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feat(dataset): add synthetic dataset generation and configuration

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SikongJueluo
2026-02-28 21:15:45 +08:00
parent f61857feba
commit 77d715a2cf
7 changed files with 473 additions and 9 deletions
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1
.gitignore vendored
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@@ -207,6 +207,7 @@ __marimo__/
# Projects
datasets/
!mini-nav/**/datasets/
data/
deps/
outputs/

2
mini-nav/configs/__init__.py
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@@ -5,6 +5,7 @@ from .config import (
from .loader import ConfigError, load_yaml, save_yaml
from .models import (
Config,
DatasetConfig,
ModelConfig,
OutputConfig,
PoolingType,
@@ -14,6 +15,7 @@ __all__ = [
# Models
"ModelConfig",
"OutputConfig",
"DatasetConfig",
"Config",
"PoolingType",
# Loader

10
mini-nav/configs/config.yaml
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@@ -5,3 +5,13 @@ model:
output:
directory: "./outputs"
dataset:
dataset_root: "datasets/InsDet-FULL"
output_dir: "datasets/InsDet-FULL/Synthesized"
num_objects_range: [3, 8]
num_scenes: 1000
object_scale_range: [0.1, 0.4]
rotation_range: [-30, 30]
overlap_threshold: 0.3
seed: 42

61
mini-nav/configs/models.py
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@@ -1,17 +1,10 @@
"""Pydantic data models for feature compressor configuration."""
from enum import Enum
from pathlib import Path
from pydantic import BaseModel, ConfigDict, Field, field_validator
class PoolingType(str, Enum):
"""Enum for pooling types."""
ATTENTION = "attention"
class ModelConfig(BaseModel):
"""Configuration for the vision model and compression."""
@@ -42,10 +35,60 @@ class OutputConfig(BaseModel):
return Path(__file__).parent.parent.parent / v
class DatasetConfig(BaseModel):
"""Configuration for synthetic dataset generation."""
model_config = ConfigDict(extra="ignore")
dataset_root: Path = (
Path(__file__).parent.parent.parent / "datasets" / "InsDet-FULL"
)
output_dir: Path = (
Path(__file__).parent.parent.parent / "datasets" / "InsDet-FULL" / "Synthesized"
)
num_objects_range: tuple[int, int] = (3, 8)
num_scenes: int = 1000
object_scale_range: tuple[float, float] = (0.1, 0.4)
rotation_range: tuple[int, int] = (-30, 30)
overlap_threshold: float = 0.3
seed: int = 42
@field_validator("dataset_root", "output_dir", mode="after")
def convert_to_absolute(cls, v: Path) -> Path:
"""
Converts the path to an absolute path relative to the project root.
This works even if the path doesn't exist on disk.
"""
if v.is_absolute():
return v
return Path(__file__).parent.parent.parent / v
@field_validator("num_objects_range", mode="after")
def validate_num_objects(cls, v: tuple[int, int]) -> tuple[int, int]:
if v[0] < 1 or v[1] < v[0]:
raise ValueError("num_objects_range must have min >= 1 and min <= max")
return v
@field_validator("object_scale_range", mode="after")
def validate_scale(cls, v: tuple[float, float]) -> tuple[float, float]:
if v[0] <= 0 or v[1] <= 0 or v[1] < v[0]:
raise ValueError(
"object_scale_range must have positive values and min <= max"
)
return v
@field_validator("overlap_threshold", mode="after")
def validate_overlap(cls, v: float) -> float:
if not 0 <= v <= 1:
raise ValueError("overlap_threshold must be between 0 and 1")
return v
class Config(BaseModel):
"""Root configuration for the feature compressor."""
model_config = ConfigDict(extra="ignore")
model: ModelConfig
output: OutputConfig
model: ModelConfig = Field(default_factory=ModelConfig)
output: OutputConfig = Field(default_factory=OutputConfig)
dataset: DatasetConfig = Field(default_factory=DatasetConfig)

8
mini-nav/datasets/__init__.py Normal file
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@@ -0,0 +1,8 @@
from .loader import SynthDataset, ValDataset
from .synthesizer import ImageSynthesizer
__all__ = [
"ImageSynthesizer",
"SynthDataset",
"ValDataset",
]

105
mini-nav/datasets/loader.py Normal file
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@@ -0,0 +1,105 @@
"""Data loaders for synthetic and validation datasets."""
from collections.abc import Iterator
from pathlib import Path
from PIL import Image
class SynthDataset:
"""Dataset loader for synthesized training images."""
def __init__(self, synth_dir: Path, annotations_suffix: str = ".txt"):
"""
Initialize the synthetic dataset loader.
Args:
synth_dir: Directory containing synthesized images and annotations
annotations_suffix: Suffix for annotation files
"""
self.synth_dir = Path(synth_dir)
self.annotations_suffix = annotations_suffix
# Find all images
self.image_files = sorted(self.synth_dir.glob("synth_*.jpg"))
def __len__(self) -> int:
return len(self.image_files)
def __getitem__(self, idx: int) -> tuple[Image.Image, list[tuple[str, int, int, int, int]]]:
"""Get a single item.
Args:
idx: Index of the item
Returns:
Tuple of (image, annotations) where annotations is a list of
(category, xmin, ymin, xmax, ymax)
"""
img_path = self.image_files[idx]
image = Image.open(img_path).convert("RGB")
# Load annotations
anno_path = img_path.with_suffix(self.annotations_suffix)
annotations: list[tuple[str, int, int, int, int]] = []
if anno_path.exists():
with open(anno_path, "r") as f:
for line in f:
line = line.strip()
if line:
parts = line.split()
if len(parts) == 5:
category = parts[0]
xmin, ymin, xmax, ymax = map(int, parts[1:])
annotations.append((category, xmin, ymin, xmax, ymax))
return image, annotations
def __iter__(self) -> Iterator[tuple[Image.Image, list[tuple[str, int, int, int, int]]]]:
"""Iterate over the dataset."""
for i in range(len(self)):
yield self[i]
class ValDataset:
"""Dataset loader for validation scene images."""
def __init__(self, scenes_dir: Path, split: str = "easy"):
"""
Initialize the validation dataset loader.
Args:
scenes_dir: Directory containing scene subdirectories
split: Scene split to load ('easy' or 'hard')
"""
self.scenes_dir = Path(scenes_dir)
self.split = split
self.split_dir = self.scenes_dir / split
if not self.split_dir.exists():
raise ValueError(f"Scene split directory not found: {self.split_dir}")
# Find all RGB images
self.image_files = sorted(self.split_dir.glob("*/rgb_*.jpg"))
def __len__(self) -> int:
return len(self.image_files)
def __getitem__(self, idx: int) -> tuple[Image.Image, Path]:
"""Get a single item.
Args:
idx: Index of the item
Returns:
Tuple of (image, scene_path)
"""
img_path = self.image_files[idx]
image = Image.open(img_path).convert("RGB")
return image, img_path.parent
def __iter__(self) -> Iterator[tuple[Image.Image, Path]]:
"""Iterate over the dataset."""
for i in range(len(self)):
yield self[i]

295
mini-nav/datasets/synthesizer.py Normal file
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@@ -0,0 +1,295 @@
"""Image synthesizer for generating synthetic object detection datasets."""
import random
from pathlib import Path
import numpy as np
from PIL import Image
from PIL.Image import Resampling
class ImageSynthesizer:
"""Synthesizes composite images from background and object images with masks."""
def __init__(
self,
dataset_root: Path,
output_dir: Path,
num_objects_range: tuple[int, int] = (3, 8),
num_scenes: int = 1000,
object_scale_range: tuple[float, float] = (0.1, 0.4),
rotation_range: tuple[int, int] = (-30, 30),
overlap_threshold: float = 0.3,
seed: int = 42,
):
"""
Initialize the image synthesizer.
Args:
dataset_root: Root directory of the dataset (InsDet-FULL)
output_dir: Directory to save synthesized images
num_objects_range: Range of number of objects per scene
num_scenes: Number of scenes to generate
object_scale_range: Range of object scale relative to background
rotation_range: Range of rotation angles in degrees
overlap_threshold: Maximum allowed overlap ratio
seed: Random seed for reproducibility
"""
self.dataset_root = Path(dataset_root)
self.output_dir = Path(output_dir)
self.num_objects_range = num_objects_range
self.num_scenes = num_scenes
self.object_scale_range = object_scale_range
self.rotation_range = rotation_range
self.overlap_threshold = overlap_threshold
self.seed = seed
self.background_dir = self.dataset_root / "Background"
self.objects_dir = self.dataset_root / "Objects"
self.scenes_dir = self.dataset_root / "Scenes"
# Will be populated on first use
self._background_categories: list[str] | None = None
self._object_categories: list[str] | None = None
@property
def background_images(self) -> list[Path]:
"""List of background image paths."""
if self._background_categories is None:
self._background_categories = sorted(
[p.name for p in self.background_dir.iterdir() if p.suffix in [".jpg", ".jpeg", ".png"]]
)
# Return as list of Path for type compatibility
return [self.background_dir / name for name in self._background_categories] # type: ignore[return-value]
@property
def object_categories(self) -> list[str]:
"""List of object categories."""
if self._object_categories is None:
self._object_categories = sorted(
[d.name for d in self.objects_dir.iterdir() if d.is_dir()]
)
return self._object_categories
def load_background(self, path: Path) -> Image.Image:
"""Load a background image.
Args:
path: Background image path
Returns:
PIL Image
"""
return Image.open(path).convert("RGB")
def load_object(self, category: str, angle: int) -> tuple[Image.Image, Image.Image]:
"""Load an object image and its mask.
Args:
category: Object category name (e.g., '099_mug_blue')
angle: Angle index (1-24)
Returns:
Tuple of (image, mask) as PIL Images
"""
img_path = self.objects_dir / category / "images" / f"{angle:03d}.jpg"
mask_path = self.objects_dir / category / "masks" / f"{angle:03d}.png"
image = Image.open(img_path).convert("RGB")
mask = Image.open(mask_path).convert("L")
return image, mask
def get_random_background(self) -> tuple[Image.Image, Path]:
"""Get a random background image.
Returns:
Tuple of (image, path)
"""
path = random.choice(self.background_images)
return self.load_background(path), path
def get_random_object(self) -> tuple[Image.Image, Image.Image, str]:
"""Get a random object with its mask.
Returns:
Tuple of (image, mask, category_name)
"""
category = random.choice(self.object_categories)
angle = random.randint(1, 24)
image, mask = self.load_object(category, angle)
return image, mask, category
def _rotate_image_and_mask(
self, image: Image.Image, mask: Image.Image, angle: float
) -> tuple[Image.Image, Image.Image]:
"""Rotate image and mask together."""
image = image.rotate(angle, resample=Resampling.BILINEAR, expand=True)
mask = mask.rotate(angle, resample=Resampling.BILINEAR, expand=True)
return image, mask
def _compute_overlap(self, box1: tuple[int, int, int, int], box2: tuple[int, int, int, int]) -> float:
"""Compute overlap ratio between two boxes.
Args:
box1: (xmin, ymin, xmax, ymax)
box2: (xmin, ymin, xmax, ymax)
Returns:
Overlap ratio (area of intersection / area of smaller box)
"""
x1_min, y1_min, x1_max, y1_max = box1
x2_min, y2_min, x2_max, y2_max = box2
# Compute intersection
inter_xmin = max(x1_min, x2_min)
inter_ymin = max(y1_min, y2_min)
inter_xmax = min(x1_max, x2_max)
inter_ymax = min(y1_max, y2_max)
if inter_xmax <= inter_xmin or inter_ymax <= inter_ymin:
return 0.0
inter_area = (inter_xmax - inter_xmin) * (inter_ymax - inter_ymin)
box1_area = (x1_max - x1_min) * (y1_max - y1_min)
box2_area = (x2_max - x2_min) * (y2_max - y2_min)
min_area = min(box1_area, box2_area)
return inter_area / min_area if min_area > 0 else 0.0
def _place_object(
self,
background: Image.Image,
obj_image: Image.Image,
obj_mask: Image.Image,
existing_boxes: list[tuple[int, int, int, int]],
scale: float,
) -> tuple[Image.Image, Image.Image, tuple[int, int, int, int]] | None:
"""Place an object on the background without exceeding overlap threshold.
Args:
background: Background PIL Image
obj_image: Object PIL Image (RGB)
obj_mask: Object PIL Image (L)
existing_boxes: List of existing object boxes
scale: Scale factor for the object
Returns:
Tuple of (new_background, updated_mask, new_box) or None if placement failed
"""
bg_w, bg_h = background.size
# Scale object
obj_w, obj_h = obj_image.size
new_w = int(obj_w * scale)
new_h = int(obj_h * scale)
if new_w <= 0 or new_h <= 0:
return None
obj_image = obj_image.resize((new_w, new_h), Resampling.LANCZOS)
obj_mask = obj_mask.resize((new_w, new_h), Resampling.LANCZOS)
# Try to find a valid position
max_attempts = 50
for _ in range(max_attempts):
# Random position
x = random.randint(0, bg_w - new_w)
y = random.randint(0, bg_h - new_h)
new_box = (x, y, x + new_w, y + new_h)
# Check overlap with existing boxes
valid = True
for existing_box in existing_boxes:
overlap = self._compute_overlap(new_box, existing_box)
if overlap > self.overlap_threshold:
valid = False
break
if valid:
# Composite object onto background
background = background.copy()
mask_array = np.array(obj_mask) / 255.0
bg_array = np.array(background)
obj_array = np.array(obj_image)
# Apply mask
mask_3d = np.stack([mask_array] * 3, axis=-1)
bg_array[y:y+new_h, x:x+new_w] = (
bg_array[y:y+new_h, x:x+new_w] * (1 - mask_3d) +
obj_array * mask_3d
)
return Image.fromarray(bg_array), obj_mask, new_box
return None
def synthesize_scene(self) -> tuple[Image.Image, list[tuple[str, int, int, int, int]]]:
"""Synthesize a single scene with random objects.
Returns:
Tuple of (synthesized_image, list of (category, xmin, ymin, xmax, ymax))
"""
random.seed(self.seed)
np.random.seed(self.seed)
# Load background
background, _ = self.get_random_background()
# Determine number of objects
num_objects = random.randint(*self.num_objects_range)
# Place objects
placed_boxes: list[tuple[int, int, int, int]] = []
annotations: list[tuple[str, int, int, int, int]] = []
for _ in range(num_objects):
# Get random object
obj_image, obj_mask, obj_category = self.get_random_object()
# Get random scale
scale = random.uniform(*self.object_scale_range)
# Get random rotation
angle = random.uniform(*self.rotation_range)
obj_image, obj_mask = self._rotate_image_and_mask(obj_image, obj_mask, angle)
# Try to place object
result = self._place_object(background, obj_image, obj_mask, placed_boxes, scale)
if result is not None:
background, _, box = result
placed_boxes.append(box)
annotations.append((obj_category, box[0], box[1], box[2], box[3]))
return background, annotations
def generate(self) -> list[Path]:
"""Generate all synthesized scenes.
Returns:
List of paths to generated images
"""
self.output_dir.mkdir(parents=True, exist_ok=True)
generated_files: list[Path] = []
for i in range(self.num_scenes):
# Update seed for each scene
random.seed(self.seed + i)
np.random.seed(self.seed + i)
image, annotations = self.synthesize_scene()
# Save image
img_path = self.output_dir / f"synth_{i:04d}.jpg"
image.save(img_path, quality=95)
# Save annotation
anno_path = self.output_dir / f"synth_{i:04d}.txt"
with open(anno_path, "w") as f:
for category, xmin, ymin, xmax, ymax in annotations:
f.write(f"{category} {xmin} {ymin} {xmax} {ymax}\n")
generated_files.append(img_path)
return generated_files