Rockchip RK3588 - OpenCL环境搭建
在上一节《Rockchip RK3588 - 基于Qt的视频监控和控制系统 》，我们介绍了实时监控的实现，在实时监控中我们需要将分辨率为1920x1080的图像缩放为指定窗口大小的图像，当采样帧率比较高时，会占用大量的CPU资源；
root@NanoPC-T6:/opt/qt-project/FloatVideo-TouchScreen# export DISPLAY=:0.0;./FloatVideo-TouchScreen -size 0.8
# 打开新的终端
root@NanoPC-T6:~# top
任务: 278 total, 2 running, 276 sleeping, 0 stopped, 0 zombie
%Cpu(s): 36.0 us, 1.9 sy, 0.0 ni, 62.1 id, 0.0 wa, 0.0 hi, 0.0 si, 0.0 st
MiB Mem : 15953.1 total, 14749.5 free, 662.4 used, 541.2 buff/cache
MiB Swap: 0.0 total, 0.0 free, 0.0 used. 14995.4 avail Mem
进程号 USER PR NI VIRT RES SHR %CPU %MEM TIME+ COMMAND
1513 root 20 0 2876120 127804 72488 S 270.9 0.8 0:27.46 FloatVideo-Touc
864 root 20 0 3345456 238548 186388 S 14.9 1.5 0:03.11 Xorg
1251 pi 20 0 1861028 76424 57116 S 2.6 0.5 0:00.96 xfwm4
......
那么我们是不是可以通过GPU来实现图像的缩放呢，在RK3588上可以使用OpenCL接口进行GPU加速。
一、OpenCL环境搭建
OpenCL（Open Computing Language开放计算语言）是一种开放的、免版税的标准，用于超级计算机、云服务器、个人计算机、移动设备和嵌入式平台中各种加速器的跨平台并行编程。
OpenCL是由Khronos Group创建和管理的。OpenCL使应用程序能够使用系统或设备中的并行处理能力，从而使应用程序运行得更快、更流畅。
1.1 工作原理
OpenCL是一种编程框架和运行时，它使程序员能够创建称为内核程序（或内核）的小程序，这些程序可以在系统中的任何处理器上并行编译和执行。处理器可以是不同类型的任意组合，包括CPU、GPU、DSP、FPGA或张量处理器，这就是为什么OpenCL经常被称为异构并行编程的解决方案。
OpenCL框架包含两个API：
platform layer API：在主机CPU上运行，首先用于使程序能够发现系统中可用的并行处理器或计算设备。通过查询哪些计算设备可用，应用程序可以在不同的系统上便携地运行—适应加速器硬件的不同组合。一旦发现了计算设备，platform layer API就允许应用程序选择并初始化它想要使用的设备；Runtime API：它使应用程序的内核程序能够为它们将要运行的计算设备编译，并行加载到这些处理器上并执行。一旦内核程序完成执行，将使用Runtime API收集结果；
为了更好适用于不同的处理器，OpenCL抽象出来了四大模型：
- 平台模型：描述了
OpenCL如何理解拓扑连接系统中的计算资源，对不同硬件及软件实现抽象，方便应用于不同设备； - 内存模型：对硬件的各种内存器进行了抽象；
- 执行模型：程序是如何在硬件上执行的；
- 编程模型：数据并行和任务并行；
1.2 平台模型
OpenCL中，需要一个主机处理器（Host），一般为CPU。而其它的硬件处理器（多核CPU/GPU/DSP等）被抽象成Compute Device；
- 每个
Compute Device包含多个Compute Unit； - 每个
Compute Unit又包含多个Processing Elements（处理单元）。
举例说明：计算设备可以是GPU，计算单元对应于GPU内部的流多处理器（streaming multiprocessors(SMs)），处理单元对应于每个SM内部的单个流处理器。处理器通常通过共享指令调度和内存资源，以及增加本地处理器间通信，将处理单元分组为计算单元，以提高实现效率。
1.3 内存模型
OpenCL内存模型定义了如何访问和共享不同内核和处理单元之间的数据。
1.3.1 内存类型
OpenCL支持以下内存类型：
Global memory： 全局内存对在上下文中执行的所有工作项可访问，主机可以使用__global关键字读取、写入和映射命令访问全局内存，在单个工作组中，全局内存是一致的；Constant memory：常量内存是用于主机分配和初始化的对象的内存区域， 所有工作项都可以以只读方式访问常量内存；Local memory： 本地内存是特定于工作组的，工作组中的工作项可以访问本地内存；使用__local关键字进行访问，对于工作组中的所有工作项来说，本地内存是一致的；Private memory：私有内存是特定于工作项的，其他工作项无法访问私有内存；
1.3.2 内存模型
OpenCL内存模型如下：
1.4 执行模型
OpenCL执行模型包括主机应用程序、上下文（context）和OpenCL内核的操作。
主机应用程序使用OpenCL命令队列将kernel和数据传输函数发送到设备以执行。
通过将命令入队到命令队列（Command Queues）中，kernel和数据传输函数可以与应用程序主机代码并行异步执行。
1.4.1 主机应用程序
主机应用程序在应用处理器上运行。主机应用程序通过为以下命令设置命令队列来管理内核的执行：
- 内存命令；
- 内核执行命令；
- 同步操作；
1.4.2 上下文
主机应用程序为内核定义上下文。上下文包括：
-
计算设备（
Compute devices）； -
内核（
Kernels）：OpenCL核心计算部分，类似C语言的代码。在需要设备执行计算任务时，数据会被推送到Compute Device，然后Compute Device的计算单元会并发执行内核程序； -
程序对象（
Programs）：Kernels的集合，OpenCL中可以使用cl_program表示； -
内存对象（
Memory Objects.）；
1.4.3 OpenCL内核的操作
Kernels在计算设备上运行。kernel是一段代码，在计算设备上与其它内核并行执行。内核的操作按以下顺序进行：
Kernels在主机应用程序中定义；- 主机应用程序将
kernel提交给计算设备执行。计算设备可以是应用处理器、GPU或其它类型的处理器； - 当主机应用程序发出提交
kernel的命令时，OpenCL创建工作项的NDRange； - 对于
NDRange中的每个元素，创建kernel的一个实例。这使得每个元素可以独立并行地进行处理。
1.5 OpenCL计算流程
对于OpenCl，利用显卡计算时，需要经历如下步骤：
- 主机应用程序进行设备初始化（获取平台和设备
id，创建上下文和命令队列）； - 编写并编译
kernel（读取内核文件->创建program对象->编译程序->创建内核） ； - 主机应用程序准备数据并传入设备（准备主机端数据，创建设备端内存对象并拷贝主机端数据）；
- 主机应用程序将
kernel提交给设备执行（传入kernel函数参数， 启动kernel函数）； - 将结果拷贝回主机应用程序；
- 后续处理；
- 释放资源。
二、OpenCL环境搭建
一个完整的OpenCL框架，从内核层到用户层，可分为四部分：
- 内核层
GPU驱动； - 用户层动态库；
- 头文件；
- 应用程序；
在《Rockchip RK3399 - Mali-T860 GPU驱动（mesa+Panfrost）》文章中我们提到GPU驱动一般分为两部分：
- 一小部分在
linux内核中； - 另外一大部分在
Userspace，在Userspace的部分向下操作内核中的驱动，向上对应用层提供标准的API接口，例如：OpenGL ES 1.1、2.0、3.0、3.1、3.2；OpenCL 1.1、1.2、2.0；Vulkan 1.0；RenderScript（受支持的API列表因二进制和GPU类型而异）；
Mail GPU IP提供商ARM公司只开放了内核部分驱动，而且这部分驱动还没有按照linux kernel的规范以DRM的框架去实现，此外Userspace部分ARM没有开源，只是以库的形式提供给购买了Mali GPU授权的SoC厂商。
2.1 内核层GPU驱动
以RK3588为例，搭载了Mail-G610。内核层GPU驱动这一部分，不需要自己移植，我们开发板所使用的的友善linux kernel 6.1已移植。
接下来的内容仅仅是作为扩展，不感兴趣忽略即可。ARM Mail G610采用的Bifrost 驱动微架构，如果需要自己移植内核层GPU驱动的话。可以从Open Source Bifrost Mali 3rd Gen GPU Architecture Kernel Drivers下载最新版本内核层GPU驱动。比如这里我下载了BX304L01B-SW-99002-r47p0-01eac0.tar；
关于如何安装该驱动可以参考：
Enable OpenCL support on Debian/hikey960；Mali kernel driver TX011-SW-99002-r5p1-00rel0 for firefly；
2.1.1 驱动入口
解压后，可以看到驱动入口文件在drivers/gpu/arm/midgard/mali_kbase_core_linux.c ，在该文件我们可以看到支持的GPU型号；
static const struct of_device_id kbase_dt_ids[] = { { .compatible = "arm,malit6xx" },
{ .compatible = "arm,mali-midgard" },
{ .compatible = "arm,mali-bifrost" },
{ .compatible = "arm,mali-valhall" },
{ /* sentinel */ } };
MODULE_DEVICE_TABLE(of, kbase_dt_ids);
static struct platform_driver kbase_platform_driver = {
.probe = kbase_platform_device_probe,
.remove = kbase_platform_device_remove,
.driver = {
.name = kbase_drv_name,
.pm = &kbase_pm_ops,
.of_match_table = of_match_ptr(kbase_dt_ids),
.probe_type = PROBE_PREFER_ASYNCHRONOUS,
},
};
module_platform_driver(kbase_platform_driver);
2.1.2 gpu设备节点
在 NanoPC上安装运行debian操作系统后，使用以下命令检查Mali GPU的设备树节点：
root@NanoPC-T6:~# apt install device-tree-compiler -y
root@NanoPC-T6:~# dtc -I fs /proc/device-tree | grep mali
在 arch/arm64/boot/dts/rockchip/rk3588s.dtsi中我们可以定位到gpu设备节点；
gpu: gpu@fb000000 {
compatible = "arm,mali-bifrost";
reg = <0x0 0xfb000000 0x0 0x200000>;
interrupts = <GIC_SPI 94 IRQ_TYPE_LEVEL_HIGH>,
<GIC_SPI 93 IRQ_TYPE_LEVEL_HIGH>,
<GIC_SPI 92 IRQ_TYPE_LEVEL_HIGH>;
interrupt-names = "GPU", "MMU", "JOB";
clocks = <&scmi_clk SCMI_CLK_GPU>, <&cru CLK_GPU_COREGROUP>,
<&cru CLK_GPU_STACKS>, <&cru CLK_GPU>;
clock-names = "clk_mali", "clk_gpu_coregroup",
"clk_gpu_stacks", "clk_gpu";
assigned-clocks = <&scmi_clk SCMI_CLK_GPU>;
assigned-clock-rates = <200000000>;
power-domains = <&power RK3588_PD_GPU>;
operating-points-v2 = <&gpu_opp_table>;
#cooling-cells = <2>;
dynamic-power-coefficient = <2982>;
upthreshold = <30>;
downdifferential = <10>;
status = "disabled";
};
在arch/arm64/boot/dts/rockchip/rk3588-nanopi6-common.dtsi中可以定位到：
&gpu {
mali-supply = <&vdd_gpu_s0>;
mem-supply = <&vdd_gpu_mem_s0>;
upthreshold = <60>;
downdifferential = <30>;
status = "okay";
};
其中：
compatible：说明了设备兼容的驱动名称，即"arm,mali-bifrost"；可以看到arm,mali-bifrost是和panfrost驱动相匹配的，因此会执行驱动的.probe函数，这里就不深入研究了；reg：指定了寄存器的基地址和大小，即基地址0xfb000000，大小为0x200000；interrupts和interrupt-names：分别指定了该设备所使用的中断号和中断的名称；clocks：指定了使用哪个时钟控制器（CRU）提供GPU时钟；power-domains：用于指定设备所属的电源域，即RK3588_PD_GPU；mali-supply：指定了GPU设备使用的电源管脚；status：指定GPU设备的状态（"okay" 表示设备正常工作）；
2.2 用户层动态库
寻找官方（Mali ARM/Rockchip ）提供的用户层动态库libmali.so。
2.2.1 Mali ARM官方下载安装libmali.so
通过浏览器进入Mali ARM官网：https://developer.arm.com/downloads/-/mali-drivers/user-space。
寻找官方提供的用户层动态库libmali.so，libmali.so一般会有不同的版本（X11，fbdev、Wayland等），其提供了OpenGL ES，EGL，OpenCL接口。
不过不幸的是：Mail ARM官网并没有看到适用于RK3588的用户层动态库，但是RK3288的倒是有，这里我们就以RK3288为例：
下载后，解压缩可以看到：
注意：上图中libEGL.so、libOpenCL.so、libGLESv2.so等库大小均为0，不难猜测libmail.so应该提供了OpenGL ES，EGL，OpenCL接口。
将libmali.so存放在ARM的/usr/lib/，同时建立软链接libOpenCL.so指向libmali.so；
root@NanoPC-T6:~# ln -s /usr/lib/libmali.so /usr/lib/libOpenCL.so
2.2.2 Rockchip官方提供的libmali.so
我们使用的友善提供的debian文件系统已经安装了libmali.so，该用户层动态库是由Rockchip官方提供的。
如何来查看是否已经安装了OpenCL库和驱动，可以通过如下命令检查是否已经安装了libmali.so ；
root@NanoPC-T6:~# find /usr -name libmali.so
/usr/lib/aarch64-linux-gnu/libmali.so
root@NanoPC-T6:~# strings /usr/lib/aarch64-linux-gnu/libmali.so | grep Mali-G610
Mali-G610
root@NanoPC-T6:~# strings /usr/lib/aarch64-linux-gnu/libmali.so | grep cl
.....
clReleaseCommandBufferKHR
clReleaseCommandQueue
clReleaseContext
clReleaseDevice
clReleaseEvent
clReleaseKernel
clReleaseMemObject
.....
root@NanoPC-T6:~# ls -l /usr/lib/aarch64-linux-gnu/libmali.so
lrwxrwxrwx 1 root root 12 7月 29 2020 /usr/lib/aarch64-linux-gnu/libmali.so -> libmali.so.1
其中/usr/lib/aarch64-linux-gnu/libmali.so是libmali.so库的路径，Mali-G610是Mali GPU驱动的版本号。
如果命令输出为空，则说明该库不是Mali GPU驱动库。如果输出包含Mali-G610 字符串，则说明该库是Mali GPU驱动库，并且版本号为Mali-G610。
此外在/usr/lib/aarch64-linux-gnu目录下包含单独的OpenGL ES，EGL，OpenCL库；
root@NanoPC-T6:/opt# ls -l /usr/lib/aarch64-linux-gnu/libOpenCL*
lrwxrwxrwx 1 root root 18 1月 12 2021 /usr/lib/aarch64-linux-gnu/libOpenCL.so.1 -> libOpenCL.so.1.0.0
-rw-r--r-- 1 root root 60856 1月 12 2021 /usr/lib/aarch64-linux-gnu/libOpenCL.so.1.0.0
root@NanoPC-T6:/opt# strings /usr/lib/aarch64-linux-gnu/libOpenCL.so.1.0.0 | grep cl
fclose
closedir
dlclose
clGetExtensionFunctionAddress
clGetPlatformIDs
clCreateContext
clCreateContextFromType
clGetGLContextInfoKHR
......
root@NanoPC-T6:/opt# ls -l /usr/lib/aarch64-linux-gnu/libEGL*
lrwxrwxrwx 1 root root 20 3月 25 2021 /usr/lib/aarch64-linux-gnu/libEGL_mesa.so.0 -> libEGL_mesa.so.0.0.0
-rw-r--r-- 1 root root 259072 3月 25 2021 /usr/lib/aarch64-linux-gnu/libEGL_mesa.so.0.0.0
lrwxrwxrwx 1 root root 11 7月 29 2020 /usr/lib/aarch64-linux-gnu/libEGL.so -> libEGL.so.1
lrwxrwxrwx 1 root root 15 7月 29 2020 /usr/lib/aarch64-linux-gnu/libEGL.so.1 -> libEGL.so.1.1.0
-rw-r--r-- 1 root root 84416 7月 29 2020 /usr/lib/aarch64-linux-gnu/libEGL.so.1.1.0
......
也可以通过如下clinfo命令查看是否已经安装OpenCL库，如果出现下图所示界面，则系统已经安装；
root@NanoPC-T6:~# aptitude install clinfo
root@NanoPC-T6:~# clinfo
arm_release_ver: g13p0-01eac0, rk_so_ver: 10
Number of platforms 1
Platform Name ARM Platform
Platform Vendor ARM
Platform Version OpenCL 3.0 v1.g13p0-01eac0.a8b6f0c7e1f83c654c60d1775112dbe4
Platform Profile FULL_PROFILE
Platform Extensions cl_khr_global_int32_base_atomics cl_khr_global_int32_extended_atomics
......
Number of devices 1
Device Name Mali-G610 r0p0
Device Vendor ARM
Device Vendor ID 0xa8670000
Device Version OpenCL 3.0 v1.g13p0-01eac0.a8b6f0c7e1f83c654c60d1775112dbe4
Device UUID 000067a8-0100-0000-0000-000000000000
Driver UUID 13833dc2-ecef-4e5b-0159-38fdaf75bfde
Valid Device LUID No
Device LUID 0000-000000000000
Device Node Mask 0
Device Numeric Version 0xc00000 (3.0.0)
Driver Version 3.0
Device OpenCL C Version OpenCL C 3.0 v1.g13p0-01eac0.a8b6f0c7e1f83c654c60d1775112dbe4
Device OpenCL C all versions OpenCL C 0x400000 (1.0
......
NULL platform behavior
clGetPlatformInfo(NULL, CL_PLATFORM_NAME, ...) ARM Platform
clGetDeviceIDs(NULL, CL_DEVICE_TYPE_ALL, ...) Success [ARM]
clCreateContext(NULL, ...) [default] Success [ARM]
clCreateContextFromType(NULL, CL_DEVICE_TYPE_DEFAULT) Success (1)
Platform Name ARM Platform
Device Name Mali-G610 r0p0 # GPU型号
clCreateContextFromType(NULL, CL_DEVICE_TYPE_CPU) No devices found in platform
clCreateContextFromType(NULL, CL_DEVICE_TYPE_GPU) Success (1)
Platform Name ARM Platform
Device Name Mali-G610 r0p0
clCreateContextFromType(NULL, CL_DEVICE_TYPE_ACCELERATOR) No devices found in platform
clCreateContextFromType(NULL, CL_DEVICE_TYPE_CUSTOM) No devices found in platform
clCreateContextFromType(NULL, CL_DEVICE_TYPE_ALL) Success (1)
Platform Name ARM Platform
Device Name Mali-G610 r0p0
ICD loader properties
ICD loader Name OpenCL ICD Loader
ICD loader Vendor OCL Icd free software
ICD loader Version 2.2.14
ICD loader Profile OpenCL 3.0
其中arm_release_ver: g13p0-01eac0, rk_so_ver: 10为驱动版本信息。
接着我们需要将建立软链接libOpenCL.so指向libmali.so；
root@NanoPC-T6:~# ln -s /usr/lib/aarch64-linux-gnu/libmali.so /usr/lib/aarch64-linux-gnu/libOpenCL.so
root@NanoPC-T6:~# ls -l /usr/lib/aarch64-linux-gnu/libOpenCL.so
lrwxrwxrwx 1 root root 37 1月 16 23:43 /usr/lib/aarch64-linux-gnu/libOpenCL.so -> /usr/lib/aarch64-linux-gnu/libmali.so
2.3 安装头文件
从官网下载头文件OpenCL-Headers：
root@NanoPC-T6:/opt# git clone https://github.com/extdomains/github.com/KhronosGroup/OpenCL-Headers.git
运行以下命令来配置构建过程，并指定安装路径为/usr：
root@NanoPC-T6:/opt/OpenCL-Headers# cmake -S . -B build -DCMAKE_INSTALL_PREFIX=/usr
-- The C compiler identification is GNU 10.2.1
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Check for working C compiler: /usr/bin/cc - skipped
-- Detecting C compile features
-- Detecting C compile features - done
-- The CXX compiler identification is GNU 10.2.1
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Check for working CXX compiler: /usr/bin/c++ - skipped
-- Detecting CXX compile features
-- Detecting CXX compile features - done
-- Found Python3: /usr/bin/python3.9 (found version "3.9.2") found components: Interpreter
-- Configuring done
-- Generating done
-- Build files have been written to: /opt/OpenCL-Headers/build
其中：
-S .：指定源代码目录的路径；-B build：指定构建目录的路径；-DCMAKE_INSTALL_PREFIX=/usr：指定cmake执行install目标时，安装的路径前缀；
如上命令会让cmake在.目录下查找CMakeLists.txt文件，并在./build目录下生成Makefile文件。
接着运行以下命令在./build目录下执行构建操作，只构建install目标，将生成的文件安装到指定的位置；
root@NanoPC-T6:/opt/OpenCL-Headers# cmake --build build --target install
Scanning dependencies of target headers_c_200
[ 0%] Building C object tests/lang_c/CMakeFiles/headers_c_200.dir/__/test_headers.c.o
[ 0%] Linking C executable headers_c_200
[ 0%] Built target headers_c_200
Scanning dependencies of target headers_c_120
[ 1%] Building C object tests/lang_c/CMakeFiles/headers_c_120.dir/__/test_headers.c.o
[ 1%] Linking C executable headers_c_120
[ 1%] Built target headers_c_120
Scanning dependencies of target cl_version_h_c_300
[ 1%] Building C object tests/lang_c/CMakeFiles/cl_version_h_c_300.dir/__/test_cl_version.h.c.o
[ 2%] Linking C executable cl_version_h_c_300
.......
[ 99%] Built target cl_egl_h_cpp_100
Scanning dependencies of target cl_gl_h_cpp_120
[100%] Building CXX object tests/lang_cpp/CMakeFiles/cl_gl_h_cpp_120.dir/test_cl_gl.h.cpp.o
[100%] Linking CXX executable cl_gl_h_cpp_120
[100%] Built target cl_gl_h_cpp_120
Install the project...
-- Install configuration: ""
-- Installing: /usr/include/CL
-- Installing: /usr/include/CL/opencl.h
-- Installing: /usr/include/CL/cl_egl.h
-- Installing: /usr/include/CL/cl_ext_intel.h
-- Installing: /usr/include/CL/cl_layer.h
-- Installing: /usr/include/CL/cl_platform.h
-- Installing: /usr/include/CL/cl_d3d10.h
-- Installing: /usr/include/CL/cl_va_api_media_sharing_intel.h
-- Installing: /usr/include/CL/cl_icd.h
-- Installing: /usr/include/CL/cl.h
-- Installing: /usr/include/CL/cl_function_types.h
-- Installing: /usr/include/CL/cl_dx9_media_sharing.h
-- Installing: /usr/include/CL/cl_dx9_media_sharing_intel.h
-- Installing: /usr/include/CL/cl_gl_ext.h
-- Installing: /usr/include/CL/cl_d3d11.h
-- Installing: /usr/include/CL/cl_version.h
-- Installing: /usr/include/CL/cl_half.h
-- Installing: /usr/include/CL/cl_ext.h
-- Installing: /usr/include/CL/cl_gl.h
-- Installing: /usr/share/cmake/OpenCLHeaders/OpenCLHeadersTargets.cmake
-- Installing: /usr/share/cmake/OpenCLHeaders/OpenCLHeadersConfig.cmake
-- Installing: /usr/share/cmake/OpenCLHeaders/OpenCLHeadersConfigVersion.cmake
-- Installing: /usr/share/pkgconfig/OpenCL-Headers.pc
头文件已经安装到/usr/include/CL目录下：
root@NanoPC-T6:/opt/OpenCL-Headers# ls -l /usr/include/CL
总用量 392
-rw-r--r-- 1 root root 8057 1月 15 00:10 cl_d3d10.h
-rw-r--r-- 1 root root 8095 1月 15 00:10 cl_d3d11.h
-rw-r--r-- 1 root root 12246 1月 15 00:10 cl_dx9_media_sharing.h
-rw-r--r-- 1 root root 959 1月 15 00:10 cl_dx9_media_sharing_intel.h
-rw-r--r-- 1 root root 5672 1月 15 00:10 cl_egl.h
-rw-r--r-- 1 root root 127490 1月 15 00:10 cl_ext.h
-rw-r--r-- 1 root root 902 1月 15 00:10 cl_ext_intel.h
-rw-r--r-- 1 root root 33387 1月 15 00:10 cl_function_types.h
-rw-r--r-- 1 root root 905 1月 15 00:10 cl_gl_ext.h
-rw-r--r-- 1 root root 12040 1月 15 00:10 cl_gl.h
-rw-r--r-- 1 root root 81631 1月 15 00:10 cl.h
-rw-r--r-- 1 root root 10430 1月 15 00:10 cl_half.h
-rw-r--r-- 1 root root 11505 1月 15 00:10 cl_icd.h
-rw-r--r-- 1 root root 3544 1月 15 00:10 cl_layer.h
-rw-r--r-- 1 root root 43430 1月 15 00:10 cl_platform.h
-rw-r--r-- 1 root root 7090 1月 15 00:10 cl_va_api_media_sharing_intel.h
-rw-r--r-- 1 root root 3125 1月 15 00:10 cl_version.h
-rw-r--r-- 1 root root 970 1月 15 00:10 opencl.h
三、OpenCL测试
此时已经有动态库和头文件，可以进行测试了。在/opt/目录下创建opencl-project文件夹;
root@NanoPC-T6:/opt# mkdir opencl-project
接着创建platform文件夹；
root@NanoPC-T6:/opt# cd opencl-project/
root@NanoPC-T6:/opt/opencl-project# mkdir platform
root@NanoPC-T6:/opt/opencl-project# cd platform
3.1 platform.cpp
在/opt/opencl-project/platform目录下编写测试代码platform.cpp；
#include <stdio.h>
#include <stdlib.h>
#include <CL/cl.h>
#define MAX_PLATFORMS 10
#define MAX_DEVICES 10
int main() {
cl_platform_id platforms[MAX_PLATFORMS];
cl_device_id devices[MAX_DEVICES];
cl_uint num_platforms, num_devices;
cl_context context;
cl_command_queue command_queue;
cl_program program;
cl_kernel kernel;
cl_int ret;
// 获取平台数量
ret = clGetPlatformIDs(MAX_PLATFORMS, platforms, &num_platforms);
if (ret != CL_SUCCESS) {
printf("Failed to get platform IDs\n");
return -1;
}
printf("Number of platforms: %u\n", num_platforms);
// 遍历打印平台信息
for (cl_uint i = 0; i < num_platforms; i++) {
char platform_name[128];
char platform_vendor[128];
ret = clGetPlatformInfo(platforms[i], CL_PLATFORM_NAME, sizeof(platform_name), platform_name, NULL);
if (ret != CL_SUCCESS) {
printf("Failed to get platform name for platform %u\n", i);
}
ret = clGetPlatformInfo(platforms[i], CL_PLATFORM_VENDOR, sizeof(platform_vendor), platform_vendor, NULL);
if (ret != CL_SUCCESS) {
printf("Failed to get platform vendor for platform %u\n", i);
}
printf("Platform %u:\n", i);
printf(" Name: %s\n", platform_name);
printf(" Vendor: %s\n", platform_vendor);
printf("\n");
}
// 获取设备数量
ret = clGetDeviceIDs(platforms[0], CL_DEVICE_TYPE_GPU, MAX_DEVICES, devices, &num_devices);
if (ret != CL_SUCCESS) {
printf("Failed to get device IDs\n");
return -1;
}
// 创建OpenCL上下文
context = clCreateContext(NULL, num_devices, devices, NULL, NULL, &ret);
if (ret != CL_SUCCESS) {
printf("Failed to create context\n");
return -1;
}
// 创建命令队列
command_queue = clCreateCommandQueue(context, devices[0], 0, &ret);
if (ret != CL_SUCCESS) {
printf("Failed to create command queue\n");
return -1;
}
// 定义和构建OpenCL内核
const char *kernel_source = "__kernel void hello_world() {\n"
" printf(\"Hello, World!\\n\");\n"
"}\n";
program = clCreateProgramWithSource(context, 1, &kernel_source, NULL, &ret);
if (ret != CL_SUCCESS) {
printf("Failed to create program\n");
return -1;
}
ret = clBuildProgram(program, num_devices, devices, NULL, NULL, NULL);
if (ret != CL_SUCCESS) {
printf("Failed to build program\n");
return -1;
}
// 创建OpenCL内核对象
kernel = clCreateKernel(program, "hello_world", &ret);
if (ret != CL_SUCCESS) {
printf("Failed to create kernel\n");
return -1;
}
// 执行内核函数
ret = clEnqueueTask(command_queue, kernel, 0, NULL, NULL);
if (ret != CL_SUCCESS) {
printf("Failed to enqueue task\n");
return -1;
}
// 等待执行完成
ret = clFinish(command_queue);
if (ret != CL_SUCCESS) {
printf("Failed to finish execution\n");
return -1;
}
printf("Kernel executed successfully\n");
// 清理资源
ret = clReleaseKernel(kernel);
ret = clReleaseProgram(program);
ret = clReleaseCommandQueue(command_queue);
ret = clReleaseContext(context);
return 0;
}
3.2 编译
这里我们介绍两种源码编译的方式。
3.2.1 直接编译
我们可以直接执行如下编译命令:
root@NanoPC-T6:/opt/opencl-project/platform# gcc platform.cpp -o platform -lmali
-lmail用于链接libmali.so库文件，-l选项指定要链接的库文件名，并在文件名前加上lib和.so的前缀和后缀。所以-lmali告诉编译器要链接的库文件名为libmali.so。
那么编译器如何知道libmali.so在哪里的呢？
- 首先搜索预定义的默认路径，如
/usr/lib和/usr/local/lib等; - 如果共享库没有在这些路径中找到，则会搜索在
/etc/ld.so.conf和/etc/ld.so.conf.d目录中指定的路径。这些路径可以包含自定义共享库路径，比如：
root@NanoPC-T6:/opt/opencl-project/platform# ls -l /etc/ld.so.conf.d/
总用量 12
-rw-r--r-- 1 root root 32 7月 29 2020 00-aarch64-mali.conf
-rw-r--r-- 1 root root 103 4月 20 2023 aarch64-linux-gnu.conf
-rw-r--r-- 1 root root 44 9月 23 2022 libc.conf
root@NanoPC-T6:/opt/opencl-project/platform# cat /etc/ld.so.conf.d/aarch64-linux-gnu.conf
# Multiarch support
/usr/local/lib/aarch64-linux-gnu
/lib/aarch64-linux-gnu
/usr/lib/aarch64-linux-gnu # 该路径下有libmali.so库文件
3.2.2 cmake编译
当然也可以使用cmake进行编译platform.cpp，接下来我们介绍cmake编译配置。
(1) 在/opt/opencl-project/platform目录下创建CMakeLists.txt：
cmake_minimum_required(VERSION 3.0)
cmake_policy(VERSION 3.0...3.18.4)
project(proj)
add_executable(platform platform.cpp)
#寻找OpenCL库 /usr/share/cmake-3.18/Modules/FindOpenCL.cmake
find_package(OpenCL REQUIRED)
#打印调试信息
MESSAGE(STATUS "Project: ${PROJECT_NAME}")
MESSAGE(STATUS "OpenCL library status:")
MESSAGE(STATUS " version: ${OpenCL_VERSION_STRING}")
MESSAGE(STATUS " libraries: ${OpenCL_LIBRARY}")
MESSAGE(STATUS " include path: ${OpenCL_INCLUDE_DIR}")
target_link_libraries(platform PRIVATE OpenCL::OpenCL)
(2) 配置构建过程：
root@NanoPC-T6:/opt/opencl-project/platform# cmake -S . -B build
-- The C compiler identification is GNU 10.2.1
-- The CXX compiler identification is GNU 10.2.1
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Check for working C compiler: /usr/bin/cc - skipped
-- Detecting C compile features
-- Detecting C compile features - done
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Check for working CXX compiler: /usr/bin/c++ - skipped
-- Detecting CXX compile features
-- Detecting CXX compile features - done
-- Looking for CL_VERSION_2_2
-- Looking for CL_VERSION_2_2 - found
-- Found OpenCL: /usr/lib/aarch64-linux-gnu/libOpenCL.so (found version "2.2")
-- Project: proj
-- OpenCL library status:
-- version: 2.2
-- libraries: /usr/lib/aarch64-linux-gnu/libOpenCL.so # 库文件路径
-- include path: /usr/include # 头文件路径
-- Configuring done
-- Generating done
-- Build files have been written to: /opt/opencl-project/platform/build
其中：
-S .：选项指定源代码目录的路径，CMake将在该路径下查找CMakeLists.txt文件；-B build：选项指定构建目录的路径；
实际上我们使用的版本是OpenCL 3.0，这里判定为2.2版本是因为cmake version 3.18.4 FindOpenCL.cmake能够识别的最大版本为2.2，其通过在CL/cl.h文件查找CL_VERSION_${VERSION}宏来判定安装的版本的。
可以通过修改/usr/share/cmake-3.18/Modules/FindOpenCL.cmake解决这个问题：
foreach(VERSION "3_0" "2_2" "2_1" "2_0" "1_2" "1_1" "1_0")
(3) 执行构建操作，生成可执行程序platform；
root@NanoPC-T6:/opt/OpenCL-Headers/exmaples# cmake --build build
Scanning dependencies of target platform
[ 50%] Building CXX object CMakeFiles/platform.dir/platform.cpp.o
In file included from /usr/include/CL/cl.h:20,
from /usr/include/CL/opencl.h:24,
from /opt/OpenCL-Headers/exmaples/platform.cpp:1:
/usr/include/CL/cl_version.h:22:104: note: ‘#pragma message: cl_version.h: CL_TARGET_OPENCL_VERSION is not defined. Defaulting to 300 (OpenCL 3.0)’
22 | #pragma message("cl_version.h: CL_TARGET_OPENCL_VERSION is not defined. Defaulting to 300 (OpenCL 3.0)")
| ^
[100%] Linking CXX executable platform
[100%] Built target platform
执行程序:
root@NanoPC-T6:/opt/opencl-project/platform# ls -l build/
总用量 48
-rw-r--r-- 1 root root 14229 1月 16 23:45 CMakeCache.txt
drwxr-xr-x 5 root root 4096 1月 16 23:46 CMakeFiles
-rw-r--r-- 1 root root 1632 1月 16 23:45 cmake_install.cmake
-rw-r--r-- 1 root root 5253 1月 16 23:45 Makefile
-rwxr-xr-x 1 root root 14248 1月 16 23:46 platform
root@NanoPC-T6:/opt/opencl-project/platform# ./build/platform
arm_release_ver: g13p0-01eac0, rk_so_ver: 10
Number of platforms: 1
Platform 0:
Name: ARM Platform
Vendor: ARM
Kernel executed successfully
四、OpenCV测试
4.1 OCL介绍
OpenCV在2011年开始与AMD合作加入OpenCL加速。因此，OpenCV-2.4.3版本包含了新的ocl模块，其中包含了一些现有OpenCV算法的OpenCL实现。也就是说，当客户端机器上有OpenCL运行时和兼容设备时，用户可以调用cv::ocl::resize()来使用加速的代码，而不是使用cv::resize()。在接下来的三年中，越来越多的函数和类被添加到ocl模块中；但它始终是一个独立的API，与OpenCV-2.x中的主要面向CPU的API并存。
在OpenCV-3.x中，架构概念已经改变为所谓的Transparent API（T-API）。在新架构中，一个单独的OpenCL加速的cv::ocl::resize() 已经从外部API中移除，而成为常规cv::resize()中的一个分支。这个分支在性能角度上自动调用，并在可行且有意义时进行优化。T-API的实现得到了AMD和Intel公司的赞助。
4.1.1 代码示例
Regular CPU code：
// initialization
VideoCapture vcap(...);
CascadeClassifier fd("haar_ff.xml");
Mat frame, frameGray;
vector<rect> faces;
for(;;){
// processing loop
vcap >> frame;
cvtColor(frame, frameGray, BGR2GRAY);
equalizeHist(frameGray, frameGray);
fd.detectMultiScale(frameGray, faces, ...);
// draw rectangles …
// show image …
}
OpenCL-aware code OpenCV-2.x：
// initialization
VideoCapture vcap(...);
ocl::OclCascadeClassifier fd("haar_ff.xml");
ocl::oclMat frame, frameGray;
Mat frameCpu;
vector<rect> faces;
for(;;){
// processing loop
vcap >> frameCpu;
frame = frameCpu;
ocl::cvtColor(frame, frameGray, BGR2GRAY);
ocl::equalizeHist(frameGray, frameGray);
fd.detectMultiScale(frameGray, faces, ...);
// draw rectangles …
// show image …
}
OpenCL-aware code OpenCV-3.x：
// initialization
VideoCapture vcap(...);
CascadeClassifier fd("haar_ff.xml");
UMat frame, frameGray;
vector<rect> faces;
for(;;){
// processing loop
vcap >> frame;
cvtColor(frame, frameGray, BGR2GRAY);
equalizeHist(frameGray, frameGray);
fd.detectMultiScale(frameGray, faces, ...);
// draw rectangles …
// show image …
}
相比于OpenCV-2.x，OpenCV-3.x封装了一个新的数据类型cv::UMat，这个数据类型能够无缝对接OpenCV的普通接口，从而最少的改动代码而最大的完成OpenCL平台的加速功能。
4.1.2 原理
如上面代码所示，只需要将原来的Mat格式换为UMat格式就可以实现OpenCV函数在OpenCL设备上加速运行，而这其中具体实施的基本原理是什么呢？接下来看一下其底层实现的基本原理，具体参看OpenCV 3.1中OpenCL部分实现的源代码（注意：每个版本代码可能略有差异）：
上图中表明了，当你使用的数据类型是UMat {dst.isUmat()}，并且开启了OpenCL使能{useOpenCL()}，那么OpenCV的接口将会跳转到OpenCL支持的设备中进行加速运行，当然你需要注意的是，在第一次使用OpenCL加速程序时，OpenCL需要编译生成对应平台的Kernel代码，而编译是需要花费大量的时间的，因此初次运行需要比较长的时间。
4.2 官方opencv-ocl
在/opt/opencl-project目录下新建opencv-ocl项目，源码位于：https://521github.com/opencv/opencv/tree/3.4.0/samples/opencl。
4.2.1 main.cpp
点击查看代码
/*
// The example of interoperability between OpenCL and OpenCV.
// This will loop through frames of video either from input media file
// or camera device and do processing of these data in OpenCL and then
// in OpenCV. In OpenCL it does inversion of pixels in left half of frame and
// in OpenCV it does bluring in the right half of frame.
*/
#include <cstdio>
#include <cstdlib>
#include <iostream>
#include <fstream>
#include <string>
#include <sstream>
#include <iomanip>
#include <stdexcept>
#define CL_USE_DEPRECATED_OPENCL_2_0_APIS // eliminate build warning
#if __APPLE__
#include <OpenCL/cl.h>
#else
#include <CL/cl.h>
#endif
#include <opencv2/core/ocl.hpp>
#include <opencv2/core/utility.hpp>
#include <opencv2/video.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>
using namespace std;
using namespace cv;
namespace opencl {
class PlatformInfo
{
public:
PlatformInfo()
{}
~PlatformInfo()
{}
cl_int QueryInfo(cl_platform_id id)
{
query_param(id, CL_PLATFORM_PROFILE, m_profile);
query_param(id, CL_PLATFORM_VERSION, m_version);
query_param(id, CL_PLATFORM_NAME, m_name);
query_param(id, CL_PLATFORM_VENDOR, m_vendor);
query_param(id, CL_PLATFORM_EXTENSIONS, m_extensions);
return CL_SUCCESS;
}
std::string Profile() { return m_profile; }
std::string Version() { return m_version; }
std::string Name() { return m_name; }
std::string Vendor() { return m_vendor; }
std::string Extensions() { return m_extensions; }
private:
cl_int query_param(cl_platform_id id, cl_platform_info param, std::string& paramStr)
{
cl_int res;
size_t psize;
cv::AutoBuffer<char> buf;
res = clGetPlatformInfo(id, param, 0, 0, &psize);
if (CL_SUCCESS != res)
throw std::runtime_error(std::string("clGetPlatformInfo failed"));
buf.resize(psize);
res = clGetPlatformInfo(id, param, psize, buf, 0);
if (CL_SUCCESS != res)
throw std::runtime_error(std::string("clGetPlatformInfo failed"));
// just in case, ensure trailing zero for ASCIIZ string
buf[psize] = 0;
paramStr = buf;
return CL_SUCCESS;
}
private:
std::string m_profile;
std::string m_version;
std::string m_name;
std::string m_vendor;
std::string m_extensions;
};
class DeviceInfo
{
public:
DeviceInfo()
{}
~DeviceInfo()
{}
cl_int QueryInfo(cl_device_id id)
{
query_param(id, CL_DEVICE_TYPE, m_type);
query_param(id, CL_DEVICE_VENDOR_ID, m_vendor_id);
query_param(id, CL_DEVICE_MAX_COMPUTE_UNITS, m_max_compute_units);
query_param(id, CL_DEVICE_MAX_WORK_ITEM_DIMENSIONS, m_max_work_item_dimensions);
query_param(id, CL_DEVICE_MAX_WORK_ITEM_SIZES, m_max_work_item_sizes);
query_param(id, CL_DEVICE_MAX_WORK_GROUP_SIZE, m_max_work_group_size);
query_param(id, CL_DEVICE_PREFERRED_VECTOR_WIDTH_CHAR, m_preferred_vector_width_char);
query_param(id, CL_DEVICE_PREFERRED_VECTOR_WIDTH_SHORT, m_preferred_vector_width_short);
query_param(id, CL_DEVICE_PREFERRED_VECTOR_WIDTH_INT, m_preferred_vector_width_int);
query_param(id, CL_DEVICE_PREFERRED_VECTOR_WIDTH_LONG, m_preferred_vector_width_long);
query_param(id, CL_DEVICE_PREFERRED_VECTOR_WIDTH_FLOAT, m_preferred_vector_width_float);
query_param(id, CL_DEVICE_PREFERRED_VECTOR_WIDTH_DOUBLE, m_preferred_vector_width_double);
#if defined(CL_VERSION_1_1)
query_param(id, CL_DEVICE_PREFERRED_VECTOR_WIDTH_HALF, m_preferred_vector_width_half);
query_param(id, CL_DEVICE_NATIVE_VECTOR_WIDTH_CHAR, m_native_vector_width_char);
query_param(id, CL_DEVICE_NATIVE_VECTOR_WIDTH_SHORT, m_native_vector_width_short);
query_param(id, CL_DEVICE_NATIVE_VECTOR_WIDTH_INT, m_native_vector_width_int);
query_param(id, CL_DEVICE_NATIVE_VECTOR_WIDTH_LONG, m_native_vector_width_long);
query_param(id, CL_DEVICE_NATIVE_VECTOR_WIDTH_FLOAT, m_native_vector_width_float);
query_param(id, CL_DEVICE_NATIVE_VECTOR_WIDTH_DOUBLE, m_native_vector_width_double);
query_param(id, CL_DEVICE_NATIVE_VECTOR_WIDTH_HALF, m_native_vector_width_half);
#endif
query_param(id, CL_DEVICE_MAX_CLOCK_FREQUENCY, m_max_clock_frequency);
query_param(id, CL_DEVICE_ADDRESS_BITS, m_address_bits);
query_param(id, CL_DEVICE_MAX_MEM_ALLOC_SIZE, m_max_mem_alloc_size);
query_param(id, CL_DEVICE_IMAGE_SUPPORT, m_image_support);
query_param(id, CL_DEVICE_MAX_READ_IMAGE_ARGS, m_max_read_image_args);
query_param(id, CL_DEVICE_MAX_WRITE_IMAGE_ARGS, m_max_write_image_args);
#if defined(CL_VERSION_2_0)
query_param(id, CL_DEVICE_MAX_READ_WRITE_IMAGE_ARGS, m_max_read_write_image_args);
#endif
query_param(id, CL_DEVICE_IMAGE2D_MAX_WIDTH, m_image2d_max_width);
query_param(id, CL_DEVICE_IMAGE2D_MAX_HEIGHT, m_image2d_max_height);
query_param(id, CL_DEVICE_IMAGE3D_MAX_WIDTH, m_image3d_max_width);
query_param(id, CL_DEVICE_IMAGE3D_MAX_HEIGHT, m_image3d_max_height);
query_param(id, CL_DEVICE_IMAGE3D_MAX_DEPTH, m_image3d_max_depth);
#if defined(CL_VERSION_1_2)
query_param(id, CL_DEVICE_IMAGE_MAX_BUFFER_SIZE, m_image_max_buffer_size);
query_param(id, CL_DEVICE_IMAGE_MAX_ARRAY_SIZE, m_image_max_array_size);
#endif
query_param(id, CL_DEVICE_MAX_SAMPLERS, m_max_samplers);
#if defined(CL_VERSION_1_2)
query_param(id, CL_DEVICE_IMAGE_PITCH_ALIGNMENT, m_image_pitch_alignment);
query_param(id, CL_DEVICE_IMAGE_BASE_ADDRESS_ALIGNMENT, m_image_base_address_alignment);
#endif
#if defined(CL_VERSION_2_0)
query_param(id, CL_DEVICE_MAX_PIPE_ARGS, m_max_pipe_args);
query_param(id, CL_DEVICE_PIPE_MAX_ACTIVE_RESERVATIONS, m_pipe_max_active_reservations);
query_param(id, CL_DEVICE_PIPE_MAX_PACKET_SIZE, m_pipe_max_packet_size);
#endif
query_param(id, CL_DEVICE_MAX_PARAMETER_SIZE, m_max_parameter_size);
query_param(id, CL_DEVICE_MEM_BASE_ADDR_ALIGN, m_mem_base_addr_align);
query_param(id, CL_DEVICE_SINGLE_FP_CONFIG, m_single_fp_config);
#if defined(CL_VERSION_1_2)
query_param(id, CL_DEVICE_DOUBLE_FP_CONFIG, m_double_fp_config);
#endif
query_param(id, CL_DEVICE_GLOBAL_MEM_CACHE_TYPE, m_global_mem_cache_type);
query_param(id, CL_DEVICE_GLOBAL_MEM_CACHELINE_SIZE, m_global_mem_cacheline_size);
query_param(id, CL_DEVICE_GLOBAL_MEM_CACHE_SIZE, m_global_mem_cache_size);
query_param(id, CL_DEVICE_GLOBAL_MEM_SIZE, m_global_mem_size);
query_param(id, CL_DEVICE_MAX_CONSTANT_BUFFER_SIZE, m_max_constant_buffer_size);
query_param(id, CL_DEVICE_MAX_CONSTANT_ARGS, m_max_constant_args);
#if defined(CL_VERSION_2_0)
query_param(id, CL_DEVICE_MAX_GLOBAL_VARIABLE_SIZE, m_max_global_variable_size);
query_param(id, CL_DEVICE_GLOBAL_VARIABLE_PREFERRED_TOTAL_SIZE, m_global_variable_preferred_total_size);
#endif
query_param(id, CL_DEVICE_LOCAL_MEM_TYPE, m_local_mem_type);
query_param(id, CL_DEVICE_LOCAL_MEM_SIZE, m_local_mem_size);
query_param(id, CL_DEVICE_ERROR_CORRECTION_SUPPORT, m_error_correction_support);
#if defined(CL_VERSION_1_1)
query_param(id, CL_DEVICE_HOST_UNIFIED_MEMORY, m_host_unified_memory);
#endif
query_param(id, CL_DEVICE_PROFILING_TIMER_RESOLUTION, m_profiling_timer_resolution);
query_param(id, CL_DEVICE_ENDIAN_LITTLE, m_endian_little);
query_param(id, CL_DEVICE_AVAILABLE, m_available);
query_param(id, CL_DEVICE_COMPILER_AVAILABLE, m_compiler_available);
#if defined(CL_VERSION_1_2)
query_param(id, CL_DEVICE_LINKER_AVAILABLE, m_linker_available);
#endif
query_param(id, CL_DEVICE_EXECUTION_CAPABILITIES, m_execution_capabilities);
query_param(id, CL_DEVICE_QUEUE_PROPERTIES, m_queue_properties);
#if defined(CL_VERSION_2_0)
query_param(id, CL_DEVICE_QUEUE_ON_HOST_PROPERTIES, m_queue_on_host_properties);
query_param(id, CL_DEVICE_QUEUE_ON_DEVICE_PROPERTIES, m_queue_on_device_properties);
query_param(id, CL_DEVICE_QUEUE_ON_DEVICE_PREFERRED_SIZE, m_queue_on_device_preferred_size);
query_param(id, CL_DEVICE_QUEUE_ON_DEVICE_MAX_SIZE, m_queue_on_device_max_size);
query_param(id, CL_DEVICE_MAX_ON_DEVICE_QUEUES, m_max_on_device_queues);
query_param(id, CL_DEVICE_MAX_ON_DEVICE_EVENTS, m_max_on_device_events);
#endif
#if defined(CL_VERSION_1_2)
query_param(id, CL_DEVICE_BUILT_IN_KERNELS, m_built_in_kernels);
#endif
query_param(id, CL_DEVICE_PLATFORM, m_platform);
query_param(id, CL_DEVICE_NAME, m_name);
query_param(id, CL_DEVICE_VENDOR, m_vendor);
query_param(id, CL_DRIVER_VERSION, m_driver_version);
query_param(id, CL_DEVICE_PROFILE, m_profile);
query_param(id, CL_DEVICE_VERSION, m_version);
#if defined(CL_VERSION_1_1)
query_param(id, CL_DEVICE_OPENCL_C_VERSION, m_opencl_c_version);
#endif
query_param(id, CL_DEVICE_EXTENSIONS, m_extensions);
#if defined(CL_VERSION_1_2)
query_param(id, CL_DEVICE_PRINTF_BUFFER_SIZE, m_printf_buffer_size);
query_param(id, CL_DEVICE_PREFERRED_INTEROP_USER_SYNC, m_preferred_interop_user_sync);
query_param(id, CL_DEVICE_PARENT_DEVICE, m_parent_device);
query_param(id, CL_DEVICE_PARTITION_MAX_SUB_DEVICES, m_partition_max_sub_devices);
query_param(id, CL_DEVICE_PARTITION_PROPERTIES, m_partition_properties);
query_param(id, CL_DEVICE_PARTITION_AFFINITY_DOMAIN, m_partition_affinity_domain);
query_param(id, CL_DEVICE_PARTITION_TYPE, m_partition_type);
query_param(id, CL_DEVICE_REFERENCE_COUNT, m_reference_count);
#endif
return CL_SUCCESS;
}
std::string Name() { return m_name; }
private:
template<typename T>
cl_int query_param(cl_device_id id, cl_device_info param, T& value)
{
cl_int res;
size_t size = 0;
res = clGetDeviceInfo(id, param, 0, 0, &size);
if (CL_SUCCESS != res && size != 0)
throw std::runtime_error(std::string("clGetDeviceInfo failed"));
if (0 == size)
return CL_SUCCESS;
if (sizeof(T) != size)
throw std::runtime_error(std::string("clGetDeviceInfo: param size mismatch"));
res = clGetDeviceInfo(id, param, size, &value, 0);
if (CL_SUCCESS != res)
throw std::runtime_error(std::string("clGetDeviceInfo failed"));
return CL_SUCCESS;
}
template<typename T>
cl_int query_param(cl_device_id id, cl_device_info param, std::vector<T>& value)
{
cl_int res;
size_t size;
res = clGetDeviceInfo(id, param, 0, 0, &size);
if (CL_SUCCESS != res)
throw std::runtime_error(std::string("clGetDeviceInfo failed"));
if (0 == size)
return CL_SUCCESS;
value.resize(size / sizeof(T));
res = clGetDeviceInfo(id, param, size, &value[0], 0);
if (CL_SUCCESS != res)
throw std::runtime_error(std::string("clGetDeviceInfo failed"));
return CL_SUCCESS;
}
cl_int query_param(cl_device_id id, cl_device_info param, std::string& value)
{
cl_int res;
size_t size;
res = clGetDeviceInfo(id, param, 0, 0, &size);
if (CL_SUCCESS != res)
throw std::runtime_error(std::string("clGetDeviceInfo failed"));
value.resize(size + 1);
res = clGetDeviceInfo(id, param, size, &value[0], 0);
if (CL_SUCCESS != res)
throw std::runtime_error(std::string("clGetDeviceInfo failed"));
// just in case, ensure trailing zero for ASCIIZ string
value[size] = 0;
return CL_SUCCESS;
}
private:
cl_device_type m_type;
cl_uint m_vendor_id;
cl_uint m_max_compute_units;
cl_uint m_max_work_item_dimensions;
std::vector<size_t> m_max_work_item_sizes;
size_t m_max_work_group_size;
cl_uint m_preferred_vector_width_char;
cl_uint m_preferred_vector_width_short;
cl_uint m_preferred_vector_width_int;
cl_uint m_preferred_vector_width_long;
cl_uint m_preferred_vector_width_float;
cl_uint m_preferred_vector_width_double;
#if defined(CL_VERSION_1_1)
cl_uint m_preferred_vector_width_half;
cl_uint m_native_vector_width_char;
cl_uint m_native_vector_width_short;
cl_uint m_native_vector_width_int;
cl_uint m_native_vector_width_long;
cl_uint m_native_vector_width_float;
cl_uint m_native_vector_width_double;
cl_uint m_native_vector_width_half;
#endif
cl_uint m_max_clock_frequency;
cl_uint m_address_bits;
cl_ulong m_max_mem_alloc_size;
cl_bool m_image_support;
cl_uint m_max_read_image_args;
cl_uint m_max_write_image_args;
#if defined(CL_VERSION_2_0)
cl_uint m_max_read_write_image_args;
#endif
size_t m_image2d_max_width;
size_t m_image2d_max_height;
size_t m_image3d_max_width;
size_t m_image3d_max_height;
size_t m_image3d_max_depth;
#if defined(CL_VERSION_1_2)
size_t m_image_max_buffer_size;
size_t m_image_max_array_size;
#endif
cl_uint m_max_samplers;
#if defined(CL_VERSION_1_2)
cl_uint m_image_pitch_alignment;
cl_uint m_image_base_address_alignment;
#endif
#if defined(CL_VERSION_2_0)
cl_uint m_max_pipe_args;
cl_uint m_pipe_max_active_reservations;
cl_uint m_pipe_max_packet_size;
#endif
size_t m_max_parameter_size;
cl_uint m_mem_base_addr_align;
cl_device_fp_config m_single_fp_config;
#if defined(CL_VERSION_1_2)
cl_device_fp_config m_double_fp_config;
#endif
cl_device_mem_cache_type m_global_mem_cache_type;
cl_uint m_global_mem_cacheline_size;
cl_ulong m_global_mem_cache_size;
cl_ulong m_global_mem_size;
cl_ulong m_max_constant_buffer_size;
cl_uint m_max_constant_args;
#if defined(CL_VERSION_2_0)
size_t m_max_global_variable_size;
size_t m_global_variable_preferred_total_size;
#endif
cl_device_local_mem_type m_local_mem_type;
cl_ulong m_local_mem_size;
cl_bool m_error_correction_support;
#if defined(CL_VERSION_1_1)
cl_bool m_host_unified_memory;
#endif
size_t m_profiling_timer_resolution;
cl_bool m_endian_little;
cl_bool m_available;
cl_bool m_compiler_available;
#if defined(CL_VERSION_1_2)
cl_bool m_linker_available;
#endif
cl_device_exec_capabilities m_execution_capabilities;
cl_command_queue_properties m_queue_properties;
#if defined(CL_VERSION_2_0)
cl_command_queue_properties m_queue_on_host_properties;
cl_command_queue_properties m_queue_on_device_properties;
cl_uint m_queue_on_device_preferred_size;
cl_uint m_queue_on_device_max_size;
cl_uint m_max_on_device_queues;
cl_uint m_max_on_device_events;
#endif
#if defined(CL_VERSION_1_2)
std::string m_built_in_kernels;
#endif
cl_platform_id m_platform;
std::string m_name;
std::string m_vendor;
std::string m_driver_version;
std::string m_profile;
std::string m_version;
#if defined(CL_VERSION_1_1)
std::string m_opencl_c_version;
#endif
std::string m_extensions;
#if defined(CL_VERSION_1_2)
size_t m_printf_buffer_size;
cl_bool m_preferred_interop_user_sync;
cl_device_id m_parent_device;
cl_uint m_partition_max_sub_devices;
std::vector<cl_device_partition_property> m_partition_properties;
cl_device_affinity_domain m_partition_affinity_domain;
std::vector<cl_device_partition_property> m_partition_type;
cl_uint m_reference_count;
#endif
};
} // namespace opencl
class App
{
public:
App(CommandLineParser& cmd);
~App();
int initOpenCL();
int initVideoSource();
int process_frame_with_open_cl(cv::Mat& frame, bool use_buffer, cl_mem* cl_buffer);
int process_cl_buffer_with_opencv(cl_mem buffer, size_t step, int rows, int cols, int type, cv::UMat& u);
int process_cl_image_with_opencv(cl_mem image, cv::UMat& u);
int run();
bool isRunning() { return m_running; }
bool doProcess() { return m_process; }
bool useBuffer() { return m_use_buffer; }
void setRunning(bool running) { m_running = running; }
void setDoProcess(bool process) { m_process = process; }
void setUseBuffer(bool use_buffer) { m_use_buffer = use_buffer; }
protected:
bool nextFrame(cv::Mat& frame) { return m_cap.read(frame); }
void handleKey(char key);
void timerStart();
void timerEnd();
std::string timeStr() const;
std::string message() const;
private:
bool m_running;
bool m_process;
bool m_use_buffer;
int64 m_t0;
int64 m_t1;
float m_time;
float m_frequency;
string m_file_name;
int m_camera_id;
cv::VideoCapture m_cap;
cv::Mat m_frame;
cv::Mat m_frameGray;
opencl::PlatformInfo m_platformInfo;
opencl::DeviceInfo m_deviceInfo;
std::vector<cl_platform_id> m_platform_ids;
cl_context m_context;
cl_device_id m_device_id;
cl_command_queue m_queue;
cl_program m_program;
cl_kernel m_kernelBuf;
cl_kernel m_kernelImg;
cl_mem m_img_src; // used as src in case processing of cl image
cl_mem m_mem_obj;
cl_event m_event;
};
App::App(CommandLineParser& cmd)
{
cout << "\nPress ESC to exit\n" << endl;
cout << "\n 'p' to toggle ON/OFF processing\n" << endl;
cout << "\n SPACE to switch between OpenCL buffer/image\n" << endl;
m_camera_id = cmd.get<int>("camera");
m_file_name = cmd.get<string>("video");
m_running = false;
m_process = false;
m_use_buffer = false;
m_t0 = 0;
m_t1 = 0;
m_time = 0.0;
m_frequency = (float)cv::getTickFrequency();
m_context = 0;
m_device_id = 0;
m_queue = 0;
m_program = 0;
m_kernelBuf = 0;
m_kernelImg = 0;
m_img_src = 0;
m_mem_obj = 0;
m_event = 0;
} // ctor
App::~App()
{
if (m_queue)
{
clFinish(m_queue);
clReleaseCommandQueue(m_queue);
m_queue = 0;
}
if (m_program)
{
clReleaseProgram(m_program);
m_program = 0;
}
if (m_img_src)
{
clReleaseMemObject(m_img_src);
m_img_src = 0;
}
if (m_mem_obj)
{
clReleaseMemObject(m_mem_obj);
m_mem_obj = 0;
}
if (m_event)
{
clReleaseEvent(m_event);
}
if (m_kernelBuf)
{
clReleaseKernel(m_kernelBuf);
m_kernelBuf = 0;
}
if (m_kernelImg)
{
clReleaseKernel(m_kernelImg);
m_kernelImg = 0;
}
if (m_device_id)
{
clReleaseDevice(m_device_id);
m_device_id = 0;
}
if (m_context)
{
clReleaseContext(m_context);
m_context = 0;
}
} // dtor
int App::initOpenCL()
{
cl_int res = CL_SUCCESS;
cl_uint num_entries = 0;
res = clGetPlatformIDs(0, 0, &num_entries);
if (CL_SUCCESS != res)
return -1;
m_platform_ids.resize(num_entries);
res = clGetPlatformIDs(num_entries, &m_platform_ids[0], 0);
if (CL_SUCCESS != res)
return -1;
unsigned int i;
// create context from first platform with GPU device
for (i = 0; i < m_platform_ids.size(); i++)
{
cl_context_properties props[] =
{
CL_CONTEXT_PLATFORM,
(cl_context_properties)(m_platform_ids[i]),
0
};
m_context = clCreateContextFromType(props, CL_DEVICE_TYPE_GPU, 0, 0, &res);
if (0 == m_context || CL_SUCCESS != res)
continue;
res = clGetContextInfo(m_context, CL_CONTEXT_DEVICES, sizeof(cl_device_id), &m_device_id, 0);
if (CL_SUCCESS != res)
return -1;
m_queue = clCreateCommandQueue(m_context, m_device_id, 0, &res);
if (0 == m_queue || CL_SUCCESS != res)
return -1;
const char* kernelSrc =
"__kernel "
"void bitwise_inv_buf_8uC1("
" __global unsigned char* pSrcDst,"
" int srcDstStep,"
" int rows,"
" int cols)"
"{"
" int x = get_global_id(0);"
" int y = get_global_id(1);"
" int idx = mad24(y, srcDstStep, x);"
" pSrcDst[idx] = ~pSrcDst[idx];"
"}"
"__kernel "
"void bitwise_inv_img_8uC1("
" read_only image2d_t srcImg,"
" write_only image2d_t dstImg)"
"{"
" int x = get_global_id(0);"
" int y = get_global_id(1);"
" int2 coord = (int2)(x, y);"
" uint4 val = read_imageui(srcImg, coord);"
" val.x = (~val.x) & 0x000000FF;"
" write_imageui(dstImg, coord, val);"
"}";
size_t len = strlen(kernelSrc);
m_program = clCreateProgramWithSource(m_context, 1, &kernelSrc, &len, &res);
if (0 == m_program || CL_SUCCESS != res)
return -1;
res = clBuildProgram(m_program, 1, &m_device_id, 0, 0, 0);
if (CL_SUCCESS != res)
return -1;
m_kernelBuf = clCreateKernel(m_program, "bitwise_inv_buf_8uC1", &res);
if (0 == m_kernelBuf || CL_SUCCESS != res)
return -1;
m_kernelImg = clCreateKernel(m_program, "bitwise_inv_img_8uC1", &res);
if (0 == m_kernelImg || CL_SUCCESS != res)
return -1;
m_platformInfo.QueryInfo(m_platform_ids[i]);
m_deviceInfo.QueryInfo(m_device_id);
// attach OpenCL context to OpenCV
cv::ocl::attachContext(m_platformInfo.Name(), m_platform_ids[i], m_context, m_device_id);
break;
}
return m_context != 0 ? CL_SUCCESS : -1;
} // initOpenCL()
int App::initVideoSource()
{
try
{
if (!m_file_name.empty() && m_camera_id == -1)
{
m_cap.open(m_file_name.c_str());
if (!m_cap.isOpened())
throw std::runtime_error(std::string("can't open video file: " + m_file_name));
}
else if (m_camera_id != -1)
{
m_cap.open(m_camera_id);
if (!m_cap.isOpened())
{
std::stringstream msg;
msg << "can't open camera: " << m_camera_id;
throw std::runtime_error(msg.str());
}
}
else
throw std::runtime_error(std::string("specify video source"));
}
catch (std::exception e)
{
cerr << "ERROR: " << e.what() << std::endl;
return -1;
}
return 0;
} // initVideoSource()
// this function is an example of "typical" OpenCL processing pipeline
// It creates OpenCL buffer or image, depending on use_buffer flag,
// from input media frame and process these data
// (inverts each pixel value in half of frame) with OpenCL kernel
int App::process_frame_with_open_cl(cv::Mat& frame, bool use_buffer, cl_mem* mem_obj)
{
cl_int res = CL_SUCCESS;
CV_Assert(mem_obj);
cl_kernel kernel = 0;
cl_mem mem = mem_obj[0];
if (0 == mem || 0 == m_img_src)
{
// allocate/delete cl memory objects every frame for the simplicity.
// in real applicaton more efficient pipeline can be built.
if (use_buffer)
{
cl_mem_flags flags = CL_MEM_READ_WRITE | CL_MEM_USE_HOST_PTR;
mem = clCreateBuffer(m_context, flags, frame.total(), frame.ptr(), &res);
if (0 == mem || CL_SUCCESS != res)
return -1;
res = clSetKernelArg(m_kernelBuf, 0, sizeof(cl_mem), &mem);
if (CL_SUCCESS != res)
return -1;
res = clSetKernelArg(m_kernelBuf, 1, sizeof(int), &frame.step[0]);
if (CL_SUCCESS != res)
return -1;
res = clSetKernelArg(m_kernelBuf, 2, sizeof(int), &frame.rows);
if (CL_SUCCESS != res)
return -1;
int cols2 = frame.cols / 2;
res = clSetKernelArg(m_kernelBuf, 3, sizeof(int), &cols2);
if (CL_SUCCESS != res)
return -1;
kernel = m_kernelBuf;
}
else
{
cl_mem_flags flags_src = CL_MEM_READ_ONLY | CL_MEM_USE_HOST_PTR;
cl_image_format fmt;
fmt.image_channel_order = CL_R;
fmt.image_channel_data_type = CL_UNSIGNED_INT8;
cl_image_desc desc_src;
desc_src.image_type = CL_MEM_OBJECT_IMAGE2D;
desc_src.image_width = frame.cols;
desc_src.image_height = frame.rows;
desc_src.image_depth = 0;
desc_src.image_array_size = 0;
desc_src.image_row_pitch = frame.step[0];
desc_src.image_slice_pitch = 0;
desc_src.num_mip_levels = 0;
desc_src.num_samples = 0;
desc_src.buffer = 0;
m_img_src = clCreateImage(m_context, flags_src, &fmt, &desc_src, frame.ptr(), &res);
if (0 == m_img_src || CL_SUCCESS != res)
return -1;
cl_mem_flags flags_dst = CL_MEM_READ_WRITE | CL_MEM_ALLOC_HOST_PTR;
cl_image_desc desc_dst;
desc_dst.image_type = CL_MEM_OBJECT_IMAGE2D;
desc_dst.image_width = frame.cols;
desc_dst.image_height = frame.rows;
desc_dst.image_depth = 0;
desc_dst.image_array_size = 0;
desc_dst.image_row_pitch = 0;
desc_dst.image_slice_pitch = 0;
desc_dst.num_mip_levels = 0;
desc_dst.num_samples = 0;
desc_dst.buffer = 0;
mem = clCreateImage(m_context, flags_dst, &fmt, &desc_dst, 0, &res);
if (0 == mem || CL_SUCCESS != res)
return -1;
size_t origin[] = { 0, 0, 0 };
size_t region[] = { (size_t)frame.cols, (size_t)frame.rows, 1 };
res = clEnqueueCopyImage(m_queue, m_img_src, mem, origin, origin, region, 0, 0, &m_event);
if (CL_SUCCESS != res)
return -1;
res = clWaitForEvents(1, &m_event);
if (CL_SUCCESS != res)
return -1;
res = clSetKernelArg(m_kernelImg, 0, sizeof(cl_mem), &m_img_src);
if (CL_SUCCESS != res)
return -1;
res = clSetKernelArg(m_kernelImg, 1, sizeof(cl_mem), &mem);
if (CL_SUCCESS != res)
return -1;
kernel = m_kernelImg;
}
}
m_event = clCreateUserEvent(m_context, &res);
if (0 == m_event || CL_SUCCESS != res)
return -1;
// process left half of frame in OpenCL
size_t size[] = { (size_t)frame.cols / 2, (size_t)frame.rows };
res = clEnqueueNDRangeKernel(m_queue, kernel, 2, 0, size, 0, 0, 0, &m_event);
if (CL_SUCCESS != res)
return -1;
res = clWaitForEvents(1, &m_event);
if (CL_SUCCESS != res)
return - 1;
mem_obj[0] = mem;
return 0;
}
// this function is an example of interoperability between OpenCL buffer
// and OpenCV UMat objects. It converts (without copying data) OpenCL buffer
// to OpenCV UMat and then do blur on these data
int App::process_cl_buffer_with_opencv(cl_mem buffer, size_t step, int rows, int cols, int type, cv::UMat& u)
{
cv::ocl::convertFromBuffer(buffer, step, rows, cols, type, u);
// process right half of frame in OpenCV
cv::Point pt(u.cols / 2, 0);
cv::Size sz(u.cols / 2, u.rows);
cv::Rect roi(pt, sz);
cv::UMat uroi(u, roi);
cv::blur(uroi, uroi, cv::Size(7, 7), cv::Point(-3, -3));
if (buffer)
clReleaseMemObject(buffer);
m_mem_obj = 0;
return 0;
}
// this function is an example of interoperability between OpenCL image
// and OpenCV UMat objects. It converts OpenCL image
// to OpenCV UMat and then do blur on these data
int App::process_cl_image_with_opencv(cl_mem image, cv::UMat& u)
{
cv::ocl::convertFromImage(image, u);
// process right half of frame in OpenCV
cv::Point pt(u.cols / 2, 0);
cv::Size sz(u.cols / 2, u.rows);
cv::Rect roi(pt, sz);
cv::UMat uroi(u, roi);
cv::blur(uroi, uroi, cv::Size(7, 7), cv::Point(-3, -3));
if (image)
clReleaseMemObject(image);
m_mem_obj = 0;
if (m_img_src)
clReleaseMemObject(m_img_src);
m_img_src = 0;
return 0;
}
int App::run()
{
if (0 != initOpenCL())
return -1;
if (0 != initVideoSource())
return -1;
Mat img_to_show;
// set running state until ESC pressed
setRunning(true);
// set process flag to show some data processing
// can be toggled on/off by 'p' button
setDoProcess(true);
// set use buffer flag,
// when it is set to true, will demo interop opencl buffer and cv::Umat,
// otherwise demo interop opencl image and cv::UMat
// can be switched on/of by SPACE button
setUseBuffer(true);
// Iterate over all frames
while (isRunning() && nextFrame(m_frame))
{
cv::cvtColor(m_frame, m_frameGray, COLOR_BGR2GRAY);
UMat uframe;
// work
timerStart();
if (doProcess())
{
process_frame_with_open_cl(m_frameGray, useBuffer(), &m_mem_obj);
if (useBuffer())
process_cl_buffer_with_opencv(
m_mem_obj, m_frameGray.step[0], m_frameGray.rows, m_frameGray.cols, m_frameGray.type(), uframe);
else
process_cl_image_with_opencv(m_mem_obj, uframe);
}
else
{
m_frameGray.copyTo(uframe);
}
timerEnd();
uframe.copyTo(img_to_show);
putText(img_to_show, "Version : " + m_platformInfo.Version(), Point(5, 30), FONT_HERSHEY_SIMPLEX, 1., Scalar(255, 100, 0), 2);
putText(img_to_show, "Name : " + m_platformInfo.Name(), Point(5, 60), FONT_HERSHEY_SIMPLEX, 1., Scalar(255, 100, 0), 2);
putText(img_to_show, "Device : " + m_deviceInfo.Name(), Point(5, 90), FONT_HERSHEY_SIMPLEX, 1., Scalar(255, 100, 0), 2);
cv::String memtype = useBuffer() ? "buffer" : "image";
putText(img_to_show, "interop with OpenCL " + memtype, Point(5, 120), FONT_HERSHEY_SIMPLEX, 1., Scalar(255, 100, 0), 2);
putText(img_to_show, "Time : " + timeStr() + " msec", Point(5, 150), FONT_HERSHEY_SIMPLEX, 1., Scalar(255, 100, 0), 2);
imshow("opencl_interop", img_to_show);
handleKey((char)waitKey(3));
}
return 0;
}
void App::handleKey(char key)
{
switch (key)
{
case 27:
setRunning(false);
break;
case ' ':
setUseBuffer(!useBuffer());
break;
case 'p':
case 'P':
setDoProcess( !doProcess() );
break;
default:
break;
}
}
inline void App::timerStart()
{
m_t0 = getTickCount();
}
inline void App::timerEnd()
{
m_t1 = getTickCount();
int64 delta = m_t1 - m_t0;
m_time = (delta / m_frequency) * 1000; // units msec
}
inline string App::timeStr() const
{
stringstream ss;
ss << std::fixed << std::setprecision(1) << m_time;
return ss.str();
}
int main(int argc, char** argv)
{
const char* keys =
"{ help h ? | | print help message }"
"{ camera c | -1 | use camera as input }"
"{ video v | | use video as input }";
CommandLineParser cmd(argc, argv, keys);
if (cmd.has("help"))
{
cmd.printMessage();
return EXIT_SUCCESS;
}
App app(cmd);
try
{
app.run();
}
catch (const cv::Exception& e)
{
cout << "error: " << e.what() << endl;
return 1;
}
catch (const std::exception& e)
{
cout << "error: " << e.what() << endl;
return 1;
}
catch (...)
{
cout << "unknown exception" << endl;
return 1;
}
return EXIT_SUCCESS;
} // main()
4.2.2 Makefile
TARGET = main
CXX = g++
CFLAGS += -I/usr/include -I/usr/local/include/opencv -I/usr/local/include/opencv2 -L/usr/lib -L/usr/local/lib -L/lib -std=c++98
CFLAGS += -lopencv_core -lopencv_objdetect -lopencv_highgui -lopencv_videoio -lopencv_imgcodecs -lopencv_imgproc -lOpenCL -lpthread -lrt
all:
@$(CXX) $(TARGET).cpp -o $(TARGET) $(CFLAGS)
clean:
rm -rf $(TARGET)
4.2.3 编译运行
root@NanoPC-T6:/opt/opencl-project/opencv-ocl# make
root@NanoPC-T6:/opt/opencl-project/opencv-ocl# ./main -c
注意：此处测试时，我们使用的OpenCV版本为3.4.14版本，安装步骤具体参考《Rockchip RK3588 - linux下Qt和opencv交叉编译环境搭建》。
如下图所示：
4.3 简单demo
由于官方给出的代码opencv-ocl，有很多和OpenCL平台相关的处理部分，看起的比较冗杂。因此这里新写一个新的基于Opencv 3.x +的OCL程序的Demo框架。
在/opt/opencl-project目录下新建opencv-ocl-demo项目。
4.3.1 main.cpp
#include <iostream>
#include <opencv2/opencv.hpp>
#include <opencv2/core/ocl.hpp>
using namespace std;
using namespace cv;
using namespace cv::ocl;
#define GPU 1
int main()
{
double t = 0.0;
// 视频捕获 这里我外接了一个摄像头，对应的设备为/dev/video1
VideoCapture vcap(1);
if (!vcap.isOpened()) {
cout << "Can not open video device" << std::endl;
return -1;
}
std::vector<cv::ocl::PlatformInfo> plats;
cv::ocl::getPlatfomsInfo(plats);
const cv::ocl::PlatformInfo *platform = &plats[0];
cout << "Platform Name:" << platform->name().c_str() << endl;
cv::ocl::Device dev;
platform->getDevice(dev,0);
cout << "Device name:" << dev.name().c_str() << endl;
#if GPU
cv::ocl::setUseOpenCL(true);
cout << "Use the OpenCL Deivice?" << cv::ocl::useOpenCL() << endl;
UMat frame, grayFrame, edges;
for(;;){
// 读取视频帧
vcap.read(frame);
t = (double)cv::getTickCount();
cv::cvtColor(frame,grayFrame,cv::COLOR_BGR2GRAY);
// 进行边缘检测
cv::Canny(grayFrame, edges, 50, 150);
t = ((double)cv::getTickCount() - t) / cv::getTickFrequency();
std::cout << "GPU Time Cost:" << t << "s" << std::endl;
putText(edges, "Platform : " + platform->name(), Point(5, 30), FONT_HERSHEY_SIMPLEX, 1., Scalar(255, 100, 0), 2);
putText(edges, "Device : " + dev.name(), Point(5, 60), FONT_HERSHEY_SIMPLEX, 1., Scalar(255, 100, 0), 2);
putText(edges, "Time : " + std::to_string(t) + " s", Point(5, 90), FONT_HERSHEY_SIMPLEX, 1., Scalar(255, 100, 0), 2);
// 显示边缘图像
cv::imshow("Edges", edges);
int key = waitKey(30);
if (key == 27 || key == 'q' || key == 'Q')
break;
}
#else
cv::ocl::setUseOpenCL(false);
cout << "Use the OpenCL Deivice?" << cv::ocl::useOpenCL() << endl;
Mat frame, grayFrame, edges;
for(;;){
// 读取视频帧
vcap.read(frame);
t = (double)cv::getTickCount();
cv::cvtColor(frame,grayFrame,cv::COLOR_RGB2GRAY);
// 进行边缘检测
cv::Canny(grayFrame, edges, 50, 150);
t = ((double)cv::getTickCount() - t) / cv::getTickFrequency();
std::cout << "CPU Time Cost:" << t << "s" << std::endl;
putText(edges, "Time : " + std::to_string(t) + " s", Point(5, 30), FONT_HERSHEY_SIMPLEX, 1., Scalar(255, 100, 0), 2);
// 显示边缘图像
cv::imshow("Edges", edges);
int key = waitKey(30);
if (key == 27 || key == 'q' || key == 'Q')
break;
}
#endif
// 关闭所有窗口
cv::destroyAllWindows();
return 0;
}
4.3.2 CMakeLists.txt
# cmake needs this line
cmake_minimum_required(VERSION 3.1)
# Define project name
project(opencv_example_project)
# Find OpenCV, you may need to set OpenCV_DIR variable
# to the absolute path to the directory containing OpenCVConfig.cmake file
# via the command line or GUI
find_package(OpenCV REQUIRED)
# If the package has been found, several variables will
# be set, you can find the full list with descriptions
# in the OpenCVConfig.cmake file.
# Print some message showing some of them
message(STATUS "OpenCV library status:")
message(STATUS " config: ${OpenCV_DIR}")
message(STATUS " version: ${OpenCV_VERSION}")
message(STATUS " libraries: ${OpenCV_LIBS}")
message(STATUS " include path: ${OpenCV_INCLUDE_DIRS}")
# Declare the executable target built from your sources
add_executable(opencv_example main.cpp)
# Link your application with OpenCV libraries
target_link_libraries(opencv_example PRIVATE ${OpenCV_LIBS})
4.3.3 GPU编译运行
此处我将USB摄像头连接到开发板的USB接口上。当宏GPU被设置为1时，编译运行：
root@NanoPC-T6:/opt/opencl-project/opencv-ocl-demo# cmake -S . -B build
-- OpenCV library status:
-- config: /usr/local/lib/cmake/opencv4
-- version: 4.9.0
-- libraries: opencv_calib3d;opencv_core;opencv_dnn;opencv_features2d;opencv_flann;opencv_gapi;opencv_highgui;opencv_imgcodecs;opencv_imgproc;opencv_ml;opencv_objdetect;opencv_photo;opencv_stitching;opencv_video;opencv_videoio
-- include path: /usr/local/include/opencv4
-- Configuring done (0.0s)
-- Generating done (0.0s)
-- Build files have been written to: /opt/opencl-project/opencv-ocl-demo/build
root@NanoPC-T6:/opt/opencl-project/opencv-ocl-demo# cmake --build build
root@NanoPC-T6:/opt/opencl-project/opencv-ocl-demo# ./build/opencv_example
[ WARN:0@1.474] global cap_gstreamer.cpp:1777 open OpenCV | GStreamer warning: Cannot query video position: status=0, value=-1, duration=-1
arm_release_ver: g13p0-01eac0, rk_so_ver: 10
Platform Name:ARM Platform
Device name:Mali-G610 r0p0
Use the OpenCL Deivice?1
GPU Time Cost:0.001845s
(opencv_example:2522): dbind-WARNING **: 22:51:47.821: AT-SPI: Error retrieving accessibility bus address: org.freedesktop.DBus.Error.ServiceUnknown: The name org.a11y.Bus was not provided by any .service files
GPU Time Cost:0.00103741s
GPU Time Cost:0.000427856s
GPU Time Cost:0.000888377s
GPU Time Cost:0.00085542s
GPU Time Cost:0.000405981s
GPU Time Cost:0.00041269s
GPU Time Cost:0.000391691s
GPU Time Cost:0.000828004s
GPU Time Cost:0.000351734s
GPU Time Cost:0.000803214s
GPU Time Cost:0.000826546s
GPU Time Cost:0.000896543s
GPU Time Cost:0.000805548s
GPU Time Cost:0.000365442s
GPU Time Cost:0.000339485s
......
并且显示图像：
注意：此处测试时，我们使用的OpenCV版本为4.9.0版本。
4.3.4 CPU编译运行
修改宏GPU为0时，编译运行：
root@NanoPC-T6:/opt/opencl-project/opencv-ocl-demo# cmake -S . -B build
-- OpenCV library status:
-- config: /usr/local/lib/cmake/opencv4
-- version: 4.9.0
-- libraries: opencv_calib3d;opencv_core;opencv_dnn;opencv_features2d;opencv_flann;opencv_gapi;opencv_highgui;opencv_imgcodecs;opencv_imgproc;opencv_ml;opencv_objdetect;opencv_photo;opencv_stitching;opencv_video;opencv_videoio
-- include path: /usr/local/include/opencv4
-- Configuring done (0.0s)
-- Generating done (0.0s)
-- Build files have been written to: /opt/opencl-project/opencv-ocl-demo/build
root@NanoPC-T6:/opt/opencl-project/opencv-ocl-demo# cmake --build build
root@NanoPC-T6:/opt/opencl-project/opencv-ocl-demo# ./build/opencv_example
[ WARN:0@1.451] global cap_gstreamer.cpp:1777 open OpenCV | GStreamer warning: Cannot query video position: status=0, value=-1, duration=-1
CPU Time Cost:0.0155965s
(opencv_example:2592): dbind-WARNING **: 22:57:10.363: AT-SPI: Error retrieving accessibility bus address: org.freedesktop.DBus.Error.ServiceUnknown: The name org.a11y.Bus was not provided by any .service files
CPU Time Cost:0.0025578s
CPU Time Cost:0.00177938s
CPU Time Cost:0.00147752s
CPU Time Cost:0.00146177s
CPU Time Cost:0.00188466s
CPU Time Cost:0.00146585s
CPU Time Cost:0.00181409s
CPU Time Cost:0.00186921s
CPU Time Cost:0.00135765s
CPU Time Cost:0.00174263s
CPU Time Cost:0.00191091s
CPU Time Cost:0.00195524s
CPU Time Cost:0.00137194s
CPU Time Cost:0.00190333s
CPU Time Cost:0.00188554s
......
并且显示图像：
根据运行的结果可以看出，CPU运行时长远远大于OpenCL平台加速后的运行时长，因此能够明显体现出加速的效果。
4.3.5 异常处理
如果程序运行出现如下错误：
[ WARN:0] OpenCV | GStreamer warning: Cannot query video position: status=0, value=-1, duration=-1
arm_release_ver: g13p0-01eac0, rk_so_ver: 10
Platform Name:ARM Platform
Device name:Mali-G610 r0p0
Use the OpenCL Deivice?1
OpenCL program build log: imgproc/color_rgb
Status -11: CL_BUILD_PROGRAM_FAILURE
-D depth=0 -D scn=3 -D PIX_PER_WI_Y=1 -D dcn=1 -D bidx=0 -D STRIPE_SIZE=1
<built-in>:131:9: error: expected member name or ';' after declaration specifiers
int32_t depth;
~~~~~~~ ^
<built-in>:1:15: note: expanded from here
#define depth 0
^
<built-in>:131:8: error: expected ';' at end of declaration list
int32_t depth;
^
error: Compiler frontend failed (error code 63)
该错误是是由于Conflict between OpenCV's local parameters and OpenCL's macro definitions #24645，大致意思就是OpenCL中定义的宏变量和OpenCV局部参数发生了冲突，这里我就不深究了。
OpenCV在4.9.0版本已经修复了这个问题，可以尝试下载该版本测试，记得首先移除之前安装的OpenCV 3.4.14；
root@NanoPC-T6:/opt/opencv-4.9.0/build# rm -rf /usr/local/lib/libopencv_*
root@NanoPC-T6:/opt/opencv-4.9.0/build# sudo cmake .. -DWITH_CAMV4L2=ON
root@NanoPC-T6:/opt/opencv-4.9.0/build# sudo make install -j7
root@NanoPC-T6:/opt/opencv-4.9.0/build# sudo /sbin/ldconfig -v
五、FloatVideo-TouchScreen优化
如果FloatVideo-TouchScreen项目需要使用OpenCL进行加速，首先我们需要将OpenCV版本从3.4.14升级到4.9.0，接着按照如下步骤进行调整。
5.1 float-video-touch-screen.pro
修改qmake配置文件：
QT += core gui
# 编译生成目标文件名称
TARGET = FloatVideo-TouchScreen
greaterThan(QT_MAJOR_VERSION, 4): QT += widgets
CONFIG += c++11
# You can make your code fail to compile if it uses deprecated APIs.
# In order to do so, uncomment the following line.
#DEFINES += QT_DISABLE_DEPRECATED_BEFORE=0x060000 # disables all the APIs deprecated before Qt 6.0.0
SOURCES += \
main.cpp \
mainwindow.cpp \
src/source/camerathread.cpp \
src/source/usbthread.cpp
HEADERS += \
mainwindow.h \
src/include/camerathread.h \
src/include/usbthread.h
FORMS += \
mainwindow.ui
TRANSLATIONS += \
float-video-touch-screen_zh_CN.ts
INCLUDEPATH += /usr/include/libusb-1.0 \
/usr/local/include/opencv \
/usr/local/include/opencv4 \ #opencv 4.9.0
#/usr/local/include/opencv2 \ #opencv 3.4.14
/usr/include/libusb-1.0 \
$$PWD/src/include \
$$PWD/lib/include \
/usr/include
LIBS += /usr/local/lib/libopencv_calib3d.so \
/usr/local/lib/libopencv_highgui.so \
/usr/local/lib/libopencv_core.so \
/usr/local/lib/libopencv_dnn.so \
/usr/local/lib/libopencv_features2d.so \
/usr/local/lib/libopencv_flann.so \
/usr/local/lib/libopencv_ml.so \
/usr/local/lib/libopencv_imgproc.so \
/usr/local/lib/libopencv_imgcodecs.so \
/usr/local/lib/libopencv_objdetect.so \
/usr/local/lib/libopencv_video.so \
/usr/local/lib/libopencv_videoio.so \
/usr/local/lib/libopencv_stitching.so \
/usr/lib/aarch64-linux-gnu/libusb-1.0.so \
/usr/lib/aarch64-linux-gnu/libudev.so \
$$PWD/lib/libScreen.so
# Default rules for deployment.
qnx: target.path = /tmp/$${TARGET}/bin
else: unix:!android: target.path = /opt/$${TARGET}/bin
!isEmpty(target.path): INSTALLS += target
DISTFILES += \
config/hid.txt \
tools/README/REMDME.md \
tools/README/gitee.md \
5.2 源码调整
此处需要对多处源码进行调整，这里就不一一展示代码了。
5.3 编译运行
可以通过如下命令进行编译，也可以通过Qt Creator工具进行编译；
root@NanoPC-T6:/opt/qt-project/FloatVideo-TouchScreen/lib# cd ..
root@NanoPC-T6:/opt/qt-project/FloatVideo-TouchScreen# mkdir build
root@NanoPC-T6:/opt/qt-project/FloatVideo-TouchScreen# cd build
root@NanoPC-T6:/opt/qt-project/FloatVideo-TouchScreen/build# qmake ..
root@NanoPC-T6:/opt/qt-project/FloatVideo-TouchScreen/build# ls -l
root@NanoPC-T6:/opt/qt-project/FloatVideo-TouchScreen/build# make
运行程序：
root@NanoPC-T6:/opt/qt-project/FloatVideo-TouchScreen/build# export DISPLAY=:0.0;./FloatVideo-TouchScreen -size 0.8
# 打开新的终端
root@NanoPC-T6:~# top
任务: 257 total, 1 running, 256 sleeping, 0 stopped, 0 zombie
%Cpu(s): 12.4 us, 0.8 sy, 0.0 ni, 86.9 id, 0.0 wa, 0.0 hi, 0.0 si, 0.0 st
MiB Mem : 15948.7 total, 14260.9 free, 643.7 used, 1044.1 buff/cache
MiB Swap: 0.0 total, 0.0 free, 0.0 used. 14980.9 avail Mem
进程号 USER PR NI VIRT RES SHR %CPU %MEM TIME+ COMMAND
7056 root 20 0 2490876 138316 102356 S 87.0 0.8 0:21.99 FloatVideo-Touc
831 root 20 0 3785368 263928 207016 S 14.6 1.6 0:54.98 Xorg
1217 pi 20 0 1862424 77360 57972 S 1.7 0.5 0:07.28 xfwm4
6888 root 0 -20 0 0 0 I 0.7 0.0 0:00.28 kworker/u17:1-mali_kbase_csf_sync_upd
6908 root 0 -20 0 0 0 D 0.7 0.0 0:00.46 kworker/u17:0+csf_scheduler_wq
5065 root 0 -20 0 0 0 I 0.3 0.0 0:00.07 kworker/7:2H-events_highpri
6901 root 20 0 0 0 0 I 0.3 0.0 0:00.11 kworker/u16:4-events_unbound
6930 root 20 0 0 0 0 D 0.3 0.0 0:00.48 kworker/4:1+events
7105 root 20 0 0 0 0 I 0.3 0.0 0:00.03 kworker/5:1-events
7106 root 20 0 12884 3656 2864 R 0.3 0.0 0:00.07 top
7109 root 0 -20 0 0 0 I 0.3 0.0 0:00.01 kworker/2:2H-events_highpri
......
可以看到CPU占用已经明显下降。此时查看GPU使用率：
root@NanoPC-T6:~# cat /sys/class/devfreq/fb000000.gpu/load
27@300000000Hz
六、代码下载
参考文章
[1] RK3588实战：调用npu加速，yolov5识别图像、ffmpeg发送到rtmp服务器
[2] 嵌入式AI应用开发实战指南—基于LubanCat-RK系列板卡
[3] RK3588边缘计算
[4] OpenCL学习笔记（四）手动编译开发库（ubuntu+gcc+rk3588）
[6] Arm Mali GPU OpenCL Developer Guide
[7] 什么是OpenCL
[8] 高性能计算
[9] OpenCL练习(一)：使用OpenCL+OpenCV进行RGB转灰度图
[10] https://opencv.org/opencl
[11] https://github.com/opencv/opencv/wiki/OpenCL-optimizations
[13] 一、Opencv-OCL编程基础
