关于python:遥感影像语义分割数据增强图像和原图同时增强

from PIL import Image, ImageFont, ImageDraw, ImageEnhance
import matplotlib.pyplot as plt
import numpy as np
import random
import random
import os
def image_rotate(image,label):

"""
对图像进行肯定角度的旋转
:param image_path:  图像门路
:param save_path:   保留门路
:param angle:       旋转角度
:return:
"""image_rotated = image.transpose(Image.ROTATE_90).convert('RGB')
label_rotated = label.transpose(Image.ROTATE_90)
return image_rotated,label_rotated

def image_rotate1(image,label):

"""
对图像进行肯定角度的旋转
:param image_path:  图像门路
:param save_path:   保留门路
:param angle:       旋转角度
:return:
"""image_rotated = image.transpose(Image.ROTATE_270).convert('RGB')
label_rotated = label.transpose(Image.ROTATE_270)
return image_rotated,label_rotated

def bright(image):

enh_bri = ImageEnhance.Brightness(image)
brightness = 1.2
image_brightened = enh_bri.enhance(brightness)
return image_brightened.convert('RGB')

def ruidu(image):

enh_sha = ImageEnhance.Sharpness(image)
sharpness = 2.3
image_sharped = enh_sha.enhance(sharpness)
return image_sharped.convert('RGB')

def sedu(image):

enh_col = ImageEnhance.Color(image)
color = 1.2
image_colored = enh_col.enhance(color)
return image_colored.convert('RGB')

def duibidu(image):

enh_con = ImageEnhance.Contrast(image)
contrast = 1.3
image_contrasted = enh_con.enhance(contrast)
return image_contrasted.convert('RGB')

def image_flip(image,label):

image_transpose = image.transpose(Image.FLIP_LEFT_RIGHT).convert('RGB')
label_transpose = label.transpose(Image.FLIP_LEFT_RIGHT)
return image_transpose,label_transpose

def image_color(Skrill 下载 image,label):

image_transpose = image.transpose(Image.FLIP_TOP_BOTTOM).convert('RGB')
label_transpose = label.transpose(Image.FLIP_TOP_BOTTOM)
return image_transpose,label_transpose

path_img = r’E:\torch-deeplabv3\pytorch-deeplab-xception-master\Waste2021\JPEGImages’
path_label = r’E:\torch-deeplabv3\pytorch-deeplab-xception-master\Waste2021\SegmentationClass’
path_new_img = r’E:\aa\torch-deeplabv3\pytorch-deeplab-xception-master\Waste2021\JPEGImages’
path_new_label = r’E:\aa\torch-deeplabv3\pytorch-deeplab-xception-master\Waste2021\SegmentationClass’
img_list = os.listdir(path_img)
label_list = os.listdir(path_label)
k=0
for i in range(len(img_list)):

img = Image.open(path_img + '/' + img_list[i])
label =Image.open(path_label + '/' + img_list[i][0:-4] + '.png')
#保留原图
img.convert('RGB').save(path_new_img + '/' + str(("%05d" % (k))) + '.jpg')
label.save(path_new_label + '/' + str(("%05d" % (k))) + '.png')
k += 1
#角度旋转第一次
img1,mask = image_rotate(img,label)
img1.save(path_new_img + '/' + str(("%05d" % (k))) + '.jpg')
mask.save(path_new_label + '/' + str(("%05d" % (k))) + '.png')
k+=1
#角度旋转第二次
img1,mask = image_rotate1(img,label)
img1.save(path_new_img + '/' + str(("%05d" % (k))) + '.jpg')
mask.save(path_new_label + '/' + str(("%05d" % (k))) + '.png')
k+=1
#调整亮度
img1 = bright(img)
img1.save(path_new_img + '/' + str(("%05d" % (k))) + '.jpg')
label.save(path_new_label + '/' + str(("%05d" % (k))) + '.png')
k+=1
#调整对比度
img1 = duibidu(img)
img1.save(path_new_img + '/' + str(("%05d" % (k))) + '.jpg')
label.save(path_new_label + '/' + str(("%05d" % (k))) + '.png')
k+=1
#调整锐度
img1 = ruidu(img)
img1.save(path_new_img + '/' + str(("%05d" % (k))) + '.jpg')
label.save(path_new_label + '/' + str(("%05d" % (k))) + '.png')
k+=1
#调整色度
img1 = sedu(img)
img1.save(path_new_img + '/' + str(("%05d" % (k))) + '.jpg')
label.save(path_new_label + '/' + str(("%05d" % (k))) + '.png')
k+=1
#左右翻转
img1,mask = image_flip(img,label)
img1.save(path_new_img + '/' + str(("%05d" % (k))) + '.jpg')
mask.save(path_new_label + '/' + str(("%05d" % (k))) + '.png')
k+=1
#高低翻转
img1,mask = image_color(img,label)
img1.save(path_new_img + '/' + str(("%05d" % (k))) + '.jpg')
mask.save(path_new_label + '/' + str(("%05d" % (k))) + '.png')
k += 1
print(img_list[i] + 'is finished')