# Stage 54 (v02): kill the rusty crotch patch on the v02 atlas — the focused re-run of what # 36_refill.py did for v01. # # blender --background --python 54_crotch_refill.py -- # # Why not just re-run 36: it rebuilds its mask from masks.npz + 00_welded.blend, and the welded # reference didn't survive the prune. It's also not needed here — the v02 body texture comes from # the 34_v04 master whose garment regions were already patch-filled clean; the ONLY colour # regression is the donor-crotch rust, and that region is a geometric box (the same box 36 used: # |x| < 0.075, 0.340 < z < 0.480, in the 0.98-unit body frame). # # Method is 36's, unchanged in the ways that mattered: # - the fill tone is a HARMONIC solve on the mesh with Dirichlet boundaries (CG, not Jacobi) — # equal to her real skin at the mask edge by construction, and seam-proof across the # torso/leg chart border the crotch straddles; # - grain transplanted from clean skin tiles so it is not a decal; # - normal flattened and rm set to surrounding-skin median over the same texels, which is what # keeps the region featureless. # Texture-only: works directly on the RIGGED master, no re-rig needed. import bpy, sys, os, time import numpy as np argv = sys.argv[sys.argv.index("--") + 1:] BLEND, OUT = argv[0], argv[1] GROW = 3 GRAIN_T = 16 FEATHER = 4 t0 = time.time() def log(m): print(f"[cr54 {time.time()-t0:6.1f}s] {m}", flush=True) bpy.ops.wm.open_mainfile(filepath=BLEND) ob = max([o for o in bpy.data.objects if o.type == 'MESH'], key=lambda o: len(o.data.vertices)) me = ob.data n_v, n_l, n_f = len(me.vertices), len(me.loops), len(me.polygons) co = np.empty(n_v * 3); me.vertices.foreach_get("co", co); co = co.reshape(-1, 3) z = co[:, 2] - co[:, 2].min() log(f"{n_v}v {n_f}f") # ---- the mask: 36's crotch box, slightly extended down the inner thigh, + grow rings ---- mask = (np.abs(co[:, 0]) < 0.075) & (z > 0.320) & (z < 0.480) ev = np.empty(len(me.edges) * 2, dtype=np.int32); me.edges.foreach_get("vertices", ev) ev = ev.reshape(-1, 2) for _ in range(GROW): hit = mask[ev[:, 0]] | mask[ev[:, 1]] mask[ev[hit, 0]] = True mask[ev[hit, 1]] = True log(f"crotch mask: {int(mask.sum())} verts") # ---- images through the material graph ---- src = {} for slot in ob.material_slots: mat = slot.material if not mat or not mat.node_tree: continue for node in mat.node_tree.nodes: if node.type != 'BSDF_PRINCIPLED': continue for sock, key in (("Base Color", "base"), ("Normal", "normal"), ("Roughness", "rm")): if sock not in node.inputs or not node.inputs[sock].links: continue nd = node.inputs[sock].links[0].from_node seen = set() while nd and nd.type != 'TEX_IMAGE' and id(nd) not in seen: seen.add(id(nd)) nxt = None for i in nd.inputs: if i.links: nxt = i.links[0].from_node break nd = nxt if nd and nd.type == 'TEX_IMAGE' and nd.image: src[key] = nd.image base = src["base"] W, H = base.size buf = np.empty(W * H * 4, dtype=np.float32) base.pixels.foreach_get(buf) tex = buf.reshape(H, W, 4) rgb = tex[:, :, :3].astype(np.float64) log(f"atlas '{base.name}' {W}x{H}") loops_v = np.empty(n_l, dtype=np.int32); me.loops.foreach_get("vertex_index", loops_v) uv = np.empty(n_l * 2); me.uv_layers.active.data.foreach_get("uv", uv); uv = uv.reshape(-1, 2) l_start = np.empty(n_f, dtype=np.int32); me.polygons.foreach_get("loop_start", l_start) l_tot = np.empty(n_f, dtype=np.int32); me.polygons.foreach_get("loop_total", l_tot) li = l_start[l_tot == 3] px = np.clip(np.round(uv[:, 0] * (W - 1)).astype(np.int32), 0, W - 1) py = np.clip(np.round(uv[:, 1] * (H - 1)).astype(np.int32), 0, H - 1) lc = rgb[py, px] vc = np.zeros((n_v, 3)) for c in range(3): vc[:, c] = np.bincount(loops_v, weights=lc[:, c], minlength=n_v) vn = np.maximum(np.bincount(loops_v, minlength=n_v), 1) vc /= vn[:, None] # ---- harmonic fill (CG on the graph Laplacian, Dirichlet boundary = her real skin) ---- o_ = np.concatenate([ev[:, 0], ev[:, 1]]) n_ = np.concatenate([ev[:, 1], ev[:, 0]]) deg = np.maximum(np.bincount(o_, minlength=n_v).astype(np.float64), 1.0) mf = mask.astype(np.float64) def A_mul(x): xm = x * mf return (deg * xm - np.bincount(o_, weights=xm[n_], minlength=n_v)) * mf fill = vc.copy() for c in range(3): known = vc[:, c] * (1.0 - mf) b = np.bincount(o_, weights=known[n_], minlength=n_v) * mf x = np.zeros(n_v) r = b - A_mul(x) p = r.copy() rs = float(r @ r); r0 = rs for it in range(4000): if rs <= max(r0 * 1e-12, 1e-20): break Ap = A_mul(p) d_ = float(p @ Ap) if abs(d_) < 1e-30: break al = rs / d_ x += al * p; r -= al * Ap rs2 = float(r @ r) p = r + (rs2 / rs) * p rs = rs2 fill[mask, c] = x[mask] log(f" ch{c}: CG {it+1} iters, residual {np.sqrt(rs/max(r0,1e-30)):.2e}") bnd = mask & (np.bincount(o_, weights=(1.0 - mf)[n_], minlength=n_v) > 0) step = np.abs(fill[bnd] - vc[bnd]).max(axis=1) log(f"boundary agreement: mean {step.mean():.4f} p99 {np.percentile(step,99):.4f}") # ---- rasterise masked faces ---- IDX = np.stack([li, li + 1, li + 2], axis=1) V = loops_v[IDX] face_any = mask[V].any(axis=1) P = np.stack([uv[IDX][:, :, 0] * (W - 1), uv[IDX][:, :, 1] * (H - 1)], axis=2) out = rgb.copy() paint = np.zeros((H, W), dtype=bool) for f in np.nonzero(face_any)[0]: p3 = P[f] x0, x1 = int(p3[:, 0].min()), int(np.ceil(p3[:, 0].max())) y0, y1 = int(p3[:, 1].min()), int(np.ceil(p3[:, 1].max())) if x1 < x0 or y1 < y0 or x1 - x0 > 512 or y1 - y0 > 512: continue det = ((p3[1, 1] - p3[2, 1]) * (p3[0, 0] - p3[2, 0]) + (p3[2, 0] - p3[1, 0]) * (p3[0, 1] - p3[2, 1])) if abs(det) < 1e-12: continue gx, gy = np.meshgrid(np.arange(max(x0, 0), min(x1, W - 1) + 1), np.arange(max(y0, 0), min(y1, H - 1) + 1)) if gx.size == 0: continue a = ((p3[1, 1] - p3[2, 1]) * (gx - p3[2, 0]) + (p3[2, 0] - p3[1, 0]) * (gy - p3[2, 1])) / det b_ = ((p3[2, 1] - p3[0, 1]) * (gx - p3[2, 0]) + (p3[0, 0] - p3[2, 0]) * (gy - p3[2, 1])) / det c_ = 1.0 - a - b_ ins = (a >= -0.02) & (b_ >= -0.02) & (c_ >= -0.02) if not ins.any(): continue aa, bb, cc = a[ins], b_[ins], c_[ins] w = aa * mf[V[f, 0]] + bb * mf[V[f, 1]] + cc * mf[V[f, 2]] col = (aa[:, None] * fill[V[f, 0]] + bb[:, None] * fill[V[f, 1]] + cc[:, None] * fill[V[f, 2]]) yy, xx = gy[ins], gx[ins] hard = w > 0.5 if hard.any(): out[yy[hard], xx[hard]] = col[hard] paint[yy[hard], xx[hard]] = True log(f"repainted {int(paint.sum())} texels ({100.0*paint.mean():.3f}%)") def box(a, r): def b1(v, ax): pad = [(0, 0)] * v.ndim pad[ax] = (r, r) cs = np.cumsum(np.pad(v, pad, mode="edge"), axis=ax) return (np.take(cs, np.arange(2 * r, cs.shape[ax]), axis=ax) - np.take(cs, np.arange(0, cs.shape[ax] - 2 * r), axis=ax)) / (2 * r) return b1(b1(a, 0), 1) # grain transplant grain = np.stack([rgb[:, :, c] - box(rgb[:, :, c], 5) for c in range(3)], axis=2) tone = np.median(out[paint], axis=0) cand = [] for ty in range(0, H - GRAIN_T, GRAIN_T): for tx in range(0, W - GRAIN_T, GRAIN_T): if paint[ty:ty + GRAIN_T, tx:tx + GRAIN_T].any(): continue t = rgb[ty:ty + GRAIN_T, tx:tx + GRAIN_T].reshape(-1, 3) if t.min() < 0.02: continue if np.abs(t.mean(axis=0) - tone).max() < 0.10: cand.append((ty, tx)) rng = np.random.RandomState(11) if cand: for ty in range(0, H - GRAIN_T + 1, GRAIN_T): for tx in range(0, W - GRAIN_T + 1, GRAIN_T): tm = paint[ty:ty + GRAIN_T, tx:tx + GRAIN_T] if not tm.any(): continue sy, sx = cand[rng.randint(len(cand))] out[ty:ty + GRAIN_T, tx:tx + GRAIN_T][tm] += \ grain[sy:sy + GRAIN_T, sx:sx + GRAIN_T][tm] * 0.85 log(f"grain from {len(cand)} tiles") # feather rim a_ = np.ones((H, W)) edge = paint.copy() for k in range(FEATHER): grown = edge.copy() grown[1:-1, 1:-1] |= (edge[:-2, 1:-1] | edge[2:, 1:-1] | edge[1:-1, :-2] | edge[1:-1, 2:]) ring = grown & ~edge a_[ring] = (k + 1) / (FEATHER + 1.0) edge = grown blend = np.where(paint, 1.0, 1.0 - a_)[:, :, None] final = np.clip(out * blend + rgb * (1 - blend), 0, 1) b4 = tex.copy() b4[:, :, :3] = final.astype(np.float32) base.pixels.foreach_set(b4.reshape(-1)) base.pack() # normal flat + rm to surrounding median over the same texels def dil(m, k): g = m.copy() for _ in range(k): n2 = g.copy() n2[1:, :] |= g[:-1, :]; n2[:-1, :] |= g[1:, :] n2[:, 1:] |= g[:, :-1]; n2[:, :-1] |= g[:, 1:] g = n2 return g soft = dil(paint, 2) for key in ("normal", "rm"): if key not in src: continue im = src[key] if tuple(im.size) != (W, H): continue a2 = np.empty(W * H * 4, dtype=np.float32) im.pixels.foreach_get(a2) arr = a2.reshape(H, W, 4) if key == "normal": arr[soft, 0] = 0.5; arr[soft, 1] = 0.5; arr[soft, 2] = 1.0 else: med = np.median(arr[~dil(paint, 8)][:, :3], axis=0) arr[soft, 0] = med[0]; arr[soft, 1] = med[1]; arr[soft, 2] = med[2] im.pixels.foreach_set(arr.reshape(-1)) im.pack() log(f" {key}: {int(soft.sum())} texels neutralised") bpy.ops.wm.save_as_mainfile(filepath=OUT) log(f"WROTE {OUT}") print("CR54_DONE")