scikit-image VS thumbor

We will use the Hugging Face transformers and diffusers libraries for inference, FiftyOne for data management and visualization, and scikit-image for evaluation metrics.

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.

scikit hog library: https://github.com/scikit-image/scikit-image/blob/main/skimage/feature/_hog.py#L302 , https://github.com/scikit-image/scikit-image/blob/main/skimage/feature/_hoghistogram.pyx

This is a good cv deep learning book with python examples https://www.manning.com/books/deep-learning-for-vision-systems. If you're pretty comfortable with the concepts of traditional image processing this is a good companion to cv2 (so you don't have to reinvent the wheel) https://scikit-image.org/

Also, don't know if you're familiar with Python, but if you need ideas for to implement for future directions : https://scikit-image.org/

There's probably something in scikit-image to do what you want, or close enough to build on.

Scikit-image The Scikit-image library is a collection of image processing algorithms that are designed to be easy to use and understand. It includes algorithms for common tasks like edge detection, feature extraction, and image restoration. If you are just starting out in image processing, then this is a good library to check out!

Python is a general-purpose programming language that provides many image processing libraries for adding image processing capabilities to digital images. Some of the most common image processing libraries in Python are OpenCV, Python Imaging Library (PIL), Scikit-image etc.

Then you would need to find what this plugin does for imshow. First thing you can see is that "interpolation" is not "bicubic" as you used, but "nearest"… but there are other settings here that are responsible for the difference of displays. (it's better that you look at the source code in your environment, as it might be slightly different)

- Image transformations | Again, use Do's $5 droplet. Install thumbor.org .Enjoy!

This is not a feature native to Cloudfront, it is something you need a dedicated service or function for. This aws blog article demonstrates a method using CloudFront Lambda@edge with a lambda that handles the image resize. Years ago before lambda@edge existed, I used an open source project thumbor behind CloudFront to handle this.

So I found this project: Thumbor This service is self-hosted, and It allows on-demand cropping, resizing, applying filters and optimizing images. It works incredibly fast and does not require a lot of resources. Take a look, and you will understand how it works.

Images - ideally use webp format, and keep images as small as possible by loading in only what you need in terms of resolution. Ie, if user is on mobile, and image is displayed in ie 320x160, don't load huge image for desktop and resize it in-browser. This can be achieved by tools like Thumbor.

You will need Thumbor to use this method. If you're unfamiliar with Thumbor then I wrote an introduction here.