# mtcnn

**Repository Path**: phyzy/mtcnn

## Basic Information

- **Project Name**: mtcnn
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-05-09
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## 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