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Repository: antabangun/coex
Branch: main
Commit: a98b37d33f37
Files: 60
Total size: 44.8 MB
Directory structure:
gitextract_5wcn44k3/
├── .gitignore
├── LICENSE
├── README.md
├── configs/
│ ├── backbone.yaml
│ └── stereo/
│ ├── cfg_coex.yaml
│ └── cfg_psm.yaml
├── dataloaders/
│ ├── __init__.py
│ └── stereo/
│ ├── KITTILoader.py
│ ├── KITTIRawLoader.py
│ ├── KITTI_submission_loader.py
│ ├── KITTIloader2012.py
│ ├── KITTIloader2015.py
│ ├── SceneFlowLoader.py
│ ├── __init__.py
│ ├── listflowfile.py
│ ├── lists/
│ │ ├── kitti2012_test.list
│ │ ├── kitti2012_train.list
│ │ ├── kitti2012_train170.list
│ │ ├── kitti2012_val24.list
│ │ ├── kitti2015_test.list
│ │ ├── kitti2015_train.list
│ │ ├── kitti2015_train180.list
│ │ ├── kitti2015_val20.list
│ │ ├── middeval3_test.list
│ │ ├── middeval3_train.list
│ │ ├── sceneflow_search_trainA.list
│ │ ├── sceneflow_search_trainB.list
│ │ ├── sceneflow_search_val.list
│ │ ├── sceneflow_test.list
│ │ └── sceneflow_train.list
│ ├── preprocess.py
│ ├── readpfm.py
│ ├── stereo_albumentation.py
│ └── transforms.py
├── demo.py
├── demo_tensorrt.py
├── demo_torchscript.py
├── environment.yml
├── logs/
│ └── stereo/
│ └── CoEx/
│ └── version_0/
│ └── checkpoints/
│ └── last.ckpt
├── models/
│ ├── __init__.py
│ └── stereo/
│ ├── CoEx.py
│ ├── CoExTRT.py
│ ├── PSMNet.py
│ ├── __init__.py
│ └── submodules/
│ ├── Submodule.py
│ ├── __init__.py
│ ├── aggregation.py
│ ├── feature.py
│ ├── regression.py
│ ├── spixel_utils/
│ │ ├── spixel.py
│ │ ├── spixel_conv.py
│ │ ├── spixel_loss.py
│ │ └── spixel_test.py
│ ├── util_conv.py
│ └── utils.py
├── stereo.py
├── torch_to_tensorrt.py
├── utils/
│ ├── __init__.py
│ └── load.py
└── zoo/
└── torchscript/
└── CoEx.pt
================================================
FILE CONTENTS
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FILE: .gitignore
================================================
./demo*
./logs/*
================================================
FILE: LICENSE
================================================
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GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
SUCH DAMAGES.
17. Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided
above cannot be given local legal effect according to their terms,
reviewing courts shall apply local law that most closely approximates
an absolute waiver of all civil liability in connection with the
Program, unless a warranty or assumption of liability accompanies a
copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
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Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short
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<program> Copyright (C) <year> <name of author>
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<https://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<https://www.gnu.org/licenses/why-not-lgpl.html>.
================================================
FILE: README.md
================================================
# CoEx
PyTorch implementation of our paper:
**Correlate-and-Excite: Real-Time Stereo Matching via Guided Cost Volume Excitation**
*Authors: [Antyanta Bangunharcana](https://antabangun.github.io/)<sup>1</sup>, Jae Won Cho<sup>2</sup>, Seokju Lee<sup>2</sup>, In So Kweon<sup>2</sup>, Kyung-Soo Kim<sup>1</sup>, Soohyun Kim<sup>1</sup>*
<sup>1</sup>MSC Lab, <sup>2</sup>RVC Lab, Korea Advanced Institute of Science and Technology (KAIST)
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021
\[[Project page](https://antabangun.github.io/projects/CoEx/)\] | \[[Paper](https://arxiv.org/abs/2304.03560)\]
We propose a Guided Cost volume Excitation (GCE) and top-k soft-argmax disparity regression for real-time and accurate stereo matching.
## Contents
- [Installation](#installation)
- [Datasets](#datasets)
- [Data for demo](#data-for-demo)
- [If you want to re-train the models](#if-you-want-to-re-train-the-models)
- [Data directories](#data-directories)
- [Demo on KITTI raw data](#demo-on-kitti-raw-data)
- [Model zoo](#model-zoo)
- [Re-training the model](#re-training-the-model)
## Installation
We recommend using [conda](https://www.anaconda.com/distribution/) for installation:
```bash
conda env create -f environment.yml
conda activate coex
```
## Update: SceneFlow model
<!-- You can download our model trained on SceneFlow dataset from here:
[SceneFlow weights](https://www.dropbox.com/s/c1v2r74tlbrrmsr/sceneflow.ckpt?dl=0)
achieving a new SceneFlow EPE of 0.596 (vs 0.69 in the paper).
We re-trained the model for 15 epochs with a learning rate of 0.001 followed by 5 epochs with a learning rate of 0.0001, without activating the stochastic weight averaging (SWA) technique. The model is trained with a batch size of 8 and fp16 precision. -->
### Model Weights
Our pre-trained SceneFlow weights can be downloaded via the following link:
- \[[**Download SceneFlow Pre-trained Weights**](https://www.dropbox.com/s/c1v2r74tlbrrmsr/sceneflow.ckpt?dl=0)\]
### Performance
Our model achieves a new SceneFlow EPE (End-Point-Error) of 0.596, improving upon the previous EPE of 0.69 reported in the original paper.
### Training Details
- The model was re-trained for a total of 20 epochs: First 15 epochs were trained with a learning rate of 0.001. The last 5 epochs were trained with a learning rate of 0.0001
- We opted not to activate the Stochastic Weight Averaging (SWA) technique during the training process.
- Batch size: 8
- Precision: fp16
## Datasets
### Data for demo
For a demo of our code on the KITTI dataset, download the "\[synced+rectified data\]" from [raw KITTI data](http://www.cvlibs.net/datasets/kitti/raw_data.php). Unzip and place the extracted folders following the directory tree below.
### If you want to re-train the models
**Sceneflow dataset**
Download the *finalpass* data of the [Sceneflow dataset](https://lmb.informatik.uni-freiburg.de/resources/datasets/SceneFlowDatasets.en.html) as well as the *Disparity* data.
**KITTI 2015**
Download [kitti15](http://www.cvlibs.net/datasets/kitti/eval_scene_flow.php?benchmark=stereo) dataset, and unzip data_scene_flow.zip, rename it as kitti15, and move it into SceneFlow directory as shown in the tree below.
**KITTI 2012**
Download [kitti12](http://www.cvlibs.net/datasets/kitti/eval_stereo_flow.php?benchmark=stereo) dataset. Unzip data_stereo_flow.zip, rename it as kitti12, and move it into SceneFlow directory as shown in the tree below.
Make sure the directory names matches the tree below so that the dataloaders can locate the files.
### Data directories
In our setup, the dataset is organized as follows
```
../../data
└── datasets
├── KITTI_raw
| ├── 2011_09_26
| │ ├── 2011_09_26_drive_0001_sync
| │ ├── 2011_09_26_drive_0002_sync
| | :
| |
| ├── 2011_09_28
| │ ├── 2011_09_28_drive_0001_sync
| │ └── 2011_09_28_drive_0002_sync
| | :
| | :
|
└── SceneFlow
├── driving
│ ├── disparity
│ └── frames_finalpass
├── flyingthings3d_final
│ ├── disparity
│ └── frames_finalpass
├── monkaa
│ ├── disparity
│ └── frames_finalpass
├── kitti12
│ ├── testing
│ └── training
└── kitti15
├── testing
└── training
```
## Demo on KITTI raw data
The pretrained KITTI model is already included in './logs'.
Run
```bash
python demo.py
```
to perform stereo matching on raw kitti sequence. Here is an example result on our system with RTX 2080Ti on Ubuntu 18.04.
<p align="center">
<img width="422" height="223" src="./imgs/coex_compress.gif" data-zoomable>
</p>
For more demo results, check out our [Project](https://antabangun.github.io/projects/CoEx/#demo) page
## Re-training the model
To re-train the model, configure './configs/stereo/cfg_yaml', e.g., batch_size, paths, device num, precision, etc. Then run
```bash
python stereo.py
```
## Citation
If you find our work useful in your research, please consider citing our paper
@inproceedings{bangunharcana2021correlate,
title={Correlate-and-Excite: Real-Time Stereo Matching via Guided Cost Volume Excitation},
author={Bangunharcana, Antyanta and Cho, Jae Won and Lee, Seokju and Kweon, In So and Kim, Kyung-Soo and Kim, Soohyun},
booktitle={2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
pages={3542--3548},
year={2021},
organization={IEEE}
}
## Acknowledgements
Part of the code is adopted from previous works: [PSMNet](https://github.com/JiaRenChang/PSMNet), [AANet](https://github.com/haofeixu/aanet), [GANet](https://github.com/feihuzhang/GANet), [SpixelFCN](https://github.com/fuy34/superpixel_fcn)
================================================
FILE: configs/backbone.yaml
================================================
channels:
mobilenetv3_large_100: [16,24,40,112,160]
mobilenetv2_120d: [24,32,40,112,192]
mobilenetv2_100: [16,24,32,96,160]
mnasnet_100: [16,24,40,96,192]
efficientnet_b0: [16,24,40,112,192]
efficientnet_b3a: [24,32,48,136,232]
mixnet_xl: [40,48,64,192,320]
dla34: [32,64,128,256,512]
layers:
mobilenetv3_large_100: [1,2,3,5,6]
mobilenetv2_120d: [1,2,3,5,6]
mobilenetv2_100: [1,2,3,5,6]
mnasnet_100: [1,2,3,5,6]
efficientnet_b0: [1,2,3,5,6]
efficientnet_b3a: [1,2,3,5,6]
mixnet_xl: [1,2,3,5,6]
dla34: [1,2,3,5,6]
================================================
FILE: configs/stereo/cfg_coex.yaml
================================================
###########################################################
device: [0]
precision: 32
###########################################################
training:
load_version: null
save_version: 0
lr: 0.001
sceneflow_max_epochs: 10
sceneflow_milestones: [7]
sceneflow_gamma: 0.1
kitti_max_epochs: 800
kitti_milestones: [30, 50, 300,]
kitti_gamma: 0.5
batch_size: 8
th: 288
tw: 576
train_on:
sceneflow: True
kitti12: True
kitti15: True
kittiraw: True
kitti360: False
paths:
sceneflow: '../../data/datasets/SceneFlow'
kitti12: '../../data/datasets/SceneFlow/kitti12/training'
kitti15: '../../data/datasets/SceneFlow/kitti15/training'
kittiraw: '../../data/datasets/KITTI_raw'
kitti360: '../../data/datasets/KITTI-360'
logging: './logs/stereo'
training_scales_weighting: [1, 0.3]
with_context: False
extract_feature: False
testing:
save_disp_imgs: True
compute_metrics: True
###########################################################
model:
name: 'CoEx'
stereo:
name: 'CoEx'
max_disparity: 192
backbone:
type: 'mobilenetv2_100'
from_scratch: False
cfg_path: './configs/backbone.yaml'
corr_volume: True
gce: True
matching_head: 1
matching_weighted: False
spixel:
branch_channels: [32,48]
aggregation:
disp_strides: 2
channels: [16,32,48]
blocks_num: [2,2,2]
regression:
top_k: 2
================================================
FILE: configs/stereo/cfg_psm.yaml
================================================
###########################################################
device: [0]
precision: 32
###########################################################
training:
load_version: null
save_version: 0
lr: 0.001
sceneflow_max_epochs: 10
sceneflow_milestones: [7]
sceneflow_gamma: 0.1
kitti_max_epochs: 800
kitti_milestones: [30, 50, 300]
kitti_gamma: 0.5
batch_size: 8
th: 288
tw: 576
train_on:
sceneflow: True
kitti12: False
kitti15: False
paths:
sceneflow: '../../data/datasets/SceneFlow'
kitti12: '../../data/datasets/SceneFlow/kitti12/training'
kitti15: '../../data/datasets/SceneFlow/kitti15/training'
logging: './logs/stereo'
training_scales_weighting: [1, 0.7, 0.5] # For PSMNet
testing:
save_disp_imgs: True
compute_metrics: True
###########################################################
model:
name: 'PSMNet'
stereo:
name: 'PSMNet'
max_disparity: 192
backbone:
type: 'mobilenetv2_100'
from_scratch: False
cfg_path: './configs/backbone.yaml'
corr_volume: False
gce: False
multiple_gce: False
regression:
top_k: 192
================================================
FILE: dataloaders/__init__.py
================================================
from .stereo import *
================================================
FILE: dataloaders/stereo/KITTILoader.py
================================================
import os
import torch
import torch.utils.data as data
import torch
import torchvision.transforms as transforms
import random
from albumentations import Compose, OneOf
from PIL import Image, ImageOps
import numpy as np
from . import preprocess
from .stereo_albumentation import RandomShiftRotate, GaussNoiseStereo, RGBShiftStereo, \
RandomBrightnessContrastStereo, random_crop, horizontal_flip
from . import transforms
from .transforms import RandomColor
import cv2
import pdb
IMG_EXTENSIONS = [
'.jpg', '.JPG', '.jpeg', '.JPEG',
'.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP',
]
def is_image_file(filename):
return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
def default_loader(path):
return cv2.imread(path)
#return Image.open(path).convert('RGB')
def disparity_loader(path):
return Image.open(path)
class ImageLoader(data.Dataset):
def __init__(self, left, right, left_disparity, calib, th=256, tw=512, shift=0, training=True, loader=default_loader, dploader= disparity_loader):
self.left = left
self.right = right
self.disp_L = left_disparity
self.calib = calib
self.loader = loader
self.dploader = dploader
self.training = training
self.th = th
self.tw = tw
self.shift = shift
def __getitem__(self, index):
batch = dict()
left = self.left[index]
right = self.right[index]
disp_L= self.disp_L[index]
calib = self.calib[index]
left_img = self.loader(left)
right_img = self.loader(right)
dataL = self.dploader(disp_L)
file = open(calib,"r")
cal = file.read()
if calib.find('kitti15')==-1:
P2 = np.array(cal.split('\n')[2].split(' ')[1:]).astype(np.float32)
P3 = np.array(cal.split('\n')[3].split(' ')[1:]).astype(np.float32)
else:
P2 = np.array(cal.split('\n')[-10].split(' ')[1:]).astype(np.float32)
P3 = np.array(cal.split('\n')[-2].split(' ')[1:]).astype(np.float32)
P2 = P2.reshape(3,4)
P3 = P3.reshape(3,4)
calib = self.kitti_calib(P2,P3)
dataL = np.ascontiguousarray(dataL,dtype=np.float32)/256
# if 'kitti15' in left:
# disp_R = disp_L.replace('occ_0','occ_1')
# dataR = self.dploader(disp_R)
# dataR = np.ascontiguousarray(dataR,dtype=np.float32)/256
if self.training:
# if 'kitti15' in left:
# left_img, right_img, dataL = horizontal_flip(left_img, right_img, dataL, dataR)
pad_h, pad_w = 384-left_img.shape[0], 1280-left_img.shape[1]
left_img = np.pad(left_img,((0,pad_h),(0,pad_w),(0,0)))
right_img = np.pad(right_img,((0,pad_h),(0,pad_w),(0,0)))
h,w,_ = left_img.shape
th, tw = self.th, self.tw
shift = random.randint(-self.shift,self.shift)
x1 = random.randint(0, w - tw)
y1 = random.randint(0, h - th)
# if x1 + shift < 0 or x1 + shift + tw > w:
shift = 0
left_img_raw = left_img[y1:y1+th,x1+shift:x1+shift+tw,:]
right_img_raw = right_img[y1:y1+th,x1:x1+tw,:]
imL_lab = cv2.cvtColor(
left_img_raw,#cv2.resize(left_img,None,None,0.25,0.25),
cv2.COLOR_BGR2LAB)
dataL = np.pad(dataL[:,:,np.newaxis],((0,pad_h),(0,pad_w),(0,0)))[:,:,0]
dataL = dataL[y1:y1 + th, x1 + shift:x1 + tw + shift]
dataL = dataL - shift
img = {'left':left_img_raw,'right':right_img_raw}
# img = self.train_aug(img)
left_img_raw, right_img_raw = img['left'], img['right']
processed = preprocess.get_transform(augment=False)
left_img = processed(left_img_raw)
right_img = processed(right_img_raw)
left_img_raw = np.transpose(left_img_raw,(2,0,1)).astype(np.float32)
right_img_raw = np.transpose(right_img_raw,(2,0,1)).astype(np.float32)
batch['imgL'], batch['imgR'], batch['disp_true'] = left_img, right_img, dataL
batch['imgLRaw'], batch['imgRRaw'], batch['imgLLab'] = left_img_raw, right_img_raw, imL_lab
batch['calib'], batch['x1'], batch['y1'] = calib, x1, y1
return batch
else:
h,w,_ = left_img.shape
imL = left_img
pad_h, pad_w = 384-h, 1280-w
# left_img_raw = left_img[h-352:h,w-1216:w,:]
# right_img_raw = right_img[h-352:h,w-1216:w,:]
left_img_raw = left_img # np.pad(left_img,((0,pad_h),(0,pad_w),(0,0)))
right_img_raw = right_img # np.pad(right_img,((0,pad_h),(0,pad_w),(0,0)))
imL_lab = cv2.cvtColor(
left_img_raw,#cv2.resize(left_img,None,None,0.25,0.25),
cv2.COLOR_BGR2LAB)
# dataL = dataL.crop((w-1216, h-352, w, h))
# dataL = np.pad(dataL,((0,pad_h),(0,pad_w)))
processed = preprocess.get_transform(augment=False)
left_img = processed(left_img_raw)
right_img = processed(right_img_raw)
batch['imgL'], batch['imgR'], batch['disp_true'] = left_img, right_img, dataL
batch['imgLLab'] = imL_lab
batch['calib'] = calib
return batch
def __len__(self):
return len(self.left)
def train_aug(self, img):
transformation = Compose([
RGBShiftStereo(always_apply=True, p_asym=0.5),
RandomBrightnessContrastStereo(always_apply=True, p_asym=0.5)
])
return transformation(**img)
# transformation = transforms.Compose([
# RandomColor()
# ])
# return transformation(img)
def kitti_calib(self, P2, P3):
t2 = np.array([P2[0,-1]/P2[0,0],P2[1,-1]/P2[1,1],P2[2,-1]])
t3 = np.array([P3[0,-1]/P3[0,0],P3[1,-1]/P3[1,1],P3[2,-1]])
t = t2-t3
baseline = np.linalg.norm(t,2)
K = P2[:,:-1]
return {'K':K,'baseline':baseline}
================================================
FILE: dataloaders/stereo/KITTIRawLoader.py
================================================
import torch.utils.data as data
from PIL import Image
import os
import os.path
import glob
import random
import numpy as np
import cv2
from . import preprocess
import pdb
IMG_EXTENSIONS = [
'.jpg', '.JPG', '.jpeg', '.JPEG',
'.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP',
]
def is_image_file(filename):
return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
def listfiles(cfg, date=None, num=None, test=False):
train_datanames = ['kittiraw', 'kitti360']
left_train = {'current': [], 'next': [], 'prev': []}
right_train = {'current': [], 'next': [], 'prev': []}
for train_dataname in train_datanames:
if cfg['training']['train_on'][train_dataname]:
left_train_, right_train_ = listtrainfiles(
cfg['training']['paths'][train_dataname],
train_dataname,
date=date,
num=num,
test=test)
left_train['current'] = left_train['current'] + left_train_['current']
right_train['current'] = right_train['current'] + right_train_['current']
left_train['prev'] = left_train['prev'] + left_train_['prev']
right_train['prev'] = right_train['prev'] + right_train_['prev']
left_train['next'] = left_train['next'] + left_train_['next']
right_train['next'] = right_train['next'] + right_train_['next']
if not test:
val_datanames = ['kitti12', 'kitti15']
left_val = {'current': []}
right_val = {'current': []}
disp_val = {'current': []}
for val_dataname in val_datanames:
left_val_, right_val_, disp_val_ = listvalfiles(cfg['training']['paths'][val_dataname])
left_val['current'] = left_val['current'] + left_val_
right_val['current'] = right_val['current'] + right_val_
disp_val['current'] = disp_val['current'] + disp_val_
# left_train['prev'] = left_train['prev'][200:201]
# left_train['current'] = left_train['current'][200:201]
# left_train['next'] = left_train['next'][200:201]
# right_train['prev'] = right_train['prev'][200:201]
# right_train['current'] = right_train['current'][200:201]
# right_train['next'] = right_train['next'][200:201]
return left_train, right_train, left_val, right_val, disp_val
else:
return left_train, right_train
def listtrainfiles(filepath, train_dataname, date=None, num=None, test=False):
if train_dataname == 'kittiraw' and not test:
with open(filepath + '/data_splits/eigen_zhou_files.txt') as f:
data_splits = f.read()
data_splits = data_splits.split('\n')
data_splits_ = []
for data_split in data_splits:
data_splits_.append(data_split.split(' ')[0])
else:
data_splits_ = None
left_fold = 'image_02' if 'raw' in filepath else 'image_00'
right_fold = 'image_03' if 'raw' in filepath else 'image_01'
data = 'data' if 'raw' in filepath else 'data_rect'
if (train_dataname == 'kittiraw' and test
and date is not None and num is not None):
date_dir_ = date
else:
date_dir_ = '20' if 'raw' in filepath else '2d'
left_train = {'current': [], 'next': [], 'prev': []}
right_train = {'current': [], 'next': [], 'prev': []}
for date_dir in os.listdir(filepath):
if date_dir_ in date_dir:
datepath = os.path.join(filepath, date_dir)
for time_dir in os.listdir(os.path.join(datepath)):
if num is not None:
if num not in time_dir:
continue
if 'sync' in time_dir:
timepath = os.path.join(datepath, time_dir)
datadir_left = '{}/{}/{}'.format(timepath, left_fold, data)
datadir_right = '{}/{}/{}'.format(timepath, right_fold, data)
img_names = os.listdir(datadir_left)
img_names.sort()
for i in range(len(img_names)-2):
if (train_dataname == 'kitti360' or data_splits_ is None or
os.path.join(*os.path.join(datadir_left, img_names[i]).split('/')[-5:]) in data_splits_):
left_train['prev'].append(os.path.join(datadir_left, img_names[i]))
left_train['current'].append(os.path.join(datadir_left, img_names[i+1]))
left_train['next'].append(os.path.join(datadir_left, img_names[i+2]))
right_train['prev'].append(os.path.join(datadir_right, img_names[i]))
right_train['current'].append(os.path.join(datadir_right, img_names[i+1]))
right_train['next'].append(os.path.join(datadir_right, img_names[i+2]))
return left_train, right_train
def listvalfiles(filepath):
left_fold = '/image_2/' if 'kitti15' in filepath else '/colored_0/'
right_fold = '/image_3/' if 'kitti15' in filepath else '/colored_1/'
disp_fold = '/disp_occ_0/' if 'kitti15' in filepath else '/disp_noc/'
image = [img for img in os.listdir(filepath+left_fold) if img.find('_10') > -1]
image.sort()
left_val = [filepath+left_fold+img for img in image]
right_val = [filepath+right_fold+img for img in image]
disp_val = [filepath+disp_fold+img for img in image]
left_val.sort()
right_val.sort()
disp_val.sort()
return left_val, right_val, disp_val
def default_loader(path):
return cv2.imread(path)
def disparity_loader(path):
return Image.open(path)
class ImageLoader(data.Dataset):
def __init__(self, left, right, cfg, disp=None, training=True, demo=False,
loader=default_loader, dploader=disparity_loader):
self.left = left
self.right = right
self.disp = disp
self.training = training
self.demo = demo
self.th, self.tw = cfg['training']['th'], cfg['training']['tw']
self.with_context = cfg['training']['with_context']
self.extract_feature = cfg['training']['extract_feature']
if self.extract_feature:
self.extractor = load_feature(cfg['training']['feature_extractor'])
self.feature_matcher = cv2.BFMatcher(
cv2.NORM_HAMMING, crossCheck=False)
self.loader = loader
self.dploader = dploader
def __getitem__(self, index):
batch = dict()
left = self.left['current'][index]
right = self.right['current'][index]
left_img = self.loader(left)
right_img = self.loader(right)
h, w, _ = left_img.shape
processed = preprocess.get_transform(augment=False)
calib_path = None
if 'KITTI_raw' in self.left['current'][index]:
calib_path = os.path.join(
*self.left['current'][index].split('/')[:-4],
'calib_cam_to_cam.txt')
elif 'KITTI-360' in self.left['current'][index]:
calib_path = os.path.join(
*self.left['current'][index].split('/')[:-4],
'calibration/perspective.txt')
if calib_path is not None:
file = open(calib_path, "r")
cal = file.read()
P2 = np.array(cal.split('\n')[-10].split(' ')[1:]).astype(np.float32)
P3 = np.array(cal.split('\n')[-2].split(' ')[1:]).astype(np.float32)
P2 = P2.reshape(3, 4)
P3 = P3.reshape(3, 4)
K, baseline = self.kitti_calib(P2, P3)
calib = {'P2': P2, 'P3': P3, 'K': K, 'baseline': baseline}
batch['calib'] = calib
if self.training:
if not self.demo:
th, tw = self.th, self.tw
else:
th, tw = h, w
if not self.demo:
x1 = random.randint(0, w - tw)
y1 = random.randint(0, h - th)
else:
x1, y1 = 0, 0
left_img = left_img[y1: y1+th, x1: x1+tw, :]
right_img = right_img[y1: y1+th, x1: x1+tw, :]
left_img_p = processed(left_img)
right_img_p = processed(right_img)
left_img_ = np.transpose(left_img, (2, 0, 1)).astype(np.float32)
right_img_ = np.transpose(right_img, (2, 0, 1)).astype(np.float32)
batch['imgL'], batch['imgR'] = left_img_p, right_img_p
batch['imgLRaw'], batch['imgRRaw'] = left_img_, right_img_
batch['x1'], batch['y1'] = x1, y1
if self.with_context:
left_prev = self.left['prev'][index]
right_prev = self.right['prev'][index]
left_img_prev = self.loader(left_prev)[y1: y1+th, x1: x1+tw, :]
right_img_prev = self.loader(right_prev)[y1: y1+th, x1: x1+tw, :]
left_img_prev_p = processed(left_img_prev)
right_img_prev_p = processed(right_img_prev)
left_img_prev_ = np.transpose(
left_img_prev, (2, 0, 1)).astype(np.float32)
right_img_prev_ = np.transpose(
right_img_prev, (2, 0, 1)).astype(np.float32)
left_next = self.left['next'][index]
right_next = self.right['next'][index]
left_img_next = self.loader(left_next)[y1: y1+th, x1: x1+tw, :]
right_img_next = self.loader(right_next)[y1: y1+th, x1: x1+tw, :]
left_img_next_p = processed(left_img_next)
right_img_next_p = processed(right_img_next)
left_img_next_ = np.transpose(
left_img_next, (2, 0, 1)).astype(np.float32)
right_img_next_ = np.transpose(
right_img_next, (2, 0, 1)).astype(np.float32)
contexts = {
'imgLPrev': left_img_prev_p, 'imgLPrevRaw': left_img_prev_,
'imgRPrev': right_img_prev_p, 'imgRPrevRaw': right_img_prev_,
'imgLNext': left_img_next_p, 'imgLNextRaw': left_img_next_,
'imgRNext': right_img_next_p, 'imgRNextRaw': right_img_next_,
}
if self.extract_feature:
kp_curr, des_curr = self.extractor.detectAndCompute(
left_img, None)
kp_prev, des_prev = self.extractor.detectAndCompute(
left_img_prev, None)
kp_next, des_next = self.extractor.detectAndCompute(
left_img_next, None)
pxscp_curr, pxscp_prev = match(
kp_curr, des_curr, kp_prev, des_prev,
self.feature_matcher)
pxscn_curr, pxscn_next = match(
kp_curr, des_curr, kp_next, des_next,
self.feature_matcher)
pxs = {
'pxscp_curr': pxscp_curr, 'pxscp_prev': pxscp_prev,
'pxscn_curr': pxscn_curr, 'pxscn_next': pxscn_next
}
contexts.update(pxs)
batch['contexts'] = contexts
else:
# left_img, right_img = left_img[:352, :1216], right_img[:352, :1216]
left_img_p = processed(left_img)
right_img_p = processed(right_img)
left_img_ = np.transpose(left_img, (2, 0, 1)).astype(np.float32)
right_img_ = np.transpose(right_img, (2, 0, 1)).astype(np.float32)
batch['imgL'], batch['imgR'] = left_img_p, right_img_p
batch['imgLRaw'], batch['imgRRaw'] = left_img_, right_img_
disp = self.disp['current'][index]
dataL = self.dploader(disp)
dataL = np.ascontiguousarray(dataL, dtype=np.float32)/256
batch['dispL'] = dataL
# # Load context images
# left_prev = self.left['prev'][index]
# left_img_prev = self.loader(left_prev)
# left_img_prev = left_img_prev[:352, :1216]
# left_img_prev_p = processed(left_img_prev)
# left_img_prev_ = np.transpose(
# left_img_prev, (2, 0, 1)).astype(np.float32)
# contexts = {
# 'imgLPrev': left_img_prev_p, 'imgLPrevRaw': left_img_prev_,
# }
# batch['contexts'] = contexts
return batch
def __len__(self):
return len(self.left['current'])
def kitti_calib(self, P2, P3):
t2 = np.array([P2[0, -1]/P2[0, 0], P2[1, -1]/P2[1, 1], P2[2, -1]])
t3 = np.array([P3[0, -1]/P3[0, 0], P3[1, -1]/P3[1, 1], P3[2, -1]])
t = t2-t3
baseline = np.linalg.norm(t, 2)
K = P2[:, :-1]
return K, baseline
def load_feature(cfg):
if cfg['type'] == 'ORB':
extractor = cv2.ORB_create(
nfeatures=cfg['max_num'],
edgeThreshold=cfg['thresh'], fastThreshold=cfg['thresh'])
elif cfg['type'] == 'AKAZE':
extractor = cv2.AKAZE_create(threshold=cfg['thresh'])
return extractor
def match(kp1, des1, kp2, des2, matcher):
raw_matches = matcher.knnMatch(des1, des2, k=2)
max_num = 1000
pxs1, pxs2 = -np.ones((max_num, 2)), -np.ones((max_num, 2))
matches = []
pxs1_, pxs2_ = [], []
for (m, n) in raw_matches:
if m.distance < 0.70*n.distance:
matches.append(m)
pxs1_.append(kp1[m.queryIdx].pt)
pxs2_.append(kp2[n.trainIdx].pt)
pxs1_, pxs2_ = np.array(pxs1_), np.array(pxs2_)
px1_len = min(max_num, pxs1_.shape[0])
px2_len = min(max_num, pxs2_.shape[0])
pxs1[:px1_len] = pxs1_[:px1_len]
pxs2[:px2_len] = pxs2_[:px2_len]
return pxs1, pxs2
================================================
FILE: dataloaders/stereo/KITTI_submission_loader.py
================================================
import torch.utils.data as data
from PIL import Image
import os
import os.path
import numpy as np
import cv2
from . import preprocess
IMG_EXTENSIONS = [
'.jpg', '.JPG', '.jpeg', '.JPEG',
'.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP',
]
def is_image_file(filename):
return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
def listfiles(filepath, dataname):
left_fold = '/image_2/' if dataname == 'kitti15' else '/colored_0/'
right_fold = '/image_3/' if dataname == 'kitti15' else '/colored_1/'
image = [img for img in os.listdir(filepath+left_fold) if img.find('_10') > -1]
image.sort()
left_test = [filepath+left_fold+img for img in image]
right_test = [filepath+right_fold+img for img in image]
calib_path = '/calib_cam_to_cam/' if dataname == 'kitti15' else '/calib/'
f = [txt for txt in os.listdir(filepath+calib_path)]
f.sort()
calib_test = [filepath+calib_path+f_ for f_ in f]
return left_test, right_test, calib_test
def default_loader(path):
return cv2.imread(path)
# return Image.open(path).convert('RGB')
def disparity_loader(path):
return Image.open(path)
class ImageLoader(data.Dataset):
def __init__(self, left, right, calib, loader=default_loader, dploader=disparity_loader):
self.left = left
self.right = right
self.calib = calib
self.loader = loader
self.dploader = dploader
def __getitem__(self, index):
batch = dict()
left = self.left[index]
right = self.right[index]
calib = self.calib[index]
left_img = self.loader(left)
right_img = self.loader(right)
file = open(calib, "r")
cal = file.read()
if calib.find('kitti15') == -1:
P2 = np.array(cal.split('\n')[2].split(' ')[1:]).astype(np.float32)
P3 = np.array(cal.split('\n')[3].split(' ')[1:]).astype(np.float32)
dataname = 'kitti12_test'
else:
P2 = np.array(cal.split('\n')[-10].split(' ')[1:]).astype(np.float32)
P3 = np.array(cal.split('\n')[-2].split(' ')[1:]).astype(np.float32)
dataname = 'kitti15_test'
filename = self.left[index].split('/')[-1].split('.')[0]
P2 = P2.reshape(3, 4)
P3 = P3.reshape(3, 4)
calib = self.kitti_calib(P2, P3)
processed = preprocess.get_transform(augment=False)
left_img = processed(left_img)
right_img = processed(right_img)
batch['imgL'], batch['imgR'] = left_img, right_img
batch['calib'], batch['dataname'], batch['filename'] = calib, dataname, filename
return batch
def __len__(self):
return len(self.left)
def kitti_calib(self, P2, P3):
t2 = np.array([P2[0, -1]/P2[0, 0], P2[1, -1]/P2[1, 1], P2[2, -1]])
t3 = np.array([P3[0, -1]/P3[0, 0], P3[1, -1]/P3[1, 1], P3[2, -1]])
t = t2-t3
baseline = np.linalg.norm(t, 2)
K = P2[:, :-1]
return {'K': K, 'baseline': baseline}
================================================
FILE: dataloaders/stereo/KITTIloader2012.py
================================================
IMG_EXTENSIONS = [
'.jpg', '.JPG', '.jpeg', '.JPEG',
'.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP',
]
def is_image_file(filename):
return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
def dataloader(filepath, returnCalib=False, trainAll=False):
train_list_f = open('dataloaders/stereo/lists/kitti2012_train170.list', 'r')
test_list_f = open('dataloaders/stereo/lists/kitti2012_val24.list', 'r')
train_list_ = train_list_f.readlines()
test_list_ = test_list_f.readlines()
if trainAll:
train_list = train_list_ + test_list_
test_list = train_list_ + test_list_
else:
train_list = train_list_
test_list = test_list_
left_train = []
right_train = []
disp_train_L = []
calib_train = []
for i in range(len(train_list)):
name = train_list[i].split('.')[0] + '.png'
left_train.append(filepath + '/colored_0/' + name)
right_train.append(filepath + '/colored_1/' + name)
disp_train_L.append(filepath + '/disp_noc/' + name)
cal = name.split('_')[0] + '.txt'
calib_train.append(filepath + '/calib/' + cal)
left_val = []
right_val = []
disp_val_L = []
calib_val = []
for i in range(len(test_list)):
name = test_list[i].split('.')[0] + '.png'
left_val.append(filepath + '/colored_0/' + name)
right_val.append(filepath + '/colored_1/' + name)
disp_val_L.append(filepath + '/disp_noc/' + name)
cal = name.split('_')[0] + '.txt'
calib_val.append(filepath + '/calib/' + cal)
return left_train, right_train, disp_train_L, calib_train, left_val, right_val, disp_val_L, calib_val
================================================
FILE: dataloaders/stereo/KITTIloader2015.py
================================================
IMG_EXTENSIONS = [
'.jpg', '.JPG', '.jpeg', '.JPEG',
'.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP',
]
def is_image_file(filename):
return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
def dataloader(filepath, returnCalib=False, trainAll=False):
train_list_f = open('dataloaders/stereo/lists/kitti2015_train180.list', 'r')
test_list_f = open('dataloaders/stereo/lists/kitti2015_val20.list', 'r')
train_list_ = train_list_f.readlines()
test_list_ = test_list_f.readlines()
if trainAll:
train_list = train_list_ + test_list_
test_list = train_list_ + test_list_
else:
train_list = train_list_
test_list = test_list_
left_train = []
right_train = []
disp_train_L = []
calib_train = []
for i in range(len(train_list)):
name = train_list[i].split('.')[0] + '.png'
left_train.append(filepath + '/image_2/' + name)
right_train.append(filepath + '/image_3/' + name)
disp_train_L.append(filepath + '/disp_occ_0/' + name)
cal = name.split('_')[0] + '.txt'
calib_train.append(filepath + '/calib_cam_to_cam/' + cal)
left_val = []
right_val = []
disp_val_L = []
calib_val = []
for i in range(len(test_list)):
name = test_list[i].split('.')[0] + '.png'
left_val.append(filepath + '/image_2/' + name)
right_val.append(filepath + '/image_3/' + name)
disp_val_L.append(filepath + '/disp_occ_0/' + name)
cal = name.split('_')[0] + '.txt'
calib_val.append(filepath + '/calib_cam_to_cam/' + cal)
return left_train, right_train, disp_train_L, calib_train, left_val, right_val, disp_val_L, calib_val
================================================
FILE: dataloaders/stereo/SceneFlowLoader.py
================================================
import os
import torch
import torch.utils.data as data
import torch
import torchvision.transforms as transforms
import random
from albumentations import Compose, OneOf
from PIL import Image, ImageOps
from . import preprocess
from .stereo_albumentation import RandomShiftRotate, GaussNoiseStereo, RGBShiftStereo, \
RandomBrightnessContrastStereo, random_crop, horizontal_flip
from . import transforms
from .transforms import RandomColor
from . import readpfm as rp
import numpy as np
import cv2
import pdb
IMG_EXTENSIONS = [
'.jpg', '.JPG', '.jpeg', '.JPEG',
'.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP',
]
def is_image_file(filename):
return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
def default_loader(path):
return cv2.imread(path)
# return Image.open(path).convert('RGB')
def disparity_loader(path):
return rp.readPFM(path)
class ImageLoader(data.Dataset):
def __init__(self, left, right,
focal, left_disparity, training,
loader=default_loader, dploader=disparity_loader,
th=256, tw=512):
self.left = left
self.right = right
self.focal = focal
self.disp_L = left_disparity
self.loader = loader
self.dploader = dploader
self.th = th
self.tw = tw
self.training = training
def __getitem__(self, index):
batch = dict()
left = self.left[index]
right = self.right[index]
disp_L = self.disp_L[index]
disp_R = disp_L.replace('left', 'right')
focal = self.focal[index]*30
K = np.array([[focal, 0, 479.5],
[0, focal, 269.5],
[0, 0, 1]])
K = torch.Tensor(K)
left_img = self.loader(left)
right_img = self.loader(right)
dataL, scaleL = self.dploader(disp_L)
dataR, scaleR = self.dploader(disp_R)
if disp_L.split('/')[-5] == 'flyingthings3d':
dataL = -dataL
dataR = -dataR
dataL = np.ascontiguousarray(dataL, dtype=np.float32)
dataR = np.ascontiguousarray(dataR, dtype=np.float32)
if self.training:
left_img, right_img, dataL = horizontal_flip(left_img, right_img, dataL, dataR)
h, w = left_img.shape[:2]
x1 = random.randint(0, w - self.tw)
y1 = random.randint(0, h - self.th)
left_img = left_img[y1: y1 + self.th, x1: x1 + self.tw]
right_img = right_img[y1: y1 + self.th, x1: x1 + self.tw]
dataL = dataL[y1:y1 + self.th, x1:x1 + self.tw]
img = {'left': left_img, 'right': right_img}
# img = self.train_aug(img)
left_img, right_img = img['left'], img['right']
processed = preprocess.get_transform(augment=True)
left_img = processed(left_img)
right_img = processed(right_img)
batch['imgL'], batch['imgR'], batch['disp_true'] = left_img, right_img, dataL
batch['K'], batch['x1'], batch['y1'] = K, x1, y1
return batch
else:
processed = preprocess.get_transform(augment=False)
left_img = processed(left_img)
right_img = processed(right_img)
batch['imgL'], batch['imgR'], batch['disp_true'] = left_img, right_img, dataL
batch['K'] = K
return batch
def __len__(self):
return len(self.left)
def train_aug(self, img):
transformation = Compose([
# RandomShiftRotate(always_apply=True),
RGBShiftStereo(always_apply=True, p_asym=0.3),
OneOf([
GaussNoiseStereo(always_apply=True, p_asym=1),
RandomBrightnessContrastStereo(always_apply=True, p_asym=0.5)
], p=1)
])
return transformation(**img)
# transformation = transforms.Compose([
# RandomColor()
# ])
# return transformation(img)
================================================
FILE: dataloaders/stereo/__init__.py
================================================
from .listflowfile import *
from .SceneFlowLoader import *
from .KITTIloader2012 import *
from .KITTIloader2015 import *
from .KITTILoader import *
from .KITTI_submission_loader import *
from .KITTIRawLoader import *
================================================
FILE: dataloaders/stereo/listflowfile.py
================================================
import torch.utils.data as data
from PIL import Image
import os
import os.path
import glob
import numpy as np
import pdb
IMG_EXTENSIONS = [
'.jpg', '.JPG', '.jpeg', '.JPEG',
'.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP',
]
def is_image_file(filename):
return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
def dataloader(filepath):
all_left_img = []
all_right_img = []
all_left_disp = []
all_focal = []
test_left_img = []
test_right_img = []
test_left_disp = []
test_focal = []
# # MONKAAS ##
monkaa_path = os.path.join(filepath, 'monkaa', 'frames_finalpass')
monkaa_disp = os.path.join(filepath, 'monkaa', 'disparity')
monkaa_dir = os.listdir(monkaa_path)
for dd in monkaa_dir:
for im in os.listdir(os.path.join(monkaa_path, dd, 'left')):
if is_image_file(os.path.join(monkaa_path, dd, 'left', im)):
all_left_img.append(os.path.join(monkaa_path, dd, 'left', im))
all_left_disp.append(os.path.join(monkaa_disp, dd, 'left', im.split(".")[0]+'.pfm'))
all_focal.append(35)
for im in os.listdir(monkaa_path+'/'+dd+'/right/'):
if is_image_file(monkaa_path+'/'+dd+'/right/'+im):
all_right_img.append(monkaa_path+'/'+dd+'/right/'+im)
# # FLYINGTHINGS 3D ##
flying_path = os.path.join(filepath, 'flyingthings3d_final', 'frames_finalpass', 'TRAIN')
flying_disp = os.path.join(filepath, 'flyingthings3d_final', 'disparity', 'TRAIN')
flying_dir = os.listdir(flying_path)
left_paths, right_paths, disp_paths = [], [], []
for dd in flying_dir:
for nn in os.listdir(os.path.join(flying_path, dd)):
for im in os.listdir(os.path.join(flying_path, dd, nn, 'left')):
if is_image_file(os.path.join(flying_path, dd, nn, 'left', im)):
left_paths.append(os.path.join(flying_path, dd, nn, 'left', im))
disp_paths.append(os.path.join(flying_disp, dd, nn, 'left', im.split(".")[0]+'.pfm'))
for im in os.listdir(os.path.join(flying_path, dd, nn, 'right')):
if is_image_file(os.path.join(flying_path, dd, nn, 'right', im)):
right_paths.append(os.path.join(flying_path, dd, nn, 'right', im))
# left_paths = glob.glob(flying_path+'/*/*/left/*.png')
# right_paths = glob.glob(flying_path+'/*/*/right/*.png')
# disp_paths = glob.glob(flying_disp+'/*/*/left/*.pfm')
flying_path_val = os.path.join(filepath, 'flyingthings3d_final', 'frames_finalpass', 'TEST')
flying_disp_val = os.path.join(filepath, 'flyingthings3d_final', 'disparity', 'TEST')
flying_dir_val = os.listdir(flying_path_val)
left_paths_val, right_paths_val, disp_paths_val = [], [], []
for dd in flying_dir_val:
for nn in os.listdir(os.path.join(flying_path_val, dd)):
for im in os.listdir(os.path.join(flying_path_val, dd, nn, 'left')):
if is_image_file(os.path.join(flying_path_val, dd, nn, 'left', im)):
left_paths_val.append(os.path.join(flying_path_val, dd, nn, 'left', im))
disp_paths_val.append(os.path.join(flying_disp_val, dd, nn, 'left', im.split(".")[0]+'.pfm'))
for im in os.listdir(os.path.join(flying_path_val, dd, nn, 'right')):
if is_image_file(os.path.join(flying_path_val, dd, nn, 'right', im)):
right_paths_val.append(os.path.join(flying_path_val, dd, nn, 'right', im))
# left_paths_val = glob.glob(flying_path_val+'/*/*/left/*.png')
# right_paths_val = glob.glob(flying_path_val+'/*/*/right/*.png')
# disp_paths_val = glob.glob(flying_disp_val+'/*/*/left/*.pfm')
left_paths.sort()
right_paths.sort()
disp_paths.sort()
left_paths_val.sort()
right_paths_val.sort()
disp_paths_val.sort()
focal = (35*np.ones(len(left_paths), dtype=int)).tolist()
all_left_img = all_left_img + left_paths
all_right_img = all_right_img + right_paths
all_left_disp = all_left_disp + disp_paths
all_focal = all_focal + focal
focal_val = (35*np.ones(len(left_paths_val), dtype=int)).tolist()
test_left_img = test_left_img + left_paths_val
test_right_img = test_right_img + right_paths_val
test_left_disp = test_left_disp + disp_paths_val
test_focal = test_focal + focal_val
# # DRIVING ##
driving_dir = os.path.join(filepath, 'driving', 'frames_finalpass')
driving_disp = os.path.join(filepath, 'driving', 'disparity')
subdir1 = ['35mm_focallength', '15mm_focallength']
subdir2 = ['scene_backwards', 'scene_forwards']
subdir3 = ['fast', 'slow']
for i in subdir1:
for j in subdir2:
for k in subdir3:
imm_l = os.listdir(os.path.join(driving_dir, i, j, k, 'left'))
for im in imm_l:
if is_image_file(os.path.join(driving_dir, i, j, k, 'left', im)):
all_left_img.append(os.path.join(driving_dir, i, j, k, 'left', im))
if i == '35mm_focallength':
all_focal.append(35)
else:
all_focal.append(15)
all_left_disp.append(os.path.join(driving_disp, i, j, k, 'left', im.split(".")[0]+'.pfm'))
if is_image_file(os.path.join(driving_dir, i, j, k, 'right', im)):
all_right_img.append(os.path.join(driving_dir, i, j, k, 'right', im))
return all_left_img, all_right_img, all_left_disp, all_focal, test_left_img, test_right_img, test_left_disp, test_focal
================================================
FILE: dataloaders/stereo/lists/kitti2012_test.list
================================================
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================================================
FILE: dataloaders/stereo/lists/kitti2012_train.list
================================================
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================================================
FILE: dataloaders/stereo/lists/kitti2012_train170.list
================================================
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================================================
FILE: dataloaders/stereo/lists/kitti2012_val24.list
================================================
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================================================
FILE: dataloaders/stereo/lists/kitti2015_test.list
================================================
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================================================
FILE: dataloaders/stereo/lists/kitti2015_train.list
================================================
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================================================
FILE: dataloaders/stereo/lists/kitti2015_train180.list
================================================
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================================================
FILE: dataloaders/stereo/lists/kitti2015_val20.list
================================================
000198_10.png
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================================================
FILE: dataloaders/stereo/lists/middeval3_test.list
================================================
Australia/im0.png
AustraliaP/im0.png
Bicycle2/im0.png
Classroom2/im0.png
Classroom2E/im0.png
Computer/im0.png
Crusade/im0.png
CrusadeP/im0.png
Djembe/im0.png
DjembeL/im0.png
Hoops/im0.png
Livingroom/im0.png
Newkuba/im0.png
Plants/im0.png
Staircase/im0.png
================================================
FILE: dataloaders/stereo/lists/middeval3_train.list
================================================
Playtable/im0.png
ArtL/im0.png
Jadeplant/im0.png
PlaytableP/im0.png
PianoL/im0.png
Piano/im0.png
Adirondack/im0.png
Teddy/im0.png
Recycle/im0.png
Motorcycle/im0.png
MotorcycleE/im0.png
Vintage/im0.png
Playroom/im0.png
Shelves/im0.png
Pipes/im0.png
================================================
FILE: dataloaders/stereo/lists/sceneflow_search_trainA.list
================================================
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TRAIN/C/0725/left/0013.png
TRAIN/C/0241/left/0007.png
TRAIN/35mm_focallength/scene_backwards/slow/left/0412.png
TRAIN/35mm_focallength/scene_backwards/slow/left/0585.png
TRAIN/C/0359/left/0013.png
TRAIN/funnyworld_x2/left/0432.png
TRAIN/A/0138/left/0007.png
TRAIN/A/0418/left/0007.png
TRAIN/C/0678/left/0013.png
TRAIN/treeflight_augmented1_x2/left/0197.png
TRAIN/C/0452/left/0008.png
TRAIN/B/0112/left/0011.png
TRAIN/funnyworld_augmented0_x2/left/0372.png
TRAIN/funnyworld_camera2_augmented1_x2/left/0135.png
TRAIN/B/0396/left/0015.png
TRAIN/A/0723/left/0011.png
TRAIN/B/0474/left/0014.png
TRAIN/C/0344/left/0014.png
TRAIN/lonetree_difftex2_x2/left/0319.png
TRAIN/funnyworld_camera2_x2/left/0333.png
TRAIN/B/0634/left/0008.png
TRAIN/35mm_focallength/scene_forwards/slow/left/0649.png
TRAIN/B/0472/left/0009.png
TRAIN/C/0455/left/0010.png
TRAIN/B/0179/left/0013.png
TRAIN/C/0323/left/0014.png
TRAIN/A/0570/left/0008.png
TRAIN/A/0346/left/0009.png
TRAIN/A/0258/left/0008.png
TRAIN/B/0363/left/0012.png
TRAIN/C/0666/left/0006.png
TRAIN/35mm_focallength/scene_forwards/slow/left/0177.png
TRAIN/B/0535/left/0014.png
TRAIN/C/0593/left/0007.png
TRAIN/C/0018/left/0012.png
TRAIN/funnyworld_x2/left/0134.png
TRAIN/A/0050/left/0006.png
TRAIN/funnyworld_x2/left/0020.png
TRAIN/15mm_focallength/scene_backwards/fast/left/0240.png
TRAIN/C/0492/left/0008.png
TRAIN/A/0447/left/0008.png
TRAIN/funnyworld_camera2_augmented1_x2/left/0201.png
TRAIN/35mm_focallength/scene_forwards/slow/left/0379.png
TRAIN/lonetree_winter_x2/left/0304.png
TRAIN/C/0456/left/0007.png
TRAIN/C/0142/left/0007.png
TRAIN/funnyworld_augmented0_x2/left/0185.png
TRAIN/funnyworld_x2/left/0168.png
TRAIN/funnyworld_augmented0_x2/left/0158.png
TRAIN/C/0101/left/0007.png
TRAIN/C/0717/left/0011.png
TRAIN/15mm_focallength/scene_backwards/fast/left/0034.png
TRAIN/funnyworld_camera2_x2/left/0102.png
TRAIN/C/0005/left/0011.png
TRAIN/B/0667/left/0009.png
TRAIN/C/0456/left/0010.png
TRAIN/A/0468/left/0009.png
TRAIN/lonetree_augmented1_x2/left/0073.png
TRAIN/A/0212/left/0010.png
TRAIN/A/0441/left/0006.png
TRAIN/C/0182/left/0014.png
TRAIN/35mm_focallength/scene_backwards/slow/left/0605.png
TRAIN/C/0680/left/0008.png
TRAIN/treeflight_augmented0_x2/left/0087.png
TRAIN/funnyworld_x2/left/0426.png
TRAIN/lonetree_x2/left/0261.png
TRAIN/A/0330/left/0008.png
TRAIN/funnyworld_camera2_x2/left/0256.png
TRAIN/15mm_focallength/scene_backwards/slow/left/0396.png
TRAIN/B/0410/left/0011.png
TRAIN/A/0096/left/0012.png
TRAIN/B/0215/left/0008.png
TRAIN/A/0488/left/0014.png
TRAIN/B/0083/left/0014.png
TRAIN/funnyworld_augmented1_x2/left/0170.png
TRAIN/B/0448/left/0010.png
TRAIN/B/0266/left/0012.png
TRAIN/A/0217/left/0015.png
TRAIN/treeflight_augmented0_x2/left/0401.png
TRAIN/35mm_focallength/scene_backwards/slow/left/0330.png
TRAIN/funnyworld_x2/left/0027.png
TRAIN/B/0215/left/0009.png
TRAIN/B/0537/left/0013.png
TRAIN/A/0088/left/0012.png
TRAIN/B/0321/left/0007.png
TRAIN/B/0042/left/0015.png
TRAIN/funnyworld_camera2_augmented1_x2/left/0191.png
TRAIN/B/0659/left/0009.png
TRAIN/35mm_focallength/scene_forwards/slow/left/0279.png
TRAIN/A/0641/left/0010.png
TRAIN/A/0471/left/0012.png
TRAIN/C/0311/left/0010.png
TRAIN/35mm_focallength/scene_forwards/fast/left/0038.png
TRAIN/C/0072/left/0012.png
TRAIN/A/0509/left/0006.png
TRAIN/A/0293/left/0013.png
TRAIN/B/0642/left/0006.png
TRAIN/A/0064/left/0006.png
TRAIN/C/0545/left/0008.png
TRAIN/funnyworld_x2/left/0086.png
TRAIN/B/0136/left/0011.png
TRAIN/C/0052/left/0006.png
TRAIN/B/0269/left/0012.png
TRAIN/C/0613/left/0008.png
TRAIN/A/0287/left/0010.png
TRAIN/A/0621/left/0015.png
TRAIN/funnyworld_camera2_augmented1_x2/left/0271.png
TRAIN/funnyworld_camera2_augmented0_x2/left/0313.png
TRAIN/C/0386/left/0009.png
TRAIN/B/0667/left/0014.png
TRAIN/B/0066/left/0009.png
TRAIN/B/0297/left/0009.png
TRAIN/B/0332/left/0008.png
TRAIN/A/0541/left/0013.png
TRAIN/C/0615/left/0014.png
TRAIN/B/0550/left/0013.png
TRAIN/35mm_focallength/scene_backwards/fast/left/0022.png
TRAIN/C/0447/left/0006.png
TRAIN/lonetree_augmented1_x2/left/0400.png
TRAIN/C/0716/left/0007.png
TRAIN/lonetree_winter_x2/left/0307.png
TRAIN/A/0093/left/0012.png
TRAIN/B/0603/left/0010.png
TRAIN/lonetree_augmented0_x2/left/0389.png
TRAIN/35mm_focallength/scene_forwards/slow/left/0050.png
TRAIN/funnyworld_camera2_augmented1_x2/left/0148.png
TRAIN/treeflight_augmented0_x2/left/0297.png
TRAIN/35mm_focallength/scene_backwards/slow/left/0514.png
TRAIN/A/0083/left/0012.png
TRAIN/15mm_focallength/scene_forwards/slow/left/0037.png
TRAIN/funnyworld_camera2_augmented1_x2/left/0379.png
TRAIN/B/0517/left/0006.png
TRAIN/B/0299/left/0010.png
TRAIN/C/0251/left/0007.png
TRAIN/C/0293/left/0006.png
TRAIN/35mm_focallength/scene_backwards/slow/left/0774.png
TRAIN/funnyworld_camera2_augmented0_x2/left/0306.png
TRAIN/B/0666/left/0013.png
TRAIN/B/0276/left/0009.png
TRAIN/B/0311/left/0009.png
TRAIN/A/0177/left/0011.png
TRAIN/A/0223/left/0012.png
TRAIN/15mm_focallength/scene_backwards/slow/left/0151.png
TRAIN/funnyworld_camera2_augmented1_x2/left/0212.png
TRAIN/A/0078/left/0009.png
TRAIN/a_rain_of_stones_x2/left/0108.png
TRAIN/C/0307/left/0006.png
TRAIN/C/0228/left/0015.png
TRAIN/C/0532/left/0010.png
TRAIN/B/0140/left/0011.png
TRAIN/B/0292/left/0015.png
TRAIN/A/0617/left/0007.png
TRAIN/35mm_focallength/scene_forwards/slow/left/0169.png
TRAIN/A/0236/left/0015.png
TRAIN/C/0051/left/0013.png
TRAIN/C/0209/left/0010.png
TRAIN/B/0748/left/0009.png
TRAIN/A/0116/left/0007.png
TRAIN/A/0743/left/0012.png
TRAIN/A/0651/left/0015.png
TRAIN/C/0510/left/0009.png
TRAIN/C/0181/left/0015.png
TRAIN/C/0235/left/0014.png
TRAIN/C/0512/left/0012.png
TRAIN/A/0397/left/0013.png
TRAIN/funnyworld_camera2_x2/left/0280.png
TRAIN/lonetree_difftex_x2/left/0333.png
TRAIN/lonetree_augmented1_x2/left/0321.png
TRAIN/funnyworld_x2/left/0319.png
TRAIN/C/0529/left/0014.png
TRAIN/B/0477/left/0011.png
TRAIN/C/0565/left/0013.png
TRAIN/A/0651/left/0011.png
TRAIN/C/0647/left/0014.png
TRAIN/B/0089/left/0013.png
TRAIN/A/0627/left/0011.png
TRAIN/B/0227/left/0013.png
TRAIN/A/0155/left/0015.png
TRAIN/C/0459/left/0009.png
TRAIN/lonetree_winter_x2/left/0338.png
TRAIN/flower_storm_x2/left/0000.png
TRAIN/B/0258/left/0014.png
TRAIN/lonetree_difftex_x2/left/0362.png
TRAIN/A/0286/left/0015.png
TRAIN/eating_x2/left/0067.png
TRAIN/family_x2/left/0085.png
TRAIN/B/0729/left/0012.png
TRAIN/A/0297/left/0010.png
TRAIN/B/0436/left/0012.png
TRAIN/A/0511/left/0012.png
TRAIN/lonetree_winter_x2/left/0414.png
TRAIN/C/0516/left/0013.png
TRAIN/flower_storm_x2/left/0087.png
TRAIN/C/0236/left/0007.png
TRAIN/lonetree_x2/left/0325.png
TRAIN/B/0185/left/0015.png
TRAIN/A/0242/left/0012.png
TRAIN/B/0059/left/0014.png
TRAIN/lonetree_winter_x2/left/0407.png
TRAIN/funnyworld_camera2_augmented1_x2/left/0422.png
TRAIN/funnyworld_augmented0_x2/left/0361.png
TRAIN/B/0638/left/0007.png
TRAIN/family_x2/left/0048.png
TRAIN/treeflight_augmented0_x2/left/0403.png
TRAIN/B/0329/left/0006.png
TRAIN/C/0238/left/0009.png
TRAIN/treeflight_augmented0_x2/left/0349.png
TRAIN/funnyworld_x2/left/0386.png
TRAIN/lonetree_augmented1_x2/left/0199.png
TRAIN/C/0189/left/0010.png
TRAIN/C/0454/left/0010.png
TRAIN/funnyworld_augmented1_x2/left/0498.png
TRAIN/15mm_focallength/scene_backwards/fast/left/0040.png
TRAIN/A/0568/left/0009.png
TRAIN/treeflight_augmented1_x2/left/0099.png
TRAIN/C/0495/left/0011.png
TRAIN/C/0683/left/0013.png
TRAIN/C/0256/left/0008.png
TRAIN/A/0420/left/0014.png
TRAIN/C/0640/left/0006.png
TRAIN/A/0039/left/0006.png
TRAIN/A/0107/left/0007.png
TRAIN/B/0014/left/0012.png
TRAIN/C/0606/left/0007.png
TRAIN/15mm_focallength/scene_backwards/slow/left/0123.png
TRAIN/15mm_focallength/scene_backwards/slow/left/0183.png
TRAIN/funnyworld_camera2_augmented1_x2/left/0441.png
TRAIN/top_view_x2/left/0013.png
TRAIN/treeflight_x2/left/0388.png
TRAIN/C/0723/left/0009.png
TRAIN/35mm_focallength/scene_backwards/slow/left/0451.png
TRAIN/35mm_focallength/scene_backwards/slow/left/0492.png
TRAIN/lonetree_x2/left/0130.png
TRAIN/B/0372/left/0008.png
TRAIN/A/0174/left/0014.png
TRAIN/A/0567/left/0006.png
TRAIN/A/0456/left/0007.png
TRAIN/funnyworld_augmented1_x2/left/0343.png
TRAIN/C/0717/left/0006.png
TRAIN/A/0288/left/0015.png
TRAIN/C/0236/left/0008.png
TRAIN/B/0206/left/0009.png
TRAIN/B/0252/left/0014.png
TRAIN/15mm_foc
gitextract_5wcn44k3/
├── .gitignore
├── LICENSE
├── README.md
├── configs/
│ ├── backbone.yaml
│ └── stereo/
│ ├── cfg_coex.yaml
│ └── cfg_psm.yaml
├── dataloaders/
│ ├── __init__.py
│ └── stereo/
│ ├── KITTILoader.py
│ ├── KITTIRawLoader.py
│ ├── KITTI_submission_loader.py
│ ├── KITTIloader2012.py
│ ├── KITTIloader2015.py
│ ├── SceneFlowLoader.py
│ ├── __init__.py
│ ├── listflowfile.py
│ ├── lists/
│ │ ├── kitti2012_test.list
│ │ ├── kitti2012_train.list
│ │ ├── kitti2012_train170.list
│ │ ├── kitti2012_val24.list
│ │ ├── kitti2015_test.list
│ │ ├── kitti2015_train.list
│ │ ├── kitti2015_train180.list
│ │ ├── kitti2015_val20.list
│ │ ├── middeval3_test.list
│ │ ├── middeval3_train.list
│ │ ├── sceneflow_search_trainA.list
│ │ ├── sceneflow_search_trainB.list
│ │ ├── sceneflow_search_val.list
│ │ ├── sceneflow_test.list
│ │ └── sceneflow_train.list
│ ├── preprocess.py
│ ├── readpfm.py
│ ├── stereo_albumentation.py
│ └── transforms.py
├── demo.py
├── demo_tensorrt.py
├── demo_torchscript.py
├── environment.yml
├── logs/
│ └── stereo/
│ └── CoEx/
│ └── version_0/
│ └── checkpoints/
│ └── last.ckpt
├── models/
│ ├── __init__.py
│ └── stereo/
│ ├── CoEx.py
│ ├── CoExTRT.py
│ ├── PSMNet.py
│ ├── __init__.py
│ └── submodules/
│ ├── Submodule.py
│ ├── __init__.py
│ ├── aggregation.py
│ ├── feature.py
│ ├── regression.py
│ ├── spixel_utils/
│ │ ├── spixel.py
│ │ ├── spixel_conv.py
│ │ ├── spixel_loss.py
│ │ └── spixel_test.py
│ ├── util_conv.py
│ └── utils.py
├── stereo.py
├── torch_to_tensorrt.py
├── utils/
│ ├── __init__.py
│ └── load.py
└── zoo/
└── torchscript/
└── CoEx.pt
SYMBOL INDEX (326 symbols across 30 files)
FILE: dataloaders/stereo/KITTILoader.py
function is_image_file (line 23) | def is_image_file(filename):
function default_loader (line 26) | def default_loader(path):
function disparity_loader (line 30) | def disparity_loader(path):
class ImageLoader (line 34) | class ImageLoader(data.Dataset):
method __init__ (line 35) | def __init__(self, left, right, left_disparity, calib, th=256, tw=512,...
method __getitem__ (line 48) | def __getitem__(self, index):
method __len__ (line 153) | def __len__(self):
method train_aug (line 156) | def train_aug(self, img):
method kitti_calib (line 168) | def kitti_calib(self, P2, P3):
FILE: dataloaders/stereo/KITTIRawLoader.py
function is_image_file (line 21) | def is_image_file(filename):
function listfiles (line 25) | def listfiles(cfg, date=None, num=None, test=False):
function listtrainfiles (line 70) | def listtrainfiles(filepath, train_dataname, date=None, num=None, test=F...
function listvalfiles (line 123) | def listvalfiles(filepath):
function default_loader (line 142) | def default_loader(path):
function disparity_loader (line 146) | def disparity_loader(path):
class ImageLoader (line 150) | class ImageLoader(data.Dataset):
method __init__ (line 151) | def __init__(self, left, right, cfg, disp=None, training=True, demo=Fa...
method __getitem__ (line 170) | def __getitem__(self, index):
method __len__ (line 323) | def __len__(self):
method kitti_calib (line 326) | def kitti_calib(self, P2, P3):
function load_feature (line 337) | def load_feature(cfg):
function match (line 348) | def match(kp1, des1, kp2, des2, matcher):
FILE: dataloaders/stereo/KITTI_submission_loader.py
function is_image_file (line 16) | def is_image_file(filename):
function listfiles (line 20) | def listfiles(filepath, dataname):
function default_loader (line 40) | def default_loader(path):
function disparity_loader (line 45) | def disparity_loader(path):
class ImageLoader (line 49) | class ImageLoader(data.Dataset):
method __init__ (line 50) | def __init__(self, left, right, calib, loader=default_loader, dploader...
method __getitem__ (line 58) | def __getitem__(self, index):
method __len__ (line 92) | def __len__(self):
method kitti_calib (line 95) | def kitti_calib(self, P2, P3):
FILE: dataloaders/stereo/KITTIloader2012.py
function is_image_file (line 8) | def is_image_file(filename):
function dataloader (line 12) | def dataloader(filepath, returnCalib=False, trainAll=False):
FILE: dataloaders/stereo/KITTIloader2015.py
function is_image_file (line 8) | def is_image_file(filename):
function dataloader (line 12) | def dataloader(filepath, returnCalib=False, trainAll=False):
FILE: dataloaders/stereo/SceneFlowLoader.py
function is_image_file (line 26) | def is_image_file(filename):
function default_loader (line 30) | def default_loader(path):
function disparity_loader (line 35) | def disparity_loader(path):
class ImageLoader (line 39) | class ImageLoader(data.Dataset):
method __init__ (line 40) | def __init__(self, left, right,
method __getitem__ (line 55) | def __getitem__(self, index):
method __len__ (line 117) | def __len__(self):
method train_aug (line 120) | def train_aug(self, img):
FILE: dataloaders/stereo/listflowfile.py
function is_image_file (line 17) | def is_image_file(filename):
function dataloader (line 21) | def dataloader(filepath):
FILE: dataloaders/stereo/preprocess.py
function scale_crop (line 23) | def scale_crop(input_size, scale_size=None, normalize=__imagenet_stats):
function scale_random_crop (line 34) | def scale_random_crop(input_size, scale_size=None, normalize=__imagenet_...
function pad_random_crop (line 46) | def pad_random_crop(input_size, scale_size=None, normalize=__imagenet_st...
function inception_preproccess (line 56) | def inception_preproccess(input_size, normalize=__imagenet_stats):
function inception_color_preproccess (line 65) | def inception_color_preproccess(input_size, normalize=__imagenet_stats):
function get_transform (line 80) | def get_transform(name='imagenet', input_size=None,
class Lighting (line 93) | class Lighting(object):
method __init__ (line 96) | def __init__(self, alphastd, eigval, eigvec):
method __call__ (line 101) | def __call__(self, img):
class Grayscale (line 114) | class Grayscale(object):
method __call__ (line 116) | def __call__(self, img):
class Saturation (line 124) | class Saturation(object):
method __init__ (line 126) | def __init__(self, var):
method __call__ (line 129) | def __call__(self, img):
class Brightness (line 135) | class Brightness(object):
method __init__ (line 137) | def __init__(self, var):
method __call__ (line 140) | def __call__(self, img):
class Contrast (line 146) | class Contrast(object):
method __init__ (line 148) | def __init__(self, var):
method __call__ (line 151) | def __call__(self, img):
class RandomOrder (line 158) | class RandomOrder(object):
method __init__ (line 162) | def __init__(self, transforms):
method __call__ (line 165) | def __call__(self, img):
class ColorJitter (line 174) | class ColorJitter(RandomOrder):
method __init__ (line 176) | def __init__(self, brightness=0.4, contrast=0.4, saturation=0.4):
FILE: dataloaders/stereo/readpfm.py
function readPFM (line 7) | def readPFM(file):
FILE: dataloaders/stereo/stereo_albumentation.py
function get_random_crop_coords (line 19) | def get_random_crop_coords(height, width, crop_height, crop_width):
function crop (line 35) | def crop(img, x1, y1, x2, y2):
function horizontal_flip (line 49) | def horizontal_flip(img_left, img_right, disp_left, disp_right):
function random_crop (line 79) | def random_crop(min_crop_height, min_crop_width, input_data, split):
class StereoTransform (line 123) | class StereoTransform(BasicTransform):
method targets (line 129) | def targets(self):
method update_params (line 135) | def update_params(self, params, **kwargs):
class RightOnlyTransform (line 144) | class RightOnlyTransform(BasicTransform):
method targets (line 150) | def targets(self):
method update_params (line 155) | def update_params(self, params, **kwargs):
class StereoTransformAsym (line 164) | class StereoTransformAsym(BasicTransform):
method __init__ (line 169) | def __init__(self, always_apply=False, p=0.5, p_asym=0.2):
method targets (line 174) | def targets(self):
method update_params (line 180) | def update_params(self, params, **kwargs):
method targets_as_params (line 189) | def targets_as_params(self):
method asym (line 192) | def asym(self):
class Normalize (line 202) | class Normalize(StereoTransform):
method __init__ (line 214) | def __init__(self, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.22...
method apply (line 221) | def apply(self, image, **params):
method get_transform_init_args_names (line 224) | def get_transform_init_args_names(self):
class ToTensor (line 228) | class ToTensor(StereoTransform):
method __init__ (line 236) | def __init__(self, always_apply=False, p=1.0):
method apply (line 239) | def apply(self, image, **params):
class ToGrayStereo (line 243) | class ToGrayStereo(StereoTransform, ToGray):
method __init__ (line 244) | def __init__(self, always_apply=False, p=0.5):
class GaussNoiseStereo (line 254) | class GaussNoiseStereo(StereoTransformAsym, GaussNoise):
method __init__ (line 267) | def __init__(self, var_limit=(10.0, 50.0), mean=0, always_apply=False,...
method apply_l (line 271) | def apply_l(self, img, gauss_l=None, **params):
method apply_r (line 274) | def apply_r(self, img, gauss_r=None, **params):
method get_params_dependent_on_targets (line 277) | def get_params_dependent_on_targets(self, params):
class RGBShiftStereo (line 298) | class RGBShiftStereo(StereoTransformAsym, RGBShift):
method __init__ (line 314) | def __init__(self, r_shift_limit=20, g_shift_limit=20, b_shift_limit=2...
method apply_l (line 318) | def apply_l(self, image, r_shift_l=0, g_shift_l=0, b_shift_l=0, **para...
method apply_r (line 321) | def apply_r(self, image, r_shift_r=0, g_shift_r=0, b_shift_r=0, **para...
method get_params_dependent_on_targets (line 324) | def get_params_dependent_on_targets(self, params):
class RandomBrightnessContrastStereo (line 348) | class RandomBrightnessContrastStereo(StereoTransformAsym, RandomBrightne...
method __init__ (line 364) | def __init__(self, brightness_limit=0.1, contrast_limit=0.1, brightnes...
method apply_l (line 369) | def apply_l(self, img, alpha_l=1.0, beta_l=0.0, **params):
method apply_r (line 372) | def apply_r(self, img, alpha_r=1.0, beta_r=0.0, **params):
method get_params_dependent_on_targets (line 375) | def get_params_dependent_on_targets(self, params):
class RandomShiftRotate (line 399) | class RandomShiftRotate(RightOnlyTransform):
method __init__ (line 410) | def __init__(self, max_shift=1.5, max_rotation=0.2, always_apply=False...
method apply (line 415) | def apply(self, img, **params):
FILE: dataloaders/stereo/transforms.py
class Compose (line 9) | class Compose(object):
method __init__ (line 10) | def __init__(self, transforms):
method __call__ (line 13) | def __call__(self, sample):
class ToTensor (line 19) | class ToTensor(object):
method __call__ (line 22) | def __call__(self, sample):
class Normalize (line 40) | class Normalize(object):
method __init__ (line 43) | def __init__(self, mean, std):
method __call__ (line 47) | def __call__(self, sample):
class RandomCrop (line 59) | class RandomCrop(object):
method __init__ (line 60) | def __init__(self, img_height, img_width, validate=False):
method __call__ (line 65) | def __call__(self, sample):
method crop_img (line 120) | def crop_img(self, img):
class RandomVerticalFlip (line 125) | class RandomVerticalFlip(object):
method __call__ (line 128) | def __call__(self, sample):
class ToPILImage (line 141) | class ToPILImage(object):
method __call__ (line 143) | def __call__(self, sample):
class ToNumpyArray (line 150) | class ToNumpyArray(object):
method __call__ (line 152) | def __call__(self, sample):
class RandomContrast (line 160) | class RandomContrast(object):
method __call__ (line 163) | def __call__(self, sample):
class RandomGamma (line 173) | class RandomGamma(object):
method __call__ (line 175) | def __call__(self, sample):
class RandomBrightness (line 185) | class RandomBrightness(object):
method __call__ (line 187) | def __call__(self, sample):
class RandomHue (line 197) | class RandomHue(object):
method __call__ (line 199) | def __call__(self, sample):
class RandomSaturation (line 209) | class RandomSaturation(object):
method __call__ (line 211) | def __call__(self, sample):
class RandomColor (line 221) | class RandomColor(object):
method __call__ (line 223) | def __call__(self, sample):
FILE: demo.py
function load_configs (line 26) | def load_configs(path):
FILE: demo_tensorrt.py
function load_configs (line 19) | def load_configs(path):
FILE: demo_torchscript.py
function load_configs (line 23) | def load_configs(path):
FILE: models/stereo/CoEx.py
class CoEx (line 16) | class CoEx(nn.Module):
method __init__ (line 17) | def __init__(self, cfg):
method forward (line 85) | def forward(self, imL, imR=None, u0=None, v0=None, training=False):
FILE: models/stereo/CoExTRT.py
class CoExTRT (line 13) | class CoExTRT(nn.Module):
method __init__ (line 14) | def __init__(self, cfg):
method forward (line 66) | def forward(self, imL):
class FeatUp (line 103) | class FeatUp(SubModule):
method __init__ (line 104) | def __init__(self, cfg):
method forward (line 117) | def forward(self, x4, x8, x16, x32):
class Feature (line 126) | class Feature(SubModule):
method __init__ (line 127) | def __init__(self, cfg):
method forward (line 146) | def forward(self, x):
class Aggregation (line 159) | class Aggregation(SubModule):
method __init__ (line 160) | def __init__(self,
method forward (line 245) | def forward(self, x4, x8, x16, x32, cost):
class Regression (line 300) | class Regression(SubModule):
method __init__ (line 301) | def __init__(self,
method forward (line 309) | def forward(self, cost, spg):
method unfold (line 332) | def unfold(self, x):
method topkpool (line 347) | def topkpool(self, cost):
class CostVolume (line 355) | class CostVolume(nn.Module):
method __init__ (line 356) | def __init__(self, maxdisp):
method forward (line 360) | def forward(self, x, y):
class AttentionCostVolume (line 379) | class AttentionCostVolume(nn.Module):
method __init__ (line 380) | def __init__(self, max_disparity, in_chan, hidden_chan, head=1, weight...
method forward (line 391) | def forward(self, imL, imR):
class BasicConv (line 405) | class BasicConv(nn.Module):
method __init__ (line 407) | def __init__(self, in_channels, out_channels, deconv=False, is_3d=Fals...
method forward (line 424) | def forward(self, x):
class BasicConvF (line 431) | class BasicConvF(nn.Module):
method __init__ (line 433) | def __init__(self, in_channels, out_channels, deconv=False, is_3d=Fals...
method forward (line 449) | def forward(self, x):
class Conv2x (line 454) | class Conv2x(nn.Module):
method __init__ (line 456) | def __init__(self, in_channels, out_channels, deconv=False, is_3d=Fals...
method forward (line 481) | def forward(self, x, rem):
FILE: models/stereo/PSMNet.py
function convbn (line 17) | def convbn(in_planes, out_planes, kernel_size, stride, pad, dilation):
function convbn_3d (line 23) | def convbn_3d(in_planes, out_planes, kernel_size, stride, pad):
class BasicBlock (line 29) | class BasicBlock(nn.Module):
method __init__ (line 31) | def __init__(self, inplanes, planes, stride, downsample, pad, dilation):
method forward (line 42) | def forward(self, x):
class disparityregression (line 54) | class disparityregression(nn.Module):
method __init__ (line 55) | def __init__(self, maxdisp):
method forward (line 59) | def forward(self, x):
class feature_extraction (line 64) | class feature_extraction(nn.Module):
method __init__ (line 65) | def __init__(self):
method _make_layer (line 100) | def _make_layer(self, block, planes, blocks, stride, pad, dilation):
method forward (line 116) | def forward(self, x):
class hourglass (line 142) | class hourglass(nn.Module):
method __init__ (line 143) | def __init__(self, inplanes):
method forward (line 163) | def forward(self, x ,presqu, postsqu):
class PSMNet (line 184) | class PSMNet(nn.Module):
method __init__ (line 185) | def __init__(self, cfg):
method forward (line 257) | def forward(self, left, right, u0=None, v0=None, training=False):
method topkpool (line 358) | def topkpool(self, cost, k):
FILE: models/stereo/submodules/Submodule.py
class SubModule (line 11) | class SubModule(nn.Module):
method __init__ (line 12) | def __init__(self):
method weight_init (line 15) | def weight_init(self):
FILE: models/stereo/submodules/aggregation.py
class Aggregation (line 14) | class Aggregation(SubModule):
method __init__ (line 15) | def __init__(self,
method forward (line 96) | def forward(self, img, cost):
FILE: models/stereo/submodules/feature.py
function convbn (line 20) | def convbn(in_planes, out_planes, kernel_size, stride, pad, dilation):
class PSMBasicBlock (line 26) | class PSMBasicBlock(nn.Module):
method __init__ (line 28) | def __init__(self, inplanes, planes, stride, downsample, pad, dilation):
method forward (line 39) | def forward(self, x):
class FeatUp (line 51) | class FeatUp(SubModule):
method __init__ (line 52) | def __init__(self, cfg):
method forward (line 66) | def forward(self, featL, featR=None):
class Feature (line 96) | class Feature(SubModule):
method __init__ (line 97) | def __init__(self, cfg):
method forward (line 225) | def forward(self, x):
method _psm_make_layer (line 307) | def _psm_make_layer(self, block, planes, blocks, stride, pad, dilation):
class unetUp (line 324) | class unetUp(nn.Module):
method __init__ (line 325) | def __init__(self, in_c1, in_c2, out_c):
method forward (line 333) | def forward(self, inputs1, inputs2): # small scale, large scale
class FeatUp2 (line 340) | class FeatUp2(SubModule):
method __init__ (line 341) | def __init__(self, cfg):
method forward (line 360) | def forward(self, featL, featR=None):
class Feature2 (line 406) | class Feature2(SubModule):
method __init__ (line 407) | def __init__(self, cfg):
method forward (line 471) | def forward(self, x):
FILE: models/stereo/submodules/regression.py
class Regression (line 11) | class Regression(SubModule):
method __init__ (line 12) | def __init__(self,
method forward (line 20) | def forward(self, cost, spg, training=False):
method topkpool (line 39) | def topkpool(self, cost, k):
class Regression2 (line 64) | class Regression2(SubModule):
method __init__ (line 65) | def __init__(self, disp_pos, disp_neg, slant):
method forward (line 74) | def forward(self, cost, disp_4b, spg=None, training=False):
method topkpool (line 100) | def topkpool(self, cost, k):
method init_grid (line 124) | def init_grid(self, ):
FILE: models/stereo/submodules/spixel_utils/spixel.py
class Args (line 14) | class Args:
method __init__ (line 15) | def __init__(self):
function init_spixel_grid (line 23) | def init_spixel_grid(train_img_height=256, train_img_width=512,
function shift9pos (line 65) | def shift9pos(input, h_shift_unit=1, w_shift_unit=1):
function poolfeat (line 89) | def poolfeat(input, prob, sp_h=2, sp_w=2):
function poolfeat_ (line 144) | def poolfeat_(input, prob, sp_h=2, sp_w=2):
function poolfeat_head (line 157) | def poolfeat_head(input, prob, sp_h=2, sp_w=2):
function poolfeat3d (line 171) | def poolfeat3d(input, prob, sp_h=2, sp_w=2):
function upfeat_original (line 184) | def upfeat_original(input, prob, up_h=2, up_w=2):
function upfeat (line 225) | def upfeat(input, prob, up_h=2, up_w=2):
function upfeatHW (line 237) | def upfeatHW(input, prob, tgt_h=2, tgt_w=2):
function upfeat_slant (line 249) | def upfeat_slant(input, prob, slant, up_h=2, up_w=2):
function upfeat_center (line 266) | def upfeat_center(input, up_h=2, up_w=2):
function upfeat3d (line 275) | def upfeat3d(input, prob, up_h=2, up_w=2):
function assign2uint8 (line 286) | def assign2uint8(assign):
function val2uint8 (line 309) | def val2uint8(mat,maxVal):
function update_spixl_map (line 315) | def update_spixl_map (spixl_map_idx_in, assig_map_in):
function get_spixel_image (line 335) | def get_spixel_image(given_img, spix_index, n_spixels = 600, b_enforce_c...
function spixlIdx (line 356) | def spixlIdx(args, b_train = False):
class AverageMeter (line 373) | class AverageMeter(object):
method __init__ (line 376) | def __init__(self):
method reset (line 379) | def reset(self):
method update (line 385) | def update(self, val, n=1):
method __repr__ (line 391) | def __repr__(self):
function batch2img (line 394) | def batch2img(img):
function build_LABXY_feat (line 405) | def build_LABXY_feat(label_in, XY_feat):
function rgb2Lab_torch (line 416) | def rgb2Lab_torch(img_in, mean_values = None):
function label2one_hot_torch (line 455) | def label2one_hot_torch(labels, C=14):
FILE: models/stereo/submodules/spixel_utils/spixel_conv.py
class spixel_conv2_5d (line 16) | class spixel_conv2_5d(nn.Module):
method __init__ (line 17) | def __init__(self, in_chan, out_chan, spixel_downsize=4):
method forward (line 39) | def forward(self, cv, sp, xy):
class spixel_upsample2_5d (line 143) | class spixel_upsample2_5d(nn.Module):
method __init__ (line 144) | def __init__(self, chan, out_dim, bnrelu=True, spixel_downsize=4):
method forward (line 171) | def forward(self, cv, sp):
class spixel_upsample2d (line 211) | class spixel_upsample2d(nn.Module):
method __init__ (line 212) | def __init__(self, ):
method forward (line 216) | def forward(self, cv, sp):
FILE: models/stereo/submodules/spixel_utils/spixel_loss.py
function compute_semantic_pos_loss (line 16) | def compute_semantic_pos_loss(prob_in, labxy_feat, pos_weight = 0.003, ...
FILE: models/stereo/submodules/spixel_utils/spixel_test.py
function test (line 20) | def test(img, spx_pred, val_spixelID, save_path=None, im_num=None, downs...
FILE: models/stereo/submodules/util_conv.py
class BasicConv (line 12) | class BasicConv(nn.Module):
method __init__ (line 14) | def __init__(self, in_channels, out_channels, deconv=False, is_3d=Fals...
method forward (line 33) | def forward(self, x):
class Conv2x (line 42) | class Conv2x(nn.Module):
method __init__ (line 44) | def __init__(self, in_channels, out_channels, deconv=False, is_3d=Fals...
method forward (line 69) | def forward(self, x, rem):
function BasicConv2d (line 84) | def BasicConv2d(in_channels, out_channels, kernel_size, stride, pad, dil...
function BasicTransposeConv2d (line 93) | def BasicTransposeConv2d(in_channels, out_channels, kernel_size, stride,...
function BasicConv3d (line 102) | def BasicConv3d(in_channels, out_channels, kernel_size, stride, pad, dil...
function BasicTransposeConv3d (line 111) | def BasicTransposeConv3d(in_channels, out_channels, kernel_size, stride,...
function conv3x3 (line 121) | def conv3x3(in_planes: int, out_planes: int, stride: int = 1, groups: in...
function conv1x1 (line 127) | def conv1x1(in_planes: int, out_planes: int, stride: int = 1) -> nn.Conv2d:
class BasicBlock (line 132) | class BasicBlock(nn.Module):
method __init__ (line 135) | def __init__(
method forward (line 162) | def forward(self, x: Tensor) -> Tensor:
class ConvBNReLU3d (line 180) | class ConvBNReLU3d(nn.Sequential):
method __init__ (line 181) | def __init__(
class InvertedResidual3d (line 199) | class InvertedResidual3d(nn.Module):
method __init__ (line 200) | def __init__(
method forward (line 231) | def forward(self, x: Tensor) -> Tensor:
class AtrousBlock (line 237) | class AtrousBlock(nn.Module):
method __init__ (line 239) | def __init__(self, in_channels, out_channels, stride=1, bn=True, relu=...
method forward (line 248) | def forward(self, x):
FILE: models/stereo/submodules/utils.py
class CostVolume (line 12) | class CostVolume(nn.Module):
method __init__ (line 13) | def __init__(self, maxdisp, glue=False, group=1):
method forward (line 21) | def forward(self, x, y, v=None):
class AttentionCostVolume (line 40) | class AttentionCostVolume(nn.Module):
method __init__ (line 41) | def __init__(self, max_disparity, in_chan, hidden_chan, head=1, weight...
method forward (line 52) | def forward(self, imL, imR):
class disparityregression (line 70) | class disparityregression(nn.Module):
method __init__ (line 71) | def __init__(self):
method forward (line 74) | def forward(self, x, reg):
class channelAtt (line 80) | class channelAtt(SubModule):
method __init__ (line 81) | def __init__(self, cv_chan, im_chan, D):
method forward (line 90) | def forward(self, cv, im):
FILE: stereo.py
class Stereo (line 37) | class Stereo(LightningModule):
method __init__ (line 39) | def __init__(self, cfg, dataname=None):
method forward (line 48) | def forward(self, imgL, imgR=None, training=False):
method training_step (line 64) | def training_step(self, batch, batch_idx):
method validation_step (line 95) | def validation_step(self, batch, batch_idx):
method test_step (line 148) | def test_step(self, batch, batch_idx):
method configure_optimizers (line 233) | def configure_optimizers(self):
method save_disp_imgs (line 243) | def save_disp_imgs(self, disp_img, filename,
function load_configs (line 264) | def load_configs(path):
function copy_dir (line 272) | def copy_dir(save_dir, name, save_version):
class StereoTRT (line 465) | class StereoTRT(Stereo):
method forward (line 467) | def forward(self, imgL):
FILE: torch_to_tensorrt.py
function load_configs (line 32) | def load_configs(path):
function postprocess (line 40) | def postprocess(outputs):
FILE: utils/load.py
function make_list (line 4) | def make_list(var, n=None):
function load_class (line 13) | def load_class(filename, paths, concat=True):
Condensed preview — 60 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (2,391K chars).
[
{
"path": ".gitignore",
"chars": 16,
"preview": "./demo*\n./logs/*"
},
{
"path": "LICENSE",
"chars": 35149,
"preview": " GNU GENERAL PUBLIC LICENSE\n Version 3, 29 June 2007\n\n Copyright (C) 2007 Free "
},
{
"path": "README.md",
"chars": 5858,
"preview": "# CoEx\n\nPyTorch implementation of our paper: \n\n\n**Correlate-and-Excite: Real-Time Stereo Matching via Guided Cost Volume"
},
{
"path": "configs/backbone.yaml",
"chars": 546,
"preview": "channels:\n mobilenetv3_large_100: [16,24,40,112,160]\n mobilenetv2_120d: [24,32,40,112,192]\n mobilenetv2_100: [16,24,3"
},
{
"path": "configs/stereo/cfg_coex.yaml",
"chars": 1470,
"preview": "###########################################################\ndevice: [0]\nprecision: 32\n\n#################################"
},
{
"path": "configs/stereo/cfg_psm.yaml",
"chars": 1150,
"preview": "###########################################################\ndevice: [0]\nprecision: 32\n\n#################################"
},
{
"path": "dataloaders/__init__.py",
"chars": 21,
"preview": "from .stereo import *"
},
{
"path": "dataloaders/stereo/KITTILoader.py",
"chars": 6322,
"preview": "import os\r\nimport torch\r\nimport torch.utils.data as data\r\nimport torch\r\nimport torchvision.transforms as transforms\r\nimp"
},
{
"path": "dataloaders/stereo/KITTIRawLoader.py",
"chars": 13749,
"preview": "import torch.utils.data as data\n\nfrom PIL import Image\nimport os\nimport os.path\nimport glob\nimport random\nimport numpy a"
},
{
"path": "dataloaders/stereo/KITTI_submission_loader.py",
"chars": 3138,
"preview": "import torch.utils.data as data\r\n\r\nfrom PIL import Image\r\nimport os\r\nimport os.path\r\nimport numpy as np\r\nimport cv2\r\nfro"
},
{
"path": "dataloaders/stereo/KITTIloader2012.py",
"chars": 1744,
"preview": "\r\nIMG_EXTENSIONS = [\r\n '.jpg', '.JPG', '.jpeg', '.JPEG',\r\n '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP',\r\n]\r\n\r\n\r"
},
{
"path": "dataloaders/stereo/KITTIloader2015.py",
"chars": 1762,
"preview": "\r\nIMG_EXTENSIONS = [\r\n '.jpg', '.JPG', '.jpeg', '.JPEG',\r\n '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP',\r\n]\r\n\r\n\r"
},
{
"path": "dataloaders/stereo/SceneFlowLoader.py",
"chars": 4180,
"preview": "import os\r\nimport torch\r\nimport torch.utils.data as data\r\nimport torch\r\nimport torchvision.transforms as transforms\r\nimp"
},
{
"path": "dataloaders/stereo/__init__.py",
"chars": 222,
"preview": "from .listflowfile import *\r\nfrom .SceneFlowLoader import *\r\nfrom .KITTIloader2012 import *\r\nfrom .KITTIloader2015 impor"
},
{
"path": "dataloaders/stereo/listflowfile.py",
"chars": 5794,
"preview": "import torch.utils.data as data\r\n\r\nfrom PIL import Image\r\nimport os\r\nimport os.path\r\nimport glob\r\nimport numpy as np\r\n\r\n"
},
{
"path": "dataloaders/stereo/lists/kitti2012_test.list",
"chars": 2730,
"preview": "000000_10.png\n000001_10.png\n000002_10.png\n000003_10.png\n000004_10.png\n000005_10.png\n000006_10.png\n000007_10.png\n000008_1"
},
{
"path": "dataloaders/stereo/lists/kitti2012_train.list",
"chars": 2716,
"preview": "000153_10.png\n000174_10.png\n000176_10.png\n000022_10.png\n000169_10.png\n000057_10.png\n000013_10.png\n000185_10.png\n000072_1"
},
{
"path": "dataloaders/stereo/lists/kitti2012_train170.list",
"chars": 2380,
"preview": "000193_10.png\n000080_10.png\n000097_10.png\n000143_10.png\n000042_10.png\n000081_10.png\n000109_10.png\n000171_10.png\n000175_1"
},
{
"path": "dataloaders/stereo/lists/kitti2012_val24.list",
"chars": 336,
"preview": "000153_10.png\n000174_10.png\n000176_10.png\n000022_10.png\n000169_10.png\n000057_10.png\n000013_10.png\n000185_10.png\n000072_1"
},
{
"path": "dataloaders/stereo/lists/kitti2015_test.list",
"chars": 2800,
"preview": "000000_10.png\n000001_10.png\n000002_10.png\n000003_10.png\n000004_10.png\n000005_10.png\n000006_10.png\n000007_10.png\n000008_1"
},
{
"path": "dataloaders/stereo/lists/kitti2015_train.list",
"chars": 2800,
"preview": "000179_10.png\n000128_10.png\n000122_10.png\n000178_10.png\n000173_10.png\n000100_10.png\n000114_10.png\n000037_10.png\n000071_1"
},
{
"path": "dataloaders/stereo/lists/kitti2015_train180.list",
"chars": 2520,
"preview": "000179_10.png\n000128_10.png\n000122_10.png\n000178_10.png\n000173_10.png\n000100_10.png\n000114_10.png\n000037_10.png\n000071_1"
},
{
"path": "dataloaders/stereo/lists/kitti2015_val20.list",
"chars": 280,
"preview": "000198_10.png\n000059_10.png\n000067_10.png\n000123_10.png\n000133_10.png\n000057_10.png\n000073_10.png\n000120_10.png\n000018_1"
},
{
"path": "dataloaders/stereo/lists/middeval3_test.list",
"chars": 256,
"preview": "Australia/im0.png\nAustraliaP/im0.png\nBicycle2/im0.png\nClassroom2/im0.png\nClassroom2E/im0.png\nComputer/im0.png\nCrusade/im"
},
{
"path": "dataloaders/stereo/lists/middeval3_train.list",
"chars": 248,
"preview": "Playtable/im0.png\nArtL/im0.png\nJadeplant/im0.png\nPlaytableP/im0.png\nPianoL/im0.png\nPiano/im0.png\nAdirondack/im0.png\nTedd"
},
{
"path": "dataloaders/stereo/lists/sceneflow_search_trainA.list",
"chars": 344877,
"preview": "TRAIN/B/0111/left/0009.png\nTRAIN/B/0061/left/0007.png\nTRAIN/C/0672/left/0011.png\nTRAIN/15mm_focallength/scene_backwards/"
},
{
"path": "dataloaders/stereo/lists/sceneflow_search_trainB.list",
"chars": 341760,
"preview": "TRAIN/A/0345/left/0014.png\nTRAIN/A/0582/left/0013.png\nTRAIN/lonetree_augmented0_x2/left/0107.png\nTRAIN/A/0186/left/0006."
},
{
"path": "dataloaders/stereo/lists/sceneflow_search_val.list",
"chars": 26000,
"preview": "TEST/B/0060/left/0009.png\nTEST/C/0048/left/0013.png\nTEST/C/0015/left/0007.png\nTEST/C/0128/left/0006.png\nTEST/B/0034/left"
},
{
"path": "dataloaders/stereo/lists/sceneflow_test.list",
"chars": 113620,
"preview": "TEST/C/0041/left/0015.png\nTEST/C/0041/left/0008.png\nTEST/C/0041/left/0006.png\nTEST/C/0041/left/0010.png\nTEST/C/0041/left"
},
{
"path": "dataloaders/stereo/lists/sceneflow_train.list",
"chars": 1219438,
"preview": "TRAIN/B/0111/left/0009.png\nTRAIN/B/0061/left/0007.png\nTRAIN/C/0672/left/0011.png\nTRAIN/15mm_focallength/scene_backwards/"
},
{
"path": "dataloaders/stereo/preprocess.py",
"chars": 5249,
"preview": "import torch\r\nimport torchvision.transforms as transforms\r\nimport random\r\n\r\nimport pdb\r\n\r\n__imagenet_stats = {'mean': [0"
},
{
"path": "dataloaders/stereo/readpfm.py",
"chars": 1024,
"preview": "import re\r\nimport numpy as np\r\nimport sys\r\nimport cv2\r\n\r\n\r\ndef readPFM(file):\r\n file = open(file, 'rb')\r\n # '''\r\n "
},
{
"path": "dataloaders/stereo/stereo_albumentation.py",
"chars": 15097,
"preview": "# Authors: Zhaoshuo Li, Xingtong Liu, Francis X. Creighton, Russell H. Taylor, and Mathias Unberath\r\n#\r\n# Copyright (c"
},
{
"path": "dataloaders/stereo/transforms.py",
"chars": 7945,
"preview": "from __future__ import division\r\nimport torch\r\nimport numpy as np\r\nfrom PIL import Image\r\nimport torchvision.transforms."
},
{
"path": "demo.py",
"chars": 3198,
"preview": "import cv2\nimport numpy as np\n\nimport torch\nfrom torch.utils.data import DataLoader\n\nfrom ruamel.yaml import YAML\n\nfrom "
},
{
"path": "demo_tensorrt.py",
"chars": 651,
"preview": "import cv2\nimport numpy as np\n\nimport torch\nfrom torch.utils.data import DataLoader\n\nfrom ruamel.yaml import YAML\n\nfrom "
},
{
"path": "demo_torchscript.py",
"chars": 2736,
"preview": "import cv2\nimport numpy as np\n\nimport torch\nfrom torch.utils.data import DataLoader\n\nfrom ruamel.yaml import YAML\n\nfrom "
},
{
"path": "environment.yml",
"chars": 454,
"preview": "name: coex\nchannels:\n - pytorch\n - nvidia\n - conda-forge\n - anaconda\n - defaults\ndependencies:\n - python=3.7\n - p"
},
{
"path": "models/__init__.py",
"chars": 21,
"preview": "from .stereo import *"
},
{
"path": "models/stereo/CoEx.py",
"chars": 5773,
"preview": "from __future__ import print_function\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nfrom torch."
},
{
"path": "models/stereo/CoExTRT.py",
"chars": 17298,
"preview": "from __future__ import print_function\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nfrom torch."
},
{
"path": "models/stereo/PSMNet.py",
"chars": 16066,
"preview": "from __future__ import print_function\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.utils.data\r\nfrom torch.autograd"
},
{
"path": "models/stereo/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "models/stereo/submodules/Submodule.py",
"chars": 1058,
"preview": "#!/usr/bin/env python\nfrom __future__ import print_function\nimport os\nimport numpy as np\nimport torch\nimport torch.nn as"
},
{
"path": "models/stereo/submodules/__init__.py",
"chars": 1,
"preview": "\n"
},
{
"path": "models/stereo/submodules/aggregation.py",
"chars": 4789,
"preview": "from typing import List\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom .util_conv import Basi"
},
{
"path": "models/stereo/submodules/feature.py",
"chars": 18668,
"preview": "#!/usr/bin/env python\nfrom __future__ import print_function\nimport os\nimport numpy as np\nfrom typing import Callable, An"
},
{
"path": "models/stereo/submodules/regression.py",
"chars": 3875,
"preview": "#!/usr/bin/env python\nimport torch\nimport torch.nn.functional as F\n\nfrom .spixel_utils import spixel\nfrom .Submodule imp"
},
{
"path": "models/stereo/submodules/spixel_utils/spixel.py",
"chars": 18455,
"preview": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nfrom skimage.segmentation import m"
},
{
"path": "models/stereo/submodules/spixel_utils/spixel_conv.py",
"chars": 9476,
"preview": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nfrom skimage.segmentation import m"
},
{
"path": "models/stereo/submodules/spixel_utils/spixel_loss.py",
"chars": 1375,
"preview": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nfrom .spixel import *\n\nimport pdb\n"
},
{
"path": "models/stereo/submodules/spixel_utils/spixel_test.py",
"chars": 1139,
"preview": "import os\nimport torch\nimport torch.nn.functional as F\nimport torch.backends.cudnn as cudnn\nimport time\nimport random\nfr"
},
{
"path": "models/stereo/submodules/util_conv.py",
"chars": 9140,
"preview": "#!/usr/bin/env python\nfrom __future__ import print_function\nfrom typing import Callable, Optional, List\nimport torch\nfro"
},
{
"path": "models/stereo/submodules/utils.py",
"chars": 2868,
"preview": "from __future__ import print_function\n\nimport torch\nimport torch.nn as nn\n\nfrom .util_conv import BasicConv\nfrom .Submod"
},
{
"path": "stereo.py",
"chars": 19321,
"preview": "import os\nimport shutil\nimport glob\nimport numpy as np\nimport cv2\nimport skimage\nimport skimage.io\n\nimport torch\nfrom to"
},
{
"path": "torch_to_tensorrt.py",
"chars": 4834,
"preview": "import cv2\nimport numpy as np\nimport os\n\nimport torch\nimport torch.nn.functional as F\nfrom torch.utils.data import DataL"
},
{
"path": "utils/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "utils/load.py",
"chars": 571,
"preview": "import importlib\n\n\ndef make_list(var, n=None):\n var = var if isinstance(var, list) else [var]\n if n is None:\n "
}
]
// ... and 2 more files (download for full content)
About this extraction
This page contains the full source code of the antabangun/coex GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 60 files (44.8 MB), approximately 581.6k tokens, and a symbol index with 326 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.