import cv2 import exifread import re from fractions import Fraction from opensfm.exif import sensor_string from opendm import get_image_size from pyproj import Proj import log import io import system import context import logging class ODM_Photo: """ ODMPhoto - a class for ODMPhotos """ def __init__(self, path_file): # general purpose self.filename = io.extract_file_from_path_file(path_file) self.width = None self.height = None # other attributes self.camera_make = '' self.camera_model = '' self.make_model = '' self.latitude = None self.longitude = None self.altitude = None # parse values from metadata self.parse_exif_values(path_file) # print log message log.ODM_DEBUG('Loaded {}'.format(self)) def __str__(self): return '{} | camera: {} | dimensions: {} x {} | lat: {} | lon: {} | alt: {}'.format( self.filename, self.make_model, self.width, self.height, self.latitude, self.longitude, self.altitude) def parse_exif_values(self, _path_file): # Disable exifread log logging.getLogger('exifread').setLevel(logging.CRITICAL) with open(_path_file, 'rb') as f: tags = exifread.process_file(f, details=False) try: if 'Image Make' in tags: self.camera_make = tags['Image Make'].values.encode('utf8') if 'Image Model' in tags: self.camera_model = tags['Image Model'].values.encode('utf8') if 'GPS GPSAltitude' in tags: self.altitude = self.float_values(tags['GPS GPSAltitude'])[0] if 'GPS GPSAltitudeRef' in tags and self.int_values(tags['GPS GPSAltitudeRef'])[0] > 0: self.altitude *= -1 if 'GPS GPSLatitude' in tags and 'GPS GPSLatitudeRef' in tags: self.latitude = self.dms_to_decimal(tags['GPS GPSLatitude'], tags['GPS GPSLatitudeRef']) if 'GPS GPSLongitude' in tags and 'GPS GPSLongitudeRef' in tags: self.longitude = self.dms_to_decimal(tags['GPS GPSLongitude'], tags['GPS GPSLongitudeRef']) except IndexError as e: log.ODM_WARNING("Cannot read EXIF tags for %s: %s" % (_path_file, e.message)) if self.camera_make and self.camera_model: self.make_model = sensor_string(self.camera_make, self.camera_model) # needed to do that since sometimes metadata contains wrong data try: self.width, self.height = get_image_size.get_image_size(_path_file) except get_image_size.UnknownImageFormat: # Fallback to slower cv2 img = cv2.imread(_path_file) self.width = img.shape[1] self.height = img.shape[0] def dms_to_decimal(self, dms, sign): """Converts dms coords to decimal degrees""" degrees, minutes, seconds = self.float_values(dms) return (-1 if sign.values[0] in 'SWsw' else 1) * ( degrees + minutes / 60 + seconds / 3600 ) def float_values(self, tag): return map(lambda v: float(v.num) / float(v.den), tag.values) def int_values(self, tag): return map(int, tag.values) class ODM_Reconstruction(object): """docstring for ODMReconstruction""" def __init__(self, photos, projstring = None, coords_file = None): self.photos = photos # list of ODM_Photos self.projection = None # Projection system the whole project will be in self.georef = None if projstring: self.projection = self.set_projection(projstring) self.georef = ODM_GeoRef(self.projection) else: self.projection = self.parse_coordinate_system(coords_file) if self.projection: self.georef = ODM_GeoRef(self.projection) def parse_coordinate_system(self, _file): """Write attributes to jobOptions from coord file""" # check for coordinate file existence if not io.file_exists(_file): log.ODM_WARNING('Could not find file %s' % _file) return with open(_file) as f: # extract reference system and utm zone from first line. # We will assume the following format: # 'WGS84 UTM 17N' or 'WGS84 UTM 17N \n' line = f.readline().rstrip() log.ODM_DEBUG('Line: %s' % line) ref = line.split(' ') # match_wgs_utm = re.search('WGS84 UTM (\d{1,2})(N|S)', line, re.I) try: if ref[0] == 'WGS84' and ref[1] == 'UTM': # match_wgs_utm: datum = ref[0] utm_pole = (ref[2][len(ref[2]) - 1]).upper() utm_zone = int(ref[2][:len(ref[2]) - 1]) proj_args = { 'proj': "utm", 'zone': utm_zone, 'datum': datum, 'no_defs': True } if utm_pole == 'S': proj_args['south'] = True return Proj(**proj_args) elif '+proj' in line: return Proj(line.strip('\'')) elif 'epsg' in line.lower(): return Proj(init=line) else: log.ODM_ERROR('Could not parse coordinates. Bad CRS supplied: %s' % line) except RuntimeError as e: log.ODM_ERROR('Uh oh! There seems to be a problem with your GCP file.\n\n' 'The line: %s\n\n' 'Is not valid. Projections that are valid include:\n' ' - EPSG:*****\n' ' - WGS84 UTM **(N|S)\n' ' - Any valid proj4 string (for example, +proj=utm +zone=32 +north +ellps=WGS84 +datum=WGS84 +units=m +no_defs)\n\n' 'Modify your GCP file and try again.' % line) raise RuntimeError(e) def set_projection(self, projstring): try: return Proj(projstring) except RuntimeError: log.ODM_EXCEPTION('Could not set projection. Please use a proj4 string') class ODM_GeoRef(object): """docstring for ODMUtmZone""" def __init__(self, projection): self.projection = projection self.epsg = None self.utm_east_offset = 0 self.utm_north_offset = 0 self.transform = [] self.gcps = [] def calculate_EPSG(self, _utm_zone, _pole): """Calculate and return the EPSG""" if _pole == 'S': return 32700 + _utm_zone elif _pole == 'N': return 32600 + _utm_zone else: log.ODM_ERROR('Unknown pole format %s' % _pole) return def calculate_EPSG(self, proj): return proj def coord_to_fractions(self, coord, refs): deg_dec = abs(float(coord)) deg = int(deg_dec) minute_dec = (deg_dec - deg) * 60 minute = int(minute_dec) sec_dec = (minute_dec - minute) * 60 sec_dec = round(sec_dec, 3) sec_denominator = 1000 sec_numerator = int(sec_dec * sec_denominator) if float(coord) >= 0: latRef = refs[0] else: latRef = refs[1] output = str(deg) + '/1 ' + str(minute) + '/1 ' + str(sec_numerator) + '/' + str(sec_denominator) return output, latRef def extract_offsets(self, _file): if not io.file_exists(_file): log.ODM_ERROR('Could not find file %s' % _file) return with open(_file) as f: offsets = f.readlines()[1].split(' ') self.utm_east_offset = float(offsets[0]) self.utm_north_offset = float(offsets[1]) def parse_transformation_matrix(self, _file): if not io.file_exists(_file): log.ODM_ERROR('Could not find file %s' % _file) return # Create a nested list for the transformation matrix with open(_file) as f: for line in f: # Handle matrix formats that either # have leading or trailing brakets or just plain numbers. line = re.sub(r"[\[\],]", "", line).strip() self.transform += [[float(i) for i in line.split()]] self.utm_east_offset = self.transform[0][3] self.utm_north_offset = self.transform[1][3] class ODM_Tree(object): def __init__(self, root_path, images_path, gcp_file = None): # root path to the project self.root_path = io.absolute_path_file(root_path) if not images_path: self.input_images = io.join_paths(self.root_path, 'images') else: self.input_images = io.absolute_path_file(images_path) # modules paths # here are defined where all modules should be located in # order to keep track all files al directories during the # whole reconstruction process. self.dataset_raw = io.join_paths(self.root_path, 'images') self.opensfm = io.join_paths(self.root_path, 'opensfm') self.mve = io.join_paths(self.root_path, 'mve') self.odm_meshing = io.join_paths(self.root_path, 'odm_meshing') self.odm_texturing = io.join_paths(self.root_path, 'odm_texturing') self.odm_25dtexturing = io.join_paths(self.root_path, 'odm_texturing_25d') self.odm_georeferencing = io.join_paths(self.root_path, 'odm_georeferencing') self.odm_25dgeoreferencing = io.join_paths(self.root_path, 'odm_25dgeoreferencing') self.odm_filterpoints = io.join_paths(self.root_path, 'odm_filterpoints') self.odm_orthophoto = io.join_paths(self.root_path, 'odm_orthophoto') # important files paths # benchmarking self.benchmarking = io.join_paths(self.root_path, 'benchmark.txt') self.dataset_list = io.join_paths(self.root_path, 'img_list.txt') # opensfm self.opensfm_tracks = io.join_paths(self.opensfm, 'tracks.csv') self.opensfm_bundle = io.join_paths(self.opensfm, 'bundle_r000.out') self.opensfm_bundle_list = io.join_paths(self.opensfm, 'list_r000.out') self.opensfm_image_list = io.join_paths(self.opensfm, 'image_list.txt') self.opensfm_reconstruction = io.join_paths(self.opensfm, 'reconstruction.json') self.opensfm_reconstruction_nvm = io.join_paths(self.opensfm, 'reconstruction.nvm') self.opensfm_model = io.join_paths(self.opensfm, 'depthmaps/merged.ply') self.opensfm_transformation = io.join_paths(self.opensfm, 'geocoords_transformation.txt') # mve self.mve_model = io.join_paths(self.mve, 'mve_dense_point_cloud.ply') self.mve_path = io.join_paths(self.opensfm, 'mve') self.mve_image_list = io.join_paths(self.mve_path, 'list.txt') self.mve_bundle = io.join_paths(self.mve_path, 'bundle/bundle.out') self.mve_views = io.join_paths(self.mve, 'views') # filter points self.filtered_point_cloud = io.join_paths(self.odm_filterpoints, "point_cloud.ply") # odm_meshing self.odm_mesh = io.join_paths(self.odm_meshing, 'odm_mesh.ply') self.odm_meshing_log = io.join_paths(self.odm_meshing, 'odm_meshing_log.txt') self.odm_25dmesh = io.join_paths(self.odm_meshing, 'odm_25dmesh.ply') self.odm_25dmeshing_log = io.join_paths(self.odm_meshing, 'odm_25dmeshing_log.txt') # texturing self.odm_texturing_undistorted_image_path = io.join_paths( self.odm_texturing, 'undistorted') self.odm_textured_model_obj = 'odm_textured_model.obj' self.odm_textured_model_mtl = 'odm_textured_model.mtl' # Log is only used by old odm_texturing self.odm_texuring_log = 'odm_texturing_log.txt' # odm_georeferencing self.odm_georeferencing_latlon = io.join_paths( self.odm_georeferencing, 'latlon.txt') self.odm_georeferencing_coords = io.join_paths( self.odm_georeferencing, 'coords.txt') self.odm_georeferencing_gcp = gcp_file or io.find('gcp_list.txt', self.root_path) self.odm_georeferencing_utm_log = io.join_paths( self.odm_georeferencing, 'odm_georeferencing_utm_log.txt') self.odm_georeferencing_log = 'odm_georeferencing_log.txt' self.odm_georeferencing_transform_file = 'odm_georeferencing_transform.txt' self.odm_georeferencing_proj = 'proj.txt' self.odm_georeferencing_model_txt_geo = 'odm_georeferencing_model_geo.txt' self.odm_georeferencing_model_obj_geo = 'odm_textured_model_geo.obj' self.odm_georeferencing_xyz_file = io.join_paths( self.odm_georeferencing, 'odm_georeferenced_model.csv') self.odm_georeferencing_las_json = io.join_paths( self.odm_georeferencing, 'las.json') self.odm_georeferencing_model_laz = io.join_paths( self.odm_georeferencing, 'odm_georeferenced_model.laz') self.odm_georeferencing_model_las = io.join_paths( self.odm_georeferencing, 'odm_georeferenced_model.las') self.odm_georeferencing_dem = io.join_paths( self.odm_georeferencing, 'odm_georeferencing_model_dem.tif') # odm_orthophoto self.odm_orthophoto_file = io.join_paths(self.odm_orthophoto, 'odm_orthophoto.png') self.odm_orthophoto_tif = io.join_paths(self.odm_orthophoto, 'odm_orthophoto.tif') self.odm_orthophoto_corners = io.join_paths(self.odm_orthophoto, 'odm_orthophoto_corners.txt') self.odm_orthophoto_log = io.join_paths(self.odm_orthophoto, 'odm_orthophoto_log.txt') self.odm_orthophoto_tif_log = io.join_paths(self.odm_orthophoto, 'gdal_translate_log.txt') self.odm_orthophoto_gdaladdo_log = io.join_paths(self.odm_orthophoto, 'gdaladdo_log.txt') def path(self, *args): return io.join_paths(self.root_path, *args)