This is a post that I have been putting off for a while, but I think the time has come to share this with the community. Two years ago I sat down to start a new project, an experiment involving image downscaling and Node.js, and since then it has become my primary open-source project.
**I wanted to generate responsive images for my website to offer a better experience. **It came to life as a set of Node.js scripts, and over the course of several iterations, evolved into an open-source package released on npm under the name Responsive Image Builder.
It was created out of necessity due to a lack of existing open-source solutions.
Let me be clear, there are a variety of image tools, loaders, and third-party services. However, none of them, in my opinion, fulfilled my needs. Furthermore, I was in love with gatsby-image and the primitive library by Michael Fogleman (which was difficult to integrate into existing solutions).
This led me to create my own solution to solve my rather unique requirements:
My goal was to glue together existing image libraries into a unified toolset that could be customised to allow the processing of images in different ways.
Psst! You can read more about the motivation behind the project here.
Today it goes by a different name that better reflects its new functionality (and partly due to a reserved package scope ️🤦♂️): Image Processing Pipeline. The processing “workflow” is now completely customisable and it has also just had a major release that refactored the internals, making it easier to implement adapters, such as the new webpack loader!
During my studies at JKU there was a task for preprocessing images for a machine learning project. It is necessary to clean the raw images before using them in a learning algorithm, so thats why we create a pre-processing function. I think it can be quite useful for others as well so I want to share a bit of my approach. The file is structured in a way that it is easy to understand and also should have a tutorial-like effect.
#image-recognition #image #image-classification #machine-learning #image-processing
In this video, I will be showing you the powerful library Jimp for image manipulation in Node.js
#jimp #image manipulation #image editing #working with images #node.js #image processing
Processing an image in order to derive some meaningful information from the image is known as image processing. It can be called a scientific study where we apply different methods or functions on images to find out what are its different features. We can enhance the image or degrade the image in order to extract unique features.
Mahotas is a computer vision and image processing library for python. It is implemented using C++ so it is fast and it operates over NumPy arrays. Currently, it has around 100 functions for computer vision and image processing.and is ever-growing.
In this article, we will explore what are the different functions and methods that are there in Mahotas which can be used for image processing.
Like any other python library, we can install mahotas using pip install mahotas.
We will import all the functionalities of mahotas and other than that we will import pylab for image display functions.
from mahotas import *
from pylab import imshow, show
We can use any image for image processing. I am using a bird image that I downloaded from google. We will use mahotas to load the image.
img = mahotas.imread('/bird.jpg')
Now we will perform different operations using mahotas and find out the important features and information about the image we are using.
#developers corner #complete guide #image analysis #image classification #image processing #image recognition #mahotas #python programming
In this video, I will be showing you the powerful library Sharp for image manipulation in Node.js.
#image manipulation #image editing #manipulation program #image processing #reduce image size