Studio Vision Daw

[Randell Magtoto]

Ifmy vision had been to bring lessons to as many people as possible, the collaboration may have been a great opportunity. But for my school, it would have distracted us from community-building activities.

I expect the vision and mission for my studio to change a lot over the first year, so for now my priority is just to get something down on paper. Once I have it written down, I make a point to revisit it regularly and hone it over time.

Vision is a plugin that lets you quickly test your GROQ queries right from the Studio. It shows up as a tool in the navigation bar when installed, and is part of the default Studio setup when running in development mode.

New projects should have the plugin installed already. For new projects, or if it is not part of your studio configuration, you can install it by adding @sanity/vision as a dependency of your project (adding it to package.json and reinstalling dependencies).

The Vision plugin allows you to quickly test a GROQ query against any of the datasets in your Content Lake. At the top of the tool, you'll find dropdowns to select your dataset, API version and perspective.

Each time you run a query (we'll see how in a moment), you'll see a fourth field at the top containing a URL for your query. This URL contains the API call to the Content Lake that's querying for your data.

If your dataset is public, that URL can be run in a browser, cURL, or an app like Postman or Insomnia, and it will return the same JSON as you see in Vision. If your dataset is private, the request must be authenticated in order to return data. The decision on dataset visibility is up to you, and can be changed if necessary.

Grove Vision AI Module Sensor represents a thumb-sized AI camera, customized sensor which has been already installed ML algorithm for people detection, and other customized models. Being easily deployed and displayed within minutes, it works under ultra-low power model, and provides two ways of singal transmission and multiple onboard modules, all of which make it perfect for getting started with AI-powered camera.

We will show you the basic function about the module, and then introduce you the customized way that you can build the ML model of your own. But before we fully apply the module to our projects, it will take us serval steps to get the module ready.

Since we have downloaded the zip Library, open your Arduino IDE, click on Sketch > Include Library > Add .ZIP Library. Choose the zip file you just downloadedand if the library install correct, you will see Library added to your libraries in the notice window. Which means the library is installed successfully.

In the foreseeable future, we will optimize and upgrade the product library for more interesting function. According to the library installation methods provided above, we here introduce you the way to upgrade.

We will update the link when the library is optimized. You can delete the original library folder in your computer's folder, then download the latest version, unzip it and put it in the Arduino IDE library directory. (...\Arduino\libraries. .... is the path you setup Arduino)

In this demo, we will detect human face and count how many people the module detects on both Seeed Studio XIAO nRF52840 Sense and Seeeduino V4.2 (Arduino UNO). Meanwhile, Seeed Studio provives a website to display what the module sees.

Step 3 (Seeed Studio XIAO). Parpare a Type-C cable and connect it to one seeed studio XIAO Series board. Plug it pin by pin into the Grove AI Module and use another Type-C cable to connect the module.

Both Type-C cable should be connected with the PC. In the end, the direction of the Type-C connector on the module should be the same as Type-C connector on the seeed studio XIAO samd21. For instance:

Open the serial monitor and set baud rate as 115200 and the result of people detection should be showed continuously. Meanwhile, the image that is captured by the module also will display on the website. The successful ongong output should be like:

ai.begin() has two arguments, the type of model and the model number. The numbering is generally different for different models. That is to say, the model number of the sample program only applies to the People Detected Model, if you use other models, then the number is no longer 0x11.

If you have encountered an unforeseen situation in actual use, or if you have used it incorrectly and have experienced an outcome other than medical treatment. Please refer to the following steps to troubleshoot and attempt to repair the module.

As shown in the figure, you can see the Bootloader version number on the first line. As of September 2023, the latest BootLoader version number should be v2.0.1. If you also check the same version number, then in principle you don't need to do the next second step.

This is the firmware that controls the BL702 chip that builds the connection between the computer and the Himax chip. The latest version of the BootLoader has now fixed the problem of Vision AI not being able to be recognised by Mac and Linux.

Open the serial monitor, enter some random characters, such as "a", click send, then the erase operation will start to execute. If you see the following message, then it proves that the firmware erase was successful. We can go to the next step.

The above three parts need to be judged and executed in turn, when the above steps are executed, you can do your operation again, if there are still problems, please contact our technical support team.

Thank you for choosing our products! We are here to provide you with different support to ensure that your experience with our products is as smooth as possible. We offer several communication channels to cater to different preferences and needs.

Azure AI Vision is a unified service that offers innovative computer vision capabilities. Give your apps the ability to analyze images, read text, and detect faces with prebuilt image tagging, text extraction with optical character recognition (OCR), and responsible facial recognition. Incorporate vision features into your projects with no machine learning experience required.

Read this 2022 commissioned study conducted by Forrester Consulting to learn how to help developers of any skill level at your organization deploy AI solutions quickly using prebuilt, production-ready cloud AI services.

No. Microsoft automatically deletes your images and videos after processing and does not train on your data to enhance the underlying models. Video data does not leave your premises, and video data is not stored on the edge where the container runs. Learn more about privacy and terms of usage.

The model customization feature of the service is optimized to quickly recognize major differences between images, so you can start prototyping your model with a small amount of data. You may start with as little as one image per label. If you have more labeled images, you may add more. Depending on the complexity of the problem and degree of accuracy required, you can continue adding additional images per label to improve your model.

You can label the images in Azure Machine Learning Studio, which is integrated with Vision Studio for easy export of labeled data. You can also label the data in the COCO file format and import the COCO file directly in Vision Studio. See documentation for details.

The model customization feature for Azure AI Vision is the next generation of Custom Vision, with improved accuracy and few-shot learning capabilities. You may continue to use Custom Vision, or you can migrate your training data to retrain your model with model customization from Azure AI Vision. See documentation for details.

After using Azure AI Vision to extract insights and text from images and video, you can use text analytics to analyze sentiment, Translator to translate text into your desired language, or Immersive Reader to read the text aloud, making it more accessible. Related services and capabilities include Azure AI Document Intelligence to extract key-value pairs and tables from documents, Azure AI Video Indexer for extracting advanced metadata from audio and video files, and Content Moderator to detect unwanted text or images.

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It is an MCU-based vision AI module powered by Himax WiseEye2, featuring Arm Cortex-M55 & Ethos-U55. TensorFlow and PyTorch frameworks are supported. Compatible with Arduino IDE and no code model deployment and immediate visualization of inference results with SenseCraft AI.

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