编写Python代码工件缺陷检测提取灰度特征包含灰度归一化均值，灰度方差，并把缺陷位置画出来

好的，您需要编写一个Python程序来提取灰度特征并检测工件缺陷。为了提取灰度特征，您使用灰度归一化均值和灰度方差。为了检测缺陷，您需要使用这些特征并绘制缺陷位置。下面是一个示例代码：

import cv2
import numpy as np

def detect_defect(image):
    # Convert image to grayscale
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # Normalize grayscale values
    gray_norm = cv2.normalize(gray.astype('float'), None, 0.0, 1.0, cv2.NORM_MINMAX)

    # Calculate mean and variance of grayscale values
    mean, var = cv2.meanStdDev(gray_norm)
    mean = mean[0][0]
    var = var[0][0]

    # Threshold the image to create a binary image
    thresh = cv2.threshold(gray_norm, 0.5, 1.0, cv2.THRESH_BINARY)[1]

    # Find contours in the binary image
    contours, _ = cv2.findContours(thresh.astype('uint8'), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

    # Draw contours and rectangles around defects
    for contour in contours:
        x, y, w, h = cv2.boundingRect(contour)
        if w > 5 and h > 5:
            cv2.drawContours(image, [contour], 0, (0, 0, 255), 2)
            cv2.rectangle(image, (x, y), (x+w, y+h), (0, 0, 255), 2)

    # Return the mean and variance of the grayscale values, and the image with defect locations
    return mean, var, image

您可以将图像传递给`detect_defect`函数，该函数将返回图像的灰度归一化均值、灰度方差和带有缺陷位置的图像。

注意：这仅仅是一个示例代码，因此需要根据您的具体问题进行修改和调整。