Question

我在Mac OS 10.9.5上使用OpenCV2.4.8.2。我有以下代码片段：

static void compute_weights(const vector<Mat>& images, vector<Mat>& weights)
{
    weights.clear();
    for (int i = 0; i < images.size(); i++) {
        Mat image = images[i];
        Mat mask = Mat::zeros(image.size(), CV_32F);
        int x_start = (i == 0) ? 0 : image.cols/2;
        int y_start = 0;
        int width = image.cols/2;
        int height = image.rows;
        Mat roi  = mask(Rect(x_start,y_start,width,height)); // Set Roi
        roi.setTo(1);
        weights.push_back(mask);
    }
}

static void blend(const vector<Mat>& inputImages, Mat& outputImage)
{
    int maxPyrIndex = 6;
    vector<Mat> weights;
    compute_weights(inputImages, weights);

    // Find the fused pyramid:
    vector<Mat> fused_pyramid;
    for (int i = 0; i < inputImages.size(); i++) {
        Mat image = inputImages[i];
        // Build Gaussian Pyramid for Weights
        vector<Mat> weight_gaussian_pyramid;
        buildPyramid(weights[i], weight_gaussian_pyramid, maxPyrIndex);

        // Build Laplacian Pyramid for original image
        Mat float_image;
        inputImages[i].convertTo(float_image, CV_32FC3, 1.0/255.0);
        vector<Mat> orig_guassian_pyramid;
        vector<Mat> orig_laplacian_pyramid;
        buildPyramid(float_image, orig_guassian_pyramid, maxPyrIndex);
        for (int j = 0; j < orig_guassian_pyramid.size() - 1; j++) {
            Mat sized_up;
            pyrUp(orig_guassian_pyramid[j+1], sized_up, Size(orig_guassian_pyramid[j].cols, orig_guassian_pyramid[j].rows));
            orig_laplacian_pyramid.push_back(orig_guassian_pyramid[j] - sized_up);
        }
        // Last Lapalcian layer is the same as the Gaussian layer
           orig_laplacian_pyramid.push_back(orig_guassian_pyramid[orig_guassian_pyramid.size()-1]);

        // Convolve laplacian original with guassian weights
        vector<Mat> convolved;
        for (int j = 0; j < maxPyrIndex + 1; j++) {
            // Create 3 channels for weight gaussian pyramid as well
            vector<Mat> gaussian_3d_vec;
            for (int k = 0; k < 3; k++) {
                gaussian_3d_vec.push_back(weight_gaussian_pyramid[j]);
            }
            Mat gaussian_3d;
            merge(gaussian_3d_vec, gaussian_3d);

            //Mat convolved_result = weight_gaussian_pyramid[j].clone();
            Mat convolved_result = gaussian_3d.clone();

            multiply(gaussian_3d, orig_laplacian_pyramid[j], convolved_result);
            convolved.push_back(convolved_result);
        }

        if (i == 0) {
            fused_pyramid = convolved;
        } else {
            for (int j = 0; j < maxPyrIndex + 1; j++) {
                fused_pyramid[j] += convolved[j];
            }
        }
    }
    // Blending
    for (int i = (int)fused_pyramid.size()-1; i > 0; i--) {
        Mat sized_up;
        pyrUp(fused_pyramid[i], sized_up, Size(fused_pyramid[i-1].cols, fused_pyramid[i-1].rows));
        fused_pyramid[i-1] += sized_up;
    }

    Mat final_color_bgr;
    fused_pyramid[0].convertTo(final_color_bgr, CV_32F, 255);
    final_color_bgr.copyTo(outputImage);

    imshow("final", outputImage);
    waitKey(0);
    imwrite(outputImagePath, outputImage);
}

此代码正在为2个图像进行一些基本的金字塔混合。关键问题与最后一行中的imshow和imwrite有关。他们给了我截然不同的结果。我为显示如此冗长/凌乱的代码而道歉，但我担心这种差异来自代码的其他部分，这些部分随后会影响imshow和imwrite。

第一张图片显示了imwrite的结果，第二张图片显示了imshow的结果，基于给出的代码。我很困惑为什么会这样。 imwrite's result imshow's result

当我这样做时，我也注意到了：

Mat float_image;
inputImages[i].convertTo(float_image, CV_32FC3, 1.0/255.0);
imshow("float image", float_image);
imshow("orig image", image);

它们显示完全相同的东西，即它们都在原始rgb图像中显示相同的图像（在图像中）。

Answer 1

IMWRITE功能

默认情况下， imwrite ，将输入图像转换为仅8位（如果是PNG，JPEG 2000和TIFF，则为16位无符号（CV_16U））单通道或使用此功能保存3通道（带'BGR'通道顺序）图像。因此，无论您为imwrite提供什么格式，它都会盲目地转换为CV_8U，范围为0（黑色） - 255（白色），采用BGR格式。

IMSHOW - 问题

因此，当注意到您的函数时，fused_pyramid[0].convertTo(final_color_bgr, CV_32F, 255); fused_pyramid已经在mat类型21（浮点CV_32F）下。您尝试使用比例因子255转换为浮点。此缩放因子255导致问题@ imshow。相反可视化，你可以直接输入fused_pyramid而不进行转换，因为它已被缩放到0.0（黑色） - 1.0（白色）之间的浮点。

希望它有所帮助。

OpenCV-2.4.8.2：imshow与imwrite不同

1 个答案: