Skip to content

This code provides depth estimation function, while supporting Web deployment, through the Web page to try to single image depth estimation.

License

Notifications You must be signed in to change notification settings

iCGY96/Depth-Estimation-Web

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

2 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Depth Map Prediction from a Single Image

This code provides depth estimation function, while supporting Web deployment, through the Web page to try to single image depth estimation.

The depth prediction model adopted is Unsupervised Monocular Depth Estimation with Left-Right Consistency

Requirements

This code was tested with Tensorflow 1.10.0, CUDA 9.0, falsk, Python 3 and Ubuntu 16.04

How to try it on an image or an videos

Make sure your first put test images or video frames into a folder, and generate a list of file names on test_files_eigen.txt

You can test an image by running:

CUDA_VISIBLE_DEVICES=0 python main.py --dataset=/path/to/images/ \
--filenames_file=/path/to/test_files_eigen.txt \
--output_directory=/path/to/output_directory \
--checkpoint_path=/path/to/models/model_city2kitti.meta

How to deploy it on Web

If you want to access the deployed web page from an external network, you should first modify the host and port for the ./visualization/app.py file.

You can deploy it on Web by running:

CUDA_VISIBLE_DEVICES=0 python app.py

Models

You can download our pre-trained models to an existing directory by running:

sh ./models/get_model.sh model_city2kitti/model_city2eigen ./models

Acknowledgements

Thanks to mrharicot, the initial code of depthmodel is references to his project monodepth.

About

This code provides depth estimation function, while supporting Web deployment, through the Web page to try to single image depth estimation.

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published