def shortcuts(request):
user = request.user if request.user.is_authenticated else None
if request.method == 'POST':
if request.POST.get('op') == 'apply':
jf = request.POST.get("filters",'{}')
filters = json.loads(jf) if jf.strip() else {}
model_pk = request.POST.get("model_pk")
video_pks = request.POST.getlist('video_pk')
target = request.POST.get('target')
frames_batch_size = request.POST.get('frames_batch_size',)
if not frames_batch_size:
frames_batch_size = defaults.DEFAULT_FRAMES_BATCH_SIZE
segments_batch_size = request.POST.get('segments_batch_size')
if not segments_batch_size:
segments_batch_size = defaults.DEFAULT_SEGMENTS_BATCH_SIZE
process_pk = view_shared.model_apply(model_pk,video_pks,filters,target,int(segments_batch_size),
int(frames_batch_size),user)
return redirect('process_detail',pk=process_pk)
elif request.POST.get('op') == 'create_retriever':
jf = request.POST.get("source_filters",'{}')
filters = json.loads(jf) if jf.strip() else {}
name = request.POST.get('name')
indexer_shasum = request.POST.get('indexer_shasum')
approximator_shasum = request.POST.get('approximator_shasum')
if approximator_shasum:
approximator_shasum = None
algorithm = Retriever.LOPQ
else:
algorithm = Retriever.EXACT
_ = view_shared.create_retriever(name,algorithm,filters,indexer_shasum,approximator_shasum,user)
return redirect('retriever_list')
else:
raise NotImplementedError(request.POST.get('op'))
else:
raise NotImplementedError("Only POST allowed")
def shortcuts(request):
user = request.user if request.user.is_authenticated else None
if request.method == 'POST':
if request.POST.get('op') == 'apply':
jf = request.POST.get("filters", '{}')
filters = json.loads(jf) if jf.strip() else {}
model_pk = request.POST.get("model_pk")
video_pks = request.POST.getlist('video_pk')
target = request.POST.get('target')
frames_batch_size = request.POST.get('frames_batch_size', )
if not frames_batch_size:
frames_batch_size = defaults.DEFAULT_FRAMES_BATCH_SIZE
segments_batch_size = request.POST.get('segments_batch_size')
if not segments_batch_size:
segments_batch_size = defaults.DEFAULT_SEGMENTS_BATCH_SIZE
process_pk = view_shared.model_apply(model_pk, video_pks, filters,
target,
int(segments_batch_size),
int(frames_batch_size), user)
return redirect('process_detail', pk=process_pk)
elif request.POST.get('op') == 'create_retriever':
jf = request.POST.get("source_filters", '{}')
filters = json.loads(jf) if jf.strip() else {}
name = request.POST.get('name')
indexer_shasum = request.POST.get('indexer_shasum')
approximator_shasum = request.POST.get('approximator_shasum')
if approximator_shasum:
approximator_shasum = None
algorithm = Retriever.LOPQ
else:
algorithm = Retriever.EXACT
_ = view_shared.create_retriever(name, algorithm, filters,
indexer_shasum,
approximator_shasum, user)
return redirect('retriever_list')
elif request.POST.get('op') == 'create_approximator_training_set':
name = request.POST.get('name')
video_pks = request.POST.getlist('video_pk')
indexer_shasum = request.POST.get('indexer_shasum')
_ = view_shared.create_approximator_training_set(
name, indexer_shasum, video_pks, user)
return redirect('training_set_list')
elif request.POST.get('op') == 'perform_approximator_training':
training_set_pk = request.POST.get('lopq_training_set_pk')
dt = TrainingSet.objects.get(pk=training_set_pk)
args = {}
args['trainer'] = "LOPQ"
args['name'] = request.POST.get('name')
args['indexer_shasum'] = dt.source_filters['indexer_shasum']
args['components'] = request.POST.get('components')
args['m'] = request.POST.get('m')
args['v'] = request.POST.get('v')
args['sub'] = request.POST.get('sub')
process_pk = view_shared.perform_training(training_set_pk, args,
user)
return redirect('process_detail', process_pk)
else:
raise NotImplementedError(request.POST.get('op'))
else:
raise NotImplementedError("Only POST allowed")
