![]() ![]() The on-board memory gets mounted as a Flash drive. If you have not installed the OpenMV firmware yet, take a look at the "Configuring the Development Environment" section which explains how to proceed in that case.ĭownload this file containing the smiley bitmap and copy it to the Flash drive that was mounted when you connected the Portenta running the OpenMV firmware. Make sure you are running the OpenMV firmware on the Portenta. For this tutorial Adobe Photoshop was used.Ĭonnect your Portenta board to your computer if you have not done so yet. You may use an image editor of your choice which supports exporting images in one of these formats. If you want to create your custom image, make sure you save it in one of the supported bitmap formats (bmp, pgm or ppm). ![]() 1 stands for a black pixel, 0 for a white one. This format consists of a matrix of zeroes and ones denoting black and white pixels. In this tutorial you will use a preloaded smiley image in the monochrome Portable Bitmap Image (.pbm) format. Image formats with an alpha layer such as PNG are not supported yet. OpenMV currently supports bmp, pgm or ppm image formats. Once you know the location of the faces in the camera image, you can overlay them with an image of your choice. The script starts by importing the sensor, image and time modules for handling the camera sensor, using machine vision algorithms and time tracking functions.Ģ print ( face_cascade ) # Prints the Haar Cascade configuration 5. Create a new script by clicking the "New File" button in the toolbar on the left side and save it as face_detection.py. For this tutorial, you will create a new script that is based on the face detection example provided by OpenMV. The Basic SetupĪttach your Portenta Vision Shield to your Portenta H7 and open the OpenMV Editor. If this is your first time using the Portenta Vision Shield and OpenMV, we recommend you to take a look at the "Configuring the Development Environment" section inside the Blob Detection tutorial to configure the development environment. Instructions Creating the Face Detection Scriptįor this tutorial, you will be using the OpenMV IDE along with the OpenMV firmware on your Portenta H7 to build the face detection script. That allows the algorithm to distinguish such images after it is being trained. The built-in Haar Cascade model for faces was trained with hundreds of images containing faces that are labeled as such and images that do not contain faces labeled differently. Fewer stages make the detection faster, while leading to more false positives. The Haar Cascade function provided by OpenMV allows to specify the amount of stages. Larger areas of the image are checked first in the earlier stages, followed by more numerous and smaller area checks in later stages. The different stages are responsible for detecting edges, lines, contrast checks and calculating pixel values in a given image. This approach uses a cascade algorithm that has multiple stages, where the output from one stage acts as additional information for the next stage in the cascade. In this tutorial, you will use a machine learning based approach called Haar Cascade to detect faces. Those algorithms can be trained to detect the desired type of object. Arduino IDE 1.8.10+ or Arduino Pro IDE 0.0.4+īy harnessing the power of machine vision algorithms, objects can be detected in a camera stream.USB-C® cable (either USB-A to USB-C® or USB-C® to USB-C®).Using MicroPython to read files from the internal Flash.Copying files to the internal Flash of the Portenta.How to use the built-in face detection algorithm of OpenMV.How to use the OpenMV IDE to run MicroPython on Portenta.This tutorial is based on the face detection example that comes with the OpenMV IDE. Think of it as building your own camera filter that puts a smile on every face it detects. In this tutorial you will build a MicroPython application with OpenMV, to use the Portenta Vision Shield to detect faces and overlay them with a custom bitmap image. ![]()
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