def qt():
import django
sys.path.append(os.path.dirname(__file__))
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "dva.settings")
django.setup()
from django.core.files.uploadedfile import SimpleUploadedFile
from dvaapp.views import handle_uploaded_file
from dvaapp.models import Video, TEvent
from dvaapp.tasks import extract_frames, perform_face_detection, perform_indexing, segment_video
for fname in glob.glob('tests/ci/*.mp4'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname,
file(fname).read(),
content_type="application/mp4")
v = handle_uploaded_file(f, name)
arguments_json = json.dumps({'sync': True})
segment_video(
TEvent.objects.create(video=v, arguments_json=arguments_json).pk)
perform_face_detection(TEvent.objects.create(video=v).pk)
args = json.dumps({
'index': 'facenet',
'target': 'regions',
'filter': {
'object_name__startswith': 'MTCNN_face'
}
})
perform_indexing(
TEvent.objects.create(video=v, arguments_json=args).pk)
def qt():
import django
sys.path.append(os.path.dirname(__file__))
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "dva.settings")
django.setup()
from django.core.files.uploadedfile import SimpleUploadedFile
from dvaapp.views import handle_uploaded_file
from dvaapp.models import Video, TEvent
from dvaapp.tasks import extract_frames,perform_face_detection,perform_face_indexing
for fname in glob.glob('tests/example*.zip'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname, file(fname).read(), content_type="application/zip")
v = handle_uploaded_file(f, name)
extract_frames(TEvent.objects.create(video=v).pk)
perform_face_detection(TEvent.objects.create(video=v).pk)
perform_face_indexing(TEvent.objects.create(video=v).pk)
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, inception_index_by_id, perform_ssd_detection_by_id,\
perform_yolo_detection_by_id, inception_index_regions_by_id, export_video_by_id, import_video_by_id,\
execute_index_subquery, perform_clustering, assign_open_images_text_tags_by_id, perform_face_detection,\
perform_face_indexing
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)
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()):
extract_frames(TEvent.objects.create(video=v).pk)
inception_index_by_id(TEvent.objects.create(video=v).pk)
if i ==0: # save travis time by just running detection on first video
perform_ssd_detection_by_id(TEvent.objects.create(video=v).pk)
perform_face_detection(TEvent.objects.create(video=v).pk)
inception_index_regions_by_id(TEvent.objects.create(video=v).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()