Rename smpl->smplpytorch.

smplpytorch/native/models/README.md

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+Here copy the .pkl model files.

smplpytorch/native/webuser/posemapper.py

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+import chumpy as ch
+import numpy as np
+import cv2
+
+
+class Rodrigues(ch.Ch):
+    dterms = 'rt'
+
+    def compute_r(self):
+        return cv2.Rodrigues(self.rt.r)[0]
+
+    def compute_dr_wrt(self, wrt):
+        if wrt is self.rt:
+            return cv2.Rodrigues(self.rt.r)[1].T
+
+
+def lrotmin(p):
+    if isinstance(p, np.ndarray):
+        p = p.ravel()[3:]
+        return np.concatenate([(cv2.Rodrigues(np.array(pp))[0] - np.eye(3)).ravel() for pp in p.reshape((-1, 3))]).ravel()
+    if p.ndim != 2 or p.shape[1] != 3:
+        p = p.reshape((-1, 3))
+    p = p[1:]
+    return ch.concatenate([(Rodrigues(pp) - ch.eye(3)).ravel() for pp in p]).ravel()
+
+
+def posemap(s):
+    if s == 'lrotmin':
+        return lrotmin
+    else:
+        raise Exception('Unknown posemapping: %s' % (str(s),))

smplpytorch/native/webuser/serialization.py

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+def ready_arguments(fname_or_dict):
+    import numpy as np
+    import pickle
+    import chumpy as ch
+    from chumpy.ch import MatVecMult
+    from smplpytorch.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