生成矫正影像 给定一张TIF格式的影像，给定一套格网重投影坐标，输出一张新的TIF影像（要求是分块存储格式Tiled TIF）； 实现为C++动态库，输出函数的接口定义如下： imgPath是输入的TIF影像路径； outImgPath是输出的TIF影像路径，要求以Tiled TIF格式存储，分块大小为256； gridWidth是原影像的格网矫正宽度，通常为64； gridPoints是原影像的格网矫正坐标，此数组中坐标点的顺序是逐行顺序存储。 假设影像宽高分别为W,H，那么gridPoints中的点数为ceil(W/64)*H； 由于矫正坐标的最小值不一定为（0，0），因而输出影像是以矫正坐标的覆盖范围来决定影像的宽高，同时要求将矫正坐标的最小值作为偏移坐标值写入到TIF数据的GeoTransform头信息中。 功能要求： 影像可能较大，因而不能完全加载全部影像到内存进行处理，所以只能分块处理； 速度较快，一张1GB的TIF数据，处理时间不能超过20秒； 自编一个程序测试上述接口；3）环境要求： C++, GDAL3.0.2，VS 2017 推荐使用GDAL3.0.2读写影像

以下是代码实现的一种思路，仅供参考：

#include "gdal_priv.h"
#include <iostream>
#include <algorithm>

using namespace std;

// 定义格网点结构体
struct GridPoint {
    double x;
    double y;
};

// 定义矩形结构体
struct Rectangle {
    double minX;
    double maxX;
    double minY;
    double maxY;
};

// 获取格网点所在的矩形范围
Rectangle getGridRect(GridPoint* gridPoints, int gridWidth, int index) {
    Rectangle rect;
    rect.minX = gridPoints[index].x - gridWidth / 2.0;
    rect.maxX = gridPoints[index].x + gridWidth / 2.0;
    rect.minY = gridPoints[index].y - gridWidth / 2.0;
    rect.maxY = gridPoints[index].y + gridWidth / 2.0;
    return rect;
}

// 获取所有格网点所在的总矩形范围
Rectangle getTotalRect(GridPoint* gridPoints, int gridWidth, int width, int height) {
    Rectangle totalRect;
    totalRect.minX = gridPoints[0].x - gridWidth / 2.0;
    totalRect.maxX = gridPoints[width / gridWidth].x + gridWidth / 2.0;
    totalRect.minY = gridPoints[0].y - gridWidth / 2.0;
    totalRect.maxY = gridPoints[height / gridWidth].y + gridWidth / 2.0;
    return totalRect;
}

// 获取影像坐标点对应的像素坐标
void getPixelCoord(GDALDataset* dataset, double x, double y, int& pixelX, int& pixelY) {
    double adfGeoTransform[6];
    dataset->GetGeoTransform(adfGeoTransform);
    pixelX = (int)((x - adfGeoTransform[0]) / adfGeoTransform[1] + 0.5);
    pixelY = (int)((y - adfGeoTransform[3]) / adfGeoTransform[5] + 0.5);
}

// 获取像素坐标对应的影像坐标点
void getImageCoord(GDALDataset* dataset, int pixelX, int pixelY, double& x, double& y) {
    double adfGeoTransform[6];
    dataset->GetGeoTransform(adfGeoTransform);
    x = adfGeoTransform[0] + pixelX * adfGeoTransform[1];
    y = adfGeoTransform[3] + pixelY * adfGeoTransform[5];
}

// 获取像素坐标所在的矩形范围
Rectangle getPixelRect(GDALDataset* dataset, int pixelX, int pixelY, int tileSize) {
    Rectangle rect;
    rect.minX = pixelX;
    rect.maxX = min(pixelX + tileSize, dataset->GetRasterXSize());
    rect.minY = pixelY;
    rect.maxY = min(pixelY + tileSize, dataset->GetRasterYSize());
    return rect;
}

// 矫正影像
void rectifyImage(const char* imgPath, const char* outImgPath, GridPoint* gridPoints, int gridWidth) {
    // 打开影像
    GDALDataset* dataset = (GDALDataset*)GDALOpen(imgPath, GA_ReadOnly);
    if (dataset == NULL) {
        cout << "Failed to open input image." << endl;
        return;
    }

    // 获取影像宽度和高度
    int width = dataset->GetRasterXSize();
    int height = dataset->GetRasterYSize();

    // 获取格网点总数
    int numGridPoints = (int)ceil(width / (double)gridWidth) * (int)ceil(height / (double)gridWidth);

    // 获取所有格网点所在的总矩形范围
    Rectangle totalRect = getTotalRect(gridPoints, gridWidth, width, height);

    // 获取输出影像的宽度和高度
    int outWidth = (int)ceil((totalRect.maxX - totalRect.minX) / gridWidth);
    int outHeight = (int)ceil((totalRect.maxY - totalRect.minY) / gridWidth);

    // 创建输出影像
    GDALDriver* driver = GetGDALDriverManager()->GetDriverByName("GTiff");
    GDALDataset* outDataset = driver->Create(outImgPath, outWidth, outHeight, dataset->GetRasterCount(), GDT_Byte, NULL);
    if (outDataset == NULL) {
        cout << "Failed to create output image." << endl;
        return;
    }

    // 设置输出影像的地理位置信息
    double adfGeoTransform[6];
    adfGeoTransform[0] = totalRect.minX;
    adfGeoTransform[1] = gridWidth;
    adfGeoTransform[2] = 0;
    adfGeoTransform[3] = totalRect.maxY;
    adfGeoTransform[4] = 0;
    adfGeoTransform[5] = -gridWidth;
    outDataset->SetGeoTransform(adfGeoTransform);

    // 逐个格网点矫正影像
    for (int i = 0; i < numGridPoints; i++) {
        // 获取格网点所在的矩形范围
        Rectangle gridRect = getGridRect(gridPoints, gridWidth, i);

        // 将矩形范围转换为像素坐标范围
        int startPixelX, startPixelY, endPixelX, endPixelY;
        getPixelCoord(dataset, gridRect.minX, gridRect.maxY, startPixelX, startPixelY);
        getPixelCoord(dataset, gridRect.maxX, gridRect.minY, endPixelX, endPixelY);

        // 将像素坐标范围分成小块，逐个处理
        const int tileSize = 256;
        for (int pixelY = startPixelY; pixelY < endPixelY; pixelY += tileSize) {
            for (int pixelX = startPixelX; pixelX < endPixelX; pixelX += tileSize) {
                // 获取像素坐标范围
                Rectangle pixelRect = getPixelRect(dataset, pixelX, pixelY, tileSize);

                // 创建内存块
                int blockWidth = pixelRect.maxX - pixelRect.minX;
                int blockHeight = pixelRect.maxY - pixelRect.minY;
                GByte* blockData = new GByte[blockWidth * blockHeight * dataset->GetRasterCount()];

                // 读取像素坐标范围内的数据
                CPLErr err = dataset->RasterIO(GF_Read, pixelRect.minX, pixelRect.minY, blockWidth, blockHeight, blockData, blockWidth, blockHeight, GDT_Byte, dataset->GetRasterCount(), NULL, 0, 0, 0);
                if (err != CE_None) {
                    cout << "Failed to read input image data." << endl;
                    delete[] blockData;
                    return;
                }

                // 将像素坐标范围转换为影像坐标范围
                double startX, startY, endX, endY;
                getImageCoord(dataset, pixelRect.minX, pixelRect.minY, startX, startY);
                getImageCoord(dataset, pixelRect.maxX, pixelRect.maxY, endX, endY);

                // 获取内存块对应的矩形范围
                Rectangle blockRect;
                blockRect.minX = max(gridRect.minX, startX);
                blockRect.maxX = min(gridRect.maxX, endX);
                blockRect.minY = max(gridRect.minY, endY);
                blockRect.maxY = min(gridRect.maxY, startY);

                // 将矩形范围转换为像素坐标范围
                int startBlockPixelX, startBlockPixelY, endBlockPixelX, endBlockPixelY;
                getPixelCoord(outDataset, blockRect.minX, blockRect.maxY, startBlockPixelX, startBlockPixelY);
                getPixelCoord(outDataset, blockRect.maxX, blockRect.minY, endBlockPixelX, endBlockPixelY);

                // 计算内存块在输出影像中的位置和大小
                int outX = startBlockPixelX - pixelRect.minX;
                int outY = startBlockPixelY - pixelRect.minY;
                int outWidth = endBlockPixelX - startBlockPixelX;
                int outHeight = endBlockPixelY - startBlockPixelY;

                // 写入数据到输出影像中
                err = outDataset->RasterIO(GF_Write, outX, outY, outWidth, outHeight, blockData, outWidth, outHeight, GDT_Byte, dataset->GetRasterCount(), NULL, 0, 0, 0);
                if (err != CE_None) {
                    cout << "Failed to write output image data." << endl;
                    delete[] blockData;
                    return;
                }

                // 释放内存块
                delete[] blockData;
            }
        }
    }

    // 关闭数据集
    GDALClose(dataset);
    GDALClose(outDataset);

    cout << "Rectification complete." << endl;
}

以下是测试程序的代码：

int main() {
    // 定义格网点数组
    const int numGridPoints = 100;
    GridPoint gridPoints[numGridPoints];
    for (int i = 0; i < numGridPoints; i++) {
        gridPoints[i].x = i * 10000.0;
        gridPoints[i].y = i * 10000.0;
    }

    // 矫正影像
    rectifyImage("input.tif", "output.tif", gridPoints, 64);

    return 0;
}