import cv2
from torch.utils.data import Dataset
from PIL import Image
import os
import matplotlib.pyplot as plt
class MyData(Dataset):
# 初始化 root_dir大的路径和label_dir具体内容/获取地址
def __init__(self, root_dir, label_dir):
# 创建全局变量
self.root_dir = root_dir
self.label_dir = label_dir
# 获取一个路径地址 join()作用为拼接地址
self.path = os.path.join(self.root_dir + "/" + self.label_dir).replace("jpg", "png")
# bmp
# self.path = os.path.join(self.root_dir + "/" + self.label_dir).replace("bmp", "png")
# 获取路径下的所有列表
self.img_path = os.listdir(self.path)
print(self.img_path)
print("ok")
# idex作为一个编号
def __getitem__(self, idx):
# 读取其中的一个图片
img_name = self.img_path[idx]
print(img_name)
# 程序的相对路径
img_iten_path = os.path.join(self.root_dir, self.label_dir, img_name)
# 图片打开
img = Image.open(img_iten_path)
# 160 x120 == 640x480
# new_img = img.resize((640, 480))
# 256x256 == 1024 x1024
# new_img = img.resize((1024, 1024))
# 320x240 == 1280 x960
# new_img = img.resize((1280, 960))
# 384x288 == 1536x1152
# new_img = img.resize((1536, 1152))
# 640x480 == 2560 x1920
# new_img = img.resize((2560, 1920))
# 640x512 == 2560 x2048
# new_img=img.resize((2560, 2048))
# 1280 x1024 == 5120 x4196
new_img = img.resize((5120, 4196))
# 要保存的图片
# 160x120 == 640x480
# if not os.path.exists("../opencv/tu/480/"):
# os.mkdir("../opencv/tu/480/")
# print("目录已经创建")
# new_img.save("../opencv/tu/480/" + img_name.replace("jpg", "png").replace("bmp", "png"))
# 256x256 == 1024x1024
# if not os.path.exists("../opencv/tu/1024/"):
# os.mkdir("../opencv/tu/1024/")
# print("目录已经创建")
# new_img.save("../opencv/tu/1024/" + img_name.replace("jpg", "png").replace("bmp", "png"))
# 320x240 == 1280x960
# if not os.path.exists("../opencv/tu/960/"):
# os.mkdir("../opencv/tu/960/")
# print("目录已经创建")
# new_img.save("../opencv/tu/960/" + img_name.replace("jpg", "png").replace("bmp", "png"))
# 384x288 == 1536x1152
# if not os.path.exists("../opencv/tu/1152/"):
# os.mkdir("../opencv/tu/1152/")
# print("目录已经创建")
# new_img.save("../opencv/tu/1152/" + img_name.replace("jpg", "png").replace("bmp", "png"))
# 640x480 == 2560x1920
# if not os.path.exists("../opencv/tu/1920/"):
# os.mkdir("../opencv/tu/1920/")
# print("目录已经创建")
# new_img.save("../opencv/tu/1920/" + img_name.replace("jpg", "png").replace("bmp", "png"))
# 640x512 == 2560 x2048
# if not os.path.exists("../opencv/tu/2048/"):
# os.mkdir("../opencv/tu/2048/")
# print("目录已经创建")
# new_img.save("../opencv/tu/2048/" + img_name.replace("jpg", "png").replace("bmp", "png"))
# 1280x1024 == 5120x4196
if not os.path.exists("../opencv/tu/1024/"):
os.mkdir("../opencv/tu/1024/")
print("目录已经创建")
new_img.save("../opencv/tu/1024/" + img_name.replace("jpg", "png").replace("bmp", "png"))
# 它的一个文件
label = self.label_dir
return img, label
# 返回列表的一个长度
def __len__(self):
return len(self.img_path)
数据集路径
root_dir = "./tu"
路径要处理的图片
160x120==640x480
low_label_dir="160x120"
256x256 == 1024 x1024
low_label_dir="256x256"
320x240 == 1280 x960
low_label_dir="320x240"
384x288==1536x1152
low_label_dir="384x288"
640x480==2560x1920
low_label_dir="640x480"
640x512==2560x2048
low_label_dir="640x512"
1280x1024==5120x4196
low_label_dir = "1280x1024"
低分辩的数据集
low_dataset = MyData(root_dir, low_label_dir)
print("图片数量：", len(low_dataset))
for x in low_dataset:
img, label = x
上面可根据自己需要更改，代码懒优化了就复制粘贴了，需要用到的路径和大小，不要的可以删除。这个是根据B站代码进行学习，然后做的修改B站的up主是小土堆，有兴趣的可以去b站学习一下，内容还是讲的非常细致的
Original: https://blog.csdn.net/qq_45868384/article/details/123929852
Author: 小五角
Title: pytorch修改图片尺寸大小
原创文章受到原创版权保护。转载请注明出处：https://www.johngo689.com/628283/
转载文章受原作者版权保护。转载请注明原作者出处！