+    float target_RMSE = 0.86f;
+    cvtColor(frame1, frame1, COLOR_BGR2GRAY);
+    cvtColor(frame2, frame2, COLOR_BGR2GRAY);
+
+    Ptr<VariationalRefinement> var_ref;
+    var_ref = createVariationalFlowRefinement();
+    var_ref->setAlpha(20.0f);
+    var_ref->setDelta(5.0f);
+    var_ref->setGamma(10.0f);
+    var_ref->setSorIterations(25);
+    var_ref->setFixedPointIterations(25);
+    Mat flow(frame1.size(), CV_32FC2);
+    flow.setTo(0.0f);
+    var_ref->calc(frame1, frame2, flow);
+    ASSERT_EQ(GT.rows, flow.rows);
+    ASSERT_EQ(GT.cols, flow.cols);
+    EXPECT_LE(calcRMSE(GT, flow), target_RMSE);
+}

modules/optflow/test/test_sparsetodenseflow.cpp → modules/optflow/test/test_OF_reproducibility.cpp

@@ -40,107 +40,120 @@
 //M*/
 
 #include "test_precomp.hpp"
-
-#include <string>
+#include <fstream>
 
 using namespace std;
+using namespace std::tr1;
 using namespace cv;
+using namespace cvtest;
+using namespace perf;
+using namespace testing;
+using namespace optflow;
 
-/* ///////////////////// sparsetodenseflow_test ///////////////////////// */
+typedef tuple<Size> OFParams;
+typedef TestWithParam<OFParams> DenseOpticalFlow_DIS;
+typedef TestWithParam<OFParams> DenseOpticalFlow_VariationalRefinement;
 
-class CV_SparseToDenseFlowTest : public cvtest::BaseTest
+TEST_P(DenseOpticalFlow_DIS, MultithreadReproducibility)
 {
-protected:
-    void run(int);
-};
-
-static bool isFlowCorrect(float u) {
-  return !cvIsNaN(u) && (fabs(u) < 1e9);
-}
-
-static float calc_rmse(Mat flow1, Mat flow2) {
-  float sum = 0;
-  int counter = 0;
-  const int rows = flow1.rows;
-  const int cols = flow1.cols;
-
-  for (int y = 0; y < rows; ++y) {
-    for (int x = 0; x < cols; ++x) {
-      Vec2f flow1_at_point = flow1.at<Vec2f>(y, x);
-      Vec2f flow2_at_point = flow2.at<Vec2f>(y, x);
-
-      float u1 = flow1_at_point[0];
-      float v1 = flow1_at_point[1];
-      float u2 = flow2_at_point[0];
-      float v2 = flow2_at_point[1];
-
-      if (isFlowCorrect(u1) && isFlowCorrect(u2) && isFlowCorrect(v1) && isFlowCorrect(v2)) {
-        sum += (u1-u2)*(u1-u2) + (v1-v2)*(v1-v2);
-        counter++;
-      }
-    }
+    double MAX_DIF = 0.01;
+    double MAX_MEAN_DIF = 0.001;
+    int loopsCount = 2;
+    RNG rng(0);
+
+    OFParams params = GetParam();
+    Size size = get<0>(params);
+
+    for (int iter = 0; iter <= loopsCount; iter++)
+    {
+        Mat frame1(size, CV_8U);
+        randu(frame1, 0, 255);
+        Mat frame2(size, CV_8U);
+        randu(frame2, 0, 255);
+
+        Ptr<DISOpticalFlow> algo = createOptFlow_DIS();
+        int psz = rng.uniform(4, 16);
+        int pstr = rng.uniform(1, psz - 1);
+        int grad_iter = rng.uniform(1, 64);
+        int var_iter = rng.uniform(0, 10);
+        bool use_mean_normalization = !!rng.uniform(0, 2);
+        bool use_spatial_propagation = !!rng.uniform(0, 2);
+        algo->setFinestScale(0);
+        algo->setPatchSize(psz);
+        algo->setPatchStride(pstr);
+        algo->setGradientDescentIterations(grad_iter);
+        algo->setVariationalRefinementIterations(var_iter);
+        algo->setUseMeanNormalization(use_mean_normalization);
+        algo->setUseSpatialPropagation(use_spatial_propagation);
+
+        cv::setNumThreads(cv::getNumberOfCPUs());
+        Mat resMultiThread;
+        algo->calc(frame1, frame2, resMultiThread);
+
+        cv::setNumThreads(1);
+        Mat resSingleThread;
+        algo->calc(frame1, frame2, resSingleThread);
+
+        EXPECT_LE(cv::norm(resSingleThread, resMultiThread, NORM_INF), MAX_DIF);
+        EXPECT_LE(cv::norm(resSingleThread, resMultiThread, NORM_L1), MAX_MEAN_DIF * frame1.total());
+
+        // resulting flow should be within the frame bounds:
+        double min_val, max_val;
+        minMaxLoc(resMultiThread, &min_val, &max_val);
+        EXPECT_LE(abs(min_val), sqrt(size.height * size.height + size.width * size.width));
+        EXPECT_LE(abs(max_val), sqrt(size.height * size.height + size.width * size.width));
     }
-  return (float)sqrt(sum / (1e-9 + counter));
 }
 
-void CV_SparseToDenseFlowTest::run(int) {
-    const float MAX_RMSE = 0.6f;
-    const string frame1_path = ts->get_data_path() + "optflow/RubberWhale1.png";
-    const string frame2_path = ts->get_data_path() + "optflow/RubberWhale2.png";
-    const string gt_flow_path = ts->get_data_path() + "optflow/RubberWhale.flo";
-
-    Mat frame1 = imread(frame1_path);
-    Mat frame2 = imread(frame2_path);
-
-    if (frame1.empty()) {
-      ts->printf(cvtest::TS::LOG, "could not read image %s\n", frame2_path.c_str());
-      ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
-      return;
-    }
-
-    if (frame2.empty()) {
-      ts->printf(cvtest::TS::LOG, "could not read image %s\n", frame2_path.c_str());
-      ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
-      return;
-    }
-
-    if (frame1.rows != frame2.rows && frame1.cols != frame2.cols) {
-      ts->printf(cvtest::TS::LOG, "images should be of equal sizes (%s and %s)",
-                 frame1_path.c_str(), frame2_path.c_str());
-      ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
-      return;
-    }
-
-    if (frame1.type() != 16 || frame2.type() != 16) {
-      ts->printf(cvtest::TS::LOG, "images should be of equal type CV_8UC3 (%s and %s)",
-                 frame1_path.c_str(), frame2_path.c_str());
-      ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
-      return;
-    }
-
-    Mat flow_gt = optflow::readOpticalFlow(gt_flow_path);
-    if(flow_gt.empty()) {
-      ts->printf(cvtest::TS::LOG, "error while reading flow data from file %s",
-                 gt_flow_path.c_str());
-      ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
-      return;
-    }
+INSTANTIATE_TEST_CASE_P(FullSet, DenseOpticalFlow_DIS, Values(szODD, szQVGA));
 
-    Mat flow;
-    optflow::calcOpticalFlowSparseToDense(frame1, frame2, flow);
-
-    float rmse = calc_rmse(flow_gt, flow);
-
-    ts->printf(cvtest::TS::LOG, "Optical flow estimation RMSE for SparseToDenseFlow algorithm : %lf\n",
-               rmse);
-
-    if (rmse > MAX_RMSE) {
-      ts->printf( cvtest::TS::LOG,
-                 "Too big rmse error : %lf ( >= %lf )\n", rmse, MAX_RMSE);
-      ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
-      return;
+TEST_P(DenseOpticalFlow_VariationalRefinement, MultithreadReproducibility)
+{
+    double MAX_DIF = 0.01;
+    double MAX_MEAN_DIF = 0.001;
+    float input_flow_rad = 5.0;
+    int loopsCount = 2;
+    RNG rng(0);
+
+    OFParams params = GetParam();
+    Size size = get<0>(params);
+
+    for (int iter = 0; iter <= loopsCount; iter++)
+    {
+        Mat frame1(size, CV_8U);
+        randu(frame1, 0, 255);
+        Mat frame2(size, CV_8U);
+        randu(frame2, 0, 255);
+        Mat flow(size, CV_32FC2);
+        randu(flow, -input_flow_rad, input_flow_rad);
+
+        Ptr<VariationalRefinement> var = createVariationalFlowRefinement();
+        var->setAlpha(rng.uniform(1.0f, 100.0f));
+        var->setGamma(rng.uniform(0.1f, 10.0f));
+        var->setDelta(rng.uniform(0.1f, 10.0f));
+        var->setSorIterations(rng.uniform(1, 20));
+        var->setFixedPointIterations(rng.uniform(1, 20));
+        var->setOmega(rng.uniform(1.01f, 1.99f));
+
+        cv::setNumThreads(cv::getNumberOfCPUs());
+        Mat resMultiThread;
+        flow.copyTo(resMultiThread);
+        var->calc(frame1, frame2, resMultiThread);
+
+        cv::setNumThreads(1);
+        Mat resSingleThread;
+        flow.copyTo(resSingleThread);
+        var->calc(frame1, frame2, resSingleThread);
+
+        EXPECT_LE(cv::norm(resSingleThread, resMultiThread, NORM_INF), MAX_DIF);
+        EXPECT_LE(cv::norm(resSingleThread, resMultiThread, NORM_L1), MAX_MEAN_DIF * frame1.total());
+
+        // resulting flow should be within the frame bounds:
+        double min_val, max_val;
+        minMaxLoc(resMultiThread, &min_val, &max_val);
+        EXPECT_LE(abs(min_val), sqrt(size.height * size.height + size.width * size.width));
+        EXPECT_LE(abs(max_val), sqrt(size.height * size.height + size.width * size.width));
     }
 }
 
-
-TEST(Video_OpticalFlowSparseToDenseFlow, accuracy) { CV_SparseToDenseFlowTest test; test.safe_run(); }
+INSTANTIATE_TEST_CASE_P(FullSet, DenseOpticalFlow_VariationalRefinement, Values(szODD, szQVGA));

modules/xfeatures2d/src/sift.cpp

@@ -579,7 +579,7 @@ static void calcSIFTDescriptor( const Mat& img, Point2f ptf, float ori, float sc
     float hist_width = SIFT_DESCR_SCL_FCTR * scl;
     int radius = cvRound(hist_width * 1.4142135623730951f * (d + 1) * 0.5f);
     // Clip the radius to the diagonal of the image to avoid autobuffer too large exception
-    radius = std::min(radius, (int) sqrt((double) img.cols*img.cols + img.rows*img.rows));
+    radius = std::min(radius, (int) sqrt(((double) img.cols)*img.cols + ((double) img.rows)*img.rows));
     cos_t /= hist_width;
     sin_t /= hist_width;