wolfhece.lazviewer.laz_viewer

Module Contents

class wolfhece.lazviewer.laz_viewer.Colors_Lazviewer

Bases: enum.Enum

Generic enumeration.

Derive from this class to define new enumerations.

ORTHO_2012_2013 = 2013

ORTHO_2015 = 2015

ORTHO_2021 = 2021

ORTHO_2023 = 2023

ORTHO_2006_2007 = 2006

CODE_2013 = 0

CODE_2023 = 2

CODE_2021_2022 = 3

FROM_FILE = 1

class wolfhece.lazviewer.laz_viewer.Classification_LAZ

test_wx()

init_2013()

init_2021_2022()

init_2023()

callback_colors()

Update from wx GUI

callback_destroy()

interactive_update_colors()

set GUI

wolfhece.lazviewer.laz_viewer.choices_laz_colormap() → list[str]

class wolfhece.lazviewer.laz_viewer.xyz_laz(fn: str = '', format: Literal[las, numpy] = 'las', to_read: bool = True)

Classe de gestion des fichiers XYZ+Class issus d’un gros fichier laz

property size

split(dir_out: str, nbparts: int)

Split file into ‘nb’ parts along X and Y

get_bounds()

test_bounds(bounds: list[list[float, float], list[float, float]])

read_bin_xyz()

Lecture d’un fichier binaire de points XYZ+classification généré par la fonction sort_grid_np Le format est une succession de trame binaire de la forme :

nbpoints (np.int32) X[nbpoints] (np.float32) ou (np.float64) Y[nbpoints] (np.float32) ou (np.float64) Z[nbpoints] (np.float32) Classif[nbpoints] (np.int8)

Il est possible de récupérer une matrice numpy shape(nbtot,4) ou un objet laspy via l’argument ‘out’ (par défaut à ‘las’)

to_las()

class wolfhece.lazviewer.laz_viewer.xyz_laz_grid(mydir: str)

Gestion d’un grid de données LAZ

_read_gridinfo(gridinfo: str) → list

scan(bounds: tuple[tuple[float, float], tuple[float, float]] | list[list[float, float], list[float, float]])

Find all points in bounds

find_files_in_bounds(bounds: tuple[tuple[float, float], tuple[float, float]] | list[list[float, float], list[float, float]])

Find all files in bounds

_split_xyz(dirout: str, nbparts: int = 10)

Split XYZ file into ‘nb’ parts along X and Y

_sort_grid_np(fn_in: str, fn_out: str, bounds: tuple[tuple[float, float], tuple[float, float]] | list[list[float, float], list[float, float]], gridsize: list[int], chunksize: int = 5000000, force_format=np.float64)

Create .bin files from .laz

class wolfhece.lazviewer.laz_viewer.xyz_laz_grids(dir_grids: str, create: bool = False)

Ensemble de grids

scan(bounds: tuple[tuple[float, float], tuple[float, float]] | list[list[float, float], list[float, float]]) → numpy.ndarray

Scan all LAZ to find used data

Parameters:

bounds (Union[tuple[tuple[float,float],tuple[float,float]], list[list[float, float],list[float, float]]]) – [[xmin,xmax], [ymin,ymax]]

Returns:

np.ndarray

find_files_in_bounds(bounds: tuple[tuple[float, float], tuple[float, float]] | list[list[float, float], list[float, float]])

copy_files_in_bounds(bounds: tuple[tuple[float, float], tuple[float, float]] | list[list[float, float], list[float, float]], dirout: str)

read_dir(dir_grids)

scan_around(xy: shapely.geometry.LineString | list[list[float], list[float]], length_buffer=5.0)

Récupération de points LAZ autour de la section

plot_laz(xy: shapely.geometry.LineString | list[list[float], list[float]], length_buffer=5.0, figax: tuple[matplotlib.figure.Figure, matplotlib.axes.Axes] = None, show=False)

Dessin des points LAZ sur un graphique Matplotlib

plot_laz_wx(xy: shapely.geometry.LineString | list[list[float], list[float]], length_buffer=5.0, show=True)

Dessin des points LAZ sur un graphique Matplotlib

create_from_laz(dir_laz: str, shape: str = None, ds: float = 50, force_format=np.float64)

create_bounds_shape(dir_laz: str, out_shape: str)

Create shape from laz files

class wolfhece.lazviewer.laz_viewer.Wolf_LAZ_Data(idx: str = '', plotted: bool = False, mapviewer=None, need_for_wx: bool = False)

Bases: wolfhece.drawing_obj.Element_To_Draw

Base class for LAZ data which can be imported in Pydraw.Mapviewer.

property associated_color

property selected

property xyz_selected: numpy.ndarray

Extract the selected points from the viewer.

Filter the selected points by codes if _select_only_codes is not empty.

property code_selected: numpy.ndarray

property num_points

Number of points

property nb_points

Number of points - alias of num_points

property nb

Number of points - alias of num_points

property right

Camera Right vector

property mvp

Model View Projection matrix

See https://en.wikipedia.org/wiki/Model%E2%80%93view%E2%80%93projection_matrix for details

property eye

eye/camera position

property lookat

Lookat position

property phi

Azimuth angle (radians)

property theta

Elevation angle (radians)

property r

Distance from lookat

property point_size

Point size in viewer – user units

property xyz

property codes

property xmin

property xmax

property ymin

property ymax

property data

Full data array (x,y,z,code)

property bounds

serialize()

Serialize class : data and attributes

deserialize(data: dict)

Deserialize class : data and attributes

saveas(fn: str)

Save class : data and attributes

load(fn: str)

Load class : data and attributes

merge(other: Wolf_LAZ_Data)

Merge two Wolf_LAZ_Data objects

filter_data(codes: list[int])

Filter data by codes

bg_color(value)

bg_color_top(value)

bg_color_bottom(value)

floor_level(value)

floor_color(value)

show_grid(value: bool)

show_axis(value: bool)

show_info(value: bool)

force_view(x, y, z=-1)

Force lookat position

_eye_pos()

Compute eye pos from lookat and r, phi, theta.

phi is the azimuth angle (radians) theta is the elevation angle (radians) r is the distance from lookat

codes_unique()

Only unique codes

create_viewer(color_code: Colors_Lazviewer = None, classification: Classification_LAZ = None)

Create a viewer for las data

interactive_update_colors()

Create a frame to interactively update colors

set_colors()

Set colors in viewer –> using attributes method (not colormap)

set_classification(classification: str = None)

Set classification of LAZ data

TODO : Check if 2020-2022 SPW campaign is the same classification as 2013

find_minmax(update=False)

Find min and max of data

from_grid(grid: xyz_laz_grids, bounds: tuple[tuple[float, float], tuple[float, float]] | list[list[float, float], list[float, float]])

Create data from grid LAZ

from_file(fn: str)

Create data from LAZ file

descimate(step: int)

Descimate data.

Conserve only one point every ‘step’ points.

Parameters:

step – step of descimation

get_data_class(key: int)

Get data with a specific code

add_pose_in_memory(key_time: float = 1.0)

Add current pose in flight memory

play_flight(tlim=[-np.inf, np.inf], repeat=False, interp='cubic_natural')

Play flight memory

set_times(times: numpy.ndarray)

Set times for flight memory

set_times_increment(increment: float)

get_times()

record_flight(dirout: str, tlim=[-np.inf, np.inf], interp='cubic_natural', fps=24, prefix: str = 'laz_', ext: str = 'png')

Record flight memory in multiple images

FIXME : FREEZE the app –> to debug

save_flight(fn: str)

Write flight memory to file JSON

load_flight(fn: str)

Load flight memory from file JSON

_callback_props()

_callback_destroy_props()

_create_props()

Create properties Wolf_Param for LAZ data

_set_new_xls()

Create a new Excel grid for selected data

OnPlot_histogram(event: wx.MouseEvent)

Plot histogram of selected data

OnPlot_histogram_grid(event: wx.MouseEvent)

Plot histogram of selected data

_plot_histogram_grid()

Histogram ONLY of selected data in grid.

The data are extracted based on the first column of the grid untile an empty cell is found.

_plot_histogram()

_selection2vector()

FIXME: must use RANSAC to compute a segment from the selected points

_OnEdit_Selection(event: wx.MouseEvent)

Get selection from viewer and create a XLS grid

_edit_selection()

_update_viewer()

Update the viewer with properties

_fill_props(full: bool = False)

Fill properties from attributes

show_properties()

Surcharged method (see Element_To_Draw) to show properties from MapViewer

wolfhece.lazviewer.laz_viewer.find_pointsXYZ(xyz: numpy.ndarray, bounds: tuple[tuple[float, float], tuple[float, float]] | list[list[float, float], list[float, float]]) → numpy.ndarray

wolfhece.lazviewer.laz_viewer.find_points(las: laspy.LasData, xb: list[float, float], yb: list[float, float]) → laspy.LasData

Get arrays which indicate invalid X, Y, or Z values

wolfhece.lazviewer.laz_viewer.read_laz(fn: str, bounds: tuple[tuple[float, float], tuple[float, float]] | list[list[float, float], list[float, float]] = None) → numpy.ndarray | laspy.LasData

Lecture d’un fichier LAZ, LAS ou NPZ

wolfhece.lazviewer.laz_viewer.xyzlaz_scandir(mydir: str, bounds: tuple[tuple[float, float], tuple[float, float]] | list[list[float, float], list[float, float]])

Scan for XYZ files

wolfhece.lazviewer.laz_viewer.laz_scandir(mydir: str, bounds: tuple[tuple[float, float], tuple[float, float]] | list[list[float, float], list[float, float]]) → list[laspy.LasData]

Scan directory and treat .laz files

wolfhece.lazviewer.laz_viewer.clip_data_xyz(dir_in: str, fn_out: str, bounds: tuple[tuple[float, float], tuple[float, float]] | list[list[float, float], list[float, float]])

Get data and write zip numpy file

wolfhece.lazviewer.laz_viewer.clip_data_laz(fn_in: str, fn_out: str, bounds: tuple[tuple[float, float], tuple[float, float]] | list[list[float, float], list[float, float]], chunksize: int = 5000000)

wolfhece.lazviewer.laz_viewer.get_concat_h(im1: PIL.Image.Image, im2: PIL.Image.Image)

Concatenate 2 images horizontally

wolfhece.lazviewer.laz_viewer.get_concat_v(im1: PIL.Image.Image, im2: PIL.Image.Image)

Concatenate 2 images vertically

wolfhece.lazviewer.laz_viewer.get_Orthos_Walonmap(bounds, fn, cat='IMAGERIE/ORTHO_2012_2013', size=3000)

Récupération des orthos depuis Walonmap fn = filename sans extension –> .png sera ajouté automatiquement

catégories possibles :

• ‘IMAGERIE/ORTHO_2012_2013’

• ‘IMAGERIE/ORTHO_2015’

• ‘IMAGERIE/ORTHO_2021’

• ‘IMAGERIE/ORTHO_2006_2007’

wolfhece.lazviewer.laz_viewer.get_colors(las: laspy.LasData, which_colors: Colors_Lazviewer, imsize=2000, fname='', palette_classif: Classification_LAZ = None)

wolfhece.lazviewer.laz_viewer.myviewer(las: numpy.ndarray | list[laspy.LasData] | laspy.LasData, which_colors: Colors_Lazviewer, fname='', palette_classif: Classification_LAZ = None)

Get viewer for las data

wolfhece.lazviewer.laz_viewer.grids