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from torch.utils import data
import os
import torch
import numpy as np
import cv2
import random
class myDataset(data.Dataset):
"""Custom data.Dataset compatible with data.DataLoader."""
def __init__(self, train_data_dir):
self.img_path = os.path.join(train_data_dir, "hair")
self.pose_path = os.path.join(train_data_dir, "pose.npy")
self.non_hair_path = os.path.join(train_data_dir, "no_hair")
self.ref_path = os.path.join(train_data_dir, "ref_hair")
self.lists = os.listdir(self.img_path)
self.len = len(self.lists)
self.pose = np.load(self.pose_path)
def __getitem__(self, index):
"""Returns one data pair (source and target)."""
# seq_len, fea_dim
random_number1 = random.randrange(0, 120)
random_number2 = random.randrange(0, 120)
while random_number2==random_number1:
random_number2 = random.randrange(0, 120)
name = self.lists[index]
hair_path = os.path.join(self.img_path, name, str(random_number1)+'.jpg')
non_hair_path = os.path.join(self.non_hair_path, name, str(random_number2)+'.jpg')
ref_folder = os.path.join(self.ref_path, name)
files = [f for f in os.listdir(ref_folder) if f.endswith('.jpg')]
ref_path = os.path.join(ref_folder, files[0])
img_hair = cv2.imread(hair_path)
img_non_hair = cv2.imread(non_hair_path)
ref_hair = cv2.imread(ref_path)
img_hair = cv2.cvtColor(img_hair, cv2.COLOR_BGR2RGB)
img_non_hair = cv2.cvtColor(img_non_hair, cv2.COLOR_BGR2RGB)
ref_hair = cv2.cvtColor(ref_hair, cv2.COLOR_BGR2RGB)
img_hair = cv2.resize(img_hair, (512, 512))
img_non_hair = cv2.resize(img_non_hair, (512, 512))
ref_hair = cv2.resize(ref_hair, (512, 512))
img_hair = (img_hair/255.0)* 2 - 1
img_non_hair = (img_non_hair/255.0)
ref_hair = (ref_hair/255.0)* 2 - 1
img_hair = torch.tensor(img_hair).permute(2, 0, 1)
img_non_hair = torch.tensor(img_non_hair).permute(2, 0, 1)
ref_hair = torch.tensor(ref_hair).permute(2, 0, 1)
pose1 = self.pose[random_number1]
pose1 = torch.tensor(pose1)
pose2 = self.pose[random_number2]
pose2 = torch.tensor(pose2)
return {
'hair_pose': pose1,
'img_hair':img_hair,
'bald_pose': pose2,
'img_non_hair':img_non_hair,
'ref_hair':ref_hair
}
def __len__(self):
return self.len
if __name__ == "__main__":
train_dataset = myDataset("./data")
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
batch_size=1,
num_workers=1,
)
for epoch in range(0, len(train_dataset)+1):
for step, batch in enumerate(train_dataloader):
print("batch[hair_pose]:", batch["hair_pose"])
print("batch[img_hair]:", batch["img_hair"])
print("batch[bald_pose]:", batch["bald_pose"])
print("batch[img_non_hair]:", batch["img_non_hair"])
print("batch[ref_hair]:", batch["ref_hair"]) |