def runInpainting(self, image, mask, folder):
basename = os.path.splitext(image['name'])[0]
outPath = VolumePath(basename + '_result.jpg')
artifactPath = VolumePath('job_artifacts')
job = docker_run.delay(
'zachmullen/inpainting:latest',
container_args=[
GirderFileIdToVolume(image['_id']),
GirderFileIdToVolume(mask['_id']), outPath, '--artifacts-dir',
artifactPath, '--progress-pipe',
ProgressPipe()
],
girder_job_title='Inpainting: %s' % image['name'],
girder_result_hooks=[
GirderUploadVolumePathToFolder(outPath,
folder['_id'],
upload_kwargs={
'reference':
json.dumps({
'inpaintedImage':
True,
'folderId':
str(folder['_id']),
})
}),
# GirderUploadVolumePathJobArtifact(artifactPath)
]).job
folder['inpaintingJobId'] = job['_id']
Folder().save(folder)
job['inpaintingImageId'] = image['_id']
job['inpaintingMaskId'] = mask['_id']
job['inpaintingFolderId'] = folder['_id']
return Job().save(job)
def prepare_task(params, user, token, index_params, opt_params, has_simple_return_file, reference):
ca = []
result_hooks = []
primary_input_name = None
# Get primary name and reference
for param in index_params:
if param.channel != 'output':
arg, name = _add_indexed_input_param(param, params, user, token)
if (name and not primary_input_name and
SLICER_TYPE_TO_GIRDER_MODEL_MAP[param.typ] != 'folder'):
primary_input_name = name
reference['userId'] = str(user['_id'])
value = _parseParamValue(param, params[param.identifier()], user, token)
itemId = value['_id']
if SLICER_TYPE_TO_GIRDER_MODEL_MAP[param.typ] == 'file':
reference['fileId'] = str(value['_id'])
itemId = value['itemId']
reference['itemId'] = str(itemId)
# optional params
for param in opt_params:
if param.channel == 'output':
ca.extend(_add_optional_output_param(param, params, user, result_hooks, reference))
else:
ca.extend(_add_optional_input_param(param, params, user, token))
if has_simple_return_file:
param_id = return_parameter_file_name + FOLDER_SUFFIX
param_name_id = return_parameter_file_name
if param_id in params and param_name_id in params:
value = params[return_parameter_file_name]
folder = params[return_parameter_file_name + FOLDER_SUFFIX]
folderModel = ModelImporter.model('folder')
instance = folderModel.load(folder, level=AccessType.WRITE, user=user)
if not instance:
raise RestException('Invalid Folder id (%s).' % (str(folder)))
ca.append('--returnparameterfile')
# Output Binding
path = VolumePath(value)
ca.append(path)
ref = reference.copy()
ref['identifier'] = 'returnparameterfile'
result_hooks.append(GirderUploadVolumePathToFolder(
path, folder, upload_kwargs={'reference': json.dumps(ref)}))
# indexed params
for param in index_params:
if param.channel == 'output':
ca.append(_add_indexed_output_param(param, params, user, result_hooks, reference))
else:
arg, name = _add_indexed_input_param(param, params, user, token)
ca.append(arg)
if name and not primary_input_name:
primary_input_name = name
return ca, result_hooks, primary_input_name
def _add_optional_output_param(param, args, user, result_hooks):
if not param.isExternalType() or not is_on_girder(param) \
or param.identifier() not in args or \
(param.identifier() + FOLDER_SUFFIX) not in args:
return []
value = args[param.identifier()]
folder = args[param.identifier() + FOLDER_SUFFIX]
container_args = []
if param.longflag:
container_args.append(param.longflag)
elif param.flag:
container_args.append(param.flag)
else:
return []
folderModel = ModelImporter.model('folder')
instance = folderModel.load(folder, level=AccessType.WRITE, user=user)
if not instance:
raise RestException('Invalid Folder id (%s).' % (str(folder)))
# Output Binding !!
path = VolumePath(value)
container_args.append(path)
result_hooks.append(GirderUploadVolumePathToFolder(path, folder))
return container_args
def actual_file():
vp = os.path.dirname(__file__)
path = os.path.join(vp, 'foo.txt')
open(path, 'a').close()
volume = BindMountVolume(vp, vp)
yield path, VolumePath('foo.txt', volume=volume)
os.unlink(path)
def run_dream3d(self, params):
"""Run Dream3D on a folder that is on girder.
Will store the output in the specified output folder.
"""
inputFolderId = params.get('inputFolderId')
outputFolderId = params.get('outputFolderId')
folder_name = 'workingDir'
volume = GirderFolderIdToVolume(inputFolderId,
volume=TemporaryVolume.default,
folder_name=folder_name)
outputDir = inputFolderId + '/' + folder_name + '/output'
volumepath = VolumePath(outputDir, volume=TemporaryVolume.default)
result = docker_run.delay(
DREAM3D_IMAGE,
pull_image=False,
container_args=[
'-c', 'bash /root/runPipelineRunner $(ls *.json | head -1)'
],
remove_container=True,
working_dir=volume,
entrypoint='bash',
girder_result_hooks=[
GirderUploadVolumePathToFolder(volumepath, outputFolderId)
])
# Set the multiscale meta data and return the job
jobId = result.job['_id']
return utils.setMultiscaleMetaData(jobId, inputFolderId,
outputFolderId)
def run_albany(self, params):
"""Run albany on a folder that is on girder.
Will store the output in the specified output folder.
"""
inputFolderId = params.get('inputFolderId')
outputFolderId = params.get('outputFolderId')
filename = 'input.yaml'
folder_name = 'workingDir'
volume = GirderFolderIdToVolume(inputFolderId,
volume=TemporaryVolume.default,
folder_name=folder_name)
outputDir = inputFolderId + '/' + folder_name + '/output.exo'
volumepath = VolumePath(outputDir, volume=TemporaryVolume.default)
result = docker_run.delay(ALBANY_IMAGE,
pull_image=False,
container_args=[filename],
entrypoint='/usr/local/albany/bin/AlbanyT',
remove_container=True,
working_dir=volume,
girder_result_hooks=[
GirderUploadVolumePathToFolder(
volumepath, outputFolderId)
])
# Set the multiscale meta data and return the job
jobId = result.job['_id']
return utils.setMultiscaleMetaData(jobId, inputFolderId,
outputFolderId)
def test_GirderUploadVolumePathToItem_transform_accepts_ObjectId(
mock_gc, bogus_volume):
vp = VolumePath('test', bogus_volume)
hash = '5a5fc09ec2231b9487ce42db'
GirderUploadVolumePathToItem(vp, ObjectId(hash), gc=mock_gc).transform()
mock_gc.uploadFileToItem.assert_called_once_with(
hash, os.path.join(BOGUS_CONTAINER_PATH, 'test'))
def _runUtm(folder, paramsFile, outputFolder):
outpath = VolumePath('__results__')
return docker_run.delay('samuelgerber/utm', container_args=[
GirderFolderIdToVolume(folder['_id']),
GirderFileIdToVolume(paramsFile['_id']),
'--workingfolder', outpath
], girder_job_title='UTM: ' + folder['name'], girder_result_hooks=[
GirderUploadVolumePathToFolder(outpath, outputFolder['_id'])
]).job
def test_docker_run_mount_idiomatic_volume(self, params):
fixture_dir = params.get('fixtureDir')
filename = 'read.txt'
mount_dir = '/mnt/test'
mount_path = os.path.join(mount_dir, filename)
volume = BindMountVolume(fixture_dir, mount_path, 'ro')
volumepath = VolumePath(filename, volume)
result = docker_run.delay(
TEST_IMAGE, pull_image=True, container_args=['read', '-p', volumepath],
remove_container=True, volumes=[volume])
return result.job
def test_docker_run_file_upload_to_item(self, params):
item_id = params.get('itemId')
contents = params.get('contents')
volumepath = VolumePath('test_file')
result = docker_run.delay(
TEST_IMAGE, pull_image=True,
container_args=['write', '-p', volumepath, '-m', contents],
remove_container=True,
girder_result_hooks=[GirderUploadVolumePathToItem(volumepath, item_id)])
return result.job
def _add_indexed_output_param(param, args, user, result_hooks):
value = args[param.identifier()]
folder = args[param.identifier() + FOLDER_SUFFIX]
folderModel = ModelImporter.model('folder')
instance = folderModel.load(folder, level=AccessType.WRITE, user=user)
if not instance:
raise RestException('Invalid Folder id (%s).' % (str(folder)))
# Output Binding !!
path = VolumePath(value)
result_hooks.append(GirderUploadVolumePathToFolder(path, folder))
return path
def prepare_task(params, user, token, index_params, opt_params,
has_simple_return_file):
ca = []
result_hooks = []
primary_input_name = None
# optional params
for param in opt_params:
if param.channel == 'output':
ca.extend(
_add_optional_output_param(param, params, user, result_hooks))
else:
ca.extend(_add_optional_input_param(param, params, user, token))
if has_simple_return_file:
param_id = return_parameter_file_name + FOLDER_SUFFIX
param_name_id = return_parameter_file_name
if param_id in params and param_name_id in params:
value = params[return_parameter_file_name]
folder = params[return_parameter_file_name + FOLDER_SUFFIX]
folderModel = ModelImporter.model('folder')
instance = folderModel.load(folder,
level=AccessType.WRITE,
user=user)
if not instance:
raise RestException('Invalid Folder id (%s).' % (str(folder)))
ca.append('--returnparameterfile')
# Output Binding !!
path = VolumePath(value)
ca.append(path)
result_hooks.append(GirderUploadVolumePathToFolder(path, folder))
# indexed params
for param in index_params:
if param.channel == 'output':
ca.append(
_add_indexed_output_param(param, params, user, result_hooks))
else:
arg, name = _add_indexed_input_param(param, params, user, token)
ca.append(arg)
if name and not primary_input_name:
primary_input_name = name
return ca, result_hooks, primary_input_name
def createOrthorectifyTask(imageFile, rpcFile):
# Set output file name based on input file name
orthoName = os.path.splitext(imageFile['name'])[0] + '_ortho.tif'
outputVolumePath = VolumePath(orthoName)
# Docker container arguments
containerArgs = [
'danesfield/tools/orthorectify.py',
# Source image
GirderFileIdToVolume(imageFile['_id'], gc=gc),
# DSM
GirderFileIdToVolume(dsmFile['_id'], gc=gc),
# Destination image
outputVolumePath,
'--dtm',
GirderFileIdToVolume(dtmFile['_id'], gc=gc),
'--raytheon-rpc',
GirderFileIdToVolume(rpcFile['_id'], gc=gc),
]
if occlusionThreshold is not None:
containerArgs.extend(
['--occlusion-thresh',
str(occlusionThreshold)])
if denoiseRadius is not None:
containerArgs.extend(['--denoise-radius', str(denoiseRadius)])
# Result hooks
# - Upload output files to output folder
# - Provide upload metadata
upload_kwargs = createUploadMetadata(jobId, stepName)
resultHooks = [
GirderUploadVolumePathToFolder(outputVolumePath,
outputFolder['_id'],
upload_kwargs=upload_kwargs,
gc=gc)
]
return docker_run.s(
**createDockerRunArguments(image=DockerImage.DANESFIELD,
containerArgs=containerArgs,
jobTitle=('[%s] Orthorectify: %s' %
(initWorkingSetName,
imageFile['name'])),
jobType=stepName,
user=requestInfo.user,
resultHooks=resultHooks))
def createConvertMsiToRgbTask(prefix, imageFile):
# Set output file name based on prefix
outputName = prefix + '_rgb_byte_image.tif'
outputVolumePath = VolumePath(outputName)
# Docker container arguments
containerArgs = [
'danesfield/tools/msi_to_rgb.py',
# Pansharpened MSI image
GirderFileIdToVolume(imageFile['_id'], gc=gc),
# Output image
outputVolumePath
]
# Enable byte option by default
if byte or byte is None:
containerArgs.append('--byte')
if alpha:
containerArgs.append('--alpha')
if rangePercentile is not None:
containerArgs.extend(['--range-percentile', str(rangePercentile)])
# TODO: Handle --big option (i.e. BIGTIFF)
# Result hooks
# - Upload output files to output folder
# - Provide upload metadata
upload_kwargs = createUploadMetadata(jobId, stepName)
resultHooks = [
GirderUploadVolumePathToFolder(
outputVolumePath,
outputFolder['_id'],
upload_kwargs=upload_kwargs,
gc=gc)
]
return docker_run.s(
**createDockerRunArguments(
image=DockerImage.DANESFIELD,
containerArgs=containerArgs,
jobTitle=('[%s] Convert MSI to RGB: %s' %
(initWorkingSetName, prefix)),
jobType=stepName,
user=requestInfo.user,
resultHooks=resultHooks
)
)
def createCropAndPansharpenTask(prefix,
msiImageFile,
panImageFile,
msiRpcFile=None,
panRpcFile=None):
# Set output directory
outputVolumePath = VolumePath('__output__')
containerArgs = [
'danesfield/tools/crop_and_pansharpen.py',
GirderFileIdToVolume(dsmFile['_id'], gc=gc), outputVolumePath,
'--pan',
GirderFileIdToVolume(panImageFile['_id'], gc=gc)
]
if panRpcFile is not None:
containerArgs.append(GirderFileIdToVolume(panRpcFile['_id'],
gc=gc))
containerArgs.extend(
['--msi',
GirderFileIdToVolume(msiImageFile['_id'], gc=gc)])
if msiRpcFile is not None:
containerArgs.append(GirderFileIdToVolume(msiRpcFile['_id'],
gc=gc))
# Result hooks
# - Upload output files to output folder
# - Provide upload metadata
upload_kwargs = createUploadMetadata(jobId, stepName)
resultHooks = [
GirderUploadVolumePathToFolder(outputVolumePath,
outputFolder['_id'],
upload_kwargs=upload_kwargs,
gc=gc)
]
return docker_run.s(**createDockerRunArguments(
image=DockerImage.DANESFIELD,
containerArgs=containerArgs,
jobTitle='[%s] Crop and pansharpen: %s' %
(initWorkingSetName, prefix),
jobType=stepName,
user=requestInfo.user,
resultHooks=resultHooks))
def createPansharpenTask(prefix, panImageFile, msiImageFile):
# Set output file name based on prefix
outputName = prefix + '_ortho_pansharpened.tif'
outputVolumePath = VolumePath(outputName)
# Docker container arguments
containerArgs = [
'gdal_pansharpen.py',
# PAN image
GirderFileIdToVolume(panImageFile['_id'], gc=gc),
# MSI image
GirderFileIdToVolume(msiImageFile['_id'], gc=gc),
# Output image
outputVolumePath
]
# Result hooks
# - Upload output files to output folder
# - Provide upload metadata
upload_kwargs = createUploadMetadata(jobId, stepName)
resultHooks = [
GirderUploadVolumePathToFolder(
outputVolumePath,
outputFolder['_id'],
upload_kwargs=upload_kwargs,
gc=gc)
]
return docker_run.s(
**createDockerRunArguments(
image=DockerImage.DANESFIELD,
containerArgs=containerArgs,
jobTitle='[%s] Pansharpen: %s' % (initWorkingSetName, prefix),
jobType=stepName,
user=requestInfo.user,
resultHooks=resultHooks
)
)
def _createThumbnail(item, preset):
# Remove previously attached thumbnails
_removeThumbnails(item, saveItem=True)
outdir = VolumePath('__thumbnails_output__')
return docker_run.delay(
'zachmullen/3d_thumbnails:latest',
container_args=[
'--angle-step',
str(_ANGLE_STEP), '--width',
str(_SIZE), '--height',
str(_SIZE), '--preset', preset,
GirderItemIdToVolume(item['_id'], item_name=item['name']), outdir
],
girder_job_title='Interactive thumbnail generation: %s' % item['name'],
girder_result_hooks=[
GirderUploadVolumePathToItem(
outdir,
item['_id'],
upload_kwargs={
'reference': json.dumps({'interactive_thumbnail': True})
})
]).job
def _createThumbnail(item):
# Remove previously attached thumbnails
_removeThumbnails(item, saveItem=True)
outdir = VolumePath('__thumbnails_output__')
return docker_run.delay(
'girder/dicom_thumbnailer:latest',
container_args=[
'--slices',
str(THUMB_SLICES), '--width',
str(THUMB_WIDTH), '--height',
str(THUMB_HEIGHT),
GirderItemIdToVolume(item['_id'], item_name=item['name']), outdir
],
girder_job_title='DICOM thumbnail generation: %s' % item['name'],
girder_result_hooks=[
GirderUploadVolumePathToItem(
outdir,
item['_id'],
upload_kwargs={
'reference': json.dumps({'interactive_thumbnail': True})
})
]).job
def run_smtk_mesh_placement(self, params):
"""Run an smtk mesh placement on a folder that is on girder.
Will store the output in the specified output folder.
"""
inputFolderId = params.get('inputFolderId')
outputFolderId = params.get('outputFolderId')
folder_name = 'workingDir'
volume = GirderFolderIdToVolume(inputFolderId,
volume=TemporaryVolume.default,
folder_name=folder_name)
outputDir = inputFolderId + '/' + folder_name + '/output/'
volumepath = VolumePath(outputDir, volume=TemporaryVolume.default)
result = docker_run.delay(
SMTK_IMAGE,
pull_image=False,
container_args=[
'-c',
('. ~/setupEnvironment; '
'python /usr/local/afrl-automation/runner.py input.json; '
'mkdir output; '
'mv input.yaml output/; '
'mv elastic.yaml output/;'
'mv *BC.exo output/')
],
entrypoint='bash',
remove_container=True,
working_dir=volume,
girder_result_hooks=[
GirderUploadVolumePathToFolder(volumepath, outputFolderId)
])
# Set the multiscale meta data and return the job
jobId = result.job['_id']
return utils.setMultiscaleMetaData(jobId, inputFolderId,
outputFolderId)
def test_GirderUploadJobArtifact_file_not_found(mock_gc, bogus_volume):
vp = VolumePath('test', bogus_volume)
GirderUploadVolumePathJobArtifact(vp, job_id='123', gc=mock_gc).transform()
mock_gc.post.assert_not_called()
def runMetrics(initWorkingSetName, stepName, requestInfo, jobId, outputFolder,
referenceFolder, referencePrefix, dtmFile, dsmFile, clsFile,
mtlFile):
"""
Run a Girder Worker job to compute metrics on output files.
Requirements:
- Danesfield Docker image is available on host
:param initWorkingSetName: The name of the top-level working set.
:type initWorkingSetName: str
:param stepName: The name of the step.
:type stepName: str (DanesfieldStep)
:param requestInfo: HTTP request and authorization info.
:type requestInfo: RequestInfo
:param jobId: Job ID.
:type jobId: str
:param outputFolder: Output folder document.
:type outputFolder: dict
:param referenceFolder: Reference directory.
:type referenceFolder: dict
:param referencePrefix: Reference file prefix.
:type referencePrefix: str
:param dtmFile: DTM file document.
:type dtmFile: dict
:param dsmFile: DSM file document.
:type dsmFile: dict
:param clsFile: CLS file document.
:type clsFile: dict
:param mtlFile: MTL file document.
:type mtlFile: dict
:returns: Job document.
"""
gc = createGirderClient(requestInfo)
if referencePrefix == "STANDARD":
# We know that there's no reference data with this selection
containerArgs = ['echo', 'No ground truth selected for scoring']
asyncResult = docker_run.delay(
**createDockerRunArguments(image=DockerImage.DANESFIELD,
containerArgs=containerArgs,
jobTitle='[%s] Run metrics' %
initWorkingSetName,
jobType=stepName,
user=requestInfo.user))
else:
# Otherwise we assume the reference data exists, and try to
# run the metrics
outputVolumePath = VolumePath('__output__')
# Docker container arguments
containerArgs = [
'danesfield/tools/run_metrics.py', '--output-dir',
outputVolumePath, '--ref-dir',
GirderFolderIdToVolume(referenceFolder['_id'], gc=gc),
'--ref-prefix', referencePrefix, '--dsm',
GirderFileIdToVolume(dsmFile['_id'], gc=gc), '--cls',
GirderFileIdToVolume(clsFile['_id'], gc=gc), '--mtl',
GirderFileIdToVolume(mtlFile['_id'], gc=gc), '--dtm',
GirderFileIdToVolume(dtmFile['_id'], gc=gc)
]
# Result hooks
# - Upload output files to output folder
# - Provide upload metadata
upload_kwargs = createUploadMetadata(jobId, stepName)
resultHooks = [
GirderUploadVolumePathToFolder(outputVolumePath,
outputFolder['_id'],
upload_kwargs=upload_kwargs,
gc=gc)
]
asyncResult = docker_run.delay(
**createDockerRunArguments(image=DockerImage.DANESFIELD,
containerArgs=containerArgs,
jobTitle='[%s] Run metrics' %
initWorkingSetName,
jobType=stepName,
user=requestInfo.user,
resultHooks=resultHooks))
# Add info for job event listeners
job = asyncResult.job
job = addJobInfo(job, jobId=jobId, stepName=stepName)
return job
def generatePointCloud(initWorkingSetName,
stepName,
requestInfo,
jobId,
outputFolder,
imageFiles,
longitude,
latitude,
longitudeWidth,
latitudeWidth):
"""
Run a Girder Worker job to generate a 3D point cloud from 2D images.
Requirements:
- P3D Girder Worker Docker image is available on host
- Host folder /mnt/GTOPO30 contains GTOPO 30 data
:param initWorkingSetName: The name of the top-level working set.
:type initWorkingSetName: str
:param stepName: The name of the step.
:type stepName: str (DanesfieldStep)
:param requestInfo: HTTP request and authorization info.
:type requestInfo: RequestInfo
:param jobId: Job ID.
:type jobId: str
:param outputFolder: Output folder document.
:type outputFolder: dict
:param imageFiles: List of input image files.
:type imageFiles: list[dict]
:param longitude:
:type longitude:
:param latitude:
:type latitude:
:param longitudeWidth:
:type longitudeWidth:
:param latitudeWidth:
:type latitudeWidth:
:returns: Job document.
"""
gc = createGirderClient(requestInfo)
# Docker volumes
volumes = [
BindMountVolume(host_path='/mnt/GTOPO30',
container_path='/P3D/GTOPO30',
mode='ro')
]
outputVolumePath = VolumePath('__output__')
# Docker container arguments
# TODO: Consider a solution where args are written to a file, in
# case of very long command lines
containerArgs = list(itertools.chain(
[
'python', '/P3D/RTN_distro/scripts/generate_point_cloud.pyc',
'--out', outputVolumePath,
'--longitude', str(longitude),
'--latitude', str(latitude),
'--longitudeWidth', str(longitudeWidth),
'--latitudeWidth', str(latitudeWidth),
'--firstProc', '0',
'--threads', '8',
'--images'
],
[GirderFileIdToVolume(imageFile['_id'], gc=gc)
for imageFile in imageFiles],
))
# Result hooks
# - Upload output files to output folder
# - Provide upload metadata
upload_kwargs = createUploadMetadata(jobId, stepName)
resultHooks = [
GirderUploadVolumePathToFolder(
outputVolumePath,
outputFolder['_id'],
upload_kwargs=upload_kwargs,
gc=gc)
]
asyncResult = docker_run.delay(
volumes=volumes,
**createDockerRunArguments(
image=DockerImage.P3D,
containerArgs=containerArgs,
jobTitle='[%s] Generate point cloud' % initWorkingSetName,
jobType=stepName,
user=requestInfo.user,
resultHooks=resultHooks
)
)
# Add info for job event listeners
job = asyncResult.job
job = addJobInfo(job, jobId=jobId, stepName=stepName)
return job
def generateDsm(initWorkingSetName, stepName, requestInfo, jobId, outputFolder,
pointCloudFile, outputPrefix):
"""
Run a Girder Worker job to generate a Digital Surface Model (DSM)
from a point cloud.
Requirements:
- Danesfield Docker image is available on host
:param initWorkingSetName: The name of the top-level working set.
:type initWorkingSetName: str
:param stepName: The name of the step.
:type stepName: str (DanesfieldStep)
:param requestInfo: HTTP request and authorization info.
:type requestInfo: RequestInfo
:param jobId: Job ID.
:type jobId: str
:param outputFolder: Output folder document.
:type outputFolder: dict
:param pointCloudFile: Point cloud file document.
:type pointCloudFile: dict
:param outputPrefix: The prefix of the output file name.
:type outputPrefix: str
:returns: Job document.
"""
gc = createGirderClient(requestInfo)
# Set output file name based on point cloud file
dsmName = outputPrefix + '_P3D_DSM.tif'
outputVolumePath = VolumePath(dsmName)
# Docker container arguments
containerArgs = [
'danesfield/tools/generate_dsm.py', outputVolumePath,
'--source_points',
GirderFileIdToVolume(pointCloudFile['_id'], gc=gc)
]
# Result hooks
# - Upload output files to output folder
# - Provide upload metadata
upload_kwargs = createUploadMetadata(jobId, stepName)
resultHooks = [
GirderUploadVolumePathToFolder(outputVolumePath,
outputFolder['_id'],
upload_kwargs=upload_kwargs,
gc=gc)
]
asyncResult = docker_run.delay(
**createDockerRunArguments(image=DockerImage.DANESFIELD,
containerArgs=containerArgs,
jobTitle=('[%s] Generate DSM: %s' %
(initWorkingSetName,
pointCloudFile['name'])),
jobType=stepName,
user=requestInfo.user,
resultHooks=resultHooks))
# Add info for job event listeners
job = asyncResult.job
job = addJobInfo(job, jobId=jobId, stepName=stepName)
return job
def getRoadVector(initWorkingSetName,
stepName,
requestInfo,
jobId,
outputFolder,
left,
bottom,
right,
top):
"""
Run a Girder Worker job to segment buildings by comparing a DSM to a DTM.
Requirements:
- Danesfield Docker image is available on host
:param initWorkingSetName: The name of the top-level working set.
:type initWorkingSetName: str
:param stepName: The name of the step.
:type stepName: str (DanesfieldStep)
:param requestInfo: HTTP request and authorization info.
:type requestInfo: RequestInfo
:param jobId: Job ID.
:type jobId: str
:param outputFolder: Output folder document.
:type outputFolder: dict
:param left: Longitude of left / westernmost side of bounding box
:type left: float
:param bottom: Latitude of bottom / southernmost side of bounding box
:type bottom: float
:param right: Longitude of right / easternmost side of bounding box
:type right: float
:param top: Latitude of top / northernmost side of bounding box
:type top: float
:returns: Job document.
"""
gc = createGirderClient(requestInfo)
# Set output file names
outputVolumePath = VolumePath('__output__')
# Docker container arguments
containerArgs = [
'danesfield/tools/get_road_vector.py',
'--left', str(left),
'--bottom', str(bottom),
'--right', str(right),
'--top', str(top),
'--output-dir', outputVolumePath,
]
# Result hooks
# - Upload output files to output folder
# - Provide upload metadata
upload_kwargs = createUploadMetadata(jobId, stepName)
resultHooks = [
GirderUploadVolumePathToFolder(
outputVolumePath,
outputFolder['_id'],
upload_kwargs=upload_kwargs,
gc=gc)
]
asyncResult = docker_run.delay(
**createDockerRunArguments(
image=DockerImage.DANESFIELD,
containerArgs=containerArgs,
jobTitle='[%s] Get OSM road vector data' % initWorkingSetName,
jobType=stepName,
user=requestInfo.user,
resultHooks=resultHooks
)
)
# Add info for job event listeners
job = asyncResult.job
job = addJobInfo(job, jobId=jobId, stepName=stepName)
return job
def computeNdvi(initWorkingSetName, stepName, requestInfo, jobId, outputFolder,
imageFiles, outputNdviFilename):
"""
Run a Girder Worker job to compute the NDVI from a set of images
Requirements:
- Danesfield Docker image is available on host
:param initWorkingSetName: The name of the top-level working set.
:type initWorkingSetName: str
:param stepName: The name of the step.
:type stepName: str (DanesfieldStep)
:param requestInfo: HTTP request and authorization info.
:type requestInfo: RequestInfo
:param jobId: Job ID.
:type jobId: str
:param outputFolder: Output folder document.
:type outputFolder: dict
:param imageFiles: List of pansharpened image files.
:type imageFiles: list[dict]
:param outputNdviFilename: Filename for output NDVI
:type outputNdviFilename: str
:returns: Job document.
"""
gc = createGirderClient(requestInfo)
# Set output file names
ndviOutputVolumePath = VolumePath(outputNdviFilename)
# Docker container arguments
containerArgs = list(
itertools.chain([
'danesfield/tools/compute_ndvi.py',
], [
GirderFileIdToVolume(imageFile['_id'], gc=gc)
for imageFile in imageFiles
], [ndviOutputVolumePath]))
# Result hooks
# - Upload output files to output folder
# - Provide upload metadata
upload_kwargs = createUploadMetadata(jobId, stepName)
resultHooks = [
GirderUploadVolumePathToFolder(ndviOutputVolumePath,
outputFolder['_id'],
upload_kwargs=upload_kwargs,
gc=gc)
]
asyncResult = docker_run.delay(
**createDockerRunArguments(image=DockerImage.DANESFIELD,
containerArgs=containerArgs,
jobTitle='[%s] Compute NDVI' %
initWorkingSetName,
jobType=stepName,
user=requestInfo.user,
resultHooks=resultHooks))
# Add info for job event listeners
job = asyncResult.job
job = addJobInfo(job, jobId=jobId, stepName=stepName)
return job
def test_GirderUploadVolumePathToItem_transform_calls_gc_uploadFile(
mock_gc, bogus_volume):
vp = VolumePath('test', bogus_volume)
GirderUploadVolumePathToItem(vp, 'BOGUS_ITEM_ID', gc=mock_gc).transform()
mock_gc.uploadFileToItem.assert_called_once_with(
'BOGUS_ITEM_ID', os.path.join(BOGUS_CONTAINER_PATH, 'test'))
def test_girderUploadVolumePathToItem_transform_returns_item_id(
mock_gc, bogus_volume):
vp = VolumePath('test', bogus_volume)
guvpti = GirderUploadVolumePathToItem(vp, 'BOGUS_ITEM_ID', gc=mock_gc)
assert guvpti.transform() == 'BOGUS_ITEM_ID'
def test_VolumePath_transform_returns_host_path_with_args(bogus_volume):
vp = VolumePath('test', bogus_volume)
assert vp.transform('TASK_DATA') == os.path.join(BOGUS_HOST_PATH, 'test')
def test_VolumePath_transform_returns_container_path_with_no_args(
bogus_volume):
vp = VolumePath('test', bogus_volume)
assert vp.transform() == os.path.join(BOGUS_CONTAINER_PATH, 'test')
def test_GirderUploadVolumePathToItem_transform_calls_gc_uploadFile(
mock_gc, bogus_volume):
vp = VolumePath('test', bogus_volume)
GirderUploadVolumePathToItem(vp, 'BOGUS_ITEM_ID', gc=mock_gc).transform()
mock_gc.uploadFileToItem.assert_called_once_with(
'BOGUS_ITEM_ID', os.path.join(BOGUS_CONTAINER_PATH, 'test'))
def test_GirderUploadVolumePathToItem_transform_accepts_ObjectId(
mock_gc, bogus_volume):
vp = VolumePath('test', bogus_volume)
hash = '5a5fc09ec2231b9487ce42db'
GirderUploadVolumePathToItem(vp, ObjectId(hash), gc=mock_gc).transform()
mock_gc.uploadFileToItem.assert_called_once_with(
hash, os.path.join(BOGUS_CONTAINER_PATH, 'test'))
@pytest.mark.parametrize('obj,expected_repr', ((VolumePath(
'test', bogus_volume
), '<girder_worker.docker.transforms.VolumePath: "test">'), (
GirderFileIdToVolume('123', gc=mock_gc()),
'<girder_worker.docker.transforms.girder.GirderFileIdToVolume: File ID=123>'
), (GirderFileIdToVolume(
'123', filename='foo.txt', gc=mock_gc()
), '<girder_worker.docker.transforms.girder.GirderFileIdToVolume: File ID=123 -> "foo.txt">'
)))
def test_docker_repr_models(obj, expected_repr):
assert obj._repr_model_() == expected_repr
