OpenDroneMap-ODM/opendm/types.py

import cv2
import exifread
import re
import os
from fractions import Fraction
from opendm import get_image_size
from opendm import location
from opendm.gcp import GCPFile
from pyproj import CRS
import xmltodict as x2d

import log
import io
import system
import context
import logging
from opendm.progress import progressbc

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.latitude = None
        self.longitude = None
        self.altitude = None
        self.band_name = 'RGB'
        self.band_index = 0

        # 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: {} | band: {} ({})'.format(
                            self.filename, self.camera_make, self.camera_model, self.width, self.height, 
                            self.latitude, self.longitude, self.altitude, self.band_name, self.band_index)

    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))

            # Extract XMP tags
            f.seek(0)
            xmp = self.get_xmp(f)

            # Find band name and camera index (if available)
            camera_index_tags = [
                    'DLS:SensorId', # Micasense
                    '@Camera:RigCameraIndex' # Parrot Sequoia
            ]

            for tags in xmp:
                if 'Camera:BandName' in tags:
                    self.band_name = str(tags['Camera:BandName']).replace(" ", "")
                else:
                    for cit in camera_index_tags:
                        if cit in tags:
                            self.band_index = int(tags[cit])
        self.width, self.height = get_image_size.get_image_size(_path_file)
        print(self)
    
    # From https://github.com/mapillary/OpenSfM/blob/master/opensfm/exif.py
    def get_xmp(self, file):
        img_str = str(file.read())
        xmp_start = img_str.find('<x:xmpmeta')
        xmp_end = img_str.find('</x:xmpmeta')

        if xmp_start < xmp_end:
            xmp_str = img_str[xmp_start:xmp_end + 12]
            xdict = x2d.parse(xmp_str)
            xdict = xdict.get('x:xmpmeta', {})
            xdict = xdict.get('rdf:RDF', {})
            xdict = xdict.get('rdf:Description', {})
            if isinstance(xdict, list):
                return xdict
            else:
                return [xdict]
        else:
            return []

    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):
    def __init__(self, photos):
        self.photos = photos
        self.georef = None
        self.gcp = None
        self.multi_camera = self.detect_multi_camera()

    def detect_multi_camera(self):
        """
        Looks at the reconstruction photos and determines if this
        is a single or multi-camera setup.
        """
        band_photos = {}
        band_indexes = {}

        for p in self.photos:
            if not p.band_name in band_photos:
                band_photos[p.band_name] = []
            if not p.band_name in band_indexes:
                band_indexes[p.band_name] = p.band_index

            band_photos[p.band_name].append(p)
            
        bands_count = len(band_photos)
        if bands_count >= 2 and bands_count <= 8:
            # Validate that all bands have the same number of images,
            # otherwise this is not a multi-camera setup
            img_per_band = len(band_photos[p.band_name])
            for band in band_photos:
                if len(band_photos[band]) != img_per_band:
                    log.ODM_ERROR("Multi-camera setup detected, but band \"%s\" (identified from \"%s\") has only %s images (instead of %s), perhaps images are missing or are corrupted. Please include all necessary files to process all bands and try again." % (band, band_photos[band][0].filename, len(band_photos[band]), img_per_band))
                    raise RuntimeError("Invalid multi-camera images")
            
            mc = []
            for band_name in band_indexes:
                mc.append({'name': band_name, 'photos': band_photos[band_name]})
            
            # Sort by band index
            mc.sort(key=lambda x: band_indexes[x['name']])

            return mc

        return None

    def is_georeferenced(self):
        return self.georef is not None

    def georeference_with_gcp(self, gcp_file, output_coords_file, output_gcp_file, rerun=False):
        if not io.file_exists(output_coords_file) or not io.file_exists(output_gcp_file) or rerun:
            gcp = GCPFile(gcp_file)
            if gcp.exists():
                # Create coords file, we'll be using this later
                # during georeferencing
                with open(output_coords_file, 'w') as f:
                    coords_header = gcp.wgs84_utm_zone()
                    f.write(coords_header + "\n")
                    log.ODM_INFO("Generated coords file from GCP: %s" % coords_header)

                # Convert GCP file to a UTM projection since the rest of the pipeline
                # does not handle other SRS well.
                rejected_entries = []
                utm_gcp = GCPFile(gcp.create_utm_copy(output_gcp_file, filenames=[p.filename for p in self.photos], rejected_entries=rejected_entries, include_extras=False))
                
                if not utm_gcp.exists():
                    raise RuntimeError("Could not project GCP file to UTM. Please double check your GCP file for mistakes.")
                
                for re in rejected_entries:
                    log.ODM_WARNING("GCP line ignored (image not found): %s" % str(re))
                
                if utm_gcp.entries_count() > 0:
                    log.ODM_INFO("%s GCP points will be used for georeferencing" % utm_gcp.entries_count())
                else:
                    raise RuntimeError("A GCP file was provided, but no valid GCP entries could be used. Note that the GCP file is case sensitive (\".JPG\" is not the same as \".jpg\").")

                self.gcp = utm_gcp
            else:
                log.ODM_WARNING("GCP file does not exist: %s" % gcp_file)
                return
        else:
            log.ODM_INFO("Coordinates file already exist: %s" % output_coords_file)
            log.ODM_INFO("GCP file already exist: %s" % output_gcp_file)
            self.gcp = GCPFile(output_gcp_file)
        
        self.georef = ODM_GeoRef.Froband_photosoordsFile(output_coords_file)
        return self.georef

    def georeference_with_gps(self, images_path, output_coords_file, rerun=False):
        try:
            if not io.file_exists(output_coords_file) or rerun:
                location.extract_utm_coords(self.photos, images_path, output_coords_file)
            else:
                log.ODM_INFO("Coordinates file already exist: %s" % output_coords_file)
            
            self.georef = ODM_GeoRef.Froband_photosoordsFile(output_coords_file)
        except:
            log.ODM_WARNING('Could not generate coordinates file. An orthophoto will not be generated.')

        self.gcp = GCPFile(None)
        return self.georef

    def save_proj_srs(self, file):
        # Save proj to file for future use (unless this 
        # dataset is not georeferenced)
        if self.is_georeferenced():
            with open(file, 'w') as f:
                f.write(self.georef.proj4())

class ODM_GeoRef(object):
    @staticmethod
    def FromProj(projstring):
        return ODM_GeoRef(CRS.from_proj4(projstring))

    @staticmethod
    def Froband_photosoordsFile(coords_file):
        # check for coordinate file existence
        if not io.file_exists(coords_file):
            log.ODM_WARNING('Could not find file %s' % coords_file)
            return

        srs = None

        with open(coords_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()
            srs = location.parse_srs_header(line)

        return ODM_GeoRef(srs)

    def __init__(self, srs):
        self.srs = srs
        self.utm_east_offset = 0
        self.utm_north_offset = 0
        self.transform = []

    def proj4(self):
        return self.srs.to_proj4()
    
    def valid_utm_offsets(self):
        return self.utm_east_offset and self.utm_north_offset

    def extract_offsets(self, geo_sys_file):
        if not io.file_exists(geo_sys_file):
            log.ODM_ERROR('Could not find file %s' % geo_sys_file)
            return

        with open(geo_sys_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, matrix_file):
        if not io.file_exists(matrix_file):
            log.ODM_ERROR('Could not find file %s' % matrix_file)
            return

        # Create a nested list for the transformation matrix
        with open(matrix_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, gcp_file = None):
        # root path to the project
        self.root_path = io.absolute_path_file(root_path)
        self.input_images = io.join_paths(self.root_path, 'images')

        # 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_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_gcp_utm = io.join_paths(self.odm_georeferencing, 'gcp_list_utm.txt')
        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_render = io.join_paths(self.odm_orthophoto, 'odm_orthophoto_render.tif')
        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')

        # Split-merge 
        self.submodels_path = io.join_paths(self.root_path, 'submodels')

        # Tiles
        self.entwine_pointcloud = self.path("entwine_pointcloud")

    def path(self, *args):
        return os.path.join(self.root_path, *args)


class ODM_Stage:
    def __init__(self, name, args, progress=0.0, **params):
        self.name = name
        self.args = args
        self.progress = progress
        self.params = params
        if self.params is None:
            self.params = {}
        self.next_stage = None
        self.prev_stage = None

    def connect(self, stage):
        self.next_stage = stage
        stage.prev_stage = self
        return stage

    def rerun(self):
        """
        Does this stage need to be rerun?
        """
        return (self.args.rerun is not None and self.args.rerun == self.name) or \
                     (self.args.rerun_all) or \
                     (self.args.rerun_from is not None and self.name in self.args.rerun_from)
    
    def run(self, outputs = {}):
        start_time = system.now_raw()
        log.ODM_INFO('Running %s stage' % self.name)

        self.process(self.args, outputs)

        # The tree variable should always be populated at this point
        if outputs.get('tree') is None:
            raise Exception("Assert violation: tree variable is missing from outputs dictionary.")

        if self.args.time:
            system.benchmark(start_time, outputs['tree'].benchmarking, self.name)

        log.ODM_INFO('Finished %s stage' % self.name)
        self.update_progress_end()

        # Last stage?
        if self.args.end_with == self.name or self.args.rerun == self.name:
            log.ODM_INFO("No more stages to run")
            return

        # Run next stage?
        elif self.next_stage is not None:
            self.next_stage.run(outputs)

    def delta_progress(self):
        if self.prev_stage:
            return max(0.0, self.progress - self.prev_stage.progress)
        else:
            return max(0.0, self.progress)
    
    def previous_stages_progress(self):
        if self.prev_stage:
            return max(0.0, self.prev_stage.progress)
        else:
            return 0.0

    def update_progress_end(self):
        self.update_progress(100.0)

    def update_progress(self, progress):
        progress = max(0.0, min(100.0, progress))
        progressbc.send_update(self.previous_stages_progress() + 
                              (self.delta_progress() / 100.0) * float(progress))

    def process(self, args, outputs):
        raise NotImplementedError