kopia lustrzana https://github.com/OpenDroneMap/ODM
Refactored band detection to use XMP tags instead of filenames
Former-commit-id: 30f1570e05
pull/1161/head
Piero Toffanin
rodzic
a5522cabef
commit
494a441ef9
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@ -314,7 +314,8 @@ void OdmOrthoPhoto::saveTIFF(const std::string &filename, GDALDataType dataType)
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exit(1);
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}
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char **papszOptions = NULL;
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GDALDatasetH hDstDS = GDALCreate( hDriver, filename.c_str(), width, height, static_cast<int>(bands.size()), dataType, papszOptions );
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GDALDatasetH hDstDS = GDALCreate( hDriver, filename.c_str(), width, height,
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static_cast<int>(bands.size()), dataType, papszOptions );
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GDALRasterBandH hBand;
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for (size_t i = 0; i < bands.size(); i++){
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@ -328,10 +329,6 @@ void OdmOrthoPhoto::saveTIFF(const std::string &filename, GDALDataType dataType)
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std::cerr << "Cannot write TIFF to " << filename << std::endl;
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exit(1);
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}
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// for (int j = 0; j < height; j++){
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// GDALRasterIO( hBand, GF_Write, 0, j, width, 1,
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// bands[i][j], width, 1, dataType, 0, 0 );
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// }
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}
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GDALClose( hDstDS );
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}
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@ -51,7 +51,7 @@ class OSFMContext:
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exit(1)
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def setup(self, args, images_path, photos, gcp_path=None, append_config = [], rerun=False):
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def setup(self, args, images_path, photos, reconstruction, append_config = [], rerun=False):
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"""
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Setup a OpenSfM project
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"""
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@ -91,13 +91,22 @@ class OSFMContext:
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except Exception as e:
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log.ODM_WARNING("Cannot set camera_models_overrides.json: %s" % str(e))
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use_bow = False
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matcher_neighbors = args.matcher_neighbors
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if matcher_neighbors != 0 and reconstruction.multi_camera is not None:
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matcher_neighbors *= len(reconstruction.multi_camera)
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log.ODM_INFO("Increasing matcher neighbors to %s to accomodate multi-camera setup" % matcher_neighbors)
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log.ODM_INFO("Multi-camera setup, using BOW matching")
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use_bow = True
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# create config file for OpenSfM
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config = [
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"use_exif_size: no",
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"feature_process_size: %s" % args.resize_to,
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"feature_min_frames: %s" % args.min_num_features,
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"processes: %s" % args.max_concurrency,
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"matching_gps_neighbors: %s" % args.matcher_neighbors,
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"matching_gps_neighbors: %s" % matcher_neighbors,
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"matching_gps_distance: %s" % args.matcher_distance,
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"depthmap_method: %s" % args.opensfm_depthmap_method,
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"depthmap_resolution: %s" % args.depthmap_resolution,
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@ -114,12 +123,16 @@ class OSFMContext:
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if not has_gps:
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log.ODM_INFO("No GPS information, using BOW matching")
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use_bow = True
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if use_bow:
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config.append("matcher_type: WORDS")
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if has_alt:
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log.ODM_INFO("Altitude data detected, enabling it for GPS alignment")
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config.append("use_altitude_tag: yes")
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gcp_path = reconstruction.gcp.gcp_path
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if has_alt or gcp_path:
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config.append("align_method: auto")
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else:
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@ -3,11 +3,11 @@ import exifread
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import re
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import os
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from fractions import Fraction
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from opensfm.exif import sensor_string
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from opendm import get_image_size
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from opendm import location
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from opendm.gcp import GCPFile
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from pyproj import CRS
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import xmltodict as x2d
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import log
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import io
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@ -28,10 +28,11 @@ class ODM_Photo:
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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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self.latitude = None
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self.longitude = None
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self.altitude = None
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self.band_name = 'RGB'
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# parse values from metadata
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self.parse_exif_values(path_file)
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@ -40,8 +41,9 @@ class ODM_Photo:
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def __str__(self):
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return '{} | camera: {} | dimensions: {} x {} | lat: {} | lon: {} | alt: {}'.format(
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self.filename, self.make_model, self.width, self.height, self.latitude, self.longitude, self.altitude)
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return '{} | camera: {} {} | dimensions: {} x {} | lat: {} | lon: {} | alt: {} | band: {}'.format(
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self.filename, self.camera_make, self.camera_model, self.width, self.height,
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self.latitude, self.longitude, self.altitude, self.band_name)
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def parse_exif_values(self, _path_file):
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# Disable exifread log
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@ -66,10 +68,36 @@ class ODM_Photo:
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except IndexError as e:
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log.ODM_WARNING("Cannot read EXIF tags for %s: %s" % (_path_file, e.message))
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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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# Extract XMP tags
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f.seek(0)
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xmp = self.get_xmp(f)
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# Find band name (if available)
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for tags in xmp:
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if 'Camera:BandName' in tags:
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self.band_name = str(tags['Camera:BandName']).replace(" ", "")
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break
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self.width, self.height = get_image_size.get_image_size(_path_file)
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# From https://github.com/mapillary/OpenSfM/blob/master/opensfm/exif.py
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def get_xmp(self, file):
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img_str = str(file.read())
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xmp_start = img_str.find('<x:xmpmeta')
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xmp_end = img_str.find('</x:xmpmeta')
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if xmp_start < xmp_end:
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xmp_str = img_str[xmp_start:xmp_end + 12]
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xdict = x2d.parse(xmp_str)
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xdict = xdict.get('x:xmpmeta', {})
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xdict = xdict.get('rdf:RDF', {})
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xdict = xdict.get('rdf:Description', {})
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if isinstance(xdict, list):
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return xdict
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else:
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return [xdict]
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else:
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return []
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def dms_to_decimal(self, dms, sign):
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"""Converts dms coords to decimal degrees"""
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@ -100,45 +128,23 @@ class ODM_Reconstruction(object):
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Looks at the reconstruction photos and determines if this
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is a single or multi-camera setup.
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"""
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supported_ext_re = r"\.(" + "|".join([e[1:] for e in context.supported_extensions]) + ")$"
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# Match filename_1.tif, filename_2.tif, filename_3.tif, ...
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#
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multi_camera_patterns = {
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'MicaSense RedEdge-M': r'^IMG_\d+_(?P<band>\d{1})',
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'Parrot Sequoia': r'^IMG_\d+_\d+_\d+_(?P<band>[A-Z]{3})',
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}
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for cam, regex in multi_camera_patterns.items():
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pattern = re.compile(regex + supported_ext_re, re.IGNORECASE)
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mc = {}
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for p in self.photos:
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matches = re.match(pattern, p.filename)
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if matches:
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band = matches.group("band")
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if not band in mc:
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mc[band] = []
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mc[band].append(p)
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mc = {}
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for p in self.photos:
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if not p.band_name in mc:
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mc[p.band_name] = []
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mc[p.band_name].append(p)
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# We support between 2 and 6 bands
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# If we matched more or less bands, we probably just
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# found filename patterns that do not match a multi-camera setup
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bands_count = len(mc)
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if bands_count >= 2 and bands_count <= 6:
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# Validate that all bands have the same number of images,
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# otherwise this is not a multi-camera setup
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img_per_band = len(mc[band])
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valid = True
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for band in mc:
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if len(mc[band]) != img_per_band:
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log.ODM_WARNING("This might be a multi-camera setup, but band \"%s\" (identified from \"%s\") has only %s images (instead of %s), perhaps images are missing or are corrupted." % (band, mc[band][0].filename, len(mc[band]), img_per_band))
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valid = False
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break
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if valid:
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return mc
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bands_count = len(mc)
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if bands_count >= 2 and bands_count <= 8:
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# Validate that all bands have the same number of images,
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# otherwise this is not a multi-camera setup
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img_per_band = len(mc[p.band_name])
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for band in mc:
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if len(mc[band]) != img_per_band:
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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, mc[band][0].filename, len(mc[band]), img_per_band))
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raise RuntimeError("Invalid multi-camera images")
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return mc
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return None
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@ -21,7 +21,7 @@ class ODMOpenSfMStage(types.ODM_Stage):
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exit(1)
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octx = OSFMContext(tree.opensfm)
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octx.setup(args, tree.dataset_raw, photos, gcp_path=reconstruction.gcp.gcp_path, rerun=self.rerun())
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octx.setup(args, tree.dataset_raw, photos, reconstruction=reconstruction, rerun=self.rerun())
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octx.extract_metadata(self.rerun())
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self.update_progress(20)
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octx.feature_matching(self.rerun())
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@ -46,7 +46,7 @@ class ODMSplitStage(types.ODM_Stage):
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"submodel_overlap: %s" % args.split_overlap,
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]
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octx.setup(args, tree.dataset_raw, photos, gcp_path=reconstruction.gcp.gcp_path, append_config=config, rerun=self.rerun())
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octx.setup(args, tree.dataset_raw, photos, reconstruction=reconstruction, append_config=config, rerun=self.rerun())
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octx.extract_metadata(self.rerun())
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self.update_progress(5)
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@ -2,8 +2,15 @@ import unittest
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from opendm import types
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class ODMPhotoMock:
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def __init__(self, filename):
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def __init__(self, filename, band_name):
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self.filename = filename
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self.band_name = band_name
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def __str__(self):
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return "%s (%s)" % (self.filename, self.band_name)
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def __repr__(self):
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return self.__str__()
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class TestTypes(unittest.TestCase):
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def setUp(self):
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@ -11,36 +18,29 @@ class TestTypes(unittest.TestCase):
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def test_reconstruction(self):
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# Multi camera setup
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micasa_redsense_files = ['IMG_0298_1.tif','IMG_0298_2.tif','IMG_0298_3.tif','IMG_0298_4.tif','IMG_0298_5.tif','IMG_0299_1.tif','IMG_0299_2.tif','IMG_0299_3.tif','IMG_0299_4.tif','IMG_0299_5.tif','IMG_0300_1.tif','IMG_0300_2.tif','IMG_0300_3.tif','IMG_0300_4.tif', 'IMG_0300_5.tif']
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photos = [ODMPhotoMock(f) for f in micasa_redsense_files]
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micasa_redsense_files = [('IMG_0298_1.tif', 'Red'), ('IMG_0298_2.tif', 'Green'), ('IMG_0298_3.tif', 'Blue'), ('IMG_0298_4.tif', 'NIR'), ('IMG_0298_5.tif', 'Rededge'),
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('IMG_0299_1.tif', 'Red'), ('IMG_0299_2.tif', 'Green'), ('IMG_0299_3.tif', 'Blue'), ('IMG_0299_4.tif', 'NIR'), ('IMG_0299_5.tif', 'Rededge'),
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('IMG_0300_1.tif', 'Red'), ('IMG_0300_2.tif', 'Green'), ('IMG_0300_3.tif', 'Blue'), ('IMG_0300_4.tif', 'NIR'), ('IMG_0300_5.tif', 'Rededge')]
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photos = [ODMPhotoMock(f, b) for f, b in micasa_redsense_files]
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recon = types.ODM_Reconstruction(photos)
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self.assertTrue(recon.multi_camera is not None)
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# Found all 5 bands
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for b in ["1", "2", "3", "4", "5"]:
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for b in ["Red", "Blue", "Green", "NIR", "Rededge"]:
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self.assertTrue(b in recon.multi_camera)
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self.assertTrue([p.filename for p in recon.multi_camera["1"]] == ['IMG_0298_1.tif', 'IMG_0299_1.tif', 'IMG_0300_1.tif'])
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self.assertTrue([p.filename for p in recon.multi_camera["Red"]] == ['IMG_0298_1.tif', 'IMG_0299_1.tif', 'IMG_0300_1.tif'])
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# Missing a file
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micasa_redsense_files = ['IMG_0298_1.tif','IMG_0298_3.tif','IMG_0298_4.tif','IMG_0298_5.tif','IMG_0299_1.tif','IMG_0299_2.tif','IMG_0299_3.tif','IMG_0299_4.tif','IMG_0299_5.tif','IMG_0300_1.tif','IMG_0300_2.tif','IMG_0300_3.tif','IMG_0300_4.tif', 'IMG_0300_5.tif']
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photos = [ODMPhotoMock(f) for f in micasa_redsense_files]
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recon = types.ODM_Reconstruction(photos)
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micasa_redsense_files = [('IMG_0298_1.tif', 'Red'), ('IMG_0298_2.tif', 'Green'), ('IMG_0298_3.tif', 'Blue'), ('IMG_0298_4.tif', 'NIR'), ('IMG_0298_5.tif', 'Rededge'),
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('IMG_0299_2.tif', 'Green'), ('IMG_0299_3.tif', 'Blue'), ('IMG_0299_4.tif', 'NIR'), ('IMG_0299_5.tif', 'Rededge'),
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('IMG_0300_1.tif', 'Red'), ('IMG_0300_2.tif', 'Green'), ('IMG_0300_3.tif', 'Blue'), ('IMG_0300_4.tif', 'NIR'), ('IMG_0300_5.tif', 'Rededge')]
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photos = [ODMPhotoMock(f, b) for f,b in micasa_redsense_files]
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self.assertRaises(RuntimeError, types.ODM_Reconstruction, photos)
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self.assertTrue(recon.multi_camera is None)
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# Parrot Sequoia pattern
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sequoia_files = ['IMG_180822_140144_0613_GRE.TIF','IMG_180822_140144_0613_NIR.TIF','IMG_180822_140144_0613_RED.TIF','IMG_180822_140144_0613_REG.TIF','IMG_180822_140146_0614_GRE.TIF','IMG_180822_140146_0614_NIR.TIF','IMG_180822_140146_0614_RED.TIF','IMG_180822_140146_0614_REG.TIF']
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photos = [ODMPhotoMock(f) for f in sequoia_files]
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recon = types.ODM_Reconstruction(photos)
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self.assertTrue(recon.multi_camera is not None)
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# Found 4 bands
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self.assertEqual(len(recon.multi_camera), 4)
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# Single camera
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dji_files = ['DJI_0018.JPG','DJI_0019.JPG','DJI_0020.JPG','DJI_0021.JPG','DJI_0022.JPG','DJI_0023.JPG']
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photos = [ODMPhotoMock(f) for f in dji_files]
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photos = [ODMPhotoMock(f, 'RGB') for f in dji_files]
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recon = types.ODM_Reconstruction(photos)
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self.assertTrue(recon.multi_camera is None)
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