TypeError: Expected Ptr＜cv::UMat＞ for argument ‘mat‘

1，报错TypeError: Expected Ptr＜cv::UMat＞ for argument ‘mat‘

2，报错时代码段如下

import cv2.cv2
import numpy as np

if __name__ == "__main__":
    img_path = "1.png"
    img = cv2.imread(img_path)
    # #获取图片的宽和高
    # width,height = img.shape[:2][::-1]
    # #将图片缩小便于显示观看
    # img_resize = cv2.resize(img,
    # (int(width*0.5),int(height*0.5)),interpolation=cv2.INTER_CUBIC)
    # cv2.imshow("img",img_resize)
    # print("img_reisze shape:{}".format(np.shape(img_resize)))
    cv2.imshow("img", img)

    #将图片转为灰度图img
    img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    #img_gray = cv2.cvtColor(img_resize,cv2.COLOR_RGB2GRAY)
    cv2.imshow("img_gray",img_gray)
    #print("img_gray shape:{}".format(np.shape(img_gray)))

    #2、固定阈值二值化
    im_at_fixed = cv2.threshold(img_gray, 255, 255, cv2.THRESH_BINARY)
    cv2.imshow("im_at_fixed", im_at_fixed)

    # 3、算术平法的自适应二值化
    im_at_mean = cv2.adaptiveThreshold(img_gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 5, 10)
    cv2.imshow("im_at_mean", im_at_mean)

    # 4、高斯加权均值法自适应二值化
    im_at_GOS = cv2.adaptiveThreshold(img_gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 5, 7)
    cv2.imshow("im_at_GOS", im_at_GOS)


    #膨胀

    #腐蚀

    print(cv2.__version__) #用于测试是否输出
    cv2.waitKey()

3，网上查了，再试了下真的是因为这个ret,（本来我还郁闷ret是干嘛的）

修改后代码段：

网上启发我的片段