def perform_face_indexing(video_id):
from dvaapp.models import Region,Frame,Video,IndexEntries
from dvalib import indexer,detector
from dvaapp.operations.video_processing import WFrame,WVideo
from django.conf import settings
from scipy import misc
face_indexer = indexer.FacenetIndexer()
dv = Video.objects.get(id=video_id)
video = WVideo(dv, settings.MEDIA_ROOT)
frames = Frame.objects.all().filter(video=dv)
wframes = [WFrame(video=video, frame_index=df.frame_index, primary_key=df.pk) for df in frames]
input_paths = {f.local_path(): f.primary_key for f in wframes}
faces_dir = '{}/{}/detections'.format(settings.MEDIA_ROOT, video_id)
indexes_dir = '{}/{}/indexes'.format(settings.MEDIA_ROOT, video_id)
face_detector = detector.FaceDetector()
aligned_paths = face_detector.detect(wframes)
logging.info(len(aligned_paths))
faces = []
faces_to_pk = {}
count = 0
for path, v in aligned_paths.iteritems():
for scaled_img, bb in v:
d = Region()
d.region_type = Region.DETECTION
d.video = dv
d.confidence = 100.0
d.frame_id = input_paths[path]
d.object_name = "mtcnn_face"
left, top, right, bottom = bb[0], bb[1], bb[2], bb[3]
d.y = top
d.x = left
d.w = right - left
d.h = bottom - top
d.save()
face_path = '{}/{}.jpg'.format(faces_dir, d.pk)
output_filename = os.path.join(faces_dir, face_path)
misc.imsave(output_filename, scaled_img)
faces.append(face_path)
faces_to_pk[face_path] = d.pk
count += 1
dv.refresh_from_db()
dv.detections = dv.detections + count
dv.save()
path_count, emb_array, entries, feat_fname, entries_fname = face_indexer.index_faces(faces, faces_to_pk,
indexes_dir, video_id)
i = IndexEntries()
i.video = dv
i.count = len(entries)
i.contains_frames = False
i.contains_detections = True
i.detection_name = "Face"
i.algorithm = 'facenet'
i.entries_file_name = entries_fname.split('/')[-1]
i.features_file_name = feat_fname.split('/')[-1]
i.save()
def get_static_detectors(self):
if DetectorTask._detectors is None:
DetectorTask._detectors = {
'coco_mobilenet':
detector.TFDetector(
model_path=model_path,
class_index_to_string=coco_class_index_to_string),
'face_mtcnn':
detector.FaceDetector()
}
return DetectorTask._detectors
def load_detector(self,cd):
if cd.pk not in DetectorTask._detectors:
if cd.detector_type == DeepModel.TFD:
DetectorTask._detectors[cd.pk] = detector.TFDetector(model_path=cd.get_model_path(),
class_index_to_string=cd.class_index_to_string)
elif cd.detector_type == DeepModel.YOLO:
DetectorTask._detectors[cd.pk] = detector.YOLODetector(cd.get_yolo_args())
elif cd.name == 'face':
DetectorTask._detectors[cd.pk] = detector.FaceDetector()
elif cd.name == 'textbox':
DetectorTask._detectors[cd.pk] = detector.TextBoxDetector(model_path=cd.get_model_path())
else:
raise ValueError,"{}".format(cd.pk)
def perform_face_indexing(video_id):
face_indexer = indexer.FacenetIndexer()
dv = Video.objects.get(id=video_id)
video = entity.WVideo(dv, settings.MEDIA_ROOT)
frames = Frame.objects.all().filter(video=dv)
wframes = [
entity.WFrame(video=video,
frame_index=df.frame_index,
primary_key=df.pk) for df in frames
]
input_paths = {f.local_path(): f.primary_key for f in wframes}
faces_dir = '{}/{}/detections'.format(settings.MEDIA_ROOT, video_id)
indexes_dir = '{}/{}/indexes'.format(settings.MEDIA_ROOT, video_id)
face_detector = detector.FaceDetector()
aligned_paths = face_detector.detect(wframes)
logging.info(len(aligned_paths))
faces = []
faces_to_pk = {}
count = 0
for path, v in aligned_paths.iteritems():
for scaled_img, bb in v:
d = Detection()
d.video = dv
d.confidence = 100.0
d.frame_id = input_paths[path]
d.object_name = "mtcnn_face"
top, left, bottom, right = bb[0], bb[1], bb[2], bb[3]
d.y = top
d.x = left
d.w = right - left
d.h = bottom - top
d.save()
face_path = '{}/{}.jpg'.format(faces_dir, d.pk)
output_filename = os.path.join(faces_dir, face_path)
misc.imsave(output_filename, scaled_img)
faces.append(face_path)
faces_to_pk[face_path] = d.pk
count += 1
dv.detections = dv.detections + count
dv.save()
path_count, emb_array, entries = face_indexer.index_faces(
faces, faces_to_pk, indexes_dir, video_id)
i = IndexEntries()
i.video = dv
i.count = len(entries)
i.contains_frames = False
i.contains_detections = True
i.detection_name = "Face"
i.algorithm = 'facenet'
i.save()
def get_static_detectors(self):
if DetectorTask._detectors is None:
detectors_root = "{}/detectors/".format(settings.MEDIA_ROOT)
DetectorTask._detectors = {
'coco':
detector.TFDetector(
model_path=detectors_root + 'coco/coco_mobilenet.pb',
class_index_to_string=coco_class_index_to_string),
'face':
detector.FaceDetector(),
'textbox':
detector.TextBoxDetector()
}
return DetectorTask._detectors
def load_detector(cls, cd):
cd.ensure()
if cd.pk not in Detectors._detectors:
if cd.detector_type == TrainedModel.TFD:
Detectors._detectors[cd.pk] = detector.TFDetector(
model_path=cd.get_model_path(),
class_index_to_string=cd.arguments['class_index_to_string']
)
elif cd.detector_type == TrainedModel.YOLO:
raise NotImplementedError("YOLO model has been removed")
elif cd.name == 'face':
Detectors._detectors[cd.pk] = detector.FaceDetector()
elif cd.name == 'textbox':
Detectors._detectors[cd.pk] = detector.TextBoxDetector(
model_path=cd.get_model_path())
else:
raise ValueError, "{}".format(cd.pk)
def load_detector(cls, cd):
cd.ensure()
if cd.pk not in Detectors._detectors:
if cd.detector_type == TrainedModel.TFD:
Detectors._detectors[cd.pk] = detector.TFDetector(
model_path=cd.get_model_path(),
class_index_to_string=cd.arguments['class_index_to_string']
)
elif cd.detector_type == TrainedModel.YOLO:
# class_names = {k: v for k, v in json.loads(self.class_names)}
# args = {'root_dir': model_dir,
# 'detector_pk': self.pk,
# 'class_names':{i: k for k, i in class_names.items()}
# }
Detectors._detectors[cd.pk] = detector.YOLODetector(
cd.get_yolo_args())
elif cd.name == 'face':
Detectors._detectors[cd.pk] = detector.FaceDetector()
elif cd.name == 'textbox':
Detectors._detectors[cd.pk] = detector.TextBoxDetector(
model_path=cd.get_model_path())
else:
raise ValueError, "{}".format(cd.pk)
def get_static_detectors(self):
if DetectorTask._detectors is None:
DetectorTask._detectors = {'coco': detector.TFDetector(model_path=model_path,class_index_to_string=coco_class_index_to_string),
'face': detector.FaceDetector(),
'textbox':detector.TextBoxDetector()}
return DetectorTask._detectors
