|Imagery Examples||Before and after examples|
|Quickstart||Get started with a Python-based quickstart tutorial|
|Task Runtime||Benchmark runtimes for the algorithm|
|Input Options||Required and optional task inputs|
|Outputs||Task outputs and example contents|
|Advanced Options||Additional information for advanced users|
|Known Issues||Issues users should be aware of|
Before: Dubai urban scene. This animation updates every five seconds to show offset of the source image that will be warped. This is especially notable in the building lean and movement of the roundabouts between the images.
After: This animation updates every five seconds and shows less building lean and less movement between the images of the roundabouts.
This script uses the Pairwise Image Registration task to co-register two images. The source image will be registered to the reference image and output to the specified directory.
from os.path import join, split import time from gbdxtools import Interface gbdx = Interface() tasks =  output_location = 'DigitalGlobe/Image2Image' # Catalog IDs pre_cat_id = '10504100003E9200' post_cat_id = '103001001C423600' # Pre-Image Auto ordering task parameters pre_order = gbdx.Task("Auto_Ordering") pre_order.inputs.cat_id = pre_cat_id pre_order.impersonation_allowed = True pre_order.persist = True pre_order.timeout = 36000 tasks += [pre_order] # Pre-Image AOP task parameters pre_aop = gbdx.Task("AOP_Strip_Processor") pre_aop.inputs.data = pre_order.outputs.s3_location.value pre_aop.outputs.data.persist = True pre_aop.outputs.data.persist_location = output_location + '/pre_aop' pre_aop.timeout = 36000 pre_aop.parts = 2 tasks += [pre_aop] # Post-Image Auto ordering task parameters post_order = gbdx.Task("Auto_Ordering") post_order.inputs.cat_id = post_cat_id post_order.impersonation_allowed = True post_order.persist = True post_order.timeout = 36000 tasks += [post_order] # Post-Image AOP task parameters post_aop = gbdx.Task("AOP_Strip_Processor") post_aop.inputs.data = post_order.outputs.s3_location.value post_aop.outputs.data.persist = True post_aop.outputs.data.persist_location = output_location + '/post_aop' post_aop.timeout = 36000 post_aop.parts = 2 tasks += [post_aop] # Get Image acquisition ID's for subdirectory in AOP pre_acq_id = split(gbdx.catalog.get_data_location(pre_cat_id))[-1][:15] post_acq_id = split(gbdx.catalog.get_data_location(post_cat_id))[-1][:15] # Set up workflow save data aop_workflow = gbdx.Workflow(tasks) aop_workflow.execute() # monitor status and set up another workflow for the image registration task done = False iteration = False while not done: try: print aop_workflow.id print aop_workflow.status if aop_workflow.complete: done = True # Image 2 Image Task tasks_1 =  # Add Pairwise Image Registration task parameters im2im_task = gbdx.Task('image2image') im2im_task.inputs.source_directory = join('s3://gbd-customer-data', gbdx.s3.info['prefix'], pre_aop.outputs.data.persist_location, pre_acq_id) im2im_task.inputs.reference_directory = join('s3://gbd-customer-data', gbdx.s3.info['prefix'], post_aop.outputs.data.persist_location, post_acq_id) im2im_task.timeout = 172799 tasks_1 += [im2im_task] i2i_workflow = gbdx.Workflow(tasks_1) i2i_workflow.savedata(im2im_task.outputs.out, location=output_location + '/im2im') i2i_workflow.execute() print(i2i_workflow.id) except Exception as e: print "Exception" if iteration != False: iteration -= 1 if iteration == 0: done = True else: iteration = 20 print(e) if not done: time.sleep(30)
There is no benchmark runtime data for this task. Standard benchmarks are sensor-specific, and this task can take images from multiple sensors.
|source_directory||YES||directory||S3 location of the Image that is layer to be warped|
|source_filename||NO||geotiff||source file must be set in the task command line if both files are located in the same directory|
|reference_directory||YES||directory||S3 location of the Image that is the base layer|
|reference_filename||NO||geotiff||reference file must be set in the task command line if both files are located in the same directory|
|boundary_directory||NO||directory||S3 location of the all the input data; only required if there is a boundary shapefile|
|boundary_filename||NO||shapefile||file that limits the areal extent of the image warping (optional)|
- Images should both be north up
- Images with different number of bands will use blue band. The following formats are assumed, but the program should work regardless.
- BGRN (Includes GeoEye-1, IKONOS and QuickBird)
- WV2 8 band
- WV3 16 band
- Images with different resolutions
- Code uses coarsest resolution for tiepoints
- Higher resolution image resampled using bilinear interpolation (just to find tiepoints)
- Input datatype can be byte, int, or float
- Working arrays are scaled to unsigned 8 bit
- Supports TIFFs (and vrts of TIFFs)
- Images must be same projection
- Images must fit in memory
- There is a 200 pixel search radius in the coarser of the two resolutions.
- Supports up to a factor of 25 resolution difference
The Pairwaise Image Registration task outputs the warped source image that is registered to the reference image.
The warped source will be placed in the output s3 bucket. This tiff image will have the same metadata as the source. It will be output with the suffix “_warped” appended to the original filename.
This Advanced Option allow the user to:
- input the source image and the reference image from the same directory
- use a boundary polygon (shapefile format) that selects the region from which tiepoints are selected; and thereby defines the extent of the image that is warped.
from gbdxtools import Interface from os.path import join import uuid gbdx = Interface() # set my s3 bucket location: my_bucket = 's3://gbd-customer-data/acct#' # create task object im2im_task = gbdx.Task('image2image') # set the values of source_directory, reference_directory im2im_task.inputs.source_directory = join(my_bucket,'short path to source image directory') im2im_task.inputs.reference_directory = join(my_bucket,'short path to reference image directory') # set the image filenames in case there are multiple image files in a directory # note that the filenames do not include a filepath im2im_task.inputs.reference_filename = 'the reference image filename with extension' im2im_task.inputs.source_filename = 'the source image filename with extension' # assuming we are using a boundary polygon, we similarly set the boundary directory and the boundary filename im2im_task.inputs.boundary_directory = join(my_bucket,'short path to boundary polygon shapefile directory') im2im_task.inputs.boundary_filename = 'the boundary polygon shapefile filename with .shp extension' # put the task in a workflow workflow = gbdx.Workflow([im2im_task]) # save the data to an output location of your choice workflow.savedata(im2im_task.outputs.out, location='path to customer S3 output directory') # Execute the Workflow workflow.execute() print workflow.id print workflow.status
Pairwise Image Registration has been verfied for source image strips and mosaics up to 12 GB in size registered to a reference image of similar size. You may encounter a limit on the size of the image that can be processed for Mosaics larger than 12 GB, because the AWS system may time out.
If you have any questions or issues with this task, please contact email@example.com .