OpenCV Project List with Abstract and Source Code - 21/22
OpenCV is a very large open source library mainly used for Image Processing, Machine Learning, and Computer vision. It has the capability of processing images and video clips to identify faces, handwriting etc. It contains more than 2500 algorithms and makes use of NumPy functions that can be implemented as real-time application. OpenCV is used widely across various organizations and supports multiple programming languages such as python, java, c++ etc. We have selected quite interesting projects in OpenCV and tried to explain each project in detail with step by step execution where every individual irrespective of their educational background can learn and execute this projects by themselves.
Project List with Step by Step execution
| S.No | Project Code | OpenCV Projects | Actions |
|---|---|---|---|
| 1 | TYTCV1001 | Capturing the Number Plates of the Vehicles Using Opencv Harcascade files. | DETAILS |
| 2 | TYTCV1002 | Click the Image Smartly using Opencv | DETAILS |
| 3 | TYTCV1003 | Color Identification in an Image using OpenCV Haarcascade files | DETAILS |
| 4 | TYTCV1004 | Alert drowsy driver using openCV | DETAILS |
| 5 | TYTCV1005 | Detecting custom objects in an image using YOLO V3 | DETAILS |
| 6 | TYTCV1006 | Recognizing the face of a person using YOLOV3 and Deep Learning | DETAILS |
| 7 | TYTCV1007 | Detect the Face and Eyes of a Person from an Image and in Real time | DETAILS |
| 8 | TYTCV1008 | Deep learning based dog breed identification using Convolutional Neural Networks with flask app | DETAILS |
| 9 | TYTCV1009 | Text extraction from image using Optical character recognition | DETAILS |
| 10 | TYTCV1010 | Classifying the Brain Tumors using CNN with Front End Framework | DETAILS |
| 11 | TYTCV1011 | Classifying the type of Flowers using Convolutional Neural Networks | DETAILS |
| 12 | TYTCV1012 | Detection of Retinal pigmentosa in paediatric age patients using CNN with Tkinter Framework | DETAILS |
| 13 | TYTCV1013 | Transfer Learning Based Kidney Stone Prediction in Patients, using RESNET50 | DETAILS |
| 14 | TYTCV1014 | Pot Hole Detection on the roads using Transfer Learning (Resnet 50) | DETAILS |
| 15 | TYTCV1015 | integrated web app for disease detection (3 diseases) | DETAILS |
| 16 | TYTCV1016 | Hand Gesture Recognition Model using Deep Learning | DETAILS |
| 17 | TYTCV1017 | Identifying the lane the vehicle is travelling. | DETAILS |
| 18 | TYTCV1018 | Image classification: Develop a system that can classify images into predefined categories. | DETAILS |
| 19 | TYTCV1019 | Object detection: Build a model that can detect and locate objects in images or videos. | DETAILS |
| 21 | TYTCV1021 | Image segmentation: Develop a method for dividing an image into multiple segments, each corresponding to a different object or background. | DETAILS |
| 22 | TYTCV1022 | Image super-resolution: Create a system that can increase the resolution of an image or video. | DETAILS |
| 23 | TYTCV1023 | Object tracking: Build a model that can follow the movement of an object in real-time video. | DETAILS |
| 25 | TYTCV1025 | Style transfer: Create a model that can transfer the style of one image onto another. | DETAILS |
| 26 | TYTCV1026 | GANs for image generation: Use generative adversarial networks (GANs) to generate new, synthetic images. | DETAILS |
| 27 | TYTCV1027 | Image captioning: Build a system that can generate a natural language description of an image. | DETAILS |
| 28 | TYTCV1028 | Handwriting recognition: Develop a model that can recognize and transcribe handwritten text. | DETAILS |
| 29 | TYTCV1029 | 3D reconstruction: Create a system that can reconstruct a 3D model of an object or scene from 2D images. | DETAILS |
| 30 | TYTCV1030 | Pose estimation: Build a model that can estimate the pose of a person or object in an image or video. | DETAILS |
| 31 | TYTCV1031 | Image denoising: Develop a method for removing noise from images. | DETAILS |
| 32 | TYTCV1032 | Image restoration: Create a system that can restore damaged or degraded images. | DETAILS |
| 33 | TYTCV1033 | Visual question answering: Build a model that can answer questions about an image or video. | DETAILS |
| 34 | TYTCV1034 | Image generation from sketches: Create a system that can generate images from hand-drawn sketches. | DETAILS |
| 35 | TYTCV1035 | Image generation from text: Develop a model that can generate images based on a textual description. | DETAILS |
| 36 | TYTCV1036 | Image colorization: Build a model that can add color to grayscale images. | DETAILS |
| 37 | TYTCV1037 | Facial expression recognition: Create a system that can identify and classify facial expressions in images or videos. | DETAILS |
| 38 | TYTCV1038 | Object recognition in video: Develop a model that can recognize and classify objects in video streams. | DETAILS |
| 39 | TYTCV1039 | Action recognition in video: Build a model that can recognize and classify actions being performed in a video. | DETAILS |
| 40 | TYTCV1040 | Scene understanding: Create a system that can understand and classify the scene or environment depicted in an image or video. | DETAILS |
| 41 | TYTCV1041 | Video stabilization: Develop a method for stabilizing shaky video footage. | DETAILS |
| 42 | TYTCV1042 | Video summarization: Build a model that can summarize long videos into shorter, highlights versions. | DETAILS |
| 43 | TYTCV1043 | Video frame interpolation: Create a system that can insert new frames into a video to increase its frame rate. | DETAILS |
| 44 | TYTCV1044 | Video super-resolution: Develop a model that can increase the resolution of a video. | DETAILS |
| 45 | TYTCV1045 | Image compression: Build a system that can compress images while maintaining quality. | DETAILS |
| 46 | TYTCV1046 | Image watermarking: Create a method for adding a visible or invisible watermark to an image. | DETAILS |
| 47 | TYTCV1047 | Image forgery detection: Develop a system that can detect if an image has been modified or manipulated. | DETAILS |
| 48 | TYTCV1048 | Image saliency detection: Build a model that can identify the most salient or important regions in an image. | DETAILS |
| 49 | TYTCV1049 | Object recognition in 3D point clouds: Create a system that can recognize and classify objects in 3D point clouds generated from lidar or depth sensors. | DETAILS |
| 50 | TYTCV1050 | Depth estimation from monocular images: Develop a model that can estimate the depth of objects in an image taken with a single camera. | DETAILS |
| 51 | TYTCV1051 | Object segmentation in 3D point clouds | DETAILS |
| 52 | TYTCV1052 | Augmented reality: Build a system that can overlay digital content on top of the real world in real-time. | DETAILS |
| 53 | TYTCV1053 | Virtual reality: Create a model that can generate immersive, interactive virtual environments. | DETAILS |
| 54 | TYTCV1054 | Image recognition for self-driving cars: Develop a system that can recognize and classify objects in the road, such as vehicles, pedestrians, and traffic signs. | DETAILS |
| 55 | TYTCV1055 | Lane detection for self-driving cars: Build a model that can detect and track the lanes on a road for autonomous vehicle navigation. | DETAILS |
| 56 | TYTCV1056 | Traffic sign recognition: Create a system that can recognize and classify traffic signs in images or video. | DETAILS |
| 57 | TYTCV1057 | Surveillance and security: Develop a model that can detect and classify suspicious activity in real-time video streams. | DETAILS |
| 58 | TYTCV1058 | Medical image analysis: Build a system that can analyze medical images, such as X-rays or CT scans, to detect abnormalities or diagnose diseases. | DETAILS |
| 59 | TYTCV1059 | Cell detection and tracking: Create a model that can detect and track individual cells in microscopy images. | DETAILS |
| 60 | TYTCV1060 | Plant disease detection: Develop a system that can identify and classify diseases in plants based on images of their leaves. | DETAILS |
| 61 | TYTCV1061 | Land use and land cover classification: Build a model that can classify land use and land cover in satellite images. | DETAILS |
| 62 | TYTCV1062 | Remote sensing: Create a system that can extract information about the Earth's surface from satellite or aerial imagery. | DETAILS |
| 63 | TYTCV1063 | Emotion recognition from facial expressions: Develop a model that can identify and classify emotions based on facial expressions in images or video. | DETAILS |
| 64 | TYTCV1064 | Sentiment analysis: Build a system that can analyze text to determine the sentiment or emotion it conveys. | DETAILS |
| 65 | TYTCV1065 | Text classification: Create a model that can classify text documents into predefined categories. | DETAILS |
| 66 | TYTCV1066 | Text generation: Develop a system that can generate natural language text based on a given prompt or input. | DETAILS |
| 67 | TYTCV1067 | Machine translation: Build a model that can translate text from one language to another. | DETAILS |
| 68 | TYTCV1068 | Image recognition for agricultural applications: Develop a system that can recognize and classify different crops, pests, or diseases in agricultural images. | DETAILS |
| 69 | TYTCV1069 | Image recognition for retail applications: Build a model that can recognize and classify products in retail images for online shopping or inventory management. | DETAILS |
| 70 | TYTCV1070 | Image recognition for industrial applications: Create a system that can recognize and classify different components or defects in industrial images. | DETAILS |
| 71 | TYTCV1071 | Image recognition for fashion applications: Develop a model that can recognize and classify different clothing and fashion accessories in images. | DETAILS |
| 72 | TYTCV1072 | Document analysis: Build a system that can extract information from documents, such as forms or contracts. | DETAILS |
| 73 | TYTCV1073 | Scene text recognition: Create a model that can recognize and extract text from natural images of text in the real world. | DETAILS |
| 74 | TYTCV1074 | Image recognition for security applications: Develop a system that can recognize and classify threats or suspicious activity in security images or video. | DETAILS |
| 75 | TYTCV1075 | Image recognition for automated quality control: Build a model that can recognize and classify defects in products or materials for quality control purposes. | DETAILS |
| 76 | TYTCV1076 | Image recognition for wildlife conservation: Create a system that can recognize and classify different species of animals in wildlife images. | DETAILS |
| 77 | TYTCV1077 | Image recognition for environmental monitoring: Develop a model that can recognize and classify different environmental features or phenomena in images or video, such as natural disasters or pollution. | DETAILS |
| 78 | TYTCV1078 | Image recognition for transportation applications: Build a model that can recognize and classify different types of vehicles, such as cars, buses, and airplanes, in images or video. | DETAILS |
| 79 | TYTCV1079 | Image recognition for weather forecasting: Create a system that can recognize and classify different weather patterns in images or video for weather forecasting purposes. | DETAILS |
| 80 | TYTCV1080 | Image recognition for geospatial applications: Develop a model that can recognize and classify different features of the Earth's surface, such as land use, vegetation, or water bodies, in satellite or aerial imagery. | DETAILS |
| 81 | TYTCV1081 | Image recognition for social media applications: Build a model that can recognize and classify different types of content, such as images, videos, or text, on social media platforms. | DETAILS |
| 82 | TYTCV1082 | Image recognition for cultural heritage applications: Create a system that can recognize and classify different cultural heritage artifacts or sites in images or video. | DETAILS |
| 83 | TYTCV1083 | Image recognition for sports applications: Develop a model that can recognize and classify different actions or events in sports images or video. | DETAILS |
| 84 | TYTCV1084 | Image recognition for entertainment applications: Build a model that can recognize and classify different types of media, such as movies, TV shows, or music, in images or video. | DETAILS |
| 85 | TYTCV1085 | Image recognition for education applications: Create a system that can recognize and classify different types of educational materials, such as textbooks, lectures, or assignments, in images or video. | DETAILS |
| 86 | TYTCV1086 | Image recognition for marketing applications: Develop a model that can recognize and classify different types of marketing materials, such as advertisements, banners, or product images, in images or video. | DETAILS |
| 87 | TYTCV1087 | Image recognition for customer service applications: Build a model that can recognize and classify different types of customer inquiries or requests in images or video. | DETAILS |
| 88 | TYTCV1088 | Image recognition for healthcare applications: Create a system that can recognize and classify different types of medical images, such as X-rays, CT scans, or MRIs, for diagnosis or treatment purposes. | DETAILS |
| 89 | TYTCV1089 | Image recognition for e-commerce applications: Develop a model that can recognize and classify different types of products in e-commerce images for online shopping or inventory management. | DETAILS |
| 90 | TYTCV1090 | Image recognition for financial applications: Build a model that can recognize and classify different types of financial documents, such as invoices, receipts, or bank statements, in images or video. | DETAILS |
| 91 | TYTCV1091 | Image recognition for insurance applications: Create a system that can recognize and classify different types of insurance documents, such as policies, claims, or assessments, in images or video. | DETAILS |
| 92 | TYTCV1092 | Image recognition for human resources applications: Develop a model that can recognize and classify different types of HR documents, such as resumes, job applications, or employee records, in images or video. | DETAILS |
| 93 | TYTCV1093 | Image recognition for legal applications: Build a model that can recognize and classify different types of legal documents, such as contracts, deeds, or court orders, in images or video. | DETAILS |
| 94 | TYTCV1094 | Image recognition for real estate applications: Create a system that can recognize and classify different types of real estate documents, such as property listings, deeds, or mortgage agreements, in images or video. | DETAILS |
| 95 | TYTCV1095 | Image recognition for travel and tourism applications: Develop a model that can recognize and classify different types of travel or tourism images, such as hotels, attractions, or landmarks, for travel planning or marketing purposes. | DETAILS |
| 96 | TYTCV1096 | Image recognition for transportation logistics applications: Build a model that can recognize and classify different types of transportation or logistics documents, such as shipping labels, manifests, or delivery receipts, in images or video. | DETAILS |
| 97 | TYTCV1097 | Image recognition for environmental monitoring applications: Create a system that can recognize and classify different types of environmental features or phenomena, such as air or water quality, in images or video. | DETAILS |
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