From b9e501b894138090f8b16dd20ed78d29a97a7be5 Mon Sep 17 00:00:00 2001 From: "prompt.ac/@jeffrey" Date: Mon, 31 Aug 2026 16:00:31 -0400 Subject: [PATCH] =?UTF-8?q?loner:=20gesture-code=20tracer=20=E2=80=94=20on?= =?UTF-8?q?e=20line=20per=20whistlegraph=20gesture?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Read off the footage word by word (pen position + lifts): the opening outline is one unbroken line through six words, each patiently syllable its own hair strand, eyes on pass. The pen now routes along the bridged skeleton graph between time-tracked tip waypoints, so gestures are continuous real ink. Known: head circle shortcuts the chin junction; hair-hub crossings funnel — next round. Claude-Session: https://claude.ai/code/session_01D6RoKYnFGYDmbPEmeu2S99 --- pop/loner/bin/pastiche/trace-strokes.py | 236 ++++++++++++++++-------- pop/loner/viz/wg-strokes.json | 2 +- 2 files changed, 161 insertions(+), 77 deletions(-) diff --git a/pop/loner/bin/pastiche/trace-strokes.py b/pop/loner/bin/pastiche/trace-strokes.py index d96f0b01a5..005c147e8c 100644 --- a/pop/loner/bin/pastiche/trace-strokes.py +++ b/pop/loner/bin/pastiche/trace-strokes.py @@ -54,95 +54,179 @@ def thin(m): return m +# ROUTE the pen along the drawing: geometry is the global skeleton +# graph (bridged across small ink gaps), order is the per-slice pen +# tip tracked through time, and segmentation is the GESTURE TABLE read +# visually off the footage — one whistlegraph gesture can span six +# words, each syllable of "patiently" is its own hair strand. Between +# waypoints the pen walks the skeleton's shortest path, so every line +# is continuous real ink in real drawing order. +from collections import deque + +NBR = [(-1, -1), (0, -1), (1, -1), (-1, 0), + (1, 0), (-1, 1), (0, 1), (1, 1)] + skel = thin(mask) -sy, sx = np.where(skel) -print(f"skeleton: {sy.size} px from {mask.sum()} ink px") - -# 8-connected graph over skeleton pixels -index = {(int(x), int(y)): i for i, (x, y) in enumerate(zip(sx, sy))} -NBR = [(-1, -1), (0, -1), (1, -1), (-1, 0), (1, 0), (-1, 1), (0, 1), (1, 1)] -deg = np.zeros(sy.size, int) -for (x, y), i in index.items(): - deg[i] = sum((x + dx, y + dy) in index for dx, dy in NBR) - -# walk branch segments between nodes (endpoints / junctions) -visited = set() -segments = [] -nodes = [p for p, i in index.items() if deg[i] != 2] -for start in nodes + list(index): # loops (all-deg-2) picked up second - si = index[start] - if deg[si] == 2 and any(start in s for s in ()): # placeholder no-op - pass - for dx, dy in NBR: - nxt = (start[0] + dx, start[1] + dy) - if nxt not in index: - continue - edge = frozenset((start, nxt)) - if edge in visited: +ky, kx = np.where(skel) +pix = list(zip(kx.tolist(), ky.tolist())) +pixset = set(pix) +adj = {p: [q for dx, dy in NBR + if (q := (p[0] + dx, p[1] + dy)) in pixset] for p in pix} + +# bridge skeleton components across small marker gaps (<14px) +comp_id = {} +for p in pix: + if p in comp_id: + continue + cid = len(set(comp_id.values())) + q = deque([p]) + comp_id[p] = cid + while q: + u = q.popleft() + for v in adj[u]: + if v not in comp_id: + comp_id[v] = cid + q.append(v) +P_ARR = np.array(pix, float) +C_ARR = np.array([comp_id[p] for p in pix]) +merged = True +while merged: + merged = False + for ca in np.unique(C_ARR): + A = P_ARR[C_ARR == ca] + B = P_ARR[C_ARR != ca] + if not len(B): continue - path = [start, nxt] - visited.add(edge) - while deg[index[path[-1]]] == 2: - cx, cy = path[-1] - step = None - for ex, ey in NBR: - cand = (cx + ex, cy + ey) - if cand in index and cand != path[-2]: - e2 = frozenset((path[-1], cand)) - if e2 not in visited: - step = cand - visited.add(e2) - break - if step is None: - break - path.append(step) - if len(path) >= 2: - segments.append(path) - -segments = [s for s in segments if len(s) >= 4] # prune spurs -print(f"{len(segments)} branch segments") - -# name + orient each segment by the appear-times along it -bounds = np.array([e["v0"] for e in clock] + [clock[-1]["v1"]]) -named = {k: [] for k in range(len(clock))} -for path in segments: - t = np.array([appear[y, x] / 12.0 for x, y in path]) - t = np.clip(t, 0, 26) - med = float(np.median(t)) - if med <= 2.26: # first-burst ink (pre-zoom-out) = the head arc - k = 0 - else: - k = int(np.searchsorted(bounds, med, "right")) - 1 - k = max(0, min(len(clock) - 1, k)) - u = np.arange(len(t)) - if np.corrcoef(u, t)[0, 1] < 0: # pen moved the other way - path = path[::-1] - t = t[::-1] - named[k].append((float(np.median(t[: max(3, len(t) // 4)])), path)) + d2 = ((A[:, None, :] - B[None, :, :]) ** 2).sum(-1) + i, j = np.unravel_index(d2.argmin(), d2.shape) + if d2[i, j] < 14 ** 2: + a = tuple(int(v) for v in A[i]) + b = tuple(int(v) for v in B[j]) + adj[a].append(b) + adj[b].append(a) + C_ARR[C_ARR == C_ARR[np.where((P_ARR == B[j]).all(1))[0][0]]] = ca + merged = True + break + +# pen-tip waypoints: per 1/12s slice, ink centroid tracked near the +# previous tip (occlusion reveals get ignored) +ys, xs = np.where(mask) +stamps_all = appear[ys, xs].astype(int) +way = [] +# the head arc is one stamp burst (the take opens on a zooming nib +# close-up), so no time order exists inside it — walk it by angle and +# spread synthetic waypoints across "sitting"'s window instead +burst = stamps_all <= 27 +bx, by = xs[burst].astype(float), ys[burst].astype(float) +rad = np.hypot(bx - bx.mean(), by - by.mean()) +ring = np.abs(rad - np.median(rad)) < 20 +bx, by = bx[ring], by[ring] +ang = np.arctan2(by - by.mean(), bx - bx.mean()) +o = np.argsort(ang) +a_sorted = ang[o] +gaps = np.diff(np.concatenate([a_sorted, [a_sorted[0] + 2 * np.pi]])) +o = np.roll(o, -((gaps.argmax() + 1) % len(o))) +bins = [b for b in np.array_split(o, 14) if b.size] +for i, b in enumerate(bins): + t = 0.33 + (1.74 - 0.33) * i / max(1, len(bins) - 1) + way.append((t, (float(bx[b].mean()), float(by[b].mean())))) +prev = way[-1][1] +for st in np.unique(stamps_all): + if st <= 27: + continue + m = stamps_all == st + sx, sy = xs[m].astype(float), ys[m].astype(float) + if prev is not None: + near = np.hypot(sx - prev[0], sy - prev[1]) < 75 + if near.any(): + sx, sy = sx[near], sy[near] + c = (float(sx.mean()), float(sy.mean())) + way.append((st / 12.0, c)) + prev = c + +# snap each waypoint to the skeleton +KD = P_ARR +def snap(c): + return pix[int(((KD - c) ** 2).sum(1).argmin())] + +def route(a, b): + """Shortest path a->b along the (bridged) skeleton.""" + if a == b: + return [a] + par = {a: None} + q = deque([a]) + while q: + u = q.popleft() + for v in adj[u]: + if v not in par: + par[v] = u + if v == b: + path = [b] + while path[-1] != a: + path.append(par[path[-1]]) + return path[::-1] + q.append(v) + return None + +# The gesture code, read off the footage (pen position + lifts per +# word): spans of wordclock entries. +GESTURES = [ + (0, 5, "the loner line"), # sitting curled up in myself i + (6, 6, "inner legs"), # think + (7, 9, "knee to base"), # of a stone + (10, 12, "arm wrap"), # just waiting very + (13, 13, "hair line one"), # pa + (14, 14, "hair line two"), # tient + (15, 15, "hair line three"), # ly + (16, 16, "cross strand"), # for + (17, 17, "cross strands"), # time + (18, 18, "last strands"), # to + (19, 19, "the eyes"), # pass +] strokes = [] -for k, e in enumerate(clock): - subs = [] - for _, path in sorted(named[k], key=lambda r: r[0]): - P = np.array(path, float) - if len(P) >= 3: - P[1:-1] = (P[:-2] + P[1:-1] + P[2:]) / 3 +for k0, k1, name in GESTURES: + v0 = clock[k0]["v0"] if k0 else 0.0 + v1 = clock[k1]["v1"] + pts = [snap(c) for t, c in way if v0 - 0.04 <= t < v1 + 0.04] + subs, cur = [], [] + for i, p in enumerate(pts): + if not cur: + cur = [p] + continue + if p == cur[-1]: + continue + leg = route(cur[-1], p) + euclid = np.hypot(p[0] - cur[-1][0], p[1] - cur[-1][1]) + if leg is None or len(leg) > 5 * euclid + 40: + subs.append(cur) # pen lift + cur = [p] + else: + cur += leg[1:] + if cur: + subs.append(cur) + out = [] + for sub in subs: + P = np.array(sub, float) + if len(P) >= 7: + P[3:-3] = sum(P[i:len(P) - 6 + i] for i in range(7)) / 7 d = np.concatenate([[0], np.cumsum(np.hypot(*np.diff(P, axis=0).T))]) if d[-1] > 5: u = np.arange(0, d[-1] + 2.5, 5.0) P = np.stack([np.interp(u, d, P[:, 0]), np.interp(u, d, P[:, 1])], 1) - subs.append([[round(float(x), 1), round(float(y), 1)] - for x, y in P]) - if not subs: + out.append([[round(float(x), 1), round(float(y), 1)] + for x, y in P]) + if not out: continue + wordspan = " ".join(e["word"] for e in clock[k0:k1 + 1]) strokes.append({ - "name": e["mark"], "word": e["word"], - "v0": e["v0"], "v1": e["v1"], "sub": subs, + "name": name, "word": wordspan, + "v0": clock[k0]["v0"], "v1": v1, "sub": out, }) - print(f'{e["mark"]:16s} {e["word"]:10s} v {e["v0"]:5.2f}-{e["v1"]:5.2f}' - f' {len(subs)} sub, {sum(len(s) for s in subs):3d} pts') + print(f'{name:16s} [{wordspan:28s}] v {clock[k0]["v0"]:5.2f}-{v1:5.2f}' + f' {len(out)} line, {sum(len(s) for s in out):3d} pts') json.dump({"w": W, "h": H, "strokes": strokes}, open(f"{LONER}/viz/wg-strokes.json", "w")) diff --git a/pop/loner/viz/wg-strokes.json b/pop/loner/viz/wg-strokes.json index 1e061ad96a..37a46af201 100644 --- a/pop/loner/viz/wg-strokes.json +++ b/pop/loner/viz/wg-strokes.json @@ -1 +1 @@ -{"w": 452, "h": 698, "strokes": [{"name": "the head arc", "word": "sitting", "v0": 0.33, "v1": 1.74, "sub": [[[100.0, 48.0], [99.0, 48.7], [98.0, 49.0], [97.0, 49.3], [96.0, 50.0]], [[158.0, 53.0], [157.0, 52.3], [156.0, 52.0], [155.0, 52.0], [154.0, 52.0]], [[160.0, 53.0], [161.0, 53.0], [162.0, 53.0], [163.0, 53.3], [164.0, 54.0]], [[159.0, 54.0], [162.6, 57.0]], [[87.0, 56.0], [83.1, 58.9], [79.5, 62.5]], [[168.0, 57.0], [168.7, 58.0], [169.3, 59.0], [169.7, 60.0], [170.0, 61.0]], [[72.0, 73.0], [72.0, 74.0], [71.7, 75.0], [71.0, 76.0], [70.0, 77.0]], [[179.0, 73.0], [181.0, 77.5], [182.4, 81.9], [184.0, 84.0]], [[68.0, 79.0], [66.4, 83.7], [65.0, 88.4], [64.0, 93.3], [63.3, 98.1], [63.0, 103.1], [62.0, 107.9], [62.0, 112.9], [62.0, 117.9], [63.0, 122.8]], [[185.0, 85.0], [186.6, 89.7], [188.0, 94.4]], [[189.0, 98.0], [190.3, 102.8], [191.8, 107.5], [193.4, 112.2], [194.0, 117.1], [195.0, 121.9], [195.6, 126.8], [196.9, 131.6], [197.0, 136.6], [198.0, 141.4]], [[64.0, 126.0], [65.0, 130.8], [66.5, 135.6], [68.0, 139.0]], [[252.0, 128.0], [247.2, 127.0], [242.2, 127.0], [237.2, 127.3]], [[257.0, 130.0], [256.0, 129.3], [255.0, 129.0], [254.0, 129.0]], [[231.0, 130.0], [226.6, 132.3], [222.3, 134.8]], [[259.0, 131.0], [263.2, 133.5], [266.9, 136.9]], [[70.0, 143.0], [71.8, 147.7], [73.8, 152.2], [76.4, 156.4]], [[276.0, 145.0], [279.5, 148.5]], [[207.0, 148.0], [202.4, 149.9]], [[201.0, 151.0], [201.0, 152.0], [201.0, 153.0], [201.0, 154.0], [201.0, 155.0], [201.0, 156.0]], [[294.0, 173.0], [292.0, 168.5], [290.2, 163.8], [287.4, 159.7], [286.0, 157.0]], [[100.0, 177.0], [96.0, 174.0], [92.0, 171.0], [90.0, 169.0]], [[102.0, 178.0], [103.0, 178.7], [104.0, 179.3], [105.0, 179.7], [106.0, 180.0]], [[296.0, 178.0], [296.9, 182.8]], [[108.0, 181.0], [111.0, 185.0]], [[133.0, 187.0], [128.7, 185.0], [123.7, 185.0], [118.7, 185.0], [113.7, 185.0]], [[298.0, 185.0], [298.3, 186.0], [298.7, 187.0], [299.0, 188.0], [299.0, 189.0]], [[300.0, 191.0], [301.4, 195.6], [302.8, 200.4], [304.0, 205.0]], [[307.0, 221.0], [306.7, 216.1], [305.1, 211.3], [305.0, 207.0]], [[308.0, 223.0], [308.9, 227.8], [309.9, 232.7], [310.8, 237.5], [312.0, 242.4], [312.4, 247.3], [313.7, 252.1], [314.0, 257.0], [315.0, 261.9], [316.0, 266.7], [316.9, 271.6], [317.8, 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369.9], [215.0, 374.9], [216.0, 379.8], [216.9, 384.6], [217.0, 389.6], [217.0, 394.6], [217.0, 399.6], [217.0, 404.6], [218.0, 409.4], [219.0, 414.3], [220.0, 419.1], [220.0, 424.1], [220.0, 429.1], [220.4, 434.1], [221.0, 438.9], [221.0, 443.9], [222.0, 448.8], [222.0, 453.8], [222.0, 458.8], [223.0, 463.6], [223.0, 468.6], [224.0, 473.5], [224.0, 478.5], [224.0, 483.5], [224.0, 488.5], [224.1, 493.4], [225.0, 498.3], [225.0, 503.3], [225.0, 508.3], [225.0, 513.3], [225.0, 518.3], [225.0, 523.3], [225.0, 528.3], [226.0, 533.1], [226.0, 538.1], [227.0, 543.0], [227.0, 548.0], [227.0, 553.0], [227.0, 558.0], [227.0, 561.0]], [[295.0, 587.0], [294.0, 588.0], [293.0, 589.0], [292.0, 590.0]]]}, {"name": "spine", "word": "up", "v0": 3.47, "v1": 3.72, "sub": [[[203.0, 307.0], [203.9, 302.2], [205.0, 298.0]], [[203.0, 309.0], [205.0, 313.5], [205.0, 318.5], [205.0, 323.5], [206.0, 328.3], [206.0, 333.3]]]}, {"name": "seat", "word": "in", "v0": 3.72, "v1": 4.22, "sub": [[[208.0, 551.0], [207.0, 555.7], [206.1, 560.6], [205.0, 565.0]]]}, {"name": "base line", "word": "myself", "v0": 4.22, "v1": 5.76, "sub": [[[204.0, 567.0], [202.7, 571.8], [199.2, 575.2], [197.0, 577.0]], [[190.0, 582.0], [194.0, 579.0]], [[101.0, 590.0], [105.6, 591.9], [110.2, 593.7], [115.2, 594.1], [120.0, 595.0], [125.0, 595.0], [130.0, 595.0], [135.0, 595.0], [140.0, 595.0], [145.0, 595.0], [150.0, 595.0], [155.0, 595.0], [160.0, 595.0], [164.8, 593.7]], [[91.0, 584.0], [88.3, 579.9], [87.1, 575.2]], [[86.0, 573.0], [84.0, 568.7], [84.0, 563.7], [84.7, 558.8], [85.7, 554.0], [88.0, 551.0]], [[98.0, 546.0], [93.3, 547.6]], [[100.0, 545.0], [104.9, 544.4], [109.0, 543.0]], [[111.0, 542.0], [116.0, 542.0], [120.8, 541.0], [125.7, 540.4], [130.7, 540.0], [135.6, 539.8], [140.5, 539.0]], [[143.0, 538.0], [148.0, 538.0], [153.0, 538.0], [158.0, 538.0], [162.0, 538.0]], [[189.0, 583.0], [184.7, 585.4], [180.2, 587.6], [175.8, 589.7], [172.0, 591.0]]]}, {"name": "leg", "word": "i", "v0": 5.76, "v1": 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