#!/usr/bin/env python """Renders an animated GIF of a LiDAR projection smoothly correcting from a miscalibrated stage back to ground truth (SE(3) interpolation between the two extrinsics), for a README/webpage hero visual. Usage: python scripts/make_correction_animation.py \\ --metadata-parquet data/train.parquet --assets-parquet assets_scratch/shards/data/frames_train-00000-of-00044.parquet \\ --sequence 00 --frame 120 --stage 1 --output docs/assets/correction_animation.gif """ from __future__ import annotations import argparse import io import sys from pathlib import Path import matplotlib matplotlib.use("Agg") import matplotlib.pyplot as plt import numpy as np import pyarrow.parquet as pq from PIL import Image from scipy.spatial.transform import Rotation, Slerp sys.path.insert(0, str(Path(__file__).resolve().parent.parent / "src")) from kitti_c2l.geometry import project_to_image, transform_points # noqa: E402 from kitti_c2l.loader.dataset import ( # noqa: E402 decode_image_png, decode_points_bin, row_extrinsic, ) MIN_DEPTH_CM = 200.0 MAX_DEPTH_CM = 8000.0 def _interpolate_transforms(r_start: np.ndarray, t_start: np.ndarray, r_end: np.ndarray, t_end: np.ndarray, num_steps: int): key_rots = Rotation.from_matrix(np.stack([r_start, r_end])) slerp = Slerp([0.0, 1.0], key_rots) fractions = np.linspace(0.0, 1.0, num_steps) rotations = slerp(fractions).as_matrix() translations = t_start[None, :] + fractions[:, None] * (t_end - t_start)[None, :] return rotations, translations, fractions def _render_frame( image: np.ndarray, points_cm: np.ndarray, rotation: np.ndarray, translation: np.ndarray, intrinsics: np.ndarray, title: str, max_points: int = 18000, rng: np.random.Generator | None = None, ) -> np.ndarray: height, width = image.shape[:2] points_cam = transform_points(points_cm, rotation, translation) pixels = project_to_image(points_cam, intrinsics) depths = points_cam[:, 2] valid = ( (depths > MIN_DEPTH_CM) & (depths < MAX_DEPTH_CM) & (pixels[:, 0] >= 0) & (pixels[:, 0] < width) & (pixels[:, 1] >= 0) & (pixels[:, 1] < height) ) pixels, depths = pixels[valid], depths[valid] if pixels.shape[0] > max_points: rng = rng or np.random.default_rng(0) keep = rng.choice(pixels.shape[0], size=max_points, replace=False) pixels, depths = pixels[keep], depths[keep] # Render at half native resolution: smaller file, and softens the raw # 64-beam scan-line banding that's distractingly sharp at full res. scale = 0.5 fig, ax = plt.subplots(figsize=(width * scale / 100, height * scale / 100 + 0.45), dpi=100) fig.subplots_adjust(top=0.88, bottom=0.0, left=0.0, right=1.0) ax.imshow(image) if pixels.shape[0] > 0: order = np.argsort(-depths) ax.scatter(pixels[order, 0], pixels[order, 1], c=depths[order] / 100.0, cmap="turbo", s=10, alpha=0.5, linewidths=0, vmin=2, vmax=80) ax.set_title(title, fontsize=10, fontweight="bold", color="#212121") ax.axis("off") ax.set_xlim(0, width) ax.set_ylim(height, 0) buf = io.BytesIO() fig.savefig(buf, format="png") plt.close(fig) buf.seek(0) return np.array(Image.open(buf).convert("RGB")) def make_animation( metadata_parquet: Path, assets_parquet: Path, sequence: str, frame: int, stage: int, output_path: Path, num_steps: int = 36, hold_frames: int = 8, fps: int = 20, ) -> Path: meta_rows = pq.read_table(metadata_parquet).to_pylist() row = next(r for r in meta_rows if r["sequence"] == sequence and r["frame_index"] == frame and r["stage"] == stage) asset_rows = pq.read_table(assets_parquet).to_pylist() frame_id = f"{sequence}_{frame:06d}" asset_row = next(r for r in asset_rows if r["frame_id"] == frame_id) image = decode_image_png(asset_row["image_png"]) points_cm = decode_points_bin(asset_row["points_bin"]) intrinsics = np.array(row["camera_intrinsics"], dtype=np.float64).reshape(3, 3) r_gt, t_gt = row_extrinsic(row, use_ground_truth=True) r_miscal, t_miscal = row_extrinsic(row, use_ground_truth=False) rotations, translations, fractions = _interpolate_transforms(r_miscal, t_miscal, r_gt, t_gt, num_steps) frames = [] for i, (rotation, translation, frac) in enumerate(zip(rotations, translations, fractions, strict=True)): progress_pct = int(round(frac * 100)) title = f"seq {sequence} frame {frame} | correcting stage {stage} miscalibration ({progress_pct}%)" frames.append(_render_frame(image, points_cm, rotation, translation, intrinsics, title)) print(f" rendered {i + 1}/{num_steps}") pil_frames = [Image.fromarray(f) for f in frames] pil_frames = [pil_frames[0]] * hold_frames + pil_frames + [pil_frames[-1]] * hold_frames # Shared adaptive palette across all frames: much smaller file than per-frame # palettes, and avoids color flicker between frames. palette_source = pil_frames[len(pil_frames) // 2].quantize(colors=192, method=Image.MEDIANCUT) pil_frames = [f.quantize(palette=palette_source, dither=Image.FLOYDSTEINBERG) for f in pil_frames] output_path.parent.mkdir(parents=True, exist_ok=True) pil_frames[0].save( output_path, save_all=True, append_images=pil_frames[1:], duration=int(1000 / fps), loop=0, optimize=True ) return output_path def main() -> int: parser = argparse.ArgumentParser() parser.add_argument("--metadata-parquet", required=True) parser.add_argument("--assets-parquet", required=True) parser.add_argument("--sequence", required=True) parser.add_argument("--frame", type=int, required=True) parser.add_argument("--stage", type=int, default=1) parser.add_argument("--output", default="docs/assets/correction_animation.gif") parser.add_argument("--num-steps", type=int, default=36) parser.add_argument("--fps", type=int, default=20) args = parser.parse_args() output_path = make_animation( Path(args.metadata_parquet), Path(args.assets_parquet), args.sequence, args.frame, args.stage, Path(args.output), num_steps=args.num_steps, fps=args.fps, ) print(f"wrote {output_path}") return 0 if __name__ == "__main__": raise SystemExit(main())