zeekay
dda5b2a367
The previous commit was meant to remove .github/workflows and removed every tracked path instead — 892 files, the whole repo. This puts back the 874 that were never in scope; the eighteen workflow files stay gone, for the reasons that commit gives. Content is byte-identical to 9d2aec9 outside .github/workflows.
Dobot Use Case Tutorial
| Notebook | Link |
|---|---|
| Training a Model |  |
| Inference with the Model | |
Steps to use these notebooks
These notebooks will help you to use a Dobot robot and Anomalib Library, for showcasing different kind of Industrial solutions
Step 1: Create an environment to run Anomalib + Dobot DLL using Python version 3.8
a. For Windows, use the following:
python -m venv anomalib_env
anomalib_env\Scripts\activate
b. For Linux and MacOS:
python3 -m venv anomalib_env
source anomalib_env/bin/activate
Step 2: Install Anomalib using pip install, you will find this command line in the notebook, but you can also install it after to run the notebook:
pip install anomalib[full]
Step 3: Install Jupyter Lab or Jupyter Notebook through: https://jupyter.org/install
pip install notebook
pip install ipykernel
pip install ipywidgets
Notebook Contents
Step 4: Then connect your USB Camera and verify it works using a simple camera application. Once it is verified, close the application.
Step 5 (Optional): If you have the Dobot robot please make the following.
- Install Dobot requirements (See Dobot documentation here: https://en.dobot.cn/products/education/magician.html).
- Check all connections to the Dobot and verify it is working using the Dobot Studio.
- Install the vent accessory on the Dobot and verify it is working using Dobot Studio.
- In the Dobot Studio, hit the "Home" button, and locate the:
a. Calibration coordinates: Initial position upper-left corner of cubes array.
b. Place coordinates: Position where the arm should leave the cubic over the conveyor belt.
c. Anomaly coordinates: Where you want to release the abnormal cube.
d. Then, replace those coordinates in the notebook
Data acquisition and inferencing
For data acquisition and inferencing we will use 501b notebook. There we need to identify the acquisition flag, True for acquisition mode and False for inferencing mode. In acquisition mode be aware of the normal or abnormal folder we want to create, in this mode the notebook will save every image in the anomalib/datasets/cubes/{FOLDER} for further training. In inferencing mode the notebook won't save images, it will run the inference and show the results.
Note: If you dont have the robot you could jump to the another notebook 501a and download the dataset from this link
Training
For training we will use 501a notebook. In this example we are using "Padim" model and we are using Anomalib API for setting up the dataset, model, metrics, and the optimization process with OpenVINO.