Image Processing Using Python: Algorithms & Tools Tutorial

This article comprehensively covers Image Processing Using Python. Understand the basics of image processing with Python, along with the tools and techniques used:

What is image processing?

The technique used for obtaining information from an image is called image processing. Through image processing, we can process thousands of images for transformation and manipulation at a single time. Through computer vision, many real-world problems like object detection, self-driving cars, and robotics can be easily solved.

Python is one of the most popular languages used in image processing. Leveraging the tools and techniques of Python, image-processing tasks are done very efficiently and effectively.

By two-dimensional spatial coordinates (x,y) an image can be represented. Also, in three dimensions, the spatial coordinates of an image can be achieved i.e. on coordinates of the x-axis, y-axis, and z-axis. The amplitude of an image at a particular value is known as the intensity of an image at that point.

Image Processing Using Python

Image Processing Using Python: Algorithm and Tools Guide

Types of Images:

  • RGB Image: In an RGB image, there are three layers of two-dimensional images. These layers are called red, blue, and green channels.
  • Grayscale Image: Only through a single channel can the images contain shades of black and white.

Classic Image Processing Algorithm

#1) Morphological Image Processing: Leveraging the opening and closing operations, we can smooth the image using morphological image processing. From the binary images, the imperfections can be removed by simple thresholding.

Dilation and erosion are the two fundamental operations that can be done using morphological image processing. Through dilation, the pixels can be added to the boundary of an object. While in erosion, we can remove the pixels from the boundary of an object in an image.

Suggested Read =>> How to Increase the Resolution of the Image

#2) Gaussian Image Processing: This technique is also known as Gaussian smoothing. Leveraging it, the noise and details contained in an image can be reduced. The Gaussian image processing blurring technique is similar to looking at an image through a translucent screen.

The Gaussian filters are low-pass filters that mostly weaken at high frequencies. The Gaussian filter gives more weight to the pixels located at the center than the other pixels located at different points. Therefore, Gaussian image filtering is used to enhance an image at different scales.

#3) Fourier Transformation: Into the sine and cosine components, the image can be broken down into Fourier transformation. An image consists of three things, i.e. magnitude, phase, and spatial frequency. Related to contrast, the magnitude can be used.

For increasing or decreasing the brightness of an image, the spatial frequency can be increased/decreased and for color information of an image, the phase needs to be checked. After the Fourier transformation of an image is done, we can use it in image filtering, compression, and reconstruction.

#4) Edge Detection: For detecting the discontinuity in the brightness of an image, an edge detection image processing technique is used.

Leveraging the Sobel edge detection algorithm, we can make separate measurements of an image using a kernel. Edge detection image processing is highly beneficial as most of the information of an image is enclosed at the edges.

#5) Wavelet Image Processing: For non-stationary signals, wavelet image processing is used. It measures the time and frequency of a wave. Through wavelet image processing, for low-frequency components, a good frequency resolution can be obtained.

While for refining the edges of an image, wavelet image processing is chosen because it does not blur the image. Only the noise is reduced in the image. Overall, the quality of an image is not degraded while applying traditional filters.

Image Processing Using Neural Networks

Consisting of nodes or neurons, the neural networks are multi-layered. For the neural network, the neurons act as processing units. The neural network consists of three layers i.e. input, hidden, and output. The neurons take the data, get themselves trained to recognize the patterns, and then afterward deliver the output.

Also Read =>> In-depth Tutorial on Neural Network Learning Rules

Image Processing Using Neural Networks

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In the above image, how a neural network recognizes an image is depicted. The images are annotated as per the category. With the assistance of algorithms, the visual characteristics of each category are determined. Following this, the models are trained to recognize each class of image. This type of model training is known as supervised learning.

Types of Neural Networks

#1) Convolutional Neural Network: For image classification and for extracting features from an image, the CNN technique is used. Through a series of layers, the input image is passed. Adjacently, there is also a pooling layer. To reduce an image according to the dimensions, a pooling layer is used. Max and average are two distinct types of pooling.

The computational power required for processing the data can be decreased using the pooling layer. The highest value obtained from an area surrounded by the kernel in an image is known as max pooling. Following this, the average of values in a part of the image occupied by a kernel is known as average pooling.

#2) Generative Adversarial Network: For the unsupervised learning approach means images with no labels, the generative adversarial network is used. The GAN consists of two models. One component is the Generator while the other is Discriminator.

With the assistance of a generator, the fake images can be made look realistic while the discriminator can distinguish between fake and real images.

The most popular GANs are Deep Convolutional, Disco GAN, Cycle GAN, Style GAN, GauGAN, Conditional GAN, and others. Face ageing, photo inpainting, super-resolution, photo blending, and clothing translation are some applications of GAN. GANs are very good for image manipulation and generation.

Image Processing Tools

The world is full of data. Through image processing tools, analysis and manipulation of images can be done. Overall, to improve the quality and for extracting the information from an image, image processing tools are used.

Developers are choosing Python for image processing because it’s popular and has free tools available for image processing tasks.

Computer vision tools for python image processing

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With the usage of deep learning, the world is altering too fast. Researchers and innovators are continuously fine-tuning the image processing area. To perform different types of tasks, a number of Python image-processing processes are available.

#1) Open CV: OpenCV is one of the largest computer vision libraries. There are around 2 million customers downloading OpenCV every week. It is one of the easiest libraries to use. OpenCV supports both Python and C++ languages.

With the key functionalities of human face detection, optical flow, and search for stereo machines, OpenCV is a cross-platform, supports Android version, and has a good in-built performance testing system. Supporting a continuous integration system, OpenCV is designed for developing open infrastructure.

Being an open-source library, it is well-optimized and designed for real-time computer-based applications. Leveraging it, most of the video and image processing jobs can be very easily done.

#2) Scikit-image: Scikit-image is an open-source library, and it leverages machine learning’s built-in functions. It contains a collection of algorithms for image processing. With a set of only a few functionality features, through Scikit we can perform a number of operations on images.

With the usage of NumPy arrays, through Scikit, we can easily rotate, rescale and apply morphological operations on an image. To implement threshold, edge detection, and Gaussian smoothing-like operations, the Scikit library is the best one to choose from.

Some Scikit-image examples are the detection of features and objects in an image, segmentation of objects, filtering and restoration, geometrical transformations, manipulating color channels, and many more.

#3) PIL/Pillow: It is one of the most powerful libraries as it supports a wide range of operations on images. Apart from rotating, resizing, grayscaling, or cropping an image, through PIL, we can get the image details such as file format, pixel format, size of an image, etc.

Adjacently, PIL can operate various other manipulating operations like uploading an image, displaying an image, or flipping an image.

#4) NumPy: Through multi-dimensional arrays, the images are represented in the NumPy library. The types of arrays used are called NdArrays. Therefore, a numpy array of three dimensions can be used for depicting a colored image.

To perform simple operations like flipping an image, extracting the content, or analyzing the image, NumPy library can be leveraged.

#5) Mahotas: Most of the algorithms in Mahotas are in C++ programming language. Having minimum dependencies Mahotas is an independent module. Only for doing the numerical calculations, Mahotas library is dependent upon the C++ compiler.

It does not require any NumPy module. Watersheds, morphological operations, thresholds, convolution, SLIC superpixels, spline interpolation, colorspace conversions, speeded-up robust features are some of the most popular and best algorithms for image processing in Mahotas library.

#6) SciPy: For processing tasks and image manipulation, SciPy is a Python core scientific module. For processing and manipulation tasks of images, SciPy can be used. The current package of SciPy includes binary morphology, linear and non-linear filtering functions, object measurements, and B-spline interpolation.

#7) Simple ITK: ITK is an open-source, cross-platform system that provides an extensive set of software tools for image analysis. ITK stands for Insight Segmentation and registration toolkit. Simple ITK is mostly available in C++, but it supports many programming languages like that of Python.

Simple ITK is an image analysis toolkit that supports operations like image segmentation, registration, and general filtering operations.

#8) PgMagick: For leveraging the GraphicsMagick library, PgMagick is a Python-based wrapper. Pymagick supports a number of tools and libraries through which reading, writing, and manipulation of images can be done. Over 88 formats that include TIFF, PDF, PNG, JPEG, JPEG-2000, DPX, GIF, PNM, etc. the PgMagick is supportable.

#9) PyCairo: Cairo is a two-dimensional graphics library for drawing vectors graphically. For the graphics library, PyCairo is a set of Python libraries. The most interesting thing to note and work with vector graphics is that whenever you resize or transform them, the vectors do not lose any clarity.

#10) SimpleCV: For building computer vision applications, SimpleCV library provides an open-source framework. For assessing SimpleCV, you do not have to learn complicated things like file format, color space, etc. You can easily access high powered computer vision libraries.

Through SimpleCV, beginners can also write simple machine vision tests. The video streams, images, cameras, and video files can be made interoperable in SimpleCV.

Basics of Image Processing in Python

Each image has its own story, and it contains a lot of information that can be used in distinct ways. For extracting meaningful information from the image, Python programming language is widely used.

#1) Install Required Library: Leveraging the pipe, we can install the required library. First, there is a need to install the required library like OpenCV, Pillow, or another one.

Install Required Library

#2) Image Open and Show: By typing the image processing Python code for upload and display of an image, the file of an image can be opened and displayed. The image can also be rotated using the code shown below:

Open and Show

#3) Rotating an image: The image can be rotated as per the need. After rotating the image, the portion of the image having no pixel values is filled with transparent pixels.

Rotating an image

#4) Resizing an image: The quality of an image either upgrades or downgrades when interpolation happens at the time of resizing. Therefore, resizing of an image should be done carefully.

Resizing an image

#5) Shifting of an image: For shifting an image from one place to another. The image can be made upwards, downwards, left, right or centrally aligned.

Shifting of an image

#6) Edge Detection: Edge detection is an image processing technique for identifying the boundaries of an object or region in an image.

There are two types of edge detection.

Sobel Edge Detection: Leveraging the Sobel operator, the edges of an image are detected. The edges are marked due to a sudden change in intensity.

Sobel Edge Detection

Canny Edge Detection: It is one of the most popular edge detection methods. Consisting of four stages i.e. extracting the edges from the image, reducing the noise, suppression of false edges and hysteresis thresholding.

Canny Edge Detection

#7) Output: Information can be retrieved about the open image.

#8) Convert and Save Image: The format of an image can be easily converted into another desired format. After that, the image can be saved by typing the code as shown below in the image:

Convert and Save Image

#9) Resize Thumbnails: The size of the image can be changed using the thumbnail method of pillow library as shown in below image:

Resize Thumbnails

#10) Converting to Grayscale Image: From the original colored image, grayscale image can be created using the code written in the image below:

Converting to Grayscale Image

#11) Output: As an output, we can get the desired image.

Python Image Analysis

#1) Introduction about Pixel: Pixel is a picture element. It is a combination of three colors i.e. red, blue, and green. In a two-dimensional grid, in the shape of a square or circular pattern, the images can be arranged.

#2) Basic Properties of Image:

Basic Properties of Image

As an output, we can receive:

Output of python image processing

#3) Logical Process to Process Pixel Values: Using logical operators, we can easily convert a number of arrays into the same size. Apart from that, through the logical process, filtering the RGB images of high-value pixels or low-value pixels can also be done very easily.

#4) Masking: For removing the background of an image or create a mask into a circular shape, some logical operations need to be added to the code. The distance from the center of an image to every border pixel is measured. After measuring the distance, code is written for masking an image.

#5) Image Processing: The color in every RGB layer has some meaning. For example, the red color indicates the altitude of a geographical data point. Meanwhile, the green color indicates the slope, and the intensity of the blue color depicts a measure of aspect in an image.

Python Image

#1) Create a Dockerfile in the Python app project: Through the Dockerfile, users can create images of the application. In the Dockerfile, instructions related to the environment and commands that need to be executed are mentioned.

Here are the four steps to be followed while dockerizing an application:

  1. Creating a docker file
  2. Creating the desired application to dockerize
  3. Building the image using the docker file
  4. Running the images

#2) Run a single Python Script: Whenever it is difficult to write a complete Dockerfile, a Python script can be made run using a Python Docker image.

#3) Multiple Python Versions in the image: In the non-python slim variant, there is an additional older Python that can be executed at /usr/bin/python. While the new usr/ local/bin/python is the default one in the $PATH. For every use case, the variant of Python is different.

#4) Types of Image Variants

  • Python: <version>: This is the default version of Python image. It is specifically designed in a way to be used as a throwaway container. This version of Python is a basic one and it provides the base to build other images.
  • Python: <version>-slim: With the minimal packages for running Python, this variant is designed. Python-slim image version does not contain any common packages as offered in the default version. Specifically for space constraints and for deploying Python images, this version is used.
  • Python: <version>-alpine: Alpine Linux is smaller than most of the distribution base images. Therefore, the images are generally smaller and thinner in size. This variant of the image is useful as the final size of the image is as small as possible. In the alpine official image, image is based on a popular alpine Linux project.
  • Python:<version>-windowsservercore: The Python windowsservercore version of the image works on Windows Server Core. It only supports Windows Server 2016, and Windows 10 Professional/Enterprise.

Python Image Processing Projects with Source Code

The image processing projects can be solved using Python programming language. For example, the Sudoku solver, barcode detection, automatically correcting image exposure, quilting images, and synthesizing texture, and signature verifying system are some of the image processing projects.

Frequently Asked Questions

Q #1) Which is the best software for image processing?

Answer: Adobe Photoshop, Inkscape, GIMP, and Pixelmator are some of the best software tools for image processing.

Q #2) How does Python remove noise from image processing?

Answer: To remove noise from an image, use the mean filter that is used. Through the mean filter, the mean of pixel values within a n*n kernel can be determined. The pixel intensity of the center element is replaced by the mean. By replacing it, the edges of an image are smoothened. Hence, the noise in the image is reduced.

Q #3) Which module of Python is used in image processing?

Answer: PIL is an open-source library that supports image processing tasks. PIL stands for Python imaging library. In distinct formats of images, the reading, rescaling, and saving of images can be done using PIL. Also, for image display, image processing, and image archives PIL can be leveraged.

Q #4) Explain Sampling and Quantization.

Answer: Sampling is the process of digitizing coordinates. While on the other hand, Quantization is the process of digitizing the amplitude or intensity. There are two types of quantization i.e. uniform and non-uniform. In uniform quantization, the level of quantization is equal and the space between them is consistent.

While in the non-uniform quantization, the level of quantization is mismatched and the relationship between them is also logarithmic.

Q #5) Explain the morphological operation in OpenCV.

Answer: On the basis of shapes and other actions, the images are manipulated in the morphological operation. An element can be created from the provided image. By either reducing the noise or smoothing out defects in an image, the image can be made clear. Both of these types are known as erosion and dilation in morphological operation.

Q #6) What is CV2 in image processing?

Answer: CV2 is a Python based open source library. For image processing, machine learning, and computer vision tasks, the CV2 library is used. CV2 library also supports Java, and C++ programming languages. For detecting faces, human handwriting, and objects in videos and photos, the Open CV2 library can be leveraged.

Q #7) Is Python good for image processing?

Answer: Python is the most mature, well-supported, and prevalent programming language. For the easy conversion of ideas into coding,

Python is the best language as it provides the developers the ease to do coding, supports fast prototyping, it is open source, direct integration with web frameworks is possible, and has a huge number of libraries for machine learning.

With all these advantages, Python is becoming an ideal choice for image-processing tasks.

Q #8) How to use OpenCV in Python for image processing?

Answer: OpenCV is an open-source library. It is used for processing images, videos, and live streams.

In the steps below, how the OpenCV can be used for image processing is described:

  • The first and foremost step is installation: To install OpenCV in the system, you need to write and run the pip command.
  • Customization of image: We can either rotate, resize or crop an image as per the need by simply writing down the code to perform different actions.
  • Thresholding: It is an image segmentation technique. For the separation of objects from the background, thresholding is done.
  • Edge detection: Through edge detection, objects and regions can be easily detected in the image.
  • Image smoothing: Leveraging smoothing algorithms like Gaussian blur, median blur, or bilateral filter, the noise can be filtered in an image.
  • Image contours: In the image, contours are called building blocks. For object shape detection, image segmentation, and motion detection, contours are used. Through the OpenCV library, the contours present in an image can be detected.
  • Save the image: The last step is to save the image to any device i.e. computer, laptop, smartphone, etc.

Q #9) Which version of Python is suitable for image processing?

Answer: The Python 3 version is best suited for image processing as this version supports modern techniques and tools of machine learning, data science, and artificial intelligence. Python 3 is a combination of powerful libraries and it can be easily mixed with other programming languages.

Python 3 version is easy to learn and adapt as it is simple and easy to understand. Apart from that, the Python 3 version is supported by a large community of developers. Therefore, for any assistance, there are a number of developers to assist and help.


In the article, we have discussed classical image processing algorithms in Python, tools, and techniques used for processing an image. By leveraging distinct Python libraries and tools, image processing tasks can be done efficiently and effectively.

Researchers and innovators are day-to-day beginning new techniques for fine-tuning the entire image processing process. In the field of image processing, deep learning is creating a huge impact.