imutils-cpp

This is a cpp implementation of popular python library imutils

You can find installation instructions here

Translation

Translation is the shifting of an image in either the x or y direction. To translate an image in OpenCV you would need to supply the (x, y)-shift, denoted as (t_x, t_y) to construct the translation matrix M:

And from there, you would need to apply the cv::warpAffine function.

Instead of manually constructing the translation matrix M and calling cv2::warpAffine, you can simply make a call to the translate function of imutils.

Example:

 // translate the image x=25 pixels to the right and y=75 pixels up
 cv::Mat translated =Convenience::translate(image,25,-75);

Output:

Rotation

Rotating an image in OpenCV is accomplished by making a call to cv2::getRotationMatrix2D and cv2::warpAffine. Further care has to be taken to supply the (x, y)-coordinate of the point the image is to be rotated about. These calculation calls can quickly add up and make your code bulky and less readable. The rotate function in imutils helps resolve this problem.

Example:

// loop over the angles to rotate the image
    int arr[]={0,90,270,360};
    for(int i:arr){
        cv::Mat rotated = Convenience::rotate(image,i);
        std::string windowname ="rotation";
        windowname+=std::to_string(i);
        cv::imshow(windowname,rotated);
    }

Resizing

Resizing an image in OpenCV is accomplished by calling the cv::resize function. However, special care needs to be taken to ensure that the aspect ratio is maintained. This resize function of imutils maintains the aspect ratio and provides the keyword arguments width and height so the image can be resized to the intended width/height while (1) maintaining aspect ratio and (2) ensuring the dimensions of the image do not have to be explicitly computed by the developer.

Another optional keyword argument, inter, can be used to specify interpolation method as well.

Example:

// loop over varying widths to resize the image to
int arr[]={400,300,200,100};
    for(int i:arr){
        cv::Mat resized = Convenience::resize(image,i);
        std::string windowname ="resized-";
        windowname+=std::to_string(i);
        cv::imshow(windowname,resized);
    }

Skeletonization

Skeletonization is the process of constructing the "topological skeleton" of an object in an image, where the object is presumed to be white on a black background. OpenCV does not provide a function to explicitly construct the skeleton, but does provide the morphological and binary functions to do so.

For convenience, the skeletonize function of imutils can be used to construct the topological skeleton of the image.

The first argument, size is the size of the structuring element kernel. An optional argument, structuring, can be used to control the structuring element -- it defaults to cv::MORPH_RECT , but can be any valid structuring element.

Example:

// skeletonize the image
cv::Mat img = cv::Mat::zeros({500,100}, CV_32F);
cv::putText(img,"thedevmanek",{5,70},cv::FONT_HERSHEY_COMPLEX,2,{255},5,cv::LINE_AA);
cv::Mat img_copy = img.clone();
cv::Mat skeletonized = Convenience::skeletonize(img,{3,3},cv::MORPH_ELLIPSE);

Output:

URL to Image

This the url_to_image function accepts a single parameter: the url of the image we want to download and convert to cv::Mat in OpenCV format.

Example:

std::string url="https://opencv.org/wp-content/uploads/2020/07/cropped-OpenCV_logo_white_600x.png";
cv::Mat logo = Convenience::urlToImager(url);
cv::imshow("urlImage",logo);

Output:

Automatic Canny Edge Detection

The Canny edge detector requires two parameters when performing hysteresis. However, tuning these two parameters to obtain an optimal edge map is non-trivial, especially when working with a dataset of images. Instead, we can use the autoCanny function which uses the median of the grayscale pixel intensities to derive the upper and lower thresholds.

Example:

   cv::Mat autoCanny = Convenience::autoCanny(image);
   cv::imshow("Auto Canny",autoCanny);

Output:

4-point Perspective Transform

A common task in computer vision and image processing is to perform a 4-point perspective transform of a ROI in an image and obtain a top-down, "birds eye view" of the ROI. The perspective module takes care of this for you.

Example

    cv::Mat fourPointTransformed = Perspective::fourPointTransformation(image,{{73, 239},{356, 117}, {475, 265}, {187, 443}});
    cv::imshow("image",image);
    cv::imshow("Transformed Image",fourPointTransformed);

Output:

(Recursively) Listing Paths to Images

The paths sub-module of imutils includes a function to recursively find images based on a root directory.

Example:

Assuming we are in the demos directory, let's list the contents of the ../demo_images:

Path path;
std::vector<std::string> dirs= path.listImages("../demo_images");
for(std::string file:dirs){
    std::cout<<file<<std::endl;

Output:

../demo_images/opencv-logo.png
../demo_images/cactus.jpg
../demo_images/shapes.png
../demo_images/bridge.jpg
../demo_images/folder with spaces/bridge.jpg
../demo_images/workspace.jpg
../demo_images/elephant.jpeg
../demo_images/notecard.png

Installation

This library is dependent on opencv and curl libraries

Dynamic Library for local use

 
$ cp /home/usr/Download/libimutils_cpp.so /home/usr/lib

or

 
$ cp /usr/Download/libimutils_cpp.so.1.0.1 /usr/lib
$ ldconfig -n -v /usr/lib

Static Library

Just install the .a file in the lib dir or the root dir of the project

Usage

Dynamic Library

g++ -Wall -L/opt/lib main.cpp -limutils_cpp

Static Library

$ g++ main.cpp libfoo.a

or

$ g++ -L. -lfoo prog.cpp libfoo.a

CMAKE

Dynamic

target_link_libraries(imutils_cpp ${OpenCV_LIBS} curl)

Static

target_link_libraries(main ${CMAKE_SOURCE_DIR}/libimutils_cpp.a)