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linux/Documentation/admin-guide/media/ipu3.rst
Bingbu Cao ce7d96a4a7 media: doc-rst: add yavta test example in ipu3 docs 
This patch add yavta test command in ipu3.rst as an example on how to
run simple ImgU test using yavta.

Signed-off-by: Bingbu Cao <bingbu.cao@intel.com>
Signed-off-by: Sakari Ailus <sakari.ailus@linux.intel.com>
Signed-off-by: Mauro Carvalho Chehab <mchehab+huawei@kernel.org>
2020-04-14 12:20:37 +02:00

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