针对图像分类的数据增强方法，离线增强，适合分类，无标签增强
针对图像分类的数据增强方法，离线增强，适合分类，无标签增强
代码：
改变路径即可使用
# 本代码主要提供一些针对图像分类的数据增强方法# 1、平移。在图像平面上对图像以一定方式进行平移。
# 2、翻转图像。沿着水平或者垂直方向翻转图像。
# 3、旋转角度。随机旋转图像一定角度; 改变图像内容的朝向。
# 4、随机颜色。包括调整图像饱和度、亮度、对比度、锐度
# 5、缩放变形图片。
# 6、二值化图像。
# 7、随机黑色块遮挡
# 8、添加噪声from PIL import Image
from PIL import ImageEnhance
from PIL import ImageChops
import os
import numpy as np# 1、图像平移
def move(img): #平移，平移尺度为offoffset = ImageChops.offset(img, np.random.randint(1, 20), np.random.randint(1, 40))return offset# 2、翻转图像
def flip(img): factor = np.random.randint(1, 3) #随机因子，随机上下或者左右翻转if factor == 1:filp_img = img.transpose(Image.FLIP_TOP_BOTTOM)else:filp_img = img.transpose(Image.FLIP_LEFT_RIGHT)return filp_img# 3、旋转角度
def rotation(img):factor = np.random.randint(1, 21) #随机旋转角度rotation_img = img.rotate(factor) return rotation_img# 4、随机颜色
def color(img): """对图像进行颜色抖动:param image: PIL的图像image:return: 有颜色色差的图像image"""random_factor = np.random.randint(5, 15) / 10. # 随机因子color_image = ImageEnhance.Color(img).enhance(random_factor) # 调整图像的饱和度random_factor = np.random.randint(8, 15) / 10. # 随机因子brightness_image = ImageEnhance.Brightness(color_image).enhance(random_factor) # 调整图像的亮度random_factor = np.random.randint(10, 13) / 10. # 随机因子contrast_image = ImageEnhance.Contrast(brightness_image).enhance(random_factor) # 调整图像对比度random_factor = np.random.randint(5, 31) / 10. # 随机因子random_color = ImageEnhance.Sharpness(contrast_image).enhance(random_factor) # 调整图像锐度return random_color # 5、缩放变形图片
def crop(img):factor_1 = np.random.randint(10, 50)factor_2 = np.random.randint(20, 50)crop_img = img.crop((img.size[0]/factor_1, img.size[1]/factor_2, img.size[0]*(factor_1-1)/factor_1, img.size[1]*(factor_2-1)/factor_2))cropResize_img = crop_img.resize((img.size[0], img.size[1]))return cropResize_img# 6、二值化图像
def convert(img):convert_img = img.convert('L')return convert_img# 7、黑色块遮挡
def paste(img):# 左上右下factor_1 = np.random.randint(20, 70)factor_2 = np.random.randint(30, 60)# 随机进行左边遮罩a = np.random.randint(1,3)if a == 2:img.paste((0,0,0),(int(img.size[0]*(factor_1-np.random.randint(2,4))/factor_1), int(img.size[1]*(np.random.randint(1,25))/factor_2), int(img.size[0]*(factor_1-np.random.randint(0,2))/factor_1),int(img.size[1]*(np.random.randint(26,50))/factor_2)))else:# 随机进行底部遮罩img.paste((0,0,0),(int(img.size[0]*(np.random.randint(1,19))/factor_1), # int(img.size[1]*(factor_2-2)/factor_2), int(img.size[1]*(factor_2-np.random.randint(3,6))/factor_2),int(img.size[0]*(np.random.randint(21,41))/factor_1),# int(img.size[1]*(factor_2-1)/factor_2)int(img.size[1]*(factor_2-np.random.randint(0,3))/factor_2)))return img# 8、随机添加黑白噪声
def salt_and_pepper_noise(img, proportion = 0.00025):noise_img = imgheight,width =noise_img.size[0],noise_img.size[1]proportion = proportion * np.random.randint(1, 50)num = int(height * width * proportion) #多少个像素点添加椒盐噪声pixels = noise_img.load()for i in range(num):w = np.random.randint(0,width-1)h = np.random.randint(0,height-1)if np.random.randint(0,2) == 1:pixels[h,w] = 0else:pixels[h,w] = 255return noise_img# 概率执行函数
def random_run(probability, func, useimage):"""以probability%的概率执行func(*args)"""list = []for i in range(probability):list.append(1) #list中放入probability个1for x in range(100 - probability):list.append(0) #剩下的位置放入0a = np.random.choice(list) #随机抽取一个if a == 0:return useimageif a == 1:image = func(useimage)return imagedef main():imageDir = "D:/adavance/resnet50/datasets/Coupling/test/norm " #要改变的图片的路径文件夹saveDir = imageDir#"D:/adavance/resnet50/datasets/TailCotterPin/test/norm_TailCotterPin" #要保存的图片的路径文件夹seed = 10 #每张初始图片要数据增强为多少张图片for name in os.listdir(imageDir):i=0for i in range(seed):i = i+1saveName = str(name[:-4]) + str(i) +".jpg"img = Image.open(os.path.join(imageDir, name))saveImage = random_run(60, flip, img) # 翻转saveImage = random_run(70, color, saveImage) # 色彩变化saveImage = random_run(30, crop, saveImage) # 裁减缩放#saveImage = random_run(30, paste, saveImage) # 添加遮罩saveImage = random_run(20, move, saveImage) # 平移saveImage = random_run(50, rotation, saveImage) # 旋转saveImage = random_run(10, convert, saveImage) # 二值化 saveImage = random_run(20, salt_and_pepper_noise, saveImage) # 添加噪声点# saveImage = random_run(90, gauss_noise, saveImage)print(type(saveImage))if saveImage != None:saveImage.save(os.path.join(saveDir, saveName))else:passprint(i)if __name__ == "__main__":main()PS: 记得备份原图，要不然出差错了，就不好恢复了
针对图像分类的数据增强方法，离线增强，适合分类，无标签增强
针对图像分类的数据增强方法，离线增强，适合分类，无标签增强
代码：
改变路径即可使用
# 本代码主要提供一些针对图像分类的数据增强方法# 1、平移。在图像平面上对图像以一定方式进行平移。
# 2、翻转图像。沿着水平或者垂直方向翻转图像。
# 3、旋转角度。随机旋转图像一定角度; 改变图像内容的朝向。
# 4、随机颜色。包括调整图像饱和度、亮度、对比度、锐度
# 5、缩放变形图片。
# 6、二值化图像。
# 7、随机黑色块遮挡
# 8、添加噪声from PIL import Image
from PIL import ImageEnhance
from PIL import ImageChops
import os
import numpy as np# 1、图像平移
def move(img): #平移，平移尺度为offoffset = ImageChops.offset(img, np.random.randint(1, 20), np.random.randint(1, 40))return offset# 2、翻转图像
def flip(img): factor = np.random.randint(1, 3) #随机因子，随机上下或者左右翻转if factor == 1:filp_img = img.transpose(Image.FLIP_TOP_BOTTOM)else:filp_img = img.transpose(Image.FLIP_LEFT_RIGHT)return filp_img# 3、旋转角度
def rotation(img):factor = np.random.randint(1, 21) #随机旋转角度rotation_img = img.rotate(factor) return rotation_img# 4、随机颜色
def color(img): """对图像进行颜色抖动:param image: PIL的图像image:return: 有颜色色差的图像image"""random_factor = np.random.randint(5, 15) / 10. # 随机因子color_image = ImageEnhance.Color(img).enhance(random_factor) # 调整图像的饱和度random_factor = np.random.randint(8, 15) / 10. # 随机因子brightness_image = ImageEnhance.Brightness(color_image).enhance(random_factor) # 调整图像的亮度random_factor = np.random.randint(10, 13) / 10. # 随机因子contrast_image = ImageEnhance.Contrast(brightness_image).enhance(random_factor) # 调整图像对比度random_factor = np.random.randint(5, 31) / 10. # 随机因子random_color = ImageEnhance.Sharpness(contrast_image).enhance(random_factor) # 调整图像锐度return random_color # 5、缩放变形图片
def crop(img):factor_1 = np.random.randint(10, 50)factor_2 = np.random.randint(20, 50)crop_img = img.crop((img.size[0]/factor_1, img.size[1]/factor_2, img.size[0]*(factor_1-1)/factor_1, img.size[1]*(factor_2-1)/factor_2))cropResize_img = crop_img.resize((img.size[0], img.size[1]))return cropResize_img# 6、二值化图像
def convert(img):convert_img = img.convert('L')return convert_img# 7、黑色块遮挡
def paste(img):# 左上右下factor_1 = np.random.randint(20, 70)factor_2 = np.random.randint(30, 60)# 随机进行左边遮罩a = np.random.randint(1,3)if a == 2:img.paste((0,0,0),(int(img.size[0]*(factor_1-np.random.randint(2,4))/factor_1), int(img.size[1]*(np.random.randint(1,25))/factor_2), int(img.size[0]*(factor_1-np.random.randint(0,2))/factor_1),int(img.size[1]*(np.random.randint(26,50))/factor_2)))else:# 随机进行底部遮罩img.paste((0,0,0),(int(img.size[0]*(np.random.randint(1,19))/factor_1), # int(img.size[1]*(factor_2-2)/factor_2), int(img.size[1]*(factor_2-np.random.randint(3,6))/factor_2),int(img.size[0]*(np.random.randint(21,41))/factor_1),# int(img.size[1]*(factor_2-1)/factor_2)int(img.size[1]*(factor_2-np.random.randint(0,3))/factor_2)))return img# 8、随机添加黑白噪声
def salt_and_pepper_noise(img, proportion = 0.00025):noise_img = imgheight,width =noise_img.size[0],noise_img.size[1]proportion = proportion * np.random.randint(1, 50)num = int(height * width * proportion) #多少个像素点添加椒盐噪声pixels = noise_img.load()for i in range(num):w = np.random.randint(0,width-1)h = np.random.randint(0,height-1)if np.random.randint(0,2) == 1:pixels[h,w] = 0else:pixels[h,w] = 255return noise_img# 概率执行函数
def random_run(probability, func, useimage):"""以probability%的概率执行func(*args)"""list = []for i in range(probability):list.append(1) #list中放入probability个1for x in range(100 - probability):list.append(0) #剩下的位置放入0a = np.random.choice(list) #随机抽取一个if a == 0:return useimageif a == 1:image = func(useimage)return imagedef main():imageDir = "D:/adavance/resnet50/datasets/Coupling/test/norm " #要改变的图片的路径文件夹saveDir = imageDir#"D:/adavance/resnet50/datasets/TailCotterPin/test/norm_TailCotterPin" #要保存的图片的路径文件夹seed = 10 #每张初始图片要数据增强为多少张图片for name in os.listdir(imageDir):i=0for i in range(seed):i = i+1saveName = str(name[:-4]) + str(i) +".jpg"img = Image.open(os.path.join(imageDir, name))saveImage = random_run(60, flip, img) # 翻转saveImage = random_run(70, color, saveImage) # 色彩变化saveImage = random_run(30, crop, saveImage) # 裁减缩放#saveImage = random_run(30, paste, saveImage) # 添加遮罩saveImage = random_run(20, move, saveImage) # 平移saveImage = random_run(50, rotation, saveImage) # 旋转saveImage = random_run(10, convert, saveImage) # 二值化 saveImage = random_run(20, salt_and_pepper_noise, saveImage) # 添加噪声点# saveImage = random_run(90, gauss_noise, saveImage)print(type(saveImage))if saveImage != None:saveImage.save(os.path.join(saveDir, saveName))else:passprint(i)if __name__ == "__main__":main()PS: 记得备份原图，要不然出差错了，就不好恢复了