361 lines
14 KiB
Python
361 lines
14 KiB
Python
from __future__ import annotations
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import argparse
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from pathlib import Path
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import numpy as np
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from PIL import Image, ImageDraw
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from repixelizer.analysis import analyze_source
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from repixelizer.continuous import (
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_build_candidate_positions,
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_build_source_detail_reference,
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_discrete_refine_output,
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_build_source_reliability,
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_edge_gradient_maps,
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_make_patch_offsets,
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_make_regular_uv,
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_relax_candidate_selection,
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_reference_match_energy,
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_representative_colors,
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_require_torch,
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_resolve_device,
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_sample_cell_patches,
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_select_colors,
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_snap_output_to_source_pixels,
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premultiply,
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)
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from repixelizer.inference import infer_lattice
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from repixelizer.io import load_rgba
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from repixelizer.metrics import source_lattice_consistency_breakdown
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from repixelizer.params import SolverHyperParams
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from repixelizer.source_reference import build_source_lattice_reference
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def _parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser(
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description="Render a source/snap/relaxed/final crop sheet for a fixed output-grid region."
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)
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parser.add_argument("--input", required=True, help="Input image path.")
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parser.add_argument("--out", required=True, help="Output PNG path.")
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parser.add_argument(
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"--cell-bbox",
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required=True,
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nargs=4,
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type=int,
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metavar=("X0", "Y0", "X1", "Y1"),
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help="Crop bounds in output-grid cell coordinates.",
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)
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parser.add_argument(
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"--panels",
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default="source,snap,relaxed,final",
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help="Comma-separated panel list. Supported: source,snap,relaxed,final",
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)
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parser.add_argument("--scale", type=int, default=16, help="Nearest-neighbor scale factor for each output cell.")
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parser.add_argument("--steps", type=int, default=64, help="Discrete refine iteration budget.")
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parser.add_argument("--device", default="auto", help="Torch device, usually auto/cpu/cuda.")
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return parser.parse_args()
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def _to_image(rgba: np.ndarray) -> Image.Image:
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rgba8 = np.clip(np.rint(rgba * 255.0), 0, 255).astype(np.uint8)
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return Image.fromarray(rgba8, mode="RGBA")
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def _grid_panel(rgba: np.ndarray, label: str, *, x0: int, y0: int, x1: int, y1: int, scale: int) -> Image.Image:
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crop = rgba[y0:y1, x0:x1]
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base = _to_image(crop).resize(((x1 - x0) * scale, (y1 - y0) * scale), Image.Resampling.NEAREST)
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canvas = Image.new("RGBA", (base.width, base.height + 20), (16, 16, 16, 255))
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canvas.alpha_composite(base, (0, 20))
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draw = ImageDraw.Draw(canvas)
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draw.text((4, 2), label, fill=(255, 255, 255, 255))
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for gx in range(x1 - x0 + 1):
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px = gx * scale
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draw.line((px, 20, px, 20 + base.height), fill=(255, 255, 255, 55), width=1)
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for gy in range(y1 - y0 + 1):
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py = 20 + gy * scale
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draw.line((0, py, base.width, py), fill=(255, 255, 255, 55), width=1)
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return canvas
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def _source_panel(source: np.ndarray, *, source_bbox: tuple[int, int, int, int], target_size: tuple[int, int], scale: int) -> Image.Image:
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sx0, sy0, sx1, sy1 = source_bbox
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source_crop = source[sy0:sy1, sx0:sx1]
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base = _to_image(source_crop).resize((target_size[0] * scale, target_size[1] * scale), Image.Resampling.NEAREST)
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canvas = Image.new("RGBA", (base.width, base.height + 20), (16, 16, 16, 255))
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canvas.alpha_composite(base, (0, 20))
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ImageDraw.Draw(canvas).text((4, 2), "Source crop", fill=(255, 255, 255, 255))
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return canvas
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def _build_focus_states(
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input_path: str | Path,
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*,
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steps: int,
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device: str,
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) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, tuple[float, float], dict[str, dict[str, float]]]:
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source = load_rgba(input_path)
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solver_params = SolverHyperParams()
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inference = infer_lattice(source, None, device=device)
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analysis = analyze_source(source, seed=7)
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torch, F = _require_torch()
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resolved_device = _resolve_device(torch, device)
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premul = premultiply(source)
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height, width = premul.shape[:2]
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cell_x = width / max(1, inference.target_width)
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cell_y = height / max(1, inference.target_height)
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source_t = torch.from_numpy(premul.transpose(2, 0, 1)[None, ...]).to(device=resolved_device, dtype=torch.float32)
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uv0 = _make_regular_uv(
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height=height,
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width=width,
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target_height=inference.target_height,
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target_width=inference.target_width,
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phase_x=inference.phase_x,
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phase_y=inference.phase_y,
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)
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uv0_t = torch.from_numpy(uv0[None, ...]).to(device=resolved_device, dtype=torch.float32)
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offsets_t = torch.from_numpy(
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_make_patch_offsets(height=height, width=width, target_height=inference.target_height, target_width=inference.target_width)
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).to(device=resolved_device, dtype=torch.float32)
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source_edge = analysis.edge_map.astype(np.float32)
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edge_grad_x, edge_grad_y = _edge_gradient_maps(source_edge)
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source_lattice_reference = build_source_lattice_reference(
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source,
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target_width=inference.target_width,
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target_height=inference.target_height,
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phase_x=inference.phase_x,
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phase_y=inference.phase_y,
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alpha_threshold=solver_params.alpha_transparent_threshold,
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edge_hint=source_edge,
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edge_grad_x_hint=edge_grad_x,
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edge_grad_y_hint=edge_grad_y,
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)
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source_detail_reference = _build_source_detail_reference(source, source_lattice_reference, solver_params)
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initial_patches = _sample_cell_patches(F, source_t, uv0_t, offsets_t)
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representative_t, _ = _representative_colors(initial_patches, solver_params)
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representative_t = representative_t.detach()
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source_reference_t = torch.from_numpy(premultiply(source_detail_reference)[None, ...]).to(
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device=resolved_device,
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dtype=torch.float32,
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)
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source_reliability_t = torch.from_numpy(_build_source_reliability(source_lattice_reference, solver_params)[None, ...]).to(
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device=resolved_device,
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dtype=torch.float32,
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)
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source_delta_x_t = (
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torch.from_numpy((premultiply(source_detail_reference)[:, 1:, :] - premultiply(source_detail_reference)[:, :-1, :])[None, ...]).to(device=resolved_device, dtype=torch.float32)
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if inference.target_width > 1
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else None
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)
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source_delta_y_t = (
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torch.from_numpy((premultiply(source_detail_reference)[1:, :, :] - premultiply(source_detail_reference)[:-1, :, :])[None, ...]).to(device=resolved_device, dtype=torch.float32)
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if inference.target_height > 1
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else None
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)
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source_delta_diag_t = (
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torch.from_numpy((premultiply(source_detail_reference)[1:, 1:, :] - premultiply(source_detail_reference)[:-1, :-1, :])[None, ...]).to(device=resolved_device, dtype=torch.float32)
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if inference.target_height > 1 and inference.target_width > 1
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else None
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)
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source_delta_anti_t = (
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torch.from_numpy((premultiply(source_detail_reference)[1:, :-1, :] - premultiply(source_detail_reference)[:-1, 1:, :])[None, ...]).to(device=resolved_device, dtype=torch.float32)
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if inference.target_height > 1 and inference.target_width > 1
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else None
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)
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snap_rgba = _snap_output_to_source_pixels(
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torch,
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source_t,
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uv0_t,
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representative_t,
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source_reference_t,
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source_reliability_t,
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source_lattice_reference,
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solver_params,
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cell_x=cell_x,
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cell_y=cell_y,
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)
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snap_t = torch.from_numpy(premultiply(snap_rgba).transpose(2, 0, 1)[None, ...]).to(
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device=resolved_device,
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dtype=torch.float32,
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)
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source_hw = source_t[0].permute(1, 2, 0)
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uv = uv0_t[0]
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representative = representative_t[0]
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anchor = snap_t[0].permute(1, 2, 0)
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output_height = representative.shape[0]
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output_width = representative.shape[1]
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candidate_levels = max(3, int(solver_params.refine_candidate_levels))
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if candidate_levels % 2 == 0:
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candidate_levels += 1
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candidate_extent = max(0.05, float(solver_params.refine_candidate_extent))
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fraction_values = np.linspace(-candidate_extent, candidate_extent, candidate_levels, dtype=np.float32)
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candidate_x, candidate_y, candidate_offset_x, candidate_offset_y = _build_candidate_positions(
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torch,
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uv,
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source_lattice_reference,
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solver_params,
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width=width,
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height=height,
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cell_x=cell_x,
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cell_y=cell_y,
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base_fraction_values=fraction_values,
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)
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candidate_colors = source_hw[candidate_y, candidate_x]
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anchor_energy = (candidate_colors - anchor[..., None, :]).abs().mean(dim=-1)
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rep_energy = (candidate_colors - representative[..., None, :]).abs().mean(dim=-1)
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source_reference_energy = (candidate_colors - source_reference_t[0][..., None, :]).abs().mean(dim=-1)
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alpha_energy = (candidate_colors[..., 3] - anchor[..., None, 3]).abs()
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distance_energy = (candidate_offset_x / max(cell_x, 1e-4)).square() + (candidate_offset_y / max(cell_y, 1e-4)).square()
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match_energy = _reference_match_energy(
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rep_energy,
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source_reference_energy,
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source_reliability_t[0],
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representative_weight=solver_params.refine_representative_match_weight,
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source_weight=solver_params.refine_source_match_weight,
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)
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relax_base_energy = (
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anchor_energy * solver_params.refine_anchor_weight * solver_params.relax_anchor_scale
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+ match_energy * solver_params.refine_representative_weight
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+ alpha_energy * solver_params.refine_alpha_weight
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+ distance_energy * solver_params.refine_distance_weight
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)
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anchor_delta_x = anchor[:, 1:, :] - anchor[:, :-1, :] if output_width > 1 else None
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anchor_delta_y = anchor[1:, :, :] - anchor[:-1, :, :] if output_height > 1 else None
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anchor_delta_diag = anchor[1:, 1:, :] - anchor[:-1, :-1, :] if output_height > 1 and output_width > 1 else None
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anchor_delta_anti = anchor[1:, :-1, :] - anchor[:-1, 1:, :] if output_height > 1 and output_width > 1 else None
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source_weight = solver_params.refine_source_delta_weight
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anchor_weight = 1.0 - source_weight
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def blend(anchor_delta: torch.Tensor | None, source_delta_t: torch.Tensor | None) -> torch.Tensor | None:
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if anchor_delta is None:
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return None
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if source_delta_t is None:
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return anchor_delta
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return anchor_delta * anchor_weight + source_delta_t[0] * source_weight
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selected, _, _, _ = _relax_candidate_selection(
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torch,
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candidate_colors,
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relax_base_energy,
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anchor,
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source_reference_t[0],
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blend(anchor_delta_x, source_delta_x_t),
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blend(anchor_delta_y, source_delta_y_t),
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blend(anchor_delta_diag, source_delta_diag_t),
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blend(anchor_delta_anti, source_delta_anti_t),
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solver_params,
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iterations=solver_params.relax_iterations,
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)
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relaxed_rgba = _select_colors(candidate_colors, selected).detach().cpu().numpy()
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final_rgba, _ = _discrete_refine_output(
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torch,
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F,
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source_t,
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uv0_t,
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representative_t,
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source_reference_t,
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source_reliability_t,
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source_lattice_reference,
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snap_t,
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source_delta_x_t,
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source_delta_y_t,
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source_delta_diag_t,
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source_delta_anti_t,
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solver_params,
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cell_x=cell_x,
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cell_y=cell_y,
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iterations=steps,
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)
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fidelity = {}
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fidelity["snap"] = source_lattice_consistency_breakdown(
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source,
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snap_rgba,
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target_width=inference.target_width,
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target_height=inference.target_height,
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phase_x=inference.phase_x,
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phase_y=inference.phase_y,
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)
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fidelity["relaxed"] = source_lattice_consistency_breakdown(
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source,
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relaxed_rgba,
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target_width=inference.target_width,
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target_height=inference.target_height,
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phase_x=inference.phase_x,
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phase_y=inference.phase_y,
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)
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fidelity["final"] = source_lattice_consistency_breakdown(
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source,
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final_rgba,
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target_width=inference.target_width,
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target_height=inference.target_height,
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phase_x=inference.phase_x,
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phase_y=inference.phase_y,
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)
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if fidelity["final"]["score"] > fidelity["snap"]["score"] + 1e-6:
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final_rgba = snap_rgba
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fidelity["final"] = fidelity["snap"]
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return source, snap_rgba, relaxed_rgba, final_rgba, (cell_x, cell_y), fidelity
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def main() -> None:
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args = _parse_args()
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x0, y0, x1, y1 = args.cell_bbox
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if x1 <= x0 or y1 <= y0:
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raise SystemExit("Invalid --cell-bbox: expected X1 > X0 and Y1 > Y0.")
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source, snap_rgba, relaxed_rgba, final_rgba, (cell_x, cell_y), fidelity = _build_focus_states(
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args.input,
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steps=args.steps,
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device=args.device,
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)
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panel_names = [name.strip().lower() for name in args.panels.split(",") if name.strip()]
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panels: list[Image.Image] = []
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source_bbox = (
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int(np.floor(x0 * cell_x)),
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int(np.floor(y0 * cell_y)),
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int(np.ceil(x1 * cell_x)),
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int(np.ceil(y1 * cell_y)),
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)
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target_size = (x1 - x0, y1 - y0)
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panel_map = {
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"source": _source_panel(source, source_bbox=source_bbox, target_size=target_size, scale=args.scale),
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"snap": _grid_panel(snap_rgba, "Snap", x0=x0, y0=y0, x1=x1, y1=y1, scale=args.scale),
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"relaxed": _grid_panel(relaxed_rgba, "Relaxed", x0=x0, y0=y0, x1=x1, y1=y1, scale=args.scale),
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"final": _grid_panel(final_rgba, "Final", x0=x0, y0=y0, x1=x1, y1=y1, scale=args.scale),
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}
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for name in panel_names:
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if name not in panel_map:
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raise SystemExit(f"Unsupported panel name: {name}")
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panels.append(panel_map[name])
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canvas = Image.new("RGBA", (sum(panel.width for panel in panels), max(panel.height for panel in panels)), (16, 16, 16, 255))
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x = 0
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for panel in panels:
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canvas.alpha_composite(panel, (x, 0))
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x += panel.width
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output_path = Path(args.out)
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output_path.parent.mkdir(parents=True, exist_ok=True)
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canvas.save(output_path)
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print(str(output_path))
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print(
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{
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"cell_bbox": [x0, y0, x1, y1],
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"source_bbox": list(source_bbox),
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"panels": panel_names,
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"scale": args.scale,
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"source_fidelity": fidelity,
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}
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)
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if __name__ == "__main__":
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main()
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