28 posts
Because some projects were developed some time ago, their programs use opencv2, while most people now have opencv4 installed. Since these projects contain a lot of code, modifying the source code would be too cumbersome, therefore considering installing multiple opencv versions side by side.
When using the OpenCV package, you may encounter ImportError: No module named cv2. I was certain that I had already installed OpenCV. After looking into it, I found that OpenCV had been installed for a different Python version than the system default, which caused the module not to be found.
CMake is a cross-platform, open-source meta-build system that can build, test, and package software. It can support multiple native build environments. More and more projects are using CMake as their build tool, making CMake a mainstream build system.
The KNN algorithm, or K-nearest neighbors algorithm, works by predicting the class of a new value x based on which class most of its K nearest neighbors belong to. The same idea applies to image recognition.
This article aims to recognize digits in camera images. Practical applications include license plate number recognition and recognizing printed digits on A4 paper in some competitions. Camera-based digit recognition consists of two steps: 1. Extract the ROI from the image, such as the rectangular license plate area or the A4 paper image. 2. Recognize digits in the ROI. Digit recognition is relatively straightforward, so this article first introduces its methods and principles.
First, download all the files in `opencv/samples/dnn/face_detector/` from OpenCV's GitHub site and overwrite the files in the local directory. Then open weights.meta4 in Notepad and download the files corresponding to the two URLs it contains.
Mean filtering uses the principle of image convolution. As shown in the figure below, when the convolution kernel is a third-order unit matrix, mean filtering is performed: each third-order submatrix of the original image has its mean computed and assigned to the center element.
An image histogram is a statistical feature of image pixel values. It is inexpensive to compute and has many advantages, including invariance to image translation, rotation, and scaling. It is widely used across image processing, especially for grayscale image thresholding, color-based image retrieval, image classification, and backprojection tracking. Common types are grayscale histograms and color histograms.
Reading and processing video is essential in image processing. In OpenCV, the main function used to read camera video is capture(). This article explains how to read camera video with OpenCV and describes the functions used to save it, including their implementation and specific details.
Common image transformation methods include image scaling, flipping, and rotation. In OpenCV, each of these image transformation operations has a corresponding function. This article introduces various image transformation methods through explanations of function definitions and concrete examples.
Normalization constrains the data to be processed, after processing (via some algorithm), within a certain range. This makes later data processing easier and also helps the program converge faster during runtime.
This article introduces how to handle keyboard and mouse input in OpenCV, the functions involved, and the parameters of each function. It also uses keyboard and mouse input handling to create several interesting small programs.
C and C++ provide the rand() and srand() functions for generating random numbers, and they can also be used when writing OpenCV code in C++. OpenCV also provides its own RNG class for generating random numbers, which is very convenient to use. This article mainly introduces how to use RNG and how to draw random lines based on the random numbers it generates.
This article introduces methods for drawing rectangles, circles, lines, and ellipses with C++ and OpenCV 4. It focuses on the definitions of OpenCV built-in functions (such as rectangle(), circle(), line(), and ellipse()) and the role of each parameter, and provides example programs for reference. It also summarizes methods for drawing polygons, including the polygon-border drawing function polylines(), the polygon-filling function fillPoly(), the function for drawing multiple polygons drawContours(), and more.
Replacing the background of a green-screen image involves ① color-space conversion, ② extracting the green-screen region, ③ inverting the green-screen region, and ④ copying the image. The various functions and code encountered along the way are also covered in detail in this article.
After splitting image channels, the output multi-channel sequence is typically stored using std::vector mv;, where mv[0], mv[1], and mv[2] correspond to the three BGR channels respectively. However, what is displayed now is essentially three single-channel images—in other words, three grayscale images. To restore intuitive color to the three images, you need to use the channel merging method described below.
OpenCV uses createTrackbar() to create a Trackbar. The function syntax and the purpose of each parameter are shown below.
Image pixel operations include read and write operations, arithmetic operations, logical operations, and more. Pixel operations are not only diverse; operations on grayscale images and color images also have their own characteristics. Operating on pixels allows us to access every pixel in an image and implement many unexpected functions.
Color space conversion function: cvtColor. GRAY: grayscale, with only one Channel for the grayscale value. BGR: the BGR color space, based on the three primary colors red, green, and blue (0~255), which are combined to form various colors. HSV: the hexagonal cone model, with Hue measured in degrees (0~180), Saturation in the range 0 ~ 255, and Value in the range 0 ~ 255.
First, download and extract OpenCV. I recommend downloading the Windows installer from the official OpenCV website. If the download is too slow, I have also uploaded the OpenCV4.5.2 installer to Aliyun Drive at https://www.aliyundrive.com/s/VGkaM7vyuck. Make sure to extract it to an English-only path.
Data generated while a program is running is temporary and is released once the program finishes. Files can be used to persist data. File operations in C++ require an #include directive.
Inheritance is one of the three core features of object-oriented programming. When defining a class, a lower-level class shares the common characteristics of the level above while also having its own characteristics.
Operator overloading redefines existing operators to give them another function, allowing them to work with different data types. This article covers overloading the addition, left-shift, increment, assignment, relational, and function-call operators.
Some private members may need to be accessed by certain functions or classes outside the class, which requires friends. The keyword for friends is fiend. Friends can be implemented in three ways: a global function as a friend, a class as a friend, or a member function as a friend.
In C++, a class's member variables and member functions are stored separately, and only non-static member variables belong to objects of the class. Each non-static member function has only one function instance, meaning that multiple objects of the same type share one block of code. How does the code distinguish which object invoked it? This is where the this pointer comes in.
Every object needs initial setup and cleanup before it is destroyed. Object initialization and cleanup are two very important safety concerns. Using an object or variable without an initial state has unknown consequences; failing to clean up an object or variable in time after use can also cause safety issues.
Encapsulation combines attributes and behaviors into a whole to represent real-world entities, while controlling access to those attributes and behaviors. When designing a class, attributes and behaviors are written together to represent an entity. The syntax is as follows.
In C++, parameters in a function's parameter list can have default values. If an argument is passed to a parameter, the supplied value is used; otherwise, the default value is used. However, if a parameter at a given position has a default argument, every parameter after it must also have a default value.
