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Load IBM Maximo Visual Inspection inference results in a dashboard

This code pattern is part of the Getting started with IBM Maximo Visual Inspection learning path.

Summary

The “Visual Inspector” application lets you leverage IBM Maximo Visual Inspection models using an IoS device. You can view the results uploaded from your mobile device in a custom dashboard. This dashboard renders the image results into an interactive table, where you can add filters, search, and export the results as a PDF file.

Description

This code pattern demonstrates a dashboard that lets you extract image analysis data from an IBM Maximo Visual Inspection instance. This extracted data can be filtered and viewed within an interactive table. The displayed data can also be exported as a PDF report. The code pattern is targeted towards users who have uploaded images to an IBM Maximo Visual Inspection instance through the Visual Inspector iOS application.

When you have completed this code pattern, you understand how to:

  • Extract information from an IBM Maximo Visual Inspection instance as a CSV file
  • How to visualize and filter the data within a web browser

Flow

Flow

  1. Upload images using the IBM Visual Inspector app.
  2. Train the image inference model in IBM Maximo Visual Inspection through the Visual Inspector app.
  3. Run the Python script to extract the inference data as a .csv file.
  4. Upload the .csv file to the dashboard and view the results.

Instructions

Find the detailed steps for this pattern in the README file. The steps show you how to:

  1. Upload the images using IBM Visual Inspector app.
  2. Clone the repository.
  3. Extract image data as a .csv file.
  4. Load the data into the dashboard.

Conclusion

This code pattern demonstrated a dashboard that lets you extract image analysis data from an IBM Maximo Visual Inspection instance. The code pattern is part of the Getting started with IBM Maximo Visual Inspection learning path. To continue with the learning path, take a look at the next pattern Build an object detection model to identify license plates from images of cars.