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deorient_cape.py

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+
+
+import os
+import sys
+
+
+os.environ["CUDA_VISIBLE_DEVICES"] = '6'
+import tensorflow as tf
+
+gpus = tf.config.experimental.list_physical_devices('GPU')
+if gpus:
+    try:
+        # Currently, memory growth needs to be the same across GPUs
+        for gpu in gpus:
+            tf.config.experimental.set_memory_growth(gpu, True)
+        logical_gpus = tf.config.experimental.list_logical_devices('GPU')
+        print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
+    except RuntimeError as e:
+        # Memory growth must be set before GPUs have been initialized
+        print(e)
+
+
+import glob
+import tqdm
+import copy
+import numpy as np
+from custom_utils.smpl import SMPL
+
+# from custom_utils.custom_utils import UV2Mesh, _maskFaces
+import plotly.graph_objects as go
+# import plotly.express as ex
+from scipy.spatial.transform import Rotation as R
+
+
+if __name__ == '__main__':
+    folder = '/home/workspace/local-data/cape_processed/train/female'
+    # folder = '/home/workspace/data/dataset/test_ready'
+    # uv2mesh = UV2Mesh(is_skirt=False)
+    
+    smpl = SMPL(use_one_hot=False, orig_smpl=True)
+
+
+    F = np.load("custom_utils/smpl_faces_orig.npy")
+
+    ids = os.listdir(os.path.join(folder, 'pose_shape'))
+    ids = [id.split('.')[0] for id in ids]
+    ids = set(ids)
+    os.makedirs(os.path.join(folder, 'mesh_posed_no_orient'), exist_ok=True)
+
+    for id in tqdm.tqdm(ids):
+
+        frames = sorted(glob.glob(os.path.join(folder, 'pose_shape', id + '.*')))
+        frames = ['.'.join(frame.split('/')[-1].split('.')[:-1]) + '.npz' for frame in frames]
+
+
+        mesh_posed = [np.load(os.path.join(folder, 'mesh_posed',  frame)) for frame in frames]
+        mesh_posed = [mesh_posed[i]['mesh_posed'] for i in range(len(frames))]
+        pose_shape = [np.load(os.path.join(folder, 'pose_shape',  frame[:-1] + 'y')) for frame in frames]
+
+        # mesh_unposed = [np.load(os.path.join(folder, 'mesh_unposed_no_shape',  frame)) for frame in frames]
+        # mesh_unposed = [mesh_unposed[i]['mesh_unposed_no_shape'] for i in range(len(frames))]
+        # shape = np.asarray(pose_shape)[:, 72:].astype(np.float32)
+        # gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
+        # shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])])
+        # mesh_unposed_shaped = [mesh_unposed[i] + shape_offsets.numpy()[i] for i in range(shape.shape[0])]
+
+
+        pose_shape_no_orient = copy.deepcopy(pose_shape)
+        for i in range(len(pose_shape)):
+            pose_shape_no_orient[i][:3] = 0
+
+        rodr = smpl.run_rodrigues(np.stack([x[:72] for x in pose_shape]).astype(np.float32)).numpy()
+
+        for i, frame in enumerate(frames):
+            r = R.from_matrix(rodr[i, 0, :, :])
+            # assert r.as_matrix() == rodr[frame, 0, :, :]
+            out = r.apply(mesh_posed[i], inverse=True)
+
+            # if i == 50:
+            #     x = smpl.run_garment(pose_shape_no_orient[i][:72][np.newaxis,:].astype(np.float32), pose_shape_no_orient[i][72:][np.newaxis,:].astype(np.float32), mesh_unposed_shaped[i][np.newaxis,:,:].astype(np.float32))[0].numpy()
+            #     y = out
+            #     faces = F
+            #     fig = go.Figure(data=[
+            #             go.Mesh3d(
+            #                 x=x[:,0],
+            #                 y=x[:,1],
+            #                 z=x[:,2],
+            #                 i = faces[:,0],
+            #                 j = faces[:,1],
+            #                 k = faces[:,2],
+            #             ),
+            #             go.Mesh3d(
+            #                 x=y[:,0],
+            #                 y=y[:,1],
+            #                 z=y[:,2],
+            #                 i = faces[:,0],
+            #                 j = faces[:,1],
+            #                 k = faces[:,2],
+            #             ),
+            #         ])
+
+            #     fig.update_layout(scene_aspectmode='manual',
+            #                     scene_aspectratio=dict(x=1, y=1, z=1))
+            #     fig.update_layout(
+            #         scene = dict(
+            #             xaxis = dict(nticks=4, range=[-1,1],),
+            #                         yaxis = dict(nticks=4, range=[-1,1],),
+            #                         zaxis = dict(nticks=4, range=[-1,1],),)
+            #                         )
+
+            #     fig.show()
+
+
+            np.savez_compressed(os.path.join(folder, 'mesh_posed_no_orient', frame), mesh_posed_no_orient=out)