Add camera parameters

data/create_syn_data.py

@@ -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_y = cam_y_ + 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)
 
     if np.linalg.norm(tcam[0:2])<1e-9:
-      Rcam = np.eye(3, dtype=np.float32) 
+      Rcam = np.eye(3, dtype=np.float32)
     else:
       Rcam = get_rotation_matrix(center, center-tcam)
 
-    tproj = tcam + basevec 
+    tproj = tcam + basevec
-    Rproj = Rcam 
+    Rproj = Rcam
 
     ret['R'].append(Rcam)
     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 = 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
       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__':
 
   np.random.seed(42)
-  
+
   # output directory
   with open('../config.json') as fp:
    config = json.load(fp)
    data_root = Path(config['DATA_ROOT'])
    shapenet_root = config['SHAPENET_ROOT']
-  
+
   data_type = 'syn'
   out_root = data_root / f'{data_type}'
   out_root.mkdir(parents=True, exist_ok=True)
@@ -228,27 +228,28 @@ if __name__=='__main__':
       except:
         pass
 
-  # load shapenet models 
+  # load shapenet models
   obj_classes = ['chair']
   objs = get_objs(shapenet_root, obj_classes)
-  
+
   # camera parameters
-  imsize = (480, 640)
+  imsize = (488, 648)
   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)]
   baseline=0.075
   blend_im = 0.6
   noise = 0
-  
+
   # capture the same static scene from different view points as a track
   track_length = 4
-  
+
   # load pattern image
   pattern_path = './kinect_pattern.png'
   pattern_crop = True
   patterns = get_patterns(pattern_path, imsizes, pattern_crop)
-  
+
   # write settings to file
   settings = {
    'imsizes': imsizes,
@@ -261,7 +262,7 @@ if __name__=='__main__':
   print(f'write settings to {out_path}')
   with open(str(out_path), 'wb') as f:
    pickle.dump(settings, f, pickle.HIGHEST_PROTOCOL)
-  
+
   # start the job
   n_samples = 2**10 + 2**13
   for idx in range(start, n_samples):