mlkit VS OpenCV

I used ML Kit pos estimation API. I followed New features in ML Kit video and building use cases playlist. I tried to understand googlesamples/mlkit and tensorflow/examples but I barely understand, where the define the processor, and I loos it when it comes to the cameraSource file. eventually I created a runnable application.

To understand the implementation of getBitmap(), please refer to BitmapUtils.java.

Next, we draw an overlay above the camera preview. There is an excellent GraphicOverlay class provided by Android ML Kit demo project. We don't need to reinvent the wheel:

Download the sample code of ML Kit vision.

MLKit is distributed by CocoaPods, no Carthage or SMP support, but at least we ask Google to add such options...

Referenceshttps https://www.kaggle.com/datasets/riyaelizashaju/skin-disease-image-dataset-balanced?fbclid=IwAR3wbTp8l5yo_5fx6HAX8Vd2-9cca3khAc8EiBGFObaALfdVid29IuB_rYE https://keras.io/api/applications/vgg/ https://www.tensorflow.org/tutorials/images/cnn?hl=th https://opencv.org/

Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks.

You'll need to plot the predictions. There are a few open source tools to do that, supervision is one you can use (https://github.com/roboflow/supervision) and opencv is another common option (https://github.com/opencv/opencv)

You might be able to achieve this with scripting tools like AutoHotkey or Python with libraries for GUI automation and image recognition (e.g., PyAutoGUI https://pyautogui.readthedocs.io/en/latest/, OpenCV https://opencv.org/).

The OpenCV4NodeJs A.I. library provides an interface for calling OpenCV routines in NodeJS.