def start(dset=None, frame_num=0):
main.initialize()
if not FOR_REAL:
if dset is None:
dataset.load_random_dataset()
else:
dataset.load_dataset(dset)
while dataset.frame_num < frame_num:
dataset.advance()
name = dset
name = os.path.split(name)[1]
custom = os.path.join('data/sets/', name, 'gt.txt')
if os.path.exists(custom):
# Try dataset directory first
fname = custom
else:
import re
# Fall back on generic ground truth file
match = re.match('.*_z(\d)m_(.*)', name)
number = int(match.groups()[0])
fname = 'data/experiments/gt/gt%d.txt' % number
with open(fname) as f:
GT = grid.gt2grid(f.read())
grid.initialize_with_groundtruth(GT)
else:
config.load('data/newest_calibration')
opennpy.align_depth_to_rgb()
dataset.setup_opencl()
def start(dset=None, frame_num=0):
main.initialize()
if not FOR_REAL:
if dset is None:
dataset.load_random_dataset()
else:
dataset.load_dataset(dset)
while dataset.frame_num < frame_num:
dataset.advance()
name = dset
name = os.path.split(name)[1]
custom = os.path.join('data/sets/', name, 'gt.txt')
if os.path.exists(custom):
# Try dataset directory first
fname = custom
else:
import re
# Fall back on generic ground truth file
match = re.match('.*_z(\d)m_(.*)', name)
number = int(match.groups()[0])
fname = 'data/experiments/gt/gt%d.txt' % number
with open(fname) as f:
GT = grid.gt2grid(f.read())
grid.initialize_with_groundtruth(GT)
else:
config.load('data/newest_calibration')
opennpy.align_depth_to_rgb()
dataset.setup_opencl()
def run_grid():
datasets = glob.glob('data/sets/study_*')
try:
shutil.rmtree(out_path)
except:
pass
os.mkdir(out_path)
for name in datasets:
#for name in ('data/sets/cube',):
dataset.load_dataset(name)
name = os.path.split(name)[1]
d = dict(name=name)
folder = os.path.join(out_path, name)
os.mkdir(folder)
global modelmat
modelmat = None
main.initialize()
import re
number = int(re.match('.*_z(\d)m_.*', name).groups()[0])
with open('data/experiments/gt/gt%d.txt' % number) as f:
GT = grid.gt2grid(f.read())
grid.initialize_with_groundtruth(GT)
total = 0
output = []
try:
while 1:
try:
dataset.advance()
except (IOError, ValueError):
break
if dataset.frame_num % 30 == 0:
print name, dataset.frame_num
t1 = time.time()
once()
t2 = time.time()
total += t2 - t1
output.append((main.R_correct.copy(), grid.occ.copy()))
except Exception as e:
print e
d['frames'] = dataset.frame_num
d['time'] = total
d['output'] = output
with open(os.path.join(folder, 'output.pkl'), 'w') as f:
pickle.dump(d, f)
with open(os.path.join(folder, 'final_output.txt'), 'w') as f:
f.write(grid.grid2gt(grid.occ))
def run_grid():
datasets = glob.glob('data/sets/study_*')
try:
shutil.rmtree(out_path)
except:
pass
os.mkdir(out_path)
for name in datasets:
#for name in ('data/sets/cube',):
dataset.load_dataset(name)
name = os.path.split(name)[1]
d = dict(name=name)
folder = os.path.join(out_path, name)
os.mkdir(folder)
global modelmat
modelmat = None
main.initialize()
import re
number = int(re.match('.*_z(\d)m_.*', name).groups()[0])
with open('data/experiments/gt/gt%d.txt' % number) as f:
GT = grid.gt2grid(f.read())
grid.initialize_with_groundtruth(GT)
total = 0
output = []
try:
while 1:
try:
dataset.advance()
except (IOError, ValueError):
break
if dataset.frame_num % 30 == 0:
print name, dataset.frame_num
t1 = time.time()
once()
t2 = time.time()
total += t2-t1
output.append((main.R_correct.copy(), grid.occ.copy()))
except Exception as e:
print e
d['frames'] = dataset.frame_num
d['time'] = total
d['output'] = output
with open(os.path.join(folder, 'output.pkl'),'w') as f:
pickle.dump(d, f)
with open(os.path.join(folder, 'final_output.txt'),'w') as f:
f.write(grid.grid2gt(grid.occ))
def run_grid():
datasets = glob.glob('data/sets/study_*')
try:
os.mkdir(out_path)
except OSError:
print "Couldn't make create output directory [%s], it may already exist." % out_path
print "Remove it and try again."
return False
for name in datasets:
dataset.load_dataset(name)
name = os.path.split(name)[1]
d = dict(name=name)
folder = os.path.join(out_path, name)
os.mkdir(folder)
main.initialize()
import re
number = int(re.match('.*_z(\d)m_.*', name).groups()[0])
with open('data/experiments/gt/gt%d.txt' % number) as f:
GT = grid.gt2grid(f.read())
grid.initialize_with_groundtruth(GT)
total = 0
output = []
try:
while 1:
try:
dataset.advance()
except (IOError, ValueError):
break
if dataset.frame_num % 30 == 0:
print name, dataset.frame_num
t1 = time.time()
once()
t2 = time.time()
total += t2-t1
output.append((main.R_correct.copy(), grid.occ.copy()))
except Exception as e:
print e
d['frames'] = dataset.frame_num
d['time'] = total
d['output'] = output
with open(os.path.join(folder, 'output.pkl'),'w') as f:
pickle.dump(d, f)
with open(os.path.join(folder, 'final_output.txt'),'w') as f:
f.write(grid.grid2gt(grid.occ))
def start(dset=None, frame_num=0):
main.initialize()
if not FOR_REAL:
if dset is None:
dataset.load_random_dataset()
else:
dataset.load_dataset(dset)
while dataset.frame_num < frame_num:
dataset.advance()
else:
config.load('data/newest_calibration')
opennpy.align_depth_to_rgb()
dataset.setup_opencl()
def start(dset=None, frame_num=0):
main.initialize()
if not FOR_REAL:
if dset is None:
dataset.load_random_dataset()
else:
dataset.load_dataset(dset)
while dataset.frame_num < frame_num:
dataset.advance()
else:
config.load('data/newest_calibration')
opennpy.align_depth_to_rgb()
dataset.setup_opencl()
def block_setup(self):
"""Initialize blockplayer stuff"""
glxcontext.makecurrent()
main.initialize()
config.load('data/newest_calibration')
opennpy.align_depth_to_rgb()
dataset.setup_opencl()
self.blocks = None
self.block_loop_quit = False
self.block_initialized = True
self.block_loop()
def start(dset=None, frame_num=0):
main.initialize()
#with open('data/experiments/collab/2011.txt') as f:
global target_model
with open('data/experiments/collab/block.txt') as f:
target_model = grid.gt2grid(f.read())
#grid.initialize_with_groundtruth(GT)
if not FOR_REAL:
if dset is None:
dataset.load_random_dataset()
else:
dataset.load_dataset(dset)
while dataset.frame_num < frame_num:
dataset.advance()
else:
config.load('data/newest_calibration')
opennpy.align_depth_to_rgb()
dataset.setup_opencl()
def start():
main.initialize()
config.load('data/newest_calibration')
opennpy.align_depth_to_rgb()
dataset.setup_opencl()