object SchemaUtil extends Serializable
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def
frameSchema(spark: SparkSession): StructType
returns a StructType representing Frame
Used in conjunction with "org.apache.spark.sql.functions.from_json" which Parses a column containing a JSON string into a StructType with the specified schema.returns a StructType representing Frame
Used in conjunction with "org.apache.spark.sql.functions.from_json" which Parses a column containing a JSON string into a StructType with the specified schema. Returns null, in the case of an unparseable string.the structType corresponds to Frame json, see com.nvidia.mdx.core.schema.frame.jFrame
An example with SGIE where object-type is Vehicle { "version": "4.0", "id": "frame-id", "@timestamp": "2018-04-11T04:59:59.828Z", "sensorId": "sensor-id", "objects": [ "957|1834|150|1918|215|Vehicle|#|sedan|Bugatti|M|blue|CA 444|California|0.8", "..........." ] }
- def frameToMessages(frame: jFrame): Array[jMessage]
- def frameToTypeBbox(frame: Frame): Array[(String, String, Bbox)]
- def frameToTypeBbox(frame: jFrame): Array[(String, String, jBbox)]
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- def jsonToNvFrame(j: String): Frame
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def
mesageSchema(spark: SparkSession): StructType
returns a StructType representing Message
Used in conjunction with "org.apache.spark.sql.functions.from_json" which Parses a column containing a JSON string into a StructType with the specified schema.returns a StructType representing Message
Used in conjunction with "org.apache.spark.sql.functions.from_json" which Parses a column containing a JSON string into a StructType with the specified schema. Returns null, in the case of an unparseable string.the structType corresponds to Message json, see com.nvidia.mdx.core.schema.jMessage
An example of Message { "messageid": "0357d37b-122c-4591-b5a6-d34ffd6e9a22", "mdsversion": "1.0", "@timestamp": "2018-01-06T08:18:29.811Z", "place": { "id": "1", "name": "endeavor", "type": "building/garage", "location": { "lat": 37.37060687475246, "lon": -121.9672466762127, "alt": 10.0 }, "parkingSpot": { "id": "Spot-649", "type": "LEV", "level": "P1", "coordinate": { "x": -15.80039606856171, "y": -42.71209355187643, "z": 3.0 } } }, "sensor": { "id": "Cam_24", "type": "Camera", "location": { "lat": 37.37097419504406, "lon": -121.96695453400918, "alt": 77.83 }, "description": "Camera Cam_24", "coordinate": { "x": -25.79694075531654, "y": -40.81336573354596, "z": 1.0 } }, "analyticsModule": { "id": "AM-id", "description": "Parking Detection", "source": "OpenALR", "version": "1.0" }, "object": { "id": "931", "vehicle": { "type": "SUV/Van", "make": "Jeep", "model": "Grand Cherokee", "color": "blue", "confidence": 0.6303990104266417, "license": "GYM0M3", "licenseState": "CA" }, "bbox": { "topleftx": -15.80039606856171, "toplefty": -42.71209355187643, "bottomrightx": -15.80039606856171, "bottomrighty": -42.71209355187643 }, "signature": [ 0.9659786686200269, 0.26463120365924153, 0.4939636991282117 ], "speed": 0.0, "direction": 72.90656542385659, "orientation": 72.90656542385659, "location": { "lat": 37.37099128359443, "lon": -121.96706774169897, "alt": 38.14 }, "coordinate": { "x": -15.80039606856171, "y": -42.71209355187643, "z": 3.0 } }, "event": { "id": "event-id", "type": "parked" }, "videoPath": "http://metrovideodev/play.html?videoPath=./videos/cam_80_capture-2017-11-07_17-12-30.mp4" }
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- def nvFrameToMessages(frame: Frame): Array[Message]
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def
puckSchema(spark: SparkSession): StructType
returns a StructType representing Puck
Used in conjunction with "org.apache.spark.sql.functions.from_json" which Parses a column containing a JSON string into a StructType with the specified schema.returns a StructType representing Puck
Used in conjunction with "org.apache.spark.sql.functions.from_json" which Parses a column containing a JSON string into a StructType with the specified schema. Returns null, in the case of an unparseable string.the structType corresponds to Puck json, see com.nvidia.mdx.core.schema.puck.Puck
An example of Puck json { "SensorId": "0080000004003b42", "GatewayTime": "2018-04-18T21:43:19.317Z", "SENtralTime": 0, "PacketCounter": 32, "ServerTime": "2018-04-18T21:43:19.317Z", "LoraWan": { "fCnt": 130, "loRaSNR": 8.8, "rssi": -91, "gwCount": 2, "rxInfo": [ { "mac": "00800000a00022f0", "rssi": -91, "loRaSNR": 8.8, "name": "00800000a00022f0", "latitude": 0, "longitude": 0, "altitude": 0 }, { "mac": "00800000a0001f92", "rssi": -104, "loRaSNR": 6.2, "name": "00800000a0001f92", "latitude": 0, "longitude": 0, "altitude": 0 } ], "txInfo": { "frequency": 903500000, "dataRate": { "modulation": "LORA", "bandwidth": 125, "spreadFactor": 9 }, "adr": false, "codeRate": "4/5" } }, "MetaError": null, "topic": "iot/KeepAlive", "CarPresence": 3, "Battery": 3.645, "Temperature": 16, "EventCounter": 0, "Latitude": 38.4209, "Longitude": -122.755257 }
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- def testSchema(spark: SparkSession): StructType
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toString(): String
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def
trajectorySchema(spark: SparkSession): StructType
this schema is incomplete, used for test purpose
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