{ // VisionModes that need to be scheduled by default go here // Basically things that are always running "InitialModeSchedules" : { // If a vision mode is not listed, it is scheduled to never run (might still be enabled) // Specify either an array of bools indicating a specific schedule, or an integer // value to process every Nth frame "Markers" : 4 // Remove with VIC-6838 }, "ColorImages" : true, // Whether color images are enabled on startup (can still be toggled later) "FaceAlbum" : "default", "FaceRecognition" : { "RunMode": "asynchronous" // "synchronous" or "asynchronous" }, "IlluminationDetector" : { "ClassifierConfigPath" : "config/engine/vision/illuminationClassifier/illuminationClassifier.json", "FeaturePercentileSubsample" : 8, // Stride for building histogram to compute percentiles "IlluminatedMinProbability" : 0.85, // Min probability to result in 'Illuminated' class "DarkenedMaxProbability" : 0.15, // Max probability to result in 'Darkened' class "AllowMovement" : true // Continue to run even if robot is moving }, "ImageQuality" : { // Auto-exposure settings "InitialExposureTime_ms" : 31, // Sent each time we request camera calibration "TargetValue" : 128, // [0,255] - Try to make targetPercentile have this value "TargetPercentile" : 0.50, // [0.0, 1.0] "CyclingTargetValues" : [5, 100, 250], // When "cycling" exposure is enabled, the target values to use [0,255] "TooDarkValue" : 20, // "Too Dark" if HighPercentile is below this "TooBrightValue" : 230, // "Too Bright" if LowPercentile is above this "LowPercentile" : 0.10, // [0.0, 1.0] (Near 0.0) "HighPercentile" : 0.90, // [0.0, 1.0] (Near 1.0) "MaxChangeFraction" : 0.0, // [0.0, inf] (Relative amount we can change current exposure/wb each update, zero disables) "SubSample" : 3, // >= 1 "MeterFromDetections" : true, // Base auto-exposure on detected markers, faces, etc, if any "TimeBeforeErrorMessage_ms" : 3000, // How long VisionSystem must detect "bad" quality before notifying game "RepeatedErrorMessageInterval_ms" : 300000 // Time between error messages once triggered }, "NumOpenCvThreads" : 0, // Number of threads to use with OpenCV. 0 means no threading according to docs. Only affects calls from VisionSystem thread. "PerformanceLogging" : { "DropStatsWindowLength_sec" : 30, // How long to average dropped image stats "TimeBetweenProfilerInfoPrints_sec" : 60, // How often to print Profiler info messages to the logs "TimeBetweenProfilerDasLogs_sec" : 600 // How often to log Profiler DAS events }, "PetTracker" : { "MaxPets" : 4, // Detectable/trackable at the same time "MinFaceSize" : 60, "MaxFaceSize" : 240, "DetectionThreshold" : 900, // 0 to 1000 "InitialSearchCycle" : 4, // 1 to 45 "NewSearchCycle" : 8, // 5 to 45 "TrackLostCount" : 2, // Number of frames to continue searching for (and reporting) a lost pet "TrackSteadiness" : 15 // 10 to 30 in Percentage change required to actually update size/position }, "MotionDetector" : { "HorizontalSize" : 0.3, // Fraction of the width of the image to be used for peripheral motion detection (right and left) "VerticalSize" : 0.3, // Fraction of the height of the image to be used for peripheral motion detection (top) "IncreaseFactor" : 500.0, // The higher this number, the more sensitive is motion detection to motion "DecreaseFactor" : 0.1, // The higher this number, the more quickly motion detection forgets about motion "MaxValue" : 3.0, // The higher this value, the sooner peripheral motion detection will be triggered "CentroidStability" : 0.6 // How quickly should peripheral motion detection track the source of motion. }, "OverheadMap" : { "NumRows" : 1000, // Size of the overhead map. Bigger sizes do not impact computation, only memory "NumCols" : 1000 }, "GroundPlaneClassifier" : { "MaxDepth" : 7, "MinSampleCount" : 10, "TruncatePrunedTree" : true, "Use1SERule" : true, "PositiveWeight" : 1.0, "OnTheFlyTrain" : false, "FileOrDirName" : "config/engine/vision/groundClassifier/deskClassifier.yaml" }, "ImageCompositing" : { "percentileForMaxIntensity" : 0.99, "imageReadyPeriod" : 4, "numImageReadyCyclesBeforeReset" : 2 }, "NeuralNets" : { // How frequently to print or log events about timing "ProfilingPrintFrequency_ms" : 10000, "ProfilingEventLogFrequency_ms" : 30000, // // Model definitions: // "Models" : [ { "modelType" : "TFLite", "networkName" : "person_detector", "graphFile" : "dfp_victor_6x6_tiny_128x128_36b906234ae4405dbf479d42d87787da.tflite", "memoryMapGraph" : 0, "labelsFile" : "person-labels.txt", "architecture" : "custom", "inputLayerName" : "input_1", "outputLayerNames" : "output_node0", "outputType" : "binary_localization", "numGridRows" : 6, "numGridCols" : 6, "useFloatInput" : 1, "useGrayscale" : 0, "inputWidth" : 128, "inputHeight" : 128, "inputScale" : 127.5, // When input is float, data is first divided by scale and then shifted "inputShift" : -1, // I.e.: float_input = data / inputScale + inputShift "minScore" : 0.5, "verbose" : 0, "pollPeriod_ms" : 10, "timeoutDuration_sec" : 10.0, "benchmarkRuns" : 0 }, { "modelType" : "TFLite", "networkName" : "hand_detector", "graphFile" : "hand_detector_fcde4ff411c44baa9e7184c733d3b943.tflite", "memoryMapGraph" : 0, "labelsFile" : "hand-labels.txt", "architecture" : "custom", "inputLayerName" : "input", "outputLayerNames" : "probabilities", "outputType" : "classification", "useFloatInput" : 1, "useGrayscale" : 0, "inputWidth" : 128, "inputHeight" : 128, "inputScale" : 1, // Scaling performed by the network "inputShift" : 0, // " "minScore" : 0.9, "verbose" : 0, "pollPeriod_ms" : 10, "timeoutDuration_sec" : 10.0, "benchmarkRuns" : 0 }, { "modelType" : "offboard", "networkName" : "offboard_person_detection", "procType" : "ObjectDetection", "commsType" : "CLAD", "inputWidth" : 640, "inputHeight" : 360, "verbose" : 0, "pollPeriod_ms" : 10, "timeoutDuration_sec" : 10.0 } //{ // // if you wanna try out efficientnet for object detection //"modelType" : "TFLite", // "networkName" : "mobilenet", // "graphFile" : "efficent-uint8-lite0.tflite", // "memoryMapGraph" : 0, // "labelsFile" : "efficientnet-labels.txt", // "architecture" : "custom", // "inputLayerName" : "images", // "outputLayerNames" : "Softmax", // "outputType" : "classification", // "useFloatInput" : 0, // "useGrayscale" : 0, // "inputWidth" : 224, // "inputHeight" : 224, // "inputScale" : 1, // When input is float, data is first divided by scale and then shifted // "inputShift" : 0, // I.e.: float_input = data / inputScale + inputShift // "minScore" : 0.5, // "verbose" : 1, // "pollPeriod_ms" : 200, // "timeoutDuration_sec" : 10.0, // "benchmarkRuns" : 0 //} // { // // mobilenet experiment which doesn't even work i don't think // "modelType" : "TFLite", // "networkName" : "mobilenet", // "graphFile" : "mobilenet_v1_1.0_224.tflite", // "memoryMapGraph" : 0, // "labelsFile" : "mobilenet-labels.txt", // "architecture" : "custom", // "inputLayerName" : "input", // "outputLayerNames" : "MobilenetV1/Predictions/Reshape_1", // "outputType" : "classification", // "useFloatInput" : 1, // "useGrayscale" : 0, // "inputWidth" : 224, // "inputHeight" : 224, // "inputScale" : 223.5, // When input is float, data is first divided by scale and then shifted // "inputShift" : -1, // I.e.: float_input = data / inputScale + inputShift // "minScore" : 0.5, // "verbose" : 0, // "pollPeriod_ms" : 200, // "timeoutDuration_sec" : 10.0, // "benchmarkRuns" : 0 // } ] // // OBJECTNESS MODEL // "ProfilingPrintFrequency_ms" : 10000, // "ProfilingEventLogFrequency_ms" : 30000, // // // // // Model definition: // // // // // Standalone TF model // "graphFile" : "objectness-mobilenet-v2-height-192-width-192-channels-3-05-2.11.pb", // "memoryMapGraph" : 0, // "labelsFile" : "person-labels.txt", // "architecture" : "custom", // "inputLayerName" : "input_1", // "outputLayerNames" : "output_node0", // "outputType" : "segmentation", // "numGridRows" : 6, // "numGridCols" : 6, // "useFloatInput" : 1, // "useGrayscale" : 0, // "inputWidth" : 192, // "inputHeight" : 192, // "inputScale" : 127.5, // "inputShift" : -1, // "minScore" : 0.5, // "verbose" : 1, // "pollPeriod_ms" : 10, // "timeoutDuration_sec" : 100.0, // "visualizationDirectory" : "objectnessResponseMap", // "benchmarkRuns" : 0 } }