https://github.com/Seanlinx/mtcnn

https://github.com/Seanlinx/mtcnn

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• Host: GitHub
• URL: https://github.com/Seanlinx/mtcnn
• Owner: Seanlinx
• Created: 2016-12-02T08:31:46.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2017-07-08T12:53:09.000Z (over 7 years ago)
• Last Synced: 2024-08-01T22:40:41.045Z (5 months ago)
• Language: Python
• Size: 9.49 MB
• Stars: 597
• Watchers: 39
• Forks: 264
• Open Issues: 59
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• Awesome-MXNet - [w/ train

README

        
## Introduction

this repository is the implementation of MTCNN in MXnet

* `core`: core routines for MTCNN training and testing.

* `tools`: utilities for training and testing

* `data`: Refer to `Data Folder Structure` for dataset reference. Usually dataset contains `images` and `imglists` 

* `model`: Folder to save training symbol and model

* `prepare_data`: scripts for generating training data for pnet, rnet and onet 

## Useful information

You're required to modify mxnet/src/regression_output-inl.h according to mxnet_diff.patch before using the code for training.

* Dataset format

The images used for training are stored in ./data/dataset_name/images/ 

The annotation file is placed in ./data/dataset_name/imglists/

    * For training:

    Each line of the annotation file states a training sample.  

    The format is:

    [path to image] [cls_label] [bbox_label]  

    cls_label: 1 for positive, 0 for negative, -1 for part face.  

    bbox_label are the offset of x1, y1, x2, y2, calculated by (xgt(ygt) - x(y)) / width(height)  

    An example would be `12/positive/28 1 -0.05 0.11 -0.05 -0.11`.  

    Note that all the strings are seperated by space.

    * For testing:

    Similar to training but only path-to-image is needed. 

* Data Folder Structure (suppose root is `data`)

```

cache (created by imdb)

-- name + image set + gt_roidb

-- results (created by detection and evaluation)

mtcnn # contains images and anno for training mtcnn

-- images

---- 12 (images of size 12 x 12, used by pnet)

---- 24 (images of size 24 x 24, used by rnet)

---- 48 (images of size 48 x 48, used by onet)

-- imglists 

---- train_12.txt

---- train_24.txt

---- train_48.txt

custom (datasets for testing) 

-- images

-- imglists

---- image_set.txt

```

* Scripts to generate training data(from wider face dataset)

    * run wider_annotations/transform.m (or transform.py) to get the annotation file of the format we need.

    * gen_pnet_data.py: obtain training samples for pnet

    * gen_hard_example.py: prepare hard examples. 

	you can set test_mode to "pnet" to get training data for rnet,

	or set test_mode to "rnet" to get training data for onet.

    * gen_imglist.py: ramdom sample images generated by gen_pnet_data.py or gen_hard_example.py to form training set.

## Results

 

![image](https://github.com/Seanlinx/mtcnn/blob/master/fddb_result.png)

    

## License

MIT LICENSE

## Reference

Kaipeng Zhang, Zhanpeng Zhang, Zhifeng Li, Yu Qiao , " Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks," IEEE Signal Processing Letter