GSoC: New background subtraction algorithm

modules/bgsegm/CMakeLists.txt

 set(the_description "Background Segmentation Algorithms")
-ocv_define_module(bgsegm opencv_core opencv_imgproc opencv_video WRAP python)
+ocv_define_module(bgsegm opencv_core opencv_imgproc opencv_video opencv_calib3d WRAP python)

modules/bgsegm/doc/bgsegm.bib

@@ -15,3 +15,13 @@
   year={2012},
   organization={IEEE}
 }
+
+@inproceedings{LGuo2016,
+  author={L. Guo and D. Xu and Z. Qiang},
+  booktitle={2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
+  title={Background Subtraction Using Local SVD Binary Pattern},
+  year={2016},
+  pages={1159-1167},
+  doi={10.1109/CVPRW.2016.148},
+  month={June}
+}

modules/bgsegm/include/opencv2/bgsegm.hpp

@@ -242,6 +242,135 @@ createBackgroundSubtractorCNT(int minPixelStability = 15,
                               int maxPixelStability = 15*60,
                               bool isParallel = true);
 
+enum LSBPCameraMotionCompensation {
+    LSBP_CAMERA_MOTION_COMPENSATION_NONE = 0,
+    LSBP_CAMERA_MOTION_COMPENSATION_LK
+};
+
+/** @brief Implementation of the different yet better algorithm which is called GSOC, as it was implemented during GSOC and was not originated from any paper.
+
+This algorithm demonstrates better performance on CDNET 2014 dataset compared to other algorithms in OpenCV.
+ */
+class CV_EXPORTS_W BackgroundSubtractorGSOC : public BackgroundSubtractor
+{
+public:
+    // BackgroundSubtractor interface
+    CV_WRAP virtual void apply(InputArray image, OutputArray fgmask, double learningRate=-1) = 0;
+
+    CV_WRAP virtual void getBackgroundImage(OutputArray backgroundImage) const = 0;
+};
+
+/** @brief Background Subtraction using Local SVD Binary Pattern. More details about the algorithm can be found at @cite LGuo2016
+ */
+class CV_EXPORTS_W BackgroundSubtractorLSBP : public BackgroundSubtractor
+{
+public:
+    // BackgroundSubtractor interface
+    CV_WRAP virtual void apply(InputArray image, OutputArray fgmask, double learningRate=-1) = 0;
+
+    CV_WRAP virtual void getBackgroundImage(OutputArray backgroundImage) const = 0;
+};
+
+/** @brief This is for calculation of the LSBP descriptors.
+ */
+class CV_EXPORTS_W BackgroundSubtractorLSBPDesc
+{
+public:
+    static void calcLocalSVDValues(OutputArray localSVDValues, const Mat& frame);
+
+    static void computeFromLocalSVDValues(OutputArray desc, const Mat& localSVDValues, const Point2i* LSBPSamplePoints);
+
+    static void compute(OutputArray desc, const Mat& frame, const Point2i* LSBPSamplePoints);
+};
+
+/** @brief Creates an instance of BackgroundSubtractorGSOC algorithm.
+
+Implementation of the different yet better algorithm which is called GSOC, as it was implemented during GSOC and was not originated from any paper.
+
+@param mc Whether to use camera motion compensation.
+@param nSamples Number of samples to maintain at each point of the frame.
+@param replaceRate Probability of replacing the old sample - how fast the model will update itself.
+@param propagationRate Probability of propagating to neighbors.
+@param hitsThreshold How many positives the sample must get before it will be considered as a possible replacement.
+@param alpha Scale coefficient for threshold.
+@param beta Bias coefficient for threshold.
+@param blinkingSupressionDecay Blinking supression decay factor.
+@param blinkingSupressionMultiplier Blinking supression multiplier.
+@param noiseRemovalThresholdFacBG Strength of the noise removal for background points.
+@param noiseRemovalThresholdFacFG Strength of the noise removal for foreground points.
+ */
+CV_EXPORTS_W Ptr<BackgroundSubtractorGSOC> createBackgroundSubtractorGSOC(int mc = LSBP_CAMERA_MOTION_COMPENSATION_NONE, int nSamples = 20, float replaceRate = 0.003f, float propagationRate = 0.01f, int hitsThreshold = 32, float alpha = 0.01f, float beta = 0.0022f, float blinkingSupressionDecay = 0.1f, float blinkingSupressionMultiplier = 0.1f, float noiseRemovalThresholdFacBG = 0.0004f, float noiseRemovalThresholdFacFG = 0.0008f);
+
+/** @brief Creates an instance of BackgroundSubtractorLSBP algorithm.
+
+Background Subtraction using Local SVD Binary Pattern. More details about the algorithm can be found at @cite LGuo2016
+
+@param mc Whether to use camera motion compensation.
+@param nSamples Number of samples to maintain at each point of the frame.
+@param LSBPRadius LSBP descriptor radius.
+@param Tlower Lower bound for T-values. See @cite LGuo2016 for details.
+@param Tupper Upper bound for T-values. See @cite LGuo2016 for details.
+@param Tinc Increase step for T-values. See @cite LGuo2016 for details.
+@param Tdec Decrease step for T-values. See @cite LGuo2016 for details.
+@param Rscale Scale coefficient for threshold values.
+@param Rincdec Increase/Decrease step for threshold values.
+@param noiseRemovalThresholdFacBG Strength of the noise removal for background points.
+@param noiseRemovalThresholdFacFG Strength of the noise removal for foreground points.
+@param LSBPthreshold Threshold for LSBP binary string.
+@param minCount Minimal number of matches for sample to be considered as foreground.
+ */
+CV_EXPORTS_W Ptr<BackgroundSubtractorLSBP> createBackgroundSubtractorLSBP(int mc = LSBP_CAMERA_MOTION_COMPENSATION_NONE, int nSamples = 20, int LSBPRadius = 16, float Tlower = 2.0f, float Tupper = 32.0f, float Tinc = 1.0f, float Tdec = 0.05f, float Rscale = 10.0f, float Rincdec = 0.005f, float noiseRemovalThresholdFacBG = 0.0004f, float noiseRemovalThresholdFacFG = 0.0008f, int LSBPthreshold = 8, int minCount = 2);
+
+/** @brief Synthetic frame sequence generator for testing background subtraction algorithms.
+
+ It will generate the moving object on top of the background.
+ It will apply some distortion to the background to make the test more complex.
+ */
+class CV_EXPORTS_W SyntheticSequenceGenerator : public Algorithm
+{
+private:
+    const double amplitude;
+    const double wavelength;
+    const double wavespeed;
+    const double objspeed;
+    unsigned timeStep;
+    Point2d pos;
+    Point2d dir;
+    Mat background;
+    Mat object;
+    RNG rng;
+
+public:
+    /** @brief Creates an instance of SyntheticSequenceGenerator.
+
+    @param background Background image for object.
+    @param object Object image which will move slowly over the background.
+    @param amplitude Amplitude of wave distortion applied to background.
+    @param wavelength Length of waves in distortion applied to background.
+    @param wavespeed How fast waves will move.
+    @param objspeed How fast object will fly over background.
+     */
+    CV_WRAP SyntheticSequenceGenerator(InputArray background, InputArray object, double amplitude, double wavelength, double wavespeed, double objspeed);
+
+    /** @brief Obtain the next frame in the sequence.
+
+    @param frame Output frame.
+    @param gtMask Output ground-truth (reference) segmentation mask object/background.
+     */
+    CV_WRAP void getNextFrame(OutputArray frame, OutputArray gtMask);
+};
+
+/** @brief Creates an instance of SyntheticSequenceGenerator.
+
+@param background Background image for object.
+@param object Object image which will move slowly over the background.
+@param amplitude Amplitude of wave distortion applied to background.
+@param wavelength Length of waves in distortion applied to background.
+@param wavespeed How fast waves will move.
+@param objspeed How fast object will fly over background.
+ */
+CV_EXPORTS_W Ptr<SyntheticSequenceGenerator> createSyntheticSequenceGenerator(InputArray background, InputArray object, double amplitude = 2.0, double wavelength = 20.0, double wavespeed = 0.2, double objspeed = 6.0);
+
 //! @}
 
 }

modules/bgsegm/samples/evaluation.py

+import argparse
+import cv2
+import glob
+import numpy as np
+import os
+import time
+
+
+# This tool is intended for evaluation of different background subtraction algorithms presented in OpenCV.
+# Several presets with different settings are available. You can see them below.
+# This tool measures quality metrics as well as speed.
+
+
+ALGORITHMS_TO_EVALUATE = [
+    (cv2.bgsegm.createBackgroundSubtractorMOG, 'MOG', {}),
+    (cv2.bgsegm.createBackgroundSubtractorGMG, 'GMG', {}),
+    (cv2.bgsegm.createBackgroundSubtractorCNT, 'CNT', {}),
+    (cv2.bgsegm.createBackgroundSubtractorLSBP, 'LSBP-vanilla', {'nSamples': 20, 'LSBPRadius': 4, 'Tlower': 2.0, 'Tupper': 200.0, 'Tinc': 1.0, 'Tdec': 0.05, 'Rscale': 5.0, 'Rincdec': 0.05, 'LSBPthreshold': 8}),
+    (cv2.bgsegm.createBackgroundSubtractorLSBP, 'LSBP-speed', {'nSamples': 10, 'LSBPRadius': 16, 'Tlower': 2.0, 'Tupper': 32.0, 'Tinc': 1.0, 'Tdec': 0.05, 'Rscale': 10.0, 'Rincdec': 0.005, 'LSBPthreshold': 8}),
+    (cv2.bgsegm.createBackgroundSubtractorLSBP, 'LSBP-quality', {'nSamples': 20, 'LSBPRadius': 16, 'Tlower': 2.0, 'Tupper': 32.0, 'Tinc': 1.0, 'Tdec': 0.05, 'Rscale': 10.0, 'Rincdec': 0.005, 'LSBPthreshold': 8}),
+    (cv2.bgsegm.createBackgroundSubtractorLSBP, 'LSBP-camera-motion-compensation', {'mc': 1}),
+    (cv2.bgsegm.createBackgroundSubtractorGSOC, 'GSOC', {}),
+    (cv2.bgsegm.createBackgroundSubtractorGSOC, 'GSOC-camera-motion-compensation', {'mc': 1})
+]
+
+
+def contains_relevant_files(root):
+    return os.path.isdir(os.path.join(root, 'groundtruth')) and os.path.isdir(os.path.join(root, 'input'))
+
+
+def find_relevant_dirs(root):
+    relevant_dirs = []
+    for d in sorted(os.listdir(root)):
+        d = os.path.join(root, d)
+        if os.path.isdir(d):
+            if contains_relevant_files(d):
+                relevant_dirs += [d]
+            else:
+                relevant_dirs += find_relevant_dirs(d)
+    return relevant_dirs
+
+
+def load_sequence(root):
+    gt_dir, frames_dir = os.path.join(root, 'groundtruth'), os.path.join(root, 'input')
+    gt = sorted(glob.glob(os.path.join(gt_dir, '*.png')))
+    f = sorted(glob.glob(os.path.join(frames_dir, '*.jpg')))
+    assert(len(gt) == len(f))
+    return gt, f
+
+
+def evaluate_algorithm(gt, frames, algo, algo_arguments):
+    bgs = algo(**algo_arguments)
+    mask = []
+    t_start = time.time()
+
+    for i in range(len(gt)):
+        frame = np.uint8(cv2.imread(frames[i], cv2.IMREAD_COLOR))
+        mask.append(bgs.apply(frame))
+
+    average_duration = (time.time() - t_start) / len(gt)
+    average_precision, average_recall, average_f1, average_accuracy = [], [], [], []
+
+    for i in range(len(gt)):
+        gt_mask = np.uint8(cv2.imread(gt[i], cv2.IMREAD_GRAYSCALE))
+        roi = ((gt_mask == 255) | (gt_mask == 0))
+        if roi.sum() > 0:
+            gt_answer, answer = gt_mask[roi], mask[i][roi]
+
+            tp = ((answer == 255) & (gt_answer == 255)).sum()
+            tn = ((answer == 0) & (gt_answer == 0)).sum()
+            fp = ((answer == 255) & (gt_answer == 0)).sum()
+            fn = ((answer == 0) & (gt_answer == 255)).sum()
+
+            if tp + fp > 0:
+                average_precision.append(float(tp) / (tp + fp))
+            if tp + fn > 0:
+                average_recall.append(float(tp) / (tp + fn))
+            if tp + fn + fp > 0:
+                average_f1.append(2.0 * tp / (2.0 * tp + fn + fp))
+            average_accuracy.append(float(tp + tn) / (tp + tn + fp + fn))
+
+    return average_duration, np.mean(average_precision), np.mean(average_recall), np.mean(average_f1), np.mean(average_accuracy)
+
+
+def evaluate_on_sequence(seq, summary):
+    gt, frames = load_sequence(seq)
+    category, video_name = os.path.basename(os.path.dirname(seq)), os.path.basename(seq)
+    print('=== %s:%s ===' % (category, video_name))
+
+    for algo, algo_name, algo_arguments in ALGORITHMS_TO_EVALUATE:
+        print('Algorithm name: %s' % algo_name)
+        sec_per_step, precision, recall, f1, accuracy = evaluate_algorithm(gt, frames, algo, algo_arguments)
+        print('Average accuracy: %.3f' % accuracy)
+        print('Average precision: %.3f' % precision)
+        print('Average recall: %.3f' % recall)
+        print('Average F1: %.3f' % f1)
+        print('Average sec. per step: %.4f' % sec_per_step)
+        print('')
+
+        if category not in summary:
+            summary[category] = {}
+        if algo_name not in summary[category]:
+            summary[category][algo_name] = []
+        summary[category][algo_name].append((precision, recall, f1, accuracy))
+
+
+def main():
+    parser = argparse.ArgumentParser(description='Evaluate all background subtractors using Change Detection 2014 dataset')
+    parser.add_argument('--dataset_path', help='Path to the directory with dataset. It may contain multiple inner directories. It will be scanned recursively.', required=True)
+    parser.add_argument('--algorithm', help='Test particular algorithm instead of all.')
+
+    args = parser.parse_args()
+    dataset_dirs = find_relevant_dirs(args.dataset_path)
+    assert len(dataset_dirs) > 0, ("Passed directory must contain at least one sequence from the Change Detection dataset. There is no relevant directories in %s. Check that this directory is correct." % (args.dataset_path))
+    if args.algorithm is not None:
+        global ALGORITHMS_TO_EVALUATE
+        ALGORITHMS_TO_EVALUATE = filter(lambda a: a[1].lower() == args.algorithm.lower(), ALGORITHMS_TO_EVALUATE)
+    summary = {}
+
+    for seq in dataset_dirs:
+        evaluate_on_sequence(seq, summary)
+
+    for category in summary:
+        for algo_name in summary[category]:
+            summary[category][algo_name] = np.mean(summary[category][algo_name], axis=0)
+
+    for category in summary:
+        print('=== SUMMARY for %s (Precision, Recall, F1, Accuracy) ===' % category)
+        for algo_name in summary[category]:
+            print('%05s: %.3f %.3f %.3f %.3f' % ((algo_name,) + tuple(summary[category][algo_name])))
+
+
+if __name__ == '__main__':
+    main()

modules/bgsegm/samples/viz.py

+import numpy as np
+import cv2
+import argparse
+import os
+
+
+def main():
+    argparser = argparse.ArgumentParser(description='Vizualization of the LSBP/GSOC background subtraction algorithm.')
+
+    argparser.add_argument('-g', '--gt', help='Directory with ground-truth frames', required=True)
+    argparser.add_argument('-f', '--frames', help='Directory with input frames', required=True)
+    argparser.add_argument('-l', '--lsbp', help='Display LSBP instead of GSOC', default=False)
+    args = argparser.parse_args()
+
+    gt = map(lambda x: os.path.join(args.gt, x), os.listdir(args.gt))
+    gt.sort()
+    f = map(lambda x: os.path.join(args.frames, x), os.listdir(args.frames))
+    f.sort()
+
+    gt = np.uint8(map(lambda x: cv2.imread(x, cv2.IMREAD_GRAYSCALE), gt))
+    f = np.uint8(map(lambda x: cv2.imread(x, cv2.IMREAD_COLOR), f))
+
+    if not args.lsbp:
+        bgs = cv2.bgsegm.createBackgroundSubtractorGSOC()
+    else:
+        bgs = cv2.bgsegm.createBackgroundSubtractorLSBP()
+
+    for i in xrange(f.shape[0]):
+        cv2.imshow('Frame', f[i])
+        cv2.imshow('Ground-truth', gt[i])
+        mask = bgs.apply(f[i])
+        bg = bgs.getBackgroundImage()
+        cv2.imshow('BG', bg)
+        cv2.imshow('Output mask', mask)
+        k = cv2.waitKey(0)
+        if k == 27:
+            break
+
+
+if __name__ == '__main__':
+    main()

modules/bgsegm/samples/viz_synthetic_seq.py

+import cv2
+import argparse
+
+
+def main():
+    argparser = argparse.ArgumentParser(description='Vizualization of the SyntheticSequenceGenerator.')
+
+    argparser.add_argument('-b', '--background', help='Background image.', required=True)
+    argparser.add_argument('-o', '--obj', help='Object image. It must be strictly smaller than background.', required=True)
+    args = argparser.parse_args()
+
+    bg = cv2.imread(args.background)
+    obj = cv2.imread(args.obj)
+    generator = cv2.bgsegm.createSyntheticSequenceGenerator(bg, obj)
+
+    while True:
+        frame, mask = generator.getNextFrame()
+        cv2.imshow('Generated frame', frame)
+        cv2.imshow('Generated mask', mask)
+        k = cv2.waitKey(int(1000.0 / 30))
+        if k == 27:
+            break
+
+
+if __name__ == '__main__':
+    main()

modules/bgsegm/src/synthetic_seq.cpp

+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  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
+//
+// Copyright (C) 2000, Intel Corporation, all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's 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.
+//
+//   * The name of the copyright holders may not 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 the Intel Corporation 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.
+//
+//M*/
+
+/**
+ * @file   synthetic_seq.cpp
+ * @author Vladislav Samsonov <vvladxx@gmail.com>
+ * @brief  Synthetic frame sequence generator for testing background subtraction algorithms.
+ *
+*/
+
+#include "precomp.hpp"
+
+namespace cv
+{
+namespace bgsegm
+{
+namespace
+{
+
+inline int clamp(int x, int l, int u) {
+    return ((x) < (l)) ? (l) : (((x) > (u)) ? (u) : (x));
+}
+
+inline int within(int a, int b, int c) {
+    return (((a) <= (b)) && ((b) <= (c))) ? 1 : 0;
+}
+
+void bilinearInterp(uint8_t* dest, double x, double y, unsigned bpp, const uint8_t** values) {
+    x = std::fmod(x, 1.0);
+    y = std::fmod(y, 1.0);
+
+    if (x < 0.0)
+        x += 1.0;
+    if (y < 0.0)
+        y += 1.0;
+
+    for (unsigned i = 0; i < bpp; i++) {
+        double m0 = (1.0 - x) * values[0][i] + x * values[1][i];
+        double m1 = (1.0 - x) * values[2][i] + x * values[3][i];
+        dest[i] = (uint8_t) ((1.0 - y) * m0 + y * m1);
+    }
+}
+
+// Static background is a way too easy test. We will add distortion to it.
+void waveDistortion(const uint8_t* src, uint8_t* dst, int width, int height, int bypp, double amplitude, double wavelength, double phase) {
+    const uint8_t zeroes[4] = {0, 0, 0, 0};
+    const long rowsiz = width * bypp;
+    const double xhsiz = (double) width / 2.0;
+    const double yhsiz = (double) height / 2.0;
+    double xscale, yscale;
+
+    if (xhsiz < yhsiz) {
+        xscale = yhsiz / xhsiz;
+        yscale = 1.0;
+    }
+    else if (xhsiz > yhsiz) {
+        xscale = 1.0;
+        yscale = xhsiz / yhsiz;
+    }
+    else {
+        xscale = 1.0;
+        yscale = 1.0;
+    }
+
+    wavelength *= 2;
+
+    for (int y = 0; y < height; y++) {
+        uint8_t* dest = dst;
+
+        for (int x = 0; x < width; x++) {
+            const double dx = x * xscale;
+            const double dy = y * yscale;
+            const double d = sqrt (dx * dx + dy * dy);
+            const double amnt = amplitude * sin(((d / wavelength) * (2.0 * M_PI) + phase));
+            const double needx = (amnt + dx) / xscale;
+            const double needy = (amnt + dy) / yscale;
+            const int xi = clamp(int(needx), 0, width - 2);
+            const int yi = clamp(int(needy), 0, height - 2);
+
+            const uint8_t* p = src + rowsiz * yi + xi * bypp;
+
+            const int x1_in = within(0, xi, width - 1);
+            const int y1_in = within(0, yi, height - 1);
+            const int x2_in = within(0, xi + 1, width - 1);
+            const int y2_in = within(0, yi + 1, height - 1);
+            const uint8_t* values[4];
+
+            if (x1_in && y1_in)
+                values[0] = p;
+            else
+                values[0] = zeroes;
+
+            if (x2_in && y1_in)
+                values[1] = p + bypp;
+            else
+                values[1] = zeroes;
+
+            if (x1_in && y2_in)
+                values[2] = p + rowsiz;
+            else
+                values[2] = zeroes;
+
+            if (x2_in && y2_in)
+                values[3] = p + bypp + rowsiz;
+            else
+                values[3] = zeroes;
+
+            bilinearInterp(dest, needx, needy, bypp, values);
+            dest += bypp;
+        }
+
+        dst += rowsiz;
+    }
+}
+
+}
+
+SyntheticSequenceGenerator::SyntheticSequenceGenerator(InputArray _background, InputArray _object, double _amplitude, double _wavelength, double _wavespeed, double _objspeed)
+: amplitude(_amplitude), wavelength(_wavelength), wavespeed(_wavespeed), objspeed(_objspeed), timeStep(0) {
+    _background.getMat().copyTo(background);
+    _object.getMat().copyTo(object);
+
+    if (background.channels() == 1) {
+        cvtColor(background, background, COLOR_GRAY2BGR);
+    }
+
+    if (object.channels() == 1) {
+        cvtColor(object, object, COLOR_GRAY2BGR);
+    }
+
+    CV_Assert(background.channels() == 3);
+    CV_Assert(object.channels() == 3);
+    CV_Assert(background.size().width > object.size().width);
+    CV_Assert(background.size().height > object.size().height);
+
+    background.convertTo(background, CV_8U);
+    object.convertTo(object, CV_8U);
+
+    pos.x = (background.size().width - object.size().width) / 2;
+    pos.y = (background.size().height - object.size().height) / 2;
+
+    const double phi = rng.uniform(0.0, CV_2PI);
+    dir.x = std::cos(phi);
+    dir.y = std::sin(phi);
+}
+
+void SyntheticSequenceGenerator::getNextFrame(OutputArray _frame, OutputArray _gtMask) {
+    CV_Assert(!background.empty() && !object.empty());
+    const Size sz = background.size();
+
+    _frame.create(sz, CV_8UC3);
+    Mat frame = _frame.getMat();
+
+    CV_Assert(background.isContinuous() && frame.isContinuous());
+
+    waveDistortion(background.ptr(), frame.ptr(), sz.width, sz.height, 3, amplitude, wavelength, double(timeStep) * wavespeed);
+
+    const Size objSz = object.size();
+
+    object.copyTo(frame(Rect(Point2i(pos), objSz)));
+
+    while (pos.x + dir.x * objspeed < 0 || pos.x + dir.x * objspeed >= sz.width - objSz.width || pos.y + dir.y * objspeed < 0 || pos.y + dir.y * objspeed >= sz.height - objSz.height) {
+        const double phi = rng.uniform(0.0, CV_2PI);
+        dir.x = std::cos(phi);
+        dir.y = std::sin(phi);
+    }
+
+    _gtMask.create(sz, CV_8U);
+    Mat gtMask = _gtMask.getMat();
+    gtMask = 0;
+    gtMask(Rect(Point2i(pos), objSz)) = 255;
+
+    pos += dir * objspeed;
+    ++timeStep;
+}
+
+Ptr<SyntheticSequenceGenerator> createSyntheticSequenceGenerator(InputArray background, InputArray object, double amplitude, double wavelength, double wavespeed, double objspeed) {
+    return makePtr<SyntheticSequenceGenerator>(background, object, amplitude, wavelength, wavespeed, objspeed);
+}
+
+}
+}

modules/bgsegm/test/test_backgroundsubtractor_lsbp.cpp

+#include "test_precomp.hpp"
+#include <set>
+
+using namespace std;
+using namespace cv;
+using namespace cvtest;
+
+static string getDataDir() { return TS::ptr()->get_data_path(); }
+
+static string getLenaImagePath() { return getDataDir() + "shared/lena.png"; }
+
+// Simple synthetic illumination invariance test
+TEST(BackgroundSubtractor_LSBP, IlluminationInvariance)
+{
+    RNG rng;
+    Mat input(100, 100, CV_32FC3);
+
+    rng.fill(input, RNG::UNIFORM, 0.0f, 0.1f);
+
+    Mat lsv1, lsv2;
+    cv::bgsegm::BackgroundSubtractorLSBPDesc::calcLocalSVDValues(lsv1, input);
+    input *= 10;
+    cv::bgsegm::BackgroundSubtractorLSBPDesc::calcLocalSVDValues(lsv2, input);
+
+    ASSERT_LE(cv::norm(lsv1, lsv2), 0.04f);
+}
+
+TEST(BackgroundSubtractor_LSBP, Correctness)
+{
+    Mat input(3, 3, CV_32FC3);
+
+    float n = 0;
+    for (int i = 0; i < 3; ++i)
+        for (int j = 0; j < 3; ++j) {
+            input.at<Point3f>(i, j) = Point3f(n, n, n);
+            ++n;
+        }
+
+    Mat lsv;
+    bgsegm::BackgroundSubtractorLSBPDesc::calcLocalSVDValues(lsv, input);
+
+    EXPECT_LE(std::abs(lsv.at<float>(1, 1) - 0.0903614f), 0.001f);
+
+    input = 1;
+    bgsegm::BackgroundSubtractorLSBPDesc::calcLocalSVDValues(lsv, input);
+
+    EXPECT_LE(std::abs(lsv.at<float>(1, 1) - 0.0f), 0.001f);
+}
+
+TEST(BackgroundSubtractor_LSBP, Discrimination)
+{
+    Point2i LSBPSamplePoints[32];
+    for (int i = 0; i < 32; ++i) {
+        const double phi = i * CV_2PI / 32.0;
+        LSBPSamplePoints[i] = Point2i(int(4 * std::cos(phi)), int(4 * std::sin(phi)));
+    }
+
+    Mat lena = imread(getLenaImagePath());
+    Mat lsv;
+
+    lena.convertTo(lena, CV_32FC3);
+
+    bgsegm::BackgroundSubtractorLSBPDesc::calcLocalSVDValues(lsv, lena);
+
+    Scalar mean, var;
+    meanStdDev(lsv, mean, var);
+
+    EXPECT_GE(mean[0], 0.02);
+    EXPECT_LE(mean[0], 0.04);
+    EXPECT_GE(var[0], 0.03);
+
+    Mat desc;
+    bgsegm::BackgroundSubtractorLSBPDesc::computeFromLocalSVDValues(desc, lsv, LSBPSamplePoints);
+    Size sz = desc.size();
+    std::set<uint32_t> distinctive_elements;
+
+    for (int i = 0; i < sz.height; ++i)
+        for (int j = 0; j < sz.width; ++j)
+            distinctive_elements.insert(desc.at<uint32_t>(i, j));
+
+    EXPECT_GE(distinctive_elements.size(), 35000U);
+}
+
+static double scoreBitwiseReduce(const Mat& mask, const Mat& gtMask, uint8_t v1, uint8_t v2) {
+    Mat result;
+    cv::bitwise_and(mask == v1, gtMask == v2, result);
+    return cv::countNonZero(result);
+}
+
+template<typename T>
+static double evaluateBGSAlgorithm(Ptr<T> bgs) {
+    Mat background = imread(getDataDir() + "shared/fruits.png");
+    Mat object = imread(getDataDir() + "shared/baboon.png");
+    cv::resize(object, object, Size(100, 100));
+    Ptr<bgsegm::SyntheticSequenceGenerator> generator = bgsegm::createSyntheticSequenceGenerator(background, object);
+
+    double f1_mean = 0;
+    unsigned total = 0;
+
+    for (int frameNum = 1; frameNum <= 400; ++frameNum) {
+        Mat frame, gtMask;
+        generator->getNextFrame(frame, gtMask);
+
+        Mat mask;
+        bgs->apply(frame, mask);
+
+        Size sz = frame.size();
+        EXPECT_EQ(sz, gtMask.size());
+        EXPECT_EQ(gtMask.size(), mask.size());
+        EXPECT_EQ(mask.type(), gtMask.type());
+        EXPECT_EQ(mask.type(), CV_8U);
+
+        // We will give the algorithm some time for the proper background model inference.
+        // Almost all background subtraction algorithms have a problem with cold start and require some time for background model initialization.
+        // So we will not count first part of the frames in the score.
+        if (frameNum > 300) {
+            const double tp = scoreBitwiseReduce(mask, gtMask, 255, 255);
+            const double fp = scoreBitwiseReduce(mask, gtMask, 255, 0);
+            const double fn = scoreBitwiseReduce(mask, gtMask, 0, 255);
+
+            if (tp + fn + fp > 0) {
+                const double f1_score = 2.0 * tp / (2.0 * tp + fn + fp);
+                f1_mean += f1_score;
+                ++total;
+            }
+        }
+    }
+
+    f1_mean /= total;
+    return f1_mean;
+}
+
+TEST(BackgroundSubtractor_LSBP, Accuracy)
+{
+    EXPECT_GE(evaluateBGSAlgorithm(bgsegm::createBackgroundSubtractorGSOC()), 0.9);
+    EXPECT_GE(evaluateBGSAlgorithm(bgsegm::createBackgroundSubtractorLSBP()), 0.25);
+}