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使用insightface实现人脸检测和人脸识别

目录

1.搭建insightface环境

2.下载insightface工程

3.人脸检测

3.1 报错1 ImportError: cannot import name 'mesh_core_cython'

4. 提取人脸特征

4.1 模型下载

4.2 提取人脸特征

1.搭建insightface环境

为了避免和服务器中的其他python版本冲突，这里使用conda创建环境，安装insightface的过程中会安装相关依赖，等待系统安装完成即可。

conda create -n insightface_chw python=3.6
conda activate insightface_chw
pip install -U insightface 
pip install -U insightface #这个命令执行两遍，我只执行一遍发现并没有安装上，只是安装了一些依赖
pip install onnxruntime-gpu
pip install albumentations

2.下载insightface工程

git clone https://github.com/deepinsight/insightface

3.人脸检测

cd ./insightface/python-package

然后运行如下脚本

import cv2
import numpy as np
import insightface
from insightface.app import FaceAnalysis
from insightface.data import get_image as ins_get_image

app = FaceAnalysis(allowed_modules=['detection'],providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
app.prepare(ctx_id=0, det_size=(640, 640))
img = ins_get_image('ldh')  #不用带后缀，图片放到./insightface/python-package/insightface/data/images
faces = app.get(img)
print("faces::::", faces)
rimg = app.draw_on(img, faces)
cv2.imwrite("./ldh_output.jpg", rimg)

faces打印结果如下

faces:::: [{'bbox': array([250.4125 , 140.93515, 439.2733 , 385.86316], dtype=float32), 'kps': array([[306.19827, 253.13426],
       [393.27582, 238.8169 ],
       [360.847  , 305.88684],
       [332.69775, 343.96198],
       [394.14444, 331.2613 ]], dtype=float32), 'det_score': 0.85925925}]

检测得到的图片是矩形框加5个关键点，如下图所示

3.1 报错1 ImportError: cannot import name 'mesh_core_cython'

from .cython import mesh_core_cython
ImportError: cannot import name 'mesh_core_cython'

上面的错误我以为是要pip install cython，结果安装上之后仍然不行，于是网上搜了一下

 我用上面的命令发现还是不行，于是我在电脑中搜了一下

find  / -iname "cython"

发现在如下目录有个这个

./insightface/python-package/insightface/thirdparty/face3d/mesh/cython

 然后我去这个目录下看了一下，发现有个setup.py，于是执行

python3 setup.py build_ext -i

报错消失。

4. 提取人脸特征

4.1 模型下载

https://github.com/deepinsight/insightface/tree/master/model_zoo

去上面的网址下载人脸识别的onnx模型，这里我下载后重命名为recg.onnx

4.2 提取人脸特征

import cv2
import numpy as np
import insightface
from insightface.app import FaceAnalysis
from insightface.data import get_image as ins_get_image



app = FaceAnalysis(allowed_modules=['detection'],providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
app.prepare(ctx_id=0, det_size=(640, 640))
img = ins_get_image('ldh')
faces = app.get(img)
print("faces::::", faces)
rimg = app.draw_on(img, faces)
cv2.imwrite("./ldh_output.jpg", rimg)



handler = insightface.model_zoo.get_model('recg.onnx', providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
handler.prepare(ctx_id=0)
img = ins_get_image('ldh')
feature = handler.get(img, faces[0])
print("size of feature:", len(feature))
print("feature:", feature)

实际在提取特征的时候，传到模型里面的是5个人脸关键点坐标信息，我们可以看一下get函数

    def get(self, img, face):
        aimg = face_align.norm_crop(img, landmark=face.kps)
        face.embedding = self.get_feat(aimg).flatten()
        return face.embedding

可以看到这里面用的是landmark=face.kps。

打印出来的人脸特征如下：

size of feature: 512
feature: [-9.25378799e-01  1.12189925e+00 -3.77961069e-01  1.53634763e+00
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  1.22991014e+00  2.59431660e-01  6.32830501e-01  7.25324899e-02
 -4.30947453e-01  3.29869479e-01 -1.40622067e+00  1.75537634e+00
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参考文献：

https://github.com/deepinsight/insightface

https://github.com/deepinsight/insightface/tree/master/python-package

来源：陈 洪 伟

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