def run():
dataset = Dataset('full_pascal_test')
train_dataset = Dataset('full_pascal_trainval')
cls = 'dog'
rtype = '1big_2small'
args = 0.5
detector = 'csc_default'
from synthetic.dataset_policy import DatasetPolicy
all_dets = DatasetPolicy.load_ext_detections(dataset, detector)
cls_ind = dataset.get_ind(cls)
dets = all_dets.filter_on_column('cls_ind',cls_ind,omit=True)
ext_det = ExternalDetectorRegions(dataset, train_dataset, cls, dets, detector, rtype, args)
img = dataset.images[13] # Just some random image...where did the get_image_by_name go?
print img.size
print ext_det.detect(img, 0)
print ext_det.detect(img, 1)
print ext_det.detect(img, 2)
def run():
dataset = Dataset('full_pascal_test')
train_dataset = Dataset('full_pascal_trainval')
cls = 'dog'
rtype = '1big_2small'
args = 0.5
detector = 'csc_default'
from synthetic.dataset_policy import DatasetPolicy
all_dets = DatasetPolicy.load_ext_detections(dataset, detector)
cls_ind = dataset.get_ind(cls)
dets = all_dets.filter_on_column('cls_ind', cls_ind, omit=True)
ext_det = ExternalDetectorRegions(dataset, train_dataset, cls, dets,
detector, rtype, args)
img = dataset.images[
13] # Just some random image...where did the get_image_by_name go?
print img.size
print ext_det.detect(img, 0)
print ext_det.detect(img, 1)
print ext_det.detect(img, 2)
def main():
parser = argparse.ArgumentParser(description="Execute different functions of our system")
parser.add_argument("--first_n", type=int, help="only take the first N images in the datasets")
parser.add_argument(
"--name", help="name for this run", default="default", choices=["default", "nolateral", "nohal", "halfsize"]
)
parser.add_argument("--force", action="store_true", default=False, help="force overwrite")
args = parser.parse_args()
print (args)
# configuration class
class config(object):
pass
cfg = config()
cfg.testname = "../ctfdet/data/finalRL/%s2_test" # object model
cfg.bottomup = False # use complete search
cfg.resize = 1.0 # resize the input image
cfg.hallucinate = True # use HOGs up to 4 pixels
cfg.initr = 1 # initial radious of the CtF search
cfg.ratio = 1 # radious at the next levels
cfg.deform = True # use deformation
cfg.usemrf = True # use lateral constraints
if args.name == "default":
cfg
# sticking with the default params
elif args.name == "nolateral":
cfg.usemrf = False
elif args.name == "nohal":
cfg.hallucinate = False
elif args.name == "halfsize":
cfg.resize = 0.5
# f**k it, do both
test_datasets = ["val", "test", "train"]
for test_dataset in test_datasets:
# Load the dataset
dataset = Dataset("full_pascal_" + test_dataset)
if args.first_n:
dataset.images = dataset.images[: args.first_n]
# create directory for storing cached detections
dirname = "./temp_data"
if os.path.exists("/u/sergeyk"):
dirname = "/u/vis/x1/sergeyk/object_detection"
dirname = dirname + "/ctfdets/%s" % (args.name)
ut.makedirs(dirname)
num_images = len(dataset.images)
for img_ind in range(comm_rank, num_images, comm_size):
# check for existing det
image = dataset.images[img_ind]
filename = os.path.join(dirname, image.name + ".npy")
if os.path.exists(filename) and not args.force:
# table = np.load(filename)[()]
continue
# read the image
imname = dataset.get_image_filename(img_ind)
img = util2.myimread(imname, resize=cfg.resize)
# compute the hog pyramid
f = pyrHOG2.pyrHOG(
img, interv=10, savedir="", notsave=True, notload=True, hallucinate=cfg.hallucinate, cformat=True
)
# for each class
all_dets = []
for ccls in dataset.classes:
t = time.time()
cls_ind = dataset.get_ind(ccls)
print "%s Img %d/%d Class: %s" % (test_dataset, img_ind + 1, num_images, ccls)
# load the class model
m = util2.load("%s%d.model" % (cfg.testname % ccls, 7))
res = []
t1 = time.time()
# for each aspect
for clm, m in enumerate(m):
# scan the image with left and right models
res.append(
pyrHOG2RL.detectflip(
f,
m,
None,
hallucinate=cfg.hallucinate,
initr=cfg.initr,
ratio=cfg.ratio,
deform=cfg.deform,
bottomup=cfg.bottomup,
usemrf=cfg.usemrf,
small=False,
cl=clm,
)
)
fuse = []
numhog = 0
# fuse the detections
for mix in res:
tr = mix[0]
fuse += mix[1]
numhog += mix[3]
rfuse = tr.rank(fuse, maxnum=300)
nfuse = tr.cluster(rfuse, ovr=0.3, inclusion=False)
# print "Number of computed HOGs:",numhog
time_elapsed = time.time() - t
print "Elapsed time: %.3f s" % time_elapsed
bboxes = [nf["bbox"] for nf in nfuse]
scores = [nf["scr"] for nf in nfuse]
assert len(bboxes) == len(scores)
if len(bboxes) > 0:
arr = np.zeros((len(bboxes), 7))
arr[:, :4] = BoundingBox.convert_arr_from_corners(np.array(bboxes))
arr[:, 4] = scores
arr[:, 5] = time_elapsed
arr[:, 6] = cls_ind
all_dets.append(arr)
cols = ["x", "y", "w", "h", "score", "time", "cls_ind"]
if len(all_dets) > 0:
all_dets = np.concatenate(all_dets, 0)
else:
all_dets = np.array([])
table = Table(all_dets, cols)
np.save(filename, table)
def main():
parser = argparse.ArgumentParser(
description='Execute different functions of our system')
parser.add_argument('--first_n',
type=int,
help='only take the first N images in the datasets')
parser.add_argument('--name',
help='name for this run',
default='default',
choices=['default', 'nolateral', 'nohal', 'halfsize'])
parser.add_argument('--force',
action='store_true',
default=False,
help='force overwrite')
args = parser.parse_args()
print(args)
#configuration class
class config(object):
pass
cfg = config()
cfg.testname = "../ctfdet/data/finalRL/%s2_test" #object model
cfg.bottomup = False #use complete search
cfg.resize = 1.0 #resize the input image
cfg.hallucinate = True #use HOGs up to 4 pixels
cfg.initr = 1 #initial radious of the CtF search
cfg.ratio = 1 #radious at the next levels
cfg.deform = True #use deformation
cfg.usemrf = True #use lateral constraints
if args.name == 'default':
cfg
# sticking with the default params
elif args.name == 'nolateral':
cfg.usemrf = False
elif args.name == 'nohal':
cfg.hallucinate = False
elif args.name == 'halfsize':
cfg.resize = 0.5
# f**k it, do both
test_datasets = ['val', 'test', 'train']
for test_dataset in test_datasets:
# Load the dataset
dataset = Dataset('full_pascal_' + test_dataset)
if args.first_n:
dataset.images = dataset.images[:args.first_n]
# create directory for storing cached detections
dirname = './temp_data'
if os.path.exists('/u/sergeyk'):
dirname = '/u/vis/x1/sergeyk/object_detection'
dirname = dirname + '/ctfdets/%s' % (args.name)
ut.makedirs(dirname)
num_images = len(dataset.images)
for img_ind in range(comm_rank, num_images, comm_size):
# check for existing det
image = dataset.images[img_ind]
filename = os.path.join(dirname, image.name + '.npy')
if os.path.exists(filename) and not args.force:
#table = np.load(filename)[()]
continue
#read the image
imname = dataset.get_image_filename(img_ind)
img = util2.myimread(imname, resize=cfg.resize)
#compute the hog pyramid
f = pyrHOG2.pyrHOG(img,
interv=10,
savedir="",
notsave=True,
notload=True,
hallucinate=cfg.hallucinate,
cformat=True)
#for each class
all_dets = []
for ccls in dataset.classes:
t = time.time()
cls_ind = dataset.get_ind(ccls)
print "%s Img %d/%d Class: %s" % (test_dataset, img_ind + 1,
num_images, ccls)
#load the class model
m = util2.load("%s%d.model" % (cfg.testname % ccls, 7))
res = []
t1 = time.time()
#for each aspect
for clm, m in enumerate(m):
#scan the image with left and right models
res.append(
pyrHOG2RL.detectflip(f,
m,
None,
hallucinate=cfg.hallucinate,
initr=cfg.initr,
ratio=cfg.ratio,
deform=cfg.deform,
bottomup=cfg.bottomup,
usemrf=cfg.usemrf,
small=False,
cl=clm))
fuse = []
numhog = 0
#fuse the detections
for mix in res:
tr = mix[0]
fuse += mix[1]
numhog += mix[3]
rfuse = tr.rank(fuse, maxnum=300)
nfuse = tr.cluster(rfuse, ovr=0.3, inclusion=False)
#print "Number of computed HOGs:",numhog
time_elapsed = time.time() - t
print "Elapsed time: %.3f s" % time_elapsed
bboxes = [nf['bbox'] for nf in nfuse]
scores = [nf['scr'] for nf in nfuse]
assert (len(bboxes) == len(scores))
if len(bboxes) > 0:
arr = np.zeros((len(bboxes), 7))
arr[:, :4] = BoundingBox.convert_arr_from_corners(
np.array(bboxes))
arr[:, 4] = scores
arr[:, 5] = time_elapsed
arr[:, 6] = cls_ind
all_dets.append(arr)
cols = ['x', 'y', 'w', 'h', 'score', 'time', 'cls_ind']
if len(all_dets) > 0:
all_dets = np.concatenate(all_dets, 0)
else:
all_dets = np.array([])
table = Table(all_dets, cols)
np.save(filename, table)
