def ci():
"""
Used in conjunction with travis for Continuous Integration testing
:return:
"""
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 dvaapp.views import handle_uploaded_file, handle_youtube_video, pull_vdn_list\
,import_vdn_dataset_url
from dvaapp.models import Video, Clusters,IndexEntries,TEvent,VDNServer, DVAPQL
from django.conf import settings
from dvaapp.operations.processing import DVAPQLProcess
from dvaapp.tasks import extract_frames, perform_indexing, export_video, import_video_by_id,\
perform_clustering, perform_analysis, perform_detection,\
segment_video, crop_regions_by_id
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)
# handle_youtube_video('world is not enough', 'https://www.youtube.com/watch?v=P-oNz3Nf50Q') # Temporarily disabled due error in travis
for i,v in enumerate(Video.objects.all()):
if v.dataset:
arguments = {'sync':True}
extract_frames(TEvent.objects.create(video=v,arguments=arguments).pk)
else:
arguments = {'sync':True}
segment_video(TEvent.objects.create(video=v,arguments=arguments).pk)
arguments = {'index': 'inception'}
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},}
crop_regions_by_id(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},}
crop_regions_by_id(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)
fname = export_video(TEvent.objects.create(video=v).pk)
f = SimpleUploadedFile(fname, file("{}/exports/{}".format(settings.MEDIA_ROOT,fname)).read(), content_type="application/zip")
vimported = handle_uploaded_file(f, fname)
import_video_by_id(TEvent.objects.create(video=vimported).pk)
dc = Clusters()
dc.indexer_algorithm = 'inception'
dc.included_index_entries_pk = [k.pk for k in IndexEntries.objects.all().filter(algorithm=dc.indexer_algorithm)]
dc.components = 32
dc.save()
clustering_task = TEvent()
clustering_task.arguments = {'clusters_id':dc.pk}
clustering_task.operation = 'perform_clustering'
clustering_task.save()
perform_clustering(clustering_task.pk)
query_dict = {
'process_type': DVAPQL.QUERY,
'image_data_b64':base64.encodestring(file('tests/query.png').read()),
'indexer_queries':[
{
'algorithm':'inception',
'count':10,
'approximate':False
}
]
}
qp = DVAPQLProcess()
qp.create_from_json(query_dict)
# execute_index_subquery(qp.indexer_queries[0].pk)
query_dict = {
'process_type': DVAPQL.QUERY,
'image_data_b64':base64.encodestring(file('tests/query.png').read()),
'indexer_queries':[
{
'algorithm':'inception',
'count':10,
'approximate':True
}
]
}
qp = DVAPQLProcess()
qp.create_from_json(query_dict)
# execute_index_subquery(qp.indexer_queries[0].pk)
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)
test_backup()
def ci():
"""
Used in conjunction with travis for Continuous Integration testing
:return:
"""
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 dvaapp.views import handle_uploaded_file, handle_youtube_video, pull_vdn_list\
,import_vdn_dataset_url
from dvaapp.models import Video, Clusters,IndexEntries,TEvent,VDNServer
from django.conf import settings
from dvaapp.operations.query_processing import QueryProcessing
from dvaapp.tasks import extract_frames, perform_indexing, export_video_by_id, import_video_by_id,\
perform_clustering, perform_analysis, perform_detection,\
segment_video, crop_regions_by_id
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)
# handle_youtube_video('world is not enough', 'https://www.youtube.com/watch?v=P-oNz3Nf50Q') # Temporarily disabled due error in travis
for i,v in enumerate(Video.objects.all()):
if v.dataset:
arguments_json = json.dumps({'sync':True})
extract_frames(TEvent.objects.create(video=v,arguments_json=arguments_json).pk)
else:
arguments_json = json.dumps({'sync':True})
segment_video(TEvent.objects.create(video=v,arguments_json=arguments_json).pk)
arguments_json = json.dumps({'index': 'inception'})
perform_indexing(TEvent.objects.create(video=v,arguments_json=arguments_json).pk)
if i ==0: # save travis time by just running detection on first video
# face_mtcnn
arguments_json = json.dumps({'detector': 'face'})
dt = TEvent.objects.create(video=v,arguments_json=arguments_json)
perform_detection(dt.pk)
arguments_json = json.dumps({'filters':{'event_id':dt.pk},})
crop_regions_by_id(TEvent.objects.create(video=v,arguments_json=arguments_json).pk)
# coco_mobilenet
arguments_json = json.dumps({'detector': 'coco'})
dt = TEvent.objects.create(video=v, arguments_json=arguments_json)
perform_detection(dt.pk)
arguments_json = json.dumps({'filters':{'event_id':dt.pk},})
crop_regions_by_id(TEvent.objects.create(video=v,arguments_json=arguments_json).pk)
# inception on crops from detector
arguments_json = json.dumps({'index':'inception','target': 'regions','filters': {'event_id': dt.pk, 'w__gte': 50, 'h__gte': 50}})
perform_indexing(TEvent.objects.create(video=v,arguments_json=arguments_json).pk)
# assign_open_images_text_tags_by_id(TEvent.objects.create(video=v).pk)
fname = export_video_by_id(TEvent.objects.create(video=v,event_type=TEvent.EXPORT).pk)
f = SimpleUploadedFile(fname, file("{}/exports/{}".format(settings.MEDIA_ROOT,fname)).read(), content_type="application/zip")
vimported = handle_uploaded_file(f, fname)
import_video_by_id(TEvent.objects.create(video=vimported).pk)
dc = Clusters()
dc.indexer_algorithm = 'inception'
dc.included_index_entries_pk = [k.pk for k in IndexEntries.objects.all().filter(algorithm=dc.indexer_algorithm)]
dc.components = 32
dc.save()
clustering_task = TEvent()
clustering_task.clustering = dc
clustering_task.event_type = TEvent.CLUSTERING
clustering_task.operation = 'perform_clustering'
clustering_task.save()
perform_clustering(clustering_task.pk)
query_dict = {
'image_data_b64':base64.encodestring(file('tests/query.png').read()),
'indexers':[
{
'algorithm':'inception',
'count':10,
'approximate':False
}
]
}
qp = QueryProcessing()
qp.create_from_json(query_dict)
# execute_index_subquery(qp.indexer_queries[0].pk)
query_dict = {
'image_data_b64':base64.encodestring(file('tests/query.png').read()),
'indexers':[
{
'algorithm':'inception',
'count':10,
'approximate':True
}
]
}
qp = QueryProcessing()
qp.create_from_json(query_dict)
# execute_index_subquery(qp.indexer_queries[0].pk)
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)
test_backup()