G2D图像处理硬件调用和测试-基于米尔全志T113-i开发板
2024-04-09
722
来源：米尔电子
本篇测评由电子工程世界的优秀测评者“jf_99374259”提供。
本文将介绍基于米尔电子MYD-YT113i开发板的G2D图像处理硬件调用和测试。
MYC-YT113i核心板及开发板
真正的国产核心板，100%国产物料认证
国产T113-i处理器配备2*Cortex-A7@1.2GHz ，RISC-V
外置DDR3接口、支持视频编解码器、HiFi4 DSP
接口丰富：视频采集接口、显示器接口、USB2.0 接口、CAN 接口、千兆以太网接口
工业级：-40℃~+85℃、尺寸37mm*39mm
邮票孔+LGA，140+50PIN
全志 T113-i 2D图形加速硬件支持情况
Supports layer size up to 2048 x 2048 pixels
Supports pre-multiply alpha image data
Supports color key
Supports two pipes Porter-Duff alpha blending
Supports multiple video formats 4:2:0, 4:2:2, 4:1:1 and multiple pixel formats (8/16/24/32 bits graphics
layer)Supports memory scan order option
Supports any format convert function
Supports 1/16× to 32× resize ratio
Supports 32-phase 8-tap horizontal anti-alias filter and 32-phase 4-tap vertical anti-alias filter
Supports window clip
Supports FillRectangle, BitBlit, StretchBlit and MaskBlit
Supports horizontal and vertical flip, clockwise 0/90/180/270 degree rotate for normal buffer
Supports horizontal flip, clockwise 0/90/270 degree rotate for LBC buffer
可以看到 g2d 硬件支持相当多的2D图像处理，包括颜色空间转换，分辨率缩放，图层叠加，旋转等
开发环境配置
基于C语言实现的YUV转RGB
这里复用之前T113-i JPG解码的函数
void yuv420sp2rgb(const unsigned char* yuv420sp, int w, int h, unsigned char* rgb)
{
const unsigned char* yptr = yuv420sp;
const unsigned char* vuptr = yuv420sp + w * h;
for (int y = 0; y < h; y += 2)
{
const unsigned char* yptr0 = yptr;
const unsigned char* yptr1 = yptr + w;
unsigned char* rgb0 = rgb;
unsigned char* rgb1 = rgb + w * 3;
int remain = w;
#define SATURATE_CAST_UCHAR(X) (unsigned char)::std::min(::std::max((int)(X), 0), 255);
for (; remain > 0; remain -= 2)
{
// R = 1.164 * yy + 1.596 * vv
// G = 1.164 * yy - 0.813 * vv - 0.391 * uu
// B = 1.164 * yy + 2.018 * uu
// R = Y + (1.370705 * (V-128))
// G = Y - (0.698001 * (V-128)) - (0.337633 * (U-128))
// B = Y + (1.732446 * (U-128))
// R = ((Y << 6) + 87.72512 * (V-128)) >> 6
// G = ((Y << 6) - 44.672064 * (V-128) - 21.608512 * (U-128)) >> 6
// B = ((Y << 6) + 110.876544 * (U-128)) >> 6
// R = ((Y << 6) + 90 * (V-128)) >> 6
// G = ((Y << 6) - 46 * (V-128) - 22 * (U-128)) >> 6
// B = ((Y << 6) + 113 * (U-128)) >> 6
// R = (yy + 90 * vv) >> 6
// G = (yy - 46 * vv - 22 * uu) >> 6
// B = (yy + 113 * uu) >> 6
int v = vuptr[0] - 128;
int u = vuptr[1] - 128;
int ruv = 90 * v;
int guv = -46 * v + -22 * u;
int buv = 113 * u;
int y00 = yptr0[0] << 6;
rgb0[0] = SATURATE_CAST_UCHAR((y00 + ruv) >> 6);
rgb0[1] = SATURATE_CAST_UCHAR((y00 + guv) >> 6);
rgb0[2] = SATURATE_CAST_UCHAR((y00 + buv) >> 6);
int y01 = yptr0[1] << 6;
rgb0[3] = SATURATE_CAST_UCHAR((y01 + ruv) >> 6);
rgb0[4] = SATURATE_CAST_UCHAR((y01 + guv) >> 6);
rgb0[5] = SATURATE_CAST_UCHAR((y01 + buv) >> 6);
int y10 = yptr1[0] << 6;
rgb1[0] = SATURATE_CAST_UCHAR((y10 + ruv) >> 6);
rgb1[1] = SATURATE_CAST_UCHAR((y10 + guv) >> 6);
rgb1[2] = SATURATE_CAST_UCHAR((y10 + buv) >> 6);
int y11 = yptr1[1] << 6;
rgb1[3] = SATURATE_CAST_UCHAR((y11 + ruv) >> 6);
rgb1[4] = SATURATE_CAST_UCHAR((y11 + guv) >> 6);
rgb1[5] = SATURATE_CAST_UCHAR((y11 + buv) >> 6);
yptr0 += 2;
yptr1 += 2;
vuptr += 2;
rgb0 += 6;
rgb1 += 6;
}
#undef SATURATE_CAST_UCHAR
yptr += 2 * w;
rgb += 2 * 3 * w;
}
}
基于ARM neon指令集优化的YUV转RGB
考虑到armv7编译器的自动neon优化能力较差，这里针对性的编写 arm neon inline assembly 实现YUV2RGB内核部分，达到最优化的性能，榨干cpu性能
void yuv420sp2rgb_neon(const unsigned char* yuv420sp, int w, int h, unsigned char* rgb)
{
const unsigned char* yptr = yuv420sp;
const unsigned char* vuptr = yuv420sp + w * h;
#if __ARM_NEON
uint8x8_t _v128 = vdup_n_u8(128);
int8x8_t _v90 = vdup_n_s8(90);
int8x8_t _v46 = vdup_n_s8(46);
int8x8_t _v22 = vdup_n_s8(22);
int8x8_t _v113 = vdup_n_s8(113);
#endif // __ARM_NEON
for (int y = 0; y < h; y += 2)
{
const unsigned char* yptr0 = yptr;
const unsigned char* yptr1 = yptr + w;
unsigned char* rgb0 = rgb;
unsigned char* rgb1 = rgb + w * 3;
#if __ARM_NEON
int nn = w >> 3;
int remain = w - (nn << 3);
#else
int remain = w;
#endif // __ARM_NEON
#if __ARM_NEON
#if __aarch64__
for (; nn > 0; nn--)
{
int16x8_t _yy0 = vreinterpretq_s16_u16(vshll_n_u8(vld1_u8(yptr0), 6));
int16x8_t _yy1 = vreinterpretq_s16_u16(vshll_n_u8(vld1_u8(yptr1), 6));
int8x8_t _vvuu = vreinterpret_s8_u8(vsub_u8(vld1_u8(vuptr), _v128));
int8x8x2_t _vvvvuuuu = vtrn_s8(_vvuu, _vvuu);
int8x8_t _vv = _vvvvuuuu.val[0];
int8x8_t _uu = _vvvvuuuu.val[1];
int16x8_t _r0 = vmlal_s8(_yy0, _vv, _v90);
int16x8_t _g0 = vmlsl_s8(_yy0, _vv, _v46);
_g0 = vmlsl_s8(_g0, _uu, _v22);
int16x8_t _b0 = vmlal_s8(_yy0, _uu, _v113);
int16x8_t _r1 = vmlal_s8(_yy1, _vv, _v90);
int16x8_t _g1 = vmlsl_s8(_yy1, _vv, _v46);
_g1 = vmlsl_s8(_g1, _uu, _v22);
int16x8_t _b1 = vmlal_s8(_yy1, _uu, _v113);
uint8x8x3_t _rgb0;
_rgb0.val[0] = vqshrun_n_s16(_r0, 6);
_rgb0.val[1] = vqshrun_n_s16(_g0, 6);
_rgb0.val[2] = vqshrun_n_s16(_b0, 6);
uint8x8x3_t _rgb1;
_rgb1.val[0] = vqshrun_n_s16(_r1, 6);
_rgb1.val[1] = vqshrun_n_s16(_g1, 6);
_rgb1.val[2] = vqshrun_n_s16(_b1, 6);
vst3_u8(rgb0, _rgb0);
vst3_u8(rgb1, _rgb1);
yptr0 += 8;
yptr1 += 8;
vuptr += 8;
rgb0 += 24;
rgb1 += 24;
}
#else
if (nn > 0)
{
asm volatile(
"0: n"
"pld [%3, #128] n"
"vld1.u8 {d2}, [%3]! n"
"vsub.s8 d2, d2, %12 n"
"pld [%1, #128] n"
"vld1.u8 {d0}, [%1]! n"
"pld [%2, #128] n"
"vld1.u8 {d1}, [%2]! n"
"vshll.u8 q2, d0, #6 n"
"vorr d3, d2, d2 n"
"vshll.u8 q3, d1, #6 n"
"vorr q9, q2, q2 n"
"vtrn.s8 d2, d3 n"
"vorr q11, q3, q3 n"
"vmlsl.s8 q9, d2, %14 n"
"vorr q8, q2, q2 n"
"vmlsl.s8 q11, d2, %14 n"
"vorr q10, q3, q3 n"
"vmlal.s8 q8, d2, %13 n"
"vmlal.s8 q2, d3, %16 n"
"vmlal.s8 q10, d2, %13 n"
"vmlsl.s8 q9, d3, %15 n"
"vmlal.s8 q3, d3, %16 n"
"vmlsl.s8 q11, d3, %15 n"
"vqshrun.s16 d24, q8, #6 n"
"vqshrun.s16 d26, q2, #6 n"
"vqshrun.s16 d4, q10, #6 n"
"vqshrun.s16 d25, q9, #6 n"
"vqshrun.s16 d6, q3, #6 n"
"vqshrun.s16 d5, q11, #6 n"
"subs %0, #1 n"
"vst3.u8 {d24-d26}, [%4]! n"
"vst3.u8 {d4-d6}, [%5]! n"
"bne 0b n"
: "=r"(nn), // %0
"=r"(yptr0), // %1
"=r"(yptr1), // %2
"=r"(vuptr), // %3
"=r"(rgb0), // %4
"=r"(rgb1) // %5
: "0"(nn),
"1"(yptr0),
"2"(yptr1),
"3"(vuptr),
"4"(rgb0),
"5"(rgb1),
"w"(_v128), // %12
"w"(_v90), // %13
"w"(_v46), // %14
"w"(_v22), // %15
"w"(_v113) // %16
: "cc", "memory", "q0", "q1", "q2", "q3", "q8", "q9", "q10", "q11", "q12", "d26");
}
#endif // __aarch64__
#endif // __ARM_NEON
#define SATURATE_CAST_UCHAR(X) (unsigned char)::std::min(::std::max((int)(X), 0), 255);
for (; remain > 0; remain -= 2)
{
// R = 1.164 * yy + 1.596 * vv
// G = 1.164 * yy - 0.813 * vv - 0.391 * uu
// B = 1.164 * yy + 2.018 * uu
// R = Y + (1.370705 * (V-128))
// G = Y - (0.698001 * (V-128)) - (0.337633 * (U-128))
// B = Y + (1.732446 * (U-128))
// R = ((Y << 6) + 87.72512 * (V-128)) >> 6
// G = ((Y << 6) - 44.672064 * (V-128) - 21.608512 * (U-128)) >> 6
// B = ((Y << 6) + 110.876544 * (U-128)) >> 6
// R = ((Y << 6) + 90 * (V-128)) >> 6
// G = ((Y << 6) - 46 * (V-128) - 22 * (U-128)) >> 6
// B = ((Y << 6) + 113 * (U-128)) >> 6
// R = (yy + 90 * vv) >> 6
// G = (yy - 46 * vv - 22 * uu) >> 6
// B = (yy + 113 * uu) >> 6
int v = vuptr[0] - 128;
int u = vuptr[1] - 128;
int ruv = 90 * v;
int guv = -46 * v + -22 * u;
int buv = 113 * u;
int y00 = yptr0[0] << 6;
rgb0[0] = SATURATE_CAST_UCHAR((y00 + ruv) >> 6);
rgb0[1] = SATURATE_CAST_UCHAR((y00 + guv) >> 6);
rgb0[2] = SATURATE_CAST_UCHAR((y00 + buv) >> 6);
int y01 = yptr0[1] << 6;
rgb0[3] = SATURATE_CAST_UCHAR((y01 + ruv) >> 6);
rgb0[4] = SATURATE_CAST_UCHAR((y01 + guv) >> 6);
rgb0[5] = SATURATE_CAST_UCHAR((y01 + buv) >> 6);
int y10 = yptr1[0] << 6;
rgb1[0] = SATURATE_CAST_UCHAR((y10 + ruv) >> 6);
rgb1[1] = SATURATE_CAST_UCHAR((y10 + guv) >> 6);
rgb1[2] = SATURATE_CAST_UCHAR((y10 + buv) >> 6);
int y11 = yptr1[1] << 6;
rgb1[3] = SATURATE_CAST_UCHAR((y11 + ruv) >> 6);
rgb1[4] = SATURATE_CAST_UCHAR((y11 + guv) >> 6);
rgb1[5] = SATURATE_CAST_UCHAR((y11 + buv) >> 6);
yptr0 += 2;
yptr1 += 2;
vuptr += 2;
rgb0 += 6;
rgb1 += 6;
}
#undef SATURATE_CAST_UCHAR
yptr += 2 * w;
rgb += 2 * 3 * w;
}
}
基于G2D图形硬件的YUV转RGB
我们先实现 dmaion buffer 管理器，参考
这里贴的代码省略了异常错误处理的逻辑，有个坑是 linux-4.9 和 linux-5.4 用法不一样，米尔电子的这个T113-i系统是linux-5.4，所以不兼容4.9内核的ioctl用法习惯
struct ion_memory
{
size_t size;
int fd;
void* virt_addr;
unsigned int phy_addr;
};
class ion_allocator
{
public:
ion_allocator();
~ion_allocator();
int open();
void close();
int alloc(size_t size, struct ion_memory* mem);
int free(struct ion_memory* mem);
int flush(struct ion_memory* mem);
public:
int ion_fd;
int cedar_fd;
};
ion_allocator::ion_allocator()
{
ion_fd = -1;
cedar_fd = -1;
}
ion_allocator::~ion_allocator()
{
close();
}
int ion_allocator::open()
{
close();
ion_fd = ::open("/dev/ion", O_RDWR);
cedar_fd = ::open("/dev/cedar_dev", O_RDONLY);
ioctl(cedar_fd, IOCTL_ENGINE_REQ, 0);
return 0;
}
void ion_allocator::close()
{
if (cedar_fd != -1)
{
ioctl(cedar_fd, IOCTL_ENGINE_REL, 0);
::close(cedar_fd);
cedar_fd = -1;
}
if (ion_fd != -1)
{
::close(ion_fd);
ion_fd = -1;
}
}
int ion_allocator::alloc(size_t size, struct ion_memory* mem)
{
struct aw_ion_new_alloc_data alloc_data;
alloc_data.len = size;
alloc_data.heap_id_mask = AW_ION_SYSTEM_HEAP_MASK;
alloc_data.flags = AW_ION_CACHED_FLAG | AW_ION_CACHED_NEEDS_SYNC_FLAG;
alloc_data.fd = 0;
alloc_data.unused = 0;
ioctl(ion_fd, AW_ION_IOC_NEW_ALLOC, &alloc_data);
void* virt_addr = mmap(NULL, size, PROT_READ|PROT_WRITE, MAP_SHARED, alloc_data.fd, 0);
struct aw_user_iommu_param iommu_param;
iommu_param.fd = alloc_data.fd;
iommu_param.iommu_addr = 0;
ioctl(cedar_fd, IOCTL_GET_IOMMU_ADDR, &iommu_param);
mem->size = size;
mem->fd = alloc_data.fd;
mem->virt_addr = virt_addr;
mem->phy_addr = iommu_param.iommu_addr;
return 0;
}
int ion_allocator::free(struct ion_memory* mem)
{
if (mem->fd == -1)
return 0;
struct aw_user_iommu_param iommu_param;
iommu_param.fd = mem->fd;
ioctl(cedar_fd, IOCTL_FREE_IOMMU_ADDR, &iommu_param);
munmap(mem->virt_addr, mem->size);
::close(mem->fd);
mem->size = 0;
mem->fd = -1;
mem->virt_addr = 0;
mem->phy_addr = 0;
return 0;
}
int ion_allocator::flush(struct ion_memory* mem)
{
struct dma_buf_sync sync;
sync.flags = DMA_BUF_SYNC_END | DMA_BUF_SYNC_RW;
ioctl(mem->fd, DMA_BUF_IOCTL_SYNC, &sync);
return 0;
}
然后再实现 G2D图形硬件 YUV转RGB 的转换器
提前分配好YUV和RGB的dmaion buffer
将YUV数据拷贝到dmaion buffer，flush cache完成同步
配置转换参数，ioctl调用G2D_CMD_BITBLT_H完成转换
flush cache完成同步，从dmaion buffer拷贝出RGB数据
释放dmaion buffer
// 步骤1
ion_allocator ion;
ion.open();
struct ion_memory yuv_ion;
ion.alloc(rgb_size, &rgb_ion);
struct ion_memory rgb_ion;
ion.alloc(yuv_size, &yuv_ion);
int g2d_fd = ::open("/dev/g2d", O_RDWR);
// 步骤2
memcpy((unsigned char*)yuv_ion.virt_addr, yuv420sp, yuv_size);
ion.flush(&yuv_ion);
// 步骤3
g2d_blt_h blit;
memset(&blit, 0, sizeof(blit));
blit.flag_h = G2D_BLT_NONE_H;
blit.src_image_h.format = G2D_FORMAT_YUV420UVC_V1U1V0U0;
blit.src_image_h.width = width;
blit.src_image_h.height = height;
blit.src_image_h.align[0] = 0;
blit.src_image_h.align[1] = 0;
blit.src_image_h.clip_rect.x = 0;
blit.src_image_h.clip_rect.y = 0;
blit.src_image_h.clip_rect.w = width;
blit.src_image_h.clip_rect.h = height;
blit.src_image_h.gamut = G2D_BT601;
blit.src_image_h.bpremul = 0;
blit.src_image_h.mode = G2D_PIXEL_ALPHA;
blit.src_image_h.use_phy_addr = 0;
blit.src_image_h.fd = yuv_ion.fd;
blit.dst_image_h.format = G2D_FORMAT_RGB888;
blit.dst_image_h.width = width;
blit.dst_image_h.height = height;
blit.dst_image_h.align[0] = 0;
blit.dst_image_h.clip_rect.x = 0;
blit.dst_image_h.clip_rect.y = 0;
blit.dst_image_h.clip_rect.w = width;
blit.dst_image_h.clip_rect.h = height;
blit.dst_image_h.gamut = G2D_BT601;
blit.dst_image_h.bpremul = 0;
blit.dst_image_h.mode = G2D_PIXEL_ALPHA;
blit.dst_image_h.use_phy_addr = 0;
blit.dst_image_h.fd = rgb_ion.fd;
ioctl(g2d_fd, G2D_CMD_BITBLT_H, &blit);
// 步骤4
ion.flush(&rgb_ion);
memcpy(rgb, (const unsigned char*)rgb_ion.virt_addr, rgb_size);
// 步骤5
ion.free(&rgb_ion);
ion.free(&yuv_ion);
ion.close();
::close(g2d_fd);
G2D图像硬件YUV转RGB测试
考虑到dmaion buffer分配和释放都比较耗时，我们提前做好，循环调用步骤3的G2D转换，统计耗时，并在top工具中查看CPU占用率
sh-4.4# LD_LIBRARY_PATH=. ./g2dtest INFO : cedarc <CedarPluginVDInit:84>: register mjpeg decoder success! this device is not whitelisted for jpeg decoder cvi this device is not whitelisted for jpeg decoder cvi this device is not whitelisted for jpeg decoder cvi this device is not whitelisted for jpeg encoder rkmpp INFO : cedarc <log_set_level:43>: Set log level to 5 from /vendor/etc/cedarc.conf ERROR : cedarc <DebugCheckConfig:316>: now cedarc log level:5 ERROR : cedarc <VideoEncCreate:241>: now cedarc log level:5 yuv420sp2rgb 46.61 yuv420sp2rgb 42.04 yuv420sp2rgb 41.32 yuv420sp2rgb 42.06 yuv420sp2rgb 41.69 yuv420sp2rgb 42.05 yuv420sp2rgb 41.29 yuv420sp2rgb 41.30 yuv420sp2rgb 42.14 yuv420sp2rgb 41.33 yuv420sp2rgb_neon 10.57 yuv420sp2rgb_neon 7.21 yuv420sp2rgb_neon 6.77 yuv420sp2rgb_neon 8.31 yuv420sp2rgb_neon 7.60 yuv420sp2rgb_neon 6.80 yuv420sp2rgb_neon 6.77 yuv420sp2rgb_neon 7.01 yuv420sp2rgb_neon 7.11 yuv420sp2rgb_neon 7.06 yuv420sp2rgb_g2d 4.32 yuv420sp2rgb_g2d 4.69 yuv420sp2rgb_g2d 4.56 yuv420sp2rgb_g2d 4.57 yuv420sp2rgb_g2d 4.52 yuv420sp2rgb_g2d 4.54 yuv420sp2rgb_g2d 4.52 yuv420sp2rgb_g2d 4.58 yuv420sp2rgb_g2d 4.60 yuv420sp2rgb_g2d 4.67
可以看到 ARM neon 的优化效果非常明显，而使用G2D图形硬件能获得进一步加速，并且能显著降低CPU占用率！
| 耗时(ms) | CPU占用率(%) | |
|---|---|---|
| C | 41.30 | 50 |
| neon | 6.77 | 50 |
| g2d | 4.32 | 12 |
转换结果对比和分析
C和neon的转换结果完全一致，但是g2d转换后的图片有明显的色差
G2D图形硬件只支持 G2D_BT601，G2D_BT709，G2D_BT2020 3种YUV系数，而JPG所使用的YUV系数是改版BT601，因此产生了色差
从g2d内核驱动中也可以得知，暂时没有方法为g2d设置自定义的YUV系数，g2d不适合用于JPG的编解码，但依然适合摄像头和视频编解码的颜色空间转换
2025-01-09
国产FPGA SoC芯选择，米尔安路飞龙重磅发布
在边缘智能、物联网、5G通信和自动驾驶等技术的快速发展下，FPGA市场需求呈现爆发式增长。国产FPGA也在这场技术浪潮中崭露头角，吸引了广大行业人士的关注。 今天，米尔电子基于安路科技最新一代国产工业级FPGA FPSoC——发布MYC-YM90XSOM模组及评估套件。该产品采用安路飞龙DR1M90，95K LEs可编程逻辑，片上集成64位2*Cortex-A35 @1GHz处理器，适用于复杂的实
2025-01-09
瑞芯微第二代8nm高性能AIOT平台,看这款板卡怎么样？
文章来源公众号：电子开发学习瑞芯微近期推出了第二代8nm高性能AIOT平台——RK3576。RK3576应用方向指向工业控制及网关，云终端，人脸识别设备，车载中控，商显等等。参数方面，内置了四核Cortex-A72+四核Cortex-A53，频率最高2.2GHz，内置ARM G52 MC3 GPU，NPU算力高达6TOPS……参数看着非常犀利，而且据说主打的就是性价比。我们近期也拿到了米尔电子推出
2024-12-26
RK3588和RK3576怎么选？
在中国半导体产业的版图中，瑞芯微作为国内SoC芯片领跑者，凭借其在处理器芯片设计领域的深厚积累和持续创新，推出很多智能应用处理器芯片，在嵌入式系统领域得到大规模的应用。RK3588和RK3576系列作为都是瑞芯微(Rockchip)高性能处理器代表，性能如何？价格如何？作为硬件产品开发的我们，这两款产品到底有什么区别呢，米尔与你一起探索。CPU性能强劲，应用场景丰富CPU 性能：RK3588采用的
2024-12-26
ST×米尔STM32MP25x线下培训会倒计时12天
STM32MP257D是ST推出的搭载了双核Cortex-A35 @1.5 GHz和Cortex-M33 @400 MHz的微处理器。米尔电子基于STM32MP257D推出了MYD-LD25X开发板，开发板配备丰富的扩展接口。为了帮助开发者更好地理解与应用STM32MP257D处理器和MYD-LD25X开发板，米尔将与ST在2025年1月7日和2025年1月10日分别于南京、北京联合举办线下培训会
2024-12-26
有奖丨米尔 全志T536开发板免费试用
米尔与全志合作发布的新品基于全志T536应用处理器的MYD-LT536-GK开发板免费试用活动来啦~~米尔提供了3块价值750元的MYD-LT536-GK开发板发起试用活动您不仅可以免费体验还可以获得京东购物卡赶快点击链接报名吧~↓↓↓评测活动报名链接：电子发烧友论坛：https://bbs.elecfans.com/try_T536.html试用活动信息报名时间：2024年12月26日-202
2024-12-20
正式发售，赋能电力和工业市场，米尔全志高性能工业级T536核心板
自发布以来，这款由米尔首发的真工业级核心板-米尔基于全志T536核心板就获得了广大关注，现正式开售：核心板278元起、开发板750元起。米尔基于米尔全志T536核心板，配备四核Cortex-A55，拥有17路串口和4路CAN口，其强劲的处理能力、丰富的接口、低功耗设计以及出色的稳定性，能够轻松应对电力与工业市场中复杂多变的应用场景，专为工控而生。MYC-LT536系列核心板采用LGA封装，存储配置
2024-12-18
人脸疲劳检测应用-米尔基于RK3576核心板/开发板
本篇源自：优秀创作者 lulugl本文将介绍基于米尔电子MYD-LR3576开发板（米尔基于瑞芯微 RK3576开发板）的人脸疲劳检测方案测试。米尔基于RK3576核心板/开发板【前言】人脸疲劳检测：一种通过分析人脸特征来判断一个人是否处于疲劳状态的技术。其原理主要基于计算机视觉和机器学习方法。当人疲劳时，面部会出现一些特征变化，如眼睛闭合程度增加、眨眼频率变慢、打哈欠、头部姿态改变等。例如，通过
2024-12-13
RK3576来了值得买吗？看看他怎么说
本文由芯板坊提供米尔的开发板我已经说过好多款式了，他们最近又发布了基于RK3576的开发板，下面我们来看看它的具体情况。这次米尔依然是核心板加扩展板的模式，我拿到手的开发板，核心板已经通过LGA贴片，焊好了。我们在选购的时候，可以选择是直接买核心板，还是买全套核心板加扩展板的开发板。米尔官方给这个开发板的定位是赋能工业AI智能化这个就得益于瑞芯微RK3576SoC了。它采用4个2.2GHz频率的C
2024-12-13
如何用OpenCV进行手势识别--基于米尔全志T527开发板
本文将介绍基于米尔电子MYD-LT527开发板（米尔基于全志T527开发板）的OpenCV手势识别方案测试。摘自优秀创作者-小火苗米尔基于全志T527开发板一、软件环境安装1.安装OpenCVsudoapt-getinstalllibopencv-devpython3-opencv2.安装pipsudoapt-getinstallpython3-pip二、OpenCV手势识别步骤1.图像获取：从摄
2024-12-13
瑞萨MCU/MPU盛会，米尔发表演讲-嵌入式处理器模组加速工业产品开发
瑞萨电子在深圳（11月30日）和上海（12月6日）的2024 MCU/MPU工业技术研讨会圆满结束。作为瑞萨电子IDH生态合作伙伴-米尔电子亮相此次研讨会，并发表题为“嵌入式处理器模组加速工业产品开发”的演讲，还展出基于RZ/G2L、RZ/G2UL、RZ/T2H的核心板开发板、技术方案等。会议现场人头攒动，热情高涨，吸引众多行业内嵌入式工程师前来探讨和交流，为嵌入式工程师获得产品设计灵感和实用方案