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发布于 2022-10-04 16:46:47

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发布于 2022-10-04 16:46:47

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PDAF：Phase Detection Auto Focus

PDAF sensor的实现原理(SPC结构)：

PDAF通过比较L/R PD pixel构成的两幅图像，PD算法会计算出当前的相位差，根据相位差和模组的PD calibration data，估算出像距，从而移动lens快速对焦，PDAF快速对焦的搜索范围[infinity，macro]主要来自于烧录的OTP中的AF段，此距离并未实际与物体的物理距离即转换后的DAC值。PDAF OTP中主要烧录以上的SPC(shield pixel calibration)用于补偿遮光后的亮度增益，DCC(defocus conversion coefficient)主要是用于将相位差转换为Lens移动的距离，DCC中数值是通用过PD Diff 与DAC的关系拟合一条曲线的斜率(即PDAF线性度斜率)

PD pixel types主要有以下三类，Dual PD、Super PD、Shield PD。目前接触使用的都是Shield PD(像素点有一半会被遮挡)，信噪比SNR一般是越大越好(S/N S表示摄像机在假设无噪声时的图像信号值，N表示摄像机本身产生的噪声值)；Shield PD 信噪比SNR一般比较差，但是能支持大尺寸像素； Dual PD指每一个像素底部的感光区域一分为二，在同一个像素内即可获得相位差，也称2PD，全像素双核对焦，PD点覆盖率100%； Super PD相邻两个像素共用一个micor lens(微透镜用于提高感光度)得到相位差信息

CMOS传感器的结构：1.微透镜 2.色彩滤镜 3.感光片(光电二极管)4.高速传输电路 (Mono sensor没有色彩滤镜，黑白)

SensorType:不同的类型主要是针对PD pixel和PD value是由sensor还是ISP处理；接触较多的Type 2 PDAF_SUPPORT_CAMSV

Sensor Type	Character	Porting guide
Type1	PD pixel corrected by sensor PD value calculate by sensor	Type1 PDAF porting
Type2	PD pixel corrected by sensor PD pixel output to ISP via VC	Type2 PDAF porting
Type3	PD pixel corrected by ISP PD pixel extracted by ISP from raw image PD pixel extracted by PDAF algo on ISP3.0	Type3 PDAF porting
Dual PD	No need to correct PD pixel PD pixel extracted by ISP, under mode 1 PD pixel output to ISP under mode 3	DualPD PDAF porting

MTK不同SensorType的总体处理流程：

MTK PDAF Flow:

vendor/mediatek/proprietary/custom/mt6853/hal/pd_buf_mgr/src/pd_buf_mgr.cpp MTK vendor/mediatek/proprietary/custom/mt6853/hal/pd_buf_mgr/src/pd_buf_mgr_open.cpp 3rd party vendor/mediatek/proprietary/hardware/mtkcam/aaa/source/common/hal3a/v3.0/HAL3AFlowCtrl.cpp vendor/mediatek/proprietary/hardware/mtkcam/aaa/source/common/utils/pdtblgen/pdtblgen.cpp vendor/mediatek/proprietary/hardware/mtkcam/aaa/source/isp_6s/af_assist_mgr.cpp vendor/mediatek/proprietary/custom/mt6853/hal/pd_buf_mgr/src/pd_buf_mgr.cpp vendor/mediatek/proprietary/custom/mt6853/hal/pd_buf_mgr/src/pd_buf_mgr/xxx_mipi_raw/pd_xxx_mipiraw.cpp vendor/mediatek/proprietary/hardware/mtkcam/drv/src/mem/common/v2/cam_cal_drv.cpp vendor/mediatek/proprietary/hardware/mtkcam/drv/src/mem/common/v2/cam_cal_helper.cpp vendor/mediatek/proprietary/custom/common/hal/imgsensor_src/camera_calibration_cam_cal.cpp kernel-4.14/drivers/misc/mediatek/cam_cal/src/common/v2/eeprom_driver.c

static struct SET_PD_BLOCK_INFO_T imgsensor_pd_info_1920_1080 =
{ 

.i4OffsetX	= 16,   // x offset of PD area
.i4OffsetY	= 12,   // y offset of PD area
.i4PitchX	= 16,   // x pitch/width of a PD block
.i4PitchY	= 16,   // y pitch/height of a PD block
.i4PairNum	= 8,    // num of pairs L/R PD pixel within a PD block
.i4SubBlkW	= 8,    // x interval of 1 pair L/R PD pixel within a PD block
.i4SubBlkH	= 4,    // y interval of 1 pair L/R PD pixel within a PD block
.i4BlockNumX = 120, // PD block number in X direction
.i4BlockNumY = 67,  // PD block number in Y direction 
.iMirrorFlip = 0,
.i4PosR =	{ 

{ 
16,13}, { 
24,13}, { 
20,17}, { 
28,17},
{ 
16,21}, { 
24,21}, { 
20,25}, { 
28,25},
},
.i4PosL =	{ 

{ 
17,13}, { 
25,13}, { 
21,17}, { 
29,17},
{ 
17,21}, { 
25,21}, { 
21,25}, { 
29,25},
},
.i4Crop = { 
 { 
0, 0}, { 
0, 0}, { 
1040, 960}, { 
0, 0}, { 
0, 0}, { 
1040,960},{ 
0, 0}, { 
0, 0}, { 
0, 0}, { 
0, 0} },
};

(1) 前4个变量和L/R的坐标可直接从PD INI文档中获取 (2) i4PairNum指一个block中有几对L/R pixel (3) i4SubBlkW 和 i4SubBlkH 分别对应PD INI文档中的 PD_DENSITY_X/Y (4) i4BlockNumX 和 i4BlockNumY 分别对应PD INI文档中的 PD_BLOCK_NUM_X_Y (5) iMirrorFlip 指出图方向与模组厂 calibration出图方向的相对方向

pd_info都是从4000*3000 尺寸上操作的，原来值的计算方式是 i4BlockNumX = ( 4000 – 16 * 2 ) / 16 = 248 i4BlockNumY = ( 3000 – 12 * 2 ) / 16 = 186

由于1920*1080是crop后的，故i4BlockNumX 和 i4BlockNumY 是需要修改的，与otp中的是不一致的 i4BlockNumX = 1920 / 16 = 120 i4BlockNumY = 1080 / 16 = 67.5 = 67

i4Crop用于记录[Scenario][Crop] -> [x_crop][y_crop] RAW_OFFSET_X = 1040 RAW_OFFSET_Y = 960 1920*1080在驱动中用于配置SensorMode 6，应该前面一个{1040, 960}不用填，是SensorMode2 i4Crop = (4000 – 1040) / 2 (3000 – 960) / 2 = 1920 1080

vendor/mediatek/proprietary/custom/mt6853/hal/pd_buf_mgr/src/pd_buf_mgr/xxx_mipi_raw/pd_xxx_mipiraw.cpp

MBOOL PD_xxxMIPIRAW::IsSupport( SPDProfile_t &iPdProfile)
{ 

if (( iPdProfile.i4SensorMode == 5) && ((iPdProfile.uImgXsz == 1920) && (iPdProfile.uImgYsz == 1080)))
{ 

m_PDBufXSz = 240;
m_PDBufYSz = 536;
if(m_PDBuf)
{ 

delete m_PDBuf;
m_PDBuf = nullptr;
}
m_PDBufSz  = m_PDBufXSz*m_PDBufYSz;
m_PDBuf    = new uint16_t [m_PDBufSz];
ret = MTRUE;
AAA_LOGD("[1080P 60fps] is Support : i4SensorMode:%d w[%d] s[%d]\n", iPdProfile.i4SensorMode,iPdProfile.uImgXsz, iPdProfile.uImgYsz);
}
...
}

m_PDXSz代表每一行传送pixel num = PitchX / DensityX * BlockNumX = 16 / 8 * 120 = 240 m_PDYSz代表传送的行数line num = PitchY / DensityY * 2 * BlockNumY = 16 / 8 *2 *67 = 536

{ 
//AF_NVRAM
{ 
 // i4HybridAFCoefs1[64]
1, // [0] hybrid_default_param
37, // [1] tracking_width
38, // [2] tracking_height
3, // [3] max_pd_win_x
3, // [4] max_pd_win_y
...
{ 
//PD_NVRAM_T
{ 
//PD_CALIBRATION_DATA_T
{ 
0},
0,
},//PD_CALIBRATION_DATA_T
{ 
//PD_ALGO_TUNING_T
//--------------------------------------------------------------------------------/
// Section: PD Block Size
// Description: Determine PD block width and height
//
// i4FocusPDSizeX (width)
// i4FocusPDSizeY (height)
// range: [0] 32 to (raw_width/x_density), [0] 24 to (raw_height/y_density)
// default:
// S5K3P8: SizeX=32, SizeY=24 (density_x=16, density_y=16)
// OV13855: SizeX=32, SizeY=48 (density_x=16, density_y=8)
// IMX258: SizeX=64, SizeY=24 (density_x=8, density_y=16)
// IMX398: SizeX=64, SizeY=48 (density_x=8, y_density_y=8)
// S5K2L8: SizeX=240, SizeY=96 (density_x=2, y_density_y=4)
// constraints: must be a multiplier of 4
// effect: A large block takes longer computation time than a small block.
//--------------------------------------------------------------------------------/
28,  // i4FocusPDSizeX
32,  // i4FocusPDSizeY
//--------------------------------------------------------------------------------/

i4FocusPDSizeY = RAW_HIGHT * tracking_height / 100 / max_pd_win_y / PD_DENSITY_Y = 1080 * 38 / 100 / 3 / 4 = 34.2 = 32 i4FocusPDSizex = RAW_WIDTH * tracking_width / 100 / max_pd_win_x / PD_DENSITY_X = 1920 * 37 / 100 / 3 / 8 = 29.6 = 28

static struct SENSOR_WINSIZE_INFO_STRUCT imgsensor_winsize_info[7] = { 

{ 
8032, 6032, 0,	  12, 8032, 6008, 4016, 3004,   8, 2, 4000, 3000, 0, 0, 4000, 3000}, //preview(4000 x 3000)
{ 
8032, 6032, 0,	  12, 8032, 6008, 4016, 3004,   8, 2, 4000, 3000, 0, 0, 4000, 3000}, //capture(4000 x 3000)
{ 
8032, 6032, 0,	  12, 8032, 6008, 4016, 3004,   8, 2, 4000, 3000, 0, 0, 4000, 3000}, // VIDEO (4000 x 3000)
{ 
8032, 6032, 0, 1568, 8032, 2896, 2008,  724, 364, 2, 1280,  720, 0, 0, 1280,  720}, // hight speed video (1280 x 720)
{ 
8032, 6032, 0,	  12, 8032, 6008, 4016, 3004,   8, 2, 4000, 3000, 0, 0, 4000, 3000}, // slim video (1280 x 720)
{ 
8032, 6032, 2080,   1932, 3872, 2168, 1936, 1084, 8, 2, 1920, 1080,0,  0, 1920, 1080}, // custom1 (1920x 1080)
{ 
8032, 6032, 0,	  14, 8032, 6004, 8032, 6004,  16, 2, 8000, 6000, 0, 0, 8000, 6000}, //remosaic (8000 x 6000)
};

8032 6032 sensor内部有效像素 crop → binning → crop 如果有还要小的尺寸还需要crop 为了保持中心一致 0 12 上下都裁剪 crop 8032-（0 * 2） 6032 -（12 * 2）= 8032 6008 再binning 4016 3004 继续上下crop 4016-（8 * 2）3004（2*2）= 4000 * 3000 最终输出 tgsize

static struct SENSOR_VC_INFO_STRUCT SENSOR_VC_INFO[4]=
{ 

/* Preview mode setting */
{ 
0x02, //VC_Num
0x0a, //VC_PixelNum
0x00, //ModeSelect /* 0:auto 1:direct */
0x00, //EXPO_Ratio /* 1/1, 1/2, 1/4, 1/8 */
0x00, //0DValue /* 0D Value */
0x00, //RG_STATSMODE /* STATS divistion mode 0:16x16 1:8x8 2:4x4 3:1x1 */
0x00, 0x2B, 0x0FA0, 0x0BB8,	// VC0 image data
0x00, 0x00, 0x0000, 0x0000,	// VC1 MVHDR
0x01, 0x30, 0x026C, 0x05D0,   // VC2 PDAF
0x00, 0x00, 0x0000, 0x0000},	// VC3 
/* Capture mode setting */ 
/* Video mode setting */
/* Custom1 mode setting */
{ 
0x02, //VC_Num
0x0a, //VC_PixelNum
0x00, //ModeSelect /* 0:auto 1:direct */
0x00, //EXPO_Ratio /* 1/1, 1/2, 1/4, 1/8 */
0x00, //0DValue /* 0D Value */
0x00, //RG_STATSMODE /* STATS divistion mode 0:16x16 1:8x8 2:4x4 3:1x1 */
0x00, 0x2B, 0x0780, 0x0438,	// VC0 image data
0x00, 0x00, 0x0000, 0x0000,	// VC1 MVHDR
0x01, 0x30, 0x012C, 0x0218,   // VC2 PDAF
0x00, 0x00, 0x0000, 0x0000},	// VC3 
};};

Type 2会使用VC(Virtual channel control)，VC的主要作用就是将数据流的数据通过不同的通道分离给不同的流程。通过每一帧都是包含图像帧+PD 虚拟帧(将Bayer数据和PD数据按照MIPI协议打包) 图像帧(Image Data VC=0 DT=0x2B RAW10) 0x0780 = 1920 0x0438 = 1080 ； PD 虚拟帧(PDAF Data VC=1 DT=0x30 通过date type区分通道) 0x012C = 120 * 2 * 10 / 8 = 300 //.i4BlockNumX = 120, // PD block number in X direction 0x0218 = 67 * 4 * 2 = 536 //.i4BlockNumY = 67, // PD block number in Y direction

PDAF线性度测试： ISO<200，对着菱形图20cm 位置下，在Confidence > 60的场景下抓取log,在log里查找PD Value与AF DAC，看他们是否是呈线性关系

插入一下OTP相关的知识

LCS Lens Shade Correction gridx gridy 垂直和网格大小，周围亮度不均匀，处理不好可能经算法会有竖条纹，光晕等
BLC BlackLevel Correction 黑电平校正 (没有做黑电平校正的的图会更亮，影响图像的对比度)
sensor输出的电压越高，电流越大，而sensor电路本算存在暗电流，导致没有光线输入时也
有一定的输出电压，所以需要把这一部分去掉,所有像素都减去一个校准值,raw10对应的OB
(Optical Black)一般为64,需要注意init setting中的寄存器修改
AWB Auto White Balance    RGB转成Bayer模式的图一般由(R Gr Gb B四个通道的值) ,AWB一般比较关注R/G B/G的值，
Gr Gb一般值都比较接近，R/G 变大时，意味着R gain减小，整幅图R分量少了，就会呈现泛绿泛蓝,
烧录的数据主要是当前模组和Golden模组(一批生产中较为平均的做基准)的R Gr Gb B的数据

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原始发表：2022年9月7日上，如有侵权请联系 cloudcommunity@tencent.com 删除

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