Computer vision is a field of artificial intelligence that deals with the ability of computers to see and understand the world around them. It is a branch of artificial intelligence that deals with the ability of computers to see and understand the world around them. While the models used in computer vision usually require a lot of data and computing power, the TensorFlow.js library makes it possible to use these models in the browser.
Introducing Scanner Cam.
What is Scanner Cam? permalink
Scanner Cam is a web application that uses the TensorFlow.js computer vision library to detect objects in real time with the camera of a mobile device or a computer.
Software Information permalink
- Project technology: React, TypeScript, TensorFlow.js, Vercel, PWA
- Industry: AI App
- Work Duration: ≈4 months
- Accessibility WCAG: AA (2.1)
- Version: 1.0
- Detect up to 80 object classes.
- Speech synthesis to talk about objects detected on camera.
- Language internationalization support in English and Spanish.
- Capabilities to work offline.
- Works with two cameras in mobile devices.
- Light and dark mode based on the user's preferred color scheme in the browser.
Development Process permalink
Importing the Neural Network Model permalink
The COCO-SSD model is a pre-trained model that can detect up to 80 object classes. It is based on the SSD (Single Shot MultiBox Detector) algorithm. The COCO-SSD model is trained on the COCO dataset.
Introducing Tensorgram permalink
Tensorgram is a mechanism that is responsible for bringing the frames of the camera to the COCO-SSD model and then painting the boxes with the detected objects on the screen.
The Tensorgram mechanism is a function that is called every time the camera sends a new frame to the app. The function receives the frame as a parameter and returns a React element that is painted on the screen.
Usually this type of function is implemented over a HTML Canvas element. But in this case, the function is implemented over a HTML Div element. This results in a better user experience.
Integrating Speech Synthesis permalink
Once ScannerCam is detecting objects in real time, it is possible that the app itself talks about the objects detected on camera. This feature is called object-to-voice and it is possible thanks to the Speech Synthesis API.
The Speech Synthesis API is a web standard that allows the web application to talk. It is a part of the Web Speech API. The Speech Synthesis API is supported by all modern browsers.
From every 5000 milliseconds, the app checks if the object-to-voice feature is active. If it is active, the app talks about the objects detected on camera.
I am identifying a person and a bycicle.
I am identifying a dog.
I am identifying an apple.
The Speech Synthesis API is supported by all modern browsers. But it is not well supported by all mobile browsers.
I discovered that the Speech Synthesis API is not supported by the Safari browser on iOS. And the same happens with the Chrome browser on iOS. This is because both browsers use the WebKit engine.
To alert the user about this, the app checks if the Speech Synthesis API is supported by the browser. If it is not supported, the app shows a message to the user:
ScannerCam: iOS devices do not support object-to-voice unfortunately. But you can use it in Android, Mac or PC.
Language internationalization permalink
The app supports two languages: English and Spanish. The language change happens by the user's preferred language in the browser.
User Interface permalink
The UI of ScannerCam was built with TailwindCSS. TailwindCSS is a utility-first CSS framework for rapidly building custom user interfaces.
It is responsive. And it's also available with a beautiful light and dark mode based on the user's preferred color scheme in the browser.
Finally I wrote more the tests to ensure that the app is working properly. The tests are located in the
tests ├── about.test.ts ├── basic.test.ts ├── model.test.ts ├── page.test.ts ├── speech.test.ts
Currently ScannerCam is continuously tested with Playwright. Playwright is a Node.js library to automate Chromium, Firefox, and WebKit with a single API.
Future plans for ScannerCam include:
- Progressive Web App (PWA) support.
- Offline-first support.*
- Unit tests with Jest (or other testing framework).
- Add more languages.
(*) Currently after the model is downloaded, the app works offline. I am thinking about how to make the app work offline from the beginning storing the model in the browser.
The source code of ScannerCam is distributed under the MIT License.