111 lines
3.9 KiB
Python
111 lines
3.9 KiB
Python
## !!MOVEd to preprocessing.py
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import os
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import sys
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import time
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import cv2
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import random
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import numpy as np
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import tensorflow as tf
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import cmapy as cm
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import plotly.graph_objects as go
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import plotly.express as ex
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from dataset import DatasetFactory, StaticDataset
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from custom_utils.custom_utils import batch_to_dict, UV2Mesh, _maskFaces
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from custom_utils.smpl import SMPL
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if __name__ == '__main__':
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os.environ["CUDA_VISIBLE_DEVICES"] = "5"
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uv2mesh = UV2Mesh()
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smpl = SMPL()
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folder = '/home/workspace/local-data/train_skirt_25'
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F = np.load("custom_utils/smpl_faces.npy")
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factory = DatasetFactory()
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signature_dynamic = { 'id': (),
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'pose_shape': (72 + 11,),
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'mesh_posed': (14475, 3,),
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'mesh_unposed': (14475, 3,),
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}
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signature_static = {
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'mesh_mask': (14475,),
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}
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dataset = factory.get_dataset(folder=folder,
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batch_size=1,
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signature=signature_dynamic,
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in_memory=False)
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static_dataset = StaticDataset(folder=folder,
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signature=signature_static)
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uv_res = 256
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os.makedirs(os.path.join(folder, id, 'mesh_unposed_no_shape'), exist_ok=True)
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for i, batch in enumerate(dataset):
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print(i)
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batch = static_dataset.expand_batch(batch_to_dict(batch, signature_dynamic))
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mesh_unposed = np.squeeze(batch['mesh_unposed'].numpy())
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mask = np.squeeze(batch['mesh_mask'].numpy()).astype(bool)
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pose, shape = batch['pose_shape'][:, :72].numpy(), batch['pose_shape'][:, 72:].numpy()
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# mesh_posed = np.squeeze(batch['mesh_posed'].numpy())
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gender = int(shape[0, -1] > 0.5)
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print(gender)
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shape_offsets = tf.tensordot(shape[:,:10], smpl.shapedirs[gender], axes=[[1], [2]]).numpy()
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mesh_unposed[mask, :] = mesh_unposed[mask, :] - np.squeeze(shape_offsets)[smpl.to_keep, :][mask, :]
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np.savez_compressed(os.path.join(folder, id, 'mesh_unposed_no_shape', id + '_' + str(i) + '.npz'), mesh_unposed_no_shape = mesh_unposed)
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# np.savez_compressed(os.path.join(folder, id, 'mesh_posed_body_no_orient', id + '_' + str(i) + '.npz'), mesh_posed_body_no_orient = T[i, :, :])
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# _, faces = _maskFaces(np.zeros((14475, 3), dtype=np.float32), F, mask)
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# x = mesh_unposed[mask, :]
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# y = mesh_unposed[mask, :] - np.squeeze(shape_offsets)[smpl.to_keep, :][mask, :]
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# # x = smpl.v_template.numpy()[0, smpl.to_keep, :]
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# # R = smpl.run_rodrigues(np.zeros((1, 72), dtype=np.float32))
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# # x = smpl.run(R, shape)[0][0]
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# # y = smpl.v_template.numpy()[gender, smpl.to_keep, :] + np.squeeze(shape_offsets)[smpl.to_keep, :]
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# # faces = F
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# fig = go.Figure(data=[
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# go.Mesh3d(
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# # 8 vertices of a cube
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# x=x[:,0],
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# y=x[:,1],
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# z=x[:,2],
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# # i, j and k give the vertices of triangles
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# i = faces[:,0],
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# j = faces[:,1],
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# k = faces[:,2]
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# ),
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# go.Mesh3d(
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# # 8 vertices of a cube
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# x=y[:,0],
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# y=y[:,1],
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# z=y[:,2],
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# # i, j and k give the vertices of triangles
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# i = faces[:,0],
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# j = faces[:,1],
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# k = faces[:,2]
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# )])
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# fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=1, z=1))
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# fig.update_layout(
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# scene = dict(
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# xaxis = dict(nticks=4, range=[-1,1],),
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# yaxis = dict(nticks=4, range=[-1,1],),
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# zaxis = dict(nticks=4, range=[-1,1],),)
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# )
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# fig.show()
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print() |