def lines_detector_hough(img,ThetaDim=None, DistStep=None, threshold=None, halfThetaWindowSize=2,
                         halfDistWindowSize=None):
    '''
    :param img: 經(jīng)過邊緣檢測得到的二值圖
    :param ThetaDim: hough空間中theta軸的刻度數(shù)量(將[0,pi)均分為多少份),反應(yīng)theta軸的粒度,越大粒度越細(xì)
    :param DistStep: hough空間中dist軸的劃分粒度,即dist軸的最小單位長度
    :param threshold: 投票表決認(rèn)定存在直線的起始閾值
    :return: 返回檢測出的所有直線的參數(shù)(theta,dist)和對應(yīng)的索引值,
    '''
    row,col= edge.shape
    if ThetaDim == None:
        ThetaDim = 90
    if DistStep == None:
        DistStep = 1
    # 計(jì)算距離分段數(shù)量
    MaxDist = np.sqrt(row ** 2 + col ** 2)
    DistDim = int(np.ceil(MaxDist / DistStep))
    if halfDistWindowSize == None:
        halfDistWindowSize = int(DistDim /50)
    # 建立投票
    accumulator = np.zeros((ThetaDim, DistDim))  # theta的范圍是[0,pi). 在這里將[0,pi)進(jìn)行了線性映射.類似的,也對Dist軸進(jìn)行了線性映射
    #
    sinTheta = [np.sin(t * np.pi / ThetaDim) for t in range(ThetaDim)]
    cosTheta = [np.cos(t * np.pi / ThetaDim) for t in range(ThetaDim)]
    #計(jì)算距離（rho）
    for i in range(row):
        for j in range(col):
            if not edge[i, j] == 0:
                for k in range(ThetaDim):
                    accumulator[k][int(round((i * cosTheta[k] + j * sinTheta[k]) * DistDim / MaxDist))] += 1
    M = accumulator.max()
#---------------------------------------
    #非極大抑制
    if threshold == None:
        threshold = int(M * 1.369/ 10)
    result = np.array(np.where(accumulator > threshold))  # 閾值化
    #獲得對應(yīng)的索引值
    temp = [[], []]
    for i in range(result.shape[1]):
        eight_neiborhood = accumulator[
                           max(0, result[0, i] - halfThetaWindowSize + 1):min(result[0, i] + halfThetaWindowSize,
                                                                              accumulator.shape[0]),
                           max(0, result[1, i] - halfDistWindowSize + 1):min(result[1, i] + halfDistWindowSize,
                                                                             accumulator.shape[1])]
        if (accumulator[result[0, i], result[1, i]] >= eight_neiborhood).all():
            temp[0].append(result[0, i])
            temp[1].append(result[1, i])
    #記錄原圖所檢測的坐標(biāo)點(diǎn)（x,y）
    result_temp= np.array(temp)
#-------------------------------------------------------------
    result = result_temp.astype(np.float64)
    result[0] = result[0] * np.pi / ThetaDim
    result[1] = result[1] * MaxDist / DistDim
    return result,result_temp

def drawLines(lines, edge, color=(255, 0, 0), err=3):
    '''
    :param lines: 檢測后的直線參數(shù)
    :param edge: 原圖
    :param color: 直線的顏色
    :param err:檢測的可接受的誤差值
    :return: 無
    '''
    if len(edge.shape) == 2:
        result = np.dstack((edge, edge, edge))
    else:
        result = edge
    Cos = np.cos(lines[0])
    Sin = np.sin(lines[0])
    for i in range(edge.shape[0]):
        for j in range(edge.shape[1]):
            e = np.abs(lines[1] - i * Cos - j * Sin)
            if (e < err).any():
                result[i, j] = color
    plt.imshow(result, cmap='gray')
    plt.axis('off')
    plt.show()

def data_img(data):
    '''
    :param data: 直線上含有的點(diǎn)（x,y）
    :return: 輸出hough空間圖像
    '''
    fig = plt.figure()  # 新建畫布
    ax = axisartist.Subplot(fig, 111)  # 使用axisartist.Subplot方法創(chuàng)建一個(gè)繪圖區(qū)對象ax
    fig.add_axes(ax)
    ax.axis[:].set_visible(False)  # 隱藏原來的實(shí)線矩形
    ax.axis["x"] = ax.new_floating_axis(0, 0, axis_direction="bottom")  # 添加x軸
    ax.axis["y"] = ax.new_floating_axis(1, 0, axis_direction="bottom")  # 添加y軸
    ax.axis["x"].set_axisline_style("->", size=1.0)  # 給x坐標(biāo)軸加箭頭
    ax.axis["y"].set_axisline_style("->", size=1.0)  # 給y坐標(biāo)軸加箭頭
    t = np.arange(-np.pi / 2, np.pi / 2, 0.1)
    ax.annotate(text='x', xy=(2 * math.pi, 0), xytext=(2 * math.pi, 0.1))  # 標(biāo)注x軸
    ax.annotate(text='y', xy=(0, 1.0), xytext=(-0.5, 1.0))  # 標(biāo)注y軸
    for i in range(data.shape[1]):
        rho = data[0][i] * np.cos(t) + data[1][i] * np.sin(t)
        plt.plot(t, rho)
    plt.show()