获得不同干扰程度的模糊图像

同时对一共父级文件夹遍历。获得对应不同干扰程度的模糊图像

# This isimport cv2
import numpy as npdef reduce_resolution(image, factor):height, width, _ = image.shape    # 获取原始图像的宽度和高度new_width = int(width / factor) # 计算新的宽度和高度new_height = int(height / factor)# 使用resize方法来缩小图像分辨率，保持尺寸不变resized_image = cv2.resize(image, (new_width, new_height), interpolation=cv2.INTER_AREA)return resized_imagedef motion_blur(image, degree=12, 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 = 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 / degreeblurred = cv2.filter2D(image, -1, motion_blur_kernel)# convert to uint8cv2.normalize(blurred, blurred, 0, 255, cv2.NORM_MINMAX)blurred = np.array(blurred, dtype=np.uint8)return blurredimg = cv2.imread(r'H:\LRFRcode\acrface-bubb\arcface-pytorch-main\ForPaper1_Images_Crop\N010\N010_0008__Gaus2.jpg')idex = 15
Image_perturbation_Gaussian_idex = 1.02*idex
Image_perturbation_Motion_idex = 3*idex
Image_perturbation_reduceresolution_idex = 1.1*idex#
Gaussian_blurred_image = cv2.GaussianBlur(img, ksize=(0,0), sigmaX=Image_perturbation_Gaussian_idex) # ksize 必须是一个正奇数,可以通过 (0, 0) 来自动计算核的大小
Motion_blurred_image = motion_blur(img, degree=Image_perturbation_Motion_idex, angle=45)
reduce_resolution_image = reduce_resolution(img, factor=Image_perturbation_reduceresolution_idex)# cv2.imshow('Original', img)
# cv2.imshow('Gaussian Filter', Gaussian_blurred_image)
# cv2.imshow('Motion Filter', Motion_blurred_image)
# cv2.imshow('reduce_resolution Filter', reduce_resolution_image)
#
# cv2.waitKey(0)
# cv2.destroyAllWindows()# 保存图像
cv2.imwrite("save/Gaussian_blurred_image" + str(Image_perturbation_Gaussian_idex) +".jpg", Gaussian_blurred_image)  # 保存降低分辨率后的图像
cv2.imwrite("save/Motion_blurred_image" + str(Image_perturbation_Motion_idex) +".jpg", Motion_blurred_image)  # 保存降低分辨率后的图像
cv2.imwrite("save/reduced_resolution_image_" + str(Image_perturbation_reduceresolution_idex) +".jpg", reduce_resolution_image)  # 保存降低分辨率后的图像