def training(request):
context = {}
context["videos"] = Video.objects.all().filter()
context["detectors"] = CustomDetector.objects.all()
if request.method == 'POST':
if request.POST.get('action') == 'estimate':
args = request.POST.get('args')
args = json.loads(args) if args.strip() else {}
args['name'] = request.POST.get('name')
args['labels'] = [k.strip() for k in request.POST.get('labels').split(',') if k.strip()]
args['object_names'] = [k.strip() for k in request.POST.get('object_names').split(',') if k.strip()]
args['excluded_videos'] = request.POST.getlist('excluded_videos')
labels = set(args['labels']) if 'labels' in args else set()
object_names = set(args['object_names']) if 'object_names' in args else set()
class_distribution, class_names, rboxes, rboxes_set, frames, i_class_names = create_detector_dataset(object_names, labels)
context["estimate"] = {
'args':args,
'class_distribution':class_distribution,
'class_names':class_names,
'rboxes':rboxes,
'rboxes_set':rboxes_set,
'frames':frames,
'i_class_names':i_class_names
}
else:
args = request.POST.get('args')
args = json.loads(args) if args.strip() else {}
args['name'] = request.POST.get('name')
args['labels'] = [k.strip() for k in request.POST.get('labels').split(',') if k.strip()]
args['object_names'] = [k.strip() for k in request.POST.get('object_names').split(',') if k.strip()]
args['excluded_videos'] = request.POST.getlist('excluded_videos')
detector = CustomDetector()
detector.name = args['name']
detector.algorithm = "yolo"
detector.arguments = json.dumps(args)
detector.save()
args['detector_pk'] = detector.pk
operation = "train_yolo_detector"
train_event = TEvent()
train_event.operation = operation
train_event.arguments = args
train_event.save()
detector.source = train_event
detector.save()
app.send_task(name=operation, args=[train_event.pk, ], queue=settings.TASK_NAMES_TO_QUEUE[operation])
return render(request, 'training.html', context)
def detections(request):
context = {}
context["videos"] = Video.objects.all().filter(
parent_query__count__isnull=True)
context["detectors"] = CustomDetector.objects.all()
detector_stats = []
for d in CustomDetector.objects.all():
class_dist = json.loads(
d.class_distribution) if d.class_distribution.strip() else {}
detector_stats.append({
'name': d.name,
'classes': class_dist,
'frames_count': d.frames_count,
'boxes_count': d.boxes_count,
'pk': d.pk
})
context["detector_stats"] = detector_stats
if request.method == 'POST':
if request.POST.get('action') == 'detect':
detector_pk = request.POST.get('detector_pk')
video_pk = request.POST.get('video_pk')
task_name = "detect_custom_objects"
apply_event = TEvent()
apply_event.video_id = video_pk
apply_event.operation = task_name
apply_event.arguments_json = json.dumps(
{'detector_pk': int(detector_pk)})
apply_event.save()
app.send_task(name=task_name,
args=[
apply_event.pk,
],
queue=settings.TASK_NAMES_TO_QUEUE[task_name])
elif request.POST.get('action') == 'estimate':
args = request.POST.get('args')
args = json.loads(args) if args.strip() else {}
args['name'] = request.POST.get('name')
args['labels'] = [
k.strip() for k in request.POST.get('labels').split(',')
if k.strip()
]
args['object_names'] = [
k.strip() for k in request.POST.get('object_names').split(',')
if k.strip()
]
args['excluded_videos'] = request.POST.getlist('excluded_videos')
labels = set(args['labels']) if 'labels' in args else set()
object_names = set(
args['object_names']) if 'object_names' in args else set()
class_distribution, class_names, rboxes, rboxes_set, frames, i_class_names = create_detector_dataset(
object_names, labels)
context["estimate"] = {
'args': args,
'class_distribution': class_distribution,
'class_names': class_names,
'rboxes': rboxes,
'rboxes_set': rboxes_set,
'frames': frames,
'i_class_names': i_class_names
}
else:
args = request.POST.get('args')
args = json.loads(args) if args.strip() else {}
args['name'] = request.POST.get('name')
args['labels'] = [
k.strip() for k in request.POST.get('labels').split(',')
if k.strip()
]
args['object_names'] = [
k.strip() for k in request.POST.get('object_names').split(',')
if k.strip()
]
args['excluded_videos'] = request.POST.getlist('excluded_videos')
detector = CustomDetector()
detector.name = args['name']
detector.algorithm = "yolo"
detector.arguments = json.dumps(args)
detector.save()
args['detector_pk'] = detector.pk
task_name = "train_yolo_detector"
train_event = TEvent()
train_event.operation = task_name
train_event.arguments_json = json.dumps(args)
train_event.save()
detector.source = train_event
detector.save()
app.send_task(name=task_name,
args=[
train_event.pk,
],
queue=settings.TASK_NAMES_TO_QUEUE[task_name])
return render(request, 'detections.html', context)
def training(request):
context = {}
context["videos"] = Video.objects.all().filter(parent_query__isnull=True)
context["detectors"] = CustomDetector.objects.all()
if request.method == 'POST':
if request.POST.get('action') == 'estimate':
args = request.POST.get('args')
args = json.loads(args) if args.strip() else {}
args['name'] = request.POST.get('name')
args['labels'] = [k.strip() for k in request.POST.get('labels').split(',') if k.strip()]
args['object_names'] = [k.strip() for k in request.POST.get('object_names').split(',') if k.strip()]
args['excluded_videos'] = request.POST.getlist('excluded_videos')
labels = set(args['labels']) if 'labels' in args else set()
object_names = set(args['object_names']) if 'object_names' in args else set()
class_distribution, class_names, rboxes, rboxes_set, frames, i_class_names = create_detector_dataset(object_names, labels)
context["estimate"] = {
'args':args,
'class_distribution':class_distribution,
'class_names':class_names,
'rboxes':rboxes,
'rboxes_set':rboxes_set,
'frames':frames,
'i_class_names':i_class_names
}
else:
args = request.POST.get('args')
args = json.loads(args) if args.strip() else {}
args['name'] = request.POST.get('name')
args['labels'] = [k.strip() for k in request.POST.get('labels').split(',') if k.strip()]
args['object_names'] = [k.strip() for k in request.POST.get('object_names').split(',') if k.strip()]
args['excluded_videos'] = request.POST.getlist('excluded_videos')
detector = CustomDetector()
detector.name = args['name']
detector.algorithm = "yolo"
detector.arguments = json.dumps(args)
detector.save()
args['detector_pk'] = detector.pk
task_name = "train_yolo_detector"
train_event = TEvent()
train_event.operation = task_name
train_event.arguments_json = json.dumps(args)
train_event.save()
detector.source = train_event
detector.save()
app.send_task(name=task_name, args=[train_event.pk, ], queue=settings.TASK_NAMES_TO_QUEUE[task_name])
return render(request, 'training.html', context)
