在 python 中使用 cv2.approxPolyDP() 绘制等高线

Question

我无法使用 cv2.approxPolyDP() 绘制形状的完整轮廓。

我得到以下结果：

但我想要这样的输出：

这是我的代码：

import cv2
im = cv2.imread('C:\Python27\Test\Targets\s1.jpg') # read picture

imgray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY) # BGR to grayscale

ret, thresh = cv2.threshold(imgray, 200, 255, cv2.THRESH_BINARY)

countours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

epsilon = 0.1 * cv2.arcLength(countours[0], True)
approx = cv2.approxPolyDP(countours[0], epsilon, True)

cv2.drawContours(im, approx, -1, (0, 255, 0), 3)
cv2.imshow("Contour", im)

cv2.waitKey(0)
cv2.destroyAllWindows()

Answer 1

我尝试了相同的代码。但是我能够得到这个：

Answer 2

在 Java 这行得通

MatOfPoint2f  newContour = new MatOfPoint2f();      
largestContour.convertTo(newContour, CvType.CV_32F);
double arcLength = Imgproc.arcLength(newContour, closed);
double epsilon = 0.2 * arcLength;
MatOfPoint2f approxCurve = new MatOfPoint2f();
Imgproc.approxPolyDP(newContour, approxCurve, epsilon, closed);

// convert back to form that can be plotted by drawContours
MatOfPoint largestOutline = new  MatOfPoint();
approxCurve.convertTo(largestOutline, CvType.CV_32S);

Answer 3

cv2.CHAIN_APPROX_SIMPLE去除所有冗余点并压缩轮廓，从而节省内存。如果您将 findContours() 函数传递给 cv2.CHAIN_APPROX_NONE 参数而不是 cv2.CHAIN_APPROX_SIMPLE，您的问题将得到解决。您的代码应更改如下：

_, countours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, 
                                                 cv2.CHAIN_APPROX_NONE)

Answer 4

很久以前的事了，但我建议你这样

contours,hierarchy = cv2.findContours(thresh, 1, 2)

contours_sizes= [(cv2.contourArea(cnt), cnt) for cnt in contours]
biggest_contour = max(contours_sizes, key=lambda x: x[0])[1]

countours = biggest_contour

这将找到图像的最大轮廓，忽略小点和噪声

在 python 中使用 cv2.approxPolyDP() 绘制等高线

Drawing contours using cv2.approxPolyDP() in python

python

opencv

image

computer-vision

opencv3.0