Top 10 Most  Interesting Image Processing Projects using Python

Are you searching for image projects? Are you working on image processing projects? Are you finding the image project topic for the final year? Are you searching for beginner-level image projects using Python? So, here, in this article, you will find image-processing projects using Python.

Image processing is performing operations on images to get some helpful information. In this process, an image is served as an input to the system, and the output is an enhanced image with some details. There are a lot of applications for image processing in many different fields.

You can implement machine learning and deep learning algorithms for image processing. Python supports image processing too. So, you can create many image processing projects using Python.

Projects provide a career path and develop your skills and strategy. If you are a beginner in Python and want to improve your skills, you should implement an image processing project. If you want a placement in your college, you should work on projects.

Let’s look at some of the top 7 image processing projects using Python.

What is Image Processing 

Image processing uses mathematical algorithms and computational techniques to manipulate and analyze digital images. The process involves acquiring, storing, processing, analyzing, and visualizing images to extract useful information.

This technique can improve the quality of images, extract relevant features, detect and classify objects, and compress images for efficient storage and transmission. Image processing applications include medical imaging, remote sensing, surveillance, entertainment, and scientific research. Image processing involves various techniques, such as filtering, image restoration, segmentation, feature extraction, and pattern recognition.

Top 10 Image Processing Projects Using Python

1. Currency Detection

The currency detection project will help a lot of people. There have been a lot of cases of fraud taking place due to fake currency transactions. Using of fake currency is increasing in many areas. This project can help you resolve and slow down such fraud. Each currency is different in its own way. You can add all the related details of any currency and make the scanner look for the same details.

For example, in the Indian currency, the value is written in both English and Hindi. There are many things to consider while distinguishing between real and fake ones. Create a model to detect the fake and original currencies and automate this model. This project can put a stop to the illegal circulation of fake currencies.

2. Real-time Surveillance

Real-time surveillance can be a useful project. You can use a smartphone to send the live feed directly. A PI cam and Raspberry PI will make this a lot easier. You can even record the video and capture images if you wish to. Raspberry PI cam has built-in support for python and works perfectly with it. It can even help you identify the motion. Some projects use Raspberry PI to track the ball or any object in motion. You can integrate many systems and modules to track faces. 

3. Face Attendance system

Teachers taking attendance and students raising their hands saying ”Present” is pretty old school, Right? You can develop a face attendance system using python. This will help you recognize the students by their faces and make the teacher’s job a little easier. However, this project will take some time and data to become precise in recognition. Recognize the student as soon as they raise their hands during attendance. This can be automated too.

A library known as OpenCV can be used in this scenario. OpenCV is built to recognize faces from an image. Using it, you can also make this project work with a live feed. You can also attach a database that stores the attendance count. This project can be a great initiative for contactless attendance, especially during this corona period.

4. Gestures and Expression Tracker

Understanding gestures and expressions make communication very easy. This project is built for the same purpose. Recognition of human gestures, expressions, and behaviors will become easier with the help of this project. Tracking it in real-time will be a little difficult.

For example, tears can signify that a person is either in pain or feeling very sad. You will have to put a lot of data signifying a particular emotion. The more the data served to the application more accurate and precise it gets to track. You will have to train your data model many times and with many subjects. Once the model is trained properly, your project could be able to predict what a human is feeling.

5. Anti-cheat System

You can create an anti-cheat system using image processing in Python. Since COVID, everything has turned online, including the exams of schools and colleges. The only drawback of taking exams online is that cheating becomes easy for students. Teachers do not support these things. It is disrespectful. To avoid this mischievous behavior of students, an anti-cheat system is required. The system will automatically find the person cheating and take action for it. This will include students trying to open another tab, looking around the room repeatedly, moving from their positions, etc. The system will be able to provide warnings and should be capable of taking images of the student cheating. This will act as proof.

6. Image Classification app

You can build an application that will automatically categorize and classify your images. You can add many images; in the output, you will get either sorted images or albums of similar ones. You can also create a column where you can see duplicate and blurred images. This app will separate all the images based on your classifications. Let’s say you have clicked many nature pics and want to separate the ones of trees, mountains, and rivers.

7. AR Filters

Many apps, such as Instagram, Snapchat, etc., provide AR filters to make your images more fun and attractive. You can build a project which can provide you with AR filters. You can add theme-based and fun filters. Since tracking a face is possible, it will be easy to implement the filters on the face too. You can set a permanent landmark where this filter will get executed on. Libraries like Numpy, pillow, and OpenCV can be used. This will be a fun project.

8. Medical Image Segmentation

Medical image segmentation separates and identifies different regions or structures of interest in medical images, such as MRI, CT, and X-ray scans. Medical image segmentation aims to extract meaningful information from the images that can aid diagnosis, treatment planning, and research.

Some popular deep learning-based medical image segmentation frameworks include U-Net, SegNet, and Mask R-CNN. These frameworks have been used in various medical imaging applications, such as brain tumor segmentation, lung segmentation, and cardiac segmentation.

9. Image Denoising Using Deep Learning

Image denoising is the process of removing noise from digital images. Noise is an undesirable and random variation in image intensity that can arise from various factors, such as the camera sensor, transmission or compression artifacts, and environmental factors.

Some popular deep learning-based image denoising methods include DnCNN, RED-Net, and Noise2Noise. These methods have achieved state-of-the-art performance in removing different types of noise from digital images.

10. Object Detection Using YOLOv5

Object detection is identifying and localizing objects of interest in digital images or videos. It has various applications, such as autonomous driving, surveillance, and robotics. YOLOv5 is a state-of-the-art deep learning-based object detection algorithm that has shown promising results in accuracy and speed.

YOLOv5 (You Only Look Once version 5) is a deep learning-based object detection algorithm that uses a single neural network to predict the bounding boxes and class probabilities of objects in an image. YOLOv5 is an improvement over its previous versions (YOLOv1-v4) in speed and accuracy. It uses a variant of the EfficientNet backbone network and advanced data augmentation techniques to achieve state-of-the-art performance.

Conclusion

Here, we covered the top 7 most interesting image processing projects using Python. Some specific pre-defined libraries and modules can help you ease your workings. So, if you want to be a Python expert, you will build an image processing project at a beginner level. Once you implement an image processing project at a beginner level, you can quickly implement intermediate and advanced-level projects.

Image processing projects are also helpful in your final year because you can impress your teacher and get a chance for campus placement.

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