我试图使用OpenCV从背景中分割弯曲的杆，然后找到其中的弯曲并计算每个弯曲之间的Angular 。
幸运的是，第一部分很简单，前景和背景之间有足够的对比。一点侵 eclipse /膨胀照顾反射/高光时分割。
第二部分是我不知道该如何处理它。
我可以很容易地检索一个轮廓（顶部和底部是非常相似的，所以任何一个将这样做），但我似乎不能弄清楚是如何得到分裂的轮廓到直的部分和弯曲杆计算Angular 。
到目前为止，我已经尝试简化轮廓，但要么我得到太多或太少的点，感觉很难针点正确的设置，以保持直的部分直和弯曲的部分简化。
这是我的输入图像（bend.png）

以下是我目前为止所做的尝试：

#!/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()

以下是我目前所得到的，但我不认为这是最好的方法：

我试着从视觉上弄清楚这一点，我的目标是沿着这些路线：

因为最终目标是计算弯曲部分之间的Angular ，所以这样做感觉更简单：

我假设拟合直线并计算相交直线对之间的Angular 可以工作：

我使用HoughLines OpenCV Python tutorial做了一个快速测试，但不管传递的参数是什么，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()

这做法是否可行呢？如果是这样，在OpenCV Python中进行直线拟合的正确方法是什么？
否则，这就是解决这个问题的最好方法？

更新遵循三木的建议，我尝试了OpenCV 3的LSD，并获得了比HoughLinesP更好的结果，但看起来仍然需要一些调整，尽管它看起来并不比cv2.createLineSegmentDetector有很多选择：