Cpt.Captain/connecting_the_dots
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Add camera parameters

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CptCaptain 2021-11-15 16:52:40 +01:00
parent 42599ee465
commit 56f2aa7d5d
1 changed files with 16 additions and 15 deletions
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31
data/create_syn_data.py
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@ -118,16 +118,16 @@ def create_data(out_root, idx, n_samples, imsize, patterns, K, baseline, blend_i
cam_x = cam_x_ + rng.uniform(-0.1,0.1) cam_x = cam_x_ + rng.uniform(-0.1,0.1)
cam_y = cam_y_ + rng.uniform(-0.1,0.1) cam_y = cam_y_ + rng.uniform(-0.1,0.1)
cam_z = cam_z_ + rng.uniform(-0.1,0.1) cam_z = cam_z_ + rng.uniform(-0.1,0.1)
tcam = np.array([cam_x, cam_y, cam_z], dtype=np.float32) tcam = np.array([cam_x, cam_y, cam_z], dtype=np.float32)
if np.linalg.norm(tcam[0:2])<1e-9: if np.linalg.norm(tcam[0:2])<1e-9:
Rcam = np.eye(3, dtype=np.float32) Rcam = np.eye(3, dtype=np.float32)
else: else:
Rcam = get_rotation_matrix(center, center-tcam) Rcam = get_rotation_matrix(center, center-tcam)
tproj = tcam + basevec tproj = tcam + basevec
Rproj = Rcam Rproj = Rcam
ret['R'].append(Rcam) ret['R'].append(Rcam)
ret['t'].append(tcam) ret['t'].append(tcam)
@ -167,7 +167,7 @@ def create_data(out_root, idx, n_samples, imsize, patterns, K, baseline, blend_i
ambient = pyrenderer.normal().copy() ambient = pyrenderer.normal().copy()
ambient = np.mean(ambient, axis=2) ambient = np.mean(ambient, axis=2)
# get the noise free IR image $J$ # get the noise free IR image $J$
im = blend_im_rnd * im + (1 - blend_im_rnd) * ambient im = blend_im_rnd * im + (1 - blend_im_rnd) * ambient
ret[f'ambient{s}'].append( ambient[None].astype(np.float32) ) ret[f'ambient{s}'].append( ambient[None].astype(np.float32) )
@ -207,13 +207,13 @@ def create_data(out_root, idx, n_samples, imsize, patterns, K, baseline, blend_i
if __name__=='__main__': if __name__=='__main__':
np.random.seed(42) np.random.seed(42)
# output directory # output directory
with open('../config.json') as fp: with open('../config.json') as fp:
config = json.load(fp) config = json.load(fp)
data_root = Path(config['DATA_ROOT']) data_root = Path(config['DATA_ROOT'])
shapenet_root = config['SHAPENET_ROOT'] shapenet_root = config['SHAPENET_ROOT']
data_type = 'syn' data_type = 'syn'
out_root = data_root / f'{data_type}' out_root = data_root / f'{data_type}'
out_root.mkdir(parents=True, exist_ok=True) out_root.mkdir(parents=True, exist_ok=True)
@ -228,27 +228,28 @@ if __name__=='__main__':
except: except:
pass pass
# load shapenet models # load shapenet models
obj_classes = ['chair'] obj_classes = ['chair']
objs = get_objs(shapenet_root, obj_classes) objs = get_objs(shapenet_root, obj_classes)
# camera parameters # camera parameters
imsize = (480, 640) imsize = (488, 648)
imsizes = [(imsize[0]//(2**s), imsize[1]//(2**s)) for s in range(4)] imsizes = [(imsize[0]//(2**s), imsize[1]//(2**s)) for s in range(4)]
K = np.array([[567.6, 0, 324.7], [0, 570.2, 250.1], [0 ,0, 1]], dtype=np.float32) # K = np.array([[567.6, 0, 324.7], [0, 570.2, 250.1], [0 ,0, 1]], dtype=np.float32)
K = np.array([[1929.5936336276382, 0, 113.66561071478046], [0, 1911.2517985448746, 473.70108079885887], [0 ,0, 1]], dtype=np.float32)
focal_lengths = [K[0,0]/(2**s) for s in range(4)] focal_lengths = [K[0,0]/(2**s) for s in range(4)]
baseline=0.075 baseline=0.075
blend_im = 0.6 blend_im = 0.6
noise = 0 noise = 0
# capture the same static scene from different view points as a track # capture the same static scene from different view points as a track
track_length = 4 track_length = 4
# load pattern image # load pattern image
pattern_path = './kinect_pattern.png' pattern_path = './kinect_pattern.png'
pattern_crop = True pattern_crop = True
patterns = get_patterns(pattern_path, imsizes, pattern_crop) patterns = get_patterns(pattern_path, imsizes, pattern_crop)
# write settings to file # write settings to file
settings = { settings = {
'imsizes': imsizes, 'imsizes': imsizes,
@ -261,7 +262,7 @@ if __name__=='__main__':
print(f'write settings to {out_path}') print(f'write settings to {out_path}')
with open(str(out_path), 'wb') as f: with open(str(out_path), 'wb') as f:
pickle.dump(settings, f, pickle.HIGHEST_PROTOCOL) pickle.dump(settings, f, pickle.HIGHEST_PROTOCOL)
# start the job # start the job
n_samples = 2**10 + 2**13 n_samples = 2**10 + 2**13
for idx in range(start, n_samples): for idx in range(start, n_samples):