def __init__(self, args):
super(YOLODetector, self).__init__()
self.model = trainer.YOLOTrainer(boxes=[],
images=[],
args=args,
test_mode=True)
self.session = None
def train_yolo_detector(task_id):
"""
:param task_id:
:return:
"""
start = TEvent.objects.get(pk=task_id)
if celery_40_bug_hack(start):
return 0
start.task_id = train_yolo_detector.request.id
start.started = True
start.operation = train_yolo_detector.name
start.save()
start_time = time.time()
args = json.loads(start.arguments_json)
labels = set(args['labels']) if 'labels' in args else set()
object_names = set(args['object_names']) if 'object_names' in args else set()
detector = CustomDetector.objects.get(pk=args['detector_pk'])
create_detector_folders(detector)
args['root_dir'] = "{}/models/{}/".format(settings.MEDIA_ROOT,detector.pk)
class_distribution, class_names, rboxes, rboxes_set, frames, i_class_names = create_detector_dataset(object_names,labels)
images, boxes = [], []
path_to_f = {}
for k,f in frames.iteritems():
path = "{}/{}/frames/{}.jpg".format(settings.MEDIA_ROOT,f.video_id,f.frame_index)
path_to_f[path] = f
images.append(path)
boxes.append(rboxes[k])
# print k,rboxes[k]
with open("{}/input.json".format(args['root_dir']),'w') as input_data:
json.dump({'boxes':boxes,'images':images,'args':args,'class_names':class_names.items()},input_data)
detector.boxes_count = sum([len(k) for k in boxes])
detector.frames_count = len(images)
detector.classes_count = len(class_names)
detector.save()
train_task = trainer.YOLOTrainer(boxes=boxes,images=images,class_names=i_class_names,args=args)
train_task.train()
detector.phase_1_log = file("{}/phase_1.log".format(args['root_dir']))
detector.phase_2_log = file("{}/phase_2.log".format(args['root_dir']))
detector.class_distribution = json.dumps(class_distribution.items())
detector.class_names = json.dumps(class_names.items())
detector.save()
results = train_task.predict()
for path, box_class, score, top, left, bottom, right in results:
r = Region()
r.region_type = r.DETECTION
r.confidence = int(100.0 * score)
r.object_name = "YOLO_{}_{}".format(detector.pk,box_class)
r.y = top
r.x = left
r.w = right - left
r.h = bottom - top
r.frame_id = path_to_f[path].pk
r.video_id = path_to_f[path].video_id
r.save()
start.completed = True
start.seconds = time.time() - start_time
start.save()
return 0
def detect_custom_objects(detector_pk, video_pk):
"""
Detection using customized trained YOLO detectors
:param detector_pk:
:param video_pk:
:return:
"""
setup_django()
from dvaapp.models import Region, Frame, CustomDetector
from django.conf import settings
from dvalib.yolo import trainer
from PIL import Image
args = {'detector_pk': int(detector_pk)}
video_pk = int(video_pk)
detector = CustomDetector.objects.get(pk=args['detector_pk'])
args['root_dir'] = "{}/detectors/{}/".format(settings.MEDIA_ROOT,
detector.pk)
class_names = {k: v for k, v in json.loads(detector.class_names)}
i_class_names = {i: k for k, i in class_names.items()}
frames = {}
for f in Frame.objects.all().filter(video_id=video_pk):
frames[f.pk] = f
images = []
path_to_f = {}
for k, f in frames.iteritems():
path = "{}/{}/frames/{}.jpg".format(settings.MEDIA_ROOT, f.video_id,
f.frame_index)
path_to_f[path] = f
images.append(path)
train_task = trainer.YOLOTrainer(boxes=[],
images=images,
class_names=i_class_names,
args=args,
test_mode=True)
results = train_task.predict()
for path, box_class, score, top, left, bottom, right in results:
r = Region()
r.region_type = r.DETECTION
r.confidence = int(100.0 * score)
r.object_name = "YOLO_{}_{}".format(detector.pk, box_class)
r.y = top
r.x = left
r.w = right - left
r.h = bottom - top
r.frame_id = path_to_f[path].pk
r.video_id = path_to_f[path].video_id
r.save()
right = r.w + r.x
bottom = r.h + r.y
img = Image.open(path)
img2 = img.crop((r.x, r.y, right, bottom))
img2.save("{}/{}/detections/{}.jpg".format(settings.MEDIA_ROOT,
video_pk, r.pk))
def train_yolo(start_pk):
"""
Train a yolo model specified in a TaskEvent.
This is necessary to ensure that the Tensorflow process exits and releases the allocated GPU memory.
:param start_pk: TEvent PK with information about lauching the training task
:return:
"""
setup_django()
from django.conf import settings
from dvaapp.models import Region, Frame, CustomDetector, TEvent
from dvaapp.shared import create_detector_folders, create_detector_dataset
from dvalib.yolo import trainer
start = TEvent.objects.get(pk=start_pk)
args = json.loads(start.arguments_json)
labels = set(args['labels']) if 'labels' in args else set()
object_names = set(args['object_names']) if 'object_names' in args else set()
detector = CustomDetector.objects.get(pk=args['detector_pk'])
create_detector_folders(detector)
args['root_dir'] = "{}/detectors/{}/".format(settings.MEDIA_ROOT,detector.pk)
class_distribution, class_names, rboxes, rboxes_set, frames, i_class_names = create_detector_dataset(object_names,labels)
images, boxes = [], []
path_to_f = {}
for k,f in frames.iteritems():
path = "{}/{}/frames/{}.jpg".format(settings.MEDIA_ROOT,f.video_id,f.frame_index)
path_to_f[path] = f
images.append(path)
boxes.append(rboxes[k])
# print k,rboxes[k]
with open("{}/input.json".format(args['root_dir']),'w') as input_data:
json.dump({'boxes':boxes,
'images':images,
'args':args,
'class_names':class_names.items(),
'class_distribution':class_distribution.items()},
input_data)
detector.boxes_count = sum([len(k) for k in boxes])
detector.frames_count = len(images)
detector.classes_count = len(class_names)
detector.save()
train_task = trainer.YOLOTrainer(boxes=boxes,images=images,class_names=i_class_names,args=args)
train_task.train()
detector.phase_1_log = file("{}/phase_1.log".format(args['root_dir'])).read()
detector.phase_2_log = file("{}/phase_2.log".format(args['root_dir'])).read()
detector.class_distribution = json.dumps(class_distribution.items())
detector.class_names = json.dumps(class_names.items())
detector.trained = True
detector.save()
results = train_task.predict()
bulk_regions = []
for path, box_class, score, top, left, bottom, right in results:
r = Region()
r.region_type = r.ANNOTATION
r.confidence = int(100.0 * score)
r.object_name = "YOLO_{}_{}".format(detector.pk,box_class)
r.y = top
r.x = left
r.w = right - left
r.h = bottom - top
r.frame_id = path_to_f[path].pk
r.video_id = path_to_f[path].video_id
bulk_regions.append(r)
Region.objects.bulk_create(bulk_regions,batch_size=1000)
folder_name = "{}/detectors/{}".format(settings.MEDIA_ROOT,detector.pk)
file_name = '{}/exports/{}.dva_detector.zip'.format(settings.MEDIA_ROOT,detector.pk)
zipper = subprocess.Popen(['zip', file_name, '-r', '.'],cwd=folder_name)
zipper.wait()
return 0
# print k,rboxes[k]
with open("{}/input.json".format(args['root_dir']), 'w') as input_data:
json.dump(
{
'boxes': boxes,
'images': images,
'args': args,
'class_names': class_names.items(),
'class_distribution': class_distribution.items()
}, input_data)
detector.boxes_count = sum([len(k) for k in boxes])
detector.frames_count = len(images)
detector.classes_count = len(class_names)
detector.save()
args['class_names'] = i_class_names
train_task = trainer.YOLOTrainer(boxes=boxes, images=images, args=args)
train_task.train()
detector.phase_1_log = file("{}/phase_1.log".format(
args['root_dir'])).read()
detector.phase_2_log = file("{}/phase_2.log".format(
args['root_dir'])).read()
detector.class_distribution = json.dumps(class_distribution.items())
detector.class_names = json.dumps(class_names.items())
detector.trained = True
detector.save()
results = train_task.predict()
bulk_regions = []
for path, box_class, score, top, left, bottom, right in results:
r = Region()
r.region_type = r.ANNOTATION
r.confidence = int(100.0 * score)
def train_yolo_detector(task_id):
"""
:param task_id:
:return:
"""
start = TEvent.objects.get(pk=task_id)
if celery_40_bug_hack(start):
return 0
start.task_id = train_yolo_detector.request.id
start.started = True
start.operation = train_yolo_detector.name
start.save()
start_time = time.time()
args = json.loads(start.arguments_json)
labels = set(args['labels']) if 'labels' in args else set()
object_names = set(
args['object_names']) if 'object_names' in args else set()
detector = CustomDetector.objects.get(pk=args['detector_pk'])
create_detector_folders(detector)
class_names = {k: i for i, k in enumerate(labels.union(object_names))}
i_class_names = {i: k for k, i in class_names.items()}
rboxes = defaultdict(list)
frames = {}
for r in Region.objects.all().filter(object_name__in=object_names):
frames[r.frame_id] = r.frame
rboxes[r.frame_id].append(
(class_names[r.object_name], r.x, r.y, r.x + r.w, r.y + r.h))
for l in AppliedLabel.objects.all().filter(label_name__in=labels):
frames[l.frame_id] = l.frame
if l.region:
r = l.region
rboxes[l.frame_id].append(
(class_names[l.label_name], r.x, r.y, r.x + r.w, r.y + r.h))
images, boxes = [], []
path_to_f = {}
for k, f in frames.iteritems():
path = "{}/{}/frames/{}.jpg".format(settings.MEDIA_ROOT, f.video_id,
f.frame_index)
path_to_f[path] = f
images.append(path)
boxes.append(rboxes[k])
# print k,rboxes[k]
with open("{}/input.json".format(args['root_dir']), 'w') as input_data:
json.dump(
{
'boxes': boxes,
'images': images,
'args': args,
'class_names': class_names.items()
}, input_data)
train_task = trainer.YOLOTrainer(boxes=boxes,
images=images,
class_names=i_class_names,
args=args)
train_task.train()
results = train_task.predict()
for path, box_class, score, top, left, bottom, right in results:
r = Region()
r.region_type = r.DETECTION
r.confidence = int(100.0 * score)
r.object_name = "YOLO_{}_{}".format(detector.pk, box_class)
r.y = top
r.x = left
r.w = right - left
r.h = bottom - top
r.frame_id = path_to_f[path].pk
r.video_id = path_to_f[path].video_id
r.save()
start.completed = True
start.seconds = time.time() - start_time
start.save()
return 0
