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#!/usr/bin/env python |
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""" |
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Registration algorithms and utility functions |
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""" |
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from __future__ import print_function |
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import numpy as np |
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from .. import BoundingBox, force_srs, extract_mask, clone |
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from .stickscale import get_stick_scale |
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from pcl.registration import gicp |
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from patty.utils import ( |
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log, |
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save, |
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downsample_voxel, |
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) |
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from patty.segmentation import ( |
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boundary_of_center_object, |
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boundary_of_drivemap, |
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boundary_of_lowest_points, |
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) |
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from sklearn.decomposition import PCA |
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def align_footprints(loose_pc, fixed_pc, |
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allow_scaling=True, |
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allow_rotation=True, |
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allow_translation=True): |
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''' |
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Align a pointcloud 'loose_pc' by placing it on top of |
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'fixed_pc' as good as poosible. Done by aligning the |
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principle axis of both pointclouds. |
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NOTE: Both pointclouds are assumed to be the footprint (or projection) |
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on the xy plane, with basically zero extent along the z-axis. |
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(allow_rotation=True) |
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The pointcloud boundary is alinged with the footprint |
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by rotating its pricipal axis in the (x,y) plane. |
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(allow_translation=True) |
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Then, it is translated so the centers of mass coincide. |
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(allow_scaling=True) |
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Finally, the pointcloud is scaled to have the same extent. |
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Arguments: |
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loose_pc : pcl.PointCloud |
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fixed_pc : pcl.PointCloud |
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allow_scaling : Bolean |
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allow_rotation : Bolean |
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allow_translation : Bolean |
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Returns: |
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rot_matrix, rot_center, scale, translation : np.array() |
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''' |
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rot_center = loose_pc.center() |
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if allow_rotation: |
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log(" - Finding rotation") |
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rot_matrix = find_rotation_xy(loose_pc, fixed_pc) |
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loose_pc.rotate(rot_matrix, origin=rot_center) |
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else: |
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log(" - Skipping rotation") |
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rot_matrix = np.eye(3) |
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if allow_scaling: |
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fixed_bb = BoundingBox(fixed_pc) # used 2x below |
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loose_bb = BoundingBox(loose_pc) |
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scale = fixed_bb.size[0:2] / loose_bb.size[0:2] |
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# take the average scale factor for the x and y dimensions |
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scale = np.mean(scale) |
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loose_pc.scale(scale, origin=rot_center) |
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log(" - Scale: %s" % scale) |
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else: |
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log(" - Skipping scale") |
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scale = 1.0 |
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if allow_translation: |
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translation = fixed_pc.center() - rot_center |
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loose_pc.translate(translation) |
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log(" - Translation: %s" % translation) |
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else: |
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log(" - Skipping translation") |
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translation = np.array([0.0, 0.0, 0.0]) |
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return rot_matrix, rot_center, scale, translation |
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def estimate_pancake_up(pointcloud): |
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''' |
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Assuming a pancake like pointcloud, the up direction is the third PCA. |
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''' |
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pca = PCA(n_components=3) |
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points = np.asarray(pointcloud) |
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pca.fit(points[:, 0:3]) |
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return pca.components_[2] |
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def _find_rotation_xy_helper(pointcloud): |
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pca = PCA(n_components=2) |
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points = np.asarray(pointcloud) |
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pca.fit(points[:, 0:2]) |
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rotxy = np.array(pca.components_) |
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# make sure the rotation is a proper rotation, ie det = +1 |
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if np.linalg.det(rotxy) < 0: |
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rotxy[:, 1] *= -1.0 |
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# create a 3D rotation around the z-axis |
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rotation = np.eye(3) |
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rotation[0:2, 0:2] = rotxy |
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return rotation |
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def find_rotation_xy(pc, ref): |
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'''Find the transformation that rotates the principal axis of the |
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pointcloud onto those of the reference. |
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Keep the z-axis pointing upwards. |
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Arguments: |
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pc: pcl.PointCloud |
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ref: pcl.PointCloud |
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Returns: |
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numpy array of shape [3,3], can be used to rotate pointclouds |
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with pc.rotate() |
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''' |
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pc_transform = _find_rotation_xy_helper(pc) |
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ref_transform = _find_rotation_xy_helper(ref) |
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return np.dot(np.linalg.inv(ref_transform), pc_transform) |
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def rotate_upwards(pc, up): |
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''' |
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Rotate the pointcloud in-place around its center, such that the |
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'up' vector points along [0,0,1] |
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Arguments: |
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pc : pcl.PointCloud |
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up : np.array([3]) |
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Returns: |
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pc : pcl.PointCloud the input pointcloud, for convenience. |
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''' |
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newz = np.array(up) |
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# Right-handed coordiante system: |
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# np.cross(x,y) = z |
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# np.cross(y,z) = x |
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# np.cross(z,x) = y |
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# normalize |
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newz /= (np.dot(newz, newz)) ** 0.5 |
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# find two orthogonal vectors to represent x and y, |
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# randomly choose a vector, and take cross product. If we're unlucky, |
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# this ones is parallel to z, so cross pruduct is zero. |
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# In that case, try another one |
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try: |
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newx = np.cross(np.array([0, 1, 0]), newz) |
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newx /= (np.dot(newx, newx)) ** 0.5 |
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newy = np.cross(newz, newx) |
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newy /= (np.dot(newy, newy)) ** 0.5 |
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except: |
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print("Alternative") |
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newy = np.cross(newz, np.array([1, 0, 0])) |
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newy /= (np.dot(newy, newy)) ** 0.5 |
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newx = np.cross(newy, newz) |
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newx /= (np.dot(newx, newx)) ** 0.5 |
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rotation = np.zeros([3, 3]) |
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rotation[0, 0] = newx[0] |
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rotation[1, 0] = newx[1] |
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rotation[2, 0] = newx[2] |
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rotation[0, 1] = newy[0] |
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rotation[1, 1] = newy[1] |
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rotation[2, 1] = newy[2] |
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rotation[0, 2] = newz[0] |
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rotation[1, 2] = newz[1] |
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rotation[2, 2] = newz[2] |
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rotation = np.linalg.inv(rotation) |
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pc.rotate(rotation, origin=pc.center()) |
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return pc |
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def initial_registration(pointcloud, up, drivemap, |
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initial_scale=None, trust_up=True): |
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""" |
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Initial registration adds an spatial reference system to the pointcloud, |
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and place the pointlcoud on top of the drivemap. The pointcloud is rotated |
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so that the up vector points along [0,0,1], and scaled such that it has the |
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right order of magnitude in size. |
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Arguments: |
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pointcloud : pcl.PointCloud |
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The high-res object to register. |
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up: np.array([3]) |
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Up direction for the pointcloud. |
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If None, assume the object is pancake shaped, and chose the |
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upvector such that it is perpendicullar to the pancake. |
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drivemap : pcl.PointCloud |
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A small part of the low-res drivemap on which to register. |
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initial_scale : float |
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if given, scale pointcloud using this value; estimate scale factor |
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from bounding boxes. |
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trust_up : Boolean, default to True |
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True: Assume the up vector is exact. |
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False: Calculate 'up' as if it was None, but orient it such that |
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np.dot( up, pancake_up ) > 0 |
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NOTE: Modifies the input pointcloud in-place, and leaves |
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it in a undefined state. |
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""" |
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log("Starting initial registration") |
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##### |
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# set scale and offset of pointcloud, drivemap, and footprint |
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# as the pointcloud is unregisterd, the coordinate system is undefined, |
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# and we lose nothing if we just copy it |
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if(hasattr(pointcloud, "offset")): |
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log(" - Dropping initial offset, was: %s" % pointcloud.offset) |
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else: |
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log(" - No initial offset") |
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force_srs(pointcloud, same_as=drivemap) |
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log(" - New offset forced to: %s" % pointcloud.offset) |
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if up is not None: |
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log(" - Rotating the pointcloud so up points along [0,0,1]") |
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if trust_up: |
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rotate_upwards(pointcloud, up) |
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log(" - Using trusted up: %s" % up) |
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else: |
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pancake_up = estimate_pancake_up(pointcloud) |
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if np.dot(up, pancake_up) < 0.0: |
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pancake_up *= -1.0 |
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log(" - Using estimated up: %s" % pancake_up) |
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rotate_upwards(pointcloud, pancake_up) |
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else: |
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log(" - No upvector, skipping") |
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if initial_scale is None: |
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bbDrivemap = BoundingBox(points=np.asarray(drivemap)) |
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bbObject = BoundingBox(points=np.asarray(pointcloud)) |
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scale = bbDrivemap.size[0:2] / bbObject.size[0:2] # ignore z-direction |
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# take the average scale factor for x and y dimensions |
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scale = np.mean(scale) |
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else: |
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# use user provided scale |
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scale = initial_scale |
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log(" - Applying rough estimation of scale factor", scale) |
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pointcloud.scale(scale) # dont care about origin of scaling |
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def coarse_registration(pointcloud, drivemap, footprint, downsample=None): |
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""" |
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Improve the initial registration. |
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Find the proper scale by looking for the red meter sticks, and calculate |
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and align the pointcloud's footprint. |
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Arguments: |
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pointcloud: pcl.PointCloud |
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The high-res object to register. |
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drivemap: pcl.PointCloud |
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A small part of the low-res drivemap on which to register |
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footprint: pcl.PointCloud |
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Pointlcloud containing the objects footprint |
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downsample: float, default=None, no resampling |
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Downsample the high-res pointcloud before footprint |
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calculation. |
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""" |
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log("Starting coarse registration") |
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### |
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# find redstick scale, and use it if possible |
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log(" - Redstick scaling") |
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allow_scaling = True |
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scale, confidence = get_stick_scale(pointcloud) |
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log(" - Red stick scale=%s confidence=%s" % (scale, confidence)) |
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if (confidence > 0.5): |
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log(" - Applying red stick scale") |
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pointcloud.scale(1.0 / scale) # dont care about origin of scaling |
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allow_scaling = False |
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else: |
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log(" - Not applying red stick scale, confidence too low") |
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##### |
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# find all the points in the drivemap along the footprint |
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# use bottom two meters of drivemap (not trees) |
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dm_boundary = boundary_of_drivemap(drivemap, footprint) |
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dm_bb = BoundingBox(dm_boundary) |
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# set footprint height from minimum value, |
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# as trees, or high object make the pc.center() too high |
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fixed_boundary = clone(footprint) |
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fp_array = np.asarray(fixed_boundary) |
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fp_array[:, 2] = dm_bb.min[2] |
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save(fixed_boundary, "fixed_bound.las") |
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##### |
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# find all the boundary points of the pointcloud |
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loose_boundary = boundary_of_center_object(pointcloud, downsample) |
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if loose_boundary is None: |
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log(" - boundary estimation failed, using lowest 30 percent of points") |
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loose_boundary = boundary_of_lowest_points(pointcloud, |
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height_fraction=0.3) |
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#### |
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# match the pointcloud boundary with the footprint boundary |
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log(" - Aligning footprints:") |
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rot_matrix, rot_center, scale, translation = align_footprints( |
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loose_boundary, fixed_boundary, |
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allow_scaling=allow_scaling, |
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allow_rotation=True, |
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allow_translation=True) |
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save(loose_boundary, "aligned_bound.las") |
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#### |
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# Apply to the main pointcloud |
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pointcloud.rotate(rot_matrix, origin=rot_center) |
366
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|
|
pointcloud.scale(scale, origin=rot_center) |
367
|
|
|
pointcloud.translate(translation) |
368
|
|
|
rot_center += translation |
369
|
|
|
|
370
|
|
|
return rot_center |
371
|
|
|
|
372
|
|
|
|
373
|
1 |
|
def _fine_registration_helper(pointcloud, drivemap, voxelsize=0.05, attempt=0): |
374
|
|
|
""" |
375
|
|
|
Perform ICP on pointcloud with drivemap, and return convergence indicator. |
376
|
|
|
Reject large translatoins. |
377
|
|
|
|
378
|
|
|
Returns: |
379
|
|
|
transf : np.array([4,4]) |
380
|
|
|
transform |
381
|
|
|
success : Boolean |
382
|
|
|
if icp was successful |
383
|
|
|
fitness : float |
384
|
|
|
sort of sum of square differences, ie. smaller is better |
385
|
|
|
""" |
386
|
|
|
#### |
387
|
|
|
# Downsample to speed up |
388
|
|
|
# use voxel filter to keep evenly distributed spatial extent |
389
|
|
|
|
390
|
|
|
log(" - Downsampling with voxel filter: %s" % voxelsize) |
391
|
|
|
pc = downsample_voxel(pointcloud, voxelsize) |
392
|
|
|
|
393
|
|
|
#### |
394
|
|
|
# Clip to drivemap to prevent outliers confusing the ICP algorithm |
395
|
|
|
|
396
|
|
|
log(" - Clipping to drivemap") |
397
|
|
|
bb = BoundingBox(drivemap) |
398
|
|
|
z = bb.center[2] |
|
|
|
|
399
|
|
|
extracted = extract_mask(pc, [bb.contains([point[0], point[1], z]) |
400
|
|
|
for point in pc]) |
401
|
|
|
|
402
|
|
|
log(" - Remaining points: %s" % len(extracted)) |
403
|
|
|
|
404
|
|
|
#### |
405
|
|
|
# GICP |
406
|
|
|
|
407
|
|
|
converged, transf, estimate, fitness = gicp(extracted, drivemap) |
408
|
|
|
|
409
|
|
|
#### |
410
|
|
|
# Dont accept large translations |
411
|
|
|
|
412
|
|
|
translation = transf[0:3, 3] |
413
|
|
|
if np.dot(translation, translation) > 5 ** 2: |
414
|
|
|
log(" - Translation too large, considering it a failure.") |
415
|
|
|
converged = False |
416
|
|
|
fitness = 1e30 |
417
|
|
|
else: |
418
|
|
|
log(" - Success, fitness: ", converged, fitness) |
419
|
|
|
|
420
|
|
|
force_srs(estimate, same_as=pointcloud) |
421
|
|
|
save(estimate, "attempt%s.las" % attempt) |
422
|
|
|
|
423
|
|
|
return transf, converged, fitness |
424
|
|
|
|
425
|
|
|
|
426
|
1 |
|
def fine_registration(pointcloud, drivemap, center, voxelsize=0.05): |
427
|
|
|
""" |
428
|
|
|
Final registration step using ICP. |
429
|
|
|
|
430
|
|
|
Find the local optimal postion of the pointcloud on the drivemap; due to |
431
|
|
|
our coarse_registration algorithm, we have to try two orientations: |
432
|
|
|
original, and rotated by 180 degrees around the z-axis. |
433
|
|
|
|
434
|
|
|
Arguments: |
435
|
|
|
pointcloud: pcl.PointCloud |
436
|
|
|
The high-res object to register. |
437
|
|
|
|
438
|
|
|
drivemap: pcl.PointCloud |
439
|
|
|
A small part of the low-res drivemap on which to register |
440
|
|
|
|
441
|
|
|
center: np.array([3]) |
442
|
|
|
Vector giving the centerpoint of the pointcloud, used to do |
443
|
|
|
the 180 degree rotations. |
444
|
|
|
|
445
|
|
|
voxelsize: float default : 0.05 |
446
|
|
|
Size in [m] of the voxel grid used for downsampling |
447
|
|
|
""" |
448
|
|
|
log("Starting fine registration") |
449
|
|
|
|
450
|
|
|
# for rotation around z-axis |
451
|
|
|
rot = np.array([[0, -1, 0], [1, 0, 0], [0, 0, 1]]) |
452
|
|
|
|
453
|
|
|
#### |
454
|
|
|
# do a ICP step for 4 orientations |
455
|
|
|
|
456
|
|
|
transf = {} |
457
|
|
|
success = {} |
458
|
|
|
fitness = {} |
459
|
|
|
for i in range(4): |
460
|
|
|
log(" - attempt: %s" % i) |
461
|
|
|
transf[i], success[i], fitness[i] = _fine_registration_helper( |
462
|
|
|
pointcloud, |
463
|
|
|
drivemap, |
464
|
|
|
attempt=i, |
465
|
|
|
voxelsize=voxelsize) |
466
|
|
|
pointcloud.rotate(rot, origin=center) |
467
|
|
|
|
468
|
|
|
#### |
469
|
|
|
# pick best |
470
|
|
|
|
471
|
|
|
best, value = min(fitness.iteritems(), key=lambda x: x[1]) |
472
|
|
|
if success[best]: |
473
|
|
|
log(" - Best attempt: %s" % best) |
474
|
|
|
if best > 0: # np.array()**0 does weird things |
475
|
|
|
pointcloud.rotate(rot**best, origin=center) |
476
|
|
|
pointcloud.transform(transf[best]) |
477
|
|
|
return |
478
|
|
|
|
479
|
|
|
# ICP failed: |
480
|
|
|
# return the pointcloud with just footprints aligned |
481
|
|
|
# no use to undo a rotation, as any orientationi is equally likely. |
482
|
|
|
log(" - Unable to do fine registration") |
483
|
|
|
|
484
|
|
|
return |
485
|
|
|
|
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