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_normals():
global ds
ds = []
try:
shutil.rmtree(out_path)
except:
pass
os.mkdir(out_path)
for i in range(1):
dataset.load_random_dataset()
dataset.advance()
name = 'dataset_%s' % str(i)
d = dict(name=name)
folder = os.path.join(out_path, name)
os.mkdir(folder)
depthL = dataset.depthL.astype('f')
pylab.figure(0)
pylab.clf()
pylab.imshow(depthL)
pylab.savefig(os.path.join(folder,'depth.jpg'))
dt = timeit.timeit(lambda: normals.normals_numpy(depthL),
number=1)
d['numpy'] = dt
n,w = normals.normals_numpy(depthL)
pylab.clf()
show_normals(n, w)
pylab.savefig(os.path.join(folder,'normals_numpy.jpg'))
dt = timeit.timeit(lambda: normals.normals_c(depthL),
number=1)
d['c'] = dt
n,w = normals.normals_c(depthL)
pylab.clf()
show_normals(n, w)
pylab.savefig(os.path.join(folder,'normals_c.jpg'))
rect = ((0,0),(640,480))
mask = np.zeros((480,640),'bool')
mask[1:-1,1:-1] = 1
normals.opencl.set_rect(rect, ((0,0),(0,0)))
dt = timeit.timeit(lambda:
normals.normals_opencl(depthL, mask, rect).wait(),
number=1)
d['opencl'] = dt
nw,_ = normals.opencl.get_normals()
n,w = nw[:,:,:3], nw[:,:,3]
pylab.clf()
show_normals(n,w)
pylab.savefig(os.path.join(folder,'normals_opencl.jpg'))
ds.append(d)
def start(dset=None, frame_num=0):
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')
dataset.setup_opencl()
def start(dset=None, frame_num=0):
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')
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 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):
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")
dataset.setup_opencl()
global R_correct
if "R_correct" in globals():
del R_correct
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 run_calib():
global ds
ds = []
try:
shutil.rmtree(out_path)
except:
pass
os.mkdir(out_path)
for i in range(1):
dataset.load_random_dataset()
table_calibration.newest_folder = dataset.current_path
name = 'dataset_%s' % str(i)
d = dict(name=name)
folder = os.path.join(out_path, name)
os.mkdir(folder)
dt = timeit.timeit(lambda: table_calibration.finish_cube_calib(),
number=1)
d['dt'] = dt
depthL, depthR = table_calibration.depthL, table_calibration.depthR
bgL, bgR = config.bgL, config.bgR
for depth, side, bg in (depthL, 'left', bgL), (depthR, 'right', bgR):
pylab.figure(0)
pylab.clf()
pylab.imshow(depth)
pylab.savefig(os.path.join(folder, 'depth_%s.jpg' % side))
pylab.clf()
pylab.imshow(bg['bgHi'])
pylab.savefig(os.path.join(folder, 'bghi_%s.jpg' % side))
pylab.imshow(bg['bgLo'])
pylab.savefig(os.path.join(folder, 'bglo_%s.jpg' % side))
ds.append(d)
def run_calib():
global ds
ds = []
try:
shutil.rmtree(out_path)
except:
pass
os.mkdir(out_path)
for i in range(1):
dataset.load_random_dataset()
table_calibration.newest_folder = dataset.current_path
name = "dataset_%s" % str(i)
d = dict(name=name)
folder = os.path.join(out_path, name)
os.mkdir(folder)
dt = timeit.timeit(lambda: table_calibration.finish_cube_calib(), number=1)
d["dt"] = dt
depthL, depthR = table_calibration.depthL, table_calibration.depthR
bgL, bgR = config.bgL, config.bgR
for depth, side, bg in (depthL, "left", bgL), (depthR, "right", bgR):
pylab.figure(0)
pylab.clf()
pylab.imshow(depth)
pylab.savefig(os.path.join(folder, "depth_%s.jpg" % side))
pylab.clf()
pylab.imshow(bg["bgHi"])
pylab.savefig(os.path.join(folder, "bghi_%s.jpg" % side))
pylab.imshow(bg["bgLo"])
pylab.savefig(os.path.join(folder, "bglo_%s.jpg" % side))
ds.append(d)
def test_tablecalib():
dataset.load_random_dataset()
dataset.advance()
table_calibration.finish_table_calib()
def test_dataset():
dataset.load_random_dataset()
def once():
dataset.advance()
depthL, depthR = dataset.depthL, dataset.depthR
maskL, rectL = preprocess.threshold_and_mask(depthL, config.bgL)
maskR, rectR = preprocess.threshold_and_mask(depthR, config.bgR)
show_mask('maskL', maskL.astype('f'), rectL)
show_mask('maskR', maskR.astype('f'), rectR)
pylab.waitforbuttonpress(0.01)
def go():
while 1:
once()
def show_backgrounds():
pylab.figure(1)
pylab.imshow(config.bgL['bgHi'])
pylab.draw()
pylab.figure(2)
pylab.clf()
pylab.imshow(config.bgR['bgHi'])
pylab.draw()
if __name__ == "__main__":
dataset.load_random_dataset()
go()
def test_tablecalib():
dataset.load_random_dataset()
dataset.advance()
def once():
dataset.advance()
depthL, depthR = dataset.depthL, dataset.depthR
maskL, rectL = preprocess.threshold_and_mask(depthL, config.bgL)
maskR, rectR = preprocess.threshold_and_mask(depthR, config.bgR)
show_mask("maskL", maskL.astype("f"), rectL)
show_mask("maskR", maskR.astype("f"), rectR)
pylab.waitforbuttonpress(0.01)
def go():
while 1:
once()
def show_backgrounds():
pylab.figure(1)
pylab.imshow(config.bgL["bgHi"])
pylab.draw()
pylab.figure(2)
pylab.clf()
pylab.imshow(config.bgR["bgHi"])
pylab.draw()
if __name__ == "__main__":
dataset.load_random_dataset()
go()
