AgentBroPet

interface:
  display_name: "AgentBroPet"
  short_description: "Create backend-flexible app pets"
  default_prompt: "Use $AgentBroPet to create an AgentBro-compatible animated pet from a concept, brand, or reference image."

Apache License
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#!/usr/bin/env python3
"""Compose or normalize an AgentBro-compatible pet spritesheet atlas."""

from __future__ import annotations

import argparse
from pathlib import Path

from PIL import Image

COLUMNS = 8
ROWS = 9
CELL_WIDTH = 192
CELL_HEIGHT = 208
ATLAS_WIDTH = COLUMNS * CELL_WIDTH
ATLAS_HEIGHT = ROWS * CELL_HEIGHT
ATLAS_ASPECT_RATIO = ATLAS_WIDTH / ATLAS_HEIGHT
ROW_SPECS = [
    ("idle", 0, 6),
    ("running-right", 1, 8),
    ("running-left", 2, 8),
    ("waving", 3, 4),
    ("jumping", 4, 5),
    ("failed", 5, 8),
    ("waiting", 6, 6),
    ("running", 7, 6),
    ("review", 8, 6),
]
IMAGE_SUFFIXES = {".png", ".webp", ".jpg", ".jpeg"}


def image_files(path: Path) -> list[Path]:
    return sorted(p for p in path.iterdir() if p.suffix.lower() in IMAGE_SUFFIXES)


def find_row_frames(root: Path, state: str, row_index: int) -> list[Path]:
    candidates = [
        root / state,
        root / f"row-{row_index}",
        root / f"row{row_index}",
        root / f"{row_index}-{state}",
    ]
    for candidate in candidates:
        if candidate.is_dir():
            files = image_files(candidate)
            if files:
                return files
    globs = [
        f"{state}_*",
        f"{state}-*",
        f"row{row_index}_*",
        f"row-{row_index}-*",
    ]
    files: list[Path] = []
    for pattern in globs:
        files.extend(p for p in root.glob(pattern) if p.suffix.lower() in IMAGE_SUFFIXES)
    return sorted(set(files))


def paste_centered(atlas: Image.Image, source: Image.Image, row: int, column: int) -> None:
    frame = source.convert("RGBA")
    if frame.size != (CELL_WIDTH, CELL_HEIGHT):
        frame.thumbnail((CELL_WIDTH, CELL_HEIGHT), Image.Resampling.LANCZOS)
    left = column * CELL_WIDTH + (CELL_WIDTH - frame.width) // 2
    top = row * CELL_HEIGHT + (CELL_HEIGHT - frame.height) // 2
    atlas.alpha_composite(frame, (left, top))


def compose_from_source_atlas(path: Path, resize_source: bool) -> Image.Image:
    with Image.open(path) as opened:
        source = opened.convert("RGBA")
    if source.size != (ATLAS_WIDTH, ATLAS_HEIGHT):
        if not resize_source:
            raise SystemExit(
                f"source atlas must be {ATLAS_WIDTH}x{ATLAS_HEIGHT}; got {source.width}x{source.height}"
            )
        source_ratio = source.width / source.height
        if abs(source_ratio - ATLAS_ASPECT_RATIO) > 0.02:
            raise SystemExit(
                "refusing to resize source atlas because its aspect ratio does not match "
                f"the Codex atlas ratio {ATLAS_ASPECT_RATIO:.3f}; got {source_ratio:.3f}. "
                "Generate exact atlas dimensions or use --frames-root."
            )
        source = source.resize((ATLAS_WIDTH, ATLAS_HEIGHT), Image.Resampling.LANCZOS)

    atlas = Image.new("RGBA", (ATLAS_WIDTH, ATLAS_HEIGHT), (0, 0, 0, 0))
    for _state, row, frame_count in ROW_SPECS:
        for column in range(frame_count):
            left = column * CELL_WIDTH
            top = row * CELL_HEIGHT
            cell = source.crop((left, top, left + CELL_WIDTH, top + CELL_HEIGHT))
            atlas.alpha_composite(cell, (left, top))
    return atlas


def compose_from_frames(root: Path) -> Image.Image:
    atlas = Image.new("RGBA", (ATLAS_WIDTH, ATLAS_HEIGHT), (0, 0, 0, 0))
    for state, row, frame_count in ROW_SPECS:
        files = find_row_frames(root, state, row)
        if len(files) < frame_count:
            raise SystemExit(
                f"{state} row needs {frame_count} frames, found {len(files)} under {root}"
            )
        for column, frame_path in enumerate(files[:frame_count]):
            with Image.open(frame_path) as frame:
                paste_centered(atlas, frame, row, column)
    return atlas


def clear_transparent_rgb(image: Image.Image) -> Image.Image:
    rgba = image.convert("RGBA")
    data = bytearray(rgba.tobytes())
    for index in range(0, len(data), 4):
        if data[index + 3] == 0:
            data[index] = 0
            data[index + 1] = 0
            data[index + 2] = 0
    return Image.frombytes("RGBA", rgba.size, bytes(data))


def save_outputs(atlas: Image.Image, output: Path, webp_output: Path | None) -> None:
    atlas = clear_transparent_rgb(atlas)
    output.parent.mkdir(parents=True, exist_ok=True)
    atlas.save(output)
    if webp_output is not None:
        webp_output.parent.mkdir(parents=True, exist_ok=True)
        atlas.save(
            webp_output,
            format="WEBP",
            lossless=True,
            quality=100,
            method=6,
            exact=True,
        )


def main() -> None:
    parser = argparse.ArgumentParser(description=__doc__)
    source = parser.add_mutually_exclusive_group(required=True)
    source.add_argument("--source-atlas")
    source.add_argument("--frames-root")
    parser.add_argument("--output", required=True)
    parser.add_argument("--webp-output")
    parser.add_argument(
        "--resize-source",
        action="store_true",
        help="Resize a lower-resolution source atlas only when it already has the Codex atlas aspect ratio.",
    )
    args = parser.parse_args()

    if args.source_atlas:
        atlas = compose_from_source_atlas(
            Path(args.source_atlas).expanduser().resolve(), args.resize_source
        )
    else:
        atlas = compose_from_frames(Path(args.frames_root).expanduser().resolve())

    save_outputs(
        atlas,
        Path(args.output).expanduser().resolve(),
        Path(args.webp_output).expanduser().resolve() if args.webp_output else None,
    )
    print(f"wrote {Path(args.output).expanduser().resolve()}")
    if args.webp_output:
        print(f"wrote {Path(args.webp_output).expanduser().resolve()}")


if __name__ == "__main__":
    main()

#!/usr/bin/env python3
"""Conditionally derive running-left by mirroring the approved running-right strip."""

from __future__ import annotations

import argparse
import json
from datetime import datetime, timezone
from pathlib import Path

from PIL import Image, ImageOps

RUNNING_FRAME_COUNT = 8


def load_manifest(run_dir: Path) -> dict[str, object]:
    path = run_dir / "imagegen-jobs.json"
    if not path.exists():
        raise SystemExit(f"job manifest not found: {path}")
    return json.loads(path.read_text(encoding="utf-8"))


def job_list(manifest: dict[str, object]) -> list[dict[str, object]]:
    jobs = manifest.get("jobs")
    if not isinstance(jobs, list):
        raise SystemExit("invalid imagegen-jobs.json: jobs must be a list")
    return [job for job in jobs if isinstance(job, dict)]


def find_job(manifest: dict[str, object], job_id: str) -> dict[str, object]:
    for job in job_list(manifest):
        if job.get("id") == job_id:
            return job
    raise SystemExit(f"unknown job id: {job_id}")


def image_metadata(path: Path) -> dict[str, object]:
    with Image.open(path) as image:
        image.verify()
    with Image.open(path) as image:
        return {
            "width": image.width,
            "height": image.height,
            "mode": image.mode,
            "format": image.format,
        }


def manifest_relative(path: Path, run_dir: Path) -> str:
    return str(path.resolve().relative_to(run_dir.resolve()))


def mirror_strip_preserving_frame_order(
    source: Image.Image,
    frame_count: int = RUNNING_FRAME_COUNT,
) -> Image.Image:
    rgba = source.convert("RGBA")
    mirrored = Image.new("RGBA", rgba.size, (0, 0, 0, 0))
    slot_width = rgba.width / frame_count
    for index in range(frame_count):
        left = round(index * slot_width)
        right = round((index + 1) * slot_width)
        mirrored.alpha_composite(
            ImageOps.mirror(rgba.crop((left, 0, right, rgba.height))),
            (left, 0),
        )
    return mirrored


def main() -> None:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("--run-dir", required=True)
    parser.add_argument(
        "--confirm-appropriate-mirror",
        action="store_true",
        help="Required after visually confirming the rightward strip can be mirrored without identity/prop issues.",
    )
    parser.add_argument(
        "--decision-note",
        required=True,
        help="Short note explaining why mirroring is acceptable for this pet.",
    )
    parser.add_argument("--force", action="store_true")
    args = parser.parse_args()

    if not args.confirm_appropriate_mirror:
        raise SystemExit("refusing to mirror without --confirm-appropriate-mirror")
    if not args.decision_note.strip():
        raise SystemExit("--decision-note must explain why mirroring is appropriate")

    run_dir = Path(args.run_dir).expanduser().resolve()
    manifest_path = run_dir / "imagegen-jobs.json"
    manifest = load_manifest(run_dir)
    right_job = find_job(manifest, "running-right")
    left_job = find_job(manifest, "running-left")

    if right_job.get("status") != "complete":
        raise SystemExit("running-right must be complete before deriving running-left")
    mirror_policy = left_job.get("mirror_policy")
    if not isinstance(mirror_policy, dict) or mirror_policy.get("may_derive_from") != "running-right":
        raise SystemExit("running-left is not configured for conditional mirroring")

    source = run_dir / "decoded" / "running-right.png"
    output = run_dir / "decoded" / "running-left.png"
    if not source.is_file():
        raise SystemExit(f"running-right decoded strip not found: {source}")
    if output.exists() and not args.force:
        raise SystemExit(f"{output} already exists; pass --force to replace it")

    output.parent.mkdir(parents=True, exist_ok=True)
    with Image.open(source) as image:
        mirrored = mirror_strip_preserving_frame_order(image)
        mirrored.save(output)

    left_job["status"] = "complete"
    left_job["source_path"] = manifest_relative(source, run_dir)
    left_job["derived_from"] = "running-right"
    left_job["completed_at"] = datetime.now(timezone.utc).isoformat()
    left_job["metadata"] = image_metadata(output)
    left_job["mirror_decision"] = {
        "approved": True,
        "approved_at": left_job["completed_at"],
        "note": args.decision_note.strip(),
        "transform": "framewise-horizontal-mirror-preserving-order",
    }
    for key in [
        "last_error",
        "repair_reason",
        "queued_at",
    ]:
        left_job.pop(key, None)

    manifest_path.write_text(json.dumps(manifest, indent=2) + "\n", encoding="utf-8")
    print(
        json.dumps(
            {
                "ok": True,
                "job_id": "running-left",
                "derived_from": "running-right",
                "output": str(output),
                "decision_note": args.decision_note.strip(),
                "transform": "framewise-horizontal-mirror-preserving-order",
            },
            indent=2,
        )
    )


if __name__ == "__main__":
    main()

#!/usr/bin/env python3
"""Extract generated horizontal row strips into 192x208 sprite frames."""

from __future__ import annotations

import argparse
import json
import math
import re
from pathlib import Path

from PIL import Image

CELL_WIDTH = 192
CELL_HEIGHT = 208
ROW_FRAME_COUNTS = {
    "idle": 6,
    "running-right": 8,
    "running-left": 8,
    "waving": 4,
    "jumping": 5,
    "failed": 8,
    "waiting": 6,
    "running": 6,
    "review": 6,
}


def parse_states(raw: str) -> list[str]:
    if raw.strip().lower() == "all":
        return list(ROW_FRAME_COUNTS)
    states = [item.strip() for item in raw.split(",") if item.strip()]
    unknown = sorted(set(states) - set(ROW_FRAME_COUNTS))
    if unknown:
        raise SystemExit(f"unknown state(s): {', '.join(unknown)}")
    return states


def parse_hex_color(value: str) -> tuple[int, int, int]:
    if not re.fullmatch(r"#[0-9a-fA-F]{6}", value):
        raise SystemExit(f"invalid chroma key color: {value}; expected #RRGGBB")
    return tuple(int(value[index : index + 2], 16) for index in (1, 3, 5))


def load_chroma_key(decoded_dir: Path, override: str | None) -> tuple[int, int, int]:
    if override:
        return parse_hex_color(override)
    request_path = decoded_dir.parent / "pet_request.json"
    if request_path.is_file():
        request = json.loads(request_path.read_text(encoding="utf-8"))
        chroma_key = request.get("chroma_key")
        if isinstance(chroma_key, dict) and isinstance(chroma_key.get("hex"), str):
            return parse_hex_color(chroma_key["hex"])
    return parse_hex_color("#00FF00")


def color_distance(
    red: int,
    green: int,
    blue: int,
    key: tuple[int, int, int],
) -> float:
    return math.sqrt((red - key[0]) ** 2 + (green - key[1]) ** 2 + (blue - key[2]) ** 2)


def remove_chroma_background(
    image: Image.Image,
    chroma_key: tuple[int, int, int],
    threshold: float,
) -> Image.Image:
    rgba = image.convert("RGBA")
    pixels = rgba.load()
    for y in range(rgba.height):
        for x in range(rgba.width):
            red, green, blue, alpha = pixels[x, y]
            if color_distance(red, green, blue, chroma_key) <= threshold:
                pixels[x, y] = (0, 0, 0, 0)
    return rgba


def fit_to_cell(image: Image.Image) -> Image.Image:
    bbox = image.getbbox()
    target = Image.new("RGBA", (CELL_WIDTH, CELL_HEIGHT), (0, 0, 0, 0))
    if bbox is None:
        return target

    sprite = image.crop(bbox)
    max_width = CELL_WIDTH - 10
    max_height = CELL_HEIGHT - 10
    scale = min(max_width / sprite.width, max_height / sprite.height, 1.0)
    if scale != 1.0:
        sprite = sprite.resize(
            (max(1, round(sprite.width * scale)), max(1, round(sprite.height * scale))),
            Image.Resampling.LANCZOS,
        )
    left = (CELL_WIDTH - sprite.width) // 2
    top = (CELL_HEIGHT - sprite.height) // 2
    target.alpha_composite(sprite, (left, top))
    return target


def fit_viewport_to_cell(image: Image.Image) -> Image.Image:
    target = Image.new("RGBA", (CELL_WIDTH, CELL_HEIGHT), (0, 0, 0, 0))
    if image.getbbox() is None:
        return target

    viewport = image.copy()
    max_width = CELL_WIDTH - 10
    max_height = CELL_HEIGHT - 10
    scale = min(max_width / viewport.width, max_height / viewport.height, 1.0)
    if scale != 1.0:
        viewport = viewport.resize(
            (max(1, round(viewport.width * scale)), max(1, round(viewport.height * scale))),
            Image.Resampling.LANCZOS,
        )
    left = (CELL_WIDTH - viewport.width) // 2
    top = (CELL_HEIGHT - viewport.height) // 2
    target.alpha_composite(viewport, (left, top))
    return target


def connected_components(image: Image.Image) -> list[dict[str, object]]:
    alpha = image.getchannel("A")
    width, height = image.size
    data = alpha.tobytes()
    visited = bytearray(width * height)
    components: list[dict[str, object]] = []

    for start, alpha_value in enumerate(data):
        if alpha_value <= 16 or visited[start]:
            continue

        stack = [start]
        visited[start] = 1
        pixels: list[int] = []
        min_x = width
        min_y = height
        max_x = 0
        max_y = 0

        while stack:
            current = stack.pop()
            pixels.append(current)
            x = current % width
            y = current // width
            min_x = min(min_x, x)
            min_y = min(min_y, y)
            max_x = max(max_x, x)
            max_y = max(max_y, y)

            if x > 0:
                neighbor = current - 1
                if not visited[neighbor] and data[neighbor] > 16:
                    visited[neighbor] = 1
                    stack.append(neighbor)
            if x + 1 < width:
                neighbor = current + 1
                if not visited[neighbor] and data[neighbor] > 16:
                    visited[neighbor] = 1
                    stack.append(neighbor)
            if y > 0:
                neighbor = current - width
                if not visited[neighbor] and data[neighbor] > 16:
                    visited[neighbor] = 1
                    stack.append(neighbor)
            if y + 1 < height:
                neighbor = current + width
                if not visited[neighbor] and data[neighbor] > 16:
                    visited[neighbor] = 1
                    stack.append(neighbor)

        components.append(
            {
                "pixels": pixels,
                "area": len(pixels),
                "bbox": (min_x, min_y, max_x + 1, max_y + 1),
                "center_x": (min_x + max_x + 1) / 2,
            }
        )

    return components


def component_group_image(
    source: Image.Image,
    components: list[dict[str, object]],
    padding: int = 4,
) -> Image.Image:
    width, height = source.size
    min_x = max(0, min(component["bbox"][0] for component in components) - padding)
    min_y = max(0, min(component["bbox"][1] for component in components) - padding)
    max_x = min(width, max(component["bbox"][2] for component in components) + padding)
    max_y = min(height, max(component["bbox"][3] for component in components) + padding)

    output = Image.new("RGBA", (max_x - min_x, max_y - min_y), (0, 0, 0, 0))
    source_pixels = source.load()
    output_pixels = output.load()
    for component in components:
        for pixel_index in component["pixels"]:
            x = pixel_index % width
            y = pixel_index // width
            output_pixels[x - min_x, y - min_y] = source_pixels[x, y]
    return output


def component_frame_groups(
    strip: Image.Image,
    frame_count: int,
) -> list[list[dict[str, object]]] | None:
    components = connected_components(strip)
    if not components:
        return None

    largest_area = max(component["area"] for component in components)
    seed_threshold = max(120, largest_area * 0.20)
    seeds = [component for component in components if component["area"] >= seed_threshold]
    if len(seeds) < frame_count:
        seeds = sorted(components, key=lambda component: component["area"], reverse=True)[
            :frame_count
        ]
    if len(seeds) < frame_count:
        return None

    seeds = sorted(
        sorted(seeds, key=lambda component: component["area"], reverse=True)[:frame_count],
        key=lambda component: component["center_x"],
    )
    seed_ids = {id(seed) for seed in seeds}
    groups: list[list[dict[str, object]]] = [[seed] for seed in seeds]
    noise_threshold = max(12, largest_area * 0.002)

    for component in components:
        if id(component) in seed_ids or component["area"] < noise_threshold:
            continue
        nearest_index = min(
            range(len(seeds)),
            key=lambda index: abs(seeds[index]["center_x"] - component["center_x"]),
        )
        groups[nearest_index].append(component)

    return groups


def extract_component_frames(strip: Image.Image, frame_count: int) -> list[Image.Image] | None:
    groups = component_frame_groups(strip, frame_count)
    if groups is None:
        return None
    return [fit_to_cell(component_group_image(strip, group)) for group in groups]


def component_bounds(components: list[dict[str, object]]) -> tuple[int, int, int, int]:
    return (
        min(component["bbox"][0] for component in components),
        min(component["bbox"][1] for component in components),
        max(component["bbox"][2] for component in components),
        max(component["bbox"][3] for component in components),
    )


def extract_slot_frames(strip: Image.Image, frame_count: int) -> list[Image.Image]:
    slot_width = strip.width / frame_count
    frames = []
    for index in range(frame_count):
        left = round(index * slot_width)
        right = round((index + 1) * slot_width)
        crop = strip.crop((left, 0, right, strip.height))
        frames.append(fit_to_cell(crop))
    return frames


def extract_stable_slot_frames(strip: Image.Image, frame_count: int) -> list[Image.Image]:
    groups = component_frame_groups(strip, frame_count)
    padding = 4
    if groups is not None:
        bboxes = [component_bounds(group) for group in groups]
        shared_top = max(0, min(bbox[1] for bbox in bboxes) - padding)
        shared_bottom = min(strip.height, max(bbox[3] for bbox in bboxes) + padding)
        viewport_width = max(bbox[2] - bbox[0] for bbox in bboxes) + padding * 2
        viewport_height = max(1, shared_bottom - shared_top)
        frames = []
        for group, bbox in zip(groups, bboxes):
            grouped = component_group_image(strip, group, padding=padding)
            grouped_top = max(0, bbox[1] - padding)
            viewport = Image.new(
                "RGBA",
                (viewport_width, viewport_height),
                (0, 0, 0, 0),
            )
            left = (viewport_width - grouped.width) // 2
            viewport.alpha_composite(grouped, (left, grouped_top - shared_top))
            frames.append(fit_viewport_to_cell(viewport))
        return frames

    bbox = strip.getbbox()
    if bbox is None:
        return [
            Image.new("RGBA", (CELL_WIDTH, CELL_HEIGHT), (0, 0, 0, 0))
            for _ in range(frame_count)
        ]

    shared_top = max(0, bbox[1] - padding)
    shared_bottom = min(strip.height, bbox[3] + padding)
    slot_width = strip.width / frame_count
    frames = []
    for index in range(frame_count):
        left = round(index * slot_width)
        right = round((index + 1) * slot_width)
        crop = strip.crop((left, shared_top, right, shared_bottom))
        frames.append(fit_viewport_to_cell(crop))
    return frames


def extract_state(
    strip_path: Path,
    state: str,
    output_root: Path,
    chroma_key: tuple[int, int, int],
    threshold: float,
    method: str,
) -> dict[str, object]:
    frame_count = ROW_FRAME_COUNTS[state]
    with Image.open(strip_path) as opened:
        strip = remove_chroma_background(opened, chroma_key, threshold)

    state_dir = output_root / state
    state_dir.mkdir(parents=True, exist_ok=True)

    frames = None
    used_method = method
    if method in {"auto", "components"}:
        frames = extract_component_frames(strip, frame_count)
        if frames is None and method == "components":
            raise SystemExit(f"could not find {frame_count} sprite components in {strip_path}")
        if frames is not None:
            used_method = "components"

    if frames is None:
        if method == "stable-slots":
            frames = extract_stable_slot_frames(strip, frame_count)
            used_method = "stable-slots"
        else:
            frames = extract_slot_frames(strip, frame_count)
            used_method = "slots"

    outputs = []
    for index, frame in enumerate(frames):
        output = state_dir / f"{index:02d}.png"
        frame.save(output)
        outputs.append(str(output))
    return {"state": state, "frames": outputs, "method": used_method}


def main() -> None:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("--decoded-dir", required=True)
    parser.add_argument("--output-dir", required=True)
    parser.add_argument("--states", default="all")
    parser.add_argument("--chroma-key", help="Override chroma key as #RRGGBB.")
    parser.add_argument("--key-threshold", type=float, default=96.0)
    parser.add_argument(
        "--method",
        choices=("auto", "components", "slots", "stable-slots"),
        default="auto",
        help="Use connected sprite components when possible, raw equal slots, or row-stable slot viewports.",
    )
    args = parser.parse_args()

    decoded_dir = Path(args.decoded_dir).expanduser().resolve()
    output_dir = Path(args.output_dir).expanduser().resolve()
    chroma_key = load_chroma_key(decoded_dir, args.chroma_key)
    states = parse_states(args.states)
    manifest = []
    for state in states:
        strip_path = decoded_dir / f"{state}.png"
        if not strip_path.is_file():
            raise SystemExit(f"missing generated strip for {state}: {strip_path}")
        manifest.append(
            extract_state(
                strip_path,
                state,
                output_dir,
                chroma_key,
                args.key_threshold,
                args.method,
            )
        )

    (output_dir / "frames-manifest.json").write_text(
        json.dumps(
            {
                "ok": True,
                "chroma_key": {
                    "hex": f"#{chroma_key[0]:02X}{chroma_key[1]:02X}{chroma_key[2]:02X}",
                    "rgb": list(chroma_key),
                    "threshold": args.key_threshold,
                },
                "rows": manifest,
            },
            indent=2,
        )
        + "\n",
        encoding="utf-8",
    )
    print(json.dumps({"ok": True, "frames_root": str(output_dir), "states": states}, indent=2))


if __name__ == "__main__":
    main()

#!/usr/bin/env python3
"""Inspect extracted AgentBro-compatible pet frames before atlas composition."""

from __future__ import annotations

import argparse
import json
import math
from pathlib import Path
from statistics import median

from PIL import Image

CELL_WIDTH = 192
CELL_HEIGHT = 208
ROW_FRAME_COUNTS = {
    "idle": 6,
    "running-right": 8,
    "running-left": 8,
    "waving": 4,
    "jumping": 5,
    "failed": 8,
    "waiting": 6,
    "running": 6,
    "review": 6,
}
IMAGE_SUFFIXES = {".png", ".webp", ".jpg", ".jpeg"}


def alpha_nonzero_count(image: Image.Image) -> int:
    alpha = image if image.mode == "L" else image.getchannel("A")
    return sum(alpha.histogram()[1:])


def edge_alpha_count(image: Image.Image, margin: int) -> int:
    alpha = image.getchannel("A")
    width, height = alpha.size
    total = 0
    for box in (
        (0, 0, width, margin),
        (0, height - margin, width, height),
        (0, 0, margin, height),
        (width - margin, 0, width, height),
    ):
        total += alpha_nonzero_count(alpha.crop(box))
    return total


def color_distance(left: tuple[int, int, int], right: tuple[int, int, int]) -> float:
    return math.sqrt(sum((left[index] - right[index]) ** 2 for index in range(3)))


def chroma_adjacent_count(
    image: Image.Image,
    chroma_key: tuple[int, int, int] | None,
    threshold: float,
) -> int:
    if chroma_key is None:
        return 0
    rgba = image.convert("RGBA")
    data = rgba.tobytes()
    count = 0
    for index in range(0, len(data), 4):
        red, green, blue, alpha = data[index : index + 4]
        if alpha > 16 and color_distance((red, green, blue), chroma_key) <= threshold:
            count += 1
    return count


def frame_files(state_dir: Path) -> list[Path]:
    if not state_dir.is_dir():
        return []
    return sorted(path for path in state_dir.iterdir() if path.suffix.lower() in IMAGE_SUFFIXES)


def load_manifest(frames_root: Path) -> dict[str, dict[str, object]]:
    manifest_path = frames_root / "frames-manifest.json"
    if not manifest_path.is_file():
        return {}
    manifest = json.loads(manifest_path.read_text(encoding="utf-8"))
    rows = manifest.get("rows", [])
    if not isinstance(rows, list):
        return {}
    return {
        row["state"]: row
        for row in rows
        if isinstance(row, dict) and isinstance(row.get("state"), str)
    }


def load_chroma_key(frames_root: Path) -> tuple[int, int, int] | None:
    manifest_path = frames_root / "frames-manifest.json"
    if not manifest_path.is_file():
        return None
    manifest = json.loads(manifest_path.read_text(encoding="utf-8"))
    chroma_key = manifest.get("chroma_key")
    if not isinstance(chroma_key, dict):
        return None
    rgb = chroma_key.get("rgb")
    if (
        not isinstance(rgb, list)
        or len(rgb) != 3
        or not all(isinstance(value, int) for value in rgb)
    ):
        return None
    return (rgb[0], rgb[1], rgb[2])


def inspect_state(
    frames_root: Path,
    state: str,
    expected_count: int,
    manifest_rows: dict[str, dict[str, object]],
    chroma_key: tuple[int, int, int] | None,
    args: argparse.Namespace,
) -> dict[str, object]:
    state_dir = frames_root / state
    files = frame_files(state_dir)
    row_errors: list[str] = []
    row_warnings: list[str] = []
    frames: list[dict[str, object]] = []
    areas: list[int] = []
    manifest_row = manifest_rows.get(state, {})
    method = manifest_row.get("method")

    if len(files) != expected_count:
        row_errors.append(f"expected {expected_count} frame files for {state}, found {len(files)}")

    if args.require_components and method and method != "components":
        if method == "stable-slots" and args.allow_stable_slots:
            row_warnings.append(
                f"{state} used extraction method stable-slots; confirm motion playback remains stable and unclipped"
            )
        else:
            row_errors.append(
                f"{state} used extraction method {method}; regenerate the row or inspect slot slicing"
            )
    elif method and method != "components":
        row_warnings.append(
            f"{state} used extraction method {method}; component extraction is preferred"
        )

    for index, frame_path in enumerate(files[:expected_count]):
        with Image.open(frame_path) as opened:
            frame = opened.convert("RGBA")
        nontransparent = alpha_nonzero_count(frame)
        bbox = frame.getbbox()
        edge_pixels = edge_alpha_count(frame, args.edge_margin)
        chroma_adjacent_pixels = chroma_adjacent_count(
            frame,
            chroma_key,
            args.chroma_adjacent_threshold,
        )
        info = {
            "index": index,
            "file": str(frame_path),
            "width": frame.width,
            "height": frame.height,
            "nontransparent_pixels": nontransparent,
            "bbox": list(bbox) if bbox else None,
            "edge_pixels": edge_pixels,
            "chroma_adjacent_pixels": chroma_adjacent_pixels,
        }
        frames.append(info)
        areas.append(nontransparent)

        if frame.size != (CELL_WIDTH, CELL_HEIGHT):
            row_errors.append(
                f"{state} frame {index:02d} is {frame.width}x{frame.height}; expected {CELL_WIDTH}x{CELL_HEIGHT}"
            )
        if nontransparent < args.min_used_pixels:
            row_errors.append(
                f"{state} frame {index:02d} is empty or too sparse ({nontransparent} pixels)"
            )
        if edge_pixels > args.edge_pixel_threshold:
            row_warnings.append(
                f"{state} frame {index:02d} has {edge_pixels} non-transparent pixels near the cell edge"
            )
        if chroma_adjacent_pixels > args.chroma_adjacent_pixel_threshold:
            row_errors.append(
                f"{state} frame {index:02d} has {chroma_adjacent_pixels} non-transparent pixels close to the chroma key"
            )

    if areas:
        row_median = median(areas)
        for index, area in enumerate(areas[:expected_count]):
            if row_median > 0 and area < row_median * args.small_outlier_ratio:
                row_warnings.append(
                    f"{state} frame {index:02d} is much smaller than the row median ({area} vs {row_median:.0f})"
                )
            if row_median > 0 and area > row_median * args.large_outlier_ratio:
                row_warnings.append(
                    f"{state} frame {index:02d} is much larger than the row median ({area} vs {row_median:.0f})"
                )

    return {
        "state": state,
        "expected_frames": expected_count,
        "actual_frames": len(files),
        "extraction_method": method,
        "ok": not row_errors,
        "errors": row_errors,
        "warnings": row_warnings,
        "frames": frames,
    }


def main() -> None:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("--frames-root", required=True)
    parser.add_argument("--json-out", required=True)
    parser.add_argument("--min-used-pixels", type=int, default=400)
    parser.add_argument("--edge-margin", type=int, default=2)
    parser.add_argument("--edge-pixel-threshold", type=int, default=24)
    parser.add_argument("--chroma-adjacent-threshold", type=float, default=150.0)
    parser.add_argument("--chroma-adjacent-pixel-threshold", type=int, default=800)
    parser.add_argument("--small-outlier-ratio", type=float, default=0.35)
    parser.add_argument("--large-outlier-ratio", type=float, default=2.75)
    parser.add_argument(
        "--require-components",
        action="store_true",
        help="Fail rows that fell back to equal-slot extraction.",
    )
    parser.add_argument(
        "--allow-stable-slots",
        action="store_true",
        help="Permit explicitly chosen stable-slots extraction while still warning for visual review.",
    )
    args = parser.parse_args()

    frames_root = Path(args.frames_root).expanduser().resolve()
    manifest_rows = load_manifest(frames_root)
    chroma_key = load_chroma_key(frames_root)
    rows = [
        inspect_state(frames_root, state, count, manifest_rows, chroma_key, args)
        for state, count in ROW_FRAME_COUNTS.items()
    ]
    errors = [error for row in rows for error in row["errors"]]
    warnings = [warning for row in rows for warning in row["warnings"]]
    result = {
        "ok": not errors,
        "frames_root": str(frames_root),
        "errors": errors,
        "warnings": warnings,
        "rows": rows,
    }

    json_out = Path(args.json_out).expanduser().resolve()
    json_out.parent.mkdir(parents=True, exist_ok=True)
    json_out.write_text(json.dumps(result, indent=2) + "\n", encoding="utf-8")
    print(json.dumps({k: v for k, v in result.items() if k != "rows"}, indent=2))
    raise SystemExit(0 if result["ok"] else 1)


if __name__ == "__main__":
    main()

#!/usr/bin/env python3
"""Create a labeled contact sheet from an AgentBro-compatible pet atlas."""

from __future__ import annotations

import argparse
from pathlib import Path

from PIL import Image, ImageDraw, ImageFont

COLUMNS = 8
ROWS = 9
CELL_WIDTH = 192
CELL_HEIGHT = 208
LABEL_HEIGHT = 22
ROW_NAMES = [
    "idle",
    "running-right",
    "running-left",
    "waving",
    "jumping",
    "failed",
    "waiting",
    "running",
    "review",
]
USED_COUNTS = [6, 8, 8, 4, 5, 8, 6, 6, 6]


def checker(size: tuple[int, int], square: int = 16) -> Image.Image:
    image = Image.new("RGB", size, "#ffffff")
    draw = ImageDraw.Draw(image)
    for y in range(0, size[1], square):
        for x in range(0, size[0], square):
            if (x // square + y // square) % 2:
                draw.rectangle((x, y, x + square - 1, y + square - 1), fill="#e8e8e8")
    return image


def main() -> None:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("atlas")
    parser.add_argument("--output", required=True)
    parser.add_argument("--scale", type=float, default=0.5)
    args = parser.parse_args()

    with Image.open(Path(args.atlas).expanduser().resolve()) as opened:
        atlas = opened.convert("RGBA")

    cell_w = max(1, round(CELL_WIDTH * args.scale))
    cell_h = max(1, round(CELL_HEIGHT * args.scale))
    width = COLUMNS * cell_w
    height = ROWS * (cell_h + LABEL_HEIGHT)
    sheet = Image.new("RGB", (width, height), "#f7f7f7")
    draw = ImageDraw.Draw(sheet)
    font = ImageFont.load_default()

    for row in range(ROWS):
        y = row * (cell_h + LABEL_HEIGHT)
        draw.rectangle((0, y, width, y + LABEL_HEIGHT - 1), fill="#111111")
        draw.text((6, y + 5), f"row {row}: {ROW_NAMES[row]}", fill="#ffffff", font=font)
        draw.text(
            (width - 92, y + 5),
            f"{USED_COUNTS[row]} frames",
            fill="#ffffff",
            font=font,
        )
        for column in range(COLUMNS):
            crop = atlas.crop(
                (
                    column * CELL_WIDTH,
                    row * CELL_HEIGHT,
                    (column + 1) * CELL_WIDTH,
                    (row + 1) * CELL_HEIGHT,
                )
            )
            crop = crop.resize((cell_w, cell_h), Image.Resampling.LANCZOS)
            bg = checker((cell_w, cell_h))
            bg.paste(crop, (0, 0), crop)
            x = column * cell_w
            sheet.paste(bg, (x, y + LABEL_HEIGHT))
            outline = "#18a058" if column < USED_COUNTS[row] else "#cc3344"
            draw.rectangle(
                (x, y + LABEL_HEIGHT, x + cell_w - 1, y + LABEL_HEIGHT + cell_h - 1),
                outline=outline,
            )
            draw.text((x + 4, y + LABEL_HEIGHT + 4), str(column), fill="#111111", font=font)

    output = Path(args.output).expanduser().resolve()
    output.parent.mkdir(parents=True, exist_ok=True)
    sheet.save(output)
    print(f"wrote {output}")


if __name__ == "__main__":
    main()

#!/usr/bin/env python3
"""Create an AgentBro-compatible pet run folder, prompts, and imagegen job manifest."""

from __future__ import annotations

import argparse
import json
import math
import re
import shutil
from datetime import datetime, timezone
from pathlib import Path

from PIL import Image
from PIL import ImageDraw

ATLAS = {"columns": 8, "rows": 9, "cell_width": 192, "cell_height": 208}
ATLAS["width"] = ATLAS["columns"] * ATLAS["cell_width"]
ATLAS["height"] = ATLAS["rows"] * ATLAS["cell_height"]

ROWS = [
    ("idle", 0, 6, "calm resting, breathing, and blinking loop"),
    ("running-right", 1, 8, "rightward drag movement loop"),
    ("running-left", 2, 8, "leftward drag movement loop"),
    ("waving", 3, 4, "greeting or attention gesture"),
    ("jumping", 4, 5, "hover or playful jump"),
    ("failed", 5, 8, "blocked, failed, or cancelled reaction"),
    ("waiting", 6, 6, "waiting for approval, help, or user input"),
    ("running", 7, 6, "active task work or processing"),
    ("review", 8, 6, "ready or completed output review"),
]

STATE_PROMPTS = {
    "idle": "Calm low-distraction resting loop: subtle breathing, tiny blink, slight head/body bob, and only quiet persona-preserving motion.",
    "running-right": "Dragging-right loop: show directional movement to the right through body and limb poses only.",
    "running-left": "Dragging-left loop: show directional movement to the left through body and limb poses only.",
    "waving": "Greeting loop: paw or limb down, raised, tilted, and returning in a friendly attention gesture.",
    "jumping": "Hover jump loop: anticipation, lift, airborne peak, descent, and settle through body height.",
    "failed": "Blocked/failed loop: slumped or deflated reaction with sad or closed eyes.",
    "waiting": "Needs-input loop: expectant asking pose for approval, help, or user input.",
    "running": "Working loop: focused active-task processing, thinking, typing, scanning, or effortful concentration; not literal foot-running, jogging, sprinting, treadmill motion, raised knees, long steps, pumping arms, or directional travel.",
    "review": "Ready-review loop: focused inspection of completed output with lean, blink, narrowed eyes, head tilt, or paw pose.",
}

STATE_REQUIREMENTS = {
    "idle": [
        "CRITICAL: idle is the low-distraction baseline state and the first frame is also used as the reduced-motion static pet.",
        "Use only subtle idle motion: gentle breathing, a tiny blink, a slight head or body bob, a very small material sway, or another quiet motion that fits the pet persona.",
        "Keep the pet essentially in the same pose, facing direction, silhouette, markings, palette, and prop state across all 6 frames.",
        "Idle variation must stay calm but still read as animation; do not repeat effectively identical copies across the loop.",
        "Do not show waving, walking, running, jumping, talking, working, reviewing, emotional reactions, large gestures, item interactions, or new props.",
        "Feet, base, body, or object anchor should remain planted or nearly planted.",
        "The first and last frames should be very close visually so the loop feels calm and does not pop.",
    ],
    "waving": [
        "Show the greeting through paw, hand, wing, or limb pose only.",
        "Do not draw wave marks, motion arcs, lines, sparkles, symbols, or floating effects around the gesture.",
    ],
    "jumping": [
        "Show the jump through pose and vertical body position only: anticipation, lift, airborne peak, descent, settle.",
        "Do not draw ground shadows, contact shadows, drop shadows, oval shadows, landing marks, dust, smears, bounce pads, or motion marks under the pet.",
        "Keep the background outside the pet perfectly flat chroma key with no darker key-colored patches.",
    ],
    "failed": [
        "Show failure through slumped pose, drooping ears/limbs, closed or sad eyes, and lower body position.",
        "Tears, small smoke puffs, or tiny stars are allowed only if attached to or overlapping the pet silhouette and kept inside the same frame slot.",
        "Do not draw red X marks, floating symbols, detached stars, separated smoke clouds, falling tear drops, dust, or other loose effects.",
    ],
    "waiting": [
        "Show that Codex needs approval, help, or user input through an expectant asking pose.",
        "Keep the motion patient and readable, without turning it into ordinary idle or review.",
    ],
    "running": [
        "Show the pet actively working or processing, as if running a task: focused posture, busy hands or paws, purposeful bobbing, thinking motion, tool or prop motion only if already part of the pet identity, or other non-locomotion activity.",
        "Do not show literal foot-running, jogging, sprinting, treadmill motion, raised knees, long steps, pumping arms, directional travel, speed lines, dust clouds, floor shadows, motion trails, or detached motion effects.",
    ],
    "review": [
        "Show review through lean, blink, narrowed eyes, head tilt, or paw/hand position.",
        "Do not add magnifying glasses, papers, code, UI, punctuation, symbols, or other new props unless they already exist in the base pet identity.",
    ],
    "running-right": [
        "Show directional drag movement to the right through body, limb, and prop movement only.",
        "The row must unmistakably face and travel right.",
        "The movement cadence must alternate visibly across the 8 frames instead of repeating one nearly static stride.",
        "Do not draw speed lines, dust clouds, floor shadows, motion trails, or detached motion effects.",
    ],
    "running-left": [
        "Show directional drag movement to the left through body, limb, and prop movement only.",
        "The row must unmistakably face and travel left.",
        "The movement cadence must alternate visibly across the 8 frames instead of repeating one nearly static stride.",
        "Do not draw speed lines, dust clouds, floor shadows, motion trails, or detached motion effects.",
    ],
}

NON_DERIVABLE_STATES = {
    "waving",
    "jumping",
    "failed",
    "waiting",
    "running",
    "review",
}

PET_SAFE_STYLE = (
    "Pet-safe sprite: compact full-body mascot, readable in a 192x208 cell, "
    "clear silhouette, simple face, stable palette/materials, and crisp edges "
    "for chroma-key extraction."
)

STYLE_PRESETS = {
    "auto": (
        "Infer the most appropriate pet-safe style from the user request and "
        "reference images, then keep that exact style consistent across every row."
    ),
    "pixel": (
        "Pixel-art-adjacent digital mascot with a chunky silhouette, simple dark "
        "outline, limited palette, flat cel shading, and visible stepped edges."
    ),
    "plush": (
        "Soft plush toy mascot with rounded stitched forms, fuzzy fabric feel, "
        "simple sewn details, and readable toy-like proportions."
    ),
    "clay": (
        "Handmade clay or polymer-clay mascot with rounded sculpted forms, soft "
        "material texture, simple features, and clean readable edges."
    ),
    "sticker": (
        "Polished sticker mascot with bold clean shapes, crisp outline, flat "
        "colors, and minimal highlight detail."
    ),
    "flat-vector": (
        "Flat vector-style mascot with simple geometric forms, crisp color areas, "
        "clean outline, and minimal shading."
    ),
    "3d-toy": (
        "Stylized 3D toy mascot with smooth rounded forms, simple materials, "
        "clear silhouette, and no photoreal complexity."
    ),
    "painterly": (
        "Painterly mascot with simplified brush texture, readable forms, stable "
        "palette, and enough edge clarity for clean extraction."
    ),
    "brand-inspired": (
        "Brand-inspired mascot using approved public or user-provided brand cues "
        "such as colors, mascot themes, and vibe while avoiding readable text or "
        "logo copying unless explicitly approved."
    ),
}

CHROMA_KEY_CANDIDATES = [
    ("magenta", "#FF00FF"),
    ("cyan", "#00FFFF"),
    ("yellow", "#FFFF00"),
    ("blue", "#0000FF"),
    ("orange", "#FF7F00"),
    ("green", "#00FF00"),
]

DEFAULT_PET_NAME = "Sprout"
CANONICAL_BASE_PATH = "references/canonical-base.png"
BRAND_DISCOVERY_PATH = "references/brand-discovery.md"
LAYOUT_GUIDE_DIR = "references/layout-guides"
LAYOUT_GUIDE_SAFE_MARGIN_X = 18
LAYOUT_GUIDE_SAFE_MARGIN_Y = 16


def slugify(value: str) -> str:
    value = value.strip().lower()
    value = re.sub(r"[^a-z0-9]+", "-", value)
    value = re.sub(r"-{2,}", "-", value)
    return value.strip("-")


def display_from_slug(value: str) -> str:
    words = [word for word in re.split(r"[^a-zA-Z0-9]+", value.strip()) if word]
    return " ".join(word.capitalize() for word in words)


def concept_words(value: str) -> list[str]:
    stop_words = {
        "a",
        "an",
        "and",
        "app",
        "based",
        "codex",
        "compact",
        "digital",
        "for",
        "from",
        "in",
        "of",
        "on",
        "pet",
        "ready",
        "small",
        "the",
        "to",
        "with",
    }
    words = [
        word.lower()
        for word in re.findall(r"[a-zA-Z0-9]+", value)
        if word.lower() not in stop_words
    ]
    return words


def infer_name(args: argparse.Namespace, reference_paths: list[Path]) -> str:
    for raw_value in [args.display_name, args.pet_name]:
        value = raw_value.strip()
        if value:
            return value

    if args.pet_id.strip():
        display = display_from_slug(args.pet_id)
        if display:
            return display

    for raw_value in [args.pet_notes, args.description, args.brand_name]:
        words = concept_words(raw_value)
        if words:
            return words[0].capitalize()

    for path in reference_paths:
        display = display_from_slug(path.stem)
        if display:
            return display

    return DEFAULT_PET_NAME


def sentence(value: str) -> str:
    value = " ".join(value.strip().split())
    if not value:
        return value
    if value[-1] not in ".!?":
        value += "."
    return value


def infer_description(args: argparse.Namespace, reference_paths: list[Path]) -> str:
    if args.description.strip():
        return sentence(args.description)
    if args.pet_notes.strip():
        return sentence(f"A compact AgentBro-compatible pet: {args.pet_notes}")
    if args.brand_name.strip():
        return sentence(f"A compact AgentBro-compatible pet inspired by {args.brand_name}")
    if reference_paths:
        return "A compact AgentBro-compatible pet based on the provided reference image."
    return "A compact original AgentBro-compatible pet ready for animation."


def infer_pet_notes(args: argparse.Namespace, reference_paths: list[Path]) -> str:
    if args.pet_notes.strip():
        return args.pet_notes.strip()
    if args.description.strip():
        return args.description.strip().rstrip(".")
    if args.brand_name.strip():
        return f"a compact mascot inspired by {args.brand_name.strip()}"
    if reference_paths:
        return "the pet shown in the reference image(s)"
    return "a compact original AgentBro-compatible pet"


def default_output_dir(pet_id: str) -> Path:
    timestamp = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ")
    return Path.cwd() / "output" / "agentbro-pet" / f"{pet_id}-{timestamp}"


def rel(path: Path, root: Path) -> str:
    return str(path.resolve().relative_to(root.resolve()))


def image_metadata(path: Path) -> dict[str, object]:
    with Image.open(path) as image:
        return {
            "path": str(path),
            "width": image.width,
            "height": image.height,
            "mode": image.mode,
            "format": image.format,
        }


def draw_dashed_line(
    draw: ImageDraw.ImageDraw,
    start: tuple[int, int],
    end: tuple[int, int],
    *,
    fill: str,
    dash: int = 8,
    gap: int = 6,
) -> None:
    x1, y1 = start
    x2, y2 = end
    if x1 == x2:
        step = dash + gap
        for y in range(min(y1, y2), max(y1, y2), step):
            draw.line((x1, y, x2, min(y + dash, max(y1, y2))), fill=fill)
        return
    if y1 == y2:
        step = dash + gap
        for x in range(min(x1, x2), max(x1, x2), step):
            draw.line((x, y1, min(x + dash, max(x1, x2)), y2), fill=fill)
        return
    raise ValueError("draw_dashed_line only supports horizontal or vertical lines")


def create_layout_guide(path: Path, state: str, frames: int) -> dict[str, object]:
    width = frames * ATLAS["cell_width"]
    height = ATLAS["cell_height"]
    cell_width = ATLAS["cell_width"]
    image = Image.new("RGB", (width, height), "#f7f7f7")
    draw = ImageDraw.Draw(image)

    for index in range(frames):
        left = index * cell_width
        right = left + cell_width - 1
        draw.rectangle((left, 0, right, height - 1), outline="#111111", width=2)

        safe_left = left + LAYOUT_GUIDE_SAFE_MARGIN_X
        safe_top = LAYOUT_GUIDE_SAFE_MARGIN_Y
        safe_right = right - LAYOUT_GUIDE_SAFE_MARGIN_X
        safe_bottom = height - 1 - LAYOUT_GUIDE_SAFE_MARGIN_Y
        draw.rectangle(
            (safe_left, safe_top, safe_right, safe_bottom),
            outline="#2f80ed",
            width=2,
        )

        center_x = left + cell_width // 2
        center_y = height // 2
        draw_dashed_line(
            draw,
            (center_x, safe_top),
            (center_x, safe_bottom),
            fill="#b8b8b8",
        )
        draw_dashed_line(
            draw,
            (safe_left, center_y),
            (safe_right, center_y),
            fill="#b8b8b8",
        )

    path.parent.mkdir(parents=True, exist_ok=True)
    image.save(path)
    return {
        "state": state,
        "path": str(path),
        "width": width,
        "height": height,
        "frames": frames,
        "cell_width": ATLAS["cell_width"],
        "cell_height": ATLAS["cell_height"],
        "safe_margin_x": LAYOUT_GUIDE_SAFE_MARGIN_X,
        "safe_margin_y": LAYOUT_GUIDE_SAFE_MARGIN_Y,
        "usage": "layout guide input only; do not copy visible guide lines into generated sprite strips",
    }


def create_layout_guides(run_dir: Path) -> list[dict[str, object]]:
    guide_dir = run_dir / LAYOUT_GUIDE_DIR
    return [
        create_layout_guide(guide_dir / f"{state}.png", state, frames)
        for state, _row, frames, _purpose in ROWS
    ]


def parse_hex_color(value: str) -> tuple[int, int, int]:
    if not re.fullmatch(r"#[0-9a-fA-F]{6}", value):
        raise SystemExit(f"invalid chroma key color: {value}; expected #RRGGBB")
    return tuple(int(value[index : index + 2], 16) for index in (1, 3, 5))


def rgb_to_hex(rgb: tuple[int, int, int]) -> str:
    return f"#{rgb[0]:02X}{rgb[1]:02X}{rgb[2]:02X}"


def color_distance(left: tuple[int, int, int], right: tuple[int, int, int]) -> float:
    return math.sqrt(sum((left[index] - right[index]) ** 2 for index in range(3)))


def sampled_reference_pixels(paths: list[Path]) -> list[tuple[int, int, int]]:
    pixels: list[tuple[int, int, int]] = []
    for path in paths:
        with Image.open(path) as opened:
            image = opened.convert("RGBA")
            image.thumbnail((128, 128), Image.Resampling.LANCZOS)
            data = image.tobytes()
            for index in range(0, len(data), 4):
                red, green, blue, alpha = data[index : index + 4]
                if alpha <= 16:
                    continue
                pixels.append((red, green, blue))

    non_background = [
        pixel
        for pixel in pixels
        if not (pixel[0] > 244 and pixel[1] > 244 and pixel[2] > 244)
    ]
    return non_background or pixels


def choose_chroma_key(reference_paths: list[Path], requested: str) -> dict[str, object]:
    if requested.lower() != "auto":
        rgb = parse_hex_color(requested)
        return {
            "hex": rgb_to_hex(rgb),
            "rgb": list(rgb),
            "name": "user-selected",
            "selection": "manual",
        }

    pixels = sampled_reference_pixels(reference_paths)
    if not pixels:
        rgb = parse_hex_color("#FF00FF")
        return {
            "hex": "#FF00FF",
            "rgb": list(rgb),
            "name": "magenta",
            "selection": "fallback",
        }

    scored: list[tuple[float, int, str, tuple[int, int, int]]] = []
    for preference_index, (name, hex_color) in enumerate(CHROMA_KEY_CANDIDATES):
        rgb = parse_hex_color(hex_color)
        distances = sorted(color_distance(rgb, pixel) for pixel in pixels)
        percentile_index = max(0, min(len(distances) - 1, int(len(distances) * 0.01)))
        scored.append((distances[percentile_index], -preference_index, name, rgb))

    score, _preference, name, rgb = max(scored)
    return {
        "hex": rgb_to_hex(rgb),
        "rgb": list(rgb),
        "name": name,
        "selection": "auto",
        "score": round(score, 2),
    }


def write_text(path: Path, text: str) -> None:
    path.parent.mkdir(parents=True, exist_ok=True)
    path.write_text(text.rstrip() + "\n", encoding="utf-8")


def resolved_style_contract(style_preset: str, raw_style_notes: str) -> str:
    style_preset = style_preset.strip().lower()
    if style_preset not in STYLE_PRESETS:
        allowed = ", ".join(sorted(STYLE_PRESETS))
        raise SystemExit(
            f"invalid style preset: {style_preset}; expected one of: {allowed}"
        )
    raw_style_notes = raw_style_notes.strip()
    preset_contract = STYLE_PRESETS[style_preset]
    if not raw_style_notes:
        return f"{PET_SAFE_STYLE} Style `{style_preset}`: {preset_contract}"
    return (
        f"{PET_SAFE_STYLE} Style `{style_preset}`: {preset_contract} "
        f"User style notes: {raw_style_notes}."
    )


def compact(value: str) -> str:
    return " ".join(value.strip().split())


def brand_inspiration_line(args: argparse.Namespace) -> str:
    brand_name = compact(args.brand_name)
    brand_brief = compact(args.brand_brief)
    if not brand_name and not brand_brief:
        return ""

    prefix = f"{brand_name}: " if brand_name else ""
    if brand_brief:
        return (
            f"{prefix}{brand_brief} Use only broad mascot-safe cues; do not copy "
            "readable logos, marks, UI screenshots, or text."
        )
    return (
        f"{prefix}Use only broad mascot-safe brand cues. Do not copy readable "
        "logos, marks, UI screenshots, or text."
    )


def base_pet_prompt(args: argparse.Namespace) -> str:
    pet_notes = args.pet_notes or "the pet shown in the reference image(s)"
    style_contract = resolved_style_contract(args.style_preset, args.style_notes)
    brand_line = brand_inspiration_line(args)
    brand_block = f"\nBrand inspiration: {brand_line}\n" if brand_line else "\n"
    chroma_key = args.chroma_key["hex"]
    chroma_name = args.chroma_key["name"]
    return f"""Create one clean full-body reference sprite for AgentBro-compatible pet {args.display_name}.

Pet identity: {pet_notes}.
Style: {style_contract}
{brand_block}
Place a single centered pose on a perfectly flat pure {chroma_name} {chroma_key} chroma-key background. Keep the full pet visible, compact, readable at 192x208, and easy to animate. Preserve approved reference identity cues. No scenery, text, borders, checkerboard transparency, shadows, glows, detached effects, or extra props. Keep {chroma_key} and close colors out of the pet, props, highlights, and effects."""


def row_prompt(
    args: argparse.Namespace, state: str, row: int, frames: int, purpose: str
) -> str:
    pet_notes = args.pet_notes or "the same pet from the approved base reference"
    style_contract = resolved_style_contract(args.style_preset, args.style_notes)
    chroma_key = args.chroma_key["hex"]
    chroma_name = args.chroma_key["name"]
    state_prompt = STATE_PROMPTS[state]
    state_requirements = "\n".join(f"- {line}" for line in STATE_REQUIREMENTS[state])
    return f"""Create one horizontal animation strip for AgentBro-compatible pet `{args.pet_id}`, state `{state}`.

Use the attached canonical base for identity. Use the attached layout guide only for slot count, spacing, centering, and padding; do not draw the guide.

Output exactly {frames} full-body frames in one left-to-right row on flat pure {chroma_name} {chroma_key}. Treat the row as {frames} invisible equal-width slots: one centered complete pose per slot, evenly spaced, with no overlap, clipping, empty slots, labels, or borders.

Identity: same pet in every frame: {pet_notes}. Preserve silhouette, face, proportions, markings, palette, material, style, and props.
Style: {style_contract}
Animation continuity: keep apparent pet scale and baseline stable within the row unless the state itself intentionally changes vertical position, such as `jumping`. Move the pose within the slot instead of redrawing the pet larger or smaller frame to frame.

State action: {state_prompt}

State requirements:
{state_requirements}

Clean extraction: crisp opaque edges, safe padding, no scenery, text, guide marks, checkerboard, shadows, glows, motion blur, speed lines, dust, detached effects, stray pixels, or chroma-key colors inside the pet."""


def retry_row_prompt(
    args: argparse.Namespace, state: str, row: int, frames: int, purpose: str
) -> str:
    pet_notes = args.pet_notes or "the canonical base pet"
    chroma_key = args.chroma_key["hex"]
    chroma_name = args.chroma_key["name"]
    state_prompt = STATE_PROMPTS[state]
    state_requirements = "\n".join(f"- {line}" for line in STATE_REQUIREMENTS[state])
    return f"""Create AgentBro-compatible pet row `{state}` for `{args.pet_id}`: exactly {frames} full-body frames in one horizontal strip on flat pure {chroma_name} {chroma_key}.

Use the attached canonical base for identity and the layout guide only for spacing. Same pet in every frame: {pet_notes}. Preserve silhouette, face, palette, material, proportions, markings, and props.

Keep apparent pet scale and baseline stable within the row unless the state itself intentionally changes vertical position, such as `jumping`.

Action: {state_prompt}

State requirements:
{state_requirements}

One centered complete pose per invisible slot. No text, boxes, guide marks, scenery, shadows, glows, motion blur, speed lines, dust, detached effects, stray pixels, or {chroma_key} colors in the pet."""


def make_jobs(
    run_dir: Path, copied_refs: list[dict[str, object]]
) -> list[dict[str, object]]:
    reference_inputs = [
        {"path": rel(Path(str(ref["copied_path"])), run_dir), "role": "pet reference"}
        for ref in copied_refs
    ]
    identity_reference_paths = [CANONICAL_BASE_PATH]
    jobs: list[dict[str, object]] = [
        {
            "id": "base",
            "kind": "base-pet",
            "status": "pending",
            "prompt_file": "prompts/base-pet.md",
            "input_images": reference_inputs,
            "output_path": "decoded/base.png",
            "depends_on": [],
            "generation_skill": "$imagegen",
            "requires_grounded_generation": bool(reference_inputs),
            "allow_prompt_only_generation": not reference_inputs,
        }
    ]
    for state, _row, frames, _purpose in ROWS:
        depends_on = ["base"]
        extra_inputs: list[dict[str, str]] = []
        derivation_policy: dict[str, object] = {
            "may_derive": False,
            "reason": "state requires its own generated animation semantics",
        }
        if state == "running-left":
            depends_on.append("running-right")
            extra_inputs.append(
                {
                    "path": "decoded/running-right.png",
                    "role": "rightward gait reference for leftward row decision",
                }
            )
            derivation_policy = {
                "may_derive": True,
                "may_derive_from": "running-right",
                "derivation": "framewise-horizontal-mirror-preserving-order",
                "requires_explicit_approval": True,
                "fallback_generation_skill": "$imagegen",
            }
        elif state not in NON_DERIVABLE_STATES:
            derivation_policy["reason"] = "no deterministic derivation is configured for this state"
        jobs.append(
            {
                "id": state,
                "kind": "row-strip",
                "status": "pending",
                "prompt_file": f"prompts/rows/{state}.md",
                "retry_prompt_file": f"prompts/row-retries/{state}.md",
                "input_images": [
                    *reference_inputs,
                    {
                        "path": f"{LAYOUT_GUIDE_DIR}/{state}.png",
                        "role": f"layout guide for {frames} frame slots; use for spacing only, do not copy guide lines",
                    },
                    {
                        "path": CANONICAL_BASE_PATH,
                        "role": "canonical identity reference",
                    },
                    *extra_inputs,
                ],
                "output_path": f"decoded/{state}.png",
                "depends_on": depends_on,
                "generation_skill": "$imagegen",
                "requires_grounded_generation": True,
                "allow_prompt_only_generation": False,
                "identity_reference_paths": identity_reference_paths,
                "parallelizable_after": depends_on,
                "derivation_policy": derivation_policy,
                "mirror_policy": derivation_policy if state == "running-left" else {},
            }
        )
    return jobs


def main() -> None:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument(
        "--pet-name",
        default="",
        help="User-facing pet name. Ask the user for this when practical; otherwise choose a short appropriate name.",
    )
    parser.add_argument(
        "--pet-id",
        default="",
        help="Stable pet folder/id slug. Defaults to the slugified pet name.",
    )
    parser.add_argument(
        "--display-name",
        default="",
        help="Display label. Defaults to the pet name.",
    )
    parser.add_argument("--description", default="")
    parser.add_argument("--reference", action="append", default=[])
    parser.add_argument("--output-dir", default="")
    parser.add_argument("--pet-notes", default="")
    parser.add_argument(
        "--brand-name",
        default="",
        help="Brand, company, or product name used for broad mascot inspiration.",
    )
    parser.add_argument(
        "--brand-brief",
        default="",
        help="Compact researched brand cue sentence for the base pet only.",
    )
    parser.add_argument(
        "--brand-source",
        action="append",
        default=[],
        help="Source URL used to produce the brand brief. May be passed multiple times.",
    )
    parser.add_argument(
        "--brand-discovery-file",
        default="",
        help="Optional markdown discovery brief to copy into the run for review.",
    )
    parser.add_argument(
        "--style-preset",
        default="auto",
        choices=sorted(STYLE_PRESETS),
        help="Pet-safe style preset to use across the base and all animation rows.",
    )
    parser.add_argument("--style-notes", default="")
    parser.add_argument(
        "--chroma-key",
        default="auto",
        help="Chroma key as #RRGGBB, or auto to choose a safe key from reference colors.",
    )
    parser.add_argument("--force", action="store_true")
    args = parser.parse_args()

    raw_reference_paths = [
        Path(raw_path).expanduser().resolve() for raw_path in args.reference
    ]
    raw_brand_discovery_path = (
        Path(args.brand_discovery_file).expanduser().resolve()
        if args.brand_discovery_file.strip()
        else None
    )

    args.display_name = infer_name(args, raw_reference_paths)
    args.pet_name = (args.pet_name or args.display_name).strip()
    args.description = infer_description(args, raw_reference_paths)
    args.pet_notes = infer_pet_notes(args, raw_reference_paths)
    args.pet_id = slugify(args.pet_id or args.pet_name or args.display_name)
    args.style_preset = args.style_preset.strip().lower()
    args.style_contract = resolved_style_contract(args.style_preset, args.style_notes)
    args.brand_name = compact(args.brand_name)
    args.brand_brief = compact(args.brand_brief)
    args.brand_source = [
        compact(source) for source in args.brand_source if compact(source)
    ]
    if not args.pet_id:
        raise SystemExit("pet id must contain at least one letter or digit")

    run_dir = (
        Path(args.output_dir).expanduser().resolve()
        if args.output_dir
        else default_output_dir(args.pet_id).resolve()
    )
    if run_dir.exists() and any(run_dir.iterdir()) and not args.force:
        raise SystemExit(
            f"{run_dir} already exists and is not empty; pass --force to reuse it"
        )
    run_dir.mkdir(parents=True, exist_ok=True)

    ref_dir = run_dir / "references"
    prompt_dir = run_dir / "prompts"
    row_prompt_dir = prompt_dir / "rows"
    row_retry_prompt_dir = prompt_dir / "row-retries"
    for directory in [
        ref_dir,
        prompt_dir,
        row_prompt_dir,
        row_retry_prompt_dir,
        run_dir / "decoded",
        run_dir / "qa",
    ]:
        directory.mkdir(parents=True, exist_ok=True)

    copied_refs: list[dict[str, object]] = []
    copied_ref_paths: list[Path] = []
    for index, source in enumerate(raw_reference_paths, start=1):
        if not source.is_file():
            raise SystemExit(f"reference not found: {source}")
        suffix = source.suffix.lower() or ".png"
        copied = ref_dir / f"reference-{index:02d}{suffix}"
        shutil.copy2(source, copied)
        meta = image_metadata(copied)
        meta["source_path"] = str(source)
        meta["copied_path"] = str(copied)
        copied_refs.append(meta)
        copied_ref_paths.append(copied)

    brand_discovery_path = ""
    if raw_brand_discovery_path is not None:
        if not raw_brand_discovery_path.is_file():
            raise SystemExit(f"brand discovery file not found: {raw_brand_discovery_path}")
        copied_discovery = run_dir / BRAND_DISCOVERY_PATH
        shutil.copy2(raw_brand_discovery_path, copied_discovery)
        brand_discovery_path = rel(copied_discovery, run_dir)

    args.chroma_key = choose_chroma_key(copied_ref_paths, args.chroma_key)
    layout_guides = create_layout_guides(run_dir)

    request = {
        "pet_id": args.pet_id,
        "display_name": args.display_name,
        "description": args.description,
        "created_at": datetime.now(timezone.utc).isoformat(),
        "atlas": ATLAS,
        "rows": [
            {"state": state, "row": row, "frames": frames, "purpose": purpose}
            for state, row, frames, purpose in ROWS
        ],
        "layout_guides": [
            {**guide, "path": rel(Path(str(guide["path"])), run_dir)}
            for guide in layout_guides
        ],
        "references": copied_refs,
        "chroma_key": args.chroma_key,
        "pet_notes": args.pet_notes,
        "style_preset": args.style_preset,
        "style_notes": args.style_notes,
        "style_contract": args.style_contract,
        "brand_name": args.brand_name,
        "brand_brief": args.brand_brief,
        "brand_sources": args.brand_source,
        "pet_safe_style": PET_SAFE_STYLE,
        "primary_generation_skill": "$imagegen",
    }
    if brand_discovery_path:
        request["brand_discovery_path"] = brand_discovery_path
    (run_dir / "pet_request.json").write_text(
        json.dumps(request, indent=2) + "\n", encoding="utf-8"
    )

    write_text(prompt_dir / "base-pet.md", base_pet_prompt(args))
    for state, row, frames, purpose in ROWS:
        write_text(
            row_prompt_dir / f"{state}.md",
            row_prompt(args, state, row, frames, purpose),
        )
        write_text(
            row_retry_prompt_dir / f"{state}.md",
            retry_row_prompt(args, state, row, frames, purpose),
        )

    jobs = {
        "schema_version": 1,
        "created_at": datetime.now(timezone.utc).isoformat(),
        "run_dir": str(run_dir),
        "primary_generation_skill": "$imagegen",
        "jobs": make_jobs(run_dir, copied_refs),
    }
    (run_dir / "imagegen-jobs.json").write_text(
        json.dumps(jobs, indent=2) + "\n", encoding="utf-8"
    )

    print(
        json.dumps(
            {
                "ok": True,
                "run_dir": str(run_dir),
                "request": str(run_dir / "pet_request.json"),
                "jobs": str(run_dir / "imagegen-jobs.json"),
                "ready_jobs": ["base"],
            },
            indent=2,
        )
    )


if __name__ == "__main__":
    main()