如何使用分水岭算法改善图像分割？

Question

如何使用分水岭算法改善图像分割？

12

我正在开发一个检测病变区域的应用程序，为此我使用grabcut来检测ROI并从图像中删除背景。然而在某些图像中它无法很好地工作。它最终无法很好地识别感兴趣区域的边界。对于这种类型的工作，watershed可以更好地识别边缘，但是我在从grabcut到watershed的过渡中遇到了困难。在处理grabcut之前，用户使用touchevent在感兴趣的图像周围标记一个矩形（伤口区域），以便算法更容易地进行工作。如下图所示。

然而，在使用其他伤口图像时，分割效果不佳，ROI检测存在缺陷。

应用程序中使用grabcut的图像

桌面上使用watershed的图像

这是代码：

private fun extractForegroundFromBackground(coordinates: Coordinates, currentPhotoPath: String): String {
    // TODO: Provide complex object that has both path and extension

    val width = bitmap?.getWidth()!!
    val height = bitmap?.getHeight()!!
    val rgba = Mat()
    val gray_mat = Mat()
    val threeChannel = Mat()
    Utils.bitmapToMat(bitmap, gray_mat)
    cvtColor(gray_mat, rgba, COLOR_RGBA2RGB)
    cvtColor(rgba, threeChannel, COLOR_RGB2GRAY)
    threshold(threeChannel, threeChannel, 100.0, 255.0, THRESH_OTSU)

    val rect = Rect(coordinates.first, coordinates.second)
    val fg = Mat(rect.size(), CvType.CV_8U)
    erode(threeChannel, fg, Mat(), Point(-1.0, -1.0), 10)
    val bg = Mat(rect.size(), CvType.CV_8U)
    dilate(threeChannel, bg, Mat(), Point(-1.0, -1.0), 5)
    threshold(bg, bg, 1.0, 128.0, THRESH_BINARY_INV)
    val markers = Mat(rgba.size(), CvType.CV_8U, Scalar(0.0))
    Core.add(fg, bg, markers)

    val marker_tempo = Mat()
    markers.convertTo(marker_tempo, CvType.CV_32S)

    watershed(rgba, marker_tempo)
    marker_tempo.convertTo(markers, CvType.CV_8U)

    val imgBmpExit = Bitmap.createBitmap(width, height, Bitmap.Config.RGB_565)
    Utils.matToBitmap(markers, imgBmpExit)

    image.setImageBitmap(imgBmpExit)


    // Run the grab cut algorithm with a rectangle (for subsequent iterations with touch-up strokes,
    // flag should be Imgproc.GC_INIT_WITH_MASK)
    //Imgproc.grabCut(srcImage, firstMask, rect, bg, fg, iterations, Imgproc.GC_INIT_WITH_RECT)

    // Create a matrix of 0s and 1s, indicating whether individual pixels are equal
    // or different between "firstMask" and "source" objects
    // Result is stored back to "firstMask"
    //Core.compare(mark, source, mark, Core.CMP_EQ)

    // Create a matrix to represent the foreground, filled with white color
    val foreground = Mat(srcImage.size(), CvType.CV_8UC3, Scalar(255.0, 255.0, 255.0))

    // Copy the foreground matrix to the first mask
    srcImage.copyTo(foreground, mark)

    // Create a red color
    val color = Scalar(255.0, 0.0, 0.0, 255.0)
    // Draw a rectangle using the coordinates of the bounding box that surrounds the foreground
    rectangle(srcImage, coordinates.first, coordinates.second, color)

    // Create a new matrix to represent the background, filled with black color
    val background = Mat(srcImage.size(), CvType.CV_8UC3, Scalar(0.0, 0.0, 0.0))

    val mask = Mat(foreground.size(), CvType.CV_8UC1, Scalar(255.0, 255.0, 255.0))
    // Convert the foreground's color space from BGR to gray scale
    cvtColor(foreground, mask, Imgproc.COLOR_BGR2GRAY)

    // Separate out regions of the mask by comparing the pixel intensity with respect to a threshold value
    threshold(mask, mask, 254.0, 255.0, Imgproc.THRESH_BINARY_INV)

    // Create a matrix to hold the final image
    val dst = Mat()
    // copy the background matrix onto the matrix that represents the final result
    background.copyTo(dst)

    val vals = Mat(1, 1, CvType.CV_8UC3, Scalar(0.0))
    // Replace all 0 values in the background matrix given the foreground mask
    background.setTo(vals, mask)

    // Add the sum of the background and foreground matrices by applying the mask
    Core.add(background, foreground, dst, mask)

    // Save the final image to storage
    Imgcodecs.imwrite(currentPhotoPath + "_tmp.png", dst)

    // Clean up used resources
    firstMask.release()
    source.release()
    //bg.release()
    //fg.release()
    vals.release()
    dst.release()

    return currentPhotoPath
}

退出：

如何更新代码以使用Watershed算法替代GrabCut？

- Carlos Diego

阅读这样长的帖子真是让人很烦恼。您可以通过使用Markdown标签调整图像大小来改善格式，例如： <img src="https://i.stack.imgur.com/nmzwj.png" width="210" height="150"> - karlphillip

1

@karlphillip 对不起，我不知道。我会纠正的。 - Carlos Diego

1个回答

网页内容由stack overflow 提供, 点击上面的

可以查看英文原文，
原文链接

- danielcahall · Accepted Answer

如何在OpenCV中使用分水岭算法的说明可以在这里找到（链接），不过该示例是用Python编写的。此外，文档中也包含一些可能有用的示例。由于您已经有了二值图像，所以只需要应用欧几里得距离变换（EDT）和分水岭函数即可。因此，您需要将以下代码：Imgproc.grabCut(srcImage, firstMask, rect, bg, fg, iterations, Imgproc.GC_INIT_WITH_RECT)替换为：

Mat dist = new Mat();
Imgproc.distanceTransform(srcImage, dist, Imgproc.DIST_L2, Imgproc.DIST_MASK_3); // use L2 for Euclidean Distance 
Mat markers = Mat.zeros(dist.size(), CvType.CV_32S);
Imgproc.watershed(dist, markers); # apply watershed to resultant image from EDT
Mat mark = Mat.zeros(markers.size(), CvType.CV_8U);
markers.convertTo(mark, CvType.CV_8UC1);
Imgproc.threshold(mark, firstMask, 0, 255, Imgproc.THRESH_BINARY + Imgproc.THRESH_OTSU); # threshold results to get binary image

阈值处理步骤在这里被描述。另外，在应用Imgproc.watershed之前，您可以选择对EDT的结果应用一些形态学操作，例如膨胀、腐蚀：

Imgproc.dilate(dist, dist, Mat.ones(3, 3, CvType.CV_8U));

如果您对处理二进制图像时的形态学操作不熟悉，可以参考OpenCV文档中提供的一些好的例子。希望这能够帮助到您！