src/superpoint_pruning/evaluation/plot_keypoints.py

import argparse
from pathlib import Path

import matplotlib.pyplot as plt
import numpy as np
import torch

from superpoint_pruning.distillation.utils import load_grayscale_image, rescale_image
from superpoint_pruning.models.superpoint import SuperPoint
from superpoint_pruning.paths import DEFAULT_IMAGE_DIR
from superpoint_pruning.pruning import add_pruning_parser_args, pruning_config_from_args

SHARED_COLOR = "lime"
ORIGINAL_ONLY_COLOR = "deepskyblue"
PRUNED_ONLY_COLOR = "orangered"


def resolve_image_path(image_dir: Path, image_name: Path) -> Path:
    if image_name.is_absolute():
        return image_name
    return image_dir / image_name


def classify_keypoints(
    original: torch.Tensor,
    pruned: torch.Tensor,
    image_width: int,
) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
    original = original.detach().cpu().round().to(torch.int64)
    pruned = pruned.detach().cpu().round().to(torch.int64)
    original_ids = original[:, 1] * image_width + original[:, 0]
    pruned_ids = pruned[:, 1] * image_width + pruned[:, 0]

    shared_mask = torch.isin(original_ids, pruned_ids)
    original_only_mask = ~shared_mask
    pruned_only_mask = ~torch.isin(pruned_ids, original_ids)

    return (
        original[shared_mask].numpy(),
        original[original_only_mask].numpy(),
        pruned[pruned_only_mask].numpy(),
    )


@torch.inference_mode()
def extract_keypoints(
    model: SuperPoint,
    image: torch.Tensor,
    scale: torch.Tensor,
) -> torch.Tensor:
    keypoints, _, _ = model(image)
    keypoints = (keypoints.to(torch.float32) + 0.5) / scale - 0.5
    return keypoints[0].detach().cpu()


def _scatter(ax, points: np.ndarray, color: str, label: str, size: int) -> None:
    if len(points) == 0:
        return
    ax.scatter(points[:, 0], points[:, 1], s=size, marker=".", color=color, label=label)


def plot_side_by_side(
    image: np.ndarray,
    original: np.ndarray,
    pruned: np.ndarray,
    output: Path,
    keypoint_size: int,
) -> None:
    fig, axes = plt.subplots(1, 2, figsize=(12, 5))
    for ax, points, title in (
        (axes[0], original, f"Original ({len(original)})"),
        (axes[1], pruned, f"Pruned ({len(pruned)})"),
    ):
        ax.imshow(image, cmap="gray")
        _scatter(ax, points, SHARED_COLOR, title, keypoint_size)
        ax.set_title(title)
        ax.axis("off")
    fig.tight_layout()
    output.parent.mkdir(parents=True, exist_ok=True)
    fig.savefig(output, dpi=150, bbox_inches="tight")
    plt.close(fig)


def plot_overlay(
    image: np.ndarray,
    original: torch.Tensor,
    pruned: torch.Tensor,
    output: Path,
    keypoint_size: int,
) -> None:
    height, width = image.shape[:2]
    shared, original_only, pruned_only = classify_keypoints(original, pruned, width)
    fig, ax = plt.subplots(figsize=(8, 6))
    ax.imshow(image, cmap="gray")
    _scatter(ax, shared, SHARED_COLOR, f"Shared ({len(shared)})", keypoint_size)
    _scatter(
        ax,
        original_only,
        ORIGINAL_ONLY_COLOR,
        f"Original only ({len(original_only)})",
        keypoint_size,
    )
    _scatter(
        ax,
        pruned_only,
        PRUNED_ONLY_COLOR,
        f"Pruned only ({len(pruned_only)})",
        keypoint_size,
    )
    ax.set_title("Shared vs unique keypoints")
    ax.axis("off")
    ax.legend(loc="upper right", framealpha=0.8)
    fig.tight_layout()
    output.parent.mkdir(parents=True, exist_ok=True)
    fig.savefig(output, dpi=150, bbox_inches="tight")
    plt.close(fig)


def add_parser_args(parser: argparse.ArgumentParser) -> None:
    parser.add_argument("--image-dir", type=Path, default=DEFAULT_IMAGE_DIR)
    parser.add_argument(
        "--image-name",
        type=Path,
        required=True,
        help="Image filename or absolute path.",
    )
    parser.add_argument("--output", type=Path, default=None, help="Output figure path.")
    parser.add_argument(
        "--overlay",
        action=argparse.BooleanOptionalAction,
        default=False,
        help="Plot both models on one image: shared, original-only, and pruned-only keypoints.",
    )
    parser.add_argument("--num-keypoints", type=int, default=512)
    parser.add_argument("--width", type=int, default=640)
    parser.add_argument("--height", type=int, default=480)
    parser.add_argument(
        "--hierarchical", action=argparse.BooleanOptionalAction, default=False
    )
    parser.add_argument(
        "--skip-refinement", action=argparse.BooleanOptionalAction, default=False
    )
    parser.add_argument("--keypoint-size", type=int, default=5)
    add_pruning_parser_args(parser)


def main(args: argparse.Namespace) -> None:
    image_path = resolve_image_path(args.image_dir, args.image_name)
    output = args.output
    if output is None:
        suffix = "overlay" if args.overlay else "keypoints"
        output = Path(f"{image_path.stem}_{suffix}.png")

    device = "cuda" if torch.cuda.is_available() else "cpu"
    pruning_config, pruning_checkpoint = pruning_config_from_args(args)

    original_image = load_grayscale_image(str(image_path))
    image, scale = rescale_image(original_image, new_size=(args.width, args.height))
    scale = torch.as_tensor(scale, device=device, dtype=torch.float32)
    batch = torch.from_numpy(image[None, None].astype(np.float32)).to(device)

    sp_original = SuperPoint(num_keypoints=args.num_keypoints).eval().to(device)
    sp_pruned = SuperPoint(
        num_keypoints=args.num_keypoints,
        hierarchical_topk=args.hierarchical,
        skip_refinement=args.skip_refinement,
    )
    if pruning_config:
        sp_pruned.prune_backbone(pruning_config)
    if pruning_checkpoint is not None:
        sp_pruned.load_pruned_weights(str(pruning_checkpoint))
    sp_pruned = sp_pruned.to(device).eval()

    original_kpts = extract_keypoints(sp_original, batch, scale)
    pruned_kpts = extract_keypoints(sp_pruned, batch, scale)

    if args.overlay:
        plot_overlay(
            original_image, original_kpts, pruned_kpts, output, args.keypoint_size
        )
    else:
        plot_side_by_side(
            original_image,
            original_kpts.numpy(),
            pruned_kpts.numpy(),
            output,
            args.keypoint_size,
        )
    print(f"Saved keypoint plot to {output.resolve()}")


if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description="Plot original and pruned SuperPoint keypoints."
    )
    add_parser_args(parser)
    main(parser.parse_args())