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