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dataset.py
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dataset.py
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import os
from os.path import join as opj
import cv2
import numpy as np
import albumentations as A
from torch.utils.data import Dataset
def imread(
p, h, w,
is_mask=False,
in_inverse_mask=False,
img=None
):
if img is None:
img = cv2.imread(p)
if not is_mask:
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (w,h))
img = (img.astype(np.float32) / 127.5) - 1.0 # [-1, 1]
else:
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img = cv2.resize(img, (w,h))
img = (img >= 128).astype(np.float32) # 0 or 1
img = img[:,:,None]
if in_inverse_mask:
img = 1-img
return img
def imread_for_albu(
p,
is_mask=False,
in_inverse_mask=False,
cloth_mask_check=False,
use_resize=False,
height=512,
width=384,
):
img = cv2.imread(p)
if use_resize:
img = cv2.resize(img, (width, height))
if not is_mask:
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
else:
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img = (img>=128).astype(np.float32)
if cloth_mask_check:
if img.sum() < 30720*4:
img = np.ones_like(img).astype(np.float32)
if in_inverse_mask:
img = 1 - img
img = np.uint8(img*255.0)
return img
def norm_for_albu(img, is_mask=False):
if not is_mask:
img = (img.astype(np.float32)/127.5) - 1.0
else:
img = img.astype(np.float32) / 255.0
img = img[:,:,None]
return img
class VITONHDDataset(Dataset):
def __init__(
self,
data_root_dir,
img_H,
img_W,
is_paired=True,
is_test=False,
is_sorted=False,
transform_size=None,
transform_color=None,
**kwargs
):
self.drd = data_root_dir
self.img_H = img_H
self.img_W = img_W
self.pair_key = "paired" if is_paired else "unpaired"
self.data_type = "train" if not is_test else "test"
self.is_test = is_test
self.resize_ratio_H = 1.0
self.resize_ratio_W = 1.0
self.resize_transform = A.Resize(img_H, img_W)
self.transform_size = None
self.transform_crop_person = None
self.transform_crop_cloth = None
self.transform_color = None
#### spatial aug >>>>
transform_crop_person_lst = []
transform_crop_cloth_lst = []
transform_size_lst = [A.Resize(int(img_H*self.resize_ratio_H), int(img_W*self.resize_ratio_W))]
if transform_size is not None:
if "hflip" in transform_size:
transform_size_lst.append(A.HorizontalFlip(p=0.5))
if "shiftscale" in transform_size:
transform_crop_person_lst.append(A.ShiftScaleRotate(rotate_limit=0, shift_limit=0.2, scale_limit=(-0.2, 0.2), border_mode=cv2.BORDER_CONSTANT, p=0.5, value=0))
transform_crop_cloth_lst.append(A.ShiftScaleRotate(rotate_limit=0, shift_limit=0.2, scale_limit=(-0.2, 0.2), border_mode=cv2.BORDER_CONSTANT, p=0.5, value=0))
self.transform_crop_person = A.Compose(
transform_crop_person_lst,
additional_targets={"agn":"image",
"agn_mask":"image",
"cloth_mask_warped":"image",
"cloth_warped":"image",
"image_densepose":"image",
"image_parse":"image",
"gt_cloth_warped_mask":"image",
}
)
self.transform_crop_cloth = A.Compose(
transform_crop_cloth_lst,
additional_targets={"cloth_mask":"image"}
)
self.transform_size = A.Compose(
transform_size_lst,
additional_targets={"agn":"image",
"agn_mask":"image",
"cloth":"image",
"cloth_mask":"image",
"cloth_mask_warped":"image",
"cloth_warped":"image",
"image_densepose":"image",
"image_parse":"image",
"gt_cloth_warped_mask":"image",
}
)
#### spatial aug <<<<
#### non-spatial aug >>>>
if transform_color is not None:
transform_color_lst = []
for t in transform_color:
if t == "hsv":
transform_color_lst.append(A.HueSaturationValue(5,5,5,p=0.5))
elif t == "bright_contrast":
transform_color_lst.append(A.RandomBrightnessContrast(brightness_limit=(-0.1, 0.02), contrast_limit=(-0.3, 0.3), p=0.5))
self.transform_color = A.Compose(
transform_color_lst,
additional_targets={"agn":"image",
"cloth":"image",
"cloth_warped":"image",
}
)
#### non-spatial aug <<<<
assert not (self.data_type == "train" and self.pair_key == "unpaired"), f"train must use paired dataset"
im_names = []
c_names = []
with open(opj(self.drd, f"{self.data_type}_pairs.txt"), "r") as f:
for line in f.readlines():
im_name, c_name = line.strip().split()
im_names.append(im_name)
c_names.append(c_name)
if is_sorted:
im_names, c_names = zip(*sorted(zip(im_names, c_names)))
self.im_names = im_names
self.c_names = dict()
self.c_names["paired"] = im_names
self.c_names["unpaired"] = c_names
def __len__(self):
return len(self.im_names)
def __getitem__(self, idx):
img_fn = self.im_names[idx]
cloth_fn = self.c_names[self.pair_key][idx]
if self.transform_size is None and self.transform_color is None:
agn = imread(
opj(self.drd, self.data_type, "agnostic-v3.2", self.im_names[idx]),
self.img_H,
self.img_W
)
agn_mask = imread(
opj(self.drd, self.data_type, "agnostic-mask", self.im_names[idx].replace(".jpg", "_mask.png")),
self.img_H,
self.img_W,
is_mask=True,
in_inverse_mask=True
)
cloth = imread(
opj(self.drd, self.data_type, "cloth", self.c_names[self.pair_key][idx]),
self.img_H,
self.img_W
)
cloth_mask = imread(
opj(self.drd, self.data_type, "cloth-mask", self.c_names[self.pair_key][idx]),
self.img_H,
self.img_W,
is_mask=True,
cloth_mask_check=True
)
gt_cloth_warped_mask = imread(
opj(self.drd, self.data_type, "gt_cloth_warped_mask", self.im_names[idx]),
self.img_H,
self.img_W,
is_mask=True
) if not self.is_test else np.zeros_like(agn_mask)
image = imread(opj(self.drd, self.data_type, "image", self.im_names[idx]), self.img_H, self.img_W)
image_densepose = imread(opj(self.drd, self.data_type, "image-densepose", self.im_names[idx]), self.img_H, self.img_W)
else:
agn = imread_for_albu(opj(self.drd, self.data_type, "agnostic-v3.2", self.im_names[idx]))
agn_mask = imread_for_albu(opj(self.drd, self.data_type, "agnostic-mask", self.im_names[idx].replace(".jpg", "_mask.png")), is_mask=True)
cloth = imread_for_albu(opj(self.drd, self.data_type, "cloth", self.c_names[self.pair_key][idx]))
cloth_mask = imread_for_albu(opj(self.drd, self.data_type, "cloth-mask", self.c_names[self.pair_key][idx]), is_mask=True, cloth_mask_check=True)
gt_cloth_warped_mask = imread_for_albu(
opj(self.drd, self.data_type, "gt_cloth_warped_mask", self.im_names[idx]),
is_mask=True
) if not self.is_test else np.zeros_like(agn_mask)
image = imread_for_albu(opj(self.drd, self.data_type, "image", self.im_names[idx]))
image_densepose = imread_for_albu(opj(self.drd, self.data_type, "image-densepose", self.im_names[idx]))
if self.transform_size is not None:
transformed = self.transform_size(
image=image,
agn=agn,
agn_mask=agn_mask,
cloth=cloth,
cloth_mask=cloth_mask,
image_densepose=image_densepose,
gt_cloth_warped_mask=gt_cloth_warped_mask,
)
image=transformed["image"]
agn=transformed["agn"]
agn_mask=transformed["agn_mask"]
image_densepose=transformed["image_densepose"]
gt_cloth_warped_mask=transformed["gt_cloth_warped_mask"]
cloth=transformed["cloth"]
cloth_mask=transformed["cloth_mask"]
if self.transform_crop_person is not None:
transformed_image = self.transform_crop_person(
image=image,
agn=agn,
agn_mask=agn_mask,
image_densepose=image_densepose,
gt_cloth_warped_mask=gt_cloth_warped_mask,
)
image=transformed_image["image"]
agn=transformed_image["agn"]
agn_mask=transformed_image["agn_mask"]
image_densepose=transformed_image["image_densepose"]
gt_cloth_warped_mask=transformed["gt_cloth_warped_mask"]
if self.transform_crop_cloth is not None:
transformed_cloth = self.transform_crop_cloth(
image=cloth,
cloth_mask=cloth_mask
)
cloth=transformed_cloth["image"]
cloth_mask=transformed_cloth["cloth_mask"]
agn_mask = 255 - agn_mask
if self.transform_color is not None:
transformed = self.transform_color(
image=image,
agn=agn,
cloth=cloth,
)
image=transformed["image"]
agn=transformed["agn"]
cloth=transformed["cloth"]
agn = agn * agn_mask[:,:,None].astype(np.float32)/255.0 + 128 * (1 - agn_mask[:,:,None].astype(np.float32)/255.0)
agn = norm_for_albu(agn)
agn_mask = norm_for_albu(agn_mask, is_mask=True)
cloth = norm_for_albu(cloth)
cloth_mask = norm_for_albu(cloth_mask, is_mask=True)
image = norm_for_albu(image)
image_densepose = norm_for_albu(image_densepose)
gt_cloth_warped_mask = norm_for_albu(gt_cloth_warped_mask, is_mask=True)
return dict(
agn=agn,
agn_mask=agn_mask,
cloth=cloth,
cloth_mask=cloth_mask,
image=image,
image_densepose=image_densepose,
gt_cloth_warped_mask=gt_cloth_warped_mask,
txt="",
img_fn=img_fn,
cloth_fn=cloth_fn,
)