Merge pull request #425 from berak:python_tracking

Author: vpisarev

modules/tracking/CMakeLists.txt

@@ -1,2 +1,2 @@
 set(the_description "Tracking API")
-ocv_define_module(tracking opencv_imgproc opencv_core opencv_video opencv_highgui opencv_datasets)
+ocv_define_module(tracking opencv_imgproc opencv_core opencv_video opencv_highgui opencv_datasets WRAP python)

modules/tracking/include/opencv2/tracking/onlineMIL.hpp

@@ -58,7 +58,7 @@ namespace cv
 
 class ClfOnlineStump;
 
-class ClfMilBoost
+class CV_EXPORTS ClfMilBoost
 {
  public:
   struct CV_EXPORTS Params

modules/tracking/include/opencv2/tracking/tracker.hpp

@@ -74,7 +74,7 @@ namespace cv
 
 /** @brief Abstract base class for TrackerFeature that represents the feature.
  */
-class CV_EXPORTS_W TrackerFeature
+class CV_EXPORTS TrackerFeature
 {
  public:
   virtual ~TrackerFeature();
@@ -131,7 +131,7 @@ TrackerFeatureSet is an aggregation of TrackerFeature
    TrackerFeature
 
  */
-class CV_EXPORTS_W TrackerFeatureSet
+class CV_EXPORTS TrackerFeatureSet
 {
  public:
 
@@ -209,7 +209,7 @@ class CV_EXPORTS_W TrackerFeatureSet
 /** @brief Abstract base class for TrackerSamplerAlgorithm that represents the algorithm for the specific
 sampler.
  */
-class CV_EXPORTS_W TrackerSamplerAlgorithm
+class CV_EXPORTS TrackerSamplerAlgorithm
 {
  public:
   /**
@@ -261,7 +261,7 @@ TrackerSampler is an aggregation of TrackerSamplerAlgorithm
 @sa
    TrackerSamplerAlgorithm
  */
-class CV_EXPORTS_W TrackerSampler
+class CV_EXPORTS TrackerSampler
 {
  public:
 
@@ -334,7 +334,7 @@ See @cite AAM \f$\hat{x}^{i}_{k}\f$ all the states candidates.
 Inherits this class with your Target state, In own implementation you can add scale variation,
 width, height, orientation, etc.
  */
-class CV_EXPORTS_W TrackerTargetState
+class CV_EXPORTS TrackerTargetState
 {
  public:
   virtual ~TrackerTargetState()
@@ -403,7 +403,7 @@ See @cite AAM State estimator
 
 See @cite AMVOT Statistical modeling (Fig. 3), Table III (generative) - IV (discriminative) - V (hybrid)
  */
-class CV_EXPORTS_W TrackerStateEstimator
+class CV_EXPORTS TrackerStateEstimator
 {
  public:
   virtual ~TrackerStateEstimator();
@@ -449,7 +449,7 @@ See @cite AAM Ak
 
 Inherits this with your TrackerModel
  */
-class CV_EXPORTS_W TrackerModel
+class CV_EXPORTS TrackerModel
 {
  public:
 
@@ -539,7 +539,7 @@ class CV_EXPORTS_W Tracker : public virtual Algorithm
 
     @return True if initialization went succesfully, false otherwise
      */
-  bool init( const Mat& image, const Rect2d& boundingBox );
+  CV_WRAP bool init( const Mat& image, const Rect2d& boundingBox );
 
   /** @brief Update the tracker, find the new most likely bounding box for the target
     @param image The current frame
@@ -550,7 +550,7 @@ class CV_EXPORTS_W Tracker : public virtual Algorithm
     current frame. Note, that latter *does not* imply that tracker has failed, maybe target is indeed
     missing from the frame (say, out of sight)
      */
-  bool update( const Mat& image, Rect2d& boundingBox );
+  CV_WRAP bool update( const Mat& image, CV_OUT Rect2d& boundingBox );
 
   /** @brief Creates a tracker by its name.
     @param trackerType Tracker type
@@ -560,7 +560,7 @@ class CV_EXPORTS_W Tracker : public virtual Algorithm
     -   "MIL" -- TrackerMIL
     -   "BOOSTING" -- TrackerBoosting
      */
-  static Ptr<Tracker> create( const String& trackerType );
+  CV_WRAP static Ptr<Tracker> create( const String& trackerType );
 
   virtual void read( const FileNode& fn )=0;
   virtual void write( FileStorage& fs ) const=0;
@@ -587,7 +587,7 @@ class CV_EXPORTS_W Tracker : public virtual Algorithm
 
 /** @brief TrackerStateEstimator based on Boosting
     */
-class CV_EXPORTS_W TrackerStateEstimatorMILBoosting : public TrackerStateEstimator
+class CV_EXPORTS TrackerStateEstimatorMILBoosting : public TrackerStateEstimator
 {
  public:
 
@@ -664,7 +664,7 @@ class CV_EXPORTS_W TrackerStateEstimatorMILBoosting : public TrackerStateEstimat
 
 /** @brief TrackerStateEstimatorAdaBoosting based on ADA-Boosting
  */
-class CV_EXPORTS_W TrackerStateEstimatorAdaBoosting : public TrackerStateEstimator
+class CV_EXPORTS TrackerStateEstimatorAdaBoosting : public TrackerStateEstimator
 {
  public:
   /** @brief Implementation of the target state for TrackerAdaBoostingTargetState
@@ -774,7 +774,7 @@ class CV_EXPORTS_W TrackerStateEstimatorAdaBoosting : public TrackerStateEstimat
 /**
  * \brief TrackerStateEstimator based on SVM
  */
-class CV_EXPORTS_W TrackerStateEstimatorSVM : public TrackerStateEstimator
+class CV_EXPORTS TrackerStateEstimatorSVM : public TrackerStateEstimator
 {
  public:
   TrackerStateEstimatorSVM();
@@ -789,7 +789,7 @@ class CV_EXPORTS_W TrackerStateEstimatorSVM : public TrackerStateEstimator
 
 /** @brief TrackerSampler based on CSC (current state centered), used by MIL algorithm TrackerMIL
  */
-class CV_EXPORTS_W TrackerSamplerCSC : public TrackerSamplerAlgorithm
+class CV_EXPORTS TrackerSamplerCSC : public TrackerSamplerAlgorithm
 {
  public:
   enum
@@ -847,7 +847,7 @@ class CV_EXPORTS_W TrackerSamplerCSC : public TrackerSamplerAlgorithm
 
 /** @brief TrackerSampler based on CS (current state), used by algorithm TrackerBoosting
  */
-class CV_EXPORTS_W TrackerSamplerCS : public TrackerSamplerAlgorithm
+class CV_EXPORTS TrackerSamplerCS : public TrackerSamplerAlgorithm
 {
  public:
   enum
@@ -914,7 +914,7 @@ It should be noted, that the definition of "similarity" between two rectangles i
 their histograms. As experiments show, tracker is *not* very succesfull if target is assumed to
 strongly change its dimensions.
  */
-class CV_EXPORTS_W TrackerSamplerPF : public TrackerSamplerAlgorithm
+class CV_EXPORTS TrackerSamplerPF : public TrackerSamplerAlgorithm
 {
 public:
   /** @brief This structure contains all the parameters that can be varied during the course of sampling
@@ -949,7 +949,7 @@ class CV_EXPORTS_W TrackerSamplerPF : public TrackerSamplerAlgorithm
 /**
  * \brief TrackerFeature based on Feature2D
  */
-class CV_EXPORTS_W TrackerFeatureFeature2d : public TrackerFeature
+class CV_EXPORTS TrackerFeatureFeature2d : public TrackerFeature
 {
  public:
 
@@ -976,7 +976,7 @@ class CV_EXPORTS_W TrackerFeatureFeature2d : public TrackerFeature
 /**
  * \brief TrackerFeature based on HOG
  */
-class CV_EXPORTS_W TrackerFeatureHOG : public TrackerFeature
+class CV_EXPORTS TrackerFeatureHOG : public TrackerFeature
 {
  public:
 
@@ -995,7 +995,7 @@ class CV_EXPORTS_W TrackerFeatureHOG : public TrackerFeature
 /** @brief TrackerFeature based on HAAR features, used by TrackerMIL and many others algorithms
 @note HAAR features implementation is copied from apps/traincascade and modified according to MIL
  */
-class CV_EXPORTS_W TrackerFeatureHAAR : public TrackerFeature
+class CV_EXPORTS TrackerFeatureHAAR : public TrackerFeature
 {
  public:
   struct CV_EXPORTS Params
@@ -1057,7 +1057,7 @@ class CV_EXPORTS_W TrackerFeatureHAAR : public TrackerFeature
 /**
  * \brief TrackerFeature based on LBP
  */
-class CV_EXPORTS_W TrackerFeatureLBP : public TrackerFeature
+class CV_EXPORTS TrackerFeatureLBP : public TrackerFeature
 {
  public:
 
@@ -1083,7 +1083,7 @@ based on @cite MIL .
 
 Original code can be found here <http://vision.ucsd.edu/~bbabenko/project_miltrack.shtml>
  */
-class CV_EXPORTS_W TrackerMIL : public Tracker
+class CV_EXPORTS TrackerMIL : public Tracker
 {
  public:
   struct CV_EXPORTS Params
@@ -1113,7 +1113,7 @@ class CV_EXPORTS_W TrackerMIL : public Tracker
 The classifier uses the surrounding background as negative examples in update step to avoid the
 drifting problem. The implementation is based on @cite OLB .
  */
-class CV_EXPORTS_W TrackerBoosting : public Tracker
+class CV_EXPORTS TrackerBoosting : public Tracker
 {
  public:
   struct CV_EXPORTS Params
@@ -1151,7 +1151,7 @@ by authors to outperform MIL). During the implementation period the code at
 <http://www.aonsquared.co.uk/node/5>, the courtesy of the author Arthur Amarra, was used for the
 reference purpose.
  */
-class CV_EXPORTS_W TrackerMedianFlow : public Tracker
+class CV_EXPORTS TrackerMedianFlow : public Tracker
 {
  public:
   struct CV_EXPORTS Params
@@ -1180,7 +1180,7 @@ The Median Flow algorithm (see cv::TrackerMedianFlow) was chosen as a tracking c
 implementation, following authors. Tracker is supposed to be able to handle rapid motions, partial
 occlusions, object absence etc.
  */
-class CV_EXPORTS_W TrackerTLD : public Tracker
+class CV_EXPORTS TrackerTLD : public Tracker
 {
  public:
   struct CV_EXPORTS Params
@@ -1202,7 +1202,7 @@ class CV_EXPORTS_W TrackerTLD : public Tracker
  * as well as the matlab implementation. For more information about KCF with color-names features, please refer to
  * <http://www.cvl.isy.liu.se/research/objrec/visualtracking/colvistrack/index.html>.
  */
-class CV_EXPORTS_W TrackerKCF : public Tracker
+class CV_EXPORTS TrackerKCF : public Tracker
 {
 public:
 	/**
@@ -1271,7 +1271,7 @@ class CV_EXPORTS_W MultiTracker
 	* In the case of trackerType is given, it will be set as the default algorithm for all trackers.
 	* @param trackerType the name of the tracker algorithm to be used
 	*/
-	MultiTracker(const String& trackerType = "");
+	CV_WRAP MultiTracker(const String& trackerType = "");
 
 	/**
 	* \brief Destructor
@@ -1284,30 +1284,30 @@ class CV_EXPORTS_W MultiTracker
 	* @param image input image
 	* @param boundingBox a rectangle represents ROI of the tracked object
 	*/
-	bool add(const Mat& image, const Rect2d& boundingBox);
+	CV_WRAP bool add(const Mat& image, const Rect2d& boundingBox);
 
 	/**
 	* \brief Add a new object to be tracked.
 	* @param trackerType the name of the tracker algorithm to be used
 	* @param image input image
 	* @param boundingBox a rectangle represents ROI of the tracked object
 	*/
-	bool add(const String& trackerType, const Mat& image, const Rect2d& boundingBox);
+	CV_WRAP bool add(const String& trackerType, const Mat& image, const Rect2d& boundingBox);
 
 	/**
 	* \brief Add a set of objects to be tracked.
 	* @param trackerType the name of the tracker algorithm to be used
 	* @param image input image
 	* @param boundingBox list of the tracked objects
 	*/
-	bool add(const String& trackerType, const Mat& image, std::vector<Rect2d> boundingBox);
+	CV_WRAP bool add(const String& trackerType, const Mat& image, std::vector<Rect2d> boundingBox);
 
 	/**
 	* \brief Add a set of objects to be tracked using the defaultAlgorithm tracker.
 	* @param image input image
 	* @param boundingBox list of the tracked objects
 	*/
-	bool add(const Mat& image, std::vector<Rect2d> boundingBox);
+	CV_WRAP bool add(const Mat& image, std::vector<Rect2d> boundingBox);
 
 	/**
 	* \brief Update the current tracking status.
@@ -1324,7 +1324,7 @@ class CV_EXPORTS_W MultiTracker
 	* @param image input image
 	* @param boundingBox the tracking result, represent a list of ROIs of the tracked objects.
 	*/
-	bool update(const Mat& image, std::vector<Rect2d> & boundingBox);
+	CV_WRAP bool update(const Mat& image, CV_OUT std::vector<Rect2d> & boundingBox);
 
 protected:
 	//!<  storage for the tracker algorithms.
@@ -1337,8 +1337,8 @@ class CV_EXPORTS_W MultiTracker
 class ROISelector {
 public:
 	Rect2d select(Mat img, bool fromCenter = true);
-	Rect2d select(const std::string& windowName, Mat img, bool showCrossair = true, bool fromCenter = true);
-	void select(const std::string& windowName, Mat img, std::vector<Rect2d> & boundingBox, bool fromCenter = true);
+	Rect2d select(const cv::String& windowName, Mat img, bool showCrossair = true, bool fromCenter = true);
+	void select(const cv::String& windowName, Mat img, std::vector<Rect2d> & boundingBox, bool fromCenter = true);
 
 	struct handlerT{
 		// basic parameters
@@ -1366,8 +1366,8 @@ class ROISelector {
 };
 
 Rect2d CV_EXPORTS_W selectROI(Mat img, bool fromCenter = true);
-Rect2d CV_EXPORTS_W selectROI(const std::string& windowName, Mat img, bool showCrossair = true, bool fromCenter = true);
-void CV_EXPORTS_W selectROI(const std::string& windowName, Mat img, std::vector<Rect2d> & boundingBox, bool fromCenter = true);
+Rect2d CV_EXPORTS_W selectROI(const cv::String& windowName, Mat img, bool showCrossair = true, bool fromCenter = true);
+void CV_EXPORTS_W selectROI(const cv::String& windowName, Mat img, std::vector<Rect2d> & boundingBox, bool fromCenter = true);
 
 
 /************************************ Multi-Tracker Classes ---By Tyan Vladimir---************************************/
@@ -1376,7 +1376,7 @@ void CV_EXPORTS_W selectROI(const std::string& windowName, Mat img, std::vector<
 
 @sa Tracker, MultiTrackerTLD
 */
-class CV_EXPORTS_W MultiTracker_Alt
+class CV_EXPORTS MultiTracker_Alt
 {
 public:
 	/** @brief Constructor for Multitracker
@@ -1433,7 +1433,7 @@ occlusions, object absence etc.
 
 @sa Tracker, MultiTracker, TrackerTLD
 */
-class CV_EXPORTS_W MultiTrackerTLD : public MultiTracker_Alt
+class CV_EXPORTS MultiTrackerTLD : public MultiTracker_Alt
 {
 public:
 	/** @brief Update all trackers from the tracking-list, find a new most likely bounding boxes for the targets by

modules/tracking/samples/multitracker.py

@@ -0,0 +1,35 @@
+import numpy as np
+import cv2
+
+cv2.namedWindow("tracking")
+camera = cv2.VideoCapture("E:/code/opencv/samples/data/768x576.avi")
+tracker = cv2.MultiTracker("MIL")
+bbox1 = (638.0,230.0,56.0,101.0)
+bbox2 = (240.0,210.0,60.0,104.0)
+bbox3 = (486.0,149.0,54.0,83.0)
+init_once = False
+
+while camera.isOpened():
+    ok, image=camera.read()
+    if not ok:
+        print 'no image read'
+        break
+
+    if not init_once:
+        # add a list of boxes:
+        ok = tracker.add(image, (bbox1,bbox2))
+        # or add single box:
+        ok = tracker.add(image, bbox3)
+        init_once = True
+
+    ok, boxes = tracker.update(image)
+    print ok, boxes
+
+    for newbox in boxes:
+        p1 = (int(newbox[0]), int(newbox[1]))
+        p2 = (int(newbox[0] + newbox[2]), int(newbox[1] + newbox[3]))
+        cv2.rectangle(image, p1, p2, (200,0,0))
+
+    cv2.imshow("tracking", image)
+    k = cv2.waitKey(1) & 0xff
+    if k == 27 : break # esc pressed

modules/tracking/samples/tracker.py

@@ -0,0 +1,30 @@
+import numpy as np
+import cv2
+
+cv2.namedWindow("tracking")
+camera = cv2.VideoCapture("E:/code/opencv/samples/data/768x576.avi")
+bbox = (638.0,230.0,56.0,101.0)
+tracker = cv2.Tracker_create("MIL")
+init_once = False
+
+while camera.isOpened():
+    ok, image=camera.read()
+    if not ok:
+        print 'no image read'
+        break
+
+    if not init_once:
+        ok = tracker.init(image, bbox)
+        init_once = True
+
+    ok, newbox = tracker.update(image)
+    print ok, newbox
+
+    if ok:
+        p1 = (int(newbox[0]), int(newbox[1]))
+        p2 = (int(newbox[0] + newbox[2]), int(newbox[1] + newbox[3]))
+        cv2.rectangle(image, p1, p2, (200,0,0))
+
+    cv2.imshow("tracking", image)
+    k = cv2.waitKey(1) & 0xff
+    if k == 27 : break # esc pressed