reference_surface.py 文件源码

def build_correspondance(self, visible_markers,camera_calibration,min_marker_perimeter,min_id_confidence):
        """
        - use all visible markers
        - fit a convex quadrangle around it
        - use quadrangle verts to establish perpective transform
        - map all markers into surface space
        - build up list of found markers and their uv coords
        """

        all_verts = [m['verts'] for m in visible_markers if m['perimeter']>=min_marker_perimeter]
        if not all_verts:
            return
        all_verts = np.array(all_verts,dtype=np.float32)
        all_verts.shape = (-1,1,2) # [vert,vert,vert,vert,vert...] with vert = [[r,c]]
        # all_verts_undistorted_normalized centered in img center flipped in y and range [-1,1]
        all_verts_undistorted_normalized = cv2.undistortPoints(all_verts, camera_calibration['camera_matrix'],camera_calibration['dist_coefs']*self.use_distortion)
        hull = cv2.convexHull(all_verts_undistorted_normalized,clockwise=False)

        #simplify until we have excatly 4 verts
        if hull.shape[0]>4:
            new_hull = cv2.approxPolyDP(hull,epsilon=1,closed=True)
            if new_hull.shape[0]>=4:
                hull = new_hull
        if hull.shape[0]>4:
            curvature = abs(GetAnglesPolyline(hull,closed=True))
            most_acute_4_threshold = sorted(curvature)[3]
            hull = hull[curvature<=most_acute_4_threshold]


        # all_verts_undistorted_normalized space is flipped in y.
        # we need to change the order of the hull vertecies
        hull = hull[[1,0,3,2],:,:]

        # now we need to roll the hull verts until we have the right orientation:
        # all_verts_undistorted_normalized space has its origin at the image center.
        # adding 1 to the coordinates puts the origin at the top left.
        distance_to_top_left = np.sqrt((hull[:,:,0]+1)**2+(hull[:,:,1]+1)**2)
        bot_left_idx = np.argmin(distance_to_top_left)+1
        hull = np.roll(hull,-bot_left_idx,axis=0)

        #based on these 4 verts we calculate the transformations into a 0,0 1,1 square space
        m_from_undistored_norm_space = m_verts_from_screen(hull)
        self.detected = True
        # map the markers vertices into the surface space (one can think of these as texture coordinates u,v)
        marker_uv_coords =  cv2.perspectiveTransform(all_verts_undistorted_normalized,m_from_undistored_norm_space)
        marker_uv_coords.shape = (-1,4,1,2) #[marker,marker...] marker = [ [[r,c]],[[r,c]] ]

        # build up a dict of discovered markers. Each with a history of uv coordinates
        for m,uv in zip (visible_markers,marker_uv_coords):
            try:
                self.markers[m['id']].add_uv_coords(uv)
            except KeyError:
                self.markers[m['id']] = Support_Marker(m['id'])
                self.markers[m['id']].add_uv_coords(uv)

        #average collection of uv correspondences accros detected markers
        self.build_up_status = sum([len(m.collected_uv_coords) for m in self.markers.values()])/float(len(self.markers))

        if self.build_up_status >= self.required_build_up:
            self.finalize_correnspondance()