This code pattern is part of the Getting started with IBM Maximo Visual Inspection learning path. Whether you are counting cars on a road or products on a conveyor belt, there are many use cases for computer vision with video. With video as input, you can use automatic labeling to create a better classifier with less manual effort. This code pattern shows you how to create and use a classifier to identify objects in motion and then track and count the objects as they enter designated regions of interest. Whether it is car traffic, people traffic, or products on a conveyer belt, there are many applications for keeping track of potential customers, actual customers, products, or other assets. With video cameras everywhere, a business can get useful information from them with some computer vision.

This code pattern is part of the Getting started with IBM Maximo Visual Inspection learning path. In this code pattern, learn how to use optical character recognition (OCR) and the IBM Maximo Visual Inspection object recognition service to identify and read license plates. Using IBM Maximo Visual Inspection and the Custom Inference Scripts, you can build an object detection model to identify license plates from images of cars. The models in the IBM Maximo Visual Inspection object recognition service can identify portions of images that represent a license plate. Then, the post custom inference script can crop this area and use open source to perform OCR on the text to return the license plate.

This code pattern is part of the Getting started with IBM Maximo Visual Inspection learning path. After a deep learning computer vision model is trained and deployed, it is often necessary to periodically (or continuously) evaluate the model with new test data. This developer code pattern provides a Jupyter Notebook that will take test images with known "ground-truth" categories and evaluate the inference results versus the truth. We will use a Jupyter Notebook to evaluate an IBM Maximo Visual Inspection image classification model. You can train a model using the provided example or test your own deployed model.

This code pattern is part of the Getting started with IBM Maximo Visual Inspection learning path. In this code pattern, learn how to deploy a customizable dashboard to visualize video and image analytics. With this customizable dashboard, you can upload images to be processed by IBM Maximo Visual Inspection (using object recognition and image classification), download the analyzed results, and view analytics through interactive graphs. When you have completed this code pattern, you understand how to build a dashboard using Vue.js and IBM Maximo Visual Inspection APIs to generate and visualize image analytics. Find the detailed steps for this pattern in the README file.

Training a visual recognition model can be repetitive and tedious. Users generally have to manually upload and label each individual image. This code pattern shows how to automate these repetitive tasks by monitoring a set of folders using a Python script. As images are added to each folder, they'll be uploaded and labeled in IBM Maximo Visual Inspection. Once enough images have been uploaded, an image recognition model will be trained.