使用处理进行 Canny 边缘检测

Question

我正在寻找处理语言中 Canny 边缘检测的复制粘贴实现。尽管我对java非常了解，但我对图像处理的了解为零，对处理的了解也很少。

一些处理专家可以告诉我是否有办法实现这个 http://www.tomgibara .com/computer-vision/CannyEdgeDetector.java 正在处理中？

Answer 1

我认为如果你从 Java 的角度来对待处理，那么一些问题就可以很容易地解决。这意味着您可以像这样使用 Java 类。

对于演示，我使用您共享的实现。

>>原始图像

>>更改的图像

>>代码

import java.awt.image.BufferedImage;
import java.util.Arrays;

PImage orig;
PImage changed;

void setup() {
  orig = loadImage("c:/temp/image.png");
  size(250, 166);

  CannyEdgeDetector detector = new CannyEdgeDetector();

  detector.setLowThreshold(0.5f);
  detector.setHighThreshold(1f);

   detector.setSourceImage((java.awt.image.BufferedImage)orig.getImage());
   detector.process();
   BufferedImage edges = detector.getEdgesImage();
   changed = new PImage(edges);
  noLoop();
}

void draw() 
{
  //image(orig, 0,0, width, height);

  image(changed, 0,0, width, height);
}

// The code below is taken from "http://www.tomgibara.com/computer-vision/CannyEdgeDetector.java" 
// I have stripped the comments for conciseness

public class CannyEdgeDetector {

    // statics

    private final static float GAUSSIAN_CUT_OFF = 0.005f;
    private final static float MAGNITUDE_SCALE = 100F;
    private final static float MAGNITUDE_LIMIT = 1000F;
    private final static int MAGNITUDE_MAX = (int) (MAGNITUDE_SCALE * MAGNITUDE_LIMIT);

    // fields

    private int height;
    private int width;
    private int picsize;
    private int[] data;
    private int[] magnitude;
    private BufferedImage sourceImage;
    private BufferedImage edgesImage;

    private float gaussianKernelRadius;
    private float lowThreshold;
    private float highThreshold;
    private int gaussianKernelWidth;
    private boolean contrastNormalized;

    private float[] xConv;
    private float[] yConv;
    private float[] xGradient;
    private float[] yGradient;

    // constructors

    /**
     * Constructs a new detector with default parameters.
     */

    public CannyEdgeDetector() {
        lowThreshold = 2.5f;
        highThreshold = 7.5f;
        gaussianKernelRadius = 2f;
        gaussianKernelWidth = 16;
        contrastNormalized = false;
    }



    public BufferedImage getSourceImage() {
        return sourceImage;
    }


    public void setSourceImage(BufferedImage image) {
        sourceImage = image;
    }


    public BufferedImage getEdgesImage() {
        return edgesImage;
    }


    public void setEdgesImage(BufferedImage edgesImage) {
        this.edgesImage = edgesImage;
    }


    public float getLowThreshold() {
        return lowThreshold;
    }


    public void setLowThreshold(float threshold) {
        if (threshold < 0) throw new IllegalArgumentException();
        lowThreshold = threshold;
    }

    public float getHighThreshold() {
        return highThreshold;
    }


    public void setHighThreshold(float threshold) {
        if (threshold < 0) throw new IllegalArgumentException();
        highThreshold = threshold;
    }

    public int getGaussianKernelWidth() {
        return gaussianKernelWidth;
    }

    public void setGaussianKernelWidth(int gaussianKernelWidth) {
        if (gaussianKernelWidth < 2) throw new IllegalArgumentException();
        this.gaussianKernelWidth = gaussianKernelWidth;
    }

    public float getGaussianKernelRadius() {
        return gaussianKernelRadius;
    }

    public void setGaussianKernelRadius(float gaussianKernelRadius) {
        if (gaussianKernelRadius < 0.1f) throw new IllegalArgumentException();
        this.gaussianKernelRadius = gaussianKernelRadius;
    }

    public boolean isContrastNormalized() {
        return contrastNormalized;
    }

    public void setContrastNormalized(boolean contrastNormalized) {
        this.contrastNormalized = contrastNormalized;
    }

    // methods

    public void process() {
        width = sourceImage.getWidth();
        height = sourceImage.getHeight();
        picsize = width * height;
        initArrays();
        readLuminance();
        if (contrastNormalized) normalizeContrast();
        computeGradients(gaussianKernelRadius, gaussianKernelWidth);
        int low = Math.round(lowThreshold * MAGNITUDE_SCALE);
        int high = Math.round( highThreshold * MAGNITUDE_SCALE);
        performHysteresis(low, high);
        thresholdEdges();
        writeEdges(data);
    }

    // private utility methods

    private void initArrays() {
        if (data == null || picsize != data.length) {
            data = new int[picsize];
            magnitude = new int[picsize];

            xConv = new float[picsize];
            yConv = new float[picsize];
            xGradient = new float[picsize];
            yGradient = new float[picsize];
        }
    }
    private void computeGradients(float kernelRadius, int kernelWidth) {

        //generate the gaussian convolution masks
        float kernel[] = new float[kernelWidth];
        float diffKernel[] = new float[kernelWidth];
        int kwidth;
        for (kwidth = 0; kwidth < kernelWidth; kwidth++) {
            float g1 = gaussian(kwidth, kernelRadius);
            if (g1 <= GAUSSIAN_CUT_OFF && kwidth >= 2) break;
            float g2 = gaussian(kwidth - 0.5f, kernelRadius);
            float g3 = gaussian(kwidth + 0.5f, kernelRadius);
            kernel[kwidth] = (g1 + g2 + g3) / 3f / (2f * (float) Math.PI * kernelRadius * kernelRadius);
            diffKernel[kwidth] = g3 - g2;
        }

        int initX = kwidth - 1;
        int maxX = width - (kwidth - 1);
        int initY = width * (kwidth - 1);
        int maxY = width * (height - (kwidth - 1));

        //perform convolution in x and y directions
        for (int x = initX; x < maxX; x++) {
            for (int y = initY; y < maxY; y += width) {
                int index = x + y;
                float sumX = data[index] * kernel[0];
                float sumY = sumX;
                int xOffset = 1;
                int yOffset = width;
                for(; xOffset < kwidth ;) {
                    sumY += kernel[xOffset] * (data[index - yOffset] + data[index + yOffset]);
                    sumX += kernel[xOffset] * (data[index - xOffset] + data[index + xOffset]);
                    yOffset += width;
                    xOffset++;
                }

                yConv[index] = sumY;
                xConv[index] = sumX;
            }

        }

        for (int x = initX; x < maxX; x++) {
            for (int y = initY; y < maxY; y += width) {
                float sum = 0f;
                int index = x + y;
                for (int i = 1; i < kwidth; i++)
                    sum += diffKernel[i] * (yConv[index - i] - yConv[index + i]);

                xGradient[index] = sum;
            }

        }

        for (int x = kwidth; x < width - kwidth; x++) {
            for (int y = initY; y < maxY; y += width) {
                float sum = 0.0f;
                int index = x + y;
                int yOffset = width;
                for (int i = 1; i < kwidth; i++) {
                    sum += diffKernel[i] * (xConv[index - yOffset] - xConv[index + yOffset]);
                    yOffset += width;
                }

                yGradient[index] = sum;
            }

        }

        initX = kwidth;
        maxX = width - kwidth;
        initY = width * kwidth;
        maxY = width * (height - kwidth);
        for (int x = initX; x < maxX; x++) {
            for (int y = initY; y < maxY; y += width) {
                int index = x + y;
                int indexN = index - width;
                int indexS = index + width;
                int indexW = index - 1;
                int indexE = index + 1;
                int indexNW = indexN - 1;
                int indexNE = indexN + 1;
                int indexSW = indexS - 1;
                int indexSE = indexS + 1;

                float xGrad = xGradient[index];
                float yGrad = yGradient[index];
                float gradMag = hypot(xGrad, yGrad);

                //perform non-maximal supression
                float nMag = hypot(xGradient[indexN], yGradient[indexN]);
                float sMag = hypot(xGradient[indexS], yGradient[indexS]);
                float wMag = hypot(xGradient[indexW], yGradient[indexW]);
                float eMag = hypot(xGradient[indexE], yGradient[indexE]);
                float neMag = hypot(xGradient[indexNE], yGradient[indexNE]);
                float seMag = hypot(xGradient[indexSE], yGradient[indexSE]);
                float swMag = hypot(xGradient[indexSW], yGradient[indexSW]);
                float nwMag = hypot(xGradient[indexNW], yGradient[indexNW]);
                float tmp;

                if (xGrad * yGrad <= (float) 0 /*(1)*/
                    ? Math.abs(xGrad) >= Math.abs(yGrad) /*(2)*/
                        ? (tmp = Math.abs(xGrad * gradMag)) >= Math.abs(yGrad * neMag - (xGrad + yGrad) * eMag) /*(3)*/
                            && tmp > Math.abs(yGrad * swMag - (xGrad + yGrad) * wMag) /*(4)*/
                        : (tmp = Math.abs(yGrad * gradMag)) >= Math.abs(xGrad * neMag - (yGrad + xGrad) * nMag) /*(3)*/
                            && tmp > Math.abs(xGrad * swMag - (yGrad + xGrad) * sMag) /*(4)*/
                    : Math.abs(xGrad) >= Math.abs(yGrad) /*(2)*/
                        ? (tmp = Math.abs(xGrad * gradMag)) >= Math.abs(yGrad * seMag + (xGrad - yGrad) * eMag) /*(3)*/
                            && tmp > Math.abs(yGrad * nwMag + (xGrad - yGrad) * wMag) /*(4)*/
                        : (tmp = Math.abs(yGrad * gradMag)) >= Math.abs(xGrad * seMag + (yGrad - xGrad) * sMag) /*(3)*/
                            && tmp > Math.abs(xGrad * nwMag + (yGrad - xGrad) * nMag) /*(4)*/
                    ) {
                    magnitude[index] = gradMag >= MAGNITUDE_LIMIT ? MAGNITUDE_MAX : (int) (MAGNITUDE_SCALE * gradMag);
                    //NOTE: The orientation of the edge is not employed by this
                    //implementation. It is a simple matter to compute it at
                    //this point as: Math.atan2(yGrad, xGrad);
                } else {
                    magnitude[index] = 0;
                }
            }
        }
    }

    private float hypot(float x, float y) {
        return (float) Math.hypot(x, y);
    }

    private float gaussian(float x, float sigma) {
        return (float) Math.exp(-(x * x) / (2f * sigma * sigma));
    }

    private void performHysteresis(int low, int high) {

        Arrays.fill(data, 0);

        int offset = 0;
        for (int y = 0; y < height; y++) {
            for (int x = 0; x < width; x++) {
                if (data[offset] == 0 && magnitude[offset] >= high) {
                    follow(x, y, offset, low);
                }
                offset++;
            }
        }
    }

    private void follow(int x1, int y1, int i1, int threshold) {
        int x0 = x1 == 0 ? x1 : x1 - 1;
        int x2 = x1 == width - 1 ? x1 : x1 + 1;
        int y0 = y1 == 0 ? y1 : y1 - 1;
        int y2 = y1 == height -1 ? y1 : y1 + 1;

        data[i1] = magnitude[i1];
        for (int x = x0; x <= x2; x++) {
            for (int y = y0; y <= y2; y++) {
                int i2 = x + y * width;
                if ((y != y1 || x != x1)
                    && data[i2] == 0 
                    && magnitude[i2] >= threshold) {
                    follow(x, y, i2, threshold);
                    return;
                }
            }
        }
    }

    private void thresholdEdges() {
        for (int i = 0; i < picsize; i++) {
            data[i] = data[i] > 0 ? -1 : 0xff000000;
        }
    }

    private int luminance(float r, float g, float b) {
        return Math.round(0.299f * r + 0.587f * g + 0.114f * b);
    }

    private void readLuminance() {
        int type = sourceImage.getType();
        if (type == BufferedImage.TYPE_INT_RGB || type == BufferedImage.TYPE_INT_ARGB) {
            int[] pixels = (int[]) sourceImage.getData().getDataElements(0, 0, width, height, null);
            for (int i = 0; i < picsize; i++) {
                int p = pixels[i];
                int r = (p & 0xff0000) >> 16;
                int g = (p & 0xff00) >> 8;
                int b = p & 0xff;
                data[i] = luminance(r, g, b);
            }
        } else if (type == BufferedImage.TYPE_BYTE_GRAY) {
            byte[] pixels = (byte[]) sourceImage.getData().getDataElements(0, 0, width, height, null);
            for (int i = 0; i < picsize; i++) {
                data[i] = (pixels[i] & 0xff);
            }
        } else if (type == BufferedImage.TYPE_USHORT_GRAY) {
            short[] pixels = (short[]) sourceImage.getData().getDataElements(0, 0, width, height, null);
            for (int i = 0; i < picsize; i++) {
                data[i] = (pixels[i] & 0xffff) / 256;
            }
        } else if (type == BufferedImage.TYPE_3BYTE_BGR) {
            byte[] pixels = (byte[]) sourceImage.getData().getDataElements(0, 0, width, height, null);
            int offset = 0;
            for (int i = 0; i < picsize; i++) {
                int b = pixels[offset++] & 0xff;
                int g = pixels[offset++] & 0xff;
                int r = pixels[offset++] & 0xff;
                data[i] = luminance(r, g, b);
            }
        } else {
            throw new IllegalArgumentException("Unsupported image type: " + type);
        }
    }

    private void normalizeContrast() {
        int[] histogram = new int[256];
        for (int i = 0; i < data.length; i++) {
            histogram[data[i]]++;
        }
        int[] remap = new int[256];
        int sum = 0;
        int j = 0;
        for (int i = 0; i < histogram.length; i++) {
            sum += histogram[i];
            int target = sum*255/picsize;
            for (int k = j+1; k <=target; k++) {
                remap[k] = i;
            }
            j = target;
        }

        for (int i = 0; i < data.length; i++) {
            data[i] = remap[data[i]];
        }
    }

    private void writeEdges(int pixels[]) {
        if (edgesImage == null) {
            edgesImage = new BufferedImage(width, height, BufferedImage.TYPE_INT_ARGB);
        }
        edgesImage.getWritableTile(0, 0).setDataElements(0, 0, width, height, pixels);
    }

}

Answer 2

我花了一些时间研究 Gibara Canny 的实现，并且我倾向于同意 Settembrini 上面的评论；除此之外，还需要改变高斯核生成的实现。

Gibara Canny 使用：

(g1 + g2 + g3) / 3f / (2f * (float) Math.PI * kernelRadius * kernelRadius)

中像素（+-0.5 像素）的平均值>(g1 + g2 + g3) / 3f 很好，但是单维方程下半部分的正确方差计算是：

(g1 + g2 + g3) / 3f / (Math. sqrt(2f * (float) Math.PI) * kernelRadius)

标准差 kernelRadius 是以下等式中的 sigma：
单向高斯

我假设 Gibara 正在尝试根据以下方程实现二维高斯：二维高斯，其中卷积是每个高斯的直接乘积。虽然这可能是可能的并且更简洁，但以下代码将通过上述方差计算正确地在两个方向上进行卷积：

  // First Convolution
  for (int x = initX; x < maxX; x++) {
    for (int y = initY; y < maxY; y += sourceImage.width) {
      int index = x + y;
      float sumX = data[index] * kernel[0];
      int xOffset = 1;
      int yOffset = sourceImage.width;
      for(; xOffset < k ;) {;
        sumX += kernel[xOffset] * (data[index - xOffset] + data[index + xOffset]);
        yOffset += sourceImage.width;
        xOffset++;
      }
      xConv[index] = sumX;
    }
  }
  // Second Convolution
  for (int x = initX; x < maxX; x++) {
    for (int y = initY; y < maxY; y += sourceImage.width) {
      int index = x + y;
      float sumY = xConv[index] * kernel[0];
      int xOffset = 1;
      int yOffset = sourceImage.width;
      for(; xOffset < k ;) {;
        sumY += xConv[xOffset] * (xConv[index - xOffset] + xConv[index + xOffset]);
        yOffset += sourceImage.width;
        xOffset++;
      }
      yConv[index] = sumY;
    }
  }

注意 yConv[] 现在是双向卷积，因此以下梯度 Sobel 计算如下如下所示：

  for (int x = initX; x < maxX; x++) {
    for (int y = initY; y < maxY; y += sourceImage.width) {
      float sum = 0f;
      int index = x + y;
      for (int i = 1; i < k; i++)
      sum += diffKernel[i] * (yConv[index - i] - yConv[index + i]);

      xGradient[index] = sum;
    }
  }

  for (int x = k; x < sourceImage.width - k; x++) {
    for (int y = initY; y < maxY; y += sourceImage.width) {
      float sum = 0.0f;
      int index = x + y;
      int yOffset = sourceImage.width;
      for (int i = 1; i < k; i++) {
        sum += diffKernel[i] * (yConv[index - yOffset] - yConv[index + yOffset]);
        yOffset += sourceImage.width;
      }

      yGradient[index] = sum;
    }
  }

Gibara 非常简洁的非极大值抑制实现要求单独计算这些梯度，但是如果您想输出具有这些梯度的图像，可以使用欧几里德距离或曼哈顿距离对它们进行求和，欧几里德距离将如下所示

// Calculate the Euclidean distance between x & y gradients prior to suppression
 int [] gradients = new int [picsize];
 for (int i = 0; i < xGradient.length; i++) {
    gradients[i] = Math.sqrt(Math.sq(xGradient[i]) + Math.sq(yGradient[i]));
 }

：有帮助，一切正常，并对我的代码表示歉意！欢迎批评指正

Answer 3

除了 Favonius 的答案之外，您可能还想尝试 Greg 的 OpenCV 处理库，您现在可以轻松地使用它通过 Sketch > 安装导入库...>>添加库...并选择OpenCV进行处理

安装库后，您可以使用FindEdges 示例：

import gab.opencv.*;

OpenCV opencv;
PImage src, canny, scharr, sobel;

void setup() {
  src = loadImage("test.jpg");
  size(src.width, src.height);

  opencv = new OpenCV(this, src);
  opencv.findCannyEdges(20,75);
  canny = opencv.getSnapshot();

  opencv.loadImage(src);
  opencv.findScharrEdges(OpenCV.HORIZONTAL);
  scharr = opencv.getSnapshot();

  opencv.loadImage(src);
  opencv.findSobelEdges(1,0);
  sobel = opencv.getSnapshot();
}


void draw() {
  pushMatrix();
  scale(0.5);
  image(src, 0, 0);
  image(canny, src.width, 0);
  image(scharr, 0, src.height);
  image(sobel, src.width, src.height);
  popMatrix();

  text("Source", 10, 25); 
  text("Canny", src.width/2 + 10, 25); 
  text("Scharr", 10, src.height/2 + 25); 
  text("Sobel", src.width/2 + 10, src.height/2 + 25);
}

Answer 4

正如我附注的那样。我前段时间研究了 Gibara Canny 的实现，发现了一些缺陷。例如，他在 x 和 y 方向上分离 1d 滤波器中的高斯滤波（这样就可以并且有效），但随后他不会应用这些滤波器的两遍（一个接一个），而只是将 SobelX 应用于 x-首轮高斯和 SobelY 到 y 首轮高斯，这当然会导致低质量梯度。因此，复制粘贴此类代码时要小心。