OpenCV VS Kornia

This library is used for image and video processing, offering functions for tasks like object detection, filtering, and transformations in computer vision.

Download the Haar cascade XML files for face and smile detection: https://github.com/opencv/opencv/tree/master/data/haarcascades

Let's open the most recent release of opencv to the date of this video capturing: https://github.com/opencv/opencv/releases/tag/4.5.1

First of all, OpenCV, an open-source computer vision library, was used as the main editing tool. This is how NuloApp is able to get the correct aspect ratio for smartphone content, and do other cool things like centering the video on the speaker so that they aren't out of frame when the aspect ratio is changed.

git clone https://github.com/opencv/opencv.git mkdir -p build && cd build cmake ../opencv make -j4 sudo make install

OpenCV: Visión por Computadora para Todos (https://dev-to-uploads.s3.amazonaws.com/uploads/articles/zxyacyoqx4h0lvem6jpj.png)

In addition to installing the YOLO implementation from Ultralytics, this also installs OpenCV for image processing.

OpenCV - The official site for OpenCV, featuring tutorials and documentation.

git clone https://github.com/opencv/opencv.git cd opencv

How to Accomplish: Use statistical analysis tools and libraries (e.g., Pandas for tabular data) to calculate and visualize these characteristics. For image datasets, custom scripts to analyze object sizes or mask distributions can be useful. Tools like OpenCV can assist in analyzing image properties, while libraries like Pandas and NumPy are excellent for tabular and numerical analysis. To address class imbalances, consider techniques like oversampling, undersampling, or synthetic data generation with SMOTE.

👉 https://github.com/kornia/kornia/releases/tag/v0.6.6

Kornia: Differential Computer Vision\ (3 comments)

Kornia - Kornia is a differentiable computer vision library for PyTorch. It consists of a set of routines and differentiable modules to solve generic computer vision problems. At its core, the package uses PyTorch as its main backend both for efficiency and to take advantage of the reverse-mode auto-differentiation to define and compute the gradient of complex functions.

Between "not being sure yet" about GPU operations in pre-processing and choosing high-end CPUs, I think you are overthinking the wrong alternative. Besides DALI, check whether you are using codecs besides nvidia/torchvision-supported jpeg and png, and if other GPU CV libraries meet your needs: torchvision kornia

Use kornia.augmentation where this problem is solved doing the augmentations in batch outside the dataloader. https://github.com/kornia/kornia