12 KiB
Executable File
12 KiB
Executable File
wsl环境
1. 安装wsl
ubuntu版本必须为18.04。 windows打开powershell 输入以下命令，不要使用cmd
wsl --install -d Ubuntu-18.04
安装参考地址：https://learn.microsoft.com/zh-cn/windows/wsl/install
2.正常安装928的开发环境，参考指南。
3.修改目录
3.1 交叉编译工具
修改cmake/ss298.cmake目录交叉编译工具路径
SET(CMAKE_C_COMPILER /opt/linux/x86-arm/aarch64-mix210-linux/bin/aarch64-mix210-linux-gcc)
SET(CMAKE_CXX_COMPILER /opt/linux/x86-arm/aarch64-mix210-linux/bin/aarch64-mix210-linux-g++)
SET(CMAKE_STRIP /opt/linux/x86-arm/aarch64-mix210-linux/bin/aarch64-mix210-linux-strip)
3.2 修改CMakeLists.txt
把全部的/home/setups/ss928_framework/ 替换为自己wsl项目目录，比如/mnt/d/ss928_framework/
4. 把虚拟机中相关目录拷贝wsl中同样的目录下
需要复制的目录为
- /usr/local/lib
- /usr/local/local
注意！！！, 注意！！！, 注意！！！,
_.--""--._
.' `.
/ O O \
| \ ^^ / |
\ `----' /
`. _______ .'
//_____\\
(( ____ ))
`------'
复制粘贴- /usr/local/lib和/usr/local/local先打成压缩包，再解压到wsl目录中，直接从虚拟机中复制出来的时候会报错不支持符号链接的主机，无法复制。
压缩并保持符号链接
cd /usr/local/lib
zip -ry ~/lib.zip ./*
cd /usr/local/include
zip -ry ~/include.zip ./*
- -r 这个选项表示递归地处理目录
- -y 保持符合链接
自行放到wsl任意目录,cd进入该目录解压。如需强制覆盖，加上-o参数
sudo unzip lib.zip -d /usr/local/lib/
sudo unzip include.zip -d /usr/local/include/
把板子上相关目录拷贝wsl中同样的目录下
需要复制到目录为
- /lib/aarch64-linux-gnu
- /usr/lib/aarch64-linux-gnu
cd /lib/aarch64-linux-gnu
zip -ry ~/lib_agnu.zip ./*
cd /usr/lib/aarch64-linux-gnu
zip -ry ~/usr_lib_agnu.zip ./*
自行放到wsl任意目录,cd进入该目录解压。如需强制覆盖，加上-o参数
sudo unzip lib_agnu.zip -d /lib/aarch64-linux-gnu/
sudo unzip usr_lib_agnu.zip -d /usr/lib/aarch64-linux-gnu/
cmake3.24版本
下载地址：https://cmake.org/files/v3.24/cmake-3.24.0-linux-aarch64.tar.gz 解压后的bin目录为可执行文件，自行放到目录中。
5.编译
mkdir build
cd build
cmake ..
make -j(nproc)
build/libss928driver.so为生成的动态库文件
执行
上传
- ss928sdk/lib 拷贝到 板端目录下
- 将生成的动态库文件
build/libss928driver.so拷贝到板端目录下 - 将om模型文件拷贝到板端目录下
- 自己要测试的图片，以及下面所属的python脚本
你最终的目录结构应该是这样的
_test_928_batch.py #脚本
best_sea_salt.om # 模型文件
lib/ # 库文件
libss928driver.so # 编译的动态库
ymj_sea_salt/ # 测试图片目录
ymj_sea_salt_result/ #结果图片目录
设置环境
# 设置环境变量能读取到lib的动态库
export LD_LIBRARY_PATH=${PWD}/lib:${PWD}/lib/npu:${PWD}/lib/svp_npu:$LD_LIBRARY_PATH
# fix ymj runtime oserror /usr/lib/aarch64-linux-gnu/libgomp.so.1: cannot allocate memory in static TLS block
export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libgomp.so.1:$LD_PRELOAD
创建conda环境
- 将下面内容复制保存为
environment.yaml, - 然后在板端执行
conda env create -f environment.yaml - 创建完毕之后，激活环境
conda activate ymj。
name: ymj
channels:
- defaults
- https://repo.anaconda.com/pkgs/main
- https://repo.anaconda.com/pkgs/r
dependencies:
- _libgcc_mutex=0.1=main
- _openmp_mutex=5.1=51_gnu
- anaconda-anon-usage=0.4.4=py312h7d20cce_100
- archspec=0.2.3=pyhd3eb1b0_0
- boltons=23.0.0=py312hd43f75c_0
- brotli-python=1.0.9=py312h419075a_8
- bzip2=1.0.8=h998d150_6
- c-ares=1.19.1=h998d150_0
- ca-certificates=2024.9.24=hd43f75c_0
- certifi=2024.8.30=py312hd43f75c_0
- cffi=1.17.1=py312hcb1262d_0
- charset-normalizer=3.3.2=pyhd3eb1b0_0
- conda=24.9.2=py312hd43f75c_0
- conda-content-trust=0.2.0=py312hd43f75c_1
- conda-libmamba-solver=24.9.0=pyhd3eb1b0_0
- conda-package-handling=2.3.0=py312hd43f75c_0
- conda-package-streaming=0.10.0=py312hd43f75c_0
- cryptography=43.0.0=py312h5077475_0
- distro=1.9.0=py312hd43f75c_0
- expat=2.6.3=h419075a_0
- fmt=9.1.0=hb8fdbf2_1
- frozendict=2.4.2=py312hd43f75c_0
- icu=73.1=h419075a_0
- idna=3.7=py312hd43f75c_0
- jsonpatch=1.33=py312hd43f75c_1
- jsonpointer=2.1=pyhd3eb1b0_0
- krb5=1.20.1=h2e2fba8_1
- ld_impl_linux-aarch64=2.40=h48e3ba3_0
- libarchive=3.7.4=h4086d46_0
- libcurl=8.9.1=hfa2bbb0_0
- libedit=3.1.20230828=h998d150_0
- libev=4.33=hfd63f10_1
- libffi=3.4.4=h419075a_1
- libgcc-ng=11.2.0=h1234567_1
- libgomp=11.2.0=h1234567_1
- libmamba=1.5.8=h0a4e7f9_3
- libmambapy=1.5.8=py312hd82f176_3
- libnghttp2=1.57.0=hb788212_0
- libsolv=0.7.24=h94b7715_1
- libssh2=1.11.0=hfa2bbb0_0
- libstdcxx-ng=11.2.0=h1234567_1
- libuuid=1.41.5=h998d150_0
- libxml2=2.13.1=h6097fa9_2
- lz4-c=1.9.4=h419075a_1
- menuinst=2.1.2=py312hd43f75c_0
- ncurses=6.4=h419075a_0
- openssl=3.0.15=h998d150_0
- packaging=24.1=py312hd43f75c_0
- pcre2=10.42=hcfaa891_1
- pip=24.2=py312hd43f75c_0
- platformdirs=3.10.0=py312hd43f75c_0
- pluggy=1.0.0=py312hd43f75c_1
- pybind11-abi=5=hd3eb1b0_0
- pycosat=0.6.6=py312h998d150_1
- pycparser=2.21=pyhd3eb1b0_0
- pysocks=1.7.1=py312hd43f75c_0
- python=3.12.7=h8edadfe_0
- readline=8.2=h998d150_0
- reproc=14.2.4=h419075a_2
- reproc-cpp=14.2.4=h419075a_2
- requests=2.32.3=py312hd43f75c_0
- ruamel.yaml=0.18.6=py312h998d150_0
- ruamel.yaml.clib=0.2.8=py312h998d150_0
- setuptools=75.1.0=py312hd43f75c_0
- sqlite=3.45.3=h998d150_0
- tk=8.6.14=h987d8db_0
- tqdm=4.66.5=py312h42ac6d5_0
- truststore=0.8.0=py312hd43f75c_0
- tzdata=2024b=h04d1e81_0
- urllib3=2.2.3=py312hd43f75c_0
- wheel=0.44.0=py312hd43f75c_0
- xz=5.4.6=h998d150_1
- yaml-cpp=0.8.0=h419075a_1
- zlib=1.2.13=h998d150_1
- zstandard=0.23.0=py312hc476304_0
- zstd=1.5.6=h6a09583_0
- pip:
- colorlog==6.9.0
- cython==3.0.11
- numpy==2.1.3
- opencv-python==4.10.0.84
- pyzmq==26.2.0
- simplejpeg==1.7.6
- vidgear==0.3.3
prefix: /root/miniconda3/envs/ymj
python执行脚本
# -- coding: utf-8 --
import os
import sys
import subprocess
import ctypes
from ctypes import *
import platform
import time
import cv2
import numpy as np
from vidgear.gears import VideoGear
from vidgear.gears import NetGear
sys.path.append(r'./')
print(platform.architecture())
libss928 = ctypes.cdll.LoadLibrary("libss928driver.so")
class _TAROBJECT_(Structure):
pass
_TAROBJECT_._fields_ = [
('x', c_int),
('y', c_int),
('width', c_int),
('height', c_int),
('label', c_int),
('prob', c_float),
('text', c_char * 256),
]
TAROBJECT = _TAROBJECT_
class _RETURN_BOJECT_(Structure):
pass
_RETURN_BOJECT_._fields_ = [
('count', c_int),
('objects', TAROBJECT * 128),
]
RETURN_BOJECT = _RETURN_BOJECT_
def LibapiSvpNpuHandleSig():
libss928.LibapiSvpNpuHandleSig.argtype = c_void_p
libss928.LibapiSvpNpuHandleSig.restype = c_uint
return libss928.LibapiSvpNpuHandleSig()
def LibapiSvpNpuAclPrepareInit():
libss928.LibapiSvpNpuAclPrepareInit.argtype = c_void_p
libss928.LibapiSvpNpuAclPrepareInit.restype = c_uint
return libss928.LibapiSvpNpuAclPrepareInit()
def LibApiSvpNpuAclPrepareExit():
libss928.LibApiSvpNpuAclPrepareExit.argtype = c_void_p
libss928.LibApiSvpNpuAclPrepareExit.restype = c_uint
return libss928.LibApiSvpNpuAclPrepareExit()
def LibApiSvpNpuLoadModel(om_model_path, model_index, is_cached):
libss928.LibApiSvpNpuLoadModel.argtype = (c_char_p, c_int, c_bool)
libss928.LibApiSvpNpuLoadModel.restype = c_uint
return libss928.LibApiSvpNpuLoadModel(om_model_path, model_index, is_cached)
def LibApiSvpNpuUnLoadModel(model_index):
libss928.LibApiSvpNpuUnLoadModel.argtype = (c_int)
libss928.LibApiSvpNpuUnLoadModel.restype = c_uint
return libss928.LibApiSvpNpuUnLoadModel(model_index)
def LibApiSvpNpuLoadDataset(model_index, src, data_len, imgin_w, imgin_h, flag):
libss928.LibApiSvpNpuLoadDataset.argtype = (c_int, c_void_p, c_int, c_int, c_int, c_int)
libss928.LibApiSvpNpuLoadDataset.restype = c_uint
return libss928.LibApiSvpNpuLoadDataset(model_index, src, data_len, imgin_w, imgin_h, flag)
def LibApiSvpNpuUnloadDataset():
libss928.LibApiSvpNpuUnloadDataset.argtype = c_void_p
libss928.LibApiSvpNpuUnloadDataset.restype = c_uint
return libss928.LibApiSvpNpuUnloadDataset()
def LibApiSvpNpuModelExecute(model_index):
libss928.LibApiSvpNpuModelExecute.argtype = (c_int)
libss928.LibApiSvpNpuModelExecute.restype = c_uint
return libss928.LibApiSvpNpuModelExecute(model_index)
def LibApiSvpNpuGetModelExecuteResult(model_index, retrunObject):
libss928.LibApiSvpNpuGetModelExecuteResult.argtype = (c_uint , c_void_p)
libss928.LibApiSvpNpuGetModelExecuteResult.restype = c_uint
return libss928.LibApiSvpNpuGetModelExecuteResult(model_index, byref(retrunObject))
def _run_sh():
os.chdir('/root/ss928_framework/build/output/')
script_path = os.path.join('/root/ss928_framework/build/output/', 'rundemo.sh')
print(script_path)
try:
result = subprocess.run(['source ' + script_path, script_path], shell=True)
print("脚本执行成功")
except subprocess.CalledProcessError as e:
print(f"脚本执行失败，返回码: {e.returncode}")
exit()
if __name__ == "__main__":
options = {
"flag": 0, "copy": False, "track": False,
"jpeg_compression": True,
"jpeg_compression_quality": 90,
"jpeg_compression_fastdct": True,
"jpeg_compression_fastupsample": True
}
# 开启Netgear服务
server = NetGear(
address="192.168.101.46",
port="5454",
protocol="tcp",
pattern=1,
logging=True,
**options
)
idxx = 0
base_path = "./ymj_sea_salt/" # 图片输入目录
result_path = "./ymj_sea_salt_result/" # 图片输出目录
om_file_name = "best_sea_salt.om"
# om_file_name = "best_hyp_add_samll_1_fp32_precision_mode_precision_mode.om"
om_file_path = "./"
# 确保输出目录存在
if not os.path.exists(result_path):
os.makedirs(result_path)
# 获取所有图片文件
image_files = [f for f in os.listdir(base_path) if f.lower().endswith(('jpg', 'jpeg', 'png'))]
for image_file in image_files:
try:
if idxx == 0:
LibapiSvpNpuHandleSig()
LibapiSvpNpuAclPrepareInit()
mpath = ctypes.c_char_p((om_file_path + om_file_name).encode('utf-8'))
LibApiSvpNpuLoadModel(mpath, 0, False)
idxx = 1
# 读取当前图片
frame = cv2.imread(os.path.join(base_path, image_file))
# frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
if frame is None:
continue
# 进行处理
ptr = frame.ctypes.data_as(np.ctypeslib.ctypes.c_void_p)
LibApiSvpNpuLoadDataset(0, ptr, frame.shape[0] * frame.shape[1] * frame.shape[2],
frame.shape[1], frame.shape[0], cv2.CV_8UC3)
LibApiSvpNpuModelExecute(0)
# 获取分析结果
return_objs = RETURN_BOJECT()
res = LibApiSvpNpuGetModelExecuteResult(0, return_objs)
print(f"LibApiSvpNpuGetModelExecuteResult.res====================================: {res}")
print(f"objs.count====================================: {return_objs.count}")
if return_objs.count > 0:
for i in range(0, return_objs.count):
if i == 128: break
tarObject = return_objs.objects[i]
if tarObject.label == 0:
frame = cv2.rectangle(frame, (tarObject.x, tarObject.y + 20),
(tarObject.x + tarObject.width, tarObject.y + 20 + tarObject.height),
(0, 0, 255), 2)
# 保存结果图像到新的目录，保持文件名不变
result_file_path = os.path.join(result_path, image_file)
cv2.imwrite(result_file_path, frame)
# 清理
LibApiSvpNpuUnloadDataset()
except KeyboardInterrupt:
break
LibApiSvpNpuUnLoadModel(0)
执行
python _test_928_batch.py