From bc7efb44c52e31794a1c84da5b7493f4197d4f62 Mon Sep 17 00:00:00 2001 From: Danny Diaz Date: Fri, 6 Oct 2023 01:37:42 -0400 Subject: [PATCH] Updating TFOD Tutorials and Fixing Blog (#223) * Updating TFOD Tutorials and Fixing Blog --- .../android_studios/android-studios.rst | 150 ---- .../android_studios/images/image22.png | Bin 79622 -> 0 bytes .../android_studios/images/image23.png | Bin 11270 -> 0 bytes .../android_studios/images/image24.png | Bin 4079 -> 0 bytes .../android_studios/images/image25.png | Bin 7497 -> 0 bytes .../android_studios/images/image26.png | Bin 31200 -> 0 bytes .../android_studios/images/image27.png | Bin 36527 -> 0 bytes .../android_studios/images/image28.png | Bin 65623 -> 0 bytes .../android_studios/images/image29.png | Bin 7016 -> 0 bytes .../android_studios/images/image30.png | Bin 7503 -> 0 bytes .../source/ftc_ml/implement/blocks/blocks.rst | 115 --- .../implement/blocks/images/image39.png | Bin 33384 -> 0 bytes .../implement/blocks/images/image40.png | 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We are working on updating this tutorial, please - bear with us as we update it. For more information on TensorFlow - for Java, see the VisionPortal - :ref:`TensorFlow Processor Initialization `. - -It is assumed that you already know how to use Android Studio. If not, -be sure to check out the :ref:`Android Studio Guide ` -document before proceeding. - -1. In Android Studio, there are three sample Opmodes to choose from. - Each has its own use case. Depending on your use case choose one (or - more) of the following. - - - `ConceptTensorFlowObjectDetection `_ - - - This opmode is designed to use the robot controller's internal - camera, if it has one, for the input for TFOD. - - - `ConceptTensorFlowObjectDetectionSwitchableCameras `_ - - - This opmode is designed to be able to switch between two - different webcams for the input for TFOD. - - - `ConceptTensorFlowObjectDetectionEasy `__ - - - This opmode is designed to use an external webcam as its input - for TFOD - -2. We will now have to move the sample opmode from the samples folder to - the teamcode folder. To do this we will first have to go to the - project view on the right. Then we will need to navigate to - - - ``FtcRobotController -> Java -> - org.firstinspires.ftc.robotcontroller -> external.samples`` - -It should look like this: - -.. figure:: images/image22.png - :align: center - -3. Select the opmode that you chose earlier and drag and drop it into - the following folder: - - - TeamCode -> Java -> org.firstinspires.ftc.teamcode - -.. - - When prompted to refactor select “refactor”. In the image below I - moved all three opmodes into my teamcode folder but that is not - necessary. You only need one opmode. - -.. figure:: images/image23.png - :align: center - -| - -.. figure:: images/image24.png - :align: center - -| - -.. figure:: images/image25.png - :align: center - -4. You will then have to open the opmode that you moved and remove the - ``@Disabled`` should be near the top of the code. - -5. The next step is to add your model (.tflite) to your assets folder. - You will find this folder in your project view on the left by going - to: - - - ``FtcRobotControler -> assets.`` - -By left- clicking on the assets folder you go to the “Open In” sub menu -from which you can choose your file explorer of choice. From there you -need to copy your model (.tflite) into the assets folder that you have -just opened. - -.. figure:: images/image26.png - :align: center - -6. You will now have to modify the name of the model that the opmode - uses. To do this change the TFOD_MODEL_ASSET variable such that - “FreightFrenzy_BCDM.tflite” is replaced with the name and fully - qualified path for the file that you uploaded earlier. The fully - qualified path to where the tflite models live can be seen in the - example below: - -``private static final String TFOD_MODEL_ASSET = -"/sdcard/FIRST/tflitemodels/myCustomFreightFrenzyModel.tflite";`` - -.. tip:: On some older models of phones, the upload path may be - different. On some phones, like the Motorola G2 phone, the model - path should instead be set to - ``private static final String TFOD_MODEL_ASSET = - "/storage/emulated/0/FIRST/tflitemodels/myCustomFreightFrenzyModel.tflite";`` - -7. Next we will have to acquire a Vuforia Key. There are detailed - instructions in each of the opmodes on acquiring such a key. - -8. After following these instructions replace the following string with - your key: - -``“ -- YOUR NEW VUFORIA KEY GOES HERE --- “`` - -9. (optional) You may find it necessary to adjust the zoom. By default - it is set to 2.5 but feel free to adjust this to your needs. Note - that this is digital zoom not optical zoom. This means that in - essence you are simply telling TFOD to ignore outer sections of the - image. - -10. If you are using ``ConceptTensorFlowObjectDetectionWebcam`` or - ``ConceptTensorFlowObjectDetectionSwitchableCameras`` you will also need - to update the device name of the camera. Simply change the device - name to correspond with your config. In the image below the name of - the webcam is “Webcam 1”. - -.. figure:: images/image27.png - :align: center - -.. figure:: images/image28.png - :align: center - -11. You will next have to update the labels. To do this find the section - of code shown below. This should match the order of the labels in - the dataset(s) that your model is based on. There should be at least - one label. - - - - -.. figure:: images/image29.png - :align: center - - Before - -| - -.. figure:: images/image30.png - :align: center - - After - -12. And you are all set to test. 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We are working on updating this tutorial, please - bear with us as we update it. For more information on using - custom TensorFlow models with Blocks, see the tutorial - `Custom TFOD Model with Blocks `__ - -It is assumed that you already know how to use Blocks. If not, be sure -to check out the :ref:`Blocks Programming -Guide ` -document before proceeding. - -1. First click the “Create New Op Mode” button in the Blocks main - management interface. - -.. figure:: images/image39.png - :align: center - -2. Select “ConceptTensorFlowObjectDetectionCustomModel” in the sample - drop down and choose whatever name you find appropriate. Then click - OK. - -.. - - .. figure:: images/image40.png - :align: center - -| - - .. note:: - Be careful of a common pitfall – as of SDK 7.0, normal - TensorFlow blocks cannot load custom tensorflow models. There are TWO - kinds of TensorFlow blocks, one type for regular models and one type - for custom models, and ALL of the blocks in each category MUST be - used together (so old programs that load regular models would need - ALL of the TensorFlow blocks replaced, not just the ones that load - the model). If you choose to not create a new Op Mode for loading the - Custom Models, and you do not replace ALL of the “normal” TensorFlow - blocks, be aware that we warned you here. - -3. You can either use a webcam or the built in camera for your video - input. If you are using the control hub you, do not have a built in - camera. If you are using the phone’s built in camera do not change - the disabled block. If you are using a webcam, disable the block of - code below the comment “The following block(first image below) uses - the device's back camera.” To disable the block right click the block - and select “Disable Block.” Then enable the following block (second - image below). Do so by right clicking the block and clicking “Enable - Block”. - -.. figure:: images/image41.png - :align: center - -.. figure:: images/image42.png - :align: center - -4. If you are using a webcam change the cameraName parameter in the - block that you just enabled to the name of your webcam. Currently it - is called “Webcam 1.” - -5. Then scroll down to the following block. - -.. figure:: images/image43.png - :align: center - -You will now have to modify the name of the model that the opmode uses. -To do this change the tfliteModelFilename string from -``“WiffleBalls.tflite”`` to the name of the file that you will upload. If -you don’t use the model uploader, you need to specify the fully -qualified path to where the tflite models live. An example of the fully -qualified path to where the tflite models live can be seen in the -example below: - -``/sdcard/FIRST/tflitemodels/myCustomFreightFrenzyModel.tflite`` - -.. tip:: On some older models of phones, the upload path may be - different. On some phones, like the Motorola G2 phone, the model - path should instead be set to - ``/storage/emulated/0/FIRST/tflitemodels/myCustomFreightFrenzyModel.tflite`` - -6. Next you will have to update the labels. This should match the order - of the labels in the dataset(s) that your model is based on. To do so - click on the gear icon and add the needed number of items. Then add - the items in your model to the labels list (seen below). This should - be in alphabetical order and match the labels in the dataset(s) that - your model is made from. - -.. figure:: images/image44.png - :align: center - -7. The next step is to upload the TFOD model that you created using - FTC-ML. To do this go to the ribbon on the top of your windows and - select “Manage”. - -.. - - .. figure:: images/image47.png - :align: center - -8. If you're using **SDK 7.0 or older**, select the “Upload TensorFlow Lite Model File” button and select - the model (.tflite). Then click upload. - -.. figure:: images/image48.png - :width: 80% - :align: center - -If you're using **SDK 7.1 and newer**, in the same location you'll find a TensorFlow Lite Model File -Manager link. Click on that link to take you to the model file manager, and upload your model. -Here you can also view other models already uploaded, rename, and delete models. - -9. And you are all set to test. After building the opmode as you would - any other opmode and select initialize and play. 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a/docs/source/ftc_ml/implement/index.rst b/docs/source/ftc_ml/implement/index.rst index ac1a0ccb..ed22332b 100644 --- a/docs/source/ftc_ml/implement/index.rst +++ b/docs/source/ftc_ml/implement/index.rst @@ -2,8 +2,7 @@ Implementing in Robot Code ============================ The basis of this tutorial will be the sample opmodes provided by *FIRST* -in the `7.0 -SDK `__. The +in the `9.0 SDK `__. The process for testing a custom TensorFlow model is quite simple. 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zGP9uod-&h8zlDOsKdC2YA0LIeMyr3nL?AEzUd;FX`xns^@c~s@e|0e{ z9d*SE2LFWSvlHgNK7yA1JDzsv!nGGr5PunfMxewpm z4SU*u{yFdO@!iug{Ar>0uj4=Wzs03yG^5PnM1Q2=5l`^vXGbX0yoCr@(D&R5^mT(R zH20(7zdqk*pPy2n&ju1*s{ipp;=u1_?}L$*e;JUKVH=W9R}q=eKK#;?zJ%?X4xU-) z)kWxIy_Xzy+Ws~&5ZrrlVI}n;9X0+l3ia6v1phatXGRgfP&YrMdxZY)eDE;>or;6& z)-5(b8VwDN!&lYZ*WSkWjf|c58`K3&LN^RRbz2>PD`>_a}DpaRe|RrOSwl&quw4?&!v!~g&Q diff --git a/docs/source/programming_resources/vision/java_tfod_opmode/java-tfod-opmode.rst b/docs/source/programming_resources/vision/java_tfod_opmode/java-tfod-opmode.rst index d0552c74..71b8d67d 100644 --- a/docs/source/programming_resources/vision/java_tfod_opmode/java-tfod-opmode.rst +++ b/docs/source/programming_resources/vision/java_tfod_opmode/java-tfod-opmode.rst @@ -1,468 +1,410 @@ -Java Sample Op Mode for TFOD -============================= - -.. warning:: - This Tutorial is outdated due to the TensorFlow updates for the - VisionPortal. We are working on updating this tutorial, please - bear with us as we update it. For more information on TensorFlow - for Java, see the VisionPortal - :ref:`TensorFlow Processor Initialization `. - -Creating the Op Mode -~~~~~~~~~~~~~~~~~~~~ - -You can use the sample “ConceptTensorFlowObjectDetection” as a template -to create your own Java op mode that uses the TensorFlow technology to -“look for” any game elements, and determine the relative location of any -identified elements. - -- If you are using a **webcam** connected to the Robot Controller - device, select “ConceptTensorFlowObjectDetectionWebcam” as the sample - op mode from the dropdown list in the New File dialog box. -- If you are using an Android smartphone’s **built-in camera**, select - “ConceptTensorFlowObjectDetection” as the sample op mode from the - dropdown list in the New File dialog box. - -Press “OK” to create the new op mode. - -.. figure:: images/onbotConceptTensorFlow.png +Java Easy Sample OpMode for TFOD +================================ + +Introduction +------------ + +This tutorial describes the “Easy” version of the FTC Java Sample OpMode +for TensorFlow Object Detection (TFOD). + +This Sample, called “ConceptTensorFlowObjectDetectionEasy.java”, can +recognize official FTC game elements and provide their visible size and +position. It uses standard/default TFOD settings. + +For the 2023-2024 game CENTERSTAGE, the game element is a hexagonal +white **Pixel**. The FTC SDK software contains a TFOD model of this +object, ready for recognition. That model was created with a Machine +Learning process described here: + +- :ref:`FIRST Machine Learning Toolchain `. + +.. figure:: images/010-TFOD-recognition.png :align: center + :width: 85% + :alt: TFOD Recognition + + Sample TFOD Recognition + +For extra points, teams may instead use their own custom TFOD models of +**Team Props**. That option is not described here. + +This tutorial shows **OnBot Java** screens. Users of **Android Studio** +can follow along, since the Sample OpMode is exactly the same. + +A different Sample OpMode shows how to set **TFOD options**, unlike the +“Easy” version which uses only standard/default TFOD settings. That +version, called “ConceptTensorFlowObjectDetection.java” has good +commenting to guide users in the Java **Builder pattern** for custom +settings. + +The “Easy” OpMode covered here does not require the user to work with +the Builder pattern, although the SDK does use it internally. - Create an Op Mode with ConceptTensorFlowObjectDetection as its template. +Creating the OpMode +------------------- -Your new op mode should appear in the editing pane of the OnBot Java -screen. +At the FTC OnBot Java browser interface, click on the large black +**plus-sign icon** “Add File”, to open the New File dialog box. -.. figure:: images/205_Java_TFOD_Sample_editor.png +.. figure:: images/020-New-File.png :align: center + :width: 85% + :alt: New File - Your newly created op mode should be available for editing through OnBot Java + New File Dialog -Initializing the System -~~~~~~~~~~~~~~~~~~~~~~~ +Specify a name for your new OpMode. Select +“ConceptTensorFlowObjectDetectionEasy” as the Sample OpMode that will be +the template for your new OpMode. -Let’s take a look at the initial statements in the op mode. Before you -start, you must first make sure you have a valid Vuforia developer -license key to initialize the Vuforia software. You can obtain a key for -free from https://developer.vuforia.com/license-manager. Once you obtain -your key, replace the VUFORIA_KEY static String with the actual license -key so the Vuforia software will be able to initialize properly. +This Sample has optional gamepad inputs, so it could be designated as a +**TeleOp** OpMode (see above). -.. code-block:: java +Click “OK” to create your new OpMode. - private static final String VUFORIA_KEY = - " -- YOUR NEW VUFORIA KEY GOES HERE --- "; +Android Studio users should follow the commented instructions to copy +this class from the Samples folder to the Teamcode folder, with a new +name. Also remove the ``@Disabled`` annotation, to make the OpMode +visible in the Driver Station list. -Also, by default the op mode is disabled. Comment out the “@Disabled” -annotation to enable your newly created op mode. +The new OpMode should appear in edit mode in your browser. -.. code-block:: java +.. figure:: images/040-Sample-Open.png + :align: center + :width: 85% + :alt: Open Sample - @TeleOp(name = "Concept: TensorFlow Object Detection", group = "Concept") - //@Disabled - public class MyTensorFlowExample extends LinearOpMode { + Opening New Sample -Since TensorFlow will receive image data from Vuforia, the op mode -attempts to create and initialize a VuforiaLocalizer object by calling -the function called “initVuforia()”: +By default, the Sample OpMode assumes you are using a webcam, configured +as “Webcam 1”. If you are using the built-in camera on your Android RC +phone, change the USE_WEBCAM Boolean from ``true`` to ``false`` (orange +oval above). -.. code-block:: java +Preliminary Testing +------------------- - // The TFObjectDetector uses the camera frames from the VuforiaLocalizer, so we create that - // first. - initVuforia(); - initTfod(); +This OpMode is ready to use – it’s the “Easy” version! -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 recognize the Duck and then use the Vuforia -library to help the robot autonomously navigate on the game field. +Click the “Build Everything” button (wrench icon at lower right), and +wait for confirmation “BUILD SUCCESSFUL”. -In this example op mode, however, the initVuforia() function does not -load any trackable image targets. +If Build is prevented by some other OpMode having errors/issues, they +must be fixed before your new OpMode can run. For a quick fix, you could +right-click on that filename and choose “Disable/Comment”. This +“comments out” all lines of code, effectively removing that file from +the Build. That file can be re-activated later with “Enable/Uncomment”. -.. code-block:: java +In Android Studio (or OnBot Java), you can open a problem class/OpMode +and type **CTRL-A** and **CTRL-/** to select and “comment out” all lines +of code. This is reversible with **CTRL-A** and **CTRL-/** again. - /** - * Initialize the Vuforia localization engine. - */ - private void initVuforia() { - /* - * Configure Vuforia by creating a Parameter object, and passing it to the Vuforia engine. - */ - VuforiaLocalizer.Parameters parameters = new VuforiaLocalizer.Parameters(); +Now run your new OpMode from the Driver Station (on the TeleOp list, if +so designated). The OpMode should recognize any CENTERSTAGE white Pixel +within the camera’s view, based on the trained TFOD model in the SDK. - parameters.vuforiaLicenseKey = VUFORIA_KEY; - parameters.cameraDirection = CameraDirection.BACK; +For a **preview** during the INIT phase, touch the Driver Station’s +3-dot menu and select **Camera Stream**. - // Instantiate the Vuforia engine - vuforia = ClassFactory.getInstance().createVuforia(parameters); +.. figure:: images/200-Sample-DS-Camera-Stream.png + :align: center + :width: 85% + :alt: DS Camera Stream + + DS Camera Stream - // Loading trackables is not necessary for the TensorFlow Object Detection engine. - } +Camera Stream is not live video; tap to refresh the image. Use the small +white arrows at lower right to expand or revert the preview size. To +close the preview, choose 3-dots and Camera Stream again. -After the Vuforia localizer is initialized, the op mode calls a method -initTfod(). This method initializes the TensorFlow Object Detection -engine. +After the DS START button is touched, the OpMode displays Telemetry for +any recognized Pixel(s): -.. code-block:: java +.. figure:: images/210-Sample-DS-Telemetry.png + :align: center + :width: 85% + :alt: DS Telemetry + + DS Telemetry Display + +The above Telemetry shows the Label name, and TFOD recognition +confidence level. It also gives the **center location** and **size** (in +pixels) of the Bounding Box, which is the colored rectangle surrounding +the recognized object. + +The pixel origin (0, 0) is at the top left corner of the image. + +Before and after DS START is touched, the Robot Controller provides a +video preview called **LiveView**. + +.. figure:: images/240-Sample-RC-LiveView.png + :align: center + :width: 85% + :alt: Sample RC LiveView + + Sample RC LiveView + +For Control Hub (with no built-in screen), plug in an HDMI monitor or +learn about ```scrcpy`` `__. The +above image is a LiveView screenshot via ``scrcpy``. + +If you don’t have a physical Pixel on hand, try pointing the camera at +this image: + +.. figure:: images/300-Sample-Pixel.png + :align: center + :width: 85% + :alt: A Pixel + + Example of a Pixel + + +Program Logic and Initialization +-------------------------------- + +During the INIT stage (before DS START is touched), this OpMode calls a +**method to initialize** the TFOD Processor and the FTC VisionPortal. +After DS START is touched, the OpMode runs a continuous loop, calling a +**method to display telemetry** about any TFOD recognitions. The OpMode +also contains two optional features to remind teams about **CPU resource +management**, useful in vision processing. + +Here’s the first method, to initialize the TFOD Processor and the FTC +VisionPortal. + +.. code:: java /** - * Initialize the TensorFlow Object Detection engine. + * Initialize the TensorFlow Object Detection processor. */ private void initTfod() { - int tfodMonitorViewId = hardwareMap.appContext.getResources().getIdentifier( - "tfodMonitorViewId", "id", hardwareMap.appContext.getPackageName()); - TFObjectDetector.Parameters tfodParameters = new TFObjectDetector.Parameters(tfodMonitorViewId); - tfodParameters.minResultConfidence = 0.8f; - tfodParameters.isModelTensorFlow2 = true; - tfodParameters.inputSize = 320; - tfod = ClassFactory.getInstance().createTFObjectDetector(tfodParameters, vuforia); - tfod.loadModelFromAsset(TFOD_MODEL_ASSET, LABELS); - } - -Note that by default, when you create a new TensorFlow object detector, -an *object tracker* is used, in addition to the TensorFlow interpreter, -to keep track of the locations of detected objects. The object tracker -*interpolates* object recognitions so that results are smoother than -they would be if the system were to solely rely on the TensorFlow -interpreter. - -If you want to turn off the object tracker, then you can set the -useObjectTracker variable of the tfodParameters object to false before -you create the TensorFlow object detector. - -.. code-block:: java - - // set useObjectTracker to false to disable object tracker. - tfodParameters.useObjectTracker = false; - -Also note that by default, the minimum detection confidence level is set -to 80%. This means that the TensorFlow library needs to have a -confidence level of 80% or higher in order to consider an object as -being detected in its field of view. You can adjust this parameter to a -higher value if you would like the system to be more selective in -identifying an object. - -.. code-block:: java - - tfodParameters.minResultConfidence = 0.8f; - -After the TensorFlow Object Detector is created it loads the TensorFlow -model data - -.. code-block:: java - - tfod.loadModelFromAsset(TFOD_MODEL_ASSET, LABELS); - -The arguments TFOD_MODEL_ASSET, LABELS are defined earlier in the op -mode and are season specific. - -.. code-block:: java - - private static final String TFOD_MODEL_ASSET = "FreightFrenzy_BCDM.tflite"; - private static final String[] LABELS = { - "Ball", - "Cube", - "Duck", - "Marker" - }; - -If a camera monitor window is enabled for the TensorFlow library, then -the confidence level for a detected target will be displayed near the -bounding box of the identified object (when the object tracker is -enabled). For example, a value of “0.92” indicates a 92% confidence that -the object has been identified correctly. - -When an object is identified by the TensorFlow library, the op mode can -read the “Left”, “Right”, “Top” and “Bottom” values associated with the -detected object. These values correspond to the location of the left, -right, top and bottom boundaries of the detection box for that object. -These values are in pixel coordinates of the image from the camera. - -The origin of the coordinate system is in the upper left-hand corner of -the image. The horizontal (x) coordinate value increases as you move -from the left to the right of the image. The vertical (y) coordinate -value increases as you move from the top to the bottom of the image. - -.. figure:: images/landscapeCoordinate.png - :align: center - The origin of the image coordinate system is located in upper left hand corner - - -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. - -Activating TensorFlow -~~~~~~~~~~~~~~~~~~~~~ - -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. - -.. code-block:: java - - /** - * Activate TensorFlow Object Detection before we wait for the start command. - * Do it here so that the Camera Stream window will have the TensorFlow annotations visible. - **/ - if (tfod != null) { - tfod.activate(); - - // The TensorFlow software will scale the input images from the camera to a lower resolution. - // This can result in lower detection accuracy at longer distances (> 55cm or 22"). - // If your target is at distance greater than 50 cm (20") you can adjust the magnification value - // to artificially zoom in to the center of image. For best results, the "aspectRatio" argument - // should be set to the value of the images used to create the TensorFlow Object Detection model - // (typically 16/9). - tfod.setZoom(2.5, 16.0/9.0); + // Create the TensorFlow processor the easy way. + tfod = TfodProcessor.easyCreateWithDefaults(); + + // Create the vision portal the easy way. + if (USE_WEBCAM) { + visionPortal = VisionPortal.easyCreateWithDefaults( + hardwareMap.get(WebcamName.class, "Webcam 1"), tfod); + } else { + visionPortal = VisionPortal.easyCreateWithDefaults( + BuiltinCameraDirection.BACK, tfod); } -Setting the Zoom Factor -~~~~~~~~~~~~~~~~~~~~~~~ - -When TensorFlow receives an image from the robot’s camera, the library -downgrades the resolution of the image (presumably to achieve a higher -detection rate). As a result, if a target is at a distance of around 24” -(61cm) or more, the detection accuracy of the system tends to diminish. -This degradation can occur, even if you have a very accurate inference -model. - -You can specify a zoom factor in your op mode to offset the effect of -this automatic scaling by the TensorFlow library. If you specify a zoom -factor, the image will be cropped by this factor and this artificially -magnified image will be passed to the TensorFlow library. The net result -is that the robot is able to detect and track an object at a -significantly larger distance. The webcams and built-in Android cameras -that are typically used by teams have high enough resolution to -allow TensorFlow to “see” an artificially magnified target clearly. - -.. code-block:: java - - // The TensorFlow software will scale the input images from the camera to a lower resolution. - // This can result in lower detection accuracy at longer distances (> 55cm or 22"). - // If your target is at distance greater than 50 cm (20") you can adjust the magnification value - // to artificially zoom in to the center of image. For best results, the "aspectRatio" argument - // should be set to the value of the images used to create the TensorFlow Object Detection model - // (typically 16/9). - tfod.setZoom(2.5, 16.0/9.0); - -If a zoom factor has been set, then the Camera Stream preview on the -Driver Station will show the cropped area that makes up the artificially -magnified image. - -.. figure:: images/235_Java_TFOD_Sample_camera_stream.png - :align: center + } // end method initTfod() - Camera Stream preview indicating magnified area. - -Iterating and Processing List of Recognized Objects -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -The op mode will then iterate until a Stop command is received. At the -beginning of each iteration, the op mode will check with the object -detector to see how many objects it recognizes in its field of view. In -the code section below, the variable “updatedRecognitions” is set to a -list of objects that were recognized using the TensorFlow technology. - -.. code-block:: java - - if (opModeIsActive()) { - while (opModeIsActive()) { - if (tfod != null) { - // getUpdatedRecognitions() will return null if no new information is available since - // the last time that call was made. - List updatedRecognitions = tfod.getUpdatedRecognitions(); - if (updatedRecognitions != null) { - telemetry.addData("# Object Detected", updatedRecognitions.size()); - - // step through the list of recognitions and display boundary info. - int i = 0; - for (Recognition recognition : updatedRecognitions) { - telemetry.addData(String.format("label (%d)", i), recognition.getLabel()); - telemetry.addData(String.format(" left,top (%d)", i), "%.03f , %.03f", - recognition.getLeft(), recognition.getTop()); - telemetry.addData(String.format(" right,bottom (%d)", i), "%.03f , %.03f", - recognition.getRight(), recognition.getBottom()); - i++; - } - telemetry.update(); - } - } - } - } +For the **TFOD Processor**, the method ``easyCreateWithDefaults()`` uses +standard default settings. Most teams don’t need to modify these, +especially for the built-in TFOD model (white Pixel). + +For the **VisionPortal**, the method ``easyCreateWithDefaults()`` +requires parameters for camera name and processor(s) used, but otherwise +uses standard default settings such as: + +- camera resolution 640 x 480 + +- non-compressed streaming format YUY2 + +- enable RC preview (called LiveView) -If the list is not empty, then the op mode iterates through the list and -sends information via telemetry about each detected object. - -Modifying the Sample to Indicate Duck Detected -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Let’s modify this sample Freight Frenzy op mode so it will set a -variable to indicate whether a Duck was detected, and show a Telemetry -message accordingly. Using the OnBot Java editor, modify the example -code so it looks like the following sample. - -Specifically, one new line initializes a Boolean variable -“isDuckDetected”, just before the “for loop” that will examine the list -of recognitions. - -Also, if the label reads “Duck” then set the variable isDuckDetected to -“true”, and send a telemetry message to indicate a Duck has been -recognized. Otherwise, or ELSE, set the variable to “false” and don’t -display the message. - -.. code-block:: java - - if (opModeIsActive()) { - while (opModeIsActive()) { - if (tfod != null) { - // getUpdatedRecognitions() will return null if no new information is available since - // the last time that call was made. - List updatedRecognitions = tfod.getUpdatedRecognitions(); - if (updatedRecognitions != null) { - telemetry.addData("# Object Detected", updatedRecognitions.size()); - - // step through the list of recognitions and display boundary info. - int i = 0; - boolean isDuckDetected = false; // ** ADDED ** - for (Recognition recognition : updatedRecognitions) { - telemetry.addData(String.format("label (%d)", i), recognition.getLabel()); - telemetry.addData(String.format(" left,top (%d)", i), "%.03f , %.03f", - recognition.getLeft(), recognition.getTop()); - telemetry.addData(String.format(" right,bottom (%d)", i), "%.03f , %.03f", - recognition.getRight(), recognition.getBottom()); - i++; - - // check label to see if the camera now sees a Duck ** ADDED ** - if (recognition.getLabel().equals("Duck")) { // ** ADDED ** - isDuckDetected = true; // ** ADDED ** - telemetry.addData("Object Detected", "Duck"); // ** ADDED ** - } else { // ** ADDED ** - isDuckDetected = false; // ** ADDED ** - } // ** ADDED ** - } - telemetry.update(); - } +- if TFOD and AprilTag processors are disabled, still display LiveView + (without annotations) + +These are good starting values for most teams. + +Telemetry Method +---------------- + +After DS START is touched, the OpMode continuously calls this method to +display telemetry about any TFOD recognitions: + +.. code:: java + + /** + * Add telemetry about TensorFlow Object Detection (TFOD) recognitions. + */ + private void telemetryTfod() { + + List currentRecognitions = tfod.getRecognitions(); + telemetry.addData("# Objects Detected", currentRecognitions.size()); + + // Step through the list of recognitions and display info for each one. + for (Recognition recognition : currentRecognitions) { + double x = (recognition.getLeft() + recognition.getRight()) / 2 ; + double y = (recognition.getTop() + recognition.getBottom()) / 2 ; + + telemetry.addData(""," "); + telemetry.addData("Image", "%s (%.0f %% Conf.)", recognition.getLabel(), recognition.getConfidence() * 100); + telemetry.addData("- Position", "%.0f / %.0f", x, y); + telemetry.addData("- Size", "%.0f x %.0f", recognition.getWidth(), recognition.getHeight()); + } // end for() loop + + } // end method telemetryTfod() + +In the first line of code, **all TFOD recognitions** are collected and +stored in a List variable. The camera might “see” more than one game +element in its field of view, even if not intended (i.e. for CENTERSTAGE +with 1 game element). + +The ``for() loop`` then iterates through that List, handling each item, +one at a time. Here the “handling” is simply processing certain TFOD +fields for DS Telemetry. + +The ``for() loop`` calculates the pixel coordinates of the **center** of +each Bounding Box (the preview’s colored rectangle around a recognized +object). + +Telemetry is created for the Driver Station, with the object’s name +(Label), recognition confidence level (percentage), and the Bounding +Box’s location and size (in pixels). + +For competition, you want to do more than display Telemetry, and you +want to exit the main OpMode loop at some point. These code +modifications are discussed in another section below. + +Resource Management +------------------- + +Vision processing is “expensive”, using much **CPU capacity and USB +bandwidth** to process millions of pixels streaming in from the camera. + +This Sample OpMode contains two optional features to remind teams about +resource management. Overall, the SDK provides :ref:`over 10 +tools ` +to manage these resources, allowing your OpMode to run effectively. + +As the first example, streaming images from the camera can be paused and +resumed. This is a very fast transition, freeing CPU resources (and +potentially USB bandwidth). + +.. code:: java + + + // Save CPU resources; can resume streaming when needed. + if (gamepad1.dpad_down) { + visionPortal.stopStreaming(); + } else if (gamepad1.dpad_up) { + visionPortal.resumeStreaming(); } - } - } -Rebuild the OnBot Java op mode and re-run it. The op mode should display -the new message, if a Duck is detected. Note that if you test this op -mode with multiple ring stacks, the order of the detected objects can -change with each iteration of your op mode. +Pressing the Dpad buttons, you can observe the off-and-on actions in the +RC preview (LiveView), described above. In your competition OpMode, +these streaming actions would be programmed, not manually controlled. -.. figure:: images/270_Java_TFOD_Sample_message.png - :align: center +The second example: after exiting the main loop, the VisionPortal is +closed. - The modified op mode should show a telemetry message if the Duck is detected +.. code:: java -You can continue modifying this sample op mode, to suit your team’s -autonomous strategy. For example, you might want to store (in a -variable) which Barcode position had the Duck. + // Save more CPU resources when camera is no longer needed. + visionPortal.close(); -Also, you must decide how the main “while loop” should actually stop -repeating, assuming the Duck’s position is discovered. (It now loops -until Stop is pressed.) For example, the loop could stop after the -camera has viewed all 3 Barcode positions. Or, if the camera’s view -includes more than one Barcode position, perhaps the Duck’s bounding box -location can provide the info you need. +Teams may consider this at any point when the VisionPortal is no longer +needed by the OpMode, freeing valuable CPU resources for other tasks. -In any case, when the op mode exits the “while loop”, your new variable -should hold the location of the Duck, which tells you the preferred -scoring level on the Alliance Shipping Hub. You op mode can continue -running, using that information. +Adjusting the Zoom Factor +------------------------- -Important Note Regarding Image Orientation -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +If the object to be recognized will be more than roughly 2 feet (61 cm) +from the camera, you might want to set the digital Zoom factor to a +value greater than 1. This tells TensorFlow to use an artificially +magnified portion of the image, which may offer more accurate +recognitions at greater distances. -If you are using a webcam with your Robot Controller, then the camera -orientation is fixed in landscape mode. However, if you are using a -smartphone camera, the system will interpret images based on the phone’s -orientation (Portrait or Landscape) at the time that the TensorFlow -object detector is created and initialized. +.. code:: java -Note that for Freight Frenzy, the default TensorFlow inference model is -optimized for a camera in landscape mode. This means that it is better -to orient your camera in landscape mode if you use this default -inference model because you will get more reliable detections. + // Indicate that only the zoomed center area of each + // image will be passed to the TensorFlow object + // detector. For no zooming, set magnification to 1.0. + tfod.setZoom(2.0); -If you execute the TensorFlow initialize command ``initTfod()`` while -the phone is in Portrait mode, then the images will be processed in -Portrait mode. +This ``setZoom()`` method can be placed in the INIT section of your +OpMode, -.. figure:: images/tfodPortrait.png - :align: center +- immediately after the call to the ``initTfod()`` method, or - If you initialize the detector in Portrait mode, then the images are processed in Portrait mode. +- as the very last command inside the ``initTfod()`` method. -The “Left” and “Right” values of an object’s bounding box correspond to -horizontal coordinate values, while the “Top” and “Bottom” values of an -object’s bounding box correspond to vertical coordinate values. +This method is **not** part of the Processor Builder pattern (used in +other TFOD Sample OpModes), so the Zoom factor can be set to other +values during the OpMode, if desired. -.. figure:: images/tfodBoundaries.png - :align: center +The “zoomed” region can be observed in the DS preview (Camera Stream) +and the RC preview (LiveView), surrounded by a greyed-out area that is +**not evaluated** by the TFOD Processor. - The “Left” and “Top” boundaries of a detection box when the image is in - Portrait mode. +Other Adjustments +----------------- -If you want to use your smartphone in Landscape mode, then make sure -that your phone is in Landscape mode when the TensorFlow object detector -is initialized. You may find that the Landscape mode is preferable for -this season’s game since it offers a wider field of view. +The Sample OpMode uses a default **minimum confidence** level of 75%. +This means the TensorFlow Processor needs a confidence level of 75% or +higher, to consider an object as “recognized” in its field of view. -.. figure:: images/tfodLandscape.png - :align: center +You can see the object name and actual confidence (as a **decimal**, +e.g. 0.96) near the Bounding Box, in the Driver Station preview (Camera +Stream) and Robot Controller preview (Liveview). - The system can also be run in Landscape mode. +.. code:: java -If the phone is in Landscape mode when the object detector is -initialized, then the images will be interpreted in Landscape mode. + // Set the minimum confidence at which to keep recognitions. + tfod.setMinResultConfidence((float) 0.75); -.. figure:: images/tfodBoundariesLandscape.png - :align: center - - The “Left” and “Top” boundaries of a detection box when the image is in Landscape mode - -Note that Android devices can be locked into Portrait Mode so that the -screen image will not rotate even if the phone is held in a Landscape -orientation. If your phone is locked in Portrait Mode, then the -TensorFlow object detector will interpret all images as Portrait images. -If you would like to use the phone in Landscape mode, then you need to -make sure your phone is set to “Auto-rotate” mode. In Auto-rotate mode, -if the phone is held in a Landscape orientation, then the screen will -auto rotate to display the contents in Landscape form. - -.. figure:: images/autorotate.png - :align: center +Adjust this parameter to a higher value if you would like the processor +to be more selective in identifying an object. - Auto-rotate must be enabled in order to operate in Landscape mode +Another option is to define, or clip, a **custom area for TFOD +evaluation**, unlike ``setZoom`` which is always centered. -Deactivating TensorFlow -~~~~~~~~~~~~~~~~~~~~~~~ +.. code:: java -When the example op mode is no longer active (i.e., when the user has -pressed the stop button on the Driver Station) the op mode will attempt -to deactivate the TensorFlow library before it’s done. It’s important to -deactivate the library to free up system resources. + // Set the number of pixels to obscure on the left, top, + // right, and bottom edges of each image passed to the + // TensorFlow object detector. The size of the images are not + // changed, but the pixels in the margins are colored black. + tfod.setClippingMargins(0, 200, 0, 0); -.. code-block:: java +Adjust the four margins as desired, in units of pixels. - if (tfod != null) { - tfod.shutdown(); - } +These methods can be placed in the INIT section of your OpMode, + +- immediately after the call to the ``initTfod()`` method, or + +- as the very last commands inside the ``initTfod()`` method. + +As with ``setZoom``, these methods are **not** part of the Processor +Builder pattern (used in other TFOD Sample OpModes), so they can be set +to other values during the OpMode, if desired. + +Modifying the Sample +-------------------- + +In this Sample OpMode, the main loop ends only when the DS STOP button +is touched. For competition, teams should **modify this code** in at +least two ways: + +- for a significant recognition, take action or store key information – + inside the ``for() loop`` + +- end the main loop based on your criteria, to continue the OpMode + +As an example, you might set a Boolean variable ``isPixelDetected`` to +``true``, if a significant recognition has occurred. + +You might also evaluate and store which randomized Spike Mark (red or +blue tape stripe) holds the white Pixel. + +Regarding the main loop, it could end after the camera views all three +Spike Marks, or after your code provides a high-confidence result. If +the camera’s view includes more than one Spike Mark position, perhaps +the white Pixel’s **Bounding Box** size and location could be useful. +Teams should consider how long to seek an acceptable recognition, and +what to do otherwise. + +In any case, the OpMode should exit the main loop and continue running, +using any stored information. +Best of luck this season! -================== +============ -Updated 10/21/21 +Questions, comments and corrections to westsiderobotics@verizon.net diff --git a/docs/source/programming_resources/vision/java_tfod_opmode_custom/images/010-TFOD-recognition.png b/docs/source/programming_resources/vision/java_tfod_opmode_custom/images/010-TFOD-recognition.png new file mode 100644 index 0000000000000000000000000000000000000000..61389e5643467eb609a47adb06f7807292e0e48d GIT binary patch literal 357922 zcmYJ4WmHsg*Y;_p1VyQ#1OW*_5a~ubMnO8IYv}Hfl9mSPmKeIb8M=od1%?Ty*IX&cDrtsEC090A(WWk%kFe=D$w%p{!HQ8_S|s$+93~oo&jK?}k^}MNUsV 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zjgDKL=DL61@}AeZ8nH}~9nE!ED=YnY#!=VEk6u-O@|l{KyoSK}{mppO^=kU-KmW7N z=I715&6ik*N-B~0@Lj*_yFFaN`?-*D)OCJ8E-sip?{CQhbSuB+`}*d_`CP7T?cVGM z>rU%$ovQXv_T!s%^5a^Xf4o=U)sKLE8vdI5e4ngozGHsKpdaq2$cWe8T(5I3WNvCN zcGj_EH}}w=>Tf>l+N^`l2={u^tZU6l`ux9~E%qUsiF2>eXn@kXPjSLZ2iuKj76sA->2#`xmNn(Py6Cccg>^E zyt&_-R` + +For the 2023-2024 game CENTERSTAGE, the official game element is a +hexagonal white **Pixel**. The FTC SDK software contains a TFOD model of +this object, ready for recognition. That default model was created with +a Machine Learning process described here: + +- :ref:`FIRST Machine Learning Toolchain` + +.. figure:: images/010-TFOD-recognition.png + :align: center + :width: 85% + :alt: TFOD Recognition + + Example Pixel Recognition using TFOD + +For extra points, FTC teams may instead use their own custom TFOD models +of game elements, called **Team Props** in CENTERSTAGE. That option is +covered in this tutorial, after showing how to use the default model. +Custom TFOD models are created by teams using the same Machine +Learning process: + +- :ref:`FIRST Machine Learning Toolchain` + +.. figure:: images/020-team-props.png + :align: center + :width: 85% + :alt: Team Props + + Example Team Props + +This tutorial shows **OnBot Java** screens. Users of **Android Studio** +can follow along with a few noted exceptions, since the Sample OpMode is +exactly the same. + +Creating the OpMode +------------------- + +At the FTC **OnBot Java** browser interface, click on the large black +**plus-sign icon** “Add File”, to open the New File dialog box. + +.. figure:: images/100-New-File.png + :align: center + :width: 85% + :alt: New File Dialog + + Example New File Dialog + +Specify a name for your new OpMode. Select +**“ConceptTensorFlowObjectDetection”** as the Sample OpMode to be the +template for your new OpMode. + +This Sample has optional gamepad inputs, so it could be designated as a +**TeleOp** OpMode (see green oval above). + +Click “OK” to create your new OpMode. + +\ **Android Studio** users should follow the commented instructions to +copy this class from the Samples folder to the Teamcode folder, with a +new name. Also remove the ``@Disabled`` annotation, to make the OpMode +visible in the Driver Station list. + +The new OpMode should appear in the editing window of OnBot Java. + +.. figure:: images/110-Sample-Open.png + :align: center + :width: 85% + :alt: Sample Open Dialog + + Sample Open Dialog + +By default, the Sample OpMode assumes you are using a webcam, configured +as “Webcam 1”. If instead you are using the built-in camera on your +Android RC phone, change the USE_WEBCAM Boolean from ``true`` to +``false`` (orange oval above). + +Preliminary Testing +------------------- + +This Sample OpMode is **ready to use**, for detecting the +default/built-in model (white Pixel for CENTERSTAGE). + +If **Android Studio** users get a DS error message “Loading model from +asset failed”, skip to the next section “Downloading the Model”. + +Click the “Build Everything” button (wrench icon at lower right), and +wait for confirmation “BUILD SUCCESSFUL”. + +If Build is prevented by some other OpMode having errors/issues, they +must be fixed before your new OpMode can run. For a quick fix, you could +right-click on that filename and choose “Disable/Comment”. This +“comments out” all lines of code, effectively removing that file from +the Build. That file can be re-activated later with “Enable/Uncomment”. + +In Android Studio (or OnBot Java), you can open a problem class/OpMode +and type **CTRL-A** and **CTRL-/** to select and “comment out” all lines +of code. This is reversible with **CTRL-A** and **CTRL-/** again. + +Now run your new OpMode from the Driver Station (in the TeleOp list, if +so designated). The OpMode should recognize any CENTERSTAGE white Pixel +within the camera’s view, based on the trained TFOD model. + +For a **preview** during the INIT phase, touch the Driver Station’s +3-dot menu and select **Camera Stream**. + +.. figure:: images/200-Sample-DS-Camera-Stream.png + :align: center + :width: 85% + :alt: Sample DS Camera Stream + + Sample DS Camera Stream + +Camera Stream is not live video; tap to refresh the image. Use the small +white arrows at bottom right to expand or revert the preview size. To +close the preview, choose 3-dots and Camera Stream again. + +After the DS START button is touched, the OpMode displays Telemetry for +any recognized Pixel(s): + +.. figure:: images/210-Sample-DS-Telemetry.png + :align: center + :width: 85% + :alt: Sample DS Telemetry + + Sample DS Telemetry + +The above Telemetry shows the Label name, and TFOD recognition +confidence level. It also gives the **center location** and **size** (in +pixels) of the Bounding Box, which is the colored rectangle surrounding +the recognized object. + +The pixel origin (0, 0) is at the top left corner of the image. + +Before and after DS START is touched, the Robot Controller provides a +video preview called **LiveView**. + +.. figure:: images/240-Sample-RC-LiveView.png + :align: center + :width: 85% + :alt: Sample RC LiveView + + Sample RC LiveView + +For Control Hub (with no built-in screen), plug in an HDMI monitor or +learn about ``scrcpy`` (https://github.com/Genymobile/scrcpy). The +above image is a LiveView screenshot via ``scrcpy``. + +If you don’t have a physical Pixel on hand, try pointing the camera at +this image: + +.. figure:: images/300-Sample-Pixel.png + :align: center + :width: 85% + :alt: Sample Pixel + + Sample Pixel + +For a larger view, right-click the image to open in a new browser tab. + +\ **Congratulations!**\ At this point the Sample OpMode and your camera +are working properly. Ready for a custom model? + +Downloading the Model +--------------------- + +Now we describe how to load a trained inference model in the form of a +TensorFlow Lite (``.tflite``) file. + +Instead of an **actual custom model**, here we use the standard FTC +model of the white Pixel from CENTERSTAGE (2023-2024). Later, your team +will follow this **same process** with your custom TFOD model, +specifying its filename and labels (objects to recognize). + +The standard ``.tflite`` file (white Pixel) is available on GitHub at +the following link: + +- CENTERSTAGE TFLite File (https://github.com/FIRST-Tech-Challenge/WikiSupport/blob/master/tensorflow/CenterStage.tflite) + +.. note:: + For competition, teams can use the + :ref:`FIRST Machine Learning Toolchain` + to train their own custom models of Team Props. Then use the process + described here; simply specify your custom model filename and labels. + + Very advanced teams could use Google's TensorFlow Object Detection + API (https://github.com/tensorflow/models/tree/master/research/object_detection) + to create their own custom inference model. + +Click the “Download Raw File” button to download the +``CenterStage.tflite`` file from GitHub to your local device +(e.g. laptop). See the green arrow. + +.. figure:: images/030-Centerstage-public-repo.png + :align: center + :width: 85% + :alt: Public Repo + + Public Repo + +Uploading to the Robot Controller +--------------------------------- + +Next, OnBot Java users will upload the TFOD model to the Robot +Controller. Connect your laptop to your Robot Controller’s wireless +network, open the Chrome browser, and navigate to the FTC “Manage” page: + +.. figure:: images/040-Manage-page.png + :align: center + :width: 85% + :alt: RC Manage Page + + Robot Controller Manage Page + +\ **Android Studio** users should instead skip to the instructions at +the bottom of this section. + +Scroll down and click on “Manage TensorFlow Lite Models”. + +.. figure:: images/050-Manage-TFLite-Models.png + :align: center + :width: 85% + :alt: TensorFlow Lite Model Management + + TensorFlow Lite Model Management + +Now click the “Upload Models” button. + +.. figure:: images/060-Upload-Models.png + :align: center + :width: 85% + :alt: Uploading Models + + Upload Models + +Click “Choose Files”, and use the dialog box to find and select the +downloaded ``CenterStage.tflite`` file. + +.. figure:: images/070-Choose-Files.png + :align: center + :width: 85% + :alt: Choose Files + + Choose Files + +Now the file will upload to the Robot Controller. The file will appear +in the list of TensorFlow models available for use in OpModes. + +.. figure:: images/080-Centerstage-tflite.png + :align: center + :width: 85% + :alt: CenterStage TFLITE Uploaded + + CENTERSTAGE TFLITE File Uploaded + +\ **Android Studio** users should instead store the TFOD model in the +project **assets** folder. At the left side, look under +``FtcRobotController`` for the folder ``assets``. If it’s missing, +right-click ``FtcRobotController``, choose ``New``, ``Directory`` and +``src\main\assets``. Right-click ``assets``, choose ``Open In`` and +``Explorer``, then copy/paste your ``.tflite`` file into that assets +folder. + +Basic OpMode Settings +--------------------- + +This Sample OpMode can now be modified, to detect the uploaded TFOD +model. + +Again, this tutorial uploaded the standard TFOD model (white Pixel for +CENTERSTAGE), just to demonstrate the process. Use the same steps for +your custom TFOD model. + +First, change the filename here: + +.. code:: java + + private static final String TFOD_MODEL_FILE = "/sdcard/FIRST/tflitemodels/myCustomModel.tflite"; + +to this: + +.. code:: java + + private static final String TFOD_MODEL_FILE = "/sdcard/FIRST/tflitemodels/CenterStage.tflite"; + +Later, you can change this filename back to the actual name of your +custom TFOD model. Here we are using the default (white Pixel) model +just downloaded. + +========= + +**Android Studio** users should instead verify or store the TFOD model +in the project **assets** folder as noted above, and use: + +.. code:: java + + private static final String TFOD_MODEL_ASSET = "CenterStage.tflite"; + +OR (for a custom model) + +.. code:: java + + private static final String TFOD_MODEL_ASSET = "MyModelStoredAsAsset.tflite"; + +========= + +For this example, the following line **does not** need to be changed: + +.. code:: java + + // Define the labels recognized in the model for TFOD (must be in training order!) + private static final String[] LABELS = { + "Pixel", + }; + +… because “Pixel” is the correct and only TFOD Label in the standard +model file. + +Later, you might have custom Labels like “myRedProp” and “myBlueProp” +(for CENTERSTAGE). The list should be in alphabetical order and contain +the labels in the dataset(s) used to make the TFOD model. + +========== + +Next, scroll down to the Java method ``initTfod()``. + +Here is the Java **Builder pattern**, used to specify various settings +for the TFOD Processor. + +.. figure:: images/140-Builder-settings.png + :align: center + :width: 85% + :alt: Builder Pattern Settings + + Builder Pattern Settings + +The **yellow ovals** indicate its distinctive features: **create** the +Processor object with ``new Builder()``, and **close/finalize** with the +``.build()`` method. + +This is the streamlined version of the Builder pattern. Notice all the +``.set`` methods are “chained” to form a single Java expression, ending +with a semicolon after ``.build()``. + +Uncomment two Builder lines, circled above in green: + +.. code:: java + + .setModelFileName(TFOD_MODEL_FILE) + .setModelLabels(LABELS) + +\ **Android Studio** users should instead uncomment the lines +``.setModelAssetName(TFOD_MODEL_ASSET)`` and +``.setModelLabels(LABELS)``. + +These Builder settings tell the TFOD Processor which model and labels to +use for evaluating camera frames. + +\ **That’s it!**\ You are ready to test this Sample OpMode again, this +time using a “custom” (uploaded) TFOD model. + +Testing with Custom Model +------------------------- + +In OnBot Java, click the “Build Everything” button (wrench icon at lower +right), and wait for confirmation “BUILD SUCCESSFUL”. + +Now run your updated OpMode from the Driver Station. The OpMode should +recognize objects within the camera’s view, based on the trained TFOD +model. + +Test the **Camera Stream** preview during the INIT phase. + +.. figure:: images/200-Sample-DS-Camera-Stream.png + :align: center + :width: 85% + :alt: Sample DS Camera Stream + + Sample DS Camera Stream + +Tap to refresh the image. Expand or revert the preview size as needed. +Close the preview, with 3-dots and Camera Stream again. + +After the DS START button is touched, the OpMode displays Telemetry for +any recognized object(s): + +.. figure:: images/210-Sample-DS-Telemetry.png + :align: center + :width: 85% + :alt: Sample DS Telemetry + + Sample DS Telemetry + +The above Telemetry shows the Label name, and TFOD recognition +confidence level. It also gives the **center location** and **size** (in +pixels) of the Bounding Box, which is the colored rectangle surrounding +the recognized object. + +Also test the RC’s video **LiveView**, using HDMI or +```scrcpy`` `__: + +.. figure:: images/240-Sample-RC-LiveView.png + :align: center + :width: 85% + :alt: Sample RC LiveView + + Sample RC LiveView + +For a large view of this standard model, right-click the image to open +in a new browser tab: + +.. figure:: images/300-Sample-Pixel.png + :align: center + :width: 85% + :alt: Sample Pixel + + Sample Pixel + +When your team creates, uploads and specifies a custom model containing +**red and blue Team Props**, the OpMode will recognize and process those +– instead of the standard model shown here. + +Program Logic and Initialization +-------------------------------- + +How does this simple OpMode work? + +- During the INIT stage (before DS START is touched), this OpMode calls + a **method to initialize** the TFOD Processor and the FTC + VisionPortal. + +- After DS START is touched, the OpMode runs a continuous loop, calling + a **method to display telemetry** about any TFOD recognitions. + +- The OpMode also contains optional features to remind teams about + **CPU resource management**, useful in vision processing. + +You’ve already seen the first part of the method ``initTfod()`` which +uses a streamlined, or “chained”, sequence of Builder commands to create +the TFOD Processor. + +The second part of that method uses regular, non-chained, Builder +commands to create the VisionPortal. + +.. code:: java + + // Create the vision portal by using a builder. + VisionPortal.Builder builder = new VisionPortal.Builder(); + + // Set the camera (webcam vs. built-in RC phone camera). + if (USE_WEBCAM) { + builder.setCamera(hardwareMap.get(WebcamName.class, "Webcam 1")); + } else { + builder.setCamera(BuiltinCameraDirection.BACK); + } + + // Choose a camera resolution. Not all cameras support all resolutions. + builder.setCameraResolution(new Size(640, 480)); + + // Enable the RC preview (LiveView). Set "false" to omit camera monitoring. + builder.enableLiveView(true); + + // Set the stream format; MJPEG uses less bandwidth than default YUY2. + builder.setStreamFormat(VisionPortal.StreamFormat.YUY2); + + // Choose whether or not LiveView stops if no processors are enabled. + // If set "true", monitor shows solid orange screen if no processors enabled. + // If set "false", monitor shows camera view without annotations. + builder.setAutoStopLiveView(false); + + // Set and enable the processor. + builder.addProcessor(tfod); + + // Build the Vision Portal, using the above settings. + visionPortal = builder.build(); + +All settings have been uncommented here, to see them more easily. + +Here the ``new Builder()`` creates a separate ``VisionPortal.Builder`` +object called ``builder``, allowing traditional/individual Java method +calls for each setting. For the streamlined “chained” TFOD process, the +``new Builder()`` operated directly on the TFOD Processor called +``tfod``, without creating a ``TfodProcessor.Builder`` object. Both +approaches are valid. + +Notice the process again **closes** with a call to the ``.build()`` +method. + +Telemetry Method +---------------- + +After DS START is touched, the OpMode continuously calls this method to +display telemetry about any TFOD recognitions: + +.. code:: java + + /** + * Add telemetry about TensorFlow Object Detection (TFOD) recognitions. + */ + private void telemetryTfod() { + + List currentRecognitions = tfod.getRecognitions(); + telemetry.addData("# Objects Detected", currentRecognitions.size()); + + // Step through the list of recognitions and display info for each one. + for (Recognition recognition : currentRecognitions) { + double x = (recognition.getLeft() + recognition.getRight()) / 2 ; + double y = (recognition.getTop() + recognition.getBottom()) / 2 ; + + telemetry.addData(""," "); + telemetry.addData("Image", "%s (%.0f %% Conf.)", recognition.getLabel(), recognition.getConfidence() * 100); + telemetry.addData("- Position", "%.0f / %.0f", x, y); + telemetry.addData("- Size", "%.0f x %.0f", recognition.getWidth(), recognition.getHeight()); + } // end for() loop + + } // end method telemetryTfod() + +In the first line of code, **all TFOD recognitions** are collected and +stored in a List variable. The camera might “see” more than one game +element in its field of view, even if not intended (i.e. for CENTERSTAGE +with 1 game element). + +The ``for() loop`` then iterates through that List, handling each item, +one at a time. Here the “handling” is simply processing certain TFOD +fields for DS Telemetry. + +The ``for() loop`` calculates the pixel coordinates of the **center** of +each Bounding Box (the preview’s colored rectangle around a recognized +object). + +Telemetry is created for the Driver Station, with the object’s name +(Label), recognition confidence level (percentage), and the Bounding +Box’s location and size (in pixels). + +For competition, you want to do more than display Telemetry, and you +want to exit the main OpMode loop at some point. These code +modifications are discussed in another section below. + +Resource Management +------------------- + +Vision processing is “expensive”, using much **CPU capacity and USB +bandwidth** to process millions of pixels streaming in from the camera. + +This Sample OpMode contains three optional features to remind teams +about resource management. Overall, the SDK provides +:ref:`over 10 tools ` +to manage these resources, allowing your OpMode to run effectively. + +As the first example, **streaming images** from the camera can be paused +and resumed. This is a very fast transition, freeing CPU resources (and +potentially USB bandwidth). + +.. code:: java + + + // Save CPU resources; can resume streaming when needed. + if (gamepad1.dpad_down) { + visionPortal.stopStreaming(); + } else if (gamepad1.dpad_up) { + visionPortal.resumeStreaming(); + } + +Pressing the Dpad buttons, you can observe the off-and-on actions in the +RC preview (LiveView), described above. In your competition OpMode, +these streaming actions would be programmed, not manually controlled. + +=========== + +The second example, commented out, similarly allows a vision processor +(TFOD and/or AprilTag) to be disabled and re-enabled: + +.. code:: java + + //Disable or re-enable the TFOD processor at any time. + visionPortal.setProcessorEnabled(tfod, true); + +Simply set the Boolean to ``false`` (to disable), or ``true`` (to +re-enable). + +=========== + +The third example: after exiting the main loop, the VisionPortal is +closed. + +.. code:: java + + // Save more CPU resources when camera is no longer needed. + visionPortal.close(); + +Teams may consider this at any point when the VisionPortal is no longer +needed by the OpMode, freeing valuable CPU resources for other tasks. + +Adjusting the Zoom Factor +------------------------- + +If the object to be recognized will be more than roughly 2 feet (61 cm) +from the camera, you might want to set the digital Zoom factor to a +value greater than 1. This tells TensorFlow to use an artificially +magnified portion of the image, which may offer more accurate +recognitions at greater distances. + +.. code:: java + + // Indicate that only the zoomed center area of each + // image will be passed to the TensorFlow object + // detector. For no zooming, set magnification to 1.0. + tfod.setZoom(2.0); + +This ``setZoom()`` method can be placed in the INIT section of your +OpMode, + +- immediately after the call to the ``initTfod()`` method, or + +- as the very last command inside the ``initTfod()`` method. + +This method is **not** part of the TFOD Processor Builder pattern, so +the Zoom factor can be set to other values during the OpMode, if +desired. + +The “zoomed” region can be observed in the DS preview (Camera Stream) +and the RC preview (LiveView), surrounded by a greyed-out area that is +**not evaluated** by the TFOD Processor. + +Other Adjustments +----------------- + +This Sample OpMode contains another adjustment, commented out: + +.. code:: java + + // Set confidence threshold for TFOD recognitions, at any time. + tfod.setMinResultConfidence(0.75f); + +The SDK uses a default **minimum confidence** level of 75%. This means +the TensorFlow Processor needs a confidence level of 75% or higher, to +consider an object as “recognized” in its field of view. + +You can see the object name and actual confidence (as a **decimal**, +e.g. 0.96) near the Bounding Box, in the Driver Station preview (Camera +Stream) and Robot Controller preview (Liveview). + +Adjust this parameter to a higher value if you want the processor to be +more selective in identifying an object. + +=========== + +Another option is to define, or clip, a **custom area for TFOD +evaluation**, unlike ``setZoom`` which is always centered. + +.. code:: java + + // Set the number of pixels to obscure on the left, top, + // right, and bottom edges of each image passed to the + // TensorFlow object detector. The size of the images are not + // changed, but the pixels in the margins are colored black. + tfod.setClippingMargins(0, 200, 0, 0); + +Adjust the four margins as desired, in units of pixels. + +These method calls can be placed in the INIT section of your OpMode, + +- immediately after the call to the ``initTfod()`` method, or + +- as the very last commands inside the ``initTfod()`` method. + +As with ``setProcessorEnabled()`` and ``setZoom()``, these methods are +**not** part of the Processor or VisionPortal Builder patterns, so they +can be set to other values during the OpMode, if desired. + +Modifying the Sample +-------------------- + +In this Sample OpMode, the main loop ends only when the DS STOP button +is touched. For CENTERSTAGE competition, teams should **modify this +code** in at least two ways: + +- for a significant recognition, take action or store key information – + inside the ``for() loop`` + +- end the main loop based on your criteria, to continue the OpMode + +As an example, you might set a Boolean variable ``isPixelDetected`` (or +``isPropDetected``) to ``true``, if a significant recognition has +occurred. + +You might also evaluate and store which randomized Spike Mark (red or +blue tape stripe) holds the white Pixel or Team Prop. + +Regarding the main loop, it could end after the camera views all three +Spike Marks, or after your code provides a high-confidence result. If +the camera’s view includes more than one Spike Mark position, perhaps +the Pixel/Prop’s **Bounding Box** size and location could be useful. +Teams should consider how long to seek an acceptable recognition, and +what to do otherwise. + +In any case, the OpMode should exit the main loop and continue running, +using any stored information. + +Best of luck this season! + +============ + +Questions, comments and corrections to westsiderobotics@verizon.net diff --git a/docs/source/programming_resources/vision/tensorflow_cs_2023/tensorflow-cs-2023.rst b/docs/source/programming_resources/vision/tensorflow_cs_2023/tensorflow-cs-2023.rst index 20d2c8b4..e8e66695 100644 --- a/docs/source/programming_resources/vision/tensorflow_cs_2023/tensorflow-cs-2023.rst +++ b/docs/source/programming_resources/vision/tensorflow_cs_2023/tensorflow-cs-2023.rst @@ -453,10 +453,7 @@ Hopefully this gives you quite a few ideas for how to approach this challenge! Using Custom TensorFlow models in Blocks and Java ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Instructions on using Custom TensorFlow Models in -:ref:`Blocks `, -:ref:`OnBot-Java `, -and :ref:`Android Studio ` can be found -in the :doc:`FTC-ML documentation <../../../ftc_ml/index>`, in the -:doc:`Implementing in Robot Code <../../../ftc_ml/implement/index>` section. +Instructions on using Custom TensorFlow Models in Blocks, OnBot-Java, and +Android Studio can be found in the :doc:`FTC-ML documentation <../../../ftc_ml/index>`, +in the :doc:`Implementing in Robot Code <../../../ftc_ml/implement/index>` section. diff --git a/docs/source/programming_resources/vision/tensorflow_ff_2021/tensorflow-ff-2021.rst b/docs/source/programming_resources/vision/tensorflow_ff_2021/tensorflow-ff-2021.rst index 562d7a7d..3df1280a 100644 --- a/docs/source/programming_resources/vision/tensorflow_ff_2021/tensorflow-ff-2021.rst +++ b/docs/source/programming_resources/vision/tensorflow_ff_2021/tensorflow-ff-2021.rst @@ -1,5 +1,5 @@ -Using TensorFlow in Freight Frenzy -=================================== +TensorFlow for FREIGHT FRENZY presented by Raytheon Technologies +================================================================ What is TensorFlow? ~~~~~~~~~~~~~~~~~~~ @@ -83,7 +83,7 @@ Click on the following links to learn more about these sample Op Modes. - :ref:`Blocks TensorFlow Object Detection Example ` - :ref:`Java TensorFlow Object Detection - Example ` + Example ` Using a Custom Inference Model ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/docs/source/programming_resources/vision/tensorflow_pp_2022/tensorflow_pp_2022.rst b/docs/source/programming_resources/vision/tensorflow_pp_2022/tensorflow_pp_2022.rst index ac148bb4..1aa45b70 100644 --- a/docs/source/programming_resources/vision/tensorflow_pp_2022/tensorflow_pp_2022.rst +++ b/docs/source/programming_resources/vision/tensorflow_pp_2022/tensorflow_pp_2022.rst @@ -384,10 +384,8 @@ top which creates a very unique pattern for TensorFlow to recognize. Be creative Using Custom TensorFlow models in Blocks and Java ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Instructions on using Custom TensorFlow Models in -:ref:`Blocks `, -:ref:`OnBot-Java `, -and :ref:`Android Studio ` can be found +Instructions on using Custom TensorFlow Models in Blocks, OnBot-Java, +and Android Studio can be found in the :doc:`FTC-ML documentation <../../../ftc_ml/index>`, in the :doc:`Implementing in Robot Code <../../../ftc_ml/implement/index>` section.

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If not, be -sure to check out the :ref:`OnBot Java -Guide ` -document before proceeding. - -1. In On Bot Java, there are three sample Opmodes to choose from. Each - has its own use case. Depending on your use case choose one (or more) - of the following. - - - ``ConceptTensorFlowObjectDetection`` - - - This opmode is designed to use the robot controller's internal - camera, if it has one, for the input for TFOD. - - - ``ConceptTensorFlowObjectDetectionSwitchableCameras`` - - - This opmode is designed to be able to switch between two - different webcams for the input for TFOD. - - - ``ConceptTensorFlowObjectDetectionWebcam`` - - - This opmode is designed to use an external webcam as its input - for TFOD - -2. Click the “Add Files” button in the top left corner. - -.. - - .. figure:: images/image31.png - :align: center - -3. In the bottom half of the popup click the drop down below “Sample”. - Then select the opmode that you chose earlier in Step 1. Then in the - “File Name” text box enter the desired name of your opmode. Then - click the blue “OK” button. - -.. - - .. figure:: images/image32.png - :align: center - -4. The next step is to upload the TFOD model that you created using - FTC-ML. To do this go to the ribbon on the top of your windows and - select “Manage”. - -.. figure:: images/image33.png - :align: center - -5. If you're using **SDK 7.0 or older**, select the “Upload TensorFlow Lite Model File” - button and select the model (.tflite). Then click upload. - -.. figure:: images/image34.png - :width: 80% - :align: center - -If you're using **SDK 7.1 and newer**, in the same location you'll find a TensorFlow Lite Model File -Manager link. Click on that link to take you to the model file manager, and upload your model. -Here you can also view other models already uploaded, rename, and delete models. - -6. Then navigate back to the “OnBotJava” page. You will now have to - modify the name of the model that the opmode uses. To do this change - the ``TFOD_MODEL_ASSET`` variable such that ``“FreightFrenzy_BCDM.tflite”`` - is replaced with the name and fully qualified path for the file that - you uploaded earlier. The fully qualified path to where the tflite - models live can be seen in the example below: - -``private static final String TFOD_MODEL_ASSET = -"/sdcard/FIRST/tflitemodels/myCustomFreightFrenzyModel.tflite";`` - -.. tip:: On some older models of phones, the upload path may be - different. On some phones, like the Motorola G2 phone, the model - path should instead be set to ``private static final String TFOD_MODEL_ASSET = "/storage/emulated/0/FIRST/tflitemodels/myCustomFreightFrenzyModel.tflite";`` - -7. Change from “loadModelFromAsset” to “loadModelFromFile” in the line - below in your opmode. - -.. figure:: images/image35.png - :align: center - -8. Modify the labels of the model such that it matches those of your - model. You need at least one label. The order should be alphabetical - and be composed of the labels in the datset(s) used to make the - model. - -.. - - .. figure:: images/image36.png - :align: center - -9. You will now need to acquire a Vuforia License Key. To do this - follow the instructions provided in the opmode. They will be found - above the line shown below. The final step will be to replace ``" -- - YOUR NEW VUFORIA KEY GOES HERE --- "`` with your actual key. - -10. (optional) You may find it necessary to adjust the zoom. By default - it is set to 2.5 but feel free to adjust this to your needs. Note - that this is digital zoom not optical zoom. This means that in - essence you are simply telling TFOD to ignore outer sections of the - image. - -.. figure:: images/image37.png - :align: center - -11. If you are using ``ConceptTensorFlowObjectDetectionWebcam`` or - ``ConceptTensorFlowObjectDetectionSwitchableCameras`` you will also need - to update the device name of the camera. Simply change the device - name to correspond with your config. In the image below the name of - the webcam is ``“Webcam 1”``. - -.. - - .. figure:: images/image38.png - :align: center - -12. And you are all set to test. After clicking the build button just - open the opmode as you would any other opmode and select initialize - and play. Have fun testing. diff --git a/docs/source/game_specific_resources/blog/blog.rst b/docs/source/game_specific_resources/blog/blog.rst new file mode 100644 index 00000000..6876e93f --- /dev/null +++ b/docs/source/game_specific_resources/blog/blog.rst @@ -0,0 +1,15 @@ +*FIRST* Tech Challenge Blog +=========================== + +The official `FIRST Tech Challenge Blog `__ +is an information resource for *FIRST* Tech Challenge teams. The Blog hosts articles +that go in-depth on topics that the +`Team Email Blasts `__ +just can't fully cover. + +.. only:: html + + .. raw:: html + + + diff --git a/docs/source/index.rst b/docs/source/index.rst index 8bc36678..d425ab68 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -41,7 +41,7 @@ to see why. :maxdepth: 1 :hidden: - *FIRST* Tech Challenge Blog + game_specific_resources/blog/blog manuals/game_manuals/game_manuals Game Q&A Forum game_specific_resources/playing_field_resources/playing_field_resources diff --git a/docs/source/programming_resources/index.rst b/docs/source/programming_resources/index.rst index fa88fd87..d207c605 100644 --- a/docs/source/programming_resources/index.rst +++ b/docs/source/programming_resources/index.rst @@ -94,7 +94,9 @@ Topics for programming with TensorFlow Object Detection (TFOD) vision/tensorflow_pp_2022/tensorflow_pp_2022 vision/tensorflow_ff_2021/tensorflow-ff-2021 vision/blocks_tfod_opmode/blocks-tfod-opmode + vision/blocks_tfod_opmode_custom/blocks-tfod-opmode-custom vision/java_tfod_opmode/java-tfod-opmode + vision/java_tfod_opmode_custom/java-tfod-opmode-custom Vision Programming ~~~~~~~~~~~~~~~~~~~ diff --git a/docs/source/programming_resources/vision/blocks_tfod_opmode/blocks-tfod-opmode.rst b/docs/source/programming_resources/vision/blocks_tfod_opmode/blocks-tfod-opmode.rst index fb11fbaf..7fd0e75b 100644 --- a/docs/source/programming_resources/vision/blocks_tfod_opmode/blocks-tfod-opmode.rst +++ b/docs/source/programming_resources/vision/blocks_tfod_opmode/blocks-tfod-opmode.rst @@ -240,7 +240,7 @@ video preview called **LiveView**. Sample RC LiveView For Control Hub (with no built-in screen), plug in an HDMI monitor or -learn about ```scrcpy`` `__. The +learn about ``scrcpy`` (https://github.com/Genymobile/scrcpy). The above image is a LiveView screenshot via ``scrcpy``. If you don’t have a physical Pixel on hand, try pointing the camera at diff --git a/docs/source/programming_resources/vision/blocks_tfod_opmode_custom/blocks-tfod-opmode-custom.rst b/docs/source/programming_resources/vision/blocks_tfod_opmode_custom/blocks-tfod-opmode-custom.rst new file mode 100644 index 00000000..f04d5b9f --- /dev/null +++ b/docs/source/programming_resources/vision/blocks_tfod_opmode_custom/blocks-tfod-opmode-custom.rst @@ -0,0 +1,320 @@ +Blocks Custom Model Sample OpMode for TFOD +========================================== + +Introduction +------------ + +This tutorial uses an FTC Blocks Sample OpMode to load and recognize a +**custom TensorFlow inference model**. + +- In this example, the “custom model” is actually the standard trained + model of the 2023-2024 CENTERSTAGE game element called a **Pixel**. This + does not affect the process described for a custom model. + +- For competition, teams would instead use their own custom models of + **Team Props**, such as ``myTeamProp_Red`` and ``myTeamProp_Blue``, + developed using the FIRST Machine Learning Toolchain here: + + - :doc:`FIRST Machine Learning Toolchain <../../../ftc_ml/index>`. + +Downloading the Model +--------------------- + +The Robot Controller allows you to load a trained inference model in the +form of a TensorFlow Lite (``.tflite``) file. + +Here we use the standard FTC ``.tflite`` file from CENTERSTAGE +(2023-2024), available on GitHub at the following link: + +- `CENTERSTAGE TFLite File `__ + +For competition, teams can use the +:doc:`FIRST Machine Learning Toolchain <../../../ftc_ml/index>` +to train their own custom models of Team Props. + +.. note:: + Very advanced teams could use Google's TensorFlow Object Detection API + (https://github.com/tensorflow/models/tree/master/research/object_detection) + to create their own custom inference model. + +Click the “Download Raw File” button to download the +``CenterStage.tflite`` file from GitHub to your local device +(e.g. laptop). See the green arrow. + +.. figure:: images/012-Centerstage-public-repo.png + :align: center + :width: 85% + :alt: Public Repo for CenterStage file + + Public repo for CenterStage tflite file + +Uploading to the Robot Controller +--------------------------------- + +After downloading the file to your laptop, you need to upload it to the +Robot Controller. Connect your laptop to your Robot Controller’s +wireless network and navigate to the FTC “Manage” page: + +.. figure:: images/020-Manage-page.png + :align: center + :width: 85% + :alt: Manage Page + + Example of the Manage Page + +Scroll down and click on “Manage TensorFlow Lite Models”. + +.. figure:: images/030-Manage-TFLite-Models.png + :align: center + :width: 85% + :alt: Managing TFLITE Models + + Manage TFLITE Models Link + +Now click the “Upload Models” button. + +.. figure:: images/040-Upload-Models.png + :align: center + :width: 85% + :alt: Upload TFLITE Model + + Upload TFLITE Models Button + +Click “Choose Files”, and use the dialog box to find and select the +downloaded ``CenterStage.tflite`` file. + +.. figure:: images/050-Choose-Files.png + :align: center + :width: 85% + :alt: Upload TFLITE Model + + Upload TFLITE Models Button + +Now the file will upload to the Robot Controller. The file will appear +in the list of TensorFlow models available for use in OpModes. + +.. figure:: images/060-Centerstage-tflite.png + :align: center + :width: 85% + :alt: Model Listed + + TFLITE Model Listed + +Creating the OpMode +------------------- + +Click on the “Blocks” tab at the top of the screen to navigate to the +Blocks Programming page. Click on the “Create New OpMode” button to +display the Create New OpMode dialog box. + +Specify a name for your new OpMode. Select +“ConceptTensorFlowObjectDetectionCustomModel” as the Sample OpMode that +will be the template for your new OpMode. + +If no webcam is configured for your REV Control Hub, the dialog box will +display a warning message (shown here). You can ignore this warning +message if you will use the built-in camera of an Android RC phone. +Click “OK” to create your new OpMode. + +.. figure:: images/createNewOpMode.png + :align: center + :width: 85% + :alt: Create OpMode + + Create New OpMode + +The new OpMode should appear in edit mode in your browser. + +.. figure:: images/100-Sample-OpMode-header.png + :align: center + :width: 85% + :alt: Sample OpMode + + Sample OpMode + +By default, the Sample OpMode assumes you are using a webcam, configured +as “Webcam 1”. If you are using the built-in camera on your Android RC +phone, change the USE_WEBCAM Boolean from ``true`` to ``false`` (green +arrow above). + +Loading the Custom Model +------------------------ + +Scroll down in the OpMode, to the Blocks Function called “initTfod”. + +In the Block with “.setModelFileName”, change the filename from +“MyCustomModel.tflite” to ``CenterStage.tflite`` – or other filename +that you uploaded to the Robot Controller. The filename must be an exact +match. See green oval below. + +.. figure:: images/120-Init-Tfod.png + :align: center + :width: 85% + :alt: Init TFOD Function + + Init TFOD Function + +When loading an inference model, you must specify a list of **labels** +that describe the known objects in the model. This is done in the next +Block, with “.setModelLabels”. + +This Sample OpMode assumes a default model with two known objects, +labeled “ball” and “cube”. The CENTERSTAGE model contains only one +object, labeled “Pixel”. + +For competition, the **Team Prop** label names might be +``myTeamProp_Red`` and/or ``myTeamProp_Blue``. + +The number of labels can be changed by clicking the small blue gear icon +for the “create list with” Block (see yellow arrow). + +.. figure:: images/145-blue-gear-delete.png + :align: center + :width: 85% + :alt: Blue Gear Delete + + Blue Gear Delete + +In the pop-up layout balloon, click on one of the list items to select +it (green arrow above). Then remove it, by pressing Delete (on +keyboard), or by dragging it to the balloon’s left-side grey zone. + +After editing that purple “list” structure, click the blue gear icon +again to close the layout balloon. Edit the remaining label to “Pixel”. + +When complete, the edited Blocks should look like this: + +.. figure:: images/147-Centerstage-Blocks.png + :align: center + :width: 85% + :alt: Adding Pixel Label + + Adding Pixel Label + +Adjusting the Zoom Factor +------------------------- + +If the object to be recognized will be more than roughly 2 feet (61 cm) +from the camera, you might want to set the digital zoom factor to a +value greater than 1. This tells TensorFlow to use an artificially +magnified portion of the image, which may offer more accurate +recognitions at greater distances. + +.. figure:: images/150-setZoom.png + :align: center + :width: 85% + :alt: Set Zoom + + Set Zoom + +Pull out the **“setZoom” Block**, found in the toolbox or palette called +“Vision”, under “TensorFlow” and “TfodProcessor” (see green oval above). +Change the magnification value as desired (green arrow). + +On REV Control Hub, the “Vision” menu appears only when the active robot +configuration contains a webcam, even if not plugged in. + +Place this Block immediately after the Block +``set myTfodProcessor to call myTfodProcessorBuilder.build``. This Block +is **not** part of the Processor Builder pattern, so the Zoom factor can +be set to other values during the OpMode, if desired. + +The “zoomed” region can be observed in the DS preview (Camera Stream) +and the RC preview (LiveView), surrounded by a greyed-out area that is +**not evaluated** by the TFOD Processor. + +Testing the OpMode +------------------ + +Click the “Save OpMode” button, then run the OpMode from the Driver +Station. The Robot Controller should use the new CENTERSTAGE inference +model to recognize and track the Pixel game element. + +For a preview during the INIT phase, touch the Driver Station’s 3-dot +menu and select **Camera Stream**. + +.. figure:: images/200-DS-Camera-Stream-Centerstage.png + :align: center + :width: 85% + :alt: DS Camera Stream + + DS Camera Stream + +Camera Stream is not live video; tap to refresh the image. Use the small +white arrows at lower right to expand or revert the preview size. To +close the preview, choose 3-dots and Camera Stream again. + +After touching the DS START button, the OpMode displays Telemetry for +any recognized Pixel(s): + +.. figure:: images/210-DS-Telemetry-Centerstage.png + :align: center + :width: 85% + :alt: DS Telemetry + + DS Telemetry + +The above Telemetry shows the label name, and TFOD confidence level. It +also gives the **center location** and **size** (in pixels) of the +Bounding Box, which is the colored rectangle surrounding the recognized +object. + +The pixel origin (0, 0) is at the top left corner of the image. + +Before and after touching DS START, the Robot Controller provides a +video preview called **LiveView**. + +.. figure:: images/240-RC-LiveView-Centerstage.png + :align: center + :width: 85% + :alt: RC LiveView + + RC LiveView + +For Control Hub (with no built-in screen), plug in an HDMI monitor or +learn about ``scrcpy`` (https://github.com/Genymobile/scrcpy). The +above image is a LiveView screenshot via ``scrcpy``. + +If you don’t have a physical Pixel on hand, try pointing the camera at +this image: + +.. figure:: images/300-Pixel.png + :align: center + :width: 85% + :alt: Sample Pixel + + Sample Pixel + +Modifying the Sample +-------------------- + +In this Sample OpMode, the main loop ends only upon touching the DS Stop +button. For competition, teams should **modify this code** in at least +two ways: + +- for a significant recognition, take action or store key information – + inside the FOR loop + +- end the main loop based on your criteria, to continue the OpMode + +As an example, you might set a Boolean variable ``isTeamPropDetected`` +to ``true``, if a significant recognition has occurred. + +You might also evaluate and store which randomized Spike Mark (red or +blue tape stripe) holds the Team Prop. + +Regarding the main loop, it could end after the camera views all three +Spike Marks, or after your code provides a high-confidence result. If +the camera’s view includes more than one Spike Mark position, perhaps +the Team Prop’s **Bounding Box** size and location could be useful. +Teams should consider how long to seek an acceptable recognition, and +what to do otherwise. + +In any case, the OpMode should exit the main loop and continue running, +using any stored information. + +Best of luck this season! + +============ + +Questions, comments and corrections to westsiderobotics@verizon.net diff --git a/docs/source/programming_resources/vision/blocks_tfod_opmode_custom/images/010-Centerstage-repo.png b/docs/source/programming_resources/vision/blocks_tfod_opmode_custom/images/010-Centerstage-repo.png new file mode 100644 index 0000000000000000000000000000000000000000..bb1d131effffaa5140f73613c49738ff48f6acdf GIT binary patch literal 53105 zcmd3tV{j$k8|O0<+qP{^?BvF_Ik9cqwrx9^XkvS!iEVrD?~mQus;%1Ecl+YrTYdYS z?$dqn;QKid3UU(gFgP$EARzDnNl_&b5HK$gkZ;CN-+}*-#Ks>3f56OzWQ9OL8scEz 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