Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/jolibrain/godd
🧠DeepDetect package for easy integration in any Go project
https://github.com/jolibrain/godd
api artificial-intelligence client deepdetect go golang machine-learning neural-network package
Last synced: about 1 month ago
JSON representation
🧠DeepDetect package for easy integration in any Go project
- Host: GitHub
- URL: https://github.com/jolibrain/godd
- Owner: jolibrain
- License: mit
- Created: 2018-10-20T07:48:33.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2021-11-29T07:26:08.000Z (about 3 years ago)
- Last Synced: 2024-06-21T06:34:20.776Z (7 months ago)
- Topics: api, artificial-intelligence, client, deepdetect, go, golang, machine-learning, neural-network, package
- Language: Go
- Size: 50.8 KB
- Stars: 5
- Watchers: 4
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[](https://forthebadge.com)[](https://forthebadge.com)
[](https://travis-ci.com/jolibrain/godd) [](https://goreportcard.com/report/github.com/jolibrain/godd) [](https://www.codacy.com/app/jolibrain/godd?utm_source=github.com&utm_medium=referral&utm_content=jolibrain/godd&utm_campaign=Badge_Grade) [](https://godoc.org/github.com/jolibrain/godd)
# GoDD
🧠[DeepDetect](https://github.com/jolibrain/deepdetect) package for easy integration in any Go project
GoDD offer a simple way to use [DeepDetect](https://github.com/jolibrain/deepdetect) in your Go software, by providing a simple interface to communicate with the different API endpoints supported by DeepDetect.
**GoDD currrently only support prediction, not training.**
# Install
`go get -u github.com/jolibrain/godd`
# Examples
[DeepDetect](https://github.com/jolibrain/deepdetect) quickstart with Docker:
`docker pull beniz/deepdetect_cpu`
`docker run -d -p 8080:8080 -v $HOME/deepdetect-models:/opt/my-models beniz/deepdetect_cpu`
`wget https://deepdetect.com/models/voc0712_dd.tar.gz`
`sudo mkdir -p $HOME/deepdetect-models/voc0712 && sudo tar -xvf voc0712_dd.tar.gz -C $HOME/deepdetect-models/voc0712`
---
Get informations on a DeepDetect instance:
```go
// Set DeepDetect host informations
const myDD = "127.0.0.1:8080"
// Retrieve informations
info, err := godd.GetInfo(myDD)
if err != nil {
fmt.Println(err.Error())
os.Exit(1)
}
// Display informations
fmt.Println(info)
// Display only the services field
fmt.Println(info.Head.Services)
```
---
Create a service:
```go
// Create a service request structure
var service godd.ServiceRequest
// Specify values for your service creation
service.Name = "imageserv"
service.Description = "object detection service"
service.Type = "supervised"
service.Mllib = "caffe"
service.Parameters.Input.Connector = "image"
service.Parameters.Input.Width = 300
service.Parameters.Input.Height = 300
service.Parameters.Mllib.Nclasses = 21
service.Model.Repository = "/opt/my-models/voc0712/"
// Send the service creation request
creationResult, err := godd.CreateService(myDD, &service)
if err != nil {
log.Fatal(err)
}
// Check if the service is created
if creationResult.Status.Code == 200 {
fmt.Println("Service creation: " + creationResult.Status.Msg)
} else {
fmt.Println("Service creation: " + creationResult.Status.Msg)
}
```
---
Predict:
```go
// Create predict structure for request parameters
var predict godd.PredictRequest
// Specify values for your prediction
predict.Service = "imageserv"
predict.Data = append(predict.Data, "https://t2.ea.ltmcdn.com/fr/images/9/0/0/les_bienfaits_d_avoir_un_chien_1009_600.jpg")
predict.Parameters.Output.Bbox = true
predict.Parameters.Output.ConfidenceThreshold = 0.1
// Execute the prediction
predictResult, err := godd.Predict(myDD, &predict)
if err != nil {
log.Fatal(err)
}
// Print data of the first object detected
if predictResult.Status.Code == 200 {
// Print the complete JSON result:
// fmt.Println(string(predictResult))
fmt.Println("Category: " + predictResult.Body.Predictions[0].Classes[0.Cat)
fmt.Println("Probability: " + strconv.FormatFloa(predictResult.Body.Predictions[0].Classes[0].Prob, 'f', 6, 64))
var bbox, _ = json.Marshal(predictResult.Body.Predictions[0].Classes[0.Bbox)
fmt.Println("Bbox: " + string(bbox))
} else {
fmt.Println("Prediction failed: " + predictResult.Status.Msg)
}
```
---
Delete a service:
```go
// Delete service
serviceDeleteStatus, err := godd.DeleteService(myDD, "imageserv")
if err != nil {
log.Fatal(err)
}
fmt.Println("Service deletion:")
fmt.Println(serviceDeleteStatus)
```
**You can see the full examples in the [examples](https://github.com/jolibrain/godd/tree/master/examples) folder.**