diff --git a/research/delighting/.gitignore b/research/delighting/.gitignore index 90d6ed7..df8b5d0 100644 --- a/research/delighting/.gitignore +++ b/research/delighting/.gitignore @@ -55,3 +55,11 @@ render047/node_modules/ render047/pnpm-lock.yaml render047/package.json results/047/boards/ + +# report 051 -- regenerable heavy artifacts (embeddings/caches gitignored, meta+metrics committed) +results/051/*.npz +results/051/delight_cache/ +catalog051/__pycache__/ +results/051/*_board.json +results/051/*_perquery.json +results/051/*_gatecurve.json diff --git a/research/delighting/catalog051/build_clean_index.py b/research/delighting/catalog051/build_clean_index.py new file mode 100644 index 0000000..f424b6b --- /dev/null +++ b/research/delighting/catalog051/build_clean_index.py @@ -0,0 +1,86 @@ +#!/usr/bin/env python3 +"""Report 051 — build the DINOv2 retrieval index over the canonical CLEAN CORPUS. + +The clean corpus (report 021/024, results/corpus/clean_manifest.json, 1,281 +images across Bullseye/Oceanside/Youghiogheny/Wissmach) is the registry the app +actually ships. In the 051 benchmark it plays two roles: + (1) a realistic DISTRACTOR pool for realpairs retrieval (the query's true + product is a Delphi SKU not in this corpus; a good index must not rank a + Bullseye look-alike above the real target), and + (2) the substrate for the query-representation ablation and the out-of-catalog + confidence-gate negatives. + +One index entry per image; product key = registry_id (clean corpus is ~1 +canonical image per SKU). Embeddings are gitignored (regenerate with this +script); the meta sidecar is small and committed. + +Raw corpus images are LOCAL-ONLY (gitignored on main); read read-only via the +absolute main-repo path, never copied or committed (report 015/019/021 posture). +""" +import argparse +import json +import os +import sys +import numpy as np + +HERE = os.path.dirname(os.path.abspath(__file__)) +sys.path.insert(0, HERE) +from embed import Embedder # noqa: E402 + +# main-repo checkout holds the gitignored corpus (same convention as +# catalog_texture_audit.resolve_default_registry) +MAIN_REPO = "/Users/dominiquepiche-meunier/Documents/vitraux" +CATALOG_DIR = os.path.join(MAIN_REPO, "frontend", "public", "assets", "catalog_images") +CLEAN_MANIFEST = os.path.join(HERE, "..", "results", "corpus", "clean_manifest.json") +OUT_DIR = os.path.join(HERE, "..", "results", "051") + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--backbone", default="dinov2-small") + ap.add_argument("--manifest", default=CLEAN_MANIFEST) + ap.add_argument("--catalog-dir", default=CATALOG_DIR) + ap.add_argument("--out", default=os.path.join(OUT_DIR, "clean_index_dinov2.npz")) + ap.add_argument("--meta-out", default=os.path.join(OUT_DIR, "clean_index_meta.json")) + ap.add_argument("--limit", type=int, default=None) + args = ap.parse_args() + + man = json.load(open(args.manifest)) + imgs = man["images"] + if args.limit: + imgs = imgs[:args.limit] + + entries, paths, missing = [], [], 0 + for im in imgs: + p = os.path.join(args.catalog_dir, im["file"]) + if not os.path.exists(p): + missing += 1 + continue + entries.append({ + "entry_id": f"clean::{im['registry_id']}", + "product_id": im["registry_id"], + "source": "clean_corpus", + "brand": im["manufacturer"], + "glass_class": im["extractor_class"], + "category": im.get("category"), + "name": im.get("name"), + "confidence": im.get("confidence"), + "file": im["file"], + }) + paths.append(p) + print(f"{len(paths)} images to embed ({missing} missing on disk)") + + emb = Embedder(backbone=args.backbone) + vecs = emb.embed(paths, normalize=True, progress=True) + + os.makedirs(os.path.dirname(args.out), exist_ok=True) + np.savez_compressed(args.out, embeddings=vecs, + entry_ids=np.array([e["entry_id"] for e in entries])) + json.dump({"backbone": args.backbone, "dim": int(vecs.shape[1]), + "n": len(entries), "entries": entries}, + open(args.meta_out, "w"), indent=1) + print(f"wrote {args.out} ({vecs.shape}) and {args.meta_out}") + + +if __name__ == "__main__": + main() diff --git a/research/delighting/catalog051/embed.py b/research/delighting/catalog051/embed.py new file mode 100644 index 0000000..1da5d04 --- /dev/null +++ b/research/delighting/catalog051/embed.py @@ -0,0 +1,100 @@ +#!/usr/bin/env python3 +"""Report 051 — shared self-supervised visual embedder for catalog retrieval. + +DINOv2-small (facebook/dinov2-small, 384-d) via HF transformers on MPS. +Verified runnable in this env before building around it (lab lesson, report 028): +loads in ~10 s, forward pass on MPS, returns a 384-d CLS/pooler embedding. + +The embedder accepts image *paths*, PIL images, or HxWx3 uint8/float arrays, so +the same code embeds catalog swatches, wild query photos, and derived query +representations (delighted-T, luma-quotient) that live only in memory. + +CLIP (openai ViT-B/32) is provided as an alternative backbone behind the same +interface for the backbone-choice ablation. +""" +import os +import sys +import numpy as np +from PIL import Image + +import torch + + +def _to_pil(x): + if isinstance(x, Image.Image): + return x.convert("RGB") + if isinstance(x, str): + return Image.open(x).convert("RGB") + if isinstance(x, np.ndarray): + a = x + if a.dtype != np.uint8: + a = np.clip(a, 0.0, 1.0) * 255.0 if a.max() <= 1.0 + 1e-6 else np.clip(a, 0, 255) + a = a.astype(np.uint8) + if a.ndim == 2: + a = np.stack([a] * 3, axis=-1) + return Image.fromarray(a).convert("RGB") + raise TypeError(f"cannot coerce {type(x)} to PIL image") + + +class Embedder: + def __init__(self, backbone="dinov2-small", device=None, batch_size=16): + self.backbone = backbone + self.batch_size = batch_size + if device is None: + device = "mps" if torch.backends.mps.is_available() else "cpu" + self.device = device + if backbone.startswith("dinov2"): + from transformers import AutoImageProcessor, AutoModel + name = f"facebook/{backbone}" + self.proc = AutoImageProcessor.from_pretrained(name) + self.model = AutoModel.from_pretrained(name).eval().to(device) + self.dim = self.model.config.hidden_size + self._kind = "dinov2" + elif backbone.startswith("clip"): + from transformers import CLIPProcessor, CLIPModel + name = "openai/clip-vit-base-patch32" + self.proc = CLIPProcessor.from_pretrained(name) + self.model = CLIPModel.from_pretrained(name).eval().to(device) + self.dim = self.model.config.projection_dim + self._kind = "clip" + else: + raise ValueError(f"unknown backbone {backbone}") + + @torch.no_grad() + def embed(self, items, normalize=True, progress=False): + """items: iterable of path|PIL|ndarray. Returns (N, dim) float32.""" + items = list(items) + out = np.zeros((len(items), self.dim), dtype=np.float32) + for start in range(0, len(items), self.batch_size): + chunk = items[start:start + self.batch_size] + pil = [_to_pil(x) for x in chunk] + if self._kind == "dinov2": + inp = self.proc(images=pil, return_tensors="pt").to(self.device) + res = self.model(**inp) + # pooler_output = layernorm(CLS); robust global descriptor + feat = res.pooler_output if res.pooler_output is not None else res.last_hidden_state[:, 0] + else: # clip + inp = self.proc(images=pil, return_tensors="pt").to(self.device) + feat = self.model.get_image_features(**inp) + feat = feat.float().cpu().numpy() + out[start:start + len(chunk)] = feat + if progress: + sys.stderr.write(f"\r embedded {start + len(chunk)}/{len(items)}") + sys.stderr.flush() + if progress: + sys.stderr.write("\n") + if normalize: + n = np.linalg.norm(out, axis=1, keepdims=True) + n[n == 0] = 1.0 + out = out / n + return out + + +if __name__ == "__main__": + # self-test + emb = Embedder() + x = (np.random.rand(300, 300, 3) * 255).astype(np.uint8) + v = emb.embed([x, x]) + print(f"backbone={emb.backbone} dim={emb.dim} device={emb.device} " + f"out={v.shape} norm={np.linalg.norm(v[0]):.3f} " + f"selfsim={float(v[0] @ v[1]):.4f}") diff --git a/research/delighting/catalog051/embed_cache.py b/research/delighting/catalog051/embed_cache.py new file mode 100644 index 0000000..641794a --- /dev/null +++ b/research/delighting/catalog051/embed_cache.py @@ -0,0 +1,55 @@ +#!/usr/bin/env python3 +"""Report 051 — path-keyed embedding cache. + +Embeds a set of images once per (backbone, representation) and memoizes to an +npz so index build, query eval, the representation ablation and the confidence +gate all reuse the same vectors. Keyed by "repr::abspath" so raw / delighted-T / +luma-quotient variants of the same file never collide. +""" +import os +import numpy as np +from embed import Embedder + + +class EmbedCache: + def __init__(self, cache_path, backbone="dinov2-small"): + self.cache_path = cache_path + self.backbone = backbone + self.vecs = {} + if os.path.exists(cache_path): + z = np.load(cache_path, allow_pickle=True) + keys = z["keys"] + arr = z["vecs"] + self.vecs = {str(k): arr[i] for i, k in enumerate(keys)} + self._emb = None + + def _embedder(self): + if self._emb is None: + self._emb = Embedder(backbone=self.backbone) + return self._emb + + def get(self, paths, repr_name="raw", transform=None, progress=True): + """Return (N, d) for paths under repr_name. `transform(path)->image` is + applied for non-raw representations (image can be path/PIL/ndarray).""" + keys = [f"{repr_name}::{os.path.abspath(p)}" for p in paths] + missing = [(i, p, k) for i, (p, k) in enumerate(zip(paths, keys)) if k not in self.vecs] + if missing: + emb = self._embedder() + items = [] + for _, p, _ in missing: + items.append(transform(p) if transform is not None else p) + new = emb.embed(items, normalize=True, progress=progress) + for (i, p, k), v in zip(missing, new): + self.vecs[k] = v.astype(np.float32) + self.save() + d = len(next(iter(self.vecs.values()))) + out = np.zeros((len(paths), d), dtype=np.float32) + for i, k in enumerate(keys): + out[i] = self.vecs[k] + return out + + def save(self): + os.makedirs(os.path.dirname(self.cache_path), exist_ok=True) + keys = list(self.vecs.keys()) + arr = np.stack([self.vecs[k] for k in keys]) if keys else np.zeros((0, 384), np.float32) + np.savez_compressed(self.cache_path, keys=np.array(keys), vecs=arr) diff --git a/research/delighting/catalog051/make_board.py b/research/delighting/catalog051/make_board.py new file mode 100644 index 0000000..b46b3ec --- /dev/null +++ b/research/delighting/catalog051/make_board.py @@ -0,0 +1,126 @@ +#!/usr/bin/env python3 +"""Report 051, scope 7 — qualitative board: query photo | top-3 retrieved | correct? + +Rows are stratified (hits, near-misses, hard misses) and rendered as small +DOWNSCALED crops only (raw catalog/realpairs photography is local-only and never +redistributed; small board thumbnails are committed, consistent with prior +reports). A green frame = top-1 correct product; the correct candidate (if in +top-3) gets a green frame; wrong candidates are framed red. +""" +import argparse +import json +import os +from PIL import Image, ImageDraw, ImageFont + +HERE = os.path.dirname(os.path.abspath(__file__)) +OUT_DIR = os.path.join(HERE, "..", "results", "051") +TH = 150 # thumbnail size +PAD = 8 +LABEL_H = 34 + + +def thumb(path, size=TH): + try: + im = Image.open(path).convert("RGB") + except Exception: + im = Image.new("RGB", (size, size), (40, 40, 40)) + im.thumbnail((size, size), Image.LANCZOS) + canvas = Image.new("RGB", (size, size), (25, 25, 25)) + canvas.paste(im, ((size - im.width) // 2, (size - im.height) // 2)) + return canvas + + +def framed(im, color, w=4): + d = ImageDraw.Draw(im) + for i in range(w): + d.rectangle([i, i, im.width - 1 - i, im.height - 1 - i], outline=color) + return im + + +def text_strip(width, lines, bg=(15, 15, 15), fg=(230, 230, 230)): + strip = Image.new("RGB", (width, LABEL_H), bg) + d = ImageDraw.Draw(strip) + try: + font = ImageFont.truetype("/System/Library/Fonts/Supplemental/Arial.ttf", 11) + except Exception: + font = ImageFont.load_default() + for i, ln in enumerate(lines[:2]): + d.text((3, 2 + i * 15), ln[:46], fill=fg, font=font) + return strip + + +def make_row(entry, topk=3): + cells = [] + # query cell + q = thumb(entry["query_path"]) + framed(q, (90, 160, 255)) + lbl = text_strip(TH, [f"QUERY {entry['query_capture']}", + (entry["query_name"] or "")[:40]]) + cells.append((q, lbl)) + for c in entry["candidates"][:topk]: + im = thumb(c.get("path")) + framed(im, (60, 200, 90) if c["correct"] else (210, 70, 70)) + src = "cat" if c.get("source") == "clean_corpus" else "rp" + cells.append((im, text_strip(TH, [f"#{'OK' if c['correct'] else 'x'} {c['score']:.3f} {src}", + (c["name"] or "")[:40]]))) + row_w = len(cells) * TH + (len(cells) + 1) * PAD + row_h = TH + LABEL_H + 2 * PAD + row = Image.new("RGB", (row_w, row_h), (0, 0, 0)) + x = PAD + for im, lbl in cells: + row.paste(im, (x, PAD)) + row.paste(lbl, (x, PAD + TH)) + x += TH + PAD + return row + + +def stratify_board(board, per_q, seed=0): + import random + rng = random.Random(seed) + hits, near, miss = [], [], [] + for b, pq in zip(board, per_q): + r = pq["rank"] + (hits if r == 1 else near if r in (2, 3) else miss).append(b) + for lst in (hits, near, miss): + rng.shuffle(lst) + return hits, near, miss + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--board", default=os.path.join(OUT_DIR, "realpairs_bench_board.json")) + ap.add_argument("--perquery", default=os.path.join(OUT_DIR, "realpairs_bench_perquery.json")) + ap.add_argument("--out", default=os.path.join(OUT_DIR, "board_qualitative.jpg")) + ap.add_argument("--rows", type=int, default=15) + args = ap.parse_args() + + board = json.load(open(args.board)) + per_q = json.load(open(args.perquery)) + hits, near, miss = stratify_board(board, per_q) + n = args.rows + sel = ([("HIT", b) for b in hits[:n // 3]] + + [("NEAR", b) for b in near[:n // 3]] + + [("MISS", b) for b in miss[:n - 2 * (n // 3)]]) + + rows = [make_row(b) for _, b in sel] + if not rows: + print("no rows"); return + W = max(r.width for r in rows) + header_h = 24 + H = header_h + sum(r.height for r in rows) + PAD + board_img = Image.new("RGB", (W, H), (0, 0, 0)) + d = ImageDraw.Draw(board_img) + d.text((6, 6), "051 wild->clean retrieval | blue=query green=correct product red=wrong (cat=corpus distractor, rp=realpairs)", + fill=(200, 200, 200)) + y = header_h + for r in rows: + board_img.paste(r, (0, y)); y += r.height + board_img.save(args.out, quality=88) + print(f"wrote {args.out} ({W}x{H}, {len(rows)} rows: " + f"{len([s for s in sel if s[0]=='HIT'])} hit / " + f"{len([s for s in sel if s[0]=='NEAR'])} near / " + f"{len([s for s in sel if s[0]=='MISS'])} miss)") + + +if __name__ == "__main__": + main() diff --git a/research/delighting/catalog051/realpairs_bench.py b/research/delighting/catalog051/realpairs_bench.py new file mode 100644 index 0000000..26daa5d --- /dev/null +++ b/research/delighting/catalog051/realpairs_bench.py @@ -0,0 +1,272 @@ +#!/usr/bin/env python3 +"""Report 051 — wild->clean catalog retrieval benchmark + confidence gate. + +Query = wild capture (window/shop) of a Delphi realpairs product. +Target = the product's clean captures (closeup/lightbox) in the index. +Index = realpairs reference captures, optionally + the 1,281-image clean corpus + as realistic distractors (a Delphi wild shot must beat Bullseye + look-alikes to be a true positive). + +Metrics: top-1 / top-5 PRODUCT accuracy, per-brand / per-capture / per-class +(opal-caution vs not) / holdout breakdowns. + +Confidence gate: in-catalog vs OUT-OF-CATALOG separation. OOC is simulated by +leave-product-out (drop the query product's reference entries, re-score). A +calibrated threshold on the top-1 cosine yields measured precision/recall for +"confidently in-catalog", plus the prior-independent AUC. Low-confidence falls +back to photo-only detection (study 050) — this script only designs the gate. +""" +import argparse +import json +import os +import numpy as np + +from rp_data import build_image_table, RP +from retrieve import Index, eval_retrieval +from embed_cache import EmbedCache + +HERE = os.path.dirname(os.path.abspath(__file__)) +OUT_DIR = os.path.join(HERE, "..", "results", "051") +CLEAN_IDX = os.path.join(OUT_DIR, "clean_index_dinov2.npz") +CLEAN_META = os.path.join(OUT_DIR, "clean_index_meta.json") + + +def auc(pos, neg): + """Probability a random positive scores above a random negative (Mann-Whitney).""" + pos = np.asarray(pos, float); neg = np.asarray(neg, float) + if len(pos) == 0 or len(neg) == 0: + return float("nan") + allv = np.concatenate([pos, neg]) + order = allv.argsort() + ranks = np.empty_like(order, float) + ranks[order] = np.arange(1, len(allv) + 1) + # average ranks for ties + _, inv, cnt = np.unique(allv, return_inverse=True, return_counts=True) + sums = np.zeros(len(cnt)); np.add.at(sums, inv, ranks) + avg = sums / cnt + ranks = avg[inv] + r_pos = ranks[:len(pos)].sum() + return float((r_pos - len(pos) * (len(pos) + 1) / 2) / (len(pos) * len(neg))) + + +def gate_sweep(pos_scores, neg_scores): + """pos = in-catalog top-1 cosine; neg = out-of-catalog top-1 cosine. + Balanced-prior precision/recall over thresholds. Returns curve + picks.""" + pos = np.asarray(pos_scores, float); neg = np.asarray(neg_scores, float) + ths = np.unique(np.concatenate([pos, neg])) + curve = [] + for t in ths: + tp = int((pos >= t).sum()); fn = int((pos < t).sum()) + fp = int((neg >= t).sum()); tn = int((neg < t).sum()) + prec = tp / (tp + fp) if (tp + fp) else 1.0 + rec = tp / (tp + fn) if (tp + fn) else 0.0 + spec = tn / (tn + fp) if (tn + fp) else 0.0 + curve.append({"t": float(t), "precision": prec, "recall": rec, + "specificity": spec, "youden": rec + spec - 1, + "f1": (2 * prec * rec / (prec + rec)) if (prec + rec) else 0.0}) + # pick: highest-recall threshold achieving >=0.90 precision; and max-Youden + p90 = [c for c in curve if c["precision"] >= 0.90] + pick_p90 = max(p90, key=lambda c: c["recall"]) if p90 else None + pick_j = max(curve, key=lambda c: c["youden"]) + return {"auc": auc(pos, neg), "n_pos": len(pos), "n_neg": len(neg), + "pos_median": float(np.median(pos)), "neg_median": float(np.median(neg)), + "pick_precision90": pick_p90, "pick_youden": pick_j, + "curve": curve} + + +def run(img_root, use_distractors=True, repr_name="raw", transform=None, + cache_path=None, eval_scope="all", tag=""): + products, images = build_image_table(img_root) + refs = [i for i in images if i["role"] == "reference"] + wilds = [i for i in images if i["role"] == "wild"] + scorable = {pid for pid, p in products.items() if p["scorable"]} + # only queries whose product is scorable (has >=1 reference on disk) + wilds = [w for w in wilds if w["product_id"] in scorable] + if eval_scope == "holdout": + wilds = [w for w in wilds if w["holdout"]] + elif eval_scope == "eval_eligible": + wilds = [w for w in wilds if not w["holdout"]] + + cache = EmbedCache(cache_path or os.path.join(OUT_DIR, "rp_embed_cache.npz")) + ref_vecs = cache.get([r["path"] for r in refs], repr_name, transform) + wild_vecs = cache.get([w["path"] for w in wilds], repr_name, transform) + + # ---- index: realpairs references (+ optional clean-corpus distractors) ---- + rp_entries = [{"entry_id": f"rp::{r['product_id']}::{r['image_key']}", + "product_id": r["product_id"], "source": "realpairs", + "brand": r["brand"], "capture_type": r["capture_type"], + "path": r["path"], + "name": products[r["product_id"]]["title"]} for r in refs] + emb_list = [ref_vecs] + ent_list = list(rp_entries) + if use_distractors and os.path.exists(CLEAN_IDX): + cz = np.load(CLEAN_IDX, allow_pickle=True) + cmeta = json.load(open(CLEAN_META))["entries"] + emb_list.append(cz["embeddings"]) + ent_list += cmeta + index = Index(np.concatenate(emb_list, axis=0), ent_list) + + gt = [w["product_id"] for w in wilds] + groupers = { + "brand": [w["brand"] for w in wilds], + "capture": [w["capture_type"] for w in wilds], + "opal_caution": ["opal" if w["opal_streaky_caution"] else "clean_id" for w in wilds], + "holdout": ["holdout" if w["holdout"] else "eval_eligible" for w in wilds], + } + metrics, per_q = eval_retrieval(index, wild_vecs, gt, topk=(1, 5), groupers=groupers) + + # ---- board data: per query, query path + top-5 candidate entries w/ paths ---- + CATALOG_DIR = os.path.join("/Users/dominiquepiche-meunier/Documents/vitraux", + "frontend", "public", "assets", "catalog_images") + + def entry_path(e): + if e.get("path"): + return e["path"] + if e.get("file"): + return os.path.join(CATALOG_DIR, e["file"]) + return None + ranked_full = index.rank_products(wild_vecs, topk=5) + board = [] + for w, r in zip(wilds, ranked_full): + cands = [] + for pid, sc, j in r["ranked"]: + e = index.entries[j] + cands.append({"product_id": pid, "score": round(sc, 4), + "source": e.get("source"), "brand": e.get("brand"), + "name": e.get("name"), "path": entry_path(e), + "correct": pid == w["product_id"]}) + board.append({"query_product_id": w["product_id"], "query_path": w["path"], + "query_capture": w["capture_type"], "query_brand": w["brand"], + "query_name": products[w["product_id"]]["title"], + "opal_caution": w["opal_streaky_caution"], + "candidates": cands}) + + # ---- confidence gate: in-catalog vs out-of-catalog (leave-product-out) ---- + in_cat_scores = [p["best_score"] for p in per_q] # product present + in_cat_correct = [p["best_score"] for p in per_q if p["top1_correct"]] + in_cat_wrong = [p["best_score"] for p in per_q if not p["top1_correct"]] + ooc_scores = [] + for w, wv in zip(wilds, wild_vecs): + idx_loo = index.drop_products([w["product_id"]]) + r = idx_loo.rank_products(wv[None, :], topk=1)[0] + ooc_scores.append(r["best_score"]) + gate = gate_sweep(in_cat_scores, ooc_scores) + # joint "confident AND top-1 correct" at the p90 pick threshold + if gate["pick_precision90"]: + t = gate["pick_precision90"]["t"] + conf = [p for p in per_q if p["best_score"] >= t] + gate["at_p90_threshold"] = { + "t": t, "n_confident": len(conf), + "frac_confident": round(len(conf) / len(per_q), 3) if per_q else 0, + "top1_acc_among_confident": round(np.mean([p["top1_correct"] for p in conf]), 3) if conf else 0, + } + + result = { + "tag": tag, "repr": repr_name, "use_distractors": use_distractors, + "eval_scope": eval_scope, + "n_scorable_products": len(scorable), + "n_reference_entries": len(refs), + "n_wild_queries": len(wilds), + "index_size": index.n, "index_products": index.n_products, + "retrieval": metrics, + "gate": {k: v for k, v in gate.items() if k != "curve"}, + "gate_score_summary": { + "in_catalog_correct_median": float(np.median(in_cat_correct)) if in_cat_correct else None, + "in_catalog_wrong_median": float(np.median(in_cat_wrong)) if in_cat_wrong else None, + "ooc_median": float(np.median(ooc_scores)) if ooc_scores else None, + "n_in_cat_correct": len(in_cat_correct), "n_in_cat_wrong": len(in_cat_wrong), + }, + } + return result, per_q, gate, board + + +def diagnostic_any_capture(img_root, use_distractors=True, cache_path=None): + """Upper-bound diagnostic: target = ANY other capture of the product + (leave-one-image-out), not just clean references. The gap vs the primary + clean-target number isolates the closeup/lightbox 'clean-reference is hard' + penalty from raw same-product matching ability.""" + products, images = build_image_table(img_root) + usable = [i for i in images if i["role"] in ("reference", "wild")] + scorable = {pid for pid, p in products.items() + if (p["n_reference"] + p["n_wild"]) >= 2} + usable = [i for i in usable if i["product_id"] in scorable] + cache = EmbedCache(cache_path or os.path.join(OUT_DIR, "rp_embed_cache.npz")) + vecs = cache.get([i["path"] for i in usable], "raw", None) + entries = [{"entry_id": f"rp::{i['product_id']}::{i['image_key']}", + "product_id": i["product_id"], "source": "realpairs", + "brand": i["brand"], "path": i["path"]} for i in usable] + emb_list, ent_list = [vecs], list(entries) + if use_distractors and os.path.exists(CLEAN_IDX): + cz = np.load(CLEAN_IDX, allow_pickle=True) + emb_list.append(cz["embeddings"]) + ent_list += json.load(open(CLEAN_META))["entries"] + index = Index(np.concatenate(emb_list, 0), ent_list) + # queries = wild only (consistent w/ primary), exclude self row + q_rows = [k for k, i in enumerate(usable) if i["role"] == "wild"] + qv = vecs[q_rows] + excl = q_rows # self row in the (realpairs-first) index is the same position + res = index.rank_products(qv, topk=5, exclude_rows=excl) + gt = [usable[k]["product_id"] for k in q_rows] + caps = [usable[k]["capture_type"] for k in q_rows] + top1 = top5 = 0 + bycap = {} + for r, g, cap in zip(res, gt, caps): + pids = [p for p, _, _ in r["ranked"]] + rank = pids.index(g) + 1 if g in pids else None + d = bycap.setdefault(cap, {"n": 0, "t1": 0, "t5": 0}) + d["n"] += 1 + if rank == 1: + top1 += 1; d["t1"] += 1 + if rank and rank <= 5: + top5 += 1; d["t5"] += 1 + n = len(gt) + return {"mode": "any_capture_leave1out", "n_queries": n, + "top1": round(top1 / n, 4), "top5": round(top5 / n, 4), + "by_capture": {c: {"n": d["n"], "top1": round(d["t1"] / d["n"], 3), + "top5": round(d["t5"] / d["n"], 3)} + for c, d in bycap.items()}} + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--img-root", default=os.path.join(RP, "data", "images")) + ap.add_argument("--repr", default="raw") + ap.add_argument("--no-distractors", action="store_true") + ap.add_argument("--scope", default="all", choices=["all", "holdout", "eval_eligible"]) + ap.add_argument("--out", default=os.path.join(OUT_DIR, "realpairs_bench.json")) + ap.add_argument("--tag", default="raw_distractors") + args = ap.parse_args() + + result, per_q, gate, board = run(args.img_root, use_distractors=not args.no_distractors, + repr_name=args.repr, eval_scope=args.scope, tag=args.tag) + os.makedirs(os.path.dirname(args.out), exist_ok=True) + json.dump(result, open(args.out, "w"), indent=1) + json.dump({"tag": args.tag, "gate_curve": gate["curve"]}, + open(args.out.replace(".json", "_gatecurve.json"), "w")) + json.dump(per_q, open(args.out.replace(".json", "_perquery.json"), "w"), indent=1, default=str) + json.dump(board, open(args.out.replace(".json", "_board.json"), "w"), indent=1, default=str) + + r = result + print(f"\n[{r['tag']}] scope={r['eval_scope']} distractors={r['use_distractors']}") + print(f" products={r['n_scorable_products']} refs={r['n_reference_entries']} " + f"queries={r['n_wild_queries']} index={r['index_size']} ({r['index_products']} prods)") + print(f" RETRIEVAL top1={r['retrieval']['top1']:.3f} top5={r['retrieval']['top5']:.3f}") + g = r["gate"]; gs = r["gate_score_summary"] + print(f" GATE AUC={g['auc']:.3f} in-cat median={g['pos_median']:.3f} OOC median={g['neg_median']:.3f}") + print(f" in-cat correct med={gs['in_catalog_correct_median']} " + f"wrong med={gs['in_catalog_wrong_median']} OOC med={gs['ooc_median']}") + if g.get("pick_precision90"): + pp = g["pick_precision90"]; at = g.get("at_p90_threshold", {}) + print(f" @prec>=0.90: t={pp['t']:.3f} recall={pp['recall']:.3f} " + f"| confident frac={at.get('frac_confident')} top1-acc-among-confident={at.get('top1_acc_among_confident')}") + print(f" per-brand top1: " + + ", ".join(f"{b}:{d['top1_acc']:.2f}(n{d['n']})" + for b, d in sorted(r['retrieval']['breakdowns']['brand'].items()))) + print(f" per-capture top1: " + + ", ".join(f"{c}:{d['top1_acc']:.2f}(n{d['n']})" + for c, d in r['retrieval']['breakdowns']['capture'].items())) + print(f"wrote {args.out}") + + +if __name__ == "__main__": + main() diff --git a/research/delighting/catalog051/relief_name_audit.py b/research/delighting/catalog051/relief_name_audit.py new file mode 100644 index 0000000..24460c3 --- /dev/null +++ b/research/delighting/catalog051/relief_name_audit.py @@ -0,0 +1,160 @@ +#!/usr/bin/env python3 +"""Report 051, scope 6 — per-SKU relief-cache: how much is a METADATA LOOKUP? + +The product idea caches a per-SKU relief preset. For many sheets the +manufacturer already NAMES the surface texture (granite / ripple / waterglass / +glue chip / hammered / muffle / baroque ...), so the preset is a pure +name->preset lookup with no vision needed. This script quantifies that fraction +over (a) the shipped registry, (b) the clean corpus, and (c) the realpairs +benchmark products, and reports the complement — smooth-named sheets that still +need per-photo relief estimation. + +We separate SURFACE-RELIEF texture words (what the relief map is about) from +COLOR/OPACITY words (opalescent, wispy, iridescent, streaky) which describe the +bulk material, not the surface height field, and must NOT be counted as relief +metadata. +""" +import argparse +import json +import os +import re +import collections + +HERE = os.path.dirname(os.path.abspath(__file__)) +MAIN_REPO = "/Users/dominiquepiche-meunier/Documents/vitraux" +REGISTRY = os.path.join(MAIN_REPO, "frontend", "public", "assets", "glass_swatch_registry.json") +CLEAN_MANIFEST = os.path.join(HERE, "..", "results", "corpus", "clean_manifest.json") +REALPAIRS = os.path.join(HERE, "..", "realpairs", "results", "manifest_033.json") +OUT = os.path.join(HERE, "..", "results", "051", "relief_name_audit.json") + +# keyword (regex, matched on lowered text) -> relief family (the cached preset key). +# Ordered; first match wins for the "primary family" tally. +RELIEF_KEYWORDS = [ + (r"glue[\s\-]?chip", "glue_chip"), + (r"herringbone", "herringbone"), + (r"waterglass|water[\s\-]glass", "waterglass"), + (r"granite", "granite"), + (r"ripple", "ripple"), + (r"stipple", "stipple"), + (r"hammer", "hammered"), + (r"corduroy|cord(?![a-z])", "corduroy"), + (r"reed(ed)?(?![a-z])", "reeded"), + (r"flemish", "flemish"), + (r"drape", "drapery"), + (r"seed(y)?(?![a-z])", "seedy"), + (r"pebble", "pebble"), + (r"crackle|crackled", "crackle"), + (r"muffle", "muffle"), + (r"baroque", "baroque"), + (r"moss", "moss"), + (r"chord", "chord"), + (r"wavolite", "wavolite"), + (r"artique", "artique"), + (r"vecchio", "vecchio"), + (r"glacier|aqualite", "glacier"), + (r"rough[\s\-]?roll|rough(?![a-z])", "rough_rolled"), + (r"textured?(?![a-z])", "generic_textured"), +] + +# category strings that are themselves a texture declaration +TEXTURE_CATEGORIES = {"Textured/Baroque", "English Muffle"} + +# words that describe bulk material, NOT surface relief — must not be counted. +NON_RELIEF_CONTROL = [ + (r"opal", "opalescent"), (r"wispy", "wispy"), (r"iridescent", "iridescent"), + (r"streaky", "streaky"), (r"mottle", "mottle"), (r"dichro", "dichroic"), +] + + +def relief_family(text): + t = (text or "").lower() + for pat, fam in RELIEF_KEYWORDS: + if re.search(pat, t): + return fam + return None + + +def audit(records, name_key, cat_key=None, label=""): + """records: list of dicts. Returns a summary.""" + n = len(records) + fam_counts = collections.Counter() + cat_texture = 0 + name_texture = 0 + either = 0 + per_brand = collections.defaultdict(lambda: [0, 0]) # brand -> [n, texture_named] + control = collections.Counter() + examples = collections.defaultdict(list) + for r in records: + nm = r.get(name_key) or "" + fam = relief_family(nm) + cat = (r.get(cat_key) if cat_key else None) + is_cat_tex = cat in TEXTURE_CATEGORIES + brand = r.get("brand") or r.get("manufacturer") or r.get("_brand") or "?" + per_brand[brand][0] += 1 + has_tex = bool(fam) or is_cat_tex + if fam: + name_texture += 1 + fam_counts[fam] += 1 + if len(examples[fam]) < 3: + examples[fam].append(nm[:70]) + if is_cat_tex: + cat_texture += 1 + if has_tex: + either += 1 + per_brand[brand][1] += 1 + for pat, c in NON_RELIEF_CONTROL: + if re.search(pat, nm.lower()): + control[c] += 1 + return { + "label": label, + "n": n, + "name_texture_hits": name_texture, + "category_texture_hits": cat_texture, + "texture_named_either": either, + "texture_named_frac": round(either / n, 4) if n else 0, + "smooth_named_needs_vision": n - either, + "smooth_named_frac": round((n - either) / n, 4) if n else 0, + "relief_family_counts": dict(fam_counts.most_common()), + "family_examples": {k: v for k, v in examples.items()}, + "non_relief_control_counts": dict(control.most_common()), + "per_brand": {b: {"n": v[0], "texture_named": v[1], + "frac": round(v[1] / v[0], 3) if v[0] else 0} + for b, v in sorted(per_brand.items())}, + } + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--out", default=OUT) + args = ap.parse_args() + results = {} + + reg = json.load(open(REGISTRY)) + results["registry"] = audit(reg, "name", "category", "shipped registry (1,269 SKUs)") + + clean = json.load(open(CLEAN_MANIFEST))["images"] + for c in clean: + c["_brand"] = c.get("manufacturer") + results["clean_corpus"] = audit(clean, "name", "category", "clean corpus (1,281 imgs)") + + if os.path.exists(REALPAIRS): + rp = json.load(open(REALPAIRS)) + for p in rp: + p["_brand"] = p.get("brand") + results["realpairs_products"] = audit(rp, "title", None, "realpairs Delphi products (254)") + + os.makedirs(os.path.dirname(args.out), exist_ok=True) + json.dump(results, open(args.out, "w"), indent=1) + + for key, s in results.items(): + print(f"\n=== {s['label']} ===") + print(f" texture-named (relief preset = metadata lookup): " + f"{s['texture_named_either']}/{s['n']} = {s['texture_named_frac']*100:.1f}%") + print(f" via name={s['name_texture_hits']} via category={s['category_texture_hits']}") + print(f" smooth-named (needs per-photo relief): {s['smooth_named_frac']*100:.1f}%") + print(f" top relief families: {list(s['relief_family_counts'].items())[:8]}") + print(f"\nwrote {args.out}") + + +if __name__ == "__main__": + main() diff --git a/research/delighting/catalog051/retrieve.py b/research/delighting/catalog051/retrieve.py new file mode 100644 index 0000000..bc58da1 --- /dev/null +++ b/research/delighting/catalog051/retrieve.py @@ -0,0 +1,118 @@ +#!/usr/bin/env python3 +"""Report 051 — retrieval index + product-level scoring. + +An Index holds L2-normalized embeddings and per-entry metadata. Scoring is +cosine similarity; PRODUCT score = max over that product's entries (max-pool is +the standard multi-view aggregation — a query need only match one of a product's +reference captures). Returns ranked products and the raw best-entry hit for +qualitative boards and the confidence gate. +""" +import json +import numpy as np + + +class Index: + def __init__(self, embeddings, entries): + assert len(embeddings) == len(entries) + self.emb = np.asarray(embeddings, dtype=np.float32) + self.entries = entries + self.product_ids = np.array([e["product_id"] for e in entries]) + # map product_id -> row indices (for product-level max-pool) + self._by_prod = {} + for i, e in enumerate(entries): + self._by_prod.setdefault(e["product_id"], []).append(i) + + @classmethod + def load(cls, npz_path, meta_path): + z = np.load(npz_path, allow_pickle=True) + meta = json.load(open(meta_path)) + return cls(z["embeddings"], meta["entries"]) + + def subset(self, keep_mask): + """Return a new Index over rows where keep_mask is True.""" + idx = np.where(keep_mask)[0] + return Index(self.emb[idx], [self.entries[i] for i in idx]) + + def drop_products(self, product_ids): + """Leave-product-out: return an Index with those products removed.""" + drop = set(product_ids) + mask = np.array([pid not in drop for pid in self.product_ids]) + return self.subset(mask) + + @property + def n(self): + return len(self.entries) + + @property + def n_products(self): + return len(self._by_prod) + + def rank_products(self, qvecs, topk=5, exclude_rows=None): + """qvecs: (Q, d) normalized. Returns list (per query) of dicts: + {ranked: [(product_id, score, best_entry_idx), ...topk], + best_entry_idx, best_score}. Product score = max-pool over entries. + exclude_rows: optional list (len Q) of a row index to suppress for that + query (leave-one-image-out, so a query cannot match its own entry).""" + qvecs = np.asarray(qvecs, dtype=np.float64) + # errstate guard: macOS Accelerate BLAS raises spurious div0/overflow + # FP flags on this matmul even though both operands are verified finite + # and outputs are exact (self-retrieval == 1.0). Data, not precision. + with np.errstate(all="ignore"): + sims = qvecs @ self.emb.astype(np.float64).T # (Q, N) + if exclude_rows is not None: + for q, row in enumerate(exclude_rows): + if row is not None: + sims[q, row] = -np.inf + results = [] + prod_list = list(self._by_prod.items()) + for q in range(sims.shape[0]): + s = sims[q] + # product-level max-pool + prod_scores = [] + for pid, rows in prod_list: + j = rows[int(np.argmax(s[rows]))] + prod_scores.append((pid, float(s[j]), j)) + prod_scores.sort(key=lambda x: -x[1]) + ranked = prod_scores[:topk] + results.append({ + "ranked": ranked, + "best_entry_idx": ranked[0][2], + "best_score": ranked[0][1], + # margin between top-1 and top-2 product (a gate feature) + "margin": (ranked[0][1] - ranked[1][1]) if len(ranked) > 1 else float("nan"), + }) + return results + + +def eval_retrieval(index, qvecs, q_product_ids, topk=(1, 5), groupers=None): + """Compute top-k product accuracy. groupers: dict name-> list[str] parallel + to queries, for per-group breakdowns (brand, class, capture, ...).""" + maxk = max(topk) + res = index.rank_products(qvecs, topk=maxk) + per_q = [] + for r, gt in zip(res, q_product_ids): + ranked_pids = [p for p, _, _ in r["ranked"]] + rank = ranked_pids.index(gt) + 1 if gt in ranked_pids else None + per_q.append({"gt": gt, "ranked_pids": ranked_pids, + "best_score": r["best_score"], "margin": r["margin"], + "top1_correct": rank == 1, + "rank": rank}) + out = {"n_queries": len(per_q)} + for k in topk: + hits = sum(1 for p in per_q if p["rank"] is not None and p["rank"] <= k) + out[f"top{k}"] = round(hits / len(per_q), 4) if per_q else 0.0 + if groupers: + out["breakdowns"] = {} + for gname, gvals in groupers.items(): + byg = {} + for p, gv in zip(per_q, gvals): + d = byg.setdefault(gv, {"n": 0, **{f"top{k}": 0 for k in topk}}) + d["n"] += 1 + for k in topk: + if p["rank"] is not None and p["rank"] <= k: + d[f"top{k}"] += 1 + for gv, d in byg.items(): + for k in topk: + d[f"top{k}_acc"] = round(d[f"top{k}"] / d["n"], 3) if d["n"] else 0.0 + out["breakdowns"][gname] = byg + return out, per_q diff --git a/research/delighting/catalog051/rp_data.py b/research/delighting/catalog051/rp_data.py new file mode 100644 index 0000000..cf96364 --- /dev/null +++ b/research/delighting/catalog051/rp_data.py @@ -0,0 +1,137 @@ +#!/usr/bin/env python3 +"""Report 051 — realpairs benchmark data assembly. + +Reads the restored 033 harvest (per-product images with capture_type), applies +the report-033 contamination screens (dataset card §9.3), reconstructs local +image paths, and splits each product's surviving images into REFERENCE (clean: +closeup/lightbox — the catalog target) and WILD (window/shop — the user's photo). + +wild -> clean is the product-identification direction: query = wild capture, +target = the product's clean capture(s). The frozen 034 holdout tag is attached +per product for comparability (retrieval here is zero-shot / training-free, so +there is no train->test leak — the tag is reported, not used to hide data). +""" +import hashlib +import json +import os + +HERE = os.path.dirname(os.path.abspath(__file__)) +RP = os.path.join(HERE, "..", "realpairs") +FROZEN_MANIFEST = os.path.join(RP, "results", "manifest_033.json") +CONTAM = os.path.join(RP, "results", "contamination_033.json") + +CLEAN_CAPTURES = {"closeup", "lightbox"} +WILD_CAPTURES = {"window", "shop", "shop_held", "standing"} +PRODUCT_KILL_FLAGS = {"non_transmissive_mirror", "multi_sheet_listing"} + + +def image_local_path(img_root, product_id, image_key): + if image_key == "hero": + return os.path.join(img_root, str(product_id), "hero_full.jpg") + if image_key.startswith("gallery_"): + n = image_key.split("_", 1)[1] + return os.path.join(img_root, str(product_id), f"g{n}_full.jpg") + return None + + +def holdout_reserved(product_id): + """Frozen 034 base rule (EVAL_PROTOCOL §3c): sha1(pid)%5==0. (The v1.1 + per-brand top-ups add 3 specific pids; included explicitly.)""" + h = int(hashlib.sha1(str(product_id).encode()).hexdigest(), 16) + topups = {"239270", "203533", "220043"} + return (h % 5 == 0) or (str(product_id) in topups) + + +def load_manifest(manifest_path=None): + return json.load(open(manifest_path or FROZEN_MANIFEST)) + + +def load_contam(contam_path=None): + return json.load(open(contam_path or CONTAM)) + + +def build_image_table(img_root, manifest_path=None, contam_path=None, + require_on_disk=True): + """Returns (products, images) where products is a dict pid->meta and images + is a list of per-image dicts with role in {reference, wild, excluded}.""" + manifest = load_manifest(manifest_path) + contam = load_contam(contam_path) + cprods = contam.get("products", {}) + + products = {} + images = [] + for p in manifest: + pid = str(p["product_id"]) + if p.get("status") != "done": + continue + pflags = set(cprods.get(pid, {}).get("flags", [])) + killed_product = bool(pflags & PRODUCT_KILL_FLAGS) + img_flags = cprods.get(pid, {}).get("images", {}) + pmeta = { + "product_id": pid, "brand": p.get("brand"), "title": p.get("title"), + "opal_streaky_caution": bool(p.get("opal_streaky_caution")), + "holdout": holdout_reserved(pid), + "product_killed": killed_product, + "product_flags": sorted(pflags), + "n_reference": 0, "n_wild": 0, + } + for im in p.get("images", []): + key = im["image_key"] + cap = im.get("capture_type") + path = image_local_path(img_root, pid, key) + on_disk = bool(path) and os.path.exists(path) + im_contam = list(img_flags.get(key, [])) + role = "excluded" + reason = None + if killed_product: + reason = "product_" + ("+".join(sorted(pflags))) + elif im_contam: + reason = "img_" + "+".join(im_contam) + elif cap in CLEAN_CAPTURES: + role = "reference" + elif cap in WILD_CAPTURES: + role = "wild" + else: + reason = f"capture_{cap}" + if require_on_disk and not on_disk: + # cannot embed what is not restored; mark separately + role = "excluded" + reason = (reason + ";" if reason else "") + "not_on_disk" + rec = { + "product_id": pid, "image_key": key, "capture_type": cap, + "capture_conf": im.get("capture_conf"), "path": path, + "on_disk": on_disk, "role": role, "exclude_reason": reason, + "brand": p.get("brand"), + "opal_streaky_caution": pmeta["opal_streaky_caution"], + "holdout": pmeta["holdout"], + } + if role == "reference": + pmeta["n_reference"] += 1 + elif role == "wild": + pmeta["n_wild"] += 1 + images.append(rec) + products[pid] = pmeta + + for pid, pm in products.items(): + pm["scorable"] = pm["n_reference"] >= 1 and pm["n_wild"] >= 1 + return products, images + + +if __name__ == "__main__": + import argparse + ap = argparse.ArgumentParser() + ap.add_argument("--img-root", default=os.path.join(RP, "data", "images")) + ap.add_argument("--manifest", default=None) + args = ap.parse_args() + prods, imgs = build_image_table(args.img_root, manifest_path=args.manifest) + on_disk = [i for i in imgs if i["on_disk"]] + ref = [i for i in imgs if i["role"] == "reference"] + wild = [i for i in imgs if i["role"] == "wild"] + scorable = [p for p in prods.values() if p["scorable"]] + print(f"products: {len(prods)} | scorable (>=1 ref & >=1 wild on disk): {len(scorable)}") + print(f"images: {len(imgs)} total, {len(on_disk)} on disk | " + f"reference={len(ref)} wild={len(wild)}") + print(f"scorable in holdout: {sum(1 for p in scorable if p['holdout'])}") + print(f"scorable opal-caution: {sum(1 for p in scorable if p['opal_streaky_caution'])}") + import collections + print("scorable per brand:", dict(collections.Counter(p['brand'] for p in scorable))) diff --git a/research/delighting/catalog051/run_all.py b/research/delighting/catalog051/run_all.py new file mode 100644 index 0000000..4661d63 --- /dev/null +++ b/research/delighting/catalog051/run_all.py @@ -0,0 +1,109 @@ +#!/usr/bin/env python3 +"""Report 051 — orchestrate the full benchmark suite in one pass. + +Runs, over the restored realpairs: + 1. raw + distractors, scope=all (PRIMARY wild->clean number + gate) + 2. raw, NO distractors (distractor-pool ablation) + 3. delighted-T + distractors (scope 3: does delighting help?) + 4. luma-quotient + distractors (scope 3: cheap normalization) + 5. raw + distractors, scope=holdout (034 holdout comparability) + 6. any-capture leave-one-out diagnostic (isolates the clean-reference gap) + +Writes each run's json under results/051/ and a combined summary_all.json. +""" +import json +import os +import sys +import numpy as np + +HERE = os.path.dirname(os.path.abspath(__file__)) +sys.path.insert(0, HERE) +import realpairs_bench as B +from transforms import DelightCache, luma_quotient +from rp_data import build_image_table, RP + +OUT_DIR = B.OUT_DIR +IMG_ROOT = os.path.join(RP, "data", "images") + + +def all_paths(): + _, images = build_image_table(IMG_ROOT) + return sorted({i["path"] for i in images if i["role"] in ("reference", "wild")}) + + +def save(result, name): + json.dump(result[0], open(os.path.join(OUT_DIR, f"{name}.json"), "w"), indent=1) + json.dump({"gate_curve": result[2]["curve"]}, + open(os.path.join(OUT_DIR, f"{name}_gatecurve.json"), "w")) + json.dump(result[1], open(os.path.join(OUT_DIR, f"{name}_perquery.json"), "w"), + indent=1, default=str) + json.dump(result[3], open(os.path.join(OUT_DIR, f"{name}_board.json"), "w"), + indent=1, default=str) + + +def headline(r): + m = r["retrieval"]; g = r["gate"]; gs = r["gate_score_summary"] + at = g.get("at_p90_threshold", {}) + return {"repr": r["repr"], "distractors": r["use_distractors"], "scope": r["eval_scope"], + "n_queries": r["n_wild_queries"], "index": r["index_size"], + "top1": m["top1"], "top5": m["top5"], "gate_auc": round(g["auc"], 4), + "in_cat_correct_med": gs["in_catalog_correct_median"], + "in_cat_wrong_med": gs["in_catalog_wrong_median"], + "ooc_med": gs["ooc_median"], + "p90_recall": (g.get("pick_precision90") or {}).get("recall"), + "p90_thresh": (g.get("pick_precision90") or {}).get("t"), + "confident_frac": at.get("frac_confident"), + "top1_among_confident": at.get("top1_acc_among_confident")} + + +def main(): + summary = {"runs": {}} + + print("== 1. raw + distractors (PRIMARY) ==") + r1 = B.run(IMG_ROOT, use_distractors=True, repr_name="raw", tag="raw_distractors") + save(r1, "bench_raw_distractors"); summary["runs"]["raw_distractors"] = headline(r1[0]) + + print("== 2. raw, no distractors ==") + r2 = B.run(IMG_ROOT, use_distractors=False, repr_name="raw", tag="raw_nodistract") + save(r2, "bench_raw_nodistract"); summary["runs"]["raw_nodistract"] = headline(r2[0]) + + print("== 3. delighted-T + distractors ==") + dc = DelightCache(os.path.join(OUT_DIR, "delight_cache")) + paths = all_paths() + print(f" delighting {len(paths)} images (cached)...") + dc.ensure(paths) + r3 = B.run(IMG_ROOT, use_distractors=True, repr_name="delight_T", + transform=dc.transform, tag="delight_distractors") + save(r3, "bench_delight_distractors"); summary["runs"]["delight_distractors"] = headline(r3[0]) + + print("== 4. luma-quotient + distractors ==") + r4 = B.run(IMG_ROOT, use_distractors=True, repr_name="luma_quotient", + transform=luma_quotient, tag="quotient_distractors") + save(r4, "bench_quotient_distractors"); summary["runs"]["quotient_distractors"] = headline(r4[0]) + + print("== 5. raw + distractors, holdout ==") + r5 = B.run(IMG_ROOT, use_distractors=True, repr_name="raw", + eval_scope="holdout", tag="raw_holdout") + save(r5, "bench_raw_holdout"); summary["runs"]["raw_holdout"] = headline(r5[0]) + + print("== 6. any-capture leave-1-out diagnostic ==") + diag = B.diagnostic_any_capture(IMG_ROOT, use_distractors=True) + summary["runs"]["any_capture_diag"] = diag + + # per-brand / per-capture / per-class breakdowns from the primary run + summary["primary_breakdowns"] = r1[0]["retrieval"]["breakdowns"] + json.dump(summary, open(os.path.join(OUT_DIR, "summary_all.json"), "w"), indent=1) + + print("\n===== SUMMARY =====") + for k, v in summary["runs"].items(): + if k == "any_capture_diag": + print(f" {k:22s} top1={v['top1']:.3f} top5={v['top5']:.3f} (n={v['n_queries']})") + else: + print(f" {k:22s} top1={v['top1']:.3f} top5={v['top5']:.3f} " + f"auc={v['gate_auc']:.3f} p90rec={v['p90_recall']} " + f"conf_frac={v['confident_frac']} conf_acc={v['top1_among_confident']}") + print(f"\nwrote {os.path.join(OUT_DIR, 'summary_all.json')}") + + +if __name__ == "__main__": + main() diff --git a/research/delighting/catalog051/run_crop_ablation.py b/research/delighting/catalog051/run_crop_ablation.py new file mode 100644 index 0000000..37fcac5 --- /dev/null +++ b/research/delighting/catalog051/run_crop_ablation.py @@ -0,0 +1,46 @@ +#!/usr/bin/env python3 +"""Report 051 — center-crop query ablation (scene-domination hypothesis). + +The primary raw numbers came in low (top-1 ~0.13) with distractors nearly +irrelevant — consistent with the wild photos' global embedding being dominated +by the SCENE (windowsill/trees/racks) rather than the sheet. References are +full-frame sheet texture. If a dumb central crop recovers a large chunk of +accuracy, the product lesson is a sheet-detection/crop stage (or asking the +user to fill the frame), not a better backbone. + +Runs: crop50 / crop50+quotient / crop30, all + distractors, and writes +results/051/summary_crop.json. +""" +import functools +import json +import os +import sys + +HERE = os.path.dirname(os.path.abspath(__file__)) +sys.path.insert(0, HERE) +import realpairs_bench as B +from transforms import center_crop, crop_then_quotient +from run_all import save, headline, IMG_ROOT, OUT_DIR + + +def main(): + summary = {} + runs = [ + ("crop50_distractors", "crop50", functools.partial(center_crop, frac=0.5)), + ("crop50q_distractors", "crop50_quotient", functools.partial(crop_then_quotient, frac=0.5)), + ("crop30_distractors", "crop30", functools.partial(center_crop, frac=0.3)), + ] + for name, repr_name, tf in runs: + print(f"== {name} ==", flush=True) + r = B.run(IMG_ROOT, use_distractors=True, repr_name=repr_name, + transform=tf, tag=name) + save(r, f"bench_{name}") + summary[name] = headline(r[0]) + print(f" top1={summary[name]['top1']:.3f} top5={summary[name]['top5']:.3f} " + f"auc={summary[name]['gate_auc']:.3f}", flush=True) + json.dump(summary, open(os.path.join(OUT_DIR, "summary_crop.json"), "w"), indent=1) + print("wrote summary_crop.json") + + +if __name__ == "__main__": + main() diff --git a/research/delighting/catalog051/transforms.py b/research/delighting/catalog051/transforms.py new file mode 100644 index 0000000..3c90da9 --- /dev/null +++ b/research/delighting/catalog051/transforms.py @@ -0,0 +1,121 @@ +#!/usr/bin/env python3 +"""Report 051 — query/index REPRESENTATIONS for the delighting ablation (scope 3). + +Three representations, all class-free so they apply to arbitrary user photos: + raw — the sRGB image as-is (baseline). + delight_T — the research classical-delighted transmission map + (research/delighting/extract.py, auto class prior, no VLM): + background removed, illumination envelope divided out. + luma_quotient — report 019's deterministic homomorphic luminance quotient + (a single Gaussian-blur log-luminance envelope divided out), + a cheap capture-normalization with no material model. + +Question: does delighting/normalization improve wild->clean retrieval? (A +product-grounded capture-invariance eval either way.) +""" +import hashlib +import os +import subprocess +import sys +import numpy as np +from PIL import Image + +HERE = os.path.dirname(os.path.abspath(__file__)) +DELIGHT_ROOT = os.path.join(HERE, "..", "delighting_root") if False else os.path.dirname(HERE) +EXTRACT = os.path.join(os.path.dirname(HERE), "extract.py") +sys.path.insert(0, os.path.dirname(HERE)) # to import extract's helpers +import extract as ex # noqa: E402 + +PYEXE = os.environ.get("RP_PYEXE", sys.executable) + + +def _key(path): + return hashlib.sha1(os.path.abspath(path).encode()).hexdigest()[:16] + + +class DelightCache: + """Runs extract.py to produce delighted T maps, cached by source-path hash.""" + def __init__(self, cache_dir, size=384): + self.cache_dir = cache_dir + self.size = size + self.link_dir = os.path.join(cache_dir, "_links") + self.out_dir = os.path.join(cache_dir, "T") + os.makedirs(self.link_dir, exist_ok=True) + os.makedirs(self.out_dir, exist_ok=True) + + def _tpath(self, path): + return os.path.join(self.out_dir, f"{_key(path)}_T.png") + + def ensure(self, paths, batch=64): + todo = [p for p in paths if not os.path.exists(self._tpath(p))] + if not todo: + return + # symlink farm with unique names so extract.py's _T.png don't collide + for start in range(0, len(todo), batch): + chunk = todo[start:start + batch] + links = [] + for p in chunk: + lp = os.path.join(self.link_dir, f"{_key(p)}.jpg") + if not os.path.exists(lp): + try: + os.symlink(os.path.abspath(p), lp) + except FileExistsError: + pass + links.append(lp) + cmd = [PYEXE, EXTRACT] + links + ["--no-vlm", "--out", self.out_dir, + "--size", str(self.size)] + subprocess.run(cmd, check=True, stdout=subprocess.DEVNULL, + stderr=subprocess.DEVNULL) + sys.stderr.write(f"\r delighted {min(start+batch,len(todo))}/{len(todo)}") + sys.stderr.flush() + sys.stderr.write("\n") + + def transform(self, path): + tp = self._tpath(path) + if not os.path.exists(tp): + self.ensure([path]) + return np.array(Image.open(tp).convert("RGB")) + + +def luma_quotient(path, size=512): + """Report 019 homomorphic normalization applied to an sRGB photo: + divide linear luminance by its log-Gaussian envelope, keep chroma, re-encode.""" + img = Image.open(path).convert("RGB") + if size: + img.thumbnail((size, size), Image.LANCZOS) + a = np.asarray(img).astype(np.float64) / 255.0 + lin = ex.srgb_to_lin(a) + Y = ex.lum(lin) + env = ex.luminance_envelope_quotient(Y) # positive scalar field + lin_norm = lin / np.clip(env, 1e-3, None)[..., None] + # rescale to keep median luminance ~ original (avoid global brightness drift) + out = ex.lin_to_srgb(np.clip(lin_norm, 0, 1)) + return (np.clip(out, 0, 1) * 255).astype(np.uint8) + + +def center_crop(path, frac=0.5, size=518): + """Central-fraction crop: a hypothesis probe for the scene-domination + failure (wild window/shop photos are mostly background; the sheet tends to + occupy the central region). frac=0.5 keeps the middle 50% per axis.""" + img = Image.open(path).convert("RGB") + w, h = img.size + cw, ch = int(w * frac), int(h * frac) + x0, y0 = (w - cw) // 2, (h - ch) // 2 + img = img.crop((x0, y0, x0 + cw, y0 + ch)) + if size: + img.thumbnail((size, size), Image.LANCZOS) + return np.asarray(img) + + +def crop_then_quotient(path, frac=0.5, size=512): + """Center-crop then 019 luma-quotient — the combined cheap normalization.""" + a = center_crop(path, frac, size=None) + img = Image.fromarray(a) + img.thumbnail((size, size), Image.LANCZOS) + a = np.asarray(img).astype(np.float64) / 255.0 + lin = ex.srgb_to_lin(a) + Y = ex.lum(lin) + env = ex.luminance_envelope_quotient(Y) + lin_norm = lin / np.clip(env, 1e-3, None)[..., None] + out = ex.lin_to_srgb(np.clip(lin_norm, 0, 1)) + return (np.clip(out, 0, 1) * 255).astype(np.uint8) diff --git a/research/delighting/catalog051/vlm_verify051.py b/research/delighting/catalog051/vlm_verify051.py new file mode 100644 index 0000000..cbadc20 --- /dev/null +++ b/research/delighting/catalog051/vlm_verify051.py @@ -0,0 +1,164 @@ +#!/usr/bin/env python3 +"""Report 051, scope 5 — VLM top-k verification as a second-stage precision boost. + +Embedding retrieval gives a shortlist; a VLM ("same physical glass product?") +can rerank it or reject a shortlist that contains no true match. We measure the +ADDED value over embedding top-1 on a stratified ~40-query budget: + A) top-1 correct — VLM should confirm the same product. + B) correct in top-2..5 — rerank opportunity (embedding missed top-1). + C) correct NOT in top-5 — VLM should answer "none" (precision on shortlists + that do not contain the answer — the OOC signal). + +Uses `claude -p` print mode, model sonnet (project memory: batch subcalls must +not inherit/burn the fable limit), --allowedTools Read (the CLI renders image +files as image blocks), read-only single call. Candidate order is shuffled per +query to remove position bias; the shuffle map is recorded. +""" +import argparse +import json +import os +import random +import subprocess +import sys + +HERE = os.path.dirname(os.path.abspath(__file__)) +OUT_DIR = os.path.join(HERE, "..", "results", "051") + +PROMPT = """You are matching stained-glass sheet photographs to a catalog. + +Image 1 is a USER PHOTO of a single glass sheet (often held up to a window or +photographed in a shop, so it may have background, a hand, glare, or a colour +cast from the lighting). + +Images 2..{n} are CATALOG reference photos of candidate glass products (clean +swatch or close-up crops). + +Decide which ONE candidate, if any, is the SAME physical glass PRODUCT as +image 1 — same colour, same opacity/opalescence, and same surface texture / +streak pattern (allowing for different lighting, crop, and scale). Different +colourway or different texture family = not a match. + +Read every image (1..{n}) with the Read tool, then respond with ONLY a JSON +object: {{"choice": , +"confidence": <0.0-1.0>}}. No other text.""" + + +def call_vlm(query_path, candidate_paths, model="sonnet", timeout=300): + paths = [query_path] + candidate_paths + numbered = "\n".join(f"{i+1}. {os.path.abspath(p)}" for i, p in enumerate(paths)) + full = (PROMPT.format(n=len(paths)) + + f"\n\nRead these files in order (1 is the user photo, 2..{len(paths)} " + f"are candidates):\n{numbered}\n\nRespond with ONLY the JSON object.") + cmd = ["claude", "-p", full, "--model", model, + "--allowedTools", "Read", "--output-format", "text"] + try: + proc = subprocess.run(cmd, capture_output=True, text=True, timeout=timeout) + except subprocess.TimeoutExpired: + return None, "timeout" + txt = proc.stdout.strip() + s, e = txt.find("{"), txt.rfind("}") + if s == -1 or e == -1: + return None, txt[:200] + try: + return json.loads(txt[s:e + 1]), None + except Exception: + return None, txt[:200] + + +def stratify(board, per_q, n_budget, seed=0): + rng = random.Random(seed) + # attach rank of gt from per_q (aligned by order) + strata = {"A_top1": [], "B_in_top5": [], "C_miss": []} + for b, pq in zip(board, per_q): + rank = pq["rank"] + if rank == 1: + strata["A_top1"].append(b) + elif rank in (2, 3, 4, 5): + strata["B_in_top5"].append(b) + else: + strata["C_miss"].append(b) + for k in strata: + rng.shuffle(strata[k]) + # aim ~ 40/40/20 split of budget + quota = {"A_top1": int(n_budget * 0.35), "B_in_top5": int(n_budget * 0.4), + "C_miss": n_budget - int(n_budget * 0.35) - int(n_budget * 0.4)} + picked = [] + for k, q in quota.items(): + picked += [(k, b) for b in strata[k][:q]] + return picked, {k: len(v) for k, v in strata.items()} + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--board", default=os.path.join(OUT_DIR, "realpairs_bench_board.json")) + ap.add_argument("--perquery", default=os.path.join(OUT_DIR, "realpairs_bench_perquery.json")) + ap.add_argument("--budget", type=int, default=40) + ap.add_argument("--model", default="sonnet") + ap.add_argument("--out", default=os.path.join(OUT_DIR, "vlm_verify.json")) + ap.add_argument("--seed", type=int, default=0) + args = ap.parse_args() + + board = json.load(open(args.board)) + per_q = json.load(open(args.perquery)) + picked, avail = stratify(board, per_q, args.budget, args.seed) + print(f"strata available: {avail}; running {len(picked)} VLM calls") + + rng = random.Random(args.seed + 1) + records = [] + for i, (stratum, b) in enumerate(picked): + cands = [c for c in b["candidates"] if c.get("path")] + order = list(range(len(cands))) + rng.shuffle(order) + shuffled = [cands[j] for j in order] + resp, err = call_vlm(b["query_path"], [c["path"] for c in shuffled], args.model) + choice_pid, choice_correct = None, None + if resp and isinstance(resp.get("choice"), int): + ch = resp["choice"] + if ch == 0: + choice_pid = "NONE" + elif 2 <= ch <= len(shuffled) + 1: + choice_pid = shuffled[ch - 2]["product_id"] + choice_correct = (choice_pid == b["query_product_id"]) + emb_top1_correct = b["candidates"][0]["correct"] if b["candidates"] else False + gt_in_shortlist = any(c["correct"] for c in cands) + rec = {"stratum": stratum, "query_product_id": b["query_product_id"], + "query_capture": b["query_capture"], "query_brand": b["query_brand"], + "vlm_choice_pid": choice_pid, "vlm_raw": resp, "err": err, + "vlm_correct": choice_correct, "emb_top1_correct": emb_top1_correct, + "gt_in_shortlist": gt_in_shortlist, + "shuffle_order": order} + records.append(rec) + sys.stderr.write(f"\r [{i+1}/{len(picked)}] {stratum} vlm={choice_pid} " + f"correct={choice_correct} err={err is not None} ") + sys.stderr.flush() + json.dump({"records": records}, open(args.out, "w"), indent=1) + sys.stderr.write("\n") + + # aggregate + def acc(rs, key): + rs = [r for r in rs if r["err"] is None] + return round(sum(1 for r in rs if r[key]) / len(rs), 3) if rs else None + ok = [r for r in records if r["err"] is None] + summary = { + "n_total": len(records), "n_ok": len(ok), "n_err": len(records) - len(ok), + "emb_top1_acc": acc(records, "emb_top1_correct"), + "vlm_choice_acc": acc(records, "vlm_correct"), + "by_stratum": {}, + } + for s in ("A_top1", "B_in_top5", "C_miss"): + rs = [r for r in records if r["stratum"] == s] + # C_miss: correct answer is "NONE"; VLM correct if it said NONE + c_none = [r for r in rs if r["err"] is None and r["vlm_choice_pid"] == "NONE"] + summary["by_stratum"][s] = { + "n": len(rs), "emb_top1_acc": acc(rs, "emb_top1_correct"), + "vlm_choice_acc": acc(rs, "vlm_correct"), + "vlm_said_none_frac": round(len(c_none) / max(1, len([r for r in rs if r["err"] is None])), 3), + } + out = {"summary": summary, "records": records} + json.dump(out, open(args.out, "w"), indent=1) + print(json.dumps(summary, indent=1)) + print(f"wrote {args.out}") + + +if __name__ == "__main__": + main() diff --git a/research/delighting/realpairs/classify.py b/research/delighting/realpairs/classify.py index b7923fd..24a5445 100644 --- a/research/delighting/realpairs/classify.py +++ b/research/delighting/realpairs/classify.py @@ -24,7 +24,7 @@ import numpy as np from PIL import Image -UA = "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) research-delighting-030 (internal eval; contact via github)" +UA = "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) research-delighting-033-restore (internal research eval, non-redistributed; contact dompm@hotmail.com)" HERO_URL = "https://images.delphiglass.com/image_new/{id}.jpg" # ~300x300 GALLERY_THUMB_URL = "https://www.delphiglass.com/syscat/image_add/{id}_{n}.jpg" # ~70x55 diff --git a/research/delighting/reports/051-catalog-retrieval.md b/research/delighting/reports/051-catalog-retrieval.md new file mode 100644 index 0000000..a8d73cb --- /dev/null +++ b/research/delighting/reports/051-catalog-retrieval.md @@ -0,0 +1,307 @@ +# 051 — Catalog product retrieval from one photo + confidence gate + +Branch `research/delighting-051-catalog-retrieval`. Code in `../catalog051/` +(`embed.py`, `build_clean_index.py`, `retrieve.py`, `rp_data.py`, +`realpairs_bench.py`, `run_all.py`, `run_crop_ablation.py`, `transforms.py`, +`vlm_verify051.py`, `make_board.py`, `relief_name_audit.py`); committed +evidence in `../results/051/` (summary_all.json, summary_crop.json, per-run +bench_*.json, failure_decomposition.json, relief_name_audit.json, +vlm_verify.json, board_raw.jpg, board_crop50.jpg). Embedding caches and +per-query/curve dumps are gitignored — regenerate with `run_all.py` then +`run_crop_ablation.py` (≈40 min on MPS, fastbook venv). + +**The product question (CTO):** when a user uploads a photo of a glass sheet, +can we RECOGNIZE which catalog product it is, so per-SKU presets (relief, now +report 050's auto-detected procedural presets, plus metadata) can be looked up +— with per-photo material estimation still handling that sheet's specific +colors/streaks? And since users will upload sheets outside the catalog: can we +CALIBRATE a "confidently in-catalog" gate, falling back to photo-only +detection (050) below threshold? + +## 0. Headline + +**Recognition: partially — 1-in-3 top-1 within a 226-product catalog, and only +after a crop stage; the naive full-frame pipeline gets 1-in-8. The calibrated +confidence gate: NO at any useful recall — in-catalog vs out-of-catalog score +distributions are near-inseparable (AUC 0.52–0.57). There IS a tiny +ultra-confident tier (cosine ≥ 0.91, ~3% of queries, 100% top-1 precision +measured) that can auto-confirm; everything else must be presented as +suggestions or fall back to 050.** Delighting the query does NOT help retrieval +— the classical-delighted T actually hurts (§4), and the win that does exist +(center-crop, 2× accuracy) is about composition, not illumination. A VLM +verification stage on the top-5 shortlist adds real precision (§6). Texture +FAMILY is largely solved by metadata + retrieval-of-the-look; the exact-SKU +colorway within a family is the hard, often visually ill-posed part (§5). + +## 1. Setup + +- **Index backbone:** DINOv2-small (`facebook/dinov2-small`, 384-d pooled CLS, + HF transformers, MPS) — verified downloadable + runnable BEFORE building + (report 028 lesson): loads in ~10 s, self-retrieval sanity 1.000. One entry + per image, product score = max-pool over the product's entries, cosine + similarity. +- **Catalog side:** the canonical clean corpus (report 021/024; + 1,281 images, Bullseye/Oceanside/Youghiogheny/Wissmach) embedded as + `results/051/clean_index_dinov2.npz` (gitignored) + committed meta. In the + benchmark it serves as the realistic distractor pool; it is also the index + the shipped app would start from. +- **Benchmark:** the report-033 real cross-capture pairs. Query = a product's + WILD captures (window 444 / shop 230); target = the same product's CLEAN + captures (closeup/lightbox) in the index. All report-033 contamination + screens applied (stock-photo dhash groups, lineup/on-white, finished-product + tail slots, mirror/multi-pack products, suspect same-photo pairs — §9.3 of + the dataset card). **674 wild queries over 176 scorable products; index = + 490 realpairs reference images (226 products with ≥1 clean capture) + the + 1,281 clean-corpus distractors = 1,771 entries / 1,507 products.** +- **Harvest restoration:** the 033 raw images (368 MB, gitignored, local-only) + were not on this machine; maintainer authorized re-running the idempotent + `harvest_033.py` under the original 033 posture (Delphi CDN only, ~0.40 + req/s measured, 62 min, descriptive UA with contact email). **Zero + attrition:** 254/254 products, 1,491/1,491 images, 100% capture-label + agreement with the frozen `manifest_033.json` — the benchmark is exactly the + dataset 033 froze. +- **Holdout discipline:** retrieval here is zero-shot (no training), so the + 034 holdout can be scored without leak; it is reported as a breakdown + (holdout products score slightly ABOVE eval-eligible — 33% vs 25% top-1 at + crop50 — so no optimistic-selection concern). + +## 2. Main result: wild→clean product retrieval + +| run (674 queries) | top-1 | top-5 | gate AUC | +|---|---:|---:|---:| +| raw photo, + distractors (PRIMARY) | 12.6% | 26.3% | 0.523 | +| raw photo, no distractors | 13.5% | 28.8% | 0.524 | +| delighted-T (extract.py), + distractors | **9.8%** | 23.7% | 0.515 | +| 019 luma-quotient, + distractors | 12.2% | 26.4% | 0.523 | +| **center-crop 50%, + distractors** | **26.6%** | **44.4%** | 0.552 | +| center-crop 50% + quotient | 26.9% | 45.7% | 0.553 | +| center-crop 30%, + distractors | 26.7% | 44.2% | 0.565 | +| center-crop 50%, no distractors | **33.2%** | **54.7%** | 0.566 | + +Reading order matters here: + +1. **The naive pipeline is weak (12.6%)** — but not because the catalog + confuses it: removing all 1,281 clean-corpus distractors buys back less + than one point. The problem is the query side. +2. **A dumb central crop DOUBLES accuracy.** Wild captures carry windowsills, + trees, racks and hands; DINOv2's global embedding is scene-dominated. + Cropping to the central 50% (where Delphi's house style — and most users + photographing "their sheet" — put the glass) recovers more than every + illumination treatment combined. **The product lesson: a sheet-detection / + crop stage (or UI guidance to fill the frame) is the single + highest-leverage component**, worth more than any delighting. +3. **At realistic catalog scale the pool costs ~6.6 points** (33.2 → 26.6) + — after cropping to pure texture, cross-brand look-alikes in the corpus + steal 34% of top-1s (vs 8% pre-crop; `failure_decomposition.json`). + Catalog growth will keep eroding exact-SKU top-1. +4. **No clean-reference penalty:** the any-capture leave-one-image-out + diagnostic (target = ANY other capture of the product, 718 queries) scores + 12.8%/28.4% — the same as clean-target. Matching a wild capture to + ANYTHING else of the same sheet is the hard part; that the reference is a + clean studio-ish closeup costs nothing extra. + +Per-brand (crop50, top-1): uro 33% (n137), clear-textured 31% (n240), kokomo +30% (n30), tiffany-today 28% (n161), **wissmach 4% (n76)** — Wissmach's +realpairs presence is almost entirely English Muffle colorways, the +family-colorway confusion case in §5. Window and shop captures score the same +(27%/26%); opal-caution products score no worse than identity-clean ones +(29% vs 25%). + +## 3. The confidence gate — the honest, load-bearing negative + +Out-of-catalog was simulated by leave-product-out: for every query, re-score +with the true product's entries removed from the index; the top-1 cosine that +remains is exactly what an out-of-catalog upload would produce against this +index. Compared against in-catalog top-1 cosines: + +| representation | AUC (in vs out) | in-cat median | OOC median | +|---|---:|---:|---:| +| raw | 0.523 | 0.663 | 0.654 | +| crop50 | 0.552 | 0.727 | 0.709 | +| crop50 no distractors | 0.566 | 0.727 | 0.685 | + +**These distributions overlap almost completely.** The reason is structural, +not a tuning miss: this catalog contains many NEAR-DUPLICATE products +(colorways of one texture line, cross-brand equivalents), so removing the true +product leaves a sibling whose score is nearly as high — out-of-catalog +queries look exactly like in-catalog queries of a sibling SKU. A cosine +threshold cannot tell "right product" from "similar product", and that is the +same reason exact-SKU top-1 is hard. + +What survives calibration: + +- **An ultra-confident auto-confirm tier exists:** at cosine ≥ 0.913 (the + measured precision-0.90 threshold, crop50), 18/674 queries (2.7%) pass and + **all 18 are top-1 correct**. Precision holds; recall is 2.7%. +- The best balanced operating point (Youden) is t = 0.817: 21.5% recall at + 61% precision — **not shippable** as an "in catalog" claim. +- Margin (top1−top2) is no better — sibling SKUs crush it by construction. + +Per the brief's instruction not to ship an uncalibrated heuristic confidence +(the 019-024 library-picker lesson): **the separation is poor and we say so +plainly.** The gate interface that follows honestly from the measurements is a +three-tier design, not a binary: + +``` +score >= 0.91 -> auto-confirm SKU (~3% of uploads, measured 100% top-1) +else -> show top-5 as *suggestions* (44% contain the right product + at crop50; VLM verification, §6, can promote one to a + confirm or reject all) +always -> run photo-only detection (050 relief presets + per-photo + T/sigma_s estimation) — retrieval AT BEST adds metadata; it + never gates the photo-only path off +``` + +050's fallback isn't a fallback at this accuracy — it's the primary path, +with retrieval as an opportunistic metadata bonus. (Texture family, though, +often doesn't need the gate at all — §7.) + +## 4. Does delighting help retrieval? No — and it costs + +The research tie-in question, answered as a product-grounded +capture-invariance eval: + +- **Classical-delighted T (extract.py, auto class prior, no VLM, 384 px): + 9.8% top-1 vs 12.6% raw — delighting HURTS by 2.8 points.** The extractor + assumes the frame IS a glass sheet; on wild frames it happily "delights" the + windowsill and foliage, and its illumination-envelope division flattens + exactly the low-frequency color statistics the embedding was using to match. + DINOv2 was trained on natural photos — it is already largely + illumination-robust; preprocessing that alters image statistics away from + the training distribution costs more than the invariance it adds. +- **019 luma-quotient: 12.2% ≈ raw (12.6%), and crop50+quotient (26.9%) ≈ + crop50 (26.6%)** — the cheap homomorphic normalization neither helps nor + hurts; the embedding had already absorbed what it removes. +- **What DOES transfer poorly is composition, not illumination** (crop 2× + win, §2). For retrieval, "delight the query" is the wrong knob; "isolate + the sheet" is the right one. Delighting remains the right tool for its + actual job (material estimation on an already-isolated sheet region — the + T/σ_s per-photo path is untouched by this finding). + +## 5. Failure modes, characterized (failure_decomposition.json + boards) + +- **Exact-SKU vs the-look:** among crop50 misses, top-1 shares the query's + brand 37% and its relief family 23%; 13% is the same product LINE, wrong + colorway. The embedding reliably lands in the right visual neighborhood and + cannot pick the sibling. `board_crop50.jpg` rows 9–11 show it concretely: + an "English Muffle Sussex Green" window shot (green foliage showing THROUGH + clear muffle glass) retrieves Noble Brass / Sage / Emerald Muffle — right + family four times over, wrong colorway every time. +- **Clear glass IS its background** (the known hard case, confirmed): a clear + textured sheet's apparent color is whatever is behind it, so wild captures + of clear products carry a color signal that is pure noise w.r.t. identity. + Texture survives; color misleads. Wissmach's 4% is this failure + concentrated. No 2-D preprocessing fixes it — the information isn't in the + photo; only texture-selective matching (patch-level, color-suppressed) or + the VLM's reasoning partially compensates. +- **Name-collisions / photo reuse:** handled upstream — clean corpus is + hash-deduplicated (021), realpairs applies the 033 dhash stock-photo screen; + the Spectrum→Oceanside relisting duplicates are on the killed side of that + screen. Residual risk: relisting-style near-duplicates ACROSS the two + corpora (a Delphi-sold Wissmach vs our Wissmach corpus) can make a "wrong" + top-1 that is actually the same physical product under another SKU; + measured top-1-steals by the corpus at crop50 (34%) will include some of + these, so the true product-level accuracy is, if anything, slightly + understated. +- **Scale/zoom mismatch** between a whole-sheet window shot and a closeup + reference crop remains unaddressed by global embeddings; multi-scale query + crops or patch-token matching are the obvious next lever (not attempted — + scope). + +## 6. VLM top-5 verification (budget ~40 calls, sonnet) + +`vlm_verify051.py`: one `claude -p` call per query — query photo + its 5 +shortlist candidates as images, forced choice "which candidate is the same +product, or none", candidate order shuffled per call. Stratified sample: +top-1-correct (A), correct-in-top-2..5 (B), correct-not-in-top-5 (C). + +40/40 calls succeeded (zero parse failures). Shortlists came from the PRIMARY +(raw) run — per-stratum rates below are measured there; applying them to +crop50's shortlists is an extrapolation, flagged as such. + +| stratum (n) | embedding top-1 | VLM choice correct | VLM said "none" | +|---|---:|---:|---:| +| A: top-1 was correct (14) | 100% | 78.6% | 0% | +| B: correct at rank 2–5 (16) | 0% | **100%** | 0% | +| C: correct not in top-5 (10) | 0% | 0% (n/a) | **70%** | + +Three measured findings: + +1. **Reranking is where the value is: 16/16 perfect promotion** of the right + candidate out of rank 2–5. Since top-5 recall is ~1.7× top-1 (44.4 vs 26.6 + at crop50), a single VLM call per upload converts most of that gap: + estimated end-to-end top-1 ≈ 0.266·0.786 + 0.178·1.00 ≈ **38.7%** at crop50 + (12-point lift; on raw shortlists, where these rates were measured, the + arithmetic gives 12.6% → 23.6%, near-doubling). +2. **The confirm direction is imperfect (78.6%):** the VLM occasionally + switches away from a correct top-1 to a sibling colorway — the same + ambiguity that limits the embedding limits the judge. Net it is still + strongly positive (its losses on A are much smaller than its gains on B). +3. **"None of these" works at 70% specificity** on hopeless shortlists — the + only measured signal in this study that meaningfully separates + shortlist-doesn't-contain-the-answer from shortlist-does (the cosine gate's + AUC is 0.55). As an out-of-catalog detector it is a second stage, not a + solution: 30% of hopeless shortlists still get a (wrong) pick, and C-stratum + here means "product exists but wasn't retrieved", which only proxies true + out-of-catalog. Cost: one multimodal call (~6 images) per upload, ~10–60 s — + fine as an async enrichment, not for a blocking UI. + +## 7. Per-SKU relief cache: mostly a metadata lookup where it matters + +`relief_name_audit.py` (results in `relief_name_audit.json`), separating +surface-RELIEF words (granite/ripple/waterglass/stipple/glue-chip/muffle/…) +from bulk-material words (opal/wispy/iridescent — NOT relief): + +| population | texture-NAMED (preset = metadata lookup) | smooth-named (needs per-photo relief) | +|---|---:|---:| +| shipped registry (1,269 SKUs) | **19.8%** | 80.2% | +| clean corpus (1,281 imgs) | 19.5% | 80.5% | +| Delphi realpairs (254 products) | **55.9%** | 44.1% | + +Top named families in the registry: stipple 55, granite 44, waterglass 35, +ripple 22, rough-rolled 21, muffle 17, hammered 10 — a ~15-preset dictionary +covers the named fifth of the registry outright. The 80% "smooth-named" +remainder is dominated by double-/thin-rolled cathedrals and opalescents whose +relief is gentle and generic — exactly where 050's auto-detected procedural +presets from the photo are the right source. **Cache design that follows:** +key presets by (manufacturer, texture-family) — populated from metadata for +named SKUs, from 050 detection for the rest; retrieval output (a SKU) resolves +to its (manufacturer, family) key, so even a WRONG-colorway-same-family +retrieval, which is the dominant near-miss mode (§5), still fetches the RIGHT +relief preset. Retrieval accuracy at the family level is far better than at +SKU level, and the preset cache only needs the family. + +## 8. What this means for the product idea + +1. **Ship the crop/sheet-isolation stage first** — it is worth 2× whatever + sits behind it, and it also feeds the per-photo material estimator. +2. **Exact-SKU identification from one wild photo is a suggestions feature, + not an auto-tag** at this accuracy (26.6% top-1 / 44.4% top-5 at realistic + scale): show top-5, let the user confirm; auto-confirm only the ~3% + ultra-confident tier; optionally spend one VLM call to promote/reject (§6). +3. **The CTO's out-of-catalog worry is justified but is the WRONG failure + axis:** out-of-catalog uploads won't be caught by score gating (AUC 0.55) + — they will look like sibling-SKU matches. The safe architecture treats + retrieval output as metadata enrichment on top of an always-on photo-only + path (050 + per-photo T/σ_s), never as a switch that turns that path off. +4. **The relief-preset half of the idea survives even when SKU-recognition + misses**, because relief keys off the texture family (§7) and family-level + agreement is the common case among misses. + +## 9. Limits & reproduction + +- Benchmark queries are Delphi's storefront photography, not real user phone + photos — one house style, mostly well-centered; real uploads are plausibly + HARDER pre-crop and no easier post-crop. Statistics-only same-product pairs + include the 31.5% opal-caution products where same-sheet identity across + captures is unverified (033 §9.4) — those queries score no worse here, but + their "correct" label is product-level, not sheet-level. +- DINOv2-small only; -base or patch-token/multi-crop matching untested + (would be the next iteration alongside a real sheet detector). CLIP path + exists behind the same interface (`embed.py --backbone clip`) but was not + benchmarked — the crop finding reorders priorities ahead of backbone shopping. +- Raw images (corpus + realpairs) are LOCAL-ONLY and gitignored; boards commit + small downscaled thumbnails only, captioned as the manufacturers'/Delphi's + photography (033 posture). +- Reproduce: `run_all.py` → `run_crop_ablation.py` → `vlm_verify051.py` + → `make_board.py` (fastbook venv python; ~40 min + VLM calls). diff --git a/research/delighting/results/051/bench_crop30_distractors.json b/research/delighting/results/051/bench_crop30_distractors.json new file mode 100644 index 0000000..77336bb --- /dev/null +++ b/research/delighting/results/051/bench_crop30_distractors.json @@ -0,0 +1,167 @@ +{ + "tag": "crop30_distractors", + "repr": "crop30", + "use_distractors": true, + "eval_scope": "all", + "n_scorable_products": 176, + "n_reference_entries": 490, + "n_wild_queries": 674, + "index_size": 1771, + "index_products": 1507, + "retrieval": { + "n_queries": 674, + "top1": 0.2671, + "top5": 0.4421, + "breakdowns": { + "brand": { + "wissmach-glass": { + "n": 76, + "top1": 8, + "top5": 15, + "top1_acc": 0.105, + "top5_acc": 0.197 + }, + "clear-textured-glass": { + "n": 240, + "top1": 78, + "top5": 125, + "top1_acc": 0.325, + "top5_acc": 0.521 + }, + "van-gogh-glass": { + "n": 12, + "top1": 2, + "top5": 3, + "top1_acc": 0.167, + "top5_acc": 0.25 + }, + "kokomo-glass": { + "n": 30, + "top1": 9, + "top5": 14, + "top1_acc": 0.3, + "top5_acc": 0.467 + }, + "armstrong-glass": { + "n": 4, + "top1": 0, + "top5": 1, + "top1_acc": 0.0, + "top5_acc": 0.25 + }, + "specialty-finish-glass": { + "n": 2, + "top1": 0, + "top5": 0, + "top1_acc": 0.0, + "top5_acc": 0.0 + }, + "uro-glass": { + "n": 137, + "top1": 38, + "top5": 67, + "top1_acc": 0.277, + "top5_acc": 0.489 + }, + "tiffany-today-glass": { + "n": 161, + "top1": 43, + "top5": 68, + "top1_acc": 0.267, + "top5_acc": 0.422 + }, + 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b/research/delighting/results/051/bench_crop50_distractors.json @@ -0,0 +1,167 @@ +{ + "tag": "crop50_distractors", + "repr": "crop50", + "use_distractors": true, + "eval_scope": "all", + "n_scorable_products": 176, + "n_reference_entries": 490, + "n_wild_queries": 674, + "index_size": 1771, + "index_products": 1507, + "retrieval": { + "n_queries": 674, + "top1": 0.2656, + "top5": 0.4436, + "breakdowns": { + "brand": { + "wissmach-glass": { + "n": 76, + "top1": 3, + "top5": 13, + "top1_acc": 0.039, + "top5_acc": 0.171 + }, + "clear-textured-glass": { + "n": 240, + "top1": 74, + "top5": 123, + "top1_acc": 0.308, + "top5_acc": 0.512 + }, + "van-gogh-glass": { + "n": 12, + "top1": 1, + "top5": 4, + "top1_acc": 0.083, + "top5_acc": 0.333 + }, + "kokomo-glass": { + "n": 30, + "top1": 9, + "top5": 13, + "top1_acc": 0.3, + "top5_acc": 0.433 + }, + "armstrong-glass": { + "n": 4, + "top1": 0, + "top5": 0, + "top1_acc": 0.0, + "top5_acc": 0.0 + }, + "specialty-finish-glass": { + "n": 2, + 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