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patty.segmentation.boundary_of_center_object() B
last analyzed 2016-03-07 10:18 UTC

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Duplication

Lines	0
Ratio	0 %

Code Coverage

Tests	1
CRAP Score	17.7179

Metric	Value
cc	4
dl	0
loc	68
ccs	1
cts	20
cp	0.05
crap	17.7179
rs	8.7864

How to fix Long Method

#!/usr/bin/env python

"""
Segmentation algorithms and utility functions
"""

from __future__ import print_function
import numpy as np
from pcl.boundaries import estimate_boundaries
from shapely.geometry.polygon import LinearRing
from shapely.geometry import asPoint

from patty import utils
from .dbscan import get_largest_dbscan_clusters
from .. import extract_mask, BoundingBox, log, save


def boundary_of_drivemap(drivemap, footprint, height=1.0, edge_width=0.25):
    """
    Construct an object boundary using the manually recorded corner points.
    Do this by finding all the points in the drivemap along the footprint.
    Use the bottom 'height' meters of the drivemap (not trees).
    Resulting pointcloud has the same SRS and offset as the input.

    Arguments:
        drivemap   : pcl.PointCloud
        footprint  : pcl.PointCloud
        height     : Cut-off height, points more than this value above the
                     lowest point of the drivemap are considered trees,
                     and dropped. default 1 m.
        edge_width : Points belong to the boundary when they are within
                     this distance from the footprint. default 0.25

    Returns:
        boundary   : pcl.PointCloud
    """

    # construct basemap as the bottom 'height' meters of the drivemap

    drivemap_array = np.asarray(drivemap)
    bb = BoundingBox(points=drivemap_array)
    basemap = extract_mask(drivemap, drivemap_array[:, 2] < bb.min[2] + height)

    # Cut band between +- edge_width around the footprint
    edge = LinearRing(np.asarray(footprint)).buffer(edge_width)
    boundary = extract_mask(basemap,
                            [edge.contains(asPoint(pnt)) for pnt in basemap])

    utils.force_srs(boundary, same_as=basemap)

    return boundary


def boundary_of_lowest_points(pc, height_fraction=0.01):
    '''
    Construct an object boundary by taking the lowest (ie. min z coordinate)
    fraction of points.
    Resulting pointcloud has the same SRS and offset as the input.

    Arguments:
        pc               : pcl.PointCloud
        height_fraction  : float

    Returns:
        boundary   : pcl.PointCloud
    '''
    array = np.asarray(pc)
    bb = BoundingBox(points=array)
    maxh = bb.min[2] + (bb.max[2] - bb.min[2]) * height_fraction

    newpc = extract_mask(pc, array[:, 2] < maxh)

    utils.force_srs(newpc, same_as=pc)

    return newpc


def boundary_of_center_object(pc,
                              downsample=None,
                              angle_threshold=0.1,
                              search_radius=0.1,
                              normal_search_radius=0.1):
    """
    Find the boundary of the main object.
    First applies dbscan to find the main object,
    then estimates its footprint by taking the pointcloud boundary.
    Resulting pointcloud has the same SRS and offset as the input.

    Arguments:
        pointcloud : pcl.PointCloud

        downsample : If given, reduce the pointcloud to given percentage
                     values should be in [0,1]

        angle_threshold : float defaults to 0.1

        search_radius : float defaults to 0.1

        normal_search_radius : float defaults to 0.1

    Returns:
        boundary : pcl.PointCloud
    """

    if downsample is not None:
        log(' - Random downsampling factor:', downsample)
        pc = utils.downsample_random(pc, downsample)
    else:
        log(' - Not downsampling')

    # Main noise supression step
    # Find largest clusters, accounting for at least 70% of the pointcloud.
    # Presumably, this is the main object.
    log(' - Starting dbscan on downsampled pointcloud')
    mainobject = get_largest_dbscan_clusters(pc, 0.7, .075, 250)
    save(mainobject, 'mainobject.las')

    boundary = estimate_boundaries(mainobject,
                                   angle_threshold=angle_threshold,
                                   search_radius=search_radius,
                                   normal_search_radius=normal_search_radius)

    boundary = extract_mask(mainobject, boundary)

    if len(boundary) == len(mainobject) or len(boundary) == 0:
        log(' - Cannot find boundary')
        return None

    # project on the xy plane, take 2th percentile height
    points = np.asarray(boundary)
    points[:, 2] = np.percentile(points[:, 2], 2)

    # Secondary noise supression step
    # some cases have multiple objects close to eachother, and we need to
    # filter some out. Assume the object is a single item; perform another
    # dbscan to select the footprint of the main item
    log(' - Starting dbscan on boundary')
    mainboundary = get_largest_dbscan_clusters(boundary, 0.5, .1, 10)

    # Evenly space out the points
    mainboundary = utils.downsample_voxel(mainboundary, voxel_size=0.1)

    utils.force_srs(mainboundary, same_as=pc)

    return mainboundary


1		#!/usr/bin/env python
2
3	1	"""
4		Segmentation algorithms and utility functions
5		"""
6
7	1	from __future__ import print_function
8	1	import numpy as np
9	1	from pcl.boundaries import estimate_boundaries
10	1	from shapely.geometry.polygon import LinearRing
11	1	from shapely.geometry import asPoint
12
13	1	from patty import utils
14	1	from .dbscan import get_largest_dbscan_clusters
15	1	from .. import extract_mask, BoundingBox, log, save
16
17
18	1	def boundary_of_drivemap(drivemap, footprint, height=1.0, edge_width=0.25):
19		"""
20		Construct an object boundary using the manually recorded corner points.
21		Do this by finding all the points in the drivemap along the footprint.
22		Use the bottom 'height' meters of the drivemap (not trees).
23		Resulting pointcloud has the same SRS and offset as the input.
24
25		Arguments:
26		drivemap : pcl.PointCloud
27		footprint : pcl.PointCloud
28		height : Cut-off height, points more than this value above the
29		lowest point of the drivemap are considered trees,
30		and dropped. default 1 m.
31		edge_width : Points belong to the boundary when they are within
32		this distance from the footprint. default 0.25
33
34		Returns:
35		boundary : pcl.PointCloud
36		"""
37
38		# construct basemap as the bottom 'height' meters of the drivemap
39
40		drivemap_array = np.asarray(drivemap)
41		bb = BoundingBox(points=drivemap_array)
42		basemap = extract_mask(drivemap, drivemap_array[:, 2] < bb.min[2] + height)
43
44		# Cut band between +- edge_width around the footprint
45		edge = LinearRing(np.asarray(footprint)).buffer(edge_width)
46		boundary = extract_mask(basemap,
47		[edge.contains(asPoint(pnt)) for pnt in basemap])
48
49		utils.force_srs(boundary, same_as=basemap)
50
51		return boundary
52
53
54	1	def boundary_of_lowest_points(pc, height_fraction=0.01):
55		'''
56		Construct an object boundary by taking the lowest (ie. min z coordinate)
57		fraction of points.
58		Resulting pointcloud has the same SRS and offset as the input.
59
60		Arguments:
61		pc : pcl.PointCloud
62		height_fraction : float
63
64		Returns:
65		boundary : pcl.PointCloud
66		'''
67		array = np.asarray(pc)
68		bb = BoundingBox(points=array)
69		maxh = bb.min[2] + (bb.max[2] - bb.min[2]) * height_fraction
70
71		newpc = extract_mask(pc, array[:, 2] < maxh)
72
73		utils.force_srs(newpc, same_as=pc)
74
75		return newpc
76
77
78	1	def boundary_of_center_object(pc,
79		downsample=None,
80		angle_threshold=0.1,
81		search_radius=0.1,
82		normal_search_radius=0.1):
83		"""
84		Find the boundary of the main object.
85		First applies dbscan to find the main object,
86		then estimates its footprint by taking the pointcloud boundary.
87		Resulting pointcloud has the same SRS and offset as the input.
88
89		Arguments:
90		pointcloud : pcl.PointCloud
91
92		downsample : If given, reduce the pointcloud to given percentage
93		values should be in [0,1]
94
95		angle_threshold : float defaults to 0.1
96
97		search_radius : float defaults to 0.1
98
99		normal_search_radius : float defaults to 0.1
100
101		Returns:
102		boundary : pcl.PointCloud
103		"""
104
105		if downsample is not None:
106		log(' - Random downsampling factor:', downsample)
107		pc = utils.downsample_random(pc, downsample)
108		else:
109		log(' - Not downsampling')
110
111		# Main noise supression step
112		# Find largest clusters, accounting for at least 70% of the pointcloud.
113		# Presumably, this is the main object.
114		log(' - Starting dbscan on downsampled pointcloud')
115		mainobject = get_largest_dbscan_clusters(pc, 0.7, .075, 250)
116		save(mainobject, 'mainobject.las')
117
118		boundary = estimate_boundaries(mainobject,
119		angle_threshold=angle_threshold,
120		search_radius=search_radius,
121		normal_search_radius=normal_search_radius)
122
123		boundary = extract_mask(mainobject, boundary)
124
125		if len(boundary) == len(mainobject) or len(boundary) == 0:
126		log(' - Cannot find boundary')
127		return None
128
129		# project on the xy plane, take 2th percentile height
130		points = np.asarray(boundary)
131		points[:, 2] = np.percentile(points[:, 2], 2)
132
133		# Secondary noise supression step
134		# some cases have multiple objects close to eachother, and we need to
135		# filter some out. Assume the object is a single item; perform another
136		# dbscan to select the footprint of the main item
137		log(' - Starting dbscan on boundary')
138		mainboundary = get_largest_dbscan_clusters(boundary, 0.5, .1, 10)
139
140		# Evenly space out the points
141		mainboundary = utils.downsample_voxel(mainboundary, voxel_size=0.1)
142
143		utils.force_srs(mainboundary, same_as=pc)
144
145		return mainboundary
146

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patty.segmentation.boundary_of_center_object() B last analyzed 2016-03-07 10:18 UTC

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patty.segmentation.boundary_of_center_object() B
last analyzed 2016-03-07 10:18 UTC