kopia lustrzana https://github.com/OpenDroneMap/ODM
462 wiersze
18 KiB
Python
462 wiersze
18 KiB
Python
import cv2
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import pyexiv2
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import re
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from fractions import Fraction
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from opensfm.exif import sensor_string
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import log
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import io
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import system
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import context
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class ODM_Photo:
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""" ODMPhoto - a class for ODMPhotos
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"""
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def __init__(self, path_file, force_focal, force_ccd):
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# general purpose
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self.path_file = path_file
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self.filename = io.extract_file_from_path_file(path_file)
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# useful attibutes
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self.width = None
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self.height = None
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self.ccd_width = None
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self.focal_length = None
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self.focal_length_px = None
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# other attributes
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self.camera_make = ''
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self.camera_model = ''
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self.make_model = ''
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# parse values from metadata
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self.parse_pyexiv2_values(self.path_file, force_focal, force_ccd)
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# compute focal length into pixels
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self.update_focal()
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# print log message
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log.ODM_DEBUG('Loaded %s | camera: %s | dimensions: %s x %s | focal: %s | ccd: %s' %
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(self.filename, self.make_model, self.width, self.height, self.focal_length, self.ccd_width))
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def update_focal(self):
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# compute focal length in pixels
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if self.focal_length and self.ccd_width:
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# take width or height as reference
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if self.width > self.height:
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# f(px) = w(px) * f(mm) / ccd(mm)
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self.focal_length_px = \
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self.width * (self.focal_length / self.ccd_width)
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else:
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# f(px) = h(px) * f(mm) / ccd(mm)
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self.focal_length_px = \
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self.height * (self.focal_length / self.ccd_width)
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def parse_pyexiv2_values(self, _path_file, _force_focal, _force_ccd):
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# read image metadata
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metadata = pyexiv2.ImageMetadata(_path_file)
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metadata.read()
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# loop over image tags
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for key in metadata:
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# try/catch tag value due to weird bug in pyexiv2
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# ValueError: invalid literal for int() with base 10: ''
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try:
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val = metadata[key].value
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# parse tag names
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if key == 'Exif.Image.Make':
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self.camera_make = val
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elif key == 'Exif.Image.Model':
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self.camera_model = val
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elif key == 'Exif.Photo.FocalLength':
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self.focal_length = float(val)
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except (pyexiv2.ExifValueError, ValueError) as e:
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pass
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except NotImplementedError as e:
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pass
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if self.camera_make and self.camera_model:
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self.make_model = sensor_string(self.camera_make, self.camera_model)
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# needed to do that since sometimes metadata contains wrong data
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img = cv2.imread(_path_file)
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self.width = img.shape[1]
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self.height = img.shape[0]
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# force focal and ccd_width with user parameter
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if _force_focal:
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self.focal_length = _force_focal
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if _force_ccd:
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self.ccd_width = _force_ccd
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# find ccd_width from file if needed
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if self.ccd_width is None and self.camera_model is not None:
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# load ccd_widths from file
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ccd_widths = system.get_ccd_widths()
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# search ccd by camera model
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key = [x for x in ccd_widths.keys() if self.make_model in x]
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# convert to float if found
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if key:
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self.ccd_width = float(ccd_widths[key[0]])
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else:
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log.ODM_WARNING('Could not find ccd_width in file. Use --force-ccd or edit the sensor_data.json '
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'file to manually input ccd width')
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# TODO: finish this class
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class ODM_Reconstruction(object):
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"""docstring for ODMReconstruction"""
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def __init__(self, arg):
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super(ODMReconstruction, self).__init__()
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self.arg = arg
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class ODM_GCPoint(object):
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"""docstring for ODMPoint"""
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def __init__(self, x, y, z):
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self.x = x
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self.y = y
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self.z = z
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class ODM_GeoRef(object):
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"""docstring for ODMUtmZone"""
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def __init__(self):
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self.datum = 'WGS84'
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self.epsg = None
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self.utm_zone = 0
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self.utm_pole = 'N'
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self.utm_east_offset = 0
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self.utm_north_offset = 0
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self.gcps = []
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def calculate_EPSG(self, _utm_zone, _pole):
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"""Calculate and return the EPSG"""
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if _pole == 'S':
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return 32700 + _utm_zone
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elif _pole == 'N':
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return 32600 + _utm_zone
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else:
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log.ODM_ERROR('Unknown pole format %s' % _pole)
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return
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def coord_to_fractions(self, coord, refs):
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deg_dec = abs(float(coord))
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deg = int(deg_dec)
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minute_dec = (deg_dec - deg) * 60
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minute = int(minute_dec)
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sec_dec = (minute_dec - minute) * 60
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sec_dec = round(sec_dec, 3)
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sec_denominator = 1000
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sec_numerator = int(sec_dec * sec_denominator)
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if float(coord) >= 0:
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latRef = refs[0]
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else:
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latRef = refs[1]
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output = str(deg) + '/1 ' + str(minute) + '/1 ' + str(sec_numerator) + '/' + str(sec_denominator)
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return output, latRef
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def convert_to_las(self, _file, _file_out, json_file):
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if not self.epsg:
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log.ODM_ERROR('Empty EPSG: Could not convert to LAS')
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return
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kwargs = {'bin': context.pdal_path,
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'f_in': _file,
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'f_out': _file_out,
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'east': self.utm_east_offset,
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'north': self.utm_north_offset,
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'epsg': self.epsg,
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'json': json_file}
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# create pipeline file transform.xml to enable transformation
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pipeline = '{{' \
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' "pipeline":[' \
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' "untransformed.ply",' \
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' {{' \
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' "type":"filters.transformation",' \
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' "matrix":"1 0 0 {east} 0 1 0 {north} 0 0 1 0 0 0 0 1"' \
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' }},' \
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' {{' \
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' "a_srs":"EPSG:{epsg}",' \
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' "filename":"transformed.las"' \
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' }}' \
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' ]' \
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'}}'.format(**kwargs)
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with open(json_file, 'w') as f:
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f.write(pipeline)
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# call pdal
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system.run('{bin}/pdal pipeline -i {json} --readers.ply.filename={f_in} '
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'--writers.las.filename={f_out}'.format(**kwargs))
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def convert_to_dem(self, _file, _file_out, pdalJSON, sample_radius, gdal_res, gdal_radius):
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# Check if exists f_in
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if not io.file_exists(_file):
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log.ODM_ERROR('LAS file does not exist')
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return False
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kwargs = {
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'bin': context.pdal_path,
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'f_in': _file,
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'sample_radius': sample_radius,
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'gdal_res': gdal_res,
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'gdal_radius': gdal_radius,
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'f_out': _file_out,
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'json': pdalJSON
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}
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pipelineJSON = '{{' \
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' "pipeline":[' \
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' "input.las",' \
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' {{' \
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' "type":"filters.sample",' \
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' "radius":"{sample_radius}"' \
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' }},' \
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' {{' \
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' "type":"filters.pmf",' \
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' "extract":"true"' \
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' }},' \
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' {{' \
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' "resolution": {gdal_res},' \
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' "radius": {gdal_radius},' \
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' "output_type":"idw",' \
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' "filename":"outputfile.tif"' \
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' }}' \
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' ]' \
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'}}'.format(**kwargs)
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with open(pdalJSON, 'w') as f:
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f.write(pipelineJSON)
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system.run('{bin}/pdal pipeline {json} --readers.las.filename={f_in} '
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'--writers.gdal.filename={f_out}'.format(**kwargs))
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if io.file_exists(kwargs['f_out']):
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return True
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else:
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return False
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def utm_to_latlon(self, _file, _photo, idx):
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gcp = self.gcps[idx]
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kwargs = {'epsg': self.epsg,
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'file': _file,
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'x': gcp.x + self.utm_east_offset,
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'y': gcp.y + self.utm_north_offset,
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'z': gcp.z}
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latlon = system.run_and_return('echo {x} {y} {z} '.format(**kwargs),
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'gdaltransform -s_srs \"EPSG:{epsg}\" '
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'-t_srs \"EPSG:4326\"'.format(**kwargs)).split()
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# Example: 83d18'16.285"W
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# Example: 41d2'11.789"N
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# Example: 0.998
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if len(latlon) == 3:
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lon_str, lat_str, alt_str = latlon
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elif len(latlon) == 2:
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lon_str, lat_str = latlon
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alt_str = ''
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else:
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log.ODM_ERROR('Something went wrong %s' % latlon)
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lat_frac = self.coord_to_fractions(latlon[1], ['N', 'S'])
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lon_frac = self.coord_to_fractions(latlon[0], ['E', 'W'])
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# read image metadata
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metadata = pyexiv2.ImageMetadata(_photo.path_file)
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metadata.read()
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# set values
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# GPS latitude
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key = 'Exif.GPSInfo.GPSLatitude'
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value = lat_frac[0].split(' ')
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log.ODM_DEBUG('lat_frac: %s %s %s' % (value[0], value[1], value[2]))
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metadata[key] = pyexiv2.ExifTag(key,
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[Fraction(value[0]),
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Fraction(value[1]),
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Fraction(value[2])])
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key = 'Exif.GPSInfo.GPSLatitudeRef'
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value = lat_frac[1]
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metadata[key] = pyexiv2.ExifTag(key, value)
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# GPS longitude
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key = 'Exif.GPSInfo.GPSLongitude'
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value = lon_frac[0].split(' ')
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metadata[key] = pyexiv2.ExifTag(key,
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[Fraction(value[0]),
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Fraction(value[1]),
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Fraction(value[2])])
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key = 'Exif.GPSInfo.GPSLongitudeRef'
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value = lon_frac[1]
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metadata[key] = pyexiv2.ExifTag(key, value)
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# GPS altitude
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altitude = abs(int(float(latlon[2]) * 100))
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key = 'Exif.GPSInfo.GPSAltitude'
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value = Fraction(altitude, 1)
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metadata[key] = pyexiv2.ExifTag(key, value)
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if latlon[2] >= 0:
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altref = '0'
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else:
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altref = '1'
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key = 'Exif.GPSInfo.GPSAltitudeRef'
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metadata[key] = pyexiv2.ExifTag(key, altref)
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# write values
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metadata.write()
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def parse_coordinate_system(self, _file):
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"""Write attributes to jobOptions from coord file"""
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# check for coordinate file existence
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if not io.file_exists(_file):
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log.ODM_ERROR('Could not find file %s' % _file)
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return
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with open(_file) as f:
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# extract reference system and utm zone from first line.
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# We will assume the following format:
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# 'WGS84 UTM 17N'
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line = f.readline()
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log.ODM_DEBUG('Line: %s' % line)
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ref = line.split(' ')
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# match_wgs_utm = re.search('WGS84 UTM (\d{1,2})(N|S)', line, re.I)
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if ref[0] == 'WGS84' and ref[1] == 'UTM': # match_wgs_utm:
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self.datum = ref[0]
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self.utm_pole = ref[2][len(ref) - 1]
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self.utm_zone = int(ref[2][:len(ref) - 1])
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# extract east and west offsets from second line.
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# We will assume the following format:
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# '440143 4588391'
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# update EPSG
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self.epsg = self.calculate_EPSG(self.utm_zone, self.utm_pole)
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# If the first line looks like "EPSG:n" or "epsg:n"
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elif ref[0].split(':')[0].lower() == 'epsg':
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self.epsg = line.split(':')[1]
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else:
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log.ODM_ERROR('Could not parse coordinates. Bad CRS supplied: %s' % line)
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return
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offsets = f.readline().split(' ')
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self.utm_east_offset = int(offsets[0])
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self.utm_north_offset = int(offsets[1])
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# parse coordinates
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lines = f.readlines()
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for l in lines:
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xyz = l.split(' ')
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if len(xyz) == 3:
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x, y, z = xyz[:3]
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elif len(xyz) == 2:
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x, y = xyz[:2]
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z = 0
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self.gcps.append(ODM_GCPoint(float(x), float(y), float(z)))
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# Write to json file
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class ODM_Tree(object):
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def __init__(self, root_path, images_path):
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# root path to the project
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self.root_path = io.absolute_path_file(root_path)
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if not images_path:
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self.input_images = io.join_paths(self.root_path, 'images')
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else:
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self.input_images = io.absolute_path_file(images_path)
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# modules paths
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# here are defined where all modules should be located in
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# order to keep track all files al directories during the
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# whole reconstruction process.
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self.dataset_raw = io.join_paths(self.root_path, 'images')
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self.dataset_resize = io.join_paths(self.root_path, 'images_resize')
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self.opensfm = io.join_paths(self.root_path, 'opensfm')
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self.pmvs = io.join_paths(self.root_path, 'pmvs')
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self.odm_meshing = io.join_paths(self.root_path, 'odm_meshing')
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self.odm_texturing = io.join_paths(self.root_path, 'odm_texturing')
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self.odm_georeferencing = io.join_paths(self.root_path, 'odm_georeferencing')
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self.odm_orthophoto = io.join_paths(self.root_path, 'odm_orthophoto')
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self.odm_pdal = io.join_paths(self.root_path, 'pdal')
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# important files paths
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# benchmarking
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self.benchmarking = io.join_paths(self.root_path, 'benchmark.txt')
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# opensfm
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self.opensfm_bundle = io.join_paths(self.opensfm, 'bundle_r000.out')
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self.opensfm_bundle_list = io.join_paths(self.opensfm, 'list_r000.out')
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self.opensfm_image_list = io.join_paths(self.opensfm, 'image_list.txt')
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self.opensfm_reconstruction = io.join_paths(self.opensfm, 'reconstruction.json')
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self.opensfm_model = io.join_paths(self.opensfm, 'depthmaps/merged.ply')
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# pmvs
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self.pmvs_rec_path = io.join_paths(self.pmvs, 'recon0')
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self.pmvs_bundle = io.join_paths(self.pmvs_rec_path, 'bundle.rd.out')
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self.pmvs_visdat = io.join_paths(self.pmvs_rec_path, 'vis.dat')
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self.pmvs_options = io.join_paths(self.pmvs_rec_path, 'pmvs_options.txt')
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self.pmvs_model = io.join_paths(self.pmvs_rec_path, 'models/option-0000.ply')
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# odm_meshing
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self.odm_mesh = io.join_paths(self.odm_meshing, 'odm_mesh.ply')
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self.odm_meshing_log = io.join_paths(self.odm_meshing, 'odm_meshing_log.txt')
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# texturing
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self.odm_texturing_undistorted_image_path = io.join_paths(
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self.odm_texturing, 'undistorted')
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self.odm_textured_model_obj = io.join_paths(
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self.odm_texturing, 'odm_textured_model.obj')
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self.odm_textured_model_mtl = io.join_paths(
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self.odm_texturing, 'odm_textured_model.mtl')
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# Log is only used by old odm_texturing
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self.odm_texuring_log = io.join_paths(
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self.odm_texturing, 'odm_texturing_log.txt')
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# odm_georeferencing
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self.odm_georeferencing_latlon = io.join_paths(
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self.odm_georeferencing, 'latlon.txt')
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self.odm_georeferencing_coords = io.join_paths(
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self.odm_georeferencing, 'coords.txt')
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self.odm_georeferencing_gcp = io.join_paths(
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self.odm_georeferencing, 'gcp_list.txt')
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self.odm_georeferencing_utm_log = io.join_paths(
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self.odm_georeferencing, 'odm_georeferencing_utm_log.txt')
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self.odm_georeferencing_log = io.join_paths(
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self.odm_georeferencing, 'odm_georeferencing_log.txt')
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self.odm_georeferencing_model_txt_geo = io.join_paths(
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self.odm_georeferencing, 'odm_georeferencing_model_geo.txt')
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self.odm_georeferencing_model_ply_geo = io.join_paths(
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self.odm_georeferencing, 'odm_georeferenced_model.ply')
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self.odm_georeferencing_model_obj_geo = io.join_paths(
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self.odm_texturing, 'odm_textured_model_geo.obj') # these files will be kept in odm_texturing/
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self.odm_georeferencing_model_mtl_geo = io.join_paths(
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self.odm_texturing, 'odm_textured_model_geo.mtl') # these files will be kept in odm_texturing/
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self.odm_georeferencing_xyz_file = io.join_paths(
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self.odm_georeferencing, 'odm_georeferenced_model.csv')
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self.odm_georeferencing_las_json = io.join_paths(
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self.odm_georeferencing, 'las.json')
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self.odm_georeferencing_model_las = io.join_paths(
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self.odm_georeferencing, 'odm_georeferenced_model.las')
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self.odm_georeferencing_dem = io.join_paths(
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self.odm_georeferencing, 'odm_georeferencing_model_dem.tif')
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self.odm_georeferencing_dem_json = io.join_paths(
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self.odm_georeferencing, 'dem.json')
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# odm_orthophoto
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self.odm_orthophoto_file = io.join_paths(self.odm_orthophoto, 'odm_orthophoto.png')
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self.odm_orthophoto_tif = io.join_paths(self.odm_orthophoto, 'odm_orthophoto.tif')
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self.odm_orthophoto_corners = io.join_paths(self.odm_orthophoto, 'odm_orthophoto_corners.txt')
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self.odm_orthophoto_log = io.join_paths(self.odm_orthophoto, 'odm_orthophoto_log.txt')
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self.odm_orthophoto_tif_log = io.join_paths(self.odm_orthophoto, 'gdal_translate_log.txt')
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