How to load images with images names using pytorch

vision
1

Hi, I am new to pytorch, please help me how to load the images with there names.
I tried this.
This my csv
file_path=‘train’
train=pd.read_csv(os.path.join(file_path,‘train.csv’))
test=pd.read_csv(os.path.join(file_path,‘test_ApKoW4T.csv’))
train.head()
|image| category|
|0|2823080.jpg |1|
|1|2870024.jpg |1|
|2|2662125.jpg |2|
|3|2900420.jpg |3|
|4|2804883.jpg |2|

These my images files
temp=[]
for img_name in train.image:
img_path=os.path.join(file_path,‘images’,img_name)
img=cv2.imread(img_path)
img=cv2.resize(img,(64,64))
temp.append(img)

train_x=np.asarray(temp)
after loading images, I did n’t understand how to convert images data and labels into tensors.

1 Like
2

torch.from_numpy() can convert numpy-data to tensor.

3

please help me how to load the data, I have csv file with image names and labels using pytorch

4

Try to have a look at this
https://pytorch.org/tutorials/beginner/data_loading_tutorial.html

1 Like
5

Hi,
This works with folders or tar files. If you use only folder name plus your file name, it will not use any function related to tar processing.

from PIL import Image
from torchvision.transforms import ToTensor, ToPILImage
import numpy as np
import random

import tarfile
import io
import os
import pandas as pd

from torch.utils.data import Dataset
import torch


class YourDataset(Dataset):
    def __init__(self, txt_path='filelist.txt', img_dir='data', transform=None):
        """
        Initialize data set as a list of IDs corresponding to each item of data set

        :param img_dir: path to image files as a uncompressed tar archive
        :param txt_path: a text file containing names of all of images line by line
        :param transform: apply some transforms like cropping, rotating, etc on input image
        """

        df = pd.read_csv(txt_path, sep=' ', index_col=0)
        self.img_names = df.index.values
        self.txt_path = txt_path
        self.img_dir = img_dir
        self.transform = transform
        self.to_tensor = ToTensor()
        self.to_pil = ToPILImage()
        self.get_image_selector = True if img_dir.__contains__('tar') else False
        self.tf = tarfile.open(self.img_dir) if self.get_image_selector else None

    def get_image_from_tar(self, name):
        """
        Gets a image by a name gathered from file list csv file

        :param name: name of targeted image
        :return: a PIL image
        """
        image = self.tf.extractfile(name)
        image = image.read()
        image = Image.open(io.BytesIO(image))
        return image

    def get_image_from_folder(self, name):
        """
        gets a image by a name gathered from file list text file

        :param name: name of targeted image
        :return: a PIL image
        """

        image = Image.open(os.path.join(self.img_dir, name))
        return image

    def __len__(self):
        """
        Return the length of data set using list of IDs

        :return: number of samples in data set
        """
        return len(self.img_names)

    def __getitem__(self, index):
        """
        Generate one item of data set.

        :param index: index of item in IDs list

        :return: a sample of data as a dict
        """

        if index == (self.__len__() - 1) and self.get_image_selector:  # close tarfile opened in __init__
            self.tf.close()

        if self.get_image_selector:  # note: we prefer to extract then process!
            X = self.get_image_from_tar(self.img_names[index])
        else:
            X = self.get_image_from_folder(self.img_names[index])

        # Get you label here using available pandas functions
        Y = #########

        if self.transform is not None:
            X = self.transform(X)
#            Y = self.transform(Y) # if your label is image too - remove if it is number

        sample = {'X': X,
                  'Y': Y}

        return sample
6 Likes
6

By the way, you can format your code by putting ``` at first and at the end of your code.
Something like this:
input:
image

output

Your code goes here
x = [1,2,3]
7

It is a good tutorial, it would be better without the landmark complexity.

8

You could be right. However, data usually come with different annotations and not only images; landmarks are example, and it would help knowing how to deal with them. I think the given landmarks’ treatment completes the picture, although data may have no landmarks.