---

前言

人脸识别项目，再走一遍。之前是公司老人留下的，没部署过，没交付过。这次重新搞一个，并且把搞过的记录下来。

---

参考旷视的insightface:
https://github.com/deepinsight/insightface/tree/master/python-package
另外一个blog:https://blog.csdn.net/u013171226/article/details/123249453

创建环境，安装库

conda activate -n insightface python=3.8
pip install insightface

把它的git 代码都拉下来：
https://github.com/deepinsight/insightface
解压到 /home/jianming_ge/workplace/zhongwaiyun/insightface/

1.开始

1.1 前置

cd /home/jianming_ge/workplace/zhongwaiyun/insightface/python-package
pip install numpy opencv-python 
pip install onnxruntime-gpu

创建一个文件：
aaa.py

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'],download=False)
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)

运行:python aaa.py
报错：

(insightface) [jianming_ge@localhost python-package]$ python aaa.py 
Traceback (most recent call last):
  File "aaa.py", line 3, in <module>
    import insightface
  File "/home/jianming_ge/workplace/zhongwaiyun/insightface/python-package/insightface/__init__.py", line 18, in <module>
    from . import app
  File "/home/jianming_ge/workplace/zhongwaiyun/insightface/python-package/insightface/app/__init__.py", line 2, in <module>
    from .mask_renderer import *
  File "/home/jianming_ge/workplace/zhongwaiyun/insightface/python-package/insightface/app/mask_renderer.py", line 8, in <module>
    from ..thirdparty import face3d
  File "/home/jianming_ge/workplace/zhongwaiyun/insightface/python-package/insightface/thirdparty/face3d/__init__.py", line 3, in <module>
    from . import mesh
  File "/home/jianming_ge/workplace/zhongwaiyun/insightface/python-package/insightface/thirdparty/face3d/mesh/__init__.py", line 9, in <module>
    from .cython import mesh_core_cython

解决：

 find  ./ -iname "cython"
 (insightface) [jianming_ge@localhost python-package]$ find  ./ -iname "cython"
./insightface/thirdparty/face3d/mesh/cython
(insightface) [jianming_ge@localhost python-package]$ cd ./insightface/thirdparty/face3d/mesh/cython/
(insightface) [jianming_ge@localhost cython]$ ls
mesh_core.cpp  mesh_core_cython.c  mesh_core_cython.cpp  mesh_core_cython.pyx  mesh_core.h  setup.py
(insightface) [jianming_ge@localhost python-package]$ cd ./insightface/thirdparty/face3d/mesh/cython/
(insightface) [jianming_ge@localhost cython]$ ls
mesh_core.cpp  mesh_core_cython.c  mesh_core_cython.cpp  mesh_core_cython.pyx  mesh_core.h  setup.py
(insightface) [jianming_ge@localhost cython]$ python setup.py build_ext -i
In file included from /home/jianming_ge/miniconda3/envs/insightface/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,
                 from /home/jianming_ge/miniconda3/envs/insightface/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,
                 from /home/jianming_ge/miniconda3/envs/insightface/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,
                 from mesh_core_cython.cpp:780:
/home/jianming_ge/miniconda3/envs/insightface/lib/python3.8/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning "Using deprecated NumPy API, disable it with " "#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it with " \

到包文件下，执行一下安装就可以了
cd ./insightface/thirdparty/face3d/mesh/cython/
python setup.py build_ext -i

1.2 再次运行，人脸检测跑通

python aaa.py

(insightface) [jianming_ge@localhost python-package]$ python aaa.py 
download_path: /home/jianming_ge/.insightface/models/buffalo_l
Downloading /home/jianming_ge/.insightface/models/buffalo_l.zip from https://github.com/deepinsight/insightface/releases/download/v0.7/buffalo_l.zip...
  0%|▏                                                                                                                                                                                     | 208/281857 [00:01<43:44, 107.30KB/s]^Z
[2]+  已停止               python aaa.py

他去下载去了，不知为什么在从git上下载特别慢，所以放弃了，想法是手动下载好，传到它所需要的目录。
Downloading /home/jianming_ge/.insightface/models/buffalo_l.zip from https://github.com/deepinsight/insightface/releases/download/v0.7/buffalo_l.zip…
代码追踪后在这里，dir_path

位置:python-package/insightface/utils/storage.py
追踪到dir_path = ~/.insightface/models/buffalo_l
建好目录，并且把权重文件放进去

(insightface) [jianming_ge@localhost python-package]$ mkdir ~/.insightface/models/buffalo_l
(insightface) [jianming_ge@localhost python-package]$ cp ~/.insightface/models/*.onnx  ~/.insightface/models/buffalo_l/
(insightface) [jianming_ge@localhost python-package]$ ls ~/.insightface/models/buffalo_l
1k3d68.onnx  2d106det.onnx  det_10g.onnx  genderage.onnx  w600k_r50.onnx

import os
import os.path as osp
import zipfile
from .download import download_file

BASE_REPO_URL = 'https://github.com/deepinsight/insightface/releases/download/v0.7'

def download(sub_dir, name, force=False, root='~/.insightface'):
    _root = os.path.expanduser(root)
    dir_path = os.path.join(_root, sub_dir, name)
    if osp.exists(dir_path) and not force:
        return dir_path
    print('download_path:', dir_path)
    zip_file_path = os.path.join(_root, sub_dir, name + '.zip')
    model_url = "%s/%s.zip"%(BASE_REPO_URL, name)
    download_file(model_url,
             path=zip_file_path,
             overwrite=True)
    if not os.path.exists(dir_path):
        os.makedirs(dir_path)
    with zipfile.ZipFile(zip_file_path) as zf:
        zf.extractall(dir_path)
    #os.remove(zip_file_path)
    return dir_path

def ensure_available(sub_dir, name, root='~/.insightface'):
    return download(sub_dir, name, force=False, root=root)

def download_onnx(sub_dir, model_file, force=False, root='~/.insightface', download_zip=False):
    _root = os.path.expanduser(root)
    model_root = osp.join(_root, sub_dir)
    new_model_file = osp.join(model_root, model_file)
    if osp.exists(new_model_file) and not force:
        return new_model_file
    if not osp.exists(model_root):
        os.makedirs(model_root)
    print('download_path:', new_model_file)
    if not download_zip:
        model_url = "%s/%s"%(BASE_REPO_URL, model_file)
        download_file(model_url,
                 path=new_model_file,
                 overwrite=True)
    else:
        model_url = "%s/%s.zip"%(BASE_REPO_URL, model_file)
        zip_file_path = new_model_file+".zip"
        download_file(model_url,
                 path=zip_file_path,
                 overwrite=True)
        with zipfile.ZipFile(zip_file_path) as zf:
            zf.extractall(model_root)
        return new_model_file

再次执行：
python aaa.py
输出：

(insightface) [jianming_ge@localhost python-package]$ python aaa.py 
Applied providers: ['CUDAExecutionProvider', 'CPUExecutionProvider'], with options: {'CPUExecutionProvider': {}, 'CUDAExecutionProvider': {'tunable_op_enabled': '0', 'cudnn_conv_use_max_workspace': '1', 'enable_cuda_graph': '0', 'do_copy_in_default_stream': '1', 'arena_extend_strategy': 'kNextPowerOfTwo', 'cudnn_conv1d_pad_to_nc1d': '0', 'gpu_external_empty_cache': '0', 'gpu_external_free': '0', 'gpu_external_alloc': '0', 'gpu_mem_limit': '18446744073709551615', 'cudnn_conv_algo_search': 'EXHAUSTIVE', 'device_id': '0'}}
model ignore: /home/jianming_ge/.insightface/models/buffalo_l/1k3d68.onnx landmark_3d_68
Applied providers: ['CUDAExecutionProvider', 'CPUExecutionProvider'], with options: {'CPUExecutionProvider': {}, 'CUDAExecutionProvider': {'tunable_op_enabled': '0', 'cudnn_conv_use_max_workspace': '1', 'enable_cuda_graph': '0', 'do_copy_in_default_stream': '1', 'arena_extend_strategy': 'kNextPowerOfTwo', 'cudnn_conv1d_pad_to_nc1d': '0', 'gpu_external_empty_cache': '0', 'gpu_external_free': '0', 'gpu_external_alloc': '0', 'gpu_mem_limit': '18446744073709551615', 'cudnn_conv_algo_search': 'EXHAUSTIVE', 'device_id': '0'}}
model ignore: /home/jianming_ge/.insightface/models/buffalo_l/2d106det.onnx landmark_2d_106
Applied providers: ['CUDAExecutionProvider', 'CPUExecutionProvider'], with options: {'CPUExecutionProvider': {}, 'CUDAExecutionProvider': {'tunable_op_enabled': '0', 'cudnn_conv_use_max_workspace': '1', 'enable_cuda_graph': '0', 'do_copy_in_default_stream': '1', 'arena_extend_strategy': 'kNextPowerOfTwo', 'cudnn_conv1d_pad_to_nc1d': '0', 'gpu_external_empty_cache': '0', 'gpu_external_free': '0', 'gpu_external_alloc': '0', 'gpu_mem_limit': '18446744073709551615', 'cudnn_conv_algo_search': 'EXHAUSTIVE', 'device_id': '0'}}
find model: /home/jianming_ge/.insightface/models/buffalo_l/det_10g.onnx detection [1, 3, '?', '?'] 127.5 128.0
Applied providers: ['CUDAExecutionProvider', 'CPUExecutionProvider'], with options: {'CPUExecutionProvider': {}, 'CUDAExecutionProvider': {'tunable_op_enabled': '0', 'cudnn_conv_use_max_workspace': '1', 'enable_cuda_graph': '0', 'do_copy_in_default_stream': '1', 'arena_extend_strategy': 'kNextPowerOfTwo', 'cudnn_conv1d_pad_to_nc1d': '0', 'gpu_external_empty_cache': '0', 'gpu_external_free': '0', 'gpu_external_alloc': '0', 'gpu_mem_limit': '18446744073709551615', 'cudnn_conv_algo_search': 'EXHAUSTIVE', 'device_id': '0'}}
model ignore: /home/jianming_ge/.insightface/models/buffalo_l/genderage.onnx genderage
Applied providers: ['CUDAExecutionProvider', 'CPUExecutionProvider'], with options: {'CPUExecutionProvider': {}, 'CUDAExecutionProvider': {'tunable_op_enabled': '0', 'cudnn_conv_use_max_workspace': '1', 'enable_cuda_graph': '0', 'do_copy_in_default_stream': '1', 'arena_extend_strategy': 'kNextPowerOfTwo', 'cudnn_conv1d_pad_to_nc1d': '0', 'gpu_external_empty_cache': '0', 'gpu_external_free': '0', 'gpu_external_alloc': '0', 'gpu_mem_limit': '18446744073709551615', 'cudnn_conv_algo_search': 'EXHAUSTIVE', 'device_id': '0'}}
model ignore: /home/jianming_ge/.insightface/models/buffalo_l/w600k_r50.onnx recognition
set det-size: (640, 640)
faces:::: [{'bbox': array([105.29808,  83.55866, 218.93301, 230.41777], dtype=float32), 'kps': array([[137.7511 , 151.92409],
       [190.89203, 143.08504],
       [171.97752, 181.22136],
       [155.1662 , 205.49452],
       [192.61273, 197.9723 ]], dtype=float32), 'det_score': 0.8469027}]

人脸检测的效果：

文件目录树也贴上来供参考：
![在这里插入图片描述](https://img-blog.csdnimg.cn/591c2829601e4a4183c437125c8f569c.png

1.3 人脸特征抽取

1.3.1 模型下载

https://github.com/deepinsight/insightface/tree/master/model_zoo
一般说来，模型越大，速度越慢，准确度越高
这个页面上的任何一个权重都可以用，我这里下载后重命名为recg.onnx
这是git 的样例，但其实是不全的。
新建bbb.py

"""
Call Recognition Models
"""
import cv2
import numpy as np
import insightface
from insightface.app import FaceAnalysis
from insightface.data import get_image as ins_get_image

handler = insightface.model_zoo.get_model('your_recognition_model.onnx')
handler.prepare(ctx_id=0)

刚开始运行肯定报错，因为你没有your_recognition_model.onnx 的权重，
通过代码追踪：它应该放在 ~/.insightface/models/your_recognition_model.onnx

1.3.2 重新跑一下检测和识别

而且权重应该放在项目目录下：

bbb.py 更新为：

"""
Call Recognition Models
"""
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'],download=False)
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)

handler = insightface.model_zoo.get_model('w600k_r50.onnx')
handler.prepare(ctx_id=0)
img = ins_get_image('ldh')
feature = handler.get(img,faces[0])
print("size 0f feature:", len(feature))
print("feature:",feature)

注意点：实际在使用过程中，

1. 先做人脸检测
2. 做特征抽取
3. 再做相似度计算

1.3.3 人脸监测返回值分析

人脸监测的返回值, 单张人脸：

faces:::: [{'bbox': array([398.00247,  76.93284, 517.12415, 217.21019], dtype=float32), 'kps': array([[420.43942, 127.73588],
       [472.40298, 119.96787],
       [440.8788 , 139.35797],
       [426.87814, 178.95143],
       [473.05652, 172.67671]], dtype=float32), 'det_score': 0.82732844}]

faces:::: [{'bbox': array([151.52905 ,   9.983715, 252.05127 , 149.92014 ], dtype=float32), 'kps': array([[179.53656 ,  65.759674],
       [225.35909 ,  66.749855],
       [202.66101 ,  89.19701 ],
       [176.11479 , 110.48839 ],
       [225.03027 , 111.73684 ]], dtype=float32), 'det_score': 0.903729}, {'bbox': array([390.146   ,  19.774494, 489.32114 , 154.33212 ], dtype=float32), 'kps': array([[417.48557 ,  73.57366 ],
       [463.98648 ,  72.462814],
       [442.4967  , 100.443985],
       [420.76508 , 118.47941 ],
       [463.62903 , 117.62505 ]], dtype=float32), 'det_score': 0.89457685}]
=============

返回一个list， len是人脸的数量
每个单元包含：
bbox 人脸框坐标
kps 人脸关键点的坐标 左眼、右眼、鼻子、嘴巴左侧、嘴巴右侧
det_score 检测的分数

人脸特征是返回一个512纬的向量：

size 0f feature: 512
feature: [-2.65807837e-01 -5.98709404e-01  1.44702032e-01  2.63355345e-01
  4.07146290e-02 -1.62199542e-01  2.32072305e-02 -2.41173565e-01
 -3.18579137e-01 -5.12303770e-01  4.71155867e-02  2.73445308e-01
 -8.21143925e-01  1.60933450e-01  2.68835127e-02 -1.19085282e-01
 -9.30740461e-02 -1.72320172e-01 -2.14248434e-01  2.86992230e-02
  3.17545533e-01  1.75784290e-01 -1.22256204e-01 -1.63961858e-01
 -8.66541564e-01  7.55451992e-02  2.67704695e-01 -5.87792039e-01
 -1.69785097e-02 -2.07043514e-02  5.06040215e-01  2.22781166e-01
 -2.63346657e-02  8.10962379e-01 -3.26032043e-01  1.26174808e-01
  3.17236811e-01  6.22351229e-01 -3.11159819e-01  4.34068412e-01
 -2.57273525e-01  2.18948558e-01  5.02837479e-01 -3.66360933e-01
 -4.49983001e-01  3.29971194e-01 -4.58328962e-01 -2.24694684e-01
 -6.20792098e-02 -2.27833763e-01 -2.73626387e-01 -1.57144777e-02
  7.91971147e-01  1.46265015e-01 -5.00584424e-01 -3.09522271e-01
  6.18827194e-02 -3.79446059e-01  4.31523114e-01 -4.84285116e-01
 -3.52546960e-01 -7.49332607e-02  6.07803948e-02  8.62523675e-01
 -4.10395920e-01 -2.44194195e-01 -3.41306150e-01 -8.33527073e-02
  5.68045788e-02 -1.97309047e-01  1.42464489e-01 -6.44236326e-01
  3.12686771e-01 -2.39409551e-01  4.39812720e-01 -4.36708421e-01
 -4.98010159e-01  1.31110698e-01 -5.03139734e-01 -4.52581614e-01
 -8.60964954e-02  1.03808708e-01 -3.57752778e-02  8.27392399e-01
  3.43307912e-01 -5.30499697e-01  2.62764663e-01  4.72977400e-01
  2.66696811e-01 -2.69342978e-02 -5.43406844e-01  3.88814628e-01
  8.32139477e-02 -7.24730372e-01  4.20132369e-01 -4.74645436e-01
 -1.48665071e-01  9.54597652e-01 -4.56728972e-02  3.51921208e-02
  3.72070462e-01 -1.53715964e-02 -8.53569135e-02 -8.37187350e-01
 -4.30278433e-03 -9.07578766e-02 -2.80029148e-01 -3.56460154e-01
 -6.20279536e-02  4.94443417e-01  1.15014195e+00 -4.20974493e-01
  4.80442680e-02 -4.37882274e-01 -1.71415120e-01 -3.32768500e-01
 -1.02889575e-01  2.16156662e-01  7.01144218e-01 -4.51926380e-01
 -4.58793879e-01  1.81827918e-02 -1.88612550e-01 -1.14988470e-02
 -1.77762285e-01 -2.05713645e-01  6.62348121e-02  1.67144701e-01
 -6.38863564e-01 -1.04559563e-01  2.81668335e-01  2.02162907e-01
 -7.78863609e-01  3.30200553e-01  5.50253451e-01  9.45175365e-02
  2.95698971e-01 -3.63492787e-01  3.95007521e-01  4.72791910e-01
 -1.13705151e-01  3.27019036e-01 -2.52318531e-01  5.03111146e-02
  7.85713866e-02 -4.40496325e-01 -1.02330513e-01 -2.04051405e-01
 -4.25574929e-01  1.15317412e-01 -2.48718798e-01  1.49252310e-01
  1.05377071e-01 -1.19814776e-01  5.92564166e-01  3.30846272e-02
  7.21025541e-02 -3.52899522e-01  7.61120737e-01 -6.52381837e-01
  6.50658727e-01 -5.15108407e-01 -1.94832787e-01 -4.66580153e-01
 -3.02693993e-01  3.60020995e-01 -6.04675889e-01  3.48266721e-01
  1.35741889e-01 -2.12851986e-01 -7.17316985e-01  1.31764725e-01
  2.28615105e-01  5.24750538e-02 -4.72336970e-02 -6.26476049e-01
 -4.56452340e-01  1.02681197e-01  3.58329445e-01 -3.19489807e-01
 -3.44365656e-01 -4.40581203e-01 -1.41268568e-02  6.94410443e-01
 -2.67537236e-01 -7.72025576e-03 -5.61997116e-01 -7.19041407e-01
  1.20449759e-01  6.45162582e-01  3.46629024e-01  1.10950135e-01
  8.20405841e-01  2.94094980e-01 -1.51019976e-01 -5.04539907e-02
 -7.21346065e-02  2.75275677e-01  4.68920708e-01 -5.11016130e-01
  1.87380016e-01  8.27821791e-02 -3.10316354e-01 -1.03935346e-01
  5.48591197e-01  9.24001709e-02 -3.89272362e-01 -1.15961254e+00
 -4.71911073e-01  3.22682679e-01 -3.27484250e-01 -2.13711888e-01
  8.46880302e-02  1.13394640e-01 -2.57271856e-01  4.57801521e-01
 -1.56833291e-01 -4.63344246e-01 -5.22429049e-02  1.63254842e-01
  3.00276607e-01  2.76694864e-01 -3.10461104e-01 -5.87711275e-01
 -1.89284272e-02  4.10056353e-01  6.57250285e-01 -8.84221718e-02
 -3.13798636e-01  5.00106215e-01 -4.48331796e-02 -5.13528764e-01
 -9.17405933e-02  7.00492561e-01 -2.52461191e-02 -4.65177923e-01
  3.35291862e-01  1.67592824e-01 -2.38635331e-01  6.97482347e-01
  1.63971394e-01 -3.29721011e-02 -1.26637876e-01  4.27806467e-01
 -5.34400046e-01 -2.61363145e-02  3.35392177e-01 -4.52100456e-01
 -1.15982279e-01 -4.80096698e-01 -1.20243188e-02 -8.81862491e-02
 -1.41368508e-01  8.21686313e-02  2.38767043e-01 -8.79521295e-02
  1.31764039e-01  9.93780792e-02  3.97302061e-01  2.62562603e-01
  5.31454384e-01  1.18952766e-01  1.63879409e-01  2.79800206e-01
  1.71809718e-01 -9.73480046e-02  5.50766960e-02  2.19342694e-01
  3.70924145e-01  2.22219989e-01 -1.48917332e-01  2.90967226e-01
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1.3.4 计算相似度

相似度计算就是用余弦函数进行计算，计算出相似度，其实就是识别的内容。

 # 计算相似度
 similarity = np.dot(feature1, feature2.T)

1.4 全流程的相似度

mmp 搞来搞去，这里有一个pipline

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

assert insightface.__version__>='0.3'

parser = argparse.ArgumentParser(description='insightface app test')
# general
parser.add_argument('--ctx', default=0, type=int, help='ctx id, <0 means using cpu')
parser.add_argument('--det-size', default=640, type=int, help='detection size')
args = parser.parse_args()

app = FaceAnalysis()
app.prepare(ctx_id=args.ctx, det_size=(args.det_size,args.det_size))

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)

# then print all-to-all face similarity
feats = []
for face in faces:
    feats.append(face.normed_embedding)
feats = np.array(feats, dtype=np.float32)
sims = np.dot(feats, feats.T)
print(sims)

执行命令：python ccc.py --ctx 0 输出，分数是1的代表最相似

[[ 1.0000002   0.06442557 -0.01041015 -0.08265568  0.03555955 -0.02871545]
 [ 0.06442557  1.0000002   0.02620501  0.09680527 -0.02184767  0.21340981]
 [-0.01041015  0.02620501  0.99999994  0.14635125  0.06818002 -0.03542848]
 [-0.08265568  0.09680527  0.14635125  1.0000001  -0.0301404  -0.01418785]
 [ 0.03555955 -0.02184767  0.06818002 -0.0301404   0.99999994  0.05765504]
 [-0.02871545  0.21340981 -0.03542848 -0.01418785  0.05765504  0.99999964]]

6个人都比一遍，肯定对角线最相似啊，内部是不是已经做了人脸矫正和对齐，就不得而知了，懂的大神可以留言一下。
跑到这里，其实就差业务化了

2. 业务化人脸识别

敬请期待
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