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R interface to the Computer Vision API
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Azure/AzureVision
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An R frontend toAzure Computer Vision andAzure Custom Vision, building on the low-level functionality provided by theAzureCognitive package. These services let you leverage the cloud to carry out visual recognition tasks using advanced image processing models, without needing powerful hardware of your own.
The primary repo for this package is athttps://github.com/Azure/AzureVision; please submit issues and PRs there. It is also mirrored at the Cloudyr org athttps://github.com/cloudyr/AzureVision. You can install the development version of the package withdevtools::install_github("Azure/AzureVision").
To communicate with the Computer Vision service, call thecomputervision_endpoint function with the service URL and key. Rather than a key, you can also supply an OAuth token obtained with the AzureAuth package.
library(AzureVision)vis<- computervision_endpoint(url="https://accountname.cognitiveservices.azure.com/",key="account_key")# images can be specified as a filename, Internet URL, or raw vectorbill_url<-"https://news.microsoft.com/uploads/2014/09/billg1_print.jpg"analyze(vis,bill_url)# $categories# name score# 1 people_ 0.953125describe(vis,bill_url)# $tags# [1] "person" "man" "suit" "clothing" "necktie" "wearing" "glasses" "looking" "holding" "standing" "older"# [12] "posing" "business" "old" "dressed" "front" "sitting" "black" "hat" "white" "sign" "phone"# $captions# text confidence# 1 Bill Gates wearing a suit and tie 0.9954072
Custom Vision defines two different types of endpoint: a training endpoint, and a prediction endpoint. To communicate with these, call thecustomvision_training_endpoint andcustomvision_prediction_endpoint functions with the service URL and key.
# training a modelcusvis<- customvision_training_endpoint(url="https://location.api.cognitive.microsoft.com/",key="training_key")# different projects can exist on the one endpointlist_projects(cusvis)# create a classification project (one tag/label per image)proj<- create_classification_project(cusvis,"myproject")img1<- dir("path/to/images/tag1",full.names=TRUE)img2<- dir("path/to/images/tag2",full.names=TRUE)add_images(proj,img1,tags="tag1")add_images(proj,img2,tags="tag2")# train the modelmod<- train_model(proj)# publish to the prediction resource (use AzureRMR framework to get resource ID)pred_res<-AzureRMR::get_azure_login("mytenant")$ get_subscription("sub_id")$ get_resource_group("rgname")$ get_cognitive_service("cusvis_prediction")publish_model(mod,"mymodel",pred_res)# get predictions from the prediction endpointcusvis_pred<- customvision_prediction_endpoint(url="https://location.api.cognitive.microsoft.com/",key="prediction_key")# must supply project ID to access the published serviceproject_id<-proj$project$idcusvis_service<- classification_service(cusvis_pred,project_id,"mymodel")predict(cusvis_service,"testimage.jpg")
You can create Computer Vision and Custom Vision resources using the AzureRMR framework.
For Computer Vision, the available service tiers areF0 (free, limited to 20 API calls per minute and 5k calls per month) andS1 (up to 10 calls per second). For Custom Vision, the tiers areF0 (free, limited to 2 projects for training and 10k transactions/month for prediction) andS0. Note that Custom Vision requires at leasttwo resources: one for training, and the other for prediction.
rg<-AzureRMR::get_azure_login("yourtenant")$ get_subscription("sub_id")$ get_resource_group("rgname")# Computer Visionrg$create_cognitive_service("myvis",service_type="ComputerVision",service_tier="S1")# Custom Vision (training and prediction)rg$create_cognitive_service("mycustvis",service_type="CustomVision",service_tier="S0")rg$create_cognitive_service("mycustvispred",service_type="CustomVision.Prediction",service_tier="S0")
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