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brand: meet Pip, the courier bird (#132)
* docs: add mascot & brand design spec (Pip the courier bird) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * docs: Pip wears a tie - update mascot spec to v5 flight loop Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * docs: add Pip brand PR implementation plan Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * docs(plan): v7 master GIF - drop frame scaling, add enclosed-hole transparency Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * docs(plan): v8 master GIF - fix hole classification (chest stays opaque) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * docs(plan): scrub stale v5 references Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * docs(plan): v10 master GIF - line-fitted envelope border clipping Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * docs(plan): label pipeline as v10 Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * docs(plan): v16 master GIF - targeted removal of gap blob Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * docs(plan): v17 master GIF - drop envelope border clipping, keep blob removal Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * docs(plan): v19 final master GIF - user-approved thin outline repair Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * feat(brand): add Pip mascot assets and regeneration pipeline Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * feat(brand): Pip takes over the README header Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * docs(spec): scrub stale scaling line Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * feat(brand): avatar, social card, and mascot sources (PNG allowlist) The global *.png personal-data rule silently excluded the mascot's source sheets and generated PNGs; allowlist the upstream-controlled assets/mascot/ paths without weakening the fork-protecting rule. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> --------- Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
This commit is contained in:
Mads Lorentzen
co-authored by
Claude Fable 5
Claude Fable 5
parent
8bee3ddb97
commit
09f0417d78
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"""Regenerate pip_flight_loop.gif from sources/chatgpt_tie_sheet.png.
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Pipeline: 2D-cluster the sheet into 6 birds, snap every pixel to the 7-color
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brand palette, order frames by wing centroid (no per-frame rescaling),
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anchor on the beak, flood-fill edge-connected background to transparent.
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Requires: pillow, numpy, scipy (maintainer tooling; not needed by CI).
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"""
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from pathlib import Path
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from PIL import Image
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import numpy as np
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from scipy import ndimage
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HERE = Path(__file__).parent
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SHEET = HERE / "sources" / "chatgpt_tie_sheet.png"
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OUT_GIF = HERE / "pip_flight_loop.gif"
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PAL = np.array([
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(42, 157, 143), (31, 111, 101), (231, 111, 81), (150, 155, 160),
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(255, 255, 255), (230, 57, 70), (34, 51, 59),
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], dtype=int)
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TRANSPARENT = 7
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CANVAS = 560
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im = Image.open(SHEET).convert("RGB")
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a_full = np.asarray(im).astype(int)
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nonwhite_full = (a_full < 245).any(axis=2)
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# 2D clustering: dilate to glue beak tips, label, keep 6 largest, sort by x
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dil = ndimage.binary_dilation(nonwhite_full, iterations=2)
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lab2, n2 = ndimage.label(dil)
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sizes = ndimage.sum(dil, lab2, range(1, n2 + 1))
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keep = np.argsort(sizes)[::-1][:6] + 1
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comps = []
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for k in keep:
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mask = (lab2 == k) & nonwhite_full
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ys, xs = np.where(mask)
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comps.append(dict(mask=mask, x0=xs.min(), x1=xs.max(), y0=ys.min(), y1=ys.max(), cx=xs.mean()))
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comps.sort(key=lambda d: d["cx"])
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assert len(comps) == 6, f"expected 6 birds, found {len(comps)}"
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def snap(arr):
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d = ((arr[:, :, None, :] - PAL[None, None, :, :]) ** 2).sum(-1)
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return PAL[d.argmin(-1)].astype(np.uint8)
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cells = []
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for comp in comps:
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pad = 8
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y0, y1 = max(0, comp["y0"] - pad), comp["y1"] + pad
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x0, x1 = max(0, comp["x0"] - pad), comp["x1"] + pad
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sub = a_full[y0:y1, x0:x1].copy()
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submask = comp["mask"][y0:y1, x0:x1]
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sub[~submask] = (255, 255, 255)
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cells.append(snap(sub))
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def color_mask(arr, ci, tol=10):
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return np.abs(arr.astype(int) - PAL[ci]).sum(-1) < tol
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infos = []
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for i, c in enumerate(cells):
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coral = color_mask(c, 2)
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lab, n = ndimage.label(coral)
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best, besty = None, 10 ** 9
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for k in range(1, n + 1):
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ys, xs = np.where(lab == k)
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if len(ys) < 80:
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continue
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if ys.mean() < besty:
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besty, best = ys.mean(), (xs, ys)
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bx, by = best[0].mean(), best[1].mean()
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dteal = color_mask(c, 1)
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wing_c = float(np.where(dteal)[0].mean()) if dteal.sum() else by
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infos.append(dict(i=i, beak=(bx, by), area=len(best[0]), wing_c=wing_c))
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for d_ in infos:
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# birds in the sheet are size-consistent (within 5%); rescaling by noisy
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# beak measurements caused a visible zoom pulse in an earlier build
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d_["scale"] = 1.0
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d_["wing_rel"] = d_["wing_c"] - d_["beak"][1]
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order_sorted = sorted(infos, key=lambda d: d["wing_rel"])
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seq = [order_sorted[0], order_sorted[2], order_sorted[4], order_sorted[5], order_sorted[3], order_sorted[1]]
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p_frames = []
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for d_ in seq:
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c = cells[d_["i"]]
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h, w = c.shape[:2]
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sc = d_["scale"]
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c2 = np.asarray(Image.fromarray(c).resize((int(w * sc), int(h * sc)), Image.NEAREST))
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beak = (d_["beak"][0] * sc, d_["beak"][1] * sc)
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canvas = np.full((CANVAS, CANVAS, 3), 255, dtype=np.uint8)
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tx, ty = int(CANVAS * 0.72), int(CANVAS * 0.38)
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px, py = int(tx - beak[0]), int(ty - beak[1])
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H2, W2 = c2.shape[:2]
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x0, y0 = max(0, px), max(0, py)
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x1, y1 = min(CANVAS, px + W2), min(CANVAS, py + H2)
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canvas[y0:y1, x0:x1] = c2[y0 - py:y0 - py + (y1 - y0), x0 - px:x0 - px + (x1 - x0)]
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nonwhite = (canvas < 250).any(axis=2)
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lab, n = ndimage.label(nonwhite)
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for k in range(1, n + 1):
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ys, xs = np.where(lab == k)
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cols = canvas[ys, xs].astype(int)
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is_gray = (np.abs(cols - PAL[3]).sum(-1) < 60).mean() > 0.6
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if len(ys) < 400 and ys.mean() < 300 and is_gray:
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canvas[ys, xs] = (255, 255, 255)
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nonwhite[ys, xs] = False
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d2 = ((canvas.astype(int)[:, :, None, :] - PAL[None, None, :, :]) ** 2).sum(-1)
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idx = d2.argmin(-1).astype(np.uint8)
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white = ~nonwhite
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wlab, wn = ndimage.label(white)
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border = set(wlab[0, :]) | set(wlab[-1, :]) | set(wlab[:, 0]) | set(wlab[:, -1])
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border.discard(0)
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idx[np.isin(wlab, list(border))] = TRANSPARENT
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# enclosed white pockets: classify by boundary composition. The chest patch
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# always borders light teal (body interior); the envelope face is >=70%
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# gray/red; true background pockets (between legs, body-envelope gap) are
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# neither. NOTE: do not use outline/dark-teal ratios here - anti-aliased
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# white-to-outline edges quantize to gray and poison those ratios.
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for wl in range(1, wn + 1):
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if wl in border:
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continue
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comp = wlab == wl
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ring = ndimage.binary_dilation(comp, iterations=2) & ~comp & nonwhite
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ridx = idx[ring]
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if len(ridx) == 0:
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continue
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teal = (ridx == 0).sum() / len(ridx) # light teal only
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envelope = np.isin(ridx, [3, 5]).sum() / len(ridx) # gray, red
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if teal >= 0.08:
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continue # chest: real content
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if envelope >= 0.70:
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continue # envelope face: real content, keep whole. (A border-clip
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# was tried here to split merged face+gap components; it misfit
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# tilted envelopes and bit into the face. The merged white reads
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# fine as-is - do not reintroduce clipping.)
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idx[comp] = TRANSPARENT
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# targeted art cleanup: the source sheet has one large outlined teal blob
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# (a vestigial appendage) drawn into the body-envelope gap of one frame.
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# Only fragments >=100px qualify - smaller teal fragments are legitimate
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# pixel-art texture, and generic cleanup rules damage the tie and feet.
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bird_mask = np.isin(idx, [0, 1])
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blab, bn = ndimage.label(bird_mask)
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if bn > 1:
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bsizes = ndimage.sum(bird_mask, blab, range(1, bn + 1))
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bmain = int(np.argmax(bsizes)) + 1
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gray_mask = idx == 3
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glab, gn = ndimage.label(gray_mask)
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a_fit, b_fit = 0.0, 10 ** 6
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if gn:
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gsizes = ndimage.sum(gray_mask, glab, range(1, gn + 1))
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env_gray = glab == (int(np.argmax(gsizes)) + 1)
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cols = np.where(env_gray.any(axis=0))[0]
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tops = env_gray.argmax(axis=0)[cols].astype(float)
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med = np.median(tops)
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good = np.abs(tops - med) <= 20
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if good.sum() >= 10:
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a_fit, b_fit = np.polyfit(cols[good], tops[good], 1)
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else:
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a_fit, b_fit = 0.0, med
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for bk in range(1, bn + 1):
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if bk == bmain or not (100 <= bsizes[bk - 1] < 600):
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continue
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ys3, xs3 = np.where(blab == bk)
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cy, cx = ys3.mean(), xs3.mean()
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border_here = a_fit * cx + b_fit
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if not (border_here - 55 <= cy < border_here):
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continue
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fmask = blab == bk
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ring = ndimage.binary_dilation(fmask, iterations=2) & ~fmask
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if not ring.any() or (idx[ring] == 6).mean() < 0.40:
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continue
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# erase the blob's own outline ring, but protect outline pixels
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# that belong to the body, chest, tie, or feet (anything near the
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# main structures) - an unprotected shell bit the body outline
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protected = ndimage.binary_dilation(
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(blab == bmain) | (idx == 4) | (idx == 2), iterations=2)
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shell = ndimage.binary_dilation(fmask, iterations=3) & (idx == 6) & ~protected
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erased = fmask | shell
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idx[fmask] = TRANSPARENT
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idx[shell] = TRANSPARENT
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# repair: the blob's boundary doubled as the local body outline, so
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# erasing it exposes bare chest white to the background. Repaint the
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# exposed content edge (white/teal within the wound area, near the
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# new transparency) as outline so the bird's underside stays closed.
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near_wound = ndimage.binary_dilation(erased, iterations=6)
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transp = idx == TRANSPARENT
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near_gap = ndimage.binary_dilation(transp, iterations=6)
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repaint = near_wound & near_gap & np.isin(idx, [0, 1, 4])
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idx[repaint] = 6
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p = Image.fromarray(idx, mode="P")
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palette = PAL.astype(np.uint8).flatten().tolist() + [255, 0, 255]
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p.putpalette(palette + [0] * (768 - len(palette)))
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p.info["transparency"] = TRANSPARENT
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p_frames.append(p)
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p_frames[0].save(OUT_GIF, save_all=True, append_images=p_frames[1:],
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duration=110, loop=0, disposal=2, transparency=TRANSPARENT, optimize=False)
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print(f"wrote {OUT_GIF} ({OUT_GIF.stat().st_size} bytes)")
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assert OUT_GIF.stat().st_size < 50_000, "README asset must stay under 50 KB"
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