Beispiel #1
0
def finish_calibration(g_pool, pupil_list, ref_list):

    if pupil_list and ref_list:
        pass
    else:
        logger.error(not_enough_data_error_msg)
        g_pool.active_calibration_plugin.notify_all({
            'subject':
            'calibration.failed',
            'reason':
            not_enough_data_error_msg,
            'timestamp':
            g_pool.get_timestamp(),
            'record':
            True
        })
        return

    camera_intrinsics = load_camera_calibration(g_pool)

    # match eye data and check if biocular and or monocular
    pupil0 = [p for p in pupil_list if p['id'] == 0]
    pupil1 = [p for p in pupil_list if p['id'] == 1]

    #TODO unify this and don't do both
    matched_binocular_data = calibrate.closest_matches_binocular(
        ref_list, pupil_list)
    matched_pupil0_data = calibrate.closest_matches_monocular(ref_list, pupil0)
    matched_pupil1_data = calibrate.closest_matches_monocular(ref_list, pupil1)

    if len(matched_pupil0_data) > len(matched_pupil1_data):
        matched_monocular_data = matched_pupil0_data
    else:
        matched_monocular_data = matched_pupil1_data

    logger.info('Collected %s monocular calibration data.' %
                len(matched_monocular_data))
    logger.info('Collected %s binocular calibration data.' %
                len(matched_binocular_data))

    mode = g_pool.detection_mapping_mode

    if mode == '3d' and not camera_intrinsics:
        mode = '2d'
        logger.warning(
            "Please calibrate your world camera using 'camera intrinsics estimation' for 3d gaze mapping."
        )

    if mode == '3d':
        hardcoded_translation0 = np.array([20, 15, -20])
        hardcoded_translation1 = np.array([-40, 15, -20])
        if matched_binocular_data:
            method = 'binocular 3d model'

            #TODO model the world as cv2 pinhole camera with distorion and focal in ceres.
            # right now we solve using a few permutations of K
            smallest_residual = 1000
            scales = list(np.linspace(0.7, 1.4, 20))
            K = camera_intrinsics["camera_matrix"]

            for s in scales:
                scale = np.ones(K.shape)
                scale[0, 0] *= s
                scale[1, 1] *= s
                camera_intrinsics["camera_matrix"] = K * scale

                ref_dir, gaze0_dir, gaze1_dir = calibrate.preprocess_3d_data(
                    matched_binocular_data,
                    camera_intrinsics=camera_intrinsics)

                if len(ref_dir) < 1 or len(gaze0_dir) < 1 or len(
                        gaze1_dir) < 1:
                    logger.error(not_enough_data_error_msg)
                    g_pool.active_calibration_plugin.notify_all({
                        'subject':
                        'calibration.failed',
                        'reason':
                        not_enough_data_error_msg,
                        'timestamp':
                        g_pool.get_timestamp(),
                        'record':
                        True
                    })
                    return

                sphere_pos0 = pupil0[-1]['sphere']['center']
                sphere_pos1 = pupil1[-1]['sphere']['center']

                initial_R0, initial_t0 = find_rigid_transform(
                    np.array(gaze0_dir) * 500,
                    np.array(ref_dir) * 500)
                initial_rotation0 = math_helper.quaternion_from_rotation_matrix(
                    initial_R0)
                initial_translation0 = np.array(initial_t0).reshape(3)

                initial_R1, initial_t1 = find_rigid_transform(
                    np.array(gaze1_dir) * 500,
                    np.array(ref_dir) * 500)
                initial_rotation1 = math_helper.quaternion_from_rotation_matrix(
                    initial_R1)
                initial_translation1 = np.array(initial_t1).reshape(3)

                eye0 = {
                    "observations": gaze0_dir,
                    "translation": hardcoded_translation0,
                    "rotation": initial_rotation0,
                    'fix': ['translation']
                }
                eye1 = {
                    "observations": gaze1_dir,
                    "translation": hardcoded_translation1,
                    "rotation": initial_rotation1,
                    'fix': ['translation']
                }
                world = {
                    "observations": ref_dir,
                    "translation": (0, 0, 0),
                    "rotation": (1, 0, 0, 0),
                    'fix': ['translation', 'rotation'],
                    'fix': ['translation', 'rotation']
                }
                initial_observers = [eye0, eye1, world]
                initial_points = np.array(ref_dir) * 500

                success, residual, observers, points = bundle_adjust_calibration(
                    initial_observers, initial_points, fix_points=False)

                if residual <= smallest_residual:
                    smallest_residual = residual
                    scales[-1] = s

            if not success:
                g_pool.active_calibration_plugin.notify_all({
                    'subject':
                    'calibration.failed',
                    'reason':
                    solver_failed_to_converge_error_msg,
                    'timestamp':
                    g_pool.get_timestamp(),
                    'record':
                    True
                })
                logger.error("Calibration solver faild to converge.")
                return

            eye0, eye1, world = observers

            t_world0 = np.array(eye0['translation'])
            R_world0 = math_helper.quaternion_rotation_matrix(
                np.array(eye0['rotation']))
            t_world1 = np.array(eye1['translation'])
            R_world1 = math_helper.quaternion_rotation_matrix(
                np.array(eye1['rotation']))

            def toWorld0(p):
                return np.dot(R_world0, p) + t_world0

            def toWorld1(p):
                return np.dot(R_world1, p) + t_world1

            points_a = []  #world coords
            points_b = []  #eye0 coords
            points_c = []  #eye1 coords
            for a, b, c, point in zip(world['observations'],
                                      eye0['observations'],
                                      eye1['observations'], points):
                line_a = np.array([0, 0, 0]), np.array(a)  #observation as line
                line_b = toWorld0(np.array([0, 0, 0])), toWorld0(
                    b)  #eye0 observation line in world coords
                line_c = toWorld1(np.array([0, 0, 0])), toWorld1(
                    c)  #eye1 observation line in world coords
                close_point_a, _ = math_helper.nearest_linepoint_to_point(
                    point, line_a)
                close_point_b, _ = math_helper.nearest_linepoint_to_point(
                    point, line_b)
                close_point_c, _ = math_helper.nearest_linepoint_to_point(
                    point, line_c)
                points_a.append(close_point_a)
                points_b.append(close_point_b)
                points_c.append(close_point_c)

            # we need to take the sphere position into account
            # orientation and translation are referring to the sphere center.
            # but we want to have it referring to the camera center
            # since the actual translation is in world coordinates, the sphere translation needs to be calculated in world coordinates
            sphere_translation = np.array(sphere_pos0)
            sphere_translation_world = np.dot(R_world0, sphere_translation)
            camera_translation = t_world0 - sphere_translation_world
            eye_camera_to_world_matrix0 = np.eye(4)
            eye_camera_to_world_matrix0[:3, :3] = R_world0
            eye_camera_to_world_matrix0[:3, 3:4] = np.reshape(
                camera_translation, (3, 1))

            sphere_translation = np.array(sphere_pos1)
            sphere_translation_world = np.dot(R_world1, sphere_translation)
            camera_translation = t_world1 - sphere_translation_world
            eye_camera_to_world_matrix1 = np.eye(4)
            eye_camera_to_world_matrix1[:3, :3] = R_world1
            eye_camera_to_world_matrix1[:3, 3:4] = np.reshape(
                camera_translation, (3, 1))

            g_pool.plugins.add(Binocular_Vector_Gaze_Mapper,
                               args={
                                   'eye_camera_to_world_matrix0':
                                   eye_camera_to_world_matrix0,
                                   'eye_camera_to_world_matrix1':
                                   eye_camera_to_world_matrix1,
                                   'camera_intrinsics': camera_intrinsics,
                                   'cal_points_3d': points,
                                   'cal_ref_points_3d': points_a,
                                   'cal_gaze_points0_3d': points_b,
                                   'cal_gaze_points1_3d': points_c
                               })

        elif matched_monocular_data:
            method = 'monocular 3d model'

            #TODO model the world as cv2 pinhole camera with distorion and focal in ceres.
            # right now we solve using a few permutations of K
            smallest_residual = 1000
            scales = list(np.linspace(0.7, 1.4, 20))
            K = camera_intrinsics["camera_matrix"]
            for s in scales:
                scale = np.ones(K.shape)
                scale[0, 0] *= s
                scale[1, 1] *= s
                camera_intrinsics["camera_matrix"] = K * scale
                ref_dir, gaze_dir, _ = calibrate.preprocess_3d_data(
                    matched_monocular_data,
                    camera_intrinsics=camera_intrinsics)
                # save_object((ref_dir,gaze_dir),os.path.join(g_pool.user_dir, "testdata"))
                if len(ref_dir) < 1 or len(gaze_dir) < 1:
                    g_pool.active_calibration_plugin.notify_all({
                        'subject':
                        'calibration.failed',
                        'reason':
                        not_enough_data_error_msg,
                        'timestamp':
                        g_pool.get_timestamp(),
                        'record':
                        True
                    })
                    logger.error(not_enough_data_error_msg + " Using:" +
                                 method)
                    return

                ### monocular calibration strategy: mimize the reprojection error by moving the world camera.
                # we fix the eye points and work in the eye coord system.
                initial_R, initial_t = find_rigid_transform(
                    np.array(ref_dir) * 500,
                    np.array(gaze_dir) * 500)
                initial_rotation = math_helper.quaternion_from_rotation_matrix(
                    initial_R)
                initial_translation = np.array(initial_t).reshape(3)
                # this problem is scale invariant so we scale to some sensical value.

                if matched_monocular_data[0]['pupil']['id'] == 0:
                    hardcoded_translation = hardcoded_translation0
                else:
                    hardcoded_translation = hardcoded_translation1

                eye = {
                    "observations": gaze_dir,
                    "translation": (0, 0, 0),
                    "rotation": (1, 0, 0, 0),
                    'fix': ['translation', 'rotation']
                }
                world = {
                    "observations": ref_dir,
                    "translation": np.dot(initial_R, -hardcoded_translation),
                    "rotation": initial_rotation,
                    'fix': ['translation']
                }
                initial_observers = [eye, world]
                initial_points = np.array(gaze_dir) * 500

                success, residual, observers, points_in_eye = bundle_adjust_calibration(
                    initial_observers, initial_points, fix_points=True)
                if residual <= smallest_residual:
                    smallest_residual = residual
                    scales[-1] = s

            eye, world = observers

            if not success:
                logger.error("Calibration solver faild to converge.")
                g_pool.active_calibration_plugin.notify_all({
                    'subject':
                    'calibration.failed',
                    'reason':
                    solver_failed_to_converge_error_msg,
                    'timestamp':
                    g_pool.get_timestamp(),
                    'record':
                    True
                })
                return

            #pose of the world in eye coords.
            rotation = np.array(world['rotation'])
            t_world = np.array(world['translation'])
            R_world = math_helper.quaternion_rotation_matrix(rotation)

            # inverse is pose of eye in world coords
            R_eye = R_world.T
            t_eye = np.dot(R_eye, -t_world)

            def toWorld(p):
                return np.dot(R_eye, p) + np.array(t_eye)

            points_in_world = [toWorld(p) for p in points_in_eye]

            points_a = []  #world coords
            points_b = []  #cam2 coords
            for a, b, point in zip(world['observations'], eye['observations'],
                                   points_in_world):

                line_a = np.array([0, 0, 0]), np.array(a)  #observation as line
                line_b = toWorld(np.array([0, 0, 0])), toWorld(
                    b)  #cam2 observation line in cam1 coords
                close_point_a, _ = math_helper.nearest_linepoint_to_point(
                    point, line_a)
                close_point_b, _ = math_helper.nearest_linepoint_to_point(
                    point, line_b)
                # print np.linalg.norm(point-close_point_a),np.linalg.norm(point-close_point_b)

                points_a.append(close_point_a)
                points_b.append(close_point_b)

            # we need to take the sphere position into account
            # orientation and translation are referring to the sphere center.
            # but we want to have it referring to the camera center
            # since the actual translation is in world coordinates, the sphere translation needs to be calculated in world coordinates
            sphere_translation = np.array(
                matched_monocular_data[-1]['pupil']['sphere']['center'])
            sphere_translation_world = np.dot(R_eye, sphere_translation)
            camera_translation = t_eye - sphere_translation_world
            eye_camera_to_world_matrix = np.eye(4)
            eye_camera_to_world_matrix[:3, :3] = R_eye
            eye_camera_to_world_matrix[:3, 3:4] = np.reshape(
                camera_translation, (3, 1))

            g_pool.plugins.add(Vector_Gaze_Mapper,
                               args={
                                   'eye_camera_to_world_matrix':
                                   eye_camera_to_world_matrix,
                                   'camera_intrinsics': camera_intrinsics,
                                   'cal_points_3d': points_in_world,
                                   'cal_ref_points_3d': points_a,
                                   'cal_gaze_points_3d': points_b,
                                   'gaze_distance': 500
                               })

        else:
            logger.error(not_enough_data_error_msg)
            g_pool.active_calibration_plugin.notify_all({
                'subject':
                'calibration.failed',
                'reason':
                not_enough_data_error_msg,
                'timestamp':
                g_pool.get_timestamp(),
                'record':
                True
            })
            return

    elif mode == '2d':
        if matched_binocular_data:
            method = 'binocular polynomial regression'
            cal_pt_cloud_binocular = calibrate.preprocess_2d_data_binocular(
                matched_binocular_data)
            cal_pt_cloud0 = calibrate.preprocess_2d_data_monocular(
                matched_pupil0_data)
            cal_pt_cloud1 = calibrate.preprocess_2d_data_monocular(
                matched_pupil1_data)

            map_fn, inliers, params = calibrate.calibrate_2d_polynomial(
                cal_pt_cloud_binocular,
                g_pool.capture.frame_size,
                binocular=True)
            if not inliers.any():
                g_pool.active_calibration_plugin.notify_all({
                    'subject':
                    'calibration.failed',
                    'reason':
                    solver_failed_to_converge_error_msg,
                    'timestamp':
                    g_pool.get_timestamp(),
                    'record':
                    True
                })
                return

            map_fn, inliers, params_eye0 = calibrate.calibrate_2d_polynomial(
                cal_pt_cloud0, g_pool.capture.frame_size, binocular=False)
            if not inliers.any():
                g_pool.active_calibration_plugin.notify_all({
                    'subject':
                    'calibration.failed',
                    'reason':
                    solver_failed_to_converge_error_msg,
                    'timestamp':
                    g_pool.get_timestamp(),
                    'record':
                    True
                })
                return

            map_fn, inliers, params_eye1 = calibrate.calibrate_2d_polynomial(
                cal_pt_cloud1, g_pool.capture.frame_size, binocular=False)
            if not inliers.any():
                g_pool.active_calibration_plugin.notify_all({
                    'subject':
                    'calibration.failed',
                    'reason':
                    solver_failed_to_converge_error_msg,
                    'timestamp':
                    g_pool.get_timestamp(),
                    'record':
                    True
                })
                return

            g_pool.plugins.add(Binocular_Gaze_Mapper,
                               args={
                                   'params': params,
                                   'params_eye0': params_eye0,
                                   'params_eye1': params_eye1
                               })

        elif matched_monocular_data:
            method = 'monocular polynomial regression'
            cal_pt_cloud = calibrate.preprocess_2d_data_monocular(
                matched_monocular_data)
            map_fn, inliers, params = calibrate.calibrate_2d_polynomial(
                cal_pt_cloud, g_pool.capture.frame_size, binocular=False)
            if not inliers.any():
                g_pool.active_calibration_plugin.notify_all({
                    'subject':
                    'calibration.failed',
                    'reason':
                    solver_failed_to_converge_error_msg,
                    'timestamp':
                    g_pool.get_timestamp(),
                    'record':
                    True
                })
                return

            g_pool.plugins.add(Monocular_Gaze_Mapper, args={'params': params})
        else:
            logger.error(not_enough_data_error_msg)
            g_pool.active_calibration_plugin.notify_all({
                'subject':
                'calibration.failed',
                'reason':
                not_enough_data_error_msg,
                'timestamp':
                g_pool.get_timestamp(),
                'record':
                True
            })
            return

    user_calibration_data = {
        'pupil_list': pupil_list,
        'ref_list': ref_list,
        'calibration_method': method
    }
    save_object(user_calibration_data,
                os.path.join(g_pool.user_dir, "user_calibration_data"))
    g_pool.active_calibration_plugin.notify_all({
        'subject':
        'calibration.successful',
        'method':
        method,
        'timestamp':
        g_pool.get_timestamp(),
        'record':
        True
    })
def finish_calibration(g_pool,pupil_list,ref_list,calibration_distance_3d = 500, force=None):
    not_enough_data_error_msg = 'Did not collect enough data during calibration.'

    if pupil_list and ref_list:
        pass
    else:
        logger.error(not_enough_data_error_msg)
        g_pool.active_calibration_plugin.notify_all({'subject':'calibration_failed','reason':not_enough_data_error_msg,'timestamp':g_pool.capture.get_timestamp(),'record':True})
        return

    camera_intrinsics = load_camera_calibration(g_pool)


    # match eye data and check if biocular and or monocular
    pupil0 = [p for p in pupil_list if p['id']==0]
    pupil1 = [p for p in pupil_list if p['id']==1]

    matched_binocular_data = calibrate.closest_matches_binocular(ref_list,pupil_list)
    matched_pupil0_data = calibrate.closest_matches_monocular(ref_list,pupil0)
    matched_pupil1_data = calibrate.closest_matches_monocular(ref_list,pupil1)

    if len(matched_pupil0_data)>len(matched_pupil1_data):
        matched_monocular_data = matched_pupil0_data
    else:
        matched_monocular_data = matched_pupil1_data
    logger.info('Collected %s monocular calibration data.'%len(matched_monocular_data))
    logger.info('Collected %s binocular calibration data.'%len(matched_binocular_data))



    if force:
        mode = force
    else:
        mode = g_pool.detection_mapping_mode

    if mode == '3d' and not camera_intrinsics:
        mode = '2d'
        logger.warning("Please calibrate your world camera using 'camera intrinsics estimation' for 3d gaze mapping.")



    if mode == '3d':
        if matched_binocular_data:
            method = 'binocular 3d model'
            cal_pt_cloud = calibrate.preprocess_3d_data_binocular(matched_binocular_data,
                                        camera_intrinsics = camera_intrinsics,
                                        calibration_distance=calibration_distance_3d )
            cal_pt_cloud = np.array(cal_pt_cloud)
            try:
                gaze_pt0_3d = cal_pt_cloud[:,0]
                gaze_pt1_3d = cal_pt_cloud[:,1]
                ref_3d = cal_pt_cloud[:,2]
            except:
                logger.error(not_enough_data_error_msg)
                g_pool.active_calibration_plugin.notify_all({'subject':'calibration_failed','reason':not_enough_data_error_msg,'timestamp':g_pool.capture.get_timestamp(),'record':True})
                return

            best_distance = 1000
            best_scale = 100
            for scale_percent in range(50,150,10):
                R0,t0 = calibrate.rigid_transform_3D( np.matrix(gaze_pt0_3d) ,np.matrix(ref_3d*(scale_percent/100.)) )
                R1,t1 = calibrate.rigid_transform_3D( np.matrix(gaze_pt1_3d) ,np.matrix(ref_3d*(scale_percent/100.)) )
                eye_to_world_matrix0  = np.matrix(np.eye(4))
                eye_to_world_matrix0[:3,:3] = R0
                eye_to_world_matrix0[:3,3:4] =  t0

                eye_to_world_matrix1  = np.matrix(np.eye(4))
                eye_to_world_matrix1[:3,:3] = R1
                eye_to_world_matrix1[:3,3:4] =  t1

                avg_distance0, dist_var0 = calibrate.calculate_residual_3D_Points( ref_3d*(scale_percent/100.), gaze_pt0_3d, eye_to_world_matrix0 )
                avg_distance1, dist_var1 = calibrate.calculate_residual_3D_Points( ref_3d*(scale_percent/100.), gaze_pt1_3d, eye_to_world_matrix1 )
                avg_distance = (avg_distance0+avg_distance1)/2.
                if avg_distance < best_distance:
                    best_distance = avg_distance
                    best_scale = scale_percent

            ref_3d *= best_scale/100.
            R0,t0 = calibrate.rigid_transform_3D( np.matrix(gaze_pt0_3d) ,np.matrix(ref_3d) )
            R1,t1 = calibrate.rigid_transform_3D( np.matrix(gaze_pt1_3d) ,np.matrix(ref_3d) )

            sphere0 = pupil0[-1]['sphere']['center']
            sphere1 = pupil1[-1]['sphere']['center']


            eye_to_world_matrix0  = np.matrix(np.eye(4))
            eye_to_world_matrix0[:3,:3] = R0
            eye_to_world_matrix0[:3,3:4] =  t0
            # eye_to_world_matrix0[:3,3:4] =  np.array((20,10,-20)).reshape(3,1)
            # eye_to_world_matrix0[:3,3:4] -=  R0 * (np.array(sphere0)*(1,-1,1)).reshape(3,1)

            eye_to_world_matrix1  = np.matrix(np.eye(4))
            eye_to_world_matrix1[:3,:3] = R1
            eye_to_world_matrix1[:3,3:4] =  t1
            # eye_to_world_matrix1[:3,3:4] =  np.array((-40,10,-20)).reshape(3,1)
            # eye_to_world_matrix1[:3,3:4] -=  R1 * (np.array(sphere1)*(1,-1,1)).reshape(3,1)

            avg_distance0, dist_var0 = calibrate.calculate_residual_3D_Points( ref_3d, gaze_pt0_3d, eye_to_world_matrix0 )
            avg_distance1, dist_var1 = calibrate.calculate_residual_3D_Points( ref_3d, gaze_pt1_3d, eye_to_world_matrix1 )
            logger.info('calibration average distance eye0: %s'%avg_distance0)
            logger.info('calibration average distance eye1: %s'%avg_distance1)

            g_pool.plugins.add(Binocular_Vector_Gaze_Mapper,args={'eye_to_world_matrix0':eye_to_world_matrix0,'eye_to_world_matrix1':eye_to_world_matrix1 , 'camera_intrinsics': camera_intrinsics , 'cal_ref_points_3d': ref_3d.tolist(), 'cal_gaze_points0_3d': gaze_pt0_3d.tolist(), 'cal_gaze_points1_3d': gaze_pt1_3d.tolist() })

        elif matched_monocular_data:
            method = 'monocular 3d model'
            cal_pt_cloud = calibrate.preprocess_3d_data_monocular(matched_monocular_data,
                                            camera_intrinsics = camera_intrinsics,
                                            calibration_distance=calibration_distance_3d )
            cal_pt_cloud = np.array(cal_pt_cloud)
            try:
                gaze_3d = cal_pt_cloud[:,0]
                ref_3d = cal_pt_cloud[:,1]
            except:
                logger.error(not_enough_data_error_msg)
                g_pool.active_calibration_plugin.notify_all({'subject':'calibration_failed','reason':not_enough_data_error_msg,'timestamp':g_pool.capture.get_timestamp(),'record':True})
                return

            best_distance = 1000
            best_scale = 100
            for scale_percent in range(50,150,10):
                #calculate transformation form eye camera to world camera
                R,t = calibrate.rigid_transform_3D( np.matrix(gaze_3d), np.matrix(ref_3d*(scale_percent/100.)) )

                eye_to_world_matrix  = np.matrix(np.eye(4))
                eye_to_world_matrix[:3,:3] = R
                eye_to_world_matrix[:3,3:4] = t

                avg_distance, dist_var = calibrate.calculate_residual_3D_Points( ref_3d*(scale_percent/100.), gaze_3d, eye_to_world_matrix )

                if avg_distance < best_distance:
                    best_distance = avg_distance
                    best_scale = scale_percent


            ref_3d *= best_scale/100.

            #calculate transformation form eye camera to world camera
            R,t = calibrate.rigid_transform_3D( np.matrix(gaze_3d), np.matrix(ref_3d) )

            eye_to_world_matrix  = np.matrix(np.eye(4))
            eye_to_world_matrix[:3,:3] = R
            eye_to_world_matrix[:3,3:4] = t
            avg_distance, dist_var = calibrate.calculate_residual_3D_Points( ref_3d, gaze_3d, eye_to_world_matrix )
            print 'best calibration average distance: ' , avg_distance
            # print 'best calibration distance variance: ' , dist_var
            g_pool.plugins.add(Vector_Gaze_Mapper,args={'eye_to_world_matrix':eye_to_world_matrix , 'camera_intrinsics': camera_intrinsics , 'cal_ref_points_3d': cal_pt_cloud[:,1].tolist(), 'cal_gaze_points_3d': cal_pt_cloud[:,0].tolist()})

        else:
            logger.error(not_enough_data_error_msg)
            g_pool.active_calibration_plugin.notify_all({'subject':'calibration_failed','reason':not_enough_data_error_msg,'timestamp':g_pool.capture.get_timestamp(),'record':True})
            return

    elif mode == '2d':
        if matched_binocular_data:
            method = 'binocular polynomial regression'
            cal_pt_cloud_binocular = calibrate.preprocess_2d_data_binocular(matched_binocular_data)
            cal_pt_cloud0 = calibrate.preprocess_2d_data_monocular(matched_pupil0_data)
            cal_pt_cloud1 = calibrate.preprocess_2d_data_monocular(matched_pupil1_data)
            map_fn,inliers,params = calibrate.calibrate_2d_polynomial(cal_pt_cloud_binocular,g_pool.capture.frame_size,binocular=True)
            map_fn,inliers,params_eye0 = calibrate.calibrate_2d_polynomial(cal_pt_cloud0,g_pool.capture.frame_size,binocular=False)
            map_fn,inliers,params_eye1 = calibrate.calibrate_2d_polynomial(cal_pt_cloud1,g_pool.capture.frame_size,binocular=False)
            g_pool.plugins.add(Binocular_Gaze_Mapper,args={'params':params, 'params_eye0':params_eye0, 'params_eye1':params_eye1})


        elif matched_monocular_data:
            method = 'monocular polynomial regression'
            cal_pt_cloud = calibrate.preprocess_2d_data_monocular(matched_monocular_data)
            map_fn,inliers,params = calibrate.calibrate_2d_polynomial(cal_pt_cloud,g_pool.capture.frame_size,binocular=False)
            g_pool.plugins.add(Simple_Gaze_Mapper,args={'params':params})
        else:
            logger.error(not_enough_data_error_msg)
            g_pool.active_calibration_plugin.notify_all({'subject':'calibration_failed','reason':not_enough_data_error_msg,'timestamp':g_pool.capture.get_timestamp(),'record':True})
            return

    user_calibration_data = {'pupil_list':pupil_list,'ref_list':ref_list,'calibration_method':method}
    save_object(user_calibration_data,os.path.join(g_pool.user_dir, "user_calibration_data"))
    g_pool.active_calibration_plugin.notify_all({'subject':'calibration_successful','method':method,'timestamp':g_pool.capture.get_timestamp(),'record':True})
def finish_calibration(g_pool,pupil_list,ref_list):

    if pupil_list and ref_list:
        pass
    else:
        logger.error(not_enough_data_error_msg)
        g_pool.active_calibration_plugin.notify_all({'subject':'calibration.failed','reason':not_enough_data_error_msg,'timestamp':g_pool.capture.get_timestamp(),'record':True})
        return

    camera_intrinsics = load_camera_calibration(g_pool)

    # match eye data and check if biocular and or monocular
    pupil0 = [p for p in pupil_list if p['id']==0]
    pupil1 = [p for p in pupil_list if p['id']==1]

    #TODO unify this and don't do both
    matched_binocular_data = calibrate.closest_matches_binocular(ref_list,pupil_list)
    matched_pupil0_data = calibrate.closest_matches_monocular(ref_list,pupil0)
    matched_pupil1_data = calibrate.closest_matches_monocular(ref_list,pupil1)

    if len(matched_pupil0_data)>len(matched_pupil1_data):
        matched_monocular_data = matched_pupil0_data
    else:
        matched_monocular_data = matched_pupil1_data

    logger.info('Collected %s monocular calibration data.'%len(matched_monocular_data))
    logger.info('Collected %s binocular calibration data.'%len(matched_binocular_data))


    mode = g_pool.detection_mapping_mode

    if mode == '3d' and not camera_intrinsics:
        mode = '2d'
        logger.warning("Please calibrate your world camera using 'camera intrinsics estimation' for 3d gaze mapping.")

    if mode == '3d':
        hardcoded_translation0  = np.array([20,15,-20])
        hardcoded_translation1  = np.array([-40,15,-20])
        if matched_binocular_data:
            method = 'binocular 3d model'

            #TODO model the world as cv2 pinhole camera with distorion and focal in ceres.
            # right now we solve using a few permutations of K
            smallest_residual = 1000
            scales = list(np.linspace(0.7,1.4,20))
            K = camera_intrinsics["camera_matrix"]

            for s in scales:
                scale = np.ones(K.shape)
                scale[0,0] *= s
                scale[1,1] *= s
                camera_intrinsics["camera_matrix"] = K*scale

                ref_dir, gaze0_dir, gaze1_dir = calibrate.preprocess_3d_data(matched_binocular_data,
                                                camera_intrinsics = camera_intrinsics )

                if len(ref_dir) < 1 or len(gaze0_dir) < 1 or len(gaze1_dir) < 1:
                    logger.error(not_enough_data_error_msg)
                    g_pool.active_calibration_plugin.notify_all({'subject':'calibration.failed','reason':not_enough_data_error_msg,'timestamp':g_pool.capture.get_timestamp(),'record':True})
                    return

                sphere_pos0 = pupil0[-1]['sphere']['center']
                sphere_pos1 = pupil1[-1]['sphere']['center']

                initial_R0,initial_t0 = find_rigid_transform(np.array(gaze0_dir)*500,np.array(ref_dir)*500)
                initial_rotation0 = math_helper.quaternion_from_rotation_matrix(initial_R0)
                initial_translation0 = np.array(initial_t0).reshape(3)

                initial_R1,initial_t1 = find_rigid_transform(np.array(gaze1_dir)*500,np.array(ref_dir)*500)
                initial_rotation1 = math_helper.quaternion_from_rotation_matrix(initial_R1)
                initial_translation1 = np.array(initial_t1).reshape(3)

                eye0 = { "observations" : gaze0_dir , "translation" : hardcoded_translation0 , "rotation" : initial_rotation0,'fix':['translation']  }
                eye1 = { "observations" : gaze1_dir , "translation" : hardcoded_translation1 , "rotation" : initial_rotation1,'fix':['translation']  }
                world = { "observations" : ref_dir , "translation" : (0,0,0) , "rotation" : (1,0,0,0),'fix':['translation','rotation'],'fix':['translation','rotation']  }
                initial_observers = [eye0,eye1,world]
                initial_points = np.array(ref_dir)*500


                success,residual, observers, points  = bundle_adjust_calibration(initial_observers , initial_points, fix_points=False )

                if residual <= smallest_residual:
                    smallest_residual = residual
                    scales[-1] = s


            if not success:
                g_pool.active_calibration_plugin.notify_all({'subject':'calibration.failed','reason':solver_failed_to_converge_error_msg,'timestamp':g_pool.capture.get_timestamp(),'record':True})
                logger.error("Calibration solver faild to converge.")
                return


            eye0,eye1,world = observers


            t_world0 = np.array(eye0['translation'])
            R_world0 = math_helper.quaternion_rotation_matrix(np.array(eye0['rotation']))
            t_world1 = np.array(eye1['translation'])
            R_world1 = math_helper.quaternion_rotation_matrix(np.array(eye1['rotation']))

            def toWorld0(p):
                return np.dot(R_world0, p)+t_world0

            def toWorld1(p):
                return np.dot(R_world1, p)+t_world1

            points_a = [] #world coords
            points_b = [] #eye0 coords
            points_c = [] #eye1 coords
            for a,b,c,point in zip(world['observations'] , eye0['observations'],eye1['observations'],points):
                line_a = np.array([0,0,0]) , np.array(a) #observation as line
                line_b = toWorld0(np.array([0,0,0])) , toWorld0(b)  #eye0 observation line in world coords
                line_c = toWorld1(np.array([0,0,0])) , toWorld1(c)  #eye1 observation line in world coords
                close_point_a,_ =  math_helper.nearest_linepoint_to_point( point , line_a )
                close_point_b,_ =  math_helper.nearest_linepoint_to_point( point , line_b )
                close_point_c,_ =  math_helper.nearest_linepoint_to_point( point , line_c )
                points_a.append(close_point_a)
                points_b.append(close_point_b)
                points_c.append(close_point_c)


            # we need to take the sphere position into account
            # orientation and translation are referring to the sphere center.
            # but we want to have it referring to the camera center
            # since the actual translation is in world coordinates, the sphere translation needs to be calculated in world coordinates
            sphere_translation = np.array( sphere_pos0 )
            sphere_translation_world = np.dot( R_world0 , sphere_translation)
            camera_translation = t_world0 - sphere_translation_world
            eye_camera_to_world_matrix0  = np.eye(4)
            eye_camera_to_world_matrix0[:3,:3] = R_world0
            eye_camera_to_world_matrix0[:3,3:4] = np.reshape(camera_translation, (3,1) )

            sphere_translation = np.array( sphere_pos1 )
            sphere_translation_world = np.dot( R_world1 , sphere_translation)
            camera_translation = t_world1 - sphere_translation_world
            eye_camera_to_world_matrix1  = np.eye(4)
            eye_camera_to_world_matrix1[:3,:3] = R_world1
            eye_camera_to_world_matrix1[:3,3:4] = np.reshape(camera_translation, (3,1) )


            g_pool.plugins.add(Binocular_Vector_Gaze_Mapper,args={
                                    'eye_camera_to_world_matrix0':eye_camera_to_world_matrix0,
                                    'eye_camera_to_world_matrix1':eye_camera_to_world_matrix1 ,
                                    'camera_intrinsics': camera_intrinsics ,
                                    'cal_points_3d': points,
                                    'cal_ref_points_3d': points_a,
                                    'cal_gaze_points0_3d': points_b,
                                    'cal_gaze_points1_3d': points_c})


        elif matched_monocular_data:
            method = 'monocular 3d model'

            #TODO model the world as cv2 pinhole camera with distorion and focal in ceres.
            # right now we solve using a few permutations of K
            smallest_residual = 1000
            scales = list(np.linspace(0.7,1.4,20))
            K = camera_intrinsics["camera_matrix"]
            for s in scales:
                scale = np.ones(K.shape)
                scale[0,0] *= s
                scale[1,1] *= s
                camera_intrinsics["camera_matrix"] = K*scale
                ref_dir , gaze_dir, _ = calibrate.preprocess_3d_data(matched_monocular_data,
                                                camera_intrinsics = camera_intrinsics )
                # save_object((ref_dir,gaze_dir),os.path.join(g_pool.user_dir, "testdata"))
                if len(ref_dir) < 1 or len(gaze_dir) < 1:
                    g_pool.active_calibration_plugin.notify_all({'subject':'calibration.failed','reason':not_enough_data_error_msg,'timestamp':g_pool.capture.get_timestamp(),'record':True})
                    logger.error(not_enough_data_error_msg + " Using:" + method)
                    return



                ### monocular calibration strategy: mimize the reprojection error by moving the world camera.
                # we fix the eye points and work in the eye coord system.
                initial_R,initial_t = find_rigid_transform(np.array(ref_dir)*500,np.array(gaze_dir)*500)
                initial_rotation = math_helper.quaternion_from_rotation_matrix(initial_R)
                initial_translation = np.array(initial_t).reshape(3)
                # this problem is scale invariant so we scale to some sensical value.


                if matched_monocular_data[0]['pupil']['id'] == 0:
                    hardcoded_translation = hardcoded_translation0
                else:
                    hardcoded_translation = hardcoded_translation1


                eye = { "observations" : gaze_dir , "translation" : (0,0,0) , "rotation" : (1,0,0,0),'fix':['translation','rotation']  }
                world = { "observations" : ref_dir , "translation" : np.dot(initial_R,-hardcoded_translation) , "rotation" : initial_rotation,'fix':['translation']  }
                initial_observers = [eye,world]
                initial_points = np.array(gaze_dir)*500


                success,residual, observers, points_in_eye  = bundle_adjust_calibration(initial_observers , initial_points, fix_points=True )
                if residual <= smallest_residual:
                    smallest_residual = residual
                    scales[-1] = s

            eye, world = observers

            if not success:
                logger.error("Calibration solver faild to converge.")
                g_pool.active_calibration_plugin.notify_all({'subject':'calibration.failed','reason':solver_failed_to_converge_error_msg,'timestamp':g_pool.capture.get_timestamp(),'record':True})
                return


            #pose of the world in eye coords.
            rotation = np.array(world['rotation'])
            t_world = np.array(world['translation'])
            R_world = math_helper.quaternion_rotation_matrix(rotation)

            # inverse is pose of eye in world coords
            R_eye = R_world.T
            t_eye = np.dot(R_eye,-t_world)



            def toWorld(p):
                return np.dot(R_eye, p)+np.array(t_eye)

            points_in_world = [toWorld(p) for p in points_in_eye]

            points_a = [] #world coords
            points_b = [] #cam2 coords
            for a,b,point in zip(world['observations'] , eye['observations'],points_in_world):

                line_a = np.array([0,0,0]) , np.array(a) #observation as line
                line_b = toWorld(np.array([0,0,0])) , toWorld(b)  #cam2 observation line in cam1 coords
                close_point_a,_ =  math_helper.nearest_linepoint_to_point( point , line_a )
                close_point_b,_ =  math_helper.nearest_linepoint_to_point( point , line_b )
                # print np.linalg.norm(point-close_point_a),np.linalg.norm(point-close_point_b)

                points_a.append(close_point_a)
                points_b.append(close_point_b)


            # we need to take the sphere position into account
            # orientation and translation are referring to the sphere center.
            # but we want to have it referring to the camera center
            # since the actual translation is in world coordinates, the sphere translation needs to be calculated in world coordinates
            sphere_translation = np.array( matched_monocular_data[-1]['pupil']['sphere']['center'] )
            sphere_translation_world = np.dot( R_eye , sphere_translation)
            camera_translation = t_eye - sphere_translation_world
            eye_camera_to_world_matrix  = np.eye(4)
            eye_camera_to_world_matrix[:3,:3] = R_eye
            eye_camera_to_world_matrix[:3,3:4] = np.reshape(camera_translation, (3,1) )


            g_pool.plugins.add(Vector_Gaze_Mapper,args=
                {'eye_camera_to_world_matrix':eye_camera_to_world_matrix ,
                'camera_intrinsics': camera_intrinsics ,
                'cal_points_3d': points_in_world,
                'cal_ref_points_3d': points_a,
                'cal_gaze_points_3d': points_b,
                'gaze_distance':500})

        else:
            logger.error(not_enough_data_error_msg)
            g_pool.active_calibration_plugin.notify_all({'subject':'calibration.failed','reason':not_enough_data_error_msg,'timestamp':g_pool.capture.get_timestamp(),'record':True})
            return

    elif mode == '2d':
        if matched_binocular_data:
            method = 'binocular polynomial regression'
            cal_pt_cloud_binocular = calibrate.preprocess_2d_data_binocular(matched_binocular_data)
            cal_pt_cloud0 = calibrate.preprocess_2d_data_monocular(matched_pupil0_data)
            cal_pt_cloud1 = calibrate.preprocess_2d_data_monocular(matched_pupil1_data)

            map_fn,inliers,params = calibrate.calibrate_2d_polynomial(cal_pt_cloud_binocular,g_pool.capture.frame_size,binocular=True)
            if not inliers.any():
                g_pool.active_calibration_plugin.notify_all({'subject':'calibration.failed','reason':solver_failed_to_converge_error_msg,'timestamp':g_pool.capture.get_timestamp(),'record':True})
                return

            map_fn,inliers,params_eye0 = calibrate.calibrate_2d_polynomial(cal_pt_cloud0,g_pool.capture.frame_size,binocular=False)
            if not inliers.any():
                g_pool.active_calibration_plugin.notify_all({'subject':'calibration.failed','reason':solver_failed_to_converge_error_msg,'timestamp':g_pool.capture.get_timestamp(),'record':True})
                return

            map_fn,inliers,params_eye1 = calibrate.calibrate_2d_polynomial(cal_pt_cloud1,g_pool.capture.frame_size,binocular=False)
            if not inliers.any():
                g_pool.active_calibration_plugin.notify_all({'subject':'calibration.failed','reason':solver_failed_to_converge_error_msg,'timestamp':g_pool.capture.get_timestamp(),'record':True})
                return

            g_pool.plugins.add(Binocular_Gaze_Mapper,args={'params':params, 'params_eye0':params_eye0, 'params_eye1':params_eye1})


        elif matched_monocular_data:
            method = 'monocular polynomial regression'
            cal_pt_cloud = calibrate.preprocess_2d_data_monocular(matched_monocular_data)
            map_fn,inliers,params = calibrate.calibrate_2d_polynomial(cal_pt_cloud,g_pool.capture.frame_size,binocular=False)
            if not inliers.any():
                g_pool.active_calibration_plugin.notify_all({'subject':'calibration.failed','reason':solver_failed_to_converge_error_msg,'timestamp':g_pool.capture.get_timestamp(),'record':True})
                return

            g_pool.plugins.add(Monocular_Gaze_Mapper,args={'params':params})
        else:
            logger.error(not_enough_data_error_msg)
            g_pool.active_calibration_plugin.notify_all({'subject':'calibration.failed','reason':not_enough_data_error_msg,'timestamp':g_pool.capture.get_timestamp(),'record':True})
            return

    user_calibration_data = {'pupil_list':pupil_list,'ref_list':ref_list,'calibration_method':method}
    save_object(user_calibration_data,os.path.join(g_pool.user_dir, "user_calibration_data"))
    g_pool.active_calibration_plugin.notify_all({'subject':'calibration.successful','method':method,'timestamp':g_pool.capture.get_timestamp(),'record':True})
def finish_calibration(g_pool,
                       pupil_list,
                       ref_list,
                       calibration_distance_3d=500,
                       force=None):
    not_enough_data_error_msg = 'Did not collect enough data during calibration.'

    if pupil_list and ref_list:
        pass
    else:
        logger.error(not_enough_data_error_msg)
        g_pool.active_calibration_plugin.notify_all({
            'subject':
            'calibration_failed',
            'reason':
            not_enough_data_error_msg,
            'timestamp':
            g_pool.capture.get_timestamp(),
            'record':
            True
        })
        return

    camera_intrinsics = load_camera_calibration(g_pool)

    # match eye data and check if biocular and or monocular
    pupil0 = [p for p in pupil_list if p['id'] == 0]
    pupil1 = [p for p in pupil_list if p['id'] == 1]

    matched_binocular_data = calibrate.closest_matches_binocular(
        ref_list, pupil_list)
    matched_pupil0_data = calibrate.closest_matches_monocular(ref_list, pupil0)
    matched_pupil1_data = calibrate.closest_matches_monocular(ref_list, pupil1)

    if len(matched_pupil0_data) > len(matched_pupil1_data):
        matched_monocular_data = matched_pupil0_data
    else:
        matched_monocular_data = matched_pupil1_data
    logger.info('Collected %s monocular calibration data.' %
                len(matched_monocular_data))
    logger.info('Collected %s binocular calibration data.' %
                len(matched_binocular_data))

    if force:
        mode = force
    else:
        mode = g_pool.detection_mapping_mode

    if mode == '3d' and not camera_intrinsics:
        mode = '2d'
        logger.warning(
            "Please calibrate your world camera using 'camera intrinsics estimation' for 3d gaze mapping."
        )

    if mode == '3d':
        if matched_binocular_data:
            method = 'binocular 3d model'
            cal_pt_cloud = calibrate.preprocess_3d_data_binocular(
                matched_binocular_data,
                camera_intrinsics=camera_intrinsics,
                calibration_distance=calibration_distance_3d)
            cal_pt_cloud = np.array(cal_pt_cloud)
            try:
                gaze_pt0_3d = cal_pt_cloud[:, 0]
                gaze_pt1_3d = cal_pt_cloud[:, 1]
                ref_3d = cal_pt_cloud[:, 2]
            except:
                logger.error(not_enough_data_error_msg)
                g_pool.active_calibration_plugin.notify_all({
                    'subject':
                    'calibration_failed',
                    'reason':
                    not_enough_data_error_msg,
                    'timestamp':
                    g_pool.capture.get_timestamp(),
                    'record':
                    True
                })
                return

            best_distance = 1000
            best_scale = 100
            for scale_percent in range(50, 150, 10):
                R0, t0 = calibrate.rigid_transform_3D(
                    np.matrix(gaze_pt0_3d),
                    np.matrix(ref_3d * (scale_percent / 100.)))
                R1, t1 = calibrate.rigid_transform_3D(
                    np.matrix(gaze_pt1_3d),
                    np.matrix(ref_3d * (scale_percent / 100.)))
                eye_to_world_matrix0 = np.matrix(np.eye(4))
                eye_to_world_matrix0[:3, :3] = R0
                eye_to_world_matrix0[:3, 3:4] = t0

                eye_to_world_matrix1 = np.matrix(np.eye(4))
                eye_to_world_matrix1[:3, :3] = R1
                eye_to_world_matrix1[:3, 3:4] = t1

                avg_distance0, dist_var0 = calibrate.calculate_residual_3D_Points(
                    ref_3d * (scale_percent / 100.), gaze_pt0_3d,
                    eye_to_world_matrix0)
                avg_distance1, dist_var1 = calibrate.calculate_residual_3D_Points(
                    ref_3d * (scale_percent / 100.), gaze_pt1_3d,
                    eye_to_world_matrix1)
                avg_distance = (avg_distance0 + avg_distance1) / 2.
                if avg_distance < best_distance:
                    best_distance = avg_distance
                    best_scale = scale_percent

            ref_3d *= best_scale / 100.
            R0, t0 = calibrate.rigid_transform_3D(np.matrix(gaze_pt0_3d),
                                                  np.matrix(ref_3d))
            R1, t1 = calibrate.rigid_transform_3D(np.matrix(gaze_pt1_3d),
                                                  np.matrix(ref_3d))

            sphere0 = pupil0[-1]['sphere']['center']
            sphere1 = pupil1[-1]['sphere']['center']

            eye_to_world_matrix0 = np.matrix(np.eye(4))
            eye_to_world_matrix0[:3, :3] = R0
            eye_to_world_matrix0[:3, 3:4] = t0
            # eye_to_world_matrix0[:3,3:4] =  np.array((20,10,-20)).reshape(3,1)
            # eye_to_world_matrix0[:3,3:4] -=  R0 * (np.array(sphere0)*(1,-1,1)).reshape(3,1)

            eye_to_world_matrix1 = np.matrix(np.eye(4))
            eye_to_world_matrix1[:3, :3] = R1
            eye_to_world_matrix1[:3, 3:4] = t1
            # eye_to_world_matrix1[:3,3:4] =  np.array((-40,10,-20)).reshape(3,1)
            # eye_to_world_matrix1[:3,3:4] -=  R1 * (np.array(sphere1)*(1,-1,1)).reshape(3,1)

            avg_distance0, dist_var0 = calibrate.calculate_residual_3D_Points(
                ref_3d, gaze_pt0_3d, eye_to_world_matrix0)
            avg_distance1, dist_var1 = calibrate.calculate_residual_3D_Points(
                ref_3d, gaze_pt1_3d, eye_to_world_matrix1)
            logger.info('calibration average distance eye0: %s' %
                        avg_distance0)
            logger.info('calibration average distance eye1: %s' %
                        avg_distance1)

            g_pool.plugins.add(Binocular_Vector_Gaze_Mapper,
                               args={
                                   'eye_to_world_matrix0':
                                   eye_to_world_matrix0,
                                   'eye_to_world_matrix1':
                                   eye_to_world_matrix1,
                                   'camera_intrinsics': camera_intrinsics,
                                   'cal_ref_points_3d': ref_3d.tolist(),
                                   'cal_gaze_points0_3d': gaze_pt0_3d.tolist(),
                                   'cal_gaze_points1_3d': gaze_pt1_3d.tolist()
                               })

        elif matched_monocular_data:
            method = 'monocular 3d model'
            cal_pt_cloud = calibrate.preprocess_3d_data_monocular(
                matched_monocular_data,
                camera_intrinsics=camera_intrinsics,
                calibration_distance=calibration_distance_3d)
            cal_pt_cloud = np.array(cal_pt_cloud)
            try:
                gaze_3d = cal_pt_cloud[:, 0]
                ref_3d = cal_pt_cloud[:, 1]
            except:
                logger.error(not_enough_data_error_msg)
                g_pool.active_calibration_plugin.notify_all({
                    'subject':
                    'calibration_failed',
                    'reason':
                    not_enough_data_error_msg,
                    'timestamp':
                    g_pool.capture.get_timestamp(),
                    'record':
                    True
                })
                return

            best_distance = 1000
            best_scale = 100
            for scale_percent in range(50, 150, 10):
                #calculate transformation form eye camera to world camera
                R, t = calibrate.rigid_transform_3D(
                    np.matrix(gaze_3d),
                    np.matrix(ref_3d * (scale_percent / 100.)))

                eye_to_world_matrix = np.matrix(np.eye(4))
                eye_to_world_matrix[:3, :3] = R
                eye_to_world_matrix[:3, 3:4] = t

                avg_distance, dist_var = calibrate.calculate_residual_3D_Points(
                    ref_3d * (scale_percent / 100.), gaze_3d,
                    eye_to_world_matrix)

                if avg_distance < best_distance:
                    best_distance = avg_distance
                    best_scale = scale_percent

            ref_3d *= best_scale / 100.

            #calculate transformation form eye camera to world camera
            R, t = calibrate.rigid_transform_3D(np.matrix(gaze_3d),
                                                np.matrix(ref_3d))

            eye_to_world_matrix = np.matrix(np.eye(4))
            eye_to_world_matrix[:3, :3] = R
            eye_to_world_matrix[:3, 3:4] = t
            avg_distance, dist_var = calibrate.calculate_residual_3D_Points(
                ref_3d, gaze_3d, eye_to_world_matrix)
            print 'best calibration average distance: ', avg_distance
            # print 'best calibration distance variance: ' , dist_var
            g_pool.plugins.add(Vector_Gaze_Mapper,
                               args={
                                   'eye_to_world_matrix':
                                   eye_to_world_matrix,
                                   'camera_intrinsics':
                                   camera_intrinsics,
                                   'cal_ref_points_3d':
                                   cal_pt_cloud[:, 1].tolist(),
                                   'cal_gaze_points_3d':
                                   cal_pt_cloud[:, 0].tolist()
                               })

        else:
            logger.error(not_enough_data_error_msg)
            g_pool.active_calibration_plugin.notify_all({
                'subject':
                'calibration_failed',
                'reason':
                not_enough_data_error_msg,
                'timestamp':
                g_pool.capture.get_timestamp(),
                'record':
                True
            })
            return

    elif mode == '2d':
        if matched_binocular_data:
            method = 'binocular polynomial regression'
            cal_pt_cloud_binocular = calibrate.preprocess_2d_data_binocular(
                matched_binocular_data)
            cal_pt_cloud0 = calibrate.preprocess_2d_data_monocular(
                matched_pupil0_data)
            cal_pt_cloud1 = calibrate.preprocess_2d_data_monocular(
                matched_pupil1_data)
            map_fn, inliers, params = calibrate.calibrate_2d_polynomial(
                cal_pt_cloud_binocular,
                g_pool.capture.frame_size,
                binocular=True)
            map_fn, inliers, params_eye0 = calibrate.calibrate_2d_polynomial(
                cal_pt_cloud0, g_pool.capture.frame_size, binocular=False)
            map_fn, inliers, params_eye1 = calibrate.calibrate_2d_polynomial(
                cal_pt_cloud1, g_pool.capture.frame_size, binocular=False)
            g_pool.plugins.add(Binocular_Gaze_Mapper,
                               args={
                                   'params': params,
                                   'params_eye0': params_eye0,
                                   'params_eye1': params_eye1
                               })

        elif matched_monocular_data:
            method = 'monocular polynomial regression'
            cal_pt_cloud = calibrate.preprocess_2d_data_monocular(
                matched_monocular_data)
            map_fn, inliers, params = calibrate.calibrate_2d_polynomial(
                cal_pt_cloud, g_pool.capture.frame_size, binocular=False)
            g_pool.plugins.add(Simple_Gaze_Mapper, args={'params': params})
        else:
            logger.error(not_enough_data_error_msg)
            g_pool.active_calibration_plugin.notify_all({
                'subject':
                'calibration_failed',
                'reason':
                not_enough_data_error_msg,
                'timestamp':
                g_pool.capture.get_timestamp(),
                'record':
                True
            })
            return

    user_calibration_data = {
        'pupil_list': pupil_list,
        'ref_list': ref_list,
        'calibration_method': method
    }
    save_object(user_calibration_data,
                os.path.join(g_pool.user_dir, "user_calibration_data"))
    g_pool.active_calibration_plugin.notify_all({
        'subject':
        'calibration_successful',
        'method':
        method,
        'timestamp':
        g_pool.capture.get_timestamp(),
        'record':
        True
    })