updating readme and requirements

README.md

@@ -1,6 +1,40 @@
 # DGC-Net: Dense Geometric Correspondence Network
 This is a PyTorch implementation of our work ["DGC-Net: Dense Geometric Correspondence Network"](https://arxiv.org/abs/1810.08393)
 
+## Installation
+- create and activate conda environment with Python 3.x
+```
+conda create -n my_fancy_env python=3.7
+source activate my_fancy_env
+```
+- install Pytorch v1.0.0 and torchvision library
+```
+pip install torch torchvision
+```
+- install all dependencies by running the following command:
+```
+pip install -r requirements.txt
+```
+
+## Getting started
+- ```eval.py``` demonstrates the results on the HPatches dataset
+To be able to run ```eval.py``` script:
+* Download an archive with pre-trained models (click) and unpack it
+in the project folder
+* Download HPatches dataset (Full image sequences). The dataset is available [here](https://github.com/hpatches/hpatches-dataset) at the end of the page
+* Run the following command:
+```
+python eval.py --image-data-path /path/to/hpatches-geometry
+```
+
+- ```train.py``` is a script to train DGC-Net/DGCM-Net model from scratch. To run this script, please follow the next procedure:
+* Download the [TokyoTimeMachine dataset](https://www.di.ens.fr/willow/research/netvlad/)
+* Run the command:
+```
+python train.py --image-data-path /path/to/TokyoTimeMachine
+```
+
+
 ## Performance on [HPatches](https://github.com/hpatches/hpatches-dataset) dataset
 Method / HPatches ID|Viewpoint 1|Viewpoint 2|Viewpoint 3|Viewpoint 4|Viewpoint 5
 :---|:---:|:---:|:---:|:---:|:---:
@@ -13,6 +47,8 @@ DGCM-Net (repo) | 2.33 | 5.62 | 9.55 | **11.59** | **16.48**
 
 Note: There is a difference in numbers presented in the original paper and obtained by the models of this repo. It might be related to the fact that both models (DGC-Net and DGCM-Net) have been trained using ```Pytorch v0.3```.
 
+More qualitative results are presented on the ["project page"](https://aaltovision.github.io/dgc-net-site/)
+
 ## How to cite
 If you use this software in your own research, please cite our publication:
 

eval.py

@@ -22,7 +22,8 @@
 # Paths
 parser.add_argument('--csv-path', type=str, default='data/csv',
                     help='path to training transformation csv folder')
-parser.add_argument('--image-path', type=str, default='data/hpatches-geometry',
+parser.add_argument('--image-data-path', type=str,
+                    default='data/hpatches-geometry',
                     help='path to folder containing training images')
 parser.add_argument('--model', type=str, default='dgc',
                     help='Model to use', choices=['dgc', 'dgcm'])
@@ -78,7 +79,7 @@
         test_dataset = \
             HPatchesDataset(csv_file=osp.join(args.csv_path,
                                               'hpatches_1_{}.csv'.format(k)),
-                            image_path_orig=args.image_path,
+                            image_path_orig=args.image_data_path,
                             transforms=dataset_transforms)
 
         test_dataloader = DataLoader(test_dataset,

requirements.txt

@@ -1,4 +1,4 @@
-termcolor=1.1.0
-opencv-python=4.0.0.21
-tqdm=4.31.1
-pandas=0.24.2
+termcolor==1.1.0
+opencv-python==4.0.0.21
+tqdm==4.31.1
+pandas==0.24.2