用Python编写一下提取工件缺陷形状特征代码

我明白了您的问题，下面是一份可能的Python代码，用于提取工件缺陷形状特征：

import cv2
import numpy as np

# 读入图像并转为灰度图像
image = cv2.imread("path/to/image.jpg")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# 阈值滤波，二值化处理
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]

# 轮廓检测，找出所有的轮廓
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

# 预处理
rect_widths = []
rect_heights = []
rect_areas = []

# 计算每个轮廓的形状特征
for contour in contours:
    x, y, w, h = cv2.boundingRect(contour)
    rect_widths.append(w)
    rect_heights.append(h)
    rect_areas.append(w * h)

# 计算平均值、方差、最小值和最大值等统计数据
avg_width = np.average(rect_widths)
avg_height = np.average(rect_heights)
avg_area = np.average(rect_areas)
stddev_width = np.std(rect_widths)
stddev_height = np.std(rect_heights)
stddev_area = np.std(rect_areas)
min_width = np.min(rect_widths)
min_height = np.min(rect_heights)
min_area = np.min(rect_areas)
max_width = np.max(rect_widths)
max_height = np.max(rect_heights)
max_area = np.max(rect_areas)

# 输出结果
print("Average width:", avg_width)
print("Average height:", avg_height)
print("Average area:", avg_area)
print("Standard deviation of width:", stddev_width)
print("Standard deviation of height:", stddev_height)
print("Standard deviation of area:", stddev_area)
print("Minimum width:", min_width)
print("Minimum height:", min_height)
print("Minimum area:", min_area)
print("Maximum width:", max_width)
print("Maximum height:", max_height)
print("Maximum area:", max_area)

这段代码使用OpenCV库实现，首先读入一张图像并转为灰度图像，然后进行阈值滤波和二值化处理。接下来使用轮廓检测算法（findContours函数）找出所有的轮廓，再计算每个轮廓的形状特征，如宽度、高度和面积等，最后统计计算平均值、方差、最小值和最大值等数据，并输出结果。