OpenCV学习笔记-轮廓特征

查找轮廓的不同特征，例如面积，周长，重心，边界框等

矩：cv.moments()

轮廓面积：cv.contourArea()

轮廓周长：cv.arcLength()

轮廓近似：cv.approxPolyDp()

边界矩形：cv.boundingRect()

最小外接矩形: cv.minAreaRect() cv.boxPoints()

最小外接圆:cv.minEnclosingCircle()

椭圆拟合:cv.ellipse()

直线拟合：cv.fitLine()

代码被我整合到一起了：

def measure_object(img):
    gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
    ret, thresh = cv.threshold(gray, 127, 255, cv.THRESH_BINARY_INV | cv.THRESH_OTSU)
    cv.imshow('thresh image', thresh)
    copyImage, contours, hireachy = cv.findContours(thresh, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
    for i, contour in enumerate(contours):
        #轮廓面积
        area = cv.contourArea(contour)
        print('contour area', area)

        #轮廓周长（弧长）
        perimeter = cv.arcLength(contour,True)
        print('contour perimeter', perimeter)

        #轮廓近似
        epsilon = 0.01 * perimeter
        approx = cv.approxPolyDP(contour, epsilon, True)
        print('approx', approx)
        cv.drawContours(img, [approx], i, (255, 0, 255), 2)

        #图像的矩 可以计算重心，面积等,返回一个字典
        M = cv.moments(contour)
        print(M)

        #重心坐标
        cx = M['m10']/M['m00']
        cy = M['m01']/M['m00']
        cv.circle(img, (np.int(cx), np.int(cy)), 3, (0, 255, 255), -1)
        print('center of gravity: (%f,%f)' % (cx,cy) )

        #边界矩阵
        x, y, w, h = cv.boundingRect(contour)
        img = cv.rectangle(img, (x, y), (x+w, y+h), (0, 0, 255), 2)
        cv.imshow('contours image', img)

        #最小外接矩形
        rect = cv.minAreaRect(contour)#[(x,y),(w,h),angle]
        print(rect)
        box = cv.boxPoints(rect) #获取到最小矩阵的四个顶点box：[[x1, y1],[x2, y2],[x3, y3],[x4, y4]]
        print(box)
        box = np.int0(box) #对box进行处理 这一步一定要进行
        print(box)
        cv.drawContours(img, [box], i, (0, 255, 0), 1) # [box]

        #最小外接圆
        (x, y), radius = cv.minEnclosingCircle(contour)
        center = (int(x), int(y))
        cv.circle(img, center, int(radius), (255, 0, 0), 2)

        #椭圆拟合,返回值其实就是旋转边界矩形的内切圆
        ellipse = cv.fitEllipse(contour)
        cv.ellipse(img, ellipse, (0, 255, 255), 2)

        #直线拟合
        rows, cols = img.shape[:2]
        [vx, vy, x, y] = cv.fitLine(contour, cv.DIST_L2, 0, 0.01, 0.01)
        left_y = int((-x*vy/vx) + y)
        right_y = int(((cols-x)*vy/vx) + y)
        cv.line(img, (cols-1, right_y), (0, left_y), (255, 255, 0), 2)
        print(i)
    cv.imshow('contours image', img)

效果图：