Name the generator Random Stroke Pattern
Align the Blender script and documentation with the stroke geometry. Verify the existing CLI output is byte-identical and the focused tests pass.
This commit is contained in:
ykcheungab
@@ -0,0 +1,507 @@
|
||||
"""Generate Random Stroke Pattern (RSP) trials from colour texture jobs.
|
||||
|
||||
Run after the colour texture command has produced its PNGs::
|
||||
|
||||
blender -b -t 2 --python-exit-code 1 \
|
||||
-P blender/scripts/random_stroke_pattern.py -- \
|
||||
--job build/textures/finger/r01/_texture_views_job.json \
|
||||
--output-dir .scratch/binary-pattern-from-color/run-01
|
||||
|
||||
This is an image-space experiment, not a print master. It accepts either a single-view
|
||||
texture job or a multiview job. Each distinct source colour is treated as one cell
|
||||
within each PNG. A split cell can have a different centre in the two print views.
|
||||
The medium- and dense-dash outputs test higher black coverage without changing
|
||||
cell positions.
|
||||
The physical-size outputs compare two stroke widths with a 0.2 mm-long,
|
||||
0.1 mm-wide capsule. Clipping and merged neighbours can change the widths of
|
||||
visible features.
|
||||
The spaced 0.12 mm trial rejects clipped or colliding marks on each print page;
|
||||
its selection can differ between the two UV views, so it is also preview-only.
|
||||
The balanced trial permits overlaps but caps nearby black coverage.
|
||||
The camera-scaled trial uses the source sensing-camera grid for a radial size
|
||||
gradient: 0.11–0.16 mm width and 0.24–0.38 mm length, edge to centre. These are
|
||||
nominal print-page sizes; clipped/merged ink still needs physical validation.
|
||||
The camera-compensated trial scales width from each camera cell's print-page
|
||||
footprint, bounded to 0.11–0.28 mm; larger far-field cells therefore receive
|
||||
larger marks. It remains a preview because image-space regions can be distorted.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import hashlib
|
||||
import json
|
||||
import math
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
import bpy
|
||||
import numpy as np
|
||||
|
||||
PHYSICAL_WIDTH_MM = {"width_0p1mm": 0.1, "width_0p12mm": 0.12, "width_0p2mm": 0.2}
|
||||
MODES = (
|
||||
"dots",
|
||||
"dashes",
|
||||
"medium_dashes",
|
||||
"dense_dashes",
|
||||
*PHYSICAL_WIDTH_MM,
|
||||
"length_0p2_width_0p1mm",
|
||||
"spaced_width_0p12mm",
|
||||
"balanced_width_0p12mm",
|
||||
"camera_scaled_width",
|
||||
"camera_compensated_width",
|
||||
)
|
||||
|
||||
|
||||
def parse_args(argv: list[str]) -> argparse.Namespace:
|
||||
parser = argparse.ArgumentParser(description=__doc__)
|
||||
parser.add_argument("--job", required=True, type=Path)
|
||||
parser.add_argument("--output-dir", required=True, type=Path)
|
||||
parser.add_argument("--seed", type=int, default=7)
|
||||
parser.add_argument("--min-cell-pixels", type=int, default=20)
|
||||
parser.add_argument(
|
||||
"--modes",
|
||||
nargs="+",
|
||||
choices=MODES,
|
||||
default=MODES,
|
||||
help="Generate only these preview variants",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--camera-width-fraction",
|
||||
type=float,
|
||||
default=0.5,
|
||||
help="Compensated stroke width as a fraction of the local print-cell span",
|
||||
)
|
||||
return parser.parse_args(argv)
|
||||
|
||||
|
||||
def cell_code(rgb: np.ndarray, seed: int) -> tuple[float, float, float, float]:
|
||||
"""Return bounded offsets, angle and size from one exact source colour."""
|
||||
digest = hashlib.blake2b(
|
||||
bytes(int(v) for v in rgb) + seed.to_bytes(8, "little", signed=True),
|
||||
digest_size=8,
|
||||
).digest()
|
||||
values = [int.from_bytes(digest[k : k + 2], "little") / 65535 for k in (0, 2, 4, 6)]
|
||||
return (
|
||||
0.66 * (values[0] - 0.5),
|
||||
0.66 * (values[1] - 0.5),
|
||||
math.pi * values[2],
|
||||
0.85 + 0.3 * values[3],
|
||||
)
|
||||
|
||||
|
||||
def read_rgb(path: Path) -> tuple[np.ndarray, int, int]:
|
||||
image = bpy.data.images.load(str(path), check_existing=False)
|
||||
try:
|
||||
width, height = image.size
|
||||
pixels = np.empty(width * height * 4, dtype=np.float32)
|
||||
image.pixels.foreach_get(pixels)
|
||||
rgb = np.clip(
|
||||
np.rint(pixels.reshape(height, width, 4)[:, :, :3] * 255), 0, 255
|
||||
).astype(np.uint8)
|
||||
return rgb, width, height
|
||||
finally:
|
||||
bpy.data.images.remove(image)
|
||||
|
||||
|
||||
def cell_regions(rgb: np.ndarray, minimum: int):
|
||||
"""Measure source-colour regions without assuming a rectangular UV cell."""
|
||||
height, width, _ = rgb.shape
|
||||
packed = (
|
||||
(rgb[:, :, 0].astype(np.uint32) << 16)
|
||||
| (rgb[:, :, 1].astype(np.uint32) << 8)
|
||||
| rgb[:, :, 2].astype(np.uint32)
|
||||
)
|
||||
colours, inverse, counts = np.unique(
|
||||
packed, return_inverse=True, return_counts=True
|
||||
)
|
||||
rows = np.repeat(np.arange(height, dtype=np.int32), width)
|
||||
cols = np.tile(np.arange(width, dtype=np.int32), height)
|
||||
n = len(colours)
|
||||
left = np.full(n, width, dtype=np.int32)
|
||||
right = np.full(n, -1, dtype=np.int32)
|
||||
bottom = np.full(n, height, dtype=np.int32)
|
||||
top = np.full(n, -1, dtype=np.int32)
|
||||
np.minimum.at(left, inverse, cols)
|
||||
np.maximum.at(right, inverse, cols)
|
||||
np.minimum.at(bottom, inverse, rows)
|
||||
np.maximum.at(top, inverse, rows)
|
||||
cx = np.bincount(inverse, weights=cols, minlength=n) / counts
|
||||
cy = np.bincount(inverse, weights=rows, minlength=n) / counts
|
||||
for index, colour in enumerate(colours):
|
||||
if colour in (0, 0xFFFFFF) or counts[index] < minimum:
|
||||
continue
|
||||
cell_width = right[index] - left[index] + 1
|
||||
cell_height = top[index] - bottom[index] + 1
|
||||
yield (
|
||||
int(colour),
|
||||
float(cx[index]),
|
||||
float(cy[index]),
|
||||
int(cell_width),
|
||||
int(cell_height),
|
||||
)
|
||||
|
||||
|
||||
def camera_cell_lookup(
|
||||
nx: int, ny: int, colour_radius: float, seed: int
|
||||
) -> dict[int, tuple[int, int] | None]:
|
||||
"""Recover the original camera-grid cell from its quantized palette colour."""
|
||||
script_dir = str(Path(__file__).resolve().parent)
|
||||
if script_dir not in sys.path:
|
||||
sys.path.insert(0, script_dir)
|
||||
from random_patch_dd02c import precompute_cell_colors
|
||||
|
||||
rng_state = np.random.get_state()
|
||||
try:
|
||||
np.random.seed(seed)
|
||||
palette = precompute_cell_colors(nx, ny, colour_radius)
|
||||
finally:
|
||||
np.random.set_state(rng_state)
|
||||
rgb = np.clip(np.rint(palette * 255), 0, 255).astype(np.uint8)
|
||||
packed = (
|
||||
(rgb[:, :, 0].astype(np.uint32) << 16)
|
||||
| (rgb[:, :, 1].astype(np.uint32) << 8)
|
||||
| rgb[:, :, 2].astype(np.uint32)
|
||||
)
|
||||
lookup: dict[int, tuple[int, int] | None] = {}
|
||||
for j in range(ny):
|
||||
for i in range(nx):
|
||||
colour = int(packed[j, i])
|
||||
lookup[colour] = None if colour in lookup else (i, j)
|
||||
return lookup
|
||||
|
||||
|
||||
def camera_scaled_size(cell: tuple[int, int], nx: int, ny: int) -> tuple[float, float]:
|
||||
"""Return nominal print width and length in mm from camera-grid position."""
|
||||
i, j = cell
|
||||
u = (i + 0.5) / nx - 0.5
|
||||
v = (j + 0.5) / ny - 0.5
|
||||
radius = min(1.0, math.hypot(u, v) / math.sqrt(0.5))
|
||||
centre_weight = 1.0 - radius**2
|
||||
return (0.11 + 0.05 * centre_weight, 0.24 + 0.14 * centre_weight)
|
||||
|
||||
|
||||
def camera_compensated_size(
|
||||
print_span_px: int, px_per_mm: float, width_fraction: float
|
||||
) -> tuple[float, float]:
|
||||
"""Return nominal print dimensions from one projected camera-cell footprint."""
|
||||
width_mm = max(0.11, min(0.28, print_span_px / px_per_mm * width_fraction))
|
||||
return width_mm, 2.4 * width_mm
|
||||
|
||||
|
||||
def draw_mark(
|
||||
canvas: np.ndarray,
|
||||
region,
|
||||
seed: int,
|
||||
mode: str,
|
||||
px_per_mm: float,
|
||||
patterned: np.ndarray | None = None,
|
||||
camera_cell: tuple[int, int] | None = None,
|
||||
grid_shape: tuple[int, int] | None = None,
|
||||
camera_width_fraction: float = 0.5,
|
||||
) -> float:
|
||||
colour, cell_x, cell_y, width, height = region
|
||||
rgb = ((colour >> 16) & 255, (colour >> 8) & 255, colour & 255)
|
||||
dx, dy, angle, scale = cell_code(np.asarray(rgb), seed)
|
||||
span = min(width, height)
|
||||
centre_x = cell_x + dx * width
|
||||
centre_y = cell_y + dy * height
|
||||
if mode == "camera_compensated_width":
|
||||
if camera_cell is None:
|
||||
raise ValueError(
|
||||
"camera-compensated marks require an unambiguous camera cell"
|
||||
)
|
||||
width_mm, length_mm = camera_compensated_size(
|
||||
span, px_per_mm, camera_width_fraction
|
||||
)
|
||||
radius = width_mm * px_per_mm / 2
|
||||
half_length = (length_mm - width_mm) * px_per_mm / 2
|
||||
elif mode == "camera_scaled_width":
|
||||
if camera_cell is None or grid_shape is None:
|
||||
raise ValueError("camera-scaled marks require an unambiguous camera cell")
|
||||
width_mm, length_mm = camera_scaled_size(camera_cell, *grid_shape)
|
||||
radius = width_mm * px_per_mm / 2
|
||||
half_length = (length_mm - width_mm) * px_per_mm / 2
|
||||
elif mode == "spaced_width_0p12mm":
|
||||
radius = 0.12 * px_per_mm / 2
|
||||
half_length = (0.30 - 0.12) * px_per_mm / 2
|
||||
elif mode == "balanced_width_0p12mm":
|
||||
radius = 0.12 * px_per_mm / 2
|
||||
half_length = 2 * radius
|
||||
elif mode == "length_0p2_width_0p1mm":
|
||||
radius = 0.1 * px_per_mm / 2
|
||||
half_length = (0.2 - 0.1) * px_per_mm / 2
|
||||
elif mode in PHYSICAL_WIDTH_MM:
|
||||
radius = PHYSICAL_WIDTH_MM[mode] * px_per_mm / 2
|
||||
half_length = 2 * radius
|
||||
else:
|
||||
radius_fraction = {
|
||||
"dots": 0.14,
|
||||
"dashes": 0.14,
|
||||
"medium_dashes": 0.16,
|
||||
"dense_dashes": 0.18,
|
||||
}[mode]
|
||||
radius = max(1.0, radius_fraction * span * scale)
|
||||
half_length_fraction = {
|
||||
"dots": 0.0,
|
||||
"dashes": 0.28,
|
||||
"medium_dashes": 0.32,
|
||||
"dense_dashes": 0.36,
|
||||
}[mode]
|
||||
half_length = half_length_fraction * span * scale
|
||||
gap_px = 0.02 * px_per_mm if mode == "spaced_width_0p12mm" else 0.0
|
||||
extent = radius + half_length + gap_px
|
||||
if patterned is not None and (
|
||||
centre_x - extent < 0
|
||||
or centre_y - extent < 0
|
||||
or centre_x + extent >= canvas.shape[1]
|
||||
or centre_y + extent >= canvas.shape[0]
|
||||
):
|
||||
return 0.0
|
||||
x0 = max(0, math.floor(centre_x - extent))
|
||||
x1 = min(canvas.shape[1], math.ceil(centre_x + extent + 1))
|
||||
y0 = max(0, math.floor(centre_y - extent))
|
||||
y1 = min(canvas.shape[0], math.ceil(centre_y + extent + 1))
|
||||
if x0 >= x1 or y0 >= y1:
|
||||
return 0.0
|
||||
yy, xx = np.ogrid[y0:y1, x0:x1]
|
||||
px = xx - centre_x
|
||||
py = yy - centre_y
|
||||
along = px * math.cos(angle) + py * math.sin(angle)
|
||||
across = -px * math.sin(angle) + py * math.cos(angle)
|
||||
distance_sq = np.maximum(np.abs(along) - half_length, 0) ** 2 + across**2
|
||||
footprint = distance_sq <= radius**2
|
||||
if patterned is not None:
|
||||
if np.any(footprint & ~patterned[y0:y1, x0:x1]):
|
||||
return 0.0
|
||||
if mode == "spaced_width_0p12mm":
|
||||
clearance = distance_sq <= (radius + gap_px) ** 2
|
||||
if np.any(canvas[y0:y1, x0:x1] & clearance):
|
||||
return 0.0
|
||||
else:
|
||||
local_radius = round(0.5 * px_per_mm)
|
||||
lx0 = max(0, round(centre_x) - local_radius)
|
||||
lx1 = min(canvas.shape[1], round(centre_x) + local_radius + 1)
|
||||
ly0 = max(0, round(centre_y) - local_radius)
|
||||
ly1 = min(canvas.shape[0], round(centre_y) + local_radius + 1)
|
||||
local_pattern = patterned[ly0:ly1, lx0:lx1]
|
||||
local_area = np.count_nonzero(local_pattern)
|
||||
if local_area == 0:
|
||||
return 0.0
|
||||
existing = np.count_nonzero(canvas[ly0:ly1, lx0:lx1] & local_pattern)
|
||||
added = np.count_nonzero(footprint & ~canvas[y0:y1, x0:x1])
|
||||
if (existing + added) / local_area > 0.45:
|
||||
return 0.0
|
||||
canvas[y0:y1, x0:x1] |= footprint
|
||||
return 2 * radius
|
||||
|
||||
|
||||
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()
|
||||
image.filepath_raw = str(path)
|
||||
image.file_format = "PNG"
|
||||
image.save()
|
||||
finally:
|
||||
bpy.data.images.remove(image)
|
||||
|
||||
|
||||
def job_sources(job: dict) -> tuple[list[tuple[str, Path, 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"]), 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"]), 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, _ in sources:
|
||||
if not source.is_file():
|
||||
raise FileNotFoundError(f"run the colour generator first: {source}")
|
||||
return sources, px_per_mm
|
||||
|
||||
|
||||
def run(
|
||||
job_path: Path,
|
||||
output_dir: Path,
|
||||
seed: int,
|
||||
minimum: int,
|
||||
modes: tuple[str, ...],
|
||||
camera_width_fraction: float,
|
||||
) -> None:
|
||||
job = json.loads(job_path.read_text(encoding="utf-8"))
|
||||
sources, px_per_mm = job_sources(job)
|
||||
targets = [
|
||||
output_dir / f"{source_id}_{mode}.png"
|
||||
for source_id, _, _ in sources
|
||||
for mode in modes
|
||||
]
|
||||
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_seed in sources:
|
||||
rgb, width, height = read_rgb(source)
|
||||
surface = np.any(rgb != 0, axis=2)
|
||||
patterned = surface & np.any(rgb != 255, axis=2)
|
||||
regions = list(cell_regions(rgb, minimum))
|
||||
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,
|
||||
)
|
||||
lookup = None
|
||||
grid_shape = None
|
||||
camera_modes = ("camera_scaled_width", "camera_compensated_width")
|
||||
if any(mode in modes for mode in camera_modes):
|
||||
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-based 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,
|
||||
)
|
||||
for mode in modes:
|
||||
marks = np.zeros((height, width), dtype=bool)
|
||||
limit = (
|
||||
patterned
|
||||
if mode
|
||||
in (
|
||||
"spaced_width_0p12mm",
|
||||
"balanced_width_0p12mm",
|
||||
"camera_scaled_width",
|
||||
"camera_compensated_width",
|
||||
)
|
||||
else None
|
||||
)
|
||||
mode_regions = [
|
||||
region
|
||||
for region in regions
|
||||
if mode not in camera_modes
|
||||
or (region[0] != 0xFF0000 and lookup[region[0]] is not None)
|
||||
]
|
||||
diameters = [
|
||||
draw_mark(
|
||||
marks,
|
||||
region,
|
||||
seed,
|
||||
mode,
|
||||
px_per_mm,
|
||||
limit,
|
||||
camera_cell=lookup[region[0]]
|
||||
if lookup is not None and mode in camera_modes
|
||||
else None,
|
||||
grid_shape=grid_shape,
|
||||
camera_width_fraction=camera_width_fraction,
|
||||
)
|
||||
for region in mode_regions
|
||||
]
|
||||
target = output_dir / f"{source_id}_{mode}.png"
|
||||
write_binary_png(target, marks, surface)
|
||||
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} -> {target}",
|
||||
flush=True,
|
||||
)
|
||||
|
||||
|
||||
def main() -> int:
|
||||
argv = sys.argv[sys.argv.index("--") + 1 :] if "--" in sys.argv else []
|
||||
args = parse_args(argv)
|
||||
if args.min_cell_pixels < 1:
|
||||
raise ValueError("--min-cell-pixels must be positive")
|
||||
if not math.isfinite(args.camera_width_fraction) or args.camera_width_fraction <= 0:
|
||||
raise ValueError("--camera-width-fraction must be finite and positive")
|
||||
run(
|
||||
args.job.resolve(),
|
||||
args.output_dir.resolve(),
|
||||
args.seed,
|
||||
args.min_cell_pixels,
|
||||
tuple(args.modes),
|
||||
args.camera_width_fraction,
|
||||
)
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
Reference in New Issue
Block a user