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Updated Blocks Sample TensorFlow Object Detection Op Mode (markdown)

FTC Engineering
2020-10-23 20:52:29 -04:00
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Blocks-Sample-TensorFlow-Object-Detection-Op-Mode.md

@ -17,7 +17,7 @@ Let's take a look at the initial blocks in the Op Mode. The first block in the
<p align="center">[[/images/Blocks-Sample-TensorFlow-Object-Detection-Op-Mode/blocksInit.png]]<br/>Initialize the Vuforia and TensorFlow libraries.<p>
In the screenshot shown above, the sample Op Mode disables the camera monitoring window on the Robot Controller. If you are using a REV Robotics Control Hub as your Robot Controller (which lacks a touch screen) you normally want to disable the camera monitoring window and use the Camera Stream function instead (described later on in this tutorial) to view the output of the webcam. If you are using an Android phone as your Robot Controller, however, you can enable the camera monitoring window so you can see the camera output on the Robot Controller's touch screen.
In the screenshot shown above, the sample Op Mode disables the camera monitoring window on the Robot Controller. If you are using a REV Robotics Control Hub as your Robot Controller (which lacks a touch screen) you normally want to disable the camera monitoring window and use the Camera Stream function instead to view the output of the webcam. If you are using an Android phone as your Robot Controller, however, you can enable the camera monitoring window so you can see the camera output on the Robot Controller's touch screen.
You can initialize both the Vuforia and the TensorFlow libraries in the same op mode. This is useful, for example, if you would like to use the TensorFlow library to determine the ring stack and then use the Vuforia library to help the robot autonomously navigate on the game field to navigate to the appropriate target zone from its starting position.
@ -36,9 +36,9 @@ The origin of the coordinate system is in the upper left-hand corner of the imag
In the landscape image above, the approximate coordinate values for the Left, Top, Right, and Bottom boundaries are 455, 191, 808, and 547 respectively (pixel coordinates). The width and height for the landscape image above is 1280 and 720 respectively.
### Iterating and Processing List of Recognized Objects
After the Op Mode waits for (and receives) a start command from the Driver Station, if the Op Mode is still active, the Op Mode will activate the TensorFlow object detector. Note that in this example, the Op Mode does not activate the Vuforia tracking feature, it only activates TensorFlow object detection. If you want to incorporate Vuforia image detection and tracking you will also need to activate (and later deactivate when you are done) the Vuforia tracking feature.
In this example, the Op Mode activates the TensorFlow object detector before waiting for the start command from the Driver Station. This is done so that the user can access the "Camera Stream" preview from the Driver Station menu while it waits for the start command. Also note that in this example, the Op Mode does not activate the Vuforia tracking feature, it only activates TensorFlow object detection. If you want to incorporate Vuforia image detection and tracking you will also need to activate (and later deactivate when you are done) the Vuforia tracking feature.
<p align="center"><img src="https://github.com/FIRST-Tech-Challenge/WikiSupport/blob/master/tensorflow/images/blocksTensorFlowActivate.png" width="400"><br/>Activate TensorFlow.<p>
<p align="center">[[/images/Blocks-Sample-TensorFlow-Object-Detection-Op-Mode/blocksTensorFlowActivate.png]]<br/>Activate TensorFlow.<p>
The Op Mode will then check with the object detector to see how many objects it recognizes in its field of view. In the screenshot below, the variable "recognitions" is set to a list of objects that were recognized using the TensorFlow technology. The Op Mode adds a telemetry message to indicate on the Driver Station how many objects were detected in the camera's field of view.