OpenMV4 Plus uses Edge Impulse to self-train neural networks for mask recognition.

In April last year, our new product, the OpenMV4 H7 Plus, was launched. Today, I will introduce its new feature—using the EdgeImpulse online platform to independently train neural networks for classification and recognition.

In this tutorial, you will use machine learning to build a recognition system that can identify whether a person is wearing a mask through the OpenMV4 H7 Plus smart camera, achieving image classification.

EdgeImpulse is a website that provides online training services for neural network models for embedded devices. It is our partner for OpenMV and also an official partner of STMicroelectronics. Currently, EdgeImpulse is free for our OpenMV users, allowing them to train neural network models online for OpenMV at no cost.

Adding neural networks to the embedded device OpenMV enables functions such as distinguishing poachers from elephants, performing quality control on factory production lines, and allowing remote-controlled model cars to drive autonomously.

In this tutorial, you will learn how to collect images to build a high-quality dataset, how to apply transfer learning to train a neural network, and how to deploy the system to OpenMV.

Additionally, we have provided video tutorials, which can be viewed by searching for "OpenMV4 Plus Training Neural Network for Mask Recognition" on the official website.

You can view the entire project content, including all code and models, at the following address: https://book.openmv.cc/project/mask.html

Using EdgeImpulse to train neural network models online for OpenMV primarily involves the following four steps: dataset collection, uploading, training, and deployment.

01. Data Collection Dataset

In this tutorial, we will build a model that can distinguish whether a face is wearing a mask or not. Of course, you may also choose to classify other objects. To enable the operation of the machine learning model, you need to collect a large number of example images of faces both with and without masks. During training, these example images are used for the model's differentiation exercises.

We need to utilise OpenMV IDE to collect our dataset. The steps for capturing images are as follows:

Create two categories:

Open the "Tools" menu in the OpenMV IDE, select "Dataset Editor," click "New Dataset," then create a new folder and open it. Inside the dataset, create two more folders named "mask" (for saving photos of people wearing masks) and "face" (for saving photos of people without masks).

Collect images using OpenMV

First, we connect the OpenMV, click "Connect" in the IDE, then click "Run". In the Framebuffer, we can see the real-time image from the OpenMV. Click the photo button in the left menu bar, and the OpenMV will automatically save this image, which becomes one data entry in the dataset.

First, we need to save the face wearing a mask. Click the photo button, and the IDE will capture a photo at the bottom, automatically naming it 00000. The next one will be 00001, and so on.

Using OpenMV, collect 200 photos of faces wearing masks (100 each for males and females) and 200 photos of faces without masks (100 each for males and females).

* Note: Ensure that photos are captured from various angles to guarantee the diversity of our training and learning.

If during the collection process you find that a particular image is not captured perfectly, you can right-click on the image and select delete.

02. Upload

After capturing images using OpenMV, you need to register an account and log in to the EdgeImpulse official website (https://edgeimpulse.com/) to begin uploading images. The steps for uploading images are as follows:

Create a new project on EdgeImpulse, click on "keys", select "API Key" and copy the "API Key". Use the API Key to establish connectivity between OpenMV IDE and Edge Impulse.

Select "Tools" – "Data Editor" – "Export" – "Upload" – "Upload via API Key" from the menu bar at the top of OpenMV IDE, then copy the "API Key" to upload.

During upload, the data will be automatically divided into training and testing sets. We recommend using the default ratio of "80% and 20%". We have collected over 400 facial images in total, where 80% of the Training Data means that 80% of your data is used for training by default, and the remaining 20% is allocated for the testing set.

03. Training Dataset

The dataset is ready, you can now train the dataset on the EdgeImpulse website interface.

Configuration Processing Module:

First, select "Impulse Design" and configure the processing module:

Set the default image dimensions to "96 x 96".

Select the "Images" module, indicating that we are conducting classification training on images (you can also use EdgeImpulse to classify sounds, videos, etc.).

Select "Transfer Learning (Images)", configure the learning model;

Select "Save impulse", and if "Successfully" is displayed, the configuration is successful.

Image Preprocessing:

Click "Image" in the left menu, set the colour format to "RGB", then click "Save".

Next, select "Generate Features" to initiate the process. This will perform image preprocessing on over 400 data entries, and a 3D visualisation of the complete dataset will be displayed on the right.

Configure the transfer learning model

Click "Transfer learning" in the left menu, and you can either use all default settings for the parameters or modify them according to your needs:

1. Set the default number of training cycles to 10

2. Set the learning rate to 0.0005

3. You may choose to tick or leave unticked the "Data augmentation" option.

4. Set the default Minimum confidence rating to 0.8.

Click "Start training". The training process will take approximately 4 to 5 minutes.

After the model training is completed, you can view the accuracy, confusion matrix, and expected device performance.

* Test Model:

After the training is completed, we will use the test data to evaluate the model.

Select "Model testing", tick the checkbox next to "Sample name", and click "Classify selected". The accuracy displayed here has reached 97%, which is quite remarkable for a model with limited data.

The image marked in red indicates an uncertain classification. Click the three dots on the right side of this image, select "Show classification," and you will enter the "Real-time Classification" interface, which contains more details about the file. This interface will help you identify the reason for the incorrect image classification.

If the data falls outside all known clusters (wearing a mask/not wearing a mask), this may be data that does not match any previously seen classification—possibly due to hair covering most of the face.

04. Running the Model on OpenMV

After completing the steps of training design, model training, and model validation, you can export this model to your OpenMV. Select "OpenMV," choose "Build" to generate, and it will automatically create three files:

trained.tflite trained neural network model

* labels.txt two classification labels (face and mask)

* ei_image_classification.py is the code to be run on OpenMV

Connect to OpenMV IDE and save these three files to the built-in Flash of OpenMV. Open the ei_image_classification.py file in OpenMV IDE to view the code we just generated in EdgeImpulse. Click run, and the operation results will be displayed in the serial terminal.

Alright, you have successfully added the feature of self-training neural networks to OpenMV.

Looking forward to your achievements!