Python is a widely-used programming language for machine learning, deep learning, and many other fields. Suppose you want to make an image recognization and prediction model. Then it’s obvious that you have do many things before making a model, like converting to grayscale, preprocessing of image e.t.c. Thus you have to know which python image modules fit for you. In this entire tutorial, you will know the best image processing library in python.
Scikit-Image converts the original image into NumPy arrays. It has many algorithms on segmentation. color manipulation, filtration , morphology, feature detection e.t.c. It is built on C Programming thus making it very fast. As a Data Scientist, you can use it for the conversion of each pixel into greyscale. You can read more from their official Scikit Image User Guide.
Pillow is the open-source librariy that supports many functionalities that some other libraries do not provide like opening, filtering, saving images. The main thing I like about it that you can resize, convert the images to other formats like jpeg, png e.t.c easily.
You must have been heard of it. This library is mostly used to build computer vision and machine learning applications. It has more than 2500 optimized algorithms. These algorithms can do many things like detecting and recognize faces, identification of objects, classification of humans in images or videos, finding similar images and many others. These features easily tell how powerful OpenCV is?
Although it is an opensource python library for scientific and mathematical computation, you can use it for image processing. It has a module scipy.ndimage that can do many general things you require for a deep learning model. It has algorithms for displaying, filtering, rotating, sharpening , classification, feature extraction and many more. You can know more from their official Scipy Documentation.
Mahotas is a computer vision and image processing library and includes many algorithms that are built using C++. Thus it makes fast for Image processing. Currently, it has more than 100 + functions for image processing like a watershed, convex points calculation, thresholding, convolution e.t.c.
OpenFace allows you to do recognize face using deep neural networks and is based on the CVPR 2015 paper FaceNet Research Paper. It is both a python and torch implementation and is an open source. OpenFace has algorithms for detecting a face from a pre-trained model in OpenCV or dlib. It Uses a deep neural network to represent (or embed) the face on a 128-dimensional unit hypersphere and use the classification techniques to complete the regonization task.
It is a Pytorch based framework for computer vision. It has many pre-trained models for face recognization and classification that many models have been implemented using it like AlexNet , ZFNet, VGG/BN-VGG , ResNet e.t.c. You can know more about their PytorchCV GitHub page.
These libraries that I have defined is the Best Image Processing Library in Python. You can use it in your own projects. All of them have different purposes. But I personally liked OpenCV and Pillow that are most popular today.
I hope you have found the answer for the Best Image Processing Library in Python. If you have any queries or want to give a suggestion on it then please contact us. We are always ready to help you.
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