Comprehensive Guide to Converting OpenCV Mat to Array and Vector in C++

Introduction

OpenCV is a widely used library for computer vision, and the cv::Mat class is fundamental for image representation. In many applications, such as high-level synthesis for Vivado HLS, it is necessary to convert Mat objects to standard C++ arrays or vectors. This article addresses this need by exploring efficient conversion techniques based on memory layout and continuity.

Understanding Memory Continuity in OpenCV Mat

The memory of a cv::Mat can be either continuous or non-continuous. Continuous memory means all data is stored in a single block, accessible via the data pointer. To check continuity, use the isContinuous() method. Matrices created by functions like imread() or clone() are typically continuous, while sub-matrices from regions of interest may not be.

Method 1: Direct Conversion for Continuous Memory

For continuous Mat objects, conversion to a vector is straightforward. The total number of elements is mat.total() * mat.channels(). Here is a code example:

std::vector<uchar> array;
if (mat.isContinuous()) {
    array.assign(mat.data, mat.data + mat.total() * mat.channels());
} else {
    // Handle non-continuous case
}

This method uses assign to copy data directly, ensuring efficient memory usage.

Method 2: Row-wise Conversion for Non-Continuous Memory

When the Mat is non-continuous, data must be accessed row by row using the ptr method. Example:

std::vector<uchar> array;
for (int i = 0; i < mat.rows; ++i) {
    array.insert(array.end(), mat.ptr<uchar>(i), mat.ptr<uchar>(i) + mat.cols * mat.channels());
}

This approach iterates through each row, appending data to the vector, suitable for any memory layout.

Handling Different Data Types

OpenCV supports various data types like CV_32F for floats. The conversion method adapts by casting pointers appropriately. For a float matrix:

std::vector<float> array;
if (mat.isContinuous()) {
    array.assign((float*)mat.data, (float*)mat.data + mat.total() * mat.channels());
} else {
    for (int i = 0; i < mat.rows; ++i) {
        array.insert(array.end(), mat.ptr<float>(i), mat.ptr<float>(i) + mat.cols * mat.channels());
    }
}

This ensures type safety and correct memory access.

Alternative Method Using Reshape and Clone

An alternative approach from supplementary answers involves reshaping the Mat to a continuous block. First, flatten the matrix:

cv::Mat flat = mat.reshape(1, mat.total() * mat.channels());
if (!mat.isContinuous()) {
    flat = flat.clone();
}
std::vector<uchar> vec(flat.data, flat.data + flat.total());

This method uses reshape to change dimensions and clone to ensure continuity, providing a concise solution.

Conclusion and Best Practices

Converting cv::Mat to arrays or vectors in C++ requires attention to memory continuity. For most cases, checking isContinuous() and using direct assignment is efficient. For non-continuous data, row-wise access or reshaping techniques are recommended. These methods are suitable for applications like Vivado HLS synthesis, ensuring compatibility and performance.