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import pathlib
from typing import Tuple
import numpy as np
import torch
import pathlib
try:
import pyspng
PYSPNG_IMPORTED = True
except ImportError:
PYSPNG_IMPORTED = False
print("Could not load pyspng. Defaulting to pillow image backend.")
from PIL import Image
from tops import logger
class FDFDataset:
def __init__(self,
dirpath,
imsize: Tuple[int],
load_keypoints: bool,
transform):
dirpath = pathlib.Path(dirpath)
self.dirpath = dirpath
self.transform = transform
self.imsize = imsize[0]
self.load_keypoints = load_keypoints
assert self.dirpath.is_dir(),\
f"Did not find dataset at: {dirpath}"
image_dir = self.dirpath.joinpath("images", str(self.imsize))
self.image_paths = list(image_dir.glob("*.png"))
assert len(self.image_paths) > 0,\
f"Did not find images in: {image_dir}"
self.image_paths.sort(key=lambda x: int(x.stem))
self.landmarks = np.load(self.dirpath.joinpath("landmarks.npy")).reshape(-1, 7, 2).astype(np.float32)
self.bounding_boxes = torch.load(self.dirpath.joinpath("bounding_box", f"{self.imsize}.torch"))
assert len(self.image_paths) == len(self.bounding_boxes)
assert len(self.image_paths) == len(self.landmarks)
logger.log(
f"Dataset loaded from: {dirpath}. Number of samples:{len(self)}, imsize={imsize}")
def get_mask(self, idx):
mask = torch.ones((1, self.imsize, self.imsize), dtype=torch.bool)
bounding_box = self.bounding_boxes[idx]
x0, y0, x1, y1 = bounding_box
mask[:, y0:y1, x0:x1] = 0
return mask
def __len__(self):
return len(self.image_paths)
def __getitem__(self, index):
impath = self.image_paths[index]
if PYSPNG_IMPORTED:
with open(impath, "rb") as fp:
im = pyspng.load(fp.read())
else:
with Image.open(impath) as fp:
im = np.array(fp)
im = torch.from_numpy(np.rollaxis(im, -1, 0))
masks = self.get_mask(index)
landmark = self.landmarks[index]
batch = {
"img": im,
"mask": masks,
}
if self.load_keypoints:
batch["keypoints"] = landmark
if self.transform is None:
return batch
return self.transform(batch)
class FDF256Dataset:
def __init__(self,
dirpath,
load_keypoints: bool,
transform):
dirpath = pathlib.Path(dirpath)
self.dirpath = dirpath
self.transform = transform
self.load_keypoints = load_keypoints
assert self.dirpath.is_dir(),\
f"Did not find dataset at: {dirpath}"
image_dir = self.dirpath.joinpath("images")
self.image_paths = list(image_dir.glob("*.png"))
assert len(self.image_paths) > 0,\
f"Did not find images in: {image_dir}"
self.image_paths.sort(key=lambda x: int(x.stem))
self.landmarks = np.load(self.dirpath.joinpath("landmarks.npy")).reshape(-1, 7, 2).astype(np.float32)
self.bounding_boxes = torch.from_numpy(np.load(self.dirpath.joinpath("bounding_box.npy")))
assert len(self.image_paths) == len(self.bounding_boxes)
assert len(self.image_paths) == len(self.landmarks)
logger.log(
f"Dataset loaded from: {dirpath}. Number of samples:{len(self)}")
def get_mask(self, idx):
mask = torch.ones((1, 256, 256), dtype=torch.bool)
bounding_box = self.bounding_boxes[idx]
x0, y0, x1, y1 = bounding_box
mask[:, y0:y1, x0:x1] = 0
return mask
def __len__(self):
return len(self.image_paths)
def __getitem__(self, index):
impath = self.image_paths[index]
if PYSPNG_IMPORTED:
with open(impath, "rb") as fp:
im = pyspng.load(fp.read())
else:
with Image.open(impath) as fp:
im = np.array(fp)
im = torch.from_numpy(np.rollaxis(im, -1, 0))
masks = self.get_mask(index)
landmark = self.landmarks[index]
batch = {
"img": im,
"mask": masks,
}
if self.load_keypoints:
batch["keypoints"] = landmark
if self.transform is None:
return batch
return self.transform(batch)
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