romaisaa / cv-toolbox

Computer Vision algorithms implementation using C++QT & opencv.

Computer-Vision-Toolbox

Table of contents:

• Introduction
• Full Project Features
• Project Structure Overview
• How to Run The Project
• Team

Introduction

Computer Vision Toolbox Application is a software solution designed to leverage the power of computer vision algorithms through an intuitive and user-friendly graphical user interface (GUI) developed using the Qt framework. This application aims to provide users with a versatile toolset for image analysis, making it suitable for a wide range of applications, including image processing, object detection,image features extraction, and more.

Project Features:

This application mainly consists of 11 tabs (pages) to provide user with all needed CV algorithms, each page has certain features that are related to each other as follows:

1. Filters, Noise and Edge Detectors page:
   Includes 3 types of noise that the user can add to grey/coloured images:

   Uniform Noise	Guassian Noise	Salt & Pepper Noise

   Includes 4 edge detector types with the ability to control kernel size and any additional option for detector:

   Sobel	Roberts	Perwitt	Canny

   Includes 3 types of filter enabling user to control kernel size:

   Average Filter	Gaussian Filter	Median Filter

   for more details about this page and results check Report 1

2. Normalization, Equalization and Thresholding page:
   Shows images histogram with options to normalize or equalize:

   Histogram	Normalization	Equalization

   Apply both global or local thresholding on uploaded image:

   Local	Global

   for more details about this page and results check Report 1

3. Frequency Domain Filters and Hybrid Image: User can upload 2 different images and determine the radius for each filter type(high pass or low pass) and then can combine the output to get hybrid image. for more details about this page and results check Report 1

4. Hough Transform Page: This page enbles users to apply Line/ Circle / Ellipse hough transform:

   Line	Circle	Ellipse

   for more details about this page and results check Report 2

5. Active Contour Page: User has the ability to select cirlce from image and tune parameters to contour objects.

   Example 1	Example 2

   for more details about this page and results check Report 2

6. Harris and Lambda Corner detector page: User can detect corners by Harris or lambda methods and change kernel size and threshold.

   Harris	Lambda

   for more details about this page and results check Report 3

7. Image Matching Methods page: User can match the image template and full image with 2 methods:

   Square Sum of Differences method	Cross-Correlation Method

   for more details about this page and results check Report 3

8. Sift Descriptors Page: User can upload one or two images to get SIFT Keypoints with options to tune, and then compute keypoints matching between images.

   

   for more details about this page and results check Report 3

9. Advanced Thresholding Page: Includes Global Thresholding with 2 methods:

   Ostu Thresholding	Optimal Thresholding

   Includes Local Thresholding and Multi-level Thresholding:

   Local Thresholding	Multi-level Thresholding

   for more details about this page and results check Report 4

10. Segmentation Methods Page: User can segment RGB images with 4 types of segmentation:

    K-Means Segmentation	Mean Shift Segmentation	Agglomerative Segmentation	Region Growing

for more details about this page and results check Report 4

11. Face Recognition and detection page:

• User can upload person or multi-person images and detect faces in images.
• User can upload a training folder that includes images named with its label.
• Once training finishes, User can upload a new image to predict the person in the image
• if the model already exists, Then the user can work directly without training
• For new training, user can determine number of PCA components to train a nearest neighbour model.
• User can upload a test folder to visualise ROC for each label.

Project Structure Overview:

main
├─ CV : header/source files related to CV algorithms implementation.
├─ docs : reports related to results and science behind.
├─ images : images used for test.
├─ models : used models in implementation
├─ pages : include ui/header / resource file related to pages design.
├─ plot : plotter used for graphs
├─ resources : resources used for app design.
├─ style : stylesheets used for app design.
└─ utilities: used for app ploting and different ui functionality.

Run the Project

1. Install opencv libaray version 4.7.0 on your machine from Here.

2. Install QT Creator on your machine from Here.

3. Open .pro file and replace by paths on your machine:

   INCLUDEPATH += C:\opencv\build\include
   LIBS += C:\opencv\release\bin\libopencv_core470.dll
   LIBS += C:\opencv\release\bin\libopencv_highgui470.dll
   LIBS += C:\opencv\release\bin\libopencv_imgcodecs470.dll
   LIBS += C:\opencv\release\bin\libopencv_imgproc470.dll
   LIBS += C:\opencv\release\bin\libopencv_features2d470.dll
   LIBS += C:\opencv\release\bin\libopencv_calib3d470.dll
   LIBS += C:\opencv\release\bin\libopencv_objdetect470.dll
   
   

4. Run the project.

Team Members:

• Kamel Mohamed
• Romaisaa Shrief
• Youssef Shaaban