Python_code_使用ImageFilter库对图片实现数据增强

Python_code_使用ImageFilter库对图片实现数据增强_28种

代码：

import os
import glob
from PIL import Image
from PIL import ImageFilter
from PIL import ImageEnhance
import matplotlib.pyplot as plt
#以下两行实现了在plt画图时，可以输出中文字符
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.unicode_minus'] = False


#对图片进行数据增强
def Image_Enhancement_img(img):
    print(img) #输出图片的路径
    #读取图像
    im = Image.open(img)

    #显示图像
    #plt.figure()
    #plt.subplot(121),plt.title("原图"),plt.imshow(im),plt.xticks([]),plt.yticks([]);

    #1. 对图像进行颜色通道的变换(5种；1-5)
    r,g,b=im.split()        #获得RGB通道数据

    #rbg
    om = Image.merge("RGB",(r,b,g)) #合并通道，参数：（色彩，新的色彩通道）
    new_filename = img[:-4] + '_rbg.png'    #print(new_filename)
    om.save(new_filename)
    #plt.subplot(122),plt.title("rbg"),plt.imshow(om),plt.xticks([]),plt.yticks([]);

    #gbr
    om = Image.merge("RGB",(g,b,r))
    new_filename = img[:-4] + '_gbr.png'
    om.save(new_filename)
    #plt.subplot(122),plt.title("gbr"),plt.imshow(om),plt.xticks([]),plt.yticks([]);

    #grb
    om = Image.merge("RGB",(g,r,b))
    new_filename = img[:-4] + '_0.png'
    om.save(new_filename)
    #plt.subplot(122),plt.title("grb"),plt.imshow(om),plt.xticks([]),plt.yticks([]);

    #bgr
    om = Image.merge("RGB",(b,g,r))
    new_filename = img[:-4] + '_1.png'
    om.save(new_filename)
    #plt.subplot(122),plt.title("bgr"),plt.imshow(om),plt.xticks([]),plt.yticks([]);

    #brg
    om = Image.merge("RGB",(b,r,g))
    new_filename = img[:-4] + '_2.png'
    om.save(new_filename)
    #plt.subplot(122),plt.title("brg"),plt.imshow(om),plt.xticks([]),plt.yticks([]);


    #第二部分：非常规的数据增强
    om = im.filter(ImageFilter.DETAIL)
    new_filename = img[:-4] + '_细节.png'
    om.save(new_filename)
    #plt.subplot(122),plt.title("细节"),plt.imshow(om),plt.xticks([]),plt.yticks([]);

    om = im.filter(ImageFilter.SMOOTH)
    new_filename = img[:-4] + '_平滑.png'
    om.save(new_filename)
    #plt.subplot(122),plt.title("平滑"),plt.imshow(om),plt.xticks([]),plt.yticks([]);

    om = im.filter(ImageFilter.SMOOTH_MORE)
    new_filename = img[:-4] + '_阈值平滑.png'
    om.save(new_filename)
    #plt.subplot(122),plt.title("阈值平滑"),plt.imshow(om),plt.xticks([]),plt.yticks([]);

    #后面带参数的锐度调整重复了
    om = im.filter(ImageFilter.SHARPEN)
    new_filename = img[:-4] + '_锐化.png'
    om.save(new_filename)
    #plt.subplot(122),plt.title("锐化"),plt.imshow(om),plt.xticks([]),plt.yticks([]);

    om = im.filter(ImageFilter.BLUR)
    new_filename = img[:-4] + '_3.png'
    om.save(new_filename)
    #plt.subplot(122),plt.title("模糊"),plt.imshow(om),plt.xticks([]),plt.yticks([]);

    om = im.filter(ImageFilter.CONTOUR) #图像的轮廓效果
    new_filename = img[:-4] + '_4.png'
    om.save(new_filename)
    #plt.subplot(122),plt.title("轮廓"),plt.imshow(om),plt.xticks([]),plt.yticks([]);


    om = im.filter(ImageFilter.FIND_EDGES)
    new_filename = img[:-4] + '_5.png'
    om.save(new_filename)
    #plt.subplot(122),plt.title("边界"),plt.imshow(om),plt.xticks([]),plt.yticks([]);

    om = im.filter(ImageFilter.EDGE_ENHANCE)
    new_filename = img[:-4] + '_6.png'
    om.save(new_filename)
    #plt.subplot(122),plt.title("边界加强"),plt.imshow(om),plt.xticks([]),plt.yticks([]);

    om = im.filter(ImageFilter.EDGE_ENHANCE_MORE)
    new_filename = img[:-4] + '_7.png'
    om.save(new_filename)
    #plt.subplot(122),plt.title("阈值边界加强"),plt.imshow(om),plt.xticks([]),plt.yticks([]);

    om = im.filter(ImageFilter.EMBOSS)
    new_filename = img[:-4] + '_8.png'
    om.save(new_filename)
    #plt.subplot(122),plt.title("浮雕"),plt.imshow(om),plt.xticks([]),plt.yticks([]);


    #第三部分：调整图像的颜色平衡，0是灰度图像，1是原图（建议在-13~13的范围内；-1，1.5，2）
    om = ImageEnhance.Color(im).enhance(-1)
    new_filename = img[:-4] + '_9.png'
    om.save(new_filename)
    #plt.subplot(122),plt.title("色彩平衡调整_参数2"),plt.imshow(om),plt.xticks([]),plt.yticks([]);

    om = ImageEnhance.Color(im).enhance(1.5)
    new_filename = img[:-4] + '_10.png'
    om.save(new_filename)

    om = ImageEnhance.Color(im).enhance(2)
    new_filename = img[:-4] + '_11.png'
    om.save(new_filename)



　　#第四部分：调整图像的亮度，enhance 中的参数可变（建议在0.3~2的范围内；0.3，0.7，1.5）
    om = ImageEnhance.Brightness(im).enhance(0.3)
    new_filename = img[:-4] + '_12.png'
    om.save(new_filename)
　　#plt.subplot(122),plt.title("图像亮度调整_参数1.3"),plt.imshow(om),plt.xticks([]),plt.yticks([]);

    om = ImageEnhance.Brightness(im).enhance(0.7)
    new_filename = img[:-4] + '_13.png'
    om.save(new_filename)

    om = ImageEnhance.Brightness(im).enhance(1.5)
    new_filename = img[:-4] + '_14.png'
    om.save(new_filename)


　　# 第五部分：调整图像的对比度，enhance 中的参数可变（建议在-2~4的范围内；-2，-1，0.5，1.5）
    om = ImageEnhance.Contrast(im).enhance(-2)
    new_filename = img[:-4] + '_15.png'
    om.save(new_filename)
　　# plt.subplot(122), plt.title("图像对比度调整_参数1.5"), plt.imshow(om), plt.xticks([]), plt.yticks([]);

    om = ImageEnhance.Contrast(im).enhance(-1)
    new_filename = img[:-4] + '_16.png'
    om.save(new_filename)

    om = ImageEnhance.Contrast(im).enhance(0.5)
    new_filename = img[:-4] + '_17.png'
    om.save(new_filename)

    om = ImageEnhance.Contrast(im).enhance(1.5)
    new_filename = img[:-4] + '_18.png'
    om.save(new_filename)


　　# 第六部分：调整图像的锐度，锐度为0是原图，参数为负是感觉和模糊效果差不多（建议选3、9、30）
    om = ImageEnhance.Sharpness(im).enhance(3)
    new_filename = img[:-4] + '_19.png'
    om.save(new_filename)
　　#plt.subplot(122), plt.title("图像锐度调整_参数30"), plt.imshow(om), plt.xticks([]), plt.yticks([]);

    om = ImageEnhance.Sharpness(im).enhance(9)
    new_filename = img[:-4] + '_20.png'
    om.save(new_filename)

    om = ImageEnhance.Sharpness(im).enhance(30)
    new_filename = img[:-4] + '_21.png'
    om.save(new_filename)

　　#plt.show()

'''主函数部分：进行函数调用'''
#图片数据所在的根目录

dataset_dir = '..\data'

for root, dirs, files in os.walk(dataset_dir):
　　'''
    root 所指的是当前正在遍历的这个文件夹的本身的地址
    dirs 是一个 list ，内容是该文件夹中所有的目录的名字(不包括子目录)
    files 同样是 list , 内容是该文件夹中所有的文件(不包括子目录)
    '''

　　for sDir in dirs:
　　# 当前文件夹下子目录的绝对路径
        imgs_list = glob.glob(os.path.join(root, sDir) + '/*.png')
        imgs_list_xml = glob.glob(os.path.join(root, sDir) + '/*.xml')
　　#print(imgs_list)

    #处理png图片
　　for i in imgs_list:
　　　　#print(i)
　　　　if '_' in i or 'png' not in i:
       　　continue
       Image_Enhancement_img(i)