# coding=utf-8
import cv2
import numpy as np
import math

# 修改图像的对比度,coefficent>0, <1降低对比度,>1提升对比度 建议0-2
def change_contrast(img, coefficent):
    imggray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    m = cv2.mean(img)[0]
    graynew = m + coefficent * (imggray - m)
    img1 = np.zeros(img.shape, np.float32)
    k = np.divide(graynew, imggray, out=np.zeros_like(graynew), where=imggray != 0)
    img1[:, :, 0] = img[:, :, 0] * k
    img1[:, :, 1] = img[:, :, 1] * k
    img1[:, :, 2] = img[:, :, 2] * k
    img1[img1 > 255] = 255
    img1[img1 < 0] = 0
    return img1.astype(np.uint8)

# 修改图像的亮度，brightness取值0～2 <1表示变暗 >1表示变亮
def change_brightness(img, brightness):
    [averB, averG, averR] = np.array(cv2.mean(img))[:-1] / 3
    k = np.ones((img.shape))
    k[:, :, 0] *= averB
    k[:, :, 1] *= averG
    k[:, :, 2] *= averR
    img = img + (brightness - 1) * k
    img[img > 255] = 255
    img[img < 0] = 0
    return img.astype(np.uint8)

# 对图像二值化并取中值滤波
def threshold_img(image):
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    retval, dst = cv2.threshold(gray, 0, 255, cv2.THRESH_OTSU)
    img_median = cv2.medianBlur(dst, 3)
    return img_median

# 腐蚀图像
def corrosion_img(image):
    # 腐蚀操作会把裂痕也消除
    kernel = np.ones((9, 9), dtype=np.uint8)
    opening = cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel, 1)
    return opening

# 找出最右侧的近似竖直直线 通过直线与水平的角度判断是否倾斜 大于容错率的竖直 小于容错率的倾斜
def lines_img(image):
    slopes = []
    yErrorRange = 3
    xErrorRange = 3
    minLength = 20
    # 创建一个LSD对象
    lsd = cv2.createLineSegmentDetector(0)
    # 执行检测结果
    dlines = lsd.detect(image)
    # 绘制检测结果
    for dline in dlines[0]:
        x0 = int(round(dline[0][0]))
        y0 = int(round(dline[0][1]))
        x1 = int(round(dline[0][2]))
        y1 = int(round(dline[0][3]))
        length = math.hypot(x1 - x0, y1 - y0)
        if (x0 > 605 and x1 > 605):
            # print((x0, y0), (x1, y1))
            # 需要根据角坐标排除 横向线，针对纵向线判断是否有倾斜
            # 过滤横线
            if not (abs(x1 - x0) > xErrorRange and abs(y1 - y0) < yErrorRange):
                # 过滤线段长度
                if not (length < minLength):
                    if x1 - x0 != 0:
                        k = abs(y1 - y0) / abs(x1 - x0)
                        angle = math.atan(k) * 180 / math.pi
                        slopes.append(angle)
                        print(angle)
                    # cv2.line(img0, (x0, y0), (x1,y1), (0,255,0), 1, cv2.LINE_AA)
    # 有小于85度的就证明胶带倾斜了
    threshold = 85
    res = any(x < threshold for x in slopes)
    if res:
        print("胶带倾斜")
    else:
        print("胶带正确")


def detectionImg():
    # 读取输入图片
    img = cv2.imread("img/a.png")
    l = 0
    c = 34
    # 亮度 -1~1
    imgBrightness = change_brightness(img, float(l - 50) / float(50))
    # 对比度 0~2
    img0 = change_contrast(imgBrightness, c / 50)
    thresholdImg = threshold_img(img0)
    corrosionImg = corrosion_img(thresholdImg)
    lines_img(corrosionImg)
    # 显示并保存结果
    cv2.imshow("gray", corrosionImg)
    cv2.imshow("lines", img0)
    cv2.waitKey(0)
    cv2.destroyAllWindows()


if __name__ == '__main__':
    detectionImg()