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"""
Data loader and module for the FDDB dataset.
https://vis-www.cs.umass.edu/fddb/
"""
from glob import glob
from typing import Any
from pathlib import Path
import torch
import pandas as pd
import PIL.Image
import torchvision.transforms
import pytorch_lightning as pl
from torch.utils.data import DataLoader, random_split
from ellipse_rcnn.utils.types import TargetDict, ImageTargetTuple, EllipseTuple
from ellipse_rcnn.utils.conics import bbox_ellipse, ellipse_to_conic_matrix, conic_center, unimodular_matrix
from ellipse_rcnn.utils.data.base import EllipseDatasetBase, collate_fn
def preprocess_label_files(root_path: str) -> dict[str, list[EllipseTuple]]:
label_files = glob(f"{root_path}/labels/*.txt")
file_paths = []
ellipse_data = []
for filename in label_files:
with open(filename) as f:
if "ellipseList" not in filename:
file_paths += [p.strip("\n") for p in f.readlines()]
else:
ellipse_data += [p.strip("\n") for p in f.readlines()]
pdf_file_paths = pd.DataFrame({"path": file_paths})
pdf_file_paths["path_idx"] = pdf_file_paths.index
pdf_ellipse_data = pd.DataFrame({"data": ellipse_data})
pdf_ellipse_data["data_idx"] = pdf_ellipse_data.index
pdf_file_data_mapping = pdf_file_paths.merge(
pdf_ellipse_data, left_on="path", right_on="data", how="left"
)
ellipse_dict: dict[str, list[EllipseTuple]] = {
str(k): [] for k in pdf_file_paths["path"]
}
for i, r in pdf_file_data_mapping.iterrows():
data_idx = r["data_idx"]
num_ellipses = int(ellipse_data[data_idx + 1])
file_path = r["path"]
for j in range(data_idx + 2, data_idx + num_ellipses + 2):
a, b, theta, x, y = [
float(v) for v in ellipse_data[j].split(" ")[:-1] if len(v) > 0
]
ellipse_params = EllipseTuple(a, b, theta, x, y)
ellipse_dict[file_path].append(ellipse_params)
return ellipse_dict
class FDDB(EllipseDatasetBase):
def __init__(
self,
root_path: str | Path,
ellipse_dict: dict[str, list[EllipseTuple]] | None = None,
transform: Any = None,
) -> None:
self.root_path = Path(root_path) if isinstance(root_path, str) else root_path
if transform is None:
self.transform = torchvision.transforms.Compose(
[
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
),
]
)
else:
self.transform = transform
self.ellipse_dict = ellipse_dict or preprocess_label_files(root_path)
def __len__(self) -> int:
return len(self.ellipse_dict)
def load_target_dict(self, index: int) -> TargetDict:
key = list(self.ellipse_dict.keys())[index]
ellipses_list = self.ellipse_dict[key]
a = torch.tensor([[e.a for e in ellipses_list]])
b = torch.tensor([[e.b for e in ellipses_list]])
theta = torch.tensor([[e.theta for e in ellipses_list]])
x = torch.tensor([[e.x for e in ellipses_list]])
y = torch.tensor([[e.y for e in ellipses_list]])
ellipse_matrices = ellipse_to_conic_matrix(a=a, b=b, x=x, y=y, theta=theta)
if torch.stack(conic_center(ellipse_matrices)).isnan().any():
raise ValueError("NaN values in ellipse matrices. Please check the data.")
if len(ellipse_matrices.shape) == 2:
ellipse_matrices = ellipse_matrices.unsqueeze(0)
boxes = bbox_ellipse(ellipse_matrices, box_type="xyxy")
num_objs = len(boxes)
labels = torch.ones((num_objs,), dtype=torch.int64)
image_id = torch.tensor([index])
area = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0])
iscrowd = torch.zeros((num_objs,), dtype=torch.int64)
target = TargetDict(
boxes=boxes,
labels=labels,
image_id=image_id,
area=area,
iscrowd=iscrowd,
ellipse_matrices=ellipse_matrices,
)
return target
def load_image(self, index: int) -> PIL.Image.Image:
key = list(self.ellipse_dict.keys())[index]
file_path = str(Path(self.root_path) / "images" / Path(key)) + ".jpg"
return PIL.Image.open(file_path)
def __getitem__(self, idx: int) -> ImageTargetTuple:
image = self.load_image(idx)
target_dict = self.load_target_dict(idx)
# If the image is grayscale, convert it to RGB
if image.mode == "L":
image = image.convert("RGB")
image = self.transform(image)
return image, target_dict
def __repr__(self) -> str:
return f"FDDB<img={len(self)}>"
def split(self, fraction: float, shuffle: bool = False) -> tuple["FDDB", "FDDB"]:
"""
Splits the dataset into two subsets based on the given fraction.
Args:
fraction (float): Fraction of the dataset for the first subset (0 < fraction < 1).
shuffle (bool): If True, dataset keys will be shuffled before splitting.
Returns:
tuple[FDDB, FDDB]: Two FDDB instances, one with the fraction of data,
and the other with the remaining data.
"""
if not (0 < fraction < 1):
raise ValueError("The fraction must be between 0 and 1.")
keys = list(self.ellipse_dict.keys())
if shuffle:
import random
random.shuffle(keys)
total_length = len(keys)
split_index = int(total_length * fraction)
subset1_keys = keys[:split_index]
subset2_keys = keys[split_index:]
subset1_ellipse_dict = {key: self.ellipse_dict[key] for key in subset1_keys}
subset2_ellipse_dict = {key: self.ellipse_dict[key] for key in subset2_keys}
subset1 = FDDB(
self.root_path, ellipse_dict=subset1_ellipse_dict, transform=self.transform
)
subset2 = FDDB(
self.root_path, ellipse_dict=subset2_ellipse_dict, transform=self.transform
)
return subset1, subset2
class FDDBLightningDataModule(pl.LightningDataModule):
def __init__(
self,
data_dir: str,
batch_size: int = 16,
train_fraction: float = 0.8,
transform: Any = None,
num_workers: int = 0,
) -> None:
super().__init__()
self.data_dir = data_dir
self.batch_size = batch_size
self.train_fraction = train_fraction
self.transform = transform
self.dataset: FDDB | None = None
self.train_dataset = None
self.val_dataset = None
self.num_workers = num_workers
def prepare_data(self) -> None:
# Ensure data preparation or downloading is done here.
pass
def setup(self, stage: str | None = None) -> None:
# Instantiate the FDDB dataset and split it into training and validation subsets.
self.dataset = FDDB(self.data_dir, transform=self.transform)
train_size = int(len(self.dataset) * self.train_fraction)
val_size = len(self.dataset) - train_size
self.train_dataset, self.val_dataset = random_split(
self.dataset, [train_size, val_size]
)
def train_dataloader(self) -> DataLoader[ImageTargetTuple]:
return DataLoader(
self.train_dataset,
batch_size=self.batch_size,
shuffle=True,
collate_fn=collate_fn,
num_workers=self.num_workers,
)
def val_dataloader(self) -> DataLoader[ImageTargetTuple]:
return DataLoader(
self.val_dataset,
batch_size=self.batch_size,
collate_fn=collate_fn,
num_workers=self.num_workers,
)
def test_dataloader(self) -> DataLoader[ImageTargetTuple]:
# Placeholder for test data; currently returns the validation dataloader as a default.
return DataLoader(
self.val_dataset, batch_size=self.batch_size, collate_fn=collate_fn
)
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