mirror of
https://gitlab.freedesktop.org/gstreamer/gstreamer.git
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63e2705e97
Split plugin into features including dynamic types which can be indiviually registered during a static build. More details here: https://gitlab.freedesktop.org/gstreamer/gst-build/-/merge_requests/199 https://gitlab.freedesktop.org/gstreamer/gstreamer/-/merge_requests/661 Part-of: <https://gitlab.freedesktop.org/gstreamer/gst-plugins-bad/-/merge_requests/2038>
775 lines
26 KiB
C++
775 lines
26 KiB
C++
/*
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* GStreamer
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* Copyright (C) 2013 Miguel Casas-Sanchez <miguelecasassanchez@gmail.com>
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* Except: Parts of code inside the preprocessor define CODE_FROM_OREILLY_BOOK,
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* which are downloaded from O'Reilly website
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* [http://examples.oreilly.com/9780596516130/]
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* and adapted. Its license reads:
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* "Oct. 3, 2008
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* Right to use this code in any way you want without warrenty, support or
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* any guarantee of it working. "
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*
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*
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* Permission is hereby granted, free of charge, to any person obtaining a
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* copy of this software and associated documentation files (the "Software"),
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* to deal in the Software without restriction, including without limitation
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* the rights to use, copy, modify, merge, publish, distribute, sublicense,
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* and/or sell copies of the Software, and to permit persons to whom the
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* Software is furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in
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* all copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
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* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
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* DEALINGS IN THE SOFTWARE.
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*
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* Alternatively, the contents of this file may be used under the
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* GNU Lesser General Public License Version 2.1 (the "LGPL"), in
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* which case the following provisions apply instead of the ones
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* mentioned above:
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*
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* This library is free software; you can redistribute it and/or
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* modify it under the terms of the GNU Library General Public
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* License as published by the Free Software Foundation; either
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* version 2 of the License, or (at your option) any later version.
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*
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* This library is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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* Library General Public License for more details.
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*
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* You should have received a copy of the GNU Library General Public
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* License along with this library; if not, write to the
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* Free Software Foundation, Inc., 51 Franklin St, Fifth Floor,
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* Boston, MA 02110-1301, USA.
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*/
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#define CODE_FROM_OREILLY_BOOK
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/**
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* SECTION:element-segmentation
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*
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* This element creates and updates a fg/bg model using one of several approaches.
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* The one called "codebook" refers to the codebook approach following the opencv
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* O'Reilly book [1] implementation of the algorithm described in K. Kim,
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* T. H. Chalidabhongse, D. Harwood and L. Davis [2]. BackgroundSubtractorMOG [3],
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* or MOG for shorts, refers to a Gaussian Mixture-based Background/Foreground
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* Segmentation Algorithm. OpenCV MOG implements the algorithm described in [4].
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* BackgroundSubtractorMOG2 [5], refers to another Gaussian Mixture-based
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* Background/Foreground segmentation algorithm. OpenCV MOG2 implements the
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* algorithm described in [6] and [7].
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*
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* [1] Learning OpenCV: Computer Vision with the OpenCV Library by Gary Bradski
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* and Adrian Kaehler, Published by O'Reilly Media, October 3, 2008
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* [2] "Real-time Foreground-Background Segmentation using Codebook Model",
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* Real-time Imaging, Volume 11, Issue 3, Pages 167-256, June 2005.
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* [3] http://opencv.itseez.com/modules/video/doc/motion_analysis_and_object_tracking.html#backgroundsubtractormog
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* [4] P. KadewTraKuPong and R. Bowden, "An improved adaptive background
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* mixture model for real-time tracking with shadow detection", Proc. 2nd
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* European Workshop on Advanced Video-Based Surveillance Systems, 2001
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* [5] http://opencv.itseez.com/modules/video/doc/motion_analysis_and_object_tracking.html#backgroundsubtractormog2
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* [6] Z.Zivkovic, "Improved adaptive Gaussian mixture model for background
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* subtraction", International Conference Pattern Recognition, UK, August, 2004.
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* [7] Z.Zivkovic, F. van der Heijden, "Efficient Adaptive Density Estimation
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* per Image Pixel for the Task of Background Subtraction", Pattern Recognition
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* Letters, vol. 27, no. 7, pages 773-780, 2006.
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*
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* ## Example launch line
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*
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* |[
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* gst-launch-1.0 v4l2src device=/dev/video0 ! videoconvert ! segmentation test-mode=true method=2 ! videoconvert ! ximagesink
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* ]|
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*/
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#ifdef HAVE_CONFIG_H
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#include <config.h>
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#endif
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#include "gstsegmentation.h"
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#include <opencv2/imgproc.hpp>
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GST_DEBUG_CATEGORY_STATIC (gst_segmentation_debug);
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#define GST_CAT_DEFAULT gst_segmentation_debug
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using namespace cv;
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/* Filter signals and args */
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enum
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{
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/* FILL ME */
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LAST_SIGNAL
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};
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enum
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{
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PROP_0,
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PROP_TEST_MODE,
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PROP_METHOD,
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PROP_LEARNING_RATE
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};
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typedef enum
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{
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METHOD_BOOK,
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METHOD_MOG,
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METHOD_MOG2
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} GstSegmentationMethod;
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#define DEFAULT_TEST_MODE FALSE
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#define DEFAULT_METHOD METHOD_MOG2
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#define DEFAULT_LEARNING_RATE 0.01
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#define GST_TYPE_SEGMENTATION_METHOD (gst_segmentation_method_get_type ())
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static GType
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gst_segmentation_method_get_type (void)
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{
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static GType etype = 0;
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if (etype == 0) {
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static const GEnumValue values[] = {
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{METHOD_BOOK, "Codebook-based segmentation (Bradski2008)", "codebook"},
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{METHOD_MOG, "Mixture-of-Gaussians segmentation (Bowden2001)", "mog"},
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{METHOD_MOG2, "Mixture-of-Gaussians segmentation (Zivkovic2004)", "mog2"},
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{0, NULL, NULL},
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};
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etype = g_enum_register_static ("GstSegmentationMethod", values);
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}
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return etype;
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}
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G_DEFINE_TYPE_WITH_CODE (GstSegmentation, gst_segmentation,
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GST_TYPE_OPENCV_VIDEO_FILTER,
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GST_DEBUG_CATEGORY_INIT (gst_segmentation_debug, "segmentation", 0,
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"Performs Foreground/Background segmentation in video sequences");
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);
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GST_ELEMENT_REGISTER_DEFINE (segmentation, "segmentation", GST_RANK_NONE,
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GST_TYPE_SEGMENTATION);
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static GstStaticPadTemplate sink_factory = GST_STATIC_PAD_TEMPLATE ("sink",
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GST_PAD_SINK,
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GST_PAD_ALWAYS,
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GST_STATIC_CAPS (GST_VIDEO_CAPS_MAKE ("RGBA")));
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static GstStaticPadTemplate src_factory = GST_STATIC_PAD_TEMPLATE ("src",
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GST_PAD_SRC,
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GST_PAD_ALWAYS,
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GST_STATIC_CAPS (GST_VIDEO_CAPS_MAKE ("RGBA")));
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static void
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gst_segmentation_set_property (GObject * object, guint prop_id,
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const GValue * value, GParamSpec * pspec);
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static void
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gst_segmentation_get_property (GObject * object, guint prop_id,
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GValue * value, GParamSpec * pspec);
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static GstFlowReturn gst_segmentation_transform_ip (GstOpencvVideoFilter *
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filter, GstBuffer * buffer, Mat img);
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static void gst_segmentation_finalize (GObject * object);
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static gboolean gst_segmentation_set_caps (GstOpencvVideoFilter * filter,
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gint in_width, gint in_height, int in_cv_type, gint out_width,
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gint out_height, int out_cv_type);
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/* Codebook algorithm + connected components functions*/
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static int update_codebook (unsigned char *p, codeBook * c,
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unsigned *cbBounds, int numChannels);
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static int clear_stale_entries (codeBook * c);
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static unsigned char background_diff (unsigned char *p, codeBook * c,
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int numChannels, int *minMod, int *maxMod);
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static void find_connected_components (Mat mask, int poly1_hull0,
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float perimScale);
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/* MOG (Mixture-of-Gaussians functions */
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static int run_mog_iteration (GstSegmentation * filter);
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static int run_mog2_iteration (GstSegmentation * filter);
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/* initialize the segmentation's class */
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static void
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gst_segmentation_class_init (GstSegmentationClass * klass)
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{
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GObjectClass *gobject_class;
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GstElementClass *element_class = GST_ELEMENT_CLASS (klass);
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GstOpencvVideoFilterClass *cvfilter_class =
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(GstOpencvVideoFilterClass *) klass;
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gobject_class = (GObjectClass *) klass;
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gobject_class->finalize = gst_segmentation_finalize;
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gobject_class->set_property = gst_segmentation_set_property;
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gobject_class->get_property = gst_segmentation_get_property;
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cvfilter_class->cv_trans_ip_func = gst_segmentation_transform_ip;
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cvfilter_class->cv_set_caps = gst_segmentation_set_caps;
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g_object_class_install_property (gobject_class, PROP_METHOD,
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g_param_spec_enum ("method",
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"Segmentation method to use",
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"Segmentation method to use",
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GST_TYPE_SEGMENTATION_METHOD, DEFAULT_METHOD,
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(GParamFlags) (G_PARAM_READWRITE | G_PARAM_STATIC_STRINGS)));
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g_object_class_install_property (gobject_class, PROP_TEST_MODE,
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g_param_spec_boolean ("test-mode", "test-mode",
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"If true, the output RGB is overwritten with the calculated foreground (white color)",
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DEFAULT_TEST_MODE, (GParamFlags)
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(GParamFlags) (G_PARAM_READWRITE | G_PARAM_STATIC_STRINGS)));
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g_object_class_install_property (gobject_class, PROP_LEARNING_RATE,
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g_param_spec_float ("learning-rate", "learning-rate",
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"Speed with which a motionless foreground pixel would become background (inverse of number of frames)",
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0, 1, DEFAULT_LEARNING_RATE, (GParamFlags) (G_PARAM_READWRITE)));
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gst_element_class_set_static_metadata (element_class,
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"Foreground/background video sequence segmentation",
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"Filter/Effect/Video",
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"Create a Foregound/Background mask applying a particular algorithm",
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"Miguel Casas-Sanchez <miguelecasassanchez@gmail.com>");
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gst_element_class_add_static_pad_template (element_class, &src_factory);
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gst_element_class_add_static_pad_template (element_class, &sink_factory);
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gst_type_mark_as_plugin_api (GST_TYPE_SEGMENTATION_METHOD, (GstPluginAPIFlags) 0);
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}
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/* initialize the new element
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* instantiate pads and add them to element
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* set pad callback functions
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* initialize instance structure
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*/
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static void
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gst_segmentation_init (GstSegmentation * filter)
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{
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filter->method = DEFAULT_METHOD;
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filter->test_mode = DEFAULT_TEST_MODE;
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filter->framecount = 0;
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filter->learning_rate = DEFAULT_LEARNING_RATE;
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gst_opencv_video_filter_set_in_place (GST_OPENCV_VIDEO_FILTER (filter), TRUE);
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}
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static void
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gst_segmentation_set_property (GObject * object, guint prop_id,
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const GValue * value, GParamSpec * pspec)
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{
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GstSegmentation *filter = GST_SEGMENTATION (object);
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switch (prop_id) {
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case PROP_METHOD:
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filter->method = g_value_get_enum (value);
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break;
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case PROP_TEST_MODE:
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filter->test_mode = g_value_get_boolean (value);
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break;
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case PROP_LEARNING_RATE:
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filter->learning_rate = g_value_get_float (value);
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break;
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default:
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G_OBJECT_WARN_INVALID_PROPERTY_ID (object, prop_id, pspec);
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break;
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}
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}
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static void
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gst_segmentation_get_property (GObject * object, guint prop_id,
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GValue * value, GParamSpec * pspec)
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{
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GstSegmentation *filter = GST_SEGMENTATION (object);
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switch (prop_id) {
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case PROP_METHOD:
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g_value_set_enum (value, filter->method);
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break;
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case PROP_TEST_MODE:
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g_value_set_boolean (value, filter->test_mode);
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break;
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case PROP_LEARNING_RATE:
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g_value_set_float (value, filter->learning_rate);
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break;
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default:
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G_OBJECT_WARN_INVALID_PROPERTY_ID (object, prop_id, pspec);
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break;
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}
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}
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static gboolean
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gst_segmentation_set_caps (GstOpencvVideoFilter * filter, gint in_width,
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gint in_height, int in_cv_type,
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gint out_width, gint out_height, int out_cv_type)
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{
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GstSegmentation *segmentation = GST_SEGMENTATION (filter);
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Size size;
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size = Size (in_width, in_height);
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segmentation->width = in_width;
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segmentation->height = in_height;
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segmentation->cvRGB.create (size, CV_8UC3);
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segmentation->cvYUV.create (size, CV_8UC3);
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segmentation->cvFG = Mat::zeros (size, CV_8UC1);
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segmentation->ch1.create (size, CV_8UC1);
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segmentation->ch2.create (size, CV_8UC1);
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segmentation->ch3.create (size, CV_8UC1);
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/* Codebook method */
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segmentation->TcodeBook = (codeBook *)
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g_malloc (sizeof (codeBook) *
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(segmentation->width * segmentation->height + 1));
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for (int j = 0; j < segmentation->width * segmentation->height; j++) {
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segmentation->TcodeBook[j].numEntries = 0;
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segmentation->TcodeBook[j].t = 0;
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}
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segmentation->learning_interval = (int) (1.0 / segmentation->learning_rate);
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/* Mixture-of-Gaussians (mog) methods */
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segmentation->mog = bgsegm::createBackgroundSubtractorMOG ();
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segmentation->mog2 = createBackgroundSubtractorMOG2 ();
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return TRUE;
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}
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/* Clean up */
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static void
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gst_segmentation_finalize (GObject * object)
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{
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GstSegmentation *filter = GST_SEGMENTATION (object);
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filter->cvRGB.release ();
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filter->cvYUV.release ();
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filter->cvFG.release ();
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filter->ch1.release ();
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filter->ch2.release ();
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filter->ch3.release ();
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filter->mog.release ();
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filter->mog2.release ();
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g_free (filter->TcodeBook);
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G_OBJECT_CLASS (gst_segmentation_parent_class)->finalize (object);
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}
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static GstFlowReturn
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gst_segmentation_transform_ip (GstOpencvVideoFilter * cvfilter,
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GstBuffer * buffer, Mat img)
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{
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GstSegmentation *filter = GST_SEGMENTATION (cvfilter);
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int j;
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filter->framecount++;
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/* Image preprocessing: color space conversion etc */
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cvtColor (img, filter->cvRGB, COLOR_RGBA2RGB);
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cvtColor (filter->cvRGB, filter->cvYUV, COLOR_RGB2YCrCb);
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/* Create and update a fg/bg model using a codebook approach following the
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* opencv O'Reilly book [1] implementation of the algo described in [2].
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*
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* [1] Learning OpenCV: Computer Vision with the OpenCV Library by Gary
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* Bradski and Adrian Kaehler, Published by O'Reilly Media, October 3, 2008
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* [2] "Real-time Foreground-Background Segmentation using Codebook Model",
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* Real-time Imaging, Volume 11, Issue 3, Pages 167-256, June 2005. */
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if (METHOD_BOOK == filter->method) {
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unsigned cbBounds[3] = { 10, 5, 5 };
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int minMod[3] = { 20, 20, 20 }, maxMod[3] = {
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20, 20, 20
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};
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if (filter->framecount < 30) {
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/* Learning background phase: update_codebook on every frame */
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for (j = 0; j < filter->width * filter->height; j++) {
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update_codebook (filter->cvYUV.data + j * 3,
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(codeBook *) & (filter->TcodeBook[j]), cbBounds, 3);
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}
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} else {
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/* this updating is responsible for FG becoming BG again */
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if (filter->framecount % filter->learning_interval == 0) {
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for (j = 0; j < filter->width * filter->height; j++) {
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update_codebook (filter->cvYUV.data + j * 3,
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(codeBook *) & (filter->TcodeBook[j]), cbBounds, 3);
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}
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}
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if (filter->framecount % 60 == 0) {
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for (j = 0; j < filter->width * filter->height; j++)
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clear_stale_entries ((codeBook *) & (filter->TcodeBook[j]));
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}
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for (j = 0; j < filter->width * filter->height; j++) {
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if (background_diff
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(filter->cvYUV.data + j * 3,
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(codeBook *) & (filter->TcodeBook[j]), 3, minMod, maxMod)) {
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filter->cvFG.data[j] = (char) 255;
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} else {
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filter->cvFG.data[j] = 0;
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}
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}
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}
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/* 3rd param is the smallest area to show: (w+h)/param , in pixels */
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find_connected_components (filter->cvFG, 1, 10000);
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}
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/* Create the foreground and background masks using BackgroundSubtractorMOG [1],
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* Gaussian Mixture-based Background/Foreground segmentation algorithm. OpenCV
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* MOG implements the algorithm described in [2].
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*
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* [1] http://opencv.itseez.com/modules/video/doc/motion_analysis_and_object_tracking.html#backgroundsubtractormog
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* [2] P. KadewTraKuPong and R. Bowden, "An improved adaptive background
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* mixture model for real-time tracking with shadow detection", Proc. 2nd
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* European Workshop on Advanced Video-Based Surveillance Systems, 2001
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*/
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else if (METHOD_MOG == filter->method) {
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run_mog_iteration (filter);
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}
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/* Create the foreground and background masks using BackgroundSubtractorMOG2
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* [1], Gaussian Mixture-based Background/Foreground segmentation algorithm.
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* OpenCV MOG2 implements the algorithm described in [2] and [3].
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*
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* [1] http://opencv.itseez.com/modules/video/doc/motion_analysis_and_object_tracking.html#backgroundsubtractormog2
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* [2] Z.Zivkovic, "Improved adaptive Gaussian mixture model for background
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* subtraction", International Conference Pattern Recognition, UK, Aug 2004.
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* [3] Z.Zivkovic, F. van der Heijden, "Efficient Adaptive Density Estimation
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* per Image Pixel for the Task of Background Subtraction", Pattern
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* Recognition Letters, vol. 27, no. 7, pages 773-780, 2006. */
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else if (METHOD_MOG2 == filter->method) {
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run_mog2_iteration (filter);
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}
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/* if we want to test_mode, just overwrite the output */
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std::vector < cv::Mat > channels (3);
|
||
|
||
if (filter->test_mode) {
|
||
cvtColor (filter->cvFG, filter->cvRGB, COLOR_GRAY2RGB);
|
||
|
||
split (filter->cvRGB, channels);
|
||
} else
|
||
split (img, channels);
|
||
|
||
channels.push_back (filter->cvFG);
|
||
|
||
/* copy anyhow the fg/bg to the alpha channel in the output image */
|
||
merge (channels, img);
|
||
|
||
|
||
return GST_FLOW_OK;
|
||
}
|
||
|
||
|
||
#ifdef CODE_FROM_OREILLY_BOOK /* See license at the beginning of the page */
|
||
/*
|
||
int update_codebook(uchar *p, codeBook &c, unsigned cbBounds)
|
||
Updates the codebook entry with a new data point
|
||
|
||
p Pointer to a YUV or HSI pixel
|
||
c Codebook for this pixel
|
||
cbBounds Learning bounds for codebook (Rule of thumb: 10)
|
||
numChannels Number of color channels we¡¯re learning
|
||
|
||
NOTES:
|
||
cvBounds must be of length equal to numChannels
|
||
|
||
RETURN
|
||
codebook index
|
||
*/
|
||
int
|
||
update_codebook (unsigned char *p, codeBook * c, unsigned *cbBounds,
|
||
int numChannels)
|
||
{
|
||
/* c->t+=1; */
|
||
unsigned int high[3], low[3];
|
||
int n, i;
|
||
int matchChannel;
|
||
|
||
for (n = 0; n < numChannels; n++) {
|
||
high[n] = p[n] + cbBounds[n];
|
||
if (high[n] > 255)
|
||
high[n] = 255;
|
||
|
||
if (p[n] > cbBounds[n])
|
||
low[n] = p[n] - cbBounds[n];
|
||
else
|
||
low[n] = 0;
|
||
}
|
||
|
||
/* SEE IF THIS FITS AN EXISTING CODEWORD */
|
||
for (i = 0; i < c->numEntries; i++) {
|
||
matchChannel = 0;
|
||
for (n = 0; n < numChannels; n++) {
|
||
if ((c->cb[i]->learnLow[n] <= *(p + n)) &&
|
||
/* Found an entry for this channel */
|
||
(*(p + n) <= c->cb[i]->learnHigh[n])) {
|
||
matchChannel++;
|
||
}
|
||
}
|
||
if (matchChannel == numChannels) { /* If an entry was found */
|
||
c->cb[i]->t_last_update = c->t;
|
||
/* adjust this codeword for the first channel */
|
||
for (n = 0; n < numChannels; n++) {
|
||
if (c->cb[i]->max[n] < *(p + n)) {
|
||
c->cb[i]->max[n] = *(p + n);
|
||
} else if (c->cb[i]->min[n] > *(p + n)) {
|
||
c->cb[i]->min[n] = *(p + n);
|
||
}
|
||
}
|
||
break;
|
||
}
|
||
}
|
||
/* OVERHEAD TO TRACK POTENTIAL STALE ENTRIES */
|
||
for (int s = 0; s < c->numEntries; s++) {
|
||
/* Track which codebook entries are going stale: */
|
||
int negRun = c->t - c->cb[s]->t_last_update;
|
||
if (c->cb[s]->stale < negRun)
|
||
c->cb[s]->stale = negRun;
|
||
}
|
||
/* ENTER A NEW CODEWORD IF NEEDED */
|
||
if (i == c->numEntries) { /* if no existing codeword found, make one */
|
||
code_element **foo =
|
||
(code_element **) g_malloc (sizeof (code_element *) *
|
||
(c->numEntries + 1));
|
||
for (int ii = 0; ii < c->numEntries; ii++) {
|
||
foo[ii] = c->cb[ii]; /* copy all pointers */
|
||
}
|
||
foo[c->numEntries] = (code_element *) g_malloc (sizeof (code_element));
|
||
if (c->numEntries)
|
||
g_free (c->cb);
|
||
c->cb = foo;
|
||
for (n = 0; n < numChannels; n++) {
|
||
c->cb[c->numEntries]->learnHigh[n] = high[n];
|
||
c->cb[c->numEntries]->learnLow[n] = low[n];
|
||
c->cb[c->numEntries]->max[n] = *(p + n);
|
||
c->cb[c->numEntries]->min[n] = *(p + n);
|
||
}
|
||
c->cb[c->numEntries]->t_last_update = c->t;
|
||
c->cb[c->numEntries]->stale = 0;
|
||
c->numEntries += 1;
|
||
}
|
||
/* SLOWLY ADJUST LEARNING BOUNDS */
|
||
for (n = 0; n < numChannels; n++) {
|
||
if (c->cb[i]->learnHigh[n] < high[n])
|
||
c->cb[i]->learnHigh[n] += 1;
|
||
if (c->cb[i]->learnLow[n] > low[n])
|
||
c->cb[i]->learnLow[n] -= 1;
|
||
}
|
||
return (i);
|
||
}
|
||
|
||
|
||
|
||
|
||
|
||
/*
|
||
int clear_stale_entries(codeBook &c)
|
||
During learning, after you've learned for some period of time,
|
||
periodically call this to clear out stale codebook entries
|
||
|
||
c Codebook to clean up
|
||
|
||
Return
|
||
number of entries cleared
|
||
*/
|
||
int
|
||
clear_stale_entries (codeBook * c)
|
||
{
|
||
int staleThresh = c->t >> 1;
|
||
int *keep = (int *) g_malloc (sizeof (int) * (c->numEntries));
|
||
int keepCnt = 0;
|
||
code_element **foo;
|
||
int k;
|
||
int numCleared;
|
||
/* SEE WHICH CODEBOOK ENTRIES ARE TOO STALE */
|
||
for (int i = 0; i < c->numEntries; i++) {
|
||
if (c->cb[i]->stale > staleThresh)
|
||
keep[i] = 0; /* Mark for destruction */
|
||
else {
|
||
keep[i] = 1; /* Mark to keep */
|
||
keepCnt += 1;
|
||
}
|
||
}
|
||
/* KEEP ONLY THE GOOD */
|
||
c->t = 0; /* Full reset on stale tracking */
|
||
foo = (code_element **) g_malloc (sizeof (code_element *) * keepCnt);
|
||
k = 0;
|
||
for (int ii = 0; ii < c->numEntries; ii++) {
|
||
if (keep[ii]) {
|
||
foo[k] = c->cb[ii];
|
||
/* We have to refresh these entries for next clearStale */
|
||
foo[k]->t_last_update = 0;
|
||
k++;
|
||
}
|
||
}
|
||
/* CLEAN UP */
|
||
g_free (keep);
|
||
g_free (c->cb);
|
||
c->cb = foo;
|
||
numCleared = c->numEntries - keepCnt;
|
||
c->numEntries = keepCnt;
|
||
return (numCleared);
|
||
}
|
||
|
||
|
||
|
||
/*
|
||
uchar background_diff( uchar *p, codeBook &c,
|
||
int minMod, int maxMod)
|
||
Given a pixel and a codebook, determine if the pixel is
|
||
covered by the codebook
|
||
|
||
p Pixel pointer (YUV interleaved)
|
||
c Codebook reference
|
||
numChannels Number of channels we are testing
|
||
maxMod Add this (possibly negative) number onto
|
||
|
||
max level when determining if new pixel is foreground
|
||
minMod Subtract this (possibly negative) number from
|
||
min level when determining if new pixel is foreground
|
||
|
||
NOTES:
|
||
minMod and maxMod must have length numChannels,
|
||
e.g. 3 channels => minMod[3], maxMod[3]. There is one min and
|
||
one max threshold per channel.
|
||
|
||
Return
|
||
0 => background, 255 => foreground
|
||
*/
|
||
unsigned char
|
||
background_diff (unsigned char *p, codeBook * c, int numChannels,
|
||
int *minMod, int *maxMod)
|
||
{
|
||
int matchChannel;
|
||
/* SEE IF THIS FITS AN EXISTING CODEWORD */
|
||
int i;
|
||
for (i = 0; i < c->numEntries; i++) {
|
||
matchChannel = 0;
|
||
for (int n = 0; n < numChannels; n++) {
|
||
if ((c->cb[i]->min[n] - minMod[n] <= *(p + n)) &&
|
||
(*(p + n) <= c->cb[i]->max[n] + maxMod[n])) {
|
||
matchChannel++; /* Found an entry for this channel */
|
||
} else {
|
||
break;
|
||
}
|
||
}
|
||
if (matchChannel == numChannels) {
|
||
break; /* Found an entry that matched all channels */
|
||
}
|
||
}
|
||
if (i >= c->numEntries)
|
||
return (255);
|
||
return (0);
|
||
}
|
||
|
||
|
||
|
||
|
||
/*
|
||
void find_connected_components(IplImage *mask, int poly1_hull0,
|
||
float perimScale, int *num,
|
||
CvRect *bbs, CvPoint *centers)
|
||
This cleans up the foreground segmentation mask derived from calls
|
||
to backgroundDiff
|
||
|
||
mask Is a grayscale (8-bit depth) “raw†mask image that
|
||
will be cleaned up
|
||
|
||
OPTIONAL PARAMETERS:
|
||
poly1_hull0 If set, approximate connected component by
|
||
(DEFAULT) polygon, or else convex hull (0)
|
||
perimScale Len = image (width+height)/perimScale. If contour
|
||
len < this, delete that contour (DEFAULT: 4)
|
||
num Maximum number of rectangles and/or centers to
|
||
return; on return, will contain number filled
|
||
(DEFAULT: NULL)
|
||
bbs Pointer to bounding box rectangle vector of
|
||
length num. (DEFAULT SETTING: NULL)
|
||
centers Pointer to contour centers vector of length
|
||
num (DEFAULT: NULL)
|
||
*/
|
||
|
||
/* Approx.threshold - the bigger it is, the simpler is the boundary */
|
||
#define CVCONTOUR_APPROX_LEVEL 1
|
||
/* How many iterations of erosion and/or dilation there should be */
|
||
#define CVCLOSE_ITR 1
|
||
static void
|
||
find_connected_components (Mat mask, int poly1_hull0, float perimScale)
|
||
{
|
||
/* Just some convenience variables */
|
||
const Scalar CVX_WHITE = CV_RGB (0xff, 0xff, 0xff);
|
||
//const Scalar CVX_BLACK = CV_RGB (0x00, 0x00, 0x00);
|
||
int idx = 0;
|
||
|
||
/* CLEAN UP RAW MASK */
|
||
morphologyEx (mask, mask, MORPH_OPEN, Mat (), Point (-1, -1), CVCLOSE_ITR);
|
||
morphologyEx (mask, mask, MORPH_CLOSE, Mat (), Point (-1, -1), CVCLOSE_ITR);
|
||
/* FIND CONTOURS AROUND ONLY BIGGER REGIONS */
|
||
|
||
std::vector < std::vector < Point > >contours;
|
||
std::vector < std::vector < Point > >to_draw;
|
||
std::vector < Vec4i > hierarchy;
|
||
findContours (mask, contours, hierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE,
|
||
Point (0, 0));
|
||
if (contours.size () == 0)
|
||
return;
|
||
|
||
for (; idx >= 0; idx = hierarchy[idx][0]) {
|
||
const std::vector < Point > &c = contours[idx];
|
||
double len = fabs (contourArea (Mat (c)));
|
||
double q = (mask.size ().height + mask.size ().width) / perimScale;
|
||
if (len >= q) {
|
||
std::vector < Point > c_new;
|
||
if (poly1_hull0) {
|
||
approxPolyDP (c, c_new, CVCONTOUR_APPROX_LEVEL, (hierarchy[idx][2] < 0
|
||
&& hierarchy[idx][3] < 0));
|
||
} else {
|
||
convexHull (c, c_new, true, true);
|
||
}
|
||
to_draw.push_back (c_new);
|
||
}
|
||
}
|
||
|
||
mask.setTo (Scalar::all (0));
|
||
if (to_draw.size () > 0) {
|
||
drawContours (mask, to_draw, -1, CVX_WHITE, FILLED);
|
||
}
|
||
|
||
}
|
||
#endif /*ifdef CODE_FROM_OREILLY_BOOK */
|
||
|
||
int
|
||
run_mog_iteration (GstSegmentation * filter)
|
||
{
|
||
/*
|
||
BackgroundSubtractorMOG [1], Gaussian Mixture-based Background/Foreground
|
||
Segmentation Algorithm. OpenCV MOG implements the algorithm described in [2].
|
||
|
||
[1] http://opencv.itseez.com/modules/video/doc/motion_analysis_and_object_tracking.html#backgroundsubtractormog
|
||
[2] P. KadewTraKuPong and R. Bowden, "An improved adaptive background
|
||
mixture model for real-time tracking with shadow detection", Proc. 2nd
|
||
European Workshop on Advanced Video-Based Surveillance Systems, 2001
|
||
*/
|
||
|
||
filter->mog->apply (filter->cvYUV, filter->cvFG, filter->learning_rate);
|
||
|
||
return (0);
|
||
}
|
||
|
||
int
|
||
run_mog2_iteration (GstSegmentation * filter)
|
||
{
|
||
/*
|
||
BackgroundSubtractorMOG2 [1], Gaussian Mixture-based Background/Foreground
|
||
segmentation algorithm. OpenCV MOG2 implements the algorithm described in
|
||
[2] and [3].
|
||
|
||
[1] http://opencv.itseez.com/modules/video/doc/motion_analysis_and_object_tracking.html#backgroundsubtractormog2
|
||
[2] Z.Zivkovic, "Improved adaptive Gaussian mixture model for background
|
||
subtraction", International Conference Pattern Recognition, UK, August, 2004.
|
||
[3] Z.Zivkovic, F. van der Heijden, "Efficient Adaptive Density Estimation per
|
||
Image Pixel for the Task of Background Subtraction", Pattern Recognition
|
||
Letters, vol. 27, no. 7, pages 773-780, 2006.
|
||
*/
|
||
|
||
filter->mog2->apply (filter->cvYUV, filter->cvFG, filter->learning_rate);
|
||
|
||
return (0);
|
||
}
|