Ciclop开源3D扫描仪软件---Horus源码分析之src\horus\engine\calibration\platform_extrinsics.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 np
from scipy import optimize

from horus import Singleton
from horus.engine.calibration.calibration import CalibrationCancel
from horus.engine.calibration.moving_calibration import MovingCalibration

import logging
logger = logging.getLogger(__name__)


class PlatformExtrinsicsError(Exception):

    def __init__(self):
        Exception.__init__(self, "PlatformExtrinsicsError")


estimated_t = [-5, 90, 320]


@Singleton
class PlatformExtrinsics(MovingCalibration):

    """Platform extrinsics algorithm:

            - Rotation matrix
            - Translation vector
    """

    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.x = []
        self.y = []
        self.z = []

    def _capture(self, angle):
        image = self.image_capture.capture_pattern()
        pose = self.image_detection.detect_pose(image)
        if pose is not None:
            plane = self.image_detection.detect_pattern_plane(pose)
            if plane is not None:
                distance, normal, corners = plane
                self.image = self.image_detection.draw_pattern(image, corners)
                if corners is not None:
                    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
        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 = fit_plane(points)
            if normal[1] > 0:
                normal = -normal
            # Fitting a circle inside the plane
            center, self.R, circle = 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))

        if self._is_calibrating and self.t is not None and \
           np.linalg.norm(self.t - estimated_t) < 100:
            response = (True, (self.R, self.t, center, point, normal,
                        [self.x, self.y, self.z], circle))

        else:
            if self._is_calibrating:
                response = (False, PlatformExtrinsicsError())
            else:
                response = (False, CalibrationCancel())

        self._is_calibrating = False
        self.image = None

        return response

    def accept(self):
        self.calibration_data.platform_rotation = self.R
        self.calibration_data.platform_translation = self.t

    def set_estimated_size(self, estimated_size):
        global estimated_t
        estimated_t = estimated_size


def distance2plane(p0, n0, p):
    return np.dot(np.array(n0), np.array(p) - np.array(p0))


def residuals_plane(parameters, data_point):
    px, py, pz, theta, phi = parameters
    nx, ny, nz = np.sin(theta) * np.cos(phi), np.sin(theta) * np.sin(phi), np.cos(theta)
    distances = [distance2plane(
        [px, py, pz], [nx, ny, nz], [x, y, z]) for x, y, z in data_point]
    return distances


def fit_plane(data):
    estimate = [0, 0, 0, 0, 0]  # px,py,pz and zeta, phi
    # you may automize this by using the center of mass data
    # note that the normal vector is given in polar coordinates
    best_fit_values, ier = optimize.leastsq(residuals_plane, estimate, args=(data))
    xF, yF, zF, tF, pF = best_fit_values

    # point  = [xF,yF,zF]
    point = data[0]
    normal = -np.array([np.sin(tF) * np.cos(pF), np.sin(tF) * np.sin(pF), np.cos(tF)])

    return point, normal


def residuals_circle(parameters, points, s, r, point):
    r_, s_, Ri = parameters
    plane_point = s_ * s + r_ * r + np.array(point)
    distance = [np.linalg.norm(plane_point - np.array([x, y, z])) for x, y, z in points]
    res = [(Ri - dist) for dist in distance]
    return res


def fit_circle(point, normal, points):
    # creating two inplane vectors
    # assuming that normal not parallel x!
    s = np.cross(np.array([1, 0, 0]), np.array(normal))
    s = s / np.linalg.norm(s)
    r = np.cross(np.array(normal), s)
    r = r / np.linalg.norm(r)  # should be normalized already, but anyhow

    # Define rotation
    R = np.array([s, r, normal]).T

    estimate_circle = [0, 0, 0]  # px,py,pz and zeta, phi
    best_circle_fit_values, ier = optimize.leastsq(
        residuals_circle, estimate_circle, args=(points, s, r, point))

    rF, sF, RiF = best_circle_fit_values

    # Synthetic Data
    center_point = sF * s + rF * r + np.array(point)
    synthetic = [list(center_point + RiF * np.cos(phi) * r + RiF * np.sin(phi) * s)
                 for phi in np.linspace(0, 2 * np.pi, 50)]
    [cxTupel, cyTupel, czTupel] = [x for x in zip(*synthetic)]

    return center_point, R, [cxTupel, cyTupel, czTupel]