Merge branch 'develop' into 'master'

Merge v1.5

See merge request Florent/idefix!2

idefix/__init__.py

@@ -9,6 +9,6 @@ Utils and production pipelines for processing LiDAR point clouds.
 
 """
 
-__all__ = ['utils', 'io', 'vxl']
+__all__ = ['utils', 'io', 'vxl', 'helpers']
 
-from . import utils, io, vxl
+from . import utils, io, vxl, helpers

idefix/helpers.py

@@ -0,0 +1,137 @@
+#!/usr/bin/env python
+# file helpers.py
+# author Florent Guiotte <florent.guiotte@irisa.fr>
+# version 0.0
+# date 24 août 2020
+"""High-level helper functions.
+
+This module contains high-level helper functions. This module shows many
+exemple on the use of idefix package and other packages (sap, rasterio,
+...) to process point clouds.
+
+"""
+
+import numpy as np
+from scipy.interpolate import griddata
+from rasterio import fill
+import sap
+import higra as hg
+
+from .vxl import get_grid, bin, squash
+
+def interpolate(raster, method='linear'):
+    """Interpolate masked raster.
+
+    Wrapper function to interpolate missing values in masked raster.
+    The 'linear', 'nearest' and 'cubic' implementation are from `Scipy`_
+    while the 'idw' (inverse distance weighting) is provided by
+    `rasterio`_.
+
+    .. _Scipy: https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.griddata.html
+    .. _rasterio: https://rasterio.readthedocs.io/en/latest/api/rasterio.fill.html
+
+    Parameters
+    ----------
+    raster : masked ndarray
+        The raster with missing values masked.
+    method : str
+        Can be 'linear', 'nearest', 'cubic' or 'idw'.
+
+    Returns
+    -------
+    out : ndarray
+        The raster with filled missing values.
+
+    """
+    if method == 'idw':
+        raster = fill.fillnodata(raster)
+    else:
+        coords = np.argwhere(~raster.mask)
+        values = raster.compressed()
+        grid   = np.argwhere(raster.mask)
+
+        raster[raster.mask] = griddata(coords, values, grid, method=method)
+
+        if method != 'nearest':
+            raster.mask = np.isnan(raster)
+            raster = interpolate(raster, 'nearest')
+
+        raster = np.array(raster)
+
+    assert not np.isnan(raster).any()
+
+    return raster
+
+def dsm(pcloud, cell_size=1., last=False):
+    """Create the digital surface model (DSM) of the point cloud.
+
+    Parameters
+    ----------
+    pcloud : recarray
+        A point cloud loaded with :mod:`idefix.io`.
+    cell_size : scalar
+        The size of the cells in meter. Cells are square. Default is 1
+        meter.
+    last : bool
+        Specifies whether the first echo (`False`) or the last echo
+        (`True`) should be taken into account. Default is `False`.
+
+    Returns
+    -------
+    dsm : ndarray
+        The DSM of the point cloud.
+
+    """
+    grid = get_grid(pcloud.spatial, cell_size)
+    vxlg = bin(grid, pcloud.spatial, pcloud.spatial[:,2], 'mean')
+    rstr = squash(vxlg, 'bottom' if last else 'top')
+    rstr = interpolate(rstr, 'idw')
+
+    return rstr
+
+def dtm_dh_filter(dsm, sigma=.5, epsilon=20000, alpha=2):
+    """Compute a digital terrain model (DTM) from a DSM.
+
+    Work best with DSM of last echo.
+
+    Parameters
+    ----------
+    dsm : ndarray
+        The DSM.
+    sigma : scalar
+        The height theshold to trigger object detection. Default is 
+        0.5 m.
+    epsilon : scalar
+        The area theshold for ground objects. All objects with surface
+        greater than epsilon are forcedto be ground. Default is 20 km².
+    alpha : scalar
+        The area threshold for horizontal noise filter. Area variations
+        smaller than alpha are removed for the detection of height
+        threshold sigma. Default is 2 m².
+
+    Returns
+    -------
+    dtm : ndarray
+        The DTM computed from the DSM.
+
+    """
+    mt = sap.MaxTree(dsm)
+    area = mt.get_attribute('area')
+    
+    area_child = hg.accumulate_parallel(mt._tree, area, hg.Accumulators.max)
+    pruned = (area - area_child) <= alpha
+
+    pruned_tree, pruned_map = hg.simplify_tree(mt._tree, pruned)
+    
+    dh = mt._alt[pruned_map] - mt._alt[pruned_map][pruned_tree.parents()]
+    remove = dh > sigma
+
+    original_map = np.zeros(mt.num_nodes(), dtype=np.int)
+    original_map[pruned_map] = np.arange(pruned_map.size)
+    original_map = hg.accumulate_and_max_sequential(mt._tree, original_map, np.arange(mt._tree.num_leaves()), hg.Accumulators.max)
+    original_remove = remove[original_map] & (area < epsilon)
+    
+    dtm = mt.reconstruct(original_remove, filtering='min')
+    
+    return dtm
+

idefix/utils.py

@@ -10,7 +10,7 @@ This module contains common utils for basic point cloud management and dataviz.
 Notes
 -----
 
-Everything should be highly tested there.
+Everything is well tested there.
 
 """
 

idefix/vxl.py

@@ -12,7 +12,6 @@ import logging
 import numpy as np
 import humanize
 from .utils import bbox
-import mayavi.mlab as mlab
 
 log = logging.getLogger(__name__)
 
@@ -30,7 +29,7 @@ def _ui_step(step, spatial):
         out_step = [step] * spatial.shape[-1]
 
     for s in out_step:
-        if s <= 0:
+        if s and s <= 0:
             msg = 'Step should be greater than 0, steps = \'{}\'.'.format(step)
             log.error(msg)
             raise ValueError(msg)
@@ -45,10 +44,12 @@ def get_grid(spatial, step):
     Parameters
     ----------
     spatial : array (m, n)
-        The spatial point cloud or the corresponding bounding box to grid.
+        The spatial point cloud or the corresponding bounding box to
+        grid.
     step : number or array or tuple
-        The step of the grid, can be a number to get an isotropic grid, or an
+        The step of the grid, can be a number to get an isotropic grid,
-        iterable of size 3 (required) to get an anisotropic grid.
+        or an iterable of size 3 (required) to get an anisotropic grid.
+        Value can be `None` to define an undivided axis.
 
     Returns
     -------
@@ -63,8 +64,11 @@ def get_grid(spatial, step):
     grid = []
     for a_min, a_max, a_s in zip(bb[0], bb[1], step):
         # Beware of float underflow
+        if a_s:
             bins = np.trunc((a_max - a_min) / a_s).astype(int) + 1
             grid += [np.linspace(a_min, a_min + bins * a_s, bins + 1)]
+        else:
+            grid += [np.array((a_min, a_max + 1))]
 
     return grid
 
@@ -91,7 +95,7 @@ def bin(grid, spatial, feature=None, method='density'):
         - 'mean': The mean of feature value in each cell.
         - 'mode': The modal (most common) in each cell. Designed for labels on
           point cloud, can be long with rich spectral data. If there is an
-          equal number of elements, then the smallest is returned.
+          equal number of elements, the smallest is returned.
         The default is 'density'.
 
     Returns
@@ -371,6 +375,8 @@ def plot(voxel_grid, vmin=None, vmax=None):
     >>> mlab.savefig(fname, magnification=4)
     >>> mlab.show()
     """
+    import mayavi.mlab as mlab
+
     points = np.where(~voxel_grid.mask)
 
     if vmin or vmax:

setup.py

@@ -8,14 +8,37 @@
 #
 # TODO details
 
-from distutils.core import setup
+import setuptools
 
-setup(name='idefix',
+with open('README.md', 'r') as fh:
-      version='1.4',
+    long_description = fh.read()
+
+setuptools.setup(
+    name='idefix',
+    version='0.1.5',
     description='Utils and processing pipelines for LiDAR point clouds',
     author='Florent Guiotte',
     author_email='florent.guiotte@uhb.fr',
     url='https://git.guiotte.fr/Florent/Idefix',
+    long_description=long_description,
+    long_description_content_type='text/markdown',
     packages=['idefix', 'idefix.tools'],
     entry_points = {'console_scripts':['txt2npz = idefix.tools.txt_to_npz:main',]},
+    classifiers=[
+        'Programming Language :: Python :: 3',
+        'License :: OSI Approved',
+        'Operating System :: OS Independent',
+    ],
+    python_requires='>=3.6',
+    install_requires=[
+        'numpy',
+        'sap',
+        'tqdm',
+        'matplotlib',
+        'pathlib',
+        'rasterio',
+        'laspy',
+        'humanize',
+        #'mayavi', Optional, for vxl.plot()
+    ],
       )

test/test_helpers.py

@@ -0,0 +1,46 @@
+#!/usr/bin/env python
+# file test_helpers.py
+# author Florent Guiotte <florent.guiotte@irisa.fr>
+# version 0.0
+# date 24 août 2020
+"""Abstract
+
+doc.
+"""
+
+import numpy as np
+import pytest
+from idefix import helpers, io
+
+@pytest.fixture
+def ma_raster():
+    rs = np.random.RandomState(42)
+    raster = rs.random((10,10))
+    raster = np.ma.array(raster, mask=raster<.1)
+    return raster
+
+@pytest.mark.parametrize('method', 
+        ['nearest', 'linear', 'cubic', 'idw'])
+def test_interpolate(ma_raster, method):
+    helpers.interpolate(ma_raster, method)
+
+def _data_pc(datadir, set_id):
+    path = datadir.join('pc{}.txt'.format(set_id))
+    data = io.load_txt(path, 'x y z i'.split())
+    return data
+
+@pytest.mark.parametrize('params', [
+    {},
+    {'cell_size': 2.},
+    {'last': True}])
+def test_dsm(datadir, params):
+    pc = _data_pc(datadir, 0)
+    dsm = helpers.dsm(pc, **params)
+
+    assert dsm is not None, 'Did not return anything...'
+    assert not np.isnan(dsm).any(), 'Some missing values in DSM'
+
+def test_dtm(ma_raster):
+    dtm = helpers.dtm_dh_filter(ma_raster)
+
+    assert dtm is not None, 'Did not return anything...'

test/test_helpers/pc0.txt

@@ -0,0 +1,8 @@
+# x y z feature
+1 1 1 2
+1 3 2 5
+1 3 2 5
+1 3 2 10
+1 3 2 20
+10 10 10 1
+5 5 5 0

test/test_vxl.py

@@ -75,6 +75,7 @@ def data_grid(datadir, set_id, step_id):
     ('0', .7, '0_7'),
     ('0', .15, '0_15'),
     ('0', [1.,1.,2.] , '1-1-2'),
+    ('0', [1.,1.,None] , '1-1-n'),
 ])
 def test_get_grid(datadir, set_id, step, grid_id):
     spatial = data_pc(datadir, set_id).spatial

test/test_vxl/pc0_grid_s1-1-n.txt

@@ -0,0 +1,3 @@
+1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0
+1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0
+1.0 11.0