# NWPU-Crowd-Sample-Code-for-Localization

**Repository Path**: USTC_HISAI/NWPU-Crowd-Sample-Code-for-Localization

## Basic Information

- **Project Name**: NWPU-Crowd-Sample-Code-for-Localization
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2021-07-09
- **Last Updated**: 2021-07-09

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# NWPU-Crowd-Sample-Code-for-Localization

---

This repo is the official crowd localization implementation of [paper](https://arxiv.org/abs/2001.03360): **NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization**. The code is developed based on [C^3 Framework](https://github.com/gjy3035/C-3-Framework). 



# Getting Started

## Preparation
- Prerequisites
  - Python 3.x
  - Pytorch 1.x: http://pytorch.org .
  - other libs in ```requirements.txt```, run ```pip install -r requirements.txt```.

- Installation
  - Clone this repo:
    ```
    git clone https://github.com/gjy3035/NWPU-Crowd-Sample-Code-for-Localization.git
    ```
    
- Data Preparation
  - Download NWPU-Crowd dataset from this [link](https://mailnwpueducn-my.sharepoint.com/personal/gjy3035_mail_nwpu_edu_cn/_layouts/15/onedrive.aspx?id=%2Fpersonal%2Fgjy3035%5Fmail%5Fnwpu%5Fedu%5Fcn%2FDocuments%2F%E8%AE%BA%E6%96%87%E5%BC%80%E6%BA%90%E6%95%B0%E6%8D%AE%2FNWPU%2DCrowd&originalPath=aHR0cHM6Ly9tYWlsbndwdWVkdWNuLW15LnNoYXJlcG9pbnQuY29tLzpmOi9nL3BlcnNvbmFsL2dqeTMwMzVfbWFpbF9ud3B1X2VkdV9jbi9Fc3ViTXA0OHd3SkRpSDBZbFQ4Mk5ZWUJtWTlMMHMtRnByckJjb2FBSmtJMXJ3P3J0aW1lPWdxTkxjV0dTMTBn). 
  - Unzip ```*zip``` files in turns and place ```images_part*``` into a folder. Finally, the folder tree is below:
  ```
    -- NWPU-Crowd
        |-- images
        |   |-- 0001.jpg
        |   |-- 0002.jpg
        |   |-- ...
        |   |-- 5109.jpg
        |-- jsons
        |   |-- 0001.json
        |   |-- 0002.json
        |   |-- ...
        |   |-- 3609.json
        |-- mats
        |   |-- 0001.mat
        |   |-- 0002.mat
        |   |-- ...
        |   |-- 3609.mat
        |-- train.txt
        |-- val.txt
        |-- test.txt
        |-- readme.md
    ```
  - Run ```./datasets/prepare_NWPU.m``` using [Matlab](https://www.mathworks.com/). 
  - Modify ```__C_NWPU.DATA_PATH``` in ```./datasets/setting/NWPU.py``` with the path of your processed data.


## Training

- Set the parameters in ```config.py``` and ```./datasets/setting/NWPU.py```.
- run ```python train.py```.
- run ```tensorboard --logdir=exp --port=6006```.

## Testing

We only provide an example to forward the model on the test set. You may need to modify it to test your models.

- Modify some key parameters in ```test.py```: 
  - Line 35: ```dataRoot```, the same as ```__C_NWPU.DATA_PATH``` in ```./datasets/setting/NWPU.py```.
  - Line 36: ```ori_data```, the original data path.
  - Line 37: ```model_path```.  
  - Line 49: GPU Id and Model Name. 
- Run ```python test.py```. 

## Evaluation and Visualization

We only provide an example to forward the model on the test set. You may need to modify it to test your models.
- ```cd eval```.
- ```python test.py```. 
- ```python vis4val.py```. 


## Pre-trained Models

We provide the pre-trained models in this [link](http://share.crowdbenchmark.com:2443/home/Pre-trained_Models_NWPU-Crowd). 

# Performance on the validation set

The overall results on val set:

|   Method   |  F1_m |  Pre  |  Rec  |
|------------|-------|-------|--------|
| RAZ_loc[1]   | 62.5| 69.2  | 56.9 |



About the leaderboard on the test set, please visit [Crowd benchmark](https://crowdbenchmark.com/nwpucrowdloc.html).  

## References

1. Recurrent Attentive Zooming for Joint Crowd Counting and Precise Localization, CPVR, 2019.


# Evaluation Scheme 

The Evaluation Python Code of the ```crowdbenchmark.com``` is shown in ```./eval/eval.py```. 

# Citation
If you find this project is useful for your research, please cite:
```
@article{gao2020nwpu,
  title={NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization},
  author={Wang, Qi and Gao, Junyu and Lin, Wei and Li, Xuelong},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  doi={10.1109/TPAMI.2020.3013269},
  year={2020}
}
```

Our code borrows a lot from the C^3 Framework, you may cite:
```
@article{gao2019c,
  title={C$^3$ Framework: An Open-source PyTorch Code for Crowd Counting},
  author={Gao, Junyu and Lin, Wei and Zhao, Bin and Wang, Dong and Gao, Chenyu and Wen, Jun},
  journal={arXiv preprint arXiv:1907.02724},
  year={2019}
}
```
If you use crowd counting models in this repo (RAZ_loc), please cite them.