def search(request):
if request.method == 'POST':
qp = DVAPQLProcess()
view_shared.create_query_from_request(qp, request)
qp.launch()
qp.wait()
qp_context = view_shared.collect(qp)
return JsonResponse(
data={
'task_id':
"",
'primary_key':
qp.process.pk,
'results':
qp_context['results'],
'regions':
qp_context['regions'],
'url':
'{}queries/{}.png'.format(settings.MEDIA_URL, qp.process.uuid)
})
def ci():
"""
Perform Continuous Integration testing using Travis
"""
import django
sys.path.append(os.path.dirname(__file__))
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "dva.settings")
django.setup()
import base64
from django.core.files.uploadedfile import SimpleUploadedFile
from dvaui.view_shared import handle_uploaded_file, pull_vdn_list \
, import_vdn_dataset_url
from dvaapp.models import Video, TEvent, VDNServer, DVAPQL, Retriever, DeepModel
from django.conf import settings
from dvaapp.processing import DVAPQLProcess
from dvaapp.tasks import perform_dataset_extraction, perform_indexing, perform_export, perform_import, \
perform_retriever_creation, perform_detection, \
perform_video_segmentation, perform_transformation
for fname in glob.glob('tests/ci/*.mp4'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname,
file(fname).read(),
content_type="video/mp4")
handle_uploaded_file(f, name, False)
if sys.platform != 'darwin':
for fname in glob.glob('tests/*.mp4'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname,
file(fname).read(),
content_type="video/mp4")
handle_uploaded_file(f, name, False)
for fname in glob.glob('tests/*.zip'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname,
file(fname).read(),
content_type="application/zip")
handle_uploaded_file(f, name)
for i, v in enumerate(Video.objects.all()):
if v.dataset:
arguments = {'sync': True}
perform_dataset_extraction(
TEvent.objects.create(video=v, arguments=arguments).pk)
else:
arguments = {'sync': True}
perform_video_segmentation(
TEvent.objects.create(video=v, arguments=arguments).pk)
arguments = {'index': 'inception', 'target': 'frames'}
perform_indexing(
TEvent.objects.create(video=v, arguments=arguments).pk)
if i == 0: # save travis time by just running detection on first video
# face_mtcnn
arguments = {'detector': 'face'}
dt = TEvent.objects.create(video=v, arguments=arguments)
perform_detection(dt.pk)
arguments = {
'filters': {
'event_id': dt.pk
},
}
perform_transformation(
TEvent.objects.create(video=v, arguments=arguments).pk)
# coco_mobilenet
arguments = {'detector': 'coco'}
dt = TEvent.objects.create(video=v, arguments=arguments)
perform_detection(dt.pk)
arguments = {
'filters': {
'event_id': dt.pk
},
}
perform_transformation(
TEvent.objects.create(video=v, arguments=arguments).pk)
# inception on crops from detector
arguments = {
'index': 'inception',
'target': 'regions',
'filters': {
'event_id': dt.pk,
'w__gte': 50,
'h__gte': 50
}
}
perform_indexing(
TEvent.objects.create(video=v, arguments=arguments).pk)
# assign_open_images_text_tags_by_id(TEvent.objects.create(video=v).pk)
temp = TEvent.objects.create(video=v,
arguments={'destination': "FILE"})
perform_export(temp.pk)
temp.refresh_from_db()
fname = temp.arguments['file_name']
f = SimpleUploadedFile(fname,
file("{}/exports/{}".format(
settings.MEDIA_ROOT, fname)).read(),
content_type="application/zip")
vimported = handle_uploaded_file(f, fname)
perform_import(
TEvent.objects.create(video=vimported,
arguments={
"source": "LOCAL"
}).pk)
dc = Retriever()
args = {}
args['components'] = 32
args['m'] = 8
args['v'] = 8
args['sub'] = 64
dc.algorithm = Retriever.LOPQ
dc.source_filters = {
'indexer_shasum':
DeepModel.objects.get(name="inception",
model_type=DeepModel.INDEXER).shasum
}
dc.arguments = args
dc.save()
clustering_task = TEvent()
clustering_task.arguments = {'retriever_pk': dc.pk}
clustering_task.operation = 'perform_retriever_creation'
clustering_task.save()
perform_retriever_creation(clustering_task.pk)
query_dict = {
'process_type':
DVAPQL.QUERY,
'image_data_b64':
base64.encodestring(file('tests/query.png').read()),
'tasks': [{
'operation': 'perform_indexing',
'arguments': {
'index':
'inception',
'target':
'query',
'next_tasks': [{
'operation': 'perform_retrieval',
'arguments': {
'count': 20,
'retriever_pk':
Retriever.objects.get(name='inception').pk
}
}]
}
}]
}
launch_workers_and_scheduler_from_environment()
qp = DVAPQLProcess()
qp.create_from_json(query_dict)
qp.launch()
qp.wait()
server, datasets, detectors = pull_vdn_list(1)
for k in datasets:
if k['name'] == 'MSCOCO_Sample_500':
print 'FOUND MSCOCO SAMPLE'
import_vdn_dataset_url(VDNServer.objects.get(pk=1), k['url'], None,
k)
'arguments': {
'index':
'inception',
'target':
'query',
'map': [{
'operation': 'perform_retrieval',
'arguments': {
'count':
15,
'retriever_pk':
Retriever.objects.get(name='inception',
algorithm=algo,
approximator_shasum=None).pk
}
}]
}
}, {
'operation': 'perform_detection',
'arguments': {
'detector': 'coco',
'target': 'query',
}
}]
}
qp = DVAPQLProcess()
qp.create_from_json(query_dict)
qp.launch()
qp.wait(timeout=400)
print QueryResults.objects.count()
{
'operation': 'perform_indexing',
'arguments': {
'index': 'inception',
'target': 'query',
'map': [
{'operation': 'perform_retrieval',
'arguments': {'count': 15, 'retriever_pk': Retriever.objects.get(name='inception',
algorithm=Retriever.EXACT).pk}
}
]
}
},
{
'operation': 'perform_detection',
'arguments': {
'detector': 'coco',
'target': 'query',
}
}
]
}
qp = DVAPQLProcess()
qp.create_from_json(query_dict)
qp.launch()
qp.wait(timeout=360)
print QueryResults.objects.count()
