下面的代码可以实现13种数据增强,出自 PIL库的 Image和 ImageEnhance,后续如果想增加扩充倍数,可直接添加。
目录
一、相关的库主要包括:
ImageEnhance.enhance(factor) 对选择属性的数值增强factor倍
ImageEnhance.Color(im) 调整图像的颜色平衡
ImageEnhance.Contrast(im) 调整图像的对比度
ImageEnhance.Brightness(im) 调整图像的亮度
ImageEnhance.Sharpness(im) 调整图像的锐度
image.rotate()方法进行旋转
image.transpose()方法进行翻转
将图片的原来文件夹和增强后的文件夹填写正确。
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- ###
- #本代码共采用了13种数据增强,如采用其他数据增强方式,可以参考本代码,随意替换。
- #imageDir 为原数据集的存放位置
- #saveDir 为数据增强后数据的存放位置
- ###
-
- def flip(root_path,img_name): #翻转图像
- img = Image.open(os.path.join(root_path, img_name))
- filp_img = img.transpose(Image.FLIP_LEFT_RIGHT)
- # filp_img.save(os.path.join(root_path,img_name.split('.')[0] + '_flip.jpg'))
- return filp_img
-
- def rotation(root_path, img_name):
- img = Image.open(os.path.join(root_path, img_name))
- rotation_img = img.rotate(20) #旋转角度20
- # rotation_img.save(os.path.join(root_path,img_name.split('.')[0] + '_rotation.jpg'))
- return rotation_img
-
- def rotation2(root_path, img_name):
- img = Image.open(os.path.join(root_path, img_name))
- rotation_img = img.rotate(10) #旋转角度10
- # rotation_img.save(os.path.join(root_path,img_name.split('.')[0] + '_rotation.jpg'))
- return rotation_img
-
- def rotation3(root_path, img_name):
- img = Image.open(os.path.join(root_path, img_name))
- rotation_img = img.rotate(90) #旋转角度90
- # rotation_img.save(os.path.join(root_path,img_name.split('.')[0] + '_rotation.jpg'))
- return rotation_img
-
- def rotation4(root_path, img_name):
- img = Image.open(os.path.join(root_path, img_name))
- rotation_img = img.rotate(180) #旋转角度180
- # rotation_img.save(os.path.join(root_path,img_name.split('.')[0] + '_rotation.jpg'))
- return rotation_img
-
- def rotation5(root_path, img_name):
- img = Image.open(os.path.join(root_path, img_name))
- rotation_img = img.rotate(45) #旋转角度45
- # rotation_img.save(os.path.join(root_path,img_name.split('.')[0] + '_rotation.jpg'))
- return rotation_img
-
-
-
- def randomColor(root_path, img_name): #随机颜色
- """
- 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
- """
- image = Image.open(os.path.join(root_path, img_name))
- random_factor = np.random.randint(0, 31) / 10. # 随机因子
- color_image = ImageEnhance.Color(image).enhance(random_factor) # 调整图像的饱和度
- random_factor = np.random.randint(10, 21) / 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, 21) / 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(0, 31) / 10. # 随机因子
- return ImageEnhance.Sharpness(contrast_image).enhance(random_factor) # 调整图像锐度
-
-
- def contrastEnhancement(root_path, img_name): # 对比度增强
- image = Image.open(os.path.join(root_path, img_name))
- enh_con = ImageEnhance.Contrast(image)
- contrast = 1.5
- image_contrasted = enh_con.enhance(contrast)
- return image_contrasted
-
- def contrastEnhancement2(root_path, img_name): # 对比度增强2
- image = Image.open(os.path.join(root_path, img_name))
- enh_con = ImageEnhance.Contrast(image)
- contrast = 1.75
- image_contrasted = enh_con.enhance(contrast)
- return image_contrasted
-
- def brightnessEnhancement(root_path,img_name):#亮度增强
- image = Image.open(os.path.join(root_path, img_name))
- enh_bri = ImageEnhance.Brightness(image)
- brightness = 1.5
- image_brightened = enh_bri.enhance(brightness)
- return image_brightened
-
- def colorEnhancement(root_path,img_name):#颜色增强
- image = Image.open(os.path.join(root_path, img_name))
- enh_col = ImageEnhance.Color(image)
- color = 1.5
- image_colored = enh_col.enhance(color)
- return image_colored
-
- def colorEnhancement2(root_path,img_name):#颜色增强->黑白图
- image = Image.open(os.path.join(root_path, img_name))
- enh_col = ImageEnhance.Color(image)
- color = 2
- image_colored = enh_col.enhance(color)
- return image_colored
-
- #锐度增强
- def colorSharpness(root_path,img_name):
- image = Image.open(os.path.join(root_path, img_name))
- enh_col = ImageEnhance.Sharpness(image)
- sharpness = 3.0
- image_colored = enh_col.enhance(sharpness)
- return image_colored
-
-
- from PIL import Image
- from PIL import ImageEnhance
- import os
- import cv2
- import numpy as np#D:\images_background\22 (2)
- imageDir="D:/学习/人工智能/孪生网络/数据集/原数据集 - 轮胎痕迹/trace.6L" #要改变的图片的路径文件夹
- saveDir="D:/学习/人工智能/孪生网络/数据集/原数据集 - 轮胎痕迹/trace.6L" #要保存的图片的路径文件夹
- #D:\trace_images01\.6LD:\学习\人工智能\孪生网络\数据集\原数据集 - 轮胎花纹\t (2)
- for name in os.listdir(imageDir):
-
- saveName= name[:-4]+"bright.jpg"
- saveImage=brightnessEnhancement(imageDir,name)#2.亮度增强
- saveImage.save(os.path.join(saveDir,saveName))
-
- saveName= name[:-4]+"flip.jpg"
- saveImage=flip(imageDir,name)#3.翻转图像
- saveImage.save(os.path.join(saveDir,saveName))
-
- saveName= name[:-4]+"rotation.jpg"
- saveImage=rotation(imageDir,name)#4.旋转角度20度
- saveImage.save(os.path.join(saveDir,saveName))
-
-
- saveName= name[:-4]+"randmcolor.jpg"
- saveImage=randomColor(imageDir,name)#5.随机颜色
- saveImage.save(os.path.join(saveDir,saveName))
-
-
- saveName= name[:-4]+"color.jpg"
- saveImage=colorEnhancement(imageDir,name)#6.颜色增强
- saveImage.save(os.path.join(saveDir,saveName))
-
-
- saveName= name[:-4]+"contrast.jpg"
- saveImage=contrastEnhancement(imageDir,name) #7.对比度增强
- saveImage.save(os.path.join(saveDir,saveName))
-
- saveName= name[:-4]+"rotation3.jpg"
- saveImage=rotation3(imageDir,name)#8.旋转角度30度
- saveImage.save(os.path.join(saveDir,saveName))
-
- saveName= name[:-4]+"rotation4.jpg"
- saveImage=rotation4(imageDir,name)#9.旋转角度45度
- saveImage.save(os.path.join(saveDir,saveName))
-
- saveName= name[:-4]+"contrast2.jpg"
- saveImage=contrastEnhancement2(imageDir,name) #10.对比度增强
- saveImage.save(os.path.join(saveDir,saveName))
-
- saveName= name[:-4]+"colorEnhancement2.jpg"
- saveImage=colorEnhancement2(imageDir,name) #11.黑白图
- saveImage.save(os.path.join(saveDir,saveName))
-
- saveName= name[:-4]+"colorSharpness.jpg"
- saveImage=colorSharpness(imageDir,name) #12.增强因子为2.0表示锐化图像。值越大图像边界越多越清晰。
- saveImage.save(os.path.join(saveDir,saveName))
-
- saveName= name[:-4]+"rotate5.jpg"
- saveImage=rotation5(imageDir,name) #13.对比度增强
- saveImage.save(os.path.join(saveDir,saveName))
-