227 lines
8.3 KiB
Python
227 lines
8.3 KiB
Python
import os
|
|
import time
|
|
import argparse
|
|
import wandb
|
|
import random
|
|
import tqdm
|
|
import scipy as sp
|
|
import cmapy as cm
|
|
import scipy.interpolate as spi
|
|
import numpy as np
|
|
import tensorflow as tf
|
|
from custom_utils.custom_utils import UV2MeshLabel, maskFacesBatch, _maskFaces
|
|
import plotly.graph_objects as go
|
|
import plotly.express as ex
|
|
from plotly.subplots import make_subplots
|
|
|
|
from utils import str_to_bool
|
|
from tf_utils import check_tf_version, allow_memory_growth, split_gpu_for_testing
|
|
# from losses import collision_penalty as collision_penalty
|
|
from losses import collision_loss as collision_penalty
|
|
from convnext.models.convnext_tf import ConvNeXt, ConvNeXtReverse, Block
|
|
from stylegan2_ae.generator import Generator
|
|
from custom_utils.graph_utils import Reconstructor_V4_Decoder as GNNDecoder
|
|
from custom_utils.graph_utils import Reconstructor_v2 as SimpleReconstructor
|
|
from dataset import DatasetFactory, StaticDataset
|
|
from custom_utils.custom_utils import batch_to_dict
|
|
from custom_utils.smpl import SMPL
|
|
|
|
|
|
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
|
|
|
|
uv2mesh = UV2MeshLabel(res=256)
|
|
|
|
|
|
def downscale_1(img):
|
|
results = [img]
|
|
for i in range(5):
|
|
results.append(tf.image.resize(img, size=np.array(img.shape[:2]) / (2 * (i + 1)), method=tf.image.ResizeMethod.LANCZOS5))
|
|
return results
|
|
|
|
def downscale_2(img):
|
|
results = [img]
|
|
for i in range(5):
|
|
# print(np.array(img.shape[:2]) / (2 ** (i + 1)))
|
|
results.append(tf.image.resize(img, size=np.array(img.shape[:2]) / (2 ** (i + 1)), method=tf.image.ResizeMethod.NEAREST_NEIGHBOR, antialias=True, preserve_aspect_ratio=True))
|
|
return results
|
|
|
|
def upscale_1(imgs):
|
|
results = []
|
|
for img in imgs:
|
|
results.append(tf.image.resize(img, size=(512, 256), method=tf.image.ResizeMethod.LANCZOS5))
|
|
return results
|
|
|
|
|
|
def fill(img, mask):
|
|
coords = np.transpose(np.stack(np.nonzero((1 - mask))), (1,0))
|
|
img_idx = np.transpose(np.stack(np.nonzero((mask))), (1,0))
|
|
img_data = img[img_idx[:,0], img_idx[:,1], :]
|
|
|
|
interpolator = spi.NearestNDInterpolator(x=img_idx, y=img_data)
|
|
img_2 = interpolator(coords)
|
|
res = np.zeros((512,256,3), dtype=np.float32)
|
|
res[coords[:,0], coords[:,1], :] = img_2
|
|
res[img_idx[:,0], img_idx[:,1], :] = img_data
|
|
|
|
return res
|
|
|
|
|
|
def fill_uv_maps(folder, type):
|
|
os.makedirs(os.path.join(folder, type + '_filled'), exist_ok=True)
|
|
mask = np.load('custom_utils/mask_256x512.npy')
|
|
|
|
frames = os.listdir(os.path.join(folder, type))
|
|
|
|
for frame in tqdm.tqdm(frames):
|
|
id, _ = frame.split('_')
|
|
# mask = np.load(os.path.join(folder, 'uv_mask', id + '.npz')); mask = mask['uv_mask']
|
|
img = np.load(os.path.join(folder, type, frame)); img = img['uv_body']
|
|
filled = fill(img, mask)
|
|
np.savez_compressed(os.path.join(folder, type + '_filled', frame), uv_body_posed = filled)
|
|
|
|
|
|
|
|
if __name__ == '__main__':
|
|
# fill_uv_maps(folder='/home/workspace/data/dataset/stylegan_jumpsuit/train', type='uv_body_posed')
|
|
|
|
# pass
|
|
# exit(0)
|
|
|
|
allow_memory_growth()
|
|
parser = argparse.ArgumentParser(description='')
|
|
parser.add_argument('--name', type=str)
|
|
args = vars(parser.parse_args())
|
|
|
|
smpl = SMPL()
|
|
|
|
|
|
factory = DatasetFactory()
|
|
signature_dynamic = { 'id': (),
|
|
'pose_shape': (72 + 11,),
|
|
# 'uv_posed': (3, 512, 256,),
|
|
# 'uv_body_posed': (3, 512, 256,),
|
|
# 'mesh_body_posed': (14475, 3,),
|
|
'mesh_posed': (14475, 3,),
|
|
# 'mesh_unposed': (14475, 3,),
|
|
# 'uv_unposed': (3, 512, 256,)
|
|
'uv_curvature': (512, 256),
|
|
'mesh_curvature': (14475)
|
|
}
|
|
signature_static = { #'uv_static': (3, 512, 256,),
|
|
# 'mesh_static': (14475, 3,),
|
|
'mesh_mask': (14475,),
|
|
'uv_mask': (512, 256,),
|
|
# 'uv_shaped_body': (3, 512, 256,),
|
|
}
|
|
|
|
dataset_train = factory.get_dataset(folder='/home/workspace/local-data/train_100',
|
|
batch_size=1,
|
|
signature=signature_dynamic)
|
|
static_dataset_train = StaticDataset(folder='/home/workspace/local-data/train_100',
|
|
signature=signature_static)
|
|
|
|
|
|
step = 0
|
|
for batch in dataset_train:
|
|
# batch = batch_to_dict(batch, signature_dynamic)
|
|
batch = static_dataset_train.expand_batch(batch_to_dict(batch, signature_dynamic))
|
|
|
|
img = tf.squeeze(batch['uv_curvature']).numpy()
|
|
coords = np.transpose(np.stack(np.nonzero((1 - batch['uv_mask'][0]).numpy().astype(bool))), (1,0))
|
|
img_idx = np.transpose(np.stack(np.nonzero((batch['uv_mask'][0]).numpy().astype(bool))), (1,0))
|
|
img_data = img[img_idx[:,0], img_idx[:,1]]
|
|
|
|
interpolator = spi.NearestNDInterpolator(x=img_idx, y=img_data)
|
|
img_2 = interpolator(coords)
|
|
res = np.zeros((512,256))
|
|
res[coords[:,0], coords[:,1]] = img_2
|
|
res[img_idx[:,0], img_idx[:,1]] = img_data
|
|
|
|
img_2 = res
|
|
|
|
i = 0
|
|
down_curv = tf.image.resize(img_2[:,:,tf.newaxis], size=np.array(img_2.shape[:2]) / (2 * (i + 1)), method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
|
|
down_curv = tf.image.resize(down_curv, size=(512, 256), method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)[:,:,0]
|
|
|
|
|
|
|
|
img_posed = tf.transpose(tf.squeeze(batch['uv_posed']), [1,2,0]).numpy()
|
|
coords = np.transpose(np.stack(np.nonzero((1 - batch['uv_mask'][0]).numpy().astype(bool))), (1,0))
|
|
img_idx = np.transpose(np.stack(np.nonzero((batch['uv_mask'][0]).numpy().astype(bool))), (1,0))
|
|
img_data = img_posed[img_idx[:,0], img_idx[:,1], :]
|
|
|
|
interpolator = spi.NearestNDInterpolator(x=img_idx, y=img_data)
|
|
img_posed_2 = interpolator(coords)
|
|
res = np.zeros((512,256,3))
|
|
res[coords[:,0], coords[:,1], :] = img_posed_2
|
|
res[img_idx[:,0], img_idx[:,1], :] = img_data
|
|
|
|
img_posed_2 = res
|
|
|
|
mesh_posed = uv2mesh(tf.transpose(img_posed_2[i], [2,0,1])[tf.newaxis,:,:,:])[0]
|
|
|
|
|
|
|
|
mask = batch['mesh_mask'].numpy().astype(bool)[0]
|
|
mesh_1 = uv2mesh(img_2[tf.newaxis,:,:])[0].numpy()[mask]
|
|
mesh_2 = uv2mesh(down_curv[tf.newaxis,:,:])[0].numpy()[mask]
|
|
F = np.load("custom_utils/smpl_faces.npy")
|
|
_, faces = _maskFaces(np.ones((14475, 3)), F, mask)
|
|
|
|
fig = make_subplots(rows=1, cols=2, start_cell="top-left")
|
|
|
|
fig.add_trace(ex.imshow((img_2) / 6).data[0], row=1, col=1)
|
|
fig.add_trace(ex.imshow((down_curv ) / 6).data[0], row=1, col=2)
|
|
|
|
fig.show()
|
|
|
|
mesh = tf.squeeze(batch['mesh_posed']).numpy()[mask]
|
|
colors = np.array([cm.color('jet', 1 - i) for i in np.linspace(0, 1, 6+1)])
|
|
v_color_1 = np.zeros((mesh.shape[0], 3))
|
|
v_color_2 = np.zeros((mesh.shape[0], 3))
|
|
for i in range(6+1):
|
|
v_color_1[mesh_1 == i, :] = colors[i]
|
|
v_color_2[mesh_2 == i, :] = colors[i]
|
|
|
|
mesh_copy = mesh.copy()
|
|
mesh_copy[:,1] += 1
|
|
fig = go.Figure(data=[
|
|
go.Mesh3d(
|
|
# 8 vertices of a cube
|
|
x=mesh[:,0],
|
|
y=mesh[:,1],
|
|
z=mesh[:,2],
|
|
# i, j and k give the vertices of triangles
|
|
i = faces[:,0],
|
|
j = faces[:,1],
|
|
k = faces[:,2],
|
|
vertexcolor=v_color_1,
|
|
text='original'
|
|
),
|
|
go.Mesh3d(
|
|
# 8 vertices of a cube
|
|
x=mesh_copy[:,0],
|
|
y=mesh_copy[:,1],
|
|
z=mesh_copy[:,2],
|
|
# i, j and k give the vertices of triangles
|
|
i = faces[:,0],
|
|
j = faces[:,1],
|
|
k = faces[:,2],
|
|
vertexcolor=v_color_2,
|
|
text='rescaled'
|
|
)
|
|
])
|
|
|
|
fig.update_layout(scene_aspectmode='manual',
|
|
scene_aspectratio=dict(x=1, y=2, z=1))
|
|
fig.update_layout(
|
|
scene = dict(
|
|
xaxis = dict(nticks=4, range=[-1,1],),
|
|
yaxis = dict(nticks=4, range=[-1,3],),
|
|
zaxis = dict(nticks=4, range=[-1,1],),)
|
|
)
|
|
|
|
fig.show()
|
|
|
|
|
|
print() |