We have mentioned it many times; Python is an excellent programming language for businesses. Google has been a supporter of Python since its inception. Today it is one of the official Google server-side languages along with C++, Go, and Java. Even Mr. Zuck of Facebook and his production team are exceptionally keen on Python and its recent development in the market. Facebook diligently uses Python image-processing libraries to maintain less code and constantly makes improvements in their going live feature on their mobile app.
If you are not from the programming field, all these websites and mobile apps may be just a feature for you, but they are results of writing and collaborating millions of lines of codes that alleviate the plight.
When we were in high school, we saw all kinds of dreams among our classmates. Some dreamed of joining the army; many wanted to be an investment banker; some wanted to step into finance and marketing.
I don’t know if you have ever paid close attention to the list of these dreams, but on observing personally, I figured out that it only consists of some defined positions. Only the jobs kids know about it. The reason behind this is that kids have not explored the market yet, but you and I both know that scope is much larger.
Out of curiosity, I asked my brother, who is currently in high school, what kinds of jobs today’s kids are looking for? And I was not amazed by the answer that most of them are looking for tech jobs in the future. The future of tech jobs is evident by the image that follows.
Being a programmer feels pretty good that jobs like software development and IT operations are also on that list of dreams.
Thanks to today’s technology, the list of kids interested in IT will get substantial with time. Most of the technologies are nothing without these programming languages. If you can order Uber from wherever you are standing on the Earth, then it’s because some programmers used ingenious methods to come up with a comprehensive mobile app by writing thousands of lines of code.
Figuring out which programming language to use for your business can be a bit tricky. You need to make sure you invest your time in walking the right path; as an eager mind, you definitely want to know which languages are most prosperous and which ones are imperfect. To be honest, there is no definite answer to which language is the BEST; however, there are some top reasons to go with Python.
Fun Fact – Python is a more searched term than Kim Kardashian on the internet in 2017. One of them is versatile, predictable, and has earned immense growing popularity, and the other one, not consistently.
Python is responsible for some significant applications in the market and is also the most preferred language for Machine Learning. With endless possibilities of ML using Python in all the major industries, it’s astonishing to see what great real-world opportunities there are for Python developers out there.
We know you might be thinking, “What about Julia?” To be honest, Julia is faster than Python, but that’s not the only aspect you should consider to establish it better than Python for Machine Learning. Julia lacks one major thing, and that is a large community supporting it. On the other hand, Python programmers are already on a high not to improve Python’s speed. The latest solutions show that ML using Python can be quicker using better optimization tools(already in development), external libraries, and third-party JIT compilers.
Today we are talking about image processing and the top 10 image-processing python libraries used in Machine Learning.
What is Image Processing in ML?
The field relating to machines that can understand images and videos is one of the major topics in the tech industry. Computer vision is making it easier for us, as we are observing many examples like self-driving cars, advanced robotics, facial recognition, etc. Essential in the middle of computer vision is image recognition, which basically means recognizing what an image represents.
If we define it technically, then image processing can be defined as the technical analysis of an image by complex algorithms to get helpful information as a result. The result of image processing is always an image, whereas, in computer vision, the output can be features or information about the image.
Why Do We Need Image Processing?
The data we collect using ML is mostly preliminary data or baseline in nature. We can’t use it in applications directly for several reasons. We ought to analyze it, pre-process it, and then it will become usable data.
Most image processing methods use machine learning models to transform images on various tasks like turning an image for optimal quality, applying artistic filters, improving specific embodiments to maximize quality for computer vision tasks.
Machine Learning is one of the most algorithmic methods in computer science. It used to take days in order to code all algorithms for machine learning. Thanks to the versatile nature of Python, because of its libraries, modules, and frameworks, we can master Machine Learning and data science.
Similarly, image processing is an element of Machine Learning which we can use to turn images as per our requirements to get valuable data. Using traditional methods can be tricky and time-consuming, but introducing image processing python libraries in ML has made things a lot easier.
Top Industries Benefiting from Image Processing and Machine Learning
Image Processing has been in the industry for a long time, and it’s time to know which industries are processing images using machine learning models and benefitting from this.
Agriculture – Images of crops can be dissected in an automated manner to pick out any shortcomings in the produce. Image processing analysis by ML models can quickly identify seeds and weeds effectively. It allows farmers to eliminate such damage-causing elements before sowing them.
Defense and Security – Security is always a big concern for every nation. Using image processing in such situations can be highly productive in uncertain situations. Recently, Indian Railways has started using image processing methods like facial recognition for identifying criminals in dense traffic. In a matter of seconds, the defense team can easily catch the person causing irregularities through the highly accurate information coming from the drone-based image capturing methods.
Waste Management – Aforesaid, artificial intelligence is looking for new ways to enter almost every industry. Using image processing and ML, waste segregation can help create a better plan for adequately managing and disposing of waste. The image can be taken for processing using ML models and identify the waste prior so different items can be disposed of separately. Germany recently started to use the waste segregation method using image processing and machine learning, and the results are promising.
Health Industry – We all know the importance of X-rays, MRIs, and other scans in the healthcare industry. Image processing using ML algorithms will bring more ease to the healthcare industry in the future. X-rays, for instance, can be subjected to an automated image analysis where deep learning models can quickly identify any kind of anomalies within seconds. Similarly, image and video graphic techniques can be used in surgical procedures to discover abnormalities within seconds.
Top 10 Image Processing Python Libraries used in Machine Learning
1. OpenCV
OpenCV is a unit of Intel and an open-source library. Soon after its release in the year 2000, it became a popular library due to its ease of use and comprehensive nature. This library is preoccupied with image processing, object detection, face detection, image segmentation, and other valuable functions in ML. Being backed by more than one thousand contributors on GitHub, this computer vision library makes image processing effortless using cunning features like grey scaling, image rotation, image translation, scaling, resizing etc.
2. SciPy
Many python developers are creating python libraries for machine learning that focus on scientific and analytical computing. With machine learning booming at a swift pace, three Python developers(Travis Oliphant, Eric Jones, and Peru Peterson) decided to merge these codes and standardize them to develop the Python library known as the SciPy library.
SciPy is focused on performing mathematics and scientific computations but can also carry out multi-dimensional image processing using its submodule known as scipy.ndimage. It offers impressive functions like image optimization, special operations, signal and image processing, fast Fourier transform, integration interpolation, etc.
3. SciKit Image
Time to tell you something unique. SciKit image is a Python-based image processing tool, but it has some of its parts written in a language called Cython. Now Cython is a programming language that is a superset of Python programming language designed to offer performance like C programming language. Something you have probably never heard before.
Among different methods, data scientists often used scikit for analysis, feature detection, filtering, segmentation, geometric transformation, color space manipulation, morphology, and more.
4. NumPy
NumPy is another image processing Python library used in machine learning. Also known as Numerical Python is an open-source library used for data manipulation and scientific computing.
We know an image is essentially an array of pixel values where each pixel represents by 1 greyscale or 3 RGB values. That makes NumPy valuable to perform image cropping, masking, or manipulating pixel values.
5. Pillow/PIL
PIL, also known as Python Imaging Library, is an open-source library for image processing that uses Python for functioning. It consists of various processes in image processing such as point operations, manipulating, filtering, image enhancement, image archives, and more.
6. SimpleITK
Simple ITK or Insight Segmentation and Registration Toolkit is a unique library out there that handles images as a set of points on a physical region in space. On the other hand, other libraries consider images as arrays. It defines the area which occupies images as origin, size, spacing, and direction cosine matrix. These modules make the process of image processing effective.
It is used for image segmentation and image registration widely by data scientists. ITK implements CMake to build the environment, as the library implements in C++ draped for Python.
7. Mahones
Mahones is an image processing and computer vision library that offers popular libraries such as binary patterns, haralick, and more. It allows you to calculate data from 2D and 3D images and can perform advanced image processing by extracting information from pictures. The most useful features of Mahotas are template matching, hit and miss thinning, morphological processing, watershed, and other 100+ functionalities.
8. Matplotlib
Matplotlib is Python Programming language’s plotting library along with its numerical mathematics extension NumPy. It is widely used for 2D visualizations and also for image processing. Matplotlib is most effective in altering images for pulling all the information out of them.
9. Pgmagick
Pgmagick is basically a Python Wrapper for GraphicsMagick. It is a proper amalgamation of tools and libraries for the manipulation of images. The best part is that it offers more than 88 formats. Pgmagick is also diligent in web applications to create new images.
10. Pytessarct
Pytessarct, also known as Python tesseract, is an OCR tool for the Python language. It is superb in recognizing and reading the text embedded in an image. It can read all image types supported by Leptonica and Pillow imaging libraries, including formats like jpg, gif, tiff, BMP, png, and more.
Conclusion
The future of image processing is promising as the top companies and governments are rapidly adopting it all across the globe. Image processing is more effective when you know how to collaborate with the best libraries from the internet. Since it has become an integral part of data science, AI, and ML for reasons like gathering information, the above list of libraries will definitely help get valuable data of the images.
As per many data scientists, the future of image processing is to find a way to scan the space for other intelligent life in the galaxy. The government of many companies is also looking to create new digital systems using advanced image processing applications and machine learning algorithms to offer better security to the people. Almost every field of our lives has been changed or impacted by improvement and advancements in the field of science and technology and image processing is going to add to that list of advancements.
We suggest first identifying your need, and based on that, you can determine the best-fit image processing library.
In case you desire some help with the technical stuff, let us know