iCGY96 / Depth-Estimation-Web

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

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.