Swiftpack.co - Package - ibm-maximo-dev/MaximoCoreMLVision

Visual Recognition with Core ML and integrate with Maximo

Classify images with Watson Visual Recognition and Core ML. Update Maximo Asset Management with the classified results. The images are classified offline using a deep neural network that is trained by Visual Recognition. The results are conveyed to Maximo using iOS OSLC SDK.

Pre-requisites

  • Make sure that you have installed Xcode 9 or later and iOS 11.0 or later. These versions are required to support Core ML.
  • Maximo 7.6.0.9 or higher is necessary for integration using Maximo iOS OSLC SDK
  • Watson Visual Recognition (VR) API key. Refer to Watson Visual Recognition

Getting the files

Use our GitHub to clone the repository locally, or download the .zip file of the repository and extract the files.

Running MaximoCoreMLVision

This project builds on the use case of identifying assets and their anamolies in a factory using iOS handheld devices, Watson VR and update anomalies identified to Maximo Asset Management System by creating a work order. This project builds and trains a Visual Recognition model (also called a classifier) to identify different colored balls at a ball manufacturing plant. It uses Apple's Core ML framework to download and manage the trained model locally on the device, uses VR APIs to classify images selected from the device camera or the photo library, and Maximo iOS OSLC SDK APIs to communicate with Maximo, including logging in and creating work orders. Further, this project uses the Watson Swift SDK to download, manage, and execute the trained model. By using the Watson Swift SDK, you don't have to learn about the underlying Core ML framework. Use Maximo iOS OSLC SDK client to communicate with Maximo.

Setting up Visual Recognition in Watson Studio

  1. Log into Watson Studio. From this link you can create an IBM Cloud account, sign up for Watson Studio, or log in.

  2. After you sign up or log in, you'll be on the Visual Recognition instance overview page in Watson Studio.

    Tip: If you lose your way in any of the following steps, click the IBM Watson logo on the top left of the page to bring you to the the Watson Studio home page. From there you can access your Visual Recognition instance by clicking the Launch tool button next to the service under "Watson services".

Training the model

  1. In Watson Studio on the Visual Recognition instance overview page, click Create Model in the Custom box.

  2. If a project is not yet associated with the Visual Recognition instance you created, a project is created. Name your project Custom Core ML and click Create.

    Tip: If no storage is defined, click refresh.

  3. Upload each .zip file of sample images from the Training Images directory onto the data pane on the right side of the page. Add the hdmi_male.zip file to your model by clicking the Browse button in the data pane. Also add the usb_male.zip, thunderbolt_male.zip, and vga_male.zip files to your model.

  4. After the files are uploaded, select Add to model from the menu next to each file, and then click Train Model.

Downloading the Watson Swift SDK

Use the Carthage dependency manager to download and build the Watson Swift SDK.

  1. Install Carthage.

  2. Open a terminal window and navigate to the Core ML Vision Custom directory.

  3. Run the following command to download and build the Watson Swift SDK:

    carthage bootstrap --platform iOS
    

Tip: Regularly download updates of the SDK so you stay in sync with any updates to this project.

Installing Maximo iOS OSLC SDK

Use pod to instance Maximo iOS OSLC SDK

Cocoapods installation

Open a terminal session and enter the following command:

sudo gem install cocoapods

Add SSH key to your GitHub account

Generate the RSA key for your GitHub user account:

ssh-keygen -t rsa -b 4096 -C git@github.ibm.com

Paste the contents of the id_rsa.pub file as described here: https://help.github.com/articles/adding-a-new-ssh-key-to-your-github-account/

Project setup

  1. In a terminal session, navigate to the directory that contains your Xcode project.

Note: If your project is already configured to use Cocoapods, you can skip the next step.

  1. Enter the following command:
pod init
  1. Type the following command to open the Podfile by using Xcode for editing:
open -a Xcode Podfile

The following code shows the default Podfile:

# Uncomment the next line to define a global platform for your project
# platform :ios, '9.0'

target '<YOUR PROJECT NAME>' do
  # Comment the next line if you're not using Swift and don't want to use dynamic frameworks
  use_frameworks!

  # Pods for <YOUR PROJECT NAME>

end

Delete the # and space before "platform" and delete the other lines that start with "#". Your Podfile now looks like the following example:

platform :ios, '9.0'

target '<YOUR PROJECT NAME>' do
  use_frameworks!

end
  1. Add the following line to your Podfile, right after "use_frameworks!":
pod 'MaximoRESTSDK', '1.0.0'
  1. Install the dependencies for your project by entering the following command in the terminal session:
pod install

After the dependencies are successfully installed, Cocoapods creates a new .xcworkspace file and a Pods folder that contains all the project's dependencies. Now, open the .xcworkspace file by using Xcode, and you are all ready to go.

Building and deploying

  1. Open MaximoCoreMLVision.xcworkspace in Xcode.
  2. Select MaximoCoreMLVision project and select MaximoCoreMLVision schema. Modify the bundle identifier to be unique within your organization. Select appropriate development 'team' and 'Signing Certificate'
  3. Run the application in the simulator or deploy on a device. 1.1. To run in the simulator, select from the dropdown next to the target on the top menu, the appropriate device, and select the Run button 1.2 To deploy on a device, connect the device to the USB port, select from the dropdown next to the target on the top menu, the device connected, and select the Run button

Copy your Model ID and API Key

  1. In Watson Studio on the custom model overview page, click your Visual Recognition instance name (it's next to Associated Service).
  2. Scroll down to find the Custom Core ML classifier you just created.
  3. Copy the Model ID of the classifier.
  4. In the Visual Recognition instance overview page in Watson Studio, click the Credentials tab, and then click View credentials. Copy the api_key or the apikey of the service.
  5. Copy the Model ID and api_key on a sheet of paper, a notepad or any other text editor. Important: Instantiation with api_key works only with Visual Recognition service instances created before May 23, 2018. Visual Recognition instances created after May 22 use IAM.

Configure Watson and Maximo

  1. Select Watson link on the Configuration row on the top of the screen
  2. Enter api_key value into VR API Key field
  3. Enter Model ID into Classifier ID field
  4. Enter 2018-05-31 into API Version field if it is not filled out
  5. Select Back link on the top menu
  6. Select Maximo link on the Configuration row on the top of the screen
  7. Enter Maximo URL in VR API Key field. Ensure this URL should be reachable from the device.
  8. Enter Admin ID and Admin Password
  9. Select Back link on the top menu

Finally.....

  1. Classify an image by clicking the camera icon and selecting a photo from your photo library. To add a custom image in the simulator, drag the image from the Finder to the simulator window.
  2. Pull new versions of the visual recognition model with the refresh button in the bottom right.

Tip: The classifier status must be Ready to use it. Check the classifier status in Watson Studio on the Visual Recognition instance overview page.

  1. By default both Maximo and Watson configurations are set to placeholder values. Before running classifications edit both configurations and add correct values.

What to do next

Create different classifiers for different use cases using Watson Studio, configure this project with the new classifiers, collect or snap images related to the classifiers and test classifications.

Resources

Watson Visual Recognition Tool

Github

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