feat(dataset): add synthetic dataset generation and configuration

mini-nav/datasets/synthesizer.py

@@ -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