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nttstar committed Jun 19, 2021
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# mypy
.mypy_cache/

.DS_Store
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You can now find heatmap based approaches under ``heatmapReg`` directory.
## Face Alignment


<div align="left">
<img src="https://insightface.ai/assets/img/custom/logo3.jpg" width="240"/>
</div>


## Introduction

These are the face alignment methods of [InsightFace](https://insightface.ai)


<div align="left">
<img src="https://insightface.ai/assets/img/custom/thumb_sdunet.png" width="600"/>
</div>


### Datasets

Please refer to [datasets](_datasets_) page for the details of face alignment datasets used for training and evaluation.

### Evaluation

Please refer to [evaluation](_evaluation_) page for the details of face alignment evaluation.


## Methods


Supported methods:

- [x] [SDUNets (BMVC'2018)](heatmap)
- [x] [SimpleRegression](coordinate_reg)



## Contributing

We appreciate all contributions to improve the face alignment model zoo of InsightFace.

You can now find coordinate regression approaches under ``coordinateReg`` directory.

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## Face Alignment


<div align="left">
<img src="https://insightface.ai/assets/img/custom/logo3.jpg" width="320"/>
</div>


## Introduction

These are the face attribute methods of [InsightFace](https://insightface.ai)


<div align="left">
<img src="https://insightface.ai/assets/img/github/t1_genderage.jpg" width="600"/>
</div>


### Datasets

Please refer to [datasets](_datasets_) page for the details of face attribute datasets used for training and evaluation.

### Evaluation

Please refer to [evaluation](_evaluation_) page for the details of face attribute evaluation.


## Methods


Supported methods:

- [x] [Gender_Age](gender_age)



## Contributing

We appreciate all contributions to improve the face attribute model zoo of InsightFace.


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import numpy as np
import insightface
from insightface.app import FaceAnalysis
from insightface.data import get_image as ins_get_image

assert insightface.__version__>='0.2'

parser = argparse.ArgumentParser(description='insightface test')
parser = argparse.ArgumentParser(description='insightface gender-age test')
# general
parser.add_argument('--ctx', default=0, type=int, help='ctx id, <0 means using cpu')
args = parser.parse_args()

app = FaceAnalysis(name='antelope')
app = FaceAnalysis(allowed_modules=['detection', 'genderage'])
app.prepare(ctx_id=args.ctx, det_size=(640,640))

img = cv2.imread('../sample-images/t1.jpg')
img = ins_get_image('t1')
faces = app.get(img)
assert len(faces)==6
rimg = app.draw_on(img, faces)
cv2.imwrite("./t1_output.jpg", rimg)
print(len(faces))
for face in faces:
print(face.bbox)
print(face.kps)
print(face.embedding.shape)
print(face.sex, face.age)

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## Challenges


<div align="left">
<img src="https://insightface.ai/assets/img/custom/logo3.jpg" width="240"/>
</div>


## Introduction

These are challenges hold by [InsightFace](https://insightface.ai)


<div align="left">
<img src="https://insightface.ai/assets/img/custom/thumb_ifrt.png" width="480"/>
</div>



## List


Supported methods:

- [LFR19 (ICCVW'2019)](iccv19-lfr)
- [MFR21 (ICCVW'2021)](iccv21-mfr)
- [IFRT](ifrt)




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*For Chinese:*

![wechat](https://github.com/deepinsight/insightface/blob/master/resources/lfr19_wechat1.jpg)
![wechat](https://insightface.ai/assets/img/github/lfr19_wechat1.jpg)

*For English:*

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