kbuild: Move -Wenum-enum-conversion to W=2

[linux.git] / Documentation / admin-guide / media / 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::
    :header-rows: 1

    * - 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 controller APIs, the ov5670 sensor is configured to send
frames in packed raw Bayer format to IPU3 CSI2 receiver.

.. code-block:: none

    # 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
    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

.. code-block:: none

    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

.. code-block:: none

    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::
    :header-rows: 1

    * - 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`.

Running mode and firmware binary selection
------------------------------------------

ImgU works based on firmware, currently the ImgU firmware support run 2 pipes
in time-sharing with single input frame data. Each pipe can run at certain mode
- "VIDEO" or "STILL", "VIDEO" mode is commonly used for video frames capture,
and "STILL" is used for still frame capture. However, you can also select
"VIDEO" to capture still frames if you want to capture images with less system
load and power. For "STILL" mode, ImgU will try to use smaller BDS factor and
output larger bayer frame for further YUV processing than "VIDEO" mode to get
high quality images. Besides, "STILL" mode need XNR3 to do noise reduction,
hence "STILL" mode will need more power and memory bandwidth than "VIDEO" mode.
TNR will be enabled in "VIDEO" mode and bypassed by "STILL" mode. ImgU is
running at "VIDEO" mode by default, the user can use v4l2 control
V4L2_CID_INTEL_IPU3_MODE (currently defined in
drivers/staging/media/ipu3/include/uapi/intel-ipu3.h) to query and set the
running mode. For user, there is no difference for buffer queueing between the
"VIDEO" and "STILL" mode, mandatory input and main output node should be
enabled and buffers need be queued, the statistics and the view-finder queues
are optional.

The firmware binary will be selected according to current running mode, such log
"using binary if_to_osys_striped " or "using binary if_to_osys_primary_striped"
could be observed if you enable the ImgU dynamic debug, the binary
if_to_osys_striped is selected for "VIDEO" and the binary
"if_to_osys_primary_striped" is selected for "STILL".


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.

.. code-block:: none

    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.

.. code-block:: none

    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 perform correction of distortions
and image filtering. It needs some extra filter and envelope 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.

.. code-block:: none

    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

You can also use yavta [#f2]_ command to do same thing as above:

.. code-block:: none

    yavta --data-prefix -Bcapture-mplane -c10 -n5 -I -s2592x1944 \
          --file=frame-#.out-f NV12 /dev/video5 & \
    yavta --data-prefix -Bcapture-mplane -c10 -n5 -I -s2592x1944 \
          --file=frame-#.vf -f NV12 /dev/video6 & \
    yavta --data-prefix -Bmeta-capture -c10 -n5 -I \
          --file=frame-#.3a /dev/video7 & \
    yavta --data-prefix -Boutput-mplane -c10 -n5 -I -s2592x1944 \
          --file=/tmp/frame-in.cio2 -f IPU3_SGRBG10 /dev/video4

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
~~~~~~~~~~~~~~~~~~

.. code-block:: none

    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
~~~~~~~~~~~~~~~~~~~~~~~~

.. code-block:: none

    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", style=filled, fillcolor=yellow, shape=cylinder]
       s [label="YUV Downscaling"]
       t [label="DDR", style=filled, fillcolor=yellow, shape=cylinder]

       { rank=same; a -> b -> c -> d -> e -> f -> g -> h -> i }
       { rank=same; j -> k -> l -> m -> n -> o -> p -> q -> s -> t}

       a -> j [style=invis, weight=10]
       i -> j
       q -> r
   }

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 struct ipu3_uapi_obgrid_param.
Linearization            This algo block uses linearization parameters to
                         address non-linearity sensor effects. The Lookup table
                         table is defined in
                         struct 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 struct ipu3_uapi_shd_config_static.
BNR                      Bayer noise reduction block removes image noise by
                         applying a bilateral filter.
                         See struct 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
                         struct 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
                         struct 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
                         struct ipu3_uapi_ccm_mat_config.
Gamma correction         Gamma correction struct 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
                         struct 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 struct 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 struct ipu3_uapi_yuvp1_chnr_config.
TCC                      Total color correction as defined in struct
                         struct 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,
                         struct ipu3_uapi_isp_xnr3_params for ISP data memory
                         and struct 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. struct ipu3_uapi_isp_tnr3_vmem_params and
                         struct 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
struct ipu3_uapi_acc_param, can be chosen to be applied by the user
space through struct struct ipu3_uapi_flags embedded in
struct 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
==========

.. [#f1] https://github.com/intel/nvt

.. [#f2] http://git.ideasonboard.org/yavta.git

.. [#f4] ImgU limitation requires an additional 16x16 for all input resolutions