def load(self):
if torch.cuda.is_available():
self.session = crnn.CRNN(32, 1, 37, 256, 1).cuda()
self.cuda = True
else:
self.session = crnn.CRNN(32, 1, 37, 256, 1)
self.session.load_state_dict(torch.load(self.model_path))
self.converter = utils.strLabelConverter(self.alphabet)
self.transformer = dataset.resizeNormalize((100, 32))
def load(self):
if torch.cuda.is_available():
self.session = crnn.CRNN(32, 1, 37, 256, 1).cuda()
self.cuda = True
else:
self.session = crnn.CRNN(32, 1, 37, 256, 1)
self.session.load_state_dict(torch.load(self.model_path))
self.converter = utils.strLabelConverter(self.alphabet)
self.transformer = dataset.resizeNormalize((100, 32))
def load(self):
logging.info("Loding CRNN model first apply will be slow")
if torch.cuda.is_available():
self.session = crnn.CRNN(32, 1, 37, 256, 1).cuda()
self.cuda = True
else:
self.session = crnn.CRNN(32, 1, 37, 256, 1)
self.session.load_state_dict(torch.load(self.model_path))
self.session.eval()
self.converter = utils.strLabelConverter(self.alphabet)
self.transformer = dataset.resizeNormalize((100, 32))
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()
