Init.

smpl/native/webuser/serialization.py

@@ -0,0 +1,39 @@
+def ready_arguments(fname_or_dict):
+    import numpy as np
+    import pickle
+    import chumpy as ch
+    from chumpy.ch import MatVecMult
+    from smpl.native.webuser.posemapper import posemap
+
+    if not isinstance(fname_or_dict, dict):
+        dd = pickle.load(open(fname_or_dict, 'rb'), encoding='latin1')
+        # dd = pickle.load(open(fname_or_dict, 'rb'))
+    else:
+        dd = fname_or_dict
+
+    want_shapemodel = 'shapedirs' in dd
+    nposeparms = dd['kintree_table'].shape[1] * 3
+
+    if 'trans' not in dd:
+        dd['trans'] = np.zeros(3)
+    if 'pose' not in dd:
+        dd['pose'] = np.zeros(nposeparms)
+    if 'shapedirs' in dd and 'betas' not in dd:
+        dd['betas'] = np.zeros(dd['shapedirs'].shape[-1])
+
+    for s in ['v_template', 'weights', 'posedirs', 'pose', 'trans', 'shapedirs', 'betas', 'J']:
+        if (s in dd) and not hasattr(dd[s], 'dterms'):
+            dd[s] = ch.array(dd[s])
+
+    if want_shapemodel:
+        dd['v_shaped'] = dd['shapedirs'].dot(dd['betas']) + dd['v_template']
+        v_shaped = dd['v_shaped']
+        J_tmpx = MatVecMult(dd['J_regressor'], v_shaped[:, 0])
+        J_tmpy = MatVecMult(dd['J_regressor'], v_shaped[:, 1])
+        J_tmpz = MatVecMult(dd['J_regressor'], v_shaped[:, 2])
+        dd['J'] = ch.vstack((J_tmpx, J_tmpy, J_tmpz)).T
+        dd['v_posed'] = v_shaped + dd['posedirs'].dot(posemap(dd['bs_type'])(dd['pose']))
+    else:
+        dd['v_posed'] = dd['v_template'] + dd['posedirs'].dot(posemap(dd['bs_type'])(dd['pose']))
+
+    return dd