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Roll out latest changes to production #3868

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Oct 3, 2024
Merged

Roll out latest changes to production #3868

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d877b07
ai camera: Fix some typos
naushir Oct 3, 2024
b1c6b24
Merge pull request #3867 from naushir/develop
nathan-contino Oct 3, 2024
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8 changes: 4 additions & 4 deletions documentation/asciidoc/accessories/ai-camera/details.adoc
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Expand Up @@ -83,14 +83,14 @@ struct CnnInputTensorInfo {

=== `rpicam-apps`

`rpicam-apps` provides an IMX500 post-processing stage base class that implements helpers for IMX500 post-processing stages: https://github.com/raspberrypi/rpicam-apps/blob/post_processing_stages/imx500_post_processing_stage.hpp[`IMX500PostProcessingStage`]. Use this base class to derive a new post-processing stage for any neural network model running on the IMX500. For an example, see https://github.com/raspberrypi/rpicam-apps/blob/post_processing_stages/imx500_mobilenet_ssd.cpp[`imx500_mobilenet_ssd.cpp`]:
`rpicam-apps` provides an IMX500 post-processing stage base class that implements helpers for IMX500 post-processing stages: https://github.com/raspberrypi/rpicam-apps/blob/main/post_processing_stages/imx500/imx500_post_processing_stage.hpp[`IMX500PostProcessingStage`]. Use this base class to derive a new post-processing stage for any neural network model running on the IMX500. For an example, see https://github.com/raspberrypi/rpicam-apps/blob/main/post_processing_stages/imx500/imx500_object_detection.cpp[`imx500_object_detection.cpp`]:

[source,cpp]
----
class ObjectInference : public IMX500PostProcessingStage
class ObjectDetection : public IMX500PostProcessingStage
{
public:
ObjectInference(RPiCamApp *app) : IMX500PostProcessingStage(app) {}
ObjectDetection(RPiCamApp *app) : IMX500PostProcessingStage(app) {}

char const *Name() const override;

Expand All @@ -102,7 +102,7 @@ public:
};
----

For every frame received by the application, the `Process()` function is called (`ObjectInference::Process()` in the above case). In this function, you can extract the output tensor for further processing or analysis:
For every frame received by the application, the `Process()` function is called (`ObjectDetection::Process()` in the above case). In this function, you can extract the output tensor for further processing or analysis:

[source,cpp]
----
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2 changes: 1 addition & 1 deletion documentation/asciidoc/accessories/ai-camera/getting-started.adoc
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Expand Up @@ -56,7 +56,7 @@ The MobileNet SSD neural network performs basic object detection, providing boun

`imx500_mobilenet_ssd.json` declares a post-processing pipeline that contains two stages:

. `imx500_mobilenet_ssd`, which picks out bounding boxes and confidence values generated by the neural network in the output tensor
. `imx500_object_detection`, which picks out bounding boxes and confidence values generated by the neural network in the output tensor
. `object_detect_draw_cv`, which draws bounding boxes and labels on the image

The MobileNet SSD tensor requires no significant post-processing on your Raspberry Pi to generate the final output of bounding boxes. All object detection runs directly on the AI Camera.
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