def sync_efs_to_s3(): setup_django() from dvaapp.models import Video,TEvent from dvaapp.tasks import sync_bucket_video_by_id for v in Video.objects.all(): e = TEvent() e.video_id = v.pk e.operation = 'sync_bucket_video_by_id' e.save() sync_bucket_video_by_id(e.pk)
def test_coco(): import django sys.path.append(os.path.dirname(__file__)) os.environ.setdefault("DJANGO_SETTINGS_MODULE", "dva.settings") django.setup() from dvaui.view_shared import handle_uploaded_file from dvaapp.tasks import perform_import, perform_region_import, perform_dataset_extraction from django.core.files.uploadedfile import SimpleUploadedFile from dvaapp.models import TEvent for fname in glob.glob('tests/ci/coco*.zip'): name = fname.split('/')[-1].split('.')[0] f = SimpleUploadedFile(fname, file(fname).read(), content_type="application/zip") v = handle_uploaded_file(f, name) dt = TEvent.objects.get(video=v, operation='perform_import') perform_import(dt.pk) dt = TEvent(video=v, operation='perform_dataset_extraction') dt.save() perform_dataset_extraction(dt.pk) shutil.copy("tests/ci/coco_regions/coco_ci_regions.json", "dva/media/ingest/coco_ci_regions.json") args = {"path": "/ingest/coco_ci_regions.json"} dt = TEvent(video=v, operation='perform_region_import', arguments=args) dt.save() perform_region_import(dt.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, create_query from dvaapp.models import Video, Clusters,IndexEntries,TEvent from django.conf import settings from dvaapp.tasks import extract_frames, perform_face_indexing, inception_index_by_id, perform_ssd_detection_by_id,\ perform_yolo_detection_by_id, inception_index_ssd_detection_by_id, export_video_by_id, import_video_by_id,\ inception_query_by_image, perform_clustering, assign_open_images_text_tags_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) 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('tomorrow never dies', 'https://www.youtube.com/watch?v=gYtz5sw98Bc') 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_yolo_detection_by_id(TEvent.objects.create(video=v).pk) perform_face_indexing(v.pk) inception_index_ssd_detection_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,dv = create_query(10,False,['inception',],[],'data:image/png;base64,'+base64.encodestring(file('tests/query.png').read())) inception_query_by_image(query.pk) query,dv = create_query(10,True,['inception',],[],'data:image/png;base64,'+base64.encodestring(file('tests/query.png').read())) inception_query_by_image(query.pk) test_backup()
def sync_efs_to_s3(): setup_django() from dvaapp.models import Video, TEvent from dvaapp.tasks import sync_bucket_video_by_id for v in Video.objects.all(): e = TEvent() e.video_id = v.pk e.operation = 'sync_bucket_video_by_id' e.save() sync_bucket_video_by_id(e.pk)
def sync_efs_to_s3(): setup_django() from dvaapp.models import Video,TEvent from dvaapp.tasks import perform_sync for v in Video.objects.all(): e = TEvent() e.video_id = v.pk e.operation = 'perform_sync' e.save() perform_sync(e.pk)
def qt_lopq(): from dvaapp.models import Retriever, Indexer, TEvent from dvaapp.tasks import perform_retriever_creation dc = Retriever() args = {'components': 32, 'm': 8, 'v': 8, 'sub': 128} dc.algorithm = Retriever.LOPQ dc.source_filters = { 'indexer_shasum': Indexer.objects.get(name="inception").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)
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()
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)
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()
#!/usr/bin/env python import django, os, sys, glob, shutil sys.path.append('../server/') os.environ.setdefault("DJANGO_SETTINGS_MODULE", "dva.settings") django.setup() from dvaui.view_shared import handle_uploaded_file from dvaapp.tasks import perform_import, perform_region_import, perform_dataset_extraction from django.core.files.uploadedfile import SimpleUploadedFile from dvaapp.models import TEvent from django.conf import settings if __name__ == '__main__': for fname in glob.glob('ci/coco*.zip'): name = fname.split('/')[-1].split('.')[0] f = SimpleUploadedFile(fname, file(fname).read(), content_type="application/zip") v = handle_uploaded_file(f, name) dt = TEvent.objects.get(video=v, operation='perform_import') perform_import(dt.pk) dt = TEvent(video=v, operation='perform_dataset_extraction', arguments={}) dt.save() perform_dataset_extraction(dt.pk) shutil.copy("ci/coco_regions/coco_ci_regions.json", "{}/ingest/coco_ci_regions.json".format(settings.MEDIA_ROOT)) args = {"path": "/ingest/coco_ci_regions.json"} dt = TEvent(video=v, operation='perform_region_import', arguments=args) dt.save() perform_region_import(dt.pk)
#!/usr/bin/env python import django, sys, glob, os, time, logging logging.basicConfig( level=logging.INFO, format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s', datefmt='%m-%d %H:%M', filename='../logs/tests.log', filemode='a') sys.path.append('../server/') os.environ.setdefault("DJANGO_SETTINGS_MODULE", "dva.settings") django.setup() from dvaapp.models import TEvent, Video from dvaapp.tasks import perform_stream_capture if __name__ == '__main__': dv = Video(name="test", url=sys.argv[-1]) dv.save() start = TEvent(video=dv, operation="perform_stream_capture", arguments={"max_time": 180}) start.save() perform_stream_capture(start.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, 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()