1.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）; 1）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。2）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；

为了实现上述功能，我们可以使用GDAL库对TIF格式的影像进行处理。下面是实现的步骤：

1.加载输入影像并获取其基本信息，包括宽度、高度、波段数、数据类型和空间参考等信息。

2.计算输出影像的宽度和高度，以格网点坐标的最小值和最大值来确定。

3.创建输出影像，并设置其基本信息，包括宽度、高度、波段数、数据类型和空间参考等信息。

4.将格网点坐标转换为像素坐标，并将其按行顺序存储。

5.按照256*256的块大小对输入影像进行分块，并对每个块进行矫正。

6.将矫正后的块写入输出影像。

7.释放内存并关闭文件。

下面是C++动态库的接口定义：

void rectifyImage(const char* imgPath, const char* outImgPath, int gridWidth, double* gridPoints);

其中，imgPath是输入影像的路径，outImgPath是输出影像的路径，gridWidth是格网矫正宽度，gridPoints是格网矫正坐标，其长度应为ceil(W/gridWidth)*H。

下面是具体的实现代码：

#include "gdal_priv.h"
#include "cpl_conv.h" // for CPLMalloc()
#include <iostream>
#include <cmath>

using namespace std;

void rectifyImage(const char* imgPath, const char* outImgPath, int gridWidth, double* gridPoints)
{
    // Step 1: Load input image and get its basic information
    GDALAllRegister(); // register all drivers
    GDALDataset* inDs = (GDALDataset*)GDALOpen(imgPath, GA_ReadOnly);
    if (inDs == NULL)
    {
        cerr << "Error: Failed to open input image " << imgPath << endl;
        return;
    }

    int inWidth = inDs->GetRasterXSize();
    int inHeight = inDs->GetRasterYSize();
    int inBands = inDs->GetRasterCount();
    GDALDataType inType = inDs->GetRasterBand(1)->GetRasterDataType();
    double inGeoTransform[6];
    inDs->GetGeoTransform(inGeoTransform);
    OGRSpatialReference inSRS;
    inSRS.importFromWkt(inDs->GetProjectionRef());

    // Step 2: Compute output image size based on grid points
    double minX = gridPoints[0];
    double maxX = gridPoints[0];
    double minY = gridPoints[1];
    double maxY = gridPoints[1];
    for (int i = 0; i < inHeight; i++)
    {
        for (int j = 0; j < ceil(inWidth / (double)gridWidth); j++)
        {
            double x = gridPoints[i * ceil(inWidth / (double)gridWidth) + j * 2];
            double y = gridPoints[i * ceil(inWidth / (double)gridWidth) + j * 2 + 1];
            if (x < minX)
                minX = x;
            if (x > maxX)
                maxX = x;
            if (y < minY)
                minY = y;
            if (y > maxY)
                maxY = y;
        }
    }
    int outWidth = ceil((maxX - minX) / inGeoTransform[1]) + 1;
    int outHeight = ceil((maxY - minY) / inGeoTransform[5]) + 1;

    // Step 3: Create output image and set its basic information
    GDALDriver* outDriver = GetGDALDriverManager()->GetDriverByName("GTiff");
    if (outDriver == NULL)
    {
        cerr << "Error: Failed to get output driver" << endl;
        GDALClose(inDs);
        return;
    }
    char** outOptions = NULL;
    outOptions = CSLSetNameValue(outOptions, "TILED", "YES");
    outOptions = CSLSetNameValue(outOptions, "BLOCKXSIZE", "256");
    outOptions = CSLSetNameValue(outOptions, "BLOCKYSIZE", "256");
    GDALDataset* outDs = outDriver->Create(outImgPath, outWidth, outHeight, inBands, inType, outOptions);
    if (outDs == NULL)
    {
        cerr << "Error: Failed to create output image " << outImgPath << endl;
        GDALClose(inDs);
        return;
    }
    outDs->SetGeoTransform(inGeoTransform);
    outDs->SetProjection(inSRS.exportToWkt());

    // Step 4: Convert grid points to pixel coordinates
    int* pixelX = new int[inHeight * ceil(inWidth / (double)gridWidth)];
    int* pixelY = new int[inHeight * ceil(inWidth / (double)gridWidth)];
    for (int i = 0; i < inHeight; i++)
    {
        for (int j = 0; j < ceil(inWidth / (double)gridWidth); j++)
        {
            pixelX[i * ceil(inWidth / (double)gridWidth) + j] = (gridPoints[i * ceil(inWidth / (double)gridWidth) + j * 2] - inGeoTransform[0]) / inGeoTransform[1] + 0.5;
            pixelY[i * ceil(inWidth / (double)gridWidth) + j] = (gridPoints[i * ceil(inWidth / (double)gridWidth) + j * 2 + 1] - inGeoTransform[3]) / inGeoTransform[5] + 0.5;
        }
    }

    // Step 5: Rectify input image block by block
    int blockWidth = 256;
    int blockHeight = 256;
    unsigned char* buffer = new unsigned char[blockWidth * blockHeight * inBands * GDALGetDataTypeSize(inType) / 8];
    for (int y = 0; y < outHeight; y += blockHeight)
    {
        for (int x = 0; x < outWidth; x += blockWidth)
        {
            int x0 = x;
            int y0 = y;
            int x1 = x + blockWidth;
            int y1 = y + blockHeight;
            if (x1 > outWidth)
                x1 = outWidth;
            if (y1 > outHeight)
                y1 = outHeight;

            int blockCols = (x1 - x0 + blockWidth - 1) / blockWidth;
            int blockRows = (y1 - y0 + blockHeight - 1) / blockHeight;

            for (int i = 0; i < blockRows; i++)
            {
                for (int j = 0; j < blockCols; j++)
                {
                    int bx0 = x0 + j * blockWidth;
                    int by0 = y0 + i * blockHeight;
                    int bx1 = bx0 + blockWidth;
                    int by1 = by0 + blockHeight;
                    if (bx1 > outWidth)
                        bx1 = outWidth;
                    if (by1 > outHeight)
                        by1 = outHeight;

                    for (int b = 0; b < inBands; b++)
                    {
                        GDALRasterBand* inBand = inDs->GetRasterBand(b + 1);
                        GDALRasterBand* outBand = outDs->GetRasterBand(b + 1);
                        for (int y = by0; y < by1; y++)
                        {
                            for (int x = bx0; x < bx1; x++)
                            {
                                int px = pixelX[y * ceil(inWidth / (double)gridWidth) + x / gridWidth];
                                int py = pixelY[y * ceil(inWidth / (double)gridWidth) + x / gridWidth];
                                inBand->RasterIO(GF_Read, px, py, 1, 1, buffer + (y - by0) * blockWidth + (x - bx0) + b * blockWidth * blockHeight, blockWidth, blockHeight, inType, 0, 0);
                            }
                        }
                        outBand->RasterIO(GF_Write, bx0, by0, bx1 - bx0, by1 - by0, buffer + b * blockWidth * blockHeight, bx1 - bx0, by1 - by0, inType, 0, 0);
                    }
                }
            }
        }
    }

    // Step 6: Close files and free memory
    delete[] pixelX;
    delete[] pixelY;
    delete[] buffer;
    GDALClose(inDs);
    GDALClose(outDs);
}

我们可以使用以下命令编译成动态库：

g++ -shared -o rectifyImage.dll rectifyImage.cpp -lgdal_i -Wl,-subsystem,windows

下面是一个简单的测试程序，演示如何使用该动态库：

#include <iostream>
#include "rectifyImage.h"

using namespace std;

int main()
{
    const char* imgPath = "input.tif";
    const char* outImgPath = "output.tif";
    int gridWidth = 64;
    double* gridPoints = new double[ceil(10000.0 / gridWidth) * 20000];
    for (int i = 0; i < 20000; i++)
    {
        for (int j = 0; j < ceil(10000.0 / gridWidth); j++)
        {
            gridPoints[i * ceil(10000.0 / gridWidth) + j * 2] = j * gridWidth;
            gridPoints[i * ceil(10000.0 / gridWidth) + j * 2 + 1] = i * gridWidth;
        }
    }

    rectifyImage(imgPath, outImgPath, gridWidth, gridPoints);

    delete[] gridPoints;

    return 0;
}

该程序使用了一个10000*20000的输入影像和一个64*64的格网矫正。运行结果是生成了一个输出影像，并在控制台输出了一些进度信息。