Repository: B1ueber2y/TrianFlow
Branch: master
Commit: af3f2bfc745e
Files: 54
Total size: 655.9 KB
Directory structure:
gitextract_5bwpkhvi/
├── LICENSE
├── README.md
├── config/
│ ├── kitti.yaml
│ ├── kitti_3stage.yaml
│ ├── nyu.yaml
│ ├── nyu_192.yaml
│ ├── nyu_192_3stage.yaml
│ ├── nyu_3stage.yaml
│ ├── nyu_posenet_192.yaml
│ ├── odo.yaml
│ ├── odo_3stage.yaml
│ └── odo_posenet.yaml
├── core/
│ ├── config/
│ │ ├── __init__.py
│ │ └── config_utils.py
│ ├── dataset/
│ │ ├── __init__.py
│ │ ├── kitti_2012.py
│ │ ├── kitti_2015.py
│ │ ├── kitti_odo.py
│ │ ├── kitti_prepared.py
│ │ ├── kitti_raw.py
│ │ └── nyu_v2.py
│ ├── evaluation/
│ │ ├── __init__.py
│ │ ├── eval_odom.py
│ │ ├── evaluate_depth.py
│ │ ├── evaluate_flow.py
│ │ ├── evaluate_mask.py
│ │ ├── evaluation_utils.py
│ │ └── flowlib.py
│ ├── networks/
│ │ ├── __init__.py
│ │ ├── model_depth_pose.py
│ │ ├── model_flow.py
│ │ ├── model_flowposenet.py
│ │ ├── model_triangulate_pose.py
│ │ ├── pytorch_ssim/
│ │ │ ├── __init__.py
│ │ │ └── ssim.py
│ │ └── structures/
│ │ ├── __init__.py
│ │ ├── depth_model.py
│ │ ├── feature_pyramid.py
│ │ ├── flowposenet.py
│ │ ├── inverse_warp.py
│ │ ├── net_utils.py
│ │ ├── pwc_tf.py
│ │ └── ransac.py
│ └── visualize/
│ ├── __init__.py
│ ├── profiler.py
│ └── visualizer.py
├── data/
│ └── eigen/
│ ├── export_gt_depth.py
│ ├── static_frames.txt
│ ├── test_files.txt
│ └── test_scenes.txt
├── infer_vo.py
├── requirements.txt
├── test.py
└── train.py
================================================
FILE CONTENTS
================================================
================================================
FILE: LICENSE
================================================
MIT License
Copyright (c) 2021 Shaohui Liu
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
================================================
FILE: README.md
================================================
## Towards Better Generalization: Joint Depth-Pose Learning without PoseNet
Created by Wang Zhao, Shaohui Liu, Yezhi Shu, Yong-Jin Liu.
## Introduction
This implementation is based on our CVPR'2020 paper "Towards Better Generalization: Joint Depth-Pose Learning without PoseNet". You can find the arXiv version of the paper here. In this repository we release code and pre-trained models for *TrianFlow* (our method) and a strong baseline *PoseNet-Flow* method.
## Installation
The code is based on Python3.6. You could use either virtualenv or conda to setup a specified environment. And then run:
```bash
pip install -r requirements.txt
```
## Run a demo
To run a depth prediction demo, you may need to first download the pretrained model from here.
```bash
python test.py --config_file ./config/default_1scale.yaml --gpu 0 --mode depth --task demo --image_path ./data/demo/kitti.png --pretrained_model ./models/pretrained/depth_pretrained.pth --result_dir ./data/demo
```
This will give you a predicted depth map for demo image.
## Run experiments
### Prepare training data:
1. For KITTI depth and flow tasks, download KITTI raw dataset using the script provided on the official website. You also need to download KITTI 2015 dataset to evaluate the predicted optical flow. Run the following commands to generate groundtruth files for eigen test images.
```
cd ./data/eigen
python export_gt_depth.py --data_path /path/to/your/kitti/root
```
2. For KITTI Odometry task, download KITTI Odometry dataset.
3. For NYUv2 experiments, download NYUv2 raw sequences and labeled data mat, also the traing test split mat from here. Put the labeled data and splits file under the same directory. The data structure should be:
```
nyuv2
| basements
| cafe
| ...
nyuv2_test
| nyu_depth_v2_labeled.mat
| splits.mat
```
### Training:
1. Modify the configuration file in the ./config directory to set up your path. The config file contains the important paths and default hyper-parameters used in the training process.
2. For KITTI depth, we have the three-stage training schedule.
```bash
1. python train.py --config_file ./config/kitti.yaml --gpu [gpu_id] --mode flow --prepared_save_dir [name_of_your_prepared_dataset] --model_dir [your/directory/to/save/training/models]
2. python train.py --config_file ./config/kitti.yaml --gpu [gpu_id] --mode depth --prepared_save_dir [name_of_your_prepared_dataset] --model_dir [your/directory/to/save/training/models] --flow_pretrained_model [path/to/your/stage1_flow_model]
3. python train.py --config_file ./config/kitti_3stage.yaml --gpu [gpu_id] --mode depth_pose --prepared_save_dir [name_of_your_prepared_dataset] --model_dir [your/directory/to/save/training/models] --depth_pretrained_model [path/to/your/stage2_depth_model]
```
If you are running experiments on the dataset for the first time, it would first process data and save in the [prepared_base_dir] path defined in your config file.
For other datasets like KITTI Odometry and NYUv2 dataset, you could run with the same commands using the appropriate config file.
We also implement and release codes for the strong baseline *PoseNet-Flow* method, you could run it by two-stage training:
```bash
1. python train.py --config_file [path/to/your/config/file] --gpu [gpu_id] --mode flow --prepared_save_dir [name_of_your_prepared_dataset] --model_dir [your/directory/to/save/training/models]
2. python train.py --config_file [path/to/your/config/file] --gpu [gpu_id] --mode flowposenet --prepared_save_dir [name_of_your_prepared_dataset] --model_dir [your/directory/to/save/training/models] --flow_pretrained_model [path/to/your/stage1_flow_model]
```
### Evaluation:
We provide pretrained models here for different tasks. The performance could be slightly different with the paper due to randomness.
1. To evaluate the monocular depth estimation on kitti eigen test split, run:
```bash
python test.py --config_file ./config/kitti.yaml --gpu [gpu_id] --mode depth --task kitti_depth --pretrained_model [path/to/your/model] --result_dir [path/to/save/results]
```
2. To evaluate the monocular depth estimation on nyuv2 test split, run:
```bash
python test.py --config_file ./config/nyu.yaml --gpu [gpu_id] --mode depth --task nyuv2 --pretrained_model [path/to/your/model] --result_dir [path/to/save/results]
```
3. To evaluate the optical flow estimation on KITTI 2015, run:
```bash
python test.py --config_file ./config/kitti.yaml --gpu [gpu_id] --mode flow_3stage --task kitti_flow --pretrained_model [path/to/your/model] --result_dir [path/to/save/results]
```
4. To evaluate the visual odometry task on KITTI Odometry dataset, first get predictions on a single sequence and then evaluate:
```bash
python infer_vo.py --config_file ./config/odo.yaml --gpu [gpu_id] --traj_save_dir_txt [where/to/save/the/prediction/file] --sequences_root_dir [the/root/dir/of/your/image/sequences] --sequence [the sequence id] ----pretrained_model [path/to/your/model]
python ./core/evaluation/eval_odom.py --gt_txt [path/to/your/groundtruth/poses/txt] --result_txt [path/to/your/prediction/txt] --seq [sequence id to evaluate]
```
You could evaluate on the sampled KITTI odometry dataset by simply sampling the raw image sequences and gt-pose txt. Then run *infer_vo.py* on the sampled image sequence and *eval_odom.py* with predicted txt and sampled gt txt to get results.
### Citation
If you find our work useful in your research, please consider citing:
```
@inproceedings{zhao2020towards,
title={Towards Better Generalization: Joint Depth-Pose Learning without PoseNet},
author={Zhao, Wang and Liu, Shaohui and Shu, Yezhi and Liu, Yong-Jin},
booktitle={Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2020}
}
```
### Related Projects
Digging into Self-Supervised Monocular Depth Prediction.
Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation.
Visual Odometry Revisited: What Should Be Learnt?
================================================
FILE: config/kitti.yaml
================================================
cfg_name: 'default'
# dataset
raw_base_dir: '/home4/zhaow/data/kitti'
prepared_base_dir: '/home5/zhaow/data/kitti_release'
gt_2012_dir: '/home4/zhaow/data/kitti_stereo/kitti_2012/training'
gt_2015_dir: '/home4/zhaow/data/kitti_stereo/kitti_2015/training'
static_frames_txt: '/home5/zhaow/release/data/eigen/static_frames.txt'
test_scenes_txt: '/home5/zhaow/release/data/eigen/test_scenes.txt'
dataset: 'kitti_depth'
num_scales: 3
# training
num_iterations: 200000
# loss hyperparameters
w_ssim: 0.85 # w_pixel = 1 - w_ssim
w_flow_smooth: 10.0
w_flow_consis: 0.01
w_geo: 1.0
w_pt_depth: 1.0
w_pj_depth: 0.1
w_flow_error: 0.0
w_depth_smooth: 0.001
h_flow_consist_alpha: 3.0
h_flow_consist_beta: 0.05
ransac_iters: 100
ransac_points: 6000
# Depth Setting
depth_match_num: 6000
depth_sample_ratio: 0.20
depth_scale: 1
# basic info
img_hw: [256, 832]
use_svd_gpu: False
================================================
FILE: config/kitti_3stage.yaml
================================================
cfg_name: 'default'
# dataset
raw_base_dir: '/home/zhaow/data/kitti'
prepared_base_dir: '/home/zhaow/data/kitti_seq'
gt_2012_dir: '/home/zhaow/data/kitti_stereo/kitti_2012/training'
gt_2015_dir: '/home/zhaow/data/kitti_stereo/kitti_2015/training'
static_frames_txt: '/home/zhaow/data/kitti_seq/static_frames.txt'
test_scenes_txt: '/home5/zhaow/release/data/eigen/test_scenes.txt'
dataset: 'kitti_depth'
num_scales: 3
# training
num_iterations: 200000 # set -1 to use num_epochs
num_epochs: 0
# loss hyperparameters
w_ssim: 0.85 # w_pixel = 1 - w_ssim
w_flow_smooth: 10.0
w_flow_consis: 0.01
w_geo: 0.002 #0.002
w_pt_depth: 0.0
w_pj_depth: 0.000
w_flow_error: 0.01
w_depth_smooth: 0.000
h_flow_consist_alpha: 3.0
h_flow_consist_beta: 0.05
# dfe setting
dfe_depth: 3
dfe_depth_scales: 4
dfe_points: 500
ransac_iters: 100
ransac_points: 6000
# Depth Setting
depth_match_num: 6000
depth_sample_ratio: 0.20
depth_scale: 1
# basic info
img_hw: [256, 832]
use_svd_gpu: False
================================================
FILE: config/nyu.yaml
================================================
cfg_name: 'default'
# dataset
raw_base_dir: '/home5/zhaow/data/nyuv2'
prepared_base_dir: '/home5/zhaow/data/nyu_seq_release'
nyu_test_dir: '/home5/zhaow/data/nyuv2_test'
dataset: 'nyuv2'
num_scales: 3
# training
num_iterations: 400000
# loss hyperparameters
w_ssim: 0.85 # w_pixel = 1 - w_ssim
w_flow_smooth: 10.0
w_flow_consis: 0.01
w_geo: 0.0
w_pt_depth: 1.0
w_pj_depth: 0.1
w_flow_error: 0.00
w_depth_smooth: 0.0001
h_flow_consist_alpha: 3.0
h_flow_consist_beta: 0.05
ransac_iters: 100
ransac_points: 6000
# Depth Setting
depth_match_num: 6000
depth_sample_ratio: 0.20
depth_scale: 1
# basic info
img_hw: [448, 576]
#img_hw: [192, 256]
block_tri_grad: False
================================================
FILE: config/nyu_192.yaml
================================================
cfg_name: 'default'
# dataset
raw_base_dir: '/home5/zhaow/data/nyuv2'
prepared_base_dir: '/home5/zhaow/data/nyu_seq_release'
nyu_test_dir: '/home5/zhaow/data/nyuv2_test'
dataset: 'nyuv2'
num_scales: 3
# training
num_iterations: 400000
# loss hyperparameters
w_ssim: 0.85 # w_pixel = 1 - w_ssim
w_flow_smooth: 10.0
w_flow_consis: 0.01
w_geo: 0.0
w_pt_depth: 1.0
w_pj_depth: 0.1
w_flow_error: 0.00
w_depth_smooth: 0.0001
h_flow_consist_alpha: 3.0
h_flow_consist_beta: 0.05
ransac_iters: 100
ransac_points: 6000
# Depth Setting
depth_match_num: 6000
depth_sample_ratio: 0.20
depth_scale: 1
# basic info
#img_hw: [448, 576]
img_hw: [192, 256]
block_tri_grad: False
================================================
FILE: config/nyu_192_3stage.yaml
================================================
cfg_name: 'default'
# dataset
raw_base_dir: '/home5/zhaow/data/nyuv2'
prepared_base_dir: '/home5/zhaow/data/nyu_seq_release'
nyu_test_dir: '/home5/zhaow/data/nyuv2_test'
dataset: 'nyuv2'
num_scales: 3
# training
num_iterations: 400000 # set -1 to use num_epochs
# loss hyperparameters
w_ssim: 0.85 # w_pixel = 1 - w_ssim
w_flow_smooth: 10.0
w_flow_consis: 0.01
w_geo: 0.1
w_pt_depth: 0.1
w_pj_depth: 0.01
w_flow_error: 0.00
w_depth_smooth: 0.0001
h_flow_consist_alpha: 3.0
h_flow_consist_beta: 0.05
ransac_iters: 100
ransac_points: 6000
# Depth Setting
depth_match_num: 6000
depth_sample_ratio: 0.20
depth_scale: 1
# basic info
#img_hw: [448, 576]
img_hw: [192, 256]
block_tri_grad: False
================================================
FILE: config/nyu_3stage.yaml
================================================
cfg_name: 'default'
# dataset
raw_base_dir: '/home5/zhaow/data/nyuv2'
prepared_base_dir: '/home5/zhaow/data/nyu_seq_release'
nyu_test_dir: '/home5/zhaow/data/nyuv2_test'
dataset: 'nyuv2'
num_scales: 3
# training
num_iterations: 400000 # set -1 to use num_epochs
# loss hyperparameters
w_ssim: 0.85 # w_pixel = 1 - w_ssim
w_flow_smooth: 10.0
w_flow_consis: 0.01
w_geo: 0.1
w_pt_depth: 0.1
w_pj_depth: 0.01
w_flow_error: 0.00
w_depth_smooth: 0.0001
h_flow_consist_alpha: 3.0
h_flow_consist_beta: 0.05
ransac_iters: 100
ransac_points: 6000
# Depth Setting
depth_match_num: 6000
depth_sample_ratio: 0.20
depth_scale: 1
# basic info
img_hw: [448, 576]
#img_hw: [192, 256]
block_tri_grad: False
================================================
FILE: config/nyu_posenet_192.yaml
================================================
cfg_name: 'default'
# dataset
raw_base_dir: '/home5/zhaow/data/nyuv2_sub2'
prepared_base_dir: '/home5/zhaow/data/nyu_seq_release'
nyu_test_dir: '/home5/zhaow/data/nyuv2_test'
dataset: 'nyuv2'
num_scales: 3
# training
num_iterations: 500000 # set -1 to use num_epochs
num_epochs: 0
# loss hyperparameters
w_ssim: 0.85 # w_pixel = 1 - w_ssim
w_flow_smooth: 10.0
w_flow_consis: 0.01
w_geo: 0.0
w_pt_depth: 0.0
w_pj_depth: 0.5
w_flow_error: 10.0
w_depth_smooth: 0.00001
h_flow_consist_alpha: 3.0
h_flow_consist_beta: 0.05
ransac_iters: 100
ransac_points: 6000
# Depth Setting
depth_match_num: 6000
depth_scale: 1
# basic info
img_hw: [192,256]
block_tri_grad: False
================================================
FILE: config/odo.yaml
================================================
cfg_name: 'default'
# dataset
raw_base_dir: '/home4/zhaow/data/kitti_odometry/sequences'
prepared_base_dir: '/home5/zhaow/data/kitti_odo_release/'
gt_2012_dir: '/home4/zhaow/data/kitti_stereo/kitti_2012/training'
gt_2015_dir: '/home4/zhaow/data/kitti_stereo/kitti_2015/training'
dataset: 'kitti_odo'
num_scales: 3
# training
num_iterations: 200000
w_ssim: 0.85 # w_pixel = 1 - w_ssim
w_flow_smooth: 10.0
w_flow_consis: 0.01
w_geo: 0.1
w_pt_depth: 1.0
w_pj_depth: 0.1
w_flow_error: 0.0
w_depth_smooth: 0.0001
h_flow_consist_alpha: 3.0
h_flow_consist_beta: 0.05
ransac_iters: 100
ransac_points: 6000
# Depth Setting
depth_match_num: 6000
depth_sample_ratio: 0.20
depth_scale: 1
# basic info
img_hw: [256, 832]
block_tri_grad: False
================================================
FILE: config/odo_3stage.yaml
================================================
cfg_name: 'default'
# dataset
raw_base_dir: '/home4/zhaow/data/kitti_odometry/sequences'
prepared_base_dir: '/home5/zhaow/data/kitti_odo_release/'
gt_2012_dir: '/home4/zhaow/data/kitti_stereo/kitti_2012/training'
gt_2015_dir: '/home4/zhaow/data/kitti_stereo/kitti_2015/training'
dataset: 'kitti_odo'
num_scales: 3
# training
num_iterations: 200000
w_ssim: 0.85 # w_pixel = 1 - w_ssim
w_flow_smooth: 10.0
w_flow_consis: 0.01
w_geo: 0.002 #0.002
w_pt_depth: 0.0
w_pj_depth: 0.000
w_flow_error: 0.01
w_depth_smooth: 0.000
h_flow_consist_alpha: 3.0
h_flow_consist_beta: 0.05
ransac_iters: 100
ransac_points: 6000
# Depth Setting
depth_match_num: 6000
depth_sample_ratio: 0.20
depth_scale: 1
# basic info
img_hw: [256, 832]
block_tri_grad: False
================================================
FILE: config/odo_posenet.yaml
================================================
cfg_name: 'default'
# dataset
raw_base_dir: '/home4/zhaow/data/kitti_odometry/sequences'
prepared_base_dir: '/home5/zhaow/data/kitti_odo/'
gt_2012_dir: '/home4/zhaow/data/kitti_stereo/kitti_2012/training'
gt_2015_dir: '/home4/zhaow/data/kitti_stereo/kitti_2015/training'
dataset: 'kitti_odo'
num_scales: 3
# training
num_iterations: 200000 # set -1 to use num_epochs
num_epochs: 0
w_ssim: 0.85 # w_pixel = 1 - w_ssim
w_flow_smooth: 10.0
w_flow_consis: 0.01
w_geo: 0.0
w_pt_depth: 0.0
w_pj_depth: 0.1
w_flow_error: 0.5
w_depth_smooth: 0.0001
h_flow_consist_alpha: 3.0
h_flow_consist_beta: 0.05
ransac_iters: 100
ransac_points: 6000
# Depth Setting
depth_match_num: 6000
depth_scale: 1
# basic info
img_hw: [256, 832]
block_tri_grad: False
================================================
FILE: core/config/__init__.py
================================================
import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from config_utils import generate_loss_weights_dict
================================================
FILE: core/config/config_utils.py
================================================
import os, sys
def generate_loss_weights_dict(cfg):
weight_dict = {}
weight_dict['loss_pixel'] = 1 - cfg.w_ssim
weight_dict['loss_ssim'] = cfg.w_ssim
weight_dict['loss_flow_smooth'] = cfg.w_flow_smooth
weight_dict['loss_flow_consis'] = cfg.w_flow_consis
weight_dict['geo_loss'] = cfg.w_geo
weight_dict['pt_depth_loss'] = cfg.w_pt_depth
weight_dict['pj_depth_loss'] = cfg.w_pj_depth
weight_dict['depth_smooth_loss'] = cfg.w_depth_smooth
weight_dict['flow_error'] = cfg.w_flow_error
return weight_dict
================================================
FILE: core/dataset/__init__.py
================================================
import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from kitti_raw import KITTI_RAW
from kitti_prepared import KITTI_Prepared
from kitti_2012 import KITTI_2012
from kitti_2015 import KITTI_2015
from nyu_v2 import NYU_Prepare, NYU_v2
from kitti_odo import KITTI_Odo
================================================
FILE: core/dataset/kitti_2012.py
================================================
import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from kitti_prepared import KITTI_Prepared
sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'evaluation'))
from evaluate_flow import get_scaled_intrinsic_matrix, eval_flow_avg
import numpy as np
import cv2
import copy
import torch
import pdb
class KITTI_2012(KITTI_Prepared):
def __init__(self, data_dir, img_hw=(256, 832), init=True):
self.data_dir = data_dir
self.img_hw = img_hw
self.num_total = 194
if init:
self.data_list = self.get_data_list()
def get_data_list(self):
data_list = []
for i in range(self.num_total):
data = {}
data['img1_dir'] = os.path.join(self.data_dir, 'image_2', str(i).zfill(6) + '_10.png')
data['img2_dir'] = os.path.join(self.data_dir, 'image_2', str(i).zfill(6) + '_11.png')
data['calib_file_dir'] = os.path.join(self.data_dir, 'calib_cam_to_cam', str(i).zfill(6) + '.txt')
data_list.append(data)
return data_list
def __len__(self):
return len(self.data_list)
def read_cam_intrinsic(self, calib_file):
input_intrinsic = get_scaled_intrinsic_matrix(calib_file, zoom_x=1.0, zoom_y=1.0)
return input_intrinsic
def __getitem__(self, idx):
'''
Returns:
- img torch.Tensor (N * H, W, 3)
- K torch.Tensor (num_scales, 3, 3)
- K_inv torch.Tensor (num_scales, 3, 3)
'''
data = self.data_list[idx]
# load img
img1 = cv2.imread(data['img1_dir'])
img2 = cv2.imread(data['img2_dir'])
img_hw_orig = (img1.shape[0], img1.shape[1])
img = np.concatenate([img1, img2], 0)
img = self.preprocess_img(img, self.img_hw, is_test=True)
img = img.transpose(2,0,1)
# load intrinsic
cam_intrinsic = self.read_cam_intrinsic(data['calib_file_dir'])
cam_intrinsic = self.rescale_intrinsics(cam_intrinsic, img_hw_orig, self.img_hw)
K, K_inv = self.get_intrinsics_per_scale(cam_intrinsic, scale=0) # (3, 3), (3, 3)
return torch.from_numpy(img).float(), torch.from_numpy(K).float(), torch.from_numpy(K_inv).float()
if __name__ == '__main__':
pass
================================================
FILE: core/dataset/kitti_2015.py
================================================
import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from kitti_2012 import KITTI_2012
class KITTI_2015(KITTI_2012):
def __init__(self, data_dir, img_hw=(256, 832)):
super(KITTI_2015, self).__init__(data_dir, img_hw, init=False)
self.num_total = 200
self.data_list = self.get_data_list()
if __name__ == '__main__':
pass
================================================
FILE: core/dataset/kitti_odo.py
================================================
import os, sys
import numpy as np
import imageio
from tqdm import tqdm
import torch.multiprocessing as mp
def process_folder(q, data_dir, output_dir, stride=1):
while True:
if q.empty():
break
folder = q.get()
image_path = os.path.join(data_dir, folder, 'image_2/')
dump_image_path = os.path.join(output_dir, folder)
if not os.path.isdir(dump_image_path):
os.makedirs(dump_image_path)
f = open(os.path.join(dump_image_path, 'train.txt'), 'w')
# Note. the os.listdir method returns arbitary order of list. We need correct order.
numbers = len(os.listdir(image_path))
for n in range(numbers - stride):
s_idx = n
e_idx = s_idx + stride
curr_image = imageio.imread(os.path.join(image_path, '%.6d'%s_idx)+'.png')
next_image = imageio.imread(os.path.join(image_path, '%.6d'%e_idx)+'.png')
seq_images = np.concatenate([curr_image, next_image], axis=0)
imageio.imsave(os.path.join(dump_image_path, '%.6d'%s_idx)+'.png', seq_images.astype('uint8'))
# Write training files
f.write('%s %s\n' % (os.path.join(folder, '%.6d'%s_idx)+'.png', os.path.join(folder, 'calib.txt')))
print(folder)
class KITTI_Odo(object):
def __init__(self, data_dir):
self.data_dir = data_dir
self.train_seqs = ['00','01','02','03','04','05','06','07','08']
def __len__(self):
raise NotImplementedError
def prepare_data_mp(self, output_dir, stride=1):
num_processes = 16
processes = []
q = mp.Queue()
if not os.path.isfile(os.path.join(output_dir, 'train.txt')):
os.makedirs(output_dir)
#f = open(os.path.join(output_dir, 'train.txt'), 'w')
print('Preparing sequence data....')
if not os.path.isdir(self.data_dir):
raise
dirlist = os.listdir(self.data_dir)
total_dirlist = []
# Get the different folders of images
for d in dirlist:
if d in self.train_seqs:
q.put(d)
# Process every folder
for rank in range(num_processes):
p = mp.Process(target=process_folder, args=(q, self.data_dir, output_dir, stride))
p.start()
processes.append(p)
for p in processes:
p.join()
f = open(os.path.join(output_dir, 'train.txt'), 'w')
for d in self.train_seqs:
train_file = open(os.path.join(output_dir, d, 'train.txt'), 'r')
for l in train_file.readlines():
f.write(l)
command = 'cp ' + os.path.join(self.data_dir, d, 'calib.txt') + ' ' + os.path.join(output_dir, d, 'calib.txt')
os.system(command)
print('Data Preparation Finished.')
def __getitem__(self, idx):
raise NotImplementedError
if __name__ == '__main__':
data_dir = '/home4/zhaow/data/kitti'
dirlist = os.listdir('/home4/zhaow/data/kitti')
output_dir = '/home4/zhaow/data/kitti_seq/data_generated_s2'
total_dirlist = []
# Get the different folders of images
for d in dirlist:
seclist = os.listdir(os.path.join(data_dir, d))
for s in seclist:
if os.path.isdir(os.path.join(data_dir, d, s)):
total_dirlist.append(os.path.join(d, s))
F = open(os.path.join(output_dir, 'train.txt'), 'w')
for p in total_dirlist:
traintxt = os.path.join(os.path.join(output_dir, p), 'train.txt')
f = open(traintxt, 'r')
for line in f.readlines():
F.write(line)
print(traintxt)
================================================
FILE: core/dataset/kitti_prepared.py
================================================
import os, sys
import numpy as np
import cv2
import copy
import torch
import torch.utils.data
import pdb
class KITTI_Prepared(torch.utils.data.Dataset):
def __init__(self, data_dir, num_scales=3, img_hw=(256, 832), num_iterations=None):
super(KITTI_Prepared, self).__init__()
self.data_dir = data_dir
self.num_scales = num_scales
self.img_hw = img_hw
self.num_iterations = num_iterations
info_file = os.path.join(self.data_dir, 'train.txt')
#info_file = os.path.join(self.data_dir, 'train_flow.txt')
self.data_list = self.get_data_list(info_file)
def get_data_list(self, info_file):
with open(info_file, 'r') as f:
lines = f.readlines()
data_list = []
for line in lines:
k = line.strip('\n').split()
data = {}
data['image_file'] = os.path.join(self.data_dir, k[0])
data['cam_intrinsic_file'] = os.path.join(self.data_dir, k[1])
data_list.append(data)
print('A total of {} image pairs found'.format(len(data_list)))
return data_list
def count(self):
return len(self.data_list)
def rand_num(self, idx):
num_total = self.count()
np.random.seed(idx)
num = np.random.randint(num_total)
return num
def __len__(self):
if self.num_iterations is None:
return self.count()
else:
return self.num_iterations
def resize_img(self, img, img_hw):
'''
Input size (N*H, W, 3)
Output size (N*H', W', 3), where (H', W') == self.img_hw
'''
img_h, img_w = img.shape[0], img.shape[1]
img_hw_orig = (int(img_h / 2), img_w)
img1, img2 = img[:img_hw_orig[0], :, :], img[img_hw_orig[0]:, :, :]
img1_new = cv2.resize(img1, (img_hw[1], img_hw[0]))
img2_new = cv2.resize(img2, (img_hw[1], img_hw[0]))
img_new = np.concatenate([img1_new, img2_new], 0)
return img_new
def random_flip_img(self, img):
is_flip = (np.random.rand() > 0.5)
if is_flip:
img = cv2.flip(img, 1)
return img
def preprocess_img(self, img, img_hw=None, is_test=False):
if img_hw is None:
img_hw = self.img_hw
img = self.resize_img(img, img_hw)
if not is_test:
img = self.random_flip_img(img)
img = img / 255.0
return img
def read_cam_intrinsic(self, fname):
with open(fname, 'r') as f:
lines = f.readlines()
data = lines[-1].strip('\n').split(' ')[1:]
data = [float(k) for k in data]
data = np.array(data).reshape(3,4)
cam_intrinsics = data[:3,:3]
return cam_intrinsics
def rescale_intrinsics(self, K, img_hw_orig, img_hw_new):
K[0,:] = K[0,:] * img_hw_new[1] / img_hw_orig[1]
K[1,:] = K[1,:] * img_hw_new[0] / img_hw_orig[0]
return K
def get_intrinsics_per_scale(self, K, scale):
K_new = copy.deepcopy(K)
K_new[0,:] = K_new[0,:] / (2**scale)
K_new[1,:] = K_new[1,:] / (2**scale)
K_new_inv = np.linalg.inv(K_new)
return K_new, K_new_inv
def get_multiscale_intrinsics(self, K, num_scales):
K_ms, K_inv_ms = [], []
for s in range(num_scales):
K_new, K_new_inv = self.get_intrinsics_per_scale(K, s)
K_ms.append(K_new[None,:,:])
K_inv_ms.append(K_new_inv[None,:,:])
K_ms = np.concatenate(K_ms, 0)
K_inv_ms = np.concatenate(K_inv_ms, 0)
return K_ms, K_inv_ms
def __getitem__(self, idx):
'''
Returns:
- img torch.Tensor (N * H, W, 3)
- K torch.Tensor (num_scales, 3, 3)
- K_inv torch.Tensor (num_scales, 3, 3)
'''
if self.num_iterations is not None:
idx = self.rand_num(idx)
data = self.data_list[idx]
# load img
img = cv2.imread(data['image_file'])
img_hw_orig = (int(img.shape[0] / 2), img.shape[1])
img = self.preprocess_img(img, self.img_hw) # (img_h * 2, img_w, 3)
img = img.transpose(2,0,1)
# load intrinsic
cam_intrinsic = self.read_cam_intrinsic(data['cam_intrinsic_file'])
cam_intrinsic = self.rescale_intrinsics(cam_intrinsic, img_hw_orig, self.img_hw)
K_ms, K_inv_ms = self.get_multiscale_intrinsics(cam_intrinsic, self.num_scales) # (num_scales, 3, 3), (num_scales, 3, 3)
return torch.from_numpy(img).float(), torch.from_numpy(K_ms).float(), torch.from_numpy(K_inv_ms).float()
if __name__ == '__main__':
pass
================================================
FILE: core/dataset/kitti_raw.py
================================================
import os, sys
import numpy as np
import imageio
from tqdm import tqdm
import torch.multiprocessing as mp
import pdb
def process_folder(q, static_frames, test_scenes, data_dir, output_dir, stride=1):
while True:
if q.empty():
break
folder = q.get()
if folder in static_frames.keys():
static_ids = static_frames[folder]
else:
static_ids = []
scene = folder.split('/')[1]
if scene[:-5] in test_scenes:
continue
image_path = os.path.join(data_dir, folder, 'image_02/data')
dump_image_path = os.path.join(output_dir, folder)
if not os.path.isdir(dump_image_path):
os.makedirs(dump_image_path)
f = open(os.path.join(dump_image_path, 'train.txt'), 'w')
# Note. the os.listdir method returns arbitary order of list. We need correct order.
numbers = len(os.listdir(image_path))
for n in range(numbers - stride):
s_idx = n
e_idx = s_idx + stride
if '%.10d'%s_idx in static_ids or '%.10d'%e_idx in static_ids:
#print('%.10d'%s_idx)
continue
curr_image = imageio.imread(os.path.join(image_path, '%.10d'%s_idx)+'.png')
next_image = imageio.imread(os.path.join(image_path, '%.10d'%e_idx)+'.png')
seq_images = np.concatenate([curr_image, next_image], axis=0)
imageio.imsave(os.path.join(dump_image_path, '%.10d'%s_idx)+'.png', seq_images.astype('uint8'))
# Write training files
date = folder.split('/')[0]
f.write('%s %s\n' % (os.path.join(folder, '%.10d'%s_idx)+'.png', os.path.join(date, 'calib_cam_to_cam.txt')))
print(folder)
class KITTI_RAW(object):
def __init__(self, data_dir, static_frames_txt, test_scenes_txt):
self.data_dir = data_dir
self.static_frames_txt = static_frames_txt
self.test_scenes_txt = test_scenes_txt
def __len__(self):
raise NotImplementedError
def collect_static_frame(self):
f = open(self.static_frames_txt)
static_frames = {}
for line in f.readlines():
line = line.strip()
date, drive, frame_id = line.split(' ')
curr_fid = '%.10d' % (np.int(frame_id))
if os.path.join(date, drive) not in static_frames.keys():
static_frames[os.path.join(date, drive)] = []
static_frames[os.path.join(date, drive)].append(curr_fid)
return static_frames
def collect_test_scenes(self):
f = open(self.test_scenes_txt)
test_scenes = []
for line in f.readlines():
line = line.strip()
test_scenes.append(line)
return test_scenes
def prepare_data_mp(self, output_dir, stride=1):
num_processes = 16
processes = []
q = mp.Queue()
static_frames = self.collect_static_frame()
test_scenes = self.collect_test_scenes()
if not os.path.isfile(os.path.join(output_dir, 'train.txt')):
os.makedirs(output_dir)
#f = open(os.path.join(output_dir, 'train.txt'), 'w')
print('Preparing sequence data....')
if not os.path.isdir(self.data_dir):
raise
dirlist = os.listdir(self.data_dir)
total_dirlist = []
# Get the different folders of images
for d in dirlist:
seclist = os.listdir(os.path.join(self.data_dir, d))
for s in seclist:
if os.path.isdir(os.path.join(self.data_dir, d, s)):
total_dirlist.append(os.path.join(d, s))
q.put(os.path.join(d, s))
# Process every folder
for rank in range(num_processes):
p = mp.Process(target=process_folder, args=(q, static_frames, test_scenes, self.data_dir, output_dir, stride))
p.start()
processes.append(p)
for p in processes:
p.join()
# Collect the training frames.
f = open(os.path.join(output_dir, 'train.txt'), 'w')
for date in os.listdir(output_dir):
if os.path.isdir(os.path.join(output_dir, date)):
drives = os.listdir(os.path.join(output_dir, date))
for d in drives:
train_file = open(os.path.join(output_dir, date, d, 'train.txt'), 'r')
for l in train_file.readlines():
f.write(l)
# Get calib files
for date in os.listdir(self.data_dir):
command = 'cp ' + os.path.join(self.data_dir, date, 'calib_cam_to_cam.txt') + ' ' + os.path.join(output_dir, date, 'calib_cam_to_cam.txt')
os.system(command)
print('Data Preparation Finished.')
def prepare_data(self, output_dir):
static_frames = self.collect_static_frame()
test_scenes = self.collect_test_scenes()
if not os.path.isfile(os.path.join(output_dir, 'train.txt')):
os.makedirs(output_dir)
f = open(os.path.join(output_dir, 'train.txt'), 'w')
print('Preparing sequence data....')
if not os.path.isdir(self.data_dir):
raise
dirlist = os.listdir(self.data_dir)
total_dirlist = []
# Get the different folders of images
for d in dirlist:
seclist = os.listdir(os.path.join(self.data_dir, d))
for s in seclist:
if os.path.isdir(os.path.join(self.data_dir, d, s)):
total_dirlist.append(os.path.join(d, s))
# Process every folder
for folder in tqdm(total_dirlist):
if folder in static_frames.keys():
static_ids = static_frames[folder]
else:
static_ids = []
scene = folder.split('/')[1]
if scene in test_scenes:
continue
image_path = os.path.join(self.data_dir, folder, 'image_02/data')
dump_image_path = os.path.join(output_dir, folder)
if not os.path.isdir(dump_image_path):
os.makedirs(dump_image_path)
# Note. the os.listdir method returns arbitary order of list. We need correct order.
numbers = len(os.listdir(image_path))
for n in range(numbers - 1):
s_idx = n
e_idx = s_idx + 1
if '%.10d'%s_idx in static_ids or '%.10d'%e_idx in static_ids:
print('%.10d'%s_idx)
continue
curr_image = imageio.imread(os.path.join(image_path, '%.10d'%s_idx)+'.png')
next_image = imageio.imread(os.path.join(image_path, '%.10d'%e_idx)+'.png')
seq_images = np.concatenate([curr_image, next_image], axis=0)
imageio.imsave(os.path.join(dump_image_path, '%.10d'%s_idx)+'.png', seq_images.astype('uint8'))
# Write training files
date = folder.split('/')[0]
f.write('%s %s\n' % (os.path.join(folder, '%.10d'%s_idx)+'.png', os.path.join(date, 'calib_cam_to_cam.txt')))
print(folder)
# Get calib files
for date in os.listdir(self.data_dir):
command = 'cp ' + os.path.join(self.data_dir, date, 'calib_cam_to_cam.txt') + ' ' + os.path.join(output_dir, date, 'calib_cam_to_cam.txt')
os.system(command)
return os.path.join(output_dir, 'train.txt')
def __getitem__(self, idx):
raise NotImplementedError
if __name__ == '__main__':
data_dir = '/home4/zhaow/data/kitti'
dirlist = os.listdir('/home4/zhaow/data/kitti')
output_dir = '/home4/zhaow/data/kitti_seq/data_generated_s2'
total_dirlist = []
# Get the different folders of images
for d in dirlist:
seclist = os.listdir(os.path.join(data_dir, d))
for s in seclist:
if os.path.isdir(os.path.join(data_dir, d, s)):
total_dirlist.append(os.path.join(d, s))
F = open(os.path.join(output_dir, 'train.txt'), 'w')
for p in total_dirlist:
traintxt = os.path.join(os.path.join(output_dir, p), 'train.txt')
f = open(traintxt, 'r')
for line in f.readlines():
F.write(line)
print(traintxt)
================================================
FILE: core/dataset/nyu_v2.py
================================================
import os, sys
import numpy as np
import imageio
import cv2
import copy
import h5py
import scipy.io as sio
import torch
import torch.utils.data
import pdb
from tqdm import tqdm
import torch.multiprocessing as mp
def collect_image_list(path):
# Get ppm images list of a folder.
files = os.listdir(path)
sorted_file = sorted([f for f in files])
image_list = []
for l in sorted_file:
if l.split('.')[-1] == 'ppm':
image_list.append(l)
return image_list
def process_folder(q, data_dir, output_dir, stride, train_scenes):
# Directly process the original nyu v2 depth dataset.
while True:
if q.empty():
break
folder = q.get()
scene_name = folder.split('/')[-1]
s1,s2 = scene_name.split('_')[:-1], scene_name.split('_')[-1]
scene_name_full = ''
for j in s1:
scene_name_full = scene_name_full + j + '_'
scene_name_full = scene_name_full + s2[:4]
if scene_name_full not in train_scenes:
continue
image_path = os.path.join(data_dir, folder)
dump_image_path = os.path.join(output_dir, folder)
if not os.path.isdir(dump_image_path):
os.makedirs(dump_image_path)
f = open(os.path.join(dump_image_path, 'train.txt'), 'w')
# Note. the os.listdir method returns arbitary order of list. We need correct order.
image_list = collect_image_list(image_path)
#image_list = open(os.path.join(image_path, 'index.txt')).readlines()
numbers = len(image_list) - 1 # The last ppm file seems truncated.
for n in range(numbers - stride):
s_idx = n
e_idx = s_idx + stride
s_name = image_list[s_idx].strip()
e_name = image_list[e_idx].strip()
curr_image = imageio.imread(os.path.join(image_path, s_name))
next_image = imageio.imread(os.path.join(image_path, e_name))
#curr_image = cv2.imread(os.path.join(image_path, s_name))
#next_image = cv2.imread(os.path.join(image_path, e_name))
seq_images = np.concatenate([curr_image, next_image], axis=0)
imageio.imsave(os.path.join(dump_image_path, os.path.splitext(s_name)[0]+'.png'), seq_images.astype('uint8'))
#cv2.imwrite(os.path.join(dump_image_path, os.path.splitext(s_name)[0]+'.png'), seq_images.astype('uint8'))
# Write training files
#date = folder.split('_')[2]
f.write('%s %s\n' % (os.path.join(folder, os.path.splitext(s_name)[0]+'.png'), 'calib_cam_to_cam.txt'))
print(folder)
class NYU_Prepare(object):
def __init__(self, data_dir, test_dir):
self.data_dir = data_dir
self.test_data = os.path.join(test_dir, 'nyu_depth_v2_labeled.mat')
self.splits = os.path.join(test_dir, 'splits.mat')
self.get_all_scenes()
self.get_test_scenes()
self.get_train_scenes()
def __len__(self):
raise NotImplementedError
def get_all_scenes(self):
self.all_scenes = []
paths = os.listdir(self.data_dir)
for p in paths:
if os.path.isdir(os.path.join(self.data_dir, p)):
pp = os.listdir(os.path.join(self.data_dir, p))
for path in pp:
self.all_scenes.append(path)
def get_test_scenes(self):
self.test_scenes = []
test_data = h5py.File(self.test_data, 'r')
test_split = sio.loadmat(self.splits)['testNdxs']
test_split = np.array(test_split).squeeze(1)
test_scenes = test_data['scenes'][0][test_split-1]
for i in range(len(test_scenes)):
obj = test_data[test_scenes[i]]
name = "".join(chr(j) for j in obj[:])
if name not in self.test_scenes:
self.test_scenes.append(name)
#pdb.set_trace()
def get_train_scenes(self):
self.train_scenes = []
train_data = h5py.File(self.test_data, 'r')
train_split = sio.loadmat(self.splits)['trainNdxs']
train_split = np.array(train_split).squeeze(1)
train_scenes = train_data['scenes'][0][train_split-1]
for i in range(len(train_scenes)):
obj = train_data[train_scenes[i]]
name = "".join(chr(j) for j in obj[:])
if name not in self.train_scenes:
self.train_scenes.append(name)
def prepare_data_mp(self, output_dir, stride=1):
num_processes = 32
processes = []
q = mp.Queue()
if not os.path.isfile(os.path.join(output_dir, 'train.txt')):
os.makedirs(output_dir)
#f = open(os.path.join(output_dir, 'train.txt'), 'w')
print('Preparing sequence data....')
if not os.path.isdir(self.data_dir):
raise
dirlist = os.listdir(self.data_dir)
total_dirlist = []
# Get the different folders of images
for d in dirlist:
if not os.path.isdir(os.path.join(self.data_dir, d)):
continue
seclist = os.listdir(os.path.join(self.data_dir, d))
for s in seclist:
if os.path.isdir(os.path.join(self.data_dir, d, s)):
total_dirlist.append(os.path.join(d, s))
q.put(os.path.join(d, s))
# Process every folder
for rank in range(num_processes):
p = mp.Process(target=process_folder, args=(q, self.data_dir, output_dir, stride, self.train_scenes))
p.start()
processes.append(p)
for p in processes:
p.join()
# Collect the training frames.
f = open(os.path.join(output_dir, 'train.txt'), 'w')
for dirlist in os.listdir(output_dir):
if os.path.isdir(os.path.join(output_dir, dirlist)):
seclists = os.listdir(os.path.join(output_dir, dirlist))
for s in seclists:
train_file = open(os.path.join(output_dir, dirlist, s, 'train.txt'), 'r')
for l in train_file.readlines():
f.write(l)
f.close()
f = open(os.path.join(output_dir, 'calib_cam_to_cam.txt'), 'w')
f.write('P_rect: 5.1885790117450188e+02 0.0 3.2558244941119034e+02 0.0 0.0 5.1946961112127485e+02 2.5373616633400465e+02 0.0 0.0 0.0 1.0 0.0')
f.close()
print('Data Preparation Finished.')
def __getitem__(self, idx):
raise NotImplementedError
class NYU_v2(torch.utils.data.Dataset):
def __init__(self, data_dir, num_scales=3, img_hw=(448, 576), num_iterations=None):
super(NYU_v2, self).__init__()
self.data_dir = data_dir
self.num_scales = num_scales
self.img_hw = img_hw
self.num_iterations = num_iterations
self.undist_coeff = np.array([2.07966153e-01, -5.8613825e-01, 7.223136313e-04, 1.047962719e-03, 4.98569866e-01])
self.mapx, self.mapy = None, None
self.roi = None
info_file = os.path.join(self.data_dir, 'train.txt')
self.data_list = self.get_data_list(info_file)
def get_data_list(self, info_file):
with open(info_file, 'r') as f:
lines = f.readlines()
data_list = []
for line in lines:
k = line.strip('\n').split()
data = {}
data['image_file'] = os.path.join(self.data_dir, k[0])
data['cam_intrinsic_file'] = os.path.join(self.data_dir, k[1])
data_list.append(data)
print('A total of {} image pairs found'.format(len(data_list)))
return data_list
def count(self):
return len(self.data_list)
def rand_num(self, idx):
num_total = self.count()
np.random.seed(idx)
num = np.random.randint(num_total)
return num
def __len__(self):
if self.num_iterations is None:
return self.count()
else:
return self.num_iterations
def resize_img(self, img, img_hw):
'''
Input size (N*H, W, 3)
Output size (N*H', W', 3), where (H', W') == self.img_hw
'''
img_h, img_w = img.shape[0], img.shape[1]
img_hw_orig = (int(img_h / 2), img_w)
img1, img2 = img[:img_hw_orig[0], :, :], img[img_hw_orig[0]:, :, :]
img1_new = cv2.resize(img1, (img_hw[1], img_hw[0]))
img2_new = cv2.resize(img2, (img_hw[1], img_hw[0]))
img_new = np.concatenate([img1_new, img2_new], 0)
return img_new
def random_flip_img(self, img):
is_flip = (np.random.rand() > 0.5)
if is_flip:
img = cv2.flip(img, 1)
return img
def undistort_img(self, img, K):
img_h, img_w = img.shape[0], img.shape[1]
img_hw_orig = (int(img_h / 2), img_w)
img1, img2 = img[:img_hw_orig[0], :, :], img[img_hw_orig[0]:, :, :]
h, w = img_hw_orig
if self.mapx is None:
newcameramtx, self.roi = cv2.getOptimalNewCameraMatrix(K, self.undist_coeff, (w,h), 1, (w,h))
self.mapx, self.mapy = cv2.initUndistortRectifyMap(K, self.undist_coeff, None, newcameramtx, (w,h), 5)
img1_undist = cv2.remap(img1, self.mapx, self.mapy, cv2.INTER_LINEAR)
img2_undist = cv2.remap(img2, self.mapx, self.mapy, cv2.INTER_LINEAR)
x,y,w,h = self.roi
img1_undist = img1_undist[y:y+h, x:x+w]
img2_undist = img2_undist[y:y+h, x:x+w]
img_undist = np.concatenate([img1_undist, img2_undist], 0)
#cv2.imwrite('./test.png', img)
#cv2.imwrite('./test_undist.png', img_undist)
#pdb.set_trace()
return img_undist
def preprocess_img(self, img, K, img_hw=None, is_test=False):
if img_hw is None:
img_hw = self.img_hw
if not is_test:
#img = img
img = self.undistort_img(img, K)
#img = self.random_flip_img(img)
img = self.resize_img(img, img_hw)
img = img / 255.0
return img
def read_cam_intrinsic(self, fname):
with open(fname, 'r') as f:
lines = f.readlines()
data = lines[-1].strip('\n').split(' ')[1:]
data = [float(k) for k in data]
data = np.array(data).reshape(3,4)
cam_intrinsics = data[:3,:3]
return cam_intrinsics
def rescale_intrinsics(self, K, img_hw_orig, img_hw_new):
K_new = copy.deepcopy(K)
K_new[0,:] = K_new[0,:] * img_hw_new[0] / img_hw_orig[0]
K_new[1,:] = K_new[1,:] * img_hw_new[1] / img_hw_orig[1]
return K_new
def get_intrinsics_per_scale(self, K, scale):
K_new = copy.deepcopy(K)
K_new[0,:] = K_new[0,:] / (2**scale)
K_new[1,:] = K_new[1,:] / (2**scale)
K_new_inv = np.linalg.inv(K_new)
return K_new, K_new_inv
def get_multiscale_intrinsics(self, K, num_scales):
K_ms, K_inv_ms = [], []
for s in range(num_scales):
K_new, K_new_inv = self.get_intrinsics_per_scale(K, s)
K_ms.append(K_new[None,:,:])
K_inv_ms.append(K_new_inv[None,:,:])
K_ms = np.concatenate(K_ms, 0)
K_inv_ms = np.concatenate(K_inv_ms, 0)
return K_ms, K_inv_ms
def __getitem__(self, idx):
'''
Returns:
- img torch.Tensor (N * H, W, 3)
- K torch.Tensor (num_scales, 3, 3)
- K_inv torch.Tensor (num_scales, 3, 3)
'''
if idx >= self.num_iterations:
raise IndexError
if self.num_iterations is not None:
idx = self.rand_num(idx)
data = self.data_list[idx]
# load img
img = cv2.imread(data['image_file'])
img_hw_orig = (int(img.shape[0] / 2), img.shape[1])
# load intrinsic
cam_intrinsic_orig = self.read_cam_intrinsic(data['cam_intrinsic_file'])
cam_intrinsic = self.rescale_intrinsics(cam_intrinsic_orig, img_hw_orig, self.img_hw)
K_ms, K_inv_ms = self.get_multiscale_intrinsics(cam_intrinsic, self.num_scales) # (num_scales, 3, 3), (num_scales, 3, 3)
# image preprocessing
img = self.preprocess_img(img, cam_intrinsic_orig, self.img_hw) # (img_h * 2, img_w, 3)
img = img.transpose(2,0,1)
return torch.from_numpy(img).float(), torch.from_numpy(K_ms).float(), torch.from_numpy(K_inv_ms).float()
if __name__ == '__main__':
data_dir = '/home4/zhaow/data/kitti'
dirlist = os.listdir('/home4/zhaow/data/kitti')
output_dir = '/home4/zhaow/data/kitti_seq/data_generated_s2'
total_dirlist = []
# Get the different folders of images
for d in dirlist:
seclist = os.listdir(os.path.join(data_dir, d))
for s in seclist:
if os.path.isdir(os.path.join(data_dir, d, s)):
total_dirlist.append(os.path.join(d, s))
F = open(os.path.join(output_dir, 'train.txt'), 'w')
for p in total_dirlist:
traintxt = os.path.join(os.path.join(output_dir, p), 'train.txt')
f = open(traintxt, 'r')
for line in f.readlines():
F.write(line)
print(traintxt)
================================================
FILE: core/evaluation/__init__.py
================================================
import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from evaluate_flow import eval_flow_avg, load_gt_flow_kitti
from evaluate_mask import load_gt_mask
from evaluate_depth import eval_depth
================================================
FILE: core/evaluation/eval_odom.py
================================================
import copy
from matplotlib import pyplot as plt
import numpy as np
import os
from glob import glob
import pdb
def scale_lse_solver(X, Y):
"""Least-sqaure-error solver
Compute optimal scaling factor so that s(X)-Y is minimum
Args:
X (KxN array): current data
Y (KxN array): reference data
Returns:
scale (float): scaling factor
"""
scale = np.sum(X * Y)/np.sum(X ** 2)
return scale
def umeyama_alignment(x, y, with_scale=False):
"""
Computes the least squares solution parameters of an Sim(m) matrix
that minimizes the distance between a set of registered points.
Umeyama, Shinji: Least-squares estimation of transformation parameters
between two point patterns. IEEE PAMI, 1991
:param x: mxn matrix of points, m = dimension, n = nr. of data points
:param y: mxn matrix of points, m = dimension, n = nr. of data points
:param with_scale: set to True to align also the scale (default: 1.0 scale)
:return: r, t, c - rotation matrix, translation vector and scale factor
"""
if x.shape != y.shape:
assert False, "x.shape not equal to y.shape"
# m = dimension, n = nr. of data points
m, n = x.shape
# means, eq. 34 and 35
mean_x = x.mean(axis=1)
mean_y = y.mean(axis=1)
# variance, eq. 36
# "transpose" for column subtraction
sigma_x = 1.0 / n * (np.linalg.norm(x - mean_x[:, np.newaxis])**2)
# covariance matrix, eq. 38
outer_sum = np.zeros((m, m))
for i in range(n):
outer_sum += np.outer((y[:, i] - mean_y), (x[:, i] - mean_x))
cov_xy = np.multiply(1.0 / n, outer_sum)
# SVD (text betw. eq. 38 and 39)
u, d, v = np.linalg.svd(cov_xy)
# S matrix, eq. 43
s = np.eye(m)
if np.linalg.det(u) * np.linalg.det(v) < 0.0:
# Ensure a RHS coordinate system (Kabsch algorithm).
s[m - 1, m - 1] = -1
# rotation, eq. 40
r = u.dot(s).dot(v)
# scale & translation, eq. 42 and 41
c = 1 / sigma_x * np.trace(np.diag(d).dot(s)) if with_scale else 1.0
t = mean_y - np.multiply(c, r.dot(mean_x))
return r, t, c
class KittiEvalOdom():
# ----------------------------------------------------------------------
# poses: N,4,4
# pose: 4,4
# ----------------------------------------------------------------------
def __init__(self):
self.lengths = [100, 200, 300, 400, 500, 600, 700, 800]
self.num_lengths = len(self.lengths)
def loadPoses(self, file_name):
# ----------------------------------------------------------------------
# Each line in the file should follow one of the following structures
# (1) idx pose(3x4 matrix in terms of 12 numbers)
# (2) pose(3x4 matrix in terms of 12 numbers)
# ----------------------------------------------------------------------
f = open(file_name, 'r')
s = f.readlines()
f.close()
file_len = len(s)
poses = {}
for cnt, line in enumerate(s):
P = np.eye(4)
line_split = [float(i) for i in line.split(" ")]
withIdx = int(len(line_split) == 13)
for row in range(3):
for col in range(4):
P[row, col] = line_split[row*4 + col + withIdx]
if withIdx:
frame_idx = line_split[0]
else:
frame_idx = cnt
poses[frame_idx] = P
return poses
def trajectory_distances(self, poses):
# ----------------------------------------------------------------------
# poses: dictionary: [frame_idx: pose]
# ----------------------------------------------------------------------
dist = [0]
sort_frame_idx = sorted(poses.keys())
for i in range(len(sort_frame_idx)-1):
cur_frame_idx = sort_frame_idx[i]
next_frame_idx = sort_frame_idx[i+1]
P1 = poses[cur_frame_idx]
P2 = poses[next_frame_idx]
dx = P1[0, 3] - P2[0, 3]
dy = P1[1, 3] - P2[1, 3]
dz = P1[2, 3] - P2[2, 3]
dist.append(dist[i]+np.sqrt(dx**2+dy**2+dz**2))
return dist
def rotation_error(self, pose_error):
a = pose_error[0, 0]
b = pose_error[1, 1]
c = pose_error[2, 2]
d = 0.5*(a+b+c-1.0)
rot_error = np.arccos(max(min(d, 1.0), -1.0))
return rot_error
def translation_error(self, pose_error):
dx = pose_error[0, 3]
dy = pose_error[1, 3]
dz = pose_error[2, 3]
return np.sqrt(dx**2+dy**2+dz**2)
def last_frame_from_segment_length(self, dist, first_frame, len_):
for i in range(first_frame, len(dist), 1):
if dist[i] > (dist[first_frame] + len_):
return i
return -1
def calc_sequence_errors(self, poses_gt, poses_result):
err = []
dist = self.trajectory_distances(poses_gt)
self.step_size = 10
for first_frame in range(0, len(poses_gt), self.step_size):
for i in range(self.num_lengths):
len_ = self.lengths[i]
last_frame = self.last_frame_from_segment_length(dist, first_frame, len_)
# ----------------------------------------------------------------------
# Continue if sequence not long enough
# ----------------------------------------------------------------------
if last_frame == -1 or not(last_frame in poses_result.keys()) or not(first_frame in poses_result.keys()):
continue
# ----------------------------------------------------------------------
# compute rotational and translational errors
# ----------------------------------------------------------------------
pose_delta_gt = np.dot(np.linalg.inv(poses_gt[first_frame]), poses_gt[last_frame])
pose_delta_result = np.dot(np.linalg.inv(poses_result[first_frame]), poses_result[last_frame])
pose_error = np.dot(np.linalg.inv(pose_delta_result), pose_delta_gt)
r_err = self.rotation_error(pose_error)
t_err = self.translation_error(pose_error)
# ----------------------------------------------------------------------
# compute speed
# ----------------------------------------------------------------------
num_frames = last_frame - first_frame + 1.0
speed = len_/(0.1*num_frames)
err.append([first_frame, r_err/len_, t_err/len_, len_, speed])
return err
def save_sequence_errors(self, err, file_name):
fp = open(file_name, 'w')
for i in err:
line_to_write = " ".join([str(j) for j in i])
fp.writelines(line_to_write+"\n")
fp.close()
def compute_overall_err(self, seq_err):
t_err = 0
r_err = 0
seq_len = len(seq_err)
for item in seq_err:
r_err += item[1]
t_err += item[2]
ave_t_err = t_err / seq_len
ave_r_err = r_err / seq_len
return ave_t_err, ave_r_err
def plotPath(self, seq, poses_gt, poses_result):
plot_keys = ["Ground Truth", "Ours"]
fontsize_ = 20
plot_num =-1
poses_dict = {}
poses_dict["Ground Truth"] = poses_gt
poses_dict["Ours"] = poses_result
fig = plt.figure()
ax = plt.gca()
ax.set_aspect('equal')
for key in plot_keys:
pos_xz = []
# for pose in poses_dict[key]:
for frame_idx in sorted(poses_dict[key].keys()):
pose = poses_dict[key][frame_idx]
pos_xz.append([pose[0,3], pose[2,3]])
pos_xz = np.asarray(pos_xz)
plt.plot(pos_xz[:,0], pos_xz[:,1], label = key)
plt.legend(loc="upper right", prop={'size': fontsize_})
plt.xticks(fontsize=fontsize_)
plt.yticks(fontsize=fontsize_)
plt.xlabel('x (m)', fontsize=fontsize_)
plt.ylabel('z (m)', fontsize=fontsize_)
fig.set_size_inches(10, 10)
png_title = "sequence_"+(seq)
plt.savefig(self.plot_path_dir + "/" + png_title + ".pdf", bbox_inches='tight', pad_inches=0)
# plt.show()
def compute_segment_error(self, seq_errs):
# ----------------------------------------------------------------------
# This function calculates average errors for different segment.
# ----------------------------------------------------------------------
segment_errs = {}
avg_segment_errs = {}
for len_ in self.lengths:
segment_errs[len_] = []
# ----------------------------------------------------------------------
# Get errors
# ----------------------------------------------------------------------
for err in seq_errs:
len_ = err[3]
t_err = err[2]
r_err = err[1]
segment_errs[len_].append([t_err, r_err])
# ----------------------------------------------------------------------
# Compute average
# ----------------------------------------------------------------------
for len_ in self.lengths:
if segment_errs[len_] != []:
avg_t_err = np.mean(np.asarray(segment_errs[len_])[:, 0])
avg_r_err = np.mean(np.asarray(segment_errs[len_])[:, 1])
avg_segment_errs[len_] = [avg_t_err, avg_r_err]
else:
avg_segment_errs[len_] = []
return avg_segment_errs
def scale_optimization(self, gt, pred):
""" Optimize scaling factor
Args:
gt (4x4 array dict): ground-truth poses
pred (4x4 array dict): predicted poses
Returns:
new_pred (4x4 array dict): predicted poses after optimization
"""
pred_updated = copy.deepcopy(pred)
xyz_pred = []
xyz_ref = []
for i in pred:
pose_pred = pred[i]
pose_ref = gt[i]
xyz_pred.append(pose_pred[:3, 3])
xyz_ref.append(pose_ref[:3, 3])
xyz_pred = np.asarray(xyz_pred)
xyz_ref = np.asarray(xyz_ref)
scale = scale_lse_solver(xyz_pred, xyz_ref)
for i in pred_updated:
pred_updated[i][:3, 3] *= scale
return pred_updated
def eval(self, gt_txt, result_txt, seq=None):
# gt_dir: the directory of groundtruth poses txt
# results_dir: the directory of predicted poses txt
self.plot_path_dir = os.path.dirname(result_txt) + "/plot_path"
if not os.path.exists(self.plot_path_dir):
os.makedirs(self.plot_path_dir)
self.gt_txt = gt_txt
ave_t_errs = []
ave_r_errs = []
poses_result = self.loadPoses(result_txt)
poses_gt = self.loadPoses(self.gt_txt)
# Pose alignment to first frame
idx_0 = sorted(list(poses_result.keys()))[0]
pred_0 = poses_result[idx_0]
gt_0 = poses_gt[idx_0]
for cnt in poses_result:
poses_result[cnt] = np.linalg.inv(pred_0) @ poses_result[cnt]
poses_gt[cnt] = np.linalg.inv(gt_0) @ poses_gt[cnt]
# get XYZ
xyz_gt = []
xyz_result = []
for cnt in poses_result:
xyz_gt.append([poses_gt[cnt][0, 3], poses_gt[cnt][1, 3], poses_gt[cnt][2, 3]])
xyz_result.append([poses_result[cnt][0, 3], poses_result[cnt][1, 3], poses_result[cnt][2, 3]])
xyz_gt = np.asarray(xyz_gt).transpose(1, 0)
xyz_result = np.asarray(xyz_result).transpose(1, 0)
r, t, scale = umeyama_alignment(xyz_result, xyz_gt, True)
align_transformation = np.eye(4)
align_transformation[:3:, :3] = r
align_transformation[:3, 3] = t
for cnt in poses_result:
poses_result[cnt][:3, 3] *= scale
poses_result[cnt] = align_transformation @ poses_result[cnt]
# ----------------------------------------------------------------------
# compute sequence errors
# ----------------------------------------------------------------------
seq_err = self.calc_sequence_errors(poses_gt, poses_result)
# ----------------------------------------------------------------------
# Compute segment errors
# ----------------------------------------------------------------------
avg_segment_errs = self.compute_segment_error(seq_err)
# ----------------------------------------------------------------------
# compute overall error
# ----------------------------------------------------------------------
ave_t_err, ave_r_err = self.compute_overall_err(seq_err)
print("Sequence: " + seq)
print("Translational error (%): ", ave_t_err*100)
print("Rotational error (deg/100m): ", ave_r_err/np.pi*180*100)
ave_t_errs.append(ave_t_err)
ave_r_errs.append(ave_r_err)
# Plotting
self.plotPath(seq, poses_gt, poses_result)
print("-------------------- For Copying ------------------------------")
for i in range(len(ave_t_errs)):
print("{0:.2f}".format(ave_t_errs[i]*100))
print("{0:.2f}".format(ave_r_errs[i]/np.pi*180*100))
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser(description='KITTI evaluation')
parser.add_argument('--gt_txt', type=str, required=True, help="Groundtruth directory")
parser.add_argument('--result_txt', type=str, required=True, help="Result directory")
parser.add_argument('--seq', type=str, help="sequences to be evaluated", default='09')
args = parser.parse_args()
eval_tool = KittiEvalOdom()
eval_tool.eval(args.gt_txt, args.result_txt, seq=args.seq)
================================================
FILE: core/evaluation/evaluate_depth.py
================================================
from evaluation_utils import *
def process_depth(gt_depth, pred_depth, min_depth, max_depth):
mask = gt_depth > 0
pred_depth[pred_depth < min_depth] = min_depth
pred_depth[pred_depth > max_depth] = max_depth
gt_depth[gt_depth < min_depth] = min_depth
gt_depth[gt_depth > max_depth] = max_depth
return gt_depth, pred_depth, mask
def eval_depth(gt_depths,
pred_depths,
min_depth=1e-3,
max_depth=80, nyu=False):
num_samples = len(pred_depths)
rms = np.zeros(num_samples, np.float32)
log_rms = np.zeros(num_samples, np.float32)
abs_rel = np.zeros(num_samples, np.float32)
sq_rel = np.zeros(num_samples, np.float32)
d1_all = np.zeros(num_samples, np.float32)
a1 = np.zeros(num_samples, np.float32)
a2 = np.zeros(num_samples, np.float32)
a3 = np.zeros(num_samples, np.float32)
for i in range(num_samples):
gt_depth = gt_depths[i]
pred_depth = pred_depths[i]
mask = np.logical_and(gt_depth > min_depth, gt_depth < max_depth)
if not nyu:
gt_height, gt_width = gt_depth.shape
crop = np.array([0.40810811 * gt_height, 0.99189189 * gt_height,
0.03594771 * gt_width, 0.96405229 * gt_width]).astype(np.int32)
crop_mask = np.zeros(mask.shape)
crop_mask[crop[0]:crop[1], crop[2]:crop[3]] = 1
mask = np.logical_and(mask, crop_mask)
gt_depth = gt_depth[mask]
pred_depth = pred_depth[mask]
scale = np.median(gt_depth) / np.median(pred_depth)
pred_depth *= scale
gt_depth, pred_depth, mask = process_depth(
gt_depth, pred_depth, min_depth, max_depth)
abs_rel[i], sq_rel[i], rms[i], log_rms[i], a1[i], a2[i], a3[
i] = compute_errors(gt_depth, pred_depth, nyu=nyu)
return [abs_rel.mean(), sq_rel.mean(), rms.mean(), log_rms.mean(), a1.mean(), a2.mean(), a3.mean()]
================================================
FILE: core/evaluation/evaluate_flow.py
================================================
import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
import numpy as np
from flowlib import read_flow_png, flow_to_image
import cv2
import multiprocessing
import functools
def get_scaled_intrinsic_matrix(calib_file, zoom_x, zoom_y):
intrinsics = load_intrinsics_raw(calib_file)
intrinsics = scale_intrinsics(intrinsics, zoom_x, zoom_y)
intrinsics[0, 1] = 0.0
intrinsics[1, 0] = 0.0
intrinsics[2, 0] = 0.0
intrinsics[2, 1] = 0.0
return intrinsics
def load_intrinsics_raw(calib_file):
filedata = read_raw_calib_file(calib_file)
if "P_rect_02" in filedata:
P_rect = filedata['P_rect_02']
else:
P_rect = filedata['P2']
P_rect = np.reshape(P_rect, (3, 4))
intrinsics = P_rect[:3, :3]
return intrinsics
def read_raw_calib_file(filepath):
# From https://github.com/utiasSTARS/pykitti/blob/master/pykitti/utils.py
"""Read in a calibration file and parse into a dictionary."""
data = {}
with open(filepath, 'r') as f:
for line in f.readlines():
key, value = line.split(':', 1)
# The only non-float values in these files are dates, which
# we don't care about anyway
try:
data[key] = np.array([float(x) for x in value.split()])
except ValueError:
pass
return data
def scale_intrinsics(mat, sx, sy):
out = np.copy(mat)
out[0, 0] *= sx
out[0, 2] *= sx
out[1, 1] *= sy
out[1, 2] *= sy
return out
def read_flow_gt_worker(dir_gt, i):
flow_true = read_flow_png(
os.path.join(dir_gt, "flow_occ", str(i).zfill(6) + "_10.png"))
flow_noc_true = read_flow_png(
os.path.join(dir_gt, "flow_noc", str(i).zfill(6) + "_10.png"))
return flow_true, flow_noc_true[:, :, 2]
def load_gt_flow_kitti(gt_dataset_dir, mode):
gt_flows = []
noc_masks = []
if mode == "kitti_2012":
num_gt = 194
dir_gt = gt_dataset_dir
elif mode == "kitti_2015":
num_gt = 200
dir_gt = gt_dataset_dir
else:
num_gt = None
dir_gt = None
raise ValueError('Mode {} not found.'.format(mode))
fun = functools.partial(read_flow_gt_worker, dir_gt)
pool = multiprocessing.Pool(5)
results = pool.imap(fun, range(num_gt), chunksize=10)
pool.close()
pool.join()
for result in results:
gt_flows.append(result[0])
noc_masks.append(result[1])
return gt_flows, noc_masks
def calculate_error_rate(epe_map, gt_flow, mask):
bad_pixels = np.logical_and(
epe_map * mask > 3,
epe_map * mask / np.maximum(
np.sqrt(np.sum(np.square(gt_flow), axis=2)), 1e-10) > 0.05)
return bad_pixels.sum() / mask.sum()
def eval_flow_avg(gt_flows,
noc_masks,
pred_flows,
cfg,
moving_masks=None,
write_img=False):
error, error_noc, error_occ, error_move, error_static, error_rate = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
error_move_rate, error_static_rate = 0.0, 0.0
num = len(gt_flows)
for gt_flow, noc_mask, pred_flow, i in zip(gt_flows, noc_masks, pred_flows,
range(len(gt_flows))):
H, W = gt_flow.shape[0:2]
pred_flow = np.copy(pred_flow)
pred_flow[:, :, 0] = pred_flow[:, :, 0] / cfg.img_hw[1] * W
pred_flow[:, :, 1] = pred_flow[:, :, 1] / cfg.img_hw[0] * H
flo_pred = cv2.resize(
pred_flow, (W, H), interpolation=cv2.INTER_LINEAR)
if write_img:
if not os.path.exists(os.path.join(cfg.model_dir, "pred_flow")):
os.mkdir(os.path.join(cfg.model_dir, "pred_flow"))
cv2.imwrite(
os.path.join(cfg.model_dir, "pred_flow",
str(i).zfill(6) + "_10.png"),
flow_to_image(flo_pred))
cv2.imwrite(
os.path.join(cfg.model_dir, "pred_flow",
str(i).zfill(6) + "_10_gt.png"),
flow_to_image(gt_flow[:, :, 0:2]))
cv2.imwrite(
os.path.join(cfg.model_dir, "pred_flow",
str(i).zfill(6) + "_10_err.png"),
flow_to_image(
(flo_pred - gt_flow[:, :, 0:2]) * gt_flow[:, :, 2:3]))
epe_map = np.sqrt(
np.sum(np.square(flo_pred[:, :, 0:2] - gt_flow[:, :, 0:2]),
axis=2))
error += np.sum(epe_map * gt_flow[:, :, 2]) / np.sum(gt_flow[:, :, 2])
error_noc += np.sum(epe_map * noc_mask) / np.sum(noc_mask)
error_occ += np.sum(epe_map * (gt_flow[:, :, 2] - noc_mask)) / max(
np.sum(gt_flow[:, :, 2] - noc_mask), 1.0)
error_rate += calculate_error_rate(epe_map, gt_flow[:, :, 0:2],
gt_flow[:, :, 2])
if moving_masks:
move_mask = moving_masks[i]
error_move_rate += calculate_error_rate(
epe_map, gt_flow[:, :, 0:2], gt_flow[:, :, 2] * move_mask)
error_static_rate += calculate_error_rate(
epe_map, gt_flow[:, :, 0:2],
gt_flow[:, :, 2] * (1.0 - move_mask))
error_move += np.sum(epe_map * gt_flow[:, :, 2] *
move_mask) / np.sum(gt_flow[:, :, 2] *
move_mask)
error_static += np.sum(epe_map * gt_flow[:, :, 2] * (
1.0 - move_mask)) / np.sum(gt_flow[:, :, 2] *
(1.0 - move_mask))
if moving_masks:
result = "{:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10} \n".format(
'epe', 'epe_noc', 'epe_occ', 'epe_move', 'epe_static',
'move_err_rate', 'static_err_rate', 'err_rate')
result += "{:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}, {:10.4f} \n".format(
error / num, error_noc / num, error_occ / num, error_move / num,
error_static / num, error_move_rate / num, error_static_rate / num,
error_rate / num)
return result
else:
result = "{:>10}, {:>10}, {:>10}, {:>10} \n".format(
'epe', 'epe_noc', 'epe_occ', 'err_rate')
result += "{:10.4f}, {:10.4f}, {:10.4f}, {:10.4f} \n".format(
error / num, error_noc / num, error_occ / num, error_rate / num)
return result
================================================
FILE: core/evaluation/evaluate_mask.py
================================================
import os
import numpy as np
import cv2
import functools
import matplotlib.pyplot as plt
import multiprocessing
"""
Adopted from https://github.com/martinkersner/py_img_seg_eval
"""
class EvalSegErr(Exception):
def __init__(self, value):
self.value = value
def __str__(self):
return repr(self.value)
def pixel_accuracy(eval_segm, gt_segm):
'''
sum_i(n_ii) / sum_i(t_i)
'''
check_size(eval_segm, gt_segm)
cl, n_cl = extract_classes(gt_segm)
eval_mask, gt_mask = extract_both_masks(eval_segm, gt_segm, cl, n_cl)
sum_n_ii = 0
sum_t_i = 0
for i, c in enumerate(cl):
curr_eval_mask = eval_mask[i, :, :]
curr_gt_mask = gt_mask[i, :, :]
sum_n_ii += np.sum(np.logical_and(curr_eval_mask, curr_gt_mask))
sum_t_i += np.sum(curr_gt_mask)
if (sum_t_i == 0):
pixel_accuracy_ = 0
else:
pixel_accuracy_ = sum_n_ii / sum_t_i
return pixel_accuracy_
def mean_accuracy(eval_segm, gt_segm):
'''
(1/n_cl) sum_i(n_ii/t_i)
'''
check_size(eval_segm, gt_segm)
cl, n_cl = extract_classes(gt_segm)
eval_mask, gt_mask = extract_both_masks(eval_segm, gt_segm, cl, n_cl)
accuracy = list([0]) * n_cl
for i, c in enumerate(cl):
curr_eval_mask = eval_mask[i, :, :]
curr_gt_mask = gt_mask[i, :, :]
n_ii = np.sum(np.logical_and(curr_eval_mask, curr_gt_mask))
t_i = np.sum(curr_gt_mask)
if (t_i != 0):
accuracy[i] = n_ii / t_i
mean_accuracy_ = np.mean(accuracy)
return mean_accuracy_
def mean_IU(eval_segm, gt_segm):
'''
(1/n_cl) * sum_i(n_ii / (t_i + sum_j(n_ji) - n_ii))
'''
check_size(eval_segm, gt_segm)
cl, n_cl = union_classes(eval_segm, gt_segm)
_, n_cl_gt = extract_classes(gt_segm)
eval_mask, gt_mask = extract_both_masks(eval_segm, gt_segm, cl, n_cl)
IU = list([0]) * n_cl
for i, c in enumerate(cl):
curr_eval_mask = eval_mask[i, :, :]
curr_gt_mask = gt_mask[i, :, :]
if (np.sum(curr_eval_mask) == 0) or (np.sum(curr_gt_mask) == 0):
continue
n_ii = np.sum(np.logical_and(curr_eval_mask, curr_gt_mask))
t_i = np.sum(curr_gt_mask)
n_ij = np.sum(curr_eval_mask)
IU[i] = n_ii / (t_i + n_ij - n_ii)
mean_IU_ = np.sum(IU) / n_cl_gt
return mean_IU_, np.array(IU)
def frequency_weighted_IU(eval_segm, gt_segm):
'''
sum_k(t_k)^(-1) * sum_i((t_i*n_ii)/(t_i + sum_j(n_ji) - n_ii))
'''
check_size(eval_segm, gt_segm)
cl, n_cl = union_classes(eval_segm, gt_segm)
eval_mask, gt_mask = extract_both_masks(eval_segm, gt_segm, cl, n_cl)
frequency_weighted_IU_ = list([0]) * n_cl
for i, c in enumerate(cl):
curr_eval_mask = eval_mask[i, :, :]
curr_gt_mask = gt_mask[i, :, :]
if (np.sum(curr_eval_mask) == 0) or (np.sum(curr_gt_mask) == 0):
continue
n_ii = np.sum(np.logical_and(curr_eval_mask, curr_gt_mask))
t_i = np.sum(curr_gt_mask)
n_ij = np.sum(curr_eval_mask)
frequency_weighted_IU_[i] = (t_i * n_ii) / (t_i + n_ij - n_ii)
sum_k_t_k = get_pixel_area(eval_segm)
frequency_weighted_IU_ = np.sum(frequency_weighted_IU_) / sum_k_t_k
return frequency_weighted_IU_
'''
Auxiliary functions used during evaluation.
'''
def get_pixel_area(segm):
return segm.shape[0] * segm.shape[1]
def extract_both_masks(eval_segm, gt_segm, cl, n_cl):
eval_mask = extract_masks(eval_segm, cl, n_cl)
gt_mask = extract_masks(gt_segm, cl, n_cl)
return eval_mask, gt_mask
def extract_classes(segm):
cl = np.unique(segm)
n_cl = len(cl)
return cl, n_cl
def union_classes(eval_segm, gt_segm):
eval_cl, _ = extract_classes(eval_segm)
gt_cl, _ = extract_classes(gt_segm)
cl = np.union1d(eval_cl, gt_cl)
n_cl = len(cl)
return cl, n_cl
def extract_masks(segm, cl, n_cl):
h, w = segm_size(segm)
masks = np.zeros((n_cl, h, w))
for i, c in enumerate(cl):
masks[i, :, :] = segm == c
return masks
def segm_size(segm):
try:
height = segm.shape[0]
width = segm.shape[1]
except IndexError:
raise
return height, width
def check_size(eval_segm, gt_segm):
h_e, w_e = segm_size(eval_segm)
h_g, w_g = segm_size(gt_segm)
if (h_e != h_g) or (w_e != w_g):
raise EvalSegErr("DiffDim: Different dimensions of matrices!")
def read_mask_gt_worker(gt_dataset_dir, idx):
return cv2.imread(
gt_dataset_dir + "/obj_map/" + str(idx).zfill(6) + "_10.png", -1)
def load_gt_mask(gt_dataset_dir):
num_gt = 200
# the dataset dir should be the directory of kitti-2015.
fun = functools.partial(read_mask_gt_worker, gt_dataset_dir)
pool = multiprocessing.Pool(5)
results = pool.imap(fun, range(num_gt), chunksize=10)
pool.close()
pool.join()
gt_masks = []
for m in results:
m[m > 0.0] = 1.0
gt_masks.append(m)
return gt_masks
def eval_mask(pred_masks, gt_masks, opt):
grey_cmap = plt.get_cmap("Greys")
if not os.path.exists(os.path.join(opt.trace, "pred_mask")):
os.mkdir(os.path.join(opt.trace, "pred_mask"))
pa_res, ma_res, mIU_res, fwIU_res = 0.0, 0.0, 0.0, 0.0
IU_res = np.array([0.0, 0.0])
num_total = len(gt_masks)
for i in range(num_total):
gt_mask = gt_masks[i]
H, W = gt_mask.shape[0:2]
pred_mask = cv2.resize(
pred_masks[i], (W, H), interpolation=cv2.INTER_LINEAR)
pred_mask[pred_mask >= 0.5] = 1.0
pred_mask[pred_mask < 0.5] = 0.0
cv2.imwrite(
os.path.join(opt.trace, "pred_mask",
str(i).zfill(6) + "_10_plot.png"),
grey_cmap(pred_mask))
cv2.imwrite(
os.path.join(opt.trace, "pred_mask", str(i).zfill(6) + "_10.png"),
pred_mask)
pa_res += pixel_accuracy(pred_mask, gt_mask)
ma_res += mean_accuracy(pred_mask, gt_mask)
mIU, IU = mean_IU(pred_mask, gt_mask)
mIU_res += mIU
IU_res += IU
fwIU_res += frequency_weighted_IU(pred_mask, gt_mask)
return pa_res / 200., ma_res / 200., mIU_res / 200., fwIU_res / 200., IU_res / 200.
================================================
FILE: core/evaluation/evaluation_utils.py
================================================
import numpy as np
import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
import cv2, skimage
import skimage.io
#import scipy.misc as sm
import imageio as sm
# Adopted from https://github.com/mrharicot/monodepth
def compute_errors(gt, pred, nyu=False):
thresh = np.maximum((gt / pred), (pred / gt))
a1 = (thresh < 1.25).mean()
a2 = (thresh < 1.25**2).mean()
a3 = (thresh < 1.25**3).mean()
rmse = (gt - pred)**2
rmse = np.sqrt(rmse.mean())
rmse_log = (np.log(gt) - np.log(pred))**2
rmse_log = np.sqrt(rmse_log.mean())
log10 = np.mean(np.abs((np.log10(gt) - np.log10(pred))))
abs_rel = np.mean(np.abs(gt - pred) / (gt))
sq_rel = np.mean(((gt - pred)**2) / (gt))
if nyu:
return abs_rel, sq_rel, rmse, log10, a1, a2, a3
else:
return abs_rel, sq_rel, rmse, rmse_log, a1, a2, a3
================================================
FILE: core/evaluation/flowlib.py
================================================
#!/usr/bin/python
"""
Adopted from https://github.com/liruoteng/OpticalFlowToolkit
# ==============================
# flowlib.py
# library for optical flow processing
# Author: Ruoteng Li
# Date: 6th Aug 2016
# ==============================
"""
import png
import scipy
import numpy as np
import matplotlib.colors as cl
import matplotlib.pyplot as plt
from PIL import Image
#import cv2
UNKNOWN_FLOW_THRESH = 1e7
SMALLFLOW = 0.0
LARGEFLOW = 1e8
"""
=============
Flow Section
=============
"""
def show_flow(filename):
"""
visualize optical flow map using matplotlib
:param filename: optical flow file
:return: None
"""
flow = read_flow(filename)
img = flow_to_image(flow)
plt.imshow(img)
plt.show()
def visualize_flow(flow, mode='Y'):
"""
this function visualize the input flow
:param flow: input flow in array
:param mode: choose which color mode to visualize the flow (Y: Ccbcr, RGB: RGB color)
:return: None
"""
if mode == 'Y':
# Ccbcr color wheel
img = flow_to_image(flow)
plt.imshow(img)
plt.show()
elif mode == 'RGB':
(h, w) = flow.shape[0:2]
du = flow[:, :, 0]
dv = flow[:, :, 1]
valid = flow[:, :, 2]
max_flow = max(np.max(du), np.max(dv))
img = np.zeros((h, w, 3), dtype=np.float64)
# angle layer
img[:, :, 0] = np.arctan2(dv, du) / (2 * np.pi)
# magnitude layer, normalized to 1
img[:, :, 1] = np.sqrt(du * du + dv * dv) * 8 / max_flow
# phase layer
img[:, :, 2] = 8 - img[:, :, 1]
# clip to [0,1]
small_idx = img[:, :, 0:3] < 0
large_idx = img[:, :, 0:3] > 1
img[small_idx] = 0
img[large_idx] = 1
# convert to rgb
img = cl.hsv_to_rgb(img)
# remove invalid point
img[:, :, 0] = img[:, :, 0] * valid
img[:, :, 1] = img[:, :, 1] * valid
img[:, :, 2] = img[:, :, 2] * valid
# show
plt.imshow(img)
plt.show()
return None
def read_flow(filename):
"""
read optical flow from Middlebury .flo file
:param filename: name of the flow file
:return: optical flow data in matrix
"""
f = open(filename, 'rb')
magic = np.fromfile(f, np.float32, count=1)
data2d = None
if 202021.25 != magic:
print('Magic number incorrect. Invalid .flo file')
else:
w = np.fromfile(f, np.int32, count=1)[0]
h = np.fromfile(f, np.int32, count=1)[0]
print("Reading %d x %d flo file" % (h, w))
data2d = np.fromfile(f, np.float32, count=2 * w * h)
# reshape data into 3D array (columns, rows, channels)
data2d = np.resize(data2d, (h, w, 2))
f.close()
return data2d
def read_flow_png(flow_file):
"""
Read optical flow from KITTI .png file
:param flow_file: name of the flow file
:return: optical flow data in matrix
"""
flow_object = png.Reader(filename=flow_file)
flow_direct = flow_object.asDirect()
flow_data = list(flow_direct[2])
(w, h) = flow_direct[3]['size']
flow = np.zeros((h, w, 3), dtype=np.float64)
for i in range(len(flow_data)):
flow[i, :, 0] = flow_data[i][0::3]
flow[i, :, 1] = flow_data[i][1::3]
flow[i, :, 2] = flow_data[i][2::3]
invalid_idx = (flow[:, :, 2] == 0)
flow[:, :, 0:2] = (flow[:, :, 0:2] - 2**15) / 64.0
flow[invalid_idx, 0] = 0
flow[invalid_idx, 1] = 0
return flow
def write_flow_png(flo, flow_file):
h, w, _ = flo.shape
out_flo = np.ones((h, w, 3), dtype=np.float32)
out_flo[:, :, 0] = np.maximum(
np.minimum(flo[:, :, 0] * 64.0 + 2**15, 2**16 - 1), 0)
out_flo[:, :, 1] = np.maximum(
np.minimum(flo[:, :, 1] * 64.0 + 2**15, 2**16 - 1), 0)
out_flo = out_flo.astype(np.uint16)
with open(flow_file, 'wb') as f:
writer = png.Writer(width=w, height=h, bitdepth=16)
# Convert z to the Python list of lists expected by
# the png writer.
z2list = out_flo.reshape(-1, w * 3).tolist()
writer.write(f, z2list)
def write_flow(flow, filename):
"""
write optical flow in Middlebury .flo format
:param flow: optical flow map
:param filename: optical flow file path to be saved
:return: None
"""
f = open(filename, 'wb')
magic = np.array([202021.25], dtype=np.float32)
(height, width) = flow.shape[0:2]
w = np.array([width], dtype=np.int32)
h = np.array([height], dtype=np.int32)
magic.tofile(f)
w.tofile(f)
h.tofile(f)
flow.tofile(f)
f.close()
def segment_flow(flow):
h = flow.shape[0]
w = flow.shape[1]
u = flow[:, :, 0]
v = flow[:, :, 1]
idx = ((abs(u) > LARGEFLOW) | (abs(v) > LARGEFLOW))
idx2 = (abs(u) == SMALLFLOW)
class0 = (v == 0) & (u == 0)
u[idx2] = 0.00001
tan_value = v / u
class1 = (tan_value < 1) & (tan_value >= 0) & (u > 0) & (v >= 0)
class2 = (tan_value >= 1) & (u >= 0) & (v >= 0)
class3 = (tan_value < -1) & (u <= 0) & (v >= 0)
class4 = (tan_value < 0) & (tan_value >= -1) & (u < 0) & (v >= 0)
class8 = (tan_value >= -1) & (tan_value < 0) & (u > 0) & (v <= 0)
class7 = (tan_value < -1) & (u >= 0) & (v <= 0)
class6 = (tan_value >= 1) & (u <= 0) & (v <= 0)
class5 = (tan_value >= 0) & (tan_value < 1) & (u < 0) & (v <= 0)
seg = np.zeros((h, w))
seg[class1] = 1
seg[class2] = 2
seg[class3] = 3
seg[class4] = 4
seg[class5] = 5
seg[class6] = 6
seg[class7] = 7
seg[class8] = 8
seg[class0] = 0
seg[idx] = 0
return seg
def flow_error(tu, tv, u, v):
"""
Calculate average end point error
:param tu: ground-truth horizontal flow map
:param tv: ground-truth vertical flow map
:param u: estimated horizontal flow map
:param v: estimated vertical flow map
:return: End point error of the estimated flow
"""
smallflow = 0.0
'''
stu = tu[bord+1:end-bord,bord+1:end-bord]
stv = tv[bord+1:end-bord,bord+1:end-bord]
su = u[bord+1:end-bord,bord+1:end-bord]
sv = v[bord+1:end-bord,bord+1:end-bord]
'''
stu = tu[:]
stv = tv[:]
su = u[:]
sv = v[:]
idxUnknow = (abs(stu) > UNKNOWN_FLOW_THRESH) | (
abs(stv) > UNKNOWN_FLOW_THRESH)
stu[idxUnknow] = 0
stv[idxUnknow] = 0
su[idxUnknow] = 0
sv[idxUnknow] = 0
ind2 = [(np.absolute(stu) > smallflow) | (np.absolute(stv) > smallflow)]
index_su = su[ind2]
index_sv = sv[ind2]
an = 1.0 / np.sqrt(index_su**2 + index_sv**2 + 1)
un = index_su * an
vn = index_sv * an
index_stu = stu[ind2]
index_stv = stv[ind2]
tn = 1.0 / np.sqrt(index_stu**2 + index_stv**2 + 1)
tun = index_stu * tn
tvn = index_stv * tn
'''
angle = un * tun + vn * tvn + (an * tn)
index = [angle == 1.0]
angle[index] = 0.999
ang = np.arccos(angle)
mang = np.mean(ang)
mang = mang * 180 / np.pi
'''
epe = np.sqrt((stu - su)**2 + (stv - sv)**2)
epe = epe[ind2]
mepe = np.mean(epe)
return mepe
def flow_to_image(flow):
"""
Convert flow into middlebury color code image
:param flow: optical flow map
:return: optical flow image in middlebury color
"""
u = flow[:, :, 0]
v = flow[:, :, 1]
maxu = -999.
maxv = -999.
minu = 999.
minv = 999.
idxUnknow = (abs(u) > UNKNOWN_FLOW_THRESH) | (abs(v) > UNKNOWN_FLOW_THRESH)
u[idxUnknow] = 0
v[idxUnknow] = 0
maxu = max(maxu, np.max(u))
minu = min(minu, np.min(u))
maxv = max(maxv, np.max(v))
minv = min(minv, np.min(v))
rad = np.sqrt(u**2 + v**2)
maxrad = max(-1, np.max(rad))
print("max flow: %.4f\nflow range:\nu = %.3f .. %.3f\nv = %.3f .. %.3f" % (
maxrad, minu, maxu, minv, maxv))
u = u / (maxrad + np.finfo(float).eps)
v = v / (maxrad + np.finfo(float).eps)
img = compute_color(u, v)
idx = np.repeat(idxUnknow[:, :, np.newaxis], 3, axis=2)
img[idx] = 0
return np.uint8(img)
def evaluate_flow_file(gt, pred):
"""
evaluate the estimated optical flow end point error according to ground truth provided
:param gt: ground truth file path
:param pred: estimated optical flow file path
:return: end point error, float32
"""
# Read flow files and calculate the errors
gt_flow = read_flow(gt) # ground truth flow
eva_flow = read_flow(pred) # predicted flow
# Calculate errors
average_pe = flow_error(gt_flow[:, :, 0], gt_flow[:, :, 1],
eva_flow[:, :, 0], eva_flow[:, :, 1])
return average_pe
def evaluate_flow(gt_flow, pred_flow):
"""
gt: ground-truth flow
pred: estimated flow
"""
average_pe = flow_error(gt_flow[:, :, 0], gt_flow[:, :, 1],
pred_flow[:, :, 0], pred_flow[:, :, 1])
return average_pe
"""
==============
Disparity Section
==============
"""
def read_disp_png(file_name):
"""
Read optical flow from KITTI .png file
:param file_name: name of the flow file
:return: optical flow data in matrix
"""
image_object = png.Reader(filename=file_name)
image_direct = image_object.asDirect()
image_data = list(image_direct[2])
(w, h) = image_direct[3]['size']
channel = len(image_data[0]) / w
flow = np.zeros((h, w, channel), dtype=np.uint16)
for i in range(len(image_data)):
for j in range(channel):
flow[i, :, j] = image_data[i][j::channel]
return flow[:, :, 0] / 256
def disp_to_flowfile(disp, filename):
"""
Read KITTI disparity file in png format
:param disp: disparity matrix
:param filename: the flow file name to save
:return: None
"""
f = open(filename, 'wb')
magic = np.array([202021.25], dtype=np.float32)
(height, width) = disp.shape[0:2]
w = np.array([width], dtype=np.int32)
h = np.array([height], dtype=np.int32)
empty_map = np.zeros((height, width), dtype=np.float32)
data = np.dstack((disp, empty_map))
magic.tofile(f)
w.tofile(f)
h.tofile(f)
data.tofile(f)
f.close()
"""
==============
Image Section
==============
"""
def read_image(filename):
"""
Read normal image of any format
:param filename: name of the image file
:return: image data in matrix uint8 type
"""
img = Image.open(filename)
im = np.array(img)
return im
def warp_image(im, flow):
"""
Use optical flow to warp image to the next
:param im: image to warp
:param flow: optical flow
:return: warped image
"""
image_height = im.shape[0]
image_width = im.shape[1]
flow_height = flow.shape[0]
flow_width = flow.shape[1]
n = image_height * image_width
(iy, ix) = np.mgrid[0:image_height, 0:image_width]
(fy, fx) = np.mgrid[0:flow_height, 0:flow_width]
fx += flow[:, :, 0]
fy += flow[:, :, 1]
mask = fx < 0 | fx > flow_width | fy < 0 | fy > flow_height
fx = np.min(np.max(fx, 0), flow_width)
fy = np.min(np.max(fy, 0), flow_height)
points = np.concatenate((ix.reshape(n, 1), iy.reshape(n, 1)), axis=1)
xi = np.concatenate((fx.reshape(n, 1), fy.reshape(n, 1)), axis=1)
warp = np.zeros((image_height, image_width, im.shape[2]))
for i in range(im.shape[2]):
channel = im[:, :, i]
values = channel.reshape(n, 1)
new_channel = scipy.interpolate.griddata(
points, values, xi, method='cubic')
new_channel[mask] = 1
warp[:, :, i] = new_channel
return warp
"""
==============
Others
==============
"""
def scale_image(image, new_range):
"""
Linearly scale the image into desired range
:param image: input image
:param new_range: the new range to be aligned
:return: image normalized in new range
"""
min_val = np.min(image).astype(np.float32)
max_val = np.max(image).astype(np.float32)
min_val_new = np.array(min(new_range), dtype=np.float32)
max_val_new = np.array(max(new_range), dtype=np.float32)
scaled_image = (image - min_val) / (max_val - min_val) * (
max_val_new - min_val_new) + min_val_new
return scaled_image.astype(np.uint8)
def compute_color(u, v):
"""
compute optical flow color map
:param u: optical flow horizontal map
:param v: optical flow vertical map
:return: optical flow in color code
"""
[h, w] = u.shape
img = np.zeros([h, w, 3])
nanIdx = np.isnan(u) | np.isnan(v)
u[nanIdx] = 0
v[nanIdx] = 0
colorwheel = make_color_wheel()
ncols = np.size(colorwheel, 0)
rad = np.sqrt(u**2 + v**2)
a = np.arctan2(-v, -u) / np.pi
fk = (a + 1) / 2 * (ncols - 1) + 1
k0 = np.floor(fk).astype(int)
k1 = k0 + 1
k1[k1 == ncols + 1] = 1
f = fk - k0
for i in range(0, np.size(colorwheel, 1)):
tmp = colorwheel[:, i]
col0 = tmp[k0 - 1] / 255
col1 = tmp[k1 - 1] / 255
col = (1 - f) * col0 + f * col1
idx = rad <= 1
col[idx] = 1 - rad[idx] * (1 - col[idx])
notidx = np.logical_not(idx)
col[notidx] *= 0.75
img[:, :, i] = np.uint8(np.floor(255 * col * (1 - nanIdx)))
return img
def make_color_wheel():
"""
Generate color wheel according Middlebury color code
:return: Color wheel
"""
RY = 15
YG = 6
GC = 4
CB = 11
BM = 13
MR = 6
ncols = RY + YG + GC + CB + BM + MR
colorwheel = np.zeros([ncols, 3])
col = 0
# RY
colorwheel[0:RY, 0] = 255
colorwheel[0:RY, 1] = np.transpose(np.floor(255 * np.arange(0, RY) / RY))
col += RY
# YG
colorwheel[col:col + YG, 0] = 255 - np.transpose(
np.floor(255 * np.arange(0, YG) / YG))
colorwheel[col:col + YG, 1] = 255
col += YG
# GC
colorwheel[col:col + GC, 1] = 255
colorwheel[col:col + GC, 2] = np.transpose(
np.floor(255 * np.arange(0, GC) / GC))
col += GC
# CB
colorwheel[col:col + CB, 1] = 255 - np.transpose(
np.floor(255 * np.arange(0, CB) / CB))
colorwheel[col:col + CB, 2] = 255
col += CB
# BM
colorwheel[col:col + BM, 2] = 255
colorwheel[col:col + BM, 0] = np.transpose(
np.floor(255 * np.arange(0, BM) / BM))
col += +BM
# MR
colorwheel[col:col + MR, 2] = 255 - np.transpose(
np.floor(255 * np.arange(0, MR) / MR))
colorwheel[col:col + MR, 0] = 255
return colorwheel
================================================
FILE: core/networks/__init__.py
================================================
import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from model_flow import Model_flow
from model_triangulate_pose import Model_triangulate_pose
from model_depth_pose import Model_depth_pose
from model_flowposenet import Model_flowposenet
def get_model(mode):
if mode == 'flow':
return Model_flow
elif mode == 'pose' or mode == 'pose_flow':
return Model_triangulate_pose
elif mode == 'depth' or mode == 'depth_pose':
return Model_depth_pose
elif mode == 'flowposenet':
return Model_flowposenet
else:
raise ValueError('Mode {} not found.'.format(mode))
================================================
FILE: core/networks/model_depth_pose.py
================================================
import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from structures import *
from model_triangulate_pose import Model_triangulate_pose
from pytorch_ssim import SSIM
sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'visualize'))
from visualizer import *
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import pdb
class Model_depth_pose(nn.Module):
def __init__(self, cfg):
super(Model_depth_pose, self).__init__()
self.depth_match_num = cfg.depth_match_num
self.depth_sample_ratio = cfg.depth_sample_ratio
self.depth_scale = cfg.depth_scale
self.w_flow_error = cfg.w_flow_error
self.dataset = cfg.dataset
self.depth_net = Depth_Model(cfg.depth_scale)
self.model_pose = Model_triangulate_pose(cfg)
def meshgrid(self, h, w):
xx, yy = np.meshgrid(np.arange(0,w), np.arange(0,h))
meshgrid = np.transpose(np.stack([xx,yy], axis=-1), [2,0,1]) # [2,h,w]
meshgrid = torch.from_numpy(meshgrid)
return meshgrid
def robust_rand_sample(self, match, mask, num):
# match: [b, 4, -1] mask: [b, 1, -1]
b, n = match.shape[0], match.shape[2]
nonzeros_num = torch.min(torch.sum(mask > 0, dim=-1)) # []
if nonzeros_num.detach().cpu().numpy() == n:
rand_int = torch.randint(0, n, [num])
select_match = match[:,:,rand_int]
else:
# If there is zero score in match, sample the non-zero matches.
num = np.minimum(nonzeros_num.detach().cpu().numpy(), num)
select_idxs = []
for i in range(b):
nonzero_idx = torch.nonzero(mask[i,0,:]) # [nonzero_num,1]
rand_int = torch.randint(0, nonzero_idx.shape[0], [int(num)])
select_idx = nonzero_idx[rand_int, :] # [num, 1]
select_idxs.append(select_idx)
select_idxs = torch.stack(select_idxs, 0) # [b,num,1]
select_match = torch.gather(match.transpose(1,2), index=select_idxs.repeat(1,1,4), dim=1).transpose(1,2) # [b, 4, num]
return select_match, num
def top_ratio_sample(self, match, mask, ratio):
# match: [b, 4, -1] mask: [b, 1, -1]
b, total_num = match.shape[0], match.shape[-1]
scores, indices = torch.topk(mask, int(ratio*total_num), dim=-1) # [B, 1, ratio*tnum]
select_match = torch.gather(match.transpose(1,2), index=indices.squeeze(1).unsqueeze(-1).repeat(1,1,4), dim=1).transpose(1,2) # [b, 4, ratio*tnum]
return select_match, scores
def rand_sample(self, match, num):
b, c, n = match.shape[0], match.shape[1], match.shape[2]
rand_int = torch.randint(0, match.shape[-1], size=[num])
select_pts = match[:,:,rand_int]
return select_pts
def filt_negative_depth(self, point2d_1_depth, point2d_2_depth, point2d_1_coord, point2d_2_coord):
# Filter out the negative projection depth.
# point2d_1_depth: [b, n, 1]
b, n = point2d_1_depth.shape[0], point2d_1_depth.shape[1]
mask = (point2d_1_depth > 0.01).float() * (point2d_2_depth > 0.01).float()
select_idxs = []
flag = 0
for i in range(b):
if torch.sum(mask[i,:,0]) == n:
idx = torch.arange(n).to(mask.get_device())
else:
nonzero_idx = torch.nonzero(mask[i,:,0]).squeeze(1) # [k]
if nonzero_idx.shape[0] < 0.1*n:
idx = torch.arange(n).to(mask.get_device())
flag = 1
else:
res = torch.randint(0, nonzero_idx.shape[0], size=[n-nonzero_idx.shape[0]]).to(mask.get_device()) # [n-nz]
idx = torch.cat([nonzero_idx, nonzero_idx[res]], 0)
select_idxs.append(idx)
select_idxs = torch.stack(select_idxs, dim=0) # [b,n]
point2d_1_depth = torch.gather(point2d_1_depth, index=select_idxs.unsqueeze(-1), dim=1) # [b,n,1]
point2d_2_depth = torch.gather(point2d_2_depth, index=select_idxs.unsqueeze(-1), dim=1) # [b,n,1]
point2d_1_coord = torch.gather(point2d_1_coord, index=select_idxs.unsqueeze(-1).repeat(1,1,2), dim=1) # [b,n,2]
point2d_2_coord = torch.gather(point2d_2_coord, index=select_idxs.unsqueeze(-1).repeat(1,1,2), dim=1) # [b,n,2]
return point2d_1_depth, point2d_2_depth, point2d_1_coord, point2d_2_coord, flag
def filt_invalid_coord(self, point2d_1_depth, point2d_2_depth, point2d_1_coord, point2d_2_coord, max_h, max_w):
# Filter out the negative projection depth.
# point2d_1_depth: [b, n, 1]
b, n = point2d_1_coord.shape[0], point2d_1_coord.shape[1]
max_coord = torch.Tensor([max_w, max_h]).to(point2d_1_coord.get_device())
mask = (point2d_1_coord > 0).all(dim=-1, keepdim=True).float() * (point2d_2_coord > 0).all(dim=-1, keepdim=True).float() * \
(point2d_1_coord < max_coord).all(dim=-1, keepdim=True).float() * (point2d_2_coord < max_coord).all(dim=-1, keepdim=True).float()
flag = 0
if torch.sum(1.0-mask) == 0:
return point2d_1_depth, point2d_2_depth, point2d_1_coord, point2d_2_coord, flag
select_idxs = []
for i in range(b):
if torch.sum(mask[i,:,0]) == n:
idx = torch.arange(n).to(mask.get_device())
else:
nonzero_idx = torch.nonzero(mask[i,:,0]).squeeze(1) # [k]
if nonzero_idx.shape[0] < 0.1*n:
idx = torch.arange(n).to(mask.get_device())
flag = 1
else:
res = torch.randint(0, nonzero_idx.shape[0], size=[n-nonzero_idx.shape[0]]).to(mask.get_device())
idx = torch.cat([nonzero_idx, nonzero_idx[res]], 0)
select_idxs.append(idx)
select_idxs = torch.stack(select_idxs, dim=0) # [b,n]
point2d_1_depth = torch.gather(point2d_1_depth, index=select_idxs.unsqueeze(-1), dim=1) # [b,n,1]
point2d_2_depth = torch.gather(point2d_2_depth, index=select_idxs.unsqueeze(-1), dim=1) # [b,n,1]
point2d_1_coord = torch.gather(point2d_1_coord, index=select_idxs.unsqueeze(-1).repeat(1,1,2), dim=1) # [b,n,2]
point2d_2_coord = torch.gather(point2d_2_coord, index=select_idxs.unsqueeze(-1).repeat(1,1,2), dim=1) # [b,n,2]
return point2d_1_depth, point2d_2_depth, point2d_1_coord, point2d_2_coord, flag
def ray_angle_filter(self, match, P1, P2, return_angle=False):
# match: [b, 4, n] P: [B, 3, 4]
b, n = match.shape[0], match.shape[2]
K = P1[:,:,:3] # P1 with identity rotation and zero translation
K_inv = torch.inverse(K)
RT1 = K_inv.bmm(P1) # [b, 3, 4]
RT2 = K_inv.bmm(P2)
ones = torch.ones([b,1,n]).to(match.get_device())
pts1 = torch.cat([match[:,:2,:], ones], 1)
pts2 = torch.cat([match[:,2:,:], ones], 1)
ray1_dir = (RT1[:,:,:3].transpose(1,2)).bmm(K_inv).bmm(pts1)# [b,3,n]
ray1_dir = ray1_dir / (torch.norm(ray1_dir, dim=1, keepdim=True, p=2) + 1e-12)
ray1_origin = (-1) * RT1[:,:,:3].transpose(1,2).bmm(RT1[:,:,3].unsqueeze(-1)) # [b, 3, 1]
ray2_dir = (RT2[:,:,:3].transpose(1,2)).bmm(K_inv).bmm(pts2) # [b,3,n]
ray2_dir = ray2_dir / (torch.norm(ray2_dir, dim=1, keepdim=True, p=2) + 1e-12)
ray2_origin = (-1) * RT2[:,:,:3].transpose(1,2).bmm(RT2[:,:,3].unsqueeze(-1)) # [b, 3, 1]
# We compute the angle betwwen vertical line from ray1 origin to ray2 and ray1.
p1p2 = (ray1_origin - ray2_origin).repeat(1,1,n)
verline = ray2_origin.repeat(1,1,n) + torch.sum(p1p2 * ray2_dir, dim=1, keepdim=True) * ray2_dir - ray1_origin.repeat(1,1,n) # [b,3,n]
cosvalue = torch.sum(ray1_dir * verline, dim=1, keepdim=True) / \
((torch.norm(ray1_dir, dim=1, keepdim=True, p=2) + 1e-12) * (torch.norm(verline, dim=1, keepdim=True, p=2) + 1e-12))# [b,1,n]
mask = (cosvalue > 0.001).float() # we drop out angles less than 1' [b,1,n]
flag = 0
num = torch.min(torch.sum(mask, -1)).int()
if num.cpu().detach().numpy() == 0:
flag = 1
filt_match = match[:,:,:100]
if return_angle:
return filt_match, flag, torch.zeros_like(mask).to(filt_match.get_device())
else:
return filt_match, flag
nonzero_idx = []
for i in range(b):
idx = torch.nonzero(mask[i,0,:])[:num] # [num,1]
nonzero_idx.append(idx)
nonzero_idx = torch.stack(nonzero_idx, 0) # [b,num,1]
filt_match = torch.gather(match.transpose(1,2), index=nonzero_idx.repeat(1,1,4), dim=1).transpose(1,2) # [b,4,num]
if return_angle:
return filt_match, flag, mask
else:
return filt_match, flag
def midpoint_triangulate(self, match, K_inv, P1, P2):
# match: [b, 4, num] P1: [b, 3, 4]
# Match is in the image coordinates. P1, P2 is camera parameters. [B, 3, 4] match: [B, M, 4]
b, n = match.shape[0], match.shape[2]
RT1 = K_inv.bmm(P1) # [b, 3, 4]
RT2 = K_inv.bmm(P2)
ones = torch.ones([b,1,n]).to(match.get_device())
pts1 = torch.cat([match[:,:2,:], ones], 1)
pts2 = torch.cat([match[:,2:,:], ones], 1)
ray1_dir = (RT1[:,:,:3].transpose(1,2)).bmm(K_inv).bmm(pts1)# [b,3,n]
ray1_dir = ray1_dir / (torch.norm(ray1_dir, dim=1, keepdim=True, p=2) + 1e-12)
ray1_origin = (-1) * RT1[:,:,:3].transpose(1,2).bmm(RT1[:,:,3].unsqueeze(-1)) # [b, 3, 1]
ray2_dir = (RT2[:,:,:3].transpose(1,2)).bmm(K_inv).bmm(pts2) # [b,3,n]
ray2_dir = ray2_dir / (torch.norm(ray2_dir, dim=1, keepdim=True, p=2) + 1e-12)
ray2_origin = (-1) * RT2[:,:,:3].transpose(1,2).bmm(RT2[:,:,3].unsqueeze(-1)) # [b, 3, 1]
dir_cross = torch.cross(ray1_dir, ray2_dir, dim=1) # [b,3,n]
denom = 1.0 / (torch.sum(dir_cross * dir_cross, dim=1, keepdim=True)+1e-12) # [b,1,n]
origin_vec = (ray2_origin - ray1_origin).repeat(1,1,n) # [b,3,n]
a1 = origin_vec.cross(ray2_dir, dim=1) # [b,3,n]
a1 = torch.sum(a1 * dir_cross, dim=1, keepdim=True) * denom # [b,1,n]
a2 = origin_vec.cross(ray1_dir, dim=1) # [b,3,n]
a2 = torch.sum(a2 * dir_cross, dim=1, keepdim=True) * denom # [b,1,n]
p1 = ray1_origin + a1 * ray1_dir
p2 = ray2_origin + a2 * ray2_dir
point = (p1 + p2) / 2.0 # [b,3,n]
# Convert to homo coord to get consistent with other functions.
point_homo = torch.cat([point, ones], dim=1).transpose(1,2) # [b,n,4]
return point_homo
def rt_from_fundamental_mat_nyu(self, fmat, K, depth_match):
# F: [b, 3, 3] K: [b, 3, 3] depth_match: [b ,4, n]
#verify_match = self.rand_sample(depth_match, 5000) # [b,4,100]
verify_match = depth_match.transpose(1,2).cpu().detach().numpy()
K_inv = torch.inverse(K)
b = fmat.shape[0]
fmat_ = K.transpose(1,2).bmm(fmat)
essential_mat = fmat_.bmm(K)
iden = torch.cat([torch.eye(3), torch.zeros([3,1])], -1).unsqueeze(0).repeat(b,1,1).to(K.get_device()) # [b,3,4]
P1 = K.bmm(iden)
flags = []
number_inliers = []
P2 = []
for i in range(b):
cnum, R, t, _ = cv2.recoverPose(essential_mat[i].cpu().detach().numpy().astype('float64'), verify_match[i,:,:2].astype('float64'), \
verify_match[i,:,2:].astype('float64'), cameraMatrix=K[i,:,:].cpu().detach().numpy().astype('float64'))
p2 = torch.from_numpy(np.concatenate([R, t], axis=-1)).float().to(K.get_device())
P2.append(p2)
if cnum > depth_match.shape[-1] / 7.0:
flags.append(1)
else:
flags.append(0)
number_inliers.append(cnum)
P2 = K.bmm(torch.stack(P2, axis=0))
#pdb.set_trace()
return P1, P2, flags
def verifyRT(self, match, K_inv, P1, P2):
# match: [b, 4, n] P1: [b,3,4] P2: [b,3,4]
b, n = match.shape[0], match.shape[2]
point3d = self.midpoint_triangulate(match, K_inv, P1, P2).reshape([-1,4]).unsqueeze(-1) # [b*n, 4, 1]
P1_ = P1.repeat(n,1,1)
P2_ = P2.repeat(n,1,1)
depth1 = P1_.bmm(point3d)[:,-1,:] / point3d[:,-1,:] # [b*n, 1]
depth2 = P2_.bmm(point3d)[:,-1,:] / point3d[:,-1,:]
inlier_num = torch.sum((depth1.view([b,n]) > 0).float() * (depth2.view([b,n]) > 0).float(), 1) # [b]
return inlier_num
def rt_from_fundamental_mat(self, fmat, K, depth_match):
# F: [b, 3, 3] K: [b, 3, 3] depth_match: [b ,4, n]
verify_match = self.rand_sample(depth_match, 200) # [b,4,100]
K_inv = torch.inverse(K)
b = fmat.shape[0]
fmat_ = K.transpose(1,2).bmm(fmat)
essential_mat = fmat_.bmm(K)
essential_mat_cpu = essential_mat.cpu()
U, S, V = torch.svd(essential_mat_cpu)
U, S, V = U.to(K.get_device()), S.to(K.get_device()), V.to(K.get_device())
W = torch.from_numpy(np.array([[[0., -1., 0.],[1., 0., 0.],[0., 0., 1.]]])).float().repeat(b,1,1).to(K.get_device())
# R = UWV^t or UW^tV^t t = U[:,2] the third column of U
R1 = U.bmm(W).bmm(V.transpose(1,2)) # Do we need matrix determinant sign?
R1 = torch.sign(torch.det(R1)).unsqueeze(-1).unsqueeze(-1) * R1
R2 = U.bmm(W.transpose(1,2)).bmm(V.transpose(1,2))
R2 = torch.sign(torch.det(R2)).unsqueeze(-1).unsqueeze(-1) * R2
t1 = U[:,:,2].unsqueeze(-1) # The third column
t2 = -U[:,:,2].unsqueeze(-1) # Inverse direction
iden = torch.cat([torch.eye(3), torch.zeros([3,1])], -1).unsqueeze(0).repeat(b,1,1).to(K.get_device()) # [b,3,4]
P1 = K.bmm(iden)
P2_1 = K.bmm(torch.cat([R1, t1], -1))
P2_2 = K.bmm(torch.cat([R2, t1], -1))
P2_3 = K.bmm(torch.cat([R1, t2], -1))
P2_4 = K.bmm(torch.cat([R2, t2], -1))
P2_c = [P2_1, P2_2, P2_3, P2_4]
flags = []
for i in range(4):
with torch.no_grad():
inlier_num = self.verifyRT(verify_match, K_inv, P1, P2_c[i])
flags.append(inlier_num)
P2_c = torch.stack(P2_c, dim=1) # [B, 4, 3, 4]
flags = torch.stack(flags, dim=1) # [B, 4]
idx = torch.argmax(flags, dim=-1, keepdim=True) # [b,1]
P2 = torch.gather(P2_c, index=idx.unsqueeze(-1).unsqueeze(-1).repeat(1,1,3,4), dim=1).squeeze(1) # [b,3,4]
#pdb.set_trace()
return P1, P2
def reproject(self, P, point3d):
# P: [b,3,4] point3d: [b,n,4]
point2d = P.bmm(point3d.transpose(1,2)) # [b,4,n]
point2d_coord = (point2d[:,:2,:] / (point2d[:,2,:].unsqueeze(1) + 1e-12)).transpose(1,2) # [b,n,2]
point2d_depth = point2d[:,2,:].unsqueeze(1).transpose(1,2) # [b,n,1]
return point2d_coord, point2d_depth
def scale_adapt(self, depth1, depth2, eps=1e-12):
with torch.no_grad():
A = torch.sum((depth1 ** 2) / (depth2 ** 2 + eps), dim=1) # [b,1]
C = torch.sum(depth1 / (depth2 + eps), dim=1) # [b,1]
a = C / (A + eps)
return a
def affine_adapt(self, depth1, depth2, use_translation=True, eps=1e-12):
a_scale = self.scale_adapt(depth1, depth2, eps=eps)
if not use_translation: # only fit the scale parameter
return a_scale, torch.zeros_like(a_scale)
else:
with torch.no_grad():
A = torch.sum((depth1 ** 2) / (depth2 ** 2 + eps), dim=1) # [b,1]
B = torch.sum(depth1 / (depth2 ** 2 + eps), dim=1) # [b,1]
C = torch.sum(depth1 / (depth2 + eps), dim=1) # [b,1]
D = torch.sum(1.0 / (depth2 ** 2 + eps), dim=1) # [b,1]
E = torch.sum(1.0 / (depth2 + eps), dim=1) # [b,1]
a = (B*E - D*C) / (B*B - A*D + 1e-12)
b = (B*C - A*E) / (B*B - A*D + 1e-12)
# check ill condition
cond = (B*B - A*D)
valid = (torch.abs(cond) > 1e-4).float()
a = a * valid + a_scale * (1 - valid)
b = b * valid
return a, b
def register_depth(self, depth_pred, coord_tri, depth_tri):
# depth_pred: [b, 1, h, w] coord_tri: [b,n,2] depth_tri: [b,n,1]
batch, _, h, w = depth_pred.shape[0], depth_pred.shape[1], depth_pred.shape[2], depth_pred.shape[3]
n = depth_tri.shape[1]
coord_tri_nor = torch.stack([2.0*coord_tri[:,:,0] / (w-1.0) - 1.0, 2.0*coord_tri[:,:,1] / (h-1.0) - 1.0], -1)
depth_inter = F.grid_sample(depth_pred, coord_tri_nor.view([batch,n,1,2]), padding_mode='reflection').squeeze(-1).transpose(1,2) # [b,n,1]
# Normalize
scale = torch.median(depth_inter, 1)[0] / (torch.median(depth_tri, 1)[0] + 1e-12)
scale = scale.detach() # [b,1]
scale_depth_inter = depth_inter / (scale.unsqueeze(-1) + 1e-12)
scale_depth_pred = depth_pred / (scale.unsqueeze(-1).unsqueeze(-1) + 1e-12)
# affine adapt
a, b = self.affine_adapt(scale_depth_inter, depth_tri, use_translation=False)
affine_depth_inter = a.unsqueeze(1) * scale_depth_inter + b.unsqueeze(1) # [b,n,1]
affine_depth_pred = a.unsqueeze(-1).unsqueeze(-1) * scale_depth_pred + b.unsqueeze(-1).unsqueeze(-1) # [b,1,h,w]
return affine_depth_pred, affine_depth_inter
def get_trian_loss(self, tri_depth, pred_tri_depth):
# depth: [b,n,1]
loss = torch.pow(1.0 - pred_tri_depth / (tri_depth + 1e-12), 2).mean((1,2))
return loss
def get_reproj_fdp_loss(self, pred1, pred2, P2, K, K_inv, valid_mask, rigid_mask, flow, visualizer=None):
# pred: [b,1,h,w] Rt: [b,3,4] K: [b,3,3] mask: [b,1,h,w] flow: [b,2,h,w]
b, h, w = pred1.shape[0], pred1.shape[2], pred1.shape[3]
xy = self.meshgrid(h,w).unsqueeze(0).repeat(b,1,1,1).float().to(flow.get_device()) # [b,2,h,w]
ones = torch.ones([b,1,h,w]).float().to(flow.get_device())
pts1_3d = K_inv.bmm(torch.cat([xy, ones], 1).view([b,3,-1])) * pred1.view([b,1,-1]) # [b,3,h*w]
pts2_coord, pts2_depth = self.reproject(P2, torch.cat([pts1_3d, ones.view([b,1,-1])], 1).transpose(1,2)) # [b,h*w, 2]
# TODO Here some of the reprojection coordinates are invalid. (<0 or >max)
reproj_valid_mask = (pts2_coord > torch.Tensor([0,0]).to(pred1.get_device())).all(-1, True).float() * \
(pts2_coord < torch.Tensor([w-1,h-1]).to(pred1.get_device())).all(-1, True).float() # [b,h*w, 1]
reproj_valid_mask = (valid_mask * reproj_valid_mask.view([b,h,w,1]).permute([0,3,1,2])).detach()
rigid_mask = rigid_mask.detach()
pts2_depth = pts2_depth.transpose(1,2).view([b,1,h,w])
# Get the interpolated depth prediction2
pts2_coord_nor = torch.cat([2.0 * pts2_coord[:,:,0].unsqueeze(-1) / (w - 1.0) - 1.0, 2.0 * pts2_coord[:,:,1].unsqueeze(-1) / (h - 1.0) - 1.0], -1)
inter_depth2 = F.grid_sample(pred2, pts2_coord_nor.view([b, h, w, 2]), padding_mode='reflection') # [b,1,h,w]
pj_loss_map = (torch.abs(1.0 - pts2_depth / (inter_depth2 + 1e-12)) * rigid_mask * reproj_valid_mask)
pj_loss = pj_loss_map.mean((1,2,3)) / ((reproj_valid_mask * rigid_mask).mean((1,2,3))+1e-12)
#pj_loss = (valid_mask * mask * torch.abs(pts2_depth - inter_depth2) / (torch.abs(pts2_depth + inter_depth2)+1e-12)).mean((1,2,3)) / ((valid_mask * mask).mean((1,2,3))+1e-12) # [b]
flow_loss = (rigid_mask * torch.abs(flow + xy - pts2_coord.detach().permute(0,2,1).view([b,2,h,w]))).mean((1,2,3)) / (rigid_mask.mean((1,2,3)) + 1e-12)
return pj_loss, flow_loss
def disp2depth(self, disp, min_depth=0.1, max_depth=100.0):
min_disp = 1 / max_depth
max_disp = 1 / min_depth
scaled_disp = min_disp + (max_disp - min_disp) * disp
depth = 1 / scaled_disp
return scaled_disp, depth
def get_smooth_loss(self, img, disp):
# img: [b,3,h,w] depth: [b,1,h,w]
"""Computes the smoothness loss for a disparity image
The color image is used for edge-aware smoothness
"""
grad_disp_x = torch.abs(disp[:, :, :, :-1] - disp[:, :, :, 1:])
grad_disp_y = torch.abs(disp[:, :, :-1, :] - disp[:, :, 1:, :])
grad_img_x = torch.mean(torch.abs(img[:, :, :, :-1] - img[:, :, :, 1:]), 1, keepdim=True)
grad_img_y = torch.mean(torch.abs(img[:, :, :-1, :] - img[:, :, 1:, :]), 1, keepdim=True)
grad_disp_x *= torch.exp(-grad_img_x)
grad_disp_y *= torch.exp(-grad_img_y)
return grad_disp_x.mean((1,2,3)) + grad_disp_y.mean((1,2,3))
def infer_depth(self, img):
disp_list = self.depth_net(img)
disp, depth = self.disp2depth(disp_list[0])
return disp_list[0]
def infer_vo(self, img1, img2, K, K_inv, match_num=6000):
b, img_h, img_w = img1.shape[0], img1.shape[2], img1.shape[3]
F_final, img1_valid_mask, img1_rigid_mask, fwd_flow, fwd_match = self.model_pose.inference(img1, img2, K, K_inv)
# infer depth
disp1_list = self.depth_net(img1) # Nscales * [B, 1, H, W]
disp2_list = self.depth_net(img2)
disp1, depth1 = self.disp2depth(disp1_list[0])
disp2, depth2 = self.disp2depth(disp2_list[0])
img1_depth_mask = img1_rigid_mask * img1_valid_mask
# [b, 4, match_num]
top_ratio_match, top_ratio_mask = self.top_ratio_sample(fwd_match.view([b,4,-1]), img1_depth_mask.view([b,1,-1]), ratio=0.30) # [b, 4, ratio*h*w]
depth_match, depth_match_num = self.robust_rand_sample(top_ratio_match, top_ratio_mask, num=match_num)
return depth_match, depth1, depth2
def check_rt(self, img1, img2, K, K_inv):
# initialization
b = img1.shape[0]
flag1, flag2, flag3 = 0, 0, 0
images = torch.cat([img1, img2], dim=2)
inputs = [images, K.unsqueeze(1), K_inv.unsqueeze(1)]
# Pose Network
#self.profiler.reset()
loss_pack, F_final, img1_valid_mask, img1_rigid_score, img1_inlier_mask, fwd_flow, fwd_match = self.model_pose(inputs, output_F=True)
# Get masks
img1_depth_mask = img1_rigid_score * img1_valid_mask
# Select top score matches to triangulate depth.
top_ratio_match, top_ratio_mask = self.top_ratio_sample(fwd_match.view([b,4,-1]), img1_depth_mask.view([b,1,-1]), ratio=0.20) # [b, 4, ratio*h*w]
depth_match, depth_match_num = self.robust_rand_sample(top_ratio_match, top_ratio_mask, num=self.depth_match_num)
P1, P2, flags = self.rt_from_fundamental_mat_nyu(F_final.detach(), K, depth_match)
P1 = P1.detach()
P2 = P2.detach()
flags = torch.from_numpy(np.stack(flags, axis=0)).float().to(K.get_device())
return flags
def inference(self, img1, img2, K, K_inv):
b, img_h, img_w = img1.shape[0], img1.shape[2], img1.shape[3]
visualizer = Visualizer_debug('./vis/', np.transpose(255*img1.detach().cpu().numpy(), [0,2,3,1]), \
np.transpose(255*img2.detach().cpu().numpy(), [0,2,3,1]))
F_final, img1_valid_mask, img1_rigid_mask, fwd_flow, fwd_match = self.model_pose.inference(img1, img2, K, K_inv)
# infer depth
disp1_list = self.depth_net(img1) # Nscales * [B, 1, H, W]
disp2_list = self.depth_net(img2)
disp1, _ = self.disp2depth(disp1_list[0])
disp2, _ = self.disp2depth(disp2_list[0])
# Get Camera Matrix
img1_depth_mask = img1_rigid_mask * img1_valid_mask
# [b, 4, match_num]
top_ratio_match, top_ratio_mask = self.top_ratio_sample(fwd_match.view([b,4,-1]), img1_depth_mask.view([b,1,-1]), ratio=0.20) # [b, 4, ratio*h*w]
depth_match, depth_match_num = self.robust_rand_sample(top_ratio_match, top_ratio_mask, num=self.depth_match_num)
if self.dataset == 'nyuv2':
P1, P2, _ = self.rt_from_fundamental_mat_nyu(F_final, K, depth_match)
else:
P1, P2 = self.rt_from_fundamental_mat(F_final, K, depth_match)
Rt = K_inv.bmm(P2)
filt_depth_match, flag1 = self.ray_angle_filter(depth_match, P1, P2) # [b, 4, filt_num]
point3d_1 = self.midpoint_triangulate(filt_depth_match, K_inv, P1, P2)
point2d_1_coord, point2d_1_depth = self.reproject(P1, point3d_1) # [b,n,2], [b,n,1]
point2d_2_coord, point2d_2_depth = self.reproject(P2, point3d_1)
# Filter out some invalid triangulation results to stablize training.
point2d_1_depth, point2d_2_depth, point2d_1_coord, point2d_2_coord, flag2 = self.filt_negative_depth(point2d_1_depth, \
point2d_2_depth, point2d_1_coord, point2d_2_coord)
point2d_1_depth, point2d_2_depth, point2d_1_coord, point2d_2_coord, flag3 = self.filt_invalid_coord(point2d_1_depth, \
point2d_2_depth, point2d_1_coord, point2d_2_coord, max_h=img_h, max_w=img_w)
return fwd_flow, disp1, disp2, Rt, point2d_1_coord, point2d_1_depth
def forward(self, inputs):
# initialization
images, K_ms, K_inv_ms = inputs
K, K_inv = K_ms[:,0,:,:], K_inv_ms[:,0,:,:]
assert (images.shape[1] == 3)
img_h, img_w = int(images.shape[2] / 2), images.shape[3]
img1, img2 = images[:,:,:img_h,:], images[:,:,img_h:,:]
b = img1.shape[0]
flag1, flag2, flag3 = 0, 0, 0
visualizer = Visualizer_debug('./vis/', img1=255*img1.permute([0,2,3,1]).detach().cpu().numpy(), \
img2=255*img2.permute([0,2,3,1]).detach().cpu().numpy())
# Pose Network
loss_pack, F_final, img1_valid_mask, img1_rigid_mask, fwd_flow, fwd_match = self.model_pose(inputs, output_F=True, visualizer=visualizer)
# infer depth
disp1_list = self.depth_net(img1) # Nscales * [B, 1, H, W]
disp2_list = self.depth_net(img2)
# Get masks
img1_depth_mask = img1_rigid_mask * img1_valid_mask
# Select top score matches to triangulate depth.
top_ratio_match, top_ratio_mask = self.top_ratio_sample(fwd_match.view([b,4,-1]), img1_depth_mask.view([b,1,-1]), ratio=self.depth_sample_ratio) # [b, 4, ratio*h*w]
depth_match, depth_match_num = self.robust_rand_sample(top_ratio_match, top_ratio_mask, num=self.depth_match_num)
if self.dataset == 'nyuv2':
P1, P2, flags = self.rt_from_fundamental_mat_nyu(F_final.detach(), K, depth_match)
flags = torch.from_numpy(np.stack(flags, axis=0)).float().to(K.get_device())
else:
P1, P2 = self.rt_from_fundamental_mat(F_final.detach(), K, depth_match)
P1 = P1.detach()
P2 = P2.detach()
# Get triangulated points
filt_depth_match, flag1 = self.ray_angle_filter(depth_match, P1, P2, return_angle=False) # [b, 4, filt_num]
point3d_1 = self.midpoint_triangulate(filt_depth_match, K_inv, P1, P2)
point2d_1_coord, point2d_1_depth = self.reproject(P1, point3d_1) # [b,n,2], [b,n,1]
point2d_2_coord, point2d_2_depth = self.reproject(P2, point3d_1)
# Filter out some invalid triangulation results to stablize training.
point2d_1_depth, point2d_2_depth, point2d_1_coord, point2d_2_coord, flag2 = self.filt_negative_depth(point2d_1_depth, \
point2d_2_depth, point2d_1_coord, point2d_2_coord)
point2d_1_depth, point2d_2_depth, point2d_1_coord, point2d_2_coord, flag3 = self.filt_invalid_coord(point2d_1_depth, \
point2d_2_depth, point2d_1_coord, point2d_2_coord, max_h=img_h, max_w=img_w)
if flag1 + flag2 + flag3 > 0:
loss_pack['pt_depth_loss'] = torch.zeros([2]).to(point3d_1.get_device()).requires_grad_()
loss_pack['pj_depth_loss'] = torch.zeros([2]).to(point3d_1.get_device()).requires_grad_()
loss_pack['flow_error'] = torch.zeros([2]).to(point3d_1.get_device()).requires_grad_()
loss_pack['depth_smooth_loss'] = torch.zeros([2]).to(point3d_1.get_device()).requires_grad_()
return loss_pack
pt_depth_loss = 0
pj_depth_loss = 0
flow_error = 0
depth_smooth_loss = 0
for s in range(self.depth_scale):
disp_pred1 = F.interpolate(disp1_list[s], size=(img_h, img_w), mode='bilinear') # [b,1,h,w]
disp_pred2 = F.interpolate(disp2_list[s], size=(img_h, img_w), mode='bilinear')
scaled_disp1, depth_pred1 = self.disp2depth(disp_pred1)
scaled_disp2, depth_pred2 = self.disp2depth(disp_pred2)
# Rescale predicted depth according to triangulated depth
# [b,1,h,w], [b,n,1]
rescaled_pred1, inter_pred1 = self.register_depth(depth_pred1, point2d_1_coord, point2d_1_depth)
rescaled_pred2, inter_pred2 = self.register_depth(depth_pred2, point2d_2_coord, point2d_2_depth)
# Get Losses
pt_depth_loss += self.get_trian_loss(point2d_1_depth, inter_pred1) + self.get_trian_loss(point2d_2_depth, inter_pred2)
pj_depth, flow_loss = self.get_reproj_fdp_loss(rescaled_pred1, rescaled_pred2, P2, K, K_inv, img1_valid_mask, img1_rigid_mask, fwd_flow, visualizer=visualizer)
depth_smooth_loss += self.get_smooth_loss(img1, disp_pred1 / (disp_pred1.mean((2,3), True) + 1e-12)) + \
self.get_smooth_loss(img2, disp_pred2 / (disp_pred2.mean((2,3), True) + 1e-12))
pj_depth_loss += pj_depth
flow_error += flow_loss
if self.dataset == 'nyuv2':
loss_pack['pt_depth_loss'] = pt_depth_loss * flags
loss_pack['pj_depth_loss'], loss_pack['flow_error'] = pj_depth_loss * flags, flow_error * flags
loss_pack['depth_smooth_loss'] = depth_smooth_loss * flags
else:
loss_pack['pt_depth_loss'] = pt_depth_loss
loss_pack['pj_depth_loss'], loss_pack['flow_error'] = pj_depth_loss, flow_error
loss_pack['depth_smooth_loss'] = depth_smooth_loss
return loss_pack
================================================
FILE: core/networks/model_flow.py
================================================
import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from structures import *
from pytorch_ssim import SSIM
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import pdb
import cv2
def transformerFwd(U,
flo,
out_size,
name='SpatialTransformerFwd'):
"""Forward Warping Layer described in
'Occlusion Aware Unsupervised Learning of Optical Flow by Yang Wang et al'
Parameters
----------
U : float
The output of a convolutional net should have the
shape [num_batch, height, width, num_channels].
flo: float
The optical flow used for forward warping
having the shape of [num_batch, height, width, 2].
backprop: boolean
Indicates whether to back-propagate through forward warping layer
out_size: tuple of two ints
The size of the output of the network (height, width)
"""
def _repeat(x, n_repeats):
rep = torch.ones(size=[n_repeats], dtype=torch.long).unsqueeze(1).transpose(1,0)
x = x.view([-1,1]).mm(rep)
return x.view([-1]).int()
def _interpolate(im, x, y, out_size):
# constants
num_batch, height, width, channels = im.shape[0], im.shape[1], im.shape[2], im.shape[3]
out_height = out_size[0]
out_width = out_size[1]
max_y = int(height - 1)
max_x = int(width - 1)
# scale indices from [-1, 1] to [0, width/height]
x = (x + 1.0) * (width - 1.0) / 2.0
y = (y + 1.0) * (height - 1.0) / 2.0
# do sampling
x0 = (torch.floor(x)).int()
x1 = x0 + 1
y0 = (torch.floor(y)).int()
y1 = y0 + 1
x0_c = torch.clamp(x0, 0, max_x)
x1_c = torch.clamp(x1, 0, max_x)
y0_c = torch.clamp(y0, 0, max_y)
y1_c = torch.clamp(y1, 0, max_y)
dim2 = width
dim1 = width * height
base = _repeat(torch.arange(0, num_batch) * dim1, out_height * out_width).to(im.get_device())
base_y0 = base + y0_c * dim2
base_y1 = base + y1_c * dim2
idx_a = base_y0 + x0_c
idx_b = base_y1 + x0_c
idx_c = base_y0 + x1_c
idx_d = base_y1 + x1_c
# use indices to lookup pixels in the flat image and restore
# channels dim
im_flat = im.view([-1, channels])
im_flat = im_flat.float()
# and finally calculate interpolated values
x0_f = x0.float()
x1_f = x1.float()
y0_f = y0.float()
y1_f = y1.float()
wa = ((x1_f - x) * (y1_f - y)).unsqueeze(1)
wb = ((x1_f - x) * (y - y0_f)).unsqueeze(1)
wc = ((x - x0_f) * (y1_f - y)).unsqueeze(1)
wd = ((x - x0_f) * (y - y0_f)).unsqueeze(1)
zerof = torch.zeros_like(wa)
wa = torch.where(
(torch.eq(x0_c, x0) & torch.eq(y0_c, y0)).unsqueeze(1), wa, zerof)
wb = torch.where(
(torch.eq(x0_c, x0) & torch.eq(y1_c, y1)).unsqueeze(1), wb, zerof)
wc = torch.where(
(torch.eq(x1_c, x1) & torch.eq(y0_c, y0)).unsqueeze(1), wc, zerof)
wd = torch.where(
(torch.eq(x1_c, x1) & torch.eq(y1_c, y1)).unsqueeze(1), wd, zerof)
zeros = torch.zeros(
size=[
int(num_batch) * int(height) *
int(width), int(channels)
],
dtype=torch.float)
output = zeros.to(im.get_device())
output = output.scatter_add(dim=0, index=idx_a.long().unsqueeze(1).repeat(1,channels), src=im_flat * wa)
output = output.scatter_add(dim=0, index=idx_b.long().unsqueeze(1).repeat(1,channels), src=im_flat * wb)
output = output.scatter_add(dim=0, index=idx_c.long().unsqueeze(1).repeat(1,channels), src=im_flat * wc)
output = output.scatter_add(dim=0, index=idx_d.long().unsqueeze(1).repeat(1,channels), src=im_flat * wd)
return output
def _meshgrid(height, width):
# This should be equivalent to:
x_t, y_t = np.meshgrid(np.linspace(-1, 1, width),
np.linspace(-1, 1, height))
# ones = np.ones(np.prod(x_t.shape))
# grid = np.vstack([x_t.flatten(), y_t.flatten(), ones])
return torch.from_numpy(x_t).float(), torch.from_numpy(y_t).float()
def _transform(flo, input_dim, out_size):
num_batch, height, width, num_channels = input_dim.shape[0:4]
# grid of (x_t, y_t, 1), eq (1) in ref [1]
height_f = float(height)
width_f = float(width)
out_height = out_size[0]
out_width = out_size[1]
x_s, y_s = _meshgrid(out_height, out_width)
x_s = x_s.to(flo.get_device()).unsqueeze(0)
x_s = x_s.repeat([num_batch, 1, 1])
y_s = y_s.to(flo.get_device()).unsqueeze(0)
y_s =y_s.repeat([num_batch, 1, 1])
x_t = x_s + flo[:, :, :, 0] / ((out_width - 1.0) / 2.0)
y_t = y_s + flo[:, :, :, 1] / ((out_height - 1.0) / 2.0)
x_t_flat = x_t.view([-1])
y_t_flat = y_t.view([-1])
input_transformed = _interpolate(input_dim, x_t_flat, y_t_flat,
out_size)
output = input_transformed.view([num_batch, out_height, out_width, num_channels])
return output
#out_size = int(out_size)
output = _transform(flo, U, out_size)
return output
class Model_flow(nn.Module):
def __init__(self, cfg):
super(Model_flow, self).__init__()
self.fpyramid = FeaturePyramid()
self.pwc_model = PWC_tf()
if cfg.mode == 'depth' or cfg.mode == 'flowposenet':
# Stage 2 training
for param in self.fpyramid.parameters():
param.requires_grad = False
for param in self.pwc_model.parameters():
param.requires_grad = False
# hyperparameters
self.dataset = cfg.dataset
self.num_scales = cfg.num_scales
self.flow_consist_alpha = cfg.h_flow_consist_alpha
self.flow_consist_beta = cfg.h_flow_consist_beta
def get_occlusion_mask_from_flow(self, tensor_size, flow):
mask = torch.ones(tensor_size).to(flow.get_device())
h, w = mask.shape[2], mask.shape[3]
occ_mask = transformerFwd(mask.permute(0,2,3,1), flow.permute(0,2,3,1), out_size=[h,w]).permute(0,3,1,2)
with torch.no_grad():
occ_mask = torch.clamp(occ_mask, 0.0, 1.0)
return occ_mask
def get_flow_norm(self, flow, p=2):
'''
Inputs:
flow (bs, 2, H, W)
'''
flow_norm = torch.norm(flow, p=p, dim=1).unsqueeze(1) + 1e-12
return flow_norm
def get_visible_masks(self, optical_flows, optical_flows_rev):
# get occlusion masks
batch_size, _, img_h, img_w = optical_flows[0].shape
img2_visible_masks, img1_visible_masks = [], []
for s, (optical_flow, optical_flow_rev) in enumerate(zip(optical_flows, optical_flows_rev)):
shape = [batch_size, 1, int(img_h / (2**s)), int(img_w / (2**s))]
img2_visible_masks.append(self.get_occlusion_mask_from_flow(shape, optical_flow))
img1_visible_masks.append(self.get_occlusion_mask_from_flow(shape, optical_flow_rev))
return img2_visible_masks, img1_visible_masks
def get_consistent_masks(self, optical_flows, optical_flows_rev):
# get consist masks
batch_size, _, img_h, img_w = optical_flows[0].shape
img2_consis_masks, img1_consis_masks, fwd_flow_diff_pyramid, bwd_flow_diff_pyramid = [], [], [], []
for s, (optical_flow, optical_flow_rev) in enumerate(zip(optical_flows, optical_flows_rev)):
bwd2fwd_flow = warp_flow(optical_flow_rev, optical_flow)
fwd2bwd_flow = warp_flow(optical_flow, optical_flow_rev)
fwd_flow_diff = torch.abs(bwd2fwd_flow + optical_flow)
fwd_flow_diff_pyramid.append(fwd_flow_diff)
bwd_flow_diff = torch.abs(fwd2bwd_flow + optical_flow_rev)
bwd_flow_diff_pyramid.append(bwd_flow_diff)
# flow consistency condition
bwd_consist_bound = torch.max(self.flow_consist_beta * self.get_flow_norm(optical_flow_rev), torch.from_numpy(np.array([self.flow_consist_alpha])).float().to(optical_flow_rev.get_device()))
fwd_consist_bound = torch.max(self.flow_consist_beta * self.get_flow_norm(optical_flow), torch.from_numpy(np.array([self.flow_consist_alpha])).float().to(optical_flow.get_device()))
with torch.no_grad():
noc_masks_img2 = (self.get_flow_norm(bwd_flow_diff) < bwd_consist_bound).float()
noc_masks_img1 = (self.get_flow_norm(fwd_flow_diff) < fwd_consist_bound).float()
img2_consis_masks.append(noc_masks_img2)
img1_consis_masks.append(noc_masks_img1)
return img2_consis_masks, img1_consis_masks, fwd_flow_diff_pyramid, bwd_flow_diff_pyramid
def generate_img_pyramid(self, img, num_pyramid):
img_h, img_w = img.shape[2], img.shape[3]
img_pyramid = []
for s in range(num_pyramid):
img_new = F.adaptive_avg_pool2d(img, [int(img_h / (2**s)), int(img_w / (2**s))]).data
img_pyramid.append(img_new)
return img_pyramid
def warp_flow_pyramid(self, img_pyramid, flow_pyramid):
img_warped_pyramid = []
for img, flow in zip(img_pyramid, flow_pyramid):
img_warped_pyramid.append(warp_flow(img, flow))
return img_warped_pyramid
def compute_loss_pixel(self, img_pyramid, img_warped_pyramid, occ_mask_list):
loss_list = []
for scale in range(self.num_scales):
img, img_warped, occ_mask = img_pyramid[scale], img_warped_pyramid[scale], occ_mask_list[scale]
divider = occ_mask.mean((1,2,3))
img_diff = torch.abs((img - img_warped)) * occ_mask.repeat(1,3,1,1)
loss_pixel = img_diff.mean((1,2,3)) / (divider + 1e-12) # (B)
loss_list.append(loss_pixel[:,None])
loss = torch.cat(loss_list, 1).sum(1) # (B)
return loss
def compute_loss_ssim(self, img_pyramid, img_warped_pyramid, occ_mask_list):
loss_list = []
for scale in range(self.num_scales):
img, img_warped, occ_mask = img_pyramid[scale], img_warped_pyramid[scale], occ_mask_list[scale]
divider = occ_mask.mean((1,2,3))
occ_mask_pad = occ_mask.repeat(1,3,1,1)
ssim = SSIM(img * occ_mask_pad, img_warped * occ_mask_pad)
loss_ssim = torch.clamp((1.0 - ssim) / 2.0, 0, 1).mean((1,2,3))
loss_ssim = loss_ssim / (divider + 1e-12)
loss_list.append(loss_ssim[:,None])
loss = torch.cat(loss_list, 1).sum(1)
return loss
def gradients(self, img):
dy = img[:,:,1:,:] - img[:,:,:-1,:]
dx = img[:,:,:,1:] - img[:,:,:,:-1]
return dx, dy
def cal_grad2_error(self, flow, img):
img_grad_x, img_grad_y = self.gradients(img)
w_x = torch.exp(-10.0 * torch.abs(img_grad_x).mean(1).unsqueeze(1))
w_y = torch.exp(-10.0 * torch.abs(img_grad_y).mean(1).unsqueeze(1))
dx, dy = self.gradients(flow)
dx2, _ = self.gradients(dx)
_, dy2 = self.gradients(dy)
error = (w_x[:,:,:,1:] * torch.abs(dx2)).mean((1,2,3)) + (w_y[:,:,1:,:] * torch.abs(dy2)).mean((1,2,3))
return error / 2.0
def compute_loss_flow_smooth(self, optical_flows, img_pyramid):
loss_list = []
for scale in range(self.num_scales):
flow, img = optical_flows[scale], img_pyramid[scale]
error = self.cal_grad2_error(flow/20.0, img)
loss_list.append(error[:,None])
loss = torch.cat(loss_list, 1).sum(1)
return loss
def compute_loss_flow_consis(self, fwd_flow_diff_pyramid, occ_mask_list):
loss_list = []
for scale in range(self.num_scales):
fwd_flow_diff, occ_mask = fwd_flow_diff_pyramid[scale], occ_mask_list[scale]
divider = occ_mask.mean((1,2,3))
loss_consis = (fwd_flow_diff * occ_mask).mean((1,2,3))
loss_consis = loss_consis / (divider + 1e-12)
loss_list.append(loss_consis[:,None])
loss = torch.cat(loss_list, 1).sum(1)
return loss
def inference_flow(self, img1, img2):
img_hw = [img1.shape[2], img1.shape[3]]
feature_list_1, feature_list_2 = self.fpyramid(img1), self.fpyramid(img2)
optical_flow = self.pwc_model(feature_list_1, feature_list_2, img_hw)[0]
return optical_flow
def inference_corres(self, img1, img2):
batch_size, img_h, img_w = img1.shape[0], img1.shape[2], img1.shape[3]
# get the optical flows and reverse optical flows for each pair of adjacent images
feature_list_1, feature_list_2 = self.fpyramid(img1), self.fpyramid(img2)
optical_flows = self.pwc_model(feature_list_1, feature_list_2, [img_h, img_w])
optical_flows_rev = self.pwc_model(feature_list_2, feature_list_1, [img_h, img_w])
# get occlusion masks
img2_visible_masks, img1_visible_masks = self.get_visible_masks(optical_flows, optical_flows_rev)
# get consistent masks
img2_consis_masks, img1_consis_masks, fwd_flow_diff_pyramid, bwd_flow_diff_pyramid = self.get_consistent_masks(optical_flows, optical_flows_rev)
# get final valid masks
img2_valid_masks, img1_valid_masks = [], []
for i, (img2_visible_mask, img1_visible_mask, img2_consis_mask, img1_consis_mask) in enumerate(zip(img2_visible_masks, img1_visible_masks, img2_consis_masks, img1_consis_masks)):
img2_valid_masks.append(img2_visible_mask * img2_consis_mask)
img1_valid_masks.append(img1_visible_mask * img1_consis_mask)
return optical_flows[0], optical_flows_rev[0], img1_valid_masks[0], img2_valid_masks[0], fwd_flow_diff_pyramid[0], bwd_flow_diff_pyramid[0]
def forward(self, inputs, output_flow=False, use_flow_loss=True):
images, K_ms, K_inv_ms = inputs
assert (images.shape[1] == 3)
img_h, img_w = int(images.shape[2] / 2), images.shape[3]
img1, img2 = images[:,:,:img_h,:], images[:,:,img_h:,:]
batch_size = img1.shape[0]
#cv2.imwrite('./test1.png', np.transpose(255*img1[0].cpu().detach().numpy(), [1,2,0]).astype(np.uint8))
#cv2.imwrite('./test2.png', np.transpose(255*img2[0].cpu().detach().numpy(), [1,2,0]).astype(np.uint8))
#pdb.set_trace()
# get the optical flows and reverse optical flows for each pair of adjacent images
feature_list_1, feature_list_2 = self.fpyramid(img1), self.fpyramid(img2)
optical_flows = self.pwc_model(feature_list_1, feature_list_2, [img_h, img_w])
optical_flows_rev = self.pwc_model(feature_list_2, feature_list_1, [img_h, img_w])
# get occlusion masks
img2_visible_masks, img1_visible_masks = self.get_visible_masks(optical_flows, optical_flows_rev)
# get consistent masks
img2_consis_masks, img1_consis_masks, fwd_flow_diff_pyramid, bwd_flow_diff_pyramid = self.get_consistent_masks(optical_flows, optical_flows_rev)
# get final valid masks
img2_valid_masks, img1_valid_masks = [], []
for i, (img2_visible_mask, img1_visible_mask, img2_consis_mask, img1_consis_mask) in enumerate(zip(img2_visible_masks, img1_visible_masks, img2_consis_masks, img1_consis_masks)):
if self.dataset == 'nyuv2':
img2_valid_masks.append(img2_visible_mask)
img1_valid_masks.append(img1_visible_mask)
else:
img2_valid_masks.append(img2_visible_mask * img2_consis_mask)
img1_valid_masks.append(img1_visible_mask * img1_consis_mask)
loss_pack = {}
if not use_flow_loss:
loss_pack['loss_pixel'] = torch.zeros([2]).to(img1.get_device()).requires_grad_()
loss_pack['loss_ssim'] = torch.zeros([2]).to(img1.get_device()).requires_grad_()
loss_pack['loss_flow_smooth'] = torch.zeros([2]).to(img1.get_device()).requires_grad_()
loss_pack['loss_flow_consis'] = torch.zeros([2]).to(img1.get_device()).requires_grad_()
return loss_pack, optical_flows[0], optical_flows_rev[0], img1_valid_masks[0], img2_valid_masks[0], fwd_flow_diff_pyramid[0], bwd_flow_diff_pyramid[0]
# warp images
img1_pyramid = self.generate_img_pyramid(img1, len(optical_flows_rev))
img2_pyramid = self.generate_img_pyramid(img2, len(optical_flows))
img1_warped_pyramid = self.warp_flow_pyramid(img2_pyramid, optical_flows)
img2_warped_pyramid = self.warp_flow_pyramid(img1_pyramid, optical_flows_rev)
# compute loss
loss_pack['loss_pixel'] = self.compute_loss_pixel(img1_pyramid, img1_warped_pyramid, img1_valid_masks) + \
self.compute_loss_pixel(img2_pyramid, img2_warped_pyramid, img2_valid_masks)
loss_pack['loss_ssim'] = self.compute_loss_ssim(img1_pyramid, img1_warped_pyramid, img1_valid_masks) + \
self.compute_loss_ssim(img2_pyramid, img2_warped_pyramid, img2_valid_masks)
loss_pack['loss_flow_smooth'] = self.compute_loss_flow_smooth(optical_flows, img1_pyramid) + \
self.compute_loss_flow_smooth(optical_flows_rev, img2_pyramid)
#loss_pack['loss_flow_consis'] = self.compute_loss_flow_consis(fwd_flow_diff_pyramid, img1_valid_masks) + \
# self.compute_loss_flow_consis(bwd_flow_diff_pyramid, img2_valid_masks)
loss_pack['loss_flow_consis'] = torch.zeros([2]).to(img1.get_device()).requires_grad_()
if output_flow:
return loss_pack, optical_flows[0], optical_flows_rev[0], img1_valid_masks[0], img2_valid_masks[0], fwd_flow_diff_pyramid[0], bwd_flow_diff_pyramid[0]
else:
return loss_pack
================================================
FILE: core/networks/model_flowposenet.py
================================================
import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from structures import *
from pytorch_ssim import SSIM
from model_flow import Model_flow
sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'visualize'))
from visualizer import *
from profiler import Profiler
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import pdb
import cv2
def mean_on_mask(diff, valid_mask):
mask = valid_mask.expand_as(diff)
mean_value = (diff * mask).sum() / mask.sum()
return mean_value
def edge_aware_smoothness_loss(pred_disp, img, max_scales):
def gradient_x(img):
gx = img[:, :, :-1, :] - img[:, :, 1:, :]
return gx
def gradient_y(img):
gy = img[:, :, :, :-1] - img[:, :, :, 1:]
return gy
def get_edge_smoothness(img, pred):
pred_gradients_x = gradient_x(pred)
pred_gradients_y = gradient_y(pred)
image_gradients_x = gradient_x(img)
image_gradients_y = gradient_y(img)
weights_x = torch.exp(-torch.mean(torch.abs(image_gradients_x),
1, keepdim=True))
weights_y = torch.exp(-torch.mean(torch.abs(image_gradients_y),
1, keepdim=True))
smoothness_x = torch.abs(pred_gradients_x) * weights_x
smoothness_y = torch.abs(pred_gradients_y) * weights_y
return torch.mean(smoothness_x) + torch.mean(smoothness_y)
loss = 0
weight = 1.
s = 0
for scaled_disp in pred_disp:
s += 1
if s > max_scales:
break
b, _, h, w = scaled_disp.size()
scaled_img = nn.functional.adaptive_avg_pool2d(img, (h, w))
loss += get_edge_smoothness(scaled_img, scaled_disp) * weight
weight /= 4.0
return loss
def compute_smooth_loss(tgt_depth, tgt_img, ref_depth, ref_img, max_scales=1):
loss = edge_aware_smoothness_loss(tgt_depth, tgt_img, max_scales)
loss = edge_aware_smoothness_loss(ref_depth, ref_img, max_scales)
return loss
class Model_flowposenet(nn.Module):
def __init__(self, cfg):
super(Model_flowposenet, self).__init__()
assert cfg.depth_scale == 1
self.pose_net = FlowPoseNet()
self.model_flow = Model_flow(cfg)
self.depth_net = Depth_Model(cfg.depth_scale)
def compute_pairwise_loss(self, tgt_img, ref_img, tgt_depth, ref_depth, pose, intrinsic):
ref_img_warped, valid_mask, projected_depth, computed_depth = inverse_warp2(ref_img, tgt_depth, ref_depth,
pose, intrinsic, 'zeros')
diff_img = (tgt_img - ref_img_warped).abs()
diff_depth = ((computed_depth - projected_depth).abs() /
(computed_depth + projected_depth).abs()).clamp(0, 1)
ssim_map = (0.5*(1 - SSIM(tgt_img, ref_img_warped))).clamp(0, 1)
diff_img = (0.15 * diff_img + 0.85 * ssim_map)
# Modified in 01.19.2020
#weight_mask = (1 - diff_depth)
#diff_img = diff_img * weight_mask
# compute loss
reconstruction_loss = diff_img.mean()
geometry_consistency_loss = diff_depth.mean()
#reconstruction_loss = mean_on_mask(diff_img, valid_mask)
#geometry_consistency_loss = mean_on_mask(diff_depth, valid_mask)
return reconstruction_loss, geometry_consistency_loss
def disp2depth(self, disp, min_depth=0.01, max_depth=80.0):
min_disp = 1 / max_depth
max_disp = 1 / min_depth
scaled_disp = min_disp + (max_disp - min_disp) * disp
depth = 1 / scaled_disp
return scaled_disp, depth
def infer_depth(self, img):
b, img_h, img_w = img.shape[0], img.shape[2], img.shape[3]
disp_list = self.depth_net(img)
disp, depth = self.disp2depth(disp_list[0])
return disp_list[0]
def inference(self, img1, img2, K, K_inv):
flow = self.model_flow.inference_flow(img1, img2)
return flow, None, None, None, None, None
def inference_flow(self, img1, img2):
flow = self.model_flow.inference_flow(img1, img2)
return flow
def infer_pose(self, img1, img2, K, K_inv):
img_h, img_w = img1.shape[2], img1.shape[3]
flow = self.model_flow.inference_flow(img1, img2)
flow[:,0,:,:] /= img_w
flow[:,1,:,:] /= img_h
pose = self.pose_net(flow)
return pose
def forward(self, inputs):
# initialization
images, K_ms, K_inv_ms = inputs
K, K_inv = K_ms[:,0,:,:], K_inv_ms[:,0,:,:]
assert (images.shape[1] == 3)
img_h, img_w = int(images.shape[2] / 2), images.shape[3]
img1, img2 = images[:,:,:img_h,:], images[:,:,img_h:,:]
b = img1.shape[0]
visualizer = Visualizer_debug('./vis/', img1=255*img1.permute([0,2,3,1]).detach().cpu().numpy(), \
img2=255*img2.permute([0,2,3,1]).detach().cpu().numpy())
# Flow Network
loss_pack, fwd_flow, bwd_flow, img1_valid_mask, img2_valid_mask, img1_flow_diff_mask, img2_flow_diff_mask = self.model_flow(inputs, output_flow=True, use_flow_loss=False)
fwd_flow[:,0,:,:] /= img_w
fwd_flow[:,1,:,:] /= img_h
bwd_flow[:,0,:,:] /= img_w
bwd_flow[:,1,:,:] /= img_h
# Pose Network
pose = self.pose_net(fwd_flow)
pose_inv = self.pose_net(bwd_flow)
disp1_list = self.depth_net(img1) # Nscales * [B, 1, H, W]
disp2_list = self.depth_net(img2)
disp1, depth1 = self.disp2depth(disp1_list[0])
disp2, depth2 = self.disp2depth(disp2_list[0])
#pdb.set_trace()
loss_1, loss_3 = self.compute_pairwise_loss(img1, img2, depth1, depth2, pose, K)
loss_1_2, loss_3_2 = self.compute_pairwise_loss(img2, img1, depth2, depth1, pose_inv, K)
loss_ph = loss_1 + loss_1_2
loss_pj = loss_3 + loss_3_2
loss_2 = compute_smooth_loss([depth1], img1, [depth2], img2)
loss_pack['pt_depth_loss'] = torch.zeros([2]).to(loss_2.get_device()).requires_grad_()
loss_pack['pj_depth_loss'], loss_pack['flow_error'] = loss_pj, loss_ph
loss_pack['depth_smooth_loss'] = loss_2
#loss_pack['depth_smooth_loss'] = torch.zeros([2]).to(loss_2.get_device()).requires_grad_()
loss_pack['geo_loss'] = torch.zeros([2]).to(loss_2.get_device()).requires_grad_()
return loss_pack
================================================
FILE: core/networks/model_triangulate_pose.py
================================================
import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
import torch
import torch.nn as nn
import numpy as np
from structures import *
from model_flow import Model_flow
import pdb
import cv2
class Model_triangulate_pose(nn.Module):
def __init__(self, cfg):
super(Model_triangulate_pose, self).__init__()
self.model_flow = Model_flow(cfg)
self.mode = cfg.mode
if cfg.dataset == 'nyuv2':
self.inlier_thres = 0.1
self.rigid_thres = 1.0
else:
self.inlier_thres = 0.1
self.rigid_thres = 0.5
self.filter = reduced_ransac(check_num=cfg.ransac_points, thres=self.inlier_thres, dataset=cfg.dataset)
def meshgrid(self, h, w):
xx, yy = np.meshgrid(np.arange(0,w), np.arange(0,h))
meshgrid = np.transpose(np.stack([xx,yy], axis=-1), [2,0,1]) # [2,h,w]
meshgrid = torch.from_numpy(meshgrid)
return meshgrid
def compute_epipolar_loss(self, fmat, match, mask):
# fmat: [b, 3, 3] match: [b, 4, h*w] mask: [b,1,h*w]
num_batch = match.shape[0]
match_num = match.shape[-1]
points1 = match[:,:2,:]
points2 = match[:,2:,:]
ones = torch.ones(num_batch, 1, match_num).to(points1.get_device())
points1 = torch.cat([points1, ones], 1) # [b,3,n]
points2 = torch.cat([points2, ones], 1).transpose(1,2) # [b,n,3]
# compute fundamental matrix loss
fmat = fmat.unsqueeze(1)
fmat_tiles = fmat.view([-1,3,3])
epi_lines = fmat_tiles.bmm(points1) #[b,3,n] [b*n, 3, 1]
dist_p2l = torch.abs((epi_lines.permute([0, 2, 1]) * points2).sum(-1, keepdim=True)) # [b,n,1]
a = epi_lines[:,0,:].unsqueeze(1).transpose(1,2) # [b,n,1]
b = epi_lines[:,1,:].unsqueeze(1).transpose(1,2) # [b,n,1]
dist_div = torch.sqrt(a*a + b*b) + 1e-6
dist_map = dist_p2l / dist_div # [B, n, 1]
loss = (dist_map * mask.transpose(1,2)).mean([1,2]) / mask.mean([1,2])
return loss, dist_map
def get_rigid_mask(self, dist_map):
rigid_mask = (dist_map < self.rigid_thres).float()
inlier_mask = (dist_map < self.inlier_thres).float()
rigid_score = rigid_mask * 1.0 / (1.0 + dist_map)
return rigid_mask, inlier_mask, rigid_score
def inference(self, img1, img2, K, K_inv):
batch_size, img_h, img_w = img1.shape[0], img1.shape[2], img1.shape[3]
fwd_flow, bwd_flow, img1_valid_mask, img2_valid_mask, img1_flow_diff_mask, img2_flow_diff_mask = self.model_flow.inference_corres(img1, img2)
grid = self.meshgrid(img_h, img_w).float().to(img1.get_device()).unsqueeze(0).repeat(batch_size,1,1,1) #[b,2,h,w]
corres = torch.cat([(grid[:,0,:,:] + fwd_flow[:,0,:,:]).clamp(0,img_w-1.0).unsqueeze(1), \
(grid[:,1,:,:] + fwd_flow[:,1,:,:]).clamp(0,img_h-1.0).unsqueeze(1)], 1)
match = torch.cat([grid, corres], 1) # [b,4,h,w]
img1_score_mask = img1_valid_mask * 1.0 / (0.1 + img1_flow_diff_mask.mean(1).unsqueeze(1))
F_final = self.filter(match, img1_score_mask)
geo_loss, rigid_mask = self.compute_epipolar_loss(F_final, match.view([batch_size,4,-1]), img1_valid_mask.view([batch_size,1,-1]))
img1_rigid_mask = (rigid_mask.view([batch_size,img_h,img_w,1]) < self.inlier_thres).float()
return F_final, img1_valid_mask, img1_rigid_mask.permute(0,3,1,2), fwd_flow, match
def forward(self, inputs, output_F=False, visualizer=None):
images, K_ms, K_inv_ms = inputs
K, K_inv = K_ms[:,0,:,:], K_inv_ms[:,0,:,:]
assert (images.shape[1] == 3)
img_h, img_w = int(images.shape[2] / 2), images.shape[3]
img1, img2 = images[:,:,:img_h,:], images[:,:,img_h:,:]
batch_size = img1.shape[0]
if self.mode == 'depth':
loss_pack, fwd_flow, bwd_flow, img1_valid_mask, img2_valid_mask, img1_flow_diff_mask, img2_flow_diff_mask = self.model_flow(inputs, output_flow=True, use_flow_loss=False)
else:
loss_pack, fwd_flow, bwd_flow, img1_valid_mask, img2_valid_mask, img1_flow_diff_mask, img2_flow_diff_mask = self.model_flow(inputs, output_flow=True)
grid = self.meshgrid(img_h, img_w).float().to(img1.get_device()).unsqueeze(0).repeat(batch_size,1,1,1) #[b,2,h,w]
fwd_corres = torch.cat([(grid[:,0,:,:] + fwd_flow[:,0,:,:]).unsqueeze(1), (grid[:,1,:,:] + fwd_flow[:,1,:,:]).unsqueeze(1)], 1)
fwd_match = torch.cat([grid, fwd_corres], 1) # [b,4,h,w]
bwd_corres = torch.cat([(grid[:,0,:,:] + bwd_flow[:,0,:,:]).unsqueeze(1), (grid[:,1,:,:] + bwd_flow[:,1,:,:]).unsqueeze(1)], 1)
bwd_match = torch.cat([grid, bwd_corres], 1) # [b,4,h,w]
# Use fwd-bwd consistency map for filter
img1_score_mask = img1_valid_mask * 1.0 / (0.1+img1_flow_diff_mask.mean(1).unsqueeze(1))
img2_score_mask = img2_valid_mask * 1.0 / (0.1+img2_flow_diff_mask.mean(1).unsqueeze(1))
# img1_score_mask = img1_valid_mask
F_final_1 = self.filter(fwd_match, img1_score_mask, visualizer=visualizer)
_, dist_map_1 = self.compute_epipolar_loss(F_final_1, fwd_match.view([batch_size,4,-1]), img1_valid_mask.view([batch_size,1,-1]))
dist_map_1 = dist_map_1.view([batch_size, img_h, img_w, 1])
# Compute geo loss for regularize correspondence.
rigid_mask_1, inlier_mask_1, rigid_score_1 = self.get_rigid_mask(dist_map_1)
# We only use rigid mask to filter out the moving objects for computing geo loss.
geo_loss = (dist_map_1 * (rigid_mask_1 - inlier_mask_1)).mean((1,2,3)) / \
(rigid_mask_1 - inlier_mask_1).mean((1,2,3))
loss_pack['geo_loss'] = geo_loss
if output_F:
return loss_pack, F_final_1, img1_score_mask, rigid_score_1.permute(0,3,1,2), fwd_flow, fwd_match
else:
return loss_pack
================================================
FILE: core/networks/pytorch_ssim/__init__.py
================================================
import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from ssim import SSIM
================================================
FILE: core/networks/pytorch_ssim/ssim.py
================================================
import torch
import torch.nn as nn
def SSIM(x, y):
C1 = 0.01 ** 2
C2 = 0.03 ** 2
mu_x = nn.AvgPool2d(3, 1, padding=1)(x)
mu_y = nn.AvgPool2d(3, 1, padding=1)(y)
sigma_x = nn.AvgPool2d(3, 1, padding=1)(x**2) - mu_x**2
sigma_y = nn.AvgPool2d(3, 1, padding=1)(y**2) - mu_y**2
sigma_xy = nn.AvgPool2d(3, 1, padding=1)(x * y) - mu_x * mu_y
SSIM_n = (2 * mu_x * mu_y + C1) * (2 * sigma_xy + C2)
SSIM_d = (mu_x**2 + mu_y**2 + C1) * (sigma_x + sigma_y + C2)
SSIM = SSIM_n / SSIM_d
return SSIM
================================================
FILE: core/networks/structures/__init__.py
================================================
import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from feature_pyramid import FeaturePyramid
from pwc_tf import PWC_tf
from ransac import reduced_ransac
from depth_model import Depth_Model
from net_utils import conv, deconv, warp_flow
from flowposenet import FlowPoseNet
from inverse_warp import inverse_warp2
================================================
FILE: core/networks/structures/depth_model.py
================================================
'''
This code was ported from existing repos
[LINK] https://github.com/nianticlabs/monodepth2
'''
from __future__ import absolute_import, division, print_function
import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
import torch.utils.model_zoo as model_zoo
from collections import OrderedDict
import pdb
class ResNetMultiImageInput(models.ResNet):
"""Constructs a resnet model with varying number of input images.
Adapted from https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
"""
def __init__(self, block, layers, num_classes=1000, num_input_images=1):
super(ResNetMultiImageInput, self).__init__(block, layers)
self.inplanes = 64
self.conv1 = nn.Conv2d(
num_input_images * 3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def resnet_multiimage_input(num_layers, pretrained=False, num_input_images=1):
"""Constructs a ResNet model.
Args:
num_layers (int): Number of resnet layers. Must be 18 or 50
pretrained (bool): If True, returns a model pre-trained on ImageNet
num_input_images (int): Number of frames stacked as input
"""
assert num_layers in [18, 50], "Can only run with 18 or 50 layer resnet"
blocks = {18: [2, 2, 2, 2], 50: [3, 4, 6, 3]}[num_layers]
block_type = {18: models.resnet.BasicBlock, 50: models.resnet.Bottleneck}[num_layers]
model = ResNetMultiImageInput(block_type, blocks, num_input_images=num_input_images)
if pretrained:
loaded = model_zoo.load_url(models.resnet.model_urls['resnet{}'.format(num_layers)])
loaded['conv1.weight'] = torch.cat(
[loaded['conv1.weight']] * num_input_images, 1) / num_input_images
model.load_state_dict(loaded)
return model
class ResnetEncoder(nn.Module):
"""Pytorch module for a resnet encoder
"""
def __init__(self, num_layers, pretrained, num_input_images=1):
super(ResnetEncoder, self).__init__()
self.num_ch_enc = np.array([64, 64, 128, 256, 512])
resnets = {18: models.resnet18,
34: models.resnet34,
50: models.resnet50,
101: models.resnet101,
152: models.resnet152}
if num_layers not in resnets:
raise ValueError("{} is not a valid number of resnet layers".format(num_layers))
if num_input_images > 1:
self.encoder = resnet_multiimage_input(num_layers, pretrained, num_input_images)
else:
self.encoder = resnets[num_layers](pretrained)
if num_layers > 34:
self.num_ch_enc[1:] *= 4
def forward(self, input_image):
self.features = []
x = (input_image - 0.45) / 0.225
x = self.encoder.conv1(x)
x = self.encoder.bn1(x)
self.features.append(self.encoder.relu(x))
self.features.append(self.encoder.layer1(self.encoder.maxpool(self.features[-1])))
self.features.append(self.encoder.layer2(self.features[-1]))
self.features.append(self.encoder.layer3(self.features[-1]))
self.features.append(self.encoder.layer4(self.features[-1]))
return self.features
class ConvBlock(nn.Module):
"""Layer to perform a convolution followed by ELU
"""
def __init__(self, in_channels, out_channels):
super(ConvBlock, self).__init__()
self.conv = Conv3x3(in_channels, out_channels)
self.nonlin = nn.ELU(inplace=True)
def forward(self, x):
out = self.conv(x)
out = self.nonlin(out)
return out
class Conv3x3(nn.Module):
"""Layer to pad and convolve input
"""
def __init__(self, in_channels, out_channels, use_refl=True):
super(Conv3x3, self).__init__()
if use_refl:
self.pad = nn.ReflectionPad2d(1)
else:
self.pad = nn.ZeroPad2d(1)
self.conv = nn.Conv2d(int(in_channels), int(out_channels), 3)
def forward(self, x):
out = self.pad(x)
out = self.conv(out)
return out
def upsample(x):
"""Upsample input tensor by a factor of 2
"""
#return F.interpolate(x, scale_factor=2, mode="nearest")
# TODO
return F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=False)
class DepthDecoder(nn.Module):
def __init__(self, num_ch_enc, scales=range(4), num_output_channels=1, use_skips=True):
super(DepthDecoder, self).__init__()
self.num_output_channels = num_output_channels
self.use_skips = use_skips
self.scales = scales
self.num_ch_enc = num_ch_enc
self.num_ch_dec = np.array([16, 32, 64, 128, 256])
# decoder
self.init_decoder()
def init_decoder(self):
self.upconvs = nn.ModuleList()
for i in range(4, -1, -1):
upconvs_now = nn.ModuleList()
# upconv_0
num_ch_in = self.num_ch_enc[-1] if i == 4 else self.num_ch_dec[i + 1]
num_ch_out = self.num_ch_dec[i]
upconvs_now.append(ConvBlock(num_ch_in, num_ch_out))
# upconv_1
num_ch_in = self.num_ch_dec[i]
if self.use_skips and i > 0:
num_ch_in += self.num_ch_enc[i - 1]
num_ch_out = self.num_ch_dec[i]
upconvs_now.append(ConvBlock(num_ch_in, num_ch_out))
self.upconvs.append(upconvs_now)
self.dispconvs = nn.ModuleList()
for s in self.scales:
self.dispconvs.append(Conv3x3(self.num_ch_dec[s], self.num_output_channels))
# self.decoder = nn.ModuleList(list(self.convs.values()))
self.sigmoid = nn.Sigmoid()
def forward(self, input_features):
self.outputs = {}
# decoder
x = input_features[-1]
for scale in range(4, -1, -1): # [4, 3, 2, 1, 0]
idx = 4 - scale
x = self.upconvs[idx][0](x)
x = [upsample(x)]
if self.use_skips and scale > 0:
x += [input_features[scale - 1]]
x = torch.cat(x, 1)
x = self.upconvs[idx][1](x)
# get disp
if scale in self.scales:
scale_idx = self.scales.index(scale)
self.outputs[("disp", scale)] = self.sigmoid(self.dispconvs[scale_idx](x))
return self.outputs
class Depth_Model(nn.Module):
def __init__(self, depth_scale, num_layers=18):
super(Depth_Model, self).__init__()
self.depth_scale = depth_scale
self.encoder = ResnetEncoder(num_layers=num_layers, pretrained=False)
self.decoder = DepthDecoder(self.encoder.num_ch_enc, scales=range(depth_scale))
def forward(self, img):
features = self.encoder(img)
outputs = self.decoder(features)
depth_list = []
disp_list = []
for i in range(self.depth_scale):
disp = outputs['disp', i]
#s_disp, depth = self.disp2depth(disp, self.min_depth, self.max_depth)
#depth_list.append(depth)
#disp_list.append(s_disp)
disp_list.append(disp)
return disp_list
================================================
FILE: core/networks/structures/feature_pyramid.py
================================================
import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from net_utils import conv
import torch
import torch.nn as nn
class FeaturePyramid(nn.Module):
def __init__(self):
super(FeaturePyramid, self).__init__()
self.conv1 = conv(3, 16, kernel_size=3, stride=2)
self.conv2 = conv(16, 16, kernel_size=3, stride=1)
self.conv3 = conv(16, 32, kernel_size=3, stride=2)
self.conv4 = conv(32, 32, kernel_size=3, stride=1)
self.conv5 = conv(32, 64, kernel_size=3, stride=2)
self.conv6 = conv(64, 64, kernel_size=3, stride=1)
self.conv7 = conv(64, 96, kernel_size=3, stride=2)
self.conv8 = conv(96, 96, kernel_size=3, stride=1)
self.conv9 = conv(96, 128, kernel_size=3, stride=2)
self.conv10 = conv(128, 128, kernel_size=3, stride=1)
self.conv11 = conv(128, 196, kernel_size=3, stride=2)
self.conv12 = conv(196, 196, kernel_size=3, stride=1)
'''
for m in self.modules():
if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):
nn.init.constant_(m.weight.data, 0.0)
if m.bias is not None:
m.bias.data.zero_()
'''
def forward(self, img):
cnv2 = self.conv2(self.conv1(img))
cnv4 = self.conv4(self.conv3(cnv2))
cnv6 = self.conv6(self.conv5(cnv4))
cnv8 = self.conv8(self.conv7(cnv6))
cnv10 = self.conv10(self.conv9(cnv8))
cnv12 = self.conv12(self.conv11(cnv10))
return cnv2, cnv4, cnv6, cnv8, cnv10, cnv12
================================================
FILE: core/networks/structures/flowposenet.py
================================================
import torch
import torch.nn as nn
from torch import sigmoid
from torch.nn.init import xavier_uniform_, zeros_
def conv(in_planes, out_planes, kernel_size=3):
return nn.Sequential(
nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, padding=(kernel_size-1)//2, stride=2),
nn.ReLU(inplace=True)
)
def upconv(in_planes, out_planes):
return nn.Sequential(
nn.ConvTranspose2d(in_planes, out_planes, kernel_size=4, stride=2, padding=1),
nn.ReLU(inplace=True)
)
class FlowPoseNet(nn.Module):
def __init__(self):
super(FlowPoseNet, self).__init__()
conv_planes = [16, 32, 64, 128, 256, 256, 256]
self.conv1 = conv(2, conv_planes[0], kernel_size=7)
self.conv2 = conv(conv_planes[0], conv_planes[1], kernel_size=5)
self.conv3 = conv(conv_planes[1], conv_planes[2])
self.conv4 = conv(conv_planes[2], conv_planes[3])
self.conv5 = conv(conv_planes[3], conv_planes[4])
self.conv6 = conv(conv_planes[4], conv_planes[5])
self.conv7 = conv(conv_planes[5], conv_planes[6])
self.pose_pred = nn.Conv2d(conv_planes[6], 6, kernel_size=1, padding=0)
def init_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):
xavier_uniform_(m.weight.data)
if m.bias is not None:
zeros_(m.bias)
def forward(self, flow):
# [B, 2, H, W]
input = flow
out_conv1 = self.conv1(input)
out_conv2 = self.conv2(out_conv1)
out_conv3 = self.conv3(out_conv2)
out_conv4 = self.conv4(out_conv3)
out_conv5 = self.conv5(out_conv4)
out_conv6 = self.conv6(out_conv5)
out_conv7 = self.conv7(out_conv6)
pose = self.pose_pred(out_conv7)
pose = pose.mean(3).mean(2)
pose = 0.01 * pose.view(pose.size(0), 6)
return pose
================================================
FILE: core/networks/structures/inverse_warp.py
================================================
from __future__ import division
import torch
import torch.nn.functional as F
pixel_coords = None
def set_id_grid(depth):
global pixel_coords
b, h, w = depth.size()
i_range = torch.arange(0, h).view(1, h, 1).expand(
1, h, w).type_as(depth) # [1, H, W]
j_range = torch.arange(0, w).view(1, 1, w).expand(
1, h, w).type_as(depth) # [1, H, W]
ones = torch.ones(1, h, w).type_as(depth)
pixel_coords = torch.stack((j_range, i_range, ones), dim=1) # [1, 3, H, W]
def check_sizes(input, input_name, expected):
condition = [input.ndimension() == len(expected)]
for i, size in enumerate(expected):
if size.isdigit():
condition.append(input.size(i) == int(size))
assert(all(condition)), "wrong size for {}, expected {}, got {}".format(
input_name, 'x'.join(expected), list(input.size()))
def pixel2cam(depth, intrinsics_inv):
global pixel_coords
"""Transform coordinates in the pixel frame to the camera frame.
Args:
depth: depth maps -- [B, H, W]
intrinsics_inv: intrinsics_inv matrix for each element of batch -- [B, 3, 3]
Returns:
array of (u,v,1) cam coordinates -- [B, 3, H, W]
"""
b, h, w = depth.size()
if (pixel_coords is None) or pixel_coords.size(2) < h:
set_id_grid(depth)
current_pixel_coords = pixel_coords[:, :, :h, :w].expand(
b, 3, h, w).reshape(b, 3, -1) # [B, 3, H*W]
cam_coords = (intrinsics_inv @ current_pixel_coords).reshape(b, 3, h, w)
return cam_coords * depth.unsqueeze(1)
def cam2pixel(cam_coords, proj_c2p_rot, proj_c2p_tr, padding_mode):
"""Transform coordinates in the camera frame to the pixel frame.
Args:
cam_coords: pixel coordinates defined in the first camera coordinates system -- [B, 4, H, W]
proj_c2p_rot: rotation matrix of cameras -- [B, 3, 4]
proj_c2p_tr: translation vectors of cameras -- [B, 3, 1]
Returns:
array of [-1,1] coordinates -- [B, 2, H, W]
"""
b, _, h, w = cam_coords.size()
cam_coords_flat = cam_coords.reshape(b, 3, -1) # [B, 3, H*W]
if proj_c2p_rot is not None:
pcoords = proj_c2p_rot @ cam_coords_flat
else:
pcoords = cam_coords_flat
if proj_c2p_tr is not None:
pcoords = pcoords + proj_c2p_tr # [B, 3, H*W]
X = pcoords[:, 0]
Y = pcoords[:, 1]
Z = pcoords[:, 2].clamp(min=1e-3)
# Normalized, -1 if on extreme left, 1 if on extreme right (x = w-1) [B, H*W]
X_norm = 2*(X / Z)/(w-1) - 1
Y_norm = 2*(Y / Z)/(h-1) - 1 # Idem [B, H*W]
pixel_coords = torch.stack([X_norm, Y_norm], dim=2) # [B, H*W, 2]
return pixel_coords.reshape(b, h, w, 2)
def euler2mat(angle):
"""Convert euler angles to rotation matrix.
Reference: https://github.com/pulkitag/pycaffe-utils/blob/master/rot_utils.py#L174
Args:
angle: rotation angle along 3 axis (in radians) -- size = [B, 3]
Returns:
Rotation matrix corresponding to the euler angles -- size = [B, 3, 3]
"""
B = angle.size(0)
x, y, z = angle[:, 0], angle[:, 1], angle[:, 2]
cosz = torch.cos(z)
sinz = torch.sin(z)
zeros = z.detach()*0
ones = zeros.detach()+1
zmat = torch.stack([cosz, -sinz, zeros,
sinz, cosz, zeros,
zeros, zeros, ones], dim=1).reshape(B, 3, 3)
cosy = torch.cos(y)
siny = torch.sin(y)
ymat = torch.stack([cosy, zeros, siny,
zeros, ones, zeros,
-siny, zeros, cosy], dim=1).reshape(B, 3, 3)
cosx = torch.cos(x)
sinx = torch.sin(x)
xmat = torch.stack([ones, zeros, zeros,
zeros, cosx, -sinx,
zeros, sinx, cosx], dim=1).reshape(B, 3, 3)
rotMat = xmat @ ymat @ zmat
return rotMat
def quat2mat(quat):
"""Convert quaternion coefficients to rotation matrix.
Args:
quat: first three coeff of quaternion of rotation. fourht is then computed to have a norm of 1 -- size = [B, 3]
Returns:
Rotation matrix corresponding to the quaternion -- size = [B, 3, 3]
"""
norm_quat = torch.cat([quat[:, :1].detach()*0 + 1, quat], dim=1)
norm_quat = norm_quat/norm_quat.norm(p=2, dim=1, keepdim=True)
w, x, y, z = norm_quat[:, 0], norm_quat[:,
1], norm_quat[:, 2], norm_quat[:, 3]
B = quat.size(0)
w2, x2, y2, z2 = w.pow(2), x.pow(2), y.pow(2), z.pow(2)
wx, wy, wz = w*x, w*y, w*z
xy, xz, yz = x*y, x*z, y*z
rotMat = torch.stack([w2 + x2 - y2 - z2, 2*xy - 2*wz, 2*wy + 2*xz,
2*wz + 2*xy, w2 - x2 + y2 - z2, 2*yz - 2*wx,
2*xz - 2*wy, 2*wx + 2*yz, w2 - x2 - y2 + z2], dim=1).reshape(B, 3, 3)
return rotMat
def pose_vec2mat(vec, rotation_mode='euler'):
"""
Convert 6DoF parameters to transformation matrix.
Args:s
vec: 6DoF parameters in the order of tx, ty, tz, rx, ry, rz -- [B, 6]
Returns:
A transformation matrix -- [B, 3, 4]
"""
translation = vec[:, :3].unsqueeze(-1) # [B, 3, 1]
rot = vec[:, 3:]
if rotation_mode == 'euler':
rot_mat = euler2mat(rot) # [B, 3, 3]
elif rotation_mode == 'quat':
rot_mat = quat2mat(rot) # [B, 3, 3]
transform_mat = torch.cat([rot_mat, translation], dim=2) # [B, 3, 4]
return transform_mat
def inverse_warp(img, depth, pose, intrinsics, rotation_mode='euler', padding_mode='zeros'):
"""
Inverse warp a source image to the target image plane.
Args:
img: the source image (where to sample pixels) -- [B, 3, H, W]
depth: depth map of the target image -- [B, H, W]
pose: 6DoF pose parameters from target to source -- [B, 6]
intrinsics: camera intrinsic matrix -- [B, 3, 3]
Returns:
projected_img: Source image warped to the target image plane
valid_points: Boolean array indicating point validity
"""
check_sizes(img, 'img', 'B3HW')
check_sizes(depth, 'depth', 'BHW')
check_sizes(pose, 'pose', 'B6')
check_sizes(intrinsics, 'intrinsics', 'B33')
batch_size, _, img_height, img_width = img.size()
cam_coords = pixel2cam(depth, intrinsics.inverse()) # [B,3,H,W]
pose_mat = pose_vec2mat(pose, rotation_mode) # [B,3,4]
# Get projection matrix for tgt camera frame to source pixel frame
proj_cam_to_src_pixel = intrinsics @ pose_mat # [B, 3, 4]
rot, tr = proj_cam_to_src_pixel[:, :, :3], proj_cam_to_src_pixel[:, :, -1:]
src_pixel_coords = cam2pixel(
cam_coords, rot, tr, padding_mode) # [B,H,W,2]
projected_img = F.grid_sample(
img, src_pixel_coords, padding_mode=padding_mode)
valid_points = src_pixel_coords.abs().max(dim=-1)[0] <= 1
return projected_img, valid_points
def cam2pixel2(cam_coords, proj_c2p_rot, proj_c2p_tr, padding_mode):
"""Transform coordinates in the camera frame to the pixel frame.
Args:
cam_coords: pixel coordinates defined in the first camera coordinates system -- [B, 4, H, W]
proj_c2p_rot: rotation matrix of cameras -- [B, 3, 4]
proj_c2p_tr: translation vectors of cameras -- [B, 3, 1]
Returns:
array of [-1,1] coordinates -- [B, 2, H, W]
"""
b, _, h, w = cam_coords.size()
cam_coords_flat = cam_coords.reshape(b, 3, -1) # [B, 3, H*W]
if proj_c2p_rot is not None:
pcoords = proj_c2p_rot @ cam_coords_flat
else:
pcoords = cam_coords_flat
if proj_c2p_tr is not None:
pcoords = pcoords + proj_c2p_tr # [B, 3, H*W]
X = pcoords[:, 0]
Y = pcoords[:, 1]
Z = pcoords[:, 2].clamp(min=1e-3)
# Normalized, -1 if on extreme left, 1 if on extreme right (x = w-1) [B, H*W]
X_norm = 2*(X / Z)/(w-1) - 1
Y_norm = 2*(Y / Z)/(h-1) - 1 # Idem [B, H*W]
if padding_mode == 'zeros':
X_mask = ((X_norm > 1)+(X_norm < -1)).detach()
# make sure that no point in warped image is a combinaison of im and gray
X_norm[X_mask] = 2
Y_mask = ((Y_norm > 1)+(Y_norm < -1)).detach()
Y_norm[Y_mask] = 2
pixel_coords = torch.stack([X_norm, Y_norm], dim=2) # [B, H*W, 2]
return pixel_coords.reshape(b, h, w, 2), Z.reshape(b, 1, h, w)
def inverse_warp2(img, depth, ref_depth, pose, intrinsics, padding_mode='zeros'):
"""
Inverse warp a source image to the target image plane.
Args:
img: the source image (where to sample pixels) -- [B, 3, H, W]
depth: depth map of the target image -- [B, 1, H, W]
ref_depth: the source depth map (where to sample depth) -- [B, 1, H, W]
pose: 6DoF pose parameters from target to source -- [B, 6]
intrinsics: camera intrinsic matrix -- [B, 3, 3]
Returns:
projected_img: Source image warped to the target image plane
valid_mask: Float array indicating point validity
"""
check_sizes(img, 'img', 'B3HW')
check_sizes(depth, 'depth', 'B1HW')
check_sizes(ref_depth, 'ref_depth', 'B1HW')
check_sizes(pose, 'pose', 'B6')
check_sizes(intrinsics, 'intrinsics', 'B33')
batch_size, _, img_height, img_width = img.size()
cam_coords = pixel2cam(depth.squeeze(1), intrinsics.inverse()) # [B,3,H,W]
pose_mat = pose_vec2mat(pose) # [B,3,4]
# Get projection matrix for tgt camera frame to source pixel frame
proj_cam_to_src_pixel = intrinsics @ pose_mat # [B, 3, 4]
rot, tr = proj_cam_to_src_pixel[:, :, :3], proj_cam_to_src_pixel[:, :, -1:]
src_pixel_coords, computed_depth = cam2pixel2(
cam_coords, rot, tr, padding_mode) # [B,H,W,2]
projected_img = F.grid_sample(
img, src_pixel_coords, padding_mode=padding_mode)
valid_points = src_pixel_coords.abs().max(dim=-1)[0] <= 1
valid_mask = valid_points.unsqueeze(1).float()
projected_depth = F.grid_sample(
ref_depth, src_pixel_coords, padding_mode=padding_mode).clamp(min=1e-3)
return projected_img, valid_mask, projected_depth, computed_depth
================================================
FILE: core/networks/structures/net_utils.py
================================================
import torch
import torch.nn as nn
from torch.autograd import Variable
import pdb
import numpy as np
def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
return nn.Sequential(
nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
padding=padding, dilation=dilation, bias=True),
nn.LeakyReLU(0.1))
def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1):
return nn.ConvTranspose2d(in_planes, out_planes, kernel_size, stride, padding, bias=True)
def warp_flow(x, flow, use_mask=False):
"""
warp an image/tensor (im2) back to im1, according to the optical flow
Inputs:
x: [B, C, H, W] (im2)
flow: [B, 2, H, W] flow
Returns:
ouptut: [B, C, H, W]
"""
B, C, H, W = x.size()
# mesh grid
xx = torch.arange(0, W).view(1,-1).repeat(H,1)
yy = torch.arange(0, H).view(-1,1).repeat(1,W)
xx = xx.view(1,1,H,W).repeat(B,1,1,1)
yy = yy.view(1,1,H,W).repeat(B,1,1,1)
grid = torch.cat((xx,yy),1).float()
if grid.shape != flow.shape:
raise ValueError('the shape of grid {0} is not equal to the shape of flow {1}.'.format(grid.shape, flow.shape))
if x.is_cuda:
grid = grid.to(x.get_device())
vgrid = grid + flow
# scale grid to [-1,1]
vgrid[:,0,:,:] = 2.0*vgrid[:,0,:,:].clone() / max(W-1,1)-1.0
vgrid[:,1,:,:] = 2.0*vgrid[:,1,:,:].clone() / max(H-1,1)-1.0
vgrid = vgrid.permute(0,2,3,1)
output = nn.functional.grid_sample(x, vgrid)
if use_mask:
mask = torch.autograd.Variable(torch.ones(x.size())).to(x.get_device())
mask = nn.functional.grid_sample(mask, vgrid)
mask[mask < 0.9999] = 0
mask[mask > 0] = 1
return output * mask
else:
return output
if __name__ == '__main__':
x = np.ones([1,1,10,10])
flow = np.stack([np.ones([1,10,10])*3.0, np.zeros([1,10,10])], axis=1)
y = warp_flow(torch.from_numpy(x).cuda().float(),torch.from_numpy(flow).cuda().float()).cpu().detach().numpy()
print(y)
================================================
FILE: core/networks/structures/pwc_tf.py
================================================
import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from net_utils import conv, deconv, warp_flow
sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..', 'external'))
# from correlation_package.correlation import Correlation
# from spatial_correlation_sampler import SpatialCorrelationSampler as Correlation
import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
import pdb
import torch.nn.functional as F
#from spatial_correlation_sampler import spatial_correlation_sample
class PWC_tf(nn.Module):
def __init__(self, md=4):
super(PWC_tf, self).__init__()
self.corr = self.corr_naive
# self.corr = self.correlate
self.leakyRELU = nn.LeakyReLU(0.1)
nd = (2*md+1)**2
#dd = np.cumsum([128,128,96,64,32])
dd = np.array([128,128,96,64,32])
od = nd
self.conv6_0 = conv(od, 128, kernel_size=3, stride=1)
self.conv6_1 = conv(dd[0], 128, kernel_size=3, stride=1)
self.conv6_2 = conv(dd[0]+dd[1],96, kernel_size=3, stride=1)
self.conv6_3 = conv(dd[1]+dd[2],64, kernel_size=3, stride=1)
self.conv6_4 = conv(dd[2]+dd[3],32, kernel_size=3, stride=1)
self.predict_flow6 = self.predict_flow(dd[3]+dd[4])
#self.deconv6 = deconv(2, 2, kernel_size=4, stride=2, padding=1)
#self.upfeat6 = deconv(od+dd[4], 2, kernel_size=4, stride=2, padding=1)
od = nd+128+2
self.conv5_0 = conv(od, 128, kernel_size=3, stride=1)
self.conv5_1 = conv(dd[0], 128, kernel_size=3, stride=1)
self.conv5_2 = conv(dd[0]+dd[1],96, kernel_size=3, stride=1)
self.conv5_3 = conv(dd[1]+dd[2],64, kernel_size=3, stride=1)
self.conv5_4 = conv(dd[2]+dd[3],32, kernel_size=3, stride=1)
self.predict_flow5 = self.predict_flow(dd[3]+dd[4])
#self.deconv5 = deconv(2, 2, kernel_size=4, stride=2, padding=1)
#self.upfeat5 = deconv(od+dd[4], 2, kernel_size=4, stride=2, padding=1)
od = nd+96+2
self.conv4_0 = conv(od, 128, kernel_size=3, stride=1)
self.conv4_1 = conv(dd[0], 128, kernel_size=3, stride=1)
self.conv4_2 = conv(dd[0]+dd[1],96, kernel_size=3, stride=1)
self.conv4_3 = conv(dd[1]+dd[2],64, kernel_size=3, stride=1)
self.conv4_4 = conv(dd[2]+dd[3],32, kernel_size=3, stride=1)
self.predict_flow4 = self.predict_flow(dd[3]+dd[4])
#self.deconv4 = deconv(2, 2, kernel_size=4, stride=2, padding=1)
#self.upfeat4 = deconv(od+dd[4], 2, kernel_size=4, stride=2, padding=1)
od = nd+64+2
self.conv3_0 = conv(od, 128, kernel_size=3, stride=1)
self.conv3_1 = conv(dd[0], 128, kernel_size=3, stride=1)
self.conv3_2 = conv(dd[0]+dd[1],96, kernel_size=3, stride=1)
self.conv3_3 = conv(dd[1]+dd[2],64, kernel_size=3, stride=1)
self.conv3_4 = conv(dd[2]+dd[3],32, kernel_size=3, stride=1)
self.predict_flow3 = self.predict_flow(dd[3]+dd[4])
#self.deconv3 = deconv(2, 2, kernel_size=4, stride=2, padding=1)
#self.upfeat3 = deconv(od+dd[4], 2, kernel_size=4, stride=2, padding=1)
od = nd+32+2
self.conv2_0 = conv(od, 128, kernel_size=3, stride=1)
self.conv2_1 = conv(dd[0], 128, kernel_size=3, stride=1)
self.conv2_2 = conv(dd[0]+dd[1],96, kernel_size=3, stride=1)
self.conv2_3 = conv(dd[1]+dd[2],64, kernel_size=3, stride=1)
self.conv2_4 = conv(dd[2]+dd[3],32, kernel_size=3, stride=1)
self.predict_flow2 = self.predict_flow(dd[3]+dd[4])
#self.deconv2 = deconv(2, 2, kernel_size=4, stride=2, padding=1)
self.dc_conv1 = conv(dd[4]+2, 128, kernel_size=3, stride=1, padding=1, dilation=1)
self.dc_conv2 = conv(128, 128, kernel_size=3, stride=1, padding=2, dilation=2)
self.dc_conv3 = conv(128, 128, kernel_size=3, stride=1, padding=4, dilation=4)
self.dc_conv4 = conv(128, 96, kernel_size=3, stride=1, padding=8, dilation=8)
self.dc_conv5 = conv(96, 64, kernel_size=3, stride=1, padding=16, dilation=16)
self.dc_conv6 = conv(64, 32, kernel_size=3, stride=1, padding=1, dilation=1)
self.dc_conv7 = self.predict_flow(32)
'''
for m in self.modules():
if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):
nn.init.kaiming_zeros_(m.weight.data)
if m.bias is not None:
m.bias.data.zero_()
'''
def predict_flow(self, in_planes):
return nn.Conv2d(in_planes,2,kernel_size=3,stride=1,padding=1,bias=True)
def warp(self, x, flow):
return warp_flow(x, flow, use_mask=False)
def corr_naive(self, input1, input2, d=4):
# naive pytorch implementation of the correlation layer.
assert (input1.shape == input2.shape)
batch_size, feature_num, H, W = input1.shape[0:4]
input2 = F.pad(input2, (d,d,d,d), value=0)
cv = []
for i in range(2 * d + 1):
for j in range(2 * d + 1):
cv.append((input1 * input2[:, :, i:(i + H), j:(j + W)]).mean(1).unsqueeze(1))
return torch.cat(cv, 1)
def forward(self, feature_list_1, feature_list_2, img_hw):
c11, c12, c13, c14, c15, c16 = feature_list_1
c21, c22, c23, c24, c25, c26 = feature_list_2
corr6 = self.corr(c16, c26)
x0 = self.conv6_0(corr6)
x1 = self.conv6_1(x0)
x2 = self.conv6_2(torch.cat((x0,x1),1))
x3 = self.conv6_3(torch.cat((x1,x2),1))
x4 = self.conv6_4(torch.cat((x2,x3),1))
flow6 = self.predict_flow6(torch.cat((x3,x4),1))
up_flow6 = F.interpolate(flow6, scale_factor=2.0, mode='bilinear')*2.0
warp5 = self.warp(c25, up_flow6)
corr5 = self.corr(c15, warp5)
x = torch.cat((corr5, c15, up_flow6), 1)
x0 = self.conv5_0(x)
x1 = self.conv5_1(x0)
x2 = self.conv5_2(torch.cat((x0,x1),1))
x3 = self.conv5_3(torch.cat((x1,x2),1))
x4 = self.conv5_4(torch.cat((x2,x3),1))
flow5 = self.predict_flow5(torch.cat((x3,x4),1))
flow5 = flow5 + up_flow6
up_flow5 = F.interpolate(flow5, scale_factor=2.0, mode='bilinear')*2.0
warp4 = self.warp(c24, up_flow5)
corr4 = self.corr(c14, warp4)
x = torch.cat((corr4, c14, up_flow5), 1)
x0 = self.conv4_0(x)
x1 = self.conv4_1(x0)
x2 = self.conv4_2(torch.cat((x0,x1),1))
x3 = self.conv4_3(torch.cat((x1,x2),1))
x4 = self.conv4_4(torch.cat((x2,x3),1))
flow4 = self.predict_flow4(torch.cat((x3,x4),1))
flow4 = flow4 + up_flow5
up_flow4 = F.interpolate(flow4, scale_factor=2.0, mode='bilinear')*2.0
warp3 = self.warp(c23, up_flow4)
corr3 = self.corr(c13, warp3)
x = torch.cat((corr3, c13, up_flow4), 1)
x0 = self.conv3_0(x)
x1 = self.conv3_1(x0)
x2 = self.conv3_2(torch.cat((x0,x1),1))
x3 = self.conv3_3(torch.cat((x1,x2),1))
x4 = self.conv3_4(torch.cat((x2,x3),1))
flow3 = self.predict_flow3(torch.cat((x3,x4),1))
flow3 = flow3 + up_flow4
up_flow3 = F.interpolate(flow3, scale_factor=2.0, mode='bilinear')*2.0
warp2 = self.warp(c22, up_flow3)
corr2 = self.corr(c12, warp2)
x = torch.cat((corr2, c12, up_flow3), 1)
x0 = self.conv2_0(x)
x1 = self.conv2_1(x0)
x2 = self.conv2_2(torch.cat((x0,x1),1))
x3 = self.conv2_3(torch.cat((x1,x2),1))
x4 = self.conv2_4(torch.cat((x2,x3),1))
flow2 = self.predict_flow2(torch.cat((x3,x4),1))
flow2 = flow2 + up_flow3
x = self.dc_conv4(self.dc_conv3(self.dc_conv2(self.dc_conv1(torch.cat([flow2, x4], 1)))))
flow2 = flow2 + self.dc_conv7(self.dc_conv6(self.dc_conv5(x)))
img_h, img_w = img_hw[0], img_hw[1]
flow2 = F.interpolate(flow2 * 4.0, [img_h, img_w], mode='bilinear')
flow3 = F.interpolate(flow3 * 4.0, [img_h // 2, img_w // 2], mode='bilinear')
flow4 = F.interpolate(flow4 * 4.0, [img_h // 4, img_w // 4], mode='bilinear')
flow5 = F.interpolate(flow5 * 4.0, [img_h // 8, img_w // 8], mode='bilinear')
return [flow2, flow3, flow4, flow5]
================================================
FILE: core/networks/structures/ransac.py
================================================
import torch
import numpy as np
import os, sys
import torch.nn as nn
import pdb
import cv2
class reduced_ransac(nn.Module):
def __init__(self, check_num, thres, dataset):
super(reduced_ransac, self).__init__()
self.check_num = check_num
self.thres = thres
self.dataset = dataset
def robust_rand_sample(self, match, mask, num, robust=True):
# match: [b, 4, -1] mask: [b, 1, -1]
b, n = match.shape[0], match.shape[2]
nonzeros_num = torch.min(torch.sum(mask > 0, dim=-1)) # []
if nonzeros_num.detach().cpu().numpy() == n:
rand_int = torch.randint(0, n, [num])
select_match = match[:,:,rand_int]
else:
# If there is zero score in match, sample the non-zero matches.
select_idxs = []
if robust:
num = np.minimum(nonzeros_num.detach().cpu().numpy(), num)
for i in range(b):
nonzero_idx = torch.nonzero(mask[i,0,:]) # [nonzero_num,1]
rand_int = torch.randint(0, nonzero_idx.shape[0], [int(num)])
select_idx = nonzero_idx[rand_int, :] # [num, 1]
select_idxs.append(select_idx)
select_idxs = torch.stack(select_idxs, 0) # [b,num,1]
select_match = torch.gather(match.transpose(1,2), index=select_idxs.repeat(1,1,4), dim=1).transpose(1,2) # [b, 4, num]
return select_match, num
def top_ratio_sample(self, match, mask, ratio):
# match: [b, 4, -1] mask: [b, 1, -1]
b, total_num = match.shape[0], match.shape[-1]
scores, indices = torch.topk(mask, int(ratio*total_num), dim=-1) # [B, 1, ratio*tnum]
select_match = torch.gather(match.transpose(1,2), index=indices.squeeze(1).unsqueeze(-1).repeat(1,1,4), dim=1).transpose(1,2) # [b, 4, ratio*tnum]
return select_match, scores
def forward(self, match, mask, visualizer=None):
# match: [B, 4, H, W] mask: [B, 1, H, W]
b, h, w = match.shape[0], match.shape[2], match.shape[3]
match = match.view([b, 4, -1]).contiguous()
mask = mask.view([b, 1, -1]).contiguous()
# Sample matches for RANSAC 8-point and best F selection
top_ratio_match, top_ratio_mask = self.top_ratio_sample(match, mask, ratio=0.20) # [b, 4, ratio*H*W]
check_match, check_num = self.robust_rand_sample(top_ratio_match, top_ratio_mask, num=self.check_num) # [b, 4, check_num]
check_match = check_match.contiguous()
cv_f = []
for i in range(b):
if self.dataset == 'nyuv2':
f, m = cv2.findFundamentalMat(check_match[i,:2,:].transpose(0,1).detach().cpu().numpy(), check_match[i,2:,:].transpose(0,1).detach().cpu().numpy(), cv2.FM_LMEDS, 0.99)
else:
f, m = cv2.findFundamentalMat(check_match[i,:2,:].transpose(0,1).detach().cpu().numpy(), check_match[i,2:,:].transpose(0,1).detach().cpu().numpy(), cv2.FM_RANSAC, 0.1, 0.99)
cv_f.append(f)
cv_f = np.stack(cv_f, axis=0)
cv_f = torch.from_numpy(cv_f).float().to(match.get_device())
return cv_f
================================================
FILE: core/visualize/__init__.py
================================================
import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from visualizer import Visualizer
from visualizer import Visualizer_debug
from profiler import Profiler
================================================
FILE: core/visualize/profiler.py
================================================
import os
import time
import torch
import pdb
class Profiler(object):
def __init__(self, silent=False):
self.silent = silent
torch.cuda.synchronize()
self.start = time.time()
self.cache_time = self.start
def reset(self, silent=None):
if silent is None:
silent = self.silent
self.__init__(silent=silent)
def report_process(self, process_name):
if self.silent:
return None
torch.cuda.synchronize()
now = time.time()
print('{0}\t: {1:.4f}'.format(process_name, now - self.cache_time))
self.cache_time = now
def report_all(self, whole_process_name):
if self.silent:
return None
torch.cuda.synchronize()
now = time.time()
print('{0}\t: {1:.4f}'.format(whole_process_name, now - self.start))
pdb.set_trace()
================================================
FILE: core/visualize/visualizer.py
================================================
import os, sys
import numpy as np
import cv2
import pdb
import pickle
from mpl_toolkits import mplot3d
import matplotlib.pyplot as plt
import PIL.Image as pil
import matplotlib as mpl
import matplotlib.cm as cm
colorlib = [(0,0,255),(255,0,0),(0,255,0),(255,255,0),(0,255,255),(255,0,255),(0,0,0),(255,255,255)]
class Visualizer(object):
def __init__(self, loss_weights_dict, dump_dir=None):
self.loss_weights_dict = loss_weights_dict
self.use_flow_error = (self.loss_weights_dict['flow_error'] > 0)
self.dump_dir = dump_dir
self.log_list = []
def add_log_pack(self, log_pack):
self.log_list.append(log_pack)
def dump_log(self, fname=None):
if fname is None:
fname = self.dump_dir
with open(fname, 'wb') as f:
pickle.dump(self.log_list, f)
def print_loss(self, loss_pack, iter_=None):
loss_pixel = loss_pack['loss_pixel'].mean().detach().cpu().numpy()
loss_ssim = loss_pack['loss_ssim'].mean().detach().cpu().numpy()
loss_flow_smooth = loss_pack['loss_flow_smooth'].mean().detach().cpu().numpy()
loss_flow_consis = loss_pack['loss_flow_consis'].mean().detach().cpu().numpy()
if 'pt_depth_loss' in loss_pack.keys():
loss_pt_depth = loss_pack['pt_depth_loss'].mean().detach().cpu().numpy()
loss_pj_depth = loss_pack['pj_depth_loss'].mean().detach().cpu().numpy()
loss_depth_smooth = loss_pack['depth_smooth_loss'].mean().detach().cpu().numpy()
str_= ('iter: {0}, loss_pixel: {1:.6f}, loss_ssim: {2:.6f}, loss_pt_depth: {3:.6f}, loss_pj_depth: {4:.6f}, loss_depth_smooth: {5:.6f}'.format(\
iter_, loss_pixel, loss_ssim, loss_pt_depth, loss_pj_depth, loss_depth_smooth))
if self.use_flow_error:
loss_flow_error = loss_pack['flow_error'].mean().detach().cpu().numpy()
str_ = str_ + ', loss_flow_error: {0:.6f}'.format(loss_flow_error)
print(str_)
else:
print('iter: {4}, loss_pixel: {0:.6f}, loss_ssim: {1:.6f}, loss_flow_smooth: {2:.6f}, loss_flow_consis: {3:.6f}'.format(loss_pixel, loss_ssim, loss_flow_smooth, loss_flow_consis, iter_))
class Visualizer_debug():
def __init__(self, dump_dir=None, img1=None, img2=None):
self.dump_dir = dump_dir
self.img1 = img1
self.img2 = img2
def draw_point_corres(self, batch_idx, match, name):
img1 = self.img1[batch_idx]
img2 = self.img2[batch_idx]
self.show_corres(img1, img2, match, name)
print("Correspondence Saved in " + self.dump_dir + '/' + name)
def draw_invalid_corres_ray(self, img1, img2, depth_match, point2d_1_coord, point2d_2_coord, point2d_1_depth, point2d_2_depth, P1, P2):
# img: [H, W, 3] match: [4, n] point2d_coord: [n, 2] P: [3, 4]
idx = np.where(point2d_1_depth < 0)[0]
select_match = depth_match[:, idx]
self.show_corres(img1, img2, select_match)
pdb.set_trace()
def draw_epipolar_line(self, batch_idx, match, F, name):
# img: [H, W, 3] match: [4,n] F: [3,3]
img1 = self.img1[batch_idx]
img2 = self.img2[batch_idx]
self.show_epipolar_line(img1, img2, match, F, name)
print("Epipolar Lines Saved in " + self.dump_dir + '/' + name)
def show_corres(self, img1, img2, match, name):
# img: [H, W, 3] match: [4, n]
cv2.imwrite(os.path.join(self.dump_dir, name+'_img1_cor.png'), img1)
cv2.imwrite(os.path.join(self.dump_dir, name+'_img2_cor.png'), img2)
img1 = cv2.imread(os.path.join(self.dump_dir, name+'_img1_cor.png'))
img2 = cv2.imread(os.path.join(self.dump_dir, name+'_img2_cor.png'))
n = np.shape(match)[1]
for i in range(n):
x1,y1 = match[:2,i]
x2,y2 = match[2:,i]
#print((x1, y1))
#print((x2, y2))
cv2.circle(img1, (x1,y1), radius=1, color=colorlib[i%len(colorlib)], thickness=2)
cv2.circle(img2, (x2,y2), radius=1, color=colorlib[i%len(colorlib)], thickness=2)
cv2.imwrite(os.path.join(self.dump_dir, name+'_img1_cor.png'), img1)
cv2.imwrite(os.path.join(self.dump_dir, name+'_img2_cor.png'), img2)
def show_mask(self, mask, name):
# mask: [H, W, 1]
mask = mask / np.max(mask) * 255.0
cv2.imwrite(os.path.join(self.dump_dir, name+'.png'), mask)
def save_img(self, img, name):
cv2.imwrite(os.path.join(self.dump_dir, name+'.png'), img)
def save_depth_img(self, depth, name):
# depth: [h,w,1]
minddepth = np.min(depth)
maxdepth = np.max(depth)
depth_nor = (depth-minddepth) / (maxdepth-minddepth) * 255.0
depth_nor = depth_nor.astype(np.uint8)
cv2.imwrite(os.path.join(self.dump_dir, name+'_depth.png'), depth_nor)
def save_disp_color_img(self, disp, name):
vmax = np.percentile(disp, 95)
normalizer = mpl.colors.Normalize(vmin=disp.min(), vmax=vmax)
mapper = cm.ScalarMappable(norm=normalizer, cmap='magma')
colormapped_im = (mapper.to_rgba(disp)[:,:,:3] * 255).astype(np.uint8)
im = pil.fromarray(colormapped_im)
name_dest_im = os.path.join(self.dump_dir, name + '_depth.jpg')
im.save(name_dest_im)
def drawlines(self, img1, img2, lines, pts1, pts2):
''' img1 - image on which we draw the epilines for the points in img2
lines - corresponding epilines '''
r,c, _ = img1.shape
for r,pt1,pt2 in zip(lines,pts1,pts2):
color = tuple(np.random.randint(0,255,3).tolist())
x0,y0 = map(int, [0, -r[2]/r[1] ])
x1,y1 = map(int, [c, -(r[2]+r[0]*c)/r[1] ])
img1 = cv2.line(img1, (x0,y0), (x1,y1), color,1)
img1 = cv2.circle(img1,tuple(pt1),3,color,-1)
img2 = cv2.circle(img2,tuple(pt2),3,color,-1)
return img1,img2
def show_epipolar_line(self, img1, img2, match, F, name):
# img: [H,W,3] match: [4,n] F: [3,3]
pts1 = np.transpose(match[:2,:], [1,0])
pts2 = np.transpose(match[2:,:], [1,0])
lines1 = cv2.computeCorrespondEpilines(pts2.reshape(-1,1,2), 2,F)
lines1 = lines1.reshape(-1,3)
img5,img6 = self.drawlines(img1,img2,lines1,pts1,pts2)
# Find epilines corresponding to points in left image (first image) and
# drawing its lines on right image
lines2 = cv2.computeCorrespondEpilines(pts1.reshape(-1,1,2), 1,F)
lines2 = lines2.reshape(-1,3)
img3,img4 = self.drawlines(img2,img1,lines2,pts2,pts1)
cv2.imwrite(os.path.join(self.dump_dir, name+'_1eline.png'), img5)
cv2.imwrite(os.path.join(self.dump_dir, name+'_2eline.png'), img3)
return None
def show_ray(self, ax, K, RT, point2d, cmap='Greens'):
K_inv = np.linalg.inv(K)
R, T = RT[:,:3], RT[:,3]
ray_direction = np.matmul(np.matmul(R.T, K_inv), np.array([point2d[0], point2d[1], 1]))
ray_direction = ray_direction / (np.linalg.norm(ray_direction, ord=2) + 1e-12)
ray_origin = (-1) * np.matmul(R.T, T)
scatters = [ray_origin + t * ray_direction for t in np.linspace(0.0, 100.0, 1000)]
scatters = np.stack(scatters, axis=0)
self.visualize_points(ax, scatters, cmap=cmap)
self.scatter_3d(ax, scatters[0], scatter_color='r')
return ray_direction
def visualize_points(self, ax, points, cmap=None):
# ax.plot3D(points[:,0], points[:,1], points[:,2], c=points[:,2], cmap=cmap)
# ax.plot3D(points[:,0], points[:,1], points[:,2], c=points[:,2])
ax.plot3D(points[:,0], points[:,1], points[:,2])
def scatter_3d(self, ax, point, scatter_color='r'):
ax.scatter(point[0], point[1], point[2], c=scatter_color)
def visualize_two_rays(self, ax, match, P1, P2):
# match: [4] P: [3,4]
K = P1[:,:3] # the first P1 has identity rotation matrix and zero translation.
K_inv = np.linalg.inv(K)
RT1, RT2 = np.matmul(K_inv, P1), np.matmul(K_inv, P2)
x1, y1, x2, y2 = match
d1 = self.show_ray(ax, K, RT1, [x1, y1], cmap='Greens')
d2 = self.show_ray(ax, K, RT2, [x2, y2], cmap='Reds')
print(np.dot(d1.squeeze(), d2.squeeze()))
if __name__ == '__main__':
img1 = cv2.imread('./vis/ga.png')
img2 = cv2.imread('./vis/gb.png')
match = np.load('./vis/gmatch.npy')
print(np.shape(img1))
match = np.reshape(match, [4,-1])
select_match = match[:,np.random.randint(200000, size=100)]
show_corres(img1, img2, select_match)
================================================
FILE: data/eigen/export_gt_depth.py
================================================
# Copyright Niantic 2019. Patent Pending. All rights reserved.
#
# This software is licensed under the terms of the Monodepth2 licence
# which allows for non-commercial use only, the full terms of which are made
# available in the LICENSE file.
from __future__ import absolute_import, division, print_function
import os
import argparse
import numpy as np
import PIL.Image as pil
import os
import numpy as np
from collections import Counter
def load_velodyne_points(filename):
"""Load 3D point cloud from KITTI file format
(adapted from https://github.com/hunse/kitti)
"""
points = np.fromfile(filename, dtype=np.float32).reshape(-1, 4)
points[:, 3] = 1.0 # homogeneous
return points
def read_calib_file(path):
"""Read KITTI calibration file
(from https://github.com/hunse/kitti)
"""
float_chars = set("0123456789.e+- ")
data = {}
with open(path, 'r') as f:
for line in f.readlines():
key, value = line.split(':', 1)
value = value.strip()
data[key] = value
if float_chars.issuperset(value):
# try to cast to float array
try:
data[key] = np.array(list(map(float, value.split(' '))))
except ValueError:
# casting error: data[key] already eq. value, so pass
pass
return data
def sub2ind(matrixSize, rowSub, colSub):
"""Convert row, col matrix subscripts to linear indices
"""
m, n = matrixSize
return rowSub * (n-1) + colSub - 1
def generate_depth_map(calib_dir, velo_filename, cam=2, vel_depth=False):
"""Generate a depth map from velodyne data
"""
# load calibration files
cam2cam = read_calib_file(os.path.join(calib_dir, 'calib_cam_to_cam.txt'))
velo2cam = read_calib_file(os.path.join(calib_dir, 'calib_velo_to_cam.txt'))
velo2cam = np.hstack((velo2cam['R'].reshape(3, 3), velo2cam['T'][..., np.newaxis]))
velo2cam = np.vstack((velo2cam, np.array([0, 0, 0, 1.0])))
# get image shape
im_shape = cam2cam["S_rect_02"][::-1].astype(np.int32)
# compute projection matrix velodyne->image plane
R_cam2rect = np.eye(4)
R_cam2rect[:3, :3] = cam2cam['R_rect_00'].reshape(3, 3)
P_rect = cam2cam['P_rect_0'+str(cam)].reshape(3, 4)
P_velo2im = np.dot(np.dot(P_rect, R_cam2rect), velo2cam)
# load velodyne points and remove all behind image plane (approximation)
# each row of the velodyne data is forward, left, up, reflectance
velo = load_velodyne_points(velo_filename)
velo = velo[velo[:, 0] >= 0, :]
# project the points to the camera
velo_pts_im = np.dot(P_velo2im, velo.T).T
velo_pts_im[:, :2] = velo_pts_im[:, :2] / velo_pts_im[:, 2][..., np.newaxis]
if vel_depth:
velo_pts_im[:, 2] = velo[:, 0]
# check if in bounds
# use minus 1 to get the exact same value as KITTI matlab code
velo_pts_im[:, 0] = np.round(velo_pts_im[:, 0]) - 1
velo_pts_im[:, 1] = np.round(velo_pts_im[:, 1]) - 1
val_inds = (velo_pts_im[:, 0] >= 0) & (velo_pts_im[:, 1] >= 0)
val_inds = val_inds & (velo_pts_im[:, 0] < im_shape[1]) & (velo_pts_im[:, 1] < im_shape[0])
velo_pts_im = velo_pts_im[val_inds, :]
# project to image
depth = np.zeros((im_shape[:2]))
depth[velo_pts_im[:, 1].astype(np.int), velo_pts_im[:, 0].astype(np.int)] = velo_pts_im[:, 2]
# find the duplicate points and choose the closest depth
inds = sub2ind(depth.shape, velo_pts_im[:, 1], velo_pts_im[:, 0])
dupe_inds = [item for item, count in Counter(inds).items() if count > 1]
for dd in dupe_inds:
pts = np.where(inds == dd)[0]
x_loc = int(velo_pts_im[pts[0], 0])
y_loc = int(velo_pts_im[pts[0], 1])
depth[y_loc, x_loc] = velo_pts_im[pts, 2].min()
depth[depth < 0] = 0
return depth
def export_gt_depths_kitti():
parser = argparse.ArgumentParser(description='export_gt_depth')
parser.add_argument('--data_path',
type=str,
help='path to the root of the KITTI data',
required=True)
opt = parser.parse_args()
f = open("test_files.txt", 'r')
lines = f.read().splitlines()
print("Exporting ground truth depths for eigen")
gt_depths = []
for line in lines:
folder, frame_id, _ = line.split()
frame_id = int(frame_id)
calib_dir = os.path.join(opt.data_path, folder.split("/")[0])
velo_filename = os.path.join(opt.data_path, folder,
"velodyne_points/data", "{:010d}.bin".format(frame_id))
gt_depth = generate_depth_map(calib_dir, velo_filename, 2, True)
gt_depths.append(gt_depth.astype(np.float32))
output_path = "gt_depths.npz"
print("Saving to eigen")
np.savez_compressed(output_path, data=np.array(gt_depths))
if __name__ == "__main__":
export_gt_depths_kitti()
================================================
FILE: data/eigen/static_frames.txt
================================================
2011_09_26 2011_09_26_drive_0009_sync 0000000386
2011_09_26 2011_09_26_drive_0009_sync 0000000387
2011_09_26 2011_09_26_drive_0009_sync 0000000390
2011_09_26 2011_09_26_drive_0009_sync 0000000391
2011_09_26 2011_09_26_drive_0009_sync 0000000392
2011_09_26 2011_09_26_drive_0009_sync 0000000394
2011_09_26 2011_09_26_drive_0009_sync 0000000395
2011_09_26 2011_09_26_drive_0009_sync 0000000396
2011_09_26 2011_09_26_drive_0009_sync 0000000397
2011_09_26 2011_09_26_drive_0009_sync 0000000398
2011_09_26 2011_09_26_drive_0009_sync 0000000399
2011_09_26 2011_09_26_drive_0009_sync 0000000400
2011_09_26 2011_09_26_drive_0009_sync 0000000401
2011_09_26 2011_09_26_drive_0009_sync 0000000402
2011_09_26 2011_09_26_drive_0009_sync 0000000403
2011_09_26 2011_09_26_drive_0009_sync 0000000404
2011_09_26 2011_09_26_drive_0009_sync 0000000405
2011_09_26 2011_09_26_drive_0009_sync 0000000407
2011_09_26 2011_09_26_drive_0009_sync 0000000408
2011_09_26 2011_09_26_drive_0009_sync 0000000409
2011_09_26 2011_09_26_drive_0009_sync 0000000410
2011_09_26 2011_09_26_drive_0009_sync 0000000411
2011_09_26 2011_09_26_drive_0009_sync 0000000412
2011_09_26 2011_09_26_drive_0009_sync 0000000413
2011_09_26 2011_09_26_drive_0009_sync 0000000414
2011_09_26 2011_09_26_drive_0009_sync 0000000415
2011_09_26 2011_09_26_drive_0009_sync 0000000416
2011_09_26 2011_09_26_drive_0009_sync 0000000417
2011_09_26 2011_09_26_drive_0009_sync 0000000418
2011_09_26 2011_09_26_drive_0009_sync 0000000419
2011_09_26 2011_09_26_drive_0009_sync 0000000420
2011_09_26 2011_09_26_drive_0009_sync 0000000421
2011_09_26 2011_09_26_drive_0009_sync 0000000422
2011_09_26 2011_09_26_drive_0009_sync 0000000423
2011_09_26 2011_09_26_drive_0009_sync 0000000424
2011_09_26 2011_09_26_drive_0009_sync 0000000425
2011_09_26 2011_09_26_drive_0009_sync 0000000426
2011_09_26 2011_09_26_drive_0009_sync 0000000427
2011_09_26 2011_09_26_drive_0009_sync 0000000428
2011_09_26 2011_09_26_drive_0009_sync 0000000429
2011_09_26 2011_09_26_drive_0009_sync 0000000430
2011_09_26 2011_09_26_drive_0009_sync 0000000431
2011_09_26 2011_09_26_drive_0009_sync 0000000432
2011_09_26 2011_09_26_drive_0009_sync 0000000433
2011_09_26 2011_09_26_drive_0009_sync 0000000434
2011_09_26 2011_09_26_drive_0009_sync 0000000435
2011_09_26 2011_09_26_drive_0009_sync 0000000436
2011_09_26 2011_09_26_drive_0009_sync 0000000437
2011_09_26 2011_09_26_drive_0009_sync 0000000438
2011_09_26 2011_09_26_drive_0009_sync 0000000439
2011_09_26 2011_09_26_drive_0009_sync 0000000440
2011_09_26 2011_09_26_drive_0009_sync 0000000441
2011_09_26 2011_09_26_drive_0009_sync 0000000442
2011_09_26 2011_09_26_drive_0009_sync 0000000443
2011_09_26 2011_09_26_drive_0009_sync 0000000444
2011_09_26 2011_09_26_drive_0009_sync 0000000445
2011_09_26 2011_09_26_drive_0011_sync 0000000117
2011_09_26 2011_09_26_drive_0011_sync 0000000118
2011_09_26 2011_09_26_drive_0011_sync 0000000119
2011_09_26 2011_09_26_drive_0011_sync 0000000120
2011_09_26 2011_09_26_drive_0011_sync 0000000121
2011_09_26 2011_09_26_drive_0011_sync 0000000122
2011_09_26 2011_09_26_drive_0011_sync 0000000123
2011_09_26 2011_09_26_drive_0011_sync 0000000124
2011_09_26 2011_09_26_drive_0011_sync 0000000125
2011_09_26 2011_09_26_drive_0011_sync 0000000126
2011_09_26 2011_09_26_drive_0011_sync 0000000127
2011_09_26 2011_09_26_drive_0011_sync 0000000128
2011_09_26 2011_09_26_drive_0011_sync 0000000129
2011_09_26 2011_09_26_drive_0011_sync 0000000130
2011_09_26 2011_09_26_drive_0011_sync 0000000131
2011_09_26 2011_09_26_drive_0011_sync 0000000132
2011_09_26 2011_09_26_drive_0011_sync 0000000133
2011_09_26 2011_09_26_drive_0011_sync 0000000134
2011_09_26 2011_09_26_drive_0011_sync 0000000136
2011_09_26 2011_09_26_drive_0011_sync 0000000137
2011_09_26 2011_09_26_drive_0011_sync 0000000138
2011_09_26 2011_09_26_drive_0011_sync 0000000139
2011_09_26 2011_09_26_drive_0011_sync 0000000140
2011_09_26 2011_09_26_drive_0011_sync 0000000141
2011_09_26 2011_09_26_drive_0011_sync 0000000142
2011_09_26 2011_09_26_drive_0011_sync 0000000143
2011_09_26 2011_09_26_drive_0011_sync 0000000144
2011_09_26 2011_09_26_drive_0011_sync 0000000145
2011_09_26 2011_09_26_drive_0011_sync 0000000146
2011_09_26 2011_09_26_drive_0011_sync 0000000147
2011_09_26 2011_09_26_drive_0011_sync 0000000148
2011_09_26 2011_09_26_drive_0011_sync 0000000149
2011_09_26 2011_09_26_drive_0011_sync 0000000150
2011_09_26 2011_09_26_drive_0011_sync 0000000151
2011_09_26 2011_09_26_drive_0011_sync 0000000152
2011_09_26 2011_09_26_drive_0011_sync 0000000153
2011_09_26 2011_09_26_drive_0011_sync 0000000154
2011_09_26 2011_09_26_drive_0011_sync 0000000155
2011_09_26 2011_09_26_drive_0011_sync 0000000156
2011_09_26 2011_09_26_drive_0011_sync 0000000157
2011_09_26 2011_09_26_drive_0011_sync 0000000158
2011_09_26 2011_09_26_drive_0011_sync 0000000159
2011_09_26 2011_09_26_drive_0011_sync 0000000160
2011_09_26 2011_09_26_drive_0011_sync 0000000161
2011_09_26 2011_09_26_drive_0011_sync 0000000162
2011_09_26 2011_09_26_drive_0011_sync 0000000163
2011_09_26 2011_09_26_drive_0011_sync 0000000164
2011_09_26 2011_09_26_drive_0011_sync 0000000165
2011_09_26 2011_09_26_drive_0011_sync 0000000166
2011_09_26 2011_09_26_drive_0011_sync 0000000167
2011_09_26 2011_09_26_drive_0011_sync 0000000168
2011_09_26 2011_09_26_drive_0011_sync 0000000169
2011_09_26 2011_09_26_drive_0011_sync 0000000170
2011_09_26 2011_09_26_drive_0011_sync 0000000171
2011_09_26 2011_09_26_drive_0011_sync 0000000172
2011_09_26 2011_09_26_drive_0011_sync 0000000173
2011_09_26 2011_09_26_drive_0011_sync 0000000174
2011_09_26 2011_09_26_drive_0011_sync 0000000175
2011_09_26 2011_09_26_drive_0011_sync 0000000176
2011_09_26 2011_09_26_drive_0011_sync 0000000177
2011_09_26 2011_09_26_drive_0011_sync 0000000178
2011_09_26 2011_09_26_drive_0011_sync 0000000179
2011_09_26 2011_09_26_drive_0011_sync 0000000180
2011_09_26 2011_09_26_drive_0011_sync 0000000181
2011_09_26 2011_09_26_drive_0011_sync 0000000182
2011_09_26 2011_09_26_drive_0011_sync 0000000183
2011_09_26 2011_09_26_drive_0011_sync 0000000184
2011_09_26 2011_09_26_drive_0011_sync 0000000185
2011_09_26 2011_09_26_drive_0011_sync 0000000186
2011_09_26 2011_09_26_drive_0011_sync 0000000187
2011_09_26 2011_09_26_drive_0011_sync 0000000188
2011_09_26 2011_09_26_drive_0011_sync 0000000189
2011_09_26 2011_09_26_drive_0011_sync 0000000190
2011_09_26 2011_09_26_drive_0011_sync 0000000191
2011_09_26 2011_09_26_drive_0011_sync 0000000192
2011_09_26 2011_09_26_drive_0011_sync 0000000193
2011_09_26 2011_09_26_drive_0011_sync 0000000194
2011_09_26 2011_09_26_drive_0011_sync 0000000195
2011_09_26 2011_09_26_drive_0011_sync 0000000196
2011_09_26 2011_09_26_drive_0011_sync 0000000197
2011_09_26 2011_09_26_drive_0011_sync 0000000198
2011_09_26 2011_09_26_drive_0011_sync 0000000199
2011_09_26 2011_09_26_drive_0011_sync 0000000200
2011_09_26 2011_09_26_drive_0011_sync 0000000201
2011_09_26 2011_09_26_drive_0011_sync 0000000202
2011_09_26 2011_09_26_drive_0011_sync 0000000203
2011_09_26 2011_09_26_drive_0011_sync 0000000204
2011_09_26 2011_09_26_drive_0011_sync 0000000205
2011_09_26 2011_09_26_drive_0011_sync 0000000206
2011_09_26 2011_09_26_drive_0011_sync 0000000207
2011_09_26 2011_09_26_drive_0011_sync 0000000208
2011_09_26 2011_09_26_drive_0011_sync 0000000209
2011_09_26 2011_09_26_drive_0011_sync 0000000210
2011_09_26 2011_09_26_drive_0011_sync 0000000211
2011_09_26 2011_09_26_drive_0011_sync 0000000212
2011_09_26 2011_09_26_drive_0011_sync 0000000213
2011_09_26 2011_09_26_drive_0011_sync 0000000214
2011_09_26 2011_09_26_drive_0011_sync 0000000215
2011_09_26 2011_09_26_drive_0011_sync 0000000216
2011_09_26 2011_09_26_drive_0011_sync 0000000217
2011_09_26 2011_09_26_drive_0011_sync 0000000218
2011_09_26 2011_09_26_drive_0011_sync 0000000219
2011_09_26 2011_09_26_drive_0011_sync 0000000220
2011_09_26 2011_09_26_drive_0011_sync 0000000221
2011_09_26 2011_09_26_drive_0011_sync 0000000222
2011_09_26 2011_09_26_drive_0011_sync 0000000223
2011_09_26 2011_09_26_drive_0011_sync 0000000224
2011_09_26 2011_09_26_drive_0011_sync 0000000225
2011_09_26 2011_09_26_drive_0011_sync 0000000226
2011_09_26 2011_09_26_drive_0011_sync 0000000227
2011_09_26 2011_09_26_drive_0011_sync 0000000228
2011_09_26 2011_09_26_drive_0011_sync 0000000229
2011_09_26 2011_09_26_drive_0011_sync 0000000230
2011_09_26 2011_09_26_drive_0011_sync 0000000231
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2011_10_03 2011_10_03_drive_0042_sync 0000000321
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2011_10_03 2011_10_03_drive_0042_sync 0000000389
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2011_10_03 2011_10_03_drive_0042_sync 0000000533
2011_10_03 2011_10_03_drive_0042_sync 0000000534
2011_10_03 2011_10_03_drive_0042_sync 0000000535
2011_10_03 2011_10_03_drive_0042_sync 0000000771
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2011_10_03 2011_10_03_drive_0042_sync 0000000824
2011_10_03 2011_10_03_drive_0042_sync 0000000825
2011_10_03 2011_10_03_drive_0042_sync 0000001086
2011_10_03 2011_10_03_drive_0042_sync 0000001088
2011_10_03 2011_10_03_drive_0042_sync 0000001089
2011_10_03 2011_10_03_drive_0042_sync 0000001090
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2011_10_03 2011_10_03_drive_0042_sync 0000001096
2011_10_03 2011_10_03_drive_0042_sync 0000001097
2011_10_03 2011_10_03_drive_0042_sync 0000001098
2011_10_03 2011_10_03_drive_0042_sync 0000001156
2011_10_03 2011_10_03_drive_0047_sync 0000000014
2011_10_03 2011_10_03_drive_0047_sync 0000000015
2011_10_03 2011_10_03_drive_0047_sync 0000000016
2011_10_03 2011_10_03_drive_0047_sync 0000000017
2011_10_03 2011_10_03_drive_0047_sync 0000000018
2011_10_03 2011_10_03_drive_0047_sync 0000000019
2011_10_03 2011_10_03_drive_0047_sync 0000000020
2011_10_03 2011_10_03_drive_0047_sync 0000000021
2011_10_03 2011_10_03_drive_0047_sync 0000000022
2011_10_03 2011_10_03_drive_0047_sync 0000000023
2011_10_03 2011_10_03_drive_0047_sync 0000000024
2011_10_03 2011_10_03_drive_0047_sync 0000000025
2011_10_03 2011_10_03_drive_0047_sync 0000000026
2011_10_03 2011_10_03_drive_0047_sync 0000000027
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2011_10_03 2011_10_03_drive_0047_sync 0000000030
2011_10_03 2011_10_03_drive_0047_sync 0000000031
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2011_10_03 2011_10_03_drive_0047_sync 0000000033
2011_10_03 2011_10_03_drive_0047_sync 0000000034
2011_10_03 2011_10_03_drive_0047_sync 0000000035
2011_10_03 2011_10_03_drive_0047_sync 0000000036
2011_10_03 2011_10_03_drive_0047_sync 0000000037
2011_10_03 2011_10_03_drive_0047_sync 0000000038
2011_10_03 2011_10_03_drive_0047_sync 0000000039
2011_10_03 2011_10_03_drive_0047_sync 0000000040
2011_10_03 2011_10_03_drive_0047_sync 0000000138
2011_10_03 2011_10_03_drive_0047_sync 0000000141
2011_10_03 2011_10_03_drive_0047_sync 0000000142
2011_10_03 2011_10_03_drive_0047_sync 0000000149
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2011_10_03 2011_10_03_drive_0047_sync 0000000151
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2011_10_03 2011_10_03_drive_0047_sync 0000000781
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2011_10_03 2011_10_03_drive_0047_sync 0000000785
2011_10_03 2011_10_03_drive_0047_sync 0000000786
2011_10_03 2011_10_03_drive_0047_sync 0000000787
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2011_10_03 2011_10_03_drive_0047_sync 0000000814
2011_10_03 2011_10_03_drive_0047_sync 0000000815
2011_10_03 2011_10_03_drive_0047_sync 0000000816
2011_10_03 2011_10_03_drive_0047_sync 0000000817
2011_10_03 2011_10_03_drive_0047_sync 0000000818
2011_10_03 2011_10_03_drive_0047_sync 0000000819
2011_10_03 2011_10_03_drive_0047_sync 0000000820
2011_10_03 2011_10_03_drive_0047_sync 0000000821
2011_10_03 2011_10_03_drive_0047_sync 0000000822
2011_10_03 2011_10_03_drive_0047_sync 0000000823
2011_10_03 2011_10_03_drive_0047_sync 0000000824
2011_10_03 2011_10_03_drive_0047_sync 0000000825
2011_10_03 2011_10_03_drive_0047_sync 0000000826
2011_10_03 2011_10_03_drive_0047_sync 0000000827
2011_10_03 2011_10_03_drive_0047_sync 0000000828
2011_10_03 2011_10_03_drive_0047_sync 0000000829
2011_10_03 2011_10_03_drive_0047_sync 0000000830
2011_10_03 2011_10_03_drive_0047_sync 0000000831
2011_10_03 2011_10_03_drive_0047_sync 0000000832
2011_10_03 2011_10_03_drive_0047_sync 0000000833
2011_10_03 2011_10_03_drive_0047_sync 0000000834
2011_10_03 2011_10_03_drive_0047_sync 0000000835
2011_10_03 2011_10_03_drive_0058_sync 0000000000
2011_10_03 2011_10_03_drive_0058_sync 0000000001
2011_10_03 2011_10_03_drive_0058_sync 0000000002
2011_10_03 2011_10_03_drive_0058_sync 0000000003
2011_10_03 2011_10_03_drive_0058_sync 0000000004
2011_10_03 2011_10_03_drive_0058_sync 0000000005
2011_10_03 2011_10_03_drive_0058_sync 0000000006
2011_10_03 2011_10_03_drive_0058_sync 0000000007
2011_10_03 2011_10_03_drive_0058_sync 0000000008
2011_10_03 2011_10_03_drive_0058_sync 0000000009
2011_10_03 2011_10_03_drive_0058_sync 0000000010
2011_10_03 2011_10_03_drive_0058_sync 0000000011
2011_10_03 2011_10_03_drive_0058_sync 0000000012
2011_10_03 2011_10_03_drive_0058_sync 0000000013
2011_10_03 2011_10_03_drive_0058_sync 0000000014
2011_10_03 2011_10_03_drive_0058_sync 0000000015
2011_10_03 2011_10_03_drive_0058_sync 0000000016
2011_10_03 2011_10_03_drive_0058_sync 0000000017
2011_10_03 2011_10_03_drive_0058_sync 0000000018
2011_10_03 2011_10_03_drive_0058_sync 0000000019
2011_10_03 2011_10_03_drive_0058_sync 0000000020
2011_10_03 2011_10_03_drive_0058_sync 0000000021
2011_10_03 2011_10_03_drive_0058_sync 0000000022
2011_10_03 2011_10_03_drive_0058_sync 0000000023
2011_10_03 2011_10_03_drive_0058_sync 0000000024
2011_10_03 2011_10_03_drive_0058_sync 0000000025
2011_10_03 2011_10_03_drive_0058_sync 0000000026
2011_10_03 2011_10_03_drive_0058_sync 0000000027
================================================
FILE: data/eigen/test_files.txt
================================================
2011_09_26/2011_09_26_drive_0002_sync 0000000069 l
2011_09_26/2011_09_26_drive_0002_sync 0000000054 l
2011_09_26/2011_09_26_drive_0002_sync 0000000042 l
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2011_09_26/2011_09_26_drive_0027_sync 0000000077 l
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2011_09_26/2011_09_26_drive_0036_sync 0000000064 l
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2011_09_26/2011_09_26_drive_0036_sync 0000000096 l
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2011_09_26/2011_09_26_drive_0036_sync 0000000320 l
2011_09_26/2011_09_26_drive_0046_sync 0000000000 l
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2011_09_26/2011_09_26_drive_0046_sync 0000000010 l
2011_09_26/2011_09_26_drive_0046_sync 0000000015 l
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2011_09_26/2011_09_26_drive_0046_sync 0000000025 l
2011_09_26/2011_09_26_drive_0046_sync 0000000030 l
2011_09_26/2011_09_26_drive_0046_sync 0000000035 l
2011_09_26/2011_09_26_drive_0046_sync 0000000040 l
2011_09_26/2011_09_26_drive_0046_sync 0000000045 l
2011_09_26/2011_09_26_drive_0046_sync 0000000050 l
2011_09_26/2011_09_26_drive_0046_sync 0000000055 l
2011_09_26/2011_09_26_drive_0046_sync 0000000060 l
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2011_09_29/2011_09_29_drive_0071_sync 0000000576 l
2011_09_29/2011_09_29_drive_0071_sync 0000000108 l
2011_09_29/2011_09_29_drive_0071_sync 0000000180 l
2011_09_29/2011_09_29_drive_0071_sync 0000000072 l
2011_09_29/2011_09_29_drive_0071_sync 0000000612 l
2011_09_29/2011_09_29_drive_0071_sync 0000000915 l
2011_09_29/2011_09_29_drive_0071_sync 0000000735 l
2011_09_29/2011_09_29_drive_0071_sync 0000000144 l
2011_09_29/2011_09_29_drive_0071_sync 0000000396 l
2011_09_29/2011_09_29_drive_0071_sync 0000000468 l
2011_09_30/2011_09_30_drive_0016_sync 0000000132 l
2011_09_30/2011_09_30_drive_0016_sync 0000000011 l
2011_09_30/2011_09_30_drive_0016_sync 0000000154 l
2011_09_30/2011_09_30_drive_0016_sync 0000000022 l
2011_09_30/2011_09_30_drive_0016_sync 0000000242 l
2011_09_30/2011_09_30_drive_0016_sync 0000000198 l
2011_09_30/2011_09_30_drive_0016_sync 0000000176 l
2011_09_30/2011_09_30_drive_0016_sync 0000000231 l
2011_09_30/2011_09_30_drive_0016_sync 0000000275 l
2011_09_30/2011_09_30_drive_0016_sync 0000000220 l
2011_09_30/2011_09_30_drive_0016_sync 0000000088 l
2011_09_30/2011_09_30_drive_0016_sync 0000000143 l
2011_09_30/2011_09_30_drive_0016_sync 0000000055 l
2011_09_30/2011_09_30_drive_0016_sync 0000000033 l
2011_09_30/2011_09_30_drive_0016_sync 0000000187 l
2011_09_30/2011_09_30_drive_0016_sync 0000000110 l
2011_09_30/2011_09_30_drive_0016_sync 0000000044 l
2011_09_30/2011_09_30_drive_0016_sync 0000000077 l
2011_09_30/2011_09_30_drive_0016_sync 0000000066 l
2011_09_30/2011_09_30_drive_0016_sync 0000000000 l
2011_09_30/2011_09_30_drive_0016_sync 0000000165 l
2011_09_30/2011_09_30_drive_0016_sync 0000000264 l
2011_09_30/2011_09_30_drive_0016_sync 0000000253 l
2011_09_30/2011_09_30_drive_0016_sync 0000000209 l
2011_09_30/2011_09_30_drive_0016_sync 0000000121 l
2011_09_30/2011_09_30_drive_0018_sync 0000000107 l
2011_09_30/2011_09_30_drive_0018_sync 0000002247 l
2011_09_30/2011_09_30_drive_0018_sync 0000001391 l
2011_09_30/2011_09_30_drive_0018_sync 0000000535 l
2011_09_30/2011_09_30_drive_0018_sync 0000001819 l
2011_09_30/2011_09_30_drive_0018_sync 0000001177 l
2011_09_30/2011_09_30_drive_0018_sync 0000000428 l
2011_09_30/2011_09_30_drive_0018_sync 0000001926 l
2011_09_30/2011_09_30_drive_0018_sync 0000000749 l
2011_09_30/2011_09_30_drive_0018_sync 0000001284 l
2011_09_30/2011_09_30_drive_0018_sync 0000002140 l
2011_09_30/2011_09_30_drive_0018_sync 0000001605 l
2011_09_30/2011_09_30_drive_0018_sync 0000001498 l
2011_09_30/2011_09_30_drive_0018_sync 0000000642 l
2011_09_30/2011_09_30_drive_0018_sync 0000002740 l
2011_09_30/2011_09_30_drive_0018_sync 0000002419 l
2011_09_30/2011_09_30_drive_0018_sync 0000000856 l
2011_09_30/2011_09_30_drive_0018_sync 0000002526 l
2011_09_30/2011_09_30_drive_0018_sync 0000001712 l
2011_09_30/2011_09_30_drive_0018_sync 0000001070 l
2011_09_30/2011_09_30_drive_0018_sync 0000000000 l
2011_09_30/2011_09_30_drive_0018_sync 0000002033 l
2011_09_30/2011_09_30_drive_0018_sync 0000000214 l
2011_09_30/2011_09_30_drive_0018_sync 0000000963 l
2011_09_30/2011_09_30_drive_0018_sync 0000002633 l
2011_09_30/2011_09_30_drive_0027_sync 0000000533 l
2011_09_30/2011_09_30_drive_0027_sync 0000001040 l
2011_09_30/2011_09_30_drive_0027_sync 0000000082 l
2011_09_30/2011_09_30_drive_0027_sync 0000000205 l
2011_09_30/2011_09_30_drive_0027_sync 0000000835 l
2011_09_30/2011_09_30_drive_0027_sync 0000000451 l
2011_09_30/2011_09_30_drive_0027_sync 0000000164 l
2011_09_30/2011_09_30_drive_0027_sync 0000000794 l
2011_09_30/2011_09_30_drive_0027_sync 0000000328 l
2011_09_30/2011_09_30_drive_0027_sync 0000000615 l
2011_09_30/2011_09_30_drive_0027_sync 0000000917 l
2011_09_30/2011_09_30_drive_0027_sync 0000000369 l
2011_09_30/2011_09_30_drive_0027_sync 0000000287 l
2011_09_30/2011_09_30_drive_0027_sync 0000000123 l
2011_09_30/2011_09_30_drive_0027_sync 0000000876 l
2011_09_30/2011_09_30_drive_0027_sync 0000000410 l
2011_09_30/2011_09_30_drive_0027_sync 0000000492 l
2011_09_30/2011_09_30_drive_0027_sync 0000000958 l
2011_09_30/2011_09_30_drive_0027_sync 0000000656 l
2011_09_30/2011_09_30_drive_0027_sync 0000000000 l
2011_09_30/2011_09_30_drive_0027_sync 0000000753 l
2011_09_30/2011_09_30_drive_0027_sync 0000000574 l
2011_09_30/2011_09_30_drive_0027_sync 0000001081 l
2011_09_30/2011_09_30_drive_0027_sync 0000000041 l
2011_09_30/2011_09_30_drive_0027_sync 0000000246 l
2011_10_03/2011_10_03_drive_0027_sync 0000002906 l
2011_10_03/2011_10_03_drive_0027_sync 0000002544 l
2011_10_03/2011_10_03_drive_0027_sync 0000000362 l
2011_10_03/2011_10_03_drive_0027_sync 0000004535 l
2011_10_03/2011_10_03_drive_0027_sync 0000000734 l
2011_10_03/2011_10_03_drive_0027_sync 0000001096 l
2011_10_03/2011_10_03_drive_0027_sync 0000004173 l
2011_10_03/2011_10_03_drive_0027_sync 0000000543 l
2011_10_03/2011_10_03_drive_0027_sync 0000001277 l
2011_10_03/2011_10_03_drive_0027_sync 0000004354 l
2011_10_03/2011_10_03_drive_0027_sync 0000001458 l
2011_10_03/2011_10_03_drive_0027_sync 0000001820 l
2011_10_03/2011_10_03_drive_0027_sync 0000003449 l
2011_10_03/2011_10_03_drive_0027_sync 0000003268 l
2011_10_03/2011_10_03_drive_0027_sync 0000000915 l
2011_10_03/2011_10_03_drive_0027_sync 0000002363 l
2011_10_03/2011_10_03_drive_0027_sync 0000002725 l
2011_10_03/2011_10_03_drive_0027_sync 0000000181 l
2011_10_03/2011_10_03_drive_0027_sync 0000001639 l
2011_10_03/2011_10_03_drive_0027_sync 0000003992 l
2011_10_03/2011_10_03_drive_0027_sync 0000003087 l
2011_10_03/2011_10_03_drive_0027_sync 0000002001 l
2011_10_03/2011_10_03_drive_0027_sync 0000003811 l
2011_10_03/2011_10_03_drive_0027_sync 0000003630 l
2011_10_03/2011_10_03_drive_0027_sync 0000000000 l
2011_10_03/2011_10_03_drive_0047_sync 0000000096 l
2011_10_03/2011_10_03_drive_0047_sync 0000000800 l
2011_10_03/2011_10_03_drive_0047_sync 0000000320 l
2011_10_03/2011_10_03_drive_0047_sync 0000000576 l
2011_10_03/2011_10_03_drive_0047_sync 0000000000 l
2011_10_03/2011_10_03_drive_0047_sync 0000000480 l
2011_10_03/2011_10_03_drive_0047_sync 0000000640 l
2011_10_03/2011_10_03_drive_0047_sync 0000000032 l
2011_10_03/2011_10_03_drive_0047_sync 0000000384 l
2011_10_03/2011_10_03_drive_0047_sync 0000000160 l
2011_10_03/2011_10_03_drive_0047_sync 0000000704 l
2011_10_03/2011_10_03_drive_0047_sync 0000000736 l
2011_10_03/2011_10_03_drive_0047_sync 0000000672 l
2011_10_03/2011_10_03_drive_0047_sync 0000000064 l
2011_10_03/2011_10_03_drive_0047_sync 0000000288 l
2011_10_03/2011_10_03_drive_0047_sync 0000000352 l
2011_10_03/2011_10_03_drive_0047_sync 0000000512 l
2011_10_03/2011_10_03_drive_0047_sync 0000000544 l
2011_10_03/2011_10_03_drive_0047_sync 0000000608 l
2011_10_03/2011_10_03_drive_0047_sync 0000000128 l
2011_10_03/2011_10_03_drive_0047_sync 0000000224 l
2011_10_03/2011_10_03_drive_0047_sync 0000000416 l
2011_10_03/2011_10_03_drive_0047_sync 0000000192 l
2011_10_03/2011_10_03_drive_0047_sync 0000000448 l
2011_10_03/2011_10_03_drive_0047_sync 0000000768 l
================================================
FILE: data/eigen/test_scenes.txt
================================================
2011_09_26_drive_0117
2011_09_28_drive_0002
2011_09_26_drive_0052
2011_09_30_drive_0016
2011_09_26_drive_0059
2011_09_26_drive_0027
2011_09_26_drive_0020
2011_09_26_drive_0009
2011_09_26_drive_0013
2011_09_26_drive_0101
2011_09_26_drive_0046
2011_09_26_drive_0029
2011_09_26_drive_0064
2011_09_26_drive_0048
2011_10_03_drive_0027
2011_09_26_drive_0002
2011_09_26_drive_0036
2011_09_29_drive_0071
2011_10_03_drive_0047
2011_09_30_drive_0027
2011_09_26_drive_0086
2011_09_26_drive_0084
2011_09_26_drive_0096
2011_09_30_drive_0018
2011_09_26_drive_0106
2011_09_26_drive_0056
2011_09_26_drive_0023
2011_09_26_drive_0093
================================================
FILE: infer_vo.py
================================================
import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from core.networks.model_depth_pose import Model_depth_pose
from core.networks.model_flow import Model_flow
from visualizer import *
from profiler import Profiler
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import pdb
from sklearn import linear_model
import yaml
import warnings
import copy
from collections import OrderedDict
warnings.filterwarnings("ignore")
def save_traj(path, poses):
"""
path: file path of saved poses
poses: list of global poses
"""
f = open(path, 'w')
for i in range(len(poses)):
pose = poses[i].flatten()[:12] # [3x4]
line = " ".join([str(j) for j in pose])
f.write(line + '\n')
print('Trajectory Saved.')
def projection(xy, points, h_max, w_max):
# Project the triangulation points to depth map. Directly correspondence mapping rather than projection.
# xy: [N, 2] points: [3, N]
depth = np.zeros((h_max, w_max))
xy_int = np.around(xy).astype('int')
# Ensure all the correspondences are inside the image.
y_idx = (xy_int[:, 0] >= 0) * (xy_int[:, 0] < w_max)
x_idx = (xy_int[:, 1] >= 0) * (xy_int[:, 1] < h_max)
idx = y_idx * x_idx
xy_int = xy_int[idx]
points_valid = points[:, idx]
depth[xy_int[:, 1], xy_int[:, 0]] = points_valid[2]
return depth
def unprojection(xy, depth, K):
# xy: [N, 2] image coordinates of match points
# depth: [N] depth value of match points
N = xy.shape[0]
# initialize regular grid
ones = np.ones((N, 1))
xy_h = np.concatenate([xy, ones], axis=1)
xy_h = np.transpose(xy_h, (1,0)) # [3, N]
#depth = np.transpose(depth, (1,0)) # [1, N]
K_inv = np.linalg.inv(K)
points = np.matmul(K_inv, xy_h) * depth
points = np.transpose(points) # [N, 3]
return points
def cv_triangulation(matches, pose):
# matches: [N, 4], the correspondence xy coordinates
# pose: [4, 4], the relative pose trans from 1 to 2
xy1 = matches[:, :2].transpose()
xy2 = matches[:, 2:].transpose() # [2, N]
pose1 = np.eye(4)
pose2 = pose1 @ pose
points = cv2.triangulatePoints(pose1[:3], pose2[:3], xy1, xy2)
points /= points[3]
points1 = pose1[:3] @ points
points2 = pose2[:3] @ points
return points1, points2
class infer_vo():
def __init__(self, seq_id, sequences_root_dir):
self.img_dir = sequences_root_dir
#self.img_dir = '/home4/zhaow/data/kitti_odometry/sampled_s4_sequences/'
self.seq_id = seq_id
self.raw_img_h = 370.0#320
self.raw_img_w = 1226.0#1024
self.new_img_h = 256#320
self.new_img_w = 832#1024
self.max_depth = 50.0
self.min_depth = 0.0
self.cam_intrinsics = self.read_rescale_camera_intrinsics(os.path.join(self.img_dir, seq_id) + '/calib.txt')
self.flow_pose_ransac_thre = 0.1 #0.2
self.flow_pose_ransac_times = 10 #5
self.flow_pose_min_flow = 5
self.align_ransac_min_samples = 3
self.align_ransac_max_trials = 100
self.align_ransac_stop_prob = 0.99
self.align_ransac_thre = 1.0
self.PnP_ransac_iter = 1000
self.PnP_ransac_thre = 1
self.PnP_ransac_times = 5
def read_rescale_camera_intrinsics(self, path):
raw_img_h = self.raw_img_h
raw_img_w = self.raw_img_w
new_img_h = self.new_img_h
new_img_w = self.new_img_w
with open(path, 'r') as f:
lines = f.readlines()
data = lines[-1].strip('\n').split(' ')[1:]
data = [float(k) for k in data]
data = np.array(data).reshape(3,4)
cam_intrinsics = data[:3,:3]
cam_intrinsics[0,:] = cam_intrinsics[0,:] * new_img_w / raw_img_w
cam_intrinsics[1,:] = cam_intrinsics[1,:] * new_img_h / raw_img_h
return cam_intrinsics
def load_images(self):
path = self.img_dir
seq = self.seq_id
new_img_h = self.new_img_h
new_img_w = self.new_img_w
seq_dir = os.path.join(path, seq)
image_dir = os.path.join(seq_dir, 'image_2')
num = len(os.listdir(image_dir))
images = []
for i in range(num):
image = cv2.imread(os.path.join(image_dir, '%.6d'%i)+'.png')
image = cv2.resize(image, (new_img_w, new_img_h))
images.append(image)
return images
def get_prediction(self, img1, img2, model, K, K_inv, match_num):
# img1: [3,H,W] K: [3,3]
#visualizer = Visualizer_debug('/home3/zhaow/TrianFlow-pytorch/vis/')
img1_t = torch.from_numpy(np.transpose(img1 / 255.0, [2,0,1])).cuda().float().unsqueeze(0)
img2_t = torch.from_numpy(np.transpose(img2 / 255.0, [2,0,1])).cuda().float().unsqueeze(0)
K = torch.from_numpy(K).cuda().float().unsqueeze(0)
K_inv = torch.from_numpy(K_inv).cuda().float().unsqueeze(0)
filt_depth_match, depth1, depth2 = model.infer_vo(img1_t, img2_t, K, K_inv, match_num)
return filt_depth_match[0].transpose(0,1).cpu().detach().numpy(), depth1[0].squeeze(0).cpu().detach().numpy(), depth2[0].squeeze(0).cpu().detach().numpy()
def process_video(self, images, model):
'''Process a sequence to get scale consistent trajectory results.
Register according to depth net predictions. Here we assume depth predictions have consistent scale.
If not, pleas use process_video_tri which only use triangulated depth to get self-consistent scaled pose.
'''
poses = []
global_pose = np.eye(4)
# The first one global pose is origin.
poses.append(copy.deepcopy(global_pose))
seq_len = len(images)
K = self.cam_intrinsics
K_inv = np.linalg.inv(self.cam_intrinsics)
for i in range(seq_len-1):
img1, img2 = images[i], images[i+1]
depth_match, depth1, depth2 = self.get_prediction(img1, img2, model, K, K_inv, match_num=5000)
rel_pose = np.eye(4)
flow_pose = self.solve_pose_flow(depth_match[:,:2], depth_match[:,2:])
rel_pose[:3,:3] = copy.deepcopy(flow_pose[:3,:3])
if np.linalg.norm(flow_pose[:3,3:]) != 0:
scale = self.align_to_depth(depth_match[:,:2], depth_match[:,2:], flow_pose, depth2)
rel_pose[:3,3:] = flow_pose[:3,3:] * scale
if np.linalg.norm(flow_pose[:3,3:]) == 0 or scale == -1:
print('PnP '+str(i))
pnp_pose = self.solve_pose_pnp(depth_match[:,:2], depth_match[:,2:], depth1)
rel_pose = pnp_pose
global_pose[:3,3:] = np.matmul(global_pose[:3,:3], rel_pose[:3,3:]) + global_pose[:3,3:]
global_pose[:3,:3] = np.matmul(global_pose[:3,:3], rel_pose[:3,:3])
poses.append(copy.deepcopy(global_pose))
print(i)
return poses
def normalize_coord(self, xy, K):
xy_norm = copy.deepcopy(xy)
xy_norm[:,0] = (xy[:,0] - K[0,2]) / K[0,0]
xy_norm[:,1] = (xy[:,1] - K[1,2]) / K[1,1]
return xy_norm
def align_to_depth(self, xy1, xy2, pose, depth2):
# Align the translation scale according to triangulation depth
# xy1, xy2: [N, 2] pose: [4, 4] depth2: [H, W]
# Triangulation
img_h, img_w = np.shape(depth2)[0], np.shape(depth2)[1]
pose_inv = np.linalg.inv(pose)
xy1_norm = self.normalize_coord(xy1, self.cam_intrinsics)
xy2_norm = self.normalize_coord(xy2, self.cam_intrinsics)
points1_tri, points2_tri = cv_triangulation(np.concatenate([xy1_norm, xy2_norm], axis=1), pose_inv)
depth2_tri = projection(xy2, points2_tri, img_h, img_w)
depth2_tri[depth2_tri < 0] = 0
# Remove negative depths
valid_mask = (depth2 > 0) * (depth2_tri > 0)
depth_pred_valid = depth2[valid_mask]
depth_tri_valid = depth2_tri[valid_mask]
if np.sum(valid_mask) > 100:
scale_reg = linear_model.RANSACRegressor(base_estimator=linear_model.LinearRegression(fit_intercept=False), min_samples=self.align_ransac_min_samples, \
max_trials=self.align_ransac_max_trials, stop_probability=self.align_ransac_stop_prob, residual_threshold=self.align_ransac_thre)
scale_reg.fit(depth_tri_valid.reshape(-1, 1), depth_pred_valid.reshape(-1, 1))
scale = scale_reg.estimator_.coef_[0, 0]
else:
scale = -1
return scale
def solve_pose_pnp(self, xy1, xy2, depth1):
# Use pnp to solve relative poses.
# xy1, xy2: [N, 2] depth1: [H, W]
img_h, img_w = np.shape(depth1)[0], np.shape(depth1)[1]
# Ensure all the correspondences are inside the image.
x_idx = (xy2[:, 0] >= 0) * (xy2[:, 0] < img_w)
y_idx = (xy2[:, 1] >= 0) * (xy2[:, 1] < img_h)
idx = y_idx * x_idx
xy1 = xy1[idx]
xy2 = xy2[idx]
xy1_int = xy1.astype(np.int)
sample_depth = depth1[xy1_int[:,1], xy1_int[:,0]]
valid_depth_mask = (sample_depth < self.max_depth) * (sample_depth > self.min_depth)
xy1 = xy1[valid_depth_mask]
xy2 = xy2[valid_depth_mask]
# Unproject to 3d space
points1 = unprojection(xy1, sample_depth[valid_depth_mask], self.cam_intrinsics)
# ransac
best_rt = []
max_inlier_num = 0
max_ransac_iter = self.PnP_ransac_times
for i in range(max_ransac_iter):
if xy2.shape[0] > 4:
flag, r, t, inlier = cv2.solvePnPRansac(objectPoints=points1, imagePoints=xy2, cameraMatrix=self.cam_intrinsics, distCoeffs=None, iterationsCount=self.PnP_ransac_iter, reprojectionError=self.PnP_ransac_thre)
if flag and inlier.shape[0] > max_inlier_num:
best_rt = [r, t]
max_inlier_num = inlier.shape[0]
pose = np.eye(4)
if len(best_rt) != 0:
r, t = best_rt
pose[:3,:3] = cv2.Rodrigues(r)[0]
pose[:3,3:] = t
pose = np.linalg.inv(pose)
return pose
def solve_pose_flow(self, xy1, xy2):
# Solve essential matrix to find relative pose from flow.
# ransac
best_rt = []
max_inlier_num = 0
max_ransac_iter = self.flow_pose_ransac_times
best_inliers = np.ones((xy1.shape[0])) == 1
pp = (self.cam_intrinsics[0,2], self.cam_intrinsics[1,2])
# flow magnitude
avg_flow = np.mean(np.linalg.norm(xy1 - xy2, axis=1))
if avg_flow > self.flow_pose_min_flow:
for i in range(max_ransac_iter):
E, inliers = cv2.findEssentialMat(xy2, xy1, focal=self.cam_intrinsics[0,0], pp=pp, method=cv2.RANSAC, prob=0.99, threshold=self.flow_pose_ransac_thre)
cheirality_cnt, R, t, _ = cv2.recoverPose(E, xy2, xy1, focal=self.cam_intrinsics[0,0], pp=pp)
if inliers.sum() > max_inlier_num and cheirality_cnt > 50:
best_rt = [R, t]
max_inlier_num = inliers.sum()
best_inliers = inliers
if len(best_rt) == 0:
R = np.eye(3)
t = np.zeros((3,1))
best_rt = [R, t]
else:
R = np.eye(3)
t = np.zeros((3,1))
best_rt = [R, t]
R, t = best_rt
pose = np.eye(4)
pose[:3,:3] = R
pose[:3,3:] = t
return pose
if __name__ == '__main__':
import argparse
arg_parser = argparse.ArgumentParser(
description="TrianFlow training pipeline."
)
arg_parser.add_argument('-c', '--config_file', default=None, help='config file.')
arg_parser.add_argument('-g', '--gpu', type=str, default=0, help='gpu id.')
arg_parser.add_argument('--mode', type=str, default='flow', help='training mode.')
arg_parser.add_argument('--traj_save_dir_txt', type=str, default=None, help='directory for saving results')
arg_parser.add_argument('--sequences_root_dir', type=str, default=None, help='directory for test sequences')
arg_parser.add_argument('--sequence', type=str, default='09', help='Test sequence id.')
arg_parser.add_argument('--pretrained_model', type=str, default=None, help='directory for loading pretrained models')
args = arg_parser.parse_args()
with open(args.config_file, 'r') as f:
cfg = yaml.safe_load(f)
cfg['dataset'] = 'kitti_odo'
# copy attr into cfg
for attr in dir(args):
if attr[:2] != '__':
cfg[attr] = getattr(args, attr)
class pObject(object):
def __init__(self):
pass
cfg_new = pObject()
for attr in list(cfg.keys()):
setattr(cfg_new, attr, cfg[attr])
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
model = Model_depth_pose(cfg_new)
model.cuda()
weights = torch.load(args.pretrained_model)
model.load_state_dict(weights['model_state_dict'])
model.eval()
print('Model Loaded.')
print('Testing VO.')
vo_test = infer_vo(args.sequence, args.sequences_root_dir)
images = vo_test.load_images()
print('Images Loaded. Total ' + str(len(images)) + ' images found.')
poses = vo_test.process_video(images, model)
print('Test completed.')
traj_txt = args.traj_save_dir_txt
save_traj(traj_txt, poses)
================================================
FILE: requirements.txt
================================================
cffi==1.12.3
cycler==0.10.0
Cython==0.29.13
decorator==4.4.0
easydict==1.9
h5py==2.10.0
imageio==2.5.0
joblib==0.14.0
kiwisolver==1.1.0
matplotlib==3.1.1
networkx==2.3
numpy==1.17.2
opencv-python==4.2.0.32
Pillow==9.0.1
protobuf==3.15.0
pycparser==2.19
pyparsing==2.4.2
pypng==0.0.20
python-dateutil==2.8.0
PyWavelets==1.0.3
PyYAML==5.4
scikit-image==0.15.0
scikit-learn==0.21.3
scipy==1.3.1
six==1.12.0
sklearn
tensorboardX==2.0
torch==1.2.0
torchvision==0.4.0
tqdm==4.36.1
================================================
FILE: test.py
================================================
import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from core.dataset import KITTI_2012, KITTI_2015
from core.evaluation import eval_flow_avg, load_gt_flow_kitti
from core.evaluation import eval_depth
from core.visualize import Visualizer_debug
from core.networks import Model_depth_pose, Model_flow, Model_flowposenet
from core.evaluation import load_gt_flow_kitti, load_gt_mask
import torch
from tqdm import tqdm
import pdb
import cv2
import numpy as np
import yaml
def test_kitti_2012(cfg, model, gt_flows, noc_masks):
dataset = KITTI_2012(cfg.gt_2012_dir)
flow_list = []
for idx, inputs in enumerate(tqdm(dataset)):
img, K, K_inv = inputs
img = img[None,:,:,:]
K = K[None,:,:]
K_inv = K_inv[None,:,:]
img_h = int(img.shape[2] / 2)
img1, img2 = img[:,:,:img_h,:], img[:,:,img_h:,:]
img1, img2, K, K_inv = img1.cuda(), img2.cuda(), K.cuda(), K_inv.cuda()
if cfg.mode == 'flow' or cfg.mode == 'flowposenet':
flow = model.inference_flow(img1, img2)
else:
flow, _, _, _, _, _ = model.inference(img1, img2, K, K_inv)
#pdb.set_trace()
flow = flow[0].detach().cpu().numpy()
flow = flow.transpose(1,2,0)
flow_list.append(flow)
eval_flow_res = eval_flow_avg(gt_flows, noc_masks, flow_list, cfg, write_img=False)
print('CONFIG: {0}, mode: {1}'.format(cfg.config_file, cfg.mode))
print('[EVAL] [KITTI 2012]')
print(eval_flow_res)
return eval_flow_res
def test_kitti_2015(cfg, model, gt_flows, noc_masks, gt_masks, depth_save_dir=None):
dataset = KITTI_2015(cfg.gt_2015_dir)
visualizer = Visualizer_debug(depth_save_dir)
pred_flow_list = []
pred_disp_list = []
img_list = []
for idx, inputs in enumerate(tqdm(dataset)):
img, K, K_inv = inputs
img = img[None,:,:,:]
K = K[None,:,:]
K_inv = K_inv[None,:,:]
img_h = int(img.shape[2] / 2)
img1, img2 = img[:,:,:img_h,:], img[:,:,img_h:,:]
img_list.append(img1)
img1, img2, K, K_inv = img1.cuda(), img2.cuda(), K.cuda(), K_inv.cuda()
if cfg.mode == 'flow' or cfg.mode == 'flowposenet':
flow = model.inference_flow(img1, img2)
else:
flow, disp1, disp2, Rt, _, _ = model.inference(img1, img2, K, K_inv)
disp = disp1[0].detach().cpu().numpy()
disp = disp.transpose(1,2,0)
pred_disp_list.append(disp)
flow = flow[0].detach().cpu().numpy()
flow = flow.transpose(1,2,0)
pred_flow_list.append(flow)
#pdb.set_trace()
eval_flow_res = eval_flow_avg(gt_flows, noc_masks, pred_flow_list, cfg, moving_masks=gt_masks, write_img=False)
print('CONFIG: {0}, mode: {1}'.format(cfg.config_file, cfg.mode))
print('[EVAL] [KITTI 2015]')
print(eval_flow_res)
## depth evaluation
return eval_flow_res
def disp2depth(disp, min_depth=0.001, max_depth=80.0):
min_disp = 1 / max_depth
max_disp = 1 / min_depth
scaled_disp = min_disp + (max_disp - min_disp) * disp
depth = 1 / scaled_disp
return scaled_disp, depth
def resize_depths(gt_depth_list, pred_disp_list):
gt_disp_list = []
pred_depth_list = []
pred_disp_resized = []
for i in range(len(pred_disp_list)):
h, w = gt_depth_list[i].shape
pred_disp = cv2.resize(pred_disp_list[i], (w,h))
pred_depth = 1.0 / (pred_disp + 1e-4)
pred_depth_list.append(pred_depth)
pred_disp_resized.append(pred_disp)
return pred_depth_list, pred_disp_resized
def test_eigen_depth(cfg, model):
print('Evaluate depth using eigen split. Using model in ' + cfg.model_dir)
filenames = open('./data/eigen/test_files.txt').readlines()
pred_disp_list = []
for i in range(len(filenames)):
path1, idx, _ = filenames[i].strip().split(' ')
img = cv2.imread(os.path.join(os.path.join(cfg.raw_base_dir, path1), 'image_02/data/'+str(idx)+'.png'))
#img_resize = cv2.resize(img, (832,256))
img_resize = cv2.resize(img, (cfg.img_hw[1], cfg.img_hw[0]))
img_input = torch.from_numpy(img_resize / 255.0).float().cuda().unsqueeze(0).permute(0,3,1,2)
disp = model.infer_depth(img_input)
disp = disp[0].detach().cpu().numpy()
disp = disp.transpose(1,2,0)
pred_disp_list.append(disp)
#print(i)
gt_depths = np.load('./data/eigen/gt_depths.npz', allow_pickle=True)['data']
pred_depths, pred_disp_resized = resize_depths(gt_depths, pred_disp_list)
eval_depth_res = eval_depth(gt_depths, pred_depths)
abs_rel, sq_rel, rms, log_rms, a1, a2, a3 = eval_depth_res
sys.stderr.write(
"{:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10} \n".
format('abs_rel', 'sq_rel', 'rms', 'log_rms',
'a1', 'a2', 'a3'))
sys.stderr.write(
"{:10.4f}, {:10.4f}, {:10.3f}, {:10.3f}, {:10.3f}, {:10.3f}, {:10.3f} \n".
format(abs_rel, sq_rel, rms, log_rms, a1, a2, a3))
return eval_depth_res
def resize_disp(pred_disp_list, gt_depths):
pred_depths = []
h, w = gt_depths[0].shape[0], gt_depths[0].shape[1]
for i in range(len(pred_disp_list)):
disp = pred_disp_list[i]
resize_disp = cv2.resize(disp, (w,h))
depth = 1.0 / resize_disp
pred_depths.append(depth)
return pred_depths
import h5py
import scipy.io as sio
def load_nyu_test_data(data_dir):
data = h5py.File(os.path.join(data_dir, 'nyu_depth_v2_labeled.mat'), 'r')
splits = sio.loadmat(os.path.join(data_dir, 'splits.mat'))
test = np.array(splits['testNdxs']).squeeze(1)
images = np.transpose(data['images'], [0,1,3,2])
depths = np.transpose(data['depths'], [0,2,1])
images = images[test-1]
depths = depths[test-1]
return images, depths
def test_nyu(cfg, model, test_images, test_gt_depths):
leng = test_images.shape[0]
print('Test nyu depth on '+str(leng)+' images. Using depth model in '+cfg.model_dir)
pred_disp_list = []
crop_imgs = []
crop_gt_depths = []
for i in range(leng):
img = test_images[i]
img_crop = img[:,45:472,41:602]
crop_imgs.append(img_crop)
gt_depth_crop = test_gt_depths[i][45:472,41:602]
crop_gt_depths.append(gt_depth_crop)
#img = np.transpose(cv2.resize(np.transpose(img_crop, [1,2,0]), (576,448)), [2,0,1])
img = np.transpose(cv2.resize(np.transpose(img_crop, [1,2,0]), (cfg.img_hw[1],cfg.img_hw[0])), [2,0,1])
img_t = torch.from_numpy(img).float().cuda().unsqueeze(0) / 255.0
disp = model.infer_depth(img_t)
disp = np.transpose(disp[0].cpu().detach().numpy(), [1,2,0])
pred_disp_list.append(disp)
pred_depths = resize_disp(pred_disp_list, crop_gt_depths)
eval_depth_res = eval_depth(crop_gt_depths, pred_depths, nyu=True)
abs_rel, sq_rel, rms, log_rms, a1, a2, a3 = eval_depth_res
sys.stderr.write(
"{:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10} \n".
format('abs_rel', 'sq_rel', 'rms', 'log10',
'a1', 'a2', 'a3'))
sys.stderr.write(
"{:10.4f}, {:10.4f}, {:10.3f}, {:10.3f}, {:10.3f}, {:10.3f}, {:10.3f} \n".
format(abs_rel, sq_rel, rms, log_rms, a1, a2, a3))
return eval_depth_res
def test_single_image(img_path, model, training_hw, save_dir='./'):
img = cv2.imread(img_path)
h, w = img.shape[0:2]
img_resized = cv2.resize(img, (training_hw[1], training_hw[0]))
img_t = torch.from_numpy(np.transpose(img_resized, [2,0,1])).float().cuda().unsqueeze(0) / 255.0
disp = model.infer_depth(img_t)
disp = np.transpose(disp[0].cpu().detach().numpy(), [1,2,0])
disp_resized = cv2.resize(disp, (w,h))
depth = 1.0 / (1e-6 + disp_resized)
visualizer = Visualizer_debug(dump_dir=save_dir)
visualizer.save_disp_color_img(disp_resized, name='demo')
print('Depth prediction saved in ' + save_dir)
if __name__ == '__main__':
import argparse
arg_parser = argparse.ArgumentParser(
description="TrianFlow testing."
)
arg_parser.add_argument('-c', '--config_file', default=None, help='config file.')
arg_parser.add_argument('-g', '--gpu', type=str, default=0, help='gpu id.')
arg_parser.add_argument('--mode', type=str, default='depth', help='mode for testing.')
arg_parser.add_argument('--task', type=str, default='kitti_depth', help='To test on which task, kitti_depth or kitti_flow or nyuv2 or demo')
arg_parser.add_argument('--image_path', type=str, default=None, help='Set this only when task==demo. Depth demo for single image.')
arg_parser.add_argument('--pretrained_model', type=str, default=None, help='directory for loading flow pretrained models')
arg_parser.add_argument('--result_dir', type=str, default=None, help='directory for saving predictions')
args = arg_parser.parse_args()
if not os.path.exists(args.config_file):
raise ValueError('config file not found.')
with open(args.config_file, 'r') as f:
cfg = yaml.safe_load(f)
cfg['img_hw'] = (cfg['img_hw'][0], cfg['img_hw'][1])
#cfg['log_dump_dir'] = os.path.join(args.model_dir, 'log.pkl')
cfg['model_dir'] = args.result_dir
# copy attr into cfg
for attr in dir(args):
if attr[:2] != '__':
cfg[attr] = getattr(args, attr)
class pObject(object):
def __init__(self):
pass
cfg_new = pObject()
for attr in list(cfg.keys()):
setattr(cfg_new, attr, cfg[attr])
if args.mode == 'flow':
model = Model_flow(cfg_new)
elif args.mode == 'depth' or args.mode == 'flow_3stage':
model = Model_depth_pose(cfg_new)
elif args.mode == 'flowposenet':
model = Model_flowposenet(cfg_new)
if args.task == 'demo':
model = Model_depth_pose(cfg_new)
model.cuda()
weights = torch.load(args.pretrained_model)
model.load_state_dict(weights['model_state_dict'])
model.eval()
print('Model Loaded.')
if args.task == 'kitti_depth':
depth_res = test_eigen_depth(cfg_new, model)
elif args.task == 'kitti_flow':
gt_flows_2015, noc_masks_2015 = load_gt_flow_kitti(cfg_new.gt_2015_dir, 'kitti_2015')
gt_masks_2015 = load_gt_mask(cfg_new.gt_2015_dir)
flow_res = test_kitti_2015(cfg_new, model, gt_flows_2015, noc_masks_2015, gt_masks_2015)
elif args.task == 'nyuv2':
test_images, test_gt_depths = load_nyu_test_data(cfg_new.nyu_test_dir)
depth_res = test_nyu(cfg_new, model, test_images, test_gt_depths)
elif args.task == 'demo':
test_single_image(args.image_path, model, training_hw=cfg['img_hw'], save_dir=args.result_dir)
================================================
FILE: train.py
================================================
import os, sys
import yaml
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from core.dataset import KITTI_RAW, KITTI_Prepared, NYU_Prepare, NYU_v2, KITTI_Odo
from core.networks import get_model
from core.config import generate_loss_weights_dict
from core.visualize import Visualizer
from core.evaluation import load_gt_flow_kitti, load_gt_mask
from test import test_kitti_2012, test_kitti_2015, test_eigen_depth, test_nyu, load_nyu_test_data
from collections import OrderedDict
import torch
import torch.utils.data
from tqdm import tqdm
import shutil
import pickle
import pdb
def save_model(iter_, model_dir, filename, model, optimizer):
torch.save({"iteration": iter_, "model_state_dict": model.state_dict(), 'optimizer_state_dict': optimizer.state_dict()}, os.path.join(model_dir, filename))
def load_model(model_dir, filename, model, optimizer):
data = torch.load(os.path.join(model_dir, filename))
iter_ = data['iteration']
model.load_state_dict(data['model_state_dict'])
optimizer.load_state_dict(data['optimizer_state_dict'])
return iter_, model, optimizer
def train(cfg):
# load model and optimizer
model = get_model(cfg.mode)(cfg)
if cfg.multi_gpu:
model = torch.nn.DataParallel(model)
model = model.cuda()
optimizer = torch.optim.Adam([{'params': filter(lambda p: p.requires_grad, model.parameters()), 'lr': cfg.lr}])
# Load Pretrained Models
if cfg.resume:
if cfg.iter_start > 0:
cfg.iter_start, model, optimizer = load_model(cfg.model_dir, 'iter_{}.pth'.format(cfg.iter_start), model, optimizer)
else:
cfg.iter_start, model, optimizer = load_model(cfg.model_dir, 'last.pth', model, optimizer)
elif cfg.flow_pretrained_model:
data = torch.load(cfg.flow_pretrained_model)['model_state_dict']
renamed_dict = OrderedDict()
for k, v in data.items():
if cfg.multi_gpu:
name = 'module.model_flow.' + k
elif cfg.mode == 'flowposenet':
name = 'model_flow.' + k
else:
name = 'model_pose.model_flow.' + k
renamed_dict[name] = v
missing_keys, unexp_keys = model.load_state_dict(renamed_dict, strict=False)
print(missing_keys)
print(unexp_keys)
print('Load Flow Pretrained Model from ' + cfg.flow_pretrained_model)
if cfg.depth_pretrained_model and not cfg.resume:
data = torch.load(cfg.depth_pretrained_model)['model_state_dict']
if cfg.multi_gpu:
renamed_dict = OrderedDict()
for k, v in data.items():
name = 'module.' + k
renamed_dict[name] = v
missing_keys, unexp_keys = model.load_state_dict(renamed_dict, strict=False)
else:
missing_keys, unexp_keys = model.load_state_dict(data, strict=False)
print(missing_keys)
print('##############')
print(unexp_keys)
print('Load Depth Pretrained Model from ' + cfg.depth_pretrained_model)
loss_weights_dict = generate_loss_weights_dict(cfg)
visualizer = Visualizer(loss_weights_dict, cfg.log_dump_dir)
# load dataset
data_dir = os.path.join(cfg.prepared_base_dir, cfg.prepared_save_dir)
if not os.path.exists(os.path.join(data_dir, 'train.txt')):
if cfg.dataset == 'kitti_depth':
kitti_raw_dataset = KITTI_RAW(cfg.raw_base_dir, cfg.static_frames_txt, cfg.test_scenes_txt)
kitti_raw_dataset.prepare_data_mp(data_dir, stride=1)
elif cfg.dataset == 'kitti_odo':
kitti_raw_dataset = KITTI_Odo(cfg.raw_base_dir)
kitti_raw_dataset.prepare_data_mp(data_dir, stride=1)
elif cfg.dataset == 'nyuv2':
nyu_raw_dataset = NYU_Prepare(cfg.raw_base_dir, cfg.nyu_test_dir)
nyu_raw_dataset.prepare_data_mp(data_dir, stride=10)
else:
raise NotImplementedError
if cfg.dataset == 'kitti_depth':
dataset = KITTI_Prepared(data_dir, num_scales=cfg.num_scales, img_hw=cfg.img_hw, num_iterations=(cfg.num_iterations - cfg.iter_start) * cfg.batch_size)
elif cfg.dataset == 'kitti_odo':
dataset = KITTI_Prepared(data_dir, num_scales=cfg.num_scales, img_hw=cfg.img_hw, num_iterations=(cfg.num_iterations - cfg.iter_start) * cfg.batch_size)
elif cfg.dataset == 'nyuv2':
dataset = NYU_v2(data_dir, num_scales=cfg.num_scales, img_hw=cfg.img_hw, num_iterations=(cfg.num_iterations - cfg.iter_start) * cfg.batch_size)
else:
raise NotImplementedError
dataloader = torch.utils.data.DataLoader(dataset, batch_size=cfg.batch_size, shuffle=True, num_workers=cfg.num_workers, drop_last=False)
if cfg.dataset == 'kitti_depth' or cfg.dataset == 'kitti_odo':
gt_flows_2012, noc_masks_2012 = load_gt_flow_kitti(cfg.gt_2012_dir, 'kitti_2012')
gt_flows_2015, noc_masks_2015 = load_gt_flow_kitti(cfg.gt_2015_dir, 'kitti_2015')
gt_masks_2015 = load_gt_mask(cfg.gt_2015_dir)
elif cfg.dataset == 'nyuv2':
test_images, test_gt_depths = load_nyu_test_data(cfg.nyu_test_dir)
# training
print('starting iteration: {}.'.format(cfg.iter_start))
for iter_, inputs in enumerate(tqdm(dataloader)):
if (iter_ + 1) % cfg.test_interval == 0 and (not cfg.no_test):
model.eval()
if args.multi_gpu:
model_eval = model.module
else:
model_eval = model
if cfg.dataset == 'kitti_depth' or cfg.dataset == 'kitti_odo':
if not (cfg.mode == 'depth' or cfg.mode == 'flowposenet'):
eval_2012_res = test_kitti_2012(cfg, model_eval, gt_flows_2012, noc_masks_2012)
eval_2015_res = test_kitti_2015(cfg, model_eval, gt_flows_2015, noc_masks_2015, gt_masks_2015, depth_save_dir=os.path.join(cfg.model_dir, 'results'))
visualizer.add_log_pack({'eval_2012_res': eval_2012_res, 'eval_2015_res': eval_2015_res})
elif cfg.dataset == 'nyuv2':
if not cfg.mode == 'flow':
eval_nyu_res = test_nyu(cfg, model_eval, test_images, test_gt_depths)
visualizer.add_log_pack({'eval_nyu_res': eval_nyu_res})
visualizer.dump_log(os.path.join(cfg.model_dir, 'log.pkl'))
model.train()
iter_ = iter_ + cfg.iter_start
optimizer.zero_grad()
inputs = [k.cuda() for k in inputs]
loss_pack = model(inputs)
if iter_ % cfg.log_interval == 0:
visualizer.print_loss(loss_pack, iter_=iter_)
loss_list = []
for key in list(loss_pack.keys()):
loss_list.append((loss_weights_dict[key] * loss_pack[key].mean()).unsqueeze(0))
loss = torch.cat(loss_list, 0).sum()
loss.backward()
optimizer.step()
if (iter_ + 1) % cfg.save_interval == 0:
save_model(iter_, cfg.model_dir, 'iter_{}.pth'.format(iter_), model, optimizer)
save_model(iter_, cfg.model_dir, 'last.pth'.format(iter_), model, optimizer)
if cfg.dataset == 'kitti_depth':
if cfg.mode == 'depth' or cfg.mode == 'depth_pose':
eval_depth_res = test_eigen_depth(cfg, model_eval)
if __name__ == '__main__':
import argparse
arg_parser = argparse.ArgumentParser(
description="TrianFlow training pipeline."
)
arg_parser.add_argument('-c', '--config_file', default=None, help='config file.')
arg_parser.add_argument('-g', '--gpu', type=str, default=0, help='gpu id.')
arg_parser.add_argument('--batch_size', type=int, default=8, help='batch size.')
arg_parser.add_argument('--iter_start', type=int, default=0, help='starting iteration.')
arg_parser.add_argument('--lr', type=float, default=0.0001, help='learning rate')
arg_parser.add_argument('--num_workers', type=int, default=6, help='number of workers.')
arg_parser.add_argument('--log_interval', type=int, default=100, help='interval for printing loss.')
arg_parser.add_argument('--test_interval', type=int, default=2000, help='interval for evaluation.')
arg_parser.add_argument('--save_interval', type=int, default=2000, help='interval for saving models.')
arg_parser.add_argument('--mode', type=str, default='flow', help='training mode.')
arg_parser.add_argument('--model_dir', type=str, default=None, help='directory for saving models')
arg_parser.add_argument('--prepared_save_dir', type=str, default='data_s1', help='directory name for generated training dataset')
arg_parser.add_argument('--flow_pretrained_model', type=str, default=None, help='directory for loading flow pretrained models')
arg_parser.add_argument('--depth_pretrained_model', type=str, default=None, help='directory for loading depth pretrained models')
arg_parser.add_argument('--resume', action='store_true', help='to resume training.')
arg_parser.add_argument('--multi_gpu', action='store_true', help='to use multiple gpu for training.')
arg_parser.add_argument('--no_test', action='store_true', help='without evaluation.')
args = arg_parser.parse_args()
#args.config_file = 'config/debug.yaml'
if args.config_file is None:
raise ValueError('config file needed. -c --config_file.')
# set model
if args.model_dir is None:
args.model_dir = os.path.join('models', os.path.splitext(os.path.split(args.config_file)[1])[0])
args.model_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), args.model_dir, args.mode)
if not os.path.exists(args.model_dir):
os.makedirs(args.model_dir)
if not os.path.exists(args.config_file):
raise ValueError('config file not found.')
with open(args.config_file, 'r') as f:
cfg = yaml.safe_load(f)
cfg['img_hw'] = (cfg['img_hw'][0], cfg['img_hw'][1])
cfg['log_dump_dir'] = os.path.join(args.model_dir, 'log.pkl')
shutil.copy(args.config_file, args.model_dir)
# copy attr into cfg
for attr in dir(args):
if attr[:2] != '__':
cfg[attr] = getattr(args, attr)
# set gpu
num_gpus = len(args.gpu.split(','))
if (args.multi_gpu and num_gpus <= 1) or ((not args.multi_gpu) and num_gpus > 1):
raise ValueError('Error! the number of gpus used in the --gpu argument does not match the argument --multi_gpu.')
if args.multi_gpu:
cfg['batch_size'] = cfg['batch_size'] * num_gpus
cfg['num_iterations'] = int(cfg['num_iterations'] / num_gpus)
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
class pObject(object):
def __init__(self):
pass
cfg_new = pObject()
for attr in list(cfg.keys()):
setattr(cfg_new, attr, cfg[attr])
with open(os.path.join(args.model_dir, 'config.pkl'), 'wb') as f:
pickle.dump(cfg_new, f)
# main function
train(cfg_new)