diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..add7c0f082bbe6a577b6257ce91f370dffa0e361 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,19 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +RigNet/binvox filter=lfs diff=lfs merge=lfs -text +RigNet/binvox.exe filter=lfs diff=lfs merge=lfs -text +RigNet/quick_start/11814_ori.fbx filter=lfs diff=lfs merge=lfs -text +RigNet/quick_start/1347_ori.fbx filter=lfs diff=lfs merge=lfs -text +RigNet/quick_start/15446_ori.fbx filter=lfs diff=lfs merge=lfs -text +RigNet/quick_start/17364_ori.fbx filter=lfs diff=lfs merge=lfs -text +RigNet/quick_start/17872_ori.fbx filter=lfs diff=lfs merge=lfs -text +RigNet/quick_start/2982_ori.fbx filter=lfs diff=lfs merge=lfs -text +RigNet/quick_start/3318_ori.fbx filter=lfs diff=lfs merge=lfs -text +RigNet/quick_start/8210_ori.fbx filter=lfs diff=lfs merge=lfs -text +RigNet/quick_start/8330_ori.fbx filter=lfs diff=lfs merge=lfs -text +RigNet/quick_start/8333_ori.fbx filter=lfs diff=lfs merge=lfs -text +RigNet/quick_start/8338_ori.fbx filter=lfs diff=lfs merge=lfs -text +RigNet/quick_start/9000_ori.fbx filter=lfs diff=lfs merge=lfs -text +RigNet/quick_start/9477_ori.fbx filter=lfs diff=lfs merge=lfs -text +RigNet/quick_start/quick_start.png filter=lfs diff=lfs merge=lfs -text diff --git a/RigNet/.gitignore b/RigNet/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..1ee2f950594010cfc3106d0943c336f2b2d3f45d --- /dev/null +++ b/RigNet/.gitignore @@ -0,0 +1,15 @@ +.idea/ +.vscode/ +__pycache__/ +utils/rigging_parser/*.pyc +logs/ +results/ +checkpoints/ +*.html +*.error +*.sbatch +*.err +*.sh +sideview.json +quick_start/*.binvox +quick_start/*_normalized* \ No newline at end of file diff --git a/RigNet/LICENSE-GPLv3.txt b/RigNet/LICENSE-GPLv3.txt new file mode 100644 index 0000000000000000000000000000000000000000..b059d641385896720b27bdbe7e40c36650a5651f --- /dev/null +++ b/RigNet/LICENSE-GPLv3.txt @@ -0,0 +1,621 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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Limitation of Liability. + + IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING +WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS +THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY +GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE +USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF +DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD +PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), +EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF +SUCH DAMAGES. + + 17. Interpretation of Sections 15 and 16. + + If the disclaimer of warranty and limitation of liability provided +above cannot be given local legal effect according to their terms, +reviewing courts shall apply local law that most closely approximates +an absolute waiver of all civil liability in connection with the +Program, unless a warranty or assumption of liability accompanies a +copy of the Program in return for a fee. + + END OF TERMS AND CONDITIONS diff --git a/RigNet/README.md b/RigNet/README.md new file mode 100644 index 0000000000000000000000000000000000000000..987d38f292e5e5b43af88002b6023fe3581ef234 --- /dev/null +++ b/RigNet/README.md @@ -0,0 +1,112 @@ +This is the code repository implementing the paper "RigNet: Neural Rigging for Articulated Characters" published on SIGGRAPH 2020 [[Project page]](https://zhan-xu.github.io/rig-net/). + +**[2021.07.20]** Another add-on for Blender, +implemented by @[L-Medici](https://github.com/L-Medici). Please check the Github [link](https://github.com/L-Medici/Rignet_blender_addon). + + +**[2020.11.23]** There is now a great add-on for Blender based on our work, +implemented by @[pKrime](https://github.com/pKrime). Please check the Github [link](https://github.com/pKrime/brignet), and the video [demo](https://www.youtube.com/watch?v=ueLlS3IoeGY&feature=youtu.be). + +## Dependecy and Setup + +The project is developed on Ubuntu 16.04 with cuda10.0 and cudnn7.6.3. +It has also been successfully tested on Windows 10. +On both platforms, we suggest to use conda virtual environment. + +#### For Linux user + +**[2023.05.21]** I have tested the code on Ubuntu 22.04, with cuda 11.3. The following commands have been updated. + +``` +conda create --name rignet python=3.7 +conda activate rignet +conda install pytorch==1.12.0 torchvision==0.13.0 cudatoolkit=11.3 -c pytorch + +# load cuda_toolkit 11.3 +export PATH=/usr/local/cuda_11.3/bin${PATH:+:${PATH}} +export LD_LIBRARY_PATH=/usr/local/cuda_11.3/lib64 + +# require g++ < 10 to install the following pytorch geometric version. +pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-1.12.0+cu113.html # this take a while +pip install torch-geometric==1.7.2 + +pip install numpy scipy matplotlib tensorboard open3d==0.9.0 opencv-python "rtree>=0.8,<0.9" trimesh[easy] # Make sure to install open3d 0.9.0. +``` + +#### For Windows user + +The code has been tested on Windows 10 with cuda 10.1. The most important difference from Linux setup is, you need to download Windows-compiled Rtree from [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#rtree), and install it by +`pip install Rtree‑0.9.4‑cp37‑cp37m‑win_amd64.whl` (64-bit system) or +`pip install Rtree‑0.9.4‑cp37‑cp37m‑win32.whl` (32-bit system). Other libraries can be installed in the same way as Linux setup instructions. + + + +## Quick start +We provide a script for quick start. First download our trained models from [here](https://drive.google.com/file/d/1gM2Lerk7a2R0g9DwlK3IvCfp8c2aFVXs/view?usp=sharing). +Put the checkpoints folder into the project folder. + +Check and run quick_start.py. We provide some examples in this script. +Due to randomness, the results might be slightly different among each run. +Generally you will get the results similar to the ones shown below: + +![results figure](quick_start/quick_start.png) + +If you want to try your own models, remember to simplify the meshes so that +the remeshed ones have vertices between 1K to 5K. I use quadratic edge collapse in MeshLab for this. +Please name the simplified meshed as *_remesh.obj. + +The predicted rigs are saved as *_rig.txt. You can combine the OBJ file and *_rig.txt into FBX format by +running maya_save_fbx.py provided by us in Maya using mayapy. (To use numpy in mayapy, download windows compiled numpy from [here](https://github.com/Eric-Vignola/numpy-for-python-2.7-64bit) and put it in mayapy library folder. For example, mine is C:\Program Files\Autodesk\Maya2019\Python\Lib\site-packages) + +## Data + +Our dataset ModelsResource-RigNetv1 has 2,703 models. +We split it into 80% for training (2,163‬ models), 10% +for validation (270 models), and 10% for testing. +All models in fbx format can be downloaded [here](https://drive.google.com/file/d/1yojBwl5eHPqgXZ1Uh4j26S-yKK-2loPu/view?usp=sharing). + +To use this dataset in this project, pre-processing is performed. +We put the pre-processed data [here](https://drive.google.com/file/d/1-B6hJ4423rw1LrTForHp7oaG5qRAbJx3/view?usp=sharing), which consists of several sub-folders. + +* obj: all meshes in OBJ format. +* rig_info: we store the rigging information into a txt file. Each txt file has four blocks. (1) Lines starting with "joint" define a joint with its 3D position. Each of joint line has four elements, which are joint_name, X, Y, and Z. (2) Line starting with "root" defines the name of root joint. (3) Lines starting with "hier" define the hierarchy of skeleton. Each hierarchy line has two elements, which are parent joint name and its child joint name. One parent joint can have multiple children joints. (4) Lines starting with "skin" define the skinning weights. Each skinning line follows the format as vertex_id, bind_joint_name_1, bind_weight_1, bind_joint_name_2, bind_weight_2 ... The vertex_id follows the vertice order in obj files in the above obj folder. +* obj_remesh: This folder contains the obj files of the remeshed models. Meshes with fewer than 1K vertices were subdivided, and those with more than 5K vertices were simplified; as a result all training and test meshes contained between 1K and 5K vertices. +* rig_info_remesh: Rigging information files corresponding to the remeshed obj. Joints, hierarchy and root are the same. The skinning is recalculated based on nearest neighbor from each remeshed vertex to original vertices. +* pretrain_attention: Pre-calculated supervision to pretrin the attention module, which are calculated by the script geometric_proc/compute_pretrain_attn.py. Each file is a N-by-1 text where N is the number of vertices corresponding to remeshed OBJ file, the i-th row stores the surpervision for vertex i. +* volumetric_geodesic: Pre-calculated volumetric geodesic distance between each vertex-bones pair. The algorithm is an approaximation, which is implemented in geometric_proc/compute_volumetric_geodesic.py. Each file is an N-by-B numpy array where N is the number of vertices corresponding to remeshed OBJ file, B is the number of bones, and (i, j) stores the volumetric geodesic distance between vertex i and bone j. +* vox: voxelized models used for inside/outside check. Obtained with [binvox](https://www.patrickmin.com/binvox/). The resolution of the grid is 88x88x88. + +After downloading the pre-processed data, one needs to create the data directly used for training/testing, please check and run our script: + +`python gen_dataset.py` + +Remember to change the root_folder to the directory you uncompress the pre-processed data. + +## Training + +Notes: As new features, we have three improvements from the paper: (1) To train the joint prediction module, now we pretrain both the regression module and the attention module, and then fine-tune them together with differentiable clustering. (2) We optimized the hyper-parameters in the fine-tuning step. (3) the input feature for skinning now includes another dimension per bone (--Lf), indicating whether this bone is a virtual leaf bone or not. (To enable control from the end-joints, we presume a virtual bone for them. Please check the code for more details.) + +1. Joint prediction: + + 1.1 Pretrain regression module: + `python -u run_joint_pretrain.py --train_folder='DATASET_DIR/train/' --val_folder='DATASET_DIR/val/' --test_folder='DATASET_DIR/test/' --checkpoint='checkpoints/pretrain_jointnet' --logdir='logs/pretrain_jointnet' --train_batch=6 --test_batch=6 --lr 5e-4 --schedule 50 --arch='jointnet'` + + 1.2 Pretrain attention module: + `python -u run_joint_pretrain.py --train_folder='DATASET_DIR/train/' --val_folder='DATASET_DIR/val/' --test_folder='DATASET_DIR/test/' --checkpoint='checkpoints/pretrain_masknet' --logdir='logs/pretrain_masknet' --train_batch=6 --test_batch=6 --lr 1e-4 --schedule 50 --arch='masknet'` + + 1.3 Finetune two modules with a clustering module: + `python -u run_joint_finetune.py --train_folder='DATASET_DIR/train/' --val_folder='DATASET_DIR/val/' --test_folder='DATASET_DIR/test/' --checkpoint='checkpoints/gcn_meanshift' --logdir='logs/gcn_meanshift' --train_batch=1 --test_batch=1 --jointnet_lr=1e-6 --masknet_lr=1e-6 --bandwidth_lr=1e-6 --epoch=50` + +2. Connectivity prediction + + 2.1 BoneNet: + `python -u run_pair_cls.py --train_folder='DATASET_DIR/train/' --val_folder='DATASET_DIR/val/' --test_folder='DATASET_DIR/test/' --checkpoint='checkpoints/bonenet' --logdir='logs/bonenet' --train_batch=6 --test_batch=6 --lr=1e-3` + + 2.2 RootNet: + `python -u run_root_cls.py --train_folder='DATASET_DIR/train/' --val_folder='DATASET_DIR/val/' --test_folder='DATASET_DIR/test/' --checkpoint='checkpoints/rootnet' --logdir='logs/rootnet' --train_batch=6 --test_batch=6 --lr=1e-3` + +3. Skinning prediction: + `python -u run_skinning.py --train_folder='DATASET_DIR/train/' --val_folder='DATASET_DIR/val/' --test_folder='DATASET_DIR/test/' --checkpoint='checkpoints/skinnet' --logdir='logs/skinnet' --train_batch=4 --test_batch=4 --lr=1e-4 --Dg --Lf` + +## License +This project is under LICENSE-GPLv3. diff --git a/RigNet/binvox b/RigNet/binvox new file mode 100644 index 0000000000000000000000000000000000000000..57f9987ffed563ebd2a6dcebf286a902e6f4550c --- /dev/null +++ b/RigNet/binvox @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:249cdfdd88a97789206c9fb4000cafbfee96fbfac90cb27023daf7511fba1376 +size 494308 diff --git a/RigNet/binvox.exe b/RigNet/binvox.exe new file mode 100644 index 0000000000000000000000000000000000000000..d6819bac77e35152e2ba9a31640808b1cefbd533 --- /dev/null +++ b/RigNet/binvox.exe @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5238bda91641eeae57cc214139ab3780b25df08ed6903a1240c76b98627db9ba +size 1930752 diff --git a/RigNet/checkpoints/bonenet/model_best.pth.tar b/RigNet/checkpoints/bonenet/model_best.pth.tar new file mode 100644 index 0000000000000000000000000000000000000000..99bfd275d6fb2aa25cf2a565cf51610d42b063cf --- /dev/null +++ b/RigNet/checkpoints/bonenet/model_best.pth.tar @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b3a87eb794542712efbc87f183ab3e39c2ae72f60b93e111582634fa1adb7e59 +size 11771879 diff --git a/RigNet/checkpoints/gcn_meanshift/model_best.pth.tar b/RigNet/checkpoints/gcn_meanshift/model_best.pth.tar new file mode 100644 index 0000000000000000000000000000000000000000..32b4847c9f0264e1f3767b896316608479547925 --- /dev/null +++ b/RigNet/checkpoints/gcn_meanshift/model_best.pth.tar @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9a1f724d46c105ef514f1a7ded93489390b16bac38738bd501d77eb71b99dd10 +size 91993316 diff --git a/RigNet/checkpoints/pretrain_jointnet/model_best.pth.tar b/RigNet/checkpoints/pretrain_jointnet/model_best.pth.tar new file mode 100644 index 0000000000000000000000000000000000000000..ee78d2b2fd8889bd1a2212036bc9537e04395722 --- /dev/null +++ b/RigNet/checkpoints/pretrain_jointnet/model_best.pth.tar @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:294e76be0a367a570c95e279af72e3a336bb4e8d6d4d386f5a5d07fa546644cd +size 45996944 diff --git a/RigNet/checkpoints/pretrain_masknet/model_best.pth.tar b/RigNet/checkpoints/pretrain_masknet/model_best.pth.tar new file mode 100644 index 0000000000000000000000000000000000000000..369935ade0f70cd3675e9b9a101aba5431c44569 --- /dev/null +++ b/RigNet/checkpoints/pretrain_masknet/model_best.pth.tar @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:11247576e2fc81f628091688bfdf88b501ec7fc5dfb3bd2375797b16fc9660c3 +size 45990776 diff --git a/RigNet/checkpoints/rootnet/model_best.pth.tar b/RigNet/checkpoints/rootnet/model_best.pth.tar new file mode 100644 index 0000000000000000000000000000000000000000..826bf1822184aa3c719f8c576fcc1203780dc67a --- /dev/null +++ b/RigNet/checkpoints/rootnet/model_best.pth.tar @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ba6eea9dad3f9fdfad3f0ef6edb550e69bfc6655f0692c2d68e57f8e65d6b86d +size 12709219 diff --git a/RigNet/checkpoints/skinnet/model_best.pth.tar b/RigNet/checkpoints/skinnet/model_best.pth.tar new file mode 100644 index 0000000000000000000000000000000000000000..d6094741b615e77a49c249ecc5022bb847e86549 --- /dev/null +++ b/RigNet/checkpoints/skinnet/model_best.pth.tar @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3ad750511957718322332fb73670646d03781aaa8e572297ef0677457901ea00 +size 44623992 diff --git a/RigNet/datasets/__init__.py b/RigNet/datasets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/RigNet/datasets/skeleton_dataset.py b/RigNet/datasets/skeleton_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..5e9975dca9208e32e889b6ba3e918968d93c17c6 --- /dev/null +++ b/RigNet/datasets/skeleton_dataset.py @@ -0,0 +1,121 @@ +#------------------------------------------------------------------------------- +# Name: skeleton_dataset.py +# Purpose: torch_geometric dataset wrapper for skeleton training and inference +# RigNet Copyright 2020 University of Massachusetts +# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License. +# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet. +#------------------------------------------------------------------------------- + +import os +import torch +import numpy as np +import glob +import itertools as it +from utils import binvox_rw +from torch_geometric.data import Data, InMemoryDataset +from torch_geometric.utils import add_self_loops + + +class SkeletonData(Data): + def __init__(self, x=None, pos=None, name=None, mask=None, joints=None, + tpl_edge_index=None, geo_edge_index=None, pairs=None, pair_attr=None): + super(SkeletonData, self).__init__() + self.x = x + self.pos = pos + self.name = name + self.mask = mask + self.joints = joints + self.tpl_edge_index = tpl_edge_index + self.geo_edge_index = geo_edge_index + self.pairs = pairs + self.pair_attr = pair_attr + + def __inc__(self, key, value): + if key == 'pairs': + return self.joints.size(0) + else: + return super(SkeletonData, self).__inc__(key, value) + + +class GraphDataset(InMemoryDataset): + def __init__(self, root): + super(GraphDataset, self).__init__(root) + self.data, self.slices = torch.load(self.processed_paths[0]) + + @property + def raw_file_names(self): + raw_v_filelist = glob.glob(os.path.join(self.root, '*_v.txt')) + return raw_v_filelist + + @property + def processed_file_names(self): + return '{:s}_skeleton_data.pt'.format(self.root.split('/')[-1]) + + def __len__(self): + return len(self.raw_paths) + + def download(self): + pass + + def sample_on_bone(self, p_pos, ch_pos): + ray = ch_pos - p_pos + bone_length = np.sqrt(np.sum((p_pos - ch_pos) ** 2)) + num_step = np.round(bone_length / 0.01) + i_step = np.arange(1, num_step + 1) + unit_step = ray / (num_step + 1e-30) + unit_step = np.repeat(unit_step[np.newaxis, :], num_step, axis=0) + res = p_pos + unit_step * i_step[:, np.newaxis] + return res + + def inside_check(self, pts, vox): + vc = (pts - vox.translate) / vox.scale * vox.dims[0] + vc = np.round(vc).astype(int) + ind1 = np.logical_and(np.all(vc >= 0, axis=1), np.all(vc < vox.dims[0], axis=1)) + vc = np.clip(vc, 0, vox.dims[0]-1) + ind2 = vox.data[vc[:, 0], vc[:, 1], vc[:, 2]] + ind = np.logical_and(ind1, ind2) + pts = pts[ind] + return pts, np.argwhere(ind).squeeze() + + def process(self): + data_list = [] + i = 0.0 + for v_filename in self.raw_paths: + print('preprecessing data complete: {:.4f}%'.format(100 * i / len(self.raw_paths))) + i += 1.0 + v = np.loadtxt(v_filename) + m = np.loadtxt(v_filename.replace('_v.txt', '_attn.txt')) + tpl_e = np.loadtxt(v_filename.replace('_v.txt', '_tpl_e.txt')).T + geo_e = np.loadtxt(v_filename.replace('_v.txt', '_geo_e.txt')).T + joints = np.loadtxt(v_filename.replace('_v.txt', '_j.txt')) + adj = np.loadtxt(v_filename.replace('_v.txt', '_adj.txt'), dtype=np.uint8) + + vox_file = v_filename.replace('_v.txt', '.binvox') + with open(vox_file, 'rb') as fvox: + vox = binvox_rw.read_as_3d_array(fvox) + pairs = list(it.combinations(range(adj.shape[0]), 2)) + pair_attr = [] + for pr in pairs: + dist = np.linalg.norm(joints[pr[0]] - joints[pr[1]]) + bone_samples = self.sample_on_bone(joints[pr[0]], joints[pr[1]]) + bone_samples_inside, _ = self.inside_check(bone_samples, vox) + outside_proportion = len(bone_samples_inside) / (len(bone_samples) + 1e-10) + attr = np.array([dist, outside_proportion, adj[pr[0], pr[1]]]) + pair_attr.append(attr) + pairs = np.array(pairs) + pair_attr = np.array(pair_attr) + name = int(v_filename.split('/')[-1].split('_')[0]) + + v = torch.from_numpy(v).float() + m = torch.from_numpy(m).long() + tpl_e = torch.from_numpy(tpl_e).long() + geo_e = torch.from_numpy(geo_e).long() + tpl_e, _ = add_self_loops(tpl_e, num_nodes=v.size(0)) + geo_e, _ = add_self_loops(geo_e, num_nodes=v.size(0)) + joints = torch.from_numpy(joints).float() + pairs = torch.from_numpy(pairs).float() + pair_attr = torch.from_numpy(pair_attr).float() + data_list.append(SkeletonData(x=v[:, 3:6], pos=v[:, 0:3], name=name, mask=m, joints=joints, + tpl_edge_index=tpl_e, geo_edge_index=geo_e, pairs=pairs, pair_attr=pair_attr)) + data, slices = self.collate(data_list) + torch.save((data, slices), self.processed_paths[0]) diff --git a/RigNet/datasets/skin_dataset.py b/RigNet/datasets/skin_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..7941c5990acc81a260e334d9d72e13b37c4bc4b2 --- /dev/null +++ b/RigNet/datasets/skin_dataset.py @@ -0,0 +1,110 @@ +#------------------------------------------------------------------------------- +# Name: skin_dataset.py +# Purpose: torch_geometric dataset wrapper for skinning training and inference +# RigNet Copyright 2020 University of Massachusetts +# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License. +# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet. +#------------------------------------------------------------------------------- + +import os +import torch +import numpy as np +import glob +from torch_geometric.data import Data, InMemoryDataset +from torch_geometric.utils import add_self_loops + + +class SkinDataset(InMemoryDataset): + def __init__(self, root): + super(SkinDataset, self).__init__(root) + self.data, self.slices = torch.load(self.processed_paths[0]) + + @property + def raw_file_names(self): + raw_v_filelist = glob.glob(os.path.join(self.root, '*_v.txt')) + return raw_v_filelist + + @property + def processed_file_names(self): + return '{:s}_skinning_data.pt'.format(self.root.split('/')[-1]) + + def __len__(self): + return len(self.raw_paths) + + def download(self): + pass + + def load_skin(self, filename): + with open(filename, 'r') as fin: + lines = fin.readlines() + bones = [] + input = [] + label = [] + nearest_bone_ids = [] + loss_mask_all = [] + for li in lines: + words = li.strip().split() + if words[0] == 'bones': + bones.append([float(w) for w in words[3:]]) + elif words[0] == 'bind': + words = [float(w) for w in words[1:]] + sample_input = [] + sample_nearest_bone_ids = [] + loss_mask = [] + for i in range(self.num_nearest_bone): + if int(words[3 * i + 1]) == -1: + ## walk-round. however words[3] may also be invalid. + sample_nearest_bone_ids.append(int(words[1])) + sample_input += bones[int(words[1])] + sample_input.append(words[2]) + sample_input.append(int(words[3])) + loss_mask.append(0) + else: + sample_nearest_bone_ids.append(int(words[3 * i + 1])) + sample_input += bones[int(words[3 * i + 1])] + sample_input.append(words[3 * i + 2]) + sample_input.append(int(words[3 * i + 3])) + loss_mask.append(1) + input.append(np.array(sample_input)[np.newaxis, :]) + nearest_bone_ids.append(np.array(sample_nearest_bone_ids)[np.newaxis, :]) + loss_mask_all.append(np.array(loss_mask)[np.newaxis, :]) + elif words[0] == 'influence': + sample_label = np.array([float(w) for w in words[1:]])[np.newaxis, :] + label.append(sample_label) + + input = np.concatenate(input, axis=0) + nearest_bone_ids = np.concatenate(nearest_bone_ids, axis=0) + label = np.concatenate(label, axis=0) + loss_mask_all = np.concatenate(loss_mask_all, axis=0) + + return input, nearest_bone_ids, label, loss_mask_all + + def process(self): + data_list = [] + self.num_nearest_bone = 5 + i = 0.0 + for v_filename in self.raw_paths: + print('preprecessing data complete: {:.4f}%'.format(100 * i / len(self.raw_paths))) + i += 1.0 + v = np.loadtxt(v_filename) + v = torch.from_numpy(v).float() + tpl_e = np.loadtxt(v_filename.replace('_v.txt', '_tpl_e.txt')).T + geo_e = np.loadtxt(v_filename.replace('_v.txt', '_geo_e.txt')).T + tpl_e = torch.from_numpy(tpl_e).long() + geo_e = torch.from_numpy(geo_e).long() + tpl_e, _ = add_self_loops(tpl_e, num_nodes=v.size(0)) + geo_e, _ = add_self_loops(geo_e, num_nodes=v.size(0)) + skin_input, skin_nn, skin_label, loss_mask = self.load_skin(v_filename.replace('_v.txt', '_skin.txt')) + + skin_input = torch.from_numpy(skin_input).float() + skin_label = torch.from_numpy(skin_label).float() + skin_nn = torch.from_numpy(skin_nn).long() + loss_mask = torch.from_numpy(loss_mask).long() + num_skin = len(skin_input) + + name = int(v_filename.split('/')[-1].split('_')[0]) + data_list.append(Data(x=v[:, 3:6], pos=v[:, 0:3], skin_input=skin_input, skin_label=skin_label, + skin_nn=skin_nn, loss_mask=loss_mask, num_skin=num_skin, name=name, + tpl_edge_index=tpl_e, geo_edge_index=geo_e)) + data, slices = self.collate(data_list) + torch.save((data, slices), self.processed_paths[0]) diff --git a/RigNet/gen_dataset.py b/RigNet/gen_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..c6854c1d2e2fa9cb3a33fb8a17913d4346dc5f35 --- /dev/null +++ b/RigNet/gen_dataset.py @@ -0,0 +1,169 @@ +#------------------------------------------------------------------------------- +# Name: gen_dataset.py +# Purpose: Script to generate data for skeleton and connectivity predition stage +# Change dataset_folder to the folder where you put the downloaded pre-processed data +# RigNet Copyright 2020 University of Massachusetts +# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License. +# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet. +#------------------------------------------------------------------------------- + +import os +import shutil +import numpy as np +import open3d as o3d +from multiprocessing import Pool +from utils.io_utils import mkdir_p +from utils.rig_parser import Info +from geometric_proc.common_ops import calc_surface_geodesic, get_bones + + +def get_tpl_edges(remesh_obj_v, remesh_obj_f): + edge_index = [] + for v in range(len(remesh_obj_v)): + face_ids = np.argwhere(remesh_obj_f == v)[:, 0] + neighbor_ids = [] + for face_id in face_ids: + for v_id in range(3): + if remesh_obj_f[face_id, v_id] != v: + neighbor_ids.append(remesh_obj_f[face_id, v_id]) + neighbor_ids = list(set(neighbor_ids)) + neighbor_ids = [np.array([v, n])[np.newaxis, :] for n in neighbor_ids] + if len(neighbor_ids) == 0: + continue + neighbor_ids = np.concatenate(neighbor_ids, axis=0) + edge_index.append(neighbor_ids) + edge_index = np.concatenate(edge_index, axis=0) + return edge_index + + +def get_geo_edges(surface_geodesic, remesh_obj_v): + edge_index = [] + surface_geodesic += 1.0 * np.eye(len(surface_geodesic)) # remove self-loop edge here + for i in range(len(remesh_obj_v)): + geodesic_ball_samples = np.argwhere(surface_geodesic[i, :] <= 0.06).squeeze(1) + if len(geodesic_ball_samples) > 10: + geodesic_ball_samples = np.random.choice(geodesic_ball_samples, 10, replace=False) + edge_index.append(np.concatenate((np.repeat(i, len(geodesic_ball_samples))[:, np.newaxis], + geodesic_ball_samples[:, np.newaxis]), axis=1)) + edge_index = np.concatenate(edge_index, axis=0) + return edge_index + + +def genDataset(process_id): + global dataset_folder + print("process ID {:d}".format(process_id)) + if process_id < 6: + model_list = np.loadtxt(os.path.join(dataset_folder, 'train_final.txt'), dtype=int) + model_list = model_list[365*process_id: 365*(process_id+1)] + split_name = 'train' + elif process_id == 6: + model_list = np.loadtxt(os.path.join(dataset_folder, 'val_final.txt'), dtype=int) + split_name = 'val' + elif process_id == 7: + model_list = np.loadtxt(os.path.join(dataset_folder, 'test_final.txt'), dtype=int) + split_name = 'test' + + mkdir_p(os.path.join(dataset_folder, split_name)) + for model_id in model_list: + remeshed_obj_filename = os.path.join(dataset_folder, 'obj_remesh/{:d}.obj'.format(model_id)) + info_filename = os.path.join(dataset_folder, 'rig_info_remesh/{:d}.txt'.format(model_id)) + remeshed_obj = o3d.io.read_triangle_mesh(remeshed_obj_filename) + remesh_obj_v = np.asarray(remeshed_obj.vertices) + if not remeshed_obj.has_vertex_normals(): + remeshed_obj.compute_vertex_normals() + remesh_obj_vn = np.asarray(remeshed_obj.vertex_normals) + remesh_obj_f = np.asarray(remeshed_obj.triangles) + rig_info = Info(info_filename) + + #vertices + vert_filename = os.path.join(dataset_folder, '{:s}/{:d}_v.txt'.format(split_name, model_id)) + input_feature = np.concatenate((remesh_obj_v, remesh_obj_vn), axis=1) + np.savetxt(vert_filename, input_feature, fmt='%.6f') + + #topology edges + edge_index = get_tpl_edges(remesh_obj_v, remesh_obj_f) + graph_filename = os.path.join(dataset_folder, '{:s}/{:d}_tpl_e.txt'.format(split_name, model_id)) + np.savetxt(graph_filename, edge_index, fmt='%d') + + # geodesic_edges + surface_geodesic = calc_surface_geodesic(remeshed_obj) + edge_index = get_geo_edges(surface_geodesic, remesh_obj_v) + graph_filename = os.path.join(dataset_folder, '{:s}/{:d}_geo_e.txt'.format(split_name, model_id)) + np.savetxt(graph_filename, edge_index, fmt='%d') + + # joints + joint_pos = rig_info.get_joint_dict() + joint_name_list = list(joint_pos.keys()) + joint_pos_list = list(joint_pos.values()) + joint_pos_list = [np.array(i) for i in joint_pos_list] + adjacent_matrix = rig_info.adjacent_matrix() + joint_filename = os.path.join(dataset_folder, '{:s}/{:d}_j.txt'.format(split_name, model_id)) + adj_filename = os.path.join(dataset_folder, '{:s}/{:d}_adj.txt'.format(split_name, model_id)) + np.savetxt(adj_filename, adjacent_matrix, fmt='%d') + np.savetxt(joint_filename, np.array(joint_pos_list), fmt='%.6f') + + # pre_trained attn + shutil.copyfile(os.path.join(dataset_folder, 'pretrain_attention/{:d}.txt'.format(model_id)), + os.path.join(dataset_folder, '{:s}/{:d}_attn.txt'.format(split_name, model_id))) + + # voxel + shutil.copyfile(os.path.join(dataset_folder, 'vox/{:d}.binvox'.format(model_id)), + os.path.join(dataset_folder, '{:s}/{:d}.binvox'.format(split_name, model_id))) + + #skinning information + num_nearest_bone = 5 + geo_dist = np.load(os.path.join(dataset_folder, "volumetric_geodesic/{:d}_volumetric_geo.npy".format(model_id))) + bone_pos, bone_names, bone_isleaf = get_bones(rig_info) + + input_samples = [] # mesh_vertex_id, (bone_id, 1 / D_g, is_leaf) * N + ground_truth_labels = [] # w_1, w_2, ..., w_N + for vert_remesh_id in range(len(remesh_obj_v)): + this_sample = [vert_remesh_id] + this_label = [] + skin = rig_info.joint_skin[vert_remesh_id] + skin_w = {} + for i in np.arange(1, len(skin), 2): + skin_w[skin[i]] = float(skin[i + 1]) + bone_id_near_to_far = np.argsort(geo_dist[vert_remesh_id, :]) + for i in range(num_nearest_bone): + if i >= len(bone_id_near_to_far): + this_sample += [-1, 0, 0] + this_label.append(0.0) + continue + bone_id = bone_id_near_to_far[i] + this_sample.append(bone_id) + this_sample.append(1.0 / (geo_dist[vert_remesh_id, bone_id] + 1e-10)) + this_sample.append(bone_isleaf[bone_id]) + start_joint_name = bone_names[bone_id][0] + if start_joint_name in skin_w: + this_label.append(skin_w[start_joint_name]) + del skin_w[start_joint_name] + else: + this_label.append(0.0) + + input_samples.append(this_sample) + ground_truth_labels.append(this_label) + + with open(os.path.join(dataset_folder, '{:s}/{:d}_skin.txt'.format(split_name, model_id)), 'w') as fout: + for i in range(len(bone_pos)): + fout.write('bones {:s} {:s} {:.6f} {:.6f} {:.6f} ' + '{:.6f} {:.6f} {:.6f}\n'.format(bone_names[i][0], bone_names[i][1], + bone_pos[i, 0], bone_pos[i, 1], bone_pos[i, 2], + bone_pos[i, 3], bone_pos[i, 4], bone_pos[i, 5])) + for i in range(len(input_samples)): + fout.write('bind {:d} '.format(input_samples[i][0])) + for j in np.arange(1, len(input_samples[i]), 3): + fout.write('{:d} {:.6f} {:d} '.format(input_samples[i][j], input_samples[i][j + 1], input_samples[i][j + 2])) + fout.write('\n') + for i in range(len(ground_truth_labels)): + fout.write('influence ') + for j in range(len(ground_truth_labels[i])): + fout.write('{:.3f} '.format(ground_truth_labels[i][j])) + fout.write('\n') + + +if __name__ == '__main__': + dataset_folder = "/media/zhanxu/4T/ModelResource_RigNetv1_preproccessed/" + p = Pool(8) + p.map(genDataset, [0, 1, 2, 3, 4, 5, 6, 7]) + #genDataset(0) diff --git a/RigNet/geometric_proc/__init__.py b/RigNet/geometric_proc/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/RigNet/geometric_proc/common_ops.py b/RigNet/geometric_proc/common_ops.py new file mode 100644 index 0000000000000000000000000000000000000000..5177adac02cfa66820c4227d991f46dd832b5995 --- /dev/null +++ b/RigNet/geometric_proc/common_ops.py @@ -0,0 +1,83 @@ +#------------------------------------------------------------------------------- +# Name: common_ops.py +# Purpose: common functions for geometry processing +# RigNet Copyright 2020 University of Massachusetts +# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License. +# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet. +#------------------------------------------------------------------------------- + +import numpy as np +import time +import open3d as o3d +from scipy.sparse import lil_matrix +from scipy.sparse.csgraph import dijkstra + + +def get_bones(skel): + """ + extract bones from skeleton struction + :param skel: input skeleton + :return: bones are B*6 array where each row consists starting and ending points of a bone + bone_name are a list of B elements, where each element consists starting and ending joint name + leaf_bones indicate if this bone is a virtual "leaf" bone. + We add virtual "leaf" bones to the leaf joints since they always have skinning weights as well + """ + bones = [] + bone_name = [] + leaf_bones = [] + this_level = [skel.root] + while this_level: + next_level = [] + for p_node in this_level: + p_pos = np.array(p_node.pos) + next_level += p_node.children + for c_node in p_node.children: + c_pos = np.array(c_node.pos) + bones.append(np.concatenate((p_pos, c_pos))[np.newaxis, :]) + bone_name.append([p_node.name, c_node.name]) + leaf_bones.append(False) + if len(c_node.children) == 0: + bones.append(np.concatenate((c_pos, c_pos))[np.newaxis, :]) + bone_name.append([c_node.name, c_node.name+'_leaf']) + leaf_bones.append(True) + this_level = next_level + bones = np.concatenate(bones, axis=0) + return bones, bone_name, leaf_bones + + +def calc_surface_geodesic(mesh): + # We denselu sample 4000 points to be more accuracy. + samples = mesh.sample_points_poisson_disk(number_of_points=4000) + pts = np.asarray(samples.points) + pts_normal = np.asarray(samples.normals) + + time1 = time.time() + N = len(pts) + verts_dist = np.sqrt(np.sum((pts[np.newaxis, ...] - pts[:, np.newaxis, :]) ** 2, axis=2)) + verts_nn = np.argsort(verts_dist, axis=1) + conn_matrix = lil_matrix((N, N), dtype=np.float32) + + for p in range(N): + nn_p = verts_nn[p, 1:6] + norm_nn_p = np.linalg.norm(pts_normal[nn_p], axis=1) + norm_p = np.linalg.norm(pts_normal[p]) + cos_similar = np.dot(pts_normal[nn_p], pts_normal[p]) / (norm_nn_p * norm_p + 1e-10) + nn_p = nn_p[cos_similar > -0.5] + conn_matrix[p, nn_p] = verts_dist[p, nn_p] + [dist, predecessors] = dijkstra(conn_matrix, directed=False, indices=range(N), + return_predecessors=True, unweighted=False) + + # replace inf distance with euclidean distance + 8 + # 6.12 is the maximal geodesic distance without considering inf, I add 8 to be safer. + inf_pos = np.argwhere(np.isinf(dist)) + if len(inf_pos) > 0: + euc_distance = np.sqrt(np.sum((pts[np.newaxis, ...] - pts[:, np.newaxis, :]) ** 2, axis=2)) + dist[inf_pos[:, 0], inf_pos[:, 1]] = 8.0 + euc_distance[inf_pos[:, 0], inf_pos[:, 1]] + + verts = np.array(mesh.vertices) + vert_pts_distance = np.sqrt(np.sum((verts[np.newaxis, ...] - pts[:, np.newaxis, :]) ** 2, axis=2)) + vert_pts_nn = np.argmin(vert_pts_distance, axis=0) + surface_geodesic = dist[vert_pts_nn, :][:, vert_pts_nn] + time2 = time.time() + print('surface geodesic calculation: {} seconds'.format((time2 - time1))) + return surface_geodesic diff --git a/RigNet/geometric_proc/compute_pretrain_attn.py b/RigNet/geometric_proc/compute_pretrain_attn.py new file mode 100644 index 0000000000000000000000000000000000000000..8b4e07b8fcf6e0b46bcb08121c8043b5b099d24f --- /dev/null +++ b/RigNet/geometric_proc/compute_pretrain_attn.py @@ -0,0 +1,279 @@ +#------------------------------------------------------------------------------- +# Name: compute_pretrain_attn.py +# Purpose: This script prepares the supervision for attention module pretraining. +# It shoots rays from each joint around the plane perpendicular to the bone, which hit the surface. +# Vertices near the valid hits are marked as 1, otherwise 0. +# RigNet Copyright 2020 University of Massachusetts +# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License. +# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet. +#------------------------------------------------------------------------------- + +""" + +""" + +import sys +sys.path.append("./") +import os +import glob +import time +import copy +import trimesh +import numpy as np +import open3d as o3d +from utils.rig_parser import Info + + +def get_perpend_vec(v): + max_dim = np.argmax(np.abs(v)) + if max_dim == 0: + u_0 = np.array([(-2.0 * v[1] - 1.0 * v[2]) / (v[0]+1e-10), 2.0, 1.0]) + elif max_dim == 1: + u_0 = np.array([1.0, (-1.0 * v[0] - 2.0 * v[2]) / (v[1]+1e-10), 2.0]) + elif max_dim == 2: + u_0 = np.array([1.0, 2.0, (-1.0 * v[0] - 2.0 * v[1]) / (v[2]+1e-10)]) + u_0 /= np.linalg.norm(u_0) + return u_0 + + +def cal_perpendicular_dir(p_pos, ch_pos): + global ray_per_sample + dirs = [] + v = (ch_pos - p_pos).squeeze() + v = v / (np.linalg.norm(v)+1e-10) + u_0 = get_perpend_vec(v) + w = np.cross(v, u_0) + w = w / (np.linalg.norm(w)+1e-10) + for angle in np.arange(0, 2*np.pi, 2*np.pi/ray_per_sample): + u = np.cos(angle) * u_0 + np.sin(angle) * w + u = u / (np.linalg.norm(u)+1e-10) + dirs.append(u[np.newaxis, :]) + dirs = np.concatenate(dirs, axis=0) + return dirs + + +def form_rays(skel): + ''' + generate rays from joints, with perpendicular direction + :param skel: input skeleton + :return: ray origins and ray directions + ''' + origins = [] + dirs = [] + this_level = [skel.root] + while this_level: + next_level = [] + for p_node in this_level: + next_level += p_node.children + p_pos = np.array(p_node.pos)[np.newaxis, :] + for c_node in p_node.children: + c_pos = np.array(c_node.pos)[np.newaxis, :] + origin_bone = np.concatenate(([p_pos, c_pos]), axis=0) + dir_bone = cal_perpendicular_dir(p_pos, c_pos) + origins.append(np.repeat(origin_bone, len(dir_bone), axis=0)) + dirs.append(np.tile(dir_bone, (len(origin_bone), 1))) + this_level = next_level + origins = np.concatenate(origins, axis=0) + dirs = np.concatenate(dirs, axis=0) + return origins, dirs + + +def shoot_rays(mesh, origins, ray_dir, debug=False, model_id=None): + ''' + shoot rays and record the first hit distance, as well as all vertices on the hit faces. + :param mesh: input mesh (trimesh) + :param origins: origin of rays + :param ray_dir: direction of rays + :return: all vertices indices on the hit face, the distance of first hit for each ray. + ''' + global ray_per_sample + RayMeshIntersector = trimesh.ray.ray_triangle.RayMeshIntersector(mesh) + locations, index_ray, index_tri = RayMeshIntersector.intersects_location(origins, ray_dir + 1e-15) + locations_per_ray = [] + index_tri_per_ray = [] + for i in range(len(ray_dir)): + locations_per_ray.append(locations[index_ray == i]) + index_tri_per_ray.append(index_tri[index_ray == i]) + all_hit_pos = [] + all_hit_tri = [] + all_hit_ori = [] + all_hit_ori_id = [] + for ori_id in np.arange(0, len(ray_dir), ray_per_sample): + hit_pos = [] + hit_tri = [] + hit_dist = [] + hit_ori_id = [] + for i in range(ray_per_sample): + ray_id = int(ori_id + i) + if len(locations_per_ray[ray_id]) > 1: + closest_hit_id = np.argmin(np.linalg.norm(locations_per_ray[ray_id] - origins[ray_id], axis=1)) + hit_pos.append(locations_per_ray[ray_id][closest_hit_id][np.newaxis, :]) + hit_dist.append(np.linalg.norm(locations_per_ray[ray_id][closest_hit_id] - origins[ray_id])) + hit_tri.append(index_tri_per_ray[ray_id][closest_hit_id]) + hit_ori_id.append(int(ori_id/ray_per_sample)) + elif len(locations_per_ray[ray_id]) == 1: + hit_pos.append(locations_per_ray[ray_id]) + hit_dist.append(np.linalg.norm(locations_per_ray[ray_id][0] - origins[ray_id])) + hit_tri.append(index_tri_per_ray[ray_id][0]) + hit_ori_id.append(int(ori_id/ray_per_sample)) + + if len(hit_pos) == 0: # no hit, pick nearby faces + hit_tri = trimesh.proximity.nearby_faces(mesh, origins[int(ori_id + 0)][np.newaxis, :])[0] + hit_vertices = mesh.faces[hit_tri].flatten() + hit_pos = [np.array(mesh.vertices[i])[np.newaxis, :] for i in hit_vertices] + hit_dist = [np.linalg.norm(hit_pos[i].squeeze() - origins[int(ori_id + 0)]) for i in range(len(hit_pos))] + hit_tri = np.repeat(hit_tri, 3) + hit_ori_id = np.repeat(int(ori_id / ray_per_sample), len(hit_tri)) + + hit_pos = np.concatenate(hit_pos, axis=0) + hit_dist = np.array(hit_dist) + hit_tri = np.array(hit_tri) + hit_ori_id = np.array(hit_ori_id) + valid_ids = np.argwhere(hit_dist < np.percentile(hit_dist, 20) * 2).squeeze(1) + hit_pos = hit_pos[valid_ids] + hit_dist = hit_dist[valid_ids] + hit_tri = hit_tri[valid_ids] + hit_ori_id = hit_ori_id[valid_ids] + + all_hit_pos.append(hit_pos) + all_hit_tri.append(hit_tri) + all_hit_ori_id.append(hit_ori_id) + all_hit_ori.append(origins[int(ori_id + 0)][np.newaxis, :]) + + all_hit_pos = np.concatenate(all_hit_pos, axis=0) + all_hit_tri = np.concatenate(all_hit_tri) + all_hit_ori_id = np.concatenate(all_hit_ori_id) + all_hit_ori = np.concatenate(all_hit_ori, axis=0) + + if debug: + import open3d as o3d + from utils.vis_utils import find_lines_from_tree, drawSphere + mesh_filename = 'dataset_folder/obj/{:d}.obj'.format(model_id) + skel = Info('dataset_folder/rig_info/{:d}.txt'.format(model_id)) + # show mesh + mesh_o3d = o3d.io.read_triangle_mesh(mesh_filename) + mesh_ls = o3d.geometry.LineSet.create_from_triangle_mesh(mesh_o3d) + mesh_ls.colors = o3d.utility.Vector3dVector([[0.8, 0.8, 0.8] for i in range(len(mesh_ls.lines))]) + # show skeleton + line_list_skel = [] + joint_pos_list = [] + find_lines_from_tree(skel.root, line_list_skel, joint_pos_list) + line_set_skel = o3d.geometry.LineSet() + line_set_skel.points = o3d.utility.Vector3dVector(joint_pos_list) + line_set_skel.lines = o3d.utility.Vector2iVector(line_list_skel) + colors = [[1.0, 0.0, 0.0] for i in range(len(line_list_skel))] + line_set_skel.colors = o3d.utility.Vector3dVector(colors) + # show ray + dpts = np.concatenate((all_hit_ori, all_hit_pos), axis=0) + dlines = o3d.geometry.LineSet() + dlines.points = o3d.utility.Vector3dVector(dpts) + dlines.lines = o3d.utility.Vector2iVector([[all_hit_ori_id[i], len(all_hit_ori) + i] for i in range(len(all_hit_ori_id))]) + colors = [[0.0, 0.0, 1.0] for i in range(len(all_hit_ori_id))] + dlines.colors = o3d.utility.Vector3dVector(colors) + vis = o3d.visualization.Visualizer() + vis.create_window() + vis.add_geometry(dlines) + vis.add_geometry(mesh_ls) + vis.add_geometry(line_set_skel) + this_level = skel.root.children + while this_level: + next_level = [] + for p_node in this_level: + vis.add_geometry(drawSphere(p_node.pos, 0.007, color=[1.0, 0.0, 0.0])) # [0.3, 0.1, 0.1] + next_level += p_node.children + this_level = next_level + vis.run() + vis.destroy_window() + + return all_hit_pos, all_hit_ori_id, all_hit_ori + + +def normalize_mesh_rig(mesh, rig): + # normalize mesh + mesh_v = np.asarray(mesh.vertices) + dims = [max(mesh_v[:, 0]) - min(mesh_v[:, 0]), + max(mesh_v[:, 1]) - min(mesh_v[:, 1]), + max(mesh_v[:, 2]) - min(mesh_v[:, 2])] + scale = 1.0 / max(dims) + pivot = np.array([(min(mesh_v[:, 0]) + max(mesh_v[:, 0])) / 2, min(mesh_v[:, 1]), + (min(mesh_v[:, 2]) + max(mesh_v[:, 2])) / 2]) + mesh_v[:, 0] -= pivot[0] + mesh_v[:, 1] -= pivot[1] + mesh_v[:, 2] -= pivot[2] + mesh_v *= scale + mesh.vertices = o3d.utility.Vector3dVector(mesh_v) + + # normalize rig + for k, v in rig.joint_pos.items(): + rig.joint_pos[k] -= pivot + rig.joint_pos[k] *= scale + this_level = [rig.root] + while this_level: + next_level = [] + for node in this_level: + node.pos = (np.array(node.pos) - pivot) * scale + node.pos = (node.pos[0], node.pos[1], node.pos[2]) + for ch in node.children: + next_level.append(ch) + this_level = next_level + + return mesh, rig + +if __name__ == '__main__': + start_id = int(sys.argv[1]) + end_id = int(sys.argv[2]) + subsampling = True # decimate mesh to speed up + + ray_per_sample = 14 # number of rays shoot from each joint + dataset_folder = "/media/zhanxu/4T/ModelResource_RigNetv1_preproccessed/" + #dataset_folder = "/home/zhanxu/Proj/RigNet_public/quick_start/tigran/" + + remesh_obj_folder = os.path.join(dataset_folder, "obj_remesh/") + info_folder = os.path.join(dataset_folder, "rig_info/") + res_folder = os.path.join(dataset_folder, "pretrain_attention/") + model_list = np.loadtxt(os.path.join(dataset_folder, "model_list.txt"), dtype=int) + #model_list = np.array([1, 2, 3, 4, 5, 6, 7], dtype=np.int) + + for model_id in model_list[start_id:end_id]: + print(model_id) + mesh = o3d.io.read_triangle_mesh(os.path.join(remesh_obj_folder, '{:d}.obj'.format(model_id))) + rig_info = Info(os.path.join(info_folder, '{:d}.txt'.format(model_id))) + mesh, rig_info = normalize_mesh_rig(mesh, rig_info) + mesh_ori = copy.deepcopy(mesh) + vtx_ori = np.asarray(mesh.vertices) + + if subsampling: + mesh = mesh.simplify_quadric_decimation(3000) + + mesh_trimesh = trimesh.Trimesh(vertices=np.asarray(mesh.vertices), faces=np.asarray(mesh.triangles), process=False) + trimesh.repair.fix_normals(mesh_trimesh) + + origins, dirs = form_rays(rig_info) + hit_pos, all_hit_ori_id, all_hit_ori = shoot_rays(mesh_trimesh, origins, dirs, debug=False, model_id=model_id) + + dist = np.sqrt(np.sum((vtx_ori[np.newaxis, ...] - hit_pos[:, np.newaxis, :])**2, axis=2)) + dist = (dist < 2e-2) + + attn = np.zeros(len(vtx_ori), np.bool) + for joint_id in np.unique(all_hit_ori_id): + num_nn = np.sum(np.sum(dist[np.argwhere(all_hit_ori_id == joint_id).squeeze(), :], axis=0) > 0) + if num_nn < 6: + # too few nearest points + id_sort = np.argsort(np.linalg.norm(vtx_ori - all_hit_ori[joint_id][np.newaxis, :], axis=1)) + attn[id_sort[0:6]] = True + else: + id_nn = np.argwhere(np.sum(dist[np.argwhere(all_hit_ori_id == joint_id).squeeze(), :], axis=0) > 0).squeeze(1) + attn[id_nn] = True + + # vis = o3d.visualization.Visualizer() + # vis.create_window() + # mesh_ls = o3d.geometry.LineSet.create_from_triangle_mesh(mesh_ori) + # mesh_ls.colors = o3d.utility.Vector3dVector([[0.8, 0.8, 0.8] for i in range(len(mesh_ls.lines))]) + # vis.add_geometry(mesh_ls) + # pcd = o3d.geometry.PointCloud(points=o3d.utility.Vector3dVector(vtx_ori[np.argwhere(attn).squeeze()])) + # pcd.paint_uniform_color([1.0, 0.0, 0.0]) + # vis.add_geometry(pcd) + # vis.run() + # vis.destroy_window() + + np.savetxt(os.path.join(res_folder, '{:d}.txt'.format(model_id)), attn, fmt='%d') \ No newline at end of file diff --git a/RigNet/geometric_proc/compute_surface_geodesic.py b/RigNet/geometric_proc/compute_surface_geodesic.py new file mode 100644 index 0000000000000000000000000000000000000000..bacca708a40106487687df678976da6da7573f44 --- /dev/null +++ b/RigNet/geometric_proc/compute_surface_geodesic.py @@ -0,0 +1,40 @@ +#------------------------------------------------------------------------------- +# Name: compute_surface_geodesic.py +# Purpose: This script calculates surface geodesic distance between all pair of vertices. +# It doesn't rely on mesh topology. Surface is densely sampled to form a graph and get shortest path between samples. +# Geodesic distance between pair of vertices are the geodesic distance between nearest samples to both vertices. +# RigNet Copyright 2020 University of Massachusetts +# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License. +# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet. +#------------------------------------------------------------------------------- + +import sys +sys.path.append("./") +import os +import glob +import numpy as np +import open3d as o3d +from multiprocessing import Pool +from geometric_proc.common_ops import calc_surface_geodesic + + +def one_process(process_id): + start_id = process_id * 340 + end_id = (process_id + 1) * 340 + print("processing {:d} to {:d}\n".format(start_id, end_id)) + + remesh_obj_folder = "/media/zhanxu/4T1/ModelResource_Dataset/obj_remesh/" + res_folder = "/media/zhanxu/4T1/ModelResource_Dataset/surface_geodesic/" + + remesh_obj_folder = glob.glob(remesh_obj_folder + '*.obj') + for remesh_obj_filename in remesh_obj_folder[start_id: end_id]: + model_id = remesh_obj_filename.split('/')[-1].split('.')[0] + print(model_id) + surface_geodesic = calc_surface_geodesic(o3d.io.read_triangle_mesh(remesh_obj_filename)) + np.save(os.path.join(res_folder, "{:s}_surface_geo.npy".format(model_id)), surface_geodesic.astype(np.float16)) + + +if __name__ == '__main__': + #one_process(0) + p = Pool(4) + p.map(one_process, [0, 1, 2, 3]) diff --git a/RigNet/geometric_proc/compute_volumetric_geodesic.py b/RigNet/geometric_proc/compute_volumetric_geodesic.py new file mode 100644 index 0000000000000000000000000000000000000000..b25d039cd6ac3f6e227996e1f988af25055d57f6 --- /dev/null +++ b/RigNet/geometric_proc/compute_volumetric_geodesic.py @@ -0,0 +1,173 @@ +#------------------------------------------------------------------------------- +# Name: compute_volumetric_geodesic.py +# Purpose: his script calculates volumetric geodesic distance between vertices and bones. +# The shortest paths start from bones, then hit all "visible" vertices w.r.t each bone, i.e. the first hit on the surface is the vertex itself. +# For "invisible" vertices, find the nearest "visible" vertices along surface by surface geodesic distance, and then go interior to the bone. +# RigNet Copyright 2020 University of Massachusetts +# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License. +# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet. +#------------------------------------------------------------------------------- + +import sys +sys.path.append("./") +import os +import trimesh +import numpy as np +import open3d as o3d +from utils.os_utils import mkdir_p +from utils.rig_parser import Info +from geometric_proc.common_ops import get_bones, calc_surface_geodesic + + +def pts2line(pts, lines): + ''' + Calculate points-to-bone distance. Point to line segment distance refer to + https://stackoverflow.com/questions/849211/shortest-distance-between-a-point-and-a-line-segment + :param pts: N*3 + :param lines: N*6, where [N,0:3] is the starting position and [N, 3:6] is the ending position + :return: origins are the neatest projected position of the point on the line. + ends are the points themselves. + dist is the distance in between, which is the distance from points to lines. + Origins and ends will be used for generate rays. + ''' + l2 = np.sum((lines[:, 3:6] - lines[:, 0:3]) ** 2, axis=1) + origins = np.zeros((len(pts) * len(lines), 3)) + ends = np.zeros((len(pts) * len(lines), 3)) + dist = np.zeros((len(pts) * len(lines))) + for l in range(len(lines)): + if np.abs(l2[l]) < 1e-8: # for zero-length edges + origins[l * len(pts):(l + 1) * len(pts)] = lines[l][0:3] + else: # for other edges + t = np.sum((pts - lines[l][0:3][np.newaxis, :]) * (lines[l][3:6] - lines[l][0:3])[np.newaxis, :], axis=1) / \ + l2[l] + t = np.clip(t, 0, 1) + t_pos = lines[l][0:3][np.newaxis, :] + t[:, np.newaxis] * (lines[l][3:6] - lines[l][0:3])[np.newaxis, :] + origins[l * len(pts):(l + 1) * len(pts)] = t_pos + ends[l * len(pts):(l + 1) * len(pts)] = pts + dist[l * len(pts):(l + 1) * len(pts)] = np.linalg.norm( + origins[l * len(pts):(l + 1) * len(pts)] - ends[l * len(pts):(l + 1) * len(pts)], axis=1) + return origins, ends, dist + + +def calc_pts2bone_visible_mat(mesh, origins, ends): + ''' + Check whether the surface point is visible by the internal bone. + Visible is defined as no occlusion on the path between. + :param mesh: + :param surface_pts: points on the surface (n*3) + :param origins: origins of rays + :param ends: ends of the rays, together with origins, we can decide the direction of the ray. + :return: binary visibility matrix (n*m), where 1 indicate the n-th surface point is visible to the m-th ray + ''' + ray_dir = ends - origins + RayMeshIntersector = trimesh.ray.ray_triangle.RayMeshIntersector(mesh) + locations, index_ray, index_tri = RayMeshIntersector.intersects_location(origins, ray_dir + 1e-15) + locations_per_ray = [locations[index_ray == i] for i in range(len(ray_dir))] + min_hit_distance = [] + for i in range(len(locations_per_ray)): + if len(locations_per_ray[i]) == 0: + min_hit_distance.append(np.linalg.norm(ray_dir[i])) + else: + min_hit_distance.append(np.min(np.linalg.norm(locations_per_ray[i] - origins[i], axis=1))) + min_hit_distance = np.array(min_hit_distance) + distance = np.linalg.norm(ray_dir, axis=1) + vis_mat = (np.abs(min_hit_distance - distance) < 1e-4) + return vis_mat + + +def show_visible_mat(mesh_filename, joint_pos, vis_mat, joint_id): + from utils.vis_utils import drawSphere + + mesh_o3d = o3d.io.read_triangle_mesh(mesh_filename) + mesh_trimesh = trimesh.load(mesh_filename) + visible = vis_mat[:, joint_id] + + mesh_ls = o3d.geometry.LineSet.create_from_triangle_mesh(mesh_o3d) + mesh_ls.colors = o3d.utility.Vector3dVector([[0.8, 0.8, 0.8] for i in range(len(mesh_ls.lines))]) + pcd = o3d.geometry.PointCloud() + pcd.points = o3d.utility.Vector3dVector(np.array(mesh_trimesh.vertices)[visible]) + pcd.colors = o3d.utility.Vector3dVector(np.repeat(np.array([[0.0, 0.0, 1.0]]), int(np.sum(visible)), axis=0)) + vis = o3d.visualization.Visualizer() + vis.create_window() + vis.add_geometry(mesh_ls) + vis.add_geometry(drawSphere(joint_pos[joint_id], 0.005, color=[1.0, 0.0, 0.0])) + vis.add_geometry(pcd) + vis.run() + vis.destroy_window() + + +def one_process(dataset_folder, start_id, end_id): + model_list = np.loadtxt(os.path.join(dataset_folder, 'model_list.txt'), dtype=int) + model_list = model_list[start_id: end_id] + remesh_obj_folder = os.path.join(dataset_folder, "obj_remesh") + mkdir_p(os.path.join(dataset_folder, "volumetric_geodesic/")) + + for model_id in model_list: + print(model_id) + if os.path.exists(os.path.join(dataset_folder, "volumetric_geodesic/{:d}_volumetric_geo.npy".format(model_id))): + continue + remeshed_obj_filename = os.path.join(dataset_folder, 'obj_remesh/{:d}.obj'.format(model_id)) + ori_obj_filename = os.path.join(dataset_folder, 'obj/{:d}.obj'.format(model_id)) + info_filename = os.path.join(dataset_folder, 'rig_info/{:d}.txt'.format(model_id)) + + pts = np.array(o3d.io.read_triangle_mesh(os.path.join(remesh_obj_folder, '{:d}.obj'.format(model_id))).vertices) + + mesh_remesh = trimesh.load(remeshed_obj_filename) + mesh_ori = trimesh.load(ori_obj_filename) + rig_info = Info(info_filename) + bones, bone_name, _ = get_bones(rig_info) + origins, ends, pts_bone_dist = pts2line(pts, bones) + + if os.path.exists(os.path.join(dataset_folder, "volumetric_geodesic/{:d}_visibility_raw.npy".format(model_id))): + pts_bone_visibility = np.load(os.path.join(dataset_folder, "volumetric_geodesic/{:d}_visibility_raw.npy".format(model_id))) + else: + # pick one mesh with fewer faces to speed up + if len(mesh_remesh.faces) < len(mesh_ori.faces): + trimesh.repair.fix_normals(mesh_remesh) + pts_bone_visibility = calc_pts2bone_visible_mat(mesh_remesh, origins, ends) + else: + trimesh.repair.fix_normals(mesh_ori) + pts_bone_visibility = calc_pts2bone_visible_mat(mesh_ori, origins, ends) + pts_bone_visibility = pts_bone_visibility.reshape(len(bones), len(pts)).transpose() + #np.save(os.path.join(dataset_folder, "volumetric_geodesic/{:d}_visibility_raw.npy".format(model_id)), pts_bone_visibility) + pts_bone_dist = pts_bone_dist.reshape(len(bones), len(pts)).transpose() + + # remove visible points which are too far + if os.path.exists(os.path.join(dataset_folder, "volumetric_geodesic/{:d}_visibility_filtered.npy".format(model_id))): + pts_bone_visibility = np.load(os.path.join(dataset_folder, "volumetric_geodesic/{:d}_visibility_filtered.npy".format(model_id))) + else: + for b in range(pts_bone_visibility.shape[1]): + visible_pts = np.argwhere(pts_bone_visibility[:, b] == 1).squeeze(1) + if len(visible_pts) == 0: + continue + threshold_b = np.percentile(pts_bone_dist[visible_pts, b], 15) + pts_bone_visibility[pts_bone_dist[:, b] > 1.3 * threshold_b, b] = False + #np.save(os.path.join(dataset_folder, "volumetric_geodesic/{:d}_visibility_filtered.npy".format(model_id)), pts_bone_visibility) + + mesh = o3d.io.read_triangle_mesh(os.path.join(remesh_obj_folder, '{:d}.obj'.format(model_id))) + surface_geodesic = calc_surface_geodesic(mesh) + + visible_matrix = np.zeros(pts_bone_visibility.shape) + visible_matrix[np.where(pts_bone_visibility == 1)] = pts_bone_dist[np.where(pts_bone_visibility == 1)] + euc_dist = np.sqrt(np.sum((pts[np.newaxis, ...] - pts[:, np.newaxis, :]) ** 2, axis=2)) + for c in range(visible_matrix.shape[1]): + unvisible_pts = np.argwhere(pts_bone_visibility[:, c] == 0).squeeze(1) + visible_pts = np.argwhere(pts_bone_visibility[:, c] == 1).squeeze(1) + if len(visible_pts) == 0: + visible_matrix[:, c] = pts_bone_dist[:, c] + continue + for r in unvisible_pts: + dist1 = np.min(surface_geodesic[r, visible_pts]) + nn_visible = visible_pts[np.argmin(surface_geodesic[r, visible_pts])] + if np.isinf(dist1): + visible_matrix[r, c] = 8.0 + pts_bone_dist[r, c] + else: + visible_matrix[r, c] = dist1 + visible_matrix[nn_visible, c] + np.save(os.path.join(dataset_folder, "volumetric_geodesic/{:d}_volumetric_geo.npy".format(model_id)), visible_matrix) + + +if __name__ == '__main__': + start_id = int(sys.argv[1]) + end_id = int(sys.argv[2]) + dataset_folder = "/media/zhanxu/4T1/ModelResource_Dataset/" + one_process(dataset_folder, start_id, end_id) diff --git a/RigNet/maya_save_fbx.py b/RigNet/maya_save_fbx.py new file mode 100644 index 0000000000000000000000000000000000000000..a94255dbf2b2222ca8fc2836e02e4c1bcc2acdad --- /dev/null +++ b/RigNet/maya_save_fbx.py @@ -0,0 +1,100 @@ +#------------------------------------------------------------------------------- +# Name: maya_save_fbx.py +# Purpose: run this scriptin maya. Assemble predicted rig (.txt) and obj +# mesh together to a FBX file +# RigNet Copyright 2020 University of Massachusetts +# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License. +# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet. +#------------------------------------------------------------------------------- +import maya.OpenMaya as om +import maya.cmds as cmds +import maya.mel as mel +import numpy as np +import pymel.core as pm +import os +import glob + + +def loadInfo(info_name, geo_name): + f_info = open(info_name,'r') + joint_pos = {} + joint_hier = {} + joint_skin = [] + for line in f_info: + word = line.split() + if word[0] == 'joints': + joint_pos[word[1]] = [float(word[2]), float(word[3]), float(word[4])] + if word[0] == 'root': + root_pos = joint_pos[word[1]] + root_name = word[1] + cmds.joint(p=(root_pos[0], root_pos[1],root_pos[2]), name = root_name) + if word[0] == 'hier': + if word[1] not in joint_hier.keys(): + joint_hier[word[1]] = [word[2]] + else: + joint_hier[word[1]].append(word[2]) + if word[0] == 'skin': + skin_item = word[1:] + joint_skin.append(skin_item) + f_info.close() + + this_level = [root_name] + while this_level: + next_level = [] + for p_node in this_level: + if p_node in joint_hier.keys(): + for c_node in joint_hier[p_node]: + cmds.select(p_node, r=True) + child_pos = joint_pos[c_node] + cmds.joint(p=(child_pos[0], child_pos[1],child_pos[2]), name = c_node) + next_level.append(c_node) + this_level = next_level + cmds.joint(root_name, e=True, oj='xyz', sao='yup', ch=True, zso=True) + cmds.skinCluster( root_name, geo_name) + #print len(joint_skin) + for i in range(len(joint_skin)): + vtx_name = geo_name + '.vtx['+joint_skin[i][0]+']' + transValue = [] + for j in range(1,len(joint_skin[i]),2): + transValue_item = (joint_skin[i][j], float(joint_skin[i][j+1])) + transValue.append(transValue_item) + #print vtx_name, transValue + cmds.skinPercent( 'skinCluster1', vtx_name, transformValue=transValue) + cmds.skinPercent( 'skinCluster1', geo_name, pruneWeights=0.01, normalize=False ) + return root_name, joint_pos + + +def getGeometryGroups(): + geo_list = [] + geometries = cmds.ls(type='surfaceShape') + for geo in geometries: + if 'ShapeOrig' in geo: + ''' + we can also use cmds.ls(geo, l=True)[0].split("|")[0] + to get the upper level node name, but stick on this way for now + ''' + geo_name = geo.replace('ShapeOrig', '') + geo_list.append(geo_name) + if not geo_list: + geo_list = cmds.ls(type='surfaceShape') + return geo_list + + +if __name__ == '__main__': + #model_id = "17872" + model_id = "smith" + print(model_id) + obj_name = 'D:\\{:s}_ori.obj'.format(model_id) + info_name = 'D:\\{:s}_ori_rig.txt'.format(model_id) + out_name = 'D:\\{:s}.fbx'.format(model_id) + + # import obj + cmds.file(new=True,force=True) + cmds.file(obj_name, o=True) + + # import info + geo_list = getGeometryGroups() + root_name, _ = loadInfo(info_name, geo_list[0]) + + # export fbx + pm.mel.FBXExport(f=out_name) diff --git a/RigNet/models/GCN.py b/RigNet/models/GCN.py new file mode 100644 index 0000000000000000000000000000000000000000..c0c0dbe75c0d4f969c3be8db6c28326bebdcaff0 --- /dev/null +++ b/RigNet/models/GCN.py @@ -0,0 +1,70 @@ +#------------------------------------------------------------------------------- +# Name: GCN.py +# Purpose: definition of joint prediction module. +# RigNet Copyright 2020 University of Massachusetts +# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License. +# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet. +#------------------------------------------------------------------------------- +import torch +from models.gcn_basic_modules import MLP, GCU +from torch_scatter import scatter_max, scatter_mean +from torch.nn import Sequential, Dropout, Linear, ReLU, Parameter + + +class JointPredNet(torch.nn.Module): + def __init__(self, out_channels, input_normal, arch, aggr='max'): + super(JointPredNet, self).__init__() + self.input_normal = input_normal + self.arch = arch + if self.input_normal: + self.input_channel = 6 + else: + self.input_channel = 3 + self.gcu_1 = GCU(in_channels=self.input_channel, out_channels=64, aggr=aggr) + self.gcu_2 = GCU(in_channels=64, out_channels=256, aggr=aggr) + self.gcu_3 = GCU(in_channels=256, out_channels=512, aggr=aggr) + # feature compression + self.mlp_glb = MLP([(64 + 256 + 512), 1024]) + self.mlp_tramsform = Sequential(MLP([1024 + self.input_channel + 64 + 256 +512, 1024, 256]), + Dropout(0.7), Linear(256, out_channels)) + if self.arch == 'jointnet': + torch.nn.init.zeros_(self.mlp_tramsform[2].weight) + torch.nn.init.zeros_(self.mlp_tramsform[2].bias) + + def forward(self, data): + if self.input_normal: + x = torch.cat([data.pos, data.x], dim=1) + else: + x = data.pos + geo_edge_index, tpl_edge_index, batch = data.geo_edge_index, data.tpl_edge_index, data.batch + + x_1 = self.gcu_1(x, tpl_edge_index, geo_edge_index) + x_2 = self.gcu_2(x_1, tpl_edge_index, geo_edge_index) + x_3 = self.gcu_3(x_2, tpl_edge_index, geo_edge_index) + x_4 = self.mlp_glb(torch.cat([x_1, x_2, x_3], dim=1)) + + x_global, _ = scatter_max(x_4, data.batch, dim=0) + #x_global_mean = scatter_mean(x_4, data.batch, dim=0) + #x_global = torch.cat([x_global_max, x_global_mean], dim=1) + x_global = torch.repeat_interleave(x_global, torch.bincount(data.batch), dim=0) + + x_5 = torch.cat([x_global, x, x_1, x_2, x_3], dim=1) + out = self.mlp_tramsform(x_5) + if self.arch == 'jointnet': + out = torch.tanh(out) + return out + + +class JOINTNET_MASKNET_MEANSHIFT(torch.nn.Module): + def __init__(self): + super(JOINTNET_MASKNET_MEANSHIFT, self).__init__() + self.jointnet = JointPredNet(3, input_normal=False, arch='jointnet', aggr='max') + self.masknet = JointPredNet(1, input_normal=False, arch='masknet', aggr='max') + self.bandwidth = Parameter(torch.Tensor(1)) + self.bandwidth.data.fill_(0.04) + + def forward(self, data): + x_offset = self.jointnet(data) + x_mask_prob_0 = self.masknet(data) + x_mask_prob = torch.sigmoid(x_mask_prob_0) + return x_offset, x_mask_prob_0, x_mask_prob, self.bandwidth diff --git a/RigNet/models/PairCls_GCN.py b/RigNet/models/PairCls_GCN.py new file mode 100644 index 0000000000000000000000000000000000000000..d5d16178e9154ee23808b8e6d8100051e71e98c6 --- /dev/null +++ b/RigNet/models/PairCls_GCN.py @@ -0,0 +1,127 @@ +#------------------------------------------------------------------------------- +# Name: PairCls_GCN.py +# Purpose: definition of connectivity prediction module. +# RigNet Copyright 2020 University of Massachusetts +# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License. +# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet. +#------------------------------------------------------------------------------- +import numpy as np +import torch +from models.gcn_basic_modules import MLP, GCU +from torch.nn import Sequential, Dropout, Linear +from torch_scatter import scatter_max +from torch_geometric.nn import PointConv, fps, radius, global_max_pool, knn_interpolate + + +class SAModule(torch.nn.Module): + def __init__(self, ratio, r, nn): + super(SAModule, self).__init__() + self.ratio = ratio + self.r = r + self.conv = PointConv(nn) + + def forward(self, x, pos, batch): + idx = fps(pos, batch, ratio=self.ratio) + row, col = radius(pos, pos[idx], self.r, batch, batch[idx], + max_num_neighbors=64) + edge_index = torch.stack([col, row], dim=0) + x = self.conv(x, (pos, pos[idx]), edge_index) + pos, batch = pos[idx], batch[idx] + return x, pos, batch + + +class GlobalSAModule(torch.nn.Module): + def __init__(self, nn): + super(GlobalSAModule, self).__init__() + self.nn = nn + + def forward(self, x, pos, batch): + x = self.nn(torch.cat([x, pos], dim=1)) + x = global_max_pool(x, batch) + pos = pos.new_zeros((x.size(0), 3)) + batch = torch.arange(x.size(0), device=batch.device) + return x, pos, batch + + +class FPModule(torch.nn.Module): + def __init__(self, k, nn): + super(FPModule, self).__init__() + self.k = k + self.nn = nn + + def forward(self, x, pos, batch, x_skip, pos_skip, batch_skip): + x = knn_interpolate(x, pos, pos_skip, batch, batch_skip, k=self.k) + if x_skip is not None: + x = torch.cat([x, x_skip], dim=1) + x = self.nn(x) + return x, pos_skip, batch_skip + + +class ShapeEncoder(torch.nn.Module): + def __init__(self, aggr='max'): + super(ShapeEncoder, self).__init__() + self.gcu_1 = GCU(in_channels=3, out_channels=64, aggr=aggr) + self.gcu_2 = GCU(in_channels=64, out_channels=128, aggr=aggr) + self.gcu_3 = GCU(in_channels=128, out_channels=256, aggr=aggr) + self.mlp_glb = MLP([(64 + 128 + 256), 256, 64]) + + def forward(self, data): + x_1 = self.gcu_1(data.pos, data.tpl_edge_index, data.geo_edge_index) + x_2 = self.gcu_2(x_1, data.tpl_edge_index, data.geo_edge_index) + x_3 = self.gcu_3(x_2, data.tpl_edge_index, data.geo_edge_index) + x_4 = self.mlp_glb(torch.cat([x_1, x_2, x_3], dim=1)) + x_global_shape, _ = scatter_max(x_4, data.batch, dim=0) + return x_global_shape + + +class JointEncoder(torch.nn.Module): + def __init__(self): + super(JointEncoder, self).__init__() + #self.mlp_1 = MLP([3, 64, 128, 1024]) + #self.mlp_2 = MLP([1024, 256, 128]) + + self.sa1_module_joints = SAModule(0.999, 0.4, MLP([3, 64, 64, 128])) + self.sa2_module_joints = SAModule(0.33, 0.6, MLP([128 + 3, 128, 128, 256])) + self.sa3_module_joints = GlobalSAModule(MLP([256 + 3, 256, 256, 512, 256, 128])) + + def forward(self, joints, joints_batch): + '''x1 = self.mlp_1(joints_norepeat) + x_glb, _ = scatter_max(x1, joints_batch, dim=0) + x_glb = self.mlp_2(x_glb) + return x_glb''' + + sa0_joints = (None, joints, joints_batch) + sa1_joints = self.sa1_module_joints(*sa0_joints) + sa2_joints = self.sa2_module_joints(*sa1_joints) + sa3_joints = self.sa3_module_joints(*sa2_joints) + x_glb_joint = sa3_joints[0] + return x_glb_joint + + +class PairCls(torch.nn.Module): + def __init__(self): + super(PairCls, self).__init__() + self.expand_joint_feature = Sequential(MLP([8, 32, 64, 128, 256])) + self.shape_encoder = ShapeEncoder() + self.joint_encoder = JointEncoder() + input_concat_dim = 448 + self.mix_transform = Sequential(MLP([input_concat_dim, 128, 64]), Dropout(0.7), Linear(64, 1)) + + def forward(self, data, permute_joints=True): + joint_feature = self.joint_encoder(data.joints, data.joints_batch) + joint_feature = torch.repeat_interleave(joint_feature, torch.bincount(data.pairs_batch), dim=0) + shape_feature = self.shape_encoder(data) + shape_feature = torch.repeat_interleave(shape_feature, torch.bincount(data.pairs_batch), dim=0) + + if permute_joints: + rand_permute = (torch.rand(len(data.pairs))>=0.5).long().to(data.pairs.device) + joints_pair = torch.cat((data.joints[torch.gather(data.pairs, dim=1, index=rand_permute.unsqueeze(dim=1)).squeeze(dim=1).long()], + data.joints[torch.gather(data.pairs, dim=1, index=1-rand_permute.unsqueeze(dim=1)).squeeze(dim=1).long()], + data.pair_attr[:, :-1]), dim=1) + else: + joints_pair = torch.cat((data.joints[data.pairs[:,0].long()], data.joints[data.pairs[:,1].long()], data.pair_attr[:, :-1]), dim=1) + pair_feature = self.expand_joint_feature(joints_pair) + pair_feature = torch.cat((shape_feature, joint_feature, pair_feature), dim=1) + pre_label = self.mix_transform(pair_feature) + gt_label = data.pair_attr[:, -1].unsqueeze(1) + return pre_label, gt_label diff --git a/RigNet/models/ROOT_GCN.py b/RigNet/models/ROOT_GCN.py new file mode 100644 index 0000000000000000000000000000000000000000..6e5d6785d4e636a6d2e0636c2175390bef4be364 --- /dev/null +++ b/RigNet/models/ROOT_GCN.py @@ -0,0 +1,136 @@ +#------------------------------------------------------------------------------- +# Name: Root_GCN.py +# Purpose: definition of root prediction module. +# RigNet Copyright 2020 University of Massachusetts +# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License. +# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet. +#------------------------------------------------------------------------------- +import torch +from models.gcn_basic_modules import MLP, GCU +from torch_scatter import scatter_max +from torch.nn import Sequential, Linear +from torch_geometric.nn import PointConv, fps, radius, global_max_pool, knn_interpolate +__all__ = ['ROOTNET'] + +class SAModule(torch.nn.Module): + def __init__(self, ratio, r, nn): + super(SAModule, self).__init__() + self.ratio = ratio + self.r = r + self.conv = PointConv(nn) + + def forward(self, x, pos, batch): + idx = fps(pos, batch, ratio=self.ratio) + row, col = radius(pos, pos[idx], self.r, batch, batch[idx], + max_num_neighbors=64) + edge_index = torch.stack([col, row], dim=0) + x = self.conv(x, (pos, pos[idx]), edge_index) + pos, batch = pos[idx], batch[idx] + return x, pos, batch + + +class GlobalSAModule(torch.nn.Module): + def __init__(self, nn): + super(GlobalSAModule, self).__init__() + self.nn = nn + + def forward(self, x, pos, batch): + x = self.nn(torch.cat([x, pos], dim=1)) + x = global_max_pool(x, batch) + pos = pos.new_zeros((x.size(0), 3)) + batch = torch.arange(x.size(0), device=batch.device) + return x, pos, batch + + +class FPModule(torch.nn.Module): + def __init__(self, k, nn): + super(FPModule, self).__init__() + self.k = k + self.nn = nn + + def forward(self, x, pos, batch, x_skip, pos_skip, batch_skip): + x = knn_interpolate(x, pos, pos_skip, batch, batch_skip, k=self.k) + if x_skip is not None: + x = torch.cat([x, x_skip], dim=1) + x = self.nn(x) + return x, pos_skip, batch_skip + + +class ShapeEncoder(torch.nn.Module): + def __init__(self, aggr='max'): + super(ShapeEncoder, self).__init__() + self.gcu_1 = GCU(in_channels=3, out_channels=64, aggr=aggr) + self.gcu_2 = GCU(in_channels=64, out_channels=128, aggr=aggr) + self.gcu_3 = GCU(in_channels=128, out_channels=256, aggr=aggr) + self.mlp_glb = MLP([(64 + 128 + 256), 128]) + #self.mlp_glb = MLP([(64 + 128 + 256), 512]) + + def forward(self, data): + x_1 = self.gcu_1(data.pos, data.tpl_edge_index, data.geo_edge_index) + x_2 = self.gcu_2(x_1, data.tpl_edge_index, data.geo_edge_index) + x_3 = self.gcu_3(x_2, data.tpl_edge_index, data.geo_edge_index) + x_4 = self.mlp_glb(torch.cat([x_1, x_2, x_3], dim=1)) + x_global, _ = scatter_max(x_4, data.batch, dim=0) + return x_global + + +class JointEncoder(torch.nn.Module): + def __init__(self): + super(JointEncoder, self).__init__() + '''self.mlp_1 = MLP([4, 64]) + self.mlp_2 = MLP([64, 128, 1024]) + self.mlp_3 = MLP([1088, 512, 256, 128, 64])''' + self.sa1_joint = SAModule(0.999, 0.4, MLP([4, 64, 64, 128])) + self.sa2_joint = SAModule(0.33, 0.6, MLP([128 + 3, 128, 128, 256])) + self.sa3_joint = GlobalSAModule(MLP([256 + 3, 256, 256, 512])) + self.fp3_joint = FPModule(1, MLP([512 + 256, 256, 256])) + self.fp2_joint = FPModule(3, MLP([256 + 128, 128, 128])) + self.fp1_joint = FPModule(3, MLP([128 + 1, 128, 128])) + + def forward(self, x, pos, batch): + '''x1= self.mlp_1(torch.cat((pos, x), dim=1)) + x2 = self.mlp_2(x1) + x_glb, _ = scatter_max(x2, batch, dim=0) + x_glb = torch.repeat_interleave(x_glb, torch.bincount(batch), dim=0) + x3 = self.mlp_3(torch.cat((x_glb, x1), dim=1)) + return x3''' + sa0_joint = (x, pos, batch) + sa1_joint = self.sa1_joint(*sa0_joint) + sa2_joint = self.sa2_joint(*sa1_joint) + sa3_joint = self.sa3_joint(*sa2_joint) + fp3_joint = self.fp3_joint(*sa3_joint, *sa2_joint) + fp2_joint = self.fp2_joint(*fp3_joint, *sa1_joint) + x_joint, _, _ = self.fp1_joint(*fp2_joint, *sa0_joint) + return x_joint + + +class ROOTNET(torch.nn.Module): + def __init__(self): + super(ROOTNET, self).__init__() + self.shape_encoder = ShapeEncoder() + self.joint_encoder = JointEncoder() + self.back_layers = Sequential(MLP([128 + 128, 200, 64]), Linear(64, 1)) + + def forward(self, data, shuffle=True): + joints_label = [] + joints_shuffle = [] + for i in range(len(torch.unique(data.joints_batch))): + joint_i = data.joints[data.joints_batch==i] + label_i = joint_i.new(torch.Size((joint_i.shape[0], 1))).zero_() + label_i[0, 0] = 1 + # random shuffle + if shuffle: + idx = torch.randperm(label_i.nelement()) + label_i = label_i[idx] + joint_i = joint_i[idx] + joints_shuffle.append(joint_i) + joints_label.append(label_i) + joints_shuffle = torch.cat(joints_shuffle, dim=0) + joints_label = torch.cat(joints_label) + + x_glb_shape = self.shape_encoder(data) + shape_feature = torch.repeat_interleave(x_glb_shape, torch.bincount(data.joints_batch), dim=0) + joint_feature = self.joint_encoder(torch.abs(joints_shuffle[:,0:1]), joints_shuffle, data.joints_batch) + x_joint = torch.cat([shape_feature, joint_feature], dim=1) + x_joint = self.back_layers(x_joint) + return x_joint, joints_label diff --git a/RigNet/models/SKINNING.py b/RigNet/models/SKINNING.py new file mode 100644 index 0000000000000000000000000000000000000000..5d0228a182733cace437c0a091f8ba18f184a838 --- /dev/null +++ b/RigNet/models/SKINNING.py @@ -0,0 +1,74 @@ +#------------------------------------------------------------------------------- +# Name: SKINNING.py +# Purpose: definition of skinning prediction module. +# RigNet Copyright 2020 University of Massachusetts +# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License. +# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet. +#------------------------------------------------------------------------------- +import numpy as np +import torch +from torch_scatter import scatter_max +from torch.nn import Sequential as Seq, Linear as Lin, ReLU, BatchNorm1d as BN, Dropout +from models.gcn_basic_modules import GCU, MLP + +__all__ = ['SKINNET', 'skinnet'] + + +class SKINNET(torch.nn.Module): + def __init__(self, nearest_bone, use_Dg, use_Lf, aggr='max'): + super(SKINNET, self).__init__() + self.num_nearest_bone = nearest_bone + self.use_Dg = use_Dg + self.use_Lf = use_Lf + if self.use_Dg and self.use_Lf: + input_dim = 3 + self.num_nearest_bone * 8 + elif self.use_Dg and not self.use_Lf: + input_dim = 3 + self.num_nearest_bone * 7 + elif self.use_Lf and not self.use_Dg: + input_dim = 3 + self.num_nearest_bone * 7 + else: + input_dim = 3 + self.num_nearest_bone * 6 + self.multi_layer_tranform1 = MLP([input_dim, 128, 64]) + self.gcu1 = GCU(in_channels=64, out_channels=512, aggr=aggr) + self.gcu2 = GCU(in_channels=512, out_channels=256, aggr=aggr) + self.gcu3 = GCU(in_channels=256, out_channels=256, aggr=aggr) + self.multi_layer_tranform2 = MLP([512, 512, 1024]) + + self.cls_branch = Seq(Lin(1024 + 256, 1024), ReLU(), BN(1024), Lin(1024, 512), ReLU(), BN(512), + Lin(512, self.num_nearest_bone)) + + def forward(self, data): + samples = data.skin_input + if self.use_Dg and self.use_Lf: + samples = samples[:, 0: 8 * self.num_nearest_bone] + elif self.use_Dg and not self.use_Lf: + samples = samples[:, np.arange(samples.shape[1]) % 8 != 7] + samples = samples[:, 0: 7 * self.num_nearest_bone] + elif self.use_Lf and not self.use_Dg: + samples = samples[:, np.arange(samples.shape[1]) % 8 != 6] + samples = samples[:, 0: 7 * self.num_nearest_bone] + else: + samples = samples[:, np.arange(samples.shape[1]) % 8 != 7] + samples = samples[:, np.arange(samples.shape[1]) % 7 != 6] + samples = samples[:, 0: 6 * self.num_nearest_bone] + + raw_input = torch.cat([data.pos, samples], dim=1) + + x_0 = self.multi_layer_tranform1(raw_input) + x_1 = self.gcu1(x_0, data.tpl_edge_index, data.geo_edge_index) + + x_global = self.multi_layer_tranform2(x_1) + x_global, _ = scatter_max(x_global, data.batch, dim=0) + + x_2 = self.gcu2(x_1, data.tpl_edge_index, data.geo_edge_index) + x_3 = self.gcu3(x_2, data.tpl_edge_index, data.geo_edge_index) + x_global = torch.repeat_interleave(x_global, torch.bincount(data.batch), dim=0) + x_4 = torch.cat([x_3, x_global], dim=1) + + skin_cls_pred = self.cls_branch(x_4) + return skin_cls_pred + + +def skinnet(nearest_bone, use_Dg, use_Lf): + model = SKINNET(nearest_bone=nearest_bone, use_Dg=use_Dg, use_Lf=use_Lf) + return model diff --git a/RigNet/models/__init__.py b/RigNet/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e75b273b993c180e2efcfd836f7343dac02292ba --- /dev/null +++ b/RigNet/models/__init__.py @@ -0,0 +1,4 @@ +from .GCN import * +from .PairCls_GCN import PairCls +from .ROOT_GCN import ROOTNET +from .SKINNING import * \ No newline at end of file diff --git a/RigNet/models/gcn_basic_modules.py b/RigNet/models/gcn_basic_modules.py new file mode 100644 index 0000000000000000000000000000000000000000..de8e6ba2ceb62bd4254ee7d0c83886a7d0231155 --- /dev/null +++ b/RigNet/models/gcn_basic_modules.py @@ -0,0 +1,62 @@ +#------------------------------------------------------------------------------- +# Name: gcn_basic_modules.py +# Purpose: basic structures (layers) used in our models +# RigNet Copyright 2020 University of Massachusetts +# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License. +# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet. +#------------------------------------------------------------------------------- +import torch +from torch_geometric.nn import MessagePassing +from torch_scatter import scatter_max, scatter_mean +from torch_geometric.utils import add_self_loops, remove_self_loops, softmax +from torch.nn import Sequential, Dropout, Linear, ReLU, BatchNorm1d, Parameter + + +def MLP(channels, batch_norm=True): + if batch_norm: + return Sequential(*[Sequential(Linear(channels[i - 1], channels[i]), ReLU(), BatchNorm1d(channels[i], momentum=0.1)) + for i in range(1, len(channels))]) + else: + return Sequential(*[Sequential(Linear(channels[i - 1], channels[i]), ReLU()) for i in range(1, len(channels))]) + + +class EdgeConv(MessagePassing): + def __init__(self, in_channels, out_channels, nn, aggr='max', **kwargs): + super(EdgeConv, self).__init__(aggr=aggr, **kwargs) + self.in_channels = in_channels + self.out_channels = out_channels + self.nn = nn + + def forward(self, x, edge_index): + """""" + x = x.unsqueeze(-1) if x.dim() == 1 else x + edge_index, _ = remove_self_loops(edge_index) + edge_index, _ = add_self_loops(edge_index, num_nodes=x.size(0)) + return self.propagate(edge_index, x=x) + + def message(self, x_i, x_j): + return self.nn(torch.cat([x_i, (x_j - x_i)], dim=1)) + + def update(self, aggr_out): + aggr_out = aggr_out.view(-1, self.out_channels) + return aggr_out + + def __repr__(self): + return '{}(nn={})'.format(self.__class__.__name__, self.nn) + + +class GCU(torch.nn.Module): + def __init__(self, in_channels, out_channels, aggr='max'): + super(GCU, self).__init__() + self.edge_conv_tpl = EdgeConv(in_channels=in_channels, out_channels=out_channels // 2, + nn=MLP([in_channels * 2, out_channels // 2, out_channels // 2]), aggr=aggr) + self.edge_conv_geo = EdgeConv(in_channels=in_channels, out_channels=out_channels // 2, + nn=MLP([in_channels * 2, out_channels // 2, out_channels // 2]), aggr=aggr) + self.mlp = MLP([out_channels, out_channels]) + + def forward(self, x, tpl_edge_index, geo_edge_index): + x_tpl = self.edge_conv_tpl(x, tpl_edge_index) + x_geo = self.edge_conv_geo(x, geo_edge_index) + x_out = torch.cat([x_tpl, x_geo], dim=1) + x_out = self.mlp(x_out) + return x_out \ No newline at end of file diff --git a/RigNet/models/supplemental_layers/__init__.py b/RigNet/models/supplemental_layers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/RigNet/models/supplemental_layers/cross_entropy_with_probs.py b/RigNet/models/supplemental_layers/cross_entropy_with_probs.py new file mode 100644 index 0000000000000000000000000000000000000000..9e9061e8634c2a25f42b49f42d56103ffe104385 --- /dev/null +++ b/RigNet/models/supplemental_layers/cross_entropy_with_probs.py @@ -0,0 +1,67 @@ +import torch +import torch.nn.functional as F + + +def cross_entropy_with_probs_snorkel(input, target, weight=None, reduction="mean"): + """Calculate cross-entropy loss when targets are probabilities (floats), not ints. + PyTorch's F.cross_entropy() method requires integer labels; it does accept + probabilistic labels. We can, however, simulate such functionality with a for loop, + calculating the loss contributed by each class and accumulating the results. + Libraries such as keras do not require this workaround, as methods like + "categorical_crossentropy" accept float labels natively. + Note that the method signature is intentionally very similar to F.cross_entropy() + so that it can be used as a drop-in replacement when target labels are changed from + from a 1D tensor of ints to a 2D tensor of probabilities. + Parameters + ---------- + input + A [num_points, num_classes] tensor of logits + target + A [num_points, num_classes] tensor of probabilistic target labels + weight + An optional [num_classes] array of weights to multiply the loss by per class + reduction + One of "none", "mean", "sum", indicating whether to return one loss per data + point, the mean loss, or the sum of losses + Returns + ------- + torch.Tensor + The calculated loss + Raises + ------ + ValueError + If an invalid reduction keyword is submitted + """ + num_points, num_classes = input.shape + # Note that t.new_zeros, t.new_full put tensor on same device as t + cum_losses = input.new_zeros(num_points) + for y in range(num_classes): + target_temp = input.new_full((num_points,), y, dtype=torch.long) + y_loss = F.cross_entropy(input, target_temp, reduction="none") + if weight is not None: + y_loss = y_loss * weight[y] + cum_losses += target[:, y].float() * y_loss + + if reduction == "none": + return cum_losses + elif reduction == "mean": + return cum_losses.mean() + elif reduction == "sum": + return cum_losses.sum() + else: + raise ValueError("Keyword 'reduction' must be one of ['none', 'mean', 'sum']") + + +def cross_entropy_with_probs(input, target, weight=None, reduction="mean"): + input_logsoftmax = F.log_softmax(input, dim=1) + cum_losses = -target * input_logsoftmax + if weight is not None: + cum_losses = cum_losses * weight + if reduction == "none": + return cum_losses + elif reduction == "mean": + return cum_losses.sum(dim=1).mean() + elif reduction == "sum": + return cum_losses.sum() + else: + raise ValueError("Keyword 'reduction' must be one of ['none', 'mean', 'sum']") diff --git a/RigNet/models/supplemental_layers/pytorch_chamfer_dist.py b/RigNet/models/supplemental_layers/pytorch_chamfer_dist.py new file mode 100644 index 0000000000000000000000000000000000000000..8d30a88606f781b52e77352fe62dded91b45a9db --- /dev/null +++ b/RigNet/models/supplemental_layers/pytorch_chamfer_dist.py @@ -0,0 +1,26 @@ +import torch +from torch_scatter import scatter_mean + + +def chamfer_distance_with_average(p1, p2): + + ''' + Calculate Chamfer Distance between two point sets + :param p1: size[1, N, D] + :param p2: size[1, M, D] + :param debug: whether need to output debug info + :return: sum of Chamfer Distance of two point sets + ''' + + assert p1.size(0) == 1 and p2.size(0) == 1 + assert p1.size(2) == p2.size(2) + p1 = p1.repeat(p2.size(1), 1, 1) + p1 = p1.transpose(0, 1) + p2 = p2.repeat(p1.size(0), 1, 1) + dist = torch.add(p1, torch.neg(p2)) + dist_norm = torch.norm(dist, 2, dim=2) + dist1 = torch.min(dist_norm, dim=1)[0] + dist2 = torch.min(dist_norm, dim=0)[0] + loss = 0.5 * ((torch.mean(dist1)) + (torch.mean(dist2))) + return loss + diff --git a/RigNet/mst_generate.py b/RigNet/mst_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..1f663133f43bbb4772c429eb76608bb674172998 --- /dev/null +++ b/RigNet/mst_generate.py @@ -0,0 +1,225 @@ +#------------------------------------------------------------------------------- +# Name: mst_generate.py +# Purpose: Generate skeleton as a tree based on predicted joints. +# RigNet Copyright 2020 University of Massachusetts +# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License. +# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet. +#------------------------------------------------------------------------------- + +import os +import cv2 +import argparse +import numpy as np +import open3d as o3d +from utils import binvox_rw +from utils.tree_utils import TreeNode +from utils.rig_parser import Skel +from utils.vis_utils import show_obj_skel, draw_shifted_pts +from utils.io_utils import readPly +from utils.cluster_utils import meanshift_cluster, nms_meanshift +from utils.mst_utils import primMST_symmetry, loadSkel_recur, increase_cost_for_outside_bone, flip, inside_check, sample_on_bone +from gen_dataset import get_geo_edges, get_tpl_edges +from geometric_proc.common_ops import calc_surface_geodesic + +import torch +from torch_geometric.data import Data +from torch_geometric.utils import add_self_loops + +from models.ROOT_GCN import ROOTNET +from models.PairCls_GCN import PairCls + +device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + + +def predict_joints(model_id, args): + """ + predict joints for a specified model + :param model_id: processed model ID number + :param args: + :return: predicted joints, and voxelized mesh + """ + vox_folder = os.path.join(args.dataset_folder, 'vox/') + mesh_folder = os.path.join(args.dataset_folder, 'obj_remesh/') + raw_pred = os.path.join(args.res_folder, '{:d}.ply'.format(model_id)) + vox_file = os.path.join(vox_folder, '{:d}.binvox'.format(model_id)) + mesh_file = os.path.join(mesh_folder, '{:d}.obj'.format(model_id)) + pred_attn = np.load(os.path.join(args.res_folder, '{:d}_attn.npy'.format(model_id))) + + with open(vox_file, 'rb') as fvox: + vox = binvox_rw.read_as_3d_array(fvox) + pred_joints = readPly(raw_pred) + pred_joints, index_inside = inside_check(pred_joints, vox) + pred_attn = pred_attn[index_inside, :] + # img = draw_shifted_pts(mesh_file, pred_joints, weights=pred_attn) + + bandwidth = np.load(os.path.join(args.res_folder, '{:d}_bandwidth.npy'.format(model_id))) + bandwidth = bandwidth[0] + pred_joints = pred_joints[pred_attn.squeeze() > 1e-3] + pred_attn = pred_attn[pred_attn.squeeze() > 1e-3] + + # reflect raw points + pred_joints_reflect = pred_joints * np.array([[-1, 1, 1]]) + pred_joints = np.concatenate((pred_joints, pred_joints_reflect), axis=0) + pred_attn = np.tile(pred_attn, (2, 1)) + # img = draw_shifted_pts(mesh_file, pred_joints, weights=pred_attn) + # cv2.imwrite(os.path.join(res_folder, '{:s}_raw.jpg'.format(model_id)), img[:, :, ::-1]) + + pred_joints = meanshift_cluster(pred_joints, bandwidth, pred_attn, max_iter=20) + Y_dist = np.sum(((pred_joints[np.newaxis, ...] - pred_joints[:, np.newaxis, :]) ** 2), axis=2) + density = np.maximum(bandwidth ** 2 - Y_dist, np.zeros(Y_dist.shape)) + # density = density * pred_attn + density = np.sum(density, axis=0) + density_sum = np.sum(density) + pred_joints_ = pred_joints[density / density_sum > args.threshold_best] + density_ = density[density / density_sum > args.threshold_best] + pred_joints_ = nms_meanshift(pred_joints_, density_, bandwidth) + pred_joints_, _ = flip(pred_joints_) + + reduce_threshold = args.threshold_best + while len(pred_joints_) < 2 and reduce_threshold > 1e-7: + # print('reducing') + reduce_threshold = reduce_threshold / 1.3 + pred_joints_ = pred_joints[density / density_sum >= reduce_threshold] + density_ = density[density / density_sum > reduce_threshold] + pred_joints_ = nms_meanshift(pred_joints_, density_, bandwidth) + pred_joints_, _ = flip(pred_joints_) + if reduce_threshold <= 1e-7: + pred_joints_ = nms_meanshift(pred_joints_, density, bandwidth) + pred_joints_, _ = flip(pred_joints_) + + pred_joints = pred_joints_ + # img = draw_shifted_pts(mesh_file, pred_joints) + # cv2.imwrite(os.path.join(res_folder, '{:d}_joint.jpg'.format(model_id)), img) + # np.save(os.path.join(res_folder, '{:d}_joint.npy'.format(model_id)), pred_joints) + return pred_joints, vox + + +def getInitId(data, model): + """ + predict root joint ID via rootnet + :param data: + :param model: + :return: + """ + with torch.no_grad(): + root_prob, _ = model(data, shuffle=False) + root_prob = torch.sigmoid(root_prob).data.cpu().numpy() + root_id = np.argmax(root_prob) + return root_id + + +def create_single_data(mesh, vox, surface_geodesic, pred_joints): + """ + create data used as input to networks, wrapped by Data structure in pytorch-gemetric library + :param mesh: input mesh loaded by open3d + :param vox: voxelized mesh + :param surface_geodesic: geodesic distance matrix of all vertices + :param pred_joints: predicted joints + :return: wrapped data structure + """ + mesh_v = np.asarray(mesh.vertices) + mesh_vn = np.asarray(mesh.vertex_normals) + mesh_f = np.asarray(mesh.triangles) + + # vertices + v = np.concatenate((mesh_v, mesh_vn), axis=1) + v = torch.from_numpy(v).float() + + # topology edges + print(" gathering topological edges.") + tpl_e = get_tpl_edges(mesh_v, mesh_f).T + tpl_e = torch.from_numpy(tpl_e).long() + tpl_e, _ = add_self_loops(tpl_e, num_nodes=v.size(0)) + + # geodesic edges + print(" gathering geodesic edges.") + geo_e = get_geo_edges(surface_geodesic, mesh_v).T + geo_e = torch.from_numpy(geo_e).long() + geo_e, _ = add_self_loops(geo_e, num_nodes=v.size(0)) + + batch = np.zeros(len(v)) + batch = torch.from_numpy(batch).long() + + pair_all = [] + for joint1_id in range(len(pred_joints)): + for joint2_id in range(joint1_id + 1, len(pred_joints)): + dist = np.linalg.norm(pred_joints[joint1_id] - pred_joints[joint2_id]) + bone_samples = sample_on_bone(pred_joints[joint1_id], pred_joints[joint2_id]) + bone_samples_inside, _ = inside_check(bone_samples, vox) + outside_proportion = len(bone_samples_inside) / (len(bone_samples) + 1e-10) + pair = np.array([joint1_id, joint2_id, dist, outside_proportion, 1]) + pair_all.append(pair) + pair_all = np.array(pair_all) + pair_all = torch.from_numpy(pair_all).float() + num_pair = len(pair_all) + num_joint = len(pred_joints) + if len(pred_joints) < len(mesh_v): + pred_joints = np.tile(pred_joints, (round(1.0 * len(mesh_v) / len(pred_joints) + 0.5), 1)) + pred_joints = pred_joints[:len(mesh_v), :] + elif len(pred_joints) > len(mesh_v): + pred_joints = pred_joints[:len(mesh_v), :] + pred_joints = torch.from_numpy(pred_joints).float() + + data = Data(x=torch.from_numpy(mesh_vn), pos=torch.from_numpy(mesh_v).float(), batch=batch, y=pred_joints, + pairs=pair_all, num_pair=[num_pair], tpl_edge_index=tpl_e, geo_edge_index=geo_e, num_joint=[num_joint]).to(device) + return data + + +def run_mst_generate(args): + """ + generate skeleton in batch + :param args: input folder path and data folder path + """ + test_list = np.loadtxt(os.path.join(args.dataset_folder, 'test_final.txt'), dtype=np.int) + root_select_model = ROOTNET() + root_select_model.to(device) + root_select_model.eval() + root_checkpoint = torch.load(args.rootnet) + root_select_model.load_state_dict(root_checkpoint['state_dict']) + connectivity_model = PairCls() + connectivity_model.to(device) + connectivity_model.eval() + conn_checkpoint = torch.load(args.bonenet) + connectivity_model.load_state_dict(conn_checkpoint['state_dict']) + + for model_id in test_list: + print(model_id) + pred_joints, vox = predict_joints(model_id, args) + mesh_filename = os.path.join(args.dataset_folder, 'obj_remesh/{:d}.obj'.format(model_id)) + mesh = o3d.io.read_triangle_mesh(mesh_filename) + surface_geodesic = calc_surface_geodesic(mesh) + data = create_single_data(mesh, vox, surface_geodesic, pred_joints) + root_id = getInitId(data, root_select_model) + with torch.no_grad(): + cost_matrix, _ = connectivity_model.forward(data) + connect_prob = torch.sigmoid(cost_matrix) + pair_idx = data.pairs.long().data.cpu().numpy() + cost_matrix = np.zeros((data.num_joint[0], data.num_joint[0])) + cost_matrix[pair_idx[:, 0], pair_idx[:, 1]] = connect_prob.data.cpu().numpy().squeeze() + cost_matrix = cost_matrix + cost_matrix.transpose() + cost_matrix = -np.log(cost_matrix+1e-10) + #cost_matrix = flip_cost_matrix(pred_joints, cost_matrix) + cost_matrix = increase_cost_for_outside_bone(cost_matrix, pred_joints, vox) + + skel = Skel() + parent, key, root_id = primMST_symmetry(cost_matrix, root_id, pred_joints) + for i in range(len(parent)): + if parent[i] == -1: + skel.root = TreeNode('root', tuple(pred_joints[i])) + break + loadSkel_recur(skel.root, i, None, pred_joints, parent) + img = show_obj_skel(mesh_filename, skel.root) + cv2.imwrite(os.path.join(args.res_folder, '{:d}_skel.jpg'.format(model_id)), img[:,:,::-1]) + skel.save(os.path.join(args.res_folder, '{:d}_skel.txt'.format(model_id))) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='') + parser.add_argument('--dataset_folder', default='/media/zhanxu/4T1/ModelResource_RigNetv1_preproccessed/', type=str) + parser.add_argument('--res_folder', default='results/gcn_meanshift/best_25/', type=str) + parser.add_argument('--rootnet', default='checkpoints/rootnet/model_best.pth.tar', type=str) + parser.add_argument('--bonenet', default='checkpoints/bonenet/model_best.pth.tar', type=str) + parser.add_argument('--threshold_best', default=1e-5, type=float) + args = parser.parse_args() + print(args) + run_mst_generate(args) diff --git a/RigNet/quick_start.py b/RigNet/quick_start.py new file mode 100644 index 0000000000000000000000000000000000000000..ba00a2507a098df239019fdae2f1c4d6f6bf33f8 --- /dev/null +++ b/RigNet/quick_start.py @@ -0,0 +1,476 @@ +# --------------------------------------------------------------------------------------------------------- +# Name: quick_start.py +# Purpose: An easy-to-use demo. Also serves as an interface of the pipeline. +# RigNet Copyright 2020 University of Massachusetts +# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License. +# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet. +# --------------------------------------------------------------------------------------------------------- + +import os +from sys import platform +import trimesh +import numpy as np +import open3d as o3d +import itertools as it + +import torch +from torch_geometric.data import Data +from torch_geometric.utils import add_self_loops + +from utils import binvox_rw +from utils.rig_parser import Skel, Info +from utils.tree_utils import TreeNode +from utils.io_utils import assemble_skel_skin +from utils.vis_utils import draw_shifted_pts, show_obj_skel, show_mesh_vox +from utils.cluster_utils import meanshift_cluster, nms_meanshift +from utils.mst_utils import increase_cost_for_outside_bone, primMST_symmetry, loadSkel_recur, inside_check, flip + +from geometric_proc.common_ops import get_bones, calc_surface_geodesic +from geometric_proc.compute_volumetric_geodesic import pts2line, calc_pts2bone_visible_mat + +from gen_dataset import get_tpl_edges, get_geo_edges +from mst_generate import sample_on_bone, getInitId +from run_skinning import post_filter + +from models.GCN import JOINTNET_MASKNET_MEANSHIFT as JOINTNET +from models.ROOT_GCN import ROOTNET +from models.PairCls_GCN import PairCls as BONENET +from models.SKINNING import SKINNET + + +def normalize_obj(mesh_v): + dims = [max(mesh_v[:, 0]) - min(mesh_v[:, 0]), + max(mesh_v[:, 1]) - min(mesh_v[:, 1]), + max(mesh_v[:, 2]) - min(mesh_v[:, 2])] + scale = 1.0 / max(dims) + pivot = np.array([(min(mesh_v[:, 0]) + max(mesh_v[:, 0])) / 2, min(mesh_v[:, 1]), + (min(mesh_v[:, 2]) + max(mesh_v[:, 2])) / 2]) + mesh_v[:, 0] -= pivot[0] + mesh_v[:, 1] -= pivot[1] + mesh_v[:, 2] -= pivot[2] + mesh_v *= scale + return mesh_v, pivot, scale + + +def create_single_data(mesh_filaname): + """ + create input data for the network. The data is wrapped by Data structure in pytorch-geometric library + :param mesh_filaname: name of the input mesh + :return: wrapped data, voxelized mesh, and geodesic distance matrix of all vertices + """ + mesh = o3d.io.read_triangle_mesh(mesh_filaname) + mesh.compute_vertex_normals() + mesh_v = np.asarray(mesh.vertices) + mesh_vn = np.asarray(mesh.vertex_normals) + mesh_f = np.asarray(mesh.triangles) + + mesh_v, translation_normalize, scale_normalize = normalize_obj(mesh_v) + mesh_normalized = o3d.geometry.TriangleMesh(vertices=o3d.utility.Vector3dVector(mesh_v), triangles=o3d.utility.Vector3iVector(mesh_f)) + o3d.io.write_triangle_mesh(mesh_filename.replace("_remesh.obj", "_normalized.obj"), mesh_normalized) + + # vertices + v = np.concatenate((mesh_v, mesh_vn), axis=1) + v = torch.from_numpy(v).float() + + # topology edges + print(" gathering topological edges.") + tpl_e = get_tpl_edges(mesh_v, mesh_f).T + tpl_e = torch.from_numpy(tpl_e).long() + tpl_e, _ = add_self_loops(tpl_e, num_nodes=v.size(0)) + + # surface geodesic distance matrix + print(" calculating surface geodesic matrix.") + surface_geodesic = calc_surface_geodesic(mesh) + + # geodesic edges + print(" gathering geodesic edges.") + geo_e = get_geo_edges(surface_geodesic, mesh_v).T + geo_e = torch.from_numpy(geo_e).long() + geo_e, _ = add_self_loops(geo_e, num_nodes=v.size(0)) + + # batch + batch = torch.zeros(len(v), dtype=torch.long) + + # voxel + if not os.path.exists(mesh_filaname.replace('_remesh.obj', '_normalized.binvox')): + if platform == "linux" or platform == "linux2": + os.system("./binvox -d 88 -pb " + mesh_filaname.replace("_remesh.obj", "_normalized.obj")) + elif platform == "win32": + os.system("binvox.exe -d 88 " + mesh_filaname.replace("_remesh.obj", "_normalized.obj")) + else: + raise Exception('Sorry, we currently only support windows and linux.') + + with open(mesh_filaname.replace('_remesh.obj', '_normalized.binvox'), 'rb') as fvox: + vox = binvox_rw.read_as_3d_array(fvox) + + data = Data(x=v[:, 3:6], pos=v[:, 0:3], tpl_edge_index=tpl_e, geo_edge_index=geo_e, batch=batch) + return data, vox, surface_geodesic, translation_normalize, scale_normalize + + +def predict_joints(input_data, vox, joint_pred_net, threshold, bandwidth=None, mesh_filename=None): + """ + Predict joints + :param input_data: wrapped input data + :param vox: voxelized mesh + :param joint_pred_net: network for predicting joints + :param threshold: density threshold to filter out shifted points + :param bandwidth: bandwidth for meanshift clustering + :param mesh_filename: mesh filename for visualization + :return: wrapped data with predicted joints, pair-wise bone representation added. + """ + data_displacement, _, attn_pred, bandwidth_pred = joint_pred_net(input_data) + y_pred = data_displacement + input_data.pos + y_pred_np = y_pred.data.cpu().numpy() + attn_pred_np = attn_pred.data.cpu().numpy() + y_pred_np, index_inside = inside_check(y_pred_np, vox) + attn_pred_np = attn_pred_np[index_inside, :] + y_pred_np = y_pred_np[attn_pred_np.squeeze() > 1e-3] + attn_pred_np = attn_pred_np[attn_pred_np.squeeze() > 1e-3] + + # symmetrize points by reflecting + y_pred_np_reflect = y_pred_np * np.array([[-1, 1, 1]]) + y_pred_np = np.concatenate((y_pred_np, y_pred_np_reflect), axis=0) + attn_pred_np = np.tile(attn_pred_np, (2, 1)) + + #img = draw_shifted_pts(mesh_filename, y_pred_np, weights=attn_pred_np) + if bandwidth is None: + bandwidth = bandwidth_pred.item() + y_pred_np = meanshift_cluster(y_pred_np, bandwidth, attn_pred_np, max_iter=40) + #img = draw_shifted_pts(mesh_filename, y_pred_np, weights=attn_pred_np) + + Y_dist = np.sum(((y_pred_np[np.newaxis, ...] - y_pred_np[:, np.newaxis, :]) ** 2), axis=2) + density = np.maximum(bandwidth ** 2 - Y_dist, np.zeros(Y_dist.shape)) + density = np.sum(density, axis=0) + density_sum = np.sum(density) + y_pred_np = y_pred_np[density / density_sum > threshold] + attn_pred_np = attn_pred_np[density / density_sum > threshold][:, 0] + density = density[density / density_sum > threshold] + + #img = draw_shifted_pts(mesh_filename, y_pred_np, weights=attn_pred_np) + pred_joints = nms_meanshift(y_pred_np, density, bandwidth) + pred_joints, _ = flip(pred_joints) + #img = draw_shifted_pts(mesh_filename, pred_joints) + + # prepare and add new data members + pairs = list(it.combinations(range(pred_joints.shape[0]), 2)) + pair_attr = [] + for pr in pairs: + dist = np.linalg.norm(pred_joints[pr[0]] - pred_joints[pr[1]]) + bone_samples = sample_on_bone(pred_joints[pr[0]], pred_joints[pr[1]]) + bone_samples_inside, _ = inside_check(bone_samples, vox) + outside_proportion = len(bone_samples_inside) / (len(bone_samples) + 1e-10) + attr = np.array([dist, outside_proportion, 1]) + pair_attr.append(attr) + pairs = np.array(pairs) + pair_attr = np.array(pair_attr) + pairs = torch.from_numpy(pairs).float() + pair_attr = torch.from_numpy(pair_attr).float() + pred_joints = torch.from_numpy(pred_joints).float() + joints_batch = torch.zeros(len(pred_joints), dtype=torch.long) + pairs_batch = torch.zeros(len(pairs), dtype=torch.long) + + input_data.joints = pred_joints + input_data.pairs = pairs + input_data.pair_attr = pair_attr + input_data.joints_batch = joints_batch + input_data.pairs_batch = pairs_batch + return input_data + + +def predict_skeleton(input_data, vox, root_pred_net, bone_pred_net, mesh_filename): + """ + Predict skeleton structure based on joints + :param input_data: wrapped data + :param vox: voxelized mesh + :param root_pred_net: network to predict root + :param bone_pred_net: network to predict pairwise connectivity cost + :param mesh_filename: meshfilename for debugging + :return: predicted skeleton structure + """ + root_id = getInitId(input_data, root_pred_net) + pred_joints = input_data.joints.data.cpu().numpy() + + with torch.no_grad(): + connect_prob, _ = bone_pred_net(input_data, permute_joints=False) + connect_prob = torch.sigmoid(connect_prob) + pair_idx = input_data.pairs.long().data.cpu().numpy() + prob_matrix = np.zeros((len(input_data.joints), len(input_data.joints))) + prob_matrix[pair_idx[:, 0], pair_idx[:, 1]] = connect_prob.data.cpu().numpy().squeeze() + prob_matrix = prob_matrix + prob_matrix.transpose() + cost_matrix = -np.log(prob_matrix + 1e-10) + cost_matrix = increase_cost_for_outside_bone(cost_matrix, pred_joints, vox) + + pred_skel = Info() + parent, key, root_id = primMST_symmetry(cost_matrix, root_id, pred_joints) + for i in range(len(parent)): + if parent[i] == -1: + pred_skel.root = TreeNode('root', tuple(pred_joints[i])) + break + loadSkel_recur(pred_skel.root, i, None, pred_joints, parent) + pred_skel.joint_pos = pred_skel.get_joint_dict() + #show_mesh_vox(mesh_filename, vox, pred_skel.root) + try: + img = show_obj_skel(mesh_filename, pred_skel.root) + except: + print("Visualization is not supported on headless servers. Please consider other headless rendering methods.") + return pred_skel + + +def calc_geodesic_matrix(bones, mesh_v, surface_geodesic, mesh_filename, subsampling=False): + """ + calculate volumetric geodesic distance from vertices to each bones + :param bones: B*6 numpy array where each row stores the starting and ending joint position of a bone + :param mesh_v: V*3 mesh vertices + :param surface_geodesic: geodesic distance matrix of all vertices + :param mesh_filename: mesh filename + :return: an approaximate volumetric geodesic distance matrix V*B, were (v,b) is the distance from vertex v to bone b + """ + + if subsampling: + mesh0 = o3d.io.read_triangle_mesh(mesh_filename) + mesh0 = mesh0.simplify_quadric_decimation(3000) + o3d.io.write_triangle_mesh(mesh_filename.replace(".obj", "_simplified.obj"), mesh0) + mesh_trimesh = trimesh.load(mesh_filename.replace(".obj", "_simplified.obj")) + subsamples_ids = np.random.choice(len(mesh_v), np.min((len(mesh_v), 1500)), replace=False) + subsamples = mesh_v[subsamples_ids, :] + surface_geodesic = surface_geodesic[subsamples_ids, :][:, subsamples_ids] + else: + mesh_trimesh = trimesh.load(mesh_filename) + subsamples = mesh_v + origins, ends, pts_bone_dist = pts2line(subsamples, bones) + pts_bone_visibility = calc_pts2bone_visible_mat(mesh_trimesh, origins, ends) + pts_bone_visibility = pts_bone_visibility.reshape(len(bones), len(subsamples)).transpose() + pts_bone_dist = pts_bone_dist.reshape(len(bones), len(subsamples)).transpose() + # remove visible points which are too far + for b in range(pts_bone_visibility.shape[1]): + visible_pts = np.argwhere(pts_bone_visibility[:, b] == 1).squeeze(1) + if len(visible_pts) == 0: + continue + threshold_b = np.percentile(pts_bone_dist[visible_pts, b], 15) + pts_bone_visibility[pts_bone_dist[:, b] > 1.3 * threshold_b, b] = False + + visible_matrix = np.zeros(pts_bone_visibility.shape) + visible_matrix[np.where(pts_bone_visibility == 1)] = pts_bone_dist[np.where(pts_bone_visibility == 1)] + for c in range(visible_matrix.shape[1]): + unvisible_pts = np.argwhere(pts_bone_visibility[:, c] == 0).squeeze(1) + visible_pts = np.argwhere(pts_bone_visibility[:, c] == 1).squeeze(1) + if len(visible_pts) == 0: + visible_matrix[:, c] = pts_bone_dist[:, c] + continue + for r in unvisible_pts: + dist1 = np.min(surface_geodesic[r, visible_pts]) + nn_visible = visible_pts[np.argmin(surface_geodesic[r, visible_pts])] + if np.isinf(dist1): + visible_matrix[r, c] = 8.0 + pts_bone_dist[r, c] + else: + visible_matrix[r, c] = dist1 + visible_matrix[nn_visible, c] + if subsampling: + nn_dist = np.sum((mesh_v[:, np.newaxis, :] - subsamples[np.newaxis, ...])**2, axis=2) + nn_ind = np.argmin(nn_dist, axis=1) + visible_matrix = visible_matrix[nn_ind, :] + os.remove(mesh_filename.replace(".obj", "_simplified.obj")) + return visible_matrix + + +def predict_skinning(input_data, pred_skel, skin_pred_net, surface_geodesic, mesh_filename, subsampling=False): + """ + predict skinning + :param input_data: wrapped input data + :param pred_skel: predicted skeleton + :param skin_pred_net: network to predict skinning weights + :param surface_geodesic: geodesic distance matrix of all vertices + :param mesh_filename: mesh filename + :return: predicted rig with skinning weights information + """ + global device, output_folder + num_nearest_bone = 5 + bones, bone_names, bone_isleaf = get_bones(pred_skel) + mesh_v = input_data.pos.data.cpu().numpy() + print(" calculating volumetric geodesic distance from vertices to bone. This step takes some time...") + geo_dist = calc_geodesic_matrix(bones, mesh_v, surface_geodesic, mesh_filename, subsampling=subsampling) + input_samples = [] # joint_pos (x, y, z), (bone_id, 1/D)*5 + loss_mask = [] + skin_nn = [] + for v_id in range(len(mesh_v)): + geo_dist_v = geo_dist[v_id] + bone_id_near_to_far = np.argsort(geo_dist_v) + this_sample = [] + this_nn = [] + this_mask = [] + for i in range(num_nearest_bone): + if i >= len(bones): + this_sample += bones[bone_id_near_to_far[0]].tolist() + this_sample.append(1.0 / (geo_dist_v[bone_id_near_to_far[0]] + 1e-10)) + this_sample.append(bone_isleaf[bone_id_near_to_far[0]]) + this_nn.append(0) + this_mask.append(0) + else: + skel_bone_id = bone_id_near_to_far[i] + this_sample += bones[skel_bone_id].tolist() + this_sample.append(1.0 / (geo_dist_v[skel_bone_id] + 1e-10)) + this_sample.append(bone_isleaf[skel_bone_id]) + this_nn.append(skel_bone_id) + this_mask.append(1) + input_samples.append(np.array(this_sample)[np.newaxis, :]) + skin_nn.append(np.array(this_nn)[np.newaxis, :]) + loss_mask.append(np.array(this_mask)[np.newaxis, :]) + + skin_input = np.concatenate(input_samples, axis=0) + loss_mask = np.concatenate(loss_mask, axis=0) + skin_nn = np.concatenate(skin_nn, axis=0) + skin_input = torch.from_numpy(skin_input).float() + input_data.skin_input = skin_input + input_data.to(device) + + skin_pred = skin_pred_net(input_data) + skin_pred = torch.softmax(skin_pred, dim=1) + skin_pred = skin_pred.data.cpu().numpy() + skin_pred = skin_pred * loss_mask + + skin_nn = skin_nn[:, 0:num_nearest_bone] + skin_pred_full = np.zeros((len(skin_pred), len(bone_names))) + for v in range(len(skin_pred)): + for nn_id in range(len(skin_nn[v, :])): + skin_pred_full[v, skin_nn[v, nn_id]] = skin_pred[v, nn_id] + print(" filtering skinning prediction") + tpl_e = input_data.tpl_edge_index.data.cpu().numpy() + skin_pred_full = post_filter(skin_pred_full, tpl_e, num_ring=1) + skin_pred_full[skin_pred_full < np.max(skin_pred_full, axis=1, keepdims=True) * 0.35] = 0.0 + skin_pred_full = skin_pred_full / (skin_pred_full.sum(axis=1, keepdims=True) + 1e-10) + skel_res = assemble_skel_skin(pred_skel, skin_pred_full) + return skel_res + + +def tranfer_to_ori_mesh(filename_ori, filename_remesh, pred_rig): + """ + convert the predicted rig of remeshed model to the rig of the original model. + Just assign skinning weight based on nearest neighbor + :param filename_ori: original mesh filename + :param filename_remesh: remeshed mesh filename + :param pred_rig: predicted rig + :return: predicted rig for original mesh + """ + mesh_remesh = o3d.io.read_triangle_mesh(filename_remesh) + mesh_ori = o3d.io.read_triangle_mesh(filename_ori) + tranfer_rig = Info() + + vert_remesh = np.asarray(mesh_remesh.vertices) + vert_ori = np.asarray(mesh_ori.vertices) + + vertice_distance = np.sqrt(np.sum((vert_ori[np.newaxis, ...] - vert_remesh[:, np.newaxis, :]) ** 2, axis=2)) + vertice_raw_id = np.argmin(vertice_distance, axis=0) # nearest vertex id on the fixed mesh for each vertex on the remeshed mesh + + tranfer_rig.root = pred_rig.root + tranfer_rig.joint_pos = pred_rig.joint_pos + new_skin = [] + for v in range(len(vert_ori)): + skin_v = [v] + v_nn = vertice_raw_id[v] + skin_v += pred_rig.joint_skin[v_nn][1:] + new_skin.append(skin_v) + tranfer_rig.joint_skin = new_skin + return tranfer_rig + + +if __name__ == '__main__': + input_folder = "quick_start/" + + # downsample_skinning is used to speed up the calculation of volumetric geodesic distance + # and to save cpu memory in skinning calculation. + # Change to False to be more accurate but less efficient. + downsample_skinning = True + + # load all weights + print("loading all networks...") + device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + + jointNet = JOINTNET() + jointNet.to(device) + jointNet.eval() + jointNet_checkpoint = torch.load('checkpoints/gcn_meanshift/model_best.pth.tar') + jointNet.load_state_dict(jointNet_checkpoint['state_dict']) + print(" joint prediction network loaded.") + + rootNet = ROOTNET() + rootNet.to(device) + rootNet.eval() + rootNet_checkpoint = torch.load('checkpoints/rootnet/model_best.pth.tar') + rootNet.load_state_dict(rootNet_checkpoint['state_dict']) + print(" root prediction network loaded.") + + boneNet = BONENET() + boneNet.to(device) + boneNet.eval() + boneNet_checkpoint = torch.load('checkpoints/bonenet/model_best.pth.tar') + boneNet.load_state_dict(boneNet_checkpoint['state_dict']) + print(" connection prediction network loaded.") + + skinNet = SKINNET(nearest_bone=5, use_Dg=True, use_Lf=True) + skinNet_checkpoint = torch.load('checkpoints/skinnet/model_best.pth.tar') + skinNet.load_state_dict(skinNet_checkpoint['state_dict']) + skinNet.to(device) + skinNet.eval() + print(" skinning prediction network loaded.") + + # Here we provide 16~17 examples. For best results, we will need to override the learned bandwidth and its associated threshold + # To process other input characters, please first try the learned bandwidth (0.0429 in the provided model), and the default threshold 1e-5. + # We also use these two default parameters for processing all test models in batch. + + #model_id, bandwidth, threshold = "smith", None, 1e-5 + model_id, bandwidth, threshold = "17872", 0.045, 0.75e-5 + #model_id, bandwidth, threshold = "8210", 0.05, 1e-5 + #model_id, bandwidth, threshold = "8330", 0.05, 0.8e-5 + #model_id, bandwidth, threshold = "9477", 0.043, 2.5e-5 + #model_id, bandwidth, threshold = "17364", 0.058, 0.3e-5 + #model_id, bandwidth, threshold = "15930", 0.055, 0.4e-5 + #model_id, bandwidth, threshold = "8333", 0.04, 2e-5 + #model_id, bandwidth, threshold = "8338", 0.052, 0.9e-5 + #model_id, bandwidth, threshold = "3318", 0.03, 0.92e-5 + #model_id, bandwidth, threshold = "15446", 0.032, 0.58e-5 + #model_id, bandwidth, threshold = "1347", 0.062, 3e-5 + #model_id, bandwidth, threshold = "11814", 0.06, 0.6e-5 + #model_id, bandwidth, threshold = "2982", 0.045, 0.3e-5 + #model_id, bandwidth, threshold = "2586", 0.05, 0.6e-5 + #model_id, bandwidth, threshold = "8184", 0.05, 0.4e-5 + #model_id, bandwidth, threshold = "9000", 0.035, 0.16e-5 + + # create data used for inferece + print("creating data for model ID {:s}".format(model_id)) + mesh_filename = os.path.join(input_folder, '{:s}_remesh.obj'.format(model_id)) + if not os.path.exists(mesh_filename): + mesh_ori_filename = os.path.join(input_folder, '{:s}_ori.obj'.format(model_id)) + mesh_ori = o3d.io.read_triangle_mesh(mesh_ori_filename) + if len(np.asarray(mesh_ori.vertices)) == 0: + print(f"Please name your input model as {model_id}_ori.obj") + exit() + mesh_remesh = mesh_ori.simplify_quadric_decimation(4000) # adjust vertices between 1K - 5K + o3d.io.write_triangle_mesh(mesh_filename, mesh_remesh) + + data, vox, surface_geodesic, translation_normalize, scale_normalize = create_single_data(mesh_filename) + data.to(device) + + print("predicting joints") + data = predict_joints(data, vox, jointNet, threshold, bandwidth=bandwidth, + mesh_filename=mesh_filename.replace("_remesh.obj", "_normalized.obj")) + data.to(device) + print("predicting connectivity") + pred_skeleton = predict_skeleton(data, vox, rootNet, boneNet, + mesh_filename=mesh_filename.replace("_remesh.obj", "_normalized.obj")) + print("predicting skinning") + pred_rig = predict_skinning(data, pred_skeleton, skinNet, surface_geodesic, + mesh_filename.replace("_remesh.obj", "_normalized.obj"), + subsampling=downsample_skinning) + + # here we reverse the normalization to the original scale and position + pred_rig.normalize(scale_normalize, -translation_normalize) + + print("Saving result") + if True: + # here we use original mesh tesselation (without remeshing) + mesh_filename_ori = os.path.join(input_folder, '{:s}_ori.obj'.format(model_id)) + pred_rig = tranfer_to_ori_mesh(mesh_filename_ori, mesh_filename, pred_rig) + pred_rig.save(mesh_filename_ori.replace('.obj', '_rig.txt')) + else: + # here we use remeshed mesh + pred_rig.save(mesh_filename.replace('.obj', '_rig.txt')) + print("Done!") diff --git a/RigNet/quick_start/11814_ori.fbx b/RigNet/quick_start/11814_ori.fbx new file mode 100644 index 0000000000000000000000000000000000000000..756df76c6b77d03695673e44ca59483a9f6b134c --- /dev/null +++ b/RigNet/quick_start/11814_ori.fbx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4916a2767e3f059bb4f78795dd3e5e58fe935ebc9ff0e9474ee9c9dda2e5e01b +size 163280 diff --git a/RigNet/quick_start/11814_ori.obj b/RigNet/quick_start/11814_ori.obj new file mode 100644 index 0000000000000000000000000000000000000000..0c9f14e319689abf6f964394330ba390d6766e14 --- /dev/null +++ b/RigNet/quick_start/11814_ori.obj @@ -0,0 +1,6954 @@ +v 0.107333 0.158298 0.351178 +v 0.116106 0.191500 0.376931 +v 0.107457 0.182814 0.396581 +v 0.095890 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1709/1709/1701 803/803/1702 513/513/1703 +f 1709/1709/1701 513/513/1703 514/514/1704 +f 1709/1709/1701 514/514/1704 803/803/1702 +f 1710/1710/1705 803/803/1702 514/514/1704 +f 1710/1710/1705 514/514/1704 515/515/1706 +f 1710/1710/1705 515/515/1706 803/803/1702 +f 1711/1711/1707 803/803/1702 515/515/1706 +f 1711/1711/1707 515/515/1706 513/513/1703 +f 1711/1711/1707 513/513/1703 803/803/1702 +s 286 +f 1712/1712/1708 804/804/1709 516/516/1710 +f 1712/1712/1708 516/516/1710 517/517/1711 +f 1712/1712/1708 517/517/1711 804/804/1709 +f 1713/1713/1712 804/804/1709 517/517/1711 +f 1713/1713/1712 517/517/1711 518/518/1713 +f 1713/1713/1712 518/518/1713 804/804/1709 +f 1714/1714/1714 804/804/1709 518/518/1713 +f 1714/1714/1714 518/518/1713 516/516/1710 +f 1714/1714/1714 516/516/1710 804/804/1709 diff --git a/RigNet/run_joint_finetune.py b/RigNet/run_joint_finetune.py new file mode 100644 index 0000000000000000000000000000000000000000..3004f5068e760a7779fcb5025cc6fc62312a6cae --- /dev/null +++ b/RigNet/run_joint_finetune.py @@ -0,0 +1,254 @@ +#------------------------------------------------------------------------------- +# Name: run_joint_finetune.py +# Purpose: Finetuning regression and attention modules togather with a meanshift module +# RigNet Copyright 2020 University of Massachusetts +# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License. +# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet. +#------------------------------------------------------------------------------- + +import sys +sys.path.append("./") +import os +import numpy as np +import shutil +import argparse + +from utils.log_utils import AverageMeter +from utils.os_utils import isdir, mkdir_p, isfile +from utils.io_utils import output_point_cloud_ply + +import torch +import torch.backends.cudnn as cudnn +from torch_geometric.data import DataLoader +from torch.utils.tensorboard import SummaryWriter + +from models.GCN import JOINTNET_MASKNET_MEANSHIFT +from datasets.skeleton_dataset import GraphDataset +from models.supplemental_layers.pytorch_chamfer_dist import chamfer_distance_with_average + +device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + + +def save_checkpoint(state, is_best, checkpoint='checkpoint', filename='checkpoint.pth.tar', snapshot=None): + filepath = os.path.join(checkpoint, filename) + torch.save(state, filepath) + + if snapshot and state['epoch'] % snapshot == 0: + shutil.copyfile(filepath, os.path.join(checkpoint, 'checkpoint_{}.pth.tar'.format(state['epoch']))) + + if is_best: + shutil.copyfile(filepath, os.path.join(checkpoint, 'model_best.pth.tar')) + + +def pairwise_distances(x, y): + #Input: x is a Nxd matrix + # y is an optional Mxd matirx + #Output: dist is a NxM matrix where dist[i,j] is the square norm between x[i,:] and y[j,:] + # if y is not given then use 'y=x'. + #i.e. dist[i,j] = ||x[i,:]-y[j,:]||^2 + x_norm = (x ** 2).sum(1).view(-1, 1) + y_t = torch.transpose(y, 0, 1) + y_norm = (y ** 2).sum(1).view(1, -1) + dist = x_norm + y_norm - 2.0 * torch.mm(x, y_t) + return torch.clamp(dist, 0.0, np.inf) + + +def meanshift_cluster(pts, bandwidth, weights, args): + """ + meanshift written in pytorch + :param pts: input points + :param weights: weight per point during clustering + :return: clustered points + """ + pts_steps = [] + for i in range(args.meanshift_step): + Y = pairwise_distances(pts, pts) + K = torch.nn.functional.relu(bandwidth ** 2 - Y) + if weights is not None: + K = K * weights + P = torch.nn.functional.normalize(K, p=1, dim=0, eps=1e-10) + P = P.transpose(0, 1) + pts = args.step_size * (torch.matmul(P, pts) - pts) + pts + pts_steps.append(pts) + return pts_steps + + +def main(args): + global device + lowest_loss = 1e20 + + # create checkpoint dir and log dir + if not isdir(args.checkpoint): + print("Create new checkpoint folder " + args.checkpoint) + mkdir_p(args.checkpoint) + if not args.resume: + if isdir(args.logdir): + shutil.rmtree(args.logdir) + mkdir_p(args.logdir) + + # create model + model = JOINTNET_MASKNET_MEANSHIFT() + model.to(device) + + optimizer = torch.optim.Adam([{'params': model.jointnet.parameters(), 'lr': args.jointnet_lr}, + {'params': model.masknet.parameters(), 'lr': args.masknet_lr}, + {'params': model.bandwidth, 'lr': args.bandwidth_lr}], + weight_decay=args.weight_decay) + + # optionally resume from a checkpoint + if args.resume: + if isfile(args.resume): + print("=> loading checkpoint '{}'".format(args.resume)) + checkpoint = torch.load(args.resume) + args.start_epoch = checkpoint['epoch'] + lowest_loss = checkpoint['lowest_loss'] + model.load_state_dict(checkpoint['state_dict']) + optimizer.load_state_dict(checkpoint['optimizer']) + print("=> loaded checkpoint '{}' (epoch {})".format(args.resume, checkpoint['epoch'])) + else: + print("=> no checkpoint found at '{}'".format(args.resume)) + else: + pretrained_masknet = torch.load(args.masknet_resume) + pretrained_jointnet = torch.load(args.jointnet_resume) + model.masknet.load_state_dict(pretrained_masknet['state_dict']) + model.jointnet.load_state_dict(pretrained_jointnet['state_dict']) + + cudnn.benchmark = True + print(' Total params: %.2fM' % (sum(p.numel() for p in model.parameters()) / 1000000.0)) + train_loader = DataLoader(GraphDataset(root=args.train_folder), batch_size=args.train_batch, shuffle=True, follow_batch=['joints']) + val_loader = DataLoader(GraphDataset(root=args.val_folder), batch_size=args.test_batch, shuffle=False, follow_batch=['joints']) + test_loader = DataLoader(GraphDataset(root=args.test_folder), batch_size=args.test_batch, shuffle=False, follow_batch=['joints']) + if args.evaluate: + print('\nEvaluation only') + test_loss = test(test_loader, model, args, save_result=True, best_epoch=args.start_epoch) + print('test_loss {:8f}'.format(test_loss)) + return + + scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, args.schedule, gamma=args.gamma) + logger = SummaryWriter(log_dir=args.logdir) + for epoch in range(args.start_epoch, args.epochs): + print('\nEpoch: %d ' % (epoch + 1)) + train_loss = train(train_loader, model, optimizer, args) + val_loss = test(val_loader, model, args) + test_loss = test(test_loader, model, args) + scheduler.step() + print('Epoch{:d}. train_loss: {:.6f}.'.format(epoch + 1, train_loss)) + print('Epoch{:d}. val_loss: {:.6f}.'.format(epoch + 1, val_loss)) + print('Epoch{:d}. test_loss: {:.6f}.'.format(epoch + 1, test_loss)) + + # remember best acc and save checkpoint + is_best = val_loss < lowest_loss + lowest_loss = min(val_loss, lowest_loss) + save_checkpoint({'epoch': epoch + 1, 'state_dict': model.state_dict(), 'lowest_loss': lowest_loss, 'optimizer': optimizer.state_dict()}, + is_best, checkpoint=args.checkpoint) + + info = {'train_loss': train_loss, 'val_loss': val_loss, 'test_loss': test_loss} + for tag, value in info.items(): + logger.add_scalar(tag, value, epoch+1) + + print("=> loading checkpoint '{}'".format(os.path.join(args.checkpoint, 'model_best.pth.tar'))) + checkpoint = torch.load(os.path.join(args.checkpoint, 'model_best.pth.tar')) + best_epoch = checkpoint['epoch'] + model.load_state_dict(checkpoint['state_dict']) + print("=> loaded checkpoint '{}' (epoch {})".format(os.path.join(args.checkpoint, 'model_best.pth.tar'), best_epoch)) + test_loss = test(test_loader, model, args, save_result=True, best_epoch=best_epoch) + print('Best epoch:\n test_loss {:8f}'.format(test_loss)) + + +def train(train_loader, model, optimizer, args): + global device + model.train() # switch to train mode + loss_meter = AverageMeter() + for data in train_loader: + data = data.to(device) + optimizer.zero_grad() + data_displacement, mask_pred_nosigmoid, mask_pred, bandwidth = model(data) + y_pred = data_displacement + data.pos + loss_total = 0.0 + for i in range(len(torch.unique(data.batch))): + joint_gt = data.joints[data.joints_batch == i, :] + y_pred_i = y_pred[data.batch == i, :] + mask_pred_i = mask_pred[data.batch == i] + loss_total += chamfer_distance_with_average(y_pred_i.unsqueeze(0), joint_gt.unsqueeze(0)) + clustered_pred = meanshift_cluster(y_pred_i, bandwidth, mask_pred_i, args) + loss_ms = 0.0 + for j in range(args.meanshift_step): + loss_ms += chamfer_distance_with_average(clustered_pred[j].unsqueeze(0), joint_gt.unsqueeze(0)) + loss_total = loss_total + args.ms_loss_weight * loss_ms / args.meanshift_step + loss_total /= len(torch.unique(data.batch)) + if args.use_bce: + mask_gt = data.mask.unsqueeze(1) + loss_total += args.bce_loss_weight * torch.nn.functional.binary_cross_entropy_with_logits(mask_pred_nosigmoid, mask_gt.float(), reduction='mean') + loss_total.backward() + optimizer.step() + loss_meter.update(loss_total.item()) + return loss_meter.avg + + +def test(test_loader, model, args, save_result=False, best_epoch=None): + global device + model.eval() # switch to test mode + loss_meter = AverageMeter() + outdir = args.checkpoint.split('/')[-1] + for data in test_loader: + data = data.to(device) + with torch.no_grad(): + data_displacement, mask_pred_nosigmoid, mask_pred, bandwidth = model(data) + y_pred = data_displacement + data.pos + loss_total = 0.0 + for i in range(len(torch.unique(data.batch))): + joint_gt = data.joints[data.joints_batch == i, :] + y_pred_i = y_pred[data.batch == i, :] + mask_pred_i = mask_pred[data.batch == i] + loss_total += chamfer_distance_with_average(y_pred_i.unsqueeze(0), joint_gt.unsqueeze(0)) + clustered_pred = meanshift_cluster(y_pred_i, bandwidth, mask_pred_i, args) + loss_ms = 0.0 + for j in range(args.meanshift_step): + loss_ms += chamfer_distance_with_average(clustered_pred[j].unsqueeze(0), joint_gt.unsqueeze(0)) + loss_total = loss_total + args.ms_loss_weight * loss_ms / args.meanshift_step + if save_result: + output_point_cloud_ply(y_pred_i, name=str(data.name[i].item()), + output_folder='results/{:s}/best_{:d}/'.format(outdir, best_epoch)) + np.save('results/{:s}/best_{:d}/{:d}_attn.npy'.format(outdir, best_epoch, data.name[i].item()), + mask_pred_i.data.to("cpu").numpy()) + np.save('results/{:s}/best_{:d}/{:d}_bandwidth.npy'.format(outdir, best_epoch, data.name[i].item()), + bandwidth.data.to("cpu").numpy()) + loss_total /= len(torch.unique(data.batch)) + if args.use_bce: + mask_gt = data.mask.unsqueeze(1) + loss_total += args.bce_loss_weight * torch.nn.functional.binary_cross_entropy_with_logits(mask_pred_nosigmoid, mask_gt.float(), reduction='mean') + loss_meter.update(loss_total.item()) + return loss_meter.avg + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='PyG DGCNN') + parser.add_argument('--start-epoch', default=0, type=int, metavar='N', help='manual epoch number (useful on restarts)') + parser.add_argument('--weight-decay', '--wd', default=1e-4, type=float, metavar='W', help='weight decay (default: 0)') + parser.add_argument('--gamma', type=float, default=0.2, help='LR is multiplied by gamma on schedule.') + parser.add_argument('--epochs', default=100, type=int, metavar='N', help='number of total epochs to run') + parser.add_argument('--schedule', type=int, nargs='+', default=[50], help='Decrease learning rate at these epochs.') + parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true', help='evaluate model on val/test set') + parser.add_argument('--train_batch', default=1, type=int, metavar='N', help='train batchsize') + parser.add_argument('--test_batch', default=1, type=int, metavar='N', help='test batchsize') + parser.add_argument('-c', '--checkpoint', default='checkpoints/test', type=str, metavar='PATH', + help='path to save checkpoint (default: checkpoint)') + parser.add_argument('--logdir', default='logs/test', type=str, metavar='LOG', help='directory to save logs') + parser.add_argument('--resume', default='', type=str, metavar='PATH', help='path to latest checkpoint (default: none)') + parser.add_argument('--train_folder', default='/media/zhanxu/4T/ModelResource_RigNetv1_preproccessed/train/', type=str, help='folder of training data') + parser.add_argument('--val_folder', default='/media/zhanxu/4T/ModelResource_RigNetv1_preproccessed/val/', type=str, help='folder of validation data') + parser.add_argument('--test_folder', default='/media/zhanxu/4T/ModelResource_RigNetv1_preproccessed/test/', type=str, help='folder of testing data') + ###################### + parser.add_argument('--jointnet_lr', default=5e-5, type=float) + parser.add_argument('--masknet_lr', default=5e-5, type=float) + parser.add_argument('--bandwidth_lr', default=1e-6, type=float) + parser.add_argument('--jointnet_resume', default='checkpoints/pretrain_jointnet/model_best.pth.tar', type=str) + parser.add_argument('--masknet_resume', default='checkpoints/pretrain_masknet/model_best.pth.tar', type=str) + parser.add_argument('--meanshift_step', default=15, type=int, help='step size for meanshift update') + parser.add_argument('--step_size', default=0.3, type=float) # step size for meanshift + parser.add_argument('--ms_loss_weight', default=2.0, type=float) # weight for chamfer loss after meanshift + parser.add_argument('--use_bce', action='store_true') # if using mask supervision during finetuning + parser.add_argument('--bce_loss_weight', default=0.1, type=float) # weight for bce loss + + print(parser.parse_args()) + main(parser.parse_args()) diff --git a/RigNet/run_joint_pretrain.py b/RigNet/run_joint_pretrain.py new file mode 100644 index 0000000000000000000000000000000000000000..aff18dee66510b24a09e86b487996e246dc4e902 --- /dev/null +++ b/RigNet/run_joint_pretrain.py @@ -0,0 +1,212 @@ +#------------------------------------------------------------------------------- +# Name: run_joint_pretrain.py +# Purpose: Pretrain regression module and attation module for joint prediction +# RigNet Copyright 2020 University of Massachusetts +# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License. +# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet. +#------------------------------------------------------------------------------- + +import sys +sys.path.append("./") +import os +import shutil +import argparse +import numpy as np + +from utils.log_utils import AverageMeter +from utils.os_utils import isdir, mkdir_p, isfile +from utils.io_utils import output_point_cloud_ply + +import torch +import torch.backends.cudnn as cudnn +from torch_geometric.data import DataLoader +from torch.utils.tensorboard import SummaryWriter + +from datasets.skeleton_dataset import GraphDataset +from models.GCN import JointPredNet +from models.supplemental_layers.pytorch_chamfer_dist import chamfer_distance_with_average + +device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + + +def save_checkpoint(state, is_best, checkpoint='checkpoint', filename='checkpoint.pth.tar', snapshot=None): + filepath = os.path.join(checkpoint, filename) + torch.save(state, filepath) + + if snapshot and state['epoch'] % snapshot == 0: + shutil.copyfile(filepath, os.path.join(checkpoint, 'checkpoint_{}.pth.tar'.format(state['epoch']))) + + if is_best: + shutil.copyfile(filepath, os.path.join(checkpoint, 'model_best.pth.tar')) + + +def main(args): + global device + lowest_loss = 1e20 + + # create checkpoint dir and log dir + if not isdir(args.checkpoint): + print("Create new checkpoint folder " + args.checkpoint) + mkdir_p(args.checkpoint) + if not args.resume: + if isdir(args.logdir): + shutil.rmtree(args.logdir) + mkdir_p(args.logdir) + + # create model + if args.arch == 'jointnet': + model = JointPredNet(out_channels=3, input_normal=args.input_normal, arch=args.arch, aggr=args.aggr) + elif args.arch == 'masknet': + model = JointPredNet(out_channels=1, input_normal=args.input_normal, arch=args.arch, aggr=args.aggr) + + model.to(device) + optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay) + + # optionally resume from a checkpoint + if args.resume: + if isfile(args.resume): + print("=> loading checkpoint '{}'".format(args.resume)) + checkpoint = torch.load(args.resume) + args.start_epoch = checkpoint['epoch'] + lowest_loss = checkpoint['lowest_loss'] + model.load_state_dict(checkpoint['state_dict']) + optimizer.load_state_dict(checkpoint['optimizer']) + print("=> loaded checkpoint '{}' (epoch {})".format(args.resume, checkpoint['epoch'])) + else: + print("=> no checkpoint found at '{}'".format(args.resume)) + + cudnn.benchmark = True + print(' Total params: %.2fM' % (sum(p.numel() for p in model.parameters()) / 1000000.0)) + train_loader = DataLoader(GraphDataset(root=args.train_folder), batch_size=args.train_batch, shuffle=True, follow_batch=['joints']) + val_loader = DataLoader(GraphDataset(root=args.val_folder), batch_size=args.test_batch, shuffle=False, follow_batch=['joints']) + test_loader = DataLoader(GraphDataset(root=args.test_folder), batch_size=args.test_batch, shuffle=False, follow_batch=['joints']) + if args.evaluate: + print('\nEvaluation only') + test_loss = test(test_loader, model, args, save_result=True, best_epoch=args.start_epoch) + print('test_loss {:8f}'.format(test_loss)) + return + + scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, args.schedule, gamma=args.gamma) + logger = SummaryWriter(log_dir=args.logdir) + for epoch in range(args.start_epoch, args.epochs): + lr = scheduler.get_last_lr() + print('\nEpoch: %d | LR: %.8f' % (epoch + 1, lr[0])) + train_loss = train(train_loader, model, optimizer, args) + val_loss = test(val_loader, model, args) + test_loss = test(test_loader, model, args) + scheduler.step() + print('Epoch{:d}. train_loss: {:.6f}.'.format(epoch + 1, train_loss)) + print('Epoch{:d}. val_loss: {:.6f}.'.format(epoch + 1, val_loss)) + print('Epoch{:d}. test_loss: {:.6f}.'.format(epoch + 1, test_loss)) + # remember best acc and save checkpoint + is_best = val_loss < lowest_loss + lowest_loss = min(val_loss, lowest_loss) + save_checkpoint({'epoch': epoch + 1, 'state_dict': model.state_dict(), 'lowest_loss': lowest_loss, 'optimizer': optimizer.state_dict()}, + is_best, checkpoint=args.checkpoint) + + info = {'train_loss': train_loss, 'val_loss': val_loss, 'test_loss': test_loss} + for tag, value in info.items(): + logger.add_scalar(tag, value, epoch+1) + + print("=> loading checkpoint '{}'".format(os.path.join(args.checkpoint, 'model_best.pth.tar'))) + checkpoint = torch.load(os.path.join(args.checkpoint, 'model_best.pth.tar')) + best_epoch = checkpoint['epoch'] + model.load_state_dict(checkpoint['state_dict']) + print("=> loaded checkpoint '{}' (epoch {})".format(os.path.join(args.checkpoint, 'model_best.pth.tar'), best_epoch)) + test_loss = test(test_loader, model, args, save_result=True, best_epoch=best_epoch) + print('Best epoch:\n test_loss {:8f}'.format(test_loss)) + + +def train(train_loader, model, optimizer, args): + global device + model.train() # switch to train mode + loss_meter = AverageMeter() + for data in train_loader: + data = data.to(device) + optimizer.zero_grad() + if args.arch == 'masknet': + mask_pred = model(data) + mask_gt = data.mask.unsqueeze(1) + loss = torch.nn.functional.binary_cross_entropy_with_logits(mask_pred, mask_gt.float(), reduction='mean') + elif args.arch == 'jointnet': + data_displacement = model(data) + y_pred = data_displacement + data.pos + loss = 0.0 + for i in range(len(torch.unique(data.joints_batch))): + joint_gt = data.joints[data.joints_batch == i, :] + y_pred_i = y_pred[data.batch == i, :] + loss += chamfer_distance_with_average(y_pred_i.unsqueeze(0), joint_gt.unsqueeze(0)) + loss /= args.train_batch + loss.backward() + optimizer.step() + loss_meter.update(loss.item()) + return loss_meter.avg + + +def test(test_loader, model, args, save_result=False, best_epoch=None): + global device + model.eval() # switch to test mode + loss_meter = AverageMeter() + outdir = args.checkpoint.split('/')[-1] + for data in test_loader: + data = data.to(device) + with torch.no_grad(): + if args.arch == 'masknet': + mask_pred = model(data) + mask_gt = data.mask.unsqueeze(1) + loss = torch.nn.functional.binary_cross_entropy_with_logits(mask_pred, mask_gt.float(), reduction='mean') + elif args.arch == 'jointnet': + data_displacement = model(data) + y_pred = data_displacement + data.pos + loss = 0.0 + for i in range(len(torch.unique(data.joints_batch))): + joint_gt = data.joints[data.joints_batch == i, :] + y_pred_i = y_pred[data.batch == i, :] + loss += chamfer_distance_with_average(y_pred_i.unsqueeze(0), joint_gt.unsqueeze(0)) + loss /= args.test_batch + loss_meter.update(loss.item()) + + if save_result: + output_folder = 'results/{:s}/best_{:d}/'.format(outdir, best_epoch) + if not os.path.exists(output_folder): + mkdir_p(output_folder) + if args.arch == 'masknet': + mask_pred = torch.sigmoid(mask_pred) + for i in range(len(torch.unique(data.batch))): + mask_pred_sample = mask_pred[data.batch == i] + np.save(os.path.join(output_folder, str(data.name[i].item()) + '_attn.npy'), mask_pred_sample.data.to("cpu").numpy()) + else: + for i in range(len(torch.unique(data.batch))): + y_pred_sample = y_pred[data.batch == i, :] + output_point_cloud_ply(y_pred_sample, name=str(data.name[i].item()), + output_folder='results/{:s}/best_{:d}/'.format(outdir, best_epoch)) + return loss_meter.avg + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='PyG DGCNN') + parser.add_argument('--arch', default='masknet') # jointnet, masknet + parser.add_argument('--start-epoch', default=0, type=int, metavar='N', help='manual epoch number (useful on restarts)') + parser.add_argument('--weight-decay', '--wd', default=1e-4, type=float, metavar='W', help='weight decay (default: 0)') + parser.add_argument('--gamma', type=float, default=0.2, help='LR is multiplied by gamma on schedule.') + parser.add_argument('--epochs', default=100, type=int, metavar='N', help='number of total epochs to run') + parser.add_argument('--lr', '--learning-rate', default=5e-4, type=float, metavar='LR', help='initial learning rate') + parser.add_argument('--schedule', type=int, nargs='+', default=[], help='Decrease learning rate at these epochs.') + parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true', help='evaluate model on val/test set') + parser.add_argument('--input_normal', action='store_true') + parser.add_argument('--aggr', default='max', type=str) + ###################### + parser.add_argument('--train_batch', default=2, type=int, metavar='N', help='train batchsize') + parser.add_argument('--test_batch', default=2, type=int, metavar='N', help='test batchsize') + parser.add_argument('-c', '--checkpoint', default='checkpoints/test', type=str, metavar='PATH', + help='path to save checkpoint (default: checkpoint)') + parser.add_argument('--logdir', default='logs/test', type=str, metavar='LOG', help='directory to save logs') + parser.add_argument('--resume', default='', type=str, metavar='PATH', help='path to latest checkpoint (default: none)') + parser.add_argument('--train_folder', default='/media/zhanxu/4T/ModelResource_RigNetv1_preproccessed/train/', + type=str, help='folder of training data') + parser.add_argument('--val_folder', default='/media/zhanxu/4T/ModelResource_RigNetv1_preproccessed/val/', + type=str, help='folder of validation data') + parser.add_argument('--test_folder', default='/media/zhanxu/4T/ModelResource_RigNetv1_preproccessed/test/', + type=str, help='folder of testing data') + print(parser.parse_args()) + main(parser.parse_args()) diff --git a/RigNet/run_pair_cls.py b/RigNet/run_pair_cls.py new file mode 100644 index 0000000000000000000000000000000000000000..72dbdbcc4f15d33135f90b6829086e16ad73dc9b --- /dev/null +++ b/RigNet/run_pair_cls.py @@ -0,0 +1,194 @@ +#------------------------------------------------------------------------------- +# Name: run_pair_cls.py +# Purpose: Train a network (bonenet) to predict pair-wise connectivity cost +# RigNet Copyright 2020 University of Massachusetts +# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License. +# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet. +#------------------------------------------------------------------------------- + +import sys +sys.path.append("./") +import os +import numpy as np +import shutil +import argparse + +import torch +import torch.backends.cudnn as cudnn +from torch_geometric.data import DataLoader +from torch.utils.tensorboard import SummaryWriter + +from models.PairCls_GCN import PairCls +from datasets.skeleton_dataset import GraphDataset +from utils.os_utils import isdir, mkdir_p, isfile +from utils.log_utils import AverageMeter + +device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + + +def save_checkpoint(state, is_best, checkpoint='checkpoint', filename='checkpoint.pth.tar', snapshot=None): + filepath = os.path.join(checkpoint, filename) + torch.save(state, filepath) + + if snapshot and state['epoch'] % snapshot == 0: + shutil.copyfile(filepath, os.path.join(checkpoint, 'checkpoint_{}.pth.tar'.format(state['epoch']))) + + if is_best: + shutil.copyfile(filepath, os.path.join(checkpoint, 'model_best.pth.tar')) + + +def main(args): + global device + lowest_loss = 1e20 + + # create checkpoint dir and log dir + if not isdir(args.checkpoint): + print("Create new checkpoint folder " + args.checkpoint) + mkdir_p(args.checkpoint) + if not args.resume: + if isdir(args.logdir): + shutil.rmtree(args.logdir) + mkdir_p(args.logdir) + + # create model + + model = PairCls() + model.to(device) + optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay) + + # optionally resume from a checkpoint + if args.resume: + if isfile(args.resume): + print("=> loading checkpoint '{}'".format(args.resume)) + checkpoint = torch.load(args.resume) + model.load_state_dict(checkpoint['state_dict']) + optimizer.load_state_dict(checkpoint['optimizer']) + args.start_epoch = checkpoint['epoch'] + lowest_loss = checkpoint['lowest_loss'] + print("=> loaded checkpoint '{}' (epoch {})".format(args.resume, checkpoint['epoch'])) + else: + print("=> no checkpoint found at '{}'".format(args.resume)) + + cudnn.benchmark = True + print(' Total params: %.2fM' % (sum(p.numel() for p in model.parameters()) / 1000000.0)) + train_loader = DataLoader(GraphDataset(root=args.train_folder), batch_size=args.train_batch, shuffle=True, follow_batch=['joints', 'pairs']) + val_loader = DataLoader(GraphDataset(root=args.val_folder), batch_size=args.test_batch, shuffle=False, follow_batch=['joints', 'pairs']) + test_loader = DataLoader(GraphDataset(root=args.test_folder), batch_size=args.test_batch, shuffle=False, follow_batch=['joints', 'pairs']) + + if args.evaluate: + print('\nEvaluation only') + test_loss = test(test_loader, model, args, save_result=True, best_epoch=args.start_epoch) + print('test_loss {:8f}'.format(test_loss)) + return + + scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, args.schedule, gamma=args.gamma) + logger = SummaryWriter(log_dir=args.logdir) + for epoch in range(args.start_epoch, args.epochs): + lr = scheduler.get_last_lr() + print('\nEpoch: %d | LR: %.8f' % (epoch + 1, lr[0])) + train_loss = train(train_loader, model, optimizer, args) + val_loss = test(val_loader, model, args) + test_loss = test(test_loader, model, args, best_epoch=epoch+1) + scheduler.step() + print('Epoch{:d}. train_loss: {:.6f}.'.format(epoch + 1, train_loss)) + print('Epoch{:d}. val_loss: {:.6f}.'.format(epoch + 1, val_loss)) + print('Epoch{:d}. test_loss: {:.6f}.'.format(epoch + 1, test_loss)) + + # remember best acc and save checkpoint + is_best = val_loss < lowest_loss + lowest_loss = min(val_loss, lowest_loss) + save_checkpoint({'epoch': epoch + 1, 'state_dict': model.state_dict(), 'lowest_loss': lowest_loss, 'optimizer': optimizer.state_dict()}, + is_best, checkpoint=args.checkpoint) + + info = {'train_loss': train_loss, 'val_loss': val_loss, 'test_loss': test_loss} + for tag, value in info.items(): + logger.add_scalar(tag, value, epoch+1) + + print("=> loading checkpoint '{}'".format(os.path.join(args.checkpoint, 'model_best.pth.tar'))) + checkpoint = torch.load(os.path.join(args.checkpoint, 'model_best.pth.tar')) + best_epoch = checkpoint['epoch'] + model.load_state_dict(checkpoint['state_dict']) + print("=> loaded checkpoint '{}' (epoch {})".format(os.path.join(args.checkpoint, 'model_best.pth.tar'), best_epoch)) + test_loss = test(test_loader, model, args, save_result=True, best_epoch=best_epoch) + print('Best epoch:\n test_loss {:8f}'.format(test_loss)) + + +def train(train_loader, model, optimizer, args): + global device + model.train() # switch to train mode + loss_meter = AverageMeter() + for data in train_loader: + data = data.to(device) + optimizer.zero_grad() + pre_label, label = model(data) + loss1 = torch.nn.functional.binary_cross_entropy_with_logits(pre_label, label, reduction='none') + topk_val, _ = torch.topk(loss1.view(-1), k=int(args.topk * len(pre_label)), dim=0, sorted=False) + loss2 = topk_val.mean() + loss = loss1.mean() + loss2 + loss.backward() + optimizer.step() + loss_meter.update(loss.item()) + return loss_meter.avg + + +def test(test_loader, model, args, save_result=False, best_epoch=None): + global device + model.eval() # switch to test mode + if save_result: + output_folder = 'results/{:s}/best_{:d}/'.format(args.checkpoint.split('/')[-1], best_epoch) + if not os.path.exists(output_folder): + mkdir_p(output_folder) + loss_meter = AverageMeter() + for data in test_loader: + data = data.to(device) + with torch.no_grad(): + pre_label, label = model(data) + loss = torch.nn.functional.binary_cross_entropy_with_logits(pre_label, label.float()) + if save_result: + connect_prob = torch.sigmoid(pre_label) + acc_joints = 0 + for i in range(len(torch.unique(data.batch))): + pair_idx = data.pairs[data.pairs_batch==i].long() + connect_prob_i = connect_prob[data.pairs_batch==i] + num_joint = len(data.joints[data.joints_batch==i]) + cost_matrix = np.zeros((num_joint, num_joint)) + pair_idx = pair_idx.to("cpu").numpy() + cost_matrix[pair_idx[:, 0]-acc_joints, pair_idx[:, 1]-acc_joints] = connect_prob_i.data.cpu().numpy().squeeze(axis=1) + cost_matrix = 1 - cost_matrix + print('saving: {:s}'.format(str(data.name[i].item()) + '_cost.npy')) + np.save(os.path.join(output_folder, str(data.name[i].item()) + '_cost.npy'), cost_matrix) + acc_joints += num_joint + loss_meter.update(loss.item()) + return loss_meter.avg + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='joint connectivity') + parser.add_argument('--arch', default='paircls') # paircls_fc, paircls_nogt, paircls_nogs + parser.add_argument('--start-epoch', default=0, type=int, metavar='N', help='manual epoch number (useful on restarts)') + parser.add_argument('--weight_decay', '--wd', default=1e-4, type=float, metavar='W', + help='weight decay (default: 1e-4)') + parser.add_argument('--gamma', type=float, default=0.2, help='LR is multiplied by gamma on schedule.') + parser.add_argument('-j', '--workers', default=1, type=int, metavar='N', + help='number of data loading workers (default: 4)') + parser.add_argument('--epochs', default=300, type=int, metavar='N', help='number of total epochs to run') + parser.add_argument('--lr', '--learning-rate', default=1e-3, type=float, metavar='LR', help='initial learning rate') + parser.add_argument('--schedule', type=int, nargs='+', default=[200], help='Decrease learning rate at these epochs.') + parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true', help='evaluate model on validation set') + #################################################################################################################### + parser.add_argument('--train_batch', default=2, type=int, metavar='N', help='train batchsize') + parser.add_argument('--test_batch', default=2, type=int, metavar='N', help='test batchsize') + parser.add_argument('-c', '--checkpoint', default='checkpoints/connect_test', type=str, metavar='PATH', + help='path to save checkpoint (default: checkpoint)') + parser.add_argument('--logdir', default='logs/connect_test', type=str, metavar='LOG', help='directory to save logs') + parser.add_argument('--resume', default='', type=str, metavar='PATH', help='path to latest checkpoint (default: none)') + parser.add_argument('--train_folder', default='/media/zhanxu/4T/ModelResource_RigNetv1_preproccessed/train/', + type=str, help='folder of training data') + parser.add_argument('--val_folder', default='/media/zhanxu/4T/ModelResource_RigNetv1_preproccessed/val/', + type=str, help='folder of validation data') + parser.add_argument('--test_folder', default='/media/zhanxu/4T/ModelResource_RigNetv1_preproccessed/test/', + type=str, help='folder of testing data') + + parser.add_argument('--topk', default=0.3, type=float, help='topk ratio for ohem') + print(parser.parse_args()) + main(parser.parse_args()) \ No newline at end of file diff --git a/RigNet/run_root_cls.py b/RigNet/run_root_cls.py new file mode 100644 index 0000000000000000000000000000000000000000..074ec406d6e88477236d4c6292b02aee18cf550f --- /dev/null +++ b/RigNet/run_root_cls.py @@ -0,0 +1,184 @@ +#------------------------------------------------------------------------------- +# Name: run_root_cls.py +# Purpose: Train a network (rootnet) to predict which joint is the root +# RigNet Copyright 2020 University of Massachusetts +# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License. +# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet. +#------------------------------------------------------------------------------- + +import os +import sys +sys.path.append("./") +import shutil +import argparse +import numpy as np + +import torch +import torch.backends.cudnn as cudnn +from torch_geometric.data import DataLoader + +from models.ROOT_GCN import ROOTNET +from utils.log_utils import AverageMeter +from utils.os_utils import isdir, mkdir_p, isfile +from torch.utils.tensorboard import SummaryWriter +from datasets.skeleton_dataset import GraphDataset + +device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + + +def save_checkpoint(state, is_best, checkpoint='checkpoint', filename='checkpoint.pth.tar', snapshot=None): + filepath = os.path.join(checkpoint, filename) + torch.save(state, filepath) + + if snapshot and state['epoch'] % snapshot == 0: + shutil.copyfile(filepath, os.path.join(checkpoint, 'checkpoint_{}.pth.tar'.format(state['epoch']))) + + if is_best: + shutil.copyfile(filepath, os.path.join(checkpoint, 'model_best.pth.tar')) + + +def main(args): + global device + best_acc = 0.0 + + # create checkpoint dir and log dir + if not isdir(args.checkpoint): + print("Create new checkpoint folder " + args.checkpoint) + mkdir_p(args.checkpoint) + if not args.resume: + if isdir(args.logdir): + shutil.rmtree(args.logdir) + mkdir_p(args.logdir) + + # create model + model = ROOTNET() + + model.to(device) + optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay) + + # optionally resume from a checkpoint + if args.resume: + if isfile(args.resume): + print("=> loading checkpoint '{}'".format(args.resume)) + checkpoint = torch.load(args.resume) + args.start_epoch = checkpoint['epoch'] + best_acc = checkpoint['best_acc'] + model.load_state_dict(checkpoint['state_dict']) + optimizer.load_state_dict(checkpoint['optimizer']) + lr = optimizer.param_groups[0]['lr'] + print("=> loaded checkpoint '{}' (epoch {})".format(args.resume, checkpoint['epoch'])) + else: + print("=> no checkpoint found at '{}'".format(args.resume)) + + cudnn.benchmark = True + print(' Total params: %.2fM' % (sum(p.numel() for p in model.parameters()) / 1000000.0)) + train_loader = DataLoader(GraphDataset(root=args.train_folder), batch_size=args.train_batch, shuffle=True, follow_batch=['joints']) + val_loader = DataLoader(GraphDataset(root=args.val_folder), batch_size=args.test_batch, shuffle=False, follow_batch=['joints']) + test_loader = DataLoader(GraphDataset(root=args.test_folder), batch_size=args.test_batch, shuffle=False, follow_batch=['joints']) + if args.evaluate: + print('\nEvaluation only') + test_loss, test_acc = test(test_loader, model) + print('test_loss {:.8f}. test_acc: {:.6f}'.format(test_loss, test_acc)) + return + scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, args.schedule, gamma=args.gamma) + logger = SummaryWriter(log_dir=args.logdir) + for epoch in range(args.start_epoch, args.epochs): + lr = scheduler.get_last_lr() + print('\nEpoch: %d | LR: %.8f' % (epoch + 1, lr[0])) + train_loss = train(train_loader, model, optimizer, args) + val_loss, val_acc = test(val_loader, model) + test_loss, test_acc = test(test_loader, model) + scheduler.step() + print('Epoch{:d}. train_loss: {:.6f}.'.format(epoch + 1, train_loss)) + print('Epoch{:d}. val_loss: {:.6f}. val_acc: {:.6f}'.format(epoch + 1, val_loss, val_acc)) + print('Epoch{:d}. test_loss: {:.6f}. test_acc: {:.6f}'.format(epoch + 1, test_loss, test_acc)) + + # remember best acc and save checkpoint + is_best = val_acc > best_acc + best_acc = max(val_acc, best_acc) + save_checkpoint({'epoch': epoch + 1, 'state_dict': model.state_dict(), 'best_acc': best_acc, + 'optimizer': optimizer.state_dict()}, is_best, checkpoint=args.checkpoint) + + info = {'train_loss': train_loss, 'val_loss': val_loss, 'val_accuracy': val_acc, + 'test_loss': test_loss, 'test_accuracy': test_acc} + for tag, value in info.items(): + logger.add_scalar(tag, value, epoch + 1) + print("=> loading checkpoint '{}'".format(os.path.join(args.checkpoint, 'model_best.pth.tar'))) + checkpoint = torch.load(os.path.join(args.checkpoint, 'model_best.pth.tar')) + best_epoch = checkpoint['epoch'] + model.load_state_dict(checkpoint['state_dict']) + print("=> loaded checkpoint '{}' (epoch {})".format(os.path.join(args.checkpoint, 'model_best.pth.tar'), best_epoch)) + test_loss, test_acc = test(test_loader, model) + print('Best epoch:\n test_loss {:8f} test_acc {:8f}'.format(test_loss, test_acc)) + + +def train(train_loader, model, optimizer, args): + global device + model.train() # switch to train mode + loss_meter = AverageMeter() + for data in train_loader: + #print(data.name) + data = data.to(device) + optimizer.zero_grad() + pre_label, label = model(data) + loss_1 = torch.nn.functional.binary_cross_entropy_with_logits(pre_label, label, reduction='none') + topk_val, _ = torch.topk(loss_1.view(-1), k=int(args.topk * len(pre_label)), dim=0, sorted=False) + loss2 = topk_val.mean() + #loss_3 = torch.nn.functional.binary_cross_entropy_with_logits(pre_label, label) + loss = loss_1.mean() + loss2 + loss.backward() + optimizer.step() + loss_meter.update(loss.item()) + return loss_meter.avg + + +def test(test_loader, model): + global device + model.eval() # switch to test mode + loss_meter = AverageMeter() + acc_total = 0.0 + count = 0.0 + for data in test_loader: + #print(data.name) + data = data.to(device) + with torch.no_grad(): + pre_label, label = model(data) + loss = torch.nn.functional.binary_cross_entropy_with_logits(pre_label, label.float()) + loss_meter.update(loss.item()) + for i in range(len(torch.unique(data.batch))): + pred_root_id = torch.argmax(pre_label[data.joints_batch==i]).item() + gt_root_id = torch.argmax(label[data.joints_batch==i]).item() + if pred_root_id == gt_root_id: + acc_total += 1.0 + count += 1.0 + return loss_meter.avg, acc_total/count + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='network for picking root') + parser.add_argument('--arch', default='rootnet') # rootnet_fc, rootnet_nogt, rootnet_nogs + parser.add_argument('--start-epoch', default=0, type=int, metavar='N', help='manual epoch number (useful on restarts)') + parser.add_argument('--weight_decay', '--wd', default=1e-4, type=float, metavar='W', help='weight decay (default: 1e-4)') + parser.add_argument('--gamma', type=float, default=0.2, help='LR is multiplied by gamma on schedule.') + parser.add_argument('-j', '--workers', default=1, type=int, metavar='N', help='number of data loading workers (default: 4)') + parser.add_argument('--epochs', default=300, type=int, metavar='N', help='number of total epochs to run') + parser.add_argument('--lr', '--learning-rate', default=1e-3, type=float, metavar='LR', help='initial learning rate') + parser.add_argument('--schedule', type=int, nargs='+', default=[200], help='Decrease learning rate at these epochs.') + parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true', help='evaluate model on validation set') + #################################################################################################################### + parser.add_argument('--train_batch', default=3, type=int, metavar='N', help='train batchsize') + parser.add_argument('--test_batch', default=3, type=int, metavar='N', help='test batchsize') + parser.add_argument('-c', '--checkpoint', default='checkpoints/test', type=str, metavar='PATH', + help='path to save checkpoint (default: checkpoint)') + parser.add_argument('--logdir', default='logs/test', type=str, metavar='LOG', help='directory to save logs') + parser.add_argument('--resume', default='', type=str, metavar='PATH', help='path to latest checkpoint (default: none)') + parser.add_argument('--train_folder', default='/media/zhanxu/4T/ModelResource_RigNetv1_preproccessed/train/', + type=str, help='folder of training data') + parser.add_argument('--val_folder', default='/media/zhanxu/4T/ModelResource_RigNetv1_preproccessed/val/', + type=str, help='folder of validation data') + parser.add_argument('--test_folder', default='/media/zhanxu/4T/ModelResource_RigNetv1_preproccessed/test/', + type=str, help='folder of testing data') + parser.add_argument('--pos_weight', default=10.0, type=float, help='weight for positive class') + parser.add_argument('--topk', default=0.3, type=float, help='topk ratio for ohem') + print(parser.parse_args()) + main(parser.parse_args()) diff --git a/RigNet/run_skinning.py b/RigNet/run_skinning.py new file mode 100644 index 0000000000000000000000000000000000000000..c63ef9cd085fd1ce953abdafa3b6cd1320c2a544 --- /dev/null +++ b/RigNet/run_skinning.py @@ -0,0 +1,247 @@ +#------------------------------------------------------------------------------- +# Name: run_skinning.py +# Purpose: Train a network to predict skinning weights +# RigNet Copyright 2020 University of Massachusetts +# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License. +# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet. +#------------------------------------------------------------------------------- +import os +import sys +sys.path.append("./") +import shutil +import argparse +import numpy as np + +from utils.log_utils import AverageMeter +from utils.os_utils import isdir, mkdir_p, isfile +from utils.io_utils import output_rigging + +import torch +import torch.backends.cudnn as cudnn +from torch_geometric.data import DataLoader +from torch.utils.tensorboard import SummaryWriter + +import models +from models.supplemental_layers.cross_entropy_with_probs import cross_entropy_with_probs +from datasets.skin_dataset import SkinDataset + +device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + + +def save_checkpoint(state, is_best, checkpoint='checkpoint', filename='checkpoint.pth.tar', snapshot=None): + filepath = os.path.join(checkpoint, filename) + torch.save(state, filepath) + + if snapshot and state['epoch'] % snapshot == 0: + shutil.copyfile(filepath, os.path.join(checkpoint, 'checkpoint_{}.pth.tar'.format(state['epoch']))) + + if is_best: + shutil.copyfile(filepath, os.path.join(checkpoint, 'model_best.pth.tar')) + + +def get_bone_names(skin_filename): + with open(skin_filename, 'r') as fin: + lines = fin.readlines() + bone_names = [] + for li in lines: + words = li.strip().split() + if words[0] == 'bones': + bone_names.append([words[1], words[2]]) + return bone_names + + +def post_filter(skin_weights, topology_edge, num_ring=1): + skin_weights_new = np.zeros_like(skin_weights) + for v in range(len(skin_weights)): + adj_verts_multi_ring = [] + current_seeds = [v] + for r in range(num_ring): + adj_verts = [] + for seed in current_seeds: + adj_edges = topology_edge[:, np.argwhere(topology_edge == seed)[:, 1]] + adj_verts_seed = list(set(adj_edges.flatten().tolist())) + adj_verts_seed.remove(seed) + adj_verts += adj_verts_seed + adj_verts_multi_ring += adj_verts + current_seeds = adj_verts + adj_verts_multi_ring = list(set(adj_verts_multi_ring)) + if v in adj_verts_multi_ring: + adj_verts_multi_ring.remove(v) + skin_weights_neighbor = [skin_weights[int(i), :][np.newaxis, :] for i in adj_verts_multi_ring] + skin_weights_neighbor = np.concatenate(skin_weights_neighbor, axis=0) + #max_bone_id = np.argmax(skin_weights[v, :]) + #if np.sum(skin_weights_neighbor[:, max_bone_id]) < 0.17 * len(skin_weights_neighbor): + # skin_weights_new[v, :] = np.mean(skin_weights_neighbor, axis=0) + #else: + # skin_weights_new[v, :] = skin_weights[v, :] + skin_weights_new[v, :] = np.mean(skin_weights_neighbor, axis=0) + + #skin_weights_new[skin_weights_new.sum(axis=1) == 0, :] = skin_weights[skin_weights_new.sum(axis=1) == 0, :] + return skin_weights_new + + +def main(args): + global device + lowest_loss = 1e20 + + # create checkpoint dir and log dir + if not isdir(args.checkpoint): + print("Create new checkpoint folder " + args.checkpoint) + mkdir_p(args.checkpoint) + if not args.resume: + if isdir(args.logdir): + shutil.rmtree(args.logdir) + mkdir_p(args.logdir) + + # create model + model = models.__dict__["skinnet"](args.nearest_bone, args.Dg, args.Lf) + model.to(device) + optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay) + + # optionally resume from a checkpoint + lr = args.lr + if args.resume: + if isfile(args.resume): + print("=> loading checkpoint '{}'".format(args.resume)) + checkpoint = torch.load(args.resume) + args.start_epoch = checkpoint['epoch'] + lowest_loss = checkpoint['lowest_loss'] + model.load_state_dict(checkpoint['state_dict']) + optimizer.load_state_dict(checkpoint['optimizer']) + lr = optimizer.param_groups[0]['lr'] + print("=> loaded checkpoint '{}' (epoch {})".format(args.resume, checkpoint['epoch'])) + else: + print("=> no checkpoint found at '{}'".format(args.resume)) + + cudnn.benchmark = True + print(' Total params: %.2fM' % (sum(p.numel() for p in model.parameters()) / 1000000.0)) + train_loader = DataLoader(SkinDataset(root=args.train_folder), batch_size=args.train_batch, shuffle=True) + val_loader = DataLoader(SkinDataset(root=args.val_folder), batch_size=args.test_batch, shuffle=False) + test_loader = DataLoader(SkinDataset(root=args.test_folder), batch_size=args.test_batch, shuffle=False) + if args.evaluate: + print('\nEvaluation only') + test_loss = test(test_loader, model, args, save_result=True) + print('test_loss {:6f}'.format(test_loss)) + return + scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, args.schedule, gamma=args.gamma) + logger = SummaryWriter(log_dir=args.logdir) + for epoch in range(args.start_epoch, args.epochs): + lr = scheduler.get_last_lr() + print('\nEpoch: %d | LR: %.8f' % (epoch + 1, lr[0])) + train_loss = train(train_loader, model, optimizer, args) + val_loss = test(val_loader, model, args) + test_loss = test(test_loader, model, args) + scheduler.step() + print('Epoch{:d}. train_loss: {:.6f}.'.format(epoch + 1, train_loss)) + print('Epoch{:d}. val_loss: {:.6f}.'.format(epoch + 1, val_loss)) + print('Epoch{:d}. test_loss: {:.6f}.'.format(epoch + 1, test_loss)) + + # remember best acc and save checkpoint + is_best = val_loss < lowest_loss + lowest_loss = min(val_loss, lowest_loss) + save_checkpoint({'epoch': epoch + 1, 'state_dict': model.state_dict(), 'lowest_loss': lowest_loss, + 'optimizer': optimizer.state_dict()}, is_best, checkpoint=args.checkpoint) + + info = {'train_loss': train_loss, 'val_loss': val_loss, 'test_loss': test_loss} + for tag, value in info.items(): + logger.add_scalar(tag, value, epoch + 1) + + +def train(train_loader, model, optimizer, args): + global device + model.train() # switch to train mode + loss_meter = AverageMeter() + for data in train_loader: + data = data.to(device) + optimizer.zero_grad() + skin_pred = model(data) + skin_gt = data.skin_label[:, 0:args.nearest_bone] + loss_mask_batch = data.loss_mask.float()[:, 0:args.nearest_bone] + skin_gt = skin_gt * loss_mask_batch + skin_gt = skin_gt / (torch.sum(torch.abs(skin_gt), dim=1, keepdim=True) + 1e-8) + vert_mask = (torch.abs(skin_gt.sum(dim=1) - 1.0) < 1e-8).float() # mask out vertices whose skinning is missing from the picked K bones. + loss = cross_entropy_with_probs(skin_pred, skin_gt, reduction='none') + loss = (loss * loss_mask_batch * vert_mask.unsqueeze(1)).sum() / (loss_mask_batch * vert_mask.unsqueeze(1)).sum() + loss.backward() + optimizer.step() + loss_meter.update(loss.item()) + return loss_meter.avg + + +def test(test_loader, model, args, save_result=False): + global device + model.eval() # switch to test mode + loss_meter = AverageMeter() + outdir = args.checkpoint.split('/')[-1] + for data in test_loader: + data = data.to(device) + with torch.no_grad(): + skin_pred = model(data) + skin_gt = data.skin_label[:, 0:args.nearest_bone] + loss_mask_batch = data.loss_mask.float()[:, 0:args.nearest_bone] + skin_gt = skin_gt * loss_mask_batch + skin_gt = skin_gt / (torch.sum(torch.abs(skin_gt), dim=1, keepdim=True) + 1e-8) + vert_mask = (torch.abs(skin_gt.sum(dim=1) - 1.0) < 1e-8).float() + loss = cross_entropy_with_probs(skin_pred, skin_gt, reduction='none') + loss = (loss * loss_mask_batch * vert_mask.unsqueeze(1)).sum() / (loss_mask_batch * vert_mask.unsqueeze(1)).sum() + loss_meter.update(loss.item()) + + if save_result: + output_folder = 'results/{:s}/'.format(outdir) + if not os.path.exists(output_folder): + mkdir_p(output_folder) + for i in range(len(torch.unique(data.batch))): + print('output result for model {:d}'.format(data.name[i].item())) + skin_pred_i = skin_pred[data.batch == i] + bone_names = get_bone_names(os.path.join(args.test_folder, "{:d}_skin.txt".format(data.name[i].item()))) + tpl_e = np.loadtxt(os.path.join(args.test_folder, "{:d}_tpl_e.txt".format(data.name[i].item()))).T + loss_mask_sample = data.loss_mask.float()[data.batch == i, 0:args.nearest_bone] + skin_pred_i = torch.softmax(skin_pred_i, dim=1) + skin_pred_i = skin_pred_i * loss_mask_sample + skin_nn_i = data.skin_nn[data.batch == i, 0:args.nearest_bone] + skin_pred_asarray = np.zeros((len(skin_pred_i), len(bone_names))) + for v in range(len(skin_pred_i)): + for nn_id in range(len(skin_nn_i[v, :])): + skin_pred_asarray[v, skin_nn_i[v, nn_id]] = skin_pred_i[v, nn_id] + skin_pred_asarray = post_filter(skin_pred_asarray, tpl_e, num_ring=1) + skin_pred_asarray[skin_pred_asarray < np.max(skin_pred_asarray, axis=1, keepdims=True) * 0.5] = 0.0 + skin_pred_asarray = skin_pred_asarray / (skin_pred_asarray.sum(axis=1, keepdims=True) + 1e-10) + with open(os.path.join(output_folder, "{:d}_bone_names.txt".format(data.name[i].item())), 'w') as fout: + for bone_name in bone_names: + fout.write("{:s} {:s}\n".format(bone_name[0], bone_name[1])) + np.save(os.path.join(output_folder, "{:d}_full_pred.npy".format(data.name[i].item())), skin_pred_asarray) + skel_filename = os.path.join(args.info_folder, "{:d}.txt".format(data.name[i].item())) + output_rigging(skel_filename, skin_pred_asarray, output_folder, data.name[i].item()) + return loss_meter.avg + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='skinning predition network') + parser.add_argument('--start-epoch', default=0, type=int, metavar='N', help='manual epoch number') + parser.add_argument('--weight_decay', '--wd', default=1e-4, type=float, metavar='W', help='weight decay (default: 1e-4)') + parser.add_argument('--gamma', type=float, default=0.5, help='LR is multiplied by gamma on schedule.') + parser.add_argument('-j', '--workers', default=1, type=int, metavar='N', help='number of data loading workers (default: 4)') + parser.add_argument('--epochs', default=200, type=int, metavar='N', help='number of total epochs to run') + parser.add_argument('--lr', '--learning-rate', default=1e-3, type=float, metavar='LR', help='initial learning rate') + parser.add_argument('--schedule', type=int, nargs='+', default=[], help='Decrease learning rate at these epochs.') + parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true', help='evaluate model on val/test set') + #################################################################################################################### + parser.add_argument('--train_batch', default=2, type=int, metavar='N', help='train batchsize') + parser.add_argument('--test_batch', default=2, type=int, metavar='N', help='test batchsize') + parser.add_argument('-c', '--checkpoint', default='checkpoints/test', type=str, metavar='PATH', + help='path to save checkpoint (default: checkpoint)') + parser.add_argument('--logdir', default='logs/test', type=str, metavar='LOG', help='directory to save logs') + parser.add_argument('--resume', default='', type=str, metavar='PATH', help='path to latest checkpoint (default: none)') + parser.add_argument('--train_folder', default='/media/zhanxu/4T/ModelResource_RigNetv1_preproccessed/train/', + type=str, help='folder of training data') + parser.add_argument('--val_folder', default='/media/zhanxu/4T/ModelResource_RigNetv1_preproccessed/val/', + type=str, help='folder of validation data') + parser.add_argument('--test_folder', default='/media/zhanxu/4T/ModelResource_RigNetv1_preproccessed/test/', + type=str, help='folder of testing data') + parser.add_argument('--nearest_bone', type=int, default=5) + parser.add_argument('--info_folder', default='/media/zhanxu/4T/ModelResource_RigNetv1_preproccessed/rig_info_remesh/', + type=str, help='folder of skeleton information') + parser.add_argument('--Dg', action='store_true', help='input inverset geodesic as addtional feature') + parser.add_argument('--Lf', action='store_true', help='input isleaf indicator as addtional feature') + print(parser.parse_args()) + main(parser.parse_args()) diff --git a/RigNet/utils/__init__.py b/RigNet/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/RigNet/utils/binvox_rw.py b/RigNet/utils/binvox_rw.py new file mode 100644 index 0000000000000000000000000000000000000000..3cf28ed4dc92607e025d8214f10f9eacdcfa2c2b --- /dev/null +++ b/RigNet/utils/binvox_rw.py @@ -0,0 +1,246 @@ +# Copyright (C) 2012 Daniel Maturana +# This file is part of binvox-rw-py. +# +# binvox-rw-py is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# binvox-rw-py is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with binvox-rw-py. If not, see . +# + + +import numpy as np +import struct + + +class Voxels(object): + """ Holds a binvox model. + data is either a three-dimensional numpy boolean array (dense representation) + or a two-dimensional numpy float array (coordinate representation). + + dims, translate and scale are the model metadata. + + dims are the voxel dimensions, e.g. [32, 32, 32] for a 32x32x32 model. + + scale and translate relate the voxels to the original model coordinates. + + To translate voxel coordinates i, j, k to original coordinates x, y, z: + + x_n = (i+.5)/dims[0] + y_n = (j+.5)/dims[1] + z_n = (k+.5)/dims[2] + x = scale*x_n + translate[0] + y = scale*y_n + translate[1] + z = scale*z_n + translate[2] + + """ + + def __init__(self, data, dims, translate, scale, axis_order): + self.data = data + self.dims = dims + self.translate = translate + self.scale = scale + assert (axis_order in ('xzy', 'xyz')) + self.axis_order = axis_order + + def clone(self): + data = self.data.copy() + dims = self.dims[:] + translate = self.translate[:] + return Voxels(data, dims, translate, self.scale, self.axis_order) + + def write(self, fp): + write(self, fp) + +def read_header(fp): + """ Read binvox header. Mostly meant for internal use. + """ + line = fp.readline().strip() + if not line.startswith(b'#binvox'): + raise IOError('Not a binvox file') + dims = list(map(int, fp.readline().strip().split(b' ')[1:])) + translate = list(map(float, fp.readline().strip().split(b' ')[1:])) + scale = list(map(float, fp.readline().strip().split(b' ')[1:]))[0] + line = fp.readline() + + return dims, translate, scale + +def read_as_3d_array(fp, fix_coords=True): + """ Read binary binvox format as array. + + Returns the model with accompanying metadata. + + Voxels are stored in a three-dimensional numpy array, which is simple and + direct, but may use a lot of memory for large models. (Storage requirements + are 8*(d^3) bytes, where d is the dimensions of the binvox model. Numpy + boolean arrays use a byte per element). + + Doesn't do any checks on input except for the '#binvox' line. + """ + dims, translate, scale = read_header(fp) + raw_data = np.frombuffer(fp.read(), dtype=np.uint8) + # if just using reshape() on the raw data: + # indexing the array as array[i,j,k], the indices map into the + # coords as: + # i -> x + # j -> z + # k -> y + # if fix_coords is true, then data is rearranged so that + # mapping is + # i -> x + # j -> y + # k -> z + values, counts = raw_data[::2], raw_data[1::2] + data = np.repeat(values, counts).astype(np.bool) + data = data.reshape(dims) + if fix_coords: + # xzy to xyz TODO the right thing + data = np.transpose(data, (0, 2, 1)) + axis_order = 'xyz' + else: + axis_order = 'xzy' + return Voxels(data, dims, translate, scale, axis_order) + +def read_as_coord_array(fp, fix_coords=True): + """ Read binary binvox format as coordinates. + + Returns binvox model with voxels in a "coordinate" representation, i.e. an + 3 x N array where N is the number of nonzero voxels. Each column + corresponds to a nonzero voxel and the 3 rows are the (x, z, y) coordinates + of the voxel. (The odd ordering is due to the way binvox format lays out + data). Note that coordinates refer to the binvox voxels, without any + scaling or translation. + + Use this to save memory if your model is very sparse (mostly empty). + + Doesn't do any checks on input except for the '#binvox' line. + """ + dims, translate, scale = read_header(fp) + raw_data = np.frombuffer(fp.read(), dtype=np.uint8) + + values, counts = raw_data[::2], raw_data[1::2] + + sz = np.prod(dims) + index, end_index = 0, 0 + end_indices = np.cumsum(counts) + indices = np.concatenate(([0], end_indices[:-1])).astype(end_indices.dtype) + + values = values.astype(np.bool) + indices = indices[values] + end_indices = end_indices[values] + + nz_voxels = [] + for index, end_index in zip(indices, end_indices): + nz_voxels.extend(range(index, end_index)) + nz_voxels = np.array(nz_voxels) + # TODO are these dims correct? + # according to docs, + # index = x * wxh + z * width + y; // wxh = width * height = d * d + + x = nz_voxels / (dims[0]*dims[1]) + zwpy = nz_voxels % (dims[0]*dims[1]) # z*w + y + z = zwpy / dims[0] + y = zwpy % dims[0] + if fix_coords: + data = np.vstack((x, y, z)) + axis_order = 'xyz' + else: + data = np.vstack((x, z, y)) + axis_order = 'xzy' + + #return Voxels(data, dims, translate, scale, axis_order) + return Voxels(np.ascontiguousarray(data), dims, translate, scale, axis_order) + +def dense_to_sparse(voxel_data, dtype=np.int): + """ From dense representation to sparse (coordinate) representation. + No coordinate reordering. + """ + if voxel_data.ndim!=3: + raise ValueError('voxel_data is wrong shape; should be 3D array.') + return np.asarray(np.nonzero(voxel_data), dtype) + +def sparse_to_dense(voxel_data, dims, dtype=np.bool): + if voxel_data.ndim!=2 or voxel_data.shape[0]!=3: + raise ValueError('voxel_data is wrong shape; should be 3xN array.') + if np.isscalar(dims): + dims = [dims]*3 + dims = np.atleast_2d(dims).T + # truncate to integers + xyz = voxel_data.astype(np.int) + # discard voxels that fall outside dims + valid_ix = ~np.any((xyz < 0) | (xyz >= dims), 0) + xyz = xyz[:,valid_ix] + out = np.zeros(dims.flatten(), dtype=dtype) + out[tuple(xyz)] = True + return out + +#def get_linear_index(x, y, z, dims): + #""" Assuming xzy order. (y increasing fastest. + #TODO ensure this is right when dims are not all same + #""" + #return x*(dims[1]*dims[2]) + z*dims[1] + y + +def bwrite(fp,s): + fp.write(s.encode()) + +def write_pair(fp,state, ctr): + fp.write(struct.pack('B',state)) + fp.write(struct.pack('B',ctr)) + +def write(voxel_model, fp): + """ Write binary binvox format. + + Note that when saving a model in sparse (coordinate) format, it is first + converted to dense format. + + Doesn't check if the model is 'sane'. + + """ + if voxel_model.data.ndim==2: + # TODO avoid conversion to dense + dense_voxel_data = sparse_to_dense(voxel_model.data, voxel_model.dims) + else: + dense_voxel_data = voxel_model.data + + bwrite(fp, '#binvox 1\n') + bwrite(fp, 'dim ' + ' '.join(map(str, voxel_model.dims)) + '\n') + bwrite(fp, 'translate ' + ' '.join(map(str, voxel_model.translate)) + '\n') + bwrite(fp, 'scale ' + str(voxel_model.scale) + '\n') + bwrite(fp, 'data\n') + if not voxel_model.axis_order in ('xzy', 'xyz'): + raise ValueError('Unsupported voxel model axis order') + + if voxel_model.axis_order=='xzy': + voxels_flat = dense_voxel_data.flatten() + elif voxel_model.axis_order=='xyz': + voxels_flat = np.transpose(dense_voxel_data, (0, 2, 1)).flatten() + + # keep a sort of state machine for writing run length encoding + state = voxels_flat[0] + ctr = 0 + for c in voxels_flat: + if c==state: + ctr += 1 + # if ctr hits max, dump + if ctr==255: + write_pair(fp, state, ctr) + ctr = 0 + else: + # if switch state, dump + write_pair(fp, state, ctr) + state = c + ctr = 1 + # flush out remainders + if ctr > 0: + write_pair(fp, state, ctr) + +if __name__ == '__main__': + import doctest + doctest.testmod() diff --git a/RigNet/utils/cluster_utils.py b/RigNet/utils/cluster_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..64f14d07c19a68e1b05b1b9b720becb48a5c5be4 --- /dev/null +++ b/RigNet/utils/cluster_utils.py @@ -0,0 +1,59 @@ +#------------------------------------------------------------------------------- +# Name: cluster_utils.py +# Purpose: utilize functions for clustering +# RigNet Copyright 2020 University of Massachusetts +# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License. +# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet. +#------------------------------------------------------------------------------- + +import sys +sys.path.append("./") +import numpy as np + + +def meanshift_cluster(pts_in, bandwidth, weights=None, max_iter=20): + """ + Meanshift clustering + :param pts_in: input points + :param bandwidth: bandwidth + :param weights: weights per pts indicting its importance in the clustering + :return: points after clustering + """ + diff = 1e10 + num_iter = 1 + while diff > 1e-3 and num_iter < max_iter: + Y = np.sum(((pts_in[np.newaxis, ...] - pts_in[:, np.newaxis, :]) ** 2), axis=2) + K = np.maximum(bandwidth**2 - Y, np.zeros(Y.shape)) + if weights is not None: + K = K * weights + row_sums = K.sum(axis=0, keepdims=True) + P = K / (row_sums + 1e-10) + P = P.transpose() + pts_in_prim = 0.3 * (np.matmul(P, pts_in) - pts_in) + pts_in + diff = np.sqrt(np.sum((pts_in_prim - pts_in)**2)) + pts_in = pts_in_prim + num_iter += 1 + return pts_in + + +def nms_meanshift(pts_in, density, bandwidth): + """ + NMS to extract modes after meanshift. Code refers to sci-kit-learn. + :param pts_in: input points + :param density: density at each point + :param bandwidth: bandwidth used in meanshift. Used here as neighbor region for NMS + :return: extracted clusters. + """ + Y = np.sum(((pts_in[np.newaxis, ...] - pts_in[:, np.newaxis, :]) ** 2), axis=2) + sorted_ids = np.argsort(density)[::-1] + unique = np.ones(len(sorted_ids), dtype=np.bool) + dist = np.sqrt(Y) + for i in sorted_ids: + if unique[i]: + neighbor_idxs = np.argwhere(dist[:, i] <= bandwidth) + unique[neighbor_idxs.squeeze()] = 0 + unique[i] = 1 # leave the current point as unique + pts_in = pts_in[unique] + return pts_in + + diff --git a/RigNet/utils/io_utils.py b/RigNet/utils/io_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..7875f71a77e8e60a9cbdf7b2fe49a8eb74c43594 --- /dev/null +++ b/RigNet/utils/io_utils.py @@ -0,0 +1,119 @@ +#------------------------------------------------------------------------------- +# Name: io_utils.py +# Purpose: utilize functions for file IO +# RigNet Copyright 2020 University of Massachusetts +# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License. +# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet. +#------------------------------------------------------------------------------- + +import os +import numpy as np +from utils.os_utils import mkdir_p +from utils.tree_utils import TreeNode +from utils.rig_parser import Info +from geometric_proc.compute_volumetric_geodesic import get_bones + + +def readPly(filename): + with open(filename, 'r') as fin: + lines = fin.readlines() + pts = [] + for li in lines[7:]: + words = li.split() + pts.append(np.array([[float(words[0]), float(words[1]), float(words[2])]])) + pts = np.concatenate(pts, axis=0) + return pts + +def writePly(pts, filename): + with open(filename, 'w') as f: + pn = pts.shape[0] + f.write('ply\n') + f.write('format ascii 1.0\n') + f.write('element vertex %d\n' % (pn) ) + f.write('property float x\n') + f.write('property float y\n') + f.write('property float z\n') + f.write('end_header\n') + for i in range(pn): + f.write('%f %f %f\n' % (pts[i, 0], pts[i, 1], pts[i, 2]) ) + + +def output_point_cloud_ply(xyzs, name, output_folder): + if not os.path.exists( output_folder ): + mkdir_p( output_folder ) + print('write: ' + os.path.join(output_folder, name + '.ply')) + with open(os.path.join(output_folder, name + '.ply'), 'w') as f: + pn = xyzs.shape[0] + f.write('ply\n') + f.write('format ascii 1.0\n') + f.write('element vertex %d\n' % (pn) ) + f.write('property float x\n') + f.write('property float y\n') + f.write('property float z\n') + f.write('end_header\n') + for i in range(pn): + f.write('%f %f %f\n' % (xyzs[i][0], xyzs[i][1], xyzs[i][2]) ) + + +def add_duplicate_joints(skel): + this_level = [skel.root] + while this_level: + next_level = [] + for p_node in this_level: + if len(p_node.children) > 1: + new_children = [] + for dup_id in range(len(p_node.children)): + p_node_new = TreeNode(p_node.name + '_dup_{:d}'.format(dup_id), p_node.pos) + p_node_new.overlap=True + p_node_new.parent = p_node + p_node_new.children = [p_node.children[dup_id]] + # for user interaction, we move overlapping joints a bit to its children + p_node_new.pos = np.array(p_node_new.pos) + 0.03 * np.linalg.norm(np.array(p_node.children[dup_id].pos) - np.array(p_node_new.pos)) + p_node_new.pos = (p_node_new.pos[0], p_node_new.pos[1], p_node_new.pos[2]) + p_node.children[dup_id].parent = p_node_new + new_children.append(p_node_new) + p_node.children = new_children + p_node.overlap = False + next_level += p_node.children + this_level = next_level + return skel + + +def mapping_bone_index(bones_old, bones_new): + bone_map = {} + for i in range(len(bones_old)): + bone_old = bones_old[i][np.newaxis, :] + dist = np.linalg.norm(bones_new - bone_old, axis=1) + ni = np.argmin(dist) + bone_map[i] = ni + return bone_map + + +def assemble_skel_skin(skel, attachment): + bones_old, bone_names_old, _ = get_bones(skel) + skel_new = add_duplicate_joints(skel) + bones_new, bone_names_new, _ = get_bones(skel_new) + bone_map = mapping_bone_index(bones_old, bones_new) + skel_new.joint_pos = skel_new.get_joint_dict() + skel_new.joint_skin = [] + + for v in range(len(attachment)): + vi_skin = [str(v)] + skw = attachment[v] + skw = skw / (np.sum(skw) + 1e-10) + for i in range(len(skw)): + if i == len(bones_old): + break + if skw[i] > 1e-5: + bind_joint_name = bone_names_new[bone_map[i]][0] + bind_weight = skw[i] + vi_skin.append(bind_joint_name) + vi_skin.append(str(bind_weight)) + skel_new.joint_skin.append(vi_skin) + return skel_new + + +def output_rigging(skel_name, attachment, output_folder, name): + skel = Info(skel_name) + skel_new = assemble_skel_skin(skel, attachment) + skel_new.save(os.path.join(output_folder, str(name) + '_rig.txt')) diff --git a/RigNet/utils/log_utils.py b/RigNet/utils/log_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..1e88e67786fb11a811cd85fd82614b8b40d93c62 --- /dev/null +++ b/RigNet/utils/log_utils.py @@ -0,0 +1,30 @@ +#------------------------------------------------------------------------------- +# Name: mst_utils.py +# Purpose: utilize class for log recording +# RigNet Copyright 2020 University of Massachusetts +# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License. +# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet. +#------------------------------------------------------------------------------- + +class AverageMeter(object): + """Computes and stores the average and current value""" + def __init__(self): + self.reset() + + def reset(self): + self.val = 0.0 + self.avg = 0.0 + self.sum = 0.0 + self.count = 0.0 + + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count + + def accumulate(self, val, n=1): + self.val = val + self.sum += val + self.count += n + self.avg = self.sum / self.count \ No newline at end of file diff --git a/RigNet/utils/mst_utils.py b/RigNet/utils/mst_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..e4cb7bf98c81dd23f035e45f8a04b6700d568dc3 --- /dev/null +++ b/RigNet/utils/mst_utils.py @@ -0,0 +1,273 @@ +#------------------------------------------------------------------------------- +# Name: mst_utils.py +# Purpose: utilize functions for skeleton generation +# RigNet Copyright 2020 University of Massachusetts +# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License. +# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet. +#------------------------------------------------------------------------------- + +import sys +import numpy as np +from utils.tree_utils import TreeNode +from utils.rig_parser import Skel + + +def inside_check(pts, vox): + """ + Check where points are inside or outside the mesh based on its voxelization. + :param pts: points to be checked + :param vox: voxelized mesh + :return: internal points, and index of them in the input array. + """ + vc = (pts - vox.translate) / vox.scale * vox.dims[0] + vc = np.round(vc).astype(int) + ind1 = np.logical_and(np.all(vc >= 0, axis=1), np.all(vc < 88, axis=1)) + vc = np.clip(vc, 0, 87) + ind2 = vox.data[vc[:, 0], vc[:, 1], vc[:, 2]] + ind = np.logical_and(ind1, ind2) + pts = pts[ind] + return pts, np.argwhere(ind).squeeze() + + +def sample_on_bone(p_pos, ch_pos): + """ + sample points on a bone + :param p_pos: parent joint position + :param ch_pos: child joint position + :return: a array of samples on this bone. + """ + ray = ch_pos - p_pos + bone_length = np.sqrt(np.sum((p_pos - ch_pos) ** 2)) + num_step = np.round(bone_length / 0.01) + i_step = np.arange(1, num_step + 1) + unit_step = ray / (num_step + 1e-30) + unit_step = np.repeat(unit_step[np.newaxis, :], num_step, axis=0) + res = p_pos + unit_step * i_step[:, np.newaxis] + return res + + +def minKey(key, mstSet, nV): + # Initilaize min value + min = sys.maxsize + for v in range(nV): + if key[v] < min and mstSet[v] == False: + min = key[v] + min_index = v + return min_index + + +def primMST(graph, init_id): + """ + Original prim MST algorithm https://www.geeksforgeeks.org/prims-minimum-spanning-tree-mst-greedy-algo-5/ + """ + nV = graph.shape[0] + # Key values used to pick minimum weight edge in cut + key = [sys.maxsize] * nV + parent = [None] * nV # Array to store constructed MST + mstSet = [False] * nV + # Make key init_id so that this vertex is picked as first vertex + key[init_id] = 0 + parent[init_id] = -1 # First node is always the root of + + for cout in range(nV): + # Pick the minimum distance vertex from + # the set of vertices not yet processed. + # u is always equal to src in first iteration + u = minKey(key, mstSet, nV) + + # Put the minimum distance vertex in + # the shortest path tree + mstSet[u] = True + + # Update dist value of the adjacent vertices + # of the picked vertex only if the current + # distance is greater than new distance and + # the vertex in not in the shotest path tree + for v in range(nV): + # graph[u][v] is non zero only for adjacent vertices of m + # mstSet[v] is false for vertices not yet included in MST + # Update the key only if graph[u][v] is smaller than key[v] + if graph[u,v] > 0 and mstSet[v] == False and key[v] > graph[u,v]: + key[v] = graph[u,v] + parent[v] = u + + return parent, key + + +def primMST_symmetry(graph, init_id, joints): + """ + my modified prim algorithm to generate a tree as symmetric as possible. + Not guaranteed to be symmetric. All heuristics. + :param graph: pairwise cost matrix + :param init_id: init node ID as root + :param joints: joint positions J*3 + :return: + """ + joint_mapping = {} + left_joint_ids = np.argwhere(joints[:, 0] < -2e-2).squeeze(1).tolist() + middle_joint_ids = np.argwhere(np.abs(joints[:, 0]) <= 2e-2).squeeze(1).tolist() + right_joint_ids = np.argwhere(joints[:, 0] > 2e-2).squeeze(1).tolist() + for i in range(len(left_joint_ids)): + joint_mapping[left_joint_ids[i]] = right_joint_ids[i] + for i in range(len(right_joint_ids)): + joint_mapping[right_joint_ids[i]] = left_joint_ids[i] + + if init_id not in middle_joint_ids: + #find nearest joint in the middle to be root + if len(middle_joint_ids) > 0: + nearest_id = np.argmin(np.linalg.norm(joints[middle_joint_ids, :] - joints[init_id, :][np.newaxis, :], axis=1)) + init_id = middle_joint_ids[nearest_id] + + nV = graph.shape[0] + # Key values used to pick minimum weight edge in cut + key = [sys.maxsize] * nV + parent = [None] * nV # Array to store constructed MST + mstSet = [False] * nV + # Make key init_id so that this vertex is picked as first vertex + key[init_id] = 0 + parent[init_id] = -1 # First node is always the root of + + while not all(mstSet): + # Pick the minimum distance vertex from + # the set of vertices not yet processed. + # u is always equal to src in first iteration + u = minKey(key, mstSet, nV) + # left cases + if u in left_joint_ids and parent[u] in middle_joint_ids: + u2 = joint_mapping[u] + if mstSet[u2] is False: + mstSet[u2] = True + parent[u2] = parent[u] + key[u2] = graph[u2, parent[u2]] + elif u in left_joint_ids and parent[u] in left_joint_ids: + u2 = joint_mapping[u] + if mstSet[u2] is False: + mstSet[u2] = True + parent[u2] = joint_mapping[parent[u]] + key[u2] = graph[u2, parent[u2]] + elif u in left_joint_ids and parent[u] in right_joint_ids: + u2 = joint_mapping[u] + if mstSet[u2] is False: + mstSet[u2] = True + parent[u2] = joint_mapping[parent[u]] + key[u2] = graph[u2, parent[u2]] + + # right cases + elif u in right_joint_ids and parent[u] in middle_joint_ids: + u2 = joint_mapping[u] + if mstSet[u2] is False: + mstSet[u2] = True + parent[u2] = parent[u] + key[u2] = graph[u2, parent[u2]] + elif u in right_joint_ids and parent[u] in right_joint_ids: + u2 = joint_mapping[u] + if mstSet[u2] is False: + mstSet[u2] = True + parent[u2] = joint_mapping[parent[u]] + key[u2] = graph[u2, parent[u2]] + elif u in right_joint_ids and parent[u] in left_joint_ids: + u2 = joint_mapping[u] + if mstSet[u2] is False: + mstSet[u2] = True + parent[u2] = joint_mapping[parent[u]] + key[u2] = graph[u2, parent[u2]] + # middle case + else: + u2 = None + + mstSet[u] = True + + # Update dist value of the adjacent vertices + # of the picked vertex only if the current + # distance is greater than new distance and + # the vertex in not in the shotest path tree + for v in range(nV): + # graph[u][v] is non zero only for adjacent vertices of m + # mstSet[v] is false for vertices not yet included in MST + # Update the key only if graph[u][v] is smaller than key[v] + if graph[u,v] > 0 and mstSet[v] == False and key[v] > graph[u,v]: + key[v] = graph[u,v] + parent[v] = u + if u2 is not None and graph[u2,v] > 0 and mstSet[v] == False and key[v] > graph[u2,v]: + key[v] = graph[u2, v] + parent[v] = u2 + + return parent, key, init_id + + +def loadSkel_recur(p_node, parent_id, joint_name, joint_pos, parent): + """ + Converst prim algorithm result to our skel/info format recursively + :param p_node: Root node + :param parent_id: parent name of current step of recursion. + :param joint_name: list of joint names + :param joint_pos: joint positions + :param parent: parent index returned by prim alg. + :return: p_node (root) will be expanded to linked with all joints + """ + for i in range(len(parent)): + if parent[i] == parent_id: + if joint_name is not None: + ch_node = TreeNode(joint_name[i], tuple(joint_pos[i])) + else: + ch_node = TreeNode('joint_{}'.format(i), tuple(joint_pos[i])) + p_node.children.append(ch_node) + ch_node.parent = p_node + loadSkel_recur(ch_node, i, joint_name, joint_pos, parent) + + +def unique_rows(a): + """ + remove repeat rows from a numpy array + """ + a = np.ascontiguousarray(a) + unique_a = np.unique(a.view([('', a.dtype)]*a.shape[1])) + return unique_a.view(a.dtype).reshape((unique_a.shape[0], a.shape[1])) + + +def increase_cost_for_outside_bone(cost_matrix, joint_pos, vox): + """ + increase connectivity cost for bones outside the meshs + """ + for i in range(len(joint_pos)): + for j in range(i+1, len(joint_pos)): + bone_samples = sample_on_bone(joint_pos[i], joint_pos[j]) + bone_samples_vox = (bone_samples - vox.translate) / vox.scale * vox.dims[0] + bone_samples_vox = np.round(bone_samples_vox).astype(int) + + ind1 = np.logical_and(np.all(bone_samples_vox >= 0, axis=1), np.all(bone_samples_vox < vox.dims[0], axis=1)) + bone_samples_vox = np.clip(bone_samples_vox, 0, vox.dims[0]-1) + ind2 = vox.data[bone_samples_vox[:, 0], bone_samples_vox[:, 1], bone_samples_vox[:, 2]] + in_flags = np.logical_and(ind1, ind2) + outside_bone_sample = np.sum(in_flags == False) + + if outside_bone_sample > 1: + cost_matrix[i, j] = 2 * outside_bone_sample + cost_matrix[j, i] = 2 * outside_bone_sample + if np.abs(joint_pos[i, 0]) < 2e-2 and np.abs(joint_pos[j, 0]) < 2e-2: + cost_matrix[i, j] *= 0.5 + cost_matrix[j, i] *= 0.5 + return cost_matrix + + +def flip(pred_joints): + """ + symmetrize the predicted joints by reflecting joints on the left half space to the right + :param pred_joints: raw predicted joints + :return: symmetrized predicted joints + """ + pred_joints_left = pred_joints[np.argwhere(pred_joints[:, 0] < -2e-2).squeeze(), :] + pred_joints_middle = pred_joints[np.argwhere(np.abs(pred_joints[:, 0]) <= 2e-2).squeeze(), :] + + if pred_joints_left.ndim == 1: + pred_joints_left = pred_joints_left[np.newaxis, :] + if pred_joints_middle.ndim == 1: + pred_joints_middle = pred_joints_middle[np.newaxis, :] + + pred_joints_middle[:, 0] = 0.0 + pred_joints_right = np.copy(pred_joints_left) + pred_joints_right[:, 0] = -pred_joints_right[:, 0] + pred_joints_res = np.concatenate((pred_joints_left, pred_joints_middle, pred_joints_right), axis=0) + side_indicator = np.concatenate((-np.ones(len(pred_joints_left)), np.zeros(len(pred_joints_middle)), np.ones(len(pred_joints_right))), axis=0) + return pred_joints_res, side_indicator + diff --git a/RigNet/utils/os_utils.py b/RigNet/utils/os_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..aabd51eb3e0d36a346a33bad6bb4028fd0f22f24 --- /dev/null +++ b/RigNet/utils/os_utils.py @@ -0,0 +1,28 @@ +#------------------------------------------------------------------------------- +# Name: mst_utils.py +# Purpose: utilize functions for system commands +# RigNet Copyright 2020 University of Massachusetts +# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License. +# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet. +#------------------------------------------------------------------------------- + +from __future__ import absolute_import + +import os +import errno + +def mkdir_p(dir_path): + try: + os.makedirs(dir_path) + except OSError as e: + if e.errno != errno.EEXIST: + raise + +def isfile(fname): + return os.path.isfile(fname) + +def isdir(dirname): + return os.path.isdir(dirname) + +def join(path, *paths): + return os.path.join(path, *paths) \ No newline at end of file diff --git a/RigNet/utils/rig_parser.py b/RigNet/utils/rig_parser.py new file mode 100644 index 0000000000000000000000000000000000000000..b959427e3e5dbf37cb1cdafa4c0510cc528d0976 --- /dev/null +++ b/RigNet/utils/rig_parser.py @@ -0,0 +1,241 @@ +#------------------------------------------------------------------------------- +# Name: rig_parser.py +# Purpose: classes for skeleton and rig +# RigNet Copyright 2020 University of Massachusetts +# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License. +# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet. +#------------------------------------------------------------------------------- + +import numpy as np +from utils.tree_utils import TreeNode +try: + import Queue as Q # ver. < 3.0 +except ImportError: + import queue as Q + + +class Info: + """ + Wrap class for rig information + """ + def __init__(self, filename=None): + self.joint_pos = {} + self.joint_skin = [] + self.root = None + if filename is not None: + self.load(filename) + + def load(self, filename): + with open(filename, 'r') as f_txt: + lines = f_txt.readlines() + for line in lines: + word = line.split() + if word[0] == 'joints': + self.joint_pos[word[1]] = [float(word[2]), float(word[3]), float(word[4])] + elif word[0] == 'root': + root_pos = self.joint_pos[word[1]] + self.root = TreeNode(word[1], (root_pos[0], root_pos[1], root_pos[2])) + elif word[0] == 'skin': + skin_item = word[1:] + self.joint_skin.append(skin_item) + self.loadHierarchy_recur(self.root, lines, self.joint_pos) + + def loadHierarchy_recur(self, node, lines, joint_pos): + for li in lines: + if li.split()[0] == 'hier' and li.split()[1] == node.name: + pos = joint_pos[li.split()[2]] + ch_node = TreeNode(li.split()[2], tuple(pos)) + node.children.append(ch_node) + ch_node.parent = node + self.loadHierarchy_recur(ch_node, lines, joint_pos) + + def save(self, filename): + with open(filename, 'w') as file_info: + for key, val in self.joint_pos.items(): + file_info.write( + 'joints {0} {1:.8f} {2:.8f} {3:.8f}\n'.format(key, val[0], val[1], val[2])) + file_info.write('root {}\n'.format(self.root.name)) + + for skw in self.joint_skin: + cur_line = 'skin {0} '.format(skw[0]) + for cur_j in range(1, len(skw), 2): + cur_line += '{0} {1:.4f} '.format(skw[cur_j], float(skw[cur_j+1])) + cur_line += '\n' + file_info.write(cur_line) + + this_level = self.root.children + while this_level: + next_level = [] + for p_node in this_level: + file_info.write('hier {0} {1}\n'.format(p_node.parent.name, p_node.name)) + next_level += p_node.children + this_level = next_level + + def save_as_skel_format(self, filename): + fout = open(filename, 'w') + this_level = [self.root] + hier_level = 1 + while this_level: + next_level = [] + for p_node in this_level: + pos = p_node.pos + parent = p_node.parent.name if p_node.parent is not None else 'None' + line = '{0} {1} {2:8f} {3:8f} {4:8f} {5}\n'.format(hier_level, p_node.name, pos[0], pos[1], pos[2], + parent) + fout.write(line) + for c_node in p_node.children: + next_level.append(c_node) + this_level = next_level + hier_level += 1 + fout.close() + + def normalize(self, scale, trans): + for k, v in self.joint_pos.items(): + self.joint_pos[k] /= scale + self.joint_pos[k] -= trans + + + this_level = [self.root] + while this_level: + next_level = [] + for node in this_level: + node.pos /= scale + node.pos = (node.pos[0] - trans[0], node.pos[1] - trans[1], node.pos[2] - trans[2]) + for ch in node.children: + next_level.append(ch) + this_level = next_level + + def get_joint_dict(self): + joint_dict = {} + this_level = [self.root] + while this_level: + next_level = [] + for node in this_level: + joint_dict[node.name] = node.pos + next_level += node.children + this_level = next_level + return joint_dict + + def adjacent_matrix(self): + joint_pos = self.get_joint_dict() + joint_name_list = list(joint_pos.keys()) + num_joint = len(joint_pos) + adj_matrix = np.zeros((num_joint, num_joint)) + this_level = [self.root] + while this_level: + next_level = [] + for p_node in this_level: + for c_node in p_node.children: + index_parent = joint_name_list.index(p_node.name) + index_children = joint_name_list.index(c_node.name) + adj_matrix[index_parent, index_children] = 1. + next_level += p_node.children + this_level = next_level + adj_matrix = adj_matrix + adj_matrix.transpose() + return adj_matrix + + +class Skel: + """ + Wrap class for skeleton topology + """ + def __init__(self, filename=None): + self.root = None + if filename is not None: + self.load(filename) + + def load(self, filename): + with open(filename, 'r') as fin: + lines = fin.readlines() + for li in lines: + words = li.split() + if words[5] == "None": + self.root = TreeNode(words[1], (float(words[2]), float(words[3]), float(words[4]))) + if len(words) == 7: + has_order = True + self.root.order = int(words[6]) + else: + has_order = False + break + self.loadSkel_recur(self.root, lines, has_order) + + def loadSkel_recur(self, node, lines, has_order): + if has_order: + ch_queue = Q.PriorityQueue() + for li in lines: + words = li.split() + if words[5] == node.name: + ch_queue.put((int(li.split()[6]), li)) + while not ch_queue.empty(): + item = ch_queue.get() + li = item[1] + ch_node = TreeNode(li.split()[1], (float(li.split()[2]), float(li.split()[3]), float(li.split()[4]))) + ch_node.order = int(li.split()[6]) + node.children.append(ch_node) + ch_node.parent = node + self.loadSkel_recur(ch_node, lines, has_order) + else: + for li in lines: + words = li.split() + if words[5] == node.name: + ch_node = TreeNode(words[1], (float(words[2]), float(words[3]), float(words[4]))) + node.children.append(ch_node) + ch_node.parent = node + self.loadSkel_recur(ch_node, lines, has_order) + + def save(self, filename): + fout = open(filename, 'w') + this_level = [self.root] + hier_level = 1 + while this_level: + next_level = [] + for p_node in this_level: + pos = p_node.pos + parent = p_node.parent.name if p_node.parent is not None else 'None' + line = '{0} {1} {2:8f} {3:8f} {4:8f} {5}\n'.format(hier_level, p_node.name, pos[0], pos[1], pos[2], parent) + fout.write(line) + for c_node in p_node.children: + next_level.append(c_node) + this_level = next_level + hier_level += 1 + fout.close() + + def normalize(self, scale, trans): + this_level = [self.root] + while this_level: + next_level = [] + for node in this_level: + node.pos /= scale + node.pos = (node.pos[0] - trans[0], node.pos[1] - trans[1], node.pos[2] - trans[2]) + for ch in node.children: + next_level.append(ch) + this_level = next_level + + def get_joint_pos(self): + joint_pos = {} + this_level = [self.root] + while this_level: + next_level = [] + for node in this_level: + joint_pos[node.name] = node.pos + next_level += node.children + this_level = next_level + return joint_pos + + def adjacent_matrix(self): + joint_pos = self.get_joint_pos() + joint_name_list = list(joint_pos.keys()) + num_joint = len(joint_pos) + adj_matrix = np.zeros((num_joint, num_joint)) + this_level = [self.root] + while this_level: + next_level = [] + for p_node in this_level: + for c_node in p_node.children: + index_parent = joint_name_list.index(p_node.name) + index_children = joint_name_list.index(c_node.name) + adj_matrix[index_parent, index_children] = 1. + next_level += p_node.children + this_level = next_level + adj_matrix = adj_matrix + adj_matrix.transpose() + return adj_matrix diff --git a/RigNet/utils/tree_utils.py b/RigNet/utils/tree_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..337a98d4a562d371accb60f76955328aee0737f1 --- /dev/null +++ b/RigNet/utils/tree_utils.py @@ -0,0 +1,20 @@ +#------------------------------------------------------------------------------- +# Name: tree_utils.py +# Purpose: classes of node (joint) and tree-node (joint with its parent and children) +# RigNet Copyright 2020 University of Massachusetts +# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License. +# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet. +#------------------------------------------------------------------------------- + + +class Node(object): + def __init__(self, name, pos): + self.name = name + self.pos = pos + + +class TreeNode(Node): + def __init__(self, name, pos): + super(TreeNode, self).__init__(name, pos) + self.children = [] + self.parent = None diff --git a/RigNet/utils/vis_utils.py b/RigNet/utils/vis_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..9fcd4fb80b294a04e24dd9d8df46085392053adb --- /dev/null +++ b/RigNet/utils/vis_utils.py @@ -0,0 +1,217 @@ +#------------------------------------------------------------------------------- +# Name: vis_utils.py +# Purpose: utilize functions for visualization, highly relied on Open3D (0.9.0) +# RigNet Copyright 2020 University of Massachusetts +# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License. +# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet. +#------------------------------------------------------------------------------- + +import os +import cv2 +import glob +import numpy as np +import open3d as o3d +import matplotlib.pyplot as plt + + +def drawSphere(center, radius, color=[0.0,0.0,0.0]): + mesh_sphere = o3d.geometry.TriangleMesh.create_sphere(radius=radius) + transform_mat = np.eye(4) + transform_mat[0:3, -1] = center + mesh_sphere.transform(transform_mat) + mesh_sphere.paint_uniform_color(color) + return mesh_sphere + + +def drawCone(bottom_center, top_position, color=[0.6, 0.6, 0.9]): + cone = o3d.geometry.TriangleMesh.create_cone(radius=0.007, height=np.linalg.norm(top_position - bottom_center)+1e-6) + line1 = np.array([0.0, 0.0, 1.0]) + line2 = (top_position - bottom_center) / (np.linalg.norm(top_position - bottom_center)+1e-6) + v = np.cross(line1, line2) + c = np.dot(line1, line2) + 1e-8 + k = np.array([[0, -v[2], v[1]], [v[2], 0, -v[0]], [-v[1], v[0], 0]]) + R = np.eye(3) + k + np.matmul(k, k) * (1 / (1 + c)) + if np.abs(c + 1.0) < 1e-4: # the above formula doesn't apply when cos(∠(𝑎,𝑏))=−1 + R = np.array([[-1, 0, 0], [0, 1, 0], [0, 0, -1]]) + T = bottom_center + 5e-3 * line2 + #print(R) + cone.transform(np.concatenate((np.concatenate((R, T[:, np.newaxis]), axis=1), np.array([[0.0, 0.0, 0.0, 1.0]])), axis=0)) + cone.paint_uniform_color(color) + return cone + + +def show_obj_skel(mesh_name, root): + vis = o3d.visualization.Visualizer() + vis.create_window() + ctr = vis.get_view_control() + + # draw mesh + mesh = o3d.io.read_triangle_mesh(mesh_name) + mesh_ls = o3d.geometry.LineSet.create_from_triangle_mesh(mesh) + mesh_ls.colors = o3d.utility.Vector3dVector([[0.8, 0.8, 0.8] for i in range(len(mesh_ls.lines))]) + vis.add_geometry(mesh_ls) + + vis.add_geometry(drawSphere(root.pos, 0.01, color=[0.1, 0.1, 0.1])) + this_level = root.children + while this_level: + next_level = [] + for p_node in this_level: + vis.add_geometry(drawSphere(p_node.pos, 0.008, color=[1.0, 0.0, 0.0])) # [0.3, 0.1, 0.1] + vis.add_geometry(drawCone(np.array(p_node.parent.pos), np.array(p_node.pos))) + next_level+=p_node.children + this_level = next_level + + #param = o3d.io.read_pinhole_camera_parameters('sideview.json') + #ctr.convert_from_pinhole_camera_parameters(param) + + vis.run() + #vis.update_geometry() + #vis.poll_events() + #vis.update_renderer() + + #param = ctr.convert_to_pinhole_camera_parameters() + #o3d.io.write_pinhole_camera_parameters('sideview.json', param) + + image = vis.capture_screen_float_buffer() + vis.destroy_window() + image = np.asarray(image) * 255 + image = image.astype(np.uint8) + return image + + +def draw_shifted_pts(mesh_name, pts, weights=None): + mesh = o3d.io.read_triangle_mesh(mesh_name) + mesh_ls = o3d.geometry.LineSet.create_from_triangle_mesh(mesh) + mesh_ls.colors = o3d.utility.Vector3dVector([[0.8, 0.8, 0.8] for i in range(len(mesh_ls.lines))]) + pred_joints = o3d.geometry.PointCloud() + pred_joints.points = o3d.utility.Vector3dVector(pts) + if weights is None: + color_joints = [[1.0, 0.0, 0.0] for i in range(len(pts))] + else: + import matplotlib.pyplot as plt + cmap = plt.cm.get_cmap('YlOrRd') + #weights = (weights - np.min(weights)) / (np.max(weights) - np.min(weights)) + #weights = 1 / (1 + np.exp(-weights)) + color_joints = cmap(weights.squeeze()) + color_joints = color_joints[:, :-1] + pred_joints.colors = o3d.utility.Vector3dVector(color_joints) + + vis = o3d.visualization.Visualizer() + vis.create_window() + ctr = vis.get_view_control() + vis.add_geometry(mesh_ls) + vis.add_geometry(pred_joints) + + param = o3d.io.read_pinhole_camera_parameters('sideview.json') + ctr.convert_from_pinhole_camera_parameters(param) + + #vis.run() + vis.update_geometry() + vis.poll_events() + vis.update_renderer() + + #param = ctr.convert_to_pinhole_camera_parameters() + #o3d.io.write_pinhole_camera_parameters('sideview.json', param) + + image = vis.capture_screen_float_buffer() + vis.destroy_window() + image = np.asarray(image) * 255 + image = image.astype(np.uint8) + return image + + +def draw_joints(mesh_name, pts): + mesh = o3d.io.read_triangle_mesh(mesh_name) + mesh_ls = o3d.geometry.LineSet.create_from_triangle_mesh(mesh) + mesh_ls.colors = o3d.utility.Vector3dVector([[0.8, 0.8, 0.8] for i in range(len(mesh_ls.lines))]) + vis = o3d.visualization.Visualizer() + vis.create_window() + ctr = vis.get_view_control() + vis.add_geometry(mesh_ls) + for joint_pos in pts: + vis.add_geometry(drawSphere(joint_pos, 0.006, color=[1.0, 0.0, 0.0])) + + param = o3d.io.read_pinhole_camera_parameters('sideview.json') + ctr.convert_from_pinhole_camera_parameters(param) + + #vis.run() + vis.update_geometry() + vis.poll_events() + vis.update_renderer() + + image = vis.capture_screen_float_buffer() + vis.destroy_window() + image = np.asarray(image) * 255 + image = image.astype(np.uint8) + return image + + +def volume_to_cubes(volume, threshold=0, dim=[1., 1., 1.]): + #o = np.array([-dim[0]/2., -dim[1]/2., -dim[2]/2.]) + o = np.array([0, 0, 0]) + step = np.array([dim[0]/volume.shape[0], dim[1]/volume.shape[1], dim[2]/volume.shape[2]]) + points = [] + lines = [] + for x in range(1, volume.shape[0]-1): + for y in range(1, volume.shape[1]-1): + for z in range(1, volume.shape[2]-1): + pos = o + np.array([x, y, z]) * step + if volume[x, y, z] > threshold: + vidx = len(points) + POS = pos + step*0.95 + xx = pos[0] + yy = pos[1] + zz = pos[2] + XX = POS[0] + YY = POS[1] + ZZ = POS[2] + + points.append(np.array([xx, yy, zz])[np.newaxis, :]) + points.append(np.array([xx, YY, zz])[np.newaxis, :]) + points.append(np.array([XX, YY, zz])[np.newaxis, :]) + points.append(np.array([XX, yy, zz])[np.newaxis, :]) + points.append(np.array([xx, yy, ZZ])[np.newaxis, :]) + points.append(np.array([xx, YY, ZZ])[np.newaxis, :]) + points.append(np.array([XX, YY, ZZ])[np.newaxis, :]) + points.append(np.array([XX, yy, ZZ])[np.newaxis, :]) + + lines.append(np.array([vidx + 1, vidx + 2])) + lines.append(np.array([vidx + 2, vidx + 6])) + lines.append(np.array([vidx + 6, vidx + 5])) + lines.append(np.array([vidx + 1, vidx + 5])) + + lines.append(np.array([vidx + 1, vidx + 1])) + lines.append(np.array([vidx + 3, vidx + 3])) + lines.append(np.array([vidx + 7, vidx + 7])) + lines.append(np.array([vidx + 5, vidx + 5])) + + lines.append(np.array([vidx + 0, vidx + 3])) + lines.append(np.array([vidx + 0, vidx + 4])) + lines.append(np.array([vidx + 4, vidx + 7])) + lines.append(np.array([vidx + 7, vidx + 3])) + + return points, lines + + +def show_mesh_vox(mesh_filename, vox): + vis = o3d.visualization.Visualizer() + vis.create_window() + + vox_pts, vox_lines = volume_to_cubes(vox.data) + vox_pts = np.concatenate(vox_pts, axis=0) + line_set_vox = o3d.geometry.LineSet() + line_set_vox.points = o3d.utility.Vector3dVector(vox_pts+np.array(vox.translate)[np.newaxis, :]) + line_set_vox.lines = o3d.utility.Vector2iVector(vox_lines) + colors = [[0.0, 0.0, 1.0] for i in range(len(vox_lines))] + line_set_vox.colors = o3d.utility.Vector3dVector(colors) + vis.add_geometry(line_set_vox) + + mesh = o3d.io.read_triangle_mesh(mesh_filename) + mesh_ls = o3d.geometry.LineSet.create_from_triangle_mesh(mesh) + mesh_ls.colors = o3d.utility.Vector3dVector([[1.0, 0.0, 0.0] for i in range(len(mesh_ls.lines))]) + vis.add_geometry(mesh_ls) + + vis.run() + vis.destroy_window() + + return