OpenCV形状检测

# 导入必要的包
import cv2


class ShapeDetector:
    def __init__(self):
        pass

    def detect(self, c):
        # 初始化形状名称并近似轮廓
        shape = "unidentified"
        peri = cv2.arcLength(c, True)
        approx = cv2.approxPolyDP(c, 0.04 * peri, True)

        # 如果形状是一个三角形，它将有3个顶点
        if len(approx) == 3:
            shape = "triangle"
        # 如果形状有4个顶点，它要么是正方形，要么是矩形
        elif len(approx) == 4:
            # 计算轮廓的包围框，并使用包围框计算高宽比
            (x, y, w, h) = cv2.boundingRect(approx)
            ar = w / float(h)
            # 正方形的长宽比大约等于1，否则，形状就是矩形
            shape = "square" if ar >= 0.95 and ar <= 1.05 else "rectangle"
        # 如果形状是一个五边形，它将有5个顶点
        elif len(approx) == 5:
            shape = "pentagon"
        # 否则，我们假设形状是一个圆
        else:
            shape = "circle"
        # 返回形状的名称
        return shape

# 导入必要的库
from utils.shapedetector import ShapeDetector
import argparse
import imutils
import cv2

# 构造参数解析并解析参数
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", default="inpaint.jpg", help="path to the input image")
args = vars(ap.parse_args())

# 加载图像并将其调整图像大小，以便更好地近似形状
image = cv2.imread(args["image"])
resized = imutils.resize(image, width=300)
ratio = image.shape[0] / float(resized.shape[0])
# 将调整后的图像转换为灰度，稍微模糊它，并阈值化
gray = cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
thresh = cv2.threshold(blurred, 60, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
# 在阈值化图像中找到轮廓并初始化形状检测器
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
                        cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
sd = ShapeDetector()

# 遍历所有轮廓
for c in cnts:
    # 计算轮廓的中心，然后仅使用轮廓检测形状的名称
    M = cv2.moments(c)
    cX = int((M["m10"] / M["m00"]) * ratio)
    cY = int((M["m01"] / M["m00"]) * ratio)
    shape = sd.detect(c)
    # 将轮廓(x, y)坐标乘以调整比例，然后在图像上绘制轮廓和形状的名称
    c = c.astype("float")
    c *= ratio
    c = c.astype("int")
    cv2.drawContours(image, [c], -1, (0, 255, 0), 2)
    cv2.putText(image, shape, (cX, cY), cv2.FONT_HERSHEY_SIMPLEX,
                0.5, (255, 255, 255), 2)
    # 显示输出图像
    cv2.imshow("Image", image)
    cv2.waitKey(0)

#include <opencv2/opencv.hpp>
using namespace std;
using namespace cv;

int main()
{

    Mat img = imread("FindingContours.png");

    //show the original image
    namedWindow("Raw");
    imshow("Raw", img);

    //converting the original image into grayscale
    Mat imgGrayScale = Mat(img.size(), CV_8UC1);
    cvtColor(img, imgGrayScale, COLOR_BGR2GRAY);

    //thresholding the grayscale image to get better results
    threshold(imgGrayScale, imgGrayScale, 128, 255, THRESH_BINARY);

    //finding all contours in the image
    vector<vector<Point> > contours;
    vector<Vec4i> hierarchy;
    findContours(imgGrayScale, contours, hierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE);


    //iterating through each contour
    for (size_t i = 0; i < contours.size(); i++)
    {
        vector<Point> approx;
        //obtain a sequence of points of contour, pointed by the variable 'contour'
        approxPolyDP(contours[i], approx, arcLength(contours[i], true) * 0.02, true);

        //if there are 3  vertices  in the contour(It should be a triangle)
        if (approx.size() == 3)
        {
            //drawing lines around the triangle
            for (int i = 0; i < 3; i++) {
                line(img, approx[i], approx[(i+1)%3], Scalar(255, 0, 0), 4);
            }
        }
        //if there are 4 vertices in the contour(It should be a quadrilateral)
        else if (approx.size() == 4)
        {
            //drawing lines around the quadrilateral
            for (int i = 0; i < 4; i++) {
                line(img, approx[i], approx[(i + 1) % 4], Scalar(0, 255, 0), 4);
            }
        }

        //if there are 7  vertices  in the contour(It should be a heptagon)
        else if (approx.size() == 7)
        {
            //drawing lines around the heptagon
            for (int i = 0; i < 7; i++) {
                line(img, approx[i], approx[(i + 1) % 7], Scalar(0, 0, 255), 4);
            } 
        }
    }

    //show the image in which identified shapes are marked   
    namedWindow("Tracked");
    imshow("Tracked", img);

    waitKey(0); //wait for a key press

    //cleaning up
    destroyAllWindows();

    return 0;
}