cotton_color/Matrox/mask.cpp

77 lines
2.5 KiB
C++

#include <opencv2/opencv.hpp>
#include <vector>
#include <iostream>
// 读取二值化的单通道一位图片并生成掩膜
std::vector<std::vector<bool>> generateMaskFromImage(const std::string& imagePath, int widthBlocks, int heightBlocks, int threshold = 10) {
// 读取图像
cv::Mat image = cv::imread(imagePath, cv::IMREAD_GRAYSCALE);
// 检查图像是否成功读取
if (image.empty()) {
std::cerr << "无法加载图像,请检查路径是否正确: " << imagePath << std::endl;
exit(EXIT_FAILURE);
}
// 确保图像是二值化的
cv::threshold(image, image, 128, 255, cv::THRESH_BINARY);
// 获取图像的宽度和高度
int imageWidth = image.cols;
int imageHeight = image.rows;
// 计算每个块的宽度和高度
int blockWidth = imageWidth / widthBlocks;
int blockHeight = imageHeight / heightBlocks;
// 创建掩膜矩阵
std::vector<std::vector<bool>> mask(heightBlocks, std::vector<bool>(widthBlocks, false));
// 遍历每个块并统计白色像素点的数量
for (int i = 0; i < heightBlocks; ++i) {
for (int j = 0; j < widthBlocks; ++j) {
// 计算块的起始和结束位置
int x_start = j * blockWidth;
int y_start = i * blockHeight;
int x_end = (j == widthBlocks - 1) ? imageWidth : (j + 1) * blockWidth;
int y_end = (i == heightBlocks - 1) ? imageHeight : (i + 1) * blockHeight;
// 提取当前块
cv::Mat block = image(cv::Rect(x_start, y_start, x_end - x_start, y_end - y_start));
// 统计块中白色像素的数量
int whitePixelCount = cv::countNonZero(block);
// 如果白色像素数大于阈值,将该块标记为 true
if (whitePixelCount > threshold) {
mask[i][j] = true;
}
}
}
return mask;
}
int main() {
// 指定图像路径
std::string imagePath = "C:\\Users\\zjc\\Desktop\\diguandai.png";
// 设置分块数量和白色像素点阈值
int widthBlocks = 24;
int heightBlocks = 24;
int threshold = 20;
// 生成掩膜
std::vector<std::vector<bool>> mask = generateMaskFromImage(imagePath, widthBlocks, heightBlocks, threshold);
// 打印掩膜结果
for (int i = 0; i < heightBlocks; ++i) {
for (int j = 0; j < widthBlocks; ++j) {
std::cout << (mask[i][j] ? "1 " : "0 ");
}
std::cout << std::endl;
}
return 0;
}