ret, binary = cv2.threshold(sobel, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) cv2.error: OpenCV(4.7.0) D:\a\opencv-python\opencv-python\opencv\modules\imgproc\src\thresh.cpp:1555: error: (-2:Unspecified error) in function 'double __cdecl cv::threshold(const class cv::_InputArray &,const class cv::_OutputArray &,double,double,int)' > THRESH_OTSU mode: > 'src_type == CV_8UC1 || src_type == CV_16UC1' > where > 'src_type' is 6 (CV_64FC1)

这个错误是因为用于二值化的图像是双精度浮点型，而`cv2.threshold`函数只支持8位或16位的灰度图像。因此，需要将图像转换为8位灰度图像，例如：

# 将图像转换为8位灰度图像
gray8 = cv2.convertScaleAbs(sobel)

# 二值化
ret, binary = cv2.threshold(gray8, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)

这里使用了`cv2.convertScaleAbs`函数将图像从双精度浮点型转换为8位灰度图像。

相关问题

import cv2import numpy as np# 加载图像img = cv2.imread('工件图像.jpg')# 转换为灰度图像gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)# 用高斯滤波去噪gray = cv2.GaussianBlur(gray, (5,5), 0)# Sobel 算子检测边缘grad_x = cv2.Sobel(gray, cv2.CV_32F, 1, 0, ksize=3)grad_y = cv2.Sobel(gray, cv2.CV_32F, 0, 1, ksize=3)# 取绝对值, 避免负数影响效果grad_x = cv2.convertScaleAbs(grad_x)grad_y = cv2.convertScaleAbs(grad_y)# 计算梯度幅值grad = cv2.addWeighted(grad_x, 0.5, grad_y, 0.5, 0)# 二值化处理ret, binary = cv2.threshold(grad, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)# 填充孔洞kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (10, 10))binary = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel)# 打印检测结果if np.count_nonzero(binary) > binary.size * 0.1: print('检测到工件缺陷！')else: print('工件无缺陷。')

这是Python语言中的两个库，cv2和numpy。cv2是OpenCV（计算机视觉）库的Python接口，可以用它来实现图片和视频的读取、处理和输出。而numpy库则是Python语言中用于科学计算的基础库，主要用来处理与数组有关的任务，例如图像处理中常用的像素点操作等。

代码错误：Traceback (most recent call last): File "C:\Users\ye\PycharmProjects\pythonProject\main.py", line 73, in <module> cv2.imshow('Car Plate', car_plate_image) cv2.error: OpenCV(4.6.0) D:\a\opencv-python\opencv-python\opencv\modules\highgui\src\window.cpp:967: error: (-215:Assertion failed) size.width>0 && size.height>0 in function 'cv::imshow'请改正

这个错误通常是因为没有检测到车牌或车牌图像大小为零导致的。请使用以下代码更改`plate_recognition`函数，以检查返回的车牌图像是否为空或大小是否为零。并在`car_plate_image`大小为零时返回`None`。

# 车牌识别函数
def plate_recognition(image):
    # 图像灰度化
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # 高斯滤波
    blur = cv2.GaussianBlur(gray, (5, 5), 0)

    # Sobel算子处理
    sobel = cv2.Sobel(blur, cv2.CV_8U, 1, 0, ksize=3)

    # 二值化处理
    ret, binary = cv2.threshold(sobel, 0, 255, cv2.THRESH_OTSU + cv2.THRESH_BINARY)

    # 膨胀和腐蚀操作
    element1 = cv2.getStructuringElement(cv2.MORPH_RECT, (30, 9))
    element2 = cv2.getStructuringElement(cv2.MORPH_RECT, (24, 6))

    dilation = cv2.dilate(binary, element2, iterations=1)
    erosion = cv2.erode(dilation, element1, iterations=1)
    dilation2 = cv2.dilate(erosion, element2, iterations=3)

    # 查找轮廓
    contours, hierarchy = cv2.findContours(dilation2, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

    # 筛选车牌轮廓
    car_plate = None
    for i in range(len(contours)):
        cnt = contours[i]
        area = cv2.contourArea(cnt)

        # 面积筛选
        if area < 2000 or area > 50000:
            continue

        # 轮廓近似
        approx = cv2.approxPolyDP(cnt, 4, True)

        # 判断是否为矩形
        if len(approx) != 4:
            continue

        # 判断矩形边长比例是否符合要求
        x, y, w, h = cv2.boundingRect(cnt)
        ratio = float(w) / float(h)
        if ratio > 8 or ratio < 2:
            continue

        # 符合要求的轮廓
        car_plate = cnt
        break

    if car_plate is None:
        return None

    # 提取车牌图像
    x, y, w, h = cv2.boundingRect(car_plate)
    car_plate_image = image[y:y+h, x:x+w]
    
    # 检查车牌图像是否为空或大小为零
    if car_plate_image is None or car_plate_image.size == 0:
        return None

    return car_plate_image

如果`car_plate_image`为空或大小为零，将返回`None`，否则将返回车牌图像。