Core Module

The Core module is the foundation of OpenCV, providing essential data structures, basic operations, and utilities that other modules depend on.

Overview

From the OpenCV source (~/workspace/source/modules/core/include/opencv2/core.hpp:62-67):

The Core module is the backbone of OpenCV, offering fundamental data structures, matrix operations, and utility functions that other modules depend on. It’s essential for handling image data, performing mathematical computations, and managing memory efficiently within the OpenCV ecosystem.

Key Components

Mat Class

N-dimensional dense array for storing images and matrices

Array Operations

Mathematical operations on arrays and matrices

Persistence

XML/YAML/JSON file I/O for data structures

Utilities

System functions, logging, and error handling

Mat - The Core Data Structure

The Mat class is OpenCV’s primary container for images and matrices.

Creating Mat Objects

#include <opencv2/core.hpp>
using namespace cv;

// Create empty matrix
Mat img;

// Create with size and type
Mat img1(480, 640, CV_8UC3);  // 8-bit, 3-channel (BGR)
Mat img2(Size(640, 480), CV_8UC1);  // 8-bit, single channel

// Create and initialize
Mat zeros = Mat::zeros(100, 100, CV_8UC1);
Mat ones = Mat::ones(100, 100, CV_32F);
Mat eye = Mat::eye(3, 3, CV_64F);

// Create from data
float data[] = {1, 2, 3, 4};
Mat m(2, 2, CV_32F, data);

Mat Properties

Mat img = imread("image.jpg");

// Dimensions
int rows = img.rows;
int cols = img.cols;
Size size = img.size();  // width x height
int channels = img.channels();

// Type information
int type = img.type();  // e.g., CV_8UC3
int depth = img.depth();  // e.g., CV_8U

// Memory
size_t step = img.step;  // bytes per row
bool continuous = img.isContinuous();

Array Operations

The core module provides extensive array operations defined in opencv2/core.hpp.

Arithmetic Operations

// Addition (core.hpp:298)
void add(InputArray src1, InputArray src2, OutputArray dst);

Mat a, b, result;
add(a, b, result);

// Also supports operator overloading
Mat c = a + b;
Mat d = a - b;
Mat e = a * 2.5;

Element-wise Operations

// Multiplication
multiply(src1, src2, dst);

// Division
divide(src1, src2, dst);

// Absolute value
absdiff(src1, src2, dst);

// Power
pow(src, power, dst);

// Square root
sqrt(src, dst);

// Logarithm
log(src, dst);

// Exponential
exp(src, dst);

Matrix Operations

// Matrix multiplication
Mat A, B, C;
C = A * B;  // Matrix product
gemm(A, B, 1, Mat(), 0, C);  // General matrix multiply

// Transpose
Mat At = A.t();
transpose(A, At);

// Inversion
Mat invA = A.inv();
invert(A, invA);

// Determinant
double det = determinant(A);

// Eigenvalues and eigenvectors
Mat eigenvalues, eigenvectors;
eigen(A, eigenvalues, eigenvectors);

// SVD (Singular Value Decomposition)
SVD svd(A);
Mat U = svd.u;
Mat W = svd.w;
Mat Vt = svd.vt;

Reduction Operations

From core.hpp:210-215, OpenCV provides reduction types:

enum ReduceTypes {
    REDUCE_SUM = 0,   // Sum of all rows/columns
    REDUCE_AVG = 1,   // Mean of all rows/columns  
    REDUCE_MAX = 2,   // Maximum value
    REDUCE_MIN = 3,   // Minimum value
    REDUCE_SUM2 = 4   // Sum of squares
};

// Example usage
Mat src, dst;
reduce(src, dst, 0, REDUCE_SUM);  // Sum along columns
reduce(src, dst, 1, REDUCE_AVG);  // Average along rows

Statistical Functions

// Min/Max
double minVal, maxVal;
Point minLoc, maxLoc;
minMaxLoc(src, &minVal, &maxVal, &minLoc, &maxLoc);

// Mean and standard deviation
Scalar meanVal = mean(src);
Scalar meanVal, stddevVal;
meanStdDev(src, meanVal, stddevVal);

// Sum
Scalar sum = sum(src);

// Count non-zero
int count = countNonZero(src);

// Norm
double n = norm(src, NORM_L2);
double n2 = norm(src1, src2, NORM_INF);

Border Handling

From core.hpp:223-244, OpenCV provides borderInterpolate() for extrapolation:

// Border types
enum BorderTypes {
    BORDER_CONSTANT,     // iiiiii|abcdefgh|iiiiiii
    BORDER_REPLICATE,    // aaaaaa|abcdefgh|hhhhhhh
    BORDER_REFLECT,      // fedcba|abcdefgh|hgfedcb
    BORDER_WRAP,         // cdefgh|abcdefgh|abcdefg
    BORDER_REFLECT_101,  // gfedcb|abcdefgh|gfedcba
    BORDER_TRANSPARENT,  // Not modified
    BORDER_ISOLATED      // Do not look outside ROI
};

// Create border (core.hpp:294)
void copyMakeBorder(InputArray src, OutputArray dst,
                    int top, int bottom, int left, int right,
                    int borderType, const Scalar& value = Scalar());

Data Persistence

Save and load data structures to XML/YAML/JSON files.

// Writing
FileStorage fs("data.yml", FileStorage::WRITE);
fs << "image" << img;
fs << "matrix" << mat;
fs << "number" << 42;
fs.release();

// Reading
FileStorage fs("data.yml", FileStorage::READ);
Mat img, mat;
int number;
fs["image"] >> img;
fs["matrix"] >> mat;
fs["number"] >> number;
fs.release();

Utility Functions

Exception Handling

From core.hpp:112-156:

// Exception class
class Exception : public std::exception {
public:
    int code;         // Error code
    String err;       // Error description
    String func;      // Function name
    String file;      // Source file
    int line;         // Line number
};

// Error handling
try {
    // OpenCV operations
} catch(const cv::Exception& e) {
    std::cerr << "Error: " << e.what() << std::endl;
}

System Information

// OpenCV version
String version = getBuildInformation();

// Number of CPU cores
int cores = getNumberOfCPUs();

// Number of threads
int threads = getNumThreads();
setNumThreads(4);

// Tick count (for performance measurement)
int64 t1 = getTickCount();
// ... operations ...
int64 t2 = getTickCount();
double time = (t2 - t1) / getTickFrequency();

Data Types

OpenCV supports various data types:

// Depth types
CV_8U   // 8-bit unsigned (0-255)
CV_8S   // 8-bit signed (-128-127)
CV_16U  // 16-bit unsigned
CV_16S  // 16-bit signed
CV_32S  // 32-bit signed integer
CV_32F  // 32-bit floating point
CV_64F  // 64-bit floating point

// Type macros: CV_<depth>C<channels>
CV_8UC1  // 8-bit, 1 channel (grayscale)
CV_8UC3  // 8-bit, 3 channels (BGR color)
CV_32FC1 // 32-bit float, 1 channel
CV_64FC3 // 64-bit float, 3 channels

Sorting

From core.hpp:158-167:

enum SortFlags {
    SORT_EVERY_ROW = 0,      // Sort each row independently
    SORT_EVERY_COLUMN = 1,   // Sort each column independently
    SORT_ASCENDING = 0,      // Ascending order
    SORT_DESCENDING = 16     // Descending order
};

// Sort array
sort(src, dst, SORT_EVERY_ROW | SORT_ASCENDING);

// Sort with indices
sortIdx(src, dst, SORT_EVERY_ROW | SORT_DESCENDING);

Memory Management

// Mat uses reference counting
Mat a = imread("image.jpg");
Mat b = a;  // Shallow copy (same data)
Mat c = a.clone();  // Deep copy (new data)

// Check reference count
int refs = a.u->refcount;

// Release (automatic with scope)
a.release();

// ROI (Region of Interest) - shares data
Rect roi(10, 10, 100, 100);
Mat roiMat = img(roi);

// Check if data is continuous
if (img.isContinuous()) {
    // Can treat as 1D array
}

Example: Matrix Operations

From samples/cpp/cout_mat.cpp:

#include <opencv2/core.hpp>
#include <iostream>

using namespace cv;
using namespace std;

int main() {
    // Create matrices
    Mat A = (Mat_<double>(3,3) << 1, 2, 3, 4, 5, 6, 7, 8, 9);
    Mat B = Mat::eye(3, 3, CV_64F);
    
    // Operations
    Mat C = A + B;
    Mat D = A * B;
    Mat At = A.t();
    
    // Display
    cout << "A = " << endl << A << endl;
    cout << "A + I = " << endl << C << endl;
    cout << "A * I = " << endl << D << endl;
    cout << "A^T = " << endl << At << endl;
    
    // Statistics
    Scalar mean_val = mean(A);
    cout << "Mean: " << mean_val[0] << endl;
    
    return 0;
}

Best Practices

Memory Efficiency:

Use Mat::clone() only when you need a deep copy
Prefer ROI over copying when working with image regions
Let Mat destructor handle memory cleanup automatically

Type Safety:

Always check Mat::type() before operations
Use convertTo() for type conversion
Verify dimensions match before matrix operations

Image Processing - Uses Mat for all operations
Image I/O - Reads/writes images as Mat
Video Analysis - Processes video frames as Mat

Source Reference

Key header file: ~/workspace/source/modules/core/include/opencv2/core.hpp See also:

opencv2/core/mat.hpp - Mat class implementation
opencv2/core/operations.hpp - Array operations
opencv2/core/persistence.hpp - File I/O

Getting Started

Core Concepts

Modules

Tutorials

Language Bindings

Platform Support

Overview

Key Components

Mat Class

Array Operations

Persistence

Utilities

Mat - The Core Data Structure

Creating Mat Objects

Mat Properties

Array Operations

Arithmetic Operations

Element-wise Operations

Matrix Operations

Reduction Operations

Statistical Functions

Border Handling

Data Persistence

Utility Functions

Exception Handling

System Information

Data Types

Sorting

Memory Management

Example: Matrix Operations

Best Practices

Source Reference

Getting Started

Core Concepts

Modules

Tutorials

Language Bindings

Platform Support

​Overview

​Key Components

Mat Class

Array Operations

Persistence

Utilities

​Mat - The Core Data Structure

​Creating Mat Objects

​Mat Properties

​Array Operations

​Arithmetic Operations

​Element-wise Operations

​Matrix Operations

​Reduction Operations

​Statistical Functions

​Border Handling

​Data Persistence

​Utility Functions

​Exception Handling

​System Information

​Data Types

​Sorting

​Memory Management

​Example: Matrix Operations

​Best Practices

​Related Modules

​Source Reference

Overview

Key Components

Mat - The Core Data Structure

Creating Mat Objects

Mat Properties

Array Operations

Arithmetic Operations

Element-wise Operations

Matrix Operations

Reduction Operations

Statistical Functions

Border Handling

Data Persistence

Utility Functions

Exception Handling

System Information

Data Types

Sorting

Memory Management

Example: Matrix Operations

Best Practices

Related Modules

Source Reference