python - 使用 Python 在 OpenCV 中检测线条和形状

Question

我一直在玩弄 OpenCV (cv2) 并检测线条和形状。说我女儿画了一幅画，像这样:

我正在尝试编写一个 Python 脚本来分析绘图并将其转换为硬线/形状，例如:

话虽这么说，我已经安装了 opencv 并尝试使用它，但除了能够在图像中绘制一条垂直线之外没有运气。到目前为止，下面是我的代码，对于我应该如何使用 opencv 执行此操作的任何指示或建议将不胜感激。

import cv2
import numpy as np

class File(object):
    def __init__(self, filename):
        self.filename = filename

    def open(self, filename=None, mode='r'):
        if filename is None:
            filename = self.filename

        return cv2.imread(filename), open(filename, mode)

    def save(self, image=None, filename_override=None):
        filename = "output/" + self.filename.split('/')[-1]

        if filename_override:
            filename = "output/" + filename_override

        return cv2.imwrite(filename, image)

class Image(object):
    def __init__(self, image):
        self.image = image

    def grayscale(self):
        return cv2.cvtColor(self.image, cv2.COLOR_BGR2GRAY)

    def edges(self):
        return cv2.Canny(self.image, 0, 255)

    def lines(self):
        lines = cv2.HoughLinesP(self.image, 1, np.pi/2, 6, None, 50, 10)
        for line in lines[0]:
            pt1 = (line[0],line[1])
            pt2 = (line[2],line[3])
            cv2.line(self.image, pt1, pt2, (0,0,255), 2)

if __name__ == '__main__':
    File = File('images/a.png')
    Image = Image(File.open()[0])
    Image.image = Image.grayscale()
    Image.lines()
    File.save(Image.image)

不幸的是，对于一个简单的方形绘图，我得到的只是:

方框中的垂直线是代码的输出。

Answer 1

这是我的尝试。它在 C++ 中，但可以很容易地移植到 python，因为大多数是 OpenCV 函数。

方法的简要概述，代码中的注释也应该有所帮助。

1. 载入图片
2. 转换为灰度
3. 二值化图像(阈值)
4. 细化，使轮廓变细并帮助findContours
5. 获取轮廓

6. 对于每个轮廓，获取凸包(以处理开放轮廓)，并根据圆度 进行分类。以不同方式处理每个形状。

   • Circle : 求最小包围圆，或最佳拟合椭圆
   • Recrangle :找到边界框，或最小定向边界框。
   • Triangle :搜索最小外接圆与原始形状的交点，因为它们会相交于三角形的三个顶点。

注意事项:

• 我需要将原始图像从具有透明度的 png 修改为 3 channel RGB。
• 细化代码来自here .还有 Python 版本。
• 圆度 定义为:A 衡量形状接近圆形的程度。例如。正六边形比正方形具有更高的圆度。定义为 (\frac{4*\pi*Area}{perimeter * perimeter})。这意味着圆形的圆度为 1，正方形的圆度为 0.785，依此类推。
• 由于轮廓的原因，每个形状可能有多个检测。这些可以根据例如交集条件过滤掉。我暂时没有在代码中插入这部分，因为它需要与查找形状的主要任务不严格相关的额外逻辑。

更新 - 刚刚注意到在 OpenCV 3.0.0 中有函数 minEnclosingTriangle .这可能有助于代替我的程序来查找三角形顶点。然而，由于在代码中插入这个函数是微不足道的，所以我将把我的过程留在代码中，以防没有 OpenCV 3.0.0。

代码:

#include <opencv2\opencv.hpp>
#include <vector>
#include <iostream>

using namespace std;
using namespace cv;

/////////////////////////////////////////////////////////////////////////////////////////////
// Thinning algorithm from here:
// https://github.com/bsdnoobz/zhang-suen-thinning
/////////////////////////////////////////////////////////////////////////////////////////////

void thinningIteration(cv::Mat& img, int iter)
{
    CV_Assert(img.channels() == 1);
    CV_Assert(img.depth() != sizeof(uchar));
    CV_Assert(img.rows > 3 && img.cols > 3);

    cv::Mat marker = cv::Mat::zeros(img.size(), CV_8UC1);

    int nRows = img.rows;
    int nCols = img.cols;

    if (img.isContinuous()) {
        nCols *= nRows;
        nRows = 1;
    }

    int x, y;
    uchar *pAbove;
    uchar *pCurr;
    uchar *pBelow;
    uchar *nw, *no, *ne;    // north (pAbove)
    uchar *we, *me, *ea;
    uchar *sw, *so, *se;    // south (pBelow)

    uchar *pDst;

    // initialize row pointers
    pAbove = NULL;
    pCurr = img.ptr<uchar>(0);
    pBelow = img.ptr<uchar>(1);

    for (y = 1; y < img.rows - 1; ++y) {
        // shift the rows up by one
        pAbove = pCurr;
        pCurr = pBelow;
        pBelow = img.ptr<uchar>(y + 1);

        pDst = marker.ptr<uchar>(y);

        // initialize col pointers
        no = &(pAbove[0]);
        ne = &(pAbove[1]);
        me = &(pCurr[0]);
        ea = &(pCurr[1]);
        so = &(pBelow[0]);
        se = &(pBelow[1]);

        for (x = 1; x < img.cols - 1; ++x) {
            // shift col pointers left by one (scan left to right)
            nw = no;
            no = ne;
            ne = &(pAbove[x + 1]);
            we = me;
            me = ea;
            ea = &(pCurr[x + 1]);
            sw = so;
            so = se;
            se = &(pBelow[x + 1]);

            int A = (*no == 0 && *ne == 1) + (*ne == 0 && *ea == 1) +
                (*ea == 0 && *se == 1) + (*se == 0 && *so == 1) +
                (*so == 0 && *sw == 1) + (*sw == 0 && *we == 1) +
                (*we == 0 && *nw == 1) + (*nw == 0 && *no == 1);
            int B = *no + *ne + *ea + *se + *so + *sw + *we + *nw;
            int m1 = iter == 0 ? (*no * *ea * *so) : (*no * *ea * *we);
            int m2 = iter == 0 ? (*ea * *so * *we) : (*no * *so * *we);

            if (A == 1 && (B >= 2 && B <= 6) && m1 == 0 && m2 == 0)
                pDst[x] = 1;
        }
    }

    img &= ~marker;
}

void thinning(const cv::Mat& src, cv::Mat& dst)
{
    dst = src.clone();
    dst /= 255;         // convert to binary image

    cv::Mat prev = cv::Mat::zeros(dst.size(), CV_8UC1);
    cv::Mat diff;

    do {
        thinningIteration(dst, 0);
        thinningIteration(dst, 1);
        cv::absdiff(dst, prev, diff);
        dst.copyTo(prev);
    } while (cv::countNonZero(diff) > 0);

    dst *= 255;
}


int main()
{
    RNG rng(123);

    // Read image
    Mat3b src = imread("path_to_image");

    // Convert to grayscale
    Mat1b gray;
    cvtColor(src, gray, COLOR_BGR2GRAY);

    // Binarize
    Mat1b bin;
    threshold(gray, bin, 127, 255, THRESH_BINARY_INV);

    // Perform thinning
    thinning(bin, bin);

    // Create result image
    Mat3b res = src.clone();

    // Find contours
    vector<vector<Point>> contours;
    findContours(bin.clone(), contours, CV_RETR_LIST, CV_CHAIN_APPROX_NONE);

    // For each contour
    for (vector<Point>& contour : contours)
    {
        // Compute convex hull
        vector<Point> hull;
        convexHull(contour, hull);

        // Compute circularity, used for shape classification
        double area = contourArea(hull);
        double perimeter = arcLength(hull, true);
        double circularity = (4 * CV_PI * area) / (perimeter * perimeter);

        // Shape classification

        if (circularity > 0.9)
        {
            // CIRCLE

            //{
            //  // Fit an ellipse ...
            //  RotatedRect rect = fitEllipse(contour);
            //  Scalar color = Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));
            //  ellipse(res, rect, color, 5);
            //}
            {
                // ... or find min enclosing circle
                Point2f center;
                float radius;
                minEnclosingCircle(contour, center, radius);
                Scalar color = Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));
                circle(res, center, radius, color, 5);
            }
        }
        else if (circularity > 0.75)
        {
            // RECTANGLE

            //{
            //  // Minimum oriented bounding box ...
            //  RotatedRect rect = minAreaRect(contour);
            //  Point2f pts[4];
            //  rect.points(pts);

            //  Scalar color = Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));
            //  for (int i = 0; i < 4; ++i)
            //  {
            //      line(res, pts[i], pts[(i + 1) % 4], color, 5);
            //  }
            //}
            {
                // ... or bounding box
                Rect box = boundingRect(contour);
                Scalar color = Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));
                rectangle(res, box, color, 5);
            }
        }
        else if (circularity > 0.7)
        {
            // TRIANGLE

            // Select the portion of the image containing only the wanted contour
            Rect roi = boundingRect(contour);
            Mat1b maskRoi(bin.rows, bin.cols, uchar(0));
            rectangle(maskRoi, roi, Scalar(255), CV_FILLED);
            Mat1b triangle(roi.height, roi.height, uchar(0));
            bin.copyTo(triangle, maskRoi);

            // Find min encolsing circle on the contour
            Point2f center;
            float radius;
            minEnclosingCircle(contour, center, radius);

            // decrease the size of the enclosing circle until it intersects the contour
            // in at least 3 different points (i.e. the 3 vertices)
            vector<vector<Point>> vertices;
            do
            {
                vertices.clear();
                radius--;

                Mat1b maskCirc(bin.rows, bin.cols, uchar(0));
                circle(maskCirc, center, radius, Scalar(255), 5);

                maskCirc &= triangle;
                findContours(maskCirc.clone(), vertices, CV_RETR_LIST, CV_CHAIN_APPROX_NONE);

            } while (vertices.size() < 3);

            // Just get the first point in each vertex blob.
            // You could get the centroid for a little better accuracy

            Scalar color = Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));
            line(res, vertices[0][0], vertices[1][0], color, 5);
            line(res, vertices[1][0], vertices[2][0], color, 5);
            line(res, vertices[2][0], vertices[0][0], color, 5);

        }
        else
        {
            cout << "Some other shape..." << endl;
        }

    }

    return 0;
}

结果(minEnclosingCircle 和 boundingRect):

结果(fitEllipse 和 minAreaRect):