This repository provides the experiment code for the paper Long-Term Visual Place Recognition Benchmark with View Direction and Data Anonymization Influences.
Here is a graphical user interface (GUI) for using VPR methods on custom datasets: https://github.com/ai4ce/VPR-GUI-Tool
To install requirements:
pip install -r requirements.txt
1. Image Anonymization
To install mseg-api:
cd segmentation
cd mseg-api
pip install -e .
Make sure that you can run python -c "import mseg" in python.
To install mseg-semantic:
cd segmentation
cd apex
pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
cd ../mseg-semantic
pip install -e .
Make sure that you can run python -c "import mseg_semantic" in python.
Finally:
input_file=/path/to/my/directory
model_name=mseg-3m
model_path=mseg_semantic/mseg-3m.pth
config=mseg_semantic/config/test/default_config_360_ms.yaml
python -u mseg_semantic/tool/universal_demo.py --config=${config} model_name {model_name} model_path ${model_path} input_file ${input_file}
2. Image Filtration
Inside the process folder, use whiteFilter.py to filter images with white pixel percentage.
1. VLAD+SURF
Modify vlad_codebook_generation.py line 157 - 170 to fit the dataset.
cd test/vlad
python vlad_codebook_generation.py
python query_image_closest_image_generation.py
*Notice: the processing may take a few hours.
2. VLAD+SuperPoint
cd test/vlad_SP
python main.py
python find_closest.py
*Notice: the processing may take a few hours.
3. NetVLAD
4. PoseNet
Copy the train_image_paths.txt and test_image_paths.txt to test/posenet.
Obtain the latitude and longtitude of training images and convert them to normalized Universal Transverse Mercator (UTM) coordinates.
cd test/posenet
python getGPS.py
python mean.py
Start training. This may take several hours. Suggestion: use slurm to run the process.
python train.py --image_path path_to_train_images/ --metadata_path trainNorm.txt
Generate the input file for testing from test_image_paths.txt.
python gen_test_txt.py
Start testing.
python single_test.py --image_path path_to_test_images/ --metadata_path test.txt --weights_path models_trainNorm/best_net.pth
The predicted normalized UTM coordinates of test images is in the image_name.txt. Match the test images with the training images based on their location.
python match.py
The matching result is in the match.txt.
5. DBoW
Copy the train_image_paths.txt and test_image_paths.txt to test/DBow3/utils. Copy and paste the content of test_image_paths.txt at the end of train_image_paths.txt and save the text file as total_images_paths.txt.
Open test/DBow3/utils/demo_general.cpp file. Change the for loop range at line 117 and line 123. Both ranges are the range of lines in total_images_paths.txt. The first for loop range is the range of test images and the second range is the range of training images. To run with multi-thread, you may run the code multiple times with small ranges of test images where the sum of ranges equals to the number of lines in test_image_paths.txt.
Compile and run the code.
cd test/DBow3
cmake .
cd utils
make
./demo_general a b
The result of each test image and its top-5 matched training images is in the output.txt.