如何分割弯杆进行角度计算？

我正在尝试使用OpenCV从它的背景中分割弯曲的杆，然后在其中找到弯曲并计算每个弯曲之间的角度 .

幸运的是，第一部分是微不足道的，前景和背景之间有足够的对比 . 在分割时，一些侵蚀/扩张会处理反射/高光 .

第二部分是我不知道如何处理它的地方 .

我可以轻松地检索轮廓（顶部和底部非常相似，所以要么会这样做），但我似乎无法弄清楚如何将轮廓分成直线部分和弯曲杆来计算角度 .

到目前为止，我已经尝试过简单地修复轮廓，但要么我得到太多或太少的点，并且感觉很难确定正确的设置以保持笔直部分和弯曲部分简化 .

这是我的输入图像（bend.png）

这是我到目前为止所尝试的内容：

#!/usr/bin/env python
import numpy as np
import cv2

threshold = 229
# erosion/dilation kernel
kernel = np.ones((5,5),np.uint8)
# contour simplification
epsilon = 0

# slider callbacks
def onThreshold(x):
    global threshold
    print "threshold = ",x
    threshold = x
def onEpsilon(x):
    global epsilon
    epsilon = x * 0.01
    print "epsilon = ",epsilon

# make a window to add sliders/preview to
cv2.namedWindow('processed')
#make some sliders
cv2.createTrackbar('threshold','processed',60,255,onThreshold)
cv2.createTrackbar('epsilon','processed',1,1000,onEpsilon)
# load image
img = cv2.imread('bend.png',0)
# continuously process for quick feedback
while 1:
    # exit on ESC key
    k = cv2.waitKey(1) & 0xFF
    if k == 27:
        break

    # Threshold
    ret,processed = cv2.threshold(img,threshold,255,0)
    # Invert
    processed = (255-processed)
    # Dilate
    processed = cv2.dilate(processed,kernel)
    processed = cv2.erode(processed,kernel)
    # Canny
    processed = cv2.Canny(processed,100,200)

    contours, hierarchy = cv2.findContours(processed,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
    if len(contours) > 0:
        approx = cv2.approxPolyDP(contours[0],epsilon,True)
        # print len(approx)
        cv2.drawContours(processed, [approx], -1, (255,255,255), 3)
        demo = img.copy()
        cv2.drawContours(demo, [approx], -1, (192,0,0), 3)
    # show result
    cv2.imshow('processed ',processed)
    cv2.imshow('demo ',demo)


# exit
cv2.destroyAllWindows()

这是我到目前为止所得到的，但我不相信这是最好的方法：

我试图在视觉上解决这个问题，我的目标是沿着以下方向：

因为最终目标是计算弯曲部件之间的角度，这样的事情会更简单：

我假设拟合线和计算相交线对之间的角度可以工作：

我使用HoughLines OpenCV Python tutorial进行了快速测试，但无论参数传递如何，我都没有得到很好的结果：

#!/usr/bin/env python
import numpy as np
import cv2

threshold = 229
minLineLength = 30
maxLineGap = 10
houghThresh = 15

# erosion/dilation kernel
kernel = np.ones((5,5),np.uint8)

# slider callbacks
def onMinLineLength(x):
    global minLineLength
    minLineLength = x
    print "minLineLength = ",x

def onMaxLineGap(x):
    global maxLineGap
    maxLineGap = x
    print "maxLineGap = ",x

def onHoughThresh(x):
    global houghThresh
    houghThresh = x
    print "houghThresh = ",x

# make a window to add sliders/preview to
cv2.namedWindow('processed')
#make some sliders
cv2.createTrackbar('minLineLength','processed',1,50,onMinLineLength)
cv2.createTrackbar('maxLineGap','processed',5,30,onMaxLineGap)
cv2.createTrackbar('houghThresh','processed',15,50,onHoughThresh)
# load image
img = cv2.imread('bend.png',0)
# continuously process for quick feedback
while 1:
    # exit on ESC key
    k = cv2.waitKey(1) & 0xFF
    if k == 27:
        break

    # Threshold
    ret,processed = cv2.threshold(img,threshold,255,0)
    # Invert
    processed = (255-processed)
    # Dilate
    processed = cv2.dilate(processed,kernel)
    processed = cv2.erode(processed,kernel)
    # Canny
    processed = cv2.Canny(processed,100,200)

    lineBottom = np.zeros(img.shape,np.uint8)

    contours, hierarchy = cv2.findContours(processed,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
    if len(contours) > 0:
        cv2.drawContours(lineBottom, contours, 0, (255,255,255), 1)

    # HoughLinesP
    houghResult = img.copy()
    lines = cv2.HoughLinesP(lineBottom,1,np.pi/180,houghThresh,minLineLength,maxLineGap)
    try:
        for x in range(0, len(lines)):
            for x1,y1,x2,y2 in lines[x]:
                cv2.line(houghResult,(x1,y1),(x2,y2),(0,255,0),2)
    except Exception as e:
        print e

    # show result
    cv2.imshow('lineBottom',lineBottom)
    cv2.imshow('houghResult ',houghResult)


# exit
cv2.destroyAllWindows()

这是一种可行的方法吗？如果是这样，在OpenCV Python中进行线拟合的正确方法是什么？

否则，这是解决这个问题的最佳方法吗？

Update 以下Miki 's advise I'已尝试使用OpenCV 3的LSD并获得了比使用 HoughLinesP 更好的结果，但看起来除了 cv2.createLineSegmentDetector 之外还有其他选项可供选择：