def assign_tags(video_id):
import django
from PIL import Image
sys.path.append(os.path.dirname(__file__))
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "dva.settings")
django.setup()
from django.conf import settings
from dvaapp.models import Video, Frame, Region
from dvalib import entity, annotator
dv = Video.objects.get(id=video_id)
frames = Frame.objects.all().filter(video=dv)
v = entity.WVideo(dvideo=dv, media_dir=settings.MEDIA_ROOT)
wframes = {
df.pk: entity.WFrame(video=v,
frame_index=df.frame_index,
primary_key=df.pk)
for df in frames
}
algorithm = annotator.OpenImagesAnnotator()
logging.info("starting annotation {}".format(algorithm.name))
for k, f in wframes.items():
tags = algorithm.apply(f.local_path())
a = Region()
a.region_type = Region.ANNOTATION
a.frame_id = k
a.video_id = video_id
a.object_name = "OpenImagesTag"
a.metadata_text = " ".join([t for t, v in tags.iteritems() if v > 0.1])
a.metadata_json = json.dumps(
{t: 100.0 * v
for t, v in tags.iteritems() if v > 0.1})
a.full_frame = True
a.save()
print a.metadata_text
def detect_text_boxes(video_pk, cpu_mode=False):
"""
Detect Text Boxes in frames for a video using CTPN, must be run in dva_ctpn container
:param detector_pk
:param video_pk
:return:
"""
setup_django()
from dvaapp.models import Region, Frame
from django.conf import settings
from PIL import Image
import sys
video_pk = int(video_pk)
sys.path.append('/opt/ctpn/CTPN/tools/')
sys.path.append('/opt/ctpn/CTPN/src/')
from cfg import Config as cfg
from other import resize_im, CaffeModel
import cv2, caffe
from detectors import TextProposalDetector, TextDetector
NET_DEF_FILE = "/opt/ctpn/CTPN/models/deploy.prototxt"
MODEL_FILE = "/opt/ctpn/CTPN/models/ctpn_trained_model.caffemodel"
if cpu_mode: # Set this to true for CPU only mode
caffe.set_mode_cpu()
else:
caffe.set_mode_gpu()
caffe.set_device(cfg.TEST_GPU_ID)
text_proposals_detector = TextProposalDetector(
CaffeModel(NET_DEF_FILE, MODEL_FILE))
text_detector = TextDetector(text_proposals_detector)
for f in Frame.objects.all().filter(video_id=video_pk):
path = "{}/{}/frames/{}.jpg".format(settings.MEDIA_ROOT, video_pk,
f.frame_index)
im = cv2.imread(path)
old_h, old_w, channels = im.shape
im, _ = resize_im(im, cfg.SCALE, cfg.MAX_SCALE)
new_h, new_w, channels = im.shape
mul_h = float(old_h) / float(new_h)
mul_w = float(old_w) / float(new_w)
text_lines = text_detector.detect(im)
for k in text_lines:
left, top, right, bottom, score = k
left, top, right, bottom = int(left * mul_w), int(
top * mul_h), int(right * mul_w), int(bottom * mul_h)
r = Region()
r.region_type = r.DETECTION
r.confidence = int(100.0 * score)
r.object_name = "CTPN_TEXTBOX"
r.y = top
r.x = left
r.w = right - left
r.h = bottom - top
r.frame_id = f.pk
r.video_id = video_pk
r.save()
right = r.w + r.x
bottom = r.h + r.y
img = Image.open(path)
img2 = img.crop((left, top, right, bottom))
img2.save("{}/{}/detections/{}.jpg".format(settings.MEDIA_ROOT,
video_pk, r.pk))
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 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 ssd_detect(video_id):
"""
This is a HACK since Tensorflow is absolutely atrocious in allocating and freeing up memory.
Once a process / session is allocated a memory it cannot be forced to clear it up.
As a result this code gets called via a subprocess which clears memory when it exits.
:param video_id:
:return:
"""
import django
from PIL import Image
sys.path.append(os.path.dirname(__file__))
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "dva.settings")
django.setup()
from django.conf import settings
from dvaapp.models import Video, Region, Frame
from dvalib import entity, detector
dv = Video.objects.get(id=video_id)
frames = Frame.objects.all().filter(video=dv)
v = entity.WVideo(dvideo=dv, media_dir=settings.MEDIA_ROOT)
wframes = {
df.pk: entity.WFrame(video=v,
frame_index=df.frame_index,
primary_key=df.pk)
for df in frames
}
detection_count = 0
algorithm = detector.SSDetector()
logging.info("starting detection {}".format(algorithm.name))
frame_detections = algorithm.detect(wframes.values())
for frame_pk, detections in frame_detections.iteritems():
for d in detections:
dd = Region()
dd.region_type = Region.DETECTION
dd.video = dv
dd.frame_id = frame_pk
dd.object_name = d['name']
dd.confidence = d['confidence']
dd.x = d['left']
dd.y = d['top']
dd.w = d['right'] - d['left']
dd.h = d['bot'] - d['top']
dd.save()
img = Image.open(wframes[frame_pk].local_path())
img2 = img.crop((d['left'], d['top'], d['right'], d['bot']))
img2.save("{}/{}/detections/{}.jpg".format(settings.MEDIA_ROOT,
video_id, dd.pk))
detection_count += 1
dv.refresh_from_db()
dv.detections = dv.detections + detection_count
dv.save()
def recognize_text(video_pk):
"""
Recognize text in regions with name CTPN_TEXTBOX using CRNN
:param detector_pk
:param video_pk
:return:
"""
setup_django()
from dvaapp.models import Region
from django.conf import settings
from PIL import Image
import sys
video_pk = int(video_pk)
import dvalib.crnn.utils as utils
import dvalib.crnn.dataset as dataset
import torch
from torch.autograd import Variable
from PIL import Image
import dvalib.crnn.models.crnn as crnn
model_path = '/root/DVA/dvalib/crnn/data/crnn.pth'
alphabet = '0123456789abcdefghijklmnopqrstuvwxyz'
model = crnn.CRNN(32, 1, 37, 256, 1)
model.load_state_dict(torch.load(model_path))
converter = utils.strLabelConverter(alphabet)
transformer = dataset.resizeNormalize((100, 32))
for r in Region.objects.all().filter(video_id=video_pk,
object_name='CTPN_TEXTBOX'):
img_path = "{}/{}/detections/{}.jpg".format(settings.MEDIA_ROOT,
video_pk, r.pk)
image = Image.open(img_path).convert('L')
image = transformer(image)
image = image.view(1, *image.size())
image = Variable(image)
model.eval()
preds = model(image)
_, preds = preds.max(2)
preds = preds.squeeze(2)
preds = preds.transpose(1, 0).contiguous().view(-1)
preds_size = Variable(torch.IntTensor([preds.size(0)]))
sim_pred = converter.decode(preds.data, preds_size.data, raw=False)
dr = Region()
dr.video_id = r.video_id
dr.object_name = "CRNN_TEXT"
dr.x = r.x
dr.y = r.y
dr.w = r.w
dr.h = r.h
dr.region_type = Region.ANNOTATION
dr.metadata_text = sim_pred
dr.frame_id = r.frame_id
dr.save()
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("{}/{}/regions/{}.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
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)
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()
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