280 lines
9.0 KiB
Python
280 lines
9.0 KiB
Python
import os
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import sys
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import time
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import argparse
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import numpy as np
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import scipy.io as sio
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import scipy as sp
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import plotly.graph_objects as go
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from scipy.spatial.transform import Rotation
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from tqdm import tqdm
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os.environ["CUDA_VISIBLE_DEVICES"] = "1"
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import tensorflow as tf
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gpus = tf.config.experimental.list_physical_devices('GPU')
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if gpus:
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try:
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for gpu in gpus:
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tf.config.experimental.set_memory_growth(gpu, True)
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except RuntimeError as e:
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print(e)
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from custom_utils.smpl import SMPL
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from custom_utils.supersmpl_io import readPC2
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# run = wandb.init(config=tf.compat.v1.flags.FLAGS, sync_tensorboard=True, project='unpose', name='new_smpl_2', notes='')
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# artifact = wandb.Artifact('code', type='code')
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# artifact.add_file('Data/unpose.py')
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# artifact.add_file('Data/verify_unposed.py')
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# artifact.add_file('FeatureLines/superSMPL/smpl.py')
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# artifact.add_file('FeatureLines/superSMPL/smpl_2.py')
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# run.log_artifact(artifact)
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def read_garment(folder, id):
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min_body = np.load(os.path.join(folder, id, id + '_minimal.npy'))
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return min_body
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def plot_3d(mesh_orig, mesh_pred, unposed, faces):
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_mesh_pred = mesh_pred.copy()
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_unposed = unposed.copy()
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_mesh_pred[:,1] += 1
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_unposed[:,1] += 3
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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=mesh_orig[:,0],
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y=mesh_orig[:,1],
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z=mesh_orig[:,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=_mesh_pred[:,0],
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y=_mesh_pred[:,1],
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z=_mesh_pred[:,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=_unposed[:,0],
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y=_unposed[:,1],
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z=_unposed[:,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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# vertexcolor=collision_color
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)
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])
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fig.update_layout(scene_aspectmode='manual',
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scene_aspectratio=dict(x=1, y=2, 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=[-1,3],),
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zaxis = dict(nticks=4, range=[-1,1],),)
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)
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return fig
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def verify():
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folder = '/home/workspace/data/dataset/cape_release/minimal_body_shape'
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F = np.load('/home/workspace/data/dataset/cape_release/misc/smpl_tris.npy')
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gender_table = np.load('/home/workspace/data/dataset/cape_release/misc/subj_genders.pkl', allow_pickle=True)
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smpl = SMPL(orig_smpl=True)
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ids = os.listdir(folder)
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for id in tqdm(ids):
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if not os.path.isdir(os.path.join(folder, id)):
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continue
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orig_body = read_garment(folder, id)
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pose = tf.constant(np.zeros((1, 72)), dtype=np.float32)
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R = smpl.run_rodrigues(pose)
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shape = np.load(os.path.join(folder, id, 'betas.npy'))
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gender = [0] if gender_table[id] == 'female' else [1]
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shape = np.concatenate((shape, gender)).astype(np.float32)
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new_body = smpl.run(R, shape[np.newaxis,:], remove=False)[0][0]
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euclid_error = tf.reduce_mean(tf.math.sqrt(tf.reduce_sum((orig_body[smpl.to_keep,:] - new_body.numpy()[smpl.to_keep,:]) ** 2, axis=-1))).numpy()
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x = orig_body
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y = new_body
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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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])
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fig.update_layout(scene_aspectmode='manual',
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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=[-1.5,1.5],),
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yaxis = dict(nticks=4, range=[-1.5,1.5],),
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zaxis = dict(nticks=4, range=[-1.5,1.5],),)
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)
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fig.show()
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print()
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if __name__ == '__main__':
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verify()
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parser = argparse.ArgumentParser()
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parser.add_argument("-f", "--folder", help = "end of range", default='/home/workspace/data/dataset/cape_release/minimal_body_shape')
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parser.add_argument("-s", "--start", help = "start of range", default=0)
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parser.add_argument("-e", "--end", help = "end of range", default=200)
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parser.add_argument("-n", "--name", help = "end of range", default='lhunor')
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args = parser.parse_args()
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F = np.load('/home/workspace/data/dataset/cape_release/misc/smpl_tris.npy')
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F = np.load('custom_utils/smpl_faces_orig.npy')
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gender_table = np.load('/home/workspace/data/dataset/cape_release/misc/subj_genders.pkl', allow_pickle=True)
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folder = args.folder
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smpl = SMPL(orig_smpl=True)
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iterations = 10000
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ids = os.listdir(folder)[int(args.start):int(args.end)]
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for id in tqdm(ids):
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if not os.path.isdir(os.path.join(folder, id)):
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continue
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min_body = read_garment(folder, id)
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min_body = min_body[smpl.to_keep, :]
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pose = tf.constant(np.zeros((1, 72)), dtype=np.float32)
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R = smpl.run_rodrigues(pose)
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zero_body = smpl.run(R, np.zeros((1, 11), np.float32), remove=False)[0]
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lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(1.0, decay_steps=40000, decay_rate=0.1, staircase=False)
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optimizer = tf.keras.optimizers.SGD(learning_rate=1.0)
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betas = tf.Variable(np.zeros((1, 10), np.float32))
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# gender = np.zeros((1,1), np.float32) #F
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gender = 0 if gender_table[id] == 'female' else 1
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@tf.function
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def shaping(shape):
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result = (tf.tensordot(shape, smpl.shapedirs[gender], axes=[[1], [2]]) + tf.reshape(smpl.v_template[gender], shape=(1,6890,3)))[0]
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result = tf.gather(result, smpl.to_keep, axis=0)
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return result
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@tf.function
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def error():
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pred = shaping(betas)
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loss = tf.reduce_mean(tf.math.abs(pred - min_body))
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return loss
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start = time.perf_counter()
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euclid_error = 10000
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while True:
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optimizer.minimize(error, [betas])
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i = optimizer.iterations.numpy()
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if i % 100 == 0:
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pred = shaping(betas)
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# loss = tf.reduce_mean(tf.reduce_sum(tf.math.abs(pred - posed) ** 2, axis=-1))
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loss = tf.reduce_mean(tf.math.abs(pred - min_body))
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euclid_error = tf.reduce_mean(tf.math.sqrt(tf.reduce_sum((min_body - pred) ** 2, axis=-1)))
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print('id: ' + id + ' iteration: ' + str(i) + ' loss: ' + str(loss.numpy()) + ' error: ' + str(euclid_error.numpy()) + ' time: ' + str(time.perf_counter() - start))
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start = time.perf_counter()
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if euclid_error < 0.0001:
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break
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if euclid_error < 0.0005 and i > 20000:
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break
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if euclid_error < 0.001 and i > 40000:
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break
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if i > 60000:
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break
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x = min_body
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y = pred
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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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])
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fig.update_layout(scene_aspectmode='manual',
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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=[-1.5,1.5],),
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yaxis = dict(nticks=4, range=[-1.5,1.5],),
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zaxis = dict(nticks=4, range=[-1.5,1.5],),)
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)
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fig.show()
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np.save(os.path.join(folder, id, 'betas_2.npy'), betas[0])
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continue
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