Add Source-guided coherent HD terrain retexturing tools
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#!/usr/bin/env python3
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"""Deterministic DERIVED terrain reconstruction, never a generator-return asset.
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The authentic opaque ordinary Source controls geometry and low-frequency colour.
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Only a bounded monochrome fine band from the parent image survives. Material
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boundaries use the engine's ScaleNx4 shape, and interpolation never mixes material
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families. This deliberately removes coarse generated stones and colour drift.
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No asset registration, frame policy, game Source or parent image is modified.
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Requires NumPy and Pillow; run with the bundled dependency Python.
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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 os
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from pathlib import Path
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import stat
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import struct
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import numpy as np
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from PIL import Image
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METHOD = 'source-nx4-material-normalized-lowpass-plus-bounded-parent-fine-luma-v1'
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DEFAULTS = dict(output_side=512, source_sigma=1.0, residual_sigma_fine=0.65,
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residual_sigma_coarse=2.2, residual_percentile=95.0,
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residual_amplitude=dict(gray=2.0, olive=1.25, soil=2.5, rim=1.5))
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FAMILY_NAMES = ('gray', 'olive', 'soil', 'rim')
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def read_regular(path):
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p = Path(path).absolute()
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fd = os.open(p, os.O_RDONLY | os.O_NOFOLLOW)
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try:
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a = os.fstat(fd)
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if not stat.S_ISREG(a.st_mode):
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raise ValueError('input must be a regular file')
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with os.fdopen(fd, 'rb', closefd=False) as f:
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data = f.read()
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b = os.fstat(fd)
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if (a.st_dev, a.st_ino, a.st_size, a.st_mtime_ns) != (b.st_dev, b.st_ino, b.st_size, b.st_mtime_ns):
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raise ValueError('input changed during read')
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finally:
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os.close(fd)
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return data, dict(path=str(p), sha256=hashlib.sha256(data).hexdigest(),
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size=a.st_size, mtime_ns=a.st_mtime_ns, mode=a.st_mode)
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def decode_source(data):
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if len(data) < 20:
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raise ValueError('short Source PIC')
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header = struct.unpack_from('<5i', data)
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kind, count, offset, width, height = header
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if (kind, count, offset) != (1, 1, 0) or not (1 <= width <= 128 and 1 <= height <= 128):
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raise ValueError('this tool supports a single uncompressed Source PIC only')
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if len(data) != 20 + width * height:
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raise ValueError('Source payload size mismatch')
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indices = np.frombuffer(data[20:], dtype=np.uint8).reshape(height, width).copy()
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if np.any(indices >= 240):
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raise ValueError('only fully opaque ordinary Source indices0..239 are supported')
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return indices
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def palette_rgb(data):
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if len(data) != 768:
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raise ValueError('palette must contain256 three-channel6-bit entries')
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a = np.minimum(np.frombuffer(data, np.uint8).reshape(256, 3).astype(np.uint16), 63)
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return ((a << 2) | (a >> 4)).astype(np.uint8)
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def material_families(indices):
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"""Explicit supported Raptor palette families; no per-asset painted mask."""
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a = np.asarray(indices)
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out = np.full(a.shape, -1, dtype=np.int8)
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out[a < 32] = 0
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out[(a >= 60) & (a <= 63)] = 1
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out[((a >= 192) & (a <= 223)) | (a == 239)] = 2
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out[(a >= 224) & (a <= 238)] = 3
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if np.any(out < 0):
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raise ValueError('Source contains unsupported material family')
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return out
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def scale_nx(indices, factor):
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"""Exact Scale2x/3x; Scale4x is two Scale2x passes, as render::upscale."""
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a = np.asarray(indices)
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if factor == 1:
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return a.copy()
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if factor == 4:
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return scale_nx(scale_nx(a, 2), 2)
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if factor not in (2, 3) or a.ndim != 2:
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raise ValueError('ScaleNx factor must be1..4 on a plane')
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h, w = a.shape
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p = np.pad(a, 1, mode='edge')
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out = np.empty((h * factor, w * factor), dtype=a.dtype)
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for y in range(h):
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for x in range(w):
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nw, n, ne, west, c, east, sw, south, se = p[y:y+3, x:x+3].flatten().tolist()
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cells = [c] * (factor * factor)
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if n != south and west != east:
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if factor == 2:
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cells = [west if west == n else c, east if n == east else c,
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west if west == south else c, east if south == east else c]
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else:
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cells = [west if west == n else c,
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n if (west == n and c != ne) or (n == east and c != nw) else c,
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east if n == east else c,
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west if (west == n and c != sw) or (west == south and c != nw) else c,
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c,
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east if (n == east and c != se) or (south == east and c != ne) else c,
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west if west == south else c,
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south if (west == south and c != se) or (south == east and c != sw) else c,
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east if south == east else c]
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out[y*factor:(y+1)*factor, x*factor:(x+1)*factor] = np.array(cells).reshape(factor, factor)
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return out
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def gaussian(a, sigma):
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a = np.asarray(a, dtype=np.float64)
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if not math.isfinite(sigma) or sigma <= 0:
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raise ValueError('sigma must be finite and positive')
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radius = int(math.ceil(3 * sigma))
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q = np.arange(-radius, radius + 1, dtype=np.float64)
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k = np.exp(-0.5 * (q / sigma) ** 2)
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k /= k.sum()
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b = a
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for axis in (0, 1):
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padding = [(0, 0)] * a.ndim
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padding[axis] = (radius, radius)
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p = np.pad(b, padding, mode='edge')
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b = np.zeros_like(a)
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for i, weight in enumerate(k):
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sl = [slice(None)] * a.ndim
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sl[axis] = slice(i, i + a.shape[axis])
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b += weight * p[tuple(sl)]
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return b
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def resize_float(a, size):
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a = np.asarray(a, np.float32)
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if a.ndim == 3:
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return np.stack([resize_float(a[..., i], size) for i in range(a.shape[2])], -1)
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return np.asarray(Image.fromarray(a).resize(size, Image.Resampling.BILINEAR), np.float64)
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def validate_parameters(p, source_shape):
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if set(p) != set(DEFAULTS):
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raise ValueError('unknown or missing reconstruction parameter')
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side = p['output_side']
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if type(side) is not int or not 4 * max(source_shape) <= side <= 2048 or side % (4 * max(source_shape)):
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raise ValueError('output must be an integer expansion of SourceNx4, at least4x')
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for name in ('source_sigma', 'residual_sigma_fine', 'residual_sigma_coarse'):
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if type(p[name]) not in (int, float) or not math.isfinite(p[name]) or not 0 < p[name] <= 8:
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raise ValueError('invalid smoothing parameter')
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if p['residual_sigma_fine'] >= p['residual_sigma_coarse']:
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raise ValueError('fine band must be narrower than coarse band')
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q = p['residual_percentile']
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if type(q) not in (int, float) or not math.isfinite(q) or not 50 <= q <= 100:
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raise ValueError('invalid residual percentile')
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if set(p['residual_amplitude']) != set(FAMILY_NAMES):
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raise ValueError('all material residual amplitudes required')
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for v in p['residual_amplitude'].values():
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if type(v) not in (int, float) or not math.isfinite(v) or not 0 <= v <= 8:
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raise ValueError('invalid bounded residual amplitude')
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def reconstruct(indices, palette, generated_rgb, parameters=None):
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p = json.loads(json.dumps(DEFAULTS if parameters is None else parameters))
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a = np.asarray(indices)
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if a.ndim != 2 or a.shape[0] != a.shape[1] or a.size == 0 or np.any(a >= 240) or np.any(a < 0):
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raise ValueError('square opaque ordinary Source required')
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validate_parameters(p, a.shape)
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guide_indices = scale_nx(a, 4)
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labels = material_families(guide_indices)
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guide = np.asarray(palette)[guide_indices].astype(np.float64)
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side = p['output_side']
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factor = side // labels.shape[0]
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owners = np.repeat(np.repeat(labels, factor, 0), factor, 1)
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base = np.zeros((side, side, 3), dtype=np.float64)
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# Normalized filtering and interpolation are performed separately per family.
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# Thus no olive/soil/rim/gray mixture can shift a Source material boundary.
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for family in np.unique(labels):
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mask = (labels == family).astype(np.float64)
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weights = resize_float(gaussian(mask, p['source_sigma']), (side, side))
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numerator = resize_float(gaussian(guide * mask[..., None], p['source_sigma']), (side, side))
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field = numerator / np.maximum(weights[..., None], 1e-12)
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base[owners == family] = field[owners == family]
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raw = np.asarray(generated_rgb)
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if raw.ndim != 3 or raw.shape[2] != 3 or raw.dtype != np.uint8:
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raise ValueError('generated input must be RGB uint8')
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parent = np.asarray(Image.fromarray(raw).resize((side, side), Image.Resampling.LANCZOS), np.float64)
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luma = parent @ np.array([0.2126, 0.7152, 0.0722])
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band = gaussian(luma, p['residual_sigma_fine']) - gaussian(luma, p['residual_sigma_coarse'])
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band -= band.mean()
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divisor = max(float(np.percentile(np.abs(band), p['residual_percentile'])), 1.0)
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fine = np.clip(band / divisor, -1, 1)
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amplitudes = np.array([p['residual_amplitude'][n] for n in FAMILY_NAMES])
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residual = fine * amplitudes[owners]
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output = np.clip(np.rint(base + residual[..., None]), 0, 255).astype(np.uint8)
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base8 = np.clip(np.rint(base), 0, 255).astype(np.uint8)
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metrics = dict(method=METHOD, source_shape=list(a.shape), output_shape=list(output.shape),
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sourceNx4_indices_sha256=hashlib.sha256(guide_indices.astype('<u2').tobytes()).hexdigest(),
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geometry_material_labels_sha256=hashlib.sha256(owners.tobytes()).hexdigest(),
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material_output_pixels={FAMILY_NAMES[int(i)]: int((owners == i).sum()) for i in np.unique(owners)},
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generated_fine_band_std=float(band.std()), fine_normalization=divisor,
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maximum_retained_luma_residual=float(np.abs(residual).max()),
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pixels_changed_from_Source_guided_base=int(np.any(output != base8, axis=2).sum()),
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mean_absolute_generated_residual=float(np.abs(residual).mean()),
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output_RGB_sha256=hashlib.sha256(output.tobytes()).hexdigest(),
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output_is_derived_not_untouched=True,
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limits='SourceNx4 family geometry, normalized Source colour, bounded generated fine luma; no generated coarse structure or hue retained. Not a material or context approval.')
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return output, base8, owners, metrics
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def main(argv=None):
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ap = argparse.ArgumentParser(description=__doc__)
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ap.add_argument('--recipe', required=True)
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ap.add_argument('--output', required=True)
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args = ap.parse_args(argv)
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recipe_path = Path(args.recipe).absolute()
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recipe_bytes, recipe_pin = read_regular(recipe_path)
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recipe = json.loads(recipe_bytes)
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if recipe.get('method') != METHOD or not isinstance(recipe.get('frames'), list) or not recipe['frames']:
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raise ValueError('wrong method or empty recipe')
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def recipe_input(binding):
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path = Path(binding['path'])
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if not path.is_absolute():
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path = recipe_path.parent / path
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return read_regular(path)
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palette_bytes, palette_pin = recipe_input(recipe['palette'])
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if palette_pin['sha256'] != recipe['palette']['sha256']:
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raise ValueError('palette identity mismatch')
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palette = palette_rgb(palette_bytes)
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prepared = []
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seen = set()
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for row in recipe['frames']:
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asset = row['asset']
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if not isinstance(asset, str) or len(asset) != 5 or any(c not in '0123456789abcdef' for c in asset) or asset in seen:
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raise ValueError('invalid or duplicate asset')
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seen.add(asset)
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source, source_pin = recipe_input(row['Source'])
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parent_bytes, parent_pin = recipe_input(row['parent_raw'])
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if source_pin['sha256'] != row['Source']['sha256'] or parent_pin['sha256'] != row['parent_raw']['sha256']:
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raise ValueError('source or parent identity mismatch')
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indices = decode_source(source)
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import io
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parent = Image.open(io.BytesIO(parent_bytes))
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if parent.convert('RGBA').getchannel('A').getextrema() != (255, 255):
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raise ValueError('parent must be wholly opaque; no alpha repair')
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crop = row['crop']
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if not isinstance(crop, list) or len(crop) != 4 or any(type(v) is not int for v in crop):
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raise ValueError('crop must be four exact integers')
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x, y, w, h = crop
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if min(x, y) < 0 or w <= 0 or h <= 0 or x+w > parent.width or y+h > parent.height:
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raise ValueError('crop outside untouched parent')
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raw = np.array(parent.convert('RGB').crop((x, y, x+w, y+h)))
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derived, base, labels, metrics = reconstruct(indices, palette, raw, recipe['parameters'])
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prepared.append((asset, derived, base, labels, metrics, source_pin, parent_pin, crop))
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out = Path(args.output).absolute()
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out.mkdir(exist_ok=False)
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tool_bytes, tool_pin = read_regular(__file__)
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results = []
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for asset, derived, base, labels, metrics, source_pin, parent_pin, crop in prepared:
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image_path = out / f'{asset}-DERIVED-source-guided-material-v1.png'
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Image.fromarray(derived).save(image_path)
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Image.fromarray(base).save(out / f'{asset}-SOURCE-GUIDED-BASE-diagnostic.png')
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Image.fromarray(labels.astype(np.uint8)).save(out / f'{asset}-SOURCE-NX4-FAMILY-geometry-diagnostic.png')
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_, output_pin = read_regular(image_path)
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results.append(dict(asset=asset, output=output_pin, Source=source_pin, parent_raw=parent_pin,
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parent_crop=crop, recipe=recipe_pin, tool=tool_pin,
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parameters=recipe['parameters'], metrics=metrics,
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lineage='DERIVED scripted cleanup; generator-return parent remains untouched'))
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for record in [recipe_pin, palette_pin] + [p for t in prepared for p in (t[5], t[6])]:
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_, now = read_regular(record['path'])
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if now != record:
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raise ValueError('input changed across cleanup')
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execution = dict(status='success', method=METHOD,
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recipe_sha256=recipe_pin['sha256'], tool_sha256=tool_pin['sha256'],
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source_sha256={r['asset']: r['Source']['sha256'] for r in results},
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parent_raw_sha256=sorted({r['parent_raw']['sha256'] for r in results}),
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output_sha256={r['asset']: r['output']['sha256'] for r in results})
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(out / 'DERIVED-LINEAGE-AND-ACTUAL-RECONSTRUCTION-METRICS.json').write_text(
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json.dumps(dict(status='DERIVED_OUTPUTS_NOT_MATERIAL_APPROVAL', method=METHOD,
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recipe=recipe_pin, tool=tool_pin, palette=palette_pin, frames=results,
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execution=execution), indent=2, sort_keys=True) + '\n')
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print(json.dumps(dict(output=str(out), frames=len(results), method=METHOD), indent=2))
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if __name__ == '__main__':
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main()
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@@ -0,0 +1,297 @@
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#!/usr/bin/env python3
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"""Deterministic DERIVED terrain reconstruction, never a generator-return asset.
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|
|
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The authentic opaque ordinary Source controls geometry and low-frequency colour.
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A bounded, coherent fine+midscale parent texture is retained as tone modulation. Material
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boundaries use the engine's ScaleNx4 shape, and interpolation never mixes material
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families. This flattens generated colour/lighting drift while retaining coherent material structure.
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No asset registration, frame policy, game Source or parent image is modified.
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Requires NumPy and Pillow; run with the bundled dependency Python.
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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 os
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from pathlib import Path
|
||||||
|
import stat
|
||||||
|
import struct
|
||||||
|
import sys
|
||||||
|
import numpy as np
|
||||||
|
import PIL
|
||||||
|
from PIL import Image
|
||||||
|
|
||||||
|
METHOD = 'source-broad-material-guidance-coherent-parent-fine-mid-logtexture-v1'
|
||||||
|
DEFAULTS = dict(output_side=512, source_sigma=3.0, residual_sigma_fine=0.65,
|
||||||
|
residual_sigma_coarse=10.0, residual_percentile=80.0,
|
||||||
|
texture_log_gain=dict(gray=0.38, olive=0.55, soil=0.34, rim=0.06))
|
||||||
|
FAMILY_NAMES = ('gray', 'olive', 'soil', 'rim')
|
||||||
|
|
||||||
|
def read_regular(path):
|
||||||
|
p = Path(path).absolute()
|
||||||
|
fd = os.open(p, os.O_RDONLY | os.O_NOFOLLOW)
|
||||||
|
try:
|
||||||
|
a = os.fstat(fd)
|
||||||
|
if not stat.S_ISREG(a.st_mode):
|
||||||
|
raise ValueError('input must be a regular file')
|
||||||
|
with os.fdopen(fd, 'rb', closefd=False) as f:
|
||||||
|
data = f.read()
|
||||||
|
b = os.fstat(fd)
|
||||||
|
if (a.st_dev, a.st_ino, a.st_size, a.st_mtime_ns) != (b.st_dev, b.st_ino, b.st_size, b.st_mtime_ns):
|
||||||
|
raise ValueError('input changed during read')
|
||||||
|
finally:
|
||||||
|
os.close(fd)
|
||||||
|
return data, dict(path=str(p), sha256=hashlib.sha256(data).hexdigest(),
|
||||||
|
size=a.st_size, mtime_ns=a.st_mtime_ns, mode=a.st_mode)
|
||||||
|
|
||||||
|
def decode_source(data):
|
||||||
|
if len(data) < 20:
|
||||||
|
raise ValueError('short Source PIC')
|
||||||
|
header = struct.unpack_from('<5i', data)
|
||||||
|
kind, count, offset, width, height = header
|
||||||
|
if (kind, count, offset) != (1, 1, 0) or not (1 <= width <= 128 and 1 <= height <= 128):
|
||||||
|
raise ValueError('this tool supports a single uncompressed Source PIC only')
|
||||||
|
if len(data) != 20 + width * height:
|
||||||
|
raise ValueError('Source payload size mismatch')
|
||||||
|
indices = np.frombuffer(data[20:], dtype=np.uint8).reshape(height, width).copy()
|
||||||
|
if np.any(indices >= 240):
|
||||||
|
raise ValueError('only fully opaque ordinary Source indices0..239 are supported')
|
||||||
|
return indices
|
||||||
|
|
||||||
|
def palette_rgb(data):
|
||||||
|
if len(data) != 768:
|
||||||
|
raise ValueError('palette must contain256 three-channel6-bit entries')
|
||||||
|
a = np.minimum(np.frombuffer(data, np.uint8).reshape(256, 3).astype(np.uint16), 63)
|
||||||
|
return ((a << 2) | (a >> 4)).astype(np.uint8)
|
||||||
|
|
||||||
|
def material_families(indices):
|
||||||
|
"""Explicit supported Raptor palette families; no per-asset painted mask."""
|
||||||
|
a = np.asarray(indices)
|
||||||
|
out = np.full(a.shape, -1, dtype=np.int8)
|
||||||
|
out[a < 32] = 0
|
||||||
|
out[(a >= 60) & (a <= 63)] = 1
|
||||||
|
out[((a >= 192) & (a <= 223)) | (a == 239)] = 2
|
||||||
|
out[(a >= 224) & (a <= 238)] = 3
|
||||||
|
if np.any(out < 0):
|
||||||
|
raise ValueError('Source contains unsupported material family')
|
||||||
|
return out
|
||||||
|
|
||||||
|
def scale_nx(indices, factor):
|
||||||
|
"""Exact Scale2x/3x; Scale4x is two Scale2x passes, as render::upscale."""
|
||||||
|
a = np.asarray(indices)
|
||||||
|
if factor == 1:
|
||||||
|
return a.copy()
|
||||||
|
if factor == 4:
|
||||||
|
return scale_nx(scale_nx(a, 2), 2)
|
||||||
|
if factor not in (2, 3) or a.ndim != 2:
|
||||||
|
raise ValueError('ScaleNx factor must be1..4 on a plane')
|
||||||
|
h, w = a.shape
|
||||||
|
p = np.pad(a, 1, mode='edge')
|
||||||
|
out = np.empty((h * factor, w * factor), dtype=a.dtype)
|
||||||
|
for y in range(h):
|
||||||
|
for x in range(w):
|
||||||
|
nw, n, ne, west, c, east, sw, south, se = p[y:y+3, x:x+3].flatten().tolist()
|
||||||
|
cells = [c] * (factor * factor)
|
||||||
|
if n != south and west != east:
|
||||||
|
if factor == 2:
|
||||||
|
cells = [west if west == n else c, east if n == east else c,
|
||||||
|
west if west == south else c, east if south == east else c]
|
||||||
|
else:
|
||||||
|
cells = [west if west == n else c,
|
||||||
|
n if (west == n and c != ne) or (n == east and c != nw) else c,
|
||||||
|
east if n == east else c,
|
||||||
|
west if (west == n and c != sw) or (west == south and c != nw) else c,
|
||||||
|
c,
|
||||||
|
east if (n == east and c != se) or (south == east and c != ne) else c,
|
||||||
|
west if west == south else c,
|
||||||
|
south if (west == south and c != se) or (south == east and c != sw) else c,
|
||||||
|
east if south == east else c]
|
||||||
|
out[y*factor:(y+1)*factor, x*factor:(x+1)*factor] = np.array(cells).reshape(factor, factor)
|
||||||
|
return out
|
||||||
|
|
||||||
|
def gaussian(a, sigma):
|
||||||
|
a = np.asarray(a, dtype=np.float64)
|
||||||
|
if not math.isfinite(sigma) or sigma <= 0:
|
||||||
|
raise ValueError('sigma must be finite and positive')
|
||||||
|
radius = int(math.ceil(3 * sigma))
|
||||||
|
q = np.arange(-radius, radius + 1, dtype=np.float64)
|
||||||
|
k = np.exp(-0.5 * (q / sigma) ** 2)
|
||||||
|
k /= k.sum()
|
||||||
|
b = a
|
||||||
|
for axis in (0, 1):
|
||||||
|
padding = [(0, 0)] * a.ndim
|
||||||
|
padding[axis] = (radius, radius)
|
||||||
|
p = np.pad(b, padding, mode='edge')
|
||||||
|
b = np.zeros_like(a)
|
||||||
|
for i, weight in enumerate(k):
|
||||||
|
sl = [slice(None)] * a.ndim
|
||||||
|
sl[axis] = slice(i, i + a.shape[axis])
|
||||||
|
b += weight * p[tuple(sl)]
|
||||||
|
return b
|
||||||
|
|
||||||
|
def resize_float(a, size):
|
||||||
|
a = np.asarray(a, np.float32)
|
||||||
|
if a.ndim == 3:
|
||||||
|
return np.stack([resize_float(a[..., i], size) for i in range(a.shape[2])], -1)
|
||||||
|
return np.asarray(Image.fromarray(a).resize(size, Image.Resampling.BILINEAR), np.float64)
|
||||||
|
|
||||||
|
def validate_parameters(p, source_shape):
|
||||||
|
if set(p) != set(DEFAULTS):
|
||||||
|
raise ValueError('unknown or missing reconstruction parameter')
|
||||||
|
side = p['output_side']
|
||||||
|
if type(side) is not int or not 4 * max(source_shape) <= side <= 2048 or side % (4 * max(source_shape)):
|
||||||
|
raise ValueError('output must be an integer expansion of SourceNx4, at least4x')
|
||||||
|
for name in ('source_sigma', 'residual_sigma_fine', 'residual_sigma_coarse'):
|
||||||
|
if type(p[name]) not in (int, float) or not math.isfinite(p[name]) or not 0 < p[name] <= 16:
|
||||||
|
raise ValueError('invalid smoothing parameter')
|
||||||
|
if p['residual_sigma_fine'] >= p['residual_sigma_coarse']:
|
||||||
|
raise ValueError('fine band must be narrower than coarse band')
|
||||||
|
q = p['residual_percentile']
|
||||||
|
if type(q) not in (int, float) or not math.isfinite(q) or not 50 <= q <= 100:
|
||||||
|
raise ValueError('invalid residual percentile')
|
||||||
|
if set(p['texture_log_gain']) != set(FAMILY_NAMES):
|
||||||
|
raise ValueError('all material residual amplitudes required')
|
||||||
|
for v in p['texture_log_gain'].values():
|
||||||
|
if type(v) not in (int, float) or not math.isfinite(v) or not 0 <= v <= 0.7:
|
||||||
|
raise ValueError('invalid bounded log-texture gain')
|
||||||
|
|
||||||
|
def reconstruct(indices, palette, generated_rgb, parameters=None):
|
||||||
|
p = json.loads(json.dumps(DEFAULTS if parameters is None else parameters))
|
||||||
|
a = np.asarray(indices)
|
||||||
|
if a.ndim != 2 or a.shape[0] != a.shape[1] or a.size == 0 or np.any(a >= 240) or np.any(a < 0):
|
||||||
|
raise ValueError('square opaque ordinary Source required')
|
||||||
|
validate_parameters(p, a.shape)
|
||||||
|
guide_indices = scale_nx(a, 4)
|
||||||
|
labels = material_families(guide_indices)
|
||||||
|
guide = np.asarray(palette)[guide_indices].astype(np.float64)
|
||||||
|
side = p['output_side']
|
||||||
|
factor = side // labels.shape[0]
|
||||||
|
owners = np.repeat(np.repeat(labels, factor, 0), factor, 1)
|
||||||
|
base = np.zeros((side, side, 3), dtype=np.float64)
|
||||||
|
quiet = np.ones((side, side), dtype=np.float64)
|
||||||
|
guide_luma = guide @ np.array([0.2126, 0.7152, 0.0722])
|
||||||
|
for family in np.unique(labels):
|
||||||
|
mask = (labels == family).astype(np.float64)
|
||||||
|
weights = resize_float(gaussian(mask, p['source_sigma']), (side, side))
|
||||||
|
numerator = resize_float(gaussian(guide * mask[..., None], p['source_sigma']), (side, side))
|
||||||
|
field = numerator / np.maximum(weights[..., None], 1e-12)
|
||||||
|
base[owners == family] = field[owners == family]
|
||||||
|
if family == 2:
|
||||||
|
second = resize_float(gaussian(guide_luma**2 * mask, p['source_sigma']), (side, side)) / np.maximum(weights, 1e-12)
|
||||||
|
first = field @ np.array([0.2126, 0.7152, 0.0722])
|
||||||
|
std = np.sqrt(np.maximum(second-first**2, 0))
|
||||||
|
# Calm Source soil (e.g.20400right) receives less texture strength.
|
||||||
|
# This map is derived from Source variance, never hand-painted.
|
||||||
|
quiet[owners == family] = (0.35+0.65*np.clip(std/8.0,0,1))[owners == family]
|
||||||
|
raw = np.asarray(generated_rgb)
|
||||||
|
if raw.ndim != 3 or raw.shape[2] != 3 or raw.dtype != np.uint8:
|
||||||
|
raise ValueError('generated input must be RGB uint8')
|
||||||
|
parent = np.asarray(Image.fromarray(raw).resize((side, side), Image.Resampling.LANCZOS), np.float64)
|
||||||
|
luma = parent @ np.array([0.2126, 0.7152, 0.0722])
|
||||||
|
band = gaussian(luma, p['residual_sigma_fine']) - gaussian(luma, p['residual_sigma_coarse'])
|
||||||
|
texture = np.zeros_like(band)
|
||||||
|
normalizers = {}
|
||||||
|
for family in np.unique(owners):
|
||||||
|
area = owners == family
|
||||||
|
centered = band-band[area].mean()
|
||||||
|
divisor = max(float(np.percentile(np.abs(centered[area]),p['residual_percentile'])),1.0)
|
||||||
|
texture[area] = np.tanh(centered[area]/divisor)
|
||||||
|
normalizers[FAMILY_NAMES[int(family)]] = divisor
|
||||||
|
strengths = np.array([p['texture_log_gain'][n]for n in FAMILY_NAMES])[owners]*quiet
|
||||||
|
gain = np.exp(strengths*texture)
|
||||||
|
output = np.clip(np.rint(base*gain[...,None]),0,255).astype(np.uint8)
|
||||||
|
base8 = np.clip(np.rint(base),0,255).astype(np.uint8)
|
||||||
|
metrics = dict(method=METHOD, source_shape=list(a.shape),output_shape=list(output.shape),
|
||||||
|
sourceNx4_indices_sha256=hashlib.sha256(guide_indices.astype('<u2').tobytes()).hexdigest(),
|
||||||
|
geometry_material_labels_sha256=hashlib.sha256(owners.tobytes()).hexdigest(),
|
||||||
|
material_output_pixels={FAMILY_NAMES[int(i)]:int((owners==i).sum())for i in np.unique(owners)},
|
||||||
|
coherent_parent_band_std=float(band.std()),per_family_normalizers=normalizers,
|
||||||
|
actual_tone_gain_min=float(gain.min()),actual_tone_gain_max=float(gain.max()),
|
||||||
|
pixels_changed_from_Source_guided_base=int(np.any(output!=base8,axis=2).sum()),
|
||||||
|
mean_absolute_parent_tone_modulation=float(np.abs(output.astype(float)-base8).mean()),
|
||||||
|
Source_derived_soil_quiet_strength_min=float(quiet[owners==2].min())if np.any(owners==2)else None,
|
||||||
|
Source_derived_soil_quiet_strength_max=float(quiet[owners==2].max())if np.any(owners==2)else None,
|
||||||
|
output_RGB_sha256=hashlib.sha256(output.tobytes()).hexdigest(),output_is_derived_not_untouched=True,
|
||||||
|
limits='Source masks/broad tones/rims; actual coherent generated fine+midscale luminance is hue-preserving multiplicative texture. No random noise; large generated hue/illumination drift discarded. Not material/context approval.')
|
||||||
|
|
||||||
|
return output, base8, owners, metrics
|
||||||
|
|
||||||
|
def main(argv=None):
|
||||||
|
ap = argparse.ArgumentParser(description=__doc__)
|
||||||
|
ap.add_argument('--recipe', required=True)
|
||||||
|
ap.add_argument('--output', required=True)
|
||||||
|
args = ap.parse_args(argv)
|
||||||
|
recipe_path = Path(args.recipe).absolute()
|
||||||
|
recipe_bytes, recipe_pin = read_regular(recipe_path)
|
||||||
|
recipe = json.loads(recipe_bytes)
|
||||||
|
if recipe.get('method') != METHOD or not isinstance(recipe.get('frames'), list) or not recipe['frames']:
|
||||||
|
raise ValueError('wrong method or empty recipe')
|
||||||
|
def recipe_input(binding):
|
||||||
|
path = Path(binding['path'])
|
||||||
|
if not path.is_absolute():
|
||||||
|
path = recipe_path.parent / path
|
||||||
|
return read_regular(path)
|
||||||
|
palette_bytes, palette_pin = recipe_input(recipe['palette'])
|
||||||
|
if palette_pin['sha256'] != recipe['palette']['sha256']:
|
||||||
|
raise ValueError('palette identity mismatch')
|
||||||
|
palette = palette_rgb(palette_bytes)
|
||||||
|
prepared = []
|
||||||
|
seen = set()
|
||||||
|
for row in recipe['frames']:
|
||||||
|
asset = row['asset']
|
||||||
|
if not isinstance(asset, str) or len(asset) != 5 or any(c not in '0123456789abcdef' for c in asset) or asset in seen:
|
||||||
|
raise ValueError('invalid or duplicate asset')
|
||||||
|
seen.add(asset)
|
||||||
|
source, source_pin = recipe_input(row['Source'])
|
||||||
|
parent_bytes, parent_pin = recipe_input(row['parent_raw'])
|
||||||
|
if source_pin['sha256'] != row['Source']['sha256'] or parent_pin['sha256'] != row['parent_raw']['sha256']:
|
||||||
|
raise ValueError('source or parent identity mismatch')
|
||||||
|
indices = decode_source(source)
|
||||||
|
import io
|
||||||
|
parent = Image.open(io.BytesIO(parent_bytes))
|
||||||
|
if parent.convert('RGBA').getchannel('A').getextrema() != (255, 255):
|
||||||
|
raise ValueError('parent must be wholly opaque; no alpha repair')
|
||||||
|
crop = row['crop']
|
||||||
|
if not isinstance(crop, list) or len(crop) != 4 or any(type(v) is not int for v in crop):
|
||||||
|
raise ValueError('crop must be four exact integers')
|
||||||
|
x, y, w, h = crop
|
||||||
|
if min(x, y) < 0 or w <= 0 or h <= 0 or x+w > parent.width or y+h > parent.height:
|
||||||
|
raise ValueError('crop outside untouched parent')
|
||||||
|
raw = np.array(parent.convert('RGB').crop((x, y, x+w, y+h)))
|
||||||
|
derived, base, labels, metrics = reconstruct(indices, palette, raw, recipe['parameters'])
|
||||||
|
prepared.append((asset, derived, base, labels, metrics, source_pin, parent_pin, crop))
|
||||||
|
out = Path(args.output).absolute()
|
||||||
|
out.mkdir(exist_ok=False)
|
||||||
|
tool_bytes, tool_pin = read_regular(__file__)
|
||||||
|
results = []
|
||||||
|
for asset, derived, base, labels, metrics, source_pin, parent_pin, crop in prepared:
|
||||||
|
image_path = out / f'{asset}-DERIVED-source-guided-material-v1.png'
|
||||||
|
Image.fromarray(derived).save(image_path)
|
||||||
|
Image.fromarray(base).save(out / f'{asset}-SOURCE-GUIDED-BASE-diagnostic.png')
|
||||||
|
Image.fromarray(labels.astype(np.uint8)).save(out / f'{asset}-SOURCE-NX4-FAMILY-geometry-diagnostic.png')
|
||||||
|
_, output_pin = read_regular(image_path)
|
||||||
|
results.append(dict(asset=asset, output=output_pin, Source=source_pin, parent_raw=parent_pin,
|
||||||
|
parent_crop=crop, recipe=recipe_pin, tool=tool_pin,
|
||||||
|
parameters=recipe['parameters'], metrics=metrics,
|
||||||
|
lineage='DERIVED scripted cleanup; generator-return parent remains untouched'))
|
||||||
|
for record in [recipe_pin, palette_pin] + [p for t in prepared for p in (t[5], t[6])]:
|
||||||
|
_, now = read_regular(record['path'])
|
||||||
|
if now != record:
|
||||||
|
raise ValueError('input changed across cleanup')
|
||||||
|
runtime = dict(python=sys.version, executable=sys.executable,
|
||||||
|
numpy=np.__version__, Pillow=PIL.__version__, platform=sys.platform)
|
||||||
|
execution = dict(status='success', method=METHOD,
|
||||||
|
recipe_sha256=recipe_pin['sha256'], tool_sha256=tool_pin['sha256'],
|
||||||
|
source_sha256={r['asset']: r['Source']['sha256'] for r in results},
|
||||||
|
parent_raw_sha256=sorted({r['parent_raw']['sha256'] for r in results}),
|
||||||
|
output_sha256={r['asset']: r['output']['sha256'] for r in results},
|
||||||
|
runtime=runtime)
|
||||||
|
(out / 'DERIVED-LINEAGE-AND-ACTUAL-RECONSTRUCTION-METRICS.json').write_text(
|
||||||
|
json.dumps(dict(status='DERIVED_OUTPUTS_NOT_MATERIAL_APPROVAL', method=METHOD,
|
||||||
|
recipe=recipe_pin, tool=tool_pin, palette=palette_pin, frames=results,
|
||||||
|
execution=execution, runtime=runtime), indent=2, sort_keys=True) + '\n')
|
||||||
|
print(json.dumps(dict(output=str(out), frames=len(results), method=METHOD), indent=2))
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
main()
|
||||||
@@ -0,0 +1,119 @@
|
|||||||
|
import copy
|
||||||
|
import json
|
||||||
|
from pathlib import Path
|
||||||
|
import struct
|
||||||
|
import sys
|
||||||
|
import tempfile
|
||||||
|
import unittest
|
||||||
|
import numpy as np
|
||||||
|
from PIL import Image
|
||||||
|
sys.path.insert(0, str(Path(__file__).resolve().parent))
|
||||||
|
import hd_terrain_cleanup as c
|
||||||
|
|
||||||
|
class TerrainCleanupTests(unittest.TestCase):
|
||||||
|
def setUp(self):
|
||||||
|
self.palette = np.zeros((256, 3), np.uint8)
|
||||||
|
for i in range(32): self.palette[i] = [4*i]*3
|
||||||
|
self.palette[60:64] = [[56,52,4],[40,40,0],[24,24,0],[12,12,0]]
|
||||||
|
self.palette[200:208] = [[85,40,36],[81,36,32],[73,32,28],[69,28,24],[60,24,20],[56,16,16],[40,12,8],[20,0,0]]
|
||||||
|
self.palette[231] = [81,60,52]
|
||||||
|
self.p = copy.deepcopy(c.DEFAULTS); self.p['output_side'] = 64
|
||||||
|
|
||||||
|
def test_constant_parent_has_no_retained_texture(self):
|
||||||
|
source = np.full((4,4),63,np.uint8)
|
||||||
|
out, base, labels, m = c.reconstruct(source,self.palette,np.full((64,64,3),180,np.uint8),self.p)
|
||||||
|
np.testing.assert_array_equal(out,base)
|
||||||
|
self.assertEqual(m['pixels_changed_from_Source_guided_base'],0)
|
||||||
|
np.testing.assert_array_equal(out,np.broadcast_to(self.palette[63],out.shape))
|
||||||
|
|
||||||
|
def test_parent_colour_and_constant_lighting_are_not_carried(self):
|
||||||
|
source = np.full((4,4),63,np.uint8)
|
||||||
|
a = np.full((64,64,3),[200,5,50],np.uint8)
|
||||||
|
b = np.full((64,64,3),[10,240,70],np.uint8)
|
||||||
|
np.testing.assert_array_equal(c.reconstruct(source,self.palette,a,self.p)[0],c.reconstruct(source,self.palette,b,self.p)[0])
|
||||||
|
|
||||||
|
def test_fine_generated_detail_is_retained_but_bounded(self):
|
||||||
|
yy,xx = np.indices((64,64)); raw = np.repeat((120+40*np.sin(xx*1.9)+20*np.sin(yy*2.1))[...,None],3,2).clip(0,255).astype(np.uint8)
|
||||||
|
out,base,labels,m = c.reconstruct(np.full((4,4),202,np.uint8),self.palette,raw,self.p)
|
||||||
|
self.assertGreater(m['pixels_changed_from_Source_guided_base'],1500)
|
||||||
|
self.assertGreater(m['mean_absolute_generated_residual'],0.5)
|
||||||
|
self.assertLessEqual(np.abs(out.astype(float)-base).max(),3)
|
||||||
|
self.assertLessEqual(m['maximum_retained_luma_residual'],2.5)
|
||||||
|
|
||||||
|
def test_exact_family_geometry_and_thin_L_are_preserved(self):
|
||||||
|
source = np.array([[27,27,231,63],[27,27,231,63],[27,27,231,231],[27,27,27,27]],np.uint8)
|
||||||
|
raw = np.random.default_rng(9).integers(0,256,(80,80,3),dtype=np.uint8)
|
||||||
|
out,base,labels,m = c.reconstruct(source,self.palette,raw,self.p)
|
||||||
|
expected = c.material_families(c.scale_nx(source,4))
|
||||||
|
np.testing.assert_array_equal(labels,np.repeat(np.repeat(expected,4,0),4,1))
|
||||||
|
self.assertTrue(np.all(out[labels==0].max(axis=1)-out[labels==0].min(axis=1)==0))
|
||||||
|
self.assertTrue(np.all(out[labels==1,2] <= 2))
|
||||||
|
self.assertTrue(np.all(out[labels==3,0] > out[labels==3,1]))
|
||||||
|
|
||||||
|
def test_smooth_source_guidance_is_not_nearest_source_copy(self):
|
||||||
|
source = np.array([[200,200,205,205]]*4,np.uint8)
|
||||||
|
out,base,_,m = c.reconstruct(source,self.palette,np.full((64,64,3),128,np.uint8),self.p)
|
||||||
|
nearest=np.repeat(np.repeat(self.palette[source],16,0),16,1)
|
||||||
|
self.assertGreater(np.any(base!=nearest,axis=2).sum(),0)
|
||||||
|
self.assertGreater(len(np.unique(base.reshape(-1,3),axis=0)),2)
|
||||||
|
|
||||||
|
def test_deterministic_pixels_and_geometry(self):
|
||||||
|
src=np.array([[27,27,27,27],[27,63,202,27],[27,231,231,27],[27,27,27,27]],np.uint8)
|
||||||
|
raw=np.random.default_rng(7).integers(0,256,(61,61,3),dtype=np.uint8)
|
||||||
|
a=c.reconstruct(src,self.palette,raw,self.p);b=c.reconstruct(src,self.palette,raw,self.p)
|
||||||
|
for x,y in zip(a[:3],b[:3]):np.testing.assert_array_equal(x,y)
|
||||||
|
self.assertEqual(a[3],b[3])
|
||||||
|
|
||||||
|
def test_scale4_is_two_scale2_and_non_nearest(self):
|
||||||
|
src=np.array([[1,1,2],[1,2,2],[3,3,2]],np.uint8)
|
||||||
|
np.testing.assert_array_equal(c.scale_nx(src,4),c.scale_nx(c.scale_nx(src,2),2))
|
||||||
|
self.assertFalse(np.array_equal(c.scale_nx(src,2),np.repeat(np.repeat(src,2,0),2,1)))
|
||||||
|
|
||||||
|
def test_decode_rejects_reserved_and_malformed_source(self):
|
||||||
|
good=struct.pack('<5i',1,1,0,2,2)+bytes([27,63,202,231])
|
||||||
|
self.assertEqual(c.decode_source(good).shape,(2,2))
|
||||||
|
for bad in [good[:-1],good[:20]+bytes([27,63,240,231]),struct.pack('<5i',2,1,0,2,2)+good[20:]]:
|
||||||
|
with self.assertRaises(ValueError):c.decode_source(bad)
|
||||||
|
|
||||||
|
def test_rejects_unknown_family_and_unsafe_parameters(self):
|
||||||
|
with self.assertRaises(ValueError):c.material_families(np.array([[100]],np.uint8))
|
||||||
|
for name,value in [('output_side',True),('output_side',15),('source_sigma',float('nan')),('residual_sigma_coarse',0.1)]:
|
||||||
|
p=copy.deepcopy(self.p);p[name]=value
|
||||||
|
with self.assertRaises(ValueError):c.reconstruct(np.full((4,4),63,np.uint8),self.palette,np.zeros((64,64,3),np.uint8),p)
|
||||||
|
|
||||||
|
def test_stable_reader_rejects_symlinks(self):
|
||||||
|
with tempfile.TemporaryDirectory()as d:
|
||||||
|
p=Path(d);(p/'a').write_bytes(b'fixed');(p/'link').symlink_to(p/'a')
|
||||||
|
with self.assertRaises(OSError):c.read_regular(p/'link')
|
||||||
|
|
||||||
|
def test_actual_cli_lineage_and_bad_crop_stops_before_mkdir(self):
|
||||||
|
import hashlib
|
||||||
|
with tempfile.TemporaryDirectory()as d:
|
||||||
|
p=Path(d);src=p/'source.pic';src.write_bytes(struct.pack('<5i',1,1,0,4,4)+bytes([63]*16))
|
||||||
|
pal=p/'palette';pal.write_bytes(bytes([0,0,0])*60+bytes([14,13,1,10,10,0,6,6,0,3,3,0])+bytes([0,0,0])*(256-64))
|
||||||
|
raw=p/'parent.png';yy,xx=np.indices((64,64));Image.fromarray(np.repeat((80+(xx%2)*80)[...,None],3,2).astype(np.uint8)).save(raw)
|
||||||
|
def binding(q):return dict(path=str(q),sha256=hashlib.sha256(q.read_bytes()).hexdigest())
|
||||||
|
recipe=dict(method=c.METHOD,palette=binding(pal),parameters=self.p,frames=[dict(asset='20400',Source=binding(src),parent_raw=binding(raw),crop=[0,0,64,64])])
|
||||||
|
rp=p/'recipe.json';rp.write_text(json.dumps(recipe));out=p/'result';c.main(['--recipe',str(rp),'--output',str(out)])
|
||||||
|
report=json.loads((out/'DERIVED-LINEAGE-AND-ACTUAL-RECONSTRUCTION-METRICS.json').read_text());row=report['frames'][0]
|
||||||
|
self.assertEqual(row['output']['sha256'],hashlib.sha256(Path(row['output']['path']).read_bytes()).hexdigest())
|
||||||
|
self.assertEqual(row['parent_raw']['sha256'],binding(raw)['sha256'])
|
||||||
|
self.assertIn('DERIVED',row['lineage'])
|
||||||
|
with Image.open(row['output']['path']) as saved:
|
||||||
|
self.assertEqual(saved.size,(64,64))
|
||||||
|
self.assertEqual(report['execution']['status'],'success')
|
||||||
|
self.assertEqual(report['execution']['source_sha256'],{'20400':binding(src)['sha256']})
|
||||||
|
self.assertEqual(report['execution']['output_sha256'],{'20400':row['output']['sha256']})
|
||||||
|
self.assertEqual(report['execution']['parent_raw_sha256'],[binding(raw)['sha256']])
|
||||||
|
portable=copy.deepcopy(recipe)
|
||||||
|
portable['palette']['path']=pal.name
|
||||||
|
portable['frames'][0]['Source']['path']=src.name
|
||||||
|
portable['frames'][0]['parent_raw']['path']=raw.name
|
||||||
|
portable_path=p/'portable.json';portable_path.write_text(json.dumps(portable))
|
||||||
|
moved=p/'relative-result';c.main(['--recipe',str(portable_path),'--output',str(moved)])
|
||||||
|
self.assertEqual((moved/Path(row['output']['path']).name).read_bytes(),Path(row['output']['path']).read_bytes())
|
||||||
|
recipe['frames'][0]['crop']=[1,0,64,64];rp.write_text(json.dumps(recipe));bad=p/'bad'
|
||||||
|
with self.assertRaises(ValueError):c.main(['--recipe',str(rp),'--output',str(bad)])
|
||||||
|
self.assertFalse(bad.exists())
|
||||||
|
|
||||||
|
if __name__ == '__main__':unittest.main()
|
||||||
@@ -0,0 +1,52 @@
|
|||||||
|
import pathlib,sys,copy,unittest
|
||||||
|
import numpy as np
|
||||||
|
sys.path.insert(0,str(pathlib.Path(__file__).resolve().parent))
|
||||||
|
import hd_terrain_retexture as c
|
||||||
|
class CoherentTextureTests(unittest.TestCase):
|
||||||
|
def setUp(self):
|
||||||
|
self.palette=np.zeros((256,3),np.uint8)
|
||||||
|
for i in range(32):self.palette[i]=[i*4]*3
|
||||||
|
self.palette[60:64]=[[56,52,4],[40,40,0],[24,24,0],[12,12,0]]
|
||||||
|
self.palette[200:208]=[[85,40,36],[81,36,32],[73,32,28],[69,28,24],[60,24,20],[56,16,16],[40,12,8],[20,0,0]];self.palette[231]=[81,60,52]
|
||||||
|
self.p=copy.deepcopy(c.DEFAULTS);self.p['output_side']=64
|
||||||
|
def test_coherent_midscale_structure_survives_not_random_noise(self):
|
||||||
|
yy,xx=np.indices((64,64));lum=(120+45*np.sin(xx*2*np.pi/20)+25*np.cos(yy*2*np.pi/27));raw=np.repeat(lum[...,None],3,2).clip(0,255).astype(np.uint8)
|
||||||
|
out,base,_,m=c.reconstruct(np.full((4,4),27,np.uint8),self.palette,raw,self.p)
|
||||||
|
correlation=np.corrcoef(out[...,0].flatten(),lum.flatten())[0,1]
|
||||||
|
self.assertGreater(correlation,.90);self.assertGreater(out[...,0].std(),3)
|
||||||
|
self.assertGreater(m['mean_absolute_parent_tone_modulation'],2)
|
||||||
|
def test_gray_achromatic_and_olive_hue_not_shifted(self):
|
||||||
|
raw=np.random.default_rng(8).integers(0,256,(64,64,3),dtype=np.uint8)
|
||||||
|
for index in [27,63]:
|
||||||
|
out,_,_,m=c.reconstruct(np.full((4,4),index,np.uint8),self.palette,raw,self.p)
|
||||||
|
if index==27:np.testing.assert_array_equal(out[...,0],out[...,2])
|
||||||
|
else:self.assertTrue(np.all(out[...,2]==0));self.assertTrue(np.all(out[...,0]==out[...,1]))
|
||||||
|
self.assertGreaterEqual(m['actual_tone_gain_min'],np.exp(-.55));self.assertLessEqual(m['actual_tone_gain_max'],np.exp(.55))
|
||||||
|
def test_source_quiet_soil_receives_less_texture(self):
|
||||||
|
raw=np.random.default_rng(1).integers(0,256,(64,64,3),dtype=np.uint8);calm=np.full((4,4),202,np.uint8);busy=np.array([[200,207]*2,[207,200]*2]*2,np.uint8)
|
||||||
|
m1=c.reconstruct(calm,self.palette,raw,self.p)[3];m2=c.reconstruct(busy,self.palette,raw,self.p)[3]
|
||||||
|
self.assertAlmostEqual(m1['Source_derived_soil_quiet_strength_max'],.35,places=5)
|
||||||
|
self.assertGreater(m2['Source_derived_soil_quiet_strength_min'],.8)
|
||||||
|
def test_source_L_and_material_geometry_exact_with_coherent_texture(self):
|
||||||
|
src=np.array([[27,27,231,63],[27,27,231,202],[27,27,231,231],[27,27,27,27]],np.uint8);raw=np.random.default_rng(4).integers(0,256,(64,64,3),dtype=np.uint8)
|
||||||
|
a=c.reconstruct(src,self.palette,raw,self.p);b=c.reconstruct(src,self.palette,raw,self.p)
|
||||||
|
expected=np.repeat(np.repeat(c.material_families(c.scale_nx(src,4)),4,0),4,1)
|
||||||
|
np.testing.assert_array_equal(a[2],expected);np.testing.assert_array_equal(a[0],b[0]);self.assertEqual(a[3],b[3])
|
||||||
|
def test_constant_parent_does_not_invent_texture(self):
|
||||||
|
out,base,_,m=c.reconstruct(np.full((4,4),63,np.uint8),self.palette,np.full((64,64,3),170,np.uint8),self.p)
|
||||||
|
np.testing.assert_array_equal(out,base);self.assertEqual(m['pixels_changed_from_Source_guided_base'],0)
|
||||||
|
def test_invalid_gain_and_material_fail(self):
|
||||||
|
p=copy.deepcopy(self.p);p['texture_log_gain']['gray']=float('inf')
|
||||||
|
with self.assertRaises(ValueError):c.reconstruct(np.full((4,4),27,np.uint8),self.palette,np.zeros((64,64,3),np.uint8),p)
|
||||||
|
with self.assertRaises(ValueError):c.reconstruct(np.full((4,4),100,np.uint8),self.palette,np.zeros((64,64,3),np.uint8),self.p)
|
||||||
|
def test_relative_cli_receipt_binds_runtime_versions(self):
|
||||||
|
import tempfile,json,struct,hashlib,PIL
|
||||||
|
from PIL import Image
|
||||||
|
with tempfile.TemporaryDirectory()as d:
|
||||||
|
root=pathlib.Path(d);src=root/'source.pic';src.write_bytes(struct.pack('<5i',1,1,0,4,4)+bytes([63]*16));pal=root/'palette.dat';pal.write_bytes((self.palette//4).tobytes());raw=root/'parent.png';Image.fromarray(np.full((64,64,3),100,np.uint8)).save(raw)
|
||||||
|
def binding(p):return dict(path=p.name,sha256=hashlib.sha256(p.read_bytes()).hexdigest())
|
||||||
|
recipe=dict(method=c.METHOD,palette=binding(pal),parameters=self.p,frames=[dict(asset='20400',Source=binding(src),parent_raw=binding(raw),crop=[0,0,64,64])]);rp=root/'recipe.json';rp.write_text(json.dumps(recipe));out=root/'out';c.main(['--recipe',str(rp),'--output',str(out)])
|
||||||
|
report=json.loads((out/'DERIVED-LINEAGE-AND-ACTUAL-RECONSTRUCTION-METRICS.json').read_text());expected=dict(python=sys.version,executable=sys.executable,numpy=np.__version__,Pillow=PIL.__version__,platform=sys.platform)
|
||||||
|
self.assertEqual(report['runtime'],expected);self.assertEqual(report['execution']['runtime'],expected)
|
||||||
|
self.assertEqual(report['execution']['output_sha256']['20400'],hashlib.sha256((out/'20400-DERIVED-source-guided-material-v1.png').read_bytes()).hexdigest())
|
||||||
|
if __name__=='__main__':unittest.main()
|
||||||
Reference in new issue
Block a user