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import os.path |
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import shutil |
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from tempfile import mkdtemp |
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import numpy as np |
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import pcl |
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from patty import utils |
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from patty.utils import downsample_voxel |
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from helpers import make_tri_pyramid_with_base |
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from sklearn.utils.extmath import cartesian |
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import unittest |
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class TestRegistrationPipeline(unittest.TestCase): |
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def setUp(self): |
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self.useLocal = False |
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if self.useLocal: |
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self.tempdir = tempdir = '.' |
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else: |
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self.tempdir = tempdir = mkdtemp(prefix='patty-analytics') |
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self.drivemapLas = os.path.join(tempdir, 'testDriveMap.las') |
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self.sourcelas = os.path.join(tempdir, 'testSource.las') |
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self.footprint_csv = os.path.join(tempdir, 'testFootprint.csv') |
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self.foutlas = os.path.join(tempdir, 'testOutput.las') |
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self.min = -10 |
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self.max = 10 |
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self.num_rows = 1000 |
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# Create plane with a pyramid |
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dm_pct = 0.5 |
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dm_rows = np.round(self.num_rows * dm_pct) |
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dm_min = self.min * dm_pct |
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dm_max = self.max * dm_pct |
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delta = dm_max / dm_rows |
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shape_side = dm_max - dm_min |
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dm_offset = [0, 0, 0] |
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self.dense_obj_offset = [3, 2, -(1 + shape_side / 2)] |
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# make drivemap |
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plane_row = np.linspace( |
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start=self.min, stop=self.max, num=self.num_rows) |
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plane_points = cartesian((plane_row, plane_row, [0])) |
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shape_points, footprint = make_tri_pyramid_with_base( |
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shape_side, delta, dm_offset) |
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np.savetxt(self.footprint_csv, footprint, fmt='%.3f', delimiter=',') |
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dm_points = np.vstack([plane_points, shape_points]) |
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plane_grid = np.zeros((dm_points.shape[0], 6), dtype=np.float32) |
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plane_grid[:, 0:3] = dm_points |
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self.drivemap_pc = pcl.PointCloudXYZRGB(plane_grid) |
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self.drivemap_pc = downsample_voxel(self.drivemap_pc, |
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voxel_size=delta * 20) |
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# utils.set_registration(self.drivemap_pc) |
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utils.save(self.drivemap_pc, self.drivemapLas) |
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# Create a simple pyramid |
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dense_grid = np.zeros((shape_points.shape[0], 6), dtype=np.float32) |
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dense_grid[:, 0:3] = shape_points + self.dense_obj_offset |
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self.source_pc = pcl.PointCloudXYZRGB(dense_grid) |
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self.source_pc = downsample_voxel(self.source_pc, voxel_size=delta * 5) |
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utils.save(self.source_pc, self.sourcelas) |
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def tearDown(self): |
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if not self.useLocal: |
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shutil.rmtree(self.tempdir, ignore_errors=True) |
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def test_pipeline(self): |
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pass |
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# # TODO: should just use shutil to run the registration.py script, and |
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# # load the result |
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# os.system( './scripts/registration.py -u testupfile.json' |
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# " " + self.sourcelas + |
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# " " + self.drivemapLas + |
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# " " + self.footprint_csv + |
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# " " + self.foutlas ) |
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# goal = utils.load( self.sourcelas) |
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# actual = np.asarray( start ) |
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# result = utils.load( self.foutlas ) |
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# target = np.asarray( result ) |
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# array_in_margin(target.min(axis=0), actual.min(axis=0), [1, 1, 1], |
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# "Lower bound of registered cloud does not" |
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# " match expectation") |
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# array_in_margin(target.max(axis=0), actual.max(axis=0), [2.5, 5.5, 2], |
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# "Upper bound of registered cloud does not" |
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# " match expectation") |
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# array_in_margin(target.mean(axis=0), actual.mean(axis=0), [1, 1, 1], |
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# "Middle point of registered cloud does not" |
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# " match expectation") |
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NLeSC /
PattyAnalytics
