ykcheungab
790 lines
31 KiB
Python
790 lines
31 KiB
Python
"""Generate Random Stroke Pattern (RSP) artwork from colour texture jobs.
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Run after the colour texture command has produced its PNGs::
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blender -b -t 2 --python-exit-code 1 \
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-P blender/scripts/random_stroke_pattern.py -- \
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--job build/textures/finger/r01/_texture_views_job.json \
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--output-dir .scratch/stroke-pattern/run-01
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Accepts either a single-view or a multiview colour job. Each distinct source colour is one cell
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of the colour artwork; a split cell can have a different centre in the two print views.
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One mode, ``camera_compensated_width``: the mark inside a cell is a capsule of ``width_mm`` swept
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``length_mm``, rotated per cell, and its width is scaled from that cell's footprint on the print
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page so a cell the projection stretches receives a proportionally larger mark. The width is then
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clamped to ``width_min_mm``..``width_max_mm``.
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When ``target_coverage`` is positive, accepted capsules grow from their endpoints into free
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space, up to twice their initial length. Width and edge clearance stay fixed. The reported
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coverage is measured only inside the eligible colour-pattern region.
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**This is an image-space experiment, not a print master.** Clipping and merged neighbours change
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the visible widths, so verify dimensions on the printed part before manufacturing.
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Tuning lives in ``design.yaml`` under ``stroke_pattern``; the CLI serialises it here as
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``--params``. With no ``--params`` the built-in ``DEFAULT_PARAMS`` apply.
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"""
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from __future__ import annotations
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import argparse
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import hashlib
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import json
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import math
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import sys
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import xml.etree.ElementTree as ET
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from pathlib import Path
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import bpy
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import numpy as np
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script_dir = str(Path(__file__).resolve().parent)
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if script_dir not in sys.path:
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sys.path.insert(0, script_dir)
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from random_patch_dd02c import save_png_image, write_pdf_vector, write_svg_vector # noqa: E402 - Blender omits the script directory from sys.path
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from stroke_geometry import capsule_segment, has_clearance # noqa: E402 - Blender script directory is on sys.path
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MODE = "camera_compensated_width"
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#: Every tunable, at the values this experiment shipped with. The CLI writes a `design.yaml`
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#: `stroke_pattern` block here as `--params`; `--seed`, `--min-cell-pixels` and
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#: `--camera-width-fraction` override individual keys for one run.
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DEFAULT_PARAMS: dict[str, float] = {
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"seed": 7,
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"min_cell_pixels": 20,
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"jitter_fraction": 0.66,
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"angle_span_deg": 180.0,
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"width_fraction": 0.5,
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"width_min_mm": 0.11,
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"width_max_mm": 0.28,
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"length_ratio": 2.4,
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"coverage_cap": 0.45,
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"neighbourhood_mm": 0.5,
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"clearance_mm": 0.0,
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"candidate_count": 1,
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"target_coverage": 0.0,
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}
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def validate_params(params: dict) -> None:
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"""Raise on a value that cannot produce artwork, naming the key.
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Ranges live here rather than only on the host side, because this script is also run
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directly under Blender, where nothing else would catch a bad `--params` file.
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"""
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for key, value in params.items():
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if not isinstance(value, (int, float)) or isinstance(value, bool):
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raise ValueError(f"params.{key}: must be a number (got {value!r})")
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if not math.isfinite(float(value)):
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raise ValueError(f"params.{key}: must be finite (got {value!r})")
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if params["min_cell_pixels"] < 1:
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raise ValueError("params.min_cell_pixels must be >= 1")
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if not isinstance(params["candidate_count"], int) or params["candidate_count"] < 1:
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raise ValueError("params.candidate_count must be an integer >= 1")
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if params["clearance_mm"] < 0:
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raise ValueError("params.clearance_mm must be >= 0")
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if not 0.0 <= params["jitter_fraction"] <= 1.0:
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raise ValueError("params.jitter_fraction must be within [0, 1]")
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if params["angle_span_deg"] < 0.0:
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raise ValueError("params.angle_span_deg must be >= 0")
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for key in ("width_fraction", "width_min_mm", "width_max_mm", "length_ratio",
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"neighbourhood_mm"):
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if params[key] <= 0.0:
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raise ValueError(f"params.{key} must be > 0")
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if params["width_min_mm"] > params["width_max_mm"]:
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raise ValueError("params.width_min_mm must be <= params.width_max_mm")
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if not 0.0 < params["coverage_cap"] <= 1.0:
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raise ValueError("params.coverage_cap must be within (0, 1]")
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if not 0.0 <= params["target_coverage"] < 1.0:
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raise ValueError("params.target_coverage must be within [0, 1)")
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def parse_args(argv: list[str]) -> argparse.Namespace:
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument("--job", required=True, type=Path)
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parser.add_argument("--output-dir", required=True, type=Path)
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parser.add_argument(
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"--params",
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type=Path,
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default=None,
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help="JSON of stroke tuning values; overrides DEFAULT_PARAMS key by key",
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)
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parser.add_argument(
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"--seed", type=int, default=None,
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help="Override params.seed for this run",
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)
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parser.add_argument(
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"--min-cell-pixels", type=int, default=None,
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help="Override params.min_cell_pixels for this run",
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)
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parser.add_argument(
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"--camera-width-fraction",
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type=float,
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default=None,
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help="Override params.width_fraction for this run",
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)
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parser.add_argument(
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"--formats", nargs="+", choices=("png", "svg", "pdf"), default=("png",)
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)
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parser.add_argument(
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"--artifact-prefix",
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default=None,
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help="Name outputs <prefix>_<source-id>.<format>",
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)
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return parser.parse_args(argv)
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def resolve_params(args: argparse.Namespace) -> dict:
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"""DEFAULT_PARAMS, then the --params file, then the per-run flag overrides."""
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params = dict(DEFAULT_PARAMS)
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if args.params is not None:
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supplied = json.loads(args.params.read_text(encoding="utf-8"))
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if not isinstance(supplied, dict):
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raise ValueError("--params must contain a JSON object")
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unknown = sorted(set(supplied) - set(DEFAULT_PARAMS))
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if unknown:
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raise ValueError(
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f"--params: unknown key(s) {unknown}; allowed: {sorted(DEFAULT_PARAMS)}"
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)
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params.update(supplied)
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for key, value in (
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("seed", args.seed),
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("min_cell_pixels", args.min_cell_pixels),
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("width_fraction", args.camera_width_fraction),
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):
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if value is not None:
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params[key] = value
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validate_params(params)
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return params
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def cell_code(
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rgb: np.ndarray, seed: int, jitter_fraction: float, angle_span_deg: float,
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candidate_index: int = 0,
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) -> tuple[float, float, float]:
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"""Return bounded offsets and angle from one exact source colour.
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Derived from the colour itself, so the same palette gives the same layout in both print
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views without storing anything.
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"""
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digest = hashlib.blake2b(
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bytes(int(v) for v in rgb) + seed.to_bytes(8, "little", signed=True)
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+ (candidate_index.to_bytes(4, "little") if candidate_index else b""),
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digest_size=8,
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).digest()
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values = [int.from_bytes(digest[k : k + 2], "little") / 65535 for k in (0, 2, 4, 6)]
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return (
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jitter_fraction * (values[0] - 0.5),
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jitter_fraction * (values[1] - 0.5),
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math.radians(angle_span_deg) * values[2],
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)
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def read_rgb(path: Path) -> tuple[np.ndarray, int, int]:
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image = bpy.data.images.load(str(path), check_existing=False)
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try:
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width, height = image.size
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pixels = np.empty(width * height * 4, dtype=np.float32)
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image.pixels.foreach_get(pixels)
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rgb = np.clip(
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np.rint(pixels.reshape(height, width, 4)[:, :, :3] * 255), 0, 255
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).astype(np.uint8)
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return rgb, width, height
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finally:
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bpy.data.images.remove(image)
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def cell_regions(rgb: np.ndarray, minimum: int):
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"""Measure source-colour regions without assuming a rectangular UV cell."""
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height, width, _ = rgb.shape
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packed = (
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(rgb[:, :, 0].astype(np.uint32) << 16)
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| (rgb[:, :, 1].astype(np.uint32) << 8)
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| rgb[:, :, 2].astype(np.uint32)
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)
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colours, inverse, counts = np.unique(
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packed, return_inverse=True, return_counts=True
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)
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rows = np.repeat(np.arange(height, dtype=np.int32), width)
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cols = np.tile(np.arange(width, dtype=np.int32), height)
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n = len(colours)
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left = np.full(n, width, dtype=np.int32)
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right = np.full(n, -1, dtype=np.int32)
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bottom = np.full(n, height, dtype=np.int32)
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top = np.full(n, -1, dtype=np.int32)
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np.minimum.at(left, inverse, cols)
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np.maximum.at(right, inverse, cols)
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np.minimum.at(bottom, inverse, rows)
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np.maximum.at(top, inverse, rows)
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cx = np.bincount(inverse, weights=cols, minlength=n) / counts
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cy = np.bincount(inverse, weights=rows, minlength=n) / counts
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for index, colour in enumerate(colours):
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if colour in (0, 0xFFFFFF) or counts[index] < minimum:
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continue
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cell_width = right[index] - left[index] + 1
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cell_height = top[index] - bottom[index] + 1
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yield (
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int(colour),
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float(cx[index]),
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float(cy[index]),
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int(cell_width),
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int(cell_height),
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)
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def camera_cell_lookup(
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nx: int, ny: int, colour_radius: float, seed: int
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) -> dict[int, tuple[int, int] | None]:
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"""Recover the original camera-grid cell from its quantized palette colour."""
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script_dir = str(Path(__file__).resolve().parent)
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if script_dir not in sys.path:
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sys.path.insert(0, script_dir)
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from random_patch_dd02c import precompute_cell_colors
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rng_state = np.random.get_state()
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try:
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np.random.seed(seed)
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palette = precompute_cell_colors(nx, ny, colour_radius)
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finally:
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np.random.set_state(rng_state)
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rgb = np.clip(np.rint(palette * 255), 0, 255).astype(np.uint8)
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packed = (
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(rgb[:, :, 0].astype(np.uint32) << 16)
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| (rgb[:, :, 1].astype(np.uint32) << 8)
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| rgb[:, :, 2].astype(np.uint32)
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)
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lookup: dict[int, tuple[int, int] | None] = {}
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for j in range(ny):
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for i in range(nx):
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colour = int(packed[j, i])
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lookup[colour] = None if colour in lookup else (i, j)
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return lookup
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def camera_compensated_size(
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print_span_px: int,
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px_per_mm: float,
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width_fraction: float,
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width_min_mm: float,
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width_max_mm: float,
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length_ratio: float,
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) -> tuple[float, float]:
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"""Nominal print width and length in mm from one projected camera-cell footprint.
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Scaling from the footprint is the point of this mode: the page is a projection, so a cell
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the projection stretches is physically larger and must receive a proportionally larger mark
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to stay in proportion. The clamp then keeps a far-field cell from growing past what the
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process can hold.
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"""
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width_mm = max(
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width_min_mm, min(width_max_mm, print_span_px / px_per_mm * width_fraction)
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)
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return width_mm, length_ratio * width_mm
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def capsule_polygon(
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centre_x: float, centre_y: float, radius: float, half_length: float, angle: float
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) -> list[tuple[float, float]]:
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"""Approximate the raster capsule boundary for native vector output."""
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points = []
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cosine, sine = math.cos(angle), math.sin(angle)
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for side, start in ((1, -math.pi / 2), (-1, math.pi / 2)):
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for index in range(25):
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theta = start + index * math.pi / 24
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local_x = side * half_length + radius * math.cos(theta)
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local_y = radius * math.sin(theta)
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points.append(
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(
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centre_x + local_x * cosine - local_y * sine,
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centre_y + local_x * sine + local_y * cosine,
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)
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)
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return points
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def draw_mark_candidate(
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canvas: np.ndarray,
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region,
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params: dict,
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px_per_mm: float,
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patterned: np.ndarray | None = None,
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camera_cell: tuple[int, int] | None = None,
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vector_shapes: list | None = None,
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accepted_capsules: list | None = None,
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candidate_index: int = 0,
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) -> float:
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"""Draw one camera-compensated capsule. Returns its printed width in pixels, or 0."""
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colour, cell_x, cell_y, width, height = region
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rgb = ((colour >> 16) & 255, (colour >> 8) & 255, colour & 255)
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dx, dy, angle = cell_code(
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np.asarray(rgb),
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params["seed"],
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params["jitter_fraction"],
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params["angle_span_deg"],
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candidate_index,
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)
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span = min(width, height)
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centre_x = cell_x + dx * width
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centre_y = cell_y + dy * height
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if camera_cell is None:
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raise ValueError(
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"camera-compensated marks require an unambiguous camera cell"
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)
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width_mm, length_mm = camera_compensated_size(
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span,
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px_per_mm,
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params["width_fraction"],
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params["width_min_mm"],
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params["width_max_mm"],
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params["length_ratio"],
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)
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radius = width_mm * px_per_mm / 2
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half_length = (length_mm - width_mm) * px_per_mm / 2
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extent = radius + half_length
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if patterned is not None and (
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centre_x - extent < 0
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or centre_y - extent < 0
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or centre_x + extent >= canvas.shape[1]
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or centre_y + extent >= canvas.shape[0]
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):
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return 0.0
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x0 = max(0, math.floor(centre_x - extent))
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x1 = min(canvas.shape[1], math.ceil(centre_x + extent + 1))
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y0 = max(0, math.floor(centre_y - extent))
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y1 = min(canvas.shape[0], math.ceil(centre_y + extent + 1))
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if x0 >= x1 or y0 >= y1:
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return 0.0
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yy, xx = np.ogrid[y0:y1, x0:x1]
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px = xx - centre_x
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py = yy - centre_y
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along = px * math.cos(angle) + py * math.sin(angle)
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across = -px * math.sin(angle) + py * math.cos(angle)
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distance_sq = np.maximum(np.abs(along) - half_length, 0) ** 2 + across**2
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footprint = distance_sq <= radius**2
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if patterned is not None:
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if np.any(footprint & ~patterned[y0:y1, x0:x1]):
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return 0.0
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local_radius = round(params["neighbourhood_mm"] * px_per_mm)
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lx0 = max(0, round(centre_x) - local_radius)
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lx1 = min(canvas.shape[1], round(centre_x) + local_radius + 1)
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ly0 = max(0, round(centre_y) - local_radius)
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ly1 = min(canvas.shape[0], round(centre_y) + local_radius + 1)
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local_pattern = patterned[ly0:ly1, lx0:lx1]
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local_area = np.count_nonzero(local_pattern)
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if local_area == 0:
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return 0.0
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existing = np.count_nonzero(canvas[ly0:ly1, lx0:lx1] & local_pattern)
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added = np.count_nonzero(footprint & ~canvas[y0:y1, x0:x1])
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if (existing + added) / local_area > params["coverage_cap"]:
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return 0.0
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capsule = (*capsule_segment(centre_x / px_per_mm, centre_y / px_per_mm,
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half_length / px_per_mm, angle), radius / px_per_mm)
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if accepted_capsules is not None and not has_clearance(
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capsule, accepted_capsules, params["clearance_mm"]
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):
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return 0.0
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canvas[y0:y1, x0:x1] |= footprint
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if accepted_capsules is not None:
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accepted_capsules.append(capsule)
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if vector_shapes is not None:
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vector_shapes.append(
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(
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capsule_polygon(centre_x, centre_y, radius, half_length, angle),
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(0.0, 0.0, 0.0, 1.0),
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)
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)
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return 2 * radius
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def draw_mark(canvas, region, params, px_per_mm, patterned=None,
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camera_cell=None, vector_shapes=None, accepted_capsules=None) -> float:
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"""Try deterministic alternatives without relaxing width or clearance."""
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for candidate_index in range(params["candidate_count"]):
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width = draw_mark_candidate(
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canvas, region, params, px_per_mm, patterned, camera_cell,
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vector_shapes, accepted_capsules, candidate_index,
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)
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if width:
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return width
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return 0.0
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def capsule_footprint(capsule, px_per_mm, shape):
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"""Raster footprint of a millimetre-space capsule, or None outside the page."""
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start, end, radius_mm = capsule
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cx = (start[0] + end[0]) * px_per_mm / 2
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cy = (start[1] + end[1]) * px_per_mm / 2
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dx = (end[0] - start[0]) * px_per_mm / 2
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dy = (end[1] - start[1]) * px_per_mm / 2
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half_length = math.hypot(dx, dy)
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angle = math.atan2(dy, dx)
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radius = radius_mm * px_per_mm
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extent = half_length + radius
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if cx - extent < 0 or cy - extent < 0 or cx + extent >= shape[1] or cy + extent >= shape[0]:
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return None
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x0, x1 = math.floor(cx - extent), min(shape[1], math.ceil(cx + extent + 1))
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y0, y1 = math.floor(cy - extent), min(shape[0], math.ceil(cy + extent + 1))
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yy, xx = np.ogrid[y0:y1, x0:x1]
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along = (xx - cx) * math.cos(angle) + (yy - cy) * math.sin(angle)
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across = -(xx - cx) * math.sin(angle) + (yy - cy) * math.cos(angle)
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footprint = np.maximum(np.abs(along) - half_length, 0) ** 2 + across**2 <= radius**2
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return (slice(y0, y1), slice(x0, x1)), footprint
|
||
|
||
|
||
def extend_marks(marks, patterned, capsules, vector_shapes, params, px_per_mm):
|
||
"""Grow existing marks into free space until target occupancy or geometry stops them."""
|
||
target = math.ceil(params["target_coverage"] * np.count_nonzero(patterned))
|
||
occupied = np.count_nonzero(marks & patterned)
|
||
if target <= occupied or not capsules:
|
||
return occupied
|
||
step_mm = 2 / px_per_mm
|
||
initial_lengths = [math.dist(start, end) + 2 * radius
|
||
for start, end, radius in capsules]
|
||
blocked = [[False, False] for _ in capsules]
|
||
while occupied < target and any(not all(pair) for pair in blocked):
|
||
for index, capsule in enumerate(capsules):
|
||
for side in (0, 1):
|
||
if blocked[index][side]:
|
||
continue
|
||
start, end, radius = capsule
|
||
length = math.dist(start, end)
|
||
if length + step_mm + 2 * radius > 2 * initial_lengths[index]:
|
||
blocked[index][side] = True
|
||
continue
|
||
ux = (end[0] - start[0]) / length
|
||
uy = (end[1] - start[1]) / length
|
||
candidate = (
|
||
(start[0] - ux * step_mm, start[1] - uy * step_mm) if side == 0 else start,
|
||
(end[0] + ux * step_mm, end[1] + uy * step_mm) if side == 1 else end,
|
||
radius,
|
||
)
|
||
if not has_clearance(candidate, capsules, params["clearance_mm"], index):
|
||
blocked[index][side] = True
|
||
continue
|
||
raster = capsule_footprint(candidate, px_per_mm, marks.shape)
|
||
if raster is None:
|
||
blocked[index][side] = True
|
||
continue
|
||
region, footprint = raster
|
||
if np.any(footprint & ~patterned[region]):
|
||
blocked[index][side] = True
|
||
continue
|
||
occupied += np.count_nonzero(footprint & ~marks[region])
|
||
marks[region] |= footprint
|
||
capsules[index] = candidate
|
||
capsule = candidate
|
||
if occupied >= target:
|
||
break
|
||
if occupied >= target:
|
||
break
|
||
if vector_shapes is not None:
|
||
vector_shapes[:] = [
|
||
(capsule_polygon((start[0] + end[0]) * px_per_mm / 2,
|
||
(start[1] + end[1]) * px_per_mm / 2,
|
||
radius * px_per_mm,
|
||
math.dist(start, end) * px_per_mm / 2,
|
||
math.atan2(end[1] - start[1], end[0] - start[0])),
|
||
(0.0, 0.0, 0.0, 1.0))
|
||
for start, end, radius in capsules
|
||
]
|
||
return occupied
|
||
|
||
|
||
def write_binary_png(path: Path, marks: np.ndarray, surface: np.ndarray) -> None:
|
||
height, width = marks.shape
|
||
rgba = np.zeros((height, width, 4), dtype=np.float32)
|
||
rgba[:, :, :3] = (surface & ~marks)[:, :, None]
|
||
rgba[:, :, 3] = 1.0
|
||
image = bpy.data.images.new(path.stem, width=width, height=height, alpha=True)
|
||
try:
|
||
image.pixels.foreach_set(rgba.ravel())
|
||
image.update()
|
||
save_png_image(image, path)
|
||
finally:
|
||
bpy.data.images.remove(image)
|
||
|
||
|
||
def job_sources(
|
||
job: dict,
|
||
) -> tuple[list[tuple[str, Path, Path | None, int | None]], float]:
|
||
"""Resolve source PNGs and physical scale from a colour texture job."""
|
||
settings = job.get("settings") or {}
|
||
schema = job.get("schema")
|
||
if schema == "orisys-sensor-design/texture-job-multiview-v1":
|
||
entries = job.get("pairs") or []
|
||
page = job.get("page") or {}
|
||
if page.get("mode") != "auto" or any(
|
||
entry.get("page", "auto") != "auto" for entry in entries
|
||
):
|
||
raise ValueError("multiview preview requires auto-sized pages")
|
||
px_per_mm = float(page.get("px_per_mm", 0))
|
||
sources = [
|
||
(
|
||
entry["id"],
|
||
Path(entry["artifacts"]["png"]),
|
||
Path(entry["artifacts"]["svg"])
|
||
if entry["artifacts"].get("svg")
|
||
else None,
|
||
entry.get("seed"),
|
||
)
|
||
for entry in entries
|
||
]
|
||
elif schema == "orisys-sensor-design/texture-job-v3":
|
||
entries = job.get("outputs") or []
|
||
vector = settings.get("vector") or {}
|
||
width_mm = float(vector.get("width_mm", 0))
|
||
height_mm = float(vector.get("height_mm", 0))
|
||
if width_mm <= 0 or height_mm <= 0:
|
||
raise ValueError(
|
||
"single-view preview requires positive vector page dimensions"
|
||
)
|
||
px_per_mm = float(settings["width"]) / width_mm
|
||
vertical_scale = float(settings["height"]) / height_mm
|
||
if not math.isclose(px_per_mm, vertical_scale, rel_tol=1e-6):
|
||
raise ValueError(
|
||
"single-view page has unequal horizontal and vertical scales"
|
||
)
|
||
sources = [
|
||
(
|
||
entry["uv_map"],
|
||
Path(entry["artifacts"]["png"]),
|
||
Path(entry["artifacts"]["svg"])
|
||
if entry["artifacts"].get("svg")
|
||
else None,
|
||
settings.get("seed"),
|
||
)
|
||
for entry in entries
|
||
]
|
||
else:
|
||
raise ValueError(f"unsupported colour texture job schema: {schema!r}")
|
||
if not sources or not math.isfinite(px_per_mm) or px_per_mm <= 0:
|
||
raise ValueError("colour texture job needs source PNGs and positive px_per_mm")
|
||
for source_id, source, _svg, _seed in sources:
|
||
if not source_id or not all(
|
||
char.isalnum() or char in "_-" for char in source_id
|
||
):
|
||
raise ValueError(f"unsafe source ID: {source_id!r}")
|
||
if not source.is_file():
|
||
raise FileNotFoundError(f"run the colour generator first: {source}")
|
||
return sources, px_per_mm
|
||
|
||
|
||
def source_mesh_polygons(
|
||
svg_path: Path | None, width: int, height: int, px_per_mm: float
|
||
) -> tuple[list[list[tuple[float, float]]], float, float]:
|
||
"""Read the source artwork's owned white ground and physical page size."""
|
||
if svg_path is None or not svg_path.is_file():
|
||
raise FileNotFoundError(
|
||
f"source colour SVG is required for vectors: {svg_path}"
|
||
)
|
||
root = ET.parse(svg_path).getroot()
|
||
width_mm = float(root.attrib["width"].removesuffix("mm"))
|
||
height_mm = float(root.attrib["height"].removesuffix("mm"))
|
||
view_box = [float(value) for value in root.attrib["viewBox"].split()]
|
||
if (
|
||
len(view_box) != 4
|
||
or view_box[:2] != [0.0, 0.0]
|
||
or view_box[2:] != [float(width), float(height)]
|
||
or not math.isfinite(width_mm)
|
||
or not math.isfinite(height_mm)
|
||
or width_mm <= 0
|
||
or height_mm <= 0
|
||
or not math.isclose(width / width_mm, px_per_mm, rel_tol=0.002)
|
||
or not math.isclose(height / height_mm, px_per_mm, rel_tol=0.002)
|
||
):
|
||
raise ValueError(f"source SVG page does not match colour PNG/job: {svg_path}")
|
||
namespace = {"svg": "http://www.w3.org/2000/svg"}
|
||
mesh = root.find(".//svg:g[@id='mesh']", namespace)
|
||
if mesh is None:
|
||
raise ValueError(f"source SVG has no owned mesh group: {svg_path}")
|
||
polygons = []
|
||
for element in mesh.findall("svg:polygon", namespace):
|
||
polygon = [
|
||
tuple(float(value) for value in point.split(","))
|
||
for point in element.attrib["points"].split()
|
||
]
|
||
if len(polygon) >= 3:
|
||
polygons.append(polygon)
|
||
if not polygons:
|
||
raise ValueError(f"source SVG has no owned mesh polygons: {svg_path}")
|
||
return polygons, width_mm, height_mm
|
||
|
||
|
||
def artifact_stem(source_id: str, prefix: str | None) -> str:
|
||
return f"{prefix}_{source_id}" if prefix else f"{source_id}_{MODE}"
|
||
|
||
|
||
def run(
|
||
job_path: Path,
|
||
output_dir: Path,
|
||
params: dict,
|
||
formats: tuple[str, ...] = ("png",),
|
||
artifact_prefix: str | None = None,
|
||
) -> None:
|
||
job = json.loads(job_path.read_text(encoding="utf-8"))
|
||
sources, px_per_mm = job_sources(job)
|
||
if artifact_prefix is not None and (
|
||
not artifact_prefix
|
||
or not all(char.isalnum() or char in "_-" for char in artifact_prefix)
|
||
):
|
||
raise ValueError("artifact prefix must be non-empty and filename-safe")
|
||
if not formats or len(formats) != len(set(formats)):
|
||
raise ValueError("formats must be non-empty and distinct")
|
||
targets = [
|
||
output_dir / f"{artifact_stem(source_id, artifact_prefix)}.{fmt}"
|
||
for source_id, _, _, _ in sources
|
||
for fmt in formats
|
||
]
|
||
for target in targets:
|
||
if target.exists():
|
||
raise FileExistsError(f"refusing to overwrite {target}")
|
||
output_dir.mkdir(parents=True, exist_ok=True)
|
||
settings = job.get("settings", {})
|
||
for source_id, source, source_svg, source_seed in sources:
|
||
rgb, width, height = read_rgb(source)
|
||
vector_page = (
|
||
source_mesh_polygons(source_svg, width, height, px_per_mm)
|
||
if "svg" in formats or "pdf" in formats
|
||
else None
|
||
)
|
||
surface = np.any(rgb != 0, axis=2)
|
||
patterned = surface & np.any(rgb != 255, axis=2)
|
||
regions = list(cell_regions(rgb, int(params["min_cell_pixels"])))
|
||
if not regions or not np.any(patterned):
|
||
raise ValueError(f"{source_id}: no coloured cell regions in source PNG")
|
||
cell_span = float(np.median([min(region[3], region[4]) for region in regions]))
|
||
print(
|
||
f"{source_id}: median cell span {cell_span:.1f} px "
|
||
f"({cell_span / px_per_mm:.3f} mm on the print page)",
|
||
flush=True,
|
||
)
|
||
# A mark is sized from the cell's footprint, so an ambiguous palette colour has no
|
||
# known footprint and is skipped rather than guessed.
|
||
grid_shape = (int(settings["nx"]), int(settings["ny"]))
|
||
pair_seed = source_seed if source_seed is not None else settings.get("seed")
|
||
if pair_seed is None:
|
||
raise ValueError("camera-compensated preview requires a seeded colour palette")
|
||
lookup = camera_cell_lookup(
|
||
*grid_shape, float(settings["color_r"]), int(pair_seed)
|
||
)
|
||
missing = [
|
||
hex(region[0])
|
||
for region in regions
|
||
if region[0] != 0xFF0000 and region[0] not in lookup
|
||
]
|
||
if missing:
|
||
raise ValueError(
|
||
f"{source_id}: camera cell missing for "
|
||
f"{len(missing)} colours: {missing[:5]}"
|
||
)
|
||
ambiguous = [
|
||
hex(region[0])
|
||
for region in regions
|
||
if region[0] != 0xFF0000 and lookup[region[0]] is None
|
||
]
|
||
if ambiguous:
|
||
print(
|
||
f"{source_id}: skip {len(ambiguous)} ambiguous palette regions "
|
||
f"{ambiguous[:5]}",
|
||
flush=True,
|
||
)
|
||
marks = np.zeros((height, width), dtype=bool)
|
||
accepted_capsules = []
|
||
vector_shapes = [] if vector_page is not None else None
|
||
mode_regions = [
|
||
region
|
||
for region in regions
|
||
if region[0] != 0xFF0000 and lookup[region[0]] is not None
|
||
]
|
||
diameters = [
|
||
draw_mark(
|
||
marks,
|
||
region,
|
||
params,
|
||
px_per_mm,
|
||
patterned,
|
||
camera_cell=lookup[region[0]],
|
||
vector_shapes=vector_shapes,
|
||
accepted_capsules=accepted_capsules,
|
||
)
|
||
for region in mode_regions
|
||
]
|
||
initial_coverage = np.count_nonzero(marks & patterned) / np.count_nonzero(patterned)
|
||
if params["target_coverage"] > initial_coverage:
|
||
extend_marks(marks, patterned, accepted_capsules, vector_shapes, params, px_per_mm)
|
||
print(f"{source_id}: endpoint extension {initial_coverage:.1%} -> "
|
||
f"{np.count_nonzero(marks & patterned) / np.count_nonzero(patterned):.1%} "
|
||
f"(target {params['target_coverage']:.1%})", flush=True)
|
||
stem = artifact_stem(source_id, artifact_prefix)
|
||
if "png" in formats:
|
||
write_binary_png(output_dir / f"{stem}.png", marks, surface)
|
||
if vector_page is not None:
|
||
mesh_polygons, width_mm, height_mm = vector_page
|
||
if "svg" in formats:
|
||
write_svg_vector(
|
||
output_dir / f"{stem}.svg",
|
||
width,
|
||
height,
|
||
width_mm,
|
||
height_mm,
|
||
mesh_polygons,
|
||
vector_shapes,
|
||
[],
|
||
(0.0, 0.0, 0.0, 1.0),
|
||
source_id,
|
||
artwork_label="stroke",
|
||
)
|
||
if "pdf" in formats:
|
||
write_pdf_vector(
|
||
output_dir / f"{stem}.pdf",
|
||
width,
|
||
height,
|
||
width_mm,
|
||
height_mm,
|
||
mesh_polygons,
|
||
vector_shapes,
|
||
[],
|
||
(0.0, 0.0, 0.0, 1.0),
|
||
artwork_label="stroke",
|
||
)
|
||
accepted = [diameter for diameter in diameters if diameter > 0]
|
||
median_width = float(np.median(accepted)) if accepted else 0.0
|
||
physical_width = (
|
||
f", about {median_width / px_per_mm:.3f} mm on the print page"
|
||
if px_per_mm > 0
|
||
else ""
|
||
)
|
||
coverage = float(
|
||
np.count_nonzero(marks & patterned) / np.count_nonzero(patterned)
|
||
)
|
||
width_range = (
|
||
f", width range {min(accepted) / px_per_mm:.3f}–"
|
||
f"{max(accepted) / px_per_mm:.3f} mm"
|
||
if accepted
|
||
else ""
|
||
)
|
||
print(
|
||
f"{source_id}: {MODE}, {len(accepted)}/{len(mode_regions)} marks, "
|
||
f"black coverage {coverage:.1%}, median mark width "
|
||
f"{median_width:.1f} px{physical_width}{width_range} -> {stem}",
|
||
flush=True,
|
||
)
|
||
|
||
|
||
def main() -> int:
|
||
argv = sys.argv[sys.argv.index("--") + 1 :] if "--" in sys.argv else []
|
||
args = parse_args(argv)
|
||
run(
|
||
args.job.resolve(),
|
||
args.output_dir.resolve(),
|
||
resolve_params(args),
|
||
tuple(args.formats),
|
||
args.artifact_prefix,
|
||
)
|
||
return 0
|
||
|
||
|
||
if __name__ == "__main__":
|
||
raise SystemExit(main())
|