如何在 Python 和 Opencv 中检测八边形形状

我正在研究一个shape检测算法opencv in python。 我正在使用库中的轮廓，并且已成功检测到一些形状：圆形、矩形和三角形。 唯一的问题是我只需要检测圆形、矩形和八边形。 此外，这个圈子正在发挥作用，但不稳定。 所以，这是我的代码：

import cv2
import numpy as np

def nothing(x):
    # any operation
    pass

cap = cv2.VideoCapture(1)

cv2.namedWindow("Trackbars")
cv2.createTrackbar("L-H", "Trackbars", 0, 180, nothing)
cv2.createTrackbar("L-S", "Trackbars", 66, 255, nothing)
cv2.createTrackbar("L-V", "Trackbars", 134, 255, nothing)
cv2.createTrackbar("U-H", "Trackbars", 180, 180, nothing)
cv2.createTrackbar("U-S", "Trackbars", 255, 255, nothing)
cv2.createTrackbar("U-V", "Trackbars", 243, 255, nothing)

font = cv2.FONT_HERSHEY_COMPLEX

while True:
    _, frame = cap.read()
    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)

    l_h = cv2.getTrackbarPos("L-H", "Trackbars")
    l_s = cv2.getTrackbarPos("L-S", "Trackbars")
    l_v = cv2.getTrackbarPos("L-V", "Trackbars")
    u_h = cv2.getTrackbarPos("U-H", "Trackbars")
    u_s = cv2.getTrackbarPos("U-S", "Trackbars")
    u_v = cv2.getTrackbarPos("U-V", "Trackbars")

    lower_yellow = np.array([l_h,l_s, l_v])
    upper_yellow = np.array([u_h, u_s, u_v])

    mask = cv2.inRange(hsv, lower_yellow, upper_yellow)
    kernel = np.ones((5, 5), np.uint8)
    mask = cv2.erode(mask, kernel)

    # Contours detection
    if int(cv2.__version__[0]) > 3:
        # Opencv 4.x.x
        contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    else:
        # Opencv 3.x.x
        _, contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

    for cnt in contours:
        area = cv2.contourArea(cnt)
        approx = cv2.approxPolyDP(cnt, 0.02*cv2.arcLength(cnt, True), True)
        x = approx.ravel()[0]
        y = approx.ravel()[1]

        if area > 400:
            cv2.drawContours(frame, [approx], 0, (0, 0, 0), 5)

            if len(approx) == 3:
                cv2.putText(frame, "Triangle", (x, y), font, 1, (0, 0, 0))
            elif len(approx) == 4:
                cv2.putText(frame, "Rectangle", (x, y), font, 1, (0, 0, 0))
            elif 10 < len(approx) < 20:
                cv2.putText(frame, "Circle", (x, y), font, 1, (0, 0, 0))

    cv2.imshow("Frame", frame)
    cv2.imshow("Mask", mask)

    key = cv2.waitKey(1)
    if key == 27:
        break

cap.release()
cv2.destroyAllWindows()

我想要的是更准确地检测八边形和圆形。

为了执行形状检测，我们可以使用轮廓近似。假设对象是simple形状，这是一种使用阈值+轮廓近似的方法。轮廓近似基于这样的假设：曲线可以通过一系列短线段来近似，这些短线段可用于确定轮廓的形状。例如，三角形有三个顶点，正方形/长方形有四个顶点，五边形有五个顶点，等等。

1. 获取二值图像。我们加载图像，转换为灰度，然后大津的门槛 https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_thresholding/py_thresholding.html#otsus-binarization以获得二值图像。

2. 检测形状。使用轮廓近似过滤查找轮廓并识别每个轮廓的形状。这可以使用以下方法完成arcLength https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_contours/py_contour_features/py_contour_features.html#contour-perimeter计算轮廓的周长并approxPolyDP https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_contours/py_contour_features/py_contour_features.html#contour-approximation以获得实际的轮廓近似值。

输入图像

标记形状

Code

import cv2

def detect_shape(c):
    # Compute perimeter of contour and perform contour approximation
    shape = ""
    peri = cv2.arcLength(c, True)
    approx = cv2.approxPolyDP(c, 0.04 * peri, True)

    # Triangle
    if len(approx) == 3:
        shape = "triangle"

    # Square or rectangle
    elif len(approx) == 4:
        (x, y, w, h) = cv2.boundingRect(approx)
        ar = w / float(h)

        # A square will have an aspect ratio that is approximately
        # equal to one, otherwise, the shape is a rectangle
        shape = "square" if ar >= 0.95 and ar <= 1.05 else "rectangle"

    # Pentagon
    elif len(approx) == 5:
        shape = "pentagon"

    # Hexagon
    elif len(approx) == 6:
        shape = "hexagon"

    # Octagon 
    elif len(approx) == 8:
        shape = "octagon"

    # Star
    elif len(approx) == 10:
        shape = "star"

    # Otherwise assume as circle or oval
    else:
        shape = "circle"

    return shape

# Load image, grayscale, Otsu's threshold
image = cv2.imread('1.png')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

# Find contours and detect shape
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    # Identify shape
    shape = detect_shape(c)

    # Find centroid and label shape name
    M = cv2.moments(c)
    cX = int(M["m10"] / M["m00"])
    cY = int(M["m01"] / M["m00"])
    cv2.putText(image, shape, (cX - 20, cY), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (36,255,12), 2)

cv2.imshow('thresh', thresh)
cv2.imshow('image', image)
cv2.waitKey()