tools/slice_generated_exercise_sheets.py - develop - DemienMedich/BodyweightBase

tools/slice_generated_exercise_sheets.py
423 строки13 KB
DemienMedich
Improve Baduanjin movement guidance
18 июл 2026, 16:20
18 июл 2026, 16:2086d0a76
from __future__ import annotations

import argparse
import json
import math
from pathlib import Path

from PIL import Image


ROOT = Path(__file__).resolve().parents[1]
SHEET_DIR = ROOT / "Assets" / "GeneratedSheets"
FRAME_DIR = ROOT / "Assets" / "ExerciseFrames"
MANIFEST_PATH = SHEET_DIR / "exercise_generation_manifest.json"
CATALOG_PATH = ROOT / "resources" / "exercises.json"


SHEETS = [
    {
        "file": "baduanjin_new_sheet.png",
        "columns": 4,
        "rows": 2,
        "cells": [
            ("baduanjin_1", 1),
            ("baduanjin_2", 1),
            ("baduanjin_3", 1),
            ("baduanjin_4", 1),
            ("baduanjin_5", 1),
            ("baduanjin_6", 1),
            ("baduanjin_7", 1),
            ("baduanjin_8", 1),
        ],
    },
    {
        "file": "baduanjin_1_motion_sheet.png",
        "columns": 2,
        "rows": 2,
        "cells": [("baduanjin_1", frame) for frame in range(1, 5)],
    },
    {
        "file": "baduanjin_2_motion_sheet.png",
        "columns": 2,
        "rows": 2,
        "cells": [("baduanjin_2", frame) for frame in range(1, 5)],
    },
    {
        "file": "baduanjin_3_motion_sheet.png",
        "columns": 2,
        "rows": 2,
        "cells": [("baduanjin_3", frame) for frame in range(1, 5)],
    },
    {
        "file": "baduanjin_4_motion_sheet.png",
        "columns": 2,
        "rows": 2,
        "cells": [("baduanjin_4", frame) for frame in range(1, 5)],
    },
    {
        "file": "qigong_forms_new_sheet.png",
        "columns": 3,
        "rows": 3,
        "cells": [
            ("wuqin_xi_crane", 1),
            ("wuqin_xi_tiger", 1),
            ("wuqin_xi_monkey", 1),
            ("wuqin_xi_bear", 1),
            ("wuqin_xi_deer", 1),
            ("zhan_zhuang", 1),
            ("yijinjing_press", 1),
            ("liuzijue_breath", 1),
            ("silk_reeling", 1),
        ],
    },
    {
        "file": "wing_chun_new_sheet.png",
        "columns": 5,
        "rows": 2,
        "cells": [
            ("siu_lim_tao", 1),
            ("chum_kiu", 1),
            ("dan_chi_sao", 1),
            ("tan_sao_drill", 1),
            ("bong_sao_drill", 1),
            ("pak_sa_drill", 1),
            ("gaun_sa_drill", 1),
            ("footer_kick", 1),
            ("chain_punch_wall", 1),
            ("wu_sau_guard", 1),
        ],
    },
    {
        "file": "siu_lim_tao_keyposes_a_sheet.png",
        "columns": 2,
        "rows": 2,
        "cells": [("siu_lim_tao", frame) for frame in range(1, 5)],
    },
    {
        "file": "siu_lim_tao_keyposes_b_sheet.png",
        "columns": 2,
        "rows": 2,
        "cells": [("siu_lim_tao", frame) for frame in range(5, 9)],
    },
    {
        "file": "tan_bong_drills_motion_sheet.png",
        "columns": 2,
        "rows": 2,
        "cells": [
            ("tan_sao_drill", 1),
            ("tan_sao_drill", 2),
            ("bong_sao_drill", 1),
            ("bong_sao_drill", 2),
        ],
    },
    {
        "file": "pak_gaun_drills_motion_sheet.png",
        "columns": 2,
        "rows": 2,
        "cells": [
            ("pak_sa_drill", 1),
            ("pak_sa_drill", 2),
            ("gaun_sa_drill", 1),
            ("gaun_sa_drill", 2),
        ],
    },
    {
        "file": "dan_chi_sao_motion_sheet.png",
        "columns": 2,
        "rows": 2,
        "cells": [("dan_chi_sao", frame) for frame in range(1, 5)],
    },
    {
        "file": "bodyweight_strength_new_sheet.png",
        "columns": 4,
        "rows": 3,
        "cells": [
            ("prisoner_squat", 1),
            ("prisoner_squat", 2),
            ("pistol_squat_assist", 1),
            ("pistol_squat_assist", 2),
            ("glute_bridge_single", 1),
            ("glute_bridge_single", 2),
            ("step_up", 1),
            ("step_up", 2),
            ("pushup_archer", 1),
            ("pushup_archer", 2),
            ("pushup_pseudo_planche", 1),
            ("pushup_pseudo_planche", 2),
        ],
    },
    {
        "file": "bodyweight_mobility_new_sheet.png",
        "columns": 4,
        "rows": 3,
        "cells": [
            ("plank_to_pike", 1),
            ("plank_to_pike", 2),
            ("l_sit_hold", 1),
            ("wall_sit", 1),
            ("cossack_squat", 1),
            ("cossack_squat", 2),
            ("single_leg_calf", 1),
            ("single_leg_calf", 2),
            ("nordic_curl_neg", 1),
            ("nordic_curl_neg", 2),
            ("inchworm", 1),
            ("inchworm", 2),
        ],
    },
    {
        "file": "inchworm_motion_sheet.png",
        "columns": 2,
        "rows": 2,
        "cells": [("inchworm", frame) for frame in range(1, 5)],
    },
    {
        "file": "warmup_arm_swing_motion_sheet.png",
        "columns": 2,
        "rows": 2,
        "cells": [("warmup_arm_swing", frame) for frame in range(1, 5)],
    },
    {
        "file": "warmup_march_place_motion_sheet.png",
        "columns": 2,
        "rows": 2,
        "cells": [("warmup_march_place", frame) for frame in range(1, 5)],
    },
    {
        "file": "dance_cardio_warmup_motion_sheet.png",
        "columns": 2,
        "rows": 2,
        "cells": [("dance_cardio_warmup", frame) for frame in range(1, 5)],
    },
    {
        "file": "warmup_shoulder_circle_motion_sheet.png",
        "columns": 2,
        "rows": 2,
        "cells": [("warmup_shoulder_circle", frame) for frame in range(1, 5)],
    },
    {
        "file": "warmup_wrist_roll_motion_sheet.png",
        "columns": 2,
        "rows": 2,
        "cells": [("warmup_wrist_roll", frame) for frame in range(1, 5)],
    },
    {
        "file": "world_greatest_stretch_sheet.png",
        "columns": 4,
        "rows": 1,
        "cells": [
            ("world_greatest_stretch", 1),
            ("world_greatest_stretch", 2),
            ("world_greatest_stretch", 3),
            ("world_greatest_stretch", 4),
        ],
    },
    {
        "file": "hip_flexor_strength_sheet.png",
        "columns": 5,
        "rows": 2,
        "cells": [
            ("standing_knee_drive", 1),
            ("standing_knee_drive", 2),
            ("high_knee_march", 1),
            ("high_knee_march", 2),
            ("supine_straight_leg_raise", 1),
            ("supine_straight_leg_raise", 2),
            ("seated_knee_tuck", 1),
            ("seated_knee_tuck", 2),
            ("dead_bug_hip_flexor_hold", 1),
            ("dead_bug_hip_flexor_hold", 2),
        ],
    },
    {
        "file": "yijkym_stance_auditfix.png",
        "columns": 1,
        "rows": 1,
        "cells": [("yijkym_stance", 1)],
    },
    {
        "file": "pistol_squat_assist_auditfix_sheet.png",
        "columns": 2,
        "rows": 1,
        "cells": [("pistol_squat_assist", 1), ("pistol_squat_assist", 2)],
    },
    {
        "file": "burpee_interval_auditfix_sheet.png",
        "columns": 5,
        "rows": 1,
        "min_aspect": 0.45,
        "cells": [("burpee_interval", frame) for frame in range(1, 6)],
    },
    {
        "file": "wall_handstand_auditfix_sheet.png",
        "columns": 4,
        "rows": 1,
        "cells": [("wall_handstand_hold", frame) for frame in range(1, 5)],
    },
    {
        "file": "pad_strike_block_auditfix_sheet.png",
        "columns": 4,
        "rows": 1,
        "cells": [("pad_strike_block_round", frame) for frame in range(1, 5)],
    },
    {
        "file": "mountain_climber_auditfix_sheet.png",
        "columns": 3,
        "rows": 1,
        "cells": [("mountain_climber", frame) for frame in range(1, 4)],
    },
    {
        "file": "cossack_squat_auditfix_sheet.png",
        "columns": 2,
        "rows": 1,
        "cells": [("cossack_squat", 1), ("cossack_squat", 2)],
    },
    {
        "file": "warmup_squat_auditfix_sheet.png",
        "columns": 2,
        "rows": 1,
        "cells": [("warmup_squat_to_stand", 1), ("warmup_squat_to_stand", 2)],
    },
    {
        "file": "middle_split_auditfix_sheet.png",
        "columns": 2,
        "rows": 1,
        "cells": [("middle_split_prep", 1), ("middle_split_prep", 2)],
    },
    {
        "file": "strikes_auditfix_grid.png",
        "columns": 3,
        "rows": 3,
        "cells": [
            ("chain_punch_round", 1),
            ("chain_punch_round", 2),
            ("chain_punch_round", 3),
            ("footer_kick", 1),
            ("footer_kick", 2),
            ("footer_kick", 3),
            ("shadow_box_round", 1),
            ("shadow_box_round", 2),
            ("shadow_box_round", 3),
        ],
    },
]


def crop_cell(image: Image.Image, columns: int, rows: int, index: int) -> Image.Image:
    cell_width = image.width / columns
    cell_height = image.height / rows
    column = index % columns
    row = index // columns
    box = (
        round(column * cell_width),
        round(row * cell_height),
        round((column + 1) * cell_width),
        round((row + 1) * cell_height),
    )
    return image.crop(box)


def pad_to_min_aspect(image: Image.Image, min_aspect: float) -> Image.Image:
    target_width = math.ceil(image.height * min_aspect)
    if image.width >= target_width:
        return image
    background = image.getpixel((0, 0))
    padded = Image.new(image.mode, (target_width, image.height), background)
    padded.paste(image, ((target_width - image.width) // 2, 0))
    return padded


def load_manifest() -> list[dict[str, object]]:
    if not MANIFEST_PATH.exists():
        return []
    with MANIFEST_PATH.open("r", encoding="utf-8") as handle:
        data = json.load(handle)
    if not isinstance(data, list):
        raise ValueError(f"Unexpected manifest shape in {MANIFEST_PATH}")
    return data


def load_catalog_metadata() -> dict[str, dict[str, object]]:
    with CATALOG_PATH.open("r", encoding="utf-8") as handle:
        catalog = json.load(handle)
    metadata: dict[str, dict[str, object]] = {}
    for exercise in catalog.get("exercises", []):
        exercise_id = exercise.get("id")
        if not exercise_id:
            continue
        metadata[str(exercise_id)] = {
            "name": exercise.get("name", ""),
            "category": exercise.get("category", ""),
            "equipment": exercise.get("equipment", ""),
        }
    return metadata


def write_manifest(manifest: list[dict[str, object]]) -> None:
    MANIFEST_PATH.write_text(
        json.dumps(manifest, ensure_ascii=False, indent=2) + "\n",
        encoding="utf-8",
    )


def main() -> None:
    parser = argparse.ArgumentParser(description="Slice generated exercise sheets into runtime frames.")
    parser.add_argument("--sheet", action="append", default=[],
                        help="Process only this sheet filename; may be repeated.")
    args = parser.parse_args()

    FRAME_DIR.mkdir(parents=True, exist_ok=True)
    manifest = load_manifest()
    catalog_metadata = load_catalog_metadata()
    by_id = {str(item.get("id")): item for item in manifest}

    written: list[Path] = []
    selected_sheets = [sheet for sheet in SHEETS if not args.sheet or sheet["file"] in args.sheet]
    if args.sheet and len(selected_sheets) != len(set(args.sheet)):
        known = {str(sheet["file"]) for sheet in selected_sheets}
        missing = sorted(set(args.sheet) - known)
        parser.error(f"unknown sheet(s): {', '.join(missing)}")

    for sheet in selected_sheets:
        sheet_path = SHEET_DIR / str(sheet["file"])
        if not sheet_path.exists():
            raise FileNotFoundError(sheet_path)

        with Image.open(sheet_path) as image:
            image = image.convert("RGB")
            cells = sheet["cells"]
            columns = int(sheet["columns"])
            rows = int(sheet["rows"])
            if len(cells) > columns * rows:
                raise ValueError(f"Too many cells configured for {sheet_path.name}")

            frame_counts: dict[str, int] = {}
            for index, (exercise_id, frame_number) in enumerate(cells):
                frame = crop_cell(image, columns, rows, index)
                if "min_aspect" in sheet:
                    frame = pad_to_min_aspect(frame, float(sheet["min_aspect"]))
                target = FRAME_DIR / f"{exercise_id}_{frame_number}.png"
                frame.save(target, optimize=True)
                written.append(target)
                frame_counts[exercise_id] = max(frame_counts.get(exercise_id, 0), frame_number)

            for exercise_id, frames in frame_counts.items():
                item = by_id.get(exercise_id, {"id": exercise_id})
                item.update({key: value for key, value in catalog_metadata.get(exercise_id, {}).items() if value})
                item["frames"] = frames
                item["source"] = sheet_path.name
                by_id[exercise_id] = item

    existing_order = [str(item.get("id")) for item in manifest if item.get("id")]
    new_ids = [exercise_id for sheet in selected_sheets for exercise_id, _ in sheet["cells"]]
    ordered_ids = existing_order + [exercise_id for exercise_id in dict.fromkeys(new_ids) if exercise_id not in existing_order]
    write_manifest([by_id[exercise_id] for exercise_id in ordered_ids if exercise_id in by_id])

    print(f"Wrote {len(written)} frames")
    for path in written:
        print(path.relative_to(ROOT))


if __name__ == "__main__":
    main()