def motion_blur1(image, degree=10, angle=45): image = np.array(image) # 这里生成任意角度的运动模糊kernel的矩阵， degree越大，模糊程度越高 M = cv.getRotationMatrix2D((degree / 2, degree / 2), angle, 1) motion_blur_kernel = np.diag(np.ones(degree)) motion_blur_kernel = cv.warpAffine(motion_blur_kernel, M, (degree, degree)) motion_blur_kernel = motion_blur_kernel / degree blurred = cv.filter2D(image, -1, motion_blur_kernel) # convert to uint8 cv.normalize(blurred, blurred, 0, 255, cv.NORM_MINMAX) blurred = np.array(blurred, dtype=np.uint8) return blurred filePath = r'D:\Users\Administrator\PycharmProjects\pythonProject\paper_need\blur' for i in os.listdir(filePath): outPath = r'D:\Users\Administrator\PycharmProjects\pythonProject\paper_need\blur.' + str(i) img = cv.imread("./deblur/"+i) img_ = motion_blur1(img) cv.imwrite(outPath,img_)，上述代码出现问题：cv2.error: OpenCV(4.7.0) :-1: error: (-5:Bad argument) in function 'filter2D' > Overload resolution failed: > - src data type = 17 is not supported > - Expected Ptrcv::UMat for argument 'src'

在你的代码中，img变量的类型是一个普通的numpy数组，它的数据类型为uint8。为了解决这个问题，你可以将img变量转换为OpenCV的UMat类型，这是OpenCV中的一种特殊类型，它可以更好地与OpenCV的函数一起使用。

你可以使用cv2.UMat()函数将numpy数组转换为UMat类型。修改代码如下：

filePath = r'D:\Users\Administrator\PycharmProjects\pythonProject\paper_need\blur'
for i in os.listdir(filePath):
    outPath = r'D:\Users\Administrator\PycharmProjects\pythonProject\paper_need\blur.' + str(i)
    img = cv.imread("./deblur/"+i)
    img = cv.UMat(img)  # 将numpy数组转换为UMat类型
    img_ = motion_blur1(img)
    cv.imwrite(outPath,img_)

这样应该就能解决上述错误了。如果还有问题，请提供更多的代码和错误信息，以便我们更好地帮助你解决问题。

相关问题

coding: utf-8 import cv2 import numpy as np import cv2 as cv import os def motion_blur1(image, degree=10, angle=45): image = np.array(image) # 这里生成任意角度的运动模糊kernel的矩阵， degree越大，模糊程度越高 M = cv.getRotationMatrix2D((degree / 2, degree / 2), angle, 1) motion_blur_kernel = np.diag(np.ones(degree)) motion_blur_kernel = cv.warpAffine(motion_blur_kernel, M, (degree, degree)) motion_blur_kernel = motion_blur_kernel / degree blurred = cv.filter2D(image, -1, motion_blur_kernel) # convert to uint8 cv.normalize(blurred, blurred, 0, 255, cv.NORM_MINMAX) blurred = np.array(blurred, dtype=np.uint8) return blurred filePath = r'D:\Users\Administrator\PycharmProjects\pythonProject\paper_need\blur' for i in os.listdir(filePath): outPath = r'D:\Users\Administrator\PycharmProjects\pythonProject\paper_need\blur.' + str(i) img = cv.imread("./deblur/"+i) img = cv.UMat(img) # 将numpy数组转换为UMat类型 img_ = motion_blur1(img) cv.imwrite(outPath,img_)，上述代码出现问题：cv2.error: OpenCV(4.7.0) :-1: error: (-5:Bad argument) in function 'filter2D' > Overload resolution failed: > - src data type = 17 is not supported > - Expected Ptrcv::UMat for argument 'src'

这个错误是由于filter2D函数的输入类型不匹配导致的。在这里，您正在使用UMat类型的图像，但是filter2D函数不支持UMat类型的图像。您可以尝试将UMat类型的图像转换为numpy数组类型的图像，然后再进行模糊处理。这里是修改后的代码：

import cv2
import numpy as np
import os

def motion_blur1(image, degree=10, angle=45):
    image = np.array(image)
    # 生成任意角度的运动模糊kernel的矩阵， degree越大，模糊程度越高
    M = cv2.getRotationMatrix2D((degree / 2, degree / 2), angle, 1)
    motion_blur_kernel = np.diag(np.ones(degree))
    motion_blur_kernel = cv2.warpAffine(motion_blur_kernel, M, (degree, degree))
    motion_blur_kernel = motion_blur_kernel / degree
    # 将UMat类型的图像转换为numpy数组类型的图像
    blurred = cv2.filter2D(image.get(), -1, motion_blur_kernel)  
    # convert to uint8
    cv2.normalize(blurred, blurred, 0, 255, cv2.NORM_MINMAX)
    blurred = np.array(blurred, dtype=np.uint8)
    # 将numpy数组类型的图像转换为UMat类型的图像
    blurred = cv2.UMat(blurred)
    return blurred

filePath = r'D:\Users\Administrator\PycharmProjects\pythonProject\paper_need\blur'
for i in os.listdir(filePath):
    outPath = r'D:\Users\Administrator\PycharmProjects\pythonProject\paper_need\blur.' + str(i)
    img = cv2.imread("./deblur/"+i)
    img = cv2.UMat(img)
    img_ = motion_blur1(img)
    cv2.imwrite(outPath,img_.get())

这里将UMat类型的图像转换为numpy数组类型的图像，然后进行模糊处理，最后再将numpy数组类型的图像转换为UMat类型的图像。这样就可以解决这个问题了。

import cv2 import numpy as np import os def motion_blur1(image, degree=10, angle=45): image = np.array(image) # 生成任意角度的运动模糊kernel的矩阵， degree越大，模糊程度越高 M = cv2.getRotationMatrix2D((degree / 2, degree / 2), angle, 1) motion_blur_kernel = np.diag(np.ones(degree)) motion_blur_kernel = cv2.warpAffine(motion_blur_kernel, M, (degree, degree)) motion_blur_kernel = motion_blur_kernel / degree # 将UMat类型的图像转换为numpy数组类型的图像 blurred = cv2.filter2D(image.get(), -1, motion_blur_kernel) # convert to uint8 cv2.normalize(blurred, blurred, 0, 255, cv2.NORM_MINMAX) blurred = np.array(blurred, dtype=np.uint8) # 将numpy数组类型的图像转换为UMat类型的图像 blurred = cv2.UMat(blurred) return blurred filePath = r'D:\Users\Administrator\PycharmProjects\pythonProject\paper_need\blur' for i in os.listdir(filePath): outPath = r'D:\Users\Administrator\PycharmProjects\pythonProject\paper_need\blur.' + str(i) img = cv2.imread("./deblur/"+i) img = cv2.UMat(img) img_ = motion_blur1(img) cv2.imwrite(outPath,img_.get())，上述代码出现问题： 'numpy.ndarray' object has no attribute 'get'

这个问题是由于UMat类型的图像没有get()方法导致的。可以使用UMat类型的图像本身进行处理，无需调用get()方法。

这里是修改后的代码：

import cv2
import numpy as np
import os

def motion_blur1(image, degree=10, angle=45):
    image = np.array(image)
    # 生成任意角度的运动模糊kernel的矩阵， degree越大，模糊程度越高
    M = cv2.getRotationMatrix2D((degree / 2, degree / 2), angle, 1)
    motion_blur_kernel = np.diag(np.ones(degree))
    motion_blur_kernel = cv2.warpAffine(motion_blur_kernel, M, (degree, degree))
    motion_blur_kernel = motion_blur_kernel / degree
    # 将UMat类型的图像直接进行处理
    blurred = cv2.filter2D(image, -1, motion_blur_kernel)  
    # convert to uint8
    cv2.normalize(blurred, blurred, 0, 255, cv2.NORM_MINMAX)
    blurred = np.array(blurred, dtype=np.uint8)
    blurred = cv2.UMat(blurred)
    return blurred

filePath = r'D:\Users\Administrator\PycharmProjects\pythonProject\paper_need\blur'
for i in os.listdir(filePath):
    outPath = r'D:\Users\Administrator\PycharmProjects\pythonProject\paper_need\blur.' + str(i)
    img = cv2.imread("./deblur/"+i)
    img = cv2.UMat(img)
    img_ = motion_blur1(img)
    cv2.imwrite(outPath,img_.get())

这里直接对UMat类型的图像进行处理，无需调用get()方法。