DOODS

Dedicated Open Object Detection Service - Yes, it's a backronym, so what...

DOODS is a GRPC service that detects objects in images. It's designed to be run as a container, optionally remotely.

API

The API uses gRPC to communicate but it has a REST gateway built in for ease of use. It supports both a single call RPC and a streaming interface. It supports very basic pre-shared key authentication if you wish to protect it. It also supports TLS encryption but is disabled by default. It uses the content-type header to automatically determine if you are connecting in REST mode or GRPC mode. It listens on port 8080 by default.

GRPC Endpoints

The protobuf API definitations are in the odrpc/odrpc.proto file. There are 3 endpoints.

• GetDetector - Get the list of configured detectors.
• Detect - Detect objects in an image - Data should be passed as raw bytes in GRPC.
• DetectStream - Detect objects in a stream of images

REST/JSON

• GET /version - Get the version
• GET /detectors - Get the list of configured detectors
• POST /detect - Detect objects in an image

{
	"detector_name": "default",
	"data": "<base64 encoded image information>",
	"detect": {
		"*": 50
	}
}

The result is returned as:

{
    "id": "test",
    "detections": [
        {
            "top": 0,
            "left": 0.05,
            "bottom": .8552,
            "right": 0.9441,
            "label": "person",
            "confidence": 87.890625
        }
    ]
}

Perform a detection. Use the default detector. (If omitted, it will use the default) The data, when using the REST interface is base64 encoded image data. DOODS can decode png, bmp and jpg. The detect object allows you to specify the list of objects to detect as defined in the labels file. You can give a min percentage match. You can also use "*" which will match anything with a minimum percentage.

Example One Liner:

echo "{\"detector_name\":\"default\", \"detect\":{\"*\":60}, \"data\":\"`cat grace_hopper.png|base64 -w0`\"}" > /tmp/postdata.json && curl -d@/tmp/postdata.json -H "Content-Type: application/json" -X POST http://localhost:8080/detect

Detectors

You should optimally pass image data in the requested size for the detector. If not, it will be automatically resized. It can read BMP, PNG and JPG as well as PPM.

TFLite

If you pass PPM image data in the right dimensions, it can be fed directly into tensorflow. This skips a couple steps for speed. You can also specify hwAccel: true in the config and it will enable Coral EdgeTPU hardware acceleration. You must also provide it an appropriate EdgeTPU model file.

Compiling

This is designed as a go module aware program and thus requires go 1.12 or better. It also relies heavily on CGO. The easiest way to compile it is to use the Dockerfile which will build a functioning docker image. It's a little large.

Configuration

The configuration can be specified in a number of ways. By default you can create a json file and call it with the -c option you can also specify environment variables that align with the config file values.

Example:

{
	"logger": {
        "level": "debug"
	}
}

Can be set via an environment variable:

LOGGER_LEVEL=debug

Options:

Setting	Description	Default
logger.level	The default logging level	"info"
logger.encoding	Logging format (console or json)	"console"
logger.color	Enable color in console mode	true
logger.disable_caller	Hide the caller source file and line number	false
logger.disable_stacktrace	Hide a stacktrace on debug logs	true
---	---	---
server.host	The host address to listen on (blank=all addresses)	""
server.port	The port number to listen on	8080
server.tls	Enable https/tls	false
server.devcert	Generate a development cert	false
server.certfile	The HTTPS/TLS server certificate	"server.crt"
server.keyfile	The HTTPS/TLS server key file	"server.key"
server.log_requests	Log API requests	true
server.profiler_enabled	Enable the profiler	false
server.profiler_path	Where should the profiler be available	"/debug"
---	---	---
pidfile	Write a pidfile (only if specified)	""
profiler.enabled	Enable the debug pprof interface	"false"
profiler.host	The profiler host address to listen on	""
profiler.port	The profiler port to listen on	"6060"
---	---	---
doods.auth_key	A pre-shared auth key. Disabled if blank	""
doods.detectors	The detector configurations

TLS/HTTPS

You can enable https by setting the config option server.tls = true and pointing it to your keyfile and certfile. To create a self-signed cert: openssl req -new -newkey rsa:2048 -days 3650 -nodes -x509 -keyout server.key -out server.crt You will need to mount these in the container and adjust the config to find them.

Detector Config

Detector config must be done with a configuration file. The default config includes one Tensorflow lite mobilenet detector.

doods:
  detectors:
    - name: default
      type: tflite
      modelFile: models/coco_ssd_mobilenet_v1_1.0_quant.tflite
      labelFile: models/coco_labels0.txt
      numThreads: 4
      numConcurrent: 4
      hwAccel: false
      timeout: 30s

The default model is downloaded from google: coco_ssd_mobilenet_v1_1.0_quant_2018_06_29

The numThreads option is the number of threads that will be available for compatible operations in a model The numConcurrent option sets the number of models that will be able to run at the same time. This should be 1 unless you have a beefy machine. The hwAccel option is used to specify that a hardware device should be used. The only device supported is the edgetpu currently If timeout is set than a detector (namely an edgetpu) that hangs for longer than the timeout will cause doods to error and exit. Generally this error is not recoverable and Doods needs to be restarted.

Detector Types Supported

• tflite - Tensorflow lite models - Supports Coral EdgeTPU if hwAccel: true and appropriate model is used
• tensorflow - Tensorflow

Examples - Clients

See the examples directory for sample clients

Docker

To run the container in docker you need to map port 8080. If you want to update the models, you need to map model files and a config to use them. docker run -it -p 8080:8080 snowzach/doods:latest

There is a script called fetch_models.sh that you can download and run to create a models directory and download several models and outputs an example.yaml config file. You could then run: docker run -it -v ./models:/opt/doods/models -v ./example.yaml:/opt/doods/config.yaml -p 8080:8080 snowzach/doods:latest

Coral EdgeTPU

If you want to run it in docker using the Coral EdgeTPU, you need to pass the device to the container with: --device /dev/bus/usb Example: docker run -it --device /dev/bus/usb -p 8080:8080 snowzach/doods:latest

Misc

Special thanks to https://github.com/mattn/go-tflite as I would have never been able to figure out all the CGO stuff. I really wanted to write this in Go but I'm not good enough at C++/CGO to do it. Most of the tflite code is taken from that repo and customized for this tool.

And special thanks to @lhelontra, @marianopeck and @PINTO0309 for help in building tensorflow and binaries for bazel on the arm.

Docker Images

There are several published Docker images that you can use

• latest - This is a multi-arch image that points to the rpi image, aarch64 and noavx image
• noavx - 64 bit x86 image that should be a highly compatible with any cpu.
• aarch64 - Arm 64 bit image
• rpi - Arm 32 bit/arm7 image optimized for the Raspberry Pi
• amd64 - 64 bit x86 image with all the fancy cpu features like avx and sse4.2

Compiling

You can compile it yourself using the native config which should optimize for whatever you have installed. Make the snowzach/doods-builder:native image followed by the snowzach/doods:native image with this command:

$ make docker CONFIG=native