From 51a48f2489ab586c55eef49f549bdc59001ccaf1 Mon Sep 17 00:00:00 2001
From: "Eric.Lee2021" <305141918@qq.com>
Date: Fri, 25 Jun 2021 23:30:23 +0800
Subject: [PATCH] add optimize_shape

---
 optimize_shape.py | 97 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 97 insertions(+)
 create mode 100644 optimize_shape.py

diff --git a/optimize_shape.py b/optimize_shape.py
new file mode 100644
index 0000000..bd7026e
--- /dev/null
+++ b/optimize_shape.py
@@ -0,0 +1,97 @@
+import argparse
+
+import numpy as np
+from tqdm import tqdm
+
+from utils import func, bone
+from utils.LM import LM_Solver
+
+
+def align_bone_len(opt_, pre_):
+    opt = opt_.copy()
+    pre = pre_.copy()
+
+    opt_align = opt.copy()
+    for i in range(opt.shape[0]):
+        ratio = pre[i][6] / opt[i][6]
+        opt_align[i] = ratio * opt_align[i]
+
+    err = np.abs(opt_align - pre).mean(0)
+
+    return err
+
+
+def main(args):
+    path=args.path
+    for dataset in args.dataset:
+        # load predictions (N*21*3)
+        print("load {}'s joint 3D".format(dataset))
+        pred_j3d = np.load("{}/{}_pre_joints.npy".format(path, dataset),allow_pickle=True)
+
+        opt_shapes = []
+        opt_bone_lens = []
+        pre_useful_bone_lens = []
+
+        # loop
+        for pred in tqdm(pred_j3d):
+            # 0 initialization
+            pose, shape = func.initiate("zero")
+
+            pre_useful_bone_len = bone.caculate_length(pred, label="useful")
+            pre_useful_bone_lens.append(pre_useful_bone_len)
+
+            # optimize here!
+            solver = LM_Solver(num_Iter=500, th_beta=shape, th_pose=pose, lb_target=pre_useful_bone_len,
+                               weight=args.weight)
+            opt_shape = solver.LM()
+            opt_shapes.append(opt_shape)
+
+            opt_bone_len = solver.get_bones(opt_shape)
+            opt_bone_lens.append(opt_bone_len)
+
+            # plt.plot(solver.get_result(), 'r')
+            # plt.show()
+
+            # break
+
+        opt_shapes = np.array(opt_shapes).reshape(-1, 10)
+        opt_bone_lens = np.array(opt_bone_lens).reshape(-1, 15)
+        pre_useful_bone_lens = np.array(pre_useful_bone_lens).reshape(-1, 15)
+
+        np.save("{}/{}_shapes.npy".format(path, dataset, args.weight), opt_shapes)
+
+        error = align_bone_len(opt_bone_lens, pre_useful_bone_lens)
+
+        print("dataset:{} weight:{} ERR sum: {}".format(dataset, args.weight, error.sum()))
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(
+        description='optimize shape params. of mano model ')
+
+    # Dataset setting
+    parser.add_argument(
+        '-ds',
+        "--dataset",
+        nargs="+",
+        default=['rhd', 'stb', 'do', 'eo'],
+        type=str,
+        help="sub datasets, should be listed in: [stb|rhd|do|eo]"
+    )
+    parser.add_argument(
+        '-wt', '--weight',
+        default=1e-5,
+        type=float,
+        metavar='weight',
+        help='weight of L2 regularizer '
+    )
+
+    parser.add_argument(
+        '-p',
+        '--path',
+        default='out_testset',
+        type=str,
+        metavar='data_root',
+        help='directory')
+
+    main(parser.parse_args())
-- 
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