社区首页 >问答首页 >使用OpenCV不正常工作的自动裁剪图像

问使用OpenCV不正常工作的自动裁剪图像
EN

Stack Overflow用户

提问于 2022-01-04 22:06:32

回答 1查看 275关注 0票数 -2

输入图像，产量裁剪得当，输入图像，输出没有适当裁剪我们正在使用C#项目中的OpenCV库执行自动裁剪操作，对于很少的示例自动裁剪图像，但是对于其他样本则没有正确裁剪。

让我们知道源代码中缺少的东西。

请找到下面的代码。

        Mat image = new Mat();
        Microsoft.Win32.OpenFileDialog openFileDlg = new Microsoft.Win32.OpenFileDialog();
        Nullable<bool> result = openFileDlg.ShowDialog();
        if (result == true)
        {
            image = Cv2.ImRead(openFileDlg.FileName);
        }
        
        Mat GrayImage = new Mat();
        Cv2.CvtColor(image, GrayImage, ColorConversionCodes.BGR2GRAY);

        Mat BlurImage = new Mat();
        Cv2.GaussianBlur(GrayImage,BlurImage, new OpenCvSharp.Size(5,5),0);

        Mat CannyImage = new Mat();
        OpenCvSharp.Point[][] contours;
        HierarchyIndex[] hierarchy;
        Cv2.Canny(BlurImage, CannyImage, 80, 150,7);

        Cv2.ImShow("Original Image", image);
        Cv2.ImShow("Canny Edged Image", CannyImage);
        Cv2.DestroyAllWindows();
       
        Cv2.FindContours(CannyImage, out contours, out hierarchy,RetrievalModes.External, ContourApproximationModes.ApproxSimple);

        image.SaveImage(@"C:\Users\TestAccount\Documents\AutoCrop_OpenCV\Output03012022\20220103.jpeg");

        var contourIndex = 0;
        var previousArea = 0;
        var biggestContourRect = Cv2.BoundingRect(contours[0]);
        while ((contourIndex >= 0))
        {
            var contour = contours[contourIndex];

            var boundingRect = Cv2.BoundingRect(contour); //Find bounding rect for each contour
            var boundingRectArea = boundingRect.Width * boundingRect.Height;
            if (boundingRectArea > previousArea)
            {
                biggestContourRect = boundingRect;
                previousArea = boundingRectArea;
            }
            
            contourIndex = hierarchy[contourIndex].Next;
        }
        

        var finalImage = new Mat(image, biggestContourRect); //Crop the image
        string outputBaseDirectory = @"C:\Users\TestAccount\Documents\AutoCrop_OpenCV\Output03012022";
        finalImage.SaveImage(string.Format("{0}{1}{2}", outputBaseDirectory, "\\Output", System.IO.Path.GetFileName(openFileDlg.FileName)));
        Cv2.ImShow("Final Image", finalImage);

opencv

image-processing

crop

回答 1

Stack Overflow用户

发布于 2022-01-05 00:03:31

与后一种不正确的作物相比，OpenCV需要更好的对比度。在试图找到轮廓之前，尝试使用阈值函数。我看到的主要问题是不正确的作物是背景，可以部分通过文件，这将阻止正确的边缘检测。

简而言之，中的--对比度需要改进，这可以用阈值来实现。

编辑

/// <summary>
        /// Performs a gradient detection function and returns the altered image
        /// </summary>
        /// <param name="mat">The Mat image to perform the functions on</param>
        /// <param name="id">ID for tests</param>
        /// <returns>The Mat image altered by the function</returns>
        public static Mat GradientDetection(Mat mat, string id = "")
        {
            Log.Information("Performing gradient detection on image");
            Mat clone = mat.Sobel(MatType.CV_32F, 1, 0);
            if (_settingsService.PIDSettings.Detector.SetTestRemoveBorder != 0)
            {
                SaveLoadHelper.SaveImage(clone, $"{id}_Sobel");
            }
            clone = clone.Abs();
            if (_settingsService.PIDSettings.Detector.SetTestRemoveBorder != 0)
            {
                SaveLoadHelper.SaveImage(clone, $"{id}_GradientDetection");
            }
            clone.MinMaxIdx(out double min, out double max);
            clone = 255 * ((clone - min) / (max - min));
            if (_settingsService.PIDSettings.Detector.SetTestRemoveBorder != 0)
            {
                SaveLoadHelper.SaveImage(clone, $"{id}_ScaledBack");
            }
            return clone;
        }

和

/// <summary>
         /// Detects lines in the image then return a mask of detected areas
         /// </summary>
         /// <param name="mat">The Mat image to run the detection on</param>
         /// <param name="rectKernel">Rectangular Kernel</param>
         /// <param name="squareKernel">Square Kernel</param>
         /// <param name="threshold">Theshold for image binarisation</param>
         /// <param name="id">ID for tests</param>
         /// <returns>The Mat image with the detections performed on it</returns>
        public static Mat LineDetection(Mat mat, Mat rectKernel, Mat squareKernel, int threshold = 65, string id = "")
        {
            Log.Information("Performing Line detection on image");

            mat = mat.MorphologyEx(MorphTypes.Close, rectKernel);
            if (_settingsService.PIDSettings.Detector.SetTestRemoveBorder != 0)
            {
                SaveLoadHelper.SaveImage(mat, $"{id}_LineDetectionStage1");
            }
            mat.ConvertTo(mat, MatType.CV_8UC1);
            mat = mat.Threshold(threshold, 255, ThresholdTypes.Binary);
            if (_settingsService.PIDSettings.Detector.SetTestRemoveBorder != 0)
            {
                SaveLoadHelper.SaveImage(mat, $"{id}_LineDetectionStage2");
            }
            mat = mat.MorphologyEx(MorphTypes.Close, squareKernel);
            //mat = mat.Erode(InputArray.KIND_SHIFT, null, 4);
            mat = mat.Erode(InputArray.KIND_SHIFT, null, 2);
            if (_settingsService.PIDSettings.Detector.SetTestRemoveBorder != 0)
            {
                SaveLoadHelper.SaveImage(mat, $"{id}_LineDetectionStage3");
            }
            return mat;
        }