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
在这里插入图片描述
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}]