Add content regarding Image Compressor, Fake Camera, and OpenCL#235
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LachlanCourt wants to merge 7 commits intomainfrom
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Add content regarding Image Compressor, Fake Camera, and OpenCL#235LachlanCourt wants to merge 7 commits intomainfrom
LachlanCourt wants to merge 7 commits intomainfrom
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LachlanCourt
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Jul 19, 2022
| #### Running OpenCL not on the robot | ||
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| The Visual Mesh works best running the OpenCL framework, however most operating systems are not set up to run this by default. Whilst running the Visual Mesh on CPU is possible, sometimes it is best to replicate an environment as close to what is running on the robot as possible. Follow the steps below to set up OpenCL on a device other than a robot. | ||
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I need to confirm that these steps are correct with a fresh install so general review is fine but these steps may change
src/book/03-guides/01-main-codebase/02-maintaining-subsystems.mdx
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ysims
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Jul 20, 2022
src/book/03-guides/01-main-codebase/02-maintaining-subsystems.mdx
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JosephusPaye
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Aug 6, 2022
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| ./b run --mount type=bind,source="/dev/dri",target="/dev/dri" <role_name> | ||
| ``` |
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Probably mention what <role_name> should be replaced with
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| To run the fake camera module, an images directory needs to exist inside the `data` directory of the module. | ||
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| 1. Create an `images` directory and add several images with filenames matching `imageXXXXXX.jpg` where "XXXXXX" refers to any numerical digits |
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Does it have to be 6 digits? If so this would help clarify:
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| 1. Create an `images` directory and add several images with filenames matching `imageXXXXXX.jpg` where "XXXXXX" refers to any numerical digits | |
| 1. Create an `images` directory and add several images with filenames matching `imageXXXXXX.jpg`, where "XXXXXX" is any 6-digit number, e.g. `000001`, `000002`, etc. |
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Doesn't have to be 6 digits, I believe it just needs to be enumerated somehow
| @@ -85,9 +124,37 @@ After updating the Visual Mesh in the NUbots repository, it should be tested bef | |||
| If you would like to see the Visual Mesh output in NUsight, you will need to log the data and run it back in NUsight using DataPlayback, since the data is too large to send over a network. Use the steps in the [DataLogging and DataPlayback guide](/guides/main/data-recording-playback) to record and playback data. Adjust the instructions for our purpose using the following hints: | |||
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| - In step 1 of Recording Data, use the [`visualmesh`](https://github.com/NUbots/NUbots/blob/main/roles/visualmesh.role) role to record the data. | |||
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| - In step 1 of Recording Data, use the [`visualmesh`](https://github.com/NUbots/NUbots/blob/main/roles/visualmesh.role) role to record the data. | |
| - In step 1 of [Recording Data](/guides/main/data-recording-playback#recording-data), use the [`visualmesh`](https://github.com/NUbots/NUbots/blob/main/roles/visualmesh.role) role to record the data. |
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I will add this to this PR 👍
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| - In step 1 of Recording Data, use the [`visualmesh`](https://github.com/NUbots/NUbots/blob/main/roles/visualmesh.role) role to record the data. | ||
| - In step 2 of Recording Data and step 4 of Playing Back Data, set `message.output.CompressedImage` to `true` and add `message.vision.VisualMesh: true` in both [`DataLogging.yaml](https://github.com/NUbots/NUbots/blob/main/module/support/logging/DataLogging/data/config/DataLogging.yaml) and [`DataPlayback.yaml](https://github.com/NUbots/NUbots/blob/main/module/support/logging/DataPlayback/data/config/DataPlayback.yaml). | ||
| - In step 2 of Recording Data and step 4 of Playing Back Data, set `message.output.CompressedImage` to `true` and add `message.vision.VisualMesh: true` in both [`DataLogging.yaml`](https://github.com/NUbots/NUbots/blob/main/module/support/logging/DataLogging/data/config/DataLogging.yaml) and [`DataPlayback.yaml`](https://github.com/NUbots/NUbots/blob/main/module/support/logging/DataPlayback/data/config/DataPlayback.yaml). |
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Suggested change
| - In step 2 of Recording Data and step 4 of Playing Back Data, set `message.output.CompressedImage` to `true` and add `message.vision.VisualMesh: true` in both [`DataLogging.yaml`](https://github.com/NUbots/NUbots/blob/main/module/support/logging/DataLogging/data/config/DataLogging.yaml) and [`DataPlayback.yaml`](https://github.com/NUbots/NUbots/blob/main/module/support/logging/DataPlayback/data/config/DataPlayback.yaml). | |
| - In step 2 of [Recording Data](/guides/main/data-recording-playback#recording-data) and step 4 of [Playing Back Data](/guides/main/data-recording-playback#playing-back-data), set `message.output.CompressedImage` to `true` and add `message.vision.VisualMesh: true` in both [`DataLogging.yaml`](https://github.com/NUbots/NUbots/blob/main/module/support/logging/DataLogging/data/config/DataLogging.yaml) and [`DataPlayback.yaml`](https://github.com/NUbots/NUbots/blob/main/module/support/logging/DataPlayback/data/config/DataPlayback.yaml). |
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| ### Image Compressors | ||
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| In order to send data to NUsight via the network, images need to be compressed. Depending on the environment different compressors can be used. [TurboJPEG](https://github.com/libjpeg-turbo/libjpeg-turbo) is the compressor that can be used on all platforms and is used primarily when submitting docker images for virtual competitions as the processor type cannot be guaranteed. [VAAPI](https://01.org/linuxmedia/vaapi) is the preferred compressor for devices running on Intel CPUs, which support the framework. |
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| In order to send data to NUsight via the network, images need to be compressed. Depending on the environment different compressors can be used. [TurboJPEG](https://github.com/libjpeg-turbo/libjpeg-turbo) is the compressor that can be used on all platforms and is used primarily when submitting docker images for virtual competitions as the processor type cannot be guaranteed. [VAAPI](https://01.org/linuxmedia/vaapi) is the preferred compressor for devices running on Intel CPUs, which support the framework. | |
| In order to send data to NUsight via the network, images need to be compressed. Depending on the environment different compressors can be used. [TurboJPEG](https://github.com/libjpeg-turbo/libjpeg-turbo) is the compressor that can be used on all platforms and is used primarily when submitting docker images for virtual competitions as the processor type cannot be guaranteed. [VAAPI](https://01.org/linuxmedia/vaapi) is the preferred compressor for devices running on Intel CPUs which support the framework, like the [CPUs in the robots](/system/hardware/overview#computing). |
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