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() from django.core.files.uploadedfile import SimpleUploadedFile from dvaapp.views import handle_uploaded_file, handle_youtube_video from dvaapp.models import Video from dvaapp.tasks import extract_frames, perform_indexing, perform_detection 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 v in Video.objects.all(): extract_frames(v.pk) perform_indexing(v.pk) # perform_detection(v.pk) detection is not performed in CI since it take long time on CPU test_backup()
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 test(ci=False): """ Run tests :param ci: if True (fab test:1) tests are run on Travis this option skips creating tasks and directly calls :return: """ 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, handle_youtube_video from dvaapp.models import Video from dvaapp.tasks import extract_frames, perform_indexing, perform_detection if ci: 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('jungle book', 'https://www.youtube.com/watch?v=C4qgAaxB_pc') for v in Video.objects.all(): extract_frames(v.pk) perform_indexing(v.pk) perform_detection(v.pk) test_backup() else: 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) 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('jungle book', 'https://www.youtube.com/watch?v=C4qgAaxB_pc')
def ci_face(): """ Used in conjunction with travis for Continuous Integration for testing face indexing :return: """ import django sys.path.append(os.path.dirname(__file__)) os.environ.setdefault("DJANGO_SETTINGS_MODULE", "dva.settings") django.setup() from dvaapp.models import Video, TEvent from dvaapp.tasks import perform_indexing for i,v in enumerate(Video.objects.all()): if i ==0: # save travis time by just running detection on first video args = { 'filter':{'object_name__startswith':'MTCNN_face'}, 'index':'facenet', 'target':'regions'} perform_indexing(TEvent.objects.create(video=v,arguments=args).pk)
def ci_face(): """ Used in conjunction with travis for Continuous Integration for testing face indexing :return: """ import django sys.path.append(os.path.dirname(__file__)) os.environ.setdefault("DJANGO_SETTINGS_MODULE", "dva.settings") django.setup() from dvaapp.models import Video, TEvent from dvaapp.tasks import perform_indexing for i,v in enumerate(Video.objects.all()): if i ==0: # save travis time by just running detection on first video args = json.dumps({ 'filter':{'object_name__startswith':'MTCNN_face'}, 'index':'facenet', 'target':'regions'}) perform_indexing(TEvent.objects.create(video=v,arguments_json=args).pk)
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)
handle_uploaded_file(f, name) if settings.DEBUG: for fname in glob.glob('data/*.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()): perform_import(TEvent.objects.get(video=v, operation='perform_import').pk) 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 == 1: # 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) print "done perform_detection" arguments = {'filters': {'event_id': dt.pk}, } perform_transformation(TEvent.objects.create(video=v, arguments=arguments).pk) print "done perform_transformation" # coco_mobilenet arguments = {'detector': 'coco'} dt = TEvent.objects.create(video=v, arguments=arguments) perform_detection(dt.pk) print "done perform_detection" arguments = {'filters': {'event_id': 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, 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(): """ 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 dvaapp.views import handle_uploaded_file, pull_vdn_list \ , import_vdn_dataset_url from dvaapp.models import Video, IndexEntries, TEvent, VDNServer, DVAPQL, Retriever from django.conf import settings from dvaapp.operations.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 = {} # # 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, k) test_backup()
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('jungle book', 'https://www.youtube.com/watch?v=C4qgAaxB_pc') for v in Video.objects.all(): extract_frames(v.pk) perform_indexing(v.pk) perform_detection(v.pk) elif sys.argv[1] == 'backup': try: os.mkdir('backups') except: pass media_dir = settings.MEDIA_ROOT db = settings.DATABASES.values()[0] pg = '/Users/aub3/PostgreSQL/pg96/bin/pg_dump' if sys.platform == 'darwin' else 'pg_dump' with open('{}/postgres.dump'.format(media_dir), 'w') as dumpfile: dump = subprocess.Popen([ pg, '--clean', '--dbname', 'postgresql://{}:{}@{}:5432/{}'.format( db['USER'], db['PASSWORD'], db['HOST'], db['NAME']) ],
import django, os from dvalib import indexer if __name__ == '__main__': os.environ.setdefault("DJANGO_SETTINGS_MODULE", "dva.settings") django.setup() from dvaapp import tasks from dvaapp.models import Video for v in Video.objects.all(): tasks.perform_indexing(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, 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()