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"""Extract context and prediction panels from a four-grid rollout mp4.
The eval videos produced by eval.py/eval_hyperbolic.py use this layout:
top-left: model-controlled rollout frame
bottom-left: dataset/reference real trajectory frame
top-right: goal frame
bottom-right: goal frame
This script crops the left panels and saves them with explicit context vs.
open-loop-prediction names so paper figures do not mix the two groups.
"""
from __future__ import annotations
import argparse
import json
from pathlib import Path
def parse_steps(value: str) -> list[int]:
steps = []
for item in str(value).split(","):
item = item.strip()
if not item:
continue
steps.append(int(item))
if not steps:
raise argparse.ArgumentTypeError("Expected at least one comma-separated step.")
return steps
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--video", required=True, type=Path, help="Path to rollout_*.mp4.")
parser.add_argument(
"--output-dir",
type=Path,
default=None,
help="Default: <video_parent>/<video_stem>_figure_frames",
)
parser.add_argument("--context-steps", type=parse_steps, default=parse_steps("0,5,10"))
parser.add_argument(
"--prediction-steps",
type=parse_steps,
default=parse_steps("15,20,25,30,35"),
)
parser.add_argument(
"--imagined-label",
default="imagined",
help="Name used for the model rollout row. Use 'rollout' if you want stricter wording.",
)
parser.add_argument(
"--save-composite",
action=argparse.BooleanOptionalAction,
default=False,
help="Also save a paper-style composite grid.",
)
parser.add_argument("--composite-name", default="context_vs_prediction_grid.png")
parser.add_argument("--dpi", type=int, default=220)
return parser.parse_args()
def read_frame(reader, index: int) -> np.ndarray:
import numpy as np
try:
frame = reader.get_data(index)
except IndexError as exc:
raise IndexError(f"Video does not contain frame index {index}.") from exc
frame = np.asarray(frame)
if frame.ndim != 3 or frame.shape[-1] < 3:
raise ValueError(f"Unexpected video frame shape at index {index}: {frame.shape}")
return frame[..., :3].astype(np.uint8, copy=False)
def crop_left_panels(frame: np.ndarray) -> dict[str, np.ndarray]:
height, width = frame.shape[:2]
if height < 2 or width < 2:
raise ValueError(f"Frame is too small to crop: {frame.shape}")
mid_h = height // 2
mid_w = width // 2
return {
"rollout": frame[:mid_h, :mid_w],
"real": frame[mid_h : 2 * mid_h, :mid_w],
}
def save_png(path: Path, frame: np.ndarray) -> None:
from PIL import Image
path.parent.mkdir(parents=True, exist_ok=True)
Image.fromarray(frame).save(path)
def extract_frames(args: argparse.Namespace) -> tuple[Path, dict]:
video_path = args.video.expanduser().resolve()
if not video_path.is_file():
raise FileNotFoundError(f"Video not found: {video_path}")
output_dir = (
args.output_dir.expanduser().resolve()
if args.output_dir is not None
else video_path.parent / f"{video_path.stem}_figure_frames"
)
context_dir = output_dir / "context_input"
prediction_dir = output_dir / "open_loop_prediction"
manifest = {
"video": str(video_path),
"layout": {
"source": "four-grid eval mp4",
"real_panel": "bottom-left",
"imagined_panel": "top-left",
},
"context_steps": list(args.context_steps),
"prediction_steps": list(args.prediction_steps),
"frames": [],
}
import imageio
reader = imageio.get_reader(video_path)
try:
groups = [
("context_input", context_dir, args.context_steps),
("open_loop_prediction", prediction_dir, args.prediction_steps),
]
for group_name, group_dir, steps in groups:
for local_index, step in enumerate(steps, start=1):
frame = read_frame(reader, int(step))
panels = crop_left_panels(frame)
entries = [
("real", panels["real"]),
(str(args.imagined_label), panels["rollout"]),
]
for row_name, panel in entries:
filename = f"{group_name}_{local_index:02d}_{row_name}_t{int(step):03d}.png"
out_path = group_dir / row_name / filename
save_png(out_path, panel)
manifest["frames"].append(
{
"group": group_name,
"index": int(local_index),
"row": row_name,
"step": int(step),
"path": str(out_path),
}
)
finally:
reader.close()
output_dir.mkdir(parents=True, exist_ok=True)
with (output_dir / "manifest.json").open("w") as handle:
json.dump(manifest, handle, indent=2)
return output_dir, manifest
def _load_manifest_image(frame_entry: dict) -> np.ndarray:
import numpy as np
from PIL import Image
return np.asarray(Image.open(frame_entry["path"]).convert("RGB"))
def save_composite(output_dir: Path, manifest: dict, composite_name: str, dpi: int) -> Path:
import matplotlib.pyplot as plt
context_steps = list(manifest["context_steps"])
prediction_steps = list(manifest["prediction_steps"])
all_steps = context_steps + prediction_steps
n_context = len(context_steps)
n_prediction = len(prediction_steps)
n_total = len(all_steps)
gap_units = 0.45
by_key = {
(entry["group"], entry["row"], int(entry["index"])): entry
for entry in manifest["frames"]
}
imagined_rows = sorted(
{
entry["row"]
for entry in manifest["frames"]
if entry["row"] != "real"
}
)
imagined_row = imagined_rows[0] if imagined_rows else "imagined"
sample = _load_manifest_image(manifest["frames"][0])
aspect = sample.shape[1] / sample.shape[0]
tile_h = 1.0
tile_w = aspect
fig_w = max(8.0, n_total * 1.35 * aspect + 2.2)
fig_h = 3.8
fig = plt.figure(figsize=(fig_w, fig_h))
left_margin = 0.10
right_margin = 0.02
top_margin = 0.20
bottom_margin = 0.18
row_gap = 0.055
available_w = 1.0 - left_margin - right_margin
available_h = 1.0 - top_margin - bottom_margin
unit_w = available_w / (n_total + gap_units)
axis_w = unit_w
axis_h = (available_h - row_gap) / 2.0
def x_for_column(column: int) -> float:
extra_gap = gap_units if column >= n_context else 0.0
return left_margin + (column + extra_gap) * unit_w
row_y = {
"real": bottom_margin + axis_h + row_gap,
imagined_row: bottom_margin,
}
context_entries = [("context_input", i + 1, step) for i, step in enumerate(context_steps)]
prediction_entries = [
("open_loop_prediction", i + 1, step)
for i, step in enumerate(prediction_steps)
]
column_entries = context_entries + prediction_entries
for col, (group_name, local_index, step) in enumerate(column_entries):
for row_name in ("real", imagined_row):
entry = by_key[(group_name, row_name, local_index)]
ax = fig.add_axes([x_for_column(col), row_y[row_name], axis_w, axis_h])
ax.imshow(_load_manifest_image(entry))
ax.set_xticks([])
ax.set_yticks([])
for spine in ax.spines.values():
spine.set_linewidth(1.2)
spine.set_color("black")
if row_name == imagined_row:
label = f"T = {step}" if col == 0 else f"{step}"
ax.set_xlabel(label, fontsize=15, labelpad=6)
context_center = (
x_for_column(0) + x_for_column(n_context - 1) + axis_w
) / 2.0
prediction_center = (
x_for_column(n_context) + x_for_column(n_total - 1) + axis_w
) / 2.0
fig.text(context_center, 0.95, "Context Input", ha="center", va="top", fontsize=19)
fig.text(
prediction_center,
0.95,
"Open Loop Prediction",
ha="center",
va="top",
fontsize=19,
)
fig.text(0.045, row_y["real"] + axis_h / 2, "Real", rotation=90, ha="center", va="center", fontsize=17)
fig.text(
0.045,
row_y[imagined_row] + axis_h / 2,
imagined_row.capitalize(),
rotation=90,
ha="center",
va="center",
fontsize=17,
)
composite_path = output_dir / composite_name
fig.savefig(composite_path, dpi=dpi, bbox_inches="tight", pad_inches=0.06)
plt.close(fig)
return composite_path
def main() -> None:
args = parse_args()
output_dir, manifest = extract_frames(args)
print(f"[extract] wrote frames to {output_dir}", flush=True)
if args.save_composite:
composite_path = save_composite(
output_dir,
manifest,
args.composite_name,
int(args.dpi),
)
print(f"[extract] wrote composite figure to {composite_path}", flush=True)
if __name__ == "__main__":
main()
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