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luni
2023-03-14 11:13:22 +00:00
parent d5bade988f
commit 5857aceecd
12 changed files with 487 additions and 71 deletions
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sample_pro_2.py
+44 -35
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@@ -41,13 +41,13 @@ from custom_utils.smpl import SMPL
from convert_to_uv_api import convert_to_uv_api
from models.upscaler import UpscalerBasicConv as Upscaler
from postprocessing import fix_collision
from models.pyramid_model_optimized import Pyramid
# from models.pyramid_model_optimized import Pyramid
# from configs.config_abl_overfit_dress_var_3 import config as config_main
# from configs.config_abl_overfit_jumpsuit_2 import config as config_main
# from configs.config_cvae_curv import config as config_curv
# from configs.iccv.config_abl_all_skirt_var import config as config_main
from configs.iccv.config_abl_skin_var import config as config_main
from configs.iccv.config_abl_all_skirt import config as config_main
from configs.iccv.config_cvae_curv import config as config_curv
# from models.cvae_model_overfit_jumpsuit import CVAE
from models.cvae_model import CVAENew as CVAE
@@ -296,52 +296,54 @@ def sampling_example(frame, camera, nr=15):
mask = batch['mesh_mask'][0].numpy().astype(bool)
_, faces = _maskFaces(np.zeros((14475, 3)), F, mask)
noise = tf.random.normal(shape=(nr, 8, 4, 128))
out = model.sample(tf.tile(batch['uv_static'], [nr,1,1,1]), tf.tile(batch['uv_body_posed'], [nr,1,1,1]), tf.tile(batch['uv_curvature'], [nr,1,1,1]), tf.tile(batch['uv_mask'], [nr,1,1]), noise)
# out,_,_ = model([tf.tile(batch['uv_unposed'], [nr,1,1,1]), tf.tile(batch['uv_static'], [nr,1,1,1]), tf.tile(batch['uv_body_posed'], [nr,1,1,1]), tf.tile(batch['uv_curvature'], [nr,1,1,1]), tf.tile(batch['uv_mask'], [nr,1,1])], training=False)
# noise = tf.random.normal(shape=(nr, 8, 4, 128))
# out = model.sample(tf.tile(batch['uv_static'], [nr,1,1,1]), tf.tile(batch['uv_body_posed'], [nr,1,1,1]), tf.tile(batch['uv_curvature'], [nr,1,1,1]), tf.tile(batch['uv_mask'], [nr,1,1]), noise)
# # out,_,_ = model([tf.tile(batch['uv_unposed'], [nr,1,1,1]), tf.tile(batch['uv_static'], [nr,1,1,1]), tf.tile(batch['uv_body_posed'], [nr,1,1,1]), tf.tile(batch['uv_curvature'], [nr,1,1,1]), tf.tile(batch['uv_mask'], [nr,1,1])], training=False)
if config_main['use_skinning']:
posed_mesh = [get_posed_mesh(uv, batch['pose_shape'], batch['mesh_body_posed'], mask) for uv in tf.unstack(out)]
else:
posed_mesh = [uv2mesh(uv[tf.newaxis,:,:,:]) for uv in tf.unstack(out)]
gt_mesh = batch['mesh_posed'][0].numpy()[mask, :]
# if config_main['use_skinning']:
# posed_mesh = [get_posed_mesh(uv, batch['pose_shape'], batch['mesh_body_posed'], mask) for uv in tf.unstack(out)]
# else:
# posed_mesh = [uv2mesh(uv[tf.newaxis,:,:,:]) for uv in tf.unstack(out)]
# gt_mesh = batch['mesh_posed'][0].numpy()[mask, :]
body = batch['mesh_body_posed'][0].numpy()
posed_mesh_fixed = [postprocess(mesh, body, faces, mask, do_smoothing=True) for mesh in posed_mesh]
# posed_mesh_fixed = [mesh[0].numpy()[mask, :] for mesh in posed_mesh]
# posed_mesh_fixed = [postprocess(mesh, body, faces, mask, do_smoothing=True) for mesh in posed_mesh]
# # posed_mesh_fixed = [mesh[0].numpy()[mask, :] for mesh in posed_mesh]
# error = [np.sqrt(((gt_mesh-m) ** 2).sum(-1)).mean() for m in posed_mesh_fixed]
# # error = [np.sqrt(((gt_mesh-m) ** 2).sum(-1)).mean() for m in posed_mesh_fixed]
# row_sampling = []
# fig = plot_fixed(gt_mesh, body, faces, camera)
# fig.write_image('/tmp/exp_temp_7.png')
# img = cv2.imread('/tmp/exp_temp_7.png')
# row_sampling.append(img[:, 100:700, :])
# for mesh in posed_mesh_fixed:
# fig = plot_fixed(mesh, body, faces, camera)
# fig.write_image('/tmp/exp_temp_7.png')
# img = cv2.imread('/tmp/exp_temp_7.png')
# row_sampling.append(img[:, 100:700, :])
# # row_sampling = []
# # fig = plot_fixed(gt_mesh, body, faces, camera)
# # fig.write_image('/tmp/exp_temp_7.png')
# # img = cv2.imread('/tmp/exp_temp_7.png')
# # row_sampling.append(img[:, 100:700, :])
# # for mesh in posed_mesh_fixed:
# # fig = plot_fixed(mesh, body, faces, camera)
# # fig.write_image('/tmp/exp_temp_7.png')
# # img = cv2.imread('/tmp/exp_temp_7.png')
# # row_sampling.append(img[:, 100:700, :])
# custom_imshow(np.concatenate(row_sampling, axis=1)).show()
os.makedirs(os.path.join(out_folder, frame), exist_ok=True)
# os.makedirs(os.path.join(out_folder, frame), exist_ok=True)
plot_fixed(np.zeros_like(gt_mesh), body, faces, camera).write_image(os.path.join(out_folder, frame, frame + '_body.png'))
plot_fixed(gt_mesh, body, faces, camera).write_image(os.path.join(out_folder, frame, frame + '_gt.png'))
for i in range(nr):
plot_fixed(posed_mesh_fixed[i], body, faces, camera).write_image(os.path.join(out_folder, frame, frame + '_' + str(i) + '.png'))
# plot_fixed(np.zeros_like(gt_mesh), body, faces, camera).write_image(os.path.join(out_folder, frame, frame + '_body.png'))
# plot_fixed(gt_mesh, body, faces, camera).write_image(os.path.join(out_folder, frame, frame + '_gt.png'))
# for i in range(nr):
# plot_fixed(posed_mesh_fixed[i], body, faces, camera).write_image(os.path.join(out_folder, frame, frame + '_' + str(i) + '.png'))
# save_stl(os.path.join(out_folder, frame, frame + '_body.stl'), body, F_body)
# save_stl(os.path.join(out_folder, frame, frame + '_gt.stl'), gt_mesh, faces.astype(np.int32))
# for i in range(nr):
# save_stl(os.path.join(out_folder, frame, frame + '_' + str(i) + '.stl'), posed_mesh_fixed[i], faces.astype(np.int32))
np.save(os.path.join(out_folder, frame, frame + '_gt.npy'), gt_mesh)
np.save(os.path.join(out_folder, frame, frame + '_body.npy'), body)
np.save(os.path.join(out_folder, frame, frame + '_faces.npy'), faces.astype(np.int32))
for i in range(nr):
np.save(os.path.join(out_folder, frame, frame + '_' + str(i) + '.npy'), posed_mesh_fixed[i])
# np.save(os.path.join(out_folder, frame, frame + '_gt.npy'), gt_mesh)
# np.save(os.path.join(out_folder, frame, frame + '_body.npy'), body)
# np.save(os.path.join(out_folder, frame, frame + '_faces.npy'), faces.astype(np.int32))
# for i in range(nr):
# np.save(os.path.join(out_folder, frame, frame + '_' + str(i) + '.npy'), posed_mesh_fixed[i])
save_stl(os.path.join(out_folder, frame + '_gt_body.stl'), body, F_body)
save_stl(os.path.join(out_folder, frame + '_posed_body.stl'), body, F_body)
# custom_imshow(np.concatenate(row_sampling, axis=1)).write_image(os.path.join(out_folder, frame + '_sampling.png'))
print()
@@ -417,6 +419,7 @@ def sampling_garment(frame, camera):
print()
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='')
parser.add_argument('--name', type=str)
@@ -476,9 +479,15 @@ if __name__ == '__main__':
# frame = '04925_272'
# frames = ['01070_13', '02276_232', '02978_242', '04008_295', '04173_193']
frames = ['02276_232', '02978_242', '04173_193']
# frames = ['00073_160', '00073_217', '00096_86', \
# '04347_100', '04360_2', '04404_41', '04442_85', '04466_59', '04620_68', '04620_129', \
# '04655_28', '04668_255', '04682_96', '04709_37', '06117_123', '06141_162']
frames = ['00265_257', '04070_263', '00239_9', '04608_170', '04372_289', '00077_106', '00205_164', '00323_97', '00185_7', '00079_250', \
'04143_56', '00240_136', '00185_33']
out_folder = 'examples/sampling_analysis'
out_folder = 'examples/qualitative/bodies'
os.makedirs(out_folder, exist_ok=True)
for frame in tqdm.tqdm(frames):
sampling_example(frame, camera_1)