create an RBG Color QImage from a GDALDataSet

create an RBG Color QImage from a GDALDataSet

本文关键字：from GDALDataSet QImage Color an RBG create      更新时间：2023-10-16

我使用GDAL读取一些图像文件，并希望使用Qt显示它们。到目前为止，我已经为GDALDataSet中的每个GDALRasterBand创建了一个灰度级QImage，但我不知道如何创建一个RGB图像。

以下是我所做的：

#include <gdal_priv.h>
#include <QtGuiQImage>
int main(int argc, char *argv[])
{
    GDALAllRegister();
    GDALDataset* dataset = static_cast<GDALDataset*>(GDALOpen("path_to_some_image.tif", GA_ReadOnly));
    int size_out = 200;
    for (int i = 1; i <= 3; ++i)
    { 
        GDALRasterBand* band = dataset->GetRasterBand(i);
        std::vector<uchar> band_data(size_out * size_out);
        band->RasterIO(GF_Read, 0, 0, size_out, size_out, band_data.data(), size_out, size_out, GDT_Byte, 0, 0);
        QImage band_image(band_data.data(), size_out, size_out, QImage::Format_Grayscale8);
        band_image.save(QString("C:\band_%1.png").arg(i));
    }
    return 0;
}

如何读取数据以便创建单个RGB QImage？

您已接近目标。第一项是QImage获取一个带有格式化标志的缓冲区。因此，该格式标志需要与您从文件加载的图像相匹配，或者需要进行转换。下面的示例假设一个4通道图像。

Q图像格式标志文档：http://doc.qt.io/qt-5/qimage.html#Format-枚举

下一个组成部分是GDAL的RasterIO方法分别处理每个波段，这意味着你必须分别交错像素，否则就会失去逐波段加载光栅的效率。

RasterIO：http://gdal.org/classGDALRasterBand.html#a30786c81246455321e96d73047b8edf1

我喜欢OpenCV的merge方法。只需为每个波段创建一个灰度图像，并将它们一起merge即可。

OpenCV合并：http://docs.opencv.org/3.0.0/d2/de8/group__core__array.html#ga61f2f2bde4a0a0154b2333ea504fab1d

例如，给定RGBA GeoTiff，

// OpenCV Libraries
#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
// GDAL Libraries
#include <gdal.h>
// QT Libraries
#include <QtGuiQImage>
using namespace cv;

int main( int argc, char* argv[] )
{
    // Initialize GDAL
    GDALAllRegister();
    // Load image
    GDALDataset* dataset = GDALOpen("path_to_some_image.tif", GA_ReadOnly);
    // Get raster image size
    int rows = dataset->GetRasterYSize();
    int cols = dataset->GetRasterXSize();
    int channels = dataset->GetRasterCount();
    // Create each separate image as grayscale
    std::vector<cv::Mat> image_list(channels, cv::Mat( rows, cols, CV_8UC1 ));
    cv::Mat output_image;
    // Iterate over each channel
    for (int i = 1; i <= channels; ++i)
    { 
        // Fetch the band
        GDALRasterBand* band = dataset->GetRasterBand(i);
        // Read the data
        band->RasterIO( GF_Read, 0, 0, 
                        cols, rows, 
                        image_list[i-1].data, 
                        cols, rows, 
                        GDT_Byte, 0, 0);
    }
    // Merge images
    cv::merge( image_list, output_image );
    // Create the QImage
    QImage qt_image( band_data.data(), 
                     cols, 
                     rows,
                     QImage::Format_RGBA8888);
    // Do some stuff with the image
    return 0;
 }

没有OpenCV，使用msmith81886代码：

// Load image
    GDALDataset* dataset = static_cast<GDALDataset*>(GDALOpen(tifFile.toLocal8Bit().data(), GA_ReadOnly));
    // Get raster image size
    int rows = dataset->GetRasterYSize();
    int cols = dataset->GetRasterXSize();
    int channels = dataset->GetRasterCount();
    std::vector<std::vector<uchar>> bandData(channels);
    for (auto& mat : bandData)
    {
        mat.resize(size_t(rows * cols));
    }
    std::vector<uchar> outputImage(size_t(4 * rows * cols));
    // Iterate over each channel
    for (int i = 1; i <= channels; ++i)
    {
        // Fetch the band
        GDALRasterBand* band = dataset->GetRasterBand(i);
        // Read the data
        band->RasterIO(GF_Read, 0, 0, cols, rows, bandData[size_t(i - 1)].data(),
            cols, rows, GDT_Byte, 0, 0);        
    }
    for (size_t i = 0, j = 0; i < outputImage.size(); i += 4, j += 1)
    {
        outputImage[i] = bandData[0][j];
        outputImage[i + 1] = bandData[1][j];
        outputImage[i + 2] = bandData[2][j];
        outputImage[i + 3] = bandData[3][j];
    }
    // Create the QImage (or even a QPixmap suitable for displaying teh image
    QImage qtImage(outputImage.data(), cols, rows, QImage::Format_RGBA8888);
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