ACPI: EC: Rework flushing of pending work

[linux/fpc-iii.git] / Documentation / media / v4l-drivers / ipu3.rst

.. 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

RAW Bayer frames go through the following ImgU pipeline HW blocks to have the
processed image output to the DDR memory.

RAW Bayer frame -> Input Feeder -> Bayer Down Scaling (BDS) -> Geometric
Distortion Correction (GDC) -> DDR

The ImgU V4L2 subdev has to be configured with the supported resolutions in all
the above HW blocks, for a given input resolution.

For a given supported resolution for an input frame, the Input Feeder, Bayer
Down Scaling and GDC blocks should be configured with the supported resolutions.
This 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