#include <iostream>
#include <fstream>
#include <vector>
#include <stdint.h>#include <opencv2/opencv.hpp> // 包含OpenCV头文件using namespace std;// 注意：确保这些值与Python脚本中生成数据的值匹配。
const int WIDTH = 1920;
const int HEIGHT = 1080;
const int CHANNELS = 1;int main() {// 用以存储从文件中读取的数据的缓冲区std::vector<uint8_t> buffer(HEIGHT * WIDTH * CHANNELS);// 打开文件std::ifstream file("hwc_1080_1920_1_uint8.bin", std::ios::binary);if (file.is_open()) {// 从文件读取数据到缓冲区中file.read(reinterpret_cast<char*>(buffer.data()), buffer.size() * sizeof(uint8_t));if (file) {std::cout << "All data read successfully." << std::endl;}else {std::cerr << "Error occurred while reading from the file." << std::endl;file.close();return 1;}file.close();std::cout << "x position: " << std::endl;cv::namedWindow("Canvas", cv::WINDOW_NORMAL);cv::Mat canvas(HEIGHT, WIDTH, CV_8UC3, cv::Scalar(255, 255, 255)); // 创建一个300x300像素的画布for(int i=0;i< WIDTH;i++){for (int j = 0; j < HEIGHT; j++) {float x_w = buffer[(j * WIDTH + i) * CHANNELS + 0]; // x_w值//std::cout << x_w << " ";cv::circle(canvas, cv::Point(i, j), 1, cv::Scalar(x_w, x_w, x_w), -1);}//std::cout << std::endl;}cv::resizeWindow("Canvas", 600, 400);cv::imshow("Canvas", canvas);cv::waitKey(0); // 等待10秒std::cout << "completed." << std::endl; 处理读取到的数据。此处你可以添加自己的逻辑代码来处理buffer中的数据。 例如，以下为访问某个特定像素的通道值的方式：//int y = 1079; // 第10行//int x = 960; // 第20列//float x_w = buffer[(y * WIDTH + x) * CHANNELS + 0]; // x_w值//float y_w = buffer[(y * WIDTH + x) * CHANNELS + 1]; // y_w值//float yaw = buffer[(y * WIDTH + x) * CHANNELS + 2]; // yaw值//float distance = buffer[(y * WIDTH + x) * CHANNELS + 3]; // distance值//std::cout << "Pixel at position (" << y << ", " << x << "): "//    << "x_w = " << x_w << ", "//    << "y_w = " << y_w << ", "//    << "yaw = " << yaw << ", "//    << "distance = " << distance << std::endl;}else {std::cerr << "Could not open the file." << std::endl;return 1;}return 0;
}