Python image processing projects with source code

Image processing projects using python with source code

  • September 26 2023
  • Bhimsen

Image processing


Image Processing or Digital Image Processing is technique to improve image quality by applying mathematical operations. Image Processing Projects involves modifying images by identification of its two dimensional signal and enhancing it by comparing with standard signal.

The second technique of image processing project is to modify characteristic parameters related to digital images. In either way you want project on image processing we can help you. Your project on image processing will be distinct and you can choose from multiple IEEE papers on image processing.

CITL offers Image Processing projects for Final year engineering and computer science students, IEEE Projects based on Image Processing, Mini Image Processing Projects. Choose your final year project on image processing from our latest 2023 / 2023 IEEE image processing projects or get help on your final year project idea and digital image processing tutorial.

Top 10 Python image processing /video processing projects with source code:

1) Here are ten real-time image-processing projects with specific applications:

  • Real-time Face Recognition: This project uses computer vision to recognize and verify people's identities in real time. For example, it can be used for access control in secure areas.

  • Real-time License Plate Recognition: This project uses image processing to recognize and read license plates in real-time. For example, it can be used for parking management or law enforcement.

  • Real-time Medical Image Analysis: This project uses image processing to analyze medical images in real-time. For example, it can be used for early detection of diseases like cancer or for monitoring vital signs during surgery.

  • Real-time Surveillance: This project uses computer vision to detect and track objects in real-time video streams for surveillance purposes. For example, it can be used for perimeter security or to monitor crowds for safety.

  • Real-time Augmented Reality: This project uses image processing to create interactive augmented reality experiences in real-time. For example, it can be used for gaming or advertising.

  • Real-time Traffic Sign Recognition: This project uses image processing to recognize and interpret traffic signs in real-time. For example, it can be used for intelligent transportation systems or for providing real-time information to drivers.

  • Real-time Optical Character Recognition (OCR): This project uses image processing to recognize and read text in real-time. For example, it can be used for automated document processing or for real-time translation.

  • Real-time Facial Expression Recognition: This project uses image processing to recognize and interpret facial expressions in real-time. For example, it can be used for analyzing customer feedback or for improving customer service.

  • Real-time Gesture Recognition: This project uses image processing to recognize and interpret hand gestures in real-time. For example, it can be used for controlling robots or for creating interactive installations.

  • Real-time Image Retrieval: This project uses image processing to retrieve similar images in real-time. For example, it can be used for visual search or for recommending products based on images.

    2) Here are the top medical image processing projects using Python.

    Medical image processing projects using Python offer significant potential in the healthcare industry and provide numerous opportunities for researchers and developers to make a difference in patient care

    Here are some top medical image processing projects using Python with their applications:

  • Segmentation of Medical Images: This project involves segmenting medical images into regions of interest using various algorithms and techniques. Applications include tumor detection, anatomical segmentation, and identification of abnormalities in MRI and CT scans.

  • Classification of Medical Images: This project involves developing machine learning models to classify medical images based on their characteristics. Applications include detecting and diagnosing diseases such as cancer, Alzheimer's, and pneumonia.

  • 3D Reconstruction of Medical Images: This project involves reconstructing 3D models of medical images from 2D slices. Applications include surgical planning, virtual reality simulations, and anatomical modeling.

  • Image Registration: This project involves aligning multiple medical images to create a composite image with enhanced features. Applications include tracking changes over time, detecting and measuring deformations, and improving the accuracy of image-guided procedures.

Top Python image processing projects List

1) AI Vision Based Social Distancing Detection

The rampant coronavirus disease 2019 (COVID-19) has brought global crisis with its deadly spread to more than 180 countries, and about 3,519,901 confirmed cases along with 247,630 deaths globally as on May 4, 2020.

The absence of any active therapeutic agents and the lack of immunity against COVID19 increases the vulnerability of the population. Since there are no vaccines available, social distancing is the only feasible approach to fight against this pandemic.

Motivated by this notion, this article proposes a deep learning based framework for automating the task of monitoring social distancing using surveillance video. The proposed framework utilizes the YOLO object detection model to segregate humans from the background and to track the identified people with the help of bounding boxes

The violation index term is proposed to quantize the non adoption of social distancing protocol. From the experimental analysis, it is observed that the YOLO with Deep sort tracking scheme displayed best results with balanced criteria to monitor the social distancing in real-time.


2) Traffic Sign Detection and Recognition Based on Image processing & Convolution Neural Network

Traffic sign recognition system (TSRS) is a significant portion of intelligent transportation system (ITS). Being able to identify traffic signs accurately and effectively can improve the driving safety. This paper brings forward a traffic sign recognition technique on the strength of deep learning, which mainly aims at the detection and classification of circular signs.

Firstly, an image is preprocessed to highlight important information. Secondly, Hough Transform is used for detecting and locating areas. Finally, the detected road traffic signs are classified based on deep learning. In this article, a traffic sign detection and identification method on account of the image processing is proposed, which is combined with convolutional neural network (CNN) to sort traffic signs.

On account of its high recognition rate, CNN can be used to realize various computer vision tasks. TensorFlow is used to implement CNN. In the German data sets, we are able to identify the circular symbol with more than 98.2% accuracy.


3) Computer Vision for Attendance and Emotion Analysis in School Settings

This paper presents facial detection and emotion analysis software developed by and for secondary students and teachers. The goal is to provide a tool that reduces the time teachers spend taking attendance while also collecting data that improves teaching practices.

Disturbing current trends regarding school shootings motivated the inclusion of emotion recognition so that teachers are able to better monitor students’ emotional states over time.

This will be accomplished by providing teachers with early warning notifications when a student significantly deviates in a negative way from their characteristic emotional profile. This project was designed to save teachers time, help teachers better address student mental health needs, and motivate students and teachers to learn more computer science, computer vision, and machine learning as they use and modify the code in their own classrooms.

Important takeaways from initial test results are that increasing training images increases the accuracy of the recognition software, and the farther away a face is from the camera, the higher the chances are that the face will be incorrectly recognized.


4) Facial Mask Detection using Semantic Segmentation

Face Detection has evolved as a very popular problem in Image processing and Computer Vision. Many new algorithms are being devised using convolutional architectures to make the algorithm as accurate as possible. These convolutional architectures have made it possible to extract even the pixel details. We aim to design a binary face classifier which can detect any face present in the frame irrespective of its alignment.

We present a method to generate accurate face segmentation masks from any arbitrary size input image. Beginning from the RGB image of any size, the method uses Predefined Training Weights of VGG – 16 Architecture for feature extraction. Training is performed through Fully Convolutional Networks to semantically segment out the faces present in that image.

Gradient Descent is used for training while Binomial Cross Entropy is used as a loss function. Further the output image from the FCN is processed to remove the unwanted noise and avoid the false predictions if any and make bounding box around the faces. Furthermore, proposed model has also shown great results in recognizing non-frontal faces. Along with this it is also able to detect multiple facial masks in a single frame. Experiments were performed on Multi Parsing Human Dataset obtaining mean pixel level accuracy of 93.884 % for the segmented face masks.


5) Human Activity Recognition using Open CV & Python

Human activities recognition has become a groundwork area of great interest because it has many significant and futuristic applications; including automated surveillance, Automated Vehicles, language interpretation and human computer interfaces (HCI). In recent time an exhaustive and in depth research has been done and progress has been made in this area.

The idea of the proposed system is a system which can be used for surveillance and monitoring applications. This paper presents a part of newer Human activity/interaction recognition onto human skeletal poses for video surveillance using one stationary camera for the recorded video data set.

The traditional surveillance cameras system requires humans to monitor the surveillance cameras for 24*7 which is oddly inefficient and expensive. Therefore, this research paper will provide the mandatory motivation for recognizing human action effectively in real-time (future work). This paper focuses on recognition of simple activity like walk, run, sit, stand by using image processing techniques.


6) Image processing based Tracking and Counting Vehicles

In this research work, we explore the vehicle detection technique that can be used for traffic surveillance systems. This system works with the integration of CCTV cameras for detecting the cars. Initial step will always be car object detection.

Haar Cascades are used for detection of car in the footage. Viola Jones Algorithm is used in training these cascade classifiers. We modify it to find unique objects in the video, by tracking each car in a selected region of interest. This is one of the fastest methods to correctly identify, track and count a car object with accuracy up to 78 percent.


7) Paddy crop disease detection using machine learning.

Now a days Farmers are facing loss in crop production due to many reasons one of the major problem for the above issue is crop diseases. Lot of farmers are committed suicide due to loss of crops from the various crop diseases. This is due to lack of knowledge about the disease and number of varieties of pesticides are available to control diseases.

But finding the most current disease, appropriate and effective pesticide to control the infected disease is difficult and requires experts advise which is time consuming and expensive.In order to solve the above issue we are using machine learning and image processing to detect the disease and provide the suitable remedy.

The remedies are giving information about which pesticide to use and how much amount of pesticide to be used for detected disease. Thus, our system intimates the farmer about the crop diseases to take further actions. The proposed system works in two phases: the first phase deals with training data sets.

This includes, training both healthy and as well as diseased data sets. The second phase deals with monitoring the crop and identifying the disease using some effective detection algorithm and provides the solution.


8) Smart Voting System through Facial Recognition

Facial recognition is a category of biometric software which works by matching the facial features. We will be studying the implementation of various algorithms in the field of secure voting methodology. There are three levels of verification which were used for the voters in our proposed system.

The first is UID verification, second is for the voter card number, and the third level of verification includes the use of various algorithms for facial recognition. In this paper, we will provide a comparative study between these algorithms, namely: Eigenface, FisherFace & SURF.


9) Efficient Deep CNN-Based Fire Detection and Localization in Video Surveillance Applications

Convolutional neural networks (CNNs) have yielded state-of-the-art performance in image classification and other computer vision tasks. Their application in fire detection systems will substantially improve detection accuracy, which will eventually minimize fire disasters and reduce the ecological and social ramifications.

However, the major concern with CNN-based fire detection systems is their implementation in real-world surveillance networks, due to their high memory and computational requirements for inference. In this paper, we propose an original, energy-friendly, and computationally efficient CNN architecture, inspired by the SqueezeNet architecture for fire detection, localization, and semantic understanding of the scene of the fire.

It uses smaller convolutional kernels and contains no dense, fully connected layers, which helps keep the computational requirements to a minimum. Despite its low computational needs, the experiment al results demonstrate that our proposed solution achieves accuracies that are comparable to other, more complex models, mainly due to its increased depth.

Moreover, this paper shows how a trade off can be reached between fire detection accuracy and efficiency, by considering the specific characteristics of the problem of interest and the variety of fire data.


10) Object Detection, convert object name to text and text to speech.

Efficient and accurate object detection has been an important topic in the advancement of computer vision systems. With the advent of deep learning techniques, the accuracy for object detection has increased drastically. The project aims to incorporate state-of-the-art technique for object detection with the goal of achieving high accuracy with a real-time performance.

A major challenge in many of the object detection systems is the dependency on other computer vision techniques for helping the deep learning based approach, which leads to slow and non-optimal performance. In this project, we use a completely deep learning based approach to solve the problem of object detection in an end-to-end fashion.

The network is trained on the most challenging publicly available dataset (PASCAL VOC), on which a object detection challenge is conducted annually. The resulting system is fast and accurate, thus aiding those applications which require object detection.

We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance.


11) What to play next? A RNN-based music recommendation system

As the title suggests, the application will be a web-based application for visually impaired persons using IVR- Interactive voice response, thus enabling everyone to control their mail accounts using their voice only and to be able to read, send, and perform all the other useful tasks. The system will prompt the user with voice commands to perform certain action and the user will respond to the same.

The main benefit of this system is that the use of keyboard is completely eliminated, the user will have to respond through voice and mouse click only. Now you must be thinking that how will a blind person will see the correct position on the screen for doing mouse clicks.

But this system will perform actions based on the clicks only that is left click or right click, it does not depends on the portion of the screen where the cursor is placed before the click giving user the freedom to click blindly anywhere on the screen.


12) An improved fatigue detection system based on behavioral characteristics of driver.

The road accidents have increased significantly. One of the major reasons for these accidents, as reported is driver fatigue. Due to continuous and longtime driving, the driver gets exhausted and drowsy which may lead to an accident. Therefore, there is a need for a system to measure the fatigue level of driver and alert him when he/she feels drowsy to avoid accidents.

Thus, we propose a system which comprises of a camera installed on the car dashboard. The camera detects the driver’s face and tracks its activity. From the driver’s face, the system observes the alteration in its facial features and uses these features to observe the fatigue level. Facial features include eyes (fast blinking or heavy eyes) and mouth (yawn detection).

Principle Component Analysis (PCA) is thus implemented to reduce the features while minimizing the amount of information lost. The parameters thus obtained are Processed through Support Vector Classifier (SVC) for classifying the fatigue level. After that classifier output is sent to the alert unit.


13) Wild animal intrusion detection using image processing & CNN.

In forest zone and agricultural field human animal conflict is a major problem where enormous amount of resources is lost and human life is in danger. Due to this People lose their crops, livestock, property, and sometimes their lives.

So this zone is to be monitored continuously to prevent entry of wild animals. With regard to this problem, we have made an effort to develop the system which will monitor the field. That is at first it will detect intrusion around the field using sensor, then camera will capture the image of the intruder and classifying them using image processing and then Taking suitable action based on the type of the intruder.

Animal attestation have been a creation stress in North America since the endless untamed life resources and increments of autos. Such issues cause different people passings, incalculable human injuries, billions of dollars in property mischief and wearisome of standard life passings constantly. To address these challenges, great vehicles must be equipped with Advanced Driver Assistance Systems (ADAS) masterminded to see hazardous animals (e.g., moose, elk and bull like), which cross the road, and alert the driver about the extraordinary setback.

In this proposition, we investigate the introduction of different picture features and gathering incorporates into animal assertion application, and structure a predictable animal area system following three criteria: perceiving check precision, revelation time and system significance use. In order to look for after high certification rate regardless low time and enormity use, a twofold stage area structure is proposed.

In the basic virtuoso, we use the LBP understanding AdaBoost count which gives the join mastermind by a great deal of zone of interests containing target animals and other false positive targets. A short time range later, the second stage rejects the phony positive ROIs by two CNN based sub-classifiers. To collect and survey the animal identifier, we make our own emerge database, which will be revived by including new perspectives. Through a wide strategy of evaluations, we note that the twofold stage system can perceive about 85% of target animals.


14) Traffic sign detection using machine learning algorithm

Deep learning based approach to solve the problem of object detection in an end-to-end fashion. The network is trained on the most challenging publicly available dataset (PASCAL VOC), on which a object detection challenge is conducted annually. The resulting system is fast and accurate, thus aiding those applications which require object detection.

We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance.


15) An Approach to Maintain Attendance using Image Processing Techniques

Nowadays, the research is growing towards the invention of new approaches. One such most attracted application is face recognition of image processing. There are several innovative technologies have been developed to take attendance. Some prominent ones are biometric, thumb impressions, access card, and fingerprints.

The method proposed in this paper is to record the attendance through image using face detection and face recognition. The proposed approach has been implemented in four steps such as face detection, labelling the detected faces, training a classifier based on labelled dataset, and face recognition.

The database has been constructed with the positive images and negative images. The complete database has been divided into training and testing set and further, processed by a classifier to recognize the faces in a classroom. The final step is to take the attendance using face recognition


16) Handwritten Digit Recognition Using Deep Learning

Handwritten digit recognition has recently been of very interest among the researchers because of the evolution of various Machine Learning, Deep Learning and Computer Vision algorithms. In this report, I compare the results of some of the most widely used Machine Learning Algorithms like SVM, KNN & RFC and with Deep Learning algorithm like multilayer CNN using Keras with Theano and Tensorflow. Using these, I was able to get the accuracy of 98.70% using CNN (Keras+Theano) as compared to 97.91% using SVM, 96.67% using KNN, 96.89% using RFC.


17) Vehicle Number Plate Detection System For Indian Vehicles.

An exponential increase in number of vehicles necessitates the use of automated systems to maintain vehicle information. The information is highly required for both management of traffic as well as reduction of crime. Number plate recognition is an effective way for automatic vehicle identification.

Some of the existing algorithms based on the principle of learning takes a lot of time and expertise before delivering satisfactory results but even then lacks in accuracy. In the proposed algorithm an efficient method for recognition for Indian vehicle number plates has been devised. The algorithm aims at addressing the problems of scaling and recognition of position of characters with a good accuracy rate of 98.07%.


18) Speed Detection Camera System Using Image Processing Techniques On Video Streams

This paper, presents a new Speed Detection Camera System (SDCS) that is applicable as a radar alternative. SDCS uses several image processing techniques on video stream in online -captured from single camera- or offline mode, which makes SDCS capable of calculating the speed of moving objects avoiding the traditional radars' problems. SDCS offers an en-expensive alternative to traditional radars with the same accuracy or even better. SDCS processes can be divided into four successive phases; first phase is Objects detection phase.

Which uses a hybrid algorithm based on combining an adaptive background subtraction technique with a three-frame differencing algorithm which ratifies the major drawback of using only adaptive background subtraction? The second phase is Objects tracking, which consists of three successive operations, Object segmentation, Object labelling, and Object canter extraction.

Objects tracking operation takes into consideration the different possible scenarios of the moving object like; Simple tracking, object has left the scene, object has entered the scene, object cross by another object, and object leaves and another one enters the scene.

Third phase is speed calculation phase, which is calculated from the number of frames consumed by the object to pass-by the scene. The final phase is Capturing Object's Picture phase, which captures the image of objects that violate the speed limits. SDCS is implemented and tested in many experiments; it proved to have achieved a satisfactory performance.


1) What are tools used to build real-time image processing projects using Python?

To build Python image processing projects, you can use a variety of tools and libraries depending on your specific project requirements. Here are some popular tools and libraries for Python image processing:

  • OpenCV: OpenCV is a popular open-source computer vision and image processing library. It provides a wide range of algorithms for image and video processing, including image filtering, segmentation, feature detection, and object tracking.

  • Pillow: Pillow is a fork of the Python Imaging Library (PIL) that provides a set of functions for image processing, such as image enhancement, image manipulation, and image filtering.

  • Scikit-Image: Scikit-Image is a Python library for image processing that provides a set of functions for image filtering, segmentation, feature detection, and object tracking.

  • NumPy: NumPy is a Python library for scientific computing that provides a set of functions for numerical operations on arrays. It can be used for image processing operations such as image resizing, image filtering, and image transformation.

  • Tensor Flow: Tensor Flow is a popular machine learning library that can be used for image classification, object detection, and other image processing tasks.

  •  Keras: Keras is a high-level neural network API that can be used with Tensor Flow or other backend libraries. It provides a simple interface for building and training neural networks for image processing tasks.

  • PyTorch:  Py Torch is another popular machine learning library that can be used for image classification, object detection, and other image processing tasks.

These tools and libraries can be combined to build various image processing projects, such as real-time face recognition, real-time object detection, and medical image analysis, among others.

2) What is the potential of image processing projects in the industry?

Image processing is a rapidly growing field with vast potential in the industry. Here are some of the ways in which image-processing projects are being used in the industry:

  • Medical Imaging: Image processing is widely used in medical imaging to detect and diagnose diseases, monitor treatment progress, and improve patient outcomes. Examples include MRI, CT scans, and ultrasound images.

  • Autonomous Vehicles: Image processing is used in autonomous vehicles for object detection and recognition, lane detection, and obstacle avoidance.

  • Surveillance and Security: Image processing is used for surveillance and security applications, including facial recognition, license plate recognition, and crowd analysis.

  • Entertainment and Gaming: Image processing is used in entertainment and gaming applications, including augmented reality, virtual reality, and game design.

  • E-commerce and Retail: Image processing is used in e-commerce and retail for product recommendation and image search.


3) What are the career opportunities in Image Processing Domain for job seekers?

As for career opportunities, image processing projects can lead to a variety of career paths, including:

  • Computer Vision Engineer: A computer vision engineer is responsible for developing algorithms and software for image processing applications.

  • Data Scientist: A data scientist uses data analysis and statistical methods to extract insights from images and other data sources.

  • Machine Learning Engineer: A machine learning engineer designs and develops algorithms that can learn from images and other data sources.

  • Robotics Engineer: A robotics engineer uses image processing to develop robots that can see and interact with the world around them.

  • Software Developer: A software developer can work on image processing projects to develop software applications that use images for various purposes.

    Overall, image processing projects offer significant potential in the industry and provide numerous career opportunities for students who develop skills in this area

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