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SPDX-License-Identifier: GPL-2.0 - -.. include:: <isonum.txt> - -=============================================================== -Intel Image Processing Unit 3 (IPU3) Imaging Unit (ImgU) driver -=============================================================== - -Copyright |copy| 2018 Intel Corporation - -Introduction -============ - -This file documents the Intel IPU3 (3rd generation Image Processing Unit) -Imaging Unit drivers located under drivers/media/pci/intel/ipu3 (CIO2) as well -as under drivers/staging/media/ipu3 (ImgU). - -The Intel IPU3 found in certain Kaby Lake (as well as certain Sky Lake) -platforms (U/Y processor lines) is made up of two parts namely the Imaging Unit -(ImgU) and the CIO2 device (MIPI CSI2 receiver). - -The CIO2 device receives the raw Bayer data from the sensors and outputs the -frames in a format that is specific to the IPU3 (for consumption by the IPU3 -ImgU). The CIO2 driver is available as drivers/media/pci/intel/ipu3/ipu3-cio2* -and is enabled through the CONFIG_VIDEO_IPU3_CIO2 config option. - -The Imaging Unit (ImgU) is responsible for processing images captured -by the IPU3 CIO2 device. The ImgU driver sources can be found under -drivers/staging/media/ipu3 directory. The driver is enabled through the -CONFIG_VIDEO_IPU3_IMGU config option. - -The two driver modules are named ipu3_csi2 and ipu3_imgu, respectively. - -The drivers has been tested on Kaby Lake platforms (U/Y processor lines). - -Both of the drivers implement V4L2, Media Controller and V4L2 sub-device -interfaces. The IPU3 CIO2 driver supports camera sensors connected to the CIO2 -MIPI CSI-2 interfaces through V4L2 sub-device sensor drivers. - -CIO2 -==== - -The CIO2 is represented as a single V4L2 subdev, which provides a V4L2 subdev -interface to the user space. There is a video node for each CSI-2 receiver, -with a single media controller interface for the entire device. - -The CIO2 contains four independent capture channel, each with its own MIPI CSI-2 -receiver and DMA engine. Each channel is modelled as a V4L2 sub-device exposed -to userspace as a V4L2 sub-device node and has two pads: - -.. tabularcolumns:: |p{0.8cm}|p{4.0cm}|p{4.0cm}| - -.. flat-table:: - - * - pad - - direction - - purpose - - * - 0 - - sink - - MIPI CSI-2 input, connected to the sensor subdev - - * - 1 - - source - - Raw video capture, connected to the V4L2 video interface - -The V4L2 video interfaces model the DMA engines. They are exposed to userspace -as V4L2 video device nodes. - -Capturing frames in raw Bayer format ------------------------------------- - -CIO2 MIPI CSI2 receiver is used to capture frames (in packed raw Bayer format) -from the raw sensors connected to the CSI2 ports. The captured frames are used -as input to the ImgU driver. - -Image processing using IPU3 ImgU requires tools such as raw2pnm [#f1]_, and -yavta [#f2]_ due to the following unique requirements and / or features specific -to IPU3. - --- The IPU3 CSI2 receiver outputs the captured frames from the sensor in packed -raw Bayer format that is specific to IPU3. - --- Multiple video nodes have to be operated simultaneously. - -Let us take the example of ov5670 sensor connected to CSI2 port 0, for a -2592x1944 image capture. - -Using the media contorller APIs, the ov5670 sensor is configured to send -frames in packed raw Bayer format to IPU3 CSI2 receiver. - -# This example assumes /dev/media0 as the CIO2 media device - -export MDEV=/dev/media0 - -# and that ov5670 sensor is connected to i2c bus 10 with address 0x36 - -export SDEV=$(media-ctl -d $MDEV -e "ov5670 10-0036") - -# Establish the link for the media devices using media-ctl [#f3]_ -media-ctl -d $MDEV -l "ov5670:0 -> ipu3-csi2 0:0[1]" - -# Set the format for the media devices -media-ctl -d $MDEV -V "ov5670:0 [fmt:SGRBG10/2592x1944]" - -media-ctl -d $MDEV -V "ipu3-csi2 0:0 [fmt:SGRBG10/2592x1944]" - -media-ctl -d $MDEV -V "ipu3-csi2 0:1 [fmt:SGRBG10/2592x1944]" - -Once the media pipeline is configured, desired sensor specific settings -(such as exposure and gain settings) can be set, using the yavta tool. - -e.g - -yavta -w 0x009e0903 444 $SDEV - -yavta -w 0x009e0913 1024 $SDEV - -yavta -w 0x009e0911 2046 $SDEV - -Once the desired sensor settings are set, frame captures can be done as below. - -e.g - -yavta --data-prefix -u -c10 -n5 -I -s2592x1944 --file=/tmp/frame-#.bin \ - -f IPU3_SGRBG10 $(media-ctl -d $MDEV -e "ipu3-cio2 0") - -With the above command, 10 frames are captured at 2592x1944 resolution, with -sGRBG10 format and output as IPU3_SGRBG10 format. - -The captured frames are available as /tmp/frame-#.bin files. - -ImgU -==== - -The ImgU is represented as two V4L2 subdevs, each of which provides a V4L2 -subdev interface to the user space. - -Each V4L2 subdev represents a pipe, which can support a maximum of 2 streams. -This helps to support advanced camera features like Continuous View Finder (CVF) -and Snapshot During Video(SDV). - -The ImgU contains two independent pipes, each modelled as a V4L2 sub-device -exposed to userspace as a V4L2 sub-device node. - -Each pipe has two sink pads and three source pads for the following purpose: - -.. tabularcolumns:: |p{0.8cm}|p{4.0cm}|p{4.0cm}| - -.. flat-table:: - - * - pad - - direction - - purpose - - * - 0 - - sink - - Input raw video stream - - * - 1 - - sink - - Processing parameters - - * - 2 - - source - - Output processed video stream - - * - 3 - - source - - Output viewfinder video stream - - * - 4 - - source - - 3A statistics - -Each pad is connected to a corresponding V4L2 video interface, exposed to -userspace as a V4L2 video device node. - -Device operation ----------------- - -With ImgU, once the input video node ("ipu3-imgu 0/1":0, in -<entity>:<pad-number> format) is queued with buffer (in packed raw Bayer -format), ImgU starts processing the buffer and produces the video output in YUV -format and statistics output on respective output nodes. The driver is expected -to have buffers ready for all of parameter, output and statistics nodes, when -input video node is queued with buffer. - -At a minimum, all of input, main output, 3A statistics and viewfinder -video nodes should be enabled for IPU3 to start image processing. - -Each ImgU V4L2 subdev has the following set of video nodes. - -input, output and viewfinder video nodes ----------------------------------------- - -The frames (in packed raw Bayer format specific to the IPU3) received by the -input video node is processed by the IPU3 Imaging Unit and are output to 2 video -nodes, with each targeting a different purpose (main output and viewfinder -output). - -Details onand the Bayer format specific to the IPU3 can be found in -:ref:`v4l2-pix-fmt-ipu3-sbggr10`. - -The driver supports V4L2 Video Capture Interface as defined at :ref:`devices`. - -Only the multi-planar API is supported. More details can be found at -:ref:`planar-apis`. - -Parameters video node ---------------------- - -The parameters video node receives the ImgU algorithm parameters that are used -to configure how the ImgU algorithms process the image. - -Details on processing parameters specific to the IPU3 can be found in -:ref:`v4l2-meta-fmt-params`. - -3A statistics video node ------------------------- - -3A statistics video node is used by the ImgU driver to output the 3A (auto -focus, auto exposure and auto white balance) statistics for the frames that are -being processed by the ImgU to user space applications. User space applications -can use this statistics data to compute the desired algorithm parameters for -the ImgU. - -Configuring the Intel IPU3 -========================== - -The IPU3 ImgU pipelines can be configured using the Media Controller, defined at -:ref:`media_controller`. - -Firmware binary selection -------------------------- - -The firmware binary is selected using the V4L2_CID_INTEL_IPU3_MODE, currently -defined in drivers/staging/media/ipu3/include/intel-ipu3.h . "VIDEO" and "STILL" -modes are available. - -Processing the image in raw Bayer format ----------------------------------------- - -Configuring ImgU V4L2 subdev for image processing -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -The ImgU V4L2 subdevs have to be configured with media controller APIs to have -all the video nodes setup correctly. - -Let us take "ipu3-imgu 0" subdev as an example. - -media-ctl -d $MDEV -r - -media-ctl -d $MDEV -l "ipu3-imgu 0 input":0 -> "ipu3-imgu 0":0[1] - -media-ctl -d $MDEV -l "ipu3-imgu 0":2 -> "ipu3-imgu 0 output":0[1] - -media-ctl -d $MDEV -l "ipu3-imgu 0":3 -> "ipu3-imgu 0 viewfinder":0[1] - -media-ctl -d $MDEV -l "ipu3-imgu 0":4 -> "ipu3-imgu 0 3a stat":0[1] - -Also the pipe mode of the corresponding V4L2 subdev should be set as desired -(e.g 0 for video mode or 1 for still mode) through the control id 0x009819a1 as -below. - -yavta -w "0x009819A1 1" /dev/v4l-subdev7 - -Certain hardware blocks in ImgU pipeline can change the frame resolution by -cropping or scaling, these hardware blocks include Input Feeder(IF), Bayer Down -Scaler (BDS) and Geometric Distortion Correction (GDC). -There is also a block which can change the frame resolution - YUV Scaler, it is -only applicable to the secondary output. - -RAW Bayer frames go through these ImgU pipeline hardware blocks and the final -processed image output to the DDR memory. - -.. kernel-figure:: ipu3_rcb.svg - :alt: ipu3 resolution blocks image - - IPU3 resolution change hardware blocks - -**Input Feeder** - -Input Feeder gets the Bayer frame data from the sensor, it can enable cropping -of lines and columns from the frame and then store pixels into device's internal -pixel buffer which are ready to readout by following blocks. - -**Bayer Down Scaler** - -Bayer Down Scaler is capable of performing image scaling in Bayer domain, the -downscale factor can be configured from 1X to 1/4X in each axis with -configuration steps of 0.03125 (1/32). - -**Geometric Distortion Correction** - -Geometric Distortion Correction is used to performe correction of distortions -and image filtering. It needs some extra filter and envelop padding pixels to -work, so the input resolution of GDC should be larger than the output -resolution. - -**YUV Scaler** - -YUV Scaler which similar with BDS, but it is mainly do image down scaling in -YUV domain, it can support up to 1/12X down scaling, but it can not be applied -to the main output. - -The ImgU V4L2 subdev has to be configured with the supported resolutions in all -the above hardware blocks, for a given input resolution. -For a given supported resolution for an input frame, the Input Feeder, Bayer -Down Scaler and GDC blocks should be configured with the supported resolutions -as each hardware block has its own alignment requirement. - -You must configure the output resolution of the hardware blocks smartly to meet -the hardware requirement along with keeping the maximum field of view. The -intermediate resolutions can be generated by specific tool - - -https://github.com/intel/intel-ipu3-pipecfg - -This tool can be used to generate intermediate resolutions. More information can -be obtained by looking at the following IPU3 ImgU configuration table. - -https://chromium.googlesource.com/chromiumos/overlays/board-overlays/+/master - -Under baseboard-poppy/media-libs/cros-camera-hal-configs-poppy/files/gcss -directory, graph_settings_ov5670.xml can be used as an example. - -The following steps prepare the ImgU pipeline for the image processing. - -1. The ImgU V4L2 subdev data format should be set by using the -VIDIOC_SUBDEV_S_FMT on pad 0, using the GDC width and height obtained above. - -2. The ImgU V4L2 subdev cropping should be set by using the -VIDIOC_SUBDEV_S_SELECTION on pad 0, with V4L2_SEL_TGT_CROP as the target, -using the input feeder height and width. - -3. The ImgU V4L2 subdev composing should be set by using the -VIDIOC_SUBDEV_S_SELECTION on pad 0, with V4L2_SEL_TGT_COMPOSE as the target, -using the BDS height and width. - -For the ov5670 example, for an input frame with a resolution of 2592x1944 -(which is input to the ImgU subdev pad 0), the corresponding resolutions -for input feeder, BDS and GDC are 2592x1944, 2592x1944 and 2560x1920 -respectively. - -Once this is done, the received raw Bayer frames can be input to the ImgU -V4L2 subdev as below, using the open source application v4l2n [#f1]_. - -For an image captured with 2592x1944 [#f4]_ resolution, with desired output -resolution as 2560x1920 and viewfinder resolution as 2560x1920, the following -v4l2n command can be used. This helps process the raw Bayer frames and produces -the desired results for the main output image and the viewfinder output, in NV12 -format. - -v4l2n --pipe=4 --load=/tmp/frame-#.bin --open=/dev/video4 ---fmt=type:VIDEO_OUTPUT_MPLANE,width=2592,height=1944,pixelformat=0X47337069 ---reqbufs=type:VIDEO_OUTPUT_MPLANE,count:1 --pipe=1 --output=/tmp/frames.out ---open=/dev/video5 ---fmt=type:VIDEO_CAPTURE_MPLANE,width=2560,height=1920,pixelformat=NV12 ---reqbufs=type:VIDEO_CAPTURE_MPLANE,count:1 --pipe=2 --output=/tmp/frames.vf ---open=/dev/video6 ---fmt=type:VIDEO_CAPTURE_MPLANE,width=2560,height=1920,pixelformat=NV12 ---reqbufs=type:VIDEO_CAPTURE_MPLANE,count:1 --pipe=3 --open=/dev/video7 ---output=/tmp/frames.3A --fmt=type:META_CAPTURE,? ---reqbufs=count:1,type:META_CAPTURE --pipe=1,2,3,4 --stream=5 - -where /dev/video4, /dev/video5, /dev/video6 and /dev/video7 devices point to -input, output, viewfinder and 3A statistics video nodes respectively. - -Converting the raw Bayer image into YUV domain ----------------------------------------------- - -The processed images after the above step, can be converted to YUV domain -as below. - -Main output frames -~~~~~~~~~~~~~~~~~~ - -raw2pnm -x2560 -y1920 -fNV12 /tmp/frames.out /tmp/frames.out.ppm - -where 2560x1920 is output resolution, NV12 is the video format, followed -by input frame and output PNM file. - -Viewfinder output frames -~~~~~~~~~~~~~~~~~~~~~~~~ - -raw2pnm -x2560 -y1920 -fNV12 /tmp/frames.vf /tmp/frames.vf.ppm - -where 2560x1920 is output resolution, NV12 is the video format, followed -by input frame and output PNM file. - -Example user space code for IPU3 -================================ - -User space code that configures and uses IPU3 is available here. - -https://chromium.googlesource.com/chromiumos/platform/arc-camera/+/master/ - -The source can be located under hal/intel directory. - -Overview of IPU3 pipeline -========================= - -IPU3 pipeline has a number of image processing stages, each of which takes a -set of parameters as input. The major stages of pipelines are shown here: - -.. kernel-render:: DOT - :alt: IPU3 ImgU Pipeline - :caption: IPU3 ImgU Pipeline Diagram - - digraph "IPU3 ImgU" { - node [shape=box] - splines="ortho" - rankdir="LR" - - a [label="Raw pixels"] - b [label="Bayer Downscaling"] - c [label="Optical Black Correction"] - d [label="Linearization"] - e [label="Lens Shading Correction"] - f [label="White Balance / Exposure / Focus Apply"] - g [label="Bayer Noise Reduction"] - h [label="ANR"] - i [label="Demosaicing"] - j [label="Color Correction Matrix"] - k [label="Gamma correction"] - l [label="Color Space Conversion"] - m [label="Chroma Down Scaling"] - n [label="Chromatic Noise Reduction"] - o [label="Total Color Correction"] - p [label="XNR3"] - q [label="TNR"] - r [label="DDR"] - - { rank=same; a -> b -> c -> d -> e -> f } - { rank=same; g -> h -> i -> j -> k -> l } - { rank=same; m -> n -> o -> p -> q -> r } - - a -> g -> m [style=invis, weight=10] - - f -> g - l -> m - } - -The table below presents a description of the above algorithms. - -======================== ======================================================= -Name Description -======================== ======================================================= -Optical Black Correction Optical Black Correction block subtracts a pre-defined - value from the respective pixel values to obtain better - image quality. - Defined in :c:type:`ipu3_uapi_obgrid_param`. -Linearization This algo block uses linearization parameters to - address non-linearity sensor effects. The Lookup table - table is defined in - :c:type:`ipu3_uapi_isp_lin_vmem_params`. -SHD Lens shading correction is used to correct spatial - non-uniformity of the pixel response due to optical - lens shading. This is done by applying a different gain - for each pixel. The gain, black level etc are - configured in :c:type:`ipu3_uapi_shd_config_static`. -BNR Bayer noise reduction block removes image noise by - applying a bilateral filter. - See :c:type:`ipu3_uapi_bnr_static_config` for details. -ANR Advanced Noise Reduction is a block based algorithm - that performs noise reduction in the Bayer domain. The - convolution matrix etc can be found in - :c:type:`ipu3_uapi_anr_config`. -DM Demosaicing converts raw sensor data in Bayer format - into RGB (Red, Green, Blue) presentation. Then add - outputs of estimation of Y channel for following stream - processing by Firmware. The struct is defined as - :c:type:`ipu3_uapi_dm_config`. -Color Correction Color Correction algo transforms sensor specific color - space to the standard "sRGB" color space. This is done - by applying 3x3 matrix defined in - :c:type:`ipu3_uapi_ccm_mat_config`. -Gamma correction Gamma correction :c:type:`ipu3_uapi_gamma_config` is a - basic non-linear tone mapping correction that is - applied per pixel for each pixel component. -CSC Color space conversion transforms each pixel from the - RGB primary presentation to YUV (Y: brightness, - UV: Luminance) presentation. This is done by applying - a 3x3 matrix defined in - :c:type:`ipu3_uapi_csc_mat_config` -CDS Chroma down sampling - After the CSC is performed, the Chroma Down Sampling - is applied for a UV plane down sampling by a factor - of 2 in each direction for YUV 4:2:0 using a 4x2 - configurable filter :c:type:`ipu3_uapi_cds_params`. -CHNR Chroma noise reduction - This block processes only the chrominance pixels and - performs noise reduction by cleaning the high - frequency noise. - See struct :c:type:`ipu3_uapi_yuvp1_chnr_config`. -TCC Total color correction as defined in struct - :c:type:`ipu3_uapi_yuvp2_tcc_static_config`. -XNR3 eXtreme Noise Reduction V3 is the third revision of - noise reduction algorithm used to improve image - quality. This removes the low frequency noise in the - captured image. Two related structs are being defined, - :c:type:`ipu3_uapi_isp_xnr3_params` for ISP data memory - and :c:type:`ipu3_uapi_isp_xnr3_vmem_params` for vector - memory. -TNR Temporal Noise Reduction block compares successive - frames in time to remove anomalies / noise in pixel - values. :c:type:`ipu3_uapi_isp_tnr3_vmem_params` and - :c:type:`ipu3_uapi_isp_tnr3_params` are defined for ISP - vector and data memory respectively. -======================== ======================================================= - -Other often encountered acronyms not listed in above table: - - ACC - Accelerator cluster - AWB_FR - Auto white balance filter response statistics - BDS - Bayer downscaler parameters - CCM - Color correction matrix coefficients - IEFd - Image enhancement filter directed - Obgrid - Optical black level compensation - OSYS - Output system configuration - ROI - Region of interest - YDS - Y down sampling - YTM - Y-tone mapping - -A few stages of the pipeline will be executed by firmware running on the ISP -processor, while many others will use a set of fixed hardware blocks also -called accelerator cluster (ACC) to crunch pixel data and produce statistics. - -ACC parameters of individual algorithms, as defined by -:c:type:`ipu3_uapi_acc_param`, can be chosen to be applied by the user -space through struct :c:type:`ipu3_uapi_flags` embedded in -:c:type:`ipu3_uapi_params` structure. For parameters that are configured as -not enabled by the user space, the corresponding structs are ignored by the -driver, in which case the existing configuration of the algorithm will be -preserved. - -References -========== - -.. [#f5] drivers/staging/media/ipu3/include/intel-ipu3.h - -.. [#f1] https://github.com/intel/nvt - -.. [#f2] http://git.ideasonboard.org/yavta.git - -.. [#f3] http://git.ideasonboard.org/?p=media-ctl.git;a=summary - -.. [#f4] ImgU limitation requires an additional 16x16 for all input resolutions |