Simple Car|Pedestrian Detector

This is only a hack.

This detector mimicks the original behavior of the Waggle image detector plugin, but without requiring full pywaggle environment.

SSD mobilenet is used as backbone network and the COCO dataset was used when trained. The pre-trained graph can be obtained from the OpenCV wiki.

This detector relies on Nvidia Triton inference server to perform the actual car/pedestrian counting. The application uses opencv to read images from an image source, generates a tensor for the image, and sends it via gRPC or HTTP to triton where the actual inference is performed. If an instance of triton is not running or accessible on the node (with model ssd_mobilenet_coco available) this application is run, it will fail and restart

For demonstration/debugging purposes, the app can be configured to expose a flask web application which displays the most recent image with annotations for the detected people and cars.

Arguments

The following arguments are available to configure the image detector:

• -f,--flask - if set flask app will run at LISTEN_IP:LISTEN_PORT
• -v,--verbose - enable verbose output
• -i,--ip or env var LISTEN_IP - listen IP address for web server if enabled. Default is 0.0.0.0
• --port or env var LISTEN_PORT - listen port for web server if enabled
• -d,--devno or env var DEVNO - device number for camera (default -1=find first available, 0=internal, 1=external), only used if CAPTURE_STRING not set
• -n,--capture-string or env var CAPTURE_STRING - any valid VideoCapture string(IP camera connection, RTSP connection string
• -c,--confidence or env var CONFIDENCE - minimum confidence score for a detection to register, default is 0.3
• -p,--publish - if flag or env var set, results of detection will be published to MQTT_BROKER_HOST:1883, default is "fluent-bit"
• -s,--sleep or env var SLEEP - after each detection, sleep for a set number of seconds, default is 1.0
• --protocol or env var PROTOCOL - protocol to send requests to triton inference server, default is HTTP, other option is gRPC
• -m,--model-name or env var MODEL_NAME - model name in triton to perform inference against, default is ssd_mobilenet_coco
• -x,--model-version or env var MODEL_VERSION - Version of model to use in triton, default is latest version
• -u,--triton-url or env var TRITON_URL - url to access triton, default is localhost:8000
• --smarter-inference-url or env var SMARTER_INFERENCE_URL - url to access smarter-inference, default is empty string. If set, triton url will be overwritten within smarter-inference inference access point
• -b,--mqtt-broker-host or env var MQTT_BROKER_HOST - host to access mqtt broker, default to fluent-bit
• --mqtt-broker-port or env var MQTT_BROKER_PORT - port to access mqtt broker, default to 1883
• -t,--mqtt-topic or env var MQTT_TOPIC - mqtt topic to post messages under, default to /demo
• --db1,--detect-car- if set will detect cars
• --db2,--detect-person - if set will detect people
• --db3,--detect-bus - if set will detect buses
• --db4,--detect-bicycle - if set will detect bicycles
• --db5,--detect-motorcycle- if set will detect motorcycles