1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
|
# Anime-Face-Detector
A Faster-RCNN based anime face detector.
This detector is trained on 6000 training samples and 641 testing samples, randomly selected from the dataset which is crawled from top 100 [pixiv daily ranking](https://www.pixiv.net/ranking.php?mode=daily).
Thanks to [OpenCV based Anime face detector](https://github.com/nagadomi/lbpcascade_animeface) written by nagadomi, which helps labelling the data.
The original implementation of Faster-RCNN using Tensorflow can be found [here](https://github.com/endernewton/tf-faster-rcnn)
## Dependencies
- Python 3.6 or 3.7
- `tensorflow` < 2.0
- `opencv-python`
- `cython` (optional, can be ignored with additional `-nms-type PY_NMS` argument)
- Pre-trained ResNet101 model
## Usage
1. Clone this repository
```bash
git clone https://github.com/qhgz2013/anime-face-detector.git
```
2. Download the pre-trained model
Google Drive: [here](https://drive.google.com/open?id=1WjBgfOUqp4sdRd9BHs4TkdH2EcBtV5ri)
Baidu Netdisk: [here](https://pan.baidu.com/s/1bvpCp1sbD7t9qnta8IhpmA)
3. Unzip the model file into `model` directory
4. Build the CPU NMS model (skip this step if use PY_NMS with argument: `-nms-type PY_NMS`)
```bash
make clean
make
```
If using Windows Power Shell, type `cmd /C make.bat` to run build script.
5. Run the demo as you want
- Visualize the result (without output path):
```bash
python main.py -i /path/to/image.jpg
```
- Save results to a json file
```bash
python main.py -i /path/to/image.jpg -o /path/to/output.json
```
Format: `{"image_path": [{"score": predicted_probability, "bbox": [min_x, min_y, max_x, max_y]}, ...], ...}`
Sample output file:
```json
{"/path/to/image.jpg": [{"score": 0.9999708, "bbox": [551.3375, 314.50253, 729.2599, 485.25674]}]}
```
- Detecting a whole directory with recursion
```bash
python main.py -i /path/to/dir -o /path/to/output.json
```
- Customize threshold
```bash
python main.py -i /path/to/image.jpg -nms 0.3 -conf 0.8
```
- Customize model path
```bash
python main.py -i /path/to/image.jpg -model /path/to/model.ckpt
```
- Customize nms type (supports CPU_NMS and PY_NMS, not supports GPU_NMS because of the complicated build process for Windows platform)
```bash
python main.py -i /path/to/image.jpg -nms-type PY_NMS
```
## Results
**Mean AP for this model: 0.9086**
Copyright info: [東方まとめ](https://www.pixiv.net/member_illust.php?mode=medium&illust_id=54275439) by [羽々斬](https://www.pixiv.net/member.php?id=2179695)
Copyright info: [【C94】桜と刀](https://www.pixiv.net/member_illust.php?mode=medium&illust_id=69797346) by [幻像黒兎](https://www.pixiv.net/member.php?id=4462245)
Copyright info: [アイドルマスター シンデレラガールズ](https://www.pixiv.net/member_illust.php?mode=medium&illust_id=69753772) by [我美蘭＠１日目 東A-40a](https://www.pixiv.net/member.php?id=2003931)
## About training
This model is directly trained by [Faster-RCNN](https://github.com/endernewton/tf-faster-rcnn), with following argument:
```bash
python tools/trainval_net.py --weight data/imagenet_weights/res101.ckpt --imdb voc_2007_trainval --imdbval voc_2007_test --iters 60000 --cfg experiments/cfgs/res101.yml --net res101 --set ANCHOR_SCALES "[4,8,16,32]" ANCHOR_RATIOS "[1]" TRAIN.STEPSIZE "[50000]"
```
## Dataset
We've uploaded the dataset to Google drive [here](https://drive.google.com/open?id=1nDPimhiwbAWc2diok-6davhubNVe82pr), dataset structure is similar to VOC2007 (used in original Faster-RCNN implementation).
## Citation and declaration
Feel free to cite this repo and dataset.
This work is not related to my research team and lab, just my personal interest.
|
