img = cv2.imread('./n1.jpg')
gray = cv2.cvtColor(img,COLOR_RGB2GRAY)  #使用颜色空间将图片转换为灰度图
imgs = cv2.Canny(gray, 100, 200)   #canny边缘检测

lines = cv2.HoughLinesP(imgs, 1, np.pi/180, 15, minLineLength = 40, maxLineGap = 20)  #霍夫变换
line = [line for line in lines]    #列表生成式得到所有的线条

def slop(lines):
    x1,y1,x2,y2 = lines[0]
    return (y2-y1)/(x2-x1)
def outlier(line, threshold):
    slops = [slop(line) for line in lines]  #列表生成式的到所有线段的斜率
    while len(lines) > 0:
        mean = np.mean(slops) #得到线段中的主要值其实就是目标的斜率
        diff = [abs(s - mean) for s in slops]    #得到所有线段斜率与目标斜率的绝对值
        idx = np.argmax(diff)  #去除绝对值中的最大值的下标
        if diff(idx) > threshold:  #果最大绝对值大于给定的阈值则剔除
            slops.pop(idx)
            lines.pop(idx)
        else:
            break
 	return lines

def fit(lines):
    x = np.ravel([[line[0][0],line[0][2]] for line in lines])
    y = np.ravel([[line[0][1],line[0][3]] for line in lines])
    poly = np.polyfit(x, y, deg=1)
    point_min = (np.min(x),np.polyval(poly, np.min(x)))
    point_max = (np.max(x),np.polyval(poly, np.max(x)))
    return np.array([point_min, point_max], dtype=np.int)
#返回值为拟合之后的一条线段由端点坐标组成的二维数组

import cv2
import numpy as np
#计算斜率
def slop(line):
    x1,y1,x2,y2 = line[0]
    return (y2-y1)/(x2-x1)
#离群值计算
def outlier(lines, threshold):
    slops = [slop(line) for line in lines]
    while len(lines) > 0:
        mean = np.mean(slops)
        diff = [abs(s - mean) for s in slops] 
        idx = np.argmax(diff)
        if diff[idx] > threshold:
            slops.pop(idx)
            lines.pop(idx)
        else:
            break
    return lines
def fit(lines):
    x = np.ravel([[line[0][0],line[0][2]] for line in lines])
    y = np.ravel([[line[0][1],line[0][3]] for line in lines])
    poly = np.polyfit(x, y, deg=1)
    point_min = (np.min(x),np.polyval(poly, np.min(x)))
    point_max = (np.max(x),np.polyval(poly, np.max(x)))
    return np.array([point_min, point_max], dtype=np.int)
img = cv2.imread('./n2.jpg')  #读取图片
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
#canny边缘检测
imgs = cv2.Canny(gray, 100, 200)
cv2.imshow('imgs',imgs)
cv2.waitKey(0)
#图像分割
#mask = np.zeros_like(imgs)
#mask = cv2.fillPoly(mask, np.array([[[0,400],[0,0],[640,0],[640,400]]]),color = 255)
#edges = cv2.bitwise_and(imgs, mask)
#霍夫变换
lines = cv2.HoughLinesP(imgs, 1, np.pi/180,15,minLineLength=40,maxLineGap=20)
Lines = [line for line in lines]
#离群值计算
lines = outlier(Lines, 0.2)
#最小二乘拟合
Line = fit(lines)
print(Line)
cv2.line(img, tuple(Line[1]), tuple(Line[0]), color=(0, 255, 255), thickness=5)
cv2.imshow('imgs',img)
cv2.waitKey(0)
cv2.destroyAllWindows()

from types import NoneType
import cv2
import numpy as np
def slop(line):
    x1,y1,x2,y2 = line[0]
    return (y2-y1)/(x2-x1)
def outlier(lines, threshold):
    slops = [slop(line) for line in lines]
    while len(lines) > 0:
        mean = np.mean(slops)
        diff = [abs(s - mean) for s in slops] 
        idx = np.argmax(diff)
        if diff[idx] > threshold:
            slops.pop(idx)
            lines.pop(idx)
        else:
            break
    return lines
def fit(lines):
    x = np.ravel([[line[0][0],line[0][2]] for line in lines])
    y = np.ravel([[line[0][1],line[0][3]] for line in lines])
    poly = np.polyfit(x, y, deg=1)
    point_min = (np.min(x),np.polyval(poly, np.min(x)))
    point_max = (np.max(x),np.polyval(poly, np.max(x)))
    return np.array([point_min, point_max], dtype=np.int32)
cat = cv2.VideoCapture(0)
cv2.namedWindow('img',cv2.WINDOW_NORMAL)
cv2.resizeWindow('img',640,480)
while True:
    ret, frame = cat.read()
    if not ret:
        break
    gray = cv2.cvtColor(frame,cv2.COLOR_RGB2GRAY)
    imgs = cv2.Canny(gray, 100, 200)
    lines = cv2.HoughLinesP(imgs, 1, np.pi/180,15,minLineLength=40,maxLineGap=20)
    if lines is None:
        continue
    Lines = [line for line in lines]
    lines = outlier(Lines, 0.2)
    Line = fit(lines)
    cv2.line(frame, tuple(Line[1]), tuple(Line[0]), color=(0, 255, 255), thickness=5)
    cv2.imshow('img',frame)
    if cv2.waitKey(10) == ord('q'):
        break
cv2.destroyAllWindows()