## (will incorporate latest updates)
* ### We just released a rendered [datasets](https://github.com/Xharlie/ShapenetRender_more_variation) of Shapenet with more view variations that contains RGB, albedo, depth and normal 2D images. Rendering scripts for both v1 and v2 are available.
Please cite our paper
[DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction (NeurIPS 2019)](https://arxiv.org/abs/1905.10711)
```
@incollection{NIPS2019_8340,
title = {DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction},
author = {Xu, Qiangeng and Wang, Weiyue and Ceylan, Duygu and Mech, Radomir and Neumann, Ulrich},
booktitle = {Advances in Neural Information Processing Systems 32},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {492--502},
year = {2019},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/8340-disn-deep-implicit-surface-network-for-high-quality-single-view-3d-reconstruction.pdf}
}
```
Primary contact: [Qiangeng Xu*](https://xharlie.github.io/)
## System Requirements
* ### GPU: 1080Ti (Other models can consider decrease the batch size if overflow)
* ### system: Ubuntu 16.04 (if your linux version can support tensorflow 1.10, it's going to be ok)
* ### tensorflow 1.10(should be able to run with 1.11, 1.12,1.13)
```
pip install trimesh==2.37.20
```
## Installation
```
cd {DISN}
mkdir checkpoint
cd checkpoint
wget https://www.dropbox.com/s/2ts7qc9w4opl4w4/SDF_DISN.tar
### or google drive: https://drive.google.com/file/d/1PEXVxXflVqWNqinSMC-hFmFdlMyoMZ7i/view?usp=sharing
### or baidu yunpan: https://pan.baidu.com/s/1Zujo84JoTcTW5dUl0AvS_w extraction code: esy9
tar -xvzf SDF_DISN.tar
rm -rf SDF_DISN.tar
cd ..
mkdir cam_est/checkpoint
cd cam_est/checkpoint
wget https://www.dropbox.com/s/hyv4lcvpfu0au9e/cam_DISN.tar
### or google drive https://drive.google.com/file/d/1S5Gh_u1C9vDvksqXDn3CP6IqsnU0hKkj/view?usp=sharing
### or baidu yunpan: https://pan.baidu.com/s/1lEHmSHA1o5lrswp0TM50qA extraction code: gbb3
tar -xvzf cam_DISN.tar
rm -rf cam_DISN.tar
cd ../../
Install whatever libary(e.g. mkl) you don't have and change corresponding libary path in your system in isosurface/LIB_PATH
```
## Demo:
* --sdf_res control the resolution of the sampled sdf, default is 64, the larger, the more fine-grained, but slower.
```
cd {DISN}
source isosurface/LIB_PATH
nohup python -u demo/demo.py --cam_est --log_dir checkpoint/SDF_DISN --cam_log_dir cam_est/checkpoint/cam_DISN --img_feat_twostream --sdf_res 256 &> log/create_sdf.log &
```
The result is demo/result.obj.
if you have dependency problems such as your mkl lib, etc. Please install the corresponding dependencies and change the path in LIB_PATH. Everyone has his/her/their own environment setting so it's impossible to instruct this step without sitting besides you and your server.
## Data Preparation
* ### file location setup:
* under preprocessing/info.json, you can change the locations of your data: the neccessary dir for the main model are :
```
"raw_dirs_v1": {
"mesh_dir": "/ssd1/datasets/ShapeNet/ShapeNetCore.v1/",
"norm_mesh_dir": "/ssd1/datasets/ShapeNet/march_cube_objs_v1/",
"rendered_dir": "/ssd1/datasets/ShapeNet/ShapeNetRendering/",
"renderedh5_dir": "/ssd1/datasets/ShapeNet/ShapeNetRenderingh5_v1/",
"sdf_dir": "/ssd1/datasets/ShapeNet/SDF_v1/"
}
```
* ### Download ShapeNetCore.v1
download the dataset following the instruction of https://www.shapenet.org/account/ (about 30GB)
```
cd {your download dir}
wget http://shapenet.cs.stanford.edu/shapenet/obj-zip/ShapeNetCore.v1.zip
unzip ShapeNetCore.v1.zip -d {your mesh_dir}
```
* ### Prepare the SDF ground truth and the marching cube reconstructed ground truth models
Download our generated sdf tar.gz from [here](https://drive.google.com/file/d/1cHDickPLKLz3smQNpOGXD2W5mkXcy1nq/view?usp=sharing) then place it at your "sdf_dir" in json; and the marching cube reconstructed ground truth models from the sdf file from [here](https://drive.google.com/drive/folders/1QGhDW335L7ra31uw5U-0V7hB-viA0JXr?usp=sharing) then place it at your "norm_mesh_dir" in your json.
If you want to generate sdf files and the reconstructed models by yourself, please follow the command lines below (Please expect the script to run for several hours). This step used this paper [Vega: non-linear fem deformable object simulator](http://run.usc.edu/vega/SinSchroederBarbic2012.pdf). Please also cite it if you use our code to generate sdf files
```
mkdir log
cd {DISN}
source isosurface/LIB_PATH
nohup python -u preprocessing/create_point_sdf_grid.py --thread_num {recommend 9} --category {default 'all', but can be single category like 'chair'} &> log/create_sdf.log &
## SDF folder takes about 9.0G, marching cube obj folder takes about 245G
```
* ### Download and generate 2d image h5 files:
* #### download 2d image following 3DR2N2[https://github.com/chrischoy/3D-R2N2], please cite their paper if you use this image tar file:
```
wget http://cvgl.stanford.edu/data2/ShapeNetRendering.tgz
untar it to {your rendered_dir}
```
* #### run h5 file generation (about 26 GB) :
```
cd {DISN}
nohup python -u preprocessing/create_img_h5.py &> log/create_imgh5.log &
```
## Camera parameters estimation network
* ### train the camera parameters estimation network:
```
### train the camera poses of the original rendered image dataset.
nohup python -u cam_est/train_sdf_cam.py --log_dir cam_est/checkpoint/{your training checkpoint dir} --gpu 0 --loss_mode 3D --learning_rate 2e-5 &> log/cam_3D_all.log &
### train the camera poses of the adding 2 more DoF augmented on the rendered image dataset.
nohup python -u cam_est/train_sdf_cam.py --log_dir cam_est/checkpoint/{your training checkpoint dir} --gpu 2 --loss_mode 3D --learning_rate 1e-4 --shift --shift_weight 2 &> log/cam_3D_shift2_all.log &
* ### if use new rendered 2d dataset:
```
### train the camera poses of the new rendered image dataset.
nohup python -u cam_est/train_sdf_cam.py --log_dir cam_est/checkpoint/{your checkpoint dir} --gpu 0 --loss_mode 3D --learning_rate 1e-4 --src_h5_dir {your new rendered images' h5 directory} --img_h 224 --img_w 224 &> log/cam_3D_easy.log &
```
* ### create h5 file of image and estimated cam parameters:
```
＃＃＃ Create img_h5 to {renderedh5_dir_est} in your info.json, the default is only generate h5 of test images and cam parameters(about 5.3GB)
nohup python -u train_sdf_cam.py --img_h5_dir {renderedh5_dir_est} --create --restore_model checkpoint/cam_3D_all --log_dir checkpoint/{your training checkpoint dir} --gpu 0--loss_mode 3D --batch_size 24 &> log/create_cam_mixloss_all.log &
```
## SDF generation network:
* ### train the sdf generation with provided camera parameters:
if train from scratch, you can load official pretrained vgg_16 by setting --restore_modelcnn; or you can --restore_model to your checkpoint to continue the training):
* support flip the background color from black to white since most online images have white background(by using --backcolorwhite)
* if use flag --cam_est, the img_h5 is loaded from {renderedh5_dir_est} instead of {renderedh5_dir}, so that we can train the generation on the estimated camera parameters
```
nohup python -u train/train_sdf.py --gpu 0 --img_feat_twostream --restore_modelcnn ./models/CNN/pretrained_model/vgg_16.ckpt --log_dir checkpoint/{your training checkpoint dir} --category all --num_sample_points 2048 --batch_size 20 --learning_rate 0.0001 --cat_limit 36000 &> log/DISN_train_all.log &
```
* ### inference sdf and create mesh objects:
* will save objs in {your training checkpoint dir}/test_objs/{sdf_res+1}_{iso}
* will save objs in {your training checkpoint dir}/test_objs/{sdf_res+1}_{iso}
* if use estimated camera post, --cam_est, will save objs in {your training checkpoint dir}/test_objs/camest_{sdf_res+1}_{iso}
* if only create chair or a single category, --category {chair or a single category}
* --sdf_res control the resolution of the sampled sdf, default is 64, the larger, the more fine-grained, but slower.
```
source isosurface/LIB_PATH
#### use ground truth camera pose
nohup python -u test/create_sdf.py --img_feat_twostream --view_num 24 --sdf_res 64 --batch_size 1 --gpu 0 --sdf_res 64 --log_dir checkpoint/{your training checkpoint dir} --iso 0.00 --category all &> log/DISN_create_all.log &
#### use estimated camera pose
nohup python -u test/create_sdf.py --img_feat_twostream --view_num 24 --sdf_res 64 --batch_size 1 --gpu 3 --sdf_res 64 --log_dir checkpoint/{your training checkpoint dir} --iso 0.00 --category all --cam_est &> log/DISN_create_all_cam.log &
```
* ### clean small objects:
* #### if the model doens't converge well, you can clean flying parts that generated by mistakes
```
nohup python -u clean_smallparts.py --src_dir checkpoint/{your training checkpoint dir}/test_objs/65_0.0 --tar_dir checkpoint/{your training checkpoint dir}/test_objs/65_0.0 --thread_n 10 &> log/DISN_clean.log &
```
## Evaluation:
### please compile models/tf_ops/ approxmatch and nn_distance and cites "A Point Set Generation Network for 3D Object Reconstruction from a Single Image"
* ### Chamfer Distance and Earth Mover Distance:
* #### cal_dir specify which obj folder to be tested, e.g. if only test watercraft, --category watercraft
```
nohup python -u test/test_cd_emd.py --img_feat_twostream --view_num 24 --num_sample_points 2048 --gpu 0 --batch_size 24 --log_dir checkpoint/{your training checkpoint dir} --cal_dir checkpoint/{your training checkpoint dir}/test_objs/65_0.0 --category all &> log/DISN_cd_emd_all.log &
```
* ### F-Score caluculation:
* cal_dir specify which obj folder to be tested, e.g. if only test watercraft, --category watercraft
also the threshold of true can be set, here we use 2.5 for default:
```
nohup python -u test/test_f_score.py --img_feat_twostream --view_num 24 --num_sample_points 2048 --gpu 0 --batch_size 24 --log_dir checkpoint/{your training checkpoint dir} --cal_dir checkpoint/{your training checkpoint dir}/test_objs/65_0.0 --category all --truethreshold 2.5 &> log/DISN_fscore_2.5.log &
```
* ### IOU caluculation:
* cal_dir specify which obj folder to be tested, e.g. if only test watercraft, --category watercraft
* --dim specify the number of voxels along each 3D dimension.
```
nohup python -u test/test_iou.py --img_feat_twostream --view_num 24 --log_dir checkpoint/{your training checkpoint dir} --cal_dir checkpoint/{your training checkpoint dir}/test_objs/65_0.0 --category all --dim 110 &> DISN_iou_all.log &
```
================================================
FILE: cam_est/model_cam.py
================================================
import tensorflow as tf
import numpy as np
import os
import tensorflow.contrib.slim as slim
from tensorflow.contrib.slim.python.slim.nets import vgg
import sys
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = os.path.dirname(BASE_DIR)
sys.path.append(os.path.join(ROOT_DIR))
sys.path.append(os.path.join(ROOT_DIR,'..'))
sys.path.append(os.path.join(ROOT_DIR, 'models'))
import posenet
def placeholder_inputs(batch_size, num_points, img_size, num_pc=2018, num_sample_pc = 2048*8, scope=''):
with tf.variable_scope(scope) as sc:
pc_pl = tf.placeholder(tf.float32, shape=(batch_size, num_pc, 3))
sample_pc_pl = tf.placeholder(tf.float32, shape=(batch_size, num_sample_pc, 3))
sample_pc_rot_pl = tf.placeholder(tf.float32, shape=(batch_size, num_sample_pc, 3))
imgs_pl = tf.placeholder(tf.float32, shape=(batch_size, img_size[0], img_size[1], 3))
sdf_value_pl = tf.placeholder(tf.float32, shape=(batch_size, num_sample_pc, 1))
sdf_sign_pl = tf.placeholder(tf.int32, shape=(batch_size, num_sample_pc))
trans_mat_pl = tf.placeholder(tf.float32, shape=(batch_size, 4, 3))
norm_params_pl = tf.placeholder(tf.float32, shape=(batch_size, 4))
regress_mat_pl = tf.placeholder(tf.float32, shape=(batch_size, 4, 3))
shifts_pl = tf.placeholder(tf.float32, shape=(batch_size, 2))
# camera intrinsic matrix
# np.array([[149.84375, 0., 68.5], [0., 149.84375, 68.5], [0., 0., 1.]], dtype=np.float32)
K = cal_K(img_size)#.T
K_pl = tf.constant(K)
K_pl = tf.expand_dims(K_pl, 0) # Convert to a len(yp) x 1 matrix.
K_pl = tf.tile(K_pl, [batch_size, 1, 1]) # Create multiple columns.
K_pl = tf.transpose(K_pl, perm=[0, 2, 1])
rot_mat_inv = np.array([[1., 0., 0., 0.],
[0., 0., 1., 0.],
[0., -1., 0., 0.],
[0., 0., 0., 1.]], dtype=np.float32)
rot_mat_inv_pl = tf.constant(rot_mat_inv)
rot_mat_inv_pl = tf.expand_dims(rot_mat_inv_pl, 0) # Convert to a len(yp) x 1 matrix.
rot_mat_inv_pl = tf.tile(rot_mat_inv_pl, [batch_size, 1, 1]) # Create multiple columns.
sdf = {}
sdf['pc'] = pc_pl
sdf['sample_pc'] = sample_pc_pl
sdf['sample_pc_rot'] = sample_pc_rot_pl
sdf['imgs'] = imgs_pl
sdf['sdf_value'] = sdf_value_pl
sdf['sdf_sign'] = sdf_sign_pl
sdf['trans_mat'] = trans_mat_pl
sdf['norm_params'] = norm_params_pl
sdf['regress_mat'] = regress_mat_pl
sdf['K'] = K_pl
sdf['rot_mat_inv'] = rot_mat_inv_pl
sdf['shifts'] = shifts_pl
return sdf
def cal_K(img_size):
F_MM = 35. # Focal length
SENSOR_SIZE_MM = 32.
PIXEL_ASPECT_RATIO = 1. # pixel_aspect_x / pixel_aspect_y
RESOLUTION_PCT = 100.
SKEW = 0.
# Calculate intrinsic matrix.
# 2 atan(35 / 2*32)
scale = RESOLUTION_PCT / 100
# print('scale', scale)
f_u = F_MM * img_size[1] * scale / SENSOR_SIZE_MM
f_v = F_MM * img_size[0] * scale * PIXEL_ASPECT_RATIO / SENSOR_SIZE_MM
# print('f_u', f_u, 'f_v', f_v)
u_0 = img_size[1] * scale / 2
v_0 = img_size[0] * scale / 2
K = np.matrix(((f_u, SKEW, u_0), (0, f_v, v_0), (0, 0, 1)), dtype=np.float32)
return K
def matrixize(xyz, batch_size):
padding = tf.eye(3, num_columns=4, batch_shape=[batch_size])
xyz1 = tf.expand_dims(tf.concat((xyz, tf.ones([batch_size, 1])), axis = 1), axis=1)
return tf.concat((padding,xyz1), axis=1)
def get_inverse_norm_matrix(norm_params, batch_size):
m = norm_params[:,3]
m_inv_padding = tf.multiply(tf.eye(3, num_columns=4, batch_shape=[batch_size]), m[:,tf.newaxis,tf.newaxis])
M_inv = tf.concat([m_inv_padding,
tf.concat([tf.zeros([batch_size, 1, 3]), tf.ones([batch_size, 1, 1])], axis= 2)], axis =1)
# M_inv =
# [[m, 0., 0., 0.],
# [0., m, 0., 0.],
# [0., 0., m, 0.],
# [0., 0., 0., 1.]]
#
T_inv_padding = tf.eye(3, num_columns=4, batch_shape=[batch_size])
xyz1 = tf.expand_dims(tf.concat((norm_params[:,:3], tf.ones([batch_size, 1])), axis=1), axis=1)
T_inv = tf.concat((T_inv_padding, xyz1), axis=1)
#
# T_inv =
# [[1.0, 0., 0., 0],
# [0., 1.0, 0., 0],
# [0., 0., 1.0, 0],
# [x., y., z., 1.]]
#
# print(M_inv.get_shape().as_list(), T_inv.get_shape().as_list())
return tf.matmul(M_inv, T_inv)
def get_model(ref_dict, num_point, is_training, bn=False, bn_decay=None, img_size = (137,137), wd=1e-5, FLAGS=None):
ref_img = ref_dict['imgs']
ref_pc = ref_dict['pc']
ref_sample_pc = ref_dict['sample_pc']
ref_sample_pc_rot = ref_dict['sample_pc_rot']
trans_mat = ref_dict['trans_mat']
K = ref_dict['K']
norm_params = ref_dict['norm_params']
rot_mat_inv = ref_dict['rot_mat_inv']
regress_mat = ref_dict['regress_mat']
gt_xyshift = ref_dict['shifts']
batch_size = ref_img.get_shape()[0].value
norm_mat_inv = get_inverse_norm_matrix(norm_params, batch_size)
# endpoints
end_points = {}
end_points['ref_pc'] = ref_pc
end_points['regress_mat'] = regress_mat
end_points['K'] = K
end_points['gt_xyshift'] = gt_xyshift
end_points['trans_mat'] = trans_mat
end_points['sample_pc'] = ref_sample_pc #* 10
# Image extract features
if ref_img.shape[1] != 224 or ref_img.shape[2] != 224:
ref_img = tf.image.resize_bilinear(ref_img, [224, 224])
else:
print("image size:", img_size)
end_points['ref_img'] = ref_img
# vgg.vgg_16.default_image_size = (224, 224)
with slim.arg_scope([slim.conv2d],
weights_regularizer=slim.l2_regularizer(wd)):
ref_feats_embedding, vgg_end_points = vgg.vgg_16(ref_img, num_classes=1024, is_training=False, scope='vgg_16', spatial_squeeze=False)
ref_feats_embedding_cnn = tf.squeeze(ref_feats_embedding, axis = [1,2])
end_points['embedding'] = ref_feats_embedding_cnn
print(vgg_end_points.keys())
with tf.variable_scope("cameraprediction") as scope:
if FLAGS.shift:
pred_rotation, pred_translation, pred_RT, pred_xyshift = posenet.get_cam_mat_shft(ref_feats_embedding_cnn, is_training, batch_size, bn, bn_decay, wd)
end_points['pred_rotation'] = pred_rotation
end_points['pred_translation'] = pred_translation
end_points['pred_RT'] = pred_RT
end_points['pred_xyshift'] = pred_xyshift
elif FLAGS.space_shift:
pred_rotation, pred_translation, pred_RT, predxyzshift = posenet.get_cam_mat_spaceshft(ref_feats_embedding_cnn, is_training, batch_size, bn, bn_decay, wd)
end_points['pred_rotation'] = pred_rotation
end_points['pred_translation'] = pred_translation
end_points['pred_RT'] = pred_RT
end_points['pred_xyshift'] = None
pred_xyshift = None
else:
pred_rotation, pred_translation, pred_RT = posenet.get_cam_mat(ref_feats_embedding_cnn, is_training, batch_size, bn, bn_decay, wd)
end_points['pred_rotation'] = pred_rotation
end_points['pred_translation'] = pred_translation
end_points['pred_RT'] = pred_RT
end_points['pred_xyshift'] = None
pred_xyshift = None
print('trans_mat', trans_mat.shape)
sample_img_points, gt_xy = get_img_points(ref_sample_pc, trans_mat, gt_xyshift, FLAGS, img_size=img_size)
end_points['sample_img_points'] = sample_img_points
end_points['gt_xy'] = gt_xy
# K, RT, rot_mat, W2O_mat, norm_mat, pred_transmat = inverse norm_mat * inverse W2O_mat * inverse rot_mat * pred_RT * K inverse
if FLAGS.space_shift:
pred_regress_mat = norm_mat_inv @ predxyzshift @ rot_mat_inv @ pred_RT
else:
pred_regress_mat = norm_mat_inv @ rot_mat_inv @ pred_RT
pred_trans_mat = pred_regress_mat @ K
pred_sample_img_points, pred_xy = get_img_points(ref_sample_pc, pred_trans_mat, pred_xyshift, FLAGS, img_size=img_size)
end_points['pred_sample_img_points'] = pred_sample_img_points
end_points['pred_trans_mat'] = pred_trans_mat
end_points['pred_regress_mat'] = pred_regress_mat
end_points['pred_xy'] = pred_xy
print("gt_xy, pred_xy", gt_xy.get_shape(), pred_xy.get_shape())
return end_points
def get_img_points(sample_pc, trans_mat_right, pred_xyshift, FLAGS, img_size = (137,137)):
# sample_pc B*N*3
size_lst = sample_pc.get_shape().as_list()
homo_pc = tf.concat((sample_pc, tf.ones((size_lst[0], size_lst[1], 1),dtype=np.float32)),axis= -1)
print("homo_pc.get_shape()", homo_pc.get_shape())
pc_xyz = tf.matmul(homo_pc, trans_mat_right)
print("pc_xyz.get_shape()", pc_xyz.get_shape()) # B * N * 3
pc_xy = tf.cast(tf.divide(pc_xyz[:,:,:2], tf.expand_dims(pc_xyz[:,:,2], axis = 2)), dtype=tf.float32)
if FLAGS.shift:
pc_xy = pc_xy + tf.tile(tf.expand_dims(pred_xyshift / 2 * FLAGS.img_h, axis=1), (1,FLAGS.num_points,1))
mintensor = tf.constant([0.0,0.0], dtype=tf.float32)
maxtensor = tf.constant([img_size[0]-1, img_size[1]-1], dtype=tf.float32)
return tf.minimum(maxtensor, tf.maximum(mintensor, pc_xy)), pc_xy
def get_loss(end_points, sdf_weight=10., regularization=True, FLAGS=None):
""" sigmoid loss+sdf value"""
sample_pc = end_points['sample_pc']
regress_mat = end_points['regress_mat']
pred_regress_mat = end_points['pred_regress_mat']
pred_RT = end_points['pred_RT']
sample_img_points = end_points['sample_img_points']
pred_sample_img_points = end_points['pred_sample_img_points']
pred_xy = end_points['pred_xy']
pred_xyshift = end_points['pred_xyshift']
gt_xyshift = end_points['gt_xyshift']
gt_xy = end_points['gt_xy']
pred_trans_mat = end_points['pred_trans_mat']
trans_mat = end_points['trans_mat']
loss = 0.
# K = end_points['K']
# pred_rotation = end_points['pred_rotation']
# pred_translation = end_points['pred_translation']
# trans_cam = end_points['trans_mat']
################
# Compute loss #
################
end_points['losses'] = {}
end_points['results'] = {}
########### camera loss
##point cloud rotation error
size_lst = sample_pc.get_shape().as_list()
homo_sample_pc = tf.concat((sample_pc, tf.ones((size_lst[0], size_lst[1], 1),dtype=np.float32)),axis= -1)
sub_3d = tf.matmul(homo_sample_pc, pred_regress_mat) - tf.matmul(homo_sample_pc, regress_mat)
rotpc_loss = tf.reduce_mean(tf.nn.l2_loss(sub_3d))
rot2d_loss = tf.reduce_mean(tf.nn.l2_loss(pred_xy - gt_xy)) / 10000.
rot2d_dist_all = tf.reduce_mean(tf.sqrt(tf.reduce_sum(tf.square(sample_img_points - pred_sample_img_points), axis = -1)), axis=1)
rot2d_dist = tf.reduce_mean(rot2d_dist_all)
rot3d_dist_all = tf.reduce_mean(tf.sqrt(tf.reduce_sum(tf.square(sub_3d), axis = -1)), axis=1)
rot3d_dist = tf.reduce_mean(rot3d_dist_all)
rotmatrix_loss = tf.reduce_mean((tf.square((pred_trans_mat-trans_mat))))
end_points['rot_homopc'] = tf.matmul(homo_sample_pc, regress_mat)
end_points['pred_rot_homopc'] = tf.matmul(homo_sample_pc, pred_regress_mat)
rotpc_loss = rotpc_loss #* 100
end_points['losses']['rotpc_loss'] = rotpc_loss
end_points['losses']['rot2d_loss'] = rot2d_loss
end_points['losses']['rot3d_dist'] = rot3d_dist
end_points['losses']['rot2d_dist'] = rot2d_dist
end_points['losses']['rotmatrix_loss'] = rotmatrix_loss
end_points['results']['rot2d_dist_all'] = rot2d_dist_all
end_points['results']['rot3d_dist_all'] = rot3d_dist_all
if FLAGS.loss_mode == "3D":
loss += rotpc_loss
elif FLAGS.loss_mode == "2D":
loss += rot2d_loss
elif FLAGS.loss_mode == "3DM":
loss += rotpc_loss + rotmatrix_loss * 0.3
elif FLAGS.loss_mode == "23D":
loss += (rotpc_loss + rot2d_loss * 0.1)
else:
loss += (rot2d_loss + rotpc_loss + rotmatrix_loss)
if FLAGS.shift:
shiftxy_loss = tf.reduce_mean(tf.nn.l2_loss(gt_xyshift - pred_xyshift))
end_points['losses']['shiftxy_loss'] = shiftxy_loss
loss+= shiftxy_loss * FLAGS.shift_weight
# ## rotation geodesic distance loss
# geodist = posenet.compute_geodesic_distance_from_two_matrices(pred_rotation, RT[:,:3,:])
# geodist_loss = tf.reduce_mean(tf.nn.l2_loss(geodist))
# geodist_loss = geodist_loss
# end_points['losses']['geodist_loss'] = geodist_loss
# loss += geodist_loss
# ## rotation mat loss
# rot_loss = tf.reduce_mean(tf.nn.l2_loss(pred_rotation - RT[:,:3,:]))
# rot_loss = 100 * rot_loss
# end_points['losses']['rot_loss'] = rot_loss
# loss += rot_loss
# ## rotation mat differencel loss
# rot_mat_diff = tf.matmul(pred_rotation, tf.transpose(RT[:,:3,:], perm=[0,2,1]))
# end_points['rot_mat_diff'] = rot_mat_diff
# rot_mat_diff -= tf.constant(np.eye(3), dtype=tf.float32)
# rot_mat_diff_loss = tf.reduce_mean(tf.nn.l2_loss(rot_mat_diff))
# rot_mat_diff_loss = 100 * rot_mat_diff_loss
# end_points['losses']['rot_mat_diff'] = rot_mat_diff_loss
# loss += rot_mat_diff_loss
# trans_loss = tf.reduce_mean(tf.abs(pred_translation - RT[:,3,:]))
# trans_loss = 100 * trans_loss
# end_points['losses']['trans_loss'] = trans_loss
# loss += trans_loss
# cam_loss = tf.reduce_mean(tf.abs(pred_RT - RT))
# cam_loss = 100 * cam_loss
# end_points['losses']['rot_loss'] = rot_loss
# end_points['losses']['cam_loss'] = cam_loss
# cam_loss = rot_mat_diff + trans_loss
# loss += cam_loss
# cam_loss = tf.reduce_mean(tf.abs(pred_cam - RT))
# end_points['losses']['cam_loss'] = cam_loss
# loss += cam_loss
# mat_diff = tf.matmul(pred_rotation, tf.transpose(pred_rotation, perm=[0,2,1]))
# mat_diff -= tf.constant(np.eye(3), dtype=tf.float32)
# mat_diff_loss = tf.nn.l2_loss(mat_diff)
# end_points['losses']['mat_diff_loss'] = mat_diff_loss
# loss += mat_diff_loss
############### weight decay
if regularization:
vgg_regularization_loss = tf.add_n(slim.losses.get_regularization_losses())
# decoder_regularization_loss = tf.add_n(tf.get_collection('regularizer'))
end_points['losses']['regularization'] = vgg_regularization_loss#(vgg_regularization_loss + decoder_regularization_loss)
loss += vgg_regularization_loss#(vgg_regularization_loss + decoder_regularization_loss)
end_points['losses']['overall_loss'] = loss
return loss, end_points
================================================
FILE: cam_est/model_cam_old.py
================================================
import tensorflow as tf
import numpy as np
import os
import tensorflow.contrib.slim as slim
from tensorflow.contrib.slim.python.slim.nets import vgg
import sys
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = os.path.dirname(BASE_DIR)
sys.path.append(os.path.join(ROOT_DIR))
sys.path.append(os.path.join(ROOT_DIR,'..'))
sys.path.append(os.path.join(ROOT_DIR , 'models'))
import posenet_old as posenet
def placeholder_inputs(batch_size, num_points, img_size, num_pc=2018, num_sample_pc = 2048*8, scope=''):
with tf.variable_scope(scope) as sc:
pc_pl = tf.placeholder(tf.float32, shape=(batch_size, num_pc, 3))
sample_pc_pl = tf.placeholder(tf.float32, shape=(batch_size, num_sample_pc, 3))
sample_pc_rot_pl = tf.placeholder(tf.float32, shape=(batch_size, num_sample_pc, 3))
imgs_pl = tf.placeholder(tf.float32, shape=(batch_size, img_size[0], img_size[1], 3))
sdf_value_pl = tf.placeholder(tf.float32, shape=(batch_size, num_sample_pc, 1))
sdf_sign_pl = tf.placeholder(tf.int32, shape=(batch_size, num_sample_pc))
trans_mat_pl = tf.placeholder(tf.float32, shape=(batch_size, 4, 3))
RT_mat_pl = tf.placeholder(tf.float32, shape=(batch_size, 4, 3))
shifts_pl = tf.placeholder(tf.float32, shape=(batch_size, 2))
# camera intrinsic matrix
K = np.array([[149.84375, 0., 68.5],[0., 149.84375, 68.5],[0., 0., 1.]], dtype=np.float32)#.T
K_pl = tf.constant(K)
K_pl = tf.expand_dims(K_pl, 0) # Convert to a len(yp) x 1 matrix.
K_pl = tf.tile(K_pl, [batch_size, 1, 1]) # Create multiple columns.
sdf = {}
sdf['pc'] = pc_pl
sdf['sample_pc'] = sample_pc_pl
sdf['sample_pc_rot'] = sample_pc_rot_pl
sdf['imgs'] = imgs_pl
sdf['sdf_value'] = sdf_value_pl
sdf['sdf_sign'] = sdf_sign_pl
sdf['trans_mat'] = trans_mat_pl
sdf['RT'] = RT_mat_pl
sdf['K'] = K_pl
sdf['shifts'] = shifts_pl
return sdf
def get_model(ref_dict, num_point, is_training, bn=False, bn_decay=None, img_size = (137,137), wd=1e-5, FLAGS=None):
ref_img = ref_dict['imgs']
ref_pc = ref_dict['pc']
ref_sample_pc = ref_dict['sample_pc']
ref_sample_pc_rot = ref_dict['sample_pc_rot']
trans_mat = ref_dict['trans_mat']
K = ref_dict['K']
RT = ref_dict['RT']
gt_xyshift = ref_dict['shifts']
batch_size = ref_img.get_shape()[0].value
# endpoints
end_points = {}
end_points['ref_pc'] = ref_pc
end_points['RT'] = RT
end_points['K'] = K
end_points['gt_xyshift'] = gt_xyshift
end_points['trans_mat'] = trans_mat
end_points['sample_pc'] = ref_sample_pc #* 10
# Image extract features
if ref_img.shape[1] != 224 or ref_img.shape[2] != 224:
ref_img = tf.image.resize_bilinear(ref_img, [224, 224])
end_points['ref_img'] = ref_img
# vgg.vgg_16.default_image_size = (224, 224)
with slim.arg_scope([slim.conv2d],
weights_regularizer=slim.l2_regularizer(wd)):
ref_feats_embedding, vgg_end_points = vgg.vgg_16(ref_img, num_classes=1024, is_training=False, scope='vgg_16', spatial_squeeze=False)
ref_feats_embedding_cnn = tf.squeeze(ref_feats_embedding, axis = [1,2])
end_points['embedding'] = ref_feats_embedding_cnn
print(vgg_end_points.keys())
with tf.variable_scope("cameraprediction") as scope:
if FLAGS.shift:
pred_rotation, pred_translation, pred_RT, pred_xyshift = posenet.get_cam_mat_shft(ref_feats_embedding_cnn, is_training, batch_size, bn, bn_decay, wd)
end_points['pred_rotation'] = pred_rotation
end_points['pred_translation'] = pred_translation
end_points['pred_RT'] = pred_RT
end_points['pred_xyshift'] = pred_xyshift
else:
pred_rotation, pred_translation, pred_RT = posenet.get_cam_mat(ref_feats_embedding_cnn, is_training, batch_size, bn, bn_decay, wd)
end_points['pred_rotation'] = pred_rotation
end_points['pred_translation'] = pred_translation
end_points['pred_RT'] = pred_RT
end_points['pred_xyshift'] = None
pred_xyshift = None
print('trans_mat', trans_mat.shape)
sample_img_points, gt_xy = get_img_points(ref_sample_pc, trans_mat, gt_xyshift, FLAGS)
end_points['sample_img_points'] = sample_img_points
end_points['gt_xy'] = gt_xy
K_transpose = tf.transpose(K, perm=[0, 2, 1])
pred_trans_mat = tf.matmul(pred_RT, K_transpose)
pred_sample_img_points, pred_xy = get_img_points(ref_sample_pc, pred_trans_mat, pred_xyshift, FLAGS)
end_points['pred_sample_img_points'] = pred_sample_img_points
end_points['pred_trans_mat'] = pred_trans_mat
end_points['pred_xy'] = pred_xy
print("gt_xy, pred_xy", gt_xy.get_shape(), pred_xy.get_shape())
return end_points
def get_img_points(sample_pc, trans_mat_right, pred_xyshift, FLAGS):
# sample_pc B*N*3
size_lst = sample_pc.get_shape().as_list()
homo_pc = tf.concat((sample_pc, tf.ones((size_lst[0], size_lst[1], 1),dtype=np.float32)),axis= -1)
print("homo_pc.get_shape()", homo_pc.get_shape())
pc_xyz = tf.matmul(homo_pc, trans_mat_right)
print("pc_xyz.get_shape()", pc_xyz.get_shape()) # B * N * 3
pc_xy = tf.cast(tf.divide(pc_xyz[:,:,:2], tf.expand_dims(pc_xyz[:,:,2], axis = 2)), dtype=tf.float32)
if FLAGS.shift:
pc_xy = pc_xy + tf.tile(tf.expand_dims(pred_xyshift / 2 * FLAGS.img_h, axis=1), (1,FLAGS.num_points,1))
mintensor = tf.constant([0.0,0.0], dtype=tf.float32)
maxtensor = tf.constant([136.0,136.0], dtype=tf.float32)
return tf.minimum(maxtensor, tf.maximum(mintensor, pc_xy)), pc_xy
def get_loss(end_points, sdf_weight=10., regularization=True, FLAGS=None):
""" sigmoid loss+sdf value"""
sample_pc = end_points['sample_pc']
RT = end_points['RT']
pred_RT = end_points['pred_RT']
sample_img_points = end_points['sample_img_points']
pred_sample_img_points = end_points['pred_sample_img_points']
pred_xy = end_points['pred_xy']
pred_xyshift = end_points['pred_xyshift']
gt_xyshift = end_points['gt_xyshift']
gt_xy = end_points['gt_xy']
pred_trans_mat = end_points['pred_trans_mat']
trans_mat = end_points['trans_mat']
loss = 0.
# K = end_points['K']
# pred_rotation = end_points['pred_rotation']
# pred_translation = end_points['pred_translation']
# trans_cam = end_points['trans_mat']
################
# Compute loss #
################
end_points['losses'] = {}
end_points['results'] = {}
########### camera loss
##point cloud rotation error
size_lst = sample_pc.get_shape().as_list()
homo_sample_pc = tf.concat((sample_pc, tf.ones((size_lst[0], size_lst[1], 1),dtype=np.float32)),axis= -1)
sub_3d = tf.matmul(homo_sample_pc, pred_RT) - tf.matmul(homo_sample_pc, RT)
rotpc_loss = tf.reduce_mean(tf.nn.l2_loss(sub_3d))
rot2d_loss = tf.reduce_mean(tf.nn.l2_loss(pred_xy - gt_xy)) / 10000.
rot2d_dist_all = tf.reduce_mean(tf.sqrt(tf.reduce_sum(tf.square(sample_img_points - pred_sample_img_points), axis = -1)), axis=1)
rot2d_dist = tf.reduce_mean(rot2d_dist_all)
rot3d_dist_all = tf.reduce_mean(tf.sqrt(tf.reduce_sum(tf.square(sub_3d), axis = -1)), axis=1)
rot3d_dist = tf.reduce_mean(rot3d_dist_all)
rotmatrix_loss = tf.reduce_mean((tf.square((pred_trans_mat-trans_mat))))
end_points['rot_homopc'] = tf.matmul(homo_sample_pc, RT)
end_points['pred_rot_homopc'] = tf.matmul(homo_sample_pc, pred_RT)
rotpc_loss = rotpc_loss #* 100
end_points['losses']['rotpc_loss'] = rotpc_loss
end_points['losses']['rot2d_loss'] = rot2d_loss
end_points['losses']['rot3d_dist'] = rot3d_dist
end_points['losses']['rot2d_dist'] = rot2d_dist
end_points['losses']['rotmatrix_loss'] = rotmatrix_loss
end_points['results']['rot2d_dist_all'] = rot2d_dist_all
end_points['results']['rot3d_dist_all'] = rot3d_dist_all
if FLAGS.loss_mode == "3D":
loss += rotpc_loss
elif FLAGS.loss_mode == "2D":
loss += rot2d_loss
elif FLAGS.loss_mode == "3DM":
loss += rotpc_loss + rotmatrix_loss * 0.3
else:
loss += rot2d_loss + rotpc_loss + rotmatrix_loss
if FLAGS.shift:
shiftxy_loss = tf.reduce_mean(tf.nn.l2_loss(gt_xyshift - pred_xyshift))
end_points['losses']['shiftxy_loss'] = shiftxy_loss
loss+= shiftxy_loss * FLAGS.shift_weight
# ## rotation geodesic distance loss
# geodist = posenet.compute_geodesic_distance_from_two_matrices(pred_rotation, RT[:,:3,:])
# geodist_loss = tf.reduce_mean(tf.nn.l2_loss(geodist))
# geodist_loss = geodist_loss
# end_points['losses']['geodist_loss'] = geodist_loss
# loss += geodist_loss
# ## rotation mat loss
# rot_loss = tf.reduce_mean(tf.nn.l2_loss(pred_rotation - RT[:,:3,:]))
# rot_loss = 100 * rot_loss
# end_points['losses']['rot_loss'] = rot_loss
# loss += rot_loss
# ## rotation mat differencel loss
# rot_mat_diff = tf.matmul(pred_rotation, tf.transpose(RT[:,:3,:], perm=[0,2,1]))
# end_points['rot_mat_diff'] = rot_mat_diff
# rot_mat_diff -= tf.constant(np.eye(3), dtype=tf.float32)
# rot_mat_diff_loss = tf.reduce_mean(tf.nn.l2_loss(rot_mat_diff))
# rot_mat_diff_loss = 100 * rot_mat_diff_loss
# end_points['losses']['rot_mat_diff'] = rot_mat_diff_loss
# loss += rot_mat_diff_loss
# trans_loss = tf.reduce_mean(tf.abs(pred_translation - RT[:,3,:]))
# trans_loss = 100 * trans_loss
# end_points['losses']['trans_loss'] = trans_loss
# loss += trans_loss
# cam_loss = tf.reduce_mean(tf.abs(pred_RT - RT))
# cam_loss = 100 * cam_loss
# end_points['losses']['rot_loss'] = rot_loss
# end_points['losses']['cam_loss'] = cam_loss
# cam_loss = rot_mat_diff + trans_loss
# loss += cam_loss
# cam_loss = tf.reduce_mean(tf.abs(pred_cam - RT))
# end_points['losses']['cam_loss'] = cam_loss
# loss += cam_loss
# mat_diff = tf.matmul(pred_rotation, tf.transpose(pred_rotation, perm=[0,2,1]))
# mat_diff -= tf.constant(np.eye(3), dtype=tf.float32)
# mat_diff_loss = tf.nn.l2_loss(mat_diff)
# end_points['losses']['mat_diff_loss'] = mat_diff_loss
# loss += mat_diff_loss
############### weight decay
if regularization:
vgg_regularization_loss = tf.add_n(slim.losses.get_regularization_losses())
# decoder_regularization_loss = tf.add_n(tf.get_collection('regularizer'))
end_points['losses']['regularization'] = vgg_regularization_loss#(vgg_regularization_loss + decoder_regularization_loss)
loss += vgg_regularization_loss#(vgg_regularization_loss + decoder_regularization_loss)
end_points['losses']['overall_loss'] = loss
return loss, end_points
================================================
FILE: cam_est/train_sdf_cam.py
================================================
import argparse
import math
from datetime import datetime
import numpy as np
import random
import tensorflow as tf
import socket
import importlib
import os
import cv2
import sys
import h5py
import time
from tensorflow.contrib.framework.python.framework import checkpoint_utils
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
print(os.path.join(os.path.dirname(BASE_DIR), 'data'))
sys.path.append(BASE_DIR) # model
sys.path.append(os.path.join(BASE_DIR, 'models'))
sys.path.append(os.path.join(BASE_DIR, 'utils'))
sys.path.append(os.path.join(BASE_DIR, 'preprocessing'))
print(os.path.join(BASE_DIR, 'preprocessing'))
sys.path.append(os.path.join(BASE_DIR, 'data'))
print(os.path.join(BASE_DIR, 'data'))
import model_cam as model
import data_sdf_h5_queue_mask_imgh5_cammat as data
import create_file_lst
slim = tf.contrib.slim
lst_dir, cats, all_cats, raw_dirs = create_file_lst.get_all_info()
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=str, default='2', help='GPU to use [default: GPU 0]')
parser.add_argument('--category', default="all", help='Which single class to train on [default: None]')
parser.add_argument('--log_dir', default='checkpoint/sdf_2d_twostream_cam_pcrot_all', help='Log dir [default: log]')
parser.add_argument('--num_points', type=int, default=1, help='Point Number [default: 2048]')
parser.add_argument('--num_sample_points', type=int, default=2048, help='Sample Point Number [default: 2048]')
parser.add_argument('--max_epoch', type=int, default=200, help='Epoch to run [default: 201]')
parser.add_argument('--batch_size', type=int, default=32, help='Batch Size during training [default: 32]')
parser.add_argument('--img_h', type=int, default=137, help='Image Height')
parser.add_argument('--img_w', type=int, default=137, help='Image Width')
parser.add_argument('--verbose_freq', type=int, default=100, help='verbose frequency')
parser.add_argument('--learning_rate', type=float, default=1e-4, help='Initial learning rate [default: 0.001]')
parser.add_argument('--momentum', type=float, default=0.9, help='Initial learning rate [default: 0.9]')
parser.add_argument('--optimizer', default='adam', help='adam or momentum [default: adam]')
parser.add_argument('--restore_model', default='', help='restore_model')
parser.add_argument('--restore_modelpn', default='', help='restore_model')#checkpoint/sdf_3dencoder_sdfbasic2/latest.ckpt
parser.add_argument('--restore_modelcnn', default='', help='restore_model')#../../models/CNN/pretrained_model/vgg_16.ckpt
parser.add_argument('--rotation', action='store_true', help='Disable random rotation during training.')
parser.add_argument('--sample', action='store_false', help='Disable sample during training.')
parser.add_argument('--img_feat', action='store_false', help='Disable sample during training.')
parser.add_argument('--splitvalid', action='store_true', help='Disable sample during training.')
parser.add_argument('--decay_step', type=int, default=200000, help='Decay step for lr decay [default: 200000]')
parser.add_argument('--decay_rate', type=float, default=0.9, help='Decay rate for lr decay [default: 0.7]')
parser.add_argument('--loss_mode', type=str, default="3D", help='loss on 3D points or 2D points')
parser.add_argument('--test', action="store_true")
parser.add_argument('--create', action="store_true")
parser.add_argument('--cat_limit', type=int, default=168000, help="balance each category, 1500 * 24 = 36000")
parser.add_argument('--img_h5_dir', type=str, default=raw_dirs["renderedh5_dir_est"], help="where to save img_h5")
parser.add_argument('--src_h5_dir', type=str, default=raw_dirs['renderedh5_dir'], help="where to read src img_h5")
parser.add_argument('--shift', action="store_true")
parser.add_argument('--shift_weight', type=float, default=0.5)
parser.add_argument('--space_shift', action="store_true")
parser.add_argument('--wd', type=float, default=2e-3, help='Decay rate for lr decay [default: 0.7]')
FLAGS = parser.parse_args()
EPOCH_CNT = 0
BATCH_SIZE = FLAGS.batch_size
NUM_POINTS = FLAGS.num_points
NUM_SAMPLE_POINTS = FLAGS.num_sample_points
MAX_EPOCH = FLAGS.max_epoch
BASE_LEARNING_RATE = FLAGS.learning_rate
GPU_INDEX = FLAGS.gpu
MOMENTUM = FLAGS.momentum
OPTIMIZER = FLAGS.optimizer
DECAY_STEP = FLAGS.decay_step
DECAY_RATE = FLAGS.decay_rate
PRETRAINED_MODEL_PATH = FLAGS.restore_model
PRETRAINED_CNN_MODEL_FILE = FLAGS.restore_modelcnn
PRETRAINED_PN_MODEL_FILE = FLAGS.restore_modelpn
LOG_DIR = FLAGS.log_dir
VV=False
os.environ["CUDA_VISIBLE_DEVICES"] = GPU_INDEX
if not os.path.exists(LOG_DIR): os.makedirs(LOG_DIR)
RESULT_PATH = os.path.join(LOG_DIR, 'train_results')
if not os.path.exists(RESULT_PATH): os.mkdir(RESULT_PATH)
VALID_RESULT_PATH = os.path.join(LOG_DIR, 'valid_results_'+str(time.time()))
TEST_RESULT_PATH = os.path.join(LOG_DIR, 'test_results_'+str(time.time()))
if not os.path.exists(VALID_RESULT_PATH): os.mkdir(VALID_RESULT_PATH)
if not os.path.exists(TEST_RESULT_PATH): os.mkdir(TEST_RESULT_PATH)
os.system('cp %s.py %s' % (os.path.splitext(model.__file__)[0], LOG_DIR))
os.system('cp %s.py %s' % (os.path.splitext(__file__)[0], LOG_DIR))
LOG_FOUT = open(os.path.join(LOG_DIR, 'log_train_%s.txt' % str(datetime.now())), 'w')
LOG_FOUT.write(str(FLAGS)+'\n')
BN_INIT_DECAY = 0.5
BN_DECAY_DECAY_RATE = 0.5
BN_DECAY_DECAY_STEP = float(DECAY_STEP)
BN_DECAY_CLIP = 0.99
IMG_SIZE = FLAGS.img_h
SDF_WEIGHT = 10.
HOSTNAME = socket.gethostname()
TRAIN_LISTINFO = []
TEST_LISTINFO = []
# "chair": "03001627",
# "bench": "02828884",
# "cabinet": "02933112",
# "car": "02958343",
# "airplane": "02691156",
# "display": "03211117",
# "lamp": "03636649",
# "speaker": "03691459",
# "rifle": "04090263",
# "sofa": "04256520",
# "table": "04379243",
# "phone": "04401088",
# "watercraft": "04530566"
CAT_LIST = ["02691156", "02828884", "02933112", "02958343", "03001627", "03211117", "03636649", "03691459", "04090263",
"04256520", "04379243", "04401088", "04530566"]
# CAT_LIST = ["03636649","04090263"]
cats_limit_train = {}
cats_limit_test = {}
cat_ids=[]
for value in CAT_LIST:
cat_ids.append(value)
cats_limit_train[value] = 0
cats_limit_test[value] = 0
for cat in CAT_LIST:
TRAIN_LST = lst_dir + '/%s_train.lst' % cat
with open(TRAIN_LST, 'r') as f:
lines = f.read().splitlines()
for line in lines:
for render in range(24):
cats_limit_train[cat] += 1
TRAIN_LISTINFO += [(cat, line.strip(), render)]
for cat in CAT_LIST:
VALID_LST = lst_dir + '/%s_test.lst' % cat
with open(VALID_LST, 'r') as f:
lines = f.read().splitlines()
for line in lines:
for render in range(24):
cats_limit_test[cat] += 1
TEST_LISTINFO += [(cat, line.strip(), render)]
info = {'rendered_dir': FLAGS.src_h5_dir,
'rendered_dir_v2': raw_dirs['renderedh5_dir_v2'],
'sdf_dir': raw_dirs['sdf_dir'],
'iso_value': 0.003}
TRAIN_DATASET = data.Pt_sdf_img(FLAGS, listinfo=TRAIN_LISTINFO, info=info, cats_limit=cats_limit_train)
VALID_DATASET = data.Pt_sdf_img(FLAGS, listinfo=TEST_LISTINFO, info=info, cats_limit=cats_limit_test)
def log_string(out_str):
LOG_FOUT.write(out_str+'\n')
LOG_FOUT.flush()
print(out_str)
def get_learning_rate(batch):
learning_rate = tf.train.exponential_decay(
BASE_LEARNING_RATE, # Base learning rate.
batch * BATCH_SIZE, # Current index into the dataset.
DECAY_STEP, # Decay step.
DECAY_RATE, # Decay rate.
staircase=True)
learning_rate = tf.maximum(learning_rate, 1e-6, name='lr') # CLIP THE LEARNING RATE!
return learning_rate
def get_bn_decay(batch):
bn_momentum = tf.train.exponential_decay(
BN_INIT_DECAY,
batch*BATCH_SIZE,
BN_DECAY_DECAY_STEP,
BN_DECAY_DECAY_RATE,
staircase=True)
bn_decay = tf.minimum(BN_DECAY_CLIP, 1 - bn_momentum)
return bn_decay
class NoStdStreams(object):
def __init__(self,stdout = None, stderr = None):
self.devnull = open(os.devnull,'w')
self._stdout = stdout or self.devnull or sys.stdout
self._stderr = stderr or self.devnull or sys.stderr
def __enter__(self):
self.old_stdout, self.old_stderr = sys.stdout, sys.stderr
self.old_stdout.flush(); self.old_stderr.flush()
sys.stdout, sys.stderr = self._stdout, self._stderr
def __exit__(self, exc_type, exc_value, traceback):
self._stdout.flush(); self._stderr.flush()
sys.stdout = self.old_stdout
sys.stderr = self.old_stderr
self.devnull.close()
def load_model(sess, LOAD_MODEL_FILE, prefixs, strict=False):
vars_in_pretrained_model = dict(checkpoint_utils.list_variables(LOAD_MODEL_FILE))
# print(vars_in_pretrained_model)
vars_in_defined_model = []
for var in tf.trainable_variables():
if isinstance(prefixs, list):
for prefix in prefixs:
if (var.op.name.startswith(prefix)) and (var.op.name in vars_in_pretrained_model.keys()) and ('logits' not in var.op.name):
if (list(var.shape) == vars_in_pretrained_model[var.op.name]):
vars_in_defined_model.append(var)
else:
if (var.op.name.startswith(prefixs)) and (var.op.name in vars_in_pretrained_model.keys()) and ('logits' not in var.op.name):
if (list(var.shape) == vars_in_pretrained_model[var.op.name]):
vars_in_defined_model.append(var)
print(vars_in_defined_model)
saver = tf.train.Saver(vars_in_defined_model)
try:
saver.restore(sess, LOAD_MODEL_FILE)
print( "Model loaded in file: %s" % (LOAD_MODEL_FILE))
except:
if strict:
print( "Fail to load modelfile: %s" % LOAD_MODEL_FILE)
return False
else:
print( "Fail loaded in file: %s" % (LOAD_MODEL_FILE))
return True
return True
def train():
log_string(LOG_DIR)
with tf.Graph().as_default():
with tf.device('/gpu:0'):
input_pls = model.placeholder_inputs(BATCH_SIZE, NUM_POINTS, (FLAGS.img_h, FLAGS.img_w), num_pc=NUM_POINTS, num_sample_pc=NUM_SAMPLE_POINTS, scope='inputs_pl')
is_training_pl = tf.placeholder(tf.bool, shape=())
print(is_training_pl)
# Note the global_step=batch parameter to minimize.
# That tells the optimizer to helpfully increment the 'batch' parameter for you every time it trains.
batch = tf.Variable(0, name='batch')
bn_decay = get_bn_decay(batch)
tf.summary.scalar('bn_decay', bn_decay)
print("--- Get model and loss")
# Get model and loss
end_points = model.get_model(input_pls, NUM_POINTS, is_training_pl, img_size = (FLAGS.img_h, FLAGS.img_w), bn=True, wd=FLAGS.wd, FLAGS=FLAGS)
loss, end_points = model.get_loss(end_points, sdf_weight=SDF_WEIGHT, FLAGS=FLAGS)
tf.summary.scalar('loss', loss)
print("--- Get training operator")
# Get training operator
learning_rate = get_learning_rate(batch)
tf.summary.scalar('learning_rate', learning_rate)
if OPTIMIZER == 'momentum':
optimizer = tf.train.MomentumOptimizer(learning_rate, momentum=MOMENTUM)
elif OPTIMIZER == 'adam':
optimizer = tf.train.AdamOptimizer(learning_rate)
# Create a session
config = tf.ConfigProto()
gpu_options = tf.GPUOptions()#per_process_gpu_memory_fraction=0.99)
config=tf.ConfigProto(gpu_options=gpu_options)
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
config.log_device_placement = False
sess = tf.Session(config=config)
# sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
# Add summary writers
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'train'), sess.graph)
test_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'test'), sess.graph)
##### all
update_variables = [x for x in tf.get_collection_ref(tf.GraphKeys.GLOBAL_VARIABLES)]
train_op = optimizer.minimize(loss, global_step=batch, var_list=update_variables)
# Init variables
init = tf.global_variables_initializer()
sess.run(init)
######### Loading Checkpoint ###############
# CNN(Pretrained from ImageNet)
if PRETRAINED_CNN_MODEL_FILE is not '':
if not load_model(sess, PRETRAINED_CNN_MODEL_FILE, 'vgg_16', strict=True):
return
if PRETRAINED_PN_MODEL_FILE is not '':
if not load_model(sess, PRETRAINED_PN_MODEL_FILE, ['refpc_reconstruction','sdfprediction'], strict=True):
return
# Overall
saver = tf.train.Saver([v for v in tf.get_collection_ref(tf.GraphKeys.GLOBAL_VARIABLES) if('lr' not in v.name) and ('batch' not in v.name)])
ckptstate = tf.train.get_checkpoint_state(PRETRAINED_MODEL_PATH)
if ckptstate is not None:
LOAD_MODEL_FILE = os.path.join(PRETRAINED_MODEL_PATH, os.path.basename(ckptstate.model_checkpoint_path))
try:
load_model(sess, LOAD_MODEL_FILE, ['vgg_16'], strict=True)
# load_model(sess, LOAD_MODEL_FILE, ['sdfprediction','vgg_16'], strict=True)
with NoStdStreams():
saver.restore(sess, LOAD_MODEL_FILE)
print( "Model loaded in file: %s" % LOAD_MODEL_FILE)
except:
print( "Fail to load overall modelfile: %s" % PRETRAINED_MODEL_PATH)
###########################################
ops = {'input_pls': input_pls,
'is_training_pl': is_training_pl,
'loss': loss,
'train_op': train_op,
'merged': merged,
'step': batch,
'end_points': end_points}
best_loss = 1e20
if FLAGS.test or FLAGS.create:
VALID_DATASET.start()
eval_one_epoch(sess, ops)
VALID_DATASET.shutdown()
else:
TRAIN_DATASET.start()
for epoch in range(MAX_EPOCH):
log_string('**** EPOCH %03d ****' % (epoch))
sys.stdout.flush()
# eval_one_epoch(sess, ops, test_writer)
train_one_epoch(sess, ops, train_writer, saver)
# epoch_loss = eval_one_epoch(sess, ops, test_writer)
# if epoch_loss < best_loss:
# best_loss = epoch_loss
# save_path = saver.save(sess, os.path.join(LOG_DIR, "best_model_epoch_%03d.ckpt"%(epoch)))
# log_string("Model saved in file: %s" % save_path)
# # Save the variables to disk.
# if epoch % 1 == 0:
# save_path = saver.save(sess, os.path.join(LOG_DIR, "model.ckpt"))
# log_string("Model saved in file: %s" % save_path)
TRAIN_DATASET.shutdown()
def pc_normalize(pc, centroid=None):
""" pc: NxC, return NxC """
l = pc.shape[0]
if centroid is None:
centroid = np.mean(pc, axis=0)
pc = pc - centroid
# m = np.max(pc, axis=0)
m = np.max(np.sqrt(np.sum(pc ** 2, axis=1)))
pc = pc / m
return pc
def train_one_epoch(sess, ops, train_writer, saver):
""" ops: dict mapping from string to tf ops """
is_training = True
# Shuffle train samples
num_batches = int(len(TRAIN_DATASET) / BATCH_SIZE)
print('num_batches', num_batches)
log_string(str(datetime.now()))
loss_all = 0.
losses = {}
for lossname in ops['end_points']['losses'].keys():
losses[lossname] = 0.
tic = time.time()
for batch_idx in range(num_batches):
start_idx = batch_idx * BATCH_SIZE
batch_data = TRAIN_DATASET.fetch()
feed_dict = {ops['is_training_pl']: is_training,
ops['input_pls']['sample_pc']: batch_data['sdf_pt'],
ops['input_pls']['trans_mat']: batch_data['trans_mat'],
ops['input_pls']['norm_params']: batch_data['norm_params'],
ops['input_pls']['regress_mat']: batch_data['regress_mat'],
ops['input_pls']['imgs']: batch_data['img'][:,:,:,:3],
ops['input_pls']['shifts']: batch_data['shifts']}
if FLAGS.rotation:
feed_dict[ops['input_pls']['sample_pc_rot']] = batch_data['sdf_pt_rot']
else:
feed_dict[ops['input_pls']['sample_pc_rot']] = batch_data['sdf_pt']
output_list = [ops['train_op'], ops['merged'], ops['step'], ops['loss'],
ops['end_points']['sample_img_points'], ops['end_points']['pred_sample_img_points'],
ops['end_points']['ref_img'], ops['end_points']['rot_homopc'], ops['end_points']['pred_rot_homopc']]
loss_list = []
for il, lossname in enumerate(losses.keys()):
loss_list += [ops['end_points']['losses'][lossname]]
outputs = sess.run(output_list + loss_list, feed_dict=feed_dict)
_, summary, step, loss_val, \
sample_img_points_val, pred_sample_img_points_val, ref_img_val, rot_homopc_val, pred_rot_homopc_val = outputs[:-len(losses)]
train_writer.add_summary(summary, step)
for il, lossname in enumerate(losses.keys()):
losses[lossname] += outputs[len(output_list)+il]
loss_all += losses['overall_loss']
save_freq = 1000
if (batch_idx + 1) % save_freq == 0:
save_path = saver.save(sess, os.path.join(LOG_DIR, "latest.ckpt"))
log_string("Model saved in file: %s" % save_path)
verbose_freq = FLAGS.verbose_freq
if (batch_idx) % verbose_freq == 0:
bid = 0
np.savetxt(os.path.join(VALID_RESULT_PATH, '%d_rot_homopc.xyz' % batch_idx), rot_homopc_val[bid,:,:])
np.savetxt(os.path.join(VALID_RESULT_PATH, '%d_pred_rot_homopc.xyz' % batch_idx), pred_rot_homopc_val[bid,:,:])
saveimg = (batch_data['img'][bid,:,:,:] * 255).astype(np.uint8)
samplept_img = sample_img_points_val[bid,...]
choice = np.random.randint(samplept_img.shape[0], size=10)
samplept_img = samplept_img[choice, ...]
pred_sample_img = pred_sample_img_points_val[bid, ...]
pred_sample_img = pred_sample_img[choice, ...]
for j in range(samplept_img.shape[0]):
x = int(samplept_img[j, 0])
y = int(samplept_img[j, 1])
cv2.circle(saveimg, (x, y), 3, (0, 255, 0, 255), -1)
for j in range(pred_sample_img.shape[0]):
x = int(pred_sample_img[j, 0])
y = int(pred_sample_img[j, 1])
cv2.circle(saveimg, (x, y), 3, (0, 0, 255, 255), -1)
cv2.imwrite(os.path.join(VALID_RESULT_PATH, '%s_%s_%s_comp.png' %
(
batch_data['cat_id'][bid], batch_data['obj_nm'][bid], batch_data['view_id'][bid])),
saveimg)
outstr = ' -- %03d / %03d -- ' % (batch_idx+1, num_batches)
for lossname in losses.keys():
outstr += '%s: %f, ' % (lossname, losses[lossname] / verbose_freq)
losses[lossname] = 0
outstr += 'time: %.02f, ' % (time.time() - tic)
tic = time.time()
log_string(outstr)
def eval_one_epoch(sess, ops):
""" ops: dict mapping from string to tf ops """
is_training = False
# Shuffle train samples
num_batches = int(len(VALID_DATASET)/BATCH_SIZE)
print('num_batches', num_batches)
print('len(VALID_DATASET)', len(VALID_DATASET))
pc3d_dist_lst = []
pc2d_dist_lst = []
losses = {}
for lossname in ops['end_points']['losses'].keys():
losses[lossname] = 0.
tic = time.time()
for batch_idx in range(num_batches):
batch_data = VALID_DATASET.fetch()
feed_dict = {ops['is_training_pl']: is_training,
ops['input_pls']['sample_pc']: batch_data['sdf_pt'],
ops['input_pls']['trans_mat']: batch_data['trans_mat'],
ops['input_pls']['norm_params']: batch_data['norm_params'],
ops['input_pls']['regress_mat']: batch_data['regress_mat'],
ops['input_pls']['imgs']: batch_data['img'][:,:,:,:3],
ops['input_pls']['shifts']: batch_data['shifts']}
if FLAGS.rotation:
feed_dict[ops['input_pls']['sample_pc_rot']] = batch_data['sdf_pt_rot']
else:
feed_dict[ops['input_pls']['sample_pc_rot']] = batch_data['sdf_pt']
output_list = [ops['loss'],
ops['end_points']['pred_trans_mat'],
ops['end_points']['pred_sample_img_points'],
ops['end_points']['sample_img_points'],
ops['end_points']['ref_img'],
ops['end_points']['rot_homopc'], ops['end_points']['pred_rot_homopc'],
ops['end_points']['results']['rot2d_dist_all'],
ops['end_points']['results']['rot3d_dist_all']]
loss_list = []
for il, lossname in enumerate(losses.keys()):
loss_list += [ops['end_points']['losses'][lossname]]
outputs = sess.run(output_list + loss_list, feed_dict=feed_dict)
loss_val, pred_trans_mat_val, pred_sample_img_points_val, sample_img_points_val, ref_img_val,\
rot_homopc_val, pred_rot_homopc_val, rot2d_dist_all_val, rot3d_dist_all_val = outputs[:-len(losses)]
for il, lossname in enumerate(losses.keys()):
if lossname == "rot2d_dist":
pc2d_dist_lst.append(outputs[len(output_list)+il])
elif lossname == "rot3d_dist":
pc3d_dist_lst.append(outputs[len(output_list)+il])
losses[lossname] += outputs[len(output_list)+il]
verbose_freq = FLAGS.verbose_freq
if (batch_idx) % verbose_freq == 0:
log_f_name = os.path.join(TEST_RESULT_PATH, "err_log.txt")
bids = range(BATCH_SIZE)
for bid in bids:
np.savetxt(os.path.join(TEST_RESULT_PATH, '%s_%s_%s_gt.xyz' %
(batch_data['cat_id'][bid], batch_data['obj_nm'][bid], batch_data['view_id'][bid])), rot_homopc_val[bid, :, :])
np.savetxt(os.path.join(TEST_RESULT_PATH, '%s_%s_%s_pred.xyz' %
(batch_data['cat_id'][bid], batch_data['obj_nm'][bid], batch_data['view_id'][bid])),
pred_rot_homopc_val[bid, :, :])
with open(log_f_name, "a") as logf:
logf.write("rot3d_dist: {}, rot2d_dist: {}, filename: {}_{}_{}_comp.png \n"
.format(rot3d_dist_all_val[bid], rot2d_dist_all_val[bid],
batch_data['cat_id'][bid], batch_data['obj_nm'][bid], batch_data['view_id'][bid]))
saveimg = (batch_data['img'][bid, :, :, :] * 255).astype(np.uint8)
# pred_saveimg = saveimg.copy()
samplept_img = sample_img_points_val[bid, ...]
choice = np.random.randint(samplept_img.shape[0], size=10)
samplept_img = samplept_img[choice, ...]
pred_sample_img = pred_sample_img_points_val[bid, ...][choice, ...]
for j in range(samplept_img.shape[0]):
x = int(samplept_img[j, 0])
y = int(samplept_img[j, 1])
cv2.circle(saveimg, (x, y), 3, (0, 255, 0, 255), -1)
for j in range(pred_sample_img.shape[0]):
x = int(pred_sample_img[j, 0])
y = int(pred_sample_img[j, 1])
cv2.circle(saveimg, (x, y), 3, (0, 0, 255, 255), -1)
cv2.imwrite(os.path.join(TEST_RESULT_PATH, '%s_%s_%s_comp.png' %
(batch_data['cat_id'][bid], batch_data['obj_nm'][bid], batch_data['view_id'][bid])), saveimg)
outstr = ' -- %03d / %03d -- ' % (batch_idx + 1, num_batches)
for lossname in losses.keys():
outstr += '%s: %f, ' % (lossname, losses[lossname] / verbose_freq)
losses[lossname] = 0
outstr += 'time: %.02f, ' % (time.time() - tic)
tic = time.time()
log_string(outstr)
if FLAGS.create:
create_img_h5(batch_data, pred_trans_mat_val)
pc2d_dist_lst = np.asarray(pc2d_dist_lst)
pc3d_dist_lst = np.asarray(pc3d_dist_lst)
print("avg 2d dist {}, max 2d dist {}, min 2d dist {}".
format(np.mean(pc2d_dist_lst), np.max(pc2d_dist_lst), np.min(pc2d_dist_lst)))
print("avg 3d dist {}, max 3d dist {}, min 3d dist {}".
format(np.mean(pc3d_dist_lst), np.max(pc3d_dist_lst), np.min(pc3d_dist_lst)))
return 1
def create_img_h5(batch_data, transmat):
# batch_data['pc'] = batch_pc
# batch_data['sdf_pt'] = batch_sdf_pt
# batch_data['sdf_pt_rot'] = batch_sdf_pt_rot
# batch_data['sdf_val'] = batch_sdf_val
# batch_data['norm_params'] = batch_norm_params
# batch_data['sdf_params'] = batch_sdf_params
# batch_data['img'] = batch_img
# # batch_data['img_mat'] = batch_img_mat
# # batch_data['img_pos'] = batch_img_pos
# batch_data['trans_mat'] = batch_trans_mat
# batch_data['cat_id'] = batch_cat_id
# batch_data['obj_nm'] = batch_obj_nm
# batch_data['view_id'] = batch_view_id
for i in range(BATCH_SIZE):
src_img_h5 = os.path.join(info['rendered_dir'], batch_data["cat_id"][i],
batch_data["obj_nm"][i], '{0:02d}'.format(batch_data['view_id'][i])+".h5")
print("src:", src_img_h5)
tar_img_dir = os.path.join(FLAGS.img_h5_dir, batch_data["cat_id"][i],
batch_data["obj_nm"][i])
os.makedirs(tar_img_dir,exist_ok=True)
tar_img_h5 = os.path.join(tar_img_dir,'{0:02d}'.format(batch_data['view_id'][i]) + ".h5")
print("tar:", tar_img_h5)
with h5py.File(src_img_h5, 'r') as h5_f:
obj_rot_mat = h5_f["obj_rot_mat"][:].astype(np.float32)
regress_mat = h5_f["regress_mat"][:].astype(np.float32)
img_arr = h5_f["img_arr"][:].astype(np.float32)
K = h5_f["K"][:].astype(np.float32)
RT = h5_f["RT"][:].astype(np.float32)
trans_mat_right = transmat[i,...]
print("transmat[i,...].shape", transmat[i,...].shape)
with h5py.File(tar_img_h5, 'w') as f1:
f1.create_dataset('img_arr', data=img_arr, compression='gzip',
dtype='uint8', compression_opts=4)
f1.create_dataset('trans_mat', data=trans_mat_right, compression='gzip',
dtype='float32', compression_opts=4)
f1.create_dataset('K', data=K, compression='gzip',
dtype='float32', compression_opts=4)
f1.create_dataset('RT', data=RT, compression='gzip',
dtype='float32', compression_opts=4)
f1.create_dataset('obj_rot_mat', data=obj_rot_mat, compression='gzip',
dtype='float32', compression_opts=4)
f1.create_dataset('regress_mat', data=regress_mat, compression='gzip',
dtype='float32', compression_opts=4)
print("write:", tar_img_h5)
def check_all_h5():
# ()
for info in TEST_LISTINFO:
cat, obj, view = info
src = os.path.join("/ssd1/datasets/ShapeNet/ShapeNetRenderingh5_v1",
cat, obj, "%02d.h5"%view)
print(src)
with h5py.File(src, 'r') as h5_f:
trans_mat = h5_f["trans_mat"][:].astype(np.float32)
if __name__ == "__main__":
try:
log_string('pid: %s'%(str(os.getpid())))
train()
LOG_FOUT.close()
except KeyboardInterrupt:
TRAIN_DATASET.shutdown()
# check_all_h5()
# nohup python -u cam_est/train_sdf_cam.py --test --restore_model /home/xharlie/dev/ProgressivePointSetGeneration/shapenet/sdf/cam/checkpoint/cam_mixloss_all_2.93 --log_dir /home/xharlie/dev/ProgressivePointSetGeneration/shapenet/sdf/cam/checkpoint/cam_mixloss_all_2.93 --gpu 0 --loss_mode 3DM --verbose_freq 1 &> log/test_cam_mixloss_all_2.93.log &
# nohup python -u cam_est/train_sdf_cam.py --test --batch_size 1 --restore_model /home/xharlie/dev/ProgressivePointSetGeneration/shapenet/sdf/cam/checkpoint/cam_mixloss_all_2.93 --log_dir /home/xharlie/dev/ProgressivePointSetGeneration/shapenet/sdf/cam/checkpoint/cam_mixloss_all_2.93 --gpu 0 --loss_mode 3DM --verbose_freq 1 &> log/test_cam_mixloss_3sample.log &
#
# nohup python -u cam_est/train_sdf_cam.py --restore_model checkpoint/cam_3D_shift_0.5 --log_dir checkpoint/cam_3D_shift_0.5 --gpu 0 --loss_mode 3D --learning_rate 1e-4 --shift --shift_weight 0.5 &> log/cam_3D_shift0.5_all.log &
#
# nohup python -u cam_est/train_sdf_cam.py --restore_model checkpoint/cam_3D_shift_0.5 --log_dir checkpoint/cam_3D_shift_1 --gpu 1 --loss_mode 3D --learning_rate 1e-4 --shift --shift_weight 1 &> log/cam_3D_shift1_all.log &
#
# nohup python -u cam_est/train_sdf_cam.py --restore_model checkpoint/cam_3D_shift_0.5 --log_dir checkpoint/cam_3D_shift_2 --gpu 2 --loss_mode 3D --learning_rate 1e-4 --shift --shift_weight 2 &> log/cam_3D_shift2_all.log &
#
# nohup python -u cam_est/train_sdf_cam.py --restore_model checkpoint/cam_3D_shift_0.5 --log_dir checkpoint/cam_3D_shift_5 --gpu 3 --loss_mode 3D --learning_rate 1e-4 --shift --shift_weight 5 &> log/cam_3D_shift5_all.log &
#
#
================================================
FILE: data/data_sdf_h5_queue.py
================================================
import numpy as np
import cv2
import random
import math
import os
import threading
import queue
import sys
import h5py
import copy
FETCH_BATCH_SIZE = 32
BATCH_SIZE = 32
HEIGHT = 192
WIDTH = 256
POINTCLOUDSIZE = 16384
OUTPUTPOINTS = 1024
REEBSIZE = 1024
def get_filelist(lst_dir, maxnverts, minsurbinvox, cats, cats_info, type):
for cat in cats:
cat_id = cats_info[cat]
inputlistfile = os.path.join(lst_dir, cat_id + type + ".lst")
with open(inputlistfile, 'r') as f:
lines = f.read().splitlines()
file_lst = [[cat_id, line.strip()] for line in lines]
return file_lst
class Pt_sdf_img(threading.Thread):
def __init__(self, FLAGS, listinfo=None, info=None, qsize=64, cats_limit=None, shuffle=True):
super(Pt_sdf_img, self).__init__()
self.queue = queue.Queue(qsize)
self.stopped = False
self.bno = 0
self.listinfo = listinfo
self.num_points = FLAGS.num_points
self.gen_num_pt = FLAGS.num_sample_points
self.batch_size = FLAGS.batch_size
self.img_dir = info['rendered_dir']
self.sdf_dir = info['sdf_dir']
self.cache = {} # from index to (point_set, cls, seg) tuple
self.cache_size = 60000
self.data_num = len(self.listinfo)
self.FLAGS = FLAGS
self.shuffle = shuffle
self.num_batches = int(self.data_num / self.FLAGS.batch_size)
self.cats_limit, self.epoch_amount = self.set_cat_limit(cats_limit)
self.data_order = list(range(len(listinfo)))
self.order = self.data_order
# def get_img_dir_old(self, cat_id, obj):
# img_dir = os.path.join(self.img_dir, cat_id, obj, "rendering")
#
# with open(img_dir + "/renderings.txt", 'r') as f:
# lines = f.read().splitlines()
# file_lst = [line.strip() for line in lines]
# return img_dir, file_lst
def set_cat_limit(self, cats_limit):
epoch_amount = 0
for cat, amount in cats_limit.items():
cats_limit[cat] = min(self.FLAGS.cat_limit, amount)
epoch_amount += cats_limit[cat]
print("epoch_amount ", epoch_amount)
print("cats_limit ", cats_limit)
return cats_limit, epoch_amount
def get_img_dir(self, cat_id, obj):
img_dir = os.path.join(self.img_dir, cat_id, obj)
return img_dir, None
def get_sdf_h5_filenm(self, cat_id, obj):
return os.path.join(self.sdf_dir, cat_id, obj, "ori_sample.h5")
def pc_normalize(self, pc, centroid=None):
""" pc: NxC, return NxC """
l = pc.shape[0]
if centroid is None:
centroid = np.mean(pc, axis=0)
pc = pc - centroid
# m = np.max(pc, axis=0)
m = np.max(np.sqrt(np.sum(pc ** 2, axis=1)))
pc = pc / m
return pc, centroid, m
def __len__(self):
return self.epoch_amount
def memory(self):
"""
Get node total memory and memory usage
"""
with open('/proc/meminfo', 'r') as mem:
ret = {}
tmp = 0
for i in mem:
sline = i.split()
if str(sline[0]) == 'MemTotal:':
ret['total'] = int(sline[1])
elif str(sline[0]) in ('MemFree:', 'Buffers:', 'Cached:'):
tmp += int(sline[1])
ret['free'] = tmp
ret['used'] = int(ret['total']) - int(ret['free'])
return ret
def getitem(self, index):
cat_id, obj, num = self.listinfo[index]
sdf_file = self.get_sdf_h5_filenm(cat_id, obj)
ori_pt, ori_sdf_val, sample_pt, sample_sdf_val, norm_params, sdf_params\
= self.get_sdf_h5(sdf_file, cat_id, obj)
img_dir, img_file_lst = self.get_img_dir(cat_id, obj)
return ori_pt, ori_sdf_val, sample_pt, sample_sdf_val, norm_params,\
sdf_params, img_dir, img_file_lst, cat_id, obj, num
def get_sdf_h5(self, sdf_h5_file, cat_id, obj):
h5_f = h5py.File(sdf_h5_file, 'r')
try:
if ('pc_sdf_original' in h5_f.keys()
and 'pc_sdf_sample' in h5_f.keys()
and 'norm_params' in h5_f.keys()):
ori_sdf = h5_f['pc_sdf_original'][:].astype(np.float32)
# sample_sdf = np.reshape(h5_f['pc_sdf_sample'][:],(ori_sdf.shape[0], -1 ,4)).astype(np.float32)
sample_sdf = h5_f['pc_sdf_sample'][:].astype(np.float32)
ori_pt = ori_sdf[:,:3]#, ori_sdf[:,3]
ori_sdf_val = None
if sample_sdf.shape[1] == 4:
sample_pt, sample_sdf_val = sample_sdf[:, :3], sample_sdf[:, 3]
else:
sample_pt, sample_sdf_val = None, sample_sdf[:, 0]
norm_params = h5_f['norm_params'][:]
sdf_params = h5_f['sdf_params'][:]
else:
raise Exception(cat_id, obj, "no sdf and sample")
finally:
h5_f.close()
return ori_pt, ori_sdf_val, sample_pt, sample_sdf_val, norm_params, sdf_params
def get_img_old(self, img_dir, num, file_lst):
params = np.loadtxt(img_dir + "/rendering_metadata.txt")
img_file = os.path.join(img_dir, file_lst[num])
# azimuth, elevation, in-plane rotation, distance, the field of view.
param = params[num, :].astype(np.float32)
cam_mat, cam_pos = self.camera_info(self.degree2rad(param))
img_arr = cv2.imread(img_file, cv2.IMREAD_UNCHANGED)[:,:,:3].astype(np.float32) / 255.
return img_arr, cam_mat, cam_pos
def get_img(self, img_dir, num):
img_h5 = os.path.join(img_dir, "%02d.h5"%num)
cam_mat, cam_pos, trans_mat, obj_rot_mat, regress_mat = None, None, None, None, None
with h5py.File(img_h5, 'r') as h5_f:
if self.FLAGS.img_feat_onestream or self.FLAGS.img_feat_twostream:
trans_mat = h5_f["trans_mat"][:].astype(np.float32)
obj_rot_mat = h5_f["obj_rot_mat"][:].astype(np.float32)
regress_mat = h5_f["regress_mat"][:].astype(np.float32)
else:
cam_mat, cam_pos = h5_f["cam_mat"][:].astype(np.float32), h5_f["cam_pos"][:].astype(np.float32)
if self.FLAGS.alpha:
img_arr = h5_f["img_arr"][:].astype(np.float32)
img_arr[:, :, :4] = img_arr[:,:,:4] / 255.
else:
img_raw = h5_f["img_arr"][:]
img_arr = img_raw[:, :, :3]
if self.FLAGS.augcolorfore or self.FLAGS.augcolorback:
r_aug = 60 * np.random.rand() - 30
g_aug = 60 * np.random.rand() - 30
b_aug = 60 * np.random.rand() - 30
if self.FLAGS.augcolorfore:
img_arr[img_raw[:, :, 3] != 0, 0] + r_aug
img_arr[img_raw[:, :, 3] != 0, 1] + g_aug
img_arr[img_raw[:, :, 3] != 0, 2] + b_aug
if self.FLAGS.backcolorwhite:
img_arr[img_raw[:, :, 3] == 0] = [255, 255, 255]
if self.FLAGS.augcolorback:
img_arr[img_raw[:, :, 3] == 0, 0] + r_aug
img_arr[img_raw[:, :, 3] == 0, 1] + g_aug
img_arr[img_raw[:, :, 3] == 0, 2] + b_aug
img_arr = np.clip(img_arr, 0, 255)
img_arr = img_arr.astype(np.float32) / 255.
return img_arr, cam_mat, cam_pos, trans_mat, obj_rot_mat, regress_mat
def degree2rad(self, params):
params[0] = np.deg2rad(params[0] + 180.0)
params[1] = np.deg2rad(params[1])
params[2] = np.deg2rad(params[2])
return params
def unit(self, v):
norm = np.linalg.norm(v)
if norm == 0:
return v
return v / norm
def camera_info(self, param):
az_mat = self.get_az(param[0])
el_mat = self.get_el(param[1])
inl_mat = self.get_inl(param[2])
cam_mat = np.transpose(np.matmul(np.matmul(inl_mat, el_mat), az_mat))
cam_pos = self.get_cam_pos(param)
return cam_mat, cam_pos
def get_cam_pos(self, param):
camX = 0
camY = 0
camZ = param[3]
cam_pos = np.array([camX, camY, camZ])
return -1 * cam_pos
def get_az(self, az):
cos = np.cos(az)
sin = np.sin(az)
mat = np.asarray([cos, 0.0, sin, 0.0, 1.0, 0.0, -1.0*sin, 0.0, cos], dtype=np.float32)
mat = np.reshape(mat, [3,3])
return mat
#
def get_el(self, el):
cos = np.cos(el)
sin = np.sin(el)
mat = np.asarray([1.0, 0.0, 0.0, 0.0, cos, -1.0*sin, 0.0, sin, cos], dtype=np.float32)
mat = np.reshape(mat, [3,3])
return mat
#
def get_inl(self, inl):
cos = np.cos(inl)
sin = np.sin(inl)
# zeros = np.zeros_like(inl)
# ones = np.ones_like(inl)
mat = np.asarray([cos, -1.0*sin, 0.0, sin, cos, 0.0, 0.0, 0.0, 1.0], dtype=np.float32)
mat = np.reshape(mat, [3,3])
return mat
def get_batch(self, index):
if index + self.batch_size > self.epoch_amount:
index = index + self.batch_size - self.epoch_amount
batch_pc = np.zeros((self.batch_size, self.num_points, 3)).astype(np.float32)
batch_sdf_pt = np.zeros((self.batch_size, self.gen_num_pt, 3)).astype(np.float32)
batch_sdf_pt_rot = np.zeros((self.batch_size, self.gen_num_pt, 3)).astype(np.float32)
batch_sdf_val = np.zeros((self.batch_size, self.gen_num_pt, 1)).astype(np.float32)
batch_norm_params = np.zeros((self.batch_size, 4)).astype(np.float32)
batch_sdf_params = np.zeros((self.batch_size, 6)).astype(np.float32)
if self.FLAGS.alpha:
batch_img = np.zeros((self.batch_size, self.FLAGS.img_h, self.FLAGS.img_w, 4), dtype=np.float32)
else:
batch_img = np.zeros((self.batch_size, self.FLAGS.img_h, self.FLAGS.img_w, 3), dtype=np.float32)
batch_regress_mat = np.zeros((self.batch_size, 4, 3), dtype=np.float32)
batch_trans_mat = np.zeros((self.batch_size, 4, 3), dtype=np.float32)
batch_cat_id = []
batch_obj_nm = []
batch_view_id = []
cnt = 0
for i in range(index, index + self.batch_size):
single_obj = self.getitem(self.order[i])
if single_obj == None:
raise Exception("single mesh is None!")
ori_pt, ori_sdf_val, sample_pt, sample_sdf_val, norm_params, sdf_params, img_dir, img_file_lst, cat_id, obj, num = single_obj
img, cam_mat, cam_pos, trans_mat, obj_rot_mat, regress_mat = self.get_img(img_dir, num)
if ori_pt is not None:
cf_ref_choice = np.random.randint(ori_pt.shape[0], size=self.num_points)
batch_pc[cnt, :, :] = ori_pt[cf_ref_choice, :]
if self.FLAGS.threedcnn:
batch_sdf_val[cnt, :, 0] = sample_sdf_val
else:
if self.gen_num_pt > sample_pt.shape[0]:
choice = np.random.randint(sample_pt.shape[0], size=self.gen_num_pt)
else:
choice = np.asarray(random.sample(range(sample_pt.shape[0]), self.gen_num_pt), dtype=np.int32)
batch_sdf_pt[cnt, ...] = sample_pt[choice, :]
batch_sdf_val[cnt, :, 0] = sample_sdf_val[choice]
if self.FLAGS.rot:
batch_sdf_pt_rot[cnt, ...] = np.dot(sample_pt[choice, :], obj_rot_mat)
else:
batch_sdf_pt_rot[cnt, ...] = sample_pt[choice, :]
batch_norm_params[cnt, ...] = norm_params
batch_sdf_params[cnt, ...] = sdf_params
else:
raise Exception("no verts or binvox")
# img, cam_mat, cam_pos = self.get_img_old(img_dir, num, img_file_lst)
batch_img[cnt, ...] = img.astype(np.float32)
batch_regress_mat[cnt, ...] = regress_mat
batch_trans_mat[cnt, ...] = trans_mat
batch_cat_id.append(cat_id)
batch_obj_nm.append(obj)
batch_view_id.append(num)
cnt += 1
batch_data = {}
batch_data['pc'] = batch_pc
batch_data['sdf_pt'] = batch_sdf_pt
batch_data['sdf_pt_rot'] = batch_sdf_pt_rot
batch_data['sdf_val'] = batch_sdf_val
batch_data['norm_params'] = batch_norm_params
batch_data['sdf_params'] = batch_sdf_params
batch_data['img'] = batch_img
batch_data['trans_mat'] = batch_trans_mat
batch_data['cat_id'] = batch_cat_id
batch_data['obj_nm'] = batch_obj_nm
batch_data['view_id'] = batch_view_id
return batch_data
def refill_data_order(self):
temp_order = copy.deepcopy(self.data_order)
cats_quota = {key: value for key, value in self.cats_limit.items()}
np.random.shuffle(temp_order)
pointer = 0
epoch_order=[]
while len(epoch_order) < self.epoch_amount:
cat_id, _, _ = self.listinfo[temp_order[pointer]]
if cats_quota[cat_id] > 0:
epoch_order.append(temp_order[pointer])
cats_quota[cat_id]-=1
pointer+=1
return epoch_order
def work(self, epoch, index):
if index == 0 and self.shuffle:
self.order = self.refill_data_order()
print("data order reordered!")
return self.get_batch(index)
def run(self):
while (self.bno // (self.num_batches* self.batch_size)) < self.FLAGS.max_epoch and not self.stopped:
self.queue.put(self.work(self.bno // (self.num_batches* self.batch_size),
self.bno % (self.num_batches * self.batch_size)))
self.bno += self.batch_size
def fetch(self):
if self.stopped:
return None
# else:
# print("queue length", self.queue.qsize())
return self.queue.get()
def shutdown(self):
self.stopped = True
while not self.queue.empty():
self.queue.get()
if __name__ == '__main__':
sys.path.append('../preprocessing/')
import create_file_lst as create
data = Pt_sdf_img(res=256, expr=1.5,
listinfo=[["03001627", "ff3581996365bdddc3bd24f986301745"],
["03001627", "ff3581996365bdddc3bd24f986301745"]],
info=create.get_all_info(), maxnverts=6000, maxntris=50000,
minsurbinvox=4096, num_points=2048, batch_size=2, normalize=False, norm_color=True)
batch1 = data.get_batch(0)
print(batch1.keys())
print(batch1["verts"].shape)
print(batch1["nverts"])
print(batch1["tris"].shape)
print(batch1["ntris"])
print(batch1["surfacebinvoxpc"].shape)
print(batch1["sdf"].shape)
print(batch1["sdf_params"])
print(batch1["img"].shape, batch1["img"][0, 64, 64, :])
print(batch1["img_cam"])
# (2048, 3)
cloud1 = batch1["surfacebinvoxpc"][0, ...]
trans1 = batch1["img_cam"][0, ...]
az1 = float(trans1[0] + 180) * math.pi / 180.0
el1 = float(trans1[1]) * math.pi / 180.0
in1 = float(trans1[2]) * math.pi / 180.0
transmatrix_az1 = [[math.cos(az1), 0, math.sin(az1)],
[0, 1, 0],
[-math.sin(az1), 0, math.cos(az1)]]
transmatrix_az1 = np.asarray(transmatrix_az1).astype(np.float32)
transmatrix_el1 = [[1, 0, 0],
[0, math.cos(el1), -math.sin(el1)],
[0, math.sin(el1), math.cos(el1)]]
transmatrix_el1 = np.asarray(transmatrix_el1).astype(np.float32)
transmatrix_in1 = [[math.cos(in1), -math.sin(in1), 0],
[math.sin(in1), math.cos(in1), 0],
[0, 0, 1]]
transmatrix_in1 = np.asarray(transmatrix_in1).astype(np.float32)
trans = np.matmul(np.matmul(transmatrix_in1, transmatrix_el1), transmatrix_az1)
translate1 = np.tile(np.expand_dims(np.asarray([-trans1[2], 0, 0]).astype(np.float32), axis=0), (2048, 1))
points = np.matmul(cloud1, trans.T)
np.savetxt("ff_rotate.xyz", points)
np.savetxt("ff.xyz", cloud1)
================================================
FILE: data/data_sdf_h5_queue_mask_imgh5_cammat.py
================================================
import numpy as np
import math
import os
import threading
import queue
import sys
import h5py
import copy
import random
import cv2
FETCH_BATCH_SIZE = 32
BATCH_SIZE = 32
HEIGHT = 192
WIDTH = 256
POINTCLOUDSIZE = 16384
OUTPUTPOINTS = 1024
REEBSIZE = 1024
def get_filelist(lst_dir, maxnverts, minsurbinvox, cats, cats_info, type):
for cat in cats:
cat_id = cats_info[cat]
inputlistfile = os.path.join(lst_dir, cat_id + type + ".lst")
with open(inputlistfile, 'r') as f:
lines = f.read().splitlines()
file_lst = [[cat_id, line.strip()] for line in lines]
return file_lst
class Pt_sdf_img(threading.Thread):
def __init__(self, FLAGS, listinfo=None, info=None, qsize=64, cats_limit=None):
super(Pt_sdf_img, self).__init__()
self.queue = queue.Queue(qsize)
self.stopped = False
self.bno = 0
self.listinfo = listinfo
self.num_points = FLAGS.num_points
self.gen_num_pt = FLAGS.num_sample_points
self.batch_size = FLAGS.batch_size
self.img_dir = info['rendered_dir']
self.img_dir_v2 = info['rendered_dir_v2']
self.sdf_dir = info['sdf_dir']
self.order = list(range(len(listinfo)))
self.cache = {} # from index to (point_set, cls, seg) tuple
self.cache_size = 60000
self.data_num = len(self.listinfo)
self.FLAGS = FLAGS
self.num_batches = int(self.data_num / self.FLAGS.batch_size)
self.fixaxismat = np.array([[-1., 0., 0.],[0., 0., 1],[ 0., -1., 0.]], dtype=np.float32)
self.CAM_ROT = np.array([[1.910685676922942e-15, 4.371138828673793e-08, 1.0],
[1.0, -4.371138828673793e-08, -0.0],
[4.371138828673793e-08, 1.0, -4.371138828673793e-08]], dtype=np.float32)
self.rot90z = np.array([[0, 1, 0],
[-1, 0,0],
[ 0,0,1]], dtype=np.float32)
self.rot90y = np.array([[0, 0, -1],
[ 0,1, 0],
[ 1,0,0]], dtype=np.float32)
self.tmp = np.array([0,0,0], dtype = np.float32)
self.count = 0
self.num_batches = int(self.data_num / self.FLAGS.batch_size)
self.cats_limit, self.epoch_amount = self.set_cat_limit(cats_limit)
self.data_order = list(range(len(listinfo)))
def set_cat_limit(self, cats_limit):
epoch_amount = 0
for cat, amount in cats_limit.items():
cats_limit[cat] = min(self.FLAGS.cat_limit, amount)
epoch_amount += cats_limit[cat]
print("epoch_amount ", epoch_amount)
print("cats_limit ", cats_limit)
return cats_limit, epoch_amount
def get_img_dir(self, cat_id, obj):
img_dir = os.path.join(self.img_dir, cat_id, obj)
img_dir_v2 = os.path.join(self.img_dir_v2, cat_id, obj, 'rendering')
return img_dir, img_dir_v2
# def get_h5_filenm(self, cat_id, obj):
# return os.path.join(self.mesh_dir, cat_id, obj + ".h5")
def get_sdf_h5_filenm(self, cat_id, obj):
return os.path.join(self.sdf_dir, cat_id, obj, "ori_sample.h5")
def pc_normalize(self, pc, centroid=None):
""" pc: NxC, return NxC """
l = pc.shape[0]
if centroid is None:
centroid = np.mean(pc, axis=0)
pc = pc - centroid
# m = np.max(pc, axis=0)
m = np.max(np.sqrt(np.sum(pc ** 2, axis=1)))
pc = pc / m
return pc, centroid, m
def __len__(self):
return self.epoch_amount
def memory(self):
"""
Get node total memory and memory usage
"""
with open('/proc/meminfo', 'r') as mem:
ret = {}
tmp = 0
for i in mem:
sline = i.split()
if str(sline[0]) == 'MemTotal:':
ret['total'] = int(sline[1])
elif str(sline[0]) in ('MemFree:', 'Buffers:', 'Cached:'):
tmp += int(sline[1])
ret['free'] = tmp
ret['used'] = int(ret['total']) - int(ret['free'])
return ret
def getitem(self, index):
cat_id, obj, num = self.listinfo[index]
sdf_file = self.get_sdf_h5_filenm(cat_id, obj)
ori_pt, ori_sdf_val, sample_pt, sample_sdf_val, norm_params, sdf_params\
= self.get_sdf_h5(sdf_file, cat_id, obj)
img_dir, img_dir_v2 = self.get_img_dir(cat_id, obj)
return ori_pt, ori_sdf_val, sample_pt, sample_sdf_val, norm_params,\
sdf_params, img_dir, img_dir_v2, cat_id, obj, num
def get_sdf_h5(self, sdf_h5_file, cat_id, obj):
h5_f = h5py.File(sdf_h5_file, 'r')
try:
if ('pc_sdf_original' in h5_f.keys()
and 'pc_sdf_sample' in h5_f.keys()
and 'norm_params' in h5_f.keys()):
ori_sdf = h5_f['pc_sdf_original'][:].astype(np.float32)
# sample_sdf = np.reshape(h5_f['pc_sdf_sample'][:],(ori_sdf.shape[0], -1 ,4)).astype(np.float32)
sample_sdf = h5_f['pc_sdf_sample'][:].astype(np.float32)
ori_pt = ori_sdf[:,:3]#, ori_sdf[:,3]
ori_sdf_val = None
sample_pt, sample_sdf_val = sample_sdf[:,:3], sample_sdf[:,3]
norm_params = h5_f['norm_params'][:]
sdf_params = h5_f['sdf_params'][:]
else:
raise Exception(cat_id, obj, "no sdf and sample")
finally:
h5_f.close()
return ori_pt, ori_sdf_val, sample_pt, sample_sdf_val, norm_params, sdf_params
def get_img_cam(self, img_dir, num):
params = np.loadtxt(img_dir + "/rendering_metadata.txt")
# img_file = os.path.join(img_dir, file_lst[num])
# azimuth, elevation, in-plane rotation, distance, the field of view.
param = params[num, :].astype(np.float32)
cam_mat, cam_pos = self.camera_info(self.degree2rad(param))
# img_arr = cv2.imread(img_file, cv2.IMREAD_UNCHANGED)[:,:,:3].astype(np.float32) / 255.
return cam_mat, cam_pos
def get_img(self, img_dir, num):
img_h5 = os.path.join(img_dir, "%02d.h5"%num)
cam_mat, cam_pos, trans_mat =None, None, None
with h5py.File(img_h5, 'r') as h5_f:
if self.FLAGS.img_feat:
trans_mat = h5_f["trans_mat"][:].astype(np.float32)
regress_mat = h5_f["regress_mat"][:].astype(np.float32)
K = h5_f["K"][:].astype(np.float32)
# self.tmp += RT[3,:]
# self.count += 1
# print (self.tmp / self.count)
# np.linalg.multi_dot([RT, rot_mat, norm_mat])
# f1.create_dataset('K', data=K, compression='gzip',
# dtype='float32', compression_opts=4)
# f1.create_dataset('RT', data=RT, compression='gzip',
# dtype='float32', compression_opts=4)
else:
cam_mat, cam_pos = h5_f["cam_mat"][:].astype(np.float32), h5_f["cam_pos"][:].astype(np.float32)
img_arr = h5_f["img_arr"][:][:,:,:4].astype(np.float32) / 255.
return img_arr, cam_mat, cam_pos, trans_mat, regress_mat
def degree2rad(self, params):
params[0] = np.deg2rad(params[0] + 180.0)
params[1] = np.deg2rad(params[1])
params[2] = np.deg2rad(params[2])
return params
def unit(self, v):
norm = np.linalg.norm(v)
if norm == 0:
return v
return v / norm
def camera_info(self, param):
az_mat = self.get_az(param[0])
el_mat = self.get_el(param[1])
inl_mat = self.get_inl(param[2])
cam_mat = np.transpose(np.matmul(np.matmul(inl_mat, el_mat), az_mat))
cam_pos = self.get_cam_pos(param)
return cam_mat, cam_pos
def get_cam_pos(self, param):
camX = 0
camY = 0
camZ = param[3]
cam_pos = np.array([camX, camY, camZ])
return -1 * cam_pos
def get_az(self, az):
cos = np.cos(az)
sin = np.sin(az)
mat = np.asarray([cos, 0.0, sin, 0.0, 1.0, 0.0, -1.0*sin, 0.0, cos], dtype=np.float32)
mat = np.reshape(mat, [3,3])
return mat
#
def get_el(self, el):
cos = np.cos(el)
sin = np.sin(el)
mat = np.asarray([1.0, 0.0, 0.0, 0.0, cos, -1.0*sin, 0.0, sin, cos], dtype=np.float32)
mat = np.reshape(mat, [3,3])
return mat
#
def get_inl(self, inl):
cos = np.cos(inl)
sin = np.sin(inl)
# zeros = np.zeros_like(inl)
# ones = np.ones_like(inl)
mat = np.asarray([cos, -1.0*sin, 0.0, sin, cos, 0.0, 0.0, 0.0, 1.0], dtype=np.float32)
mat = np.reshape(mat, [3,3])
return mat
def get_batch(self, index, istrain = True):
if index + self.batch_size > self.epoch_amount:
index = index + self.batch_size - self.epoch_amount
batch_pc = np.zeros((self.batch_size, self.num_points, 3)).astype(np.float32)
batch_sdf_pt = np.zeros((self.batch_size, self.gen_num_pt, 3)).astype(np.float32)
batch_sdf_pt_rot = np.zeros((self.batch_size, self.gen_num_pt, 3)).astype(np.float32)
batch_sdf_val = np.zeros((self.batch_size, self.gen_num_pt, 1)).astype(np.float32)
batch_norm_params = np.zeros((self.batch_size, 4)).astype(np.float32)
batch_sdf_params = np.zeros((self.batch_size, 6)).astype(np.float32)
batch_img = np.zeros((self.batch_size, self.FLAGS.img_h, self.FLAGS.img_w, 4), dtype=np.float32)
batch_img_mat = np.zeros((self.batch_size, 3, 3), dtype=np.float32)
batch_img_pos = np.zeros((self.batch_size, 3), dtype=np.float32)
batch_trans_mat = np.zeros((self.batch_size, 4, 3), dtype=np.float32)
batch_regress_mat = np.zeros((self.batch_size, 4, 3), dtype=np.float32)
batch_shifts = np.zeros((self.batch_size, 2), dtype=np.float32)
batch_cat_id = []
batch_obj_nm = []
batch_view_id = []
cnt = 0
for i in range(index, index + self.batch_size):
single_obj = self.getitem(self.order[i])
if single_obj == None:
raise Exception("single mesh is None!")
ori_pt, ori_sdf_val, sample_pt, sample_sdf_val, norm_params, sdf_params, img_dir, img_dir_v2, cat_id, obj, num = single_obj
img, cam_mat, cam_pos, trans_mat, regress_mat = self.get_img(img_dir, num)
if self.FLAGS.shift:
alpha_pixels = np.argwhere(img[:, :, 3] > 0.00)
y_shift_top = min(alpha_pixels[:, 0])
y_shift_down = self.FLAGS.img_h - max(alpha_pixels[:, 0])
x_shift_left = min(alpha_pixels[:,1])
x_shift_right = self.FLAGS.img_w - max(alpha_pixels[:,1])
y_shift = random.randrange(-y_shift_top * 0.7, y_shift_down * 0.7, 1)
x_shift = random.randrange(-x_shift_left * 0.7, x_shift_right * 0.7, 1)
# print(y_shift_top,y_shift_down,y_shift)
# print(x_shift_left,x_shift_right,x_shift)
img_new = np.zeros((self.FLAGS.img_h, self.FLAGS.img_w, 4), dtype=np.float32)
new_alpha_pixels = np.stack((alpha_pixels[:,0] + y_shift, alpha_pixels[:,1] + x_shift), axis=1)
img_new[new_alpha_pixels[:, 0], new_alpha_pixels[:, 1], :] = img[alpha_pixels[:, 0], alpha_pixels[:, 1], :]
# cv2.imwrite(os.path.join("/home/xharlie/dev/DISN_codebase/pnt_vis", 'org_%s_%d.png' % (img_dir.split("/")[-1],num)), np.asarray(img*255).astype(np.uint8))
# cv2.imwrite(os.path.join("/home/xharlie/dev/DISN_codebase/pnt_vis", 'shifted_%s_%d.png' % (img_dir.split("/")[-1],num)), np.asarray(img_new*255).astype(np.uint8))
# print(os.path.join("/home/xharlie/dev/DISN_codebase/pnt_vis", 'org_%s_%d.png' % (img_dir.split("/")[-1],num)))
# print(os.path.join("/home/xharlie/dev/DISN_codebase/pnt_vis", 'shifted_%s_%d.png' % (img_dir.split("/")[-1],num)))
if ori_pt is not None:
cf_ref_choice = np.random.randint(ori_pt.shape[0], size=self.num_points)
batch_pc[cnt, :, :] = ori_pt[cf_ref_choice, :]
# if self.FLAGS.threedcnn:
# batch_sdf_pt[cnt, ...] = sample_pt
# batch_sdf_val[cnt, :, 0] = sample_sdf_val
# else:
choice = np.random.randint(sample_pt.shape[0], size=self.gen_num_pt)
if self.FLAGS.rotation:
batch_sdf_pt[cnt, ...] = sample_pt[choice, :]
# R = np.dot(self.CAM_ROT, RT[:3,:3].T).T
pc_rot = batch_sdf_pt[cnt, ...].copy()
# pc_rot[:,0] = -pc_rot[:,2]
# pc_rot[:,1] = -pc_rot[:,0]
# pc_rot[:,2] = pc_rot[:,1]
R, _ = self.get_img_cam(img_dir_v2, num)
R = np.dot(self.rot90y, R)
# R = self.rot90y
batch_sdf_pt_rot[cnt, ...] = np.dot(batch_sdf_pt[cnt, ...], R)
else:
batch_sdf_pt[cnt, ...] = sample_pt[choice, :]
batch_sdf_val[cnt, :, 0] = sample_sdf_val[choice]
batch_norm_params[cnt, ...] = norm_params
batch_sdf_params[cnt, ...] = sdf_params
else:
raise Exception("no verts or binvox")
# img, cam_mat, cam_pos = self.get_img_old(img_dir, num, img_file_lst)
if self.FLAGS.shift:
batch_img[cnt, ...] = img_new.astype(np.float32)
batch_shifts[cnt, ...] = np.asarray([x_shift, y_shift], dtype=np.float32) * 2 / (self.FLAGS.img_h)
else:
batch_img[cnt, ...] = img.astype(np.float32)
# batch_img[cnt, ...] = self.normalize_color(img.astype(np.float32))
# batch_img_mat[cnt, ...] = cam_mat
# batch_img_pos[cnt, ...] = cam_pos
batch_trans_mat[cnt, ...] = trans_mat
batch_regress_mat[cnt, ...] = regress_mat
batch_cat_id.append(cat_id)
batch_obj_nm.append(obj)
batch_view_id.append(num)
cnt += 1
batch_data = {}
batch_data['pc'] = batch_pc
batch_data['sdf_pt'] = batch_sdf_pt
batch_data['sdf_pt_rot'] = batch_sdf_pt_rot
batch_data['sdf_val'] = batch_sdf_val
batch_data['norm_params'] = batch_norm_params
batch_data['sdf_params'] = batch_sdf_params
batch_data['img'] = batch_img
# batch_data['img_mat'] = batch_img_mat
# batch_data['img_pos'] = batch_img_pos
batch_data['trans_mat'] = batch_trans_mat
batch_data['regress_mat'] = batch_regress_mat
batch_data['cat_id'] = batch_cat_id
batch_data['obj_nm'] = batch_obj_nm
batch_data['view_id'] = batch_view_id
batch_data['shifts'] = batch_shifts
return batch_data
def refill_data_order(self):
temp_order = copy.deepcopy(self.data_order)
cats_quota = {key: value for key, value in self.cats_limit.items()}
np.random.shuffle(temp_order)
pointer = 0
epoch_order=[]
while len(epoch_order) < self.epoch_amount:
cat_id, _, _ = self.listinfo[temp_order[pointer]]
if cats_quota[cat_id] > 0:
epoch_order.append(temp_order[pointer])
cats_quota[cat_id]-=1
pointer+=1
return epoch_order
def work(self, epoch, index):
if index == 0:
self.order = self.refill_data_order()
print("data order reordered!")
return self.get_batch(index)
def run(self):
while (self.bno // (self.num_batches* self.batch_size)) < self.FLAGS.max_epoch and not self.stopped:
self.queue.put(self.work(self.bno // (self.num_batches* self.batch_size),
self.bno % (self.num_batches * self.batch_size)))
self.bno += self.batch_size
def fetch(self):
if self.stopped:
return None
# else:
# print("queue length", self.queue.qsize())
return self.queue.get()
def shutdown(self):
self.stopped = True
while not self.queue.empty():
self.queue.get()
if __name__ == '__main__':
sys.path.append('../preprocessing/')
import create_file_lst as create
data = Pt_sdf_img(res=256, expr=1.5,
listinfo=[["03001627", "ff3581996365bdddc3bd24f986301745"],
["03001627", "ff3581996365bdddc3bd24f986301745"]],
info=create.get_all_info(), maxnverts=6000, maxntris=50000,
minsurbinvox=4096, num_points=2048, batch_size=2, normalize=False, norm_color=True)
batch1 = data.get_batch(0)
print(batch1.keys())
print(batch1["verts"].shape)
print(batch1["nverts"])
print(batch1["tris"].shape)
print(batch1["ntris"])
print(batch1["surfacebinvoxpc"].shape)
print(batch1["sdf"].shape)
print(batch1["sdf_params"])
print(batch1["img"].shape, batch1["img"][0, 64, 64, :])
print(batch1["img_cam"])
# (2048, 3)
cloud1 = batch1["surfacebinvoxpc"][0, ...]
trans1 = batch1["img_cam"][0, ...]
az1 = float(trans1[0] + 180) * math.pi / 180.0
el1 = float(trans1[1]) * math.pi / 180.0
in1 = float(trans1[2]) * math.pi / 180.0
transmatrix_az1 = [[math.cos(az1), 0, math.sin(az1)],
[0, 1, 0],
[-math.sin(az1), 0, math.cos(az1)]]
transmatrix_az1 = np.asarray(transmatrix_az1).astype(np.float32)
transmatrix_el1 = [[1, 0, 0],
[0, math.cos(el1), -math.sin(el1)],
[0, math.sin(el1), math.cos(el1)]]
transmatrix_el1 = np.asarray(transmatrix_el1).astype(np.float32)
transmatrix_in1 = [[math.cos(in1), -math.sin(in1), 0],
[math.sin(in1), math.cos(in1), 0],
[0, 0, 1]]
transmatrix_in1 = np.asarray(transmatrix_in1).astype(np.float32)
trans = np.matmul(np.matmul(transmatrix_in1, transmatrix_el1), transmatrix_az1)
translate1 = np.tile(np.expand_dims(np.asarray([-trans1[2], 0, 0]).astype(np.float32), axis=0), (2048, 1))
points = np.matmul(cloud1, trans.T)
np.savetxt("ff_rotate.xyz", points)
np.savetxt("ff.xyz", cloud1)
# def get_img(img_dir, num):
# img_h5 = os.path.join(img_dir, "%02d.h5"%num)
# cam_mat, cam_pos, trans_mat =None, None, None
# with h5py.File(img_h5, 'r') as h5_f:
# trans_mat = h5_f["trans_mat"][:].astype(np.float32)
# RT = h5_f["regress_mat"][:].astype(np.float32)
# K = h5_f["K"][:].astype(np.float32)
# img_arr = h5_f["img_arr"][:][:,:,:4].astype(np.float32) / 255.
# return img_arr, cam_mat, cam_pos, trans_mat, RT
#
#
# img, cam_mat, cam_pos, trans_mat, RT = get_img("/ssd1/datasets/ShapeNet/ShapeNetRenderingh5_v1/03636649/7fa0f8d0da975ea0f323a65d99f15033", 8)
# alpha_pixels = np.argwhere(img[:, :, 3] > 0.00)
# y_shift_top = min(alpha_pixels[:, 0])
# y_shift_down = 137 - max(alpha_pixels[:, 0])
#
# x_shift_left = min(alpha_pixels[:, 1])
# x_shift_right = 137 - max(alpha_pixels[:, 1])
# y_shift = random.randrange(-y_shift_top, y_shift_down, 1)
# x_shift = random.randrange(-x_shift_left, x_shift_right, 1)
# print(y_shift_top, y_shift_down, y_shift)
# print(x_shift_left, x_shift_right, x_shift)
# img_new = np.zeros((137,137, 4), dtype=np.float32)
# new_alpha_pixels = np.stack((alpha_pixels[:, 0] + y_shift, alpha_pixels[:, 1] + x_shift), axis=1)
# print(new_alpha_pixels.shape, alpha_pixels.shape, new_alpha_pixels - alpha_pixels)
# # for i in range(new_alpha_pixels.shape[0]):
# # img_new[new_alpha_pixels[i,0],new_alpha_pixels[i,1],:] = img[alpha_pixels[i,0],alpha_pixels[i,1],:]
# img_new[new_alpha_pixels[:, 0], new_alpha_pixels[:, 1], :] = img[alpha_pixels[:, 0], alpha_pixels[:, 1], :]
# cv2.imwrite(os.path.join("/home/xharlie/dev/DISN_codebase/pnt_vis", 'org_%s_%d.png' % ("7fa0f8d0da975ea0f323a65d99f15033",8)), np.asarray(img*255).astype(np.uint8))
# cv2.imwrite(os.path.join("/home/xharlie/dev/DISN_codebase/pnt_vis", 'shifted_%s_%d.png' % ("7fa0f8d0da975ea0f323a65d99f15033",8)), np.asarray(img_new*255).astype(np.uint8))
# print(os.path.join("/home/xharlie/dev/DISN_codebase/pnt_vis", 'org_%s_%d.png' % ("7fa0f8d0da975ea0f323a65d99f15033",8)))
# print(os.path.join("/home/xharlie/dev/DISN_codebase/pnt_vis", 'shifted_%s_%d.png' % ("7fa0f8d0da975ea0f323a65d99f15033",8)))
================================================
FILE: data/filelists/02691156_test.lst
================================================
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================================================
FILE: data/filelists/02691156_train.lst
================================================
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================================================
FILE: data/filelists/02828884_test.lst
================================================
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================================================
FILE: data/filelists/02828884_train.lst
================================================
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================================================
FILE: data/filelists/02933112_test.lst
================================================
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================================================
FILE: data/filelists/02933112_train.lst
================================================
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================================================
FILE: data/filelists/02958343_test.lst
================================================
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================================================
FILE: data/filelists/02958343_train.lst
================================================
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================================================
FILE: data/filelists/03001627_test.lst
================================================
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FILE: data/filelists/03001627_train.lst
================================================
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================================================
FILE: data/filelists/03211117_test.lst
================================================
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================================================
FILE: data/filelists/03211117_train.lst
================================================
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================================================
FILE: data/filelists/03636649_test.lst
================================================
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================================================
FILE: data/filelists/03636649_train.lst
================================================
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================================================
FILE: data/filelists/03691459_test.lst
================================================
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================================================
FILE: data/filelists/03691459_train.lst
================================================
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================================================
FILE: data/filelists/04090263_test.lst
================================================
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================================================
FILE: data/filelists/04090263_train.lst
================================================
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================================================
FILE: data/filelists/04256520_test.lst
================================================
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================================================
FILE: data/filelists/04256520_train.lst
================================================
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================================================
FILE: data/filelists/04379243_test.lst
================================================
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================================================
FILE: data/filelists/04379243_train.lst
================================================
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================================================
FILE: data/filelists/04401088_test.lst
================================================
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================================================
FILE: data/filelists/04401088_train.lst
================================================
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================================================
FILE: data/filelists/04530566_test.lst
================================================
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================================================
FILE: data/filelists/04530566_train.lst
================================================
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================================================
FILE: demo/demo.py
================================================
import argparse
from datetime import datetime
import numpy as np
import random
import tensorflow as tf
import socket
import os
import sys
import h5py
import struct
BASE_DIR = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
sys.path.append(BASE_DIR) # model
sys.path.append(os.path.join(BASE_DIR, 'models'))
sys.path.append(os.path.join(BASE_DIR, 'cam_est'))
sys.path.append(os.path.join(BASE_DIR, 'data'))
sys.path.append(os.path.join(BASE_DIR, 'utils'))
sys.path.append(os.path.join(BASE_DIR, 'preprocessing'))
import model_normalization as model
import model_cam_old as model_cam
from concurrent.futures import ThreadPoolExecutor
import create_file_lst
import cv2
slim = tf.contrib.slim
lst_dir, cats, all_cats, raw_dirs = create_file_lst.get_all_info()
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=str, default='0', help='GPU to use [default: GPU 0]')
parser.add_argument('--max_epoch', type=int, default=1, help='Epoch to run [default: 201]')
parser.add_argument('--img_h', type=int, default=137, help='Image Height')
parser.add_argument('--img_w', type=int, default=137, help='Image Width')
parser.add_argument('--batch_size', type=int, default=1, help='Batch Size during training [default: 32]')
parser.add_argument('--learning_rate', type=float, default=1e-4, help='Initial learning rate [default: 0.001]')
parser.add_argument('--decay_step', type=int, default=200000, help='Decay step for lr decay [default: 200000]')
parser.add_argument('--decay_rate', type=float, default=0.9, help='Decay rate for lr decay [default: 0.7]')
parser.add_argument('--num_classes', type=int, default=1024, help='vgg dim')
parser.add_argument('--num_points', type=int, default=1, help='Point Number [default: 2048]')
parser.add_argument('--sdf_res', type=int, default=64, help='sdf grid')
parser.add_argument('--alpha', action='store_true')
parser.add_argument('--rot', action='store_true')
parser.add_argument('--tanh', action='store_true')
parser.add_argument('--cat_limit', type=int, default=168000, help="balance each category, 1500 * 24 = 36000")
parser.add_argument('--multi_view', action='store_true')
parser.add_argument('--num_sample_points', type=int, default=1, help='Sample Point Number [default: 2048]')
parser.add_argument('--shift', action="store_true")
parser.add_argument('--loss_mode', type=str, default="3D", help='loss on 3D points or 2D points')
parser.add_argument('--log_dir', default='checkpoint/SDF_DISN', help='Log dir [default: log]')
parser.add_argument('--cam_log_dir', default='./cam_est/checkpoint/cam_DISN', help='Log dir [default: log]')
parser.add_argument('--test_lst_dir', default=lst_dir, help='test mesh data list')
parser.add_argument('--iso', type=float, default=0.0, help='iso value')
parser.add_argument('--threedcnn', action='store_true')
parser.add_argument('--img_feat_onestream', action='store_true')
parser.add_argument('--img_feat_twostream', action='store_true')
parser.add_argument('--category', default="all", help='Which single class to train on [default: None]')
parser.add_argument('--binary', action='store_true')
parser.add_argument('--create_obj', action='store_true', help="create_obj or test accuracy on test set")
parser.add_argument('--store', action='store_true')
parser.add_argument('--view_num', type=int, default=24, help="how many views do you want to create for each obj")
parser.add_argument('--cam_est', action='store_true', help="if you are using the estimated camera image h5")
parser.add_argument('--augcolorfore', action='store_true')
parser.add_argument('--augcolorback', action='store_true')
parser.add_argument('--backcolorwhite', action='store_true')
FLAGS = parser.parse_args()
print('pid: %s'%(str(os.getpid())))
print(FLAGS)
EPOCH_CNT = 0
NUM_POINTS = FLAGS.num_points
BATCH_SIZE = FLAGS.batch_size
RESOLUTION = FLAGS.sdf_res+1
TOTAL_POINTS = RESOLUTION * RESOLUTION * RESOLUTION
if FLAGS.img_feat_twostream:
SPLIT_SIZE = int(np.ceil(TOTAL_POINTS / 214669.0))
elif FLAGS.threedcnn :
SPLIT_SIZE = 1
else:
SPLIT_SIZE = int(np.ceil(TOTAL_POINTS / 274625.0))
NUM_SAMPLE_POINTS = int(np.ceil(TOTAL_POINTS / SPLIT_SIZE))
GPU_INDEX = FLAGS.gpu
PRETRAINED_MODEL_PATH = FLAGS.log_dir
LOG_DIR = FLAGS.log_dir
SDF_WEIGHT = 10.
os.environ["CUDA_VISIBLE_DEVICES"] = GPU_INDEX
if not os.path.exists(LOG_DIR): os.makedirs(LOG_DIR)
if FLAGS.cam_est:
RESULT_OBJ_PATH = os.path.join("./demo/")
print("RESULT_OBJ_PATH: ",RESULT_OBJ_PATH)
else:
RESULT_OBJ_PATH = os.path.join("./demo/")
LOG_FOUT = open(os.path.join(LOG_DIR, 'log_test.txt'), 'w')
LOG_FOUT.write(str(FLAGS)+'\n')
IMG_SIZE = FLAGS.img_h
HOSTNAME = socket.gethostname()
print("HOSTNAME:", HOSTNAME)
def log_string(out_str):
LOG_FOUT.write(out_str+'\n')
LOG_FOUT.flush()
print(out_str)
if FLAGS.threedcnn:
info = {'rendered_dir': raw_dirs["renderedh5_dir_v2"],
'sdf_dir': raw_dirs["3dnnsdf_dir"]}
elif FLAGS.img_feat_onestream or FLAGS.img_feat_twostream:
info = {'rendered_dir': raw_dirs["renderedh5_dir"],
'sdf_dir': raw_dirs["sdf_dir"]}
if FLAGS.cam_est:
info['rendered_dir']= raw_dirs["renderedh5_dir_est"]
else:
info = {'rendered_dir': raw_dirs["renderedh5_dir_v2"],
'sdf_dir': raw_dirs['sdf_dir_v2']}
# cam_gt=[326.421594487, 29.0316186116, 0, 0.790311739218, 25]
def create():
log_string(LOG_DIR)
batch_data = read_img_get_transmat()
input_pls = model.placeholder_inputs(BATCH_SIZE, NUM_POINTS, (IMG_SIZE, IMG_SIZE),
num_sample_pc=NUM_SAMPLE_POINTS, scope='inputs_pl', FLAGS=FLAGS)
is_training_pl = tf.placeholder(tf.bool, shape=())
print(is_training_pl)
batch = tf.Variable(0, name='batch')
print("--- Get model and loss")
# Get model and loss
end_points = model.get_model(input_pls, NUM_POINTS, is_training_pl, bn=False,FLAGS=FLAGS)
loss, end_points = model.get_loss(end_points,
sdf_weight=SDF_WEIGHT, num_sample_points=NUM_SAMPLE_POINTS, FLAGS=FLAGS)
# Create a session
gpu_options = tf.GPUOptions() # per_process_gpu_memory_fraction=0.99
config = tf.ConfigProto(gpu_options=gpu_options)
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
config.log_device_placement = False
sess = tf.Session(config=config)
init = tf.global_variables_initializer()
sess.run(init)
######### Loading Checkpoint ###############
saver = tf.train.Saver([v for v in tf.get_collection_ref(tf.GraphKeys.GLOBAL_VARIABLES) if
('lr' not in v.name) and ('batch' not in v.name)])
ckptstate = tf.train.get_checkpoint_state(PRETRAINED_MODEL_PATH)
if ckptstate is not None:
LOAD_MODEL_FILE = os.path.join(PRETRAINED_MODEL_PATH, os.path.basename(ckptstate.model_checkpoint_path))
try:
# load_model(sess, PRETRAINED_PN_MODEL_FILE, ['refpc_reconstruction','sdfprediction','vgg_16'], strict=True)
with NoStdStreams():
saver.restore(sess, LOAD_MODEL_FILE)
print("Model loaded in file: %s" % LOAD_MODEL_FILE)
except:
print("Fail to load overall modelfile: %s" % PRETRAINED_MODEL_PATH)
###########################################
ops = {'input_pls': input_pls,
'is_training_pl': is_training_pl,
'loss': loss,
'step': batch,
'end_points': end_points}
test_one_epoch(sess, ops, batch_data)
class NoStdStreams(object):
def __init__(self,stdout = None, stderr = None):
self.devnull = open(os.devnull,'w')
self._stdout = stdout or self.devnull or sys.stdout
self._stderr = stderr or self.devnull or sys.stderr
def __enter__(self):
self.old_stdout, self.old_stderr = sys.stdout, sys.stderr
self.old_stdout.flush(); self.old_stderr.flush()
sys.stdout, sys.stderr = self._stdout, self._stderr
def __exit__(self, exc_type, exc_value, traceback):
self._stdout.flush(); self._stderr.flush()
sys.stdout = self.old_stdout
sys.stderr = self.old_stderr
self.devnull.close()
def cam_evl(img_arr):
with tf.Graph().as_default():
with tf.device('/gpu:0'):
input_pls = model_cam.placeholder_inputs(1, NUM_POINTS, (IMG_SIZE, IMG_SIZE), num_pc=NUM_POINTS,
num_sample_pc=1, scope='inputs_pl')
is_training_pl = tf.placeholder(tf.bool, shape=())
print(is_training_pl)
# Note the global_step=batch parameter to minimize.
# That tells the optimizer to helpfully increment the 'batch' parameter for you every time it trains.
batch = tf.Variable(0, name='batch')
print("--- cam Get model_cam and loss")
# Get model and loss
end_points = model_cam.get_model(input_pls, NUM_POINTS, is_training_pl, img_size=(IMG_SIZE, IMG_SIZE), bn=False, wd=2e-3, FLAGS=FLAGS)
loss, end_points = model_cam.get_loss(end_points, sdf_weight=SDF_WEIGHT, FLAGS=FLAGS)
tf.summary.scalar('loss', loss)
print("--- Get training operator")
# Get training operator
# Create a session
config = tf.ConfigProto()
gpu_options = tf.GPUOptions() # per_process_gpu_memory_fraction=0.99)
config = tf.ConfigProto(gpu_options=gpu_options)
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
config.log_device_placement = False
sess = tf.Session(config=config)
# Init variables
init = tf.global_variables_initializer()
sess.run(init)
######### Loading Checkpoint ###############
saver = tf.train.Saver([v for v in tf.get_collection_ref(tf.GraphKeys.GLOBAL_VARIABLES) if
('lr' not in v.name) and ('batch' not in v.name)])
ckptstate = tf.train.get_checkpoint_state(FLAGS.cam_log_dir)
if ckptstate is not None:
LOAD_MODEL_FILE = os.path.join(FLAGS.cam_log_dir, os.path.basename(ckptstate.model_checkpoint_path))
try:
with NoStdStreams():
saver.restore(sess, LOAD_MODEL_FILE)
print("model_cam loaded in file: %s" % LOAD_MODEL_FILE)
except:
print("Fail to load overall modelfile: %s" % LOAD_MODEL_FILE)
###########################################
ops = {'input_pls': input_pls,
'is_training_pl': is_training_pl,
'step': batch,
'end_points': end_points}
is_training = False
batch_data = img_arr
feed_dict = {ops['is_training_pl']: is_training,
ops['input_pls']['imgs']: batch_data}
pred_trans_mat_val = sess.run(ops['end_points']['pred_trans_mat'], feed_dict=feed_dict)
print("pred_trans_mat_val", pred_trans_mat_val)
return pred_trans_mat_val
def read_img_get_transmat():
img_file = "./demo/03001627_17e916fc863540ee3def89b32cef8e45_20.png"
img_arr = cv2.imread(img_file, cv2.IMREAD_UNCHANGED).astype(np.uint8)[:, :, :3]
batch_img = np.asarray([img_arr.astype(np.float32) / 255.])
batch_data = {}
batch_data['img'] = batch_img
if FLAGS.cam_est:
print("here we use our cam est network to estimate cam parameters:")
batch_data['trans_mat'] = cam_evl(batch_img)
else:
print("here we use gt cam parameters")
batch_data['trans_mat'] = np.asarray(
[[[-68.453156, 5.5086656, -0.37556022],
[-17.138561 , -84.685486 , -0.250198 ],
[-47.284092 , -3.6569588 , 0.2493176 ],
[101.133705 , 101.34268 , 1.4305686 ]]], dtype=np.float32)
batch_data['sdf_params'] = np.array([[-1, -1, -1, 1, 1, 1]]) # only useful if we want to compare it with gt, here random set some value
return batch_data
def test_one_epoch(sess, ops, batch_data):
""" ops: dict mapping from string to tf ops """
is_training = False
# Shuffle train samples
log_string(str(datetime.now()))
losses = {}
for lossname in ops['end_points']['losses'].keys():
losses[lossname] = 0
with ThreadPoolExecutor(max_workers=4) as executor:
extra_pts = np.zeros((1, SPLIT_SIZE * NUM_SAMPLE_POINTS - TOTAL_POINTS, 3), dtype=np.float32)
batch_points = np.zeros((SPLIT_SIZE, 0, NUM_SAMPLE_POINTS, 3), dtype=np.float32)
if not FLAGS.threedcnn:
for b in range(BATCH_SIZE):
sdf_params = batch_data['sdf_params'][b]
x_ = np.linspace(sdf_params[0], sdf_params[3], num=RESOLUTION)
y_ = np.linspace(sdf_params[1], sdf_params[4], num=RESOLUTION)
z_ = np.linspace(sdf_params[2], sdf_params[5], num=RESOLUTION)
z, y, x = np.meshgrid(z_, y_, x_, indexing='ij')
x = np.expand_dims(x, 3)
y = np.expand_dims(y, 3)
z = np.expand_dims(z, 3)
all_pts = np.concatenate((x, y, z), axis=3).astype(np.float32)
all_pts = all_pts.reshape(1, -1, 3)
all_pts = np.concatenate((all_pts, extra_pts), axis=1).reshape(SPLIT_SIZE, 1, -1, 3)
print('all_pts', all_pts.shape)
batch_points = np.concatenate((batch_points, all_pts), axis=1)
pred_sdf_val_all = np.zeros((SPLIT_SIZE, BATCH_SIZE, NUM_SAMPLE_POINTS, 2 if FLAGS.binary else 1))
for sp in range(SPLIT_SIZE):
if FLAGS.threedcnn:
feed_dict = {ops['is_training_pl']: is_training,
ops['input_pls']['imgs']: batch_data['img']}
else:
feed_dict = {ops['is_training_pl']: is_training,
ops['input_pls']['sample_pc']: batch_points[sp,...].reshape(BATCH_SIZE, -1, 3),
ops['input_pls']['sample_pc_rot']: batch_points[sp,...].reshape(BATCH_SIZE, -1, 3),
ops['input_pls']['imgs']: batch_data['img'],
ops['input_pls']['trans_mat']: batch_data['trans_mat']}
output_list = [ops['end_points']['pred_sdf'], ops['end_points']['ref_img'],
ops['end_points']['sample_img_points']]
pred_sdf_val, ref_img_val, sample_img_points_val = sess.run(output_list, feed_dict=feed_dict)
pred_sdf_val_all[sp,:,:,:] = pred_sdf_val
pred_sdf_val_all = np.swapaxes(pred_sdf_val_all,0,1) # B, S, NUM SAMPLE, 1 or 2
pred_sdf_val_all = pred_sdf_val_all.reshape((BATCH_SIZE,-1,2 if FLAGS.binary else 1))[:, :TOTAL_POINTS, :]
if FLAGS.binary:
expo = np.exp(pred_sdf_val_all)
prob = expo[:,:,1] / np.sum(expo, axis = 2)
result = (prob - 0.5) / 10.
print("result.shape", result.shape)
else:
result = pred_sdf_val_all / SDF_WEIGHT
for b in range(BATCH_SIZE):
print("submit create_obj")
executor.submit(create_obj, result[b], batch_data['sdf_params'][b], RESULT_OBJ_PATH,
FLAGS.iso)
def to_binary(res, pos, pred_sdf_val_all, sdf_file):
f_sdf_bin = open(sdf_file, 'wb')
f_sdf_bin.write(struct.pack('i', -res)) # write an int
f_sdf_bin.write(struct.pack('i', res)) # write an int
f_sdf_bin.write(struct.pack('i', res)) # write an int
positions = struct.pack('d' * len(pos), *pos)
f_sdf_bin.write(positions)
val = struct.pack('=%sf'%pred_sdf_val_all.shape[0], *(pred_sdf_val_all))
f_sdf_bin.write(val)
f_sdf_bin.close()
def create_obj(pred_sdf_val, sdf_params, dir, i):
obj_nm = "result"
cube_obj_file = os.path.join(dir, obj_nm+".obj")
sdf_file = os.path.join(dir, obj_nm+".dist")
to_binary((RESOLUTION-1), sdf_params, pred_sdf_val, sdf_file)
create_one_cube_obj("./isosurface/computeMarchingCubes", i, sdf_file, cube_obj_file)
command_str = "rm -rf " + sdf_file
print("command:", command_str)
os.system(command_str)
def create_one_cube_obj(marching_cube_command, i, sdf_file, cube_obj_file):
command_str = marching_cube_command + " " + sdf_file + " " + cube_obj_file + " -i " + str(i)
print("command:", command_str)
os.system(command_str)
return cube_obj_file
def get_sdf_h5(sdf_h5_file, cat_id, obj):
h5_f = h5py.File(sdf_h5_file, 'r')
try:
if ('pc_sdf_original' in h5_f.keys()
and 'pc_sdf_sample' in h5_f.keys()
and 'norm_params' in h5_f.keys()):
ori_sdf = h5_f['pc_sdf_original'][:].astype(np.float32)
# sample_sdf = np.reshape(h5_f['pc_sdf_sample'][:],(ori_sdf.shape[0], -1 ,4)).astype(np.float32)
sample_sdf = h5_f['pc_sdf_sample'][:].astype(np.float32)
ori_pt = ori_sdf[:,:3]#, ori_sdf[:,3]
ori_sdf_val = None
if sample_sdf.shape[1] == 4:
sample_pt, sample_sdf_val = sample_sdf[:,:3], sample_sdf[:,3]
else:
sample_pt, sample_sdf_val = None, sample_sdf[:, 0]
norm_params = h5_f['norm_params'][:]
sdf_params = h5_f['sdf_params'][:]
else:
raise Exception(cat_id, obj, "no sdf and sample")
finally:
h5_f.close()
return ori_pt, ori_sdf_val, sample_pt, sample_sdf_val, norm_params, sdf_params
if __name__ == "__main__":
# 1. create all categories / some of the categories:
create()
# 2. create single obj, just run python -u create_sdf.py
# ori_pt, ori_sdf_val, sample_pt, sample_sdf_val, norm_params, sdf_params = \
# get_sdf_h5("/ssd1/datasets/ShapeNet/SDF_full/64_expr_1.2/03001627/47cd848a5584867b1e8791c225564ae0/ori_sample.h5",
# "03001627", "47cd848a5584867b1e8791c225564ae0")
# create_obj(sample_sdf_val, sdf_params, "send/",
# "03001627", "97cd4ed02e022ce7174150bd56e389a8", "111", 0.00)
================================================
FILE: demo/result.obj
================================================
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================================================
FILE: isosurface/LIB_PATH
================================================
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:./isosurface/:/home/xharlie/dev/isosurface/tbb/tbb2018_20180822oss/lib/intel64/gcc4.7:/opt/intel/lib/intel64:/opt/intel/mkl/lib/intel64:/usr/local/lib64:/usr/local/lib:/usr/local/cuda/lib64
================================================
FILE: isosurface/tbb/pstl2018_20180822oss/CHANGES
================================================
------------------------------------------------------------------------
The list of most significant changes made over time in Parallel STL.
Parallel STL 20180619 release
PSTL_VERSION == 106
Features / APIs:
- More algorithms support parallel and vector execution policies:
adjacent_difference, partition, reverse, reverse_copy, rotate_copy,
stable_partition.
- More algorithms support parallel execution policies:
inplace_merge, partial_sort_copy.
- Split algorithm declarations and implementation by files.
(by Thomas Rodgers).
- CMake support - Preview Feature
(by Amit Prakash Ambasta and Henry Schreiner)
------------------------------------------------------------------------
Parallel STL release within Intel(R) Parallel Studio XE 2018 Update 3
PSTL_VERSION == 105
Features / APIs:
- More algorithms support parallel and vector execution policies:
is_partitioned, lexicographical_compare, swap_ranges.
- Added specialization of parallel_transform_scan pattern for better
performance with floating point types.
- Arranged iterator types in public interface according to the standard
(by Thomas Rodgers).
Bugs fixed:
- Fixed an error in parallel version of partial_sort algorithm.
- Fixed an error in equal algorithm connected with values comparison.
------------------------------------------------------------------------
Parallel STL 20180329 release
PSTL_VERSION == 104
Features / APIs:
- More algorithms support parallel and vector execution policies:
find_first_of, is_heap, is_heap_until, replace, replace_if.
- More algorithms support vector execution policies:
remove, remove_if.
- More algorithms support parallel execution policies:
partial_sort.
------------------------------------------------------------------------
Parallel STL release within Intel(R) Parallel Studio XE 2018 Update 2
PSTL_VERSION == 103
Features / APIs:
- More algorithms support parallel and/or vector execution policies:
find_end, merge, search, search_n
------------------------------------------------------------------------
Parallel STL 20171127 release
PSTL_VERSION == 102
Features / APIs:
- Added Parallel STL version macros:
PSTL_VERSION, PSTL_VERSION_MAJOR, PSTL_VERSION_MINOR.
- More algorithms support parallel and vector execution policies:
move, partition_copy, mismatch.
- More algorithms support parallel execution policies:
min_element, max_element, minmax_element.
------------------------------------------------------------------------
Parallel STL release within Intel(R) Parallel Studio XE 2018 Update 1
Features / APIs:
- More algorithms support parallel and vector execution policies:
destroy, destroy_n, uninitialized_copy, uninitialized_copy_n,
uninitialized_default_construct, uninitialized_default_construct_n,
uninitialized_fill, uninitialized_fill_n, uninitialized_move,
uninitialized_move_n, uninitialized_value_construct,
uninitialized_value_construct_n.
- Improved performance in find_end and search algorithms.
- Added macro PSTL_USE_NONTEMPORAL_STORES that can improve performance
of copy, copy_n, fill, fill_n, generate, generate_n algorithms with
unseq and par_unseq policies; by default the macro is not defined.
Bugs fixed:
- Fixed transform_inclusive_scan to correctly process the first element.
- Fixed compile time error in sort algorithm when used with zip_iterator
and some other custom iterator types.
- Fixed several algorithms to allow use of non-const functors.
------------------------------------------------------------------------
Parallel STL release within Intel(R) Parallel Studio XE 2018
Features / APIs:
- Aligned the implementation with the draft N4659 of the C++ standard.
In particular, inner_product no longer supports execution policies.
- reduce and transform_reduce support unseq and par_unseq execution
policies if std::plus<> is used for reduction.
- Added counting_iterator and zip_iterator to support advanced use cases.
To use, include pstl/iterators.h header file.
- Added macro PSTL_USE_PARALLEL_POLICIES to control usage of parallel
execution policies (par, par_unseq); by default these are enabled.
Examples:
- Added dot_product and convex_hull samples to demonstrate Parallel STL
usage.
------------------------------------------------------------------------
Parallel STL beta update release
Features / APIs:
- sequenced_policy is supported by all C++17 algorithms.
- Added other execution policies support for inner_product algorithm.
------------------------------------------------------------------------
Parallel STL beta initial release
Features / APIs:
- Implemented C++17 execution policies: sequenced_policy,
parallel_policy, parallel_unsequenced_policy.
- Implemented unsequenced_policy based on the ISO C++ working group
paper P0076R3.
- sequenced_policy is supported by all except a few C++17 algorithms.
- Other execution policies are supported by the following algorithms:
adjacent_find, all_of, any_of, copy, copy_if, copy_n, count,
count_if, equal, exclusive_scan, fill, fill_n, find, find_if,
find_if_not, for_each, for_each_n, generate, generate_n,
inclusive_scan, none_of, is_sorted, is_sorted_until, reduce,
remove_copy, remove_copy_if, sort, stable_sort, transform,
transform_exclusive_scan, transform_inclusive_scan,
transform_reduce, unique_copy.
Documentation:
- Initial set of documents: Getting Started Guide, Release Notes.
Examples:
- Added gamma_correction sample to demonstrate Parallel STL usage.
------------------------------------------------------------------------
================================================
FILE: isosurface/tbb/pstl2018_20180822oss/CMakeLists.txt
================================================
# Copyright (c) 2018 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
#
#
#
cmake_minimum_required(VERSION 3.1)
set(PARALLELSTL_VERSION_FILE "include/pstl/internal/pstl_config.h")
file(STRINGS "${PARALLELSTL_VERSION_FILE}" PARALLELSTL_VERSION_SOURCE REGEX "#define PSTL_VERSION .*$")
string(REGEX MATCH "#define PSTL_VERSION (.*)$" PARALLELSTL_VERSION_SOURCE "${PARALLELSTL_VERSION_SOURCE}")
math(EXPR VERSION_MAJOR "${PARALLELSTL_VERSION_SOURCE} / 100")
math(EXPR VERSION_MINOR "${PARALLELSTL_VERSION_SOURCE} % 100")
project(ParallelSTL VERSION ${VERSION_MAJOR}.${VERSION_MINOR} LANGUAGES CXX)
option(PARALLELSTL_USE_PARALLEL_POLICIES "Enable parallel policies" ON)
set(PARALLELSTL_BACKEND "tbb" CACHE STRING "Threading backend; defaults to TBB")
include(CMakePackageConfigHelpers)
add_library(ParallelSTL INTERFACE)
add_library(pstl::ParallelSTL ALIAS ParallelSTL)
if (PARALLELSTL_USE_PARALLEL_POLICIES)
if (PARALLELSTL_BACKEND STREQUAL "tbb")
find_package(TBB 2018 REQUIRED tbb)
message(STATUS "Parallel STL uses TBB ${TBB_VERSION} (interface version: ${TBB_INTERFACE_VERSION})")
target_link_libraries(ParallelSTL INTERFACE TBB::tbb)
else()
if (TARGET ${PARALLELSTL_BACKEND})
target_link_libraries(ParallelSTL INTERFACE ${PARALLELSTL_BACKEND})
else()
find_package(${PARALLELSTL_BACKEND} REQUIRED)
target_link_libraries(ParallelSTL INTERFACE ${${PARALLELSTL_BACKEND}_IMPORTED_TARGETS})
endif()
endif()
else()
target_add_definitions(ParallelSTL INTERFACE PSTL_USE_PARALLEL_POLICIES=0)
endif()
target_include_directories(ParallelSTL
INTERFACE
$
Parallel STL.
convex_hull sample
This example demonstrates computing the convex_hull of a set of points on the plane based on the quickhull algorithm.
See https://en.wikipedia.org/wiki/Quickhull for more information.
The example generates a set of random 2D points by std::generate. Then performs the quickhull algorithm on it.
Left and right most points are found by std::minmax_element.
On each step points on the right side of oriented line are copied by std::copy_if and the farthest point is found by std::max_element.
Correctness of the convex hull is checked by std::any_of algorithm with counting iterators.
The output of the example application is a CSV file with points of the convex hull.
System Requirements
For the most up-to-date system requirements, see the release notes.
Files
- convex_hull.cpp
- Implementation of the quickhull algorithm based on Parallel STL.
- utils.h
- Utility code: template
point, random points generator. - Makefile
- Makefile for building the example.
Directories
- msvs
- Contains a Microsoft* Visual Studio* IDE workspace for building and running the example (Windows* OS systems only).
Build instructions
To use Parallel STL, set up the environment by calling the pstlvars script (if you use a command line) or set the %PSTLROOT%
environment variable pointing to the <pstl_installdir> folder (in Microsoft* Visual Studio* IDE on Windows* OS)
and add the <pstl_installdir>/pstl/include folder to the compiler include paths.
Use the Makefile to build the example on the command line.
Use the msvs/convex_hull.sln project file to build the example in the Microsoft* Visual Studio* IDE (Windows* systems only).
Usage
- convex_hull or convex_hull.exe
- Outputs the result convex hull ConvexHull.csv
Legal Information
Intel and the Intel logo are trademarks of Intel Corporation in the U.S. and/or other countries.
* Other names and brands may be claimed as the property of others.
© 2017, Intel Corporation
Parallel STL.
Dot_product sample
This example demonstrates computing the dot product of two vectors.
See Dot_product for more information.
The example computes a dot product (std::transform_reduce) of a couple of vectors filled randomly by floating numbers (std::generate).
The output of the example is the dot product of the couple of vectors.
System Requirements
For the most up-to-date system requirements, see the release notes.
Files
- dot_product.cpp
- Implementation of the dot product based on Parallel STL.
- Makefile
- Makefile for building the example.
Directories
- msvs
- Contains a Microsoft* Visual Studio* IDE workspace for building and running the example (Windows* OS systems only).
Build instructions
To use Parallel STL, set up the environment by calling the pstlvars script (if you use a command line) or set the %PSTLROOT%
environment variable pointing to the <pstl_installdir> folder (in Microsoft* Visual Studio* IDE on Windows* OS)
and add the <pstl_installdir>/pstl/include folder to the compiler include paths.
Use the Makefile to build the example on the command line.
Use the msvs/dot_product.sln project file to build the example in the Microsoft* Visual Studio* IDE (Windows* systems only).
Usage
- dot_product or dot_product.exe
Legal Information
Intel and the Intel logo are trademarks of Intel Corporation in the U.S. and/or other countries.
* Other names and brands may be claimed as the property of others.
© 2017, Intel Corporation
Parallel STL.
Gamma_correction sample
This example demonstrates gamma correction — a nonlinear operation used to encode and decode the luminance of each image pixel.
See https://en.wikipedia.org/wiki/Gamma_correction for more information.
The example creates a fractal image in memory and performs gamma correction on it.
The output of the example application is a BMP image with corrected luminance.
This example uses C++11 lambda expressions. Specifying a compiler option such as -std=c++11 or similar might be necessary in order to build the example.
For more information, please refer to the documentation for the compiler you use.
System Requirements
For the most up-to-date system requirements, see the release notes.
Files
- gamma_correction.cpp
- Implementation of the gamma correction algorithm based on Parallel STL.
- utils.cpp
- Utility code (class
image) to write an image to disk as a BMP file. - utils.h
- Declaration of the
imageclass. - Makefile
- Makefile for building the example.
Directories
Build instructions
To use Parallel STL, set up the environment by calling the pstlvars script (if you use a command line) or set the %PSTLROOT%
environment variable pointing to the <pstl_installdir> folder (in Microsoft* Visual Studio* IDE on Windows* OS)
and add the <pstl_installdir>/pstl/include folder to the compiler include paths.
Use the Makefile to build the example on the command line.
Use the msvs/gamma_correction.sln project file to build the example in the Microsoft* Visual Studio* IDE (Windows* systems only).
Usage
- gamma_correction or gamma_correction.exe
- Outputs the original fractal image image_1.bmp and the fractal image with corrected luminance image_1_gamma.bmp
Legal Information
Intel and the Intel logo are trademarks of Intel Corporation in the U.S. and/or other countries.
* Other names and brands may be claimed as the property of others.
© 2017, Intel Corporation