![]() In case if you have any queries or suggestions do reach out.1 React + TypeScript: Face detection with Tensorflow 2 UI Components website Released!. **- Here your structure JSON_RES should match output typeĬopy_data_to_ref( exporting is_data = json_res Lr_json_deserializer->deserialize( EXPORTING json = response IMPORTING abap = json_res ). Response = lo_response->get_string_data( ). Lo_response = lo_rest_client->if_rest_client~get_response_entity( ). Lo_rest_client->if_rest_resource~post( lo_request ). Lo_request = lo_rest_client->if_rest_client~create_request_entity( ). Uri = lv_url " URI String (in the Form of /path?query-string) Request = lo_http_client->request " HTTP Framework (iHTTP) HTTP Request IF lo_http_client IS BOUND AND lo_rest_client IS BOUND. Lo_http_client->request->set_version( if_http_request=>co_protocol_version_1_0 ). ![]() Lo_rest_client TYPE REF TO cl_rest_http_client,ĭATA lr_json_deserializer TYPE REF TO cl_trex_json_deserializer.ĭestination = 'MLRFC' " Logical destination (specified in function call)Ĭlient = lo_http_client " HTTP Client Abstraction In the GW service method /IWBEP/IF_MGW_APPL_SRV_RUNTIME~CREATE_STREAM, wrote the below code to hit the service and parse the output DATA: lo_http_client TYPE REF TO if_http_client, Create an RFC destination to the GCP ML Engine URL where the model is hosted and listening for HTTP requests.The GW service reads the image’s binary data in an attachment GW service and triggered POST REST hit on the Google ML engine to get back the results from our neural network hosted in Google ML engine. Once this is done, your model will be ready to take input image as binary and return back results The final part of this was the backend SAP GW service: Since there are many steps involved in deploying the newly created model to GCP Machine learning engine, it is not shown here. SAP UI5 code to trigger attachment GW service, to carry file binary data to SAP GW sap.ui.define(, function(BaseController) Next: In order to have our UI5 webcam image be recognized by our python script we need to upload the created Tensorflow model. Var data = canvas.toDataURL('image/jpeg') Take image from webcam -> Data is stored in a variable window.filebinaryĬtx.drawImage(video, 0,0, canvas.width, canvas.height) Get the canvas and obtain a context forĬanvas = document.getElementById("m圜anvas") SAP UI5 HTML IFRAME screen embedded inside an xml view to invoke camera and take picture. ![]() So lets start, we will go one by one here with as much screen shots as possible: Please note: That some terminologies like Python and Tensorflow might be new to you, hence you may refer to external links/ papers to understand more about Python, Tensorflow, Convolutional nets etc. ![]() Account on Google cloud platform to host the final Neural network model.Tensorflow knowledge to train a Convolutional neural network on Python –. ![]() Knowledge of implementing Convolutional neural nets ( Deep learning ).SAP UI5 over WebIDE to build webcam screen.We integrated below list of technologies/ skillset to implement the same: The blog post is slightly huge considering the complexity of the scenario, so please forgive me for the same □ I am pretty excited here to describe a very interesting and complex implementation we did to demonstrate integration of SAP with Google ML engine and Tensorflow, bringing user experience to an entirely new level.Īn end user can take a picture from the webcam of his computer through a UI5 screen and in real time the system should be able to recognize the person/object in the image – harnessing the power of Deep learning and Tensorflow in SAP. Welcoming you all to the world of Deep Learning ! This application was also demo’ed at Sapphire 2017. ![]()
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |