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# This file is a part of the HiRISE DTM Importer for Blender
#
# Copyright (C) 2017 Arizona Board of Regents on behalf of the Planetary Image
# Research Laboratory, Lunar and Planetary Laboratory at the University of
# Arizona.
#
# This program is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by the Free
# Software Foundation, either version 3 of the License, or (at your option)
# any later version.
#
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY
# or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
# for more details.
#
# You should have received a copy of the GNU General Public License along
# with this program. If not, see <http://www.gnu.org/licenses/>.
"""Objects for importing HiRISE DTMs."""
import numpy as np
from .. import pvl
class DTM:
"""
HiRISE Digital Terrain Model
This class imports a HiRISE DTM from a Planetary Data Systems (PDS)
compliant .IMG file.
Parameters
----------
path : str
terrain_resolution : float, optional
Controls the resolution the DTM is read at. This should be a float
in the range [0.01, 1.0] (and will be constrained to this range). A
value of 1.0 will result in the DTM being read at full resolution. A
value of 0.01 will result in the DTM being read at 1/100th resolution.
Default is 1.0 (no downsampling).
Todo
----
* Use GDAL for importing the DTM if it is installed for this Python
environment. If/when I have the time to do this, it probably
warrants breaking out separate importer classes. The benefits of
doing this are pretty substantial, though:
+ More reliable (doesn't rely on my PVL parser for finding the
valid values in the DTM, for locating the starting position of
the elevation data in the .IMG file)
+ Other, better, downsampling algorithms are already built in.
+ Would make this much better at general PDS DTM importing,
currently some of the import code is specific to HiRISE DTMs.
"""
# Special constants in our data:
# NULL : No data at this point.
# LRS : Low Representation Saturation
# LIS : Low Instrument Saturation
# HRS : High Representation Saturation
# HIS : High Insturment Saturation
SPECIAL_VALUES = {
"NULL": np.fromstring(b'\xFF\x7F\xFF\xFB', dtype='>f4')[0],
"LRS": np.fromstring(b'\xFF\x7F\xFF\xFC', dtype='>f4')[0],
"LIS": np.fromstring(b'\xFF\x7F\xFF\xFD', dtype='>f4')[0],
"HRS": np.fromstring(b'\xFF\x7F\xFF\xFE', dtype='>f4')[0],
"HIS": np.fromstring(b'\xFF\x7F\xFF\xFF', dtype='>f4')[0]
}
def __init__(self, path, terrain_resolution=1.0):
self.path = path
self.terrain_resolution = terrain_resolution
self.label = self._read_label()
self.data = self._read_data()
def _read_label(self):
"""Returns a dict-like representation of a PVL label"""
return pvl.load(self.path)
def _read_data(self):
"""
Reads elevation data from a PDS .IMG file.
Notes
-----
* Uses nearest-neighbor to downsample data.
Todo
----
* Add other downsampling algorithms.
"""
h, w = self.image_resolution
max_samples = int(w - w % self.bin_size)
data = np.zeros(self.shape)
with open(self.path, 'rb') as f:
# Seek to the first byte of data
start_byte = self._get_data_start()
f.seek(start_byte)
# Iterate over each row of the data
for r in range(data.shape[0]):
# Each iteration, seek to the right location before
# reading a row. We determine this location as the
# first byte of data PLUS a offset which we calculate as the
# product of:
#
# 4, the number of bytes in a single record
# r, the current row index
# w, the number of records in a row of the DTM
# bin_size, the number of records in a bin
#
# This is where we account for skipping over rows.
offset = int(4 * r * w * self.bin_size)
f.seek(start_byte + offset)
# Read a row
row = np.fromfile(f, dtype=np.float32, count=max_samples)
# This is where we account for skipping over columns.
data[r] = row[::self.bin_size]
data = self._process_invalid_data(data)
return data
def _get_data_start(self):
"""Gets the start position of the DTM data block"""
label_length = self.label['RECORD_BYTES']
num_labels = self.label.get('LABEL_RECORDS', 1)
return int(label_length * num_labels)
def _process_invalid_data(self, data):
"""Sets any 'NULL' elevation values to np.NaN"""
invalid_data_mask = (data <= self.SPECIAL_VALUES['NULL'])
data[invalid_data_mask] = np.NaN
return data
@property
def map_size(self):
"""Geographic size of the bounding box around the DTM"""
scale = self.map_scale * self.unit_scale
w = self.image_resolution[0] * scale
h = self.image_resolution[1] * scale
return (w, h)
@property
def mesh_scale(self):
"""Geographic spacing between mesh vertices"""
return self.bin_size * self.map_scale * self.unit_scale
@property
def map_info(self):
"""Map Projection metadata"""
return self.label['IMAGE_MAP_PROJECTION']
@property
def map_scale(self):
"""Geographic spacing between DTM posts"""
map_scale = self.map_info.get('MAP_SCALE', None)
return getattr(map_scale, 'value', 1.0)
@property
def map_units(self):
"""Geographic unit for spacing between DTM posts"""
map_scale = self.map_info.get('MAP_SCALE', None)
return getattr(map_scale, 'units', None)
@property
def unit_scale(self):
"""
The function that creates a Blender mesh from this object will assume
that the height values passed into it are in meters --- this
property is a multiplier to convert DTM-units to meters.
"""
scaling_factors = {
'KM/PIXEL': 1000,
'METERS/PIXEL': 1
}
return scaling_factors.get(self.map_units, 1.0)
@property
def terrain_resolution(self):
"""Vertex spacing, meters"""
return self._terrain_resolution
@terrain_resolution.setter
def terrain_resolution(self, t):
self._terrain_resolution = np.clip(t, 0.01, 1.0)
@property
def bin_size(self):
"""The width of the (square) downsampling bin"""
return int(np.ceil(1 / self.terrain_resolution))
@property
def image_stats(self):
"""Image statistics from the original DTM label"""
return self.label['IMAGE']
@property
def image_resolution(self):
"""(Line, Sample) resolution of the original DTM"""
return (self.image_stats['LINES'], self.image_stats['LINE_SAMPLES'])
@property
def size(self):
"""Number of posts in our reduced DTM"""
return self.shape[0] * self.shape[1]
@property
def shape(self):
"""Shape of our reduced DTM"""
num_rows = self.image_resolution[0] // self.bin_size
num_cols = self.image_resolution[1] // self.bin_size
return (num_rows, num_cols)
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