文章目录
- 一、torchvision 图像数据读取 [0, 1]
- 二、torchvision 的 Transform
- 三、读取图像数据类
- 3.1 class torchvision.datasets.ImageFolder 默认读取图像数据方法：
- 3.2 自定义数据读取方法
运行环境安装 Anaconda | python ==3.6.6
conda install pytorch -c pytorch
pip install config
pip install tqdm #包装迭代器，显示进度条
pip install torchvision
pip install scikit-image 一、torchvision 图像数据读取 [0, 1]
import torchvision.transforms as transforms
transforms 模块提供了一般的图像转换操作类。
class torchvision.transforms.ToTensor
功能：
把shape=(H x W x C) 的像素值为 [0, 255] 的 PIL.Image 和 numpy.ndarray
转换成shape=(C x H x W)的像素值范围为
[0.0, 1.0]
的 torch.FloatTensor。
class torchvision.transforms.Normalize(mean, std)
功能：
此转换类作用于torch.*Tensor。给定均值(R, G, B)和标准差(R, G, B)，用公式channel = (channel - mean) / std进行规范化。
import torchvision
import torchvision.transforms as transforms
import cv2
import numpy as np
from PIL import Image
img_path = "./data/timg.jpg"
# 引入transforms.ToTensor()功能： range [0, 255] -> [0.0,1.0]
transform1 = transforms.Compose([transforms.ToTensor()])
# 直接读取：numpy.ndarray
img = cv2.imread(img_path)
print("img = ", img[0]) #只输出其中一个通道
print("img.shape = ", img.shape)
# 归一化，转化为numpy.ndarray并显示
img1 = transform1(img)
img2 = img1.numpy()*255
img2 = img2.astype('uint8')
img2 = np.transpose(img2 , (1,2,0))
print("img1 = ", img1)
cv2.imshow('img2 ', img2 )
cv2.waitKey()
# PIL 读取图像
img = Image.open(img_path).convert('RGB') # 读取图像
img2 = transform1(img) # 归一化到 [0.0,1.0]
print("img2 = ",img2) #转化为PILImage并显示
img_2 = transforms.ToPILImage()(img2).convert('RGB')
print("img_2 = ",img_2)
img_2.show()
从上到下依次输出：---------------------------------------------
img = [[197 203 202]
[195 203 202]
...
[200 208 207]
[200 208 207]]
img.shape = (362, 434, 3)
img1 = tensor([[[0.7725, 0.7647, 0.7686, ..., 0.7804, 0.7843, 0.7843],
[0.7765, 0.7725, 0.7686, ..., 0.7686, 0.7608, 0.7569],
[0.7843, 0.7725, 0.7686, ..., 0.7725, 0.7686, 0.7569],
...,
img_transform = tensor([[[0.7922, 0.7922, 0.7961, ..., 0.8078, 0.8118, 0.8118],
[0.7961, 0.8000, 0.7961, ..., 0.7922, 0.7882, 0.7843],
[0.8039, 0.8000, 0.7961, ..., 0.8118, 0.8039, 0.7922],
..., 
transforms.Compose 归一化到 [-1.0, 1.0 ]
transform2 = transforms.Compose([transforms.ToTensor()])
transforms.Normalize(mean = (0.5, 0.5, 0.5), std = (0.5, 0.5, 0.5))]) 二、torchvision 的 Transform
在深度学习时关于图像的数据读取：由于Tensorflow不支持与numpy的无缝切换，导致难以使用现成的pandas等格式化数据读取工具，造成了很多不必要的麻烦，而pytorch解决了这个问题。
pytorch自定义读取数据和进行Transform的部分请见文档：
http://pytorch.org/tutorials/beginner/data_loading_tutorial.html
但是按照文档中所描述所完成的自定义Dataset只能够使用自定义的Transform步骤，而torchvision包中已经给我们提供了很多图像transform步骤的实现，为了使用这些已经实现的Transform步骤，我们可以使用如下方法定义Dataset：
from __future__ import print_function, division
import os
import torch
import pandas as pd
from PIL import Image
import numpy as np
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
class FaceLandmarkDataset(Dataset):
def __len__(self) -> int:
return len(self.landmarks_frame)
def __init__(self, csv_file: str, root_dir: str, transform=None) -> None:
super().__init__()
self.landmarks_frame = pd.read_csv(csv_file)
self.root_dir = root_dir
self.transform = transform
def __getitem__(self, index:int):
img_name = self.landmarks_frame.ix[index, 0]
img_path = os.path.join('./faces', img_name)
with Image.open(img_path) as img:
image = img.convert('RGB')
landmarks = self.landmarks_frame.as_matrix()[index, 1:].astype('float')
landmarks = np.reshape(landmarks,newshape=(-1,2))
if self.transform is not None:
image = self.transform(image)
return image, landmarks
########################以上为数据读取类（返回：image,landmarks）###############################
trans = transforms.Compose(transforms = [transforms.RandomSizedCrop(size=128),
transforms.ToTensor()])
face_dataset = FaceLandmarkDataset(csv_file='faces/face_landmarks.csv',
root_dir='faces', transform= trans)
loader = DataLoader(dataset = face_dataset,
batch_size=4,
shuffle=True,
num_workers=4) 三、读取图像数据类
3.1 class torchvision.datasets.ImageFolder 默认读取图像数据方法：
-
__init__
-
classes, class_to_idx = find_classes(root)
:得到分类的类别名（classes）和类别名与数字类别的映射关系字典（class_to_idx）
其中 classes (list): List of the class names.
其中 class_to_idx (dict): Dict with items (class_name, class_index).
-
imgs = make_dataset(root, class_to_idx)
得到imags列表。
其中 imgs (list): List of (image path, class_index) tuples
每个值是一个tuple，每个tuple包含两个元素：图像路径和标签
-
__getitem__
-
path, target = self.imgs[index]
-
img = self.loader(path)
-
img = self.transform(img)
-
target = self.target_transform(target)
-
__len__
-
return len(self.imgs)
class ImageFolder(data.Dataset):
"""默认图像数据目录结构
root
.
├──dog
| ├──001.png
| ├──002.png
| └──...
└──cat
| ├──001.png
| ├──002.png
| └──...
└──...
"""
def __init__(self, root, transform=None, target_transform=None,
loader=default_loader):
classes, class_to_idx = find_classes(root)
imgs = make_dataset(root, class_to_idx)
if len(imgs) == 0:
raise(RuntimeError("Found 0 images in subfolders of: " + root + "\n"
"Supported image extensions are: " + ",".join(IMG_EXTENSIONS)))
self.root = root
self.imgs = imgs
self.classes = classes
self.class_to_idx = class_to_idx
self.transform = transform
self.target_transform = target_transform
self.loader = loader
def __getitem__(self, index):
"""
index (int): Index
Returns:tuple: (image, target) where target is class_index of the target class.
"""
path, target = self.imgs[index]
img = self.loader(path)
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
target = self.target_transform(target)
return img, target
def __len__(self):
return len(self.imgs) 图像获取
__getitem__
def pil_loader(path): # 一般采用pil_loader函数。
with open(path, 'rb') as f:
with Image.open(f) as img:
return img.convert('RGB')
def accimage_loader(path):
import accimage
try:
return accimage.Image(path)
except IOError:
# Potentially a decoding problem, fall back to PIL.Image
return pil_loader(path)
def default_loader(path):
from torchvision import get_image_backend
if get_image_backend() == 'accimage':
return accimage_loader(path)
else:
return pil_loader(path) 3.2 自定义数据读取方法
-
__init__（）
-
img_path 里面为所有图像数据（包括训练和测试）
txt_path 里面有 train.txt和val.txt两个文件：txt文件中每行都是图像路径，tab键，标签。
- 其中 self.img_name 和 self.img_label 的读取方式就跟你数据的存放方式有关（需要调整的地方)
-
__getitem__（）
-
Transform
class customData(Dataset):
def __init__(self, img_path, txt_path, dataset = '',data_transforms=None, loader = default_loader):
with open(txt_path) as input_file:
"""
关于json文件解析：
https://blog.csdn.net/wsp_1138886114/article/details/83302339
txt文件解析如下，具体文本解析具体分析，没有定数
"""
lines = input_file.readlines()
self.img_name = [os.path.join(img_path, line.strip().split('\t')[0]) for line in lines]
self.img_label = [int(line.strip().split('\t')[-1]) for line in lines]
self.data_transforms = data_transforms
self.dataset = dataset
self.loader = loader
def __len__(self):
return len(self.img_name)
def __getitem__(self, item):
img_name = self.img_name[item]
label = self.img_label[item]
img = self.loader(img_name)
if self.data_transforms is not None:
try:
img = self.data_transforms[self.dataset](img)
except:
print("Cannot transform image: {}".format(img_name))
return img, label
#####################以上为图像数据读取，返回（img, label）#########################
# 保证image_datasets与torchvision.datasets.ImageFolder类返回的数据类型一样
image_datasets = {x: customData(img_path='/ImagePath',
txt_path=('/TxtFile/' + x + '.txt'),
data_transforms=data_transforms,
dataset=x) for x in ['train', 'val']}
#用torch.utils.data.DataLoader类，将这个batch的图像数据和标签都分别封装成Tensor。
dataloders = {x: torch.utils.data.DataLoader(image_datasets[x],
batch_size=batch_size,
shuffle=True) for x in ['train', 'val']}
# 模型保存
torch.save(model, 'output/resnet_epoch{}.pkl'.format(epoch)) ![Kafka：Topic概念与API介绍[图]](data:image/gif;base64,R0lGODlhAQABAIAAAP///wAAACwAAAAAAQABAAACAkQBADs=)