We are using an updated automatic batch processing script to process large amounts of data. We have success with target detection but the errors on the targets are huge. We are also experiencing the well documented 'doming' effect.
Moving from version 1.1 to 1.4, I want to make sure we are 'optimizing' the photo alignment and cameras properly. After photo alignment and target detection, we are using 'optimizeCameras'
Match photos (perform image matching for the chunk) and align camera (perform photo alignment for chunk)
chunk.matchPhotos(accuracy=PhotoScan.HighAccuracy, generic_preselection=True)
chunk.alignCameras()
doc.save()
### Detect markers
chunk.detectMarkers(type=PhotoScan.CircularTarget12bit, tolerance=80)
chunk.loadReference(coord_path, format=PhotoScan.ReferenceFormatCSV, skip_rows=1, columns="nxyz",
delimiter=",") # you can alter the columns order and delimiter according to the input csv format
chunk.updateTransform()
chunk.marker_location_accuracy = PhotoScan.Vector(
[0.001, 0.001, 0.001]) # you can input different accuracy along every axis
print('Fini les cilbes debut seuil')
doc.save()
print('debut optimisation')
### Perform optimization of point cloud/camera parameters
chunk.optimizeCameras()
Help is greatly appreciated!