We provide the solution to different Computer Vision problems solved with OpenCV. Since this repository is realized following the path of the Computer Vision course @ University of Padua, the algorithms proposed go from a basic "getting started" with OpenCV to more advanced examples.
Each folder contains a project, use the following commands to compile and run it:
make
./main
To get familiar with OpenCV, we start with a simple thresholding algorithm, implemented in segmenting_threshold.
The main program, given an image and a set of thresholds, one for each BGR channel, works as follows:
- Shows the image on a window;
- Captures the left click of the mouse and computes the mean RGB color over a square of size 9x9 pixels centered on the left click;
- Segments the mean color selected by applying a static threshold to the three channels R, G and B;
- Applies a new color to the selected regions (we use BGR = (37,201,92)).
An example running the code with ./main roma.jpeg 50 50 50:
In histogram_equalization we implement a histogram equalization algorithm in RGB and BGR2HSV color spaces.
Given an image, the program computes the histogram of each channel of the image, in order to and apply equalization to the three channels independently in the case of RGB and to the last channel for what concerns BGR2HSV. We plot the histograms of the original image, the equalized image and save the result in a new folder.
An example follows: original photo, RGB equalization HSV equalization
In filtering we provide an API and a test code to perform median, gaussian and bilateral filtering of a given image. Parameters can be tuned by means of trackbars once the image is shown.
An example of interface follows:
In edge detection we test the Canny edge detector, the Hough transform for lines detection and the Hough circles detector. In particular we provide the capability to tune the parameters of the Canny edge detector, once these are fixed we can use the outcome of Canny to detect lines and circles.
An example of ouput follows:
