Question

如何通过轮廓中心线？我有轮廓的中心坐标。

Answer 1

这是您解决此问题的方法-

原始图片-

结果图片-

您首先需要进行基本过滤并找到轮廓。然后-

1）找出轮廓区域（minAreaRect）
2）从轮廓中提取点（BoxPoints）
3）将其转换为numpy数组（np.array）
4）订购积分（perspective.order_points）
5）取出左上角，右上角，右下角和左下角（tl，tr，br，bl）=框（第52行）
6）计算中点（（point1 + point2）/ 2）
7）画线（第76行）

这是它的代码

# import the necessary packages
from scipy.spatial import distance as dist
from imutils import perspective
from imutils import contours
import numpy as np
import imutils
import cv2

# Method to find the mid point
def midpoint(ptA, ptB):
    return ((ptA[0] + ptB[0]) * 0.5, (ptA[1] + ptB[1]) * 0.5)

# load the image, convert it to grayscale, and blur it slightly
image = cv2.imread("test.png")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (7, 7), 0)

# perform edge detection, then perform a dilation + erosion to
# close gaps in between object edges
edged = cv2.Canny(gray, 50, 100)
edged = cv2.dilate(edged, None, iterations=1)
edged = cv2.erode(edged, None, iterations=1)

# find contours in the edge map
cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,
    cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]

# loop over the contours individually
for c in cnts:
    # This is to ignore that small hair countour which is not big enough
    if cv2.contourArea(c) < 1000:
        continue

    # compute the rotated bounding box of the contour
    box = cv2.minAreaRect(c)
    box = cv2.cv.BoxPoints(box) if imutils.is_cv2() else cv2.boxPoints(box)
    box = np.array(box, dtype="int")

    # order the points in the contour such that they appear
    # in top-left, top-right, bottom-right, and bottom-left
    # order, then draw the outline of the rotated bounding
    # box
    box = perspective.order_points(box)
    # draw the contours on the image
    orig = image.copy()
    cv2.drawContours(orig, [box.astype("int")], -1, (0, 255, 0), 3)

    # unpack the ordered bounding box, then compute the midpoint
    # between the top-left and top-right coordinates, followed by
    # the midpoint between bottom-left and bottom-right coordinates
    (tl, tr, br, bl) = box
    (tltrX, tltrY) = midpoint(tl, tr)
    (blbrX, blbrY) = midpoint(bl, br)

    # compute the midpoint between the top-left and top-right points,
    # followed by the midpoint between the top-righ and bottom-right
    (tlblX, tlblY) = midpoint(tl, bl)
    (trbrX, trbrY) = midpoint(tr, br)

    # draw and write the midpoints on the image
    cv2.circle(orig, (int(tltrX), int(tltrY)), 5, (255, 0, 0), -1)
    cv2.putText(orig, "({},{})".format(tltrX, tltrY), (int(tltrX - 50), int(tltrY - 10) - 20),
            cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,0,0), 2)
    cv2.circle(orig, (int(blbrX), int(blbrY)), 5, (255, 0, 0), -1)
    cv2.putText(orig, "({},{})".format(blbrX, blbrY), (int(blbrX - 50), int(blbrY - 10) - 20),
            cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,0,0), 2)
    cv2.circle(orig, (int(tlblX), int(tlblY)), 5, (255, 0, 0), -1)
    cv2.putText(orig, "({},{})".format(tlblX, tlblY), (int(tlblX - 50), int(tlblY - 10) - 20),
            cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,0,0), 2)
    cv2.circle(orig, (int(trbrX), int(trbrY)), 5, (255, 0, 0), -1)
    cv2.putText(orig, "({},{})".format(trbrX, trbrY), (int(trbrX     - 50), int(trbrY - 10) - 20),
            cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,0,0), 2)

    # draw lines between the midpoints
    cv2.line(orig, (int(tltrX), int(tltrY)), (int(blbrX), int(blbrY)),
        (255, 0, 255), 2)
    cv2.line(orig, (int(tlblX), int(tlblY)), (int(trbrX), int(trbrY)),
        (255, 0, 255), 2)

    # compute the Euclidean distance between the midpoints
    dA = dist.euclidean((tltrX, tltrY), (blbrX, blbrY))
    dB = dist.euclidean((tlblX, tlblY), (trbrX, trbrY))

    # loop over the original points
    for (xA, yA) in list(box):
        # draw circles corresponding to the current points and
        cv2.circle(orig, (int(xA), int(yA)), 5, (0,0,255), -1)
        cv2.putText(orig, "({},{})".format(xA, yA), (int(xA - 50), int(yA - 10) - 20),
            cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,0,0), 2)

        # show the output image, resize it as per your requirements
        cv2.imshow("Image", orig) 

    cv2.waitKey(0)

在opencv中使用质心点绘制线

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