Texas Instruments AM6x Kupanga Maupangiri Ogwiritsa Ntchito Makamera Angapo

Kuyang'anira Chitetezo: Enhances surveillance coverage, object tracking, and recognition accuracy.
Kuzungulira View: Enables stereo vision for tasks like obstacle detection and object manipulation.

Cabin Recorder ndi Camera Mirror System: Amapereka kufalikira kwakutali ndikuchotsa mawanga akhungu.
Kujambula Zachipatala: Offers enhanced precision in surgical navigation and endoscopy.

Drones ndi Kujambula Kwamlengalenga: Capture high-resolution images from different angles for various applications.

Connecting Multiple CSI-2 Cameras to the SoC:
To connect multiple CSI-2 cameras to the SoC, follow the guidelines provided in the user manual. Ensure proper alignment and connection of each camera to the designated ports on the SoC.

Chidziwitso cha Ntchito
Kupanga Mapulogalamu Amakamera Angapo pa AM6x

Jianzhong Xu, Qutaiba Saleh

ABSTRACT
This report describes application development using multiple CSI-2 cameras on the AM6x family of devices. A reference design of object detection with deep learning on 4 cameras on the AM62A SoC is presented with performance analysis. General principles of the design apply to other SoCs with a CSI-2 interface, such as AM62x and AM62P.

Mawu Oyamba

Makamera ophatikizika amagwira ntchito yofunika kwambiri pamawonekedwe amakono. Kugwiritsa ntchito makamera angapo pamakina kumakulitsa luso la machitidwewa ndikupangitsa kuthekera komwe sikungatheke ndi kamera imodzi. M'munsimu muli ena akaleamples of applications using multiple embedded cameras:

Kuyang'anira Chitetezo: Makamera angapo oyikidwa mwanzeru amapereka kuwunika kokwanira. Iwo amathandiza panoramic views, reduce blind spots, and enhance the accuracy of object tracking and recognition, improving overall security measures.
Kuzungulira View: Multiple cameras are used to create a stereo vision setup, enabling three-dimensional information and the estimation of depth. This is crucial for tasks such as obstacle detection in autonomous vehicles, precise object manipulation in robotics, and enhanced realism of augmented reality experiences.

Cabin Recorder and Camera Mirror System: A car cabin recorder with multiple cameras can provide more coverage using a single processor. Similarly, a camera mirror system with two or more cameras can expand the driver’s field of view ndi kuchotsa madontho akhungu kumbali zonse za galimoto.
Medical Imaging: Multiple cameras can be used in medical imaging for tasks like surgical navigation, providing surgeons with multiple perspectives for enhanced precision. In endoscopy, multiple cameras enable a thorough examination of internal organs.

Drones and Aerial Imaging: Drones often come equipped with multiple cameras to capture high-resolution images or videos from different angles. This is useful in applications like aerial photography, agriculture monitoring, and land surveying.
With the advancement of microprocessors, multiple cameras can be integrated into a single System-on-Chip.
(SoC) to provide compact and efficient solutions. The AM62Ax SoC, with high-performance video/vision processing and deep learning acceleration, is an ideal device for the above-mentioned use cases. Another AM6x device, the AM62P, is built for high-performance embedded 3D display applications. Equipped with 3D graphics acceleration, the AM62P can easily stitch together the images from multiple cameras and produce a high-resolution panoramic view. Zinthu zatsopano za AM62A/AM62P SoC zaperekedwa m'mabuku osiyanasiyana, monga [4], [5], [6], ndi zina zotero. Cholemba ichi sichidzabwereza mafotokozedwe azinthuzo koma m'malo mwake chimayang'ana kuphatikiza makamera angapo a CSI-2 muzojambula zojambulidwa pa AM62A/AM62P.

Table 1-1 ikuwonetsa kusiyana kwakukulu pakati pa AM62A ndi AM62P pokhudzana ndi kukonza zithunzi.

Gulu 1-1. Kusiyana Pakati pa AM62A ndi AM62P mu Kukonza Zithunzi

SoC	AM62A	Chithunzi cha AM62P
Mtundu wa Kamera Wothandizira	With or without a built-in ISP	Ndi Built-in ISP
Kamera Zotulutsa Data	Yaiwisi/YUV/RGB	YUV/RGB
ISP HWA	Inde	Ayi
Kuphunzira Mwakuya HWA	Inde	Ayi
Zithunzi za 3-D HWA	Ayi	Inde

Kulumikiza Makamera Angapo CSI-2 ku SoC
Kamera ya Camera pa AM6x SoC ili ndi zigawo zotsatirazi, monga zikuwonetsera Chithunzi 2-1:

MIPI D-PHY Receiver: receives video streams from external cameras, supporting up to 1.5 Gbps per data lane for 4 lanes.

CSI-2 Receiver (RX): receives video streams from the D-PHY receiver and either directly sends the streams to the ISP or dumps the data to DDR memory. This module supports up to 16 virtual channels.
SHIM: a DMA wrapper that enables sending the captured streams to memory over DMA. Multiple DMA contexts can be created by this wrapper, with each context corresponding to a virtual channel of the CSI-2 Receiver.

Multiple cameras can be supported on the AM6x through the use of virtual channels of CSI-2 RX, even though there is only one CSI-2 RX interface on the SoC. An external CSI-2 aggregating component is needed to combine multiple camera streams and send them to a single SoC. Two types of CSI-2 aggregating solutions can be used, described in the following sections.

CSI-2 Aggregator Using SerDes
Njira imodzi yophatikizira makanema angapo amakamera ndikugwiritsa ntchito njira ya serializing ndi deserializing (SerDes). Deta ya CSI-2 kuchokera ku kamera iliyonse imasinthidwa ndi serializer ndikusamutsidwa kudzera pa chingwe. The deserializer imalandira deta yonse yosakanizidwa kuchokera ku zingwe (chingwe chimodzi pa kamera), imatembenuza mitsinje kubwerera ku data ya CSI-2, kenako imatumiza mtsinje wa CSI-2 wosakanikirana ku mawonekedwe amodzi a CSI-2 RX pa SoC. Kamera iliyonse yamakamera imadziwika ndi njira yapadera. Yankho lophatikizikali limapereka phindu lowonjezera lololeza kulumikizana kwakutali mpaka 15m kuchokera pamakamera kupita ku SoC.

FPD-Link kapena V3-Link serializers ndi deserializers (SerDes), yothandizidwa mu AM6x Linux SDK, ndi matekinoloje otchuka kwambiri amtundu uwu wa CSI-2 aggregating solution. Ma FPD-Link ndi V3-Link deserializers ali ndi mayendedwe kumbuyo omwe angagwiritsidwe ntchito kutumiza ma siginecha olumikizirana kuti alumikizitse makamera onse, monga tafotokozera mu [7].
Chithunzi 2-2 chikuwonetsa zakaleample la kugwiritsa ntchito ma SerDes kulumikiza makamera angapo ku AM6x SoC imodzi.

Wakaleample of this aggregating solution can be found in the Arducam V3Link Camera Solution Kit. This kit has a deserializer hub which aggregates 4 CSI-2 camera streams, as well as 4 pairs of V3link serializers and IMX219 cameras, including FAKRA coaxial cables and 22-pin FPC cables. The reference design discussed later is built on this kit.

CSI-2 Aggregator without Using SerDes
Mtundu uwu wa aggregator ukhoza kugwirizanitsa mwachindunji ndi makamera angapo a MIPI CSI-2 ndikusonkhanitsa deta kuchokera ku makamera onse kupita kumtsinje umodzi wa CSI-2.

Chithunzi 2-3 chikuwonetsa zakaleample of such a system. This type of aggregating solution does not use any serializer/deserializer but is limited by the maximum distance of CSI-2 data transfer, which is up to 30cm. The AM6x Linux SDK does not support this type of CSI-2 aggregator

Kuyambitsa Makamera Angapo mu Mapulogalamu

Camera Subsystem Software Architecture
Chithunzi 3-1 chikuwonetsa chojambula chapamwamba kwambiri cha pulogalamu yojambula kamera mu AM62A/AM62P Linux SDK, yogwirizana ndi dongosolo la HW pa Chithunzi 2-2.

Zomangamanga zamapulogalamuwa zimathandizira SoC kuti ilandire makamera angapo pogwiritsa ntchito SerDes, monga zikuwonekera pa Chithunzi 2-2. FPD-Link/V3-Link SerDes imapereka adilesi yapadera ya I2C ndi njira yeniyeni ku kamera iliyonse. Chophimba chamtengo wapadera chamtengo chiyenera kupangidwa ndi adilesi yapadera ya I2C pa kamera iliyonse. Dalaivala wa CSI-2 RX amazindikira kamera iliyonse pogwiritsa ntchito nambala yapadera ya tchanelo ndikupanga mawonekedwe a DMA pamakina a kamera. Kanema wamavidiyo amapangidwa pamtundu uliwonse wa DMA. Deta kuchokera ku kamera iliyonse imalandiridwa ndikusungidwa pogwiritsa ntchito DMA mpaka kukumbukira moyenerera. Mapulogalamu ogwiritsira ntchito malo amagwiritsa ntchito mavidiyo omwe amafanana ndi kamera iliyonse kuti apeze deta ya kamera. Eksamples of using this software architecture are given in Chapter 4 – Reference Design.
Dalaivala aliyense wa sensor yemwe amagwirizana ndi V4L2 chimango amatha kulumikiza ndikusewera pamamangidwe awa. Onani [8] momwe mungaphatikizire dalaivala watsopano wa sensor mu Linux SDK.

Image Pipeline Software Architecture

The AM6x Linux SDK provides the GStreamer (GST) framework, which can be used in the ser space to integrate the image processing components for various applications. The Hardware Accelerators (HWA) on the SoC, such as the Vision Pre-processing Accelerator (VPAC) or ISP, video encoder/decoder, and deep learning compute engine, are accessed through GST plugins. VPAC (ISP) yokha ili ndi midadada ingapo, kuphatikiza Vision Imaging Sub-System (VISS), Lens Distortion Correction (LDC), ndi Multiscalar (MSC), iliyonse yogwirizana ndi GST plugin.

Figure 3-2 shows the block diagram of a typical image pipeline from the camera to encoding or deep
learning applications on AM62A. For more details about the end-to-end data flow, refer to the EdgeAI SDK documentation.

For AM62P, the image pipeline is simpler because there is no ISP on AM62P.

Ndi kanema wa kanema wopangidwa pamakamera aliwonse, payipi yazithunzi yochokera ku GStreamer imalola kukonzanso zolowetsa zingapo zamakamera (zolumikizidwa kudzera mu mawonekedwe omwewo a CSI-2 RX) nthawi imodzi. Mapangidwe ogwiritsira ntchito GStreamer pamakamera ambiri amaperekedwa m'mutu wotsatira.

Reference Design

Mutuwu ukuwonetsa mawonekedwe ogwiritsira ntchito makamera angapo pa AM62A EVM, pogwiritsa ntchito Arducam V3Link Camera Solution Kit kulumikiza makamera a 4 CSI-2 ku AM62A ndikuyendetsa kuzindikira kwa chinthu pamakamera onse anayi.

Makamera Othandizidwa
The Arducam V3Link kit works with both FPD-Link/V3-Link-based cameras and Raspberry Pi-compatible CSI-2 cameras. The following cameras have been tested:

D3 Engineering D3RCM-IMX390-953
Leopard Imaging LI-OV2312-FPDLINKIII-110H

IMX219 cameras in the Arducam V3Link Camera Solution Kit

Setting up Four IMX219 Cameras
Follow the instructions provided in the AM62A Starter Kit EVM Quick Start Guide to set up the SK-AM62A-LP EVM (AM62A SK) and ArduCam V3Link Camera Solution Quick Start Guide to connect the cameras to AM62A SK through the V3Link kit. Make sure the pins on the flex cables, cameras, V3Link board, and AM62A SK are all aligned properly.

Figure 4-1 shows the setup used for the reference design in this report. The main components in the setup include:

1X SK-AM62A-LP EVM board
1X Arducam V3Link d-ch adapter board
FPC cable connecting Arducam V3Link to SK-AM62A

4X V3Link camera adapters (serializers)
4X RF coaxial cables to connect V3Link serializers to V3Link d-ch kit
4X IMX219 Cameras

4X CSI-2 22-pin cables to connect cameras to serializers
Cables: HDMI cable, USB-C to power SK-AM62A-LP and 12V power sourced for V3Link d-ch kit)
Other components not shown in Figure 4-1: micro-SD card, micro-USB cable to access SK-AM62A-LP, and Ethernet for streaming

Configuring Cameras and CSI-2 RX Interface
Set up the software according to the instructions provided in the Arducam V3Link Quick Start Guide. After running the camera setup script, setup-imx219.sh, the camera’s format, the CSI-2 RX interface format, and the routes from each camera to the corresponding video node will be configured properly. Four video nodes are created for the four IMX219 cameras. Command “v4l2-ctl –list-devices” displays all the V4L2 video devices, as shown below:

There are 6 video nodes and 1 media node under tiscsi2rx. Each video node corresponds to a DMA context allocated by the CSI2 RX driver. Out of the 6 video nodes, 4 are used for the 4 IMX219 cameras, as shown in the media pipe topology below:

Monga tawonetsera pamwambapa, media entity 30102000.ticsi2rx ili ndi 6 ma source pads, koma 4 oyamba okha ndi omwe amagwiritsidwa ntchito, iliyonse pa IMX219 imodzi. Media pipe topology ingathenso kufotokozedwa mwatsatanetsatane. Thamangani lamulo ili kuti mupange kadontho file:

Then run the command below on a Linux host PC to generate a PNG file:

Chithunzi 4-2 ndi chithunzi chopangidwa pogwiritsa ntchito malamulo omwe aperekedwa pamwambapa. Zomwe zili muzojambula zamapulogalamu a Chithunzi 3-1 zitha kupezeka mu graph iyi.

Streaming from Four Cameras
Ndi zida zonse ndi mapulogalamu akukhazikitsidwa moyenera, mapulogalamu amakamera angapo amatha kuthamanga kuchokera pamalo ogwiritsira ntchito. Kwa AM62A, ISP iyenera kusinthidwa kuti ipange chithunzi chabwino. Onani maupangiri a AM6xA ISP Tuning amomwe mungapangire ma ISP. Ndime zotsatirazi zikupereka exampkutsitsa deta ya kamera kuwonetsero, kusuntha deta ya kamera ku netiweki, ndikusunga deta ya kamera files.

Streaming Camera Data to Display
Chofunikira pamakina amakamera ambiriwa ndikutsitsa makanema kuchokera ku makamera onse kupita ku chiwonetsero cholumikizidwa ndi SoC yomweyo. Zotsatirazi ndi bomba la GStreamer example yotsatsira IMX219 inayi pachiwonetsero (ma manambala a node ya kanema ndi manambala a v4l-subdev papaipi zitha kusintha kuchokera kuyambiranso mpaka kuyambiranso).

Streaming Camera Data through Ethernet
M'malo mokhamukira pachiwonetsero cholumikizidwa ndi SoC yomweyo, deta ya kamera imathanso kuseweredwa kudzera pa Ethernet. Mbali yolandila ikhoza kukhala purosesa ina ya AM62A/AM62P kapena PC yolandila. Chotsatira ndi example yotsatsira deta ya kamera kudzera pa Ethernet (pogwiritsa ntchito makamera awiri kuti akhale osavuta) (zindikirani pulogalamu yowonjezera ya encoder yomwe ikugwiritsidwa ntchito papaipi):

Chotsatira ndi example la kulandira deta ya kamera ndikusunthira kuwonetsero pa purosesa ina ya AM62A/AM62P:

Storing Camera Data to Files
Instead of streaming to a display or through a network, the camera data can be stored in local files. Mapaipi omwe ali pansipa amasunga deta ya kamera iliyonse ku a file (kugwiritsa ntchito makamera awiri ngati example kwa kuphweka).

Multicamera Deep Learning Inference

AM62A ili ndi accelerator yakuya (C7x-MMA) yokhala ndi ma TOPS awiri, omwe amatha kuyendetsa mitundu yosiyanasiyana ya maphunzilo ozama a magulu, kuzindikira zinthu, magawo a semantic, ndi zina. Gawoli likuwonetsa momwe AM62A ingagwiritsire ntchito nthawi imodzi mitundu inayi yophunzirira mozama pamakamera anayi osiyanasiyana.

Kusankhidwa Kwachitsanzo
The TI’s EdgeAI-ModelZoo provides hundreds of state-of-the-art models, which are converted/exported from their original training frameworks to an anembedded-friendlyy format so that they can be offloaded to the C7x-MMA deep learning accelerator. The cloud-based Edge AI Studio Model Analyzer provides an easy-to-use “Model Selection” tool. It is dynamically updated to include all models supported in TI EdgeAI-ModelZoo. The tool requires no previous experience and provides an easy-to-use interface to enter the features required in the desired model.

The TFL-OD-2000-ssd-mobV1-coco-mlperf was selected for this multi-camera deep learning experiment. This multi-object detection model is developed in the TensorFlow framework with a 300×300 input resolution. Table 4-1 shows the important features of this model when trained on the cCOCO dataset with about 80 different classes.

Gulu 4-1. Yang'anani Mbali za Model TFL-OD-2000-ssd-mobV1-coco-mlperf.

Chitsanzo	Ntchito	Kusamvana	FPS	mAP 50% Accuracy On COCO	Latency/Fremu (ms)	DDR BW Utilization (MB/ Frame)
TFL-OD-2000-ssd- mobV1-coco-mlperf	Multi Object Detection	300 × 300	~ 152	15.9	6.5	18.839

Pipeline Setup
Figure 4-3 shows the 4-camera deep learning GStreamer pipeline. TI provides a suite of GStreamer plugins zomwe zimalola kutsitsa zina zama media media komanso kuphunzira mozama kwa ma accelerator a hardware. Ena exampza izi plugins zikuphatikizapo tiovxisp, tiovxmultiscaler, tiovxmosaic, ndi tidlinferer. Mapaipi mu Chithunzi 4-3 akuphatikizapo zonse zofunika plugins for a multipath GStreamer pipeline for 4-camera inputs, each with media preprocess, deep learning inference, and postprocess. The duplicated plugins njira iliyonse ya kamera imayikidwa mu graph kuti iwonetsedwe mosavuta.
The available hardware resources are evenly distributed among the four camera paths. For instance, AM62A contains two image multiscalers: MSC0 and MSC1. The pipeline explicitly dedicates MSC0 to process camera 1 and camera 2 paths, while MSC1 is dedicated to camera 3 and camera 4.

The output of the four camera pipelines is scaled down and concatenated together using the tiovxmosaic plugin. The output is displayed on a single screen. Figure 4-4 shows the output of the four cameras with a deep learning model running object detection. Each pipeline (camera) is running at 30 FPS and a total of 120 FPS.

Chotsatira ndi cholembera chathunthu chankhani yophunzirira mozama yamakamera ambiri yomwe ikuwonetsedwa pa Chithunzi 4-3.

Kusanthula Kachitidwe

The setup with four cameras using the V3Link board and the AM62A SK was tested in various application scenarios, including directly displaying on a screen, streaming over Ethernet (four UDP channels), recording to 4 separate files, and with deep learning inference. In each experiment, we monitored the frame rate and the utilization of CPU cores to explore the whole system’s capabilities.

Monga tawonera kale pa Chithunzi 4-4, payipi yophunzirira mwakuya imagwiritsa ntchito pulogalamu yowonjezera ya tiperfoverlay GStreamer kuwonetsa zolemera za CPU ngati graph ya bar pansi pazenera. Mwachikhazikitso, graph imasinthidwa masekondi awiri aliwonse kuti iwonetse katunduyo ngati peresenti yogwiritsira ntchitotage. In addition to the tiperfoverlay GStreamer plugin, the perf_stats tool is a second option to show core performance directly on the terminal with an option for saving to a file. This tool is more accurate compared to the tTiperfoverlayas the latter adds extra load on theARMm cores and the DDR to draw the graph and overlay it on the screen. The perf_stats tool is mainly used to collect hardware utilization results in all of the test cases shown in this document. Some of the important processing cores and accelerators studied in these tests include the main processors (four A53 Arm cores @ 1.25GHz), the deep learning accelerator (C7x-MMA @ 850MHz), the VPAC (ISP) with VISS and multiscalers (MSC0 and MSC1), and DDR operations.

Table 5-1 shows the performance and resource utilization when using AM62A with four cameras for three use cases, including streaming four cameras to a display, streaming over Ethernet, and recording to four separate files. Two tests are implemented in each use case: with the camera only and with deep learning inference. In addition, the first row in Table 5-1 shows hardware utilizations when only the operating system was running on AM62A without any user applications. This is used as a baseline to compare against when evaluating hardware utilizations of the other test cases. As shown in the table, the four cameras with deep learning and screen display operated at 30 FPS each ,with a total of 120 FPS for the four cameras. This high frame rate is achieved with only 86% of the deep learning accelerator (C7x-MMA) full capacity. In addition, it is important to note that the deep learning accelerator was clocked at 850MHz instead of 1000MHz in these experiments, which is about only 85% of its maximum performance.

Gulu 5-1. Performance (FPS) ndi Resource Utilization of AM62A ikagwiritsidwa ntchito ndi 4 IMX219 Makamera a Screen Display, Ethernet Stream, Record to Files, ndi Kuchita Zophunzirira Mwakuya

Ntchito n	Pipeline (operation )	Zotulutsa	FPS avg pipeline s	FPS zonse	MPUs A53s @ 1.25 GHz [%]	MCU R5 [%]	DLA (C7x- MMA) @ 850 MHz [%]	VISS [%]	MSC0 [%]	MSC1 [%]	DDR Rd [MB/s]	DDR Wr [MB/s]	DDR Total [MB/s]
Palibe App	Baseline No operation	NA	NA	NA	1.87	1	0	0	0	0	560	19	579
Kamera kokha	Mtsinje to Screen	Chophimba	30	120	12	12	0	70	61	60	1015	757	1782
	Stream over Ethernet	UDP: 4 ports 1920×1080	30	120	23	6	0	70	0	0	2071	1390	3461
	Lembani ku files	4 files 1920×1080	30	120	25	3	0	70	0	0	2100	1403	3503
Cam with Deep learning	Deep learning: Object detection MobV1- coco	Chophimba	30	120	38	25	86	71	85	82	2926	1676	4602
	Deep learning: Object detection MobV1- coco and Stream over Ethernet	UDP: 4 ports 1920×1080	28	112	84	20	99	66	65	72	4157	2563	6720
	Deep learning: Object detection MobV1- coco and record to files	4 files 1920×1080	28	112	87	22	98	75	82	61	2024	2458	6482

Chidule
This application report describes how to implement multi-camera applications on the AM6x family of devices. A reference design based on Arducam’s V3Link Camera Solution Kit and AM62A SK EVM is provided in the report, with several camera applications using four IMX219 cameras, such as streaming and object detection. Users are encouraged to acquire the V3Link Camera Solution Kit from Arducam and replicate these examples. The report also provides a detailed analysis of the performance of AM62A while using four cameras under various configurations, including displaying to a screen, streaming over Ethernet, and recording to files. It also showsAM62A’sA capability of performing deep learning inference on four separate camera streams in parallel. If there are any questions about running these examples, perekani zofunsira pa TI E2E forum.

Maumboni

AM62A Starter Kit EVM Quick Start Guide

ArduCam V3Link Camera Solution Quick Start Guide
Edge AI SDK documentation for AM62A
Edge AI Smart Cameras Using Energy-Efficient AM62A Processor

Camera Mirror Systems on AM62A
Driver and Occupancy Monitoring Systems on AM62A
Quad Channel Camera Application for Surround View and CMS Camera Systems

AM62Ax Linux Academy on Enabling CIS-2 Sensor
Edge AI ModelZoo
Edge AI Studio

Perf_stats tool

TI Parts Referred in This Application Note:

https://www.ti.com/product/AM62A7

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kupanga, kutsimikizira, ndi kuyesa pulogalamu yanu, ndi
ensuring your application meets applicable standards, and any other safety, security, regulatory, or other requirements.

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Mafunso Ofunsidwa Kawirikawiri

Q: Kodi ndingagwiritse ntchito kamera yamtundu uliwonse ndi zida za AM6x?

The AM6x family supports different camera types, including those with or without built-in ISP. Refer to the specifications for more details on supported camera types.

: What are the main differences between AM62A and AM62P in image processing?

The key variations include supported camera types, camera output data, presence of ISP HWA, Deep Learning HWA, and 3-D Graphics HWA. Refer to the specifications section for a detailed comparison.

Zolemba / Zothandizira

Texas Instruments AM6x Kupanga Makamera Angapo [pdf] Buku Logwiritsa Ntchito
AM62A, AM62P, AM6x Kupanga Makamera Angapo, AM6x, Kupanga Makamera Angapo, Makamera Angapo, Kamera

Maumboni

Buku Logwiritsa Ntchito

Texas Instruments AM6x Developing Multiple Cameras

Zofotokozera