--- /dev/null
+/*
+ * GStreamer
+ * Copyright (C) 2013 Miguel Casas-Sanchez <miguelecasassanchez@gmail.com>
+ * Except: Parts of code inside the preprocessor define CODE_FROM_OREILLY_BOOK,
+ * which are downloaded from O'Reilly website
+ * [http://examples.oreilly.com/9780596516130/]
+ * and adapted. Its license reads:
+ * "Oct. 3, 2008
+ * Right to use this code in any way you want without warrenty, support or
+ * any guarentee of it working. "
+ *
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a
+ * copy of this software and associated documentation files (the "Software"),
+ * to deal in the Software without restriction, including without limitation
+ * the rights to use, copy, modify, merge, publish, distribute, sublicense,
+ * and/or sell copies of the Software, and to permit persons to whom the
+ * Software is furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in
+ * all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+ * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+ * DEALINGS IN THE SOFTWARE.
+ *
+ * Alternatively, the contents of this file may be used under the
+ * GNU Lesser General Public License Version 2.1 (the "LGPL"), in
+ * which case the following provisions apply instead of the ones
+ * mentioned above:
+ *
+ * This library is free software; you can redistribute it and/or
+ * modify it under the terms of the GNU Library General Public
+ * License as published by the Free Software Foundation; either
+ * version 2 of the License, or (at your option) any later version.
+ *
+ * This library is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
+ * Library General Public License for more details.
+ *
+ * You should have received a copy of the GNU Library General Public
+ * License along with this library; if not, write to the
+ * Free Software Foundation, Inc., 51 Franklin St, Fifth Floor,
+ * Boston, MA 02110-1301, USA.
+ */
+#define CODE_FROM_OREILLY_BOOK
+
+/**
+ * SECTION:element-segmentation
+ *
+ * This element creates and updates a fg/bg model using one of several approaches.
+ * The one called "codebook" refers to the codebook approach following the opencv
+ * O'Reilly book [1] implementation of the algorithm described in K. Kim,
+ * T. H. Chalidabhongse, D. Harwood and L. Davis [2]. BackgroundSubtractorMOG [3],
+ * or MOG for shorts, refers to a Gaussian Mixture-based Background/Foreground
+ * Segmentation Algorithm. OpenCV MOG implements the algorithm described in [4].
+ * BackgroundSubtractorMOG2 [5], refers to another Gaussian Mixture-based
+ * Background/Foreground segmentation algorithm. OpenCV MOG2 implements the
+ * algorithm described in [6] and [7].
+ *
+ * [1] Learning OpenCV: Computer Vision with the OpenCV Library by Gary Bradski
+ * and Adrian Kaehler, Published by O'Reilly Media, October 3, 2008
+ * [2] "Real-time Foreground-Background Segmentation using Codebook Model",
+ * Real-time Imaging, Volume 11, Issue 3, Pages 167-256, June 2005.
+ * [3] http://opencv.itseez.com/modules/video/doc/motion_analysis_and_object_tracking.html#backgroundsubtractormog
+ * [4] 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
+ * [5] http://opencv.itseez.com/modules/video/doc/motion_analysis_and_object_tracking.html#backgroundsubtractormog2
+ * [6] Z.Zivkovic, "Improved adaptive Gausian mixture model for background
+ * subtraction", International Conference Pattern Recognition, UK, August, 2004.
+ * [7] 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.
+ *
+ * <refsect2>
+ * <title>Example launch line</title>
+ * |[
+ * gst-launch-1.0 v4l2src device=/dev/video0 ! videoconvert ! video/x-raw,width=320,height=240 ! videoconvert ! segmentation test-mode=true method=2 ! videoconvert ! ximagesink
+ * ]|
+ * </refsect2>
+ */
+
+#ifdef HAVE_CONFIG_H
+#include <config.h>
+#endif
+
+#include <gst/gst.h>
+
+#include "gstsegmentation.h"
+
+GST_DEBUG_CATEGORY_STATIC (gst_segmentation_debug);
+#define GST_CAT_DEFAULT gst_segmentation_debug
+
+/* Filter signals and args */
+enum
+{
+ /* FILL ME */
+ LAST_SIGNAL
+};
+
+enum
+{
+ PROP_0,
+ PROP_TEST_MODE,
+ PROP_METHOD,
+ PROP_LEARNING_RATE
+};
+typedef enum
+{
+ METHOD_BOOK,
+ METHOD_MOG,
+ METHOD_MOG2
+} GstSegmentationMethod;
+
+#define DEFAULT_TEST_MODE FALSE
+#define DEFAULT_METHOD METHOD_MOG2
+#define DEFAULT_LEARNING_RATE 0.01
+
+#define GST_TYPE_SEGMENTATION_METHOD (gst_segmentation_method_get_type ())
+static GType
+gst_segmentation_method_get_type (void)
+{
+ static GType etype = 0;
+ if (etype == 0) {
+ static const GEnumValue values[] = {
+ {METHOD_BOOK, "Codebook-based segmentation (Bradski2008)", "codebook"},
+ {METHOD_MOG, "Mixture-of-Gaussians segmentation (Bowden2001)", "mog"},
+ {METHOD_MOG2, "Mixture-of-Gaussians segmentation (Zivkovic2004)", "mog2"},
+ {0, NULL, NULL},
+ };
+ etype = g_enum_register_static ("GstSegmentationMethod", values);
+ }
+ return etype;
+}
+
+G_DEFINE_TYPE (GstSegmentation, gst_segmentation, GST_TYPE_VIDEO_FILTER);
+static GstStaticPadTemplate sink_factory = GST_STATIC_PAD_TEMPLATE ("sink",
+ GST_PAD_SINK,
+ GST_PAD_ALWAYS,
+ GST_STATIC_CAPS (GST_VIDEO_CAPS_MAKE ("RGBA")));
+
+static GstStaticPadTemplate src_factory = GST_STATIC_PAD_TEMPLATE ("src",
+ GST_PAD_SRC,
+ GST_PAD_ALWAYS,
+ GST_STATIC_CAPS (GST_VIDEO_CAPS_MAKE ("RGBA")));
+
+
+static void gst_segmentation_set_property (GObject * object, guint prop_id,
+ const GValue * value, GParamSpec * pspec);
+static void gst_segmentation_get_property (GObject * object, guint prop_id,
+ GValue * value, GParamSpec * pspec);
+
+static GstFlowReturn gst_segmentation_transform_ip (GstVideoFilter * btrans,
+ GstVideoFrame *frame);
+
+static gboolean gst_segmentation_stop (GstBaseTransform * basesrc);
+static gboolean gst_segmentation_set_info(GstVideoFilter *filter,
+ GstCaps *incaps, GstVideoInfo *in_info,
+ GstCaps *outcaps, GstVideoInfo *out_info);
+static void gst_segmentation_release_all_pointers (GstSegmentation * filter);
+
+/* Codebook algorithm + connected components functions*/
+static int update_codebook (unsigned char *p, codeBook * c,
+ unsigned *cbBounds, int numChannels);
+static int clear_stale_entries (codeBook * c);
+static unsigned char background_diff (unsigned char *p, codeBook * c,
+ int numChannels, int *minMod, int *maxMod);
+static void find_connected_components (IplImage * mask, int poly1_hull0,
+ float perimScale, CvMemStorage * mem_storage, CvSeq * contours);
+
+/* MOG (Mixture-of-Gaussians functions */
+static int initialise_mog (GstSegmentation * filter);
+static int run_mog_iteration (GstSegmentation * filter);
+static int run_mog2_iteration (GstSegmentation * filter);
+static int finalise_mog (GstSegmentation * filter);
+
+/* initialize the segmentation's class */
+static void
+gst_segmentation_class_init (GstSegmentationClass * klass)
+{
+ GObjectClass *gobject_class;
+ GstElementClass *element_class = GST_ELEMENT_CLASS (klass);
+ GstBaseTransformClass *basesrc_class = GST_BASE_TRANSFORM_CLASS (klass);
+ GstVideoFilterClass *video_class = (GstVideoFilterClass*) klass;
+
+ gobject_class = (GObjectClass *) klass;
+
+ gobject_class->set_property = gst_segmentation_set_property;
+ gobject_class->get_property = gst_segmentation_get_property;
+
+ basesrc_class->stop = gst_segmentation_stop;
+
+ video_class->transform_frame_ip = gst_segmentation_transform_ip;
+ video_class->set_info = gst_segmentation_set_info;
+
+ g_object_class_install_property (gobject_class, PROP_METHOD,
+ g_param_spec_enum ("method",
+ "Segmentation method to use",
+ "Segmentation method to use",
+ GST_TYPE_SEGMENTATION_METHOD, DEFAULT_METHOD,
+ (GParamFlags) (G_PARAM_READWRITE | G_PARAM_STATIC_STRINGS)));
+
+ g_object_class_install_property (gobject_class, PROP_TEST_MODE,
+ g_param_spec_boolean ("test-mode", "test-mode",
+ "If true, the output RGB is overwritten with the calculated foreground (white color)",
+ DEFAULT_TEST_MODE, (GParamFlags)
+ (GParamFlags) (G_PARAM_READWRITE | G_PARAM_STATIC_STRINGS)));
+
+ g_object_class_install_property (gobject_class, PROP_LEARNING_RATE,
+ g_param_spec_float ("learning-rate", "learning-rate",
+ "Speed with which a motionless foreground pixel would become background (inverse of number of frames)",
+ 0, 1, DEFAULT_LEARNING_RATE, (GParamFlags) (G_PARAM_READWRITE)));
+
+ gst_element_class_set_static_metadata (element_class,
+ "Foreground/background video sequence segmentation",
+ "Filter/Effect/Video",
+ "Create a Foregound/Background mask applying a particular algorithm",
+ "Miguel Casas-Sanchez <miguelecasassanchez@gmail.com>");
+
+ gst_element_class_add_pad_template (element_class,
+ gst_static_pad_template_get (&src_factory));
+ gst_element_class_add_pad_template (element_class,
+ gst_static_pad_template_get (&sink_factory));
+
+}
+
+/* initialize the new element
+ * instantiate pads and add them to element
+ * set pad calback functions
+ * initialize instance structure
+ */
+static void
+gst_segmentation_init (GstSegmentation * filter)
+{
+ filter->method = DEFAULT_METHOD;
+ filter->test_mode = DEFAULT_TEST_MODE;
+ filter->framecount = 0;
+ filter->learning_rate = DEFAULT_LEARNING_RATE;
+ gst_base_transform_set_in_place (GST_BASE_TRANSFORM (filter), TRUE);
+}
+
+
+static void
+gst_segmentation_set_property (GObject * object, guint prop_id,
+ const GValue * value, GParamSpec * pspec)
+{
+ GstSegmentation *filter = GST_SEGMENTATION (object);
+
+ switch (prop_id) {
+ case PROP_METHOD:
+ filter->method = g_value_get_enum (value);
+ break;
+ case PROP_TEST_MODE:
+ filter->test_mode = g_value_get_boolean (value);
+ break;
+ case PROP_LEARNING_RATE:
+ filter->learning_rate = g_value_get_float (value);
+ break;
+ default:
+ G_OBJECT_WARN_INVALID_PROPERTY_ID (object, prop_id, pspec);
+ break;
+ }
+}
+
+static void
+gst_segmentation_get_property (GObject * object, guint prop_id,
+ GValue * value, GParamSpec * pspec)
+{
+ GstSegmentation *filter = GST_SEGMENTATION (object);
+
+ switch (prop_id) {
+ case PROP_METHOD:
+ g_value_set_enum (value, filter->method);
+ break;
+ case PROP_TEST_MODE:
+ g_value_set_boolean (value, filter->test_mode);
+ break;
+ case PROP_LEARNING_RATE:
+ g_value_set_float (value, filter->learning_rate);
+ break;
+ default:
+ G_OBJECT_WARN_INVALID_PROPERTY_ID (object, prop_id, pspec);
+ break;
+ }
+}
+
+/* GstElement vmethod implementations */
+/* this function handles the link with other elements */
+static gboolean
+gst_segmentation_set_info(GstVideoFilter *filter,
+ GstCaps *incaps, GstVideoInfo *in_info,
+ GstCaps *outcaps, GstVideoInfo *out_info)
+{
+ GstSegmentation *segmentation = GST_SEGMENTATION (filter);
+ CvSize size;
+
+ size = cvSize (in_info->width, in_info->height);
+ segmentation->width = in_info->width;
+ segmentation->height = in_info->height;
+ /* If cvRGB is already allocated, it means there's a cap modification, */
+ /* so release first all the images. */
+ if (NULL != segmentation->cvRGBA)
+ gst_segmentation_release_all_pointers (segmentation);
+
+ segmentation->cvRGBA = cvCreateImageHeader (size, IPL_DEPTH_8U, 4);
+
+ segmentation->cvRGB = cvCreateImage (size, IPL_DEPTH_8U, 3);
+ segmentation->cvYUV = cvCreateImage (size, IPL_DEPTH_8U, 3);
+
+ segmentation->cvFG = cvCreateImage (size, IPL_DEPTH_8U, 1);
+ cvZero (segmentation->cvFG);
+
+ segmentation->ch1 = cvCreateImage (size, IPL_DEPTH_8U, 1);
+ segmentation->ch2 = cvCreateImage (size, IPL_DEPTH_8U, 1);
+ segmentation->ch3 = cvCreateImage (size, IPL_DEPTH_8U, 1);
+
+ /* Codebook method */
+ segmentation->TcodeBook = (codeBook *)
+ g_malloc (sizeof (codeBook) *
+ (segmentation->width * segmentation->height + 1));
+ for (int j = 0; j < segmentation->width * segmentation->height; j++) {
+ segmentation->TcodeBook[j].numEntries = 0;
+ segmentation->TcodeBook[j].t = 0;
+ }
+ segmentation->learning_interval = (int) (1.0 / segmentation->learning_rate);
+
+ /* Mixture-of-Gaussians (mog) methods */
+ initialise_mog (segmentation);
+
+ return TRUE;
+}
+
+/* Clean up */
+static gboolean
+gst_segmentation_stop (GstBaseTransform * basesrc)
+{
+ GstSegmentation *filter = GST_SEGMENTATION (basesrc);
+
+ if (filter->cvRGBA != NULL)
+ gst_segmentation_release_all_pointers (filter);
+
+ return TRUE;
+}
+
+static void
+gst_segmentation_release_all_pointers (GstSegmentation * filter)
+{
+ cvReleaseImage (&filter->cvRGBA);
+ cvReleaseImage (&filter->cvRGB);
+ cvReleaseImage (&filter->cvYUV);
+ cvReleaseImage (&filter->cvFG);
+ cvReleaseImage (&filter->ch1);
+ cvReleaseImage (&filter->ch2);
+ cvReleaseImage (&filter->ch3);
+
+ g_free (filter->TcodeBook);
+ finalise_mog (filter);
+}
+
+static GstFlowReturn
+gst_segmentation_transform_ip (GstVideoFilter * btrans, GstVideoFrame * frame)
+{
+ GstSegmentation *filter = GST_SEGMENTATION (btrans);
+ int j;
+
+ /* get image data from the input, which is RGBA */
+ filter->cvRGBA->imageData = (char *) GST_VIDEO_FRAME_COMP_DATA (frame, 0);
+ filter->cvRGBA->widthStep = GST_VIDEO_FRAME_COMP_STRIDE (frame, 0);
+ filter->framecount++;
+
+ /* Image preprocessing: color space conversion etc */
+ cvCvtColor (filter->cvRGBA, filter->cvRGB, CV_RGBA2RGB);
+ cvCvtColor (filter->cvRGB, filter->cvYUV, CV_RGB2YCrCb);
+
+ /* Create and update a fg/bg model using a codebook approach following the
+ * opencv O'Reilly book [1] implementation of the algo described in [2].
+ *
+ * [1] Learning OpenCV: Computer Vision with the OpenCV Library by Gary
+ * Bradski and Adrian Kaehler, Published by O'Reilly Media, October 3, 2008
+ * [2] "Real-time Foreground-Background Segmentation using Codebook Model",
+ * Real-time Imaging, Volume 11, Issue 3, Pages 167-256, June 2005. */
+ if (METHOD_BOOK == filter->method) {
+ unsigned cbBounds[3] = { 10, 5, 5 };
+ int minMod[3] = { 20, 20, 20 }, maxMod[3] = {
+ 20, 20, 20};
+
+ if (filter->framecount < 30) {
+ /* Learning background phase: update_codebook on every frame */
+ for (j = 0; j < filter->width * filter->height; j++) {
+ update_codebook ((unsigned char *) filter->cvYUV->imageData + j * 3,
+ (codeBook *) & (filter->TcodeBook[j]), cbBounds, 3);
+ }
+ } else {
+ /* this updating is responsible for FG becoming BG again */
+ if (filter->framecount % filter->learning_interval == 0) {
+ for (j = 0; j < filter->width * filter->height; j++) {
+ update_codebook ((uchar *) filter->cvYUV->imageData + j * 3,
+ (codeBook *) & (filter->TcodeBook[j]), cbBounds, 3);
+ }
+ }
+ if (filter->framecount % 60 == 0) {
+ for (j = 0; j < filter->width * filter->height; j++)
+ clear_stale_entries ((codeBook *) & (filter->TcodeBook[j]));
+ }
+
+ for (j = 0; j < filter->width * filter->height; j++) {
+ if (background_diff
+ ((uchar *) filter->cvYUV->imageData + j * 3,
+ (codeBook *) & (filter->TcodeBook[j]), 3, minMod, maxMod)) {
+ filter->cvFG->imageData[j] = 255;
+ } else {
+ filter->cvFG->imageData[j] = 0;
+ }
+ }
+ }
+
+ /* 3rd param is the smallest area to show: (w+h)/param , in pixels */
+ find_connected_components (filter->cvFG, 1, 10000,
+ filter->mem_storage, filter->contours);
+
+ }
+ /* Create the foreground and background masks using 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
+ */
+ else if (METHOD_MOG == filter->method) {
+ run_mog_iteration (filter);
+ }
+ /* Create the foreground and background masks using 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 Gausian mixture model for background
+ * subtraction", International Conference Pattern Recognition, UK, Aug 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. */
+ else if (METHOD_MOG2 == filter->method) {
+ run_mog2_iteration (filter);
+ }
+
+ /* if we want to test_mode, just overwrite the output */
+ if (filter->test_mode) {
+ cvCvtColor (filter->cvFG, filter->cvRGB, CV_GRAY2RGB);
+
+ cvSplit (filter->cvRGB, filter->ch1, filter->ch2, filter->ch3, NULL);
+ } else
+ cvSplit (filter->cvRGBA, filter->ch1, filter->ch2, filter->ch3, NULL);
+
+ /* copy anyhow the fg/bg to the alpha channel in the output image */
+ cvMerge (filter->ch1, filter->ch2, filter->ch3, filter->cvFG, filter->cvRGBA);
+
+
+ return GST_FLOW_OK;
+}
+
+/* entry point to initialize the plug-in
+ * initialize the plug-in itself
+ * register the element factories and other features
+ */
+gboolean
+gst_segmentation_plugin_init (GstPlugin * plugin)
+{
+ GST_DEBUG_CATEGORY_INIT (gst_segmentation_debug, "segmentation",
+ 0, "Performs Foreground/Background segmentation in video sequences");
+
+ return gst_element_register (plugin, "segmentation", GST_RANK_NONE,
+ GST_TYPE_SEGMENTATION);
+}
+
+
+
+#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;
+ low[n] = *(p + n) - *(cbBounds + n);
+ if (low[n] < 0)
+ 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 Subract 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) “rawâ€\9d 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 (IplImage * mask, int poly1_hull0, float perimScale,
+ CvMemStorage * mem_storage, CvSeq * contours)
+{
+ CvContourScanner scanner;
+ CvSeq *c;
+ int numCont = 0;
+ /* Just some convenience variables */
+ const CvScalar CVX_WHITE = CV_RGB (0xff, 0xff, 0xff);
+ const CvScalar CVX_BLACK = CV_RGB (0x00, 0x00, 0x00);
+
+ /* CLEAN UP RAW MASK */
+ cvMorphologyEx (mask, mask, 0, 0, CV_MOP_OPEN, CVCLOSE_ITR);
+ cvMorphologyEx (mask, mask, 0, 0, CV_MOP_CLOSE, CVCLOSE_ITR);
+ /* FIND CONTOURS AROUND ONLY BIGGER REGIONS */
+ if (mem_storage == NULL) {
+ mem_storage = cvCreateMemStorage (0);
+ } else {
+ cvClearMemStorage (mem_storage);
+ }
+
+ scanner = cvStartFindContours (mask, mem_storage, sizeof (CvContour),
+ CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, cvPoint (0, 0));
+
+ while ((c = cvFindNextContour (scanner)) != NULL) {
+ double len = cvContourArea (c, CV_WHOLE_SEQ, 0);
+ /* calculate perimeter len threshold: */
+ double q = (mask->height + mask->width) / perimScale;
+ /* Get rid of blob if its perimeter is too small: */
+ if (len < q) {
+ cvSubstituteContour (scanner, NULL);
+ } else {
+ /* Smooth its edges if its large enough */
+ CvSeq *c_new;
+ if (poly1_hull0) {
+ /* Polygonal approximation */
+ c_new =
+ cvApproxPoly (c, sizeof (CvContour), mem_storage, CV_POLY_APPROX_DP,
+ CVCONTOUR_APPROX_LEVEL, 0);
+ } else {
+ /* Convex Hull of the segmentation */
+ c_new = cvConvexHull2 (c, mem_storage, CV_CLOCKWISE, 1);
+ }
+ cvSubstituteContour (scanner, c_new);
+ numCont++;
+ }
+ }
+ contours = cvEndFindContours (&scanner);
+
+ /* PAINT THE FOUND REGIONS BACK INTO THE IMAGE */
+ cvZero (mask);
+ /* DRAW PROCESSED CONTOURS INTO THE MASK */
+ for (c = contours; c != NULL; c = c->h_next)
+ cvDrawContours (mask, c, CVX_WHITE, CVX_BLACK, -1, CV_FILLED, 8, cvPoint (0,
+ 0));
+}
+#endif /*ifdef CODE_FROM_OREILLY_BOOK */
+
+
+int
+initialise_mog (GstSegmentation * filter)
+{
+ filter->img_input_as_cvMat = (void *) new cv::Mat (filter->cvYUV, false);
+ filter->img_fg_as_cvMat = (void *) new cv::Mat (filter->cvFG, false);
+
+ filter->mog = (void *) new cv::BackgroundSubtractorMOG ();
+ filter->mog2 = (void *) new cv::BackgroundSubtractorMOG2 ();
+
+ return (0);
+}
+
+int
+run_mog_iteration (GstSegmentation * filter)
+{
+ ((cv::Mat *) filter->img_input_as_cvMat)->data =
+ (uchar *) filter->cvYUV->imageData;
+ ((cv::Mat *) filter->img_fg_as_cvMat)->data =
+ (uchar *) filter->cvFG->imageData;
+
+ /*
+ 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
+ */
+
+ (*((cv::BackgroundSubtractorMOG *) filter->mog)) (*((cv::Mat *) filter->
+ img_input_as_cvMat), *((cv::Mat *) filter->img_fg_as_cvMat),
+ filter->learning_rate);
+
+ return (0);
+}
+
+int
+run_mog2_iteration (GstSegmentation * filter)
+{
+ ((cv::Mat *) filter->img_input_as_cvMat)->data =
+ (uchar *) filter->cvYUV->imageData;
+ ((cv::Mat *) filter->img_fg_as_cvMat)->data =
+ (uchar *) filter->cvFG->imageData;
+
+ /*
+ 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 Gausian 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.
+ */
+
+ (*((cv::BackgroundSubtractorMOG *) filter->mog2)) (*((cv::Mat *) filter->
+ img_input_as_cvMat), *((cv::Mat *) filter->img_fg_as_cvMat),
+ filter->learning_rate);
+
+ return (0);
+}
+
+int
+finalise_mog (GstSegmentation * filter)
+{
+ delete (cv::Mat *) filter->img_input_as_cvMat;
+ delete (cv::Mat *) filter->img_fg_as_cvMat;
+ delete (cv::BackgroundSubtractorMOG *) filter->mog;
+ delete (cv::BackgroundSubtractorMOG2 *) filter->mog2;
+ return (0);
+}
projects / platform / upstream / gstreamer.git / commitdiff