# 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
```bahs
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
```bash
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


```text
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
```bash
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
```bash
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

```bash
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
```bash
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
```bash
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
```text
_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**
```bash
# 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**
```python
# -- 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**
```bash
python _test_928_batch.py
```