initial commit

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

.gitignore

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+# Python build
+.eggs/
+gradio.egg-info/*
+!gradio.egg-info/requires.txt
+!gradio.egg-info/PKG-INFO
+dist/
+*.pyc
+__pycache__/
+*.py[cod]
+*$py.class
+build/
+
+# JS build
+gradio/templates/frontend
+# Secrets
+.env
+
+# Gradio run artifacts
+*.db
+*.sqlite3
+gradio/launches.json
+flagged/
+gradio_cached_examples/
+
+# Tests
+.coverage
+coverage.xml
+test.txt
+
+# Demos
+demo/tmp.zip
+demo/files/*.avi
+demo/files/*.mp4
+
+# Etc
+.idea/*
+.DS_Store
+*.bak
+workspace.code-workspace
+*.h5
+.vscode/
+
+# log files
+.pnpm-debug.log
+venv/
+*.db-journal

PIFu/.gitignore

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+checkpoints/*

PIFu/LICENSE.txt

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+MIT License
+
+Copyright (c) 2019 Shunsuke Saito, Zeng Huang, and Ryota Natsume
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+anyabagomo
+
+-------------------- LICENSE FOR ResBlk Image Encoder -----------------------
+Copyright (c) 2017, Jun-Yan Zhu and Taesung Park
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

PIFu/README.md

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+# PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization
+
+[![report](https://img.shields.io/badge/arxiv-report-red)](https://arxiv.org/abs/1905.05172) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1GFSsqP2BWz4gtq0e-nki00ZHSirXwFyY)
+
+News:
+* \[2020/05/04\] Added EGL rendering option for training data generation. Now you can create your own training data with headless machines!
+* \[2020/04/13\] Demo with Google Colab (incl. visualization) is available. Special thanks to [@nanopoteto](https://github.com/nanopoteto)!!!
+* \[2020/02/26\] License is updated to MIT license! Enjoy!
+
+This repository contains a pytorch implementation of "[PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization](https://arxiv.org/abs/1905.05172)".
+
+[Project Page](https://shunsukesaito.github.io/PIFu/)
+![Teaser Image](https://shunsukesaito.github.io/PIFu/resources/images/teaser.png)
+
+If you find the code useful in your research, please consider citing the paper.
+
+```
+@InProceedings{saito2019pifu,
+author = {Saito, Shunsuke and Huang, Zeng and Natsume, Ryota and Morishima, Shigeo and Kanazawa, Angjoo and Li, Hao},
+title = {PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization},
+booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
+month = {October},
+year = {2019}
+}
+```
+
+
+This codebase provides: 
+- test code
+- training code
+- data generation code
+
+## Requirements
+- Python 3
+- [PyTorch](https://pytorch.org/) tested on 1.4.0
+- json
+- PIL
+- skimage
+- tqdm
+- numpy
+- cv2
+
+for training and data generation
+- [trimesh](https://trimsh.org/) with [pyembree](https://github.com/scopatz/pyembree)
+- [pyexr](https://github.com/tvogels/pyexr)
+- PyOpenGL
+- freeglut (use `sudo apt-get install freeglut3-dev` for ubuntu users)
+- (optional) egl related packages for rendering with headless machines. (use `apt install libgl1-mesa-dri libegl1-mesa libgbm1` for ubuntu users)
+
+Warning: I found that outdated NVIDIA drivers may cause errors with EGL. If you want to try out the EGL version, please update your NVIDIA driver to the latest!!
+
+## Windows demo installation instuction
+
+- Install [miniconda](https://docs.conda.io/en/latest/miniconda.html)
+- Add `conda` to PATH
+- Install [git bash](https://git-scm.com/downloads)
+- Launch `Git\bin\bash.exe`
+- `eval "$(conda shell.bash hook)"` then `conda activate my_env` because of [this](https://github.com/conda/conda-build/issues/3371)
+- Automatic `env create -f environment.yml` (look [this](https://github.com/conda/conda/issues/3417))
+- OR manually setup [environment](https://towardsdatascience.com/a-guide-to-conda-environments-bc6180fc533)
+    - `conda create —name pifu python` where `pifu` is name of your environment
+    - `conda activate`
+    - `conda install pytorch torchvision cudatoolkit=10.1 -c pytorch`
+    - `conda install pillow`
+    - `conda install scikit-image`
+    - `conda install tqdm`
+    - `conda install -c menpo opencv`
+- Download [wget.exe](https://eternallybored.org/misc/wget/)
+- Place it into `Git\mingw64\bin`
+- `sh ./scripts/download_trained_model.sh`
+- Remove background from your image ([this](https://www.remove.bg/), for example)
+- Create black-white mask .png
+- Replace original from sample_images/
+- Try it out - `sh ./scripts/test.sh`
+- Download [Meshlab](http://www.meshlab.net/) because of [this](https://github.com/shunsukesaito/PIFu/issues/1)
+- Open .obj file in Meshlab
+
+
+## Demo
+Warning: The released model is trained with mostly upright standing scans with weak perspectie projection and the pitch angle of 0 degree. Reconstruction quality may degrade for images highly deviated from trainining data.
+1. run the following script to download the pretrained models from the following link and copy them under `./PIFu/checkpoints/`.
+```
+sh ./scripts/download_trained_model.sh
+```
+
+2. run the following script. the script creates a textured `.obj` file under `./PIFu/eval_results/`. You may need to use `./apps/crop_img.py` to roughly align an input image and the corresponding mask to the training data for better performance. For background removal, you can use any off-the-shelf tools such as [removebg](https://www.remove.bg/).
+```
+sh ./scripts/test.sh
+```
+
+## Demo on Google Colab
+If you do not have a setup to run PIFu, we offer Google Colab version to give it a try, allowing you to run PIFu in the cloud, free of charge. Try our Colab demo using the following notebook: 
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1GFSsqP2BWz4gtq0e-nki00ZHSirXwFyY)
+
+## Data Generation (Linux Only)
+While we are unable to release the full training data due to the restriction of commertial scans, we provide rendering code using free models in [RenderPeople](https://renderpeople.com/free-3d-people/).
+This tutorial uses `rp_dennis_posed_004` model. Please download the model from [this link](https://renderpeople.com/sample/free/rp_dennis_posed_004_OBJ.zip) and unzip the content under a folder named `rp_dennis_posed_004_OBJ`. The same process can be applied to other RenderPeople data.
+
+Warning: the following code becomes extremely slow without [pyembree](https://github.com/scopatz/pyembree). Please make sure you install pyembree.
+
+1. run the following script to compute spherical harmonics coefficients for [precomputed radiance transfer (PRT)](https://sites.fas.harvard.edu/~cs278/papers/prt.pdf). In a nutshell, PRT is used to account for accurate light transport including ambient occlusion without compromising online rendering time, which significantly improves the photorealism compared with [a common sperical harmonics rendering using surface normals](https://cseweb.ucsd.edu/~ravir/papers/envmap/envmap.pdf). This process has to be done once for each obj file.
+```
+python -m apps.prt_util -i {path_to_rp_dennis_posed_004_OBJ}
+```
+
+2. run the following script. Under the specified data path, the code creates folders named `GEO`, `RENDER`, `MASK`, `PARAM`, `UV_RENDER`, `UV_MASK`, `UV_NORMAL`, and `UV_POS`. Note that you may need to list validation subjects to exclude from training in `{path_to_training_data}/val.txt` (this tutorial has only one subject and leave it empty). If you wish to render images with headless servers equipped with NVIDIA GPU, add -e to enable EGL rendering.
+```
+python -m apps.render_data -i {path_to_rp_dennis_posed_004_OBJ} -o {path_to_training_data} [-e]
+```
+
+## Training (Linux Only)
+
+Warning: the following code becomes extremely slow without [pyembree](https://github.com/scopatz/pyembree). Please make sure you install pyembree.
+
+1. run the following script to train the shape module. The intermediate results and checkpoints are saved under `./results` and `./checkpoints` respectively. You can add `--batch_size` and `--num_sample_input` flags to adjust the batch size and the number of sampled points based on available GPU memory.
+```
+python -m apps.train_shape --dataroot {path_to_training_data} --random_flip --random_scale --random_trans
+```
+
+2. run the following script to train the color module. 
+```
+python -m apps.train_color --dataroot {path_to_training_data} --num_sample_inout 0 --num_sample_color 5000 --sigma 0.1 --random_flip --random_scale --random_trans
+```
+
+## Related Research
+**[Monocular Real-Time Volumetric Performance Capture (ECCV 2020)](https://project-splinter.github.io/)**  
+*Ruilong Li\*, Yuliang Xiu\*, Shunsuke Saito, Zeng Huang, Kyle Olszewski, Hao Li*
+
+The first real-time PIFu by accelerating reconstruction and rendering!!
+
+**[PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization (CVPR 2020)](https://shunsukesaito.github.io/PIFuHD/)**  
+*Shunsuke Saito, Tomas Simon, Jason Saragih, Hanbyul Joo*
+
+We further improve the quality of reconstruction by leveraging multi-level approach!
+
+**[ARCH: Animatable Reconstruction of Clothed Humans (CVPR 2020)](https://arxiv.org/pdf/2004.04572.pdf)**  
+*Zeng Huang, Yuanlu Xu, Christoph Lassner, Hao Li, Tony Tung*
+
+Learning PIFu in canonical space for animatable avatar generation!
+
+**[Robust 3D Self-portraits in Seconds (CVPR 2020)](http://www.liuyebin.com/portrait/portrait.html)**  
+*Zhe Li, Tao Yu, Chuanyu Pan, Zerong Zheng, Yebin Liu*
+
+They extend PIFu to RGBD + introduce "PIFusion" utilizing PIFu reconstruction for non-rigid fusion.
+
+**[Learning to Infer Implicit Surfaces without 3d Supervision (NeurIPS 2019)](http://papers.nips.cc/paper/9039-learning-to-infer-implicit-surfaces-without-3d-supervision.pdf)**  
+*Shichen Liu, Shunsuke Saito, Weikai Chen, Hao Li*
+
+We answer to the question of "how can we learn implicit function if we don't have 3D ground truth?"
+
+**[SiCloPe: Silhouette-Based Clothed People (CVPR 2019, best paper finalist)](https://arxiv.org/pdf/1901.00049.pdf)**  
+*Ryota Natsume\*, Shunsuke Saito\*, Zeng Huang, Weikai Chen, Chongyang Ma, Hao Li, Shigeo Morishima*
+
+Our first attempt to reconstruct 3D clothed human body with texture from a single image!
+
+**[Deep Volumetric Video from Very Sparse Multi-view Performance Capture (ECCV 2018)](http://openaccess.thecvf.com/content_ECCV_2018/papers/Zeng_Huang_Deep_Volumetric_Video_ECCV_2018_paper.pdf)**  
+*Zeng Huang, Tianye Li, Weikai Chen, Yajie Zhao, Jun Xing, Chloe LeGendre, Linjie Luo, Chongyang Ma, Hao Li*
+
+Implict surface learning for sparse view human performance capture!
+
+------
+
+
+
+For commercial queries, please contact:
+
+Hao Li: hao@hao-li.com ccto: saitos@usc.edu Baker!!

PIFu/apps/crop_img.py

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+import os
+import cv2
+import numpy as np 
+
+from pathlib import Path
+import argparse
+
+def get_bbox(msk):
+    rows = np.any(msk, axis=1)
+    cols = np.any(msk, axis=0)
+    rmin, rmax = np.where(rows)[0][[0,-1]]
+    cmin, cmax = np.where(cols)[0][[0,-1]]
+
+    return rmin, rmax, cmin, cmax
+
+def process_img(img, msk, bbox=None):
+    if bbox is None:
+        bbox = get_bbox(msk > 100)
+    cx = (bbox[3] + bbox[2])//2
+    cy = (bbox[1] + bbox[0])//2
+
+    w = img.shape[1]
+    h = img.shape[0]
+    height = int(1.138*(bbox[1] - bbox[0]))
+    hh = height//2
+
+    # crop
+    dw = min(cx, w-cx, hh)
+    if cy-hh < 0:
+        img = cv2.copyMakeBorder(img,hh-cy,0,0,0,cv2.BORDER_CONSTANT,value=[0,0,0])    
+        msk = cv2.copyMakeBorder(msk,hh-cy,0,0,0,cv2.BORDER_CONSTANT,value=0) 
+        cy = hh
+    if cy+hh > h:
+        img = cv2.copyMakeBorder(img,0,cy+hh-h,0,0,cv2.BORDER_CONSTANT,value=[0,0,0])    
+        msk = cv2.copyMakeBorder(msk,0,cy+hh-h,0,0,cv2.BORDER_CONSTANT,value=0)    
+    img = img[cy-hh:(cy+hh),cx-dw:cx+dw,:]
+    msk = msk[cy-hh:(cy+hh),cx-dw:cx+dw]
+    dw = img.shape[0] - img.shape[1]
+    if dw != 0:
+        img = cv2.copyMakeBorder(img,0,0,dw//2,dw//2,cv2.BORDER_CONSTANT,value=[0,0,0])    
+        msk = cv2.copyMakeBorder(msk,0,0,dw//2,dw//2,cv2.BORDER_CONSTANT,value=0)    
+    img = cv2.resize(img, (512, 512))
+    msk = cv2.resize(msk, (512, 512))
+
+    kernel = np.ones((3,3),np.uint8)
+    msk = cv2.erode((255*(msk > 100)).astype(np.uint8), kernel, iterations = 1)
+
+    return img, msk
+
+def main():
+    '''
+    given foreground mask, this script crops and resizes an input image and mask for processing.
+    '''
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-i', '--input_image', type=str, help='if the image has alpha channel, it will be used as mask')
+    parser.add_argument('-m', '--input_mask', type=str)
+    parser.add_argument('-o', '--out_path', type=str, default='./sample_images')
+    args = parser.parse_args()
+
+    img = cv2.imread(args.input_image, cv2.IMREAD_UNCHANGED)
+    if img.shape[2] == 4:
+        msk = img[:,:,3:]
+        img = img[:,:,:3]
+    else:
+        msk = cv2.imread(args.input_mask, cv2.IMREAD_GRAYSCALE)
+
+    img_new, msk_new = process_img(img, msk)
+
+    img_name = Path(args.input_image).stem
+
+    cv2.imwrite(os.path.join(args.out_path, img_name + '.png'), img_new)
+    cv2.imwrite(os.path.join(args.out_path, img_name + '_mask.png'), msk_new)
+
+if __name__ == "__main__":
+    main()

PIFu/apps/eval.py

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+import sys
+import os
+
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+ROOT_PATH = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+import time
+import json
+import numpy as np
+import torch
+from torch.utils.data import DataLoader
+
+from lib.options import BaseOptions
+from lib.mesh_util import *
+from lib.sample_util import *
+from lib.train_util import *
+from lib.model import *
+
+from PIL import Image
+import torchvision.transforms as transforms
+import glob
+import tqdm
+
+# get options
+opt = BaseOptions().parse()
+
+class Evaluator:
+    def __init__(self, opt, projection_mode='orthogonal'):
+        self.opt = opt
+        self.load_size = self.opt.loadSize
+        self.to_tensor = transforms.Compose([
+            transforms.Resize(self.load_size),
+            transforms.ToTensor(),
+            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
+        ])
+        # set cuda
+        cuda = torch.device('cuda:%d' % opt.gpu_id) if torch.cuda.is_available() else torch.device('cpu')
+
+        # create net
+        netG = HGPIFuNet(opt, projection_mode).to(device=cuda)
+        print('Using Network: ', netG.name)
+
+        if opt.load_netG_checkpoint_path:
+            netG.load_state_dict(torch.load(opt.load_netG_checkpoint_path, map_location=cuda))
+
+        if opt.load_netC_checkpoint_path is not None:
+            print('loading for net C ...', opt.load_netC_checkpoint_path)
+            netC = ResBlkPIFuNet(opt).to(device=cuda)
+            netC.load_state_dict(torch.load(opt.load_netC_checkpoint_path, map_location=cuda))
+        else:
+            netC = None
+
+        os.makedirs(opt.results_path, exist_ok=True)
+        os.makedirs('%s/%s' % (opt.results_path, opt.name), exist_ok=True)
+
+        opt_log = os.path.join(opt.results_path, opt.name, 'opt.txt')
+        with open(opt_log, 'w') as outfile:
+            outfile.write(json.dumps(vars(opt), indent=2))
+
+        self.cuda = cuda
+        self.netG = netG
+        self.netC = netC
+
+    def load_image(self, image_path, mask_path):
+        # Name
+        img_name = os.path.splitext(os.path.basename(image_path))[0]
+        # Calib
+        B_MIN = np.array([-1, -1, -1])
+        B_MAX = np.array([1, 1, 1])
+        projection_matrix = np.identity(4)
+        projection_matrix[1, 1] = -1
+        calib = torch.Tensor(projection_matrix).float()
+        # Mask
+        mask = Image.open(mask_path).convert('L')
+        mask = transforms.Resize(self.load_size)(mask)
+        mask = transforms.ToTensor()(mask).float()
+        # image
+        image = Image.open(image_path).convert('RGB')
+        image = self.to_tensor(image)
+        image = mask.expand_as(image) * image
+        return {
+            'name': img_name,
+            'img': image.unsqueeze(0),
+            'calib': calib.unsqueeze(0),
+            'mask': mask.unsqueeze(0),
+            'b_min': B_MIN,
+            'b_max': B_MAX,
+        }
+
+    def eval(self, data, use_octree=False):
+        '''
+        Evaluate a data point
+        :param data: a dict containing at least ['name'], ['image'], ['calib'], ['b_min'] and ['b_max'] tensors.
+        :return:
+        '''
+        opt = self.opt
+        with torch.no_grad():
+            self.netG.eval()
+            if self.netC:
+                self.netC.eval()
+            save_path = '%s/%s/result_%s.obj' % (opt.results_path, opt.name, data['name'])
+            if self.netC:
+                gen_mesh_color(opt, self.netG, self.netC, self.cuda, data, save_path, use_octree=use_octree)
+            else:
+                gen_mesh(opt, self.netG, self.cuda, data, save_path, use_octree=use_octree)
+
+
+if __name__ == '__main__':
+    evaluator = Evaluator(opt)
+
+    test_images = glob.glob(os.path.join(opt.test_folder_path, '*'))
+    test_images = [f for f in test_images if ('png' in f or 'jpg' in f) and (not 'mask' in f)]
+    test_masks = [f[:-4]+'_mask.png' for f in test_images]
+
+    print("num; ", len(test_masks))
+
+    for image_path, mask_path in tqdm.tqdm(zip(test_images, test_masks)):
+        try:
+            print(image_path, mask_path)
+            data = evaluator.load_image(image_path, mask_path)
+            evaluator.eval(data, True)
+        except Exception as e:
+           print("error:", e.args)

PIFu/apps/eval_spaces.py

@@ -0,0 +1,130 @@
+import sys
+import os
+
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+ROOT_PATH = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+import time
+import json
+import numpy as np
+import torch
+from torch.utils.data import DataLoader
+
+from lib.options import BaseOptions
+from lib.mesh_util import *
+from lib.sample_util import *
+from lib.train_util import *
+from lib.model import *
+
+from PIL import Image
+import torchvision.transforms as transforms
+import glob
+import tqdm
+
+import trimesh
+# get options
+opt = BaseOptions().parse()
+
+class Evaluator:
+    def __init__(self, opt, projection_mode='orthogonal'):
+        self.opt = opt
+        self.load_size = self.opt.loadSize
+        self.to_tensor = transforms.Compose([
+            transforms.Resize(self.load_size),
+            transforms.ToTensor(),
+            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
+        ])
+        # set cuda
+        cuda = torch.device('cuda:%d' % opt.gpu_id) if torch.cuda.is_available() else torch.device('cpu')
+
+        # create net
+        netG = HGPIFuNet(opt, projection_mode).to(device=cuda)
+        print('Using Network: ', netG.name)
+
+        if opt.load_netG_checkpoint_path:
+            netG.load_state_dict(torch.load(opt.load_netG_checkpoint_path, map_location=cuda))
+
+        if opt.load_netC_checkpoint_path is not None:
+            print('loading for net C ...', opt.load_netC_checkpoint_path)
+            netC = ResBlkPIFuNet(opt).to(device=cuda)
+            netC.load_state_dict(torch.load(opt.load_netC_checkpoint_path, map_location=cuda))
+        else:
+            netC = None
+
+        os.makedirs(opt.results_path, exist_ok=True)
+        os.makedirs('%s/%s' % (opt.results_path, opt.name), exist_ok=True)
+
+        opt_log = os.path.join(opt.results_path, opt.name, 'opt.txt')
+        with open(opt_log, 'w') as outfile:
+            outfile.write(json.dumps(vars(opt), indent=2))
+
+        self.cuda = cuda
+        self.netG = netG
+        self.netC = netC
+
+    def load_image(self, image_path, mask_path):
+        # Name
+        img_name = os.path.splitext(os.path.basename(image_path))[0]
+        # Calib
+        B_MIN = np.array([-1, -1, -1])
+        B_MAX = np.array([1, 1, 1])
+        projection_matrix = np.identity(4)
+        projection_matrix[1, 1] = -1
+        calib = torch.Tensor(projection_matrix).float()
+        # Mask
+        mask = Image.open(mask_path).convert('L')
+        mask = transforms.Resize(self.load_size)(mask)
+        mask = transforms.ToTensor()(mask).float()
+        # image
+        image = Image.open(image_path).convert('RGB')
+        image = self.to_tensor(image)
+        image = mask.expand_as(image) * image
+        return {
+            'name': img_name,
+            'img': image.unsqueeze(0),
+            'calib': calib.unsqueeze(0),
+            'mask': mask.unsqueeze(0),
+            'b_min': B_MIN,
+            'b_max': B_MAX,
+        }
+
+    def eval(self, data, use_octree=False):
+        '''
+        Evaluate a data point
+        :param data: a dict containing at least ['name'], ['image'], ['calib'], ['b_min'] and ['b_max'] tensors.
+        :return:
+        '''
+        opt = self.opt
+        with torch.no_grad():
+            self.netG.eval()
+            if self.netC:
+                self.netC.eval()
+            save_path = '%s/%s/result_%s.obj' % (opt.results_path, opt.name, data['name'])
+            if self.netC:
+                gen_mesh_color(opt, self.netG, self.netC, self.cuda, data, save_path, use_octree=use_octree)
+            else:
+                gen_mesh(opt, self.netG, self.cuda, data, save_path, use_octree=use_octree)
+
+
+if __name__ == '__main__':
+    evaluator = Evaluator(opt)
+
+    results_path = opt.results_path
+    name = opt.name
+    test_image_path = opt.img_path
+    test_mask_path = test_image_path[:-4] +'_mask.png'
+    test_img_name = os.path.splitext(os.path.basename(test_image_path))[0]
+    print("test_image: ", test_image_path)
+    print("test_mask: ", test_mask_path)
+
+    try:
+        data = evaluator.load_image(test_image_path, test_mask_path)
+        evaluator.eval(data, True)
+        mesh = trimesh.load(f'{results_path}/{name}/result_{test_img_name}.obj')
+        mesh.apply_transform([[1, 0, 0, 0],
+        [0, 1, 0, 0],
+        [0, 0, -1, 0],
+        [0, 0, 0, 1]])
+        mesh.export(file_obj=f'{results_path}/{name}/result_{test_img_name}.glb')
+    except Exception as e:
+        print("error:", e.args)

PIFu/apps/prt_util.py

@@ -0,0 +1,142 @@
+import os
+import trimesh
+import numpy as np
+import math
+from scipy.special import sph_harm
+import argparse
+from tqdm import tqdm
+
+def factratio(N, D):
+    if N >= D:
+        prod = 1.0
+        for i in range(D+1, N+1):
+            prod *= i
+        return prod
+    else:
+        prod = 1.0
+        for i in range(N+1, D+1):
+            prod *= i
+        return 1.0 / prod
+
+def KVal(M, L):
+    return math.sqrt(((2 * L + 1) / (4 * math.pi)) * (factratio(L - M, L + M)))
+
+def AssociatedLegendre(M, L, x):
+    if M < 0 or M > L or np.max(np.abs(x)) > 1.0:
+        return np.zeros_like(x)
+    
+    pmm = np.ones_like(x)
+    if M > 0:
+        somx2 = np.sqrt((1.0 + x) * (1.0 - x))
+        fact = 1.0
+        for i in range(1, M+1):
+            pmm = -pmm * fact * somx2
+            fact = fact + 2
+    
+    if L == M:
+        return pmm
+    else:
+        pmmp1 = x * (2 * M + 1) * pmm
+        if L == M+1:
+            return pmmp1
+        else:
+            pll = np.zeros_like(x)
+            for i in range(M+2, L+1):
+                pll = (x * (2 * i - 1) * pmmp1 - (i + M - 1) * pmm) / (i - M)
+                pmm = pmmp1
+                pmmp1 = pll
+            return pll
+
+def SphericalHarmonic(M, L, theta, phi):
+    if M > 0:
+        return math.sqrt(2.0) * KVal(M, L) * np.cos(M * phi) * AssociatedLegendre(M, L, np.cos(theta))
+    elif M < 0:
+        return math.sqrt(2.0) * KVal(-M, L) * np.sin(-M * phi) * AssociatedLegendre(-M, L, np.cos(theta))
+    else:
+        return KVal(0, L) * AssociatedLegendre(0, L, np.cos(theta))
+
+def save_obj(mesh_path, verts):
+    file = open(mesh_path, 'w')    
+    for v in verts:
+        file.write('v %.4f %.4f %.4f\n' % (v[0], v[1], v[2]))
+    file.close()
+
+def sampleSphericalDirections(n):
+    xv = np.random.rand(n,n)
+    yv = np.random.rand(n,n)
+    theta = np.arccos(1-2 * xv)
+    phi = 2.0 * math.pi * yv
+
+    phi = phi.reshape(-1)
+    theta = theta.reshape(-1)
+
+    vx = -np.sin(theta) * np.cos(phi)
+    vy = -np.sin(theta) * np.sin(phi)
+    vz = np.cos(theta)
+    return np.stack([vx, vy, vz], 1), phi, theta
+
+def getSHCoeffs(order, phi, theta):
+    shs = []
+    for n in range(0, order+1):
+        for m in range(-n,n+1):
+            s = SphericalHarmonic(m, n, theta, phi)
+            shs.append(s)
+    
+    return np.stack(shs, 1)
+
+def computePRT(mesh_path, n, order):
+    mesh = trimesh.load(mesh_path, process=False)
+    vectors_orig, phi, theta = sampleSphericalDirections(n)
+    SH_orig = getSHCoeffs(order, phi, theta)
+
+    w = 4.0 * math.pi / (n*n)
+
+    origins = mesh.vertices
+    normals = mesh.vertex_normals
+    n_v = origins.shape[0]
+
+    origins = np.repeat(origins[:,None], n, axis=1).reshape(-1,3)
+    normals = np.repeat(normals[:,None], n, axis=1).reshape(-1,3)
+    PRT_all = None
+    for i in tqdm(range(n)):
+        SH = np.repeat(SH_orig[None,(i*n):((i+1)*n)], n_v, axis=0).reshape(-1,SH_orig.shape[1])
+        vectors = np.repeat(vectors_orig[None,(i*n):((i+1)*n)], n_v, axis=0).reshape(-1,3)
+
+        dots = (vectors * normals).sum(1)
+        front = (dots > 0.0)
+
+        delta = 1e-3*min(mesh.bounding_box.extents)
+        hits = mesh.ray.intersects_any(origins + delta * normals, vectors)
+        nohits = np.logical_and(front, np.logical_not(hits))
+
+        PRT = (nohits.astype(np.float) * dots)[:,None] * SH
+        
+        if PRT_all is not None:
+            PRT_all += (PRT.reshape(-1, n, SH.shape[1]).sum(1))
+        else:
+            PRT_all = (PRT.reshape(-1, n, SH.shape[1]).sum(1))
+
+    PRT = w * PRT_all
+
+    # NOTE: trimesh sometimes break the original vertex order, but topology will not change.
+    # when loading PRT in other program, use the triangle list from trimesh.
+    return PRT, mesh.faces
+
+def testPRT(dir_path, n=40):
+    if dir_path[-1] == '/':
+        dir_path = dir_path[:-1]
+    sub_name = dir_path.split('/')[-1][:-4]
+    obj_path = os.path.join(dir_path, sub_name + '_100k.obj')
+    os.makedirs(os.path.join(dir_path, 'bounce'), exist_ok=True)
+
+    PRT, F = computePRT(obj_path, n, 2)
+    np.savetxt(os.path.join(dir_path, 'bounce', 'bounce0.txt'), PRT, fmt='%.8f')
+    np.save(os.path.join(dir_path, 'bounce', 'face.npy'), F)
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-i', '--input', type=str, default='/home/shunsuke/Downloads/rp_dennis_posed_004_OBJ')
+    parser.add_argument('-n', '--n_sample', type=int, default=40, help='squared root of number of sampling. the higher, the more accurate, but slower')
+    args = parser.parse_args()
+
+    testPRT(args.input)

PIFu/apps/render_data.py

@@ -0,0 +1,290 @@
+#from data.config import raw_dataset, render_dataset, archive_dataset, model_list, zip_path
+
+from lib.renderer.camera import Camera
+import numpy as np
+from lib.renderer.mesh import load_obj_mesh, compute_tangent, compute_normal, load_obj_mesh_mtl
+from lib.renderer.camera import Camera
+import os
+import cv2
+import time
+import math
+import random
+import pyexr
+import argparse
+from tqdm import tqdm
+
+
+def make_rotate(rx, ry, rz):
+    sinX = np.sin(rx)
+    sinY = np.sin(ry)
+    sinZ = np.sin(rz)
+
+    cosX = np.cos(rx)
+    cosY = np.cos(ry)
+    cosZ = np.cos(rz)
+
+    Rx = np.zeros((3,3))
+    Rx[0, 0] = 1.0
+    Rx[1, 1] = cosX
+    Rx[1, 2] = -sinX
+    Rx[2, 1] = sinX
+    Rx[2, 2] = cosX
+
+    Ry = np.zeros((3,3))
+    Ry[0, 0] = cosY
+    Ry[0, 2] = sinY
+    Ry[1, 1] = 1.0
+    Ry[2, 0] = -sinY
+    Ry[2, 2] = cosY
+
+    Rz = np.zeros((3,3))
+    Rz[0, 0] = cosZ
+    Rz[0, 1] = -sinZ
+    Rz[1, 0] = sinZ
+    Rz[1, 1] = cosZ
+    Rz[2, 2] = 1.0
+
+    R = np.matmul(np.matmul(Rz,Ry),Rx)
+    return R
+
+def rotateSH(SH, R):
+    SHn = SH
+    
+    # 1st order
+    SHn[1] = R[1,1]*SH[1] - R[1,2]*SH[2] + R[1,0]*SH[3]
+    SHn[2] = -R[2,1]*SH[1] + R[2,2]*SH[2] - R[2,0]*SH[3]
+    SHn[3] = R[0,1]*SH[1] - R[0,2]*SH[2] + R[0,0]*SH[3]
+
+    # 2nd order
+    SHn[4:,0] = rotateBand2(SH[4:,0],R)
+    SHn[4:,1] = rotateBand2(SH[4:,1],R)
+    SHn[4:,2] = rotateBand2(SH[4:,2],R)
+
+    return SHn
+
+def rotateBand2(x, R):
+    s_c3 = 0.94617469575
+    s_c4 = -0.31539156525
+    s_c5 = 0.54627421529
+
+    s_c_scale = 1.0/0.91529123286551084
+    s_c_scale_inv = 0.91529123286551084
+
+    s_rc2 = 1.5853309190550713*s_c_scale
+    s_c4_div_c3 = s_c4/s_c3
+    s_c4_div_c3_x2 = (s_c4/s_c3)*2.0
+
+    s_scale_dst2 = s_c3 * s_c_scale_inv
+    s_scale_dst4 = s_c5 * s_c_scale_inv
+
+    sh0 =  x[3] + x[4] + x[4] - x[1]
+    sh1 =  x[0] + s_rc2*x[2] +  x[3] + x[4]
+    sh2 =  x[0]
+    sh3 = -x[3]
+    sh4 = -x[1]
+    
+    r2x = R[0][0] + R[0][1]
+    r2y = R[1][0] + R[1][1]
+    r2z = R[2][0] + R[2][1]
+    
+    r3x = R[0][0] + R[0][2]
+    r3y = R[1][0] + R[1][2]
+    r3z = R[2][0] + R[2][2]
+    
+    r4x = R[0][1] + R[0][2]
+    r4y = R[1][1] + R[1][2]
+    r4z = R[2][1] + R[2][2]
+    
+    sh0_x = sh0 * R[0][0]
+    sh0_y = sh0 * R[1][0]
+    d0 = sh0_x * R[1][0]
+    d1 = sh0_y * R[2][0]
+    d2 = sh0 * (R[2][0] * R[2][0] + s_c4_div_c3)
+    d3 = sh0_x * R[2][0]
+    d4 = sh0_x * R[0][0] - sh0_y * R[1][0]
+    
+    sh1_x = sh1 * R[0][2]
+    sh1_y = sh1 * R[1][2]
+    d0 += sh1_x * R[1][2]
+    d1 += sh1_y * R[2][2]
+    d2 += sh1 * (R[2][2] * R[2][2] + s_c4_div_c3)
+    d3 += sh1_x * R[2][2]
+    d4 += sh1_x * R[0][2] - sh1_y * R[1][2]
+    
+    sh2_x = sh2 * r2x
+    sh2_y = sh2 * r2y
+    d0 += sh2_x * r2y
+    d1 += sh2_y * r2z
+    d2 += sh2 * (r2z * r2z + s_c4_div_c3_x2)
+    d3 += sh2_x * r2z
+    d4 += sh2_x * r2x - sh2_y * r2y
+    
+    sh3_x = sh3 * r3x
+    sh3_y = sh3 * r3y
+    d0 += sh3_x * r3y
+    d1 += sh3_y * r3z
+    d2 += sh3 * (r3z * r3z + s_c4_div_c3_x2)
+    d3 += sh3_x * r3z
+    d4 += sh3_x * r3x - sh3_y * r3y
+    
+    sh4_x = sh4 * r4x
+    sh4_y = sh4 * r4y
+    d0 += sh4_x * r4y
+    d1 += sh4_y * r4z
+    d2 += sh4 * (r4z * r4z + s_c4_div_c3_x2)
+    d3 += sh4_x * r4z
+    d4 += sh4_x * r4x - sh4_y * r4y
+
+    dst = x
+    dst[0] = d0
+    dst[1] = -d1
+    dst[2] = d2 * s_scale_dst2
+    dst[3] = -d3
+    dst[4] = d4 * s_scale_dst4
+
+    return dst
+
+def render_prt_ortho(out_path, folder_name, subject_name, shs, rndr, rndr_uv, im_size, angl_step=4, n_light=1, pitch=[0]):
+    cam = Camera(width=im_size, height=im_size)
+    cam.ortho_ratio = 0.4 * (512 / im_size)
+    cam.near = -100
+    cam.far = 100
+    cam.sanity_check()
+
+    # set path for obj, prt
+    mesh_file = os.path.join(folder_name, subject_name + '_100k.obj')
+    if not os.path.exists(mesh_file):
+        print('ERROR: obj file does not exist!!', mesh_file)
+        return 
+    prt_file = os.path.join(folder_name, 'bounce', 'bounce0.txt')
+    if not os.path.exists(prt_file):
+        print('ERROR: prt file does not exist!!!', prt_file)
+        return
+    face_prt_file = os.path.join(folder_name, 'bounce', 'face.npy')
+    if not os.path.exists(face_prt_file):
+        print('ERROR: face prt file does not exist!!!', prt_file)
+        return
+    text_file = os.path.join(folder_name, 'tex', subject_name + '_dif_2k.jpg')
+    if not os.path.exists(text_file):
+        print('ERROR: dif file does not exist!!', text_file)
+        return             
+
+    texture_image = cv2.imread(text_file)
+    texture_image = cv2.cvtColor(texture_image, cv2.COLOR_BGR2RGB)
+
+    vertices, faces, normals, faces_normals, textures, face_textures = load_obj_mesh(mesh_file, with_normal=True, with_texture=True)
+    vmin = vertices.min(0)
+    vmax = vertices.max(0)
+    up_axis = 1 if (vmax-vmin).argmax() == 1 else 2
+    
+    vmed = np.median(vertices, 0)
+    vmed[up_axis] = 0.5*(vmax[up_axis]+vmin[up_axis])
+    y_scale = 180/(vmax[up_axis] - vmin[up_axis])
+
+    rndr.set_norm_mat(y_scale, vmed)
+    rndr_uv.set_norm_mat(y_scale, vmed)
+
+    tan, bitan = compute_tangent(vertices, faces, normals, textures, face_textures)
+    prt = np.loadtxt(prt_file)
+    face_prt = np.load(face_prt_file)
+    rndr.set_mesh(vertices, faces, normals, faces_normals, textures, face_textures, prt, face_prt, tan, bitan)    
+    rndr.set_albedo(texture_image)
+
+    rndr_uv.set_mesh(vertices, faces, normals, faces_normals, textures, face_textures, prt, face_prt, tan, bitan)   
+    rndr_uv.set_albedo(texture_image)
+
+    os.makedirs(os.path.join(out_path, 'GEO', 'OBJ', subject_name),exist_ok=True)
+    os.makedirs(os.path.join(out_path, 'PARAM', subject_name),exist_ok=True)
+    os.makedirs(os.path.join(out_path, 'RENDER', subject_name),exist_ok=True)
+    os.makedirs(os.path.join(out_path, 'MASK', subject_name),exist_ok=True)
+    os.makedirs(os.path.join(out_path, 'UV_RENDER', subject_name),exist_ok=True)
+    os.makedirs(os.path.join(out_path, 'UV_MASK', subject_name),exist_ok=True)
+    os.makedirs(os.path.join(out_path, 'UV_POS', subject_name),exist_ok=True)
+    os.makedirs(os.path.join(out_path, 'UV_NORMAL', subject_name),exist_ok=True)
+
+    if not os.path.exists(os.path.join(out_path, 'val.txt')):
+        f = open(os.path.join(out_path, 'val.txt'), 'w')
+        f.close()
+
+    # copy obj file
+    cmd = 'cp %s %s' % (mesh_file, os.path.join(out_path, 'GEO', 'OBJ', subject_name))
+    print(cmd)
+    os.system(cmd)
+
+    for p in pitch:
+        for y in tqdm(range(0, 360, angl_step)):
+            R = np.matmul(make_rotate(math.radians(p), 0, 0), make_rotate(0, math.radians(y), 0))
+            if up_axis == 2:
+                R = np.matmul(R, make_rotate(math.radians(90),0,0))
+
+            rndr.rot_matrix = R
+            rndr_uv.rot_matrix = R
+            rndr.set_camera(cam)
+            rndr_uv.set_camera(cam)
+
+            for j in range(n_light):
+                sh_id = random.randint(0,shs.shape[0]-1)
+                sh = shs[sh_id]
+                sh_angle = 0.2*np.pi*(random.random()-0.5)
+                sh = rotateSH(sh, make_rotate(0, sh_angle, 0).T)
+
+                dic = {'sh': sh, 'ortho_ratio': cam.ortho_ratio, 'scale': y_scale, 'center': vmed, 'R': R}
+                
+                rndr.set_sh(sh)        
+                rndr.analytic = False
+                rndr.use_inverse_depth = False
+                rndr.display()
+
+                out_all_f = rndr.get_color(0)
+                out_mask = out_all_f[:,:,3]
+                out_all_f = cv2.cvtColor(out_all_f, cv2.COLOR_RGBA2BGR)
+
+                np.save(os.path.join(out_path, 'PARAM', subject_name, '%d_%d_%02d.npy'%(y,p,j)),dic)
+                cv2.imwrite(os.path.join(out_path, 'RENDER', subject_name, '%d_%d_%02d.jpg'%(y,p,j)),255.0*out_all_f)
+                cv2.imwrite(os.path.join(out_path, 'MASK', subject_name, '%d_%d_%02d.png'%(y,p,j)),255.0*out_mask)
+
+                rndr_uv.set_sh(sh)
+                rndr_uv.analytic = False
+                rndr_uv.use_inverse_depth = False
+                rndr_uv.display()
+
+                uv_color = rndr_uv.get_color(0)
+                uv_color = cv2.cvtColor(uv_color, cv2.COLOR_RGBA2BGR)
+                cv2.imwrite(os.path.join(out_path, 'UV_RENDER', subject_name, '%d_%d_%02d.jpg'%(y,p,j)),255.0*uv_color)
+
+                if y == 0 and j == 0 and p == pitch[0]:
+                    uv_pos = rndr_uv.get_color(1)
+                    uv_mask = uv_pos[:,:,3]
+                    cv2.imwrite(os.path.join(out_path, 'UV_MASK', subject_name, '00.png'),255.0*uv_mask)
+
+                    data = {'default': uv_pos[:,:,:3]} # default is a reserved name
+                    pyexr.write(os.path.join(out_path, 'UV_POS', subject_name, '00.exr'), data) 
+
+                    uv_nml = rndr_uv.get_color(2)
+                    uv_nml = cv2.cvtColor(uv_nml, cv2.COLOR_RGBA2BGR)
+                    cv2.imwrite(os.path.join(out_path, 'UV_NORMAL', subject_name, '00.png'),255.0*uv_nml)
+
+
+if __name__ == '__main__':
+    shs = np.load('./env_sh.npy')
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-i', '--input', type=str, default='/home/shunsuke/Downloads/rp_dennis_posed_004_OBJ')
+    parser.add_argument('-o', '--out_dir', type=str, default='/home/shunsuke/Documents/hf_human')
+    parser.add_argument('-m', '--ms_rate', type=int, default=1, help='higher ms rate results in less aliased output. MESA renderer only supports ms_rate=1.')
+    parser.add_argument('-e', '--egl',  action='store_true', help='egl rendering option. use this when rendering with headless server with NVIDIA GPU')
+    parser.add_argument('-s', '--size',  type=int, default=512, help='rendering image size')
+    args = parser.parse_args()
+
+    # NOTE: GL context has to be created before any other OpenGL function loads.
+    from lib.renderer.gl.init_gl import initialize_GL_context
+    initialize_GL_context(width=args.size, height=args.size, egl=args.egl)
+
+    from lib.renderer.gl.prt_render import PRTRender
+    rndr = PRTRender(width=args.size, height=args.size, ms_rate=args.ms_rate, egl=args.egl)
+    rndr_uv = PRTRender(width=args.size, height=args.size, uv_mode=True, egl=args.egl)
+
+    if args.input[-1] == '/':
+        args.input = args.input[:-1]
+    subject_name = args.input.split('/')[-1][:-4]
+    render_prt_ortho(args.out_dir, args.input, subject_name, shs, rndr, rndr_uv, args.size, 1, 1, pitch=[0])

PIFu/apps/train_color.py

@@ -0,0 +1,191 @@
+import sys
+import os
+
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+ROOT_PATH = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+import time
+import json
+import numpy as np
+import cv2
+import random
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+
+from lib.options import BaseOptions
+from lib.mesh_util import *
+from lib.sample_util import *
+from lib.train_util import *
+from lib.data import *
+from lib.model import *
+from lib.geometry import index
+
+# get options
+opt = BaseOptions().parse()
+
+def train_color(opt):
+    # set cuda
+    cuda = torch.device('cuda:%d' % opt.gpu_id)
+
+    train_dataset = TrainDataset(opt, phase='train')
+    test_dataset = TrainDataset(opt, phase='test')
+
+    projection_mode = train_dataset.projection_mode
+
+    # create data loader
+    train_data_loader = DataLoader(train_dataset,
+                                   batch_size=opt.batch_size, shuffle=not opt.serial_batches,
+                                   num_workers=opt.num_threads, pin_memory=opt.pin_memory)
+
+    print('train data size: ', len(train_data_loader))
+
+    # NOTE: batch size should be 1 and use all the points for evaluation
+    test_data_loader = DataLoader(test_dataset,
+                                  batch_size=1, shuffle=False,
+                                  num_workers=opt.num_threads, pin_memory=opt.pin_memory)
+    print('test data size: ', len(test_data_loader))
+
+    # create net
+    netG = HGPIFuNet(opt, projection_mode).to(device=cuda)
+
+    lr = opt.learning_rate
+
+    # Always use resnet for color regression
+    netC = ResBlkPIFuNet(opt).to(device=cuda)
+    optimizerC = torch.optim.Adam(netC.parameters(), lr=opt.learning_rate)
+
+    def set_train():
+        netG.eval()
+        netC.train()
+
+    def set_eval():
+        netG.eval()
+        netC.eval()
+
+    print('Using NetworkG: ', netG.name, 'networkC: ', netC.name)
+
+    # load checkpoints
+    if opt.load_netG_checkpoint_path is not None:
+        print('loading for net G ...', opt.load_netG_checkpoint_path)
+        netG.load_state_dict(torch.load(opt.load_netG_checkpoint_path, map_location=cuda))
+    else:
+        model_path_G = '%s/%s/netG_latest' % (opt.checkpoints_path, opt.name)
+        print('loading for net G ...', model_path_G)
+        netG.load_state_dict(torch.load(model_path_G, map_location=cuda))
+
+    if opt.load_netC_checkpoint_path is not None:
+        print('loading for net C ...', opt.load_netC_checkpoint_path)
+        netC.load_state_dict(torch.load(opt.load_netC_checkpoint_path, map_location=cuda))
+
+    if opt.continue_train:
+        if opt.resume_epoch < 0:
+            model_path_C = '%s/%s/netC_latest' % (opt.checkpoints_path, opt.name)
+        else:
+            model_path_C = '%s/%s/netC_epoch_%d' % (opt.checkpoints_path, opt.name, opt.resume_epoch)
+
+        print('Resuming from ', model_path_C)
+        netC.load_state_dict(torch.load(model_path_C, map_location=cuda))
+
+    os.makedirs(opt.checkpoints_path, exist_ok=True)
+    os.makedirs(opt.results_path, exist_ok=True)
+    os.makedirs('%s/%s' % (opt.checkpoints_path, opt.name), exist_ok=True)
+    os.makedirs('%s/%s' % (opt.results_path, opt.name), exist_ok=True)
+
+    opt_log = os.path.join(opt.results_path, opt.name, 'opt.txt')
+    with open(opt_log, 'w') as outfile:
+        outfile.write(json.dumps(vars(opt), indent=2))
+
+    # training
+    start_epoch = 0 if not opt.continue_train else max(opt.resume_epoch,0)
+    for epoch in range(start_epoch, opt.num_epoch):
+        epoch_start_time = time.time()
+
+        set_train()
+        iter_data_time = time.time()
+        for train_idx, train_data in enumerate(train_data_loader):
+            iter_start_time = time.time()
+            # retrieve the data
+            image_tensor = train_data['img'].to(device=cuda)
+            calib_tensor = train_data['calib'].to(device=cuda)
+            color_sample_tensor = train_data['color_samples'].to(device=cuda)
+
+            image_tensor, calib_tensor = reshape_multiview_tensors(image_tensor, calib_tensor)
+
+            if opt.num_views > 1:
+                color_sample_tensor = reshape_sample_tensor(color_sample_tensor, opt.num_views)
+
+            rgb_tensor = train_data['rgbs'].to(device=cuda)
+
+            with torch.no_grad():
+                netG.filter(image_tensor)
+            resC, error = netC.forward(image_tensor, netG.get_im_feat(), color_sample_tensor, calib_tensor, labels=rgb_tensor)
+
+            optimizerC.zero_grad()
+            error.backward()
+            optimizerC.step()
+
+            iter_net_time = time.time()
+            eta = ((iter_net_time - epoch_start_time) / (train_idx + 1)) * len(train_data_loader) - (
+                    iter_net_time - epoch_start_time)
+
+            if train_idx % opt.freq_plot == 0:
+                print(
+                    'Name: {0} | Epoch: {1} | {2}/{3} | Err: {4:.06f} | LR: {5:.06f} | dataT: {6:.05f} | netT: {7:.05f} | ETA: {8:02d}:{9:02d}'.format(
+                        opt.name, epoch, train_idx, len(train_data_loader),
+                        error.item(),
+                        lr,
+                        iter_start_time - iter_data_time,
+                        iter_net_time - iter_start_time, int(eta // 60),
+                        int(eta - 60 * (eta // 60))))
+
+            if train_idx % opt.freq_save == 0 and train_idx != 0:
+                torch.save(netC.state_dict(), '%s/%s/netC_latest' % (opt.checkpoints_path, opt.name))
+                torch.save(netC.state_dict(), '%s/%s/netC_epoch_%d' % (opt.checkpoints_path, opt.name, epoch))
+
+            if train_idx % opt.freq_save_ply == 0:
+                save_path = '%s/%s/pred_col.ply' % (opt.results_path, opt.name)
+                rgb = resC[0].transpose(0, 1).cpu() * 0.5 + 0.5
+                points = color_sample_tensor[0].transpose(0, 1).cpu()
+                save_samples_rgb(save_path, points.detach().numpy(), rgb.detach().numpy())
+
+            iter_data_time = time.time()
+
+        #### test
+        with torch.no_grad():
+            set_eval()
+
+            if not opt.no_num_eval:
+                test_losses = {}
+                print('calc error (test) ...')
+                test_color_error = calc_error_color(opt, netG, netC, cuda, test_dataset, 100)
+                print('eval test | color error:', test_color_error)
+                test_losses['test_color'] = test_color_error
+
+                print('calc error (train) ...')
+                train_dataset.is_train = False
+                train_color_error = calc_error_color(opt, netG, netC, cuda, train_dataset, 100)
+                train_dataset.is_train = True
+                print('eval train | color error:', train_color_error)
+                test_losses['train_color'] = train_color_error
+
+            if not opt.no_gen_mesh:
+                print('generate mesh (test) ...')
+                for gen_idx in tqdm(range(opt.num_gen_mesh_test)):
+                    test_data = random.choice(test_dataset)
+                    save_path = '%s/%s/test_eval_epoch%d_%s.obj' % (
+                        opt.results_path, opt.name, epoch, test_data['name'])
+                    gen_mesh_color(opt, netG, netC, cuda, test_data, save_path)
+
+                print('generate mesh (train) ...')
+                train_dataset.is_train = False
+                for gen_idx in tqdm(range(opt.num_gen_mesh_test)):
+                    train_data = random.choice(train_dataset)
+                    save_path = '%s/%s/train_eval_epoch%d_%s.obj' % (
+                        opt.results_path, opt.name, epoch, train_data['name'])
+                    gen_mesh_color(opt, netG, netC, cuda, train_data, save_path)
+                train_dataset.is_train = True
+
+if __name__ == '__main__':
+    train_color(opt)

PIFu/apps/train_shape.py

@@ -0,0 +1,183 @@
+import sys
+import os
+
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+ROOT_PATH = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+import time
+import json
+import numpy as np
+import cv2
+import random
+import torch
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+
+from lib.options import BaseOptions
+from lib.mesh_util import *
+from lib.sample_util import *
+from lib.train_util import *
+from lib.data import *
+from lib.model import *
+from lib.geometry import index
+
+# get options
+opt = BaseOptions().parse()
+
+def train(opt):
+    # set cuda
+    cuda = torch.device('cuda:%d' % opt.gpu_id)
+
+    train_dataset = TrainDataset(opt, phase='train')
+    test_dataset = TrainDataset(opt, phase='test')
+
+    projection_mode = train_dataset.projection_mode
+
+    # create data loader
+    train_data_loader = DataLoader(train_dataset,
+                                   batch_size=opt.batch_size, shuffle=not opt.serial_batches,
+                                   num_workers=opt.num_threads, pin_memory=opt.pin_memory)
+
+    print('train data size: ', len(train_data_loader))
+
+    # NOTE: batch size should be 1 and use all the points for evaluation
+    test_data_loader = DataLoader(test_dataset,
+                                  batch_size=1, shuffle=False,
+                                  num_workers=opt.num_threads, pin_memory=opt.pin_memory)
+    print('test data size: ', len(test_data_loader))
+
+    # create net
+    netG = HGPIFuNet(opt, projection_mode).to(device=cuda)
+    optimizerG = torch.optim.RMSprop(netG.parameters(), lr=opt.learning_rate, momentum=0, weight_decay=0)
+    lr = opt.learning_rate
+    print('Using Network: ', netG.name)
+    
+    def set_train():
+        netG.train()
+
+    def set_eval():
+        netG.eval()
+
+    # load checkpoints
+    if opt.load_netG_checkpoint_path is not None:
+        print('loading for net G ...', opt.load_netG_checkpoint_path)
+        netG.load_state_dict(torch.load(opt.load_netG_checkpoint_path, map_location=cuda))
+
+    if opt.continue_train:
+        if opt.resume_epoch < 0:
+            model_path = '%s/%s/netG_latest' % (opt.checkpoints_path, opt.name)
+        else:
+            model_path = '%s/%s/netG_epoch_%d' % (opt.checkpoints_path, opt.name, opt.resume_epoch)
+        print('Resuming from ', model_path)
+        netG.load_state_dict(torch.load(model_path, map_location=cuda))
+
+    os.makedirs(opt.checkpoints_path, exist_ok=True)
+    os.makedirs(opt.results_path, exist_ok=True)
+    os.makedirs('%s/%s' % (opt.checkpoints_path, opt.name), exist_ok=True)
+    os.makedirs('%s/%s' % (opt.results_path, opt.name), exist_ok=True)
+
+    opt_log = os.path.join(opt.results_path, opt.name, 'opt.txt')
+    with open(opt_log, 'w') as outfile:
+        outfile.write(json.dumps(vars(opt), indent=2))
+
+    # training
+    start_epoch = 0 if not opt.continue_train else max(opt.resume_epoch,0)
+    for epoch in range(start_epoch, opt.num_epoch):
+        epoch_start_time = time.time()
+
+        set_train()
+        iter_data_time = time.time()
+        for train_idx, train_data in enumerate(train_data_loader):
+            iter_start_time = time.time()
+
+            # retrieve the data
+            image_tensor = train_data['img'].to(device=cuda)
+            calib_tensor = train_data['calib'].to(device=cuda)
+            sample_tensor = train_data['samples'].to(device=cuda)
+
+            image_tensor, calib_tensor = reshape_multiview_tensors(image_tensor, calib_tensor)
+
+            if opt.num_views > 1:
+                sample_tensor = reshape_sample_tensor(sample_tensor, opt.num_views)
+
+            label_tensor = train_data['labels'].to(device=cuda)
+
+            res, error = netG.forward(image_tensor, sample_tensor, calib_tensor, labels=label_tensor)
+
+            optimizerG.zero_grad()
+            error.backward()
+            optimizerG.step()
+
+            iter_net_time = time.time()
+            eta = ((iter_net_time - epoch_start_time) / (train_idx + 1)) * len(train_data_loader) - (
+                    iter_net_time - epoch_start_time)
+
+            if train_idx % opt.freq_plot == 0:
+                print(
+                    'Name: {0} | Epoch: {1} | {2}/{3} | Err: {4:.06f} | LR: {5:.06f} | Sigma: {6:.02f} | dataT: {7:.05f} | netT: {8:.05f} | ETA: {9:02d}:{10:02d}'.format(
+                        opt.name, epoch, train_idx, len(train_data_loader), error.item(), lr, opt.sigma,
+                                                                            iter_start_time - iter_data_time,
+                                                                            iter_net_time - iter_start_time, int(eta // 60),
+                        int(eta - 60 * (eta // 60))))
+
+            if train_idx % opt.freq_save == 0 and train_idx != 0:
+                torch.save(netG.state_dict(), '%s/%s/netG_latest' % (opt.checkpoints_path, opt.name))
+                torch.save(netG.state_dict(), '%s/%s/netG_epoch_%d' % (opt.checkpoints_path, opt.name, epoch))
+
+            if train_idx % opt.freq_save_ply == 0:
+                save_path = '%s/%s/pred.ply' % (opt.results_path, opt.name)
+                r = res[0].cpu()
+                points = sample_tensor[0].transpose(0, 1).cpu()
+                save_samples_truncted_prob(save_path, points.detach().numpy(), r.detach().numpy())
+
+            iter_data_time = time.time()
+
+        # update learning rate
+        lr = adjust_learning_rate(optimizerG, epoch, lr, opt.schedule, opt.gamma)
+
+        #### test
+        with torch.no_grad():
+            set_eval()
+
+            if not opt.no_num_eval:
+                test_losses = {}
+                print('calc error (test) ...')
+                test_errors = calc_error(opt, netG, cuda, test_dataset, 100)
+                print('eval test MSE: {0:06f} IOU: {1:06f} prec: {2:06f} recall: {3:06f}'.format(*test_errors))
+                MSE, IOU, prec, recall = test_errors
+                test_losses['MSE(test)'] = MSE
+                test_losses['IOU(test)'] = IOU
+                test_losses['prec(test)'] = prec
+                test_losses['recall(test)'] = recall
+
+                print('calc error (train) ...')
+                train_dataset.is_train = False
+                train_errors = calc_error(opt, netG, cuda, train_dataset, 100)
+                train_dataset.is_train = True
+                print('eval train MSE: {0:06f} IOU: {1:06f} prec: {2:06f} recall: {3:06f}'.format(*train_errors))
+                MSE, IOU, prec, recall = train_errors
+                test_losses['MSE(train)'] = MSE
+                test_losses['IOU(train)'] = IOU
+                test_losses['prec(train)'] = prec
+                test_losses['recall(train)'] = recall
+
+            if not opt.no_gen_mesh:
+                print('generate mesh (test) ...')
+                for gen_idx in tqdm(range(opt.num_gen_mesh_test)):
+                    test_data = random.choice(test_dataset)
+                    save_path = '%s/%s/test_eval_epoch%d_%s.obj' % (
+                        opt.results_path, opt.name, epoch, test_data['name'])
+                    gen_mesh(opt, netG, cuda, test_data, save_path)
+
+                print('generate mesh (train) ...')
+                train_dataset.is_train = False
+                for gen_idx in tqdm(range(opt.num_gen_mesh_test)):
+                    train_data = random.choice(train_dataset)
+                    save_path = '%s/%s/train_eval_epoch%d_%s.obj' % (
+                        opt.results_path, opt.name, epoch, train_data['name'])
+                    gen_mesh(opt, netG, cuda, train_data, save_path)
+                train_dataset.is_train = True
+
+
+if __name__ == '__main__':
+    train(opt)

PIFu/environment.yml

@@ -0,0 +1,19 @@
+name: PIFu
+channels:
+- pytorch
+- defaults
+dependencies:
+- opencv
+- pytorch
+- json
+- pyexr
+- cv2
+- PIL
+- skimage
+- tqdm
+- pyembree
+- shapely
+- rtree
+- xxhash
+- trimesh
+- PyOpenGL

PIFu/inputs/.gitignore

@@ -0,0 +1,2 @@
+*
+!.gitignore

PIFu/lib/colab_util.py

@@ -0,0 +1,114 @@
+import io
+import os
+import torch
+from skimage.io import imread
+import numpy as np
+import cv2
+from tqdm import tqdm_notebook as tqdm
+import base64
+from IPython.display import HTML
+
+# Util function for loading meshes
+from pytorch3d.io import load_objs_as_meshes
+
+from IPython.display import HTML
+from base64 import b64encode
+
+# Data structures and functions for rendering
+from pytorch3d.structures import Meshes
+from pytorch3d.renderer import (
+    look_at_view_transform,
+    OpenGLOrthographicCameras, 
+    PointLights, 
+    DirectionalLights, 
+    Materials, 
+    RasterizationSettings, 
+    MeshRenderer, 
+    MeshRasterizer,  
+    SoftPhongShader,
+    HardPhongShader,
+    TexturesVertex
+)
+
+def set_renderer():
+    # Setup
+    device = torch.device("cuda:0")
+    torch.cuda.set_device(device)
+
+    # Initialize an OpenGL perspective camera.
+    R, T = look_at_view_transform(2.0, 0, 180) 
+    cameras = OpenGLOrthographicCameras(device=device, R=R, T=T)
+
+    raster_settings = RasterizationSettings(
+        image_size=512, 
+        blur_radius=0.0, 
+        faces_per_pixel=1, 
+        bin_size = None, 
+        max_faces_per_bin = None
+    )
+
+    lights = PointLights(device=device, location=((2.0, 2.0, 2.0),))
+
+    renderer = MeshRenderer(
+        rasterizer=MeshRasterizer(
+            cameras=cameras, 
+            raster_settings=raster_settings
+        ),
+        shader=HardPhongShader(
+            device=device, 
+            cameras=cameras,
+            lights=lights
+        )
+    )
+    return renderer
+
+def get_verts_rgb_colors(obj_path):
+  rgb_colors = []
+
+  f = open(obj_path)
+  lines = f.readlines()
+  for line in lines:
+    ls = line.split(' ')
+    if len(ls) == 7:
+      rgb_colors.append(ls[-3:])
+
+  return np.array(rgb_colors, dtype='float32')[None, :, :]
+
+def generate_video_from_obj(obj_path, video_path, renderer):
+    # Setup
+    device = torch.device("cuda:0")
+    torch.cuda.set_device(device)
+
+    # Load obj file
+    verts_rgb_colors = get_verts_rgb_colors(obj_path)
+    verts_rgb_colors = torch.from_numpy(verts_rgb_colors).to(device)
+    textures = TexturesVertex(verts_features=verts_rgb_colors)
+    wo_textures = TexturesVertex(verts_features=torch.ones_like(verts_rgb_colors)*0.75)
+
+    # Load obj
+    mesh = load_objs_as_meshes([obj_path], device=device)
+
+    # Set mesh
+    vers = mesh._verts_list
+    faces = mesh._faces_list
+    mesh_w_tex = Meshes(vers, faces, textures)
+    mesh_wo_tex = Meshes(vers, faces, wo_textures)
+
+    # create VideoWriter
+    fourcc = cv2. VideoWriter_fourcc(*'MP4V')
+    out = cv2.VideoWriter(video_path, fourcc, 20.0, (1024,512))
+
+    for i in tqdm(range(90)):
+        R, T = look_at_view_transform(1.8, 0, i*4, device=device)
+        images_w_tex = renderer(mesh_w_tex, R=R, T=T)
+        images_w_tex = np.clip(images_w_tex[0, ..., :3].cpu().numpy(), 0.0, 1.0)[:, :, ::-1] * 255
+        images_wo_tex = renderer(mesh_wo_tex, R=R, T=T)
+        images_wo_tex = np.clip(images_wo_tex[0, ..., :3].cpu().numpy(), 0.0, 1.0)[:, :, ::-1] * 255
+        image = np.concatenate([images_w_tex, images_wo_tex], axis=1)
+        out.write(image.astype('uint8'))
+    out.release()
+
+def video(path):
+    mp4 = open(path,'rb').read()
+    data_url = "data:video/mp4;base64," + b64encode(mp4).decode()
+    return HTML('<video width=500 controls loop> <source src="%s" type="video/mp4"></video>' % data_url)

PIFu/lib/data/BaseDataset.py

@@ -0,0 +1,46 @@
+from torch.utils.data import Dataset
+import random
+
+
+class BaseDataset(Dataset):
+    '''
+    This is the Base Datasets.
+    Itself does nothing and is not runnable.
+    Check self.get_item function to see what it should return.
+    '''
+
+    @staticmethod
+    def modify_commandline_options(parser, is_train):
+        return parser
+
+    def __init__(self, opt, phase='train'):
+        self.opt = opt
+        self.is_train = self.phase == 'train'
+        self.projection_mode = 'orthogonal'  # Declare projection mode here
+
+    def __len__(self):
+        return 0
+
+    def get_item(self, index):
+        # In case of a missing file or IO error, switch to a random sample instead
+        try:
+            res = {
+                'name': None,  # name of this subject
+                'b_min': None,  # Bounding box (x_min, y_min, z_min) of target space
+                'b_max': None,  # Bounding box (x_max, y_max, z_max) of target space
+
+                'samples': None,  # [3, N] samples
+                'labels': None,  # [1, N] labels
+
+                'img': None,  # [num_views, C, H, W] input images
+                'calib': None,  # [num_views, 4, 4] calibration matrix
+                'extrinsic': None,  # [num_views, 4, 4] extrinsic matrix
+                'mask': None,  # [num_views, 1, H, W] segmentation masks
+            }
+            return res
+        except:
+            print("Requested index %s has missing files. Using a random sample instead." % index)
+            return self.get_item(index=random.randint(0, self.__len__() - 1))
+
+    def __getitem__(self, index):
+        return self.get_item(index)

PIFu/lib/data/EvalDataset.py

@@ -0,0 +1,166 @@
+from torch.utils.data import Dataset
+import numpy as np
+import os
+import random
+import torchvision.transforms as transforms
+from PIL import Image, ImageOps
+import cv2
+import torch
+from PIL.ImageFilter import GaussianBlur
+import trimesh
+import cv2
+
+
+class EvalDataset(Dataset):
+    @staticmethod
+    def modify_commandline_options(parser):
+        return parser
+
+    def __init__(self, opt, root=None):
+        self.opt = opt
+        self.projection_mode = 'orthogonal'
+
+        # Path setup
+        self.root = self.opt.dataroot
+        if root is not None:
+            self.root = root
+        self.RENDER = os.path.join(self.root, 'RENDER')
+        self.MASK = os.path.join(self.root, 'MASK')
+        self.PARAM = os.path.join(self.root, 'PARAM')
+        self.OBJ = os.path.join(self.root, 'GEO', 'OBJ')
+
+        self.phase = 'val'
+        self.load_size = self.opt.loadSize
+
+        self.num_views = self.opt.num_views
+
+        self.max_view_angle = 360
+        self.interval = 1
+        self.subjects = self.get_subjects()
+
+        # PIL to tensor
+        self.to_tensor = transforms.Compose([
+            transforms.Resize(self.load_size),
+            transforms.ToTensor(),
+            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
+        ])
+
+    def get_subjects(self):
+        var_file = os.path.join(self.root, 'val.txt')
+        if os.path.exists(var_file):
+            var_subjects = np.loadtxt(var_file, dtype=str)
+            return sorted(list(var_subjects))
+        all_subjects = os.listdir(self.RENDER)
+        return sorted(list(all_subjects))
+
+    def __len__(self):
+        return len(self.subjects) * self.max_view_angle // self.interval
+
+    def get_render(self, subject, num_views, view_id=None, random_sample=False):
+        '''
+        Return the render data
+        :param subject: subject name
+        :param num_views: how many views to return
+        :param view_id: the first view_id. If None, select a random one.
+        :return:
+            'img': [num_views, C, W, H] images
+            'calib': [num_views, 4, 4] calibration matrix
+            'extrinsic': [num_views, 4, 4] extrinsic matrix
+            'mask': [num_views, 1, W, H] masks
+        '''
+        # For now we only have pitch = 00. Hard code it here
+        pitch = 0
+        # Select a random view_id from self.max_view_angle if not given
+        if view_id is None:
+            view_id = np.random.randint(self.max_view_angle)
+        # The ids are an even distribution of num_views around view_id
+        view_ids = [(view_id + self.max_view_angle // num_views * offset) % self.max_view_angle
+                    for offset in range(num_views)]
+        if random_sample:
+            view_ids = np.random.choice(self.max_view_angle, num_views, replace=False)
+
+        calib_list = []
+        render_list = []
+        mask_list = []
+        extrinsic_list = []
+
+        for vid in view_ids:
+            param_path = os.path.join(self.PARAM, subject, '%d_%02d.npy' % (vid, pitch))
+            render_path = os.path.join(self.RENDER, subject, '%d_%02d.jpg' % (vid, pitch))
+            mask_path = os.path.join(self.MASK, subject, '%d_%02d.png' % (vid, pitch))
+
+            # loading calibration data
+            param = np.load(param_path)
+            # pixel unit / world unit
+            ortho_ratio = param.item().get('ortho_ratio')
+            # world unit / model unit
+            scale = param.item().get('scale')
+            # camera center world coordinate
+            center = param.item().get('center')
+            # model rotation
+            R = param.item().get('R')
+
+            translate = -np.matmul(R, center).reshape(3, 1)
+            extrinsic = np.concatenate([R, translate], axis=1)
+            extrinsic = np.concatenate([extrinsic, np.array([0, 0, 0, 1]).reshape(1, 4)], 0)
+            # Match camera space to image pixel space
+            scale_intrinsic = np.identity(4)
+            scale_intrinsic[0, 0] = scale / ortho_ratio
+            scale_intrinsic[1, 1] = -scale / ortho_ratio
+            scale_intrinsic[2, 2] = -scale / ortho_ratio
+            # Match image pixel space to image uv space
+            uv_intrinsic = np.identity(4)
+            uv_intrinsic[0, 0] = 1.0 / float(self.opt.loadSize // 2)
+            uv_intrinsic[1, 1] = 1.0 / float(self.opt.loadSize // 2)
+            uv_intrinsic[2, 2] = 1.0 / float(self.opt.loadSize // 2)
+            # Transform under image pixel space
+            trans_intrinsic = np.identity(4)
+
+            mask = Image.open(mask_path).convert('L')
+            render = Image.open(render_path).convert('RGB')
+
+            intrinsic = np.matmul(trans_intrinsic, np.matmul(uv_intrinsic, scale_intrinsic))
+            calib = torch.Tensor(np.matmul(intrinsic, extrinsic)).float()
+            extrinsic = torch.Tensor(extrinsic).float()
+
+            mask = transforms.Resize(self.load_size)(mask)
+            mask = transforms.ToTensor()(mask).float()
+            mask_list.append(mask)
+
+            render = self.to_tensor(render)
+            render = mask.expand_as(render) * render
+
+            render_list.append(render)
+            calib_list.append(calib)
+            extrinsic_list.append(extrinsic)
+
+        return {
+            'img': torch.stack(render_list, dim=0),
+            'calib': torch.stack(calib_list, dim=0),
+            'extrinsic': torch.stack(extrinsic_list, dim=0),
+            'mask': torch.stack(mask_list, dim=0)
+        }
+
+    def get_item(self, index):
+        # In case of a missing file or IO error, switch to a random sample instead
+        try:
+            sid = index % len(self.subjects)
+            vid = (index // len(self.subjects)) * self.interval
+            # name of the subject 'rp_xxxx_xxx'
+            subject = self.subjects[sid]
+            res = {
+                'name': subject,
+                'mesh_path': os.path.join(self.OBJ, subject + '.obj'),
+                'sid': sid,
+                'vid': vid,
+            }
+            render_data = self.get_render(subject, num_views=self.num_views, view_id=vid,
+                                          random_sample=self.opt.random_multiview)
+            res.update(render_data)
+            return res
+        except Exception as e:
+            print(e)
+            return self.get_item(index=random.randint(0, self.__len__() - 1))
+
+    def __getitem__(self, index):
+        return self.get_item(index)

PIFu/lib/data/TrainDataset.py

@@ -0,0 +1,390 @@
+from torch.utils.data import Dataset
+import numpy as np
+import os
+import random
+import torchvision.transforms as transforms
+from PIL import Image, ImageOps
+import cv2
+import torch
+from PIL.ImageFilter import GaussianBlur
+import trimesh
+import logging
+
+log = logging.getLogger('trimesh')
+log.setLevel(40)
+
+def load_trimesh(root_dir):
+    folders = os.listdir(root_dir)
+    meshs = {}
+    for i, f in enumerate(folders):
+        sub_name = f
+        meshs[sub_name] = trimesh.load(os.path.join(root_dir, f, '%s_100k.obj' % sub_name))
+
+    return meshs
+
+def save_samples_truncted_prob(fname, points, prob):
+    '''
+    Save the visualization of sampling to a ply file.
+    Red points represent positive predictions.
+    Green points represent negative predictions.
+    :param fname: File name to save
+    :param points: [N, 3] array of points
+    :param prob: [N, 1] array of predictions in the range [0~1]
+    :return:
+    '''
+    r = (prob > 0.5).reshape([-1, 1]) * 255
+    g = (prob < 0.5).reshape([-1, 1]) * 255
+    b = np.zeros(r.shape)
+
+    to_save = np.concatenate([points, r, g, b], axis=-1)
+    return np.savetxt(fname,
+                      to_save,
+                      fmt='%.6f %.6f %.6f %d %d %d',
+                      comments='',
+                      header=(
+                          'ply\nformat ascii 1.0\nelement vertex {:d}\nproperty float x\nproperty float y\nproperty float z\nproperty uchar red\nproperty uchar green\nproperty uchar blue\nend_header').format(
+                          points.shape[0])
+                      )
+
+
+class TrainDataset(Dataset):
+    @staticmethod
+    def modify_commandline_options(parser, is_train):
+        return parser
+
+    def __init__(self, opt, phase='train'):
+        self.opt = opt
+        self.projection_mode = 'orthogonal'
+
+        # Path setup
+        self.root = self.opt.dataroot
+        self.RENDER = os.path.join(self.root, 'RENDER')
+        self.MASK = os.path.join(self.root, 'MASK')
+        self.PARAM = os.path.join(self.root, 'PARAM')
+        self.UV_MASK = os.path.join(self.root, 'UV_MASK')
+        self.UV_NORMAL = os.path.join(self.root, 'UV_NORMAL')
+        self.UV_RENDER = os.path.join(self.root, 'UV_RENDER')
+        self.UV_POS = os.path.join(self.root, 'UV_POS')
+        self.OBJ = os.path.join(self.root, 'GEO', 'OBJ')
+
+        self.B_MIN = np.array([-128, -28, -128])
+        self.B_MAX = np.array([128, 228, 128])
+
+        self.is_train = (phase == 'train')
+        self.load_size = self.opt.loadSize
+
+        self.num_views = self.opt.num_views
+
+        self.num_sample_inout = self.opt.num_sample_inout
+        self.num_sample_color = self.opt.num_sample_color
+
+        self.yaw_list = list(range(0,360,1))
+        self.pitch_list = [0]
+        self.subjects = self.get_subjects()
+
+        # PIL to tensor
+        self.to_tensor = transforms.Compose([
+            transforms.Resize(self.load_size),
+            transforms.ToTensor(),
+            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
+        ])
+
+        # augmentation
+        self.aug_trans = transforms.Compose([
+            transforms.ColorJitter(brightness=opt.aug_bri, contrast=opt.aug_con, saturation=opt.aug_sat,
+                                   hue=opt.aug_hue)
+        ])
+
+        self.mesh_dic = load_trimesh(self.OBJ)
+
+    def get_subjects(self):
+        all_subjects = os.listdir(self.RENDER)
+        var_subjects = np.loadtxt(os.path.join(self.root, 'val.txt'), dtype=str)
+        if len(var_subjects) == 0:
+            return all_subjects
+
+        if self.is_train:
+            return sorted(list(set(all_subjects) - set(var_subjects)))
+        else:
+            return sorted(list(var_subjects))
+
+    def __len__(self):
+        return len(self.subjects) * len(self.yaw_list) * len(self.pitch_list)
+
+    def get_render(self, subject, num_views, yid=0, pid=0, random_sample=False):
+        '''
+        Return the render data
+        :param subject: subject name
+        :param num_views: how many views to return
+        :param view_id: the first view_id. If None, select a random one.
+        :return:
+            'img': [num_views, C, W, H] images
+            'calib': [num_views, 4, 4] calibration matrix
+            'extrinsic': [num_views, 4, 4] extrinsic matrix
+            'mask': [num_views, 1, W, H] masks
+        '''
+        pitch = self.pitch_list[pid]
+
+        # The ids are an even distribution of num_views around view_id
+        view_ids = [self.yaw_list[(yid + len(self.yaw_list) // num_views * offset) % len(self.yaw_list)]
+                    for offset in range(num_views)]
+        if random_sample:
+            view_ids = np.random.choice(self.yaw_list, num_views, replace=False)
+
+        calib_list = []
+        render_list = []
+        mask_list = []
+        extrinsic_list = []
+
+        for vid in view_ids:
+            param_path = os.path.join(self.PARAM, subject, '%d_%d_%02d.npy' % (vid, pitch, 0))
+            render_path = os.path.join(self.RENDER, subject, '%d_%d_%02d.jpg' % (vid, pitch, 0))
+            mask_path = os.path.join(self.MASK, subject, '%d_%d_%02d.png' % (vid, pitch, 0))
+
+            # loading calibration data
+            param = np.load(param_path, allow_pickle=True)
+            # pixel unit / world unit
+            ortho_ratio = param.item().get('ortho_ratio')
+            # world unit / model unit
+            scale = param.item().get('scale')
+            # camera center world coordinate
+            center = param.item().get('center')
+            # model rotation
+            R = param.item().get('R')
+
+            translate = -np.matmul(R, center).reshape(3, 1)
+            extrinsic = np.concatenate([R, translate], axis=1)
+            extrinsic = np.concatenate([extrinsic, np.array([0, 0, 0, 1]).reshape(1, 4)], 0)
+            # Match camera space to image pixel space
+            scale_intrinsic = np.identity(4)
+            scale_intrinsic[0, 0] = scale / ortho_ratio
+            scale_intrinsic[1, 1] = -scale / ortho_ratio
+            scale_intrinsic[2, 2] = scale / ortho_ratio
+            # Match image pixel space to image uv space
+            uv_intrinsic = np.identity(4)
+            uv_intrinsic[0, 0] = 1.0 / float(self.opt.loadSize // 2)
+            uv_intrinsic[1, 1] = 1.0 / float(self.opt.loadSize // 2)
+            uv_intrinsic[2, 2] = 1.0 / float(self.opt.loadSize // 2)
+            # Transform under image pixel space
+            trans_intrinsic = np.identity(4)
+
+            mask = Image.open(mask_path).convert('L')
+            render = Image.open(render_path).convert('RGB')
+
+            if self.is_train:
+                # Pad images
+                pad_size = int(0.1 * self.load_size)
+                render = ImageOps.expand(render, pad_size, fill=0)
+                mask = ImageOps.expand(mask, pad_size, fill=0)
+
+                w, h = render.size
+                th, tw = self.load_size, self.load_size
+
+                # random flip
+                if self.opt.random_flip and np.random.rand() > 0.5:
+                    scale_intrinsic[0, 0] *= -1
+                    render = transforms.RandomHorizontalFlip(p=1.0)(render)
+                    mask = transforms.RandomHorizontalFlip(p=1.0)(mask)
+
+                # random scale
+                if self.opt.random_scale:
+                    rand_scale = random.uniform(0.9, 1.1)
+                    w = int(rand_scale * w)
+                    h = int(rand_scale * h)
+                    render = render.resize((w, h), Image.BILINEAR)
+                    mask = mask.resize((w, h), Image.NEAREST)
+                    scale_intrinsic *= rand_scale
+                    scale_intrinsic[3, 3] = 1
+
+                # random translate in the pixel space
+                if self.opt.random_trans:
+                    dx = random.randint(-int(round((w - tw) / 10.)),
+                                        int(round((w - tw) / 10.)))
+                    dy = random.randint(-int(round((h - th) / 10.)),
+                                        int(round((h - th) / 10.)))
+                else:
+                    dx = 0
+                    dy = 0
+
+                trans_intrinsic[0, 3] = -dx / float(self.opt.loadSize // 2)
+                trans_intrinsic[1, 3] = -dy / float(self.opt.loadSize // 2)
+
+                x1 = int(round((w - tw) / 2.)) + dx
+                y1 = int(round((h - th) / 2.)) + dy
+
+                render = render.crop((x1, y1, x1 + tw, y1 + th))
+                mask = mask.crop((x1, y1, x1 + tw, y1 + th))
+
+                render = self.aug_trans(render)
+
+                # random blur
+                if self.opt.aug_blur > 0.00001:
+                    blur = GaussianBlur(np.random.uniform(0, self.opt.aug_blur))
+                    render = render.filter(blur)
+
+            intrinsic = np.matmul(trans_intrinsic, np.matmul(uv_intrinsic, scale_intrinsic))
+            calib = torch.Tensor(np.matmul(intrinsic, extrinsic)).float()
+            extrinsic = torch.Tensor(extrinsic).float()
+
+            mask = transforms.Resize(self.load_size)(mask)
+            mask = transforms.ToTensor()(mask).float()
+            mask_list.append(mask)
+
+            render = self.to_tensor(render)
+            render = mask.expand_as(render) * render
+
+            render_list.append(render)
+            calib_list.append(calib)
+            extrinsic_list.append(extrinsic)
+
+        return {
+            'img': torch.stack(render_list, dim=0),
+            'calib': torch.stack(calib_list, dim=0),
+            'extrinsic': torch.stack(extrinsic_list, dim=0),
+            'mask': torch.stack(mask_list, dim=0)
+        }
+
+    def select_sampling_method(self, subject):
+        if not self.is_train:
+            random.seed(1991)
+            np.random.seed(1991)
+            torch.manual_seed(1991)
+        mesh = self.mesh_dic[subject]
+        surface_points, _ = trimesh.sample.sample_surface(mesh, 4 * self.num_sample_inout)
+        sample_points = surface_points + np.random.normal(scale=self.opt.sigma, size=surface_points.shape)
+
+        # add random points within image space
+        length = self.B_MAX - self.B_MIN
+        random_points = np.random.rand(self.num_sample_inout // 4, 3) * length + self.B_MIN
+        sample_points = np.concatenate([sample_points, random_points], 0)
+        np.random.shuffle(sample_points)
+
+        inside = mesh.contains(sample_points)
+        inside_points = sample_points[inside]
+        outside_points = sample_points[np.logical_not(inside)]
+
+        nin = inside_points.shape[0]
+        inside_points = inside_points[
+                        :self.num_sample_inout // 2] if nin > self.num_sample_inout // 2 else inside_points
+        outside_points = outside_points[
+                         :self.num_sample_inout // 2] if nin > self.num_sample_inout // 2 else outside_points[
+                                                                                               :(self.num_sample_inout - nin)]
+
+        samples = np.concatenate([inside_points, outside_points], 0).T
+        labels = np.concatenate([np.ones((1, inside_points.shape[0])), np.zeros((1, outside_points.shape[0]))], 1)
+
+        # save_samples_truncted_prob('out.ply', samples.T, labels.T)
+        # exit()
+
+        samples = torch.Tensor(samples).float()
+        labels = torch.Tensor(labels).float()
+        
+        del mesh
+
+        return {
+            'samples': samples,
+            'labels': labels
+        }
+
+
+    def get_color_sampling(self, subject, yid, pid=0):
+        yaw = self.yaw_list[yid]
+        pitch = self.pitch_list[pid]
+        uv_render_path = os.path.join(self.UV_RENDER, subject, '%d_%d_%02d.jpg' % (yaw, pitch, 0))
+        uv_mask_path = os.path.join(self.UV_MASK, subject, '%02d.png' % (0))
+        uv_pos_path = os.path.join(self.UV_POS, subject, '%02d.exr' % (0))
+        uv_normal_path = os.path.join(self.UV_NORMAL, subject, '%02d.png' % (0))
+
+        # Segmentation mask for the uv render.
+        # [H, W] bool
+        uv_mask = cv2.imread(uv_mask_path)
+        uv_mask = uv_mask[:, :, 0] != 0
+        # UV render. each pixel is the color of the point.
+        # [H, W, 3] 0 ~ 1 float
+        uv_render = cv2.imread(uv_render_path)
+        uv_render = cv2.cvtColor(uv_render, cv2.COLOR_BGR2RGB) / 255.0
+
+        # Normal render. each pixel is the surface normal of the point.
+        # [H, W, 3] -1 ~ 1 float
+        uv_normal = cv2.imread(uv_normal_path)
+        uv_normal = cv2.cvtColor(uv_normal, cv2.COLOR_BGR2RGB) / 255.0
+        uv_normal = 2.0 * uv_normal - 1.0
+        # Position render. each pixel is the xyz coordinates of the point
+        uv_pos = cv2.imread(uv_pos_path, 2 | 4)[:, :, ::-1]
+
+        ### In these few lines we flattern the masks, positions, and normals
+        uv_mask = uv_mask.reshape((-1))
+        uv_pos = uv_pos.reshape((-1, 3))
+        uv_render = uv_render.reshape((-1, 3))
+        uv_normal = uv_normal.reshape((-1, 3))
+
+        surface_points = uv_pos[uv_mask]
+        surface_colors = uv_render[uv_mask]
+        surface_normal = uv_normal[uv_mask]
+
+        if self.num_sample_color:
+            sample_list = random.sample(range(0, surface_points.shape[0] - 1), self.num_sample_color)
+            surface_points = surface_points[sample_list].T
+            surface_colors = surface_colors[sample_list].T
+            surface_normal = surface_normal[sample_list].T
+
+        # Samples are around the true surface with an offset
+        normal = torch.Tensor(surface_normal).float()
+        samples = torch.Tensor(surface_points).float() \
+                  + torch.normal(mean=torch.zeros((1, normal.size(1))), std=self.opt.sigma).expand_as(normal) * normal
+
+        # Normalized to [-1, 1]
+        rgbs_color = 2.0 * torch.Tensor(surface_colors).float() - 1.0
+
+        return {
+            'color_samples': samples,
+            'rgbs': rgbs_color
+        }
+
+    def get_item(self, index):
+        # In case of a missing file or IO error, switch to a random sample instead
+        # try:
+        sid = index % len(self.subjects)
+        tmp = index // len(self.subjects)
+        yid = tmp % len(self.yaw_list)
+        pid = tmp // len(self.yaw_list)
+
+        # name of the subject 'rp_xxxx_xxx'
+        subject = self.subjects[sid]
+        res = {
+            'name': subject,
+            'mesh_path': os.path.join(self.OBJ, subject + '.obj'),
+            'sid': sid,
+            'yid': yid,
+            'pid': pid,
+            'b_min': self.B_MIN,
+            'b_max': self.B_MAX,
+        }
+        render_data = self.get_render(subject, num_views=self.num_views, yid=yid, pid=pid,
+                                        random_sample=self.opt.random_multiview)
+        res.update(render_data)
+
+        if self.opt.num_sample_inout:
+            sample_data = self.select_sampling_method(subject)
+            res.update(sample_data)
+        
+        # img = np.uint8((np.transpose(render_data['img'][0].numpy(), (1, 2, 0)) * 0.5 + 0.5)[:, :, ::-1] * 255.0)
+        # rot = render_data['calib'][0,:3, :3]
+        # trans = render_data['calib'][0,:3, 3:4]
+        # pts = torch.addmm(trans, rot, sample_data['samples'][:, sample_data['labels'][0] > 0.5])  # [3, N]
+        # pts = 0.5 * (pts.numpy().T + 1.0) * render_data['img'].size(2)
+        # for p in pts:
+        #     img = cv2.circle(img, (p[0], p[1]), 2, (0,255,0), -1)
+        # cv2.imshow('test', img)
+        # cv2.waitKey(1)
+
+        if self.num_sample_color:
+            color_data = self.get_color_sampling(subject, yid=yid, pid=pid)
+            res.update(color_data)
+        return res
+        # except Exception as e:
+        #     print(e)
+        #     return self.get_item(index=random.randint(0, self.__len__() - 1))
+
+    def __getitem__(self, index):
+        return self.get_item(index)

PIFu/lib/data/__init__.py

@@ -0,0 +1,2 @@
+from .EvalDataset import EvalDataset
+from .TrainDataset import TrainDataset

PIFu/lib/ext_transform.py

@@ -0,0 +1,78 @@
+import random
+
+import numpy as np
+from skimage.filters import gaussian
+import torch
+from PIL import Image, ImageFilter
+
+
+class RandomVerticalFlip(object):
+    def __call__(self, img):
+        if random.random() < 0.5:
+            return img.transpose(Image.FLIP_TOP_BOTTOM)
+        return img
+
+
+class DeNormalize(object):
+    def __init__(self, mean, std):
+        self.mean = mean
+        self.std = std
+
+    def __call__(self, tensor):
+        for t, m, s in zip(tensor, self.mean, self.std):
+            t.mul_(s).add_(m)
+        return tensor
+
+
+class MaskToTensor(object):
+    def __call__(self, img):
+        return torch.from_numpy(np.array(img, dtype=np.int32)).long()
+
+
+class FreeScale(object):
+    def __init__(self, size, interpolation=Image.BILINEAR):
+        self.size = tuple(reversed(size))  # size: (h, w)
+        self.interpolation = interpolation
+
+    def __call__(self, img):
+        return img.resize(self.size, self.interpolation)
+
+
+class FlipChannels(object):
+    def __call__(self, img):
+        img = np.array(img)[:, :, ::-1]
+        return Image.fromarray(img.astype(np.uint8))
+
+
+class RandomGaussianBlur(object):
+    def __call__(self, img):
+        sigma = 0.15 + random.random() * 1.15
+        blurred_img = gaussian(np.array(img), sigma=sigma, multichannel=True)
+        blurred_img *= 255
+        return Image.fromarray(blurred_img.astype(np.uint8))
+
+# Lighting data augmentation take from here - https://github.com/eladhoffer/convNet.pytorch/blob/master/preprocess.py
+
+
+class Lighting(object):
+    """Lighting noise(AlexNet - style PCA - based noise)"""
+
+    def __init__(self, alphastd, 
+                 eigval=(0.2175, 0.0188, 0.0045), 
+                 eigvec=((-0.5675, 0.7192, 0.4009),
+                         (-0.5808, -0.0045, -0.8140),
+                         (-0.5836, -0.6948, 0.4203))):
+        self.alphastd = alphastd
+        self.eigval = torch.Tensor(eigval)
+        self.eigvec = torch.Tensor(eigvec)
+
+    def __call__(self, img):
+        if self.alphastd == 0:
+            return img
+
+        alpha = img.new().resize_(3).normal_(0, self.alphastd)
+        rgb = self.eigvec.type_as(img).clone()\
+            .mul(alpha.view(1, 3).expand(3, 3))\
+            .mul(self.eigval.view(1, 3).expand(3, 3))\
+            .sum(1).squeeze()
+        return img.add(rgb.view(3, 1, 1).expand_as(img))

PIFu/lib/geometry.py

@@ -0,0 +1,55 @@
+import torch
+
+
+def index(feat, uv):
+    '''
+
+    :param feat: [B, C, H, W] image features
+    :param uv: [B, 2, N] uv coordinates in the image plane, range [-1, 1]
+    :return: [B, C, N] image features at the uv coordinates
+    '''
+    uv = uv.transpose(1, 2)  # [B, N, 2]
+    uv = uv.unsqueeze(2)  # [B, N, 1, 2]
+    # NOTE: for newer PyTorch, it seems that training results are degraded due to implementation diff in F.grid_sample
+    # for old versions, simply remove the aligned_corners argument.
+    samples = torch.nn.functional.grid_sample(feat, uv, align_corners=True)  # [B, C, N, 1]
+    return samples[:, :, :, 0]  # [B, C, N]
+
+
+def orthogonal(points, calibrations, transforms=None):
+    '''
+    Compute the orthogonal projections of 3D points into the image plane by given projection matrix
+    :param points: [B, 3, N] Tensor of 3D points
+    :param calibrations: [B, 4, 4] Tensor of projection matrix
+    :param transforms: [B, 2, 3] Tensor of image transform matrix
+    :return: xyz: [B, 3, N] Tensor of xyz coordinates in the image plane
+    '''
+    rot = calibrations[:, :3, :3]
+    trans = calibrations[:, :3, 3:4]
+    pts = torch.baddbmm(trans, rot, points)  # [B, 3, N]
+    if transforms is not None:
+        scale = transforms[:2, :2]
+        shift = transforms[:2, 2:3]
+        pts[:, :2, :] = torch.baddbmm(shift, scale, pts[:, :2, :])
+    return pts
+
+
+def perspective(points, calibrations, transforms=None):
+    '''
+    Compute the perspective projections of 3D points into the image plane by given projection matrix
+    :param points: [Bx3xN] Tensor of 3D points
+    :param calibrations: [Bx4x4] Tensor of projection matrix
+    :param transforms: [Bx2x3] Tensor of image transform matrix
+    :return: xy: [Bx2xN] Tensor of xy coordinates in the image plane
+    '''
+    rot = calibrations[:, :3, :3]
+    trans = calibrations[:, :3, 3:4]
+    homo = torch.baddbmm(trans, rot, points)  # [B, 3, N]
+    xy = homo[:, :2, :] / homo[:, 2:3, :]
+    if transforms is not None:
+        scale = transforms[:2, :2]
+        shift = transforms[:2, 2:3]
+        xy = torch.baddbmm(shift, scale, xy)
+
+    xyz = torch.cat([xy, homo[:, 2:3, :]], 1)
+    return xyz

PIFu/lib/mesh_util.py

@@ -0,0 +1,91 @@
+from skimage import measure
+import numpy as np
+import torch
+from .sdf import create_grid, eval_grid_octree, eval_grid
+from skimage import measure
+
+
+def reconstruction(net, cuda, calib_tensor,
+                   resolution, b_min, b_max,
+                   use_octree=False, num_samples=10000, transform=None):
+    '''
+    Reconstruct meshes from sdf predicted by the network.
+    :param net: a BasePixImpNet object. call image filter beforehead.
+    :param cuda: cuda device
+    :param calib_tensor: calibration tensor
+    :param resolution: resolution of the grid cell
+    :param b_min: bounding box corner [x_min, y_min, z_min]
+    :param b_max: bounding box corner [x_max, y_max, z_max]
+    :param use_octree: whether to use octree acceleration
+    :param num_samples: how many points to query each gpu iteration
+    :return: marching cubes results.
+    '''
+    # First we create a grid by resolution
+    # and transforming matrix for grid coordinates to real world xyz
+    coords, mat = create_grid(resolution, resolution, resolution,
+                              b_min, b_max, transform=transform)
+
+    # Then we define the lambda function for cell evaluation
+    def eval_func(points):
+        points = np.expand_dims(points, axis=0)
+        points = np.repeat(points, net.num_views, axis=0)
+        samples = torch.from_numpy(points).to(device=cuda).float()
+        net.query(samples, calib_tensor)
+        pred = net.get_preds()[0][0]
+        return pred.detach().cpu().numpy()
+
+    # Then we evaluate the grid
+    if use_octree:
+        sdf = eval_grid_octree(coords, eval_func, num_samples=num_samples)
+    else:
+        sdf = eval_grid(coords, eval_func, num_samples=num_samples)
+
+    # Finally we do marching cubes
+    try:
+        verts, faces, normals, values = measure.marching_cubes_lewiner(sdf, 0.5)
+        # transform verts into world coordinate system
+        verts = np.matmul(mat[:3, :3], verts.T) + mat[:3, 3:4]
+        verts = verts.T
+        return verts, faces, normals, values
+    except:
+        print('error cannot marching cubes')
+        return -1
+
+
+def save_obj_mesh(mesh_path, verts, faces):
+    file = open(mesh_path, 'w')
+
+    for v in verts:
+        file.write('v %.4f %.4f %.4f\n' % (v[0], v[1], v[2]))
+    for f in faces:
+        f_plus = f + 1
+        file.write('f %d %d %d\n' % (f_plus[0], f_plus[2], f_plus[1]))
+    file.close()
+
+
+def save_obj_mesh_with_color(mesh_path, verts, faces, colors):
+    file = open(mesh_path, 'w')
+
+    for idx, v in enumerate(verts):
+        c = colors[idx]
+        file.write('v %.4f %.4f %.4f %.4f %.4f %.4f\n' % (v[0], v[1], v[2], c[0], c[1], c[2]))
+    for f in faces:
+        f_plus = f + 1
+        file.write('f %d %d %d\n' % (f_plus[0], f_plus[2], f_plus[1]))
+    file.close()
+
+
+def save_obj_mesh_with_uv(mesh_path, verts, faces, uvs):
+    file = open(mesh_path, 'w')
+
+    for idx, v in enumerate(verts):
+        vt = uvs[idx]
+        file.write('v %.4f %.4f %.4f\n' % (v[0], v[1], v[2]))
+        file.write('vt %.4f %.4f\n' % (vt[0], vt[1]))
+
+    for f in faces:
+        f_plus = f + 1
+        file.write('f %d/%d %d/%d %d/%d\n' % (f_plus[0], f_plus[0],
+                                              f_plus[2], f_plus[2],
+                                              f_plus[1], f_plus[1]))
+    file.close()

PIFu/lib/model/BasePIFuNet.py

@@ -0,0 +1,76 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ..geometry import index, orthogonal, perspective
+
+class BasePIFuNet(nn.Module):
+    def __init__(self,
+                 projection_mode='orthogonal',
+                 error_term=nn.MSELoss(),
+                 ):
+        """
+        :param projection_mode:
+        Either orthogonal or perspective.
+        It will call the corresponding function for projection.
+        :param error_term:
+        nn Loss between the predicted [B, Res, N] and the label [B, Res, N]
+        """
+        super(BasePIFuNet, self).__init__()
+        self.name = 'base'
+
+        self.error_term = error_term
+
+        self.index = index
+        self.projection = orthogonal if projection_mode == 'orthogonal' else perspective
+
+        self.preds = None
+        self.labels = None
+
+    def forward(self, points, images, calibs, transforms=None):
+        '''
+        :param points: [B, 3, N] world space coordinates of points
+        :param images: [B, C, H, W] input images
+        :param calibs: [B, 3, 4] calibration matrices for each image
+        :param transforms: Optional [B, 2, 3] image space coordinate transforms
+        :return: [B, Res, N] predictions for each point
+        '''
+        self.filter(images)
+        self.query(points, calibs, transforms)
+        return self.get_preds()
+
+    def filter(self, images):
+        '''
+        Filter the input images
+        store all intermediate features.
+        :param images: [B, C, H, W] input images
+        '''
+        None
+
+    def query(self, points, calibs, transforms=None, labels=None):
+        '''
+        Given 3D points, query the network predictions for each point.
+        Image features should be pre-computed before this call.
+        store all intermediate features.
+        query() function may behave differently during training/testing.
+        :param points: [B, 3, N] world space coordinates of points
+        :param calibs: [B, 3, 4] calibration matrices for each image
+        :param transforms: Optional [B, 2, 3] image space coordinate transforms
+        :param labels: Optional [B, Res, N] gt labeling
+        :return: [B, Res, N] predictions for each point
+        '''
+        None
+
+    def get_preds(self):
+        '''
+        Get the predictions from the last query
+        :return: [B, Res, N] network prediction for the last query
+        '''
+        return self.preds
+
+    def get_error(self):
+        '''
+        Get the network loss from the last query
+        :return: loss term
+        '''
+        return self.error_term(self.preds, self.labels)

PIFu/lib/model/ConvFilters.py

@@ -0,0 +1,112 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision.models.resnet as resnet
+import torchvision.models.vgg as vgg
+
+
+class MultiConv(nn.Module):
+    def __init__(self, filter_channels):
+        super(MultiConv, self).__init__()
+        self.filters = []
+
+        for l in range(0, len(filter_channels) - 1):
+            self.filters.append(
+                nn.Conv2d(filter_channels[l], filter_channels[l + 1], kernel_size=4, stride=2))
+            self.add_module("conv%d" % l, self.filters[l])
+
+    def forward(self, image):
+        '''
+        :param image: [BxC_inxHxW] tensor of input image
+        :return: list of [BxC_outxHxW] tensors of output features
+        '''
+        y = image
+        # y = F.relu(self.bn0(self.conv0(y)), True)
+        feat_pyramid = [y]
+        for i, f in enumerate(self.filters):
+            y = f(y)
+            if i != len(self.filters) - 1:
+                y = F.leaky_relu(y)
+            # y = F.max_pool2d(y, kernel_size=2, stride=2)
+            feat_pyramid.append(y)
+        return feat_pyramid
+
+
+class Vgg16(torch.nn.Module):
+    def __init__(self):
+        super(Vgg16, self).__init__()
+        vgg_pretrained_features = vgg.vgg16(pretrained=True).features
+        self.slice1 = torch.nn.Sequential()
+        self.slice2 = torch.nn.Sequential()
+        self.slice3 = torch.nn.Sequential()
+        self.slice4 = torch.nn.Sequential()
+        self.slice5 = torch.nn.Sequential()
+
+        for x in range(4):
+            self.slice1.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(4, 9):
+            self.slice2.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(9, 16):
+            self.slice3.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(16, 23):
+            self.slice4.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(23, 30):
+            self.slice5.add_module(str(x), vgg_pretrained_features[x])
+
+    def forward(self, X):
+        h = self.slice1(X)
+        h_relu1_2 = h
+        h = self.slice2(h)
+        h_relu2_2 = h
+        h = self.slice3(h)
+        h_relu3_3 = h
+        h = self.slice4(h)
+        h_relu4_3 = h
+        h = self.slice5(h)
+        h_relu5_3 = h
+
+        return [h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3]
+
+
+class ResNet(nn.Module):
+    def __init__(self, model='resnet18'):
+        super(ResNet, self).__init__()
+
+        if model == 'resnet18':
+            net = resnet.resnet18(pretrained=True)
+        elif model == 'resnet34':
+            net = resnet.resnet34(pretrained=True)
+        elif model == 'resnet50':
+            net = resnet.resnet50(pretrained=True)
+        else:
+            raise NameError('Unknown Fan Filter setting!')
+
+        self.conv1 = net.conv1
+
+        self.pool = net.maxpool
+        self.layer0 = nn.Sequential(net.conv1, net.bn1, net.relu)
+        self.layer1 = net.layer1
+        self.layer2 = net.layer2
+        self.layer3 = net.layer3
+        self.layer4 = net.layer4
+
+    def forward(self, image):
+        '''
+        :param image: [BxC_inxHxW] tensor of input image
+        :return: list of [BxC_outxHxW] tensors of output features
+        '''
+
+        y = image
+        feat_pyramid = []
+        y = self.layer0(y)
+        feat_pyramid.append(y)
+        y = self.layer1(self.pool(y))
+        feat_pyramid.append(y)
+        y = self.layer2(y)
+        feat_pyramid.append(y)
+        y = self.layer3(y)
+        feat_pyramid.append(y)
+        y = self.layer4(y)
+        feat_pyramid.append(y)
+
+        return feat_pyramid

PIFu/lib/model/ConvPIFuNet.py

@@ -0,0 +1,99 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from .BasePIFuNet import BasePIFuNet
+from .SurfaceClassifier import SurfaceClassifier
+from .DepthNormalizer import DepthNormalizer
+from .ConvFilters import *
+from ..net_util import init_net
+
+class ConvPIFuNet(BasePIFuNet):
+    '''
+    Conv Piximp network is the standard 3-phase network that we will use.
+    The image filter is a pure multi-layer convolutional network,
+    while during feature extraction phase all features in the pyramid at the projected location
+    will be aggregated.
+    It does the following:
+        1. Compute image feature pyramids and store it in self.im_feat_list
+        2. Calculate calibration and indexing on each of the feat, and append them together
+        3. Classification.
+    '''
+
+    def __init__(self,
+                 opt,
+                 projection_mode='orthogonal',
+                 error_term=nn.MSELoss(),
+                 ):
+        super(ConvPIFuNet, self).__init__(
+            projection_mode=projection_mode,
+            error_term=error_term)
+
+        self.name = 'convpifu'
+
+        self.opt = opt
+        self.num_views = self.opt.num_views
+
+        self.image_filter = self.define_imagefilter(opt)
+
+        self.surface_classifier = SurfaceClassifier(
+            filter_channels=self.opt.mlp_dim,
+            num_views=self.opt.num_views,
+            no_residual=self.opt.no_residual,
+            last_op=nn.Sigmoid())
+
+        self.normalizer = DepthNormalizer(opt)
+
+        # This is a list of [B x Feat_i x H x W] features
+        self.im_feat_list = []
+
+        init_net(self)
+
+    def define_imagefilter(self, opt):
+        net = None
+        if opt.netIMF == 'multiconv':
+            net = MultiConv(opt.enc_dim)
+        elif 'resnet' in opt.netIMF:
+            net = ResNet(model=opt.netIMF)
+        elif opt.netIMF == 'vgg16':
+            net = Vgg16()
+        else:
+            raise NotImplementedError('model name [%s] is not recognized' % opt.imf_type)
+
+        return net
+
+    def filter(self, images):
+        '''
+        Filter the input images
+        store all intermediate features.
+        :param images: [B, C, H, W] input images
+        '''
+        self.im_feat_list = self.image_filter(images)
+
+    def query(self, points, calibs, transforms=None, labels=None):
+        '''
+        Given 3D points, query the network predictions for each point.
+        Image features should be pre-computed before this call.
+        store all intermediate features.
+        query() function may behave differently during training/testing.
+        :param points: [B, 3, N] world space coordinates of points
+        :param calibs: [B, 3, 4] calibration matrices for each image
+        :param transforms: Optional [B, 2, 3] image space coordinate transforms
+        :param labels: Optional [B, Res, N] gt labeling
+        :return: [B, Res, N] predictions for each point
+        '''
+        if labels is not None:
+            self.labels = labels
+
+        xyz = self.projection(points, calibs, transforms)
+        xy = xyz[:, :2, :]
+        z = xyz[:, 2:3, :]
+
+        z_feat = self.normalizer(z)
+
+        # This is a list of [B, Feat_i, N] features
+        point_local_feat_list = [self.index(im_feat, xy) for im_feat in self.im_feat_list]
+        point_local_feat_list.append(z_feat)
+        # [B, Feat_all, N]
+        point_local_feat = torch.cat(point_local_feat_list, 1)
+
+        self.preds = self.surface_classifier(point_local_feat)

PIFu/lib/model/DepthNormalizer.py

@@ -0,0 +1,18 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class DepthNormalizer(nn.Module):
+    def __init__(self, opt):
+        super(DepthNormalizer, self).__init__()
+        self.opt = opt
+
+    def forward(self, z, calibs=None, index_feat=None):
+        '''
+        Normalize z_feature
+        :param z_feat: [B, 1, N] depth value for z in the image coordinate system
+        :return:
+        '''
+        z_feat = z * (self.opt.loadSize // 2) / self.opt.z_size
+        return z_feat

PIFu/lib/model/HGFilters.py

@@ -0,0 +1,146 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from ..net_util import *
+
+
+class HourGlass(nn.Module):
+    def __init__(self, num_modules, depth, num_features, norm='batch'):
+        super(HourGlass, self).__init__()
+        self.num_modules = num_modules
+        self.depth = depth
+        self.features = num_features
+        self.norm = norm
+
+        self._generate_network(self.depth)
+
+    def _generate_network(self, level):
+        self.add_module('b1_' + str(level), ConvBlock(self.features, self.features, norm=self.norm))
+
+        self.add_module('b2_' + str(level), ConvBlock(self.features, self.features, norm=self.norm))
+
+        if level > 1:
+            self._generate_network(level - 1)
+        else:
+            self.add_module('b2_plus_' + str(level), ConvBlock(self.features, self.features, norm=self.norm))
+
+        self.add_module('b3_' + str(level), ConvBlock(self.features, self.features, norm=self.norm))
+
+    def _forward(self, level, inp):
+        # Upper branch
+        up1 = inp
+        up1 = self._modules['b1_' + str(level)](up1)
+
+        # Lower branch
+        low1 = F.avg_pool2d(inp, 2, stride=2)
+        low1 = self._modules['b2_' + str(level)](low1)
+
+        if level > 1:
+            low2 = self._forward(level - 1, low1)
+        else:
+            low2 = low1
+            low2 = self._modules['b2_plus_' + str(level)](low2)
+
+        low3 = low2
+        low3 = self._modules['b3_' + str(level)](low3)
+
+        # NOTE: for newer PyTorch (1.3~), it seems that training results are degraded due to implementation diff in F.grid_sample
+        # if the pretrained model behaves weirdly, switch with the commented line.
+        # NOTE: I also found that "bicubic" works better.
+        up2 = F.interpolate(low3, scale_factor=2, mode='bicubic', align_corners=True)
+        # up2 = F.interpolate(low3, scale_factor=2, mode='nearest)
+
+        return up1 + up2
+
+    def forward(self, x):
+        return self._forward(self.depth, x)
+
+
+class HGFilter(nn.Module):
+    def __init__(self, opt):
+        super(HGFilter, self).__init__()
+        self.num_modules = opt.num_stack
+
+        self.opt = opt
+
+        # Base part
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
+
+        if self.opt.norm == 'batch':
+            self.bn1 = nn.BatchNorm2d(64)
+        elif self.opt.norm == 'group':
+            self.bn1 = nn.GroupNorm(32, 64)
+
+        if self.opt.hg_down == 'conv64':
+            self.conv2 = ConvBlock(64, 64, self.opt.norm)
+            self.down_conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1)
+        elif self.opt.hg_down == 'conv128':
+            self.conv2 = ConvBlock(64, 128, self.opt.norm)
+            self.down_conv2 = nn.Conv2d(128, 128, kernel_size=3, stride=2, padding=1)
+        elif self.opt.hg_down == 'ave_pool':
+            self.conv2 = ConvBlock(64, 128, self.opt.norm)
+        else:
+            raise NameError('Unknown Fan Filter setting!')
+
+        self.conv3 = ConvBlock(128, 128, self.opt.norm)
+        self.conv4 = ConvBlock(128, 256, self.opt.norm)
+
+        # Stacking part
+        for hg_module in range(self.num_modules):
+            self.add_module('m' + str(hg_module), HourGlass(1, opt.num_hourglass, 256, self.opt.norm))
+
+            self.add_module('top_m_' + str(hg_module), ConvBlock(256, 256, self.opt.norm))
+            self.add_module('conv_last' + str(hg_module),
+                            nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0))
+            if self.opt.norm == 'batch':
+                self.add_module('bn_end' + str(hg_module), nn.BatchNorm2d(256))
+            elif self.opt.norm == 'group':
+                self.add_module('bn_end' + str(hg_module), nn.GroupNorm(32, 256))
+                
+            self.add_module('l' + str(hg_module), nn.Conv2d(256,
+                                                            opt.hourglass_dim, kernel_size=1, stride=1, padding=0))
+
+            if hg_module < self.num_modules - 1:
+                self.add_module(
+                    'bl' + str(hg_module), nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0))
+                self.add_module('al' + str(hg_module), nn.Conv2d(opt.hourglass_dim,
+                                                                 256, kernel_size=1, stride=1, padding=0))
+
+    def forward(self, x):
+        x = F.relu(self.bn1(self.conv1(x)), True)
+        tmpx = x
+        if self.opt.hg_down == 'ave_pool':
+            x = F.avg_pool2d(self.conv2(x), 2, stride=2)
+        elif self.opt.hg_down in ['conv64', 'conv128']:
+            x = self.conv2(x)
+            x = self.down_conv2(x)
+        else:
+            raise NameError('Unknown Fan Filter setting!')
+
+        normx = x
+
+        x = self.conv3(x)
+        x = self.conv4(x)
+
+        previous = x
+
+        outputs = []
+        for i in range(self.num_modules):
+            hg = self._modules['m' + str(i)](previous)
+
+            ll = hg
+            ll = self._modules['top_m_' + str(i)](ll)
+
+            ll = F.relu(self._modules['bn_end' + str(i)]
+                        (self._modules['conv_last' + str(i)](ll)), True)
+
+            # Predict heatmaps
+            tmp_out = self._modules['l' + str(i)](ll)
+            outputs.append(tmp_out)
+
+            if i < self.num_modules - 1:
+                ll = self._modules['bl' + str(i)](ll)
+                tmp_out_ = self._modules['al' + str(i)](tmp_out)
+                previous = previous + ll + tmp_out_
+
+        return outputs, tmpx.detach(), normx

PIFu/lib/model/HGPIFuNet.py

@@ -0,0 +1,142 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from .BasePIFuNet import BasePIFuNet
+from .SurfaceClassifier import SurfaceClassifier
+from .DepthNormalizer import DepthNormalizer
+from .HGFilters import *
+from ..net_util import init_net
+
+
+class HGPIFuNet(BasePIFuNet):
+    '''
+    HG PIFu network uses Hourglass stacks as the image filter.
+    It does the following:
+        1. Compute image feature stacks and store it in self.im_feat_list
+            self.im_feat_list[-1] is the last stack (output stack)
+        2. Calculate calibration
+        3. If training, it index on every intermediate stacks,
+            If testing, it index on the last stack.
+        4. Classification.
+        5. During training, error is calculated on all stacks.
+    '''
+
+    def __init__(self,
+                 opt,
+                 projection_mode='orthogonal',
+                 error_term=nn.MSELoss(),
+                 ):
+        super(HGPIFuNet, self).__init__(
+            projection_mode=projection_mode,
+            error_term=error_term)
+
+        self.name = 'hgpifu'
+
+        self.opt = opt
+        self.num_views = self.opt.num_views
+
+        self.image_filter = HGFilter(opt)
+
+        self.surface_classifier = SurfaceClassifier(
+            filter_channels=self.opt.mlp_dim,
+            num_views=self.opt.num_views,
+            no_residual=self.opt.no_residual,
+            last_op=nn.Sigmoid())
+
+        self.normalizer = DepthNormalizer(opt)
+
+        # This is a list of [B x Feat_i x H x W] features
+        self.im_feat_list = []
+        self.tmpx = None
+        self.normx = None
+
+        self.intermediate_preds_list = []
+
+        init_net(self)
+
+    def filter(self, images):
+        '''
+        Filter the input images
+        store all intermediate features.
+        :param images: [B, C, H, W] input images
+        '''
+        self.im_feat_list, self.tmpx, self.normx = self.image_filter(images)
+        # If it is not in training, only produce the last im_feat
+        if not self.training:
+            self.im_feat_list = [self.im_feat_list[-1]]
+
+    def query(self, points, calibs, transforms=None, labels=None):
+        '''
+        Given 3D points, query the network predictions for each point.
+        Image features should be pre-computed before this call.
+        store all intermediate features.
+        query() function may behave differently during training/testing.
+        :param points: [B, 3, N] world space coordinates of points
+        :param calibs: [B, 3, 4] calibration matrices for each image
+        :param transforms: Optional [B, 2, 3] image space coordinate transforms
+        :param labels: Optional [B, Res, N] gt labeling
+        :return: [B, Res, N] predictions for each point
+        '''
+        if labels is not None:
+            self.labels = labels
+
+        xyz = self.projection(points, calibs, transforms)
+        xy = xyz[:, :2, :]
+        z = xyz[:, 2:3, :]
+
+        in_img = (xy[:, 0] >= -1.0) & (xy[:, 0] <= 1.0) & (xy[:, 1] >= -1.0) & (xy[:, 1] <= 1.0)
+
+        z_feat = self.normalizer(z, calibs=calibs)
+
+        if self.opt.skip_hourglass:
+            tmpx_local_feature = self.index(self.tmpx, xy)
+
+        self.intermediate_preds_list = []
+
+        for im_feat in self.im_feat_list:
+            # [B, Feat_i + z, N]
+            point_local_feat_list = [self.index(im_feat, xy), z_feat]
+
+            if self.opt.skip_hourglass:
+                point_local_feat_list.append(tmpx_local_feature)
+
+            point_local_feat = torch.cat(point_local_feat_list, 1)
+
+            # out of image plane is always set to 0
+            pred = in_img[:,None].float() * self.surface_classifier(point_local_feat)
+            self.intermediate_preds_list.append(pred)
+
+        self.preds = self.intermediate_preds_list[-1]
+
+    def get_im_feat(self):
+        '''
+        Get the image filter
+        :return: [B, C_feat, H, W] image feature after filtering
+        '''
+        return self.im_feat_list[-1]
+
+    def get_error(self):
+        '''
+        Hourglass has its own intermediate supervision scheme
+        '''
+        error = 0
+        for preds in self.intermediate_preds_list:
+            error += self.error_term(preds, self.labels)
+        error /= len(self.intermediate_preds_list)
+        
+        return error
+
+    def forward(self, images, points, calibs, transforms=None, labels=None):
+        # Get image feature
+        self.filter(images)
+
+        # Phase 2: point query
+        self.query(points=points, calibs=calibs, transforms=transforms, labels=labels)
+
+        # get the prediction
+        res = self.get_preds()
+        
+        # get the error
+        error = self.get_error()
+
+        return res, error

PIFu/lib/model/ResBlkPIFuNet.py

@@ -0,0 +1,201 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from .BasePIFuNet import BasePIFuNet
+import functools
+from .SurfaceClassifier import SurfaceClassifier
+from .DepthNormalizer import DepthNormalizer
+from ..net_util import *
+
+
+class ResBlkPIFuNet(BasePIFuNet):
+    def __init__(self, opt,
+                 projection_mode='orthogonal'):
+        if opt.color_loss_type == 'l1':
+            error_term = nn.L1Loss()
+        elif opt.color_loss_type == 'mse':
+            error_term = nn.MSELoss()
+
+        super(ResBlkPIFuNet, self).__init__(
+            projection_mode=projection_mode,
+            error_term=error_term)
+
+        self.name = 'respifu'
+        self.opt = opt
+
+        norm_type = get_norm_layer(norm_type=opt.norm_color)
+        self.image_filter = ResnetFilter(opt, norm_layer=norm_type)
+
+        self.surface_classifier = SurfaceClassifier(
+            filter_channels=self.opt.mlp_dim_color,
+            num_views=self.opt.num_views,
+            no_residual=self.opt.no_residual,
+            last_op=nn.Tanh())
+
+        self.normalizer = DepthNormalizer(opt)
+
+        init_net(self)
+
+    def filter(self, images):
+        '''
+        Filter the input images
+        store all intermediate features.
+        :param images: [B, C, H, W] input images
+        '''
+        self.im_feat = self.image_filter(images)
+
+    def attach(self, im_feat):
+        self.im_feat = torch.cat([im_feat, self.im_feat], 1)
+
+    def query(self, points, calibs, transforms=None, labels=None):
+        '''
+        Given 3D points, query the network predictions for each point.
+        Image features should be pre-computed before this call.
+        store all intermediate features.
+        query() function may behave differently during training/testing.
+        :param points: [B, 3, N] world space coordinates of points
+        :param calibs: [B, 3, 4] calibration matrices for each image
+        :param transforms: Optional [B, 2, 3] image space coordinate transforms
+        :param labels: Optional [B, Res, N] gt labeling
+        :return: [B, Res, N] predictions for each point
+        '''
+        if labels is not None:
+            self.labels = labels
+
+        xyz = self.projection(points, calibs, transforms)
+        xy = xyz[:, :2, :]
+        z = xyz[:, 2:3, :]
+
+        z_feat = self.normalizer(z)
+
+        # This is a list of [B, Feat_i, N] features
+        point_local_feat_list = [self.index(self.im_feat, xy), z_feat]
+        # [B, Feat_all, N]
+        point_local_feat = torch.cat(point_local_feat_list, 1)
+
+        self.preds = self.surface_classifier(point_local_feat)
+
+    def forward(self, images, im_feat, points, calibs, transforms=None, labels=None):
+        self.filter(images)
+
+        self.attach(im_feat)
+
+        self.query(points, calibs, transforms, labels)
+
+        res = self.get_preds()
+        error = self.get_error()
+
+        return res, error
+
+class ResnetBlock(nn.Module):
+    """Define a Resnet block"""
+
+    def __init__(self, dim, padding_type, norm_layer, use_dropout, use_bias, last=False):
+        """Initialize the Resnet block
+        A resnet block is a conv block with skip connections
+        We construct a conv block with build_conv_block function,
+        and implement skip connections in <forward> function.
+        Original Resnet paper: https://arxiv.org/pdf/1512.03385.pdf
+        """
+        super(ResnetBlock, self).__init__()
+        self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, use_dropout, use_bias, last)
+
+    def build_conv_block(self, dim, padding_type, norm_layer, use_dropout, use_bias, last=False):
+        """Construct a convolutional block.
+        Parameters:
+            dim (int)           -- the number of channels in the conv layer.
+            padding_type (str)  -- the name of padding layer: reflect | replicate | zero
+            norm_layer          -- normalization layer
+            use_dropout (bool)  -- if use dropout layers.
+            use_bias (bool)     -- if the conv layer uses bias or not
+        Returns a conv block (with a conv layer, a normalization layer, and a non-linearity layer (ReLU))
+        """
+        conv_block = []
+        p = 0
+        if padding_type == 'reflect':
+            conv_block += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv_block += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+
+        conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), norm_layer(dim), nn.ReLU(True)]
+        if use_dropout:
+            conv_block += [nn.Dropout(0.5)]
+
+        p = 0
+        if padding_type == 'reflect':
+            conv_block += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv_block += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+        if last:
+            conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias)]
+        else:
+            conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), norm_layer(dim)]
+
+        return nn.Sequential(*conv_block)
+
+    def forward(self, x):
+        """Forward function (with skip connections)"""
+        out = x + self.conv_block(x)  # add skip connections
+        return out
+
+
+class ResnetFilter(nn.Module):
+    """Resnet-based generator that consists of Resnet blocks between a few downsampling/upsampling operations.
+    We adapt Torch code and idea from Justin Johnson's neural style transfer project(https://github.com/jcjohnson/fast-neural-style)
+    """
+
+    def __init__(self, opt, input_nc=3, output_nc=256, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False,
+                 n_blocks=6, padding_type='reflect'):
+        """Construct a Resnet-based generator
+        Parameters:
+            input_nc (int)      -- the number of channels in input images
+            output_nc (int)     -- the number of channels in output images
+            ngf (int)           -- the number of filters in the last conv layer
+            norm_layer          -- normalization layer
+            use_dropout (bool)  -- if use dropout layers
+            n_blocks (int)      -- the number of ResNet blocks
+            padding_type (str)  -- the name of padding layer in conv layers: reflect | replicate | zero
+        """
+        assert (n_blocks >= 0)
+        super(ResnetFilter, self).__init__()
+        if type(norm_layer) == functools.partial:
+            use_bias = norm_layer.func == nn.InstanceNorm2d
+        else:
+            use_bias = norm_layer == nn.InstanceNorm2d
+
+        model = [nn.ReflectionPad2d(3),
+                 nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0, bias=use_bias),
+                 norm_layer(ngf),
+                 nn.ReLU(True)]
+
+        n_downsampling = 2
+        for i in range(n_downsampling):  # add downsampling layers
+            mult = 2 ** i
+            model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1, bias=use_bias),
+                      norm_layer(ngf * mult * 2),
+                      nn.ReLU(True)]
+
+        mult = 2 ** n_downsampling
+        for i in range(n_blocks):  # add ResNet blocks
+            if i == n_blocks - 1:
+                model += [ResnetBlock(ngf * mult, padding_type=padding_type, norm_layer=norm_layer,
+                                      use_dropout=use_dropout, use_bias=use_bias, last=True)]
+            else:
+                model += [ResnetBlock(ngf * mult, padding_type=padding_type, norm_layer=norm_layer,
+                                      use_dropout=use_dropout, use_bias=use_bias)]
+
+        if opt.use_tanh:
+            model += [nn.Tanh()]
+        self.model = nn.Sequential(*model)
+
+    def forward(self, input):
+        """Standard forward"""
+        return self.model(input)

PIFu/lib/model/SurfaceClassifier.py

@@ -0,0 +1,71 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class SurfaceClassifier(nn.Module):
+    def __init__(self, filter_channels, num_views=1, no_residual=True, last_op=None):
+        super(SurfaceClassifier, self).__init__()
+
+        self.filters = []
+        self.num_views = num_views
+        self.no_residual = no_residual
+        filter_channels = filter_channels
+        self.last_op = last_op
+
+        if self.no_residual:
+            for l in range(0, len(filter_channels) - 1):
+                self.filters.append(nn.Conv1d(
+                    filter_channels[l],
+                    filter_channels[l + 1],
+                    1))
+                self.add_module("conv%d" % l, self.filters[l])
+        else:
+            for l in range(0, len(filter_channels) - 1):
+                if 0 != l:
+                    self.filters.append(
+                        nn.Conv1d(
+                            filter_channels[l] + filter_channels[0],
+                            filter_channels[l + 1],
+                            1))
+                else:
+                    self.filters.append(nn.Conv1d(
+                        filter_channels[l],
+                        filter_channels[l + 1],
+                        1))
+
+                self.add_module("conv%d" % l, self.filters[l])
+
+    def forward(self, feature):
+        '''
+
+        :param feature: list of [BxC_inxHxW] tensors of image features
+        :param xy: [Bx3xN] tensor of (x,y) coodinates in the image plane
+        :return: [BxC_outxN] tensor of features extracted at the coordinates
+        '''
+
+        y = feature
+        tmpy = feature
+        for i, f in enumerate(self.filters):
+            if self.no_residual:
+                y = self._modules['conv' + str(i)](y)
+            else:
+                y = self._modules['conv' + str(i)](
+                    y if i == 0
+                    else torch.cat([y, tmpy], 1)
+                )
+            if i != len(self.filters) - 1:
+                y = F.leaky_relu(y)
+
+            if self.num_views > 1 and i == len(self.filters) // 2:
+                y = y.view(
+                    -1, self.num_views, y.shape[1], y.shape[2]
+                ).mean(dim=1)
+                tmpy = feature.view(
+                    -1, self.num_views, feature.shape[1], feature.shape[2]
+                ).mean(dim=1)
+
+        if self.last_op:
+            y = self.last_op(y)
+
+        return y

PIFu/lib/model/VhullPIFuNet.py

@@ -0,0 +1,70 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from .BasePIFuNet import BasePIFuNet
+
+
+class VhullPIFuNet(BasePIFuNet):
+    '''
+    Vhull Piximp network is a minimal network demonstrating how the template works
+    also, it helps debugging the training/test schemes
+    It does the following:
+        1. Compute the masks of images and stores under self.im_feats
+        2. Calculate calibration and indexing
+        3. Return if the points fall into the intersection of all masks
+    '''
+
+    def __init__(self,
+                 num_views,
+                 projection_mode='orthogonal',
+                 error_term=nn.MSELoss(),
+                 ):
+        super(VhullPIFuNet, self).__init__(
+            projection_mode=projection_mode,
+            error_term=error_term)
+        self.name = 'vhull'
+
+        self.num_views = num_views
+
+        self.im_feat = None
+
+    def filter(self, images):
+        '''
+        Filter the input images
+        store all intermediate features.
+        :param images: [B, C, H, W] input images
+        '''
+        # If the image has alpha channel, use the alpha channel
+        if images.shape[1] > 3:
+            self.im_feat = images[:, 3:4, :, :]
+        # Else, tell if it's not white
+        else:
+            self.im_feat = images[:, 0:1, :, :]
+
+    def query(self, points, calibs, transforms=None, labels=None):
+        '''
+        Given 3D points, query the network predictions for each point.
+        Image features should be pre-computed before this call.
+        store all intermediate features.
+        query() function may behave differently during training/testing.
+        :param points: [B, 3, N] world space coordinates of points
+        :param calibs: [B, 3, 4] calibration matrices for each image
+        :param transforms: Optional [B, 2, 3] image space coordinate transforms
+        :param labels: Optional [B, Res, N] gt labeling
+        :return: [B, Res, N] predictions for each point
+        '''
+        if labels is not None:
+            self.labels = labels
+
+        xyz = self.projection(points, calibs, transforms)
+        xy = xyz[:, :2, :]
+
+        point_local_feat = self.index(self.im_feat, xy)
+        local_shape = point_local_feat.shape
+        point_feat = point_local_feat.view(
+            local_shape[0] // self.num_views,
+            local_shape[1] * self.num_views,
+            -1)
+        pred = torch.prod(point_feat, dim=1)
+
+        self.preds = pred.unsqueeze(1)

PIFu/lib/model/__init__.py

@@ -0,0 +1,5 @@
+from .BasePIFuNet import BasePIFuNet
+from .VhullPIFuNet import VhullPIFuNet
+from .ConvPIFuNet import ConvPIFuNet
+from .HGPIFuNet import HGPIFuNet
+from .ResBlkPIFuNet import ResBlkPIFuNet

PIFu/lib/net_util.py

@@ -0,0 +1,396 @@
+import torch
+from torch.nn import init
+import torch.nn as nn
+import torch.nn.functional as F
+import functools
+
+import numpy as np
+from .mesh_util import *
+from .sample_util import *
+from .geometry import index
+import cv2
+from PIL import Image
+from tqdm import tqdm
+
+
+def reshape_multiview_tensors(image_tensor, calib_tensor):
+    # Careful here! Because we put single view and multiview together,
+    # the returned tensor.shape is 5-dim: [B, num_views, C, W, H]
+    # So we need to convert it back to 4-dim [B*num_views, C, W, H]
+    # Don't worry classifier will handle multi-view cases
+    image_tensor = image_tensor.view(
+        image_tensor.shape[0] * image_tensor.shape[1],
+        image_tensor.shape[2],
+        image_tensor.shape[3],
+        image_tensor.shape[4]
+    )
+    calib_tensor = calib_tensor.view(
+        calib_tensor.shape[0] * calib_tensor.shape[1],
+        calib_tensor.shape[2],
+        calib_tensor.shape[3]
+    )
+
+    return image_tensor, calib_tensor
+
+
+def reshape_sample_tensor(sample_tensor, num_views):
+    if num_views == 1:
+        return sample_tensor
+    # Need to repeat sample_tensor along the batch dim num_views times
+    sample_tensor = sample_tensor.unsqueeze(dim=1)
+    sample_tensor = sample_tensor.repeat(1, num_views, 1, 1)
+    sample_tensor = sample_tensor.view(
+        sample_tensor.shape[0] * sample_tensor.shape[1],
+        sample_tensor.shape[2],
+        sample_tensor.shape[3]
+    )
+    return sample_tensor
+
+
+def gen_mesh(opt, net, cuda, data, save_path, use_octree=True):
+    image_tensor = data['img'].to(device=cuda)
+    calib_tensor = data['calib'].to(device=cuda)
+
+    net.filter(image_tensor)
+
+    b_min = data['b_min']
+    b_max = data['b_max']
+    try:
+        save_img_path = save_path[:-4] + '.png'
+        save_img_list = []
+        for v in range(image_tensor.shape[0]):
+            save_img = (np.transpose(image_tensor[v].detach().cpu().numpy(), (1, 2, 0)) * 0.5 + 0.5)[:, :, ::-1] * 255.0
+            save_img_list.append(save_img)
+        save_img = np.concatenate(save_img_list, axis=1)
+        Image.fromarray(np.uint8(save_img[:,:,::-1])).save(save_img_path)
+
+        verts, faces, _, _ = reconstruction(
+            net, cuda, calib_tensor, opt.resolution, b_min, b_max, use_octree=use_octree)
+        verts_tensor = torch.from_numpy(verts.T).unsqueeze(0).to(device=cuda).float()
+        xyz_tensor = net.projection(verts_tensor, calib_tensor[:1])
+        uv = xyz_tensor[:, :2, :]
+        color = index(image_tensor[:1], uv).detach().cpu().numpy()[0].T
+        color = color * 0.5 + 0.5
+        save_obj_mesh_with_color(save_path, verts, faces, color)
+    except Exception as e:
+        print(e)
+        print('Can not create marching cubes at this time.')
+
+def gen_mesh_color(opt, netG, netC, cuda, data, save_path, use_octree=True):
+    image_tensor = data['img'].to(device=cuda)
+    calib_tensor = data['calib'].to(device=cuda)
+
+    netG.filter(image_tensor)
+    netC.filter(image_tensor)
+    netC.attach(netG.get_im_feat())
+
+    b_min = data['b_min']
+    b_max = data['b_max']
+    try:
+        save_img_path = save_path[:-4] + '.png'
+        save_img_list = []
+        for v in range(image_tensor.shape[0]):
+            save_img = (np.transpose(image_tensor[v].detach().cpu().numpy(), (1, 2, 0)) * 0.5 + 0.5)[:, :, ::-1] * 255.0
+            save_img_list.append(save_img)
+        save_img = np.concatenate(save_img_list, axis=1)
+        Image.fromarray(np.uint8(save_img[:,:,::-1])).save(save_img_path)
+
+        verts, faces, _, _ = reconstruction(
+            netG, cuda, calib_tensor, opt.resolution, b_min, b_max, use_octree=use_octree)
+
+        # Now Getting colors
+        verts_tensor = torch.from_numpy(verts.T).unsqueeze(0).to(device=cuda).float()
+        verts_tensor = reshape_sample_tensor(verts_tensor, opt.num_views)
+
+        color = np.zeros(verts.shape)
+        interval = opt.num_sample_color
+        for i in range(len(color) // interval):
+            left = i * interval
+            right = i * interval + interval
+            if i == len(color) // interval - 1:
+                right = -1
+            netC.query(verts_tensor[:, :, left:right], calib_tensor)
+            rgb = netC.get_preds()[0].detach().cpu().numpy() * 0.5 + 0.5
+            color[left:right] = rgb.T
+
+        save_obj_mesh_with_color(save_path, verts, faces, color)
+    except Exception as e:
+        print(e)
+        print('Can not create marching cubes at this time.')
+
+def adjust_learning_rate(optimizer, epoch, lr, schedule, gamma):
+    """Sets the learning rate to the initial LR decayed by schedule"""
+    if epoch in schedule:
+        lr *= gamma
+        for param_group in optimizer.param_groups:
+            param_group['lr'] = lr
+    return lr
+
+
+def compute_acc(pred, gt, thresh=0.5):
+    '''
+    return:
+        IOU, precision, and recall
+    '''
+    with torch.no_grad():
+        vol_pred = pred > thresh
+        vol_gt = gt > thresh
+
+        union = vol_pred | vol_gt
+        inter = vol_pred & vol_gt
+
+        true_pos = inter.sum().float()
+
+        union = union.sum().float()
+        if union == 0:
+            union = 1
+        vol_pred = vol_pred.sum().float()
+        if vol_pred == 0:
+            vol_pred = 1
+        vol_gt = vol_gt.sum().float()
+        if vol_gt == 0:
+            vol_gt = 1
+        return true_pos / union, true_pos / vol_pred, true_pos / vol_gt
+
+
+def calc_error(opt, net, cuda, dataset, num_tests):
+    if num_tests > len(dataset):
+        num_tests = len(dataset)
+    with torch.no_grad():
+        erorr_arr, IOU_arr, prec_arr, recall_arr = [], [], [], []
+        for idx in tqdm(range(num_tests)):
+            data = dataset[idx * len(dataset) // num_tests]
+            # retrieve the data
+            image_tensor = data['img'].to(device=cuda)
+            calib_tensor = data['calib'].to(device=cuda)
+            sample_tensor = data['samples'].to(device=cuda).unsqueeze(0)
+            if opt.num_views > 1:
+                sample_tensor = reshape_sample_tensor(sample_tensor, opt.num_views)
+            label_tensor = data['labels'].to(device=cuda).unsqueeze(0)
+
+            res, error = net.forward(image_tensor, sample_tensor, calib_tensor, labels=label_tensor)
+
+            IOU, prec, recall = compute_acc(res, label_tensor)
+
+            # print(
+            #     '{0}/{1} | Error: {2:06f} IOU: {3:06f} prec: {4:06f} recall: {5:06f}'
+            #         .format(idx, num_tests, error.item(), IOU.item(), prec.item(), recall.item()))
+            erorr_arr.append(error.item())
+            IOU_arr.append(IOU.item())
+            prec_arr.append(prec.item())
+            recall_arr.append(recall.item())
+
+    return np.average(erorr_arr), np.average(IOU_arr), np.average(prec_arr), np.average(recall_arr)
+
+def calc_error_color(opt, netG, netC, cuda, dataset, num_tests):
+    if num_tests > len(dataset):
+        num_tests = len(dataset)
+    with torch.no_grad():
+        error_color_arr = []
+
+        for idx in tqdm(range(num_tests)):
+            data = dataset[idx * len(dataset) // num_tests]
+            # retrieve the data
+            image_tensor = data['img'].to(device=cuda)
+            calib_tensor = data['calib'].to(device=cuda)
+            color_sample_tensor = data['color_samples'].to(device=cuda).unsqueeze(0)
+
+            if opt.num_views > 1:
+                color_sample_tensor = reshape_sample_tensor(color_sample_tensor, opt.num_views)
+
+            rgb_tensor = data['rgbs'].to(device=cuda).unsqueeze(0)
+
+            netG.filter(image_tensor)
+            _, errorC = netC.forward(image_tensor, netG.get_im_feat(), color_sample_tensor, calib_tensor, labels=rgb_tensor)
+
+            # print('{0}/{1} | Error inout: {2:06f} | Error color: {3:06f}'
+            #       .format(idx, num_tests, errorG.item(), errorC.item()))
+            error_color_arr.append(errorC.item())
+
+    return np.average(error_color_arr)
+
+
+def conv3x3(in_planes, out_planes, strd=1, padding=1, bias=False):
+    "3x3 convolution with padding"
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3,
+                     stride=strd, padding=padding, bias=bias)
+
+def init_weights(net, init_type='normal', init_gain=0.02):
+    """Initialize network weights.
+
+    Parameters:
+        net (network)   -- network to be initialized
+        init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal
+        init_gain (float)    -- scaling factor for normal, xavier and orthogonal.
+
+    We use 'normal' in the original pix2pix and CycleGAN paper. But xavier and kaiming might
+    work better for some applications. Feel free to try yourself.
+    """
+
+    def init_func(m):  # define the initialization function
+        classname = m.__class__.__name__
+        if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):
+            if init_type == 'normal':
+                init.normal_(m.weight.data, 0.0, init_gain)
+            elif init_type == 'xavier':
+                init.xavier_normal_(m.weight.data, gain=init_gain)
+            elif init_type == 'kaiming':
+                init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
+            elif init_type == 'orthogonal':
+                init.orthogonal_(m.weight.data, gain=init_gain)
+            else:
+                raise NotImplementedError('initialization method [%s] is not implemented' % init_type)
+            if hasattr(m, 'bias') and m.bias is not None:
+                init.constant_(m.bias.data, 0.0)
+        elif classname.find(
+                'BatchNorm2d') != -1:  # BatchNorm Layer's weight is not a matrix; only normal distribution applies.
+            init.normal_(m.weight.data, 1.0, init_gain)
+            init.constant_(m.bias.data, 0.0)
+
+    print('initialize network with %s' % init_type)
+    net.apply(init_func)  # apply the initialization function <init_func>
+
+
+def init_net(net, init_type='normal', init_gain=0.02, gpu_ids=[]):
+    """Initialize a network: 1. register CPU/GPU device (with multi-GPU support); 2. initialize the network weights
+    Parameters:
+        net (network)      -- the network to be initialized
+        init_type (str)    -- the name of an initialization method: normal | xavier | kaiming | orthogonal
+        gain (float)       -- scaling factor for normal, xavier and orthogonal.
+        gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2
+
+    Return an initialized network.
+    """
+    if len(gpu_ids) > 0:
+        assert (torch.cuda.is_available())
+        net.to(gpu_ids[0])
+        net = torch.nn.DataParallel(net, gpu_ids)  # multi-GPUs
+    init_weights(net, init_type, init_gain=init_gain)
+    return net
+
+
+def imageSpaceRotation(xy, rot):
+    '''
+    args:
+        xy: (B, 2, N) input
+        rot: (B, 2) x,y axis rotation angles
+
+    rotation center will be always image center (other rotation center can be represented by additional z translation)
+    '''
+    disp = rot.unsqueeze(2).sin().expand_as(xy)
+    return (disp * xy).sum(dim=1)
+
+
+def cal_gradient_penalty(netD, real_data, fake_data, device, type='mixed', constant=1.0, lambda_gp=10.0):
+    """Calculate the gradient penalty loss, used in WGAN-GP paper https://arxiv.org/abs/1704.00028
+
+    Arguments:
+        netD (network)              -- discriminator network
+        real_data (tensor array)    -- real images
+        fake_data (tensor array)    -- generated images from the generator
+        device (str)                -- GPU / CPU: from torch.device('cuda:{}'.format(self.gpu_ids[0])) if self.gpu_ids else torch.device('cpu')
+        type (str)                  -- if we mix real and fake data or not [real | fake | mixed].
+        constant (float)            -- the constant used in formula ( | |gradient||_2 - constant)^2
+        lambda_gp (float)           -- weight for this loss
+
+    Returns the gradient penalty loss
+    """
+    if lambda_gp > 0.0:
+        if type == 'real':  # either use real images, fake images, or a linear interpolation of two.
+            interpolatesv = real_data
+        elif type == 'fake':
+            interpolatesv = fake_data
+        elif type == 'mixed':
+            alpha = torch.rand(real_data.shape[0], 1)
+            alpha = alpha.expand(real_data.shape[0], real_data.nelement() // real_data.shape[0]).contiguous().view(
+                *real_data.shape)
+            alpha = alpha.to(device)
+            interpolatesv = alpha * real_data + ((1 - alpha) * fake_data)
+        else:
+            raise NotImplementedError('{} not implemented'.format(type))
+        interpolatesv.requires_grad_(True)
+        disc_interpolates = netD(interpolatesv)
+        gradients = torch.autograd.grad(outputs=disc_interpolates, inputs=interpolatesv,
+                                        grad_outputs=torch.ones(disc_interpolates.size()).to(device),
+                                        create_graph=True, retain_graph=True, only_inputs=True)
+        gradients = gradients[0].view(real_data.size(0), -1)  # flat the data
+        gradient_penalty = (((gradients + 1e-16).norm(2, dim=1) - constant) ** 2).mean() * lambda_gp  # added eps
+        return gradient_penalty, gradients
+    else:
+        return 0.0, None
+
+def get_norm_layer(norm_type='instance'):
+    """Return a normalization layer
+    Parameters:
+        norm_type (str) -- the name of the normalization layer: batch | instance | none
+    For BatchNorm, we use learnable affine parameters and track running statistics (mean/stddev).
+    For InstanceNorm, we do not use learnable affine parameters. We do not track running statistics.
+    """
+    if norm_type == 'batch':
+        norm_layer = functools.partial(nn.BatchNorm2d, affine=True, track_running_stats=True)
+    elif norm_type == 'instance':
+        norm_layer = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=False)
+    elif norm_type == 'group':
+        norm_layer = functools.partial(nn.GroupNorm, 32)
+    elif norm_type == 'none':
+        norm_layer = None
+    else:
+        raise NotImplementedError('normalization layer [%s] is not found' % norm_type)
+    return norm_layer
+
+class Flatten(nn.Module):
+    def forward(self, input):
+        return input.view(input.size(0), -1)
+
+class ConvBlock(nn.Module):
+    def __init__(self, in_planes, out_planes, norm='batch'):
+        super(ConvBlock, self).__init__()
+        self.conv1 = conv3x3(in_planes, int(out_planes / 2))
+        self.conv2 = conv3x3(int(out_planes / 2), int(out_planes / 4))
+        self.conv3 = conv3x3(int(out_planes / 4), int(out_planes / 4))
+
+        if norm == 'batch':
+            self.bn1 = nn.BatchNorm2d(in_planes)
+            self.bn2 = nn.BatchNorm2d(int(out_planes / 2))
+            self.bn3 = nn.BatchNorm2d(int(out_planes / 4))
+            self.bn4 = nn.BatchNorm2d(in_planes)
+        elif norm == 'group':
+            self.bn1 = nn.GroupNorm(32, in_planes)
+            self.bn2 = nn.GroupNorm(32, int(out_planes / 2))
+            self.bn3 = nn.GroupNorm(32, int(out_planes / 4))
+            self.bn4 = nn.GroupNorm(32, in_planes)
+        
+        if in_planes != out_planes:
+            self.downsample = nn.Sequential(
+                self.bn4,
+                nn.ReLU(True),
+                nn.Conv2d(in_planes, out_planes,
+                          kernel_size=1, stride=1, bias=False),
+            )
+        else:
+            self.downsample = None
+
+    def forward(self, x):
+        residual = x
+
+        out1 = self.bn1(x)
+        out1 = F.relu(out1, True)
+        out1 = self.conv1(out1)
+
+        out2 = self.bn2(out1)
+        out2 = F.relu(out2, True)
+        out2 = self.conv2(out2)
+
+        out3 = self.bn3(out2)
+        out3 = F.relu(out3, True)
+        out3 = self.conv3(out3)
+
+        out3 = torch.cat((out1, out2, out3), 1)
+
+        if self.downsample is not None:
+            residual = self.downsample(residual)
+
+        out3 += residual
+
+        return out3
+  

PIFu/lib/options.py

@@ -0,0 +1,157 @@
+import argparse
+import os
+
+
+class BaseOptions():
+    def __init__(self):
+        self.initialized = False
+
+    def initialize(self, parser):
+        # Datasets related
+        g_data = parser.add_argument_group('Data')
+        g_data.add_argument('--dataroot', type=str, default='./data',
+                            help='path to images (data folder)')
+
+        g_data.add_argument('--loadSize', type=int, default=512, help='load size of input image')
+
+        # Experiment related
+        g_exp = parser.add_argument_group('Experiment')
+        g_exp.add_argument('--name', type=str, default='example',
+                           help='name of the experiment. It decides where to store samples and models')
+        g_exp.add_argument('--debug', action='store_true', help='debug mode or not')
+
+        g_exp.add_argument('--num_views', type=int, default=1, help='How many views to use for multiview network.')
+        g_exp.add_argument('--random_multiview', action='store_true', help='Select random multiview combination.')
+
+        # Training related
+        g_train = parser.add_argument_group('Training')
+        g_train.add_argument('--gpu_id', type=int, default=0, help='gpu id for cuda')
+        g_train.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0  0,1,2, 0,2, -1 for CPU mode')
+
+        g_train.add_argument('--num_threads', default=1, type=int, help='# sthreads for loading data')
+        g_train.add_argument('--serial_batches', action='store_true',
+                             help='if true, takes images in order to make batches, otherwise takes them randomly')
+        g_train.add_argument('--pin_memory', action='store_true', help='pin_memory')
+        
+        g_train.add_argument('--batch_size', type=int, default=2, help='input batch size')
+        g_train.add_argument('--learning_rate', type=float, default=1e-3, help='adam learning rate')
+        g_train.add_argument('--learning_rateC', type=float, default=1e-3, help='adam learning rate')
+        g_train.add_argument('--num_epoch', type=int, default=100, help='num epoch to train')
+
+        g_train.add_argument('--freq_plot', type=int, default=10, help='freqency of the error plot')
+        g_train.add_argument('--freq_save', type=int, default=50, help='freqency of the save_checkpoints')
+        g_train.add_argument('--freq_save_ply', type=int, default=100, help='freqency of the save ply')
+       
+        g_train.add_argument('--no_gen_mesh', action='store_true')
+        g_train.add_argument('--no_num_eval', action='store_true')
+        
+        g_train.add_argument('--resume_epoch', type=int, default=-1, help='epoch resuming the training')
+        g_train.add_argument('--continue_train', action='store_true', help='continue training: load the latest model')
+
+        # Testing related
+        g_test = parser.add_argument_group('Testing')
+        g_test.add_argument('--resolution', type=int, default=256, help='# of grid in mesh reconstruction')
+        g_test.add_argument('--test_folder_path', type=str, default=None, help='the folder of test image')
+
+        # Sampling related
+        g_sample = parser.add_argument_group('Sampling')
+        g_sample.add_argument('--sigma', type=float, default=5.0, help='perturbation standard deviation for positions')
+
+        g_sample.add_argument('--num_sample_inout', type=int, default=5000, help='# of sampling points')
+        g_sample.add_argument('--num_sample_color', type=int, default=0, help='# of sampling points')
+
+        g_sample.add_argument('--z_size', type=float, default=200.0, help='z normalization factor')
+
+        # Model related
+        g_model = parser.add_argument_group('Model')
+        # General
+        g_model.add_argument('--norm', type=str, default='group',
+                             help='instance normalization or batch normalization or group normalization')
+        g_model.add_argument('--norm_color', type=str, default='instance',
+                             help='instance normalization or batch normalization or group normalization')
+
+        # hg filter specify
+        g_model.add_argument('--num_stack', type=int, default=4, help='# of hourglass')
+        g_model.add_argument('--num_hourglass', type=int, default=2, help='# of stacked layer of hourglass')
+        g_model.add_argument('--skip_hourglass', action='store_true', help='skip connection in hourglass')
+        g_model.add_argument('--hg_down', type=str, default='ave_pool', help='ave pool || conv64 || conv128')
+        g_model.add_argument('--hourglass_dim', type=int, default='256', help='256 | 512')
+
+        # Classification General
+        g_model.add_argument('--mlp_dim', nargs='+', default=[257, 1024, 512, 256, 128, 1], type=int,
+                             help='# of dimensions of mlp')
+        g_model.add_argument('--mlp_dim_color', nargs='+', default=[513, 1024, 512, 256, 128, 3],
+                             type=int, help='# of dimensions of color mlp')
+
+        g_model.add_argument('--use_tanh', action='store_true',
+                             help='using tanh after last conv of image_filter network')
+
+        # for train
+        parser.add_argument('--random_flip', action='store_true', help='if random flip')
+        parser.add_argument('--random_trans', action='store_true', help='if random flip')
+        parser.add_argument('--random_scale', action='store_true', help='if random flip')
+        parser.add_argument('--no_residual', action='store_true', help='no skip connection in mlp')
+        parser.add_argument('--schedule', type=int, nargs='+', default=[60, 80],
+                            help='Decrease learning rate at these epochs.')
+        parser.add_argument('--gamma', type=float, default=0.1, help='LR is multiplied by gamma on schedule.')
+        parser.add_argument('--color_loss_type', type=str, default='l1', help='mse | l1')
+
+        # for eval
+        parser.add_argument('--val_test_error', action='store_true', help='validate errors of test data')
+        parser.add_argument('--val_train_error', action='store_true', help='validate errors of train data')
+        parser.add_argument('--gen_test_mesh', action='store_true', help='generate test mesh')
+        parser.add_argument('--gen_train_mesh', action='store_true', help='generate train mesh')
+        parser.add_argument('--all_mesh', action='store_true', help='generate meshs from all hourglass output')
+        parser.add_argument('--num_gen_mesh_test', type=int, default=1,
+                            help='how many meshes to generate during testing')
+
+        # path
+        parser.add_argument('--checkpoints_path', type=str, default='./checkpoints', help='path to save checkpoints')
+        parser.add_argument('--load_netG_checkpoint_path', type=str, default=None, help='path to save checkpoints')
+        parser.add_argument('--load_netC_checkpoint_path', type=str, default=None, help='path to save checkpoints')
+        parser.add_argument('--results_path', type=str, default='./results', help='path to save results ply')
+        parser.add_argument('--load_checkpoint_path', type=str, help='path to save results ply')
+        parser.add_argument('--single', type=str, default='', help='single data for training')
+        # for single image reconstruction
+        parser.add_argument('--mask_path', type=str, help='path for input mask')
+        parser.add_argument('--img_path', type=str, help='path for input image')
+
+        # aug
+        group_aug = parser.add_argument_group('aug')
+        group_aug.add_argument('--aug_alstd', type=float, default=0.0, help='augmentation pca lighting alpha std')
+        group_aug.add_argument('--aug_bri', type=float, default=0.0, help='augmentation brightness')
+        group_aug.add_argument('--aug_con', type=float, default=0.0, help='augmentation contrast')
+        group_aug.add_argument('--aug_sat', type=float, default=0.0, help='augmentation saturation')
+        group_aug.add_argument('--aug_hue', type=float, default=0.0, help='augmentation hue')
+        group_aug.add_argument('--aug_blur', type=float, default=0.0, help='augmentation blur')
+
+        # special tasks
+        self.initialized = True
+        return parser
+
+    def gather_options(self):
+        # initialize parser with basic options
+        if not self.initialized:
+            parser = argparse.ArgumentParser(
+                formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+            parser = self.initialize(parser)
+
+        self.parser = parser
+
+        return parser.parse_args()
+
+    def print_options(self, opt):
+        message = ''
+        message += '----------------- Options ---------------\n'
+        for k, v in sorted(vars(opt).items()):
+            comment = ''
+            default = self.parser.get_default(k)
+            if v != default:
+                comment = '\t[default: %s]' % str(default)
+            message += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment)
+        message += '----------------- End -------------------'
+        print(message)
+
+    def parse(self):
+        opt = self.gather_options()
+        return opt

PIFu/lib/renderer/camera.py

@@ -0,0 +1,207 @@
+import cv2
+import numpy as np
+
+from .glm import ortho
+
+
+class Camera:
+    def __init__(self, width=1600, height=1200):
+        # Focal Length
+        # equivalent 50mm
+        focal = np.sqrt(width * width + height * height)
+        self.focal_x = focal
+        self.focal_y = focal
+        # Principal Point Offset
+        self.principal_x = width / 2
+        self.principal_y = height / 2
+        # Axis Skew
+        self.skew = 0
+        # Image Size
+        self.width = width
+        self.height = height
+
+        self.near = 1
+        self.far = 10
+
+        # Camera Center
+        self.center = np.array([0, 0, 1.6])
+        self.direction = np.array([0, 0, -1])
+        self.right = np.array([1, 0, 0])
+        self.up = np.array([0, 1, 0])
+
+        self.ortho_ratio = None
+
+    def sanity_check(self):
+        self.center = self.center.reshape([-1])
+        self.direction = self.direction.reshape([-1])
+        self.right = self.right.reshape([-1])
+        self.up = self.up.reshape([-1])
+
+        assert len(self.center) == 3
+        assert len(self.direction) == 3
+        assert len(self.right) == 3
+        assert len(self.up) == 3
+
+    @staticmethod
+    def normalize_vector(v):
+        v_norm = np.linalg.norm(v)
+        return v if v_norm == 0 else v / v_norm
+
+    def get_real_z_value(self, z):
+        z_near = self.near
+        z_far = self.far
+        z_n = 2.0 * z - 1.0
+        z_e = 2.0 * z_near * z_far / (z_far + z_near - z_n * (z_far - z_near))
+        return z_e
+
+    def get_rotation_matrix(self):
+        rot_mat = np.eye(3)
+        s = self.right
+        s = self.normalize_vector(s)
+        rot_mat[0, :] = s
+        u = self.up
+        u = self.normalize_vector(u)
+        rot_mat[1, :] = -u
+        rot_mat[2, :] = self.normalize_vector(self.direction)
+
+        return rot_mat
+
+    def get_translation_vector(self):
+        rot_mat = self.get_rotation_matrix()
+        trans = -np.dot(rot_mat, self.center)
+        return trans
+
+    def get_intrinsic_matrix(self):
+        int_mat = np.eye(3)
+
+        int_mat[0, 0] = self.focal_x
+        int_mat[1, 1] = self.focal_y
+        int_mat[0, 1] = self.skew
+        int_mat[0, 2] = self.principal_x
+        int_mat[1, 2] = self.principal_y
+
+        return int_mat
+
+    def get_projection_matrix(self):
+        ext_mat = self.get_extrinsic_matrix()
+        int_mat = self.get_intrinsic_matrix()
+
+        return np.matmul(int_mat, ext_mat)
+
+    def get_extrinsic_matrix(self):
+        rot_mat = self.get_rotation_matrix()
+        int_mat = self.get_intrinsic_matrix()
+        trans = self.get_translation_vector()
+
+        extrinsic = np.eye(4)
+        extrinsic[:3, :3] = rot_mat
+        extrinsic[:3, 3] = trans
+
+        return extrinsic[:3, :]
+
+    def set_rotation_matrix(self, rot_mat):
+        self.direction = rot_mat[2, :]
+        self.up = -rot_mat[1, :]
+        self.right = rot_mat[0, :]
+
+    def set_intrinsic_matrix(self, int_mat):
+        self.focal_x = int_mat[0, 0]
+        self.focal_y = int_mat[1, 1]
+        self.skew = int_mat[0, 1]
+        self.principal_x = int_mat[0, 2]
+        self.principal_y = int_mat[1, 2]
+
+    def set_projection_matrix(self, proj_mat):
+        res = cv2.decomposeProjectionMatrix(proj_mat)
+        int_mat, rot_mat, camera_center_homo = res[0], res[1], res[2]
+        camera_center = camera_center_homo[0:3] / camera_center_homo[3]
+        camera_center = camera_center.reshape(-1)
+        int_mat = int_mat / int_mat[2][2]
+
+        self.set_intrinsic_matrix(int_mat)
+        self.set_rotation_matrix(rot_mat)
+        self.center = camera_center
+
+        self.sanity_check()
+
+    def get_gl_matrix(self):
+        z_near = self.near
+        z_far = self.far
+        rot_mat = self.get_rotation_matrix()
+        int_mat = self.get_intrinsic_matrix()
+        trans = self.get_translation_vector()
+
+        extrinsic = np.eye(4)
+        extrinsic[:3, :3] = rot_mat
+        extrinsic[:3, 3] = trans
+        axis_adj = np.eye(4)
+        axis_adj[2, 2] = -1
+        axis_adj[1, 1] = -1
+        model_view = np.matmul(axis_adj, extrinsic)
+
+        projective = np.zeros([4, 4])
+        projective[:2, :2] = int_mat[:2, :2]
+        projective[:2, 2:3] = -int_mat[:2, 2:3]
+        projective[3, 2] = -1
+        projective[2, 2] = (z_near + z_far)
+        projective[2, 3] = (z_near * z_far)
+
+        if self.ortho_ratio is None:
+            ndc = ortho(0, self.width, 0, self.height, z_near, z_far)
+            perspective = np.matmul(ndc, projective)
+        else:
+            perspective = ortho(-self.width * self.ortho_ratio / 2, self.width * self.ortho_ratio / 2,
+                                -self.height * self.ortho_ratio / 2, self.height * self.ortho_ratio / 2,
+                                z_near, z_far)
+
+        return perspective, model_view
+
+
+def KRT_from_P(proj_mat, normalize_K=True):
+    res = cv2.decomposeProjectionMatrix(proj_mat)
+    K, Rot, camera_center_homog = res[0], res[1], res[2]
+    camera_center = camera_center_homog[0:3] / camera_center_homog[3]
+    trans = -Rot.dot(camera_center)
+    if normalize_K:
+        K = K / K[2][2]
+    return K, Rot, trans
+
+
+def MVP_from_P(proj_mat, width, height, near=0.1, far=10000):
+    '''
+    Convert OpenCV camera calibration matrix to OpenGL projection and model view matrix
+    :param proj_mat: OpenCV camera projeciton matrix
+    :param width: Image width
+    :param height: Image height
+    :param near: Z near value
+    :param far: Z far value
+    :return: OpenGL projection matrix and model view matrix
+    '''
+    res = cv2.decomposeProjectionMatrix(proj_mat)
+    K, Rot, camera_center_homog = res[0], res[1], res[2]
+    camera_center = camera_center_homog[0:3] / camera_center_homog[3]
+    trans = -Rot.dot(camera_center)
+    K = K / K[2][2]
+
+    extrinsic = np.eye(4)
+    extrinsic[:3, :3] = Rot
+    extrinsic[:3, 3:4] = trans
+    axis_adj = np.eye(4)
+    axis_adj[2, 2] = -1
+    axis_adj[1, 1] = -1
+    model_view = np.matmul(axis_adj, extrinsic)
+
+    zFar = far
+    zNear = near
+    projective = np.zeros([4, 4])
+    projective[:2, :2] = K[:2, :2]
+    projective[:2, 2:3] = -K[:2, 2:3]
+    projective[3, 2] = -1
+    projective[2, 2] = (zNear + zFar)
+    projective[2, 3] = (zNear * zFar)
+
+    ndc = ortho(0, width, 0, height, zNear, zFar)
+
+    perspective = np.matmul(ndc, projective)
+
+    return perspective, model_view

PIFu/lib/renderer/gl/cam_render.py

@@ -0,0 +1,48 @@
+from .render import Render
+
+GLUT = None
+
+class CamRender(Render):
+    def __init__(self, width=1600, height=1200, name='Cam Renderer',
+                 program_files=['simple.fs', 'simple.vs'], color_size=1, ms_rate=1, egl=False):
+        Render.__init__(self, width, height, name, program_files, color_size, ms_rate=ms_rate, egl=egl)
+        self.camera = None
+
+        if not egl:
+            global GLUT
+            import OpenGL.GLUT as GLUT
+            GLUT.glutDisplayFunc(self.display)
+            GLUT.glutKeyboardFunc(self.keyboard)
+
+    def set_camera(self, camera):
+        self.camera = camera
+        self.projection_matrix, self.model_view_matrix = camera.get_gl_matrix()
+
+    def keyboard(self, key, x, y):
+        # up
+        eps = 1
+        # print(key)
+        if key == b'w':
+            self.camera.center += eps * self.camera.direction
+        elif key == b's':
+            self.camera.center -= eps * self.camera.direction
+        if key == b'a':
+            self.camera.center -= eps * self.camera.right
+        elif key == b'd':
+            self.camera.center += eps * self.camera.right
+        if key == b' ':
+            self.camera.center += eps * self.camera.up
+        elif key == b'x':
+            self.camera.center -= eps * self.camera.up
+        elif key == b'i':
+            self.camera.near += 0.1 * eps
+            self.camera.far += 0.1 * eps
+        elif key == b'o':
+            self.camera.near -= 0.1 * eps
+            self.camera.far -= 0.1 * eps
+
+        self.projection_matrix, self.model_view_matrix = self.camera.get_gl_matrix()
+
+    def show(self):
+        if GLUT is not None:
+            GLUT.glutMainLoop()

PIFu/lib/renderer/gl/data/prt.fs

@@ -0,0 +1,153 @@
+#version 330
+
+uniform vec3 SHCoeffs[9];
+uniform uint analytic;
+
+uniform uint hasNormalMap;
+uniform uint hasAlbedoMap;
+
+uniform sampler2D AlbedoMap;
+uniform sampler2D NormalMap;
+
+in VertexData {
+    vec3 Position;
+    vec3 Depth;
+    vec3 ModelNormal;
+    vec2 Texcoord;
+    vec3 Tangent;
+    vec3 Bitangent;
+    vec3 PRT1;
+    vec3 PRT2;
+    vec3 PRT3;
+} VertexIn;
+
+layout (location = 0) out vec4 FragColor;
+layout (location = 1) out vec4 FragNormal;
+layout (location = 2) out vec4 FragPosition;
+layout (location = 3) out vec4 FragAlbedo;
+layout (location = 4) out vec4 FragShading;
+layout (location = 5) out vec4 FragPRT1;
+layout (location = 6) out vec4 FragPRT2;
+layout (location = 7) out vec4 FragPRT3;
+
+vec4 gammaCorrection(vec4 vec, float g)
+{
+    return vec4(pow(vec.x, 1.0/g), pow(vec.y, 1.0/g), pow(vec.z, 1.0/g), vec.w);
+}
+
+vec3 gammaCorrection(vec3 vec, float g)
+{
+    return vec3(pow(vec.x, 1.0/g), pow(vec.y, 1.0/g), pow(vec.z, 1.0/g));
+}
+
+void evaluateH(vec3 n, out float H[9])
+{
+    float c1 = 0.429043, c2 = 0.511664,
+        c3 = 0.743125, c4 = 0.886227, c5 = 0.247708;
+
+    H[0] = c4;
+    H[1] = 2.0 * c2 * n[1];
+    H[2] = 2.0 * c2 * n[2];
+    H[3] = 2.0 * c2 * n[0];
+    H[4] = 2.0 * c1 * n[0] * n[1];
+    H[5] = 2.0 * c1 * n[1] * n[2];
+    H[6] = c3 * n[2] * n[2] - c5;
+    H[7] = 2.0 * c1 * n[2] * n[0];
+    H[8] = c1 * (n[0] * n[0] - n[1] * n[1]);
+}
+
+vec3 evaluateLightingModel(vec3 normal)
+{
+    float H[9];
+    evaluateH(normal, H);
+    vec3 res = vec3(0.0);
+    for (int i = 0; i < 9; i++) {
+        res += H[i] * SHCoeffs[i];
+    }
+    return res;
+}
+
+// nC: coarse geometry normal, nH: fine normal from normal map
+vec3 evaluateLightingModelHybrid(vec3 nC, vec3 nH, mat3 prt)
+{
+    float HC[9], HH[9];
+    evaluateH(nC, HC);
+    evaluateH(nH, HH);
+    
+    vec3 res = vec3(0.0);
+    vec3 shadow = vec3(0.0);
+    vec3 unshadow = vec3(0.0);
+    for(int i = 0; i < 3; ++i){
+        for(int j = 0; j < 3; ++j){
+            int id = i*3+j;
+            res += HH[id]* SHCoeffs[id];
+            shadow += prt[i][j] * SHCoeffs[id];
+            unshadow += HC[id] * SHCoeffs[id];
+        }
+    }
+    vec3 ratio = clamp(shadow/unshadow,0.0,1.0);
+    res = ratio * res;
+
+    return res;
+}
+
+vec3 evaluateLightingModelPRT(mat3 prt)
+{
+    vec3 res = vec3(0.0);
+    for(int i = 0; i < 3; ++i){
+        for(int j = 0; j < 3; ++j){
+            res += prt[i][j] * SHCoeffs[i*3+j];
+        }
+    }
+
+    return res;
+}
+
+void main()
+{
+    vec2 uv = VertexIn.Texcoord;
+    vec3 nC = normalize(VertexIn.ModelNormal);
+    vec3 nml = nC;
+    mat3 prt = mat3(VertexIn.PRT1, VertexIn.PRT2, VertexIn.PRT3);
+
+    if(hasAlbedoMap == uint(0))
+        FragAlbedo = vec4(1.0);
+    else
+        FragAlbedo = texture(AlbedoMap, uv);//gammaCorrection(texture(AlbedoMap, uv), 1.0/2.2);
+
+    if(hasNormalMap == uint(0))
+    {
+        if(analytic == uint(0))
+            FragShading = vec4(evaluateLightingModelPRT(prt), 1.0f);
+        else
+            FragShading = vec4(evaluateLightingModel(nC), 1.0f);
+    }
+    else
+    {
+        vec3 n_tan = normalize(texture(NormalMap, uv).rgb*2.0-vec3(1.0));
+
+        mat3 TBN = mat3(normalize(VertexIn.Tangent),normalize(VertexIn.Bitangent),nC);
+        vec3 nH = normalize(TBN * n_tan);
+
+        if(analytic == uint(0))
+            FragShading = vec4(evaluateLightingModelHybrid(nC,nH,prt),1.0f);
+        else
+            FragShading = vec4(evaluateLightingModel(nH), 1.0f);
+    
+        nml = nH;
+    }
+
+    FragShading = gammaCorrection(FragShading, 2.2);
+    FragColor = clamp(FragAlbedo * FragShading, 0.0, 1.0);
+    FragNormal = vec4(0.5*(nml+vec3(1.0)), 1.0);
+    FragPosition = vec4(VertexIn.Position,VertexIn.Depth.x);
+    FragShading = vec4(clamp(0.5*FragShading.xyz, 0.0, 1.0),1.0);
+    // FragColor = gammaCorrection(clamp(FragAlbedo * FragShading, 0.0, 1.0),2.2);
+    // FragNormal = vec4(0.5*(nml+vec3(1.0)), 1.0);
+    // FragPosition = vec4(VertexIn.Position,VertexIn.Depth.x);
+    // FragShading = vec4(gammaCorrection(clamp(0.5*FragShading.xyz, 0.0, 1.0),2.2),1.0);
+    // FragAlbedo = gammaCorrection(FragAlbedo,2.2);
+    FragPRT1 = vec4(VertexIn.PRT1,1.0);
+    FragPRT2 = vec4(VertexIn.PRT2,1.0);
+    FragPRT3 = vec4(VertexIn.PRT3,1.0);
+}

PIFu/lib/renderer/gl/data/prt.vs

@@ -0,0 +1,167 @@
+#version 330
+
+layout (location = 0) in vec3 a_Position;
+layout (location = 1) in vec3 a_Normal;
+layout (location = 2) in vec2 a_TextureCoord;
+layout (location = 3) in vec3 a_Tangent;
+layout (location = 4) in vec3 a_Bitangent;
+layout (location = 5) in vec3 a_PRT1;
+layout (location = 6) in vec3 a_PRT2;
+layout (location = 7) in vec3 a_PRT3;
+
+out VertexData {
+    vec3 Position;
+    vec3 Depth;
+    vec3 ModelNormal;
+    vec2 Texcoord;
+    vec3 Tangent;
+    vec3 Bitangent;
+    vec3 PRT1;
+    vec3 PRT2;
+    vec3 PRT3;
+} VertexOut;
+
+uniform mat3 RotMat;
+uniform mat4 NormMat;
+uniform mat4 ModelMat;
+uniform mat4 PerspMat;
+
+float s_c3 = 0.94617469575; // (3*sqrt(5))/(4*sqrt(pi))
+float s_c4 = -0.31539156525;// (-sqrt(5))/(4*sqrt(pi))
+float s_c5 = 0.54627421529; // (sqrt(15))/(4*sqrt(pi))
+
+float s_c_scale = 1.0/0.91529123286551084;
+float s_c_scale_inv = 0.91529123286551084;
+
+float s_rc2 = 1.5853309190550713*s_c_scale;
+float s_c4_div_c3 = s_c4/s_c3;
+float s_c4_div_c3_x2 = (s_c4/s_c3)*2.0;
+
+float s_scale_dst2 = s_c3 * s_c_scale_inv;
+float s_scale_dst4 = s_c5 * s_c_scale_inv;
+
+void OptRotateBand0(float x[1], mat3 R, out float dst[1])
+{
+    dst[0] = x[0];
+}
+
+// 9 multiplies
+void OptRotateBand1(float x[3], mat3 R, out float dst[3])
+{
+    // derived from  SlowRotateBand1
+    dst[0] = ( R[1][1])*x[0] + (-R[1][2])*x[1] + ( R[1][0])*x[2];
+    dst[1] = (-R[2][1])*x[0] + ( R[2][2])*x[1] + (-R[2][0])*x[2];
+    dst[2] = ( R[0][1])*x[0] + (-R[0][2])*x[1] + ( R[0][0])*x[2];
+}
+
+// 48 multiplies
+void OptRotateBand2(float x[5], mat3 R, out float dst[5])
+{
+    // Sparse matrix multiply
+    float sh0 =  x[3] + x[4] + x[4] - x[1];
+    float sh1 =  x[0] + s_rc2*x[2] +  x[3] + x[4];
+    float sh2 =  x[0];
+    float sh3 = -x[3];
+    float sh4 = -x[1];
+    
+    // Rotations.  R0 and R1 just use the raw matrix columns
+    float r2x = R[0][0] + R[0][1];
+    float r2y = R[1][0] + R[1][1];
+    float r2z = R[2][0] + R[2][1];
+    
+    float r3x = R[0][0] + R[0][2];
+    float r3y = R[1][0] + R[1][2];
+    float r3z = R[2][0] + R[2][2];
+    
+    float r4x = R[0][1] + R[0][2];
+    float r4y = R[1][1] + R[1][2];
+    float r4z = R[2][1] + R[2][2];
+    
+    // dense matrix multiplication one column at a time
+    
+    // column 0
+    float sh0_x = sh0 * R[0][0];
+    float sh0_y = sh0 * R[1][0];
+    float d0 = sh0_x * R[1][0];
+    float d1 = sh0_y * R[2][0];
+    float d2 = sh0 * (R[2][0] * R[2][0] + s_c4_div_c3);
+    float d3 = sh0_x * R[2][0];
+    float d4 = sh0_x * R[0][0] - sh0_y * R[1][0];
+    
+    // column 1
+    float sh1_x = sh1 * R[0][2];
+    float sh1_y = sh1 * R[1][2];
+    d0 += sh1_x * R[1][2];
+    d1 += sh1_y * R[2][2];
+    d2 += sh1 * (R[2][2] * R[2][2] + s_c4_div_c3);
+    d3 += sh1_x * R[2][2];
+    d4 += sh1_x * R[0][2] - sh1_y * R[1][2];
+    
+    // column 2
+    float sh2_x = sh2 * r2x;
+    float sh2_y = sh2 * r2y;
+    d0 += sh2_x * r2y;
+    d1 += sh2_y * r2z;
+    d2 += sh2 * (r2z * r2z + s_c4_div_c3_x2);
+    d3 += sh2_x * r2z;
+    d4 += sh2_x * r2x - sh2_y * r2y;
+    
+    // column 3
+    float sh3_x = sh3 * r3x;
+    float sh3_y = sh3 * r3y;
+    d0 += sh3_x * r3y;
+    d1 += sh3_y * r3z;
+    d2 += sh3 * (r3z * r3z + s_c4_div_c3_x2);
+    d3 += sh3_x * r3z;
+    d4 += sh3_x * r3x - sh3_y * r3y;
+    
+    // column 4
+    float sh4_x = sh4 * r4x;
+    float sh4_y = sh4 * r4y;
+    d0 += sh4_x * r4y;
+    d1 += sh4_y * r4z;
+    d2 += sh4 * (r4z * r4z + s_c4_div_c3_x2);
+    d3 += sh4_x * r4z;
+    d4 += sh4_x * r4x - sh4_y * r4y;
+    
+    // extra multipliers
+    dst[0] = d0;
+    dst[1] = -d1;
+    dst[2] = d2 * s_scale_dst2;
+    dst[3] = -d3;
+    dst[4] = d4 * s_scale_dst4;
+}
+
+void main()
+{
+    // normalization
+    vec3 pos = (NormMat * vec4(a_Position,1.0)).xyz;
+
+    mat3 R = mat3(ModelMat) * RotMat;
+    VertexOut.ModelNormal = (R * a_Normal);
+    VertexOut.Position = R * pos;
+    VertexOut.Texcoord = a_TextureCoord;
+    VertexOut.Tangent = (R * a_Tangent);
+    VertexOut.Bitangent = (R * a_Bitangent);
+    float PRT0, PRT1[3], PRT2[5];
+    PRT0 = a_PRT1[0];
+    PRT1[0] = a_PRT1[1];
+    PRT1[1] = a_PRT1[2];
+    PRT1[2] = a_PRT2[0];
+    PRT2[0] = a_PRT2[1];
+    PRT2[1] = a_PRT2[2];
+    PRT2[2] = a_PRT3[0];
+    PRT2[3] = a_PRT3[1];
+    PRT2[4] = a_PRT3[2];
+
+    OptRotateBand1(PRT1, R, PRT1);
+    OptRotateBand2(PRT2, R, PRT2);
+
+    VertexOut.PRT1 = vec3(PRT0,PRT1[0],PRT1[1]);
+    VertexOut.PRT2 = vec3(PRT1[2],PRT2[0],PRT2[1]);
+    VertexOut.PRT3 = vec3(PRT2[2],PRT2[3],PRT2[4]);
+
+    gl_Position = PerspMat * ModelMat * vec4(RotMat * pos, 1.0);
+    
+    VertexOut.Depth = vec3(gl_Position.z / gl_Position.w);
+}

PIFu/lib/renderer/gl/data/prt_uv.fs

@@ -0,0 +1,141 @@
+#version 330
+
+uniform vec3 SHCoeffs[9];
+uniform uint analytic;
+
+uniform uint hasNormalMap;
+uniform uint hasAlbedoMap;
+
+uniform sampler2D AlbedoMap;
+uniform sampler2D NormalMap;
+
+in VertexData {
+    vec3 Position;
+    vec3 ModelNormal;
+    vec3 CameraNormal;
+    vec2 Texcoord;
+    vec3 Tangent;
+    vec3 Bitangent;
+    vec3 PRT1;
+    vec3 PRT2;
+    vec3 PRT3;
+} VertexIn;
+
+layout (location = 0) out vec4 FragColor;
+layout (location = 1) out vec4 FragPosition;
+layout (location = 2) out vec4 FragNormal;
+
+vec4 gammaCorrection(vec4 vec, float g)
+{
+    return vec4(pow(vec.x, 1.0/g), pow(vec.y, 1.0/g), pow(vec.z, 1.0/g), vec.w);
+}
+
+vec3 gammaCorrection(vec3 vec, float g)
+{
+    return vec3(pow(vec.x, 1.0/g), pow(vec.y, 1.0/g), pow(vec.z, 1.0/g));
+}
+
+void evaluateH(vec3 n, out float H[9])
+{
+    float c1 = 0.429043, c2 = 0.511664,
+        c3 = 0.743125, c4 = 0.886227, c5 = 0.247708;
+
+    H[0] = c4;
+    H[1] = 2.0 * c2 * n[1];
+    H[2] = 2.0 * c2 * n[2];
+    H[3] = 2.0 * c2 * n[0];
+    H[4] = 2.0 * c1 * n[0] * n[1];
+    H[5] = 2.0 * c1 * n[1] * n[2];
+    H[6] = c3 * n[2] * n[2] - c5;
+    H[7] = 2.0 * c1 * n[2] * n[0];
+    H[8] = c1 * (n[0] * n[0] - n[1] * n[1]);
+}
+
+vec3 evaluateLightingModel(vec3 normal)
+{
+    float H[9];
+    evaluateH(normal, H);
+    vec3 res = vec3(0.0);
+    for (int i = 0; i < 9; i++) {
+        res += H[i] * SHCoeffs[i];
+    }
+    return res;
+}
+
+// nC: coarse geometry normal, nH: fine normal from normal map
+vec3 evaluateLightingModelHybrid(vec3 nC, vec3 nH, mat3 prt)
+{
+    float HC[9], HH[9];
+    evaluateH(nC, HC);
+    evaluateH(nH, HH);
+    
+    vec3 res = vec3(0.0);
+    vec3 shadow = vec3(0.0);
+    vec3 unshadow = vec3(0.0);
+    for(int i = 0; i < 3; ++i){
+        for(int j = 0; j < 3; ++j){
+            int id = i*3+j;
+            res += HH[id]* SHCoeffs[id];
+            shadow += prt[i][j] * SHCoeffs[id];
+            unshadow += HC[id] * SHCoeffs[id];
+        }
+    }
+    vec3 ratio = clamp(shadow/unshadow,0.0,1.0);
+    res = ratio * res;
+
+    return res;
+}
+
+vec3 evaluateLightingModelPRT(mat3 prt)
+{
+    vec3 res = vec3(0.0);
+    for(int i = 0; i < 3; ++i){
+        for(int j = 0; j < 3; ++j){
+            res += prt[i][j] * SHCoeffs[i*3+j];
+        }
+    }
+
+    return res;
+}
+
+void main()
+{
+    vec2 uv = VertexIn.Texcoord;
+    vec3 nM = normalize(VertexIn.ModelNormal);
+    vec3 nC = normalize(VertexIn.CameraNormal);
+    vec3 nml = nC;
+    mat3 prt = mat3(VertexIn.PRT1, VertexIn.PRT2, VertexIn.PRT3);
+
+    vec4 albedo, shading;
+    if(hasAlbedoMap == uint(0))
+        albedo = vec4(1.0);
+    else
+        albedo = texture(AlbedoMap, uv);//gammaCorrection(texture(AlbedoMap, uv), 1.0/2.2);
+
+    if(hasNormalMap == uint(0))
+    {
+        if(analytic == uint(0))
+            shading = vec4(evaluateLightingModelPRT(prt), 1.0f);
+        else
+            shading = vec4(evaluateLightingModel(nC), 1.0f);
+    }
+    else
+    {
+        vec3 n_tan = normalize(texture(NormalMap, uv).rgb*2.0-vec3(1.0));
+
+        mat3 TBN = mat3(normalize(VertexIn.Tangent),normalize(VertexIn.Bitangent),nC);
+        vec3 nH = normalize(TBN * n_tan);
+
+        if(analytic == uint(0))
+            shading = vec4(evaluateLightingModelHybrid(nC,nH,prt),1.0f);
+        else
+            shading = vec4(evaluateLightingModel(nH), 1.0f);
+    
+        nml = nH;
+    }
+
+    shading = gammaCorrection(shading, 2.2);
+    FragColor = clamp(albedo * shading, 0.0, 1.0);
+    FragPosition = vec4(VertexIn.Position,1.0);
+    FragNormal = vec4(0.5*(nM+vec3(1.0)),1.0);
+}

PIFu/lib/renderer/gl/data/prt_uv.vs

@@ -0,0 +1,168 @@
+#version 330
+
+layout (location = 0) in vec3 a_Position;
+layout (location = 1) in vec3 a_Normal;
+layout (location = 2) in vec2 a_TextureCoord;
+layout (location = 3) in vec3 a_Tangent;
+layout (location = 4) in vec3 a_Bitangent;
+layout (location = 5) in vec3 a_PRT1;
+layout (location = 6) in vec3 a_PRT2;
+layout (location = 7) in vec3 a_PRT3;
+
+out VertexData {
+    vec3 Position;
+    vec3 ModelNormal;
+    vec3 CameraNormal;
+    vec2 Texcoord;
+    vec3 Tangent;
+    vec3 Bitangent;
+    vec3 PRT1;
+    vec3 PRT2;
+    vec3 PRT3;
+} VertexOut;
+
+uniform mat3 RotMat;
+uniform mat4 NormMat;
+uniform mat4 ModelMat;
+uniform mat4 PerspMat;
+
+#define pi 3.1415926535897932384626433832795
+
+float s_c3 = 0.94617469575; // (3*sqrt(5))/(4*sqrt(pi))
+float s_c4 = -0.31539156525;// (-sqrt(5))/(4*sqrt(pi))
+float s_c5 = 0.54627421529; // (sqrt(15))/(4*sqrt(pi))
+
+float s_c_scale = 1.0/0.91529123286551084;
+float s_c_scale_inv = 0.91529123286551084;
+
+float s_rc2 = 1.5853309190550713*s_c_scale;
+float s_c4_div_c3 = s_c4/s_c3;
+float s_c4_div_c3_x2 = (s_c4/s_c3)*2.0;
+
+float s_scale_dst2 = s_c3 * s_c_scale_inv;
+float s_scale_dst4 = s_c5 * s_c_scale_inv;
+
+void OptRotateBand0(float x[1], mat3 R, out float dst[1])
+{
+    dst[0] = x[0];
+}
+
+// 9 multiplies
+void OptRotateBand1(float x[3], mat3 R, out float dst[3])
+{
+    // derived from  SlowRotateBand1
+    dst[0] = ( R[1][1])*x[0] + (-R[1][2])*x[1] + ( R[1][0])*x[2];
+    dst[1] = (-R[2][1])*x[0] + ( R[2][2])*x[1] + (-R[2][0])*x[2];
+    dst[2] = ( R[0][1])*x[0] + (-R[0][2])*x[1] + ( R[0][0])*x[2];
+}
+
+// 48 multiplies
+void OptRotateBand2(float x[5], mat3 R, out float dst[5])
+{
+    // Sparse matrix multiply
+    float sh0 =  x[3] + x[4] + x[4] - x[1];
+    float sh1 =  x[0] + s_rc2*x[2] +  x[3] + x[4];
+    float sh2 =  x[0];
+    float sh3 = -x[3];
+    float sh4 = -x[1];
+    
+    // Rotations.  R0 and R1 just use the raw matrix columns
+    float r2x = R[0][0] + R[0][1];
+    float r2y = R[1][0] + R[1][1];
+    float r2z = R[2][0] + R[2][1];
+    
+    float r3x = R[0][0] + R[0][2];
+    float r3y = R[1][0] + R[1][2];
+    float r3z = R[2][0] + R[2][2];
+    
+    float r4x = R[0][1] + R[0][2];
+    float r4y = R[1][1] + R[1][2];
+    float r4z = R[2][1] + R[2][2];
+    
+    // dense matrix multiplication one column at a time
+    
+    // column 0
+    float sh0_x = sh0 * R[0][0];
+    float sh0_y = sh0 * R[1][0];
+    float d0 = sh0_x * R[1][0];
+    float d1 = sh0_y * R[2][0];
+    float d2 = sh0 * (R[2][0] * R[2][0] + s_c4_div_c3);
+    float d3 = sh0_x * R[2][0];
+    float d4 = sh0_x * R[0][0] - sh0_y * R[1][0];
+    
+    // column 1
+    float sh1_x = sh1 * R[0][2];
+    float sh1_y = sh1 * R[1][2];
+    d0 += sh1_x * R[1][2];
+    d1 += sh1_y * R[2][2];
+    d2 += sh1 * (R[2][2] * R[2][2] + s_c4_div_c3);
+    d3 += sh1_x * R[2][2];
+    d4 += sh1_x * R[0][2] - sh1_y * R[1][2];
+    
+    // column 2
+    float sh2_x = sh2 * r2x;
+    float sh2_y = sh2 * r2y;
+    d0 += sh2_x * r2y;
+    d1 += sh2_y * r2z;
+    d2 += sh2 * (r2z * r2z + s_c4_div_c3_x2);
+    d3 += sh2_x * r2z;
+    d4 += sh2_x * r2x - sh2_y * r2y;
+    
+    // column 3
+    float sh3_x = sh3 * r3x;
+    float sh3_y = sh3 * r3y;
+    d0 += sh3_x * r3y;
+    d1 += sh3_y * r3z;
+    d2 += sh3 * (r3z * r3z + s_c4_div_c3_x2);
+    d3 += sh3_x * r3z;
+    d4 += sh3_x * r3x - sh3_y * r3y;
+    
+    // column 4
+    float sh4_x = sh4 * r4x;
+    float sh4_y = sh4 * r4y;
+    d0 += sh4_x * r4y;
+    d1 += sh4_y * r4z;
+    d2 += sh4 * (r4z * r4z + s_c4_div_c3_x2);
+    d3 += sh4_x * r4z;
+    d4 += sh4_x * r4x - sh4_y * r4y;
+    
+    // extra multipliers
+    dst[0] = d0;
+    dst[1] = -d1;
+    dst[2] = d2 * s_scale_dst2;
+    dst[3] = -d3;
+    dst[4] = d4 * s_scale_dst4;
+}
+
+void main()
+{ 
+    // normalization
+    mat3 R = mat3(ModelMat) * RotMat;
+    VertexOut.ModelNormal = a_Normal;
+    VertexOut.CameraNormal = (R * a_Normal);
+    VertexOut.Position = a_Position;
+    VertexOut.Texcoord = a_TextureCoord;
+    VertexOut.Tangent = (R * a_Tangent);
+    VertexOut.Bitangent = (R * a_Bitangent);
+    float PRT0, PRT1[3], PRT2[5];
+    PRT0 = a_PRT1[0];
+    PRT1[0] = a_PRT1[1];
+    PRT1[1] = a_PRT1[2];
+    PRT1[2] = a_PRT2[0];
+    PRT2[0] = a_PRT2[1];
+    PRT2[1] = a_PRT2[2];
+    PRT2[2] = a_PRT3[0];
+    PRT2[3] = a_PRT3[1];
+    PRT2[4] = a_PRT3[2];
+
+    OptRotateBand1(PRT1, R, PRT1);
+    OptRotateBand2(PRT2, R, PRT2);
+
+    VertexOut.PRT1 = vec3(PRT0,PRT1[0],PRT1[1]);
+    VertexOut.PRT2 = vec3(PRT1[2],PRT2[0],PRT2[1]);
+    VertexOut.PRT3 = vec3(PRT2[2],PRT2[3],PRT2[4]);
+
+    gl_Position = vec4(a_TextureCoord, 0.0, 1.0) - vec4(0.5, 0.5, 0, 0);
+    gl_Position[0] *= 2.0;
+    gl_Position[1] *= 2.0;
+}

PIFu/lib/renderer/gl/data/quad.fs

@@ -0,0 +1,11 @@
+#version 330 core
+out vec4 FragColor;
+
+in vec2 TexCoord;
+
+uniform sampler2D screenTexture;
+
+void main()
+{
+    FragColor = texture(screenTexture, TexCoord);
+}

PIFu/lib/renderer/gl/data/quad.vs

@@ -0,0 +1,11 @@
+#version 330 core
+layout (location = 0) in vec2 aPos;
+layout (location = 1) in vec2 aTexCoord;
+
+out vec2 TexCoord;
+
+void main()
+{
+    gl_Position = vec4(aPos.x, aPos.y, 0.0, 1.0);
+    TexCoord = aTexCoord;
+}

PIFu/lib/renderer/gl/framework.py

@@ -0,0 +1,90 @@
+# Mario Rosasco, 2016
+# adapted from framework.cpp, Copyright (C) 2010-2012 by Jason L. McKesson
+# This file is licensed under the MIT License.
+#
+# NB: Unlike in the framework.cpp organization, the main loop is contained
+# in the tutorial files, not in this framework file. Additionally, a copy of
+# this module file must exist in the same directory as the tutorial files
+# to be imported properly.
+
+import os
+from OpenGL.GL import *
+
+# Function that creates and compiles shaders according to the given type (a GL enum value) and
+# shader program (a file containing a GLSL program).
+def loadShader(shaderType, shaderFile):
+    # check if file exists, get full path name
+    strFilename = findFileOrThrow(shaderFile)
+    shaderData = None
+    with open(strFilename, 'r') as f:
+        shaderData = f.read()
+
+    shader = glCreateShader(shaderType)
+    glShaderSource(shader, shaderData)  # note that this is a simpler function call than in C
+
+    # This shader compilation is more explicit than the one used in
+    # framework.cpp, which relies on a glutil wrapper function.
+    # This is made explicit here mainly to decrease dependence on pyOpenGL
+    # utilities and wrappers, which docs caution may change in future versions.
+    glCompileShader(shader)
+
+    status = glGetShaderiv(shader, GL_COMPILE_STATUS)
+    if status == GL_FALSE:
+        # Note that getting the error log is much simpler in Python than in C/C++
+        # and does not require explicit handling of the string buffer
+        strInfoLog = glGetShaderInfoLog(shader)
+        strShaderType = ""
+        if shaderType is GL_VERTEX_SHADER:
+            strShaderType = "vertex"
+        elif shaderType is GL_GEOMETRY_SHADER:
+            strShaderType = "geometry"
+        elif shaderType is GL_FRAGMENT_SHADER:
+            strShaderType = "fragment"
+
+        print("Compilation failure for " + strShaderType + " shader:\n" + str(strInfoLog))
+
+    return shader
+
+
+# Function that accepts a list of shaders, compiles them, and returns a handle to the compiled program
+def createProgram(shaderList):
+    program = glCreateProgram()
+
+    for shader in shaderList:
+        glAttachShader(program, shader)
+
+    glLinkProgram(program)
+
+    status = glGetProgramiv(program, GL_LINK_STATUS)
+    if status == GL_FALSE:
+        # Note that getting the error log is much simpler in Python than in C/C++
+        # and does not require explicit handling of the string buffer
+        strInfoLog = glGetProgramInfoLog(program)
+        print("Linker failure: \n" + str(strInfoLog))
+
+    for shader in shaderList:
+        glDetachShader(program, shader)
+
+    return program
+
+
+# Helper function to locate and open the target file (passed in as a string).
+# Returns the full path to the file as a string.
+def findFileOrThrow(strBasename):
+    # Keep constant names in C-style convention, for readability
+    # when comparing to C(/C++) code.
+    if os.path.isfile(strBasename):
+        return strBasename
+
+    LOCAL_FILE_DIR = "data" + os.sep
+    GLOBAL_FILE_DIR = os.path.dirname(os.path.abspath(__file__)) + os.sep + "data" + os.sep
+
+    strFilename = LOCAL_FILE_DIR + strBasename
+    if os.path.isfile(strFilename):
+        return strFilename
+
+    strFilename = GLOBAL_FILE_DIR + strBasename
+    if os.path.isfile(strFilename):
+        return strFilename
+
+    raise IOError('Could not find target file ' + strBasename)

PIFu/lib/renderer/gl/glcontext.py

@@ -0,0 +1,142 @@
+"""Headless GPU-accelerated OpenGL context creation on Google Colaboratory.
+
+Typical usage:
+
+    # Optional PyOpenGL configuratiopn can be done here.
+    # import OpenGL
+    # OpenGL.ERROR_CHECKING = True
+
+    # 'glcontext' must be imported before any OpenGL.* API.
+    from lucid.misc.gl.glcontext import create_opengl_context
+
+    # Now it's safe to import OpenGL and EGL functions
+    import OpenGL.GL as gl
+
+    # create_opengl_context() creates a GL context that is attached to an
+    # offscreen surface of the specified size. Note that rendering to buffers
+    # of other sizes and formats is still possible with OpenGL Framebuffers.
+    #
+    # Users are expected to directly use the EGL API in case more advanced
+    # context management is required.
+    width, height = 640, 480
+    create_opengl_context((width, height))
+
+    # OpenGL context is available here.
+
+"""
+
+from __future__ import print_function
+
+# pylint: disable=unused-import,g-import-not-at-top,g-statement-before-imports
+
+try:
+  import OpenGL
+except:
+  print('This module depends on PyOpenGL.')
+  print('Please run "\033[1m!pip install -q pyopengl\033[0m" '
+        'prior importing this module.')
+  raise
+
+import ctypes
+from ctypes import pointer, util
+import os
+
+os.environ['PYOPENGL_PLATFORM'] = 'egl'
+
+# OpenGL loading workaround.
+#
+# * PyOpenGL tries to load libGL, but we need libOpenGL, see [1,2].
+#   This could have been solved by a symlink libGL->libOpenGL, but:
+#
+# * Python 2.7 can't find libGL and linEGL due to a bug (see [3])
+#   in ctypes.util, that was only wixed in Python 3.6.
+#
+# So, the only solution I've found is to monkeypatch ctypes.util
+# [1] https://devblogs.nvidia.com/egl-eye-opengl-visualization-without-x-server/
+# [2] https://devblogs.nvidia.com/linking-opengl-server-side-rendering/
+# [3] https://bugs.python.org/issue9998
+_find_library_old = ctypes.util.find_library
+try:
+
+  def _find_library_new(name):
+    return {
+        'GL': 'libOpenGL.so',
+        'EGL': 'libEGL.so',
+    }.get(name, _find_library_old(name))
+  util.find_library = _find_library_new
+  import OpenGL.GL as gl
+  import OpenGL.EGL as egl
+  from OpenGL import error
+  from OpenGL.EGL.EXT.device_base import egl_get_devices
+  from OpenGL.raw.EGL.EXT.platform_device import EGL_PLATFORM_DEVICE_EXT
+except:
+  print('Unable to load OpenGL libraries. '
+        'Make sure you use GPU-enabled backend.')
+  print('Press "Runtime->Change runtime type" and set '
+        '"Hardware accelerator" to GPU.')
+  raise
+finally:
+  util.find_library = _find_library_old
+
+def create_initialized_headless_egl_display():
+  """Creates an initialized EGL display directly on a device."""
+  for device in egl_get_devices():
+    display = egl.eglGetPlatformDisplayEXT(EGL_PLATFORM_DEVICE_EXT, device, None)
+
+    if display != egl.EGL_NO_DISPLAY and egl.eglGetError() == egl.EGL_SUCCESS:
+      # `eglInitialize` may or may not raise an exception on failure depending
+      # on how PyOpenGL is configured. We therefore catch a `GLError` and also
+      # manually check the output of `eglGetError()` here.
+      try:
+        initialized = egl.eglInitialize(display, None, None)
+      except error.GLError:
+        pass
+      else:
+        if initialized == egl.EGL_TRUE and egl.eglGetError() == egl.EGL_SUCCESS:
+          return display
+  return egl.EGL_NO_DISPLAY
+
+def create_opengl_context(surface_size=(640, 480)):
+  """Create offscreen OpenGL context and make it current.
+
+  Users are expected to directly use EGL API in case more advanced
+  context management is required.
+
+  Args:
+    surface_size: (width, height), size of the offscreen rendering surface.
+  """
+  egl_display = create_initialized_headless_egl_display()
+  if egl_display == egl.EGL_NO_DISPLAY:
+    raise ImportError('Cannot initialize a headless EGL display.')
+
+  major, minor = egl.EGLint(), egl.EGLint()
+  egl.eglInitialize(egl_display, pointer(major), pointer(minor))
+
+  config_attribs = [
+      egl.EGL_SURFACE_TYPE, egl.EGL_PBUFFER_BIT, egl.EGL_BLUE_SIZE, 8,
+      egl.EGL_GREEN_SIZE, 8, egl.EGL_RED_SIZE, 8, egl.EGL_DEPTH_SIZE, 24,
+      egl.EGL_RENDERABLE_TYPE, egl.EGL_OPENGL_BIT, egl.EGL_NONE
+  ]
+  config_attribs = (egl.EGLint * len(config_attribs))(*config_attribs)
+
+  num_configs = egl.EGLint()
+  egl_cfg = egl.EGLConfig()
+  egl.eglChooseConfig(egl_display, config_attribs, pointer(egl_cfg), 1,
+                      pointer(num_configs))
+
+  width, height = surface_size
+  pbuffer_attribs = [
+      egl.EGL_WIDTH,
+      width,
+      egl.EGL_HEIGHT,
+      height,
+      egl.EGL_NONE,
+  ]
+  pbuffer_attribs = (egl.EGLint * len(pbuffer_attribs))(*pbuffer_attribs)
+  egl_surf = egl.eglCreatePbufferSurface(egl_display, egl_cfg, pbuffer_attribs)
+
+  egl.eglBindAPI(egl.EGL_OPENGL_API)
+
+  egl_context = egl.eglCreateContext(egl_display, egl_cfg, egl.EGL_NO_CONTEXT,
+                                     None)
+  egl.eglMakeCurrent(egl_display, egl_surf, egl_surf, egl_context)

PIFu/lib/renderer/gl/init_gl.py

@@ -0,0 +1,24 @@
+_glut_window = None
+_context_inited = None
+
+def initialize_GL_context(width=512, height=512, egl=False):
+    '''
+    default context uses GLUT
+    '''
+    if not egl:
+        import OpenGL.GLUT as GLUT      
+        display_mode = GLUT.GLUT_DOUBLE | GLUT.GLUT_RGB | GLUT.GLUT_DEPTH
+        global _glut_window
+        if _glut_window is None:
+            GLUT.glutInit()
+            GLUT.glutInitDisplayMode(display_mode)
+            GLUT.glutInitWindowSize(width, height)
+            GLUT.glutInitWindowPosition(0, 0)
+            _glut_window = GLUT.glutCreateWindow("My Render.")
+    else:
+        from .glcontext import create_opengl_context
+        global _context_inited
+        if _context_inited is None:
+            create_opengl_context((width, height))
+            _context_inited = True
+