Ciclop开源3D扫描仪软件---Horus源码分析之src\horus\engine\calibration\combo_calibration.py

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# -*- coding: utf-8 -*-# This file is part of the Horus Project__author__ = 'Jes煤s Arroyo Torrens <[e-mail]>'__copyright__ = 'Copyright (C) 2014-2016 Mundo Reader S.L.'__license__ = 'GNU General Public License v2 http://www.gnu.org/licenses/gpl2.html'import numpy as npfrom horus import Singletonfrom horus.engine.calibration.calibration import CalibrationCancelfrom horus.engine.calibration.moving_calibration import MovingCalibrationfrom horus.engine.calibration import laser_triangulation, platform_extrinsicsimport logginglogger = logging.getLogger(__name__)class ComboCalibrationError(Exception):    def __init__(self):        Exception.__init__(self, "ComboCalibrationError")@Singletonclass ComboCalibration(MovingCalibration):    """Combine Laser Triangulation and Platform Extrinsics calibration in one"""    def __init__(self):        self.image = None        self.has_image = False        MovingCalibration.__init__(self)    def _initialize(self):        self.image = None        self.has_image = True        self.image_capture.stream = False        self._point_cloud = [None, None]        self.x = []        self.y = []        self.z = []    def _capture(self, angle):        image = self.image_capture.capture_pattern()        pose = self.image_detection.detect_pose(image)        plane = self.image_detection.detect_pattern_plane(pose)        if plane is not None:            distance, normal, corners = plane            # Laser triangulation            if (angle > 65 and angle < 115):                self.image_capture.flush_laser()                self.image_capture.flush_laser()                for i in xrange(2):                    image = self.image_capture.capture_laser(i)                    image = self.image_detection.pattern_mask(image, corners)                    self.image = image                    points_2d, _ = self.laser_segmentation.compute_2d_points(image)                    point_3d = self.point_cloud_generation.compute_camera_point_cloud(                        points_2d, distance, normal)                    if self._point_cloud[i] is None:                        self._point_cloud[i] = point_3d.T                    else:                        self._point_cloud[i] = np.concatenate(                            (self._point_cloud[i], point_3d.T))            # Platform extrinsics            origin = corners[self.pattern.columns * (self.pattern.rows - 1)][0]            origin = np.array([[origin[0]], [origin[1]]])            t = self.point_cloud_generation.compute_camera_point_cloud(                origin, distance, normal)            if t is not None:                self.x += [t[0][0]]                self.y += [t[1][0]]                self.z += [t[2][0]]        else:            self.image = image    def _calibrate(self):        self.has_image = False        self.image_capture.stream = True        # Laser triangulation        # Save point clouds        for i in xrange(2):            laser_triangulation.save_point_cloud('PC' + str(i) + '.ply', self._point_cloud[i])        self.distance = [None, None]        self.normal = [None, None]        self.std = [None, None]        # Compute planes        for i in xrange(2):            if self._is_calibrating:                plane = laser_triangulation.compute_plane(i, self._point_cloud[i])                self.distance[i], self.normal[i], self.std[i] = plane        # Platform extrinsics        self.t = None        self.x = np.array(self.x)        self.y = np.array(self.y)        self.z = np.array(self.z)        points = zip(self.x, self.y, self.z)        if len(points) > 4:            # Fitting a plane            point, normal = platform_extrinsics.fit_plane(points)            if normal[1] > 0:                normal = -normal            # Fitting a circle inside the plane            center, self.R, circle = platform_extrinsics.fit_circle(point, normal, points)            # Get real origin            self.t = center - self.pattern.origin_distance * np.array(normal)            logger.info("Platform calibration ")            logger.info(" Translation: " + str(self.t))            logger.info(" Rotation: " + str(self.R).replace('\n', ''))            logger.info(" Normal: " + str(normal))        # Return response        result = True        if self._is_calibrating:            if self.t is not None and \               np.linalg.norm(self.t - platform_extrinsics.estimated_t) < 100:                response_platform_extrinsics = (                    self.R, self.t, center, point, normal, [self.x, self.y, self.z], circle)            else:                result = False            if self.std[0] < 1.0 and self.std[1] < 1.0 and \               self.normal[0] is not None and self.normal[1] is not None:                response_laser_triangulation = ((self.distance[0], self.normal[0], self.std[0]),                                                (self.distance[1], self.normal[1], self.std[1]))            else:                result = False            if result:                response = (True, (response_platform_extrinsics, response_laser_triangulation))            else:                response = (False, ComboCalibrationError())        else:            response = (False, CalibrationCancel())        self._is_calibrating = False        self.image = None        return response    def accept(self):        for i in xrange(2):            self.calibration_data.laser_planes[i].distance = self.distance[i]            self.calibration_data.laser_planes[i].normal = self.normal[i]        self.calibration_data.platform_rotation = self.R        self.calibration_data.platform_translation = self.t