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import torch
from torch import Tensor
from torchvision import transforms
import cv2
from torch.utils.data import Dataset, DataLoader
import os
from PIL import Image
import pandas as pd
# 3264 x 2448
DATA_DIR = "data/image/train"
labels = os.listdir(DATA_DIR)
label2id = {label:id for id, label in enumerate(labels)}
def compile_image_df(data_dir:str, split_at = 0.9)-> pd.DataFrame:
dirs = os.listdir(data_dir)
columns=['Image_ID','Species']
train = pd.DataFrame(columns=columns)
val = pd.DataFrame(columns=columns)
for dir in dirs:
imgs = [(f"{data_dir}/{dir}/{img}", dir) for img in list(os.listdir(f"{data_dir}/{dir}"))]
length = len(imgs)
train_count = int(length * split_at)
train = pd.concat([train, pd.DataFrame(imgs[:train_count],columns=columns)])
val = pd.concat([val, pd.DataFrame(imgs[train_count:],columns=columns)])
return train, val
class TimberDataset(Dataset):
def __init__(self,
dataframe: pd.DataFrame,
is_train=False,
transform=None,
device=torch.device('cuda' if torch.cuda.is_available() else 'cpu')) -> None:
super().__init__()
self.dataframe = dataframe
self.is_train = is_train
self.transform = transform
self.device = device
def __len__(self) -> int:
return len(self.dataframe)
def __getitem__(self, idx: list[int]|Tensor):
if torch.is_tensor(idx):
idx = idx.tolist()
img_name = os.path.join(self.dataframe.iloc[idx,0])
image = cv2.imread(img_name)
image = Image.fromarray(image)
label = self.dataframe.iloc[idx,1]
label = label2id[label]
label = torch.tensor(int(label))
if self.transform:
image = self.transform(image)
return image.to(self.device), label.to(self.device)
def build_dataloader(
train_ratio = 0.9,
img_size = (640,640),
batch_size = 12,
) -> tuple[DataLoader,DataLoader]:
train_df, val_df = compile_image_df(DATA_DIR, split_at=train_ratio)
transform = transforms.Compose([
transforms.Resize(img_size),
transforms.ToTensor(),
])
train_loader = DataLoader(TimberDataset(train_df, is_train=True,transform=transform),
shuffle=True,
batch_size=batch_size)
val_loader = DataLoader(TimberDataset(val_df, is_train=True,transform=transform),
batch_size=batch_size)
return train_loader,val_loader |