'NoneType' object has no attribute 'convert'

问题遇到的现象和发生背景

用imread读取不到图片，搜了好多方法都不行

代码

import cv2
import numpy as np
from PIL import Image


def image_preprocessing():
    # 读取图片
    img = cv2.imread('3.jpg',IMREAD_GRAYSCALE=1).convert("RGB")
    # =====================图像处理======================== #
    # 转换成灰度图像!)
    gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    # 进行高斯滤波
    gauss_img = cv2.GaussianBlur(gray_img, (5, 5), 0, 0, cv2.BORDER_DEFAULT)

    # 边缘检测
    img_edge1 = cv2.Canny(gauss_img, 100, 200)

    # ==================================================== #
    # =====================图像分割======================== #

    # 获取原始图像的宽和高
    high = img.shape[0]
    width = img.shape[1]

    # 分别初始化高和宽的和
    add_width = np.zeros(high, dtype=int)
    add_high = np.zeros(width, dtype=int)

    # 计算每一行的灰度图的值的和
    for h in range(high):
        for w in range(width):
            add_width[h] = add_width[h] + img_edge1[h][w]

    # 计算每一列的值的和
    for w in range(width):
        for h in range(high):
            add_high[w] = add_high[w] + img_edge1[h][w]

    # 初始化上下边界为宽度总值最大的值的索引
    acount_high_up = np.argmax(add_width)
    acount_high_down = np.argmax(add_width)

    # 将上边界坐标值上移，直到没有遇到白色点停止，此为数字的上边界
    while add_width[acount_high_up] != 0:
        acount_high_up = acount_high_up + 1

    # 将下边界坐标值下移，直到没有遇到白色点停止，此为数字的下边界
    while add_width[acount_high_down] != 0:
        acount_high_down = acount_high_down - 1

    # 初始化左右边界为宽度总值最大的值的索引
    acount_width_left = np.argmax(add_high)
    acount_width_right = np.argmax(add_high)

    # 将左边界坐标值左移，直到没有遇到白色点停止，此为数字的左边界
    while add_high[acount_width_left] != 0:
        acount_width_left = acount_width_left - 1

    # 将右边界坐标值右移，直到没有遇到白色点停止，此为数字的右边界
    while add_high[acount_width_right] != 0:
        acount_width_right = acount_width_right + 1

    # 求出宽和高的间距
    width_spacing = acount_width_right - acount_width_left
    high_spacing = acount_high_up - acount_high_down

    # 求出宽和高的间距差
    poor = width_spacing - high_spacing

    # 将数字进行正方形分割，目的是方便之后进行图像压缩
    if poor > 0:
        tailor_image = img[acount_high_down - poor // 2 - 5:acount_high_up + poor - poor // 2 + 5,
                       acount_width_left - 5:acount_width_right + 5]
    else:
        tailor_image = img[acount_high_down - 5:acount_high_up + 5,
                       acount_width_left + poor // 2 - 5:acount_width_right - poor + poor // 2 + 5]

    # ==================================================== #
    # ======================小图处理======================= #

    # 将裁剪后的图片进行灰度化
    gray_img = cv2.cvtColor(tailor_image, cv2.COLOR_BGR2GRAY)

    # 高斯去噪
    gauss_img = cv2.GaussianBlur(gray_img, (5, 5), 0, 0, cv2.BORDER_DEFAULT)

    # 将图像形状调整到28*28大小
    zoom_image = cv2.resize(gauss_img, (28, 28))

    # 获取图像的高和宽
    high = zoom_image.shape[0]
    wide = zoom_image.shape[1]

    # 将图像每个点的灰度值进行阈值比较
    for h in range(high):
        for w in range(wide):

            # 若灰度值大于100，则判断为背景并赋值0，否则将深灰度值变白处理
            if zoom_image[h][w] > 100:
                zoom_image[h][w] = 0
            else:
                zoom_image[h][w] = 255 - zoom_image[h][w]

    return zoom_image

运行结果及报错内容

看一下你的图片3.jpg是不是在代码运行的同级目录下哦，这里的错误信息应该是没有读取到图片，所以opencv读完以后是None

你可以把图片名称换成绝对路径。