Founder: "obomprogramador.com". Full-stack dev/ AI Egineer/ Professional Writer/ M.Sc. Rio de Janeir
For people with vision problems.
It's in Portuguese, but you can remove the translation and leve it talking in english. Just change line #81 of script
Finally I finished the audible object detector proof of concept. The goal is to create something that can be used by people with visual needs. This is a proof of concept, or an MVP.
In this demo, I'm using Yolo (You Only Look Once), with python and OpenCV. I was inspired by the Adrian Rosebrock article to create this PoC.
It is still an unfinished project, but I decided to share it for you to help me and develop your own solutions.
I'm using Google's gTTS library to transcribe text to audio.
You will need:
To connect an HC-SR04 sensor to the Raspberry PI, follow the instructions in this article. The image of the article is this:
I used the GPIOs: 17 (TRIGGER) and 24 (ECHO). In the image, he used: 18 (TRIGGER) and 24 (ECHO).
Connect the switch by connecting the circuit ground (GND) and the GPIO 25. When you press the Switch, this GPIO will change the state and command a photo.
Clone the Darknet project (git clone https://github.com/pjreddie/darknet) and copy following files to yolo folder:
Click on this link and download the yolov3.weights file and save it in the yolo folder.
conda env create -f ./env.yml conda activate object
To execute, just run the script simple_detector.py:
If you want, you can pass the path of an image file to test. I attached 2 images for you to test.
Oh, and I created a JSON Dictionary to translate the names of the objects found (to Portuguese), but if you are an english speaker, just use the original names.
Install the conda environment: env-armhf.yml.
By pressing the switch the device will take a photo and tell you the objects that are in it and the distance to the closest object (see the video).
Read the OpenCV installation to see how to install the rest of the components on your Raspberry PI.
Previously published at https://github.com/cleuton/audio_object_recognizer/blob/master/english.md