| """Score Mirage (Cactus) on the synthetic poker table dataset. |
| |
| Modes: "describe" (objects + OCR text lines) and, if the Mirage API supports it, |
| "forms" scoring over card enums. Reports: card-read accuracy (OCR text that |
| matches a card glyph set), card-bbox recall via IoU matching to ground truth, |
| pot-text read rate, and per-image timing. |
| """ |
| import argparse |
| import json |
| import re |
| import time |
| from pathlib import Path |
|
|
|
|
| def iou(a, b): |
| ax0, ay0, ax1, ay1 = a |
| bx0, by0, bx1, by1 = b |
| ix = max(0, min(ax1, bx1) - max(ax0, bx0)) |
| iy = max(0, min(ay1, by1) - max(ay0, by0)) |
| inter = ix * iy |
| if inter <= 0: |
| return 0.0 |
| return inter / (((ax1 - ax0) * (ay1 - ay0)) + ((bx1 - bx0) * (by1 - by0)) - inter) |
|
|
|
|
| def norm_rank(r): |
| return {"T": "10", "A": "A", "K": "K", "Q": "Q", "J": "J"}.get(r, r) |
|
|
|
|
| def card_matches(text, card): |
| """Tolerant: rank (3..10,J..A) and suit symbol both present, any order.""" |
| rank, suit = card[:-1], card[-1] |
| t = text.replace(" ", "").upper() |
| r_ok = rank.upper() in t if rank != "10" else ("10" in t or "T" in t.upper().replace("10", "")) |
| s_ok = suit in text or suit in t |
| return r_ok and s_ok |
|
|
|
|
| def main(): |
| ap = argparse.ArgumentParser() |
| ap.add_argument("--images", default="dataset/images") |
| ap.add_argument("--labels", default="dataset/labels") |
| ap.add_argument("--limit", type=int, default=0, help="0 = all") |
| ap.add_argument("--out", default="results_mirage.json") |
| ap.add_argument("--scale", type=float, default=1.0, |
| help="upscale factor for the image before inference; " |
| "ground-truth bboxes are scaled to match for IoU") |
| ap.add_argument("--sections", default="objects,text") |
| args = ap.parse_args() |
|
|
| from cactus import Mirage |
| model = Mirage() |
| print("mirage loaded", flush=True) |
|
|
| sections = [s for s in args.sections.split(",") if s] |
| img_dir, lbl_dir = Path(args.images), Path(args.labels) |
| names = sorted(p.name for p in img_dir.glob("*.png")) |
| if args.limit: |
| names = names[: args.limit] |
|
|
| |
| per_image = [] |
| card_hits = card_true = iou_hits = iou_true = pot_hits = pot_true = 0 |
| reads = [] |
| t_start = time.time() |
| for i, name in enumerate(names): |
| lab = json.loads((lbl_dir / name.replace(".png", ".json")).read_text()) |
| from PIL import Image as PILImage |
| img = PILImage.open(img_dir / name).convert("RGB") |
| sf = args.scale |
| if sf != 1.0: |
| img = img.resize((int(img.width * sf), int(img.height * sf)), |
| PILImage.LANCZOS) |
| t0 = time.time() |
| try: |
| res = model.describe(img, opts={ |
| "sections": sections, "conf": 0.4, "text_conf": 0.3, |
| "max_objects": 30, "max_text_lines": 30, "max_tokens": 700, |
| "nms": True}) |
| except TypeError: |
| res = model.describe(img, {"sections": sections, "conf": 0.4, |
| "text_conf": 0.3, "max_objects": 30, |
| "max_text_lines": 30, "max_tokens": 700, |
| "nms": True}) |
| dt = time.time() - t0 |
| ocr_lines = [l.get("string", "") for l in (res.get("text") or [])] |
| obj_names = [o.get("name", "") for o in (res.get("objects") or [])] |
| obj_boxes = [o.get("bbox") for o in (res.get("objects") or []) if o.get("bbox")] |
| ocr_boxes = [l.get("bbox") for l in (res.get("text") or []) if l.get("bbox")] |
|
|
| |
| gt_cards = [c["card"] for c in lab["card_bboxes"]] |
| img_hits = 0 |
| img_ious = [] |
| matched = set() |
| for gt in gt_cards: |
| hit = any(card_matches(t, gt) for t in ocr_lines) or \ |
| any(card_matches(t, gt) for t in obj_names) |
| img_hits += hit |
| |
| best = 0.0 |
| best_j = -1 |
| gt_box = next(c["bbox"] for c in lab["card_bboxes"] if c["card"] == gt) |
| if sf != 1.0: |
| gt_box = [v * sf for v in gt_box] |
| for j, ob in enumerate(obj_boxes + ocr_boxes): |
| v = iou(gt_box, ob) if ob else 0.0 |
| if v > best: |
| best, best_j = v, j |
| img_ious.append(best) |
| card_hits += img_hits |
| card_true += len(gt_cards) |
| iou_hits += sum(1 for v in img_ious if v > 0.4) |
| iou_true += len(gt_cards) |
| pot_true += 1 |
| if any(str(lab["pot"]) in t for t in ocr_lines): |
| pot_hits += 1 |
| per_image.append({"image": name, "cards": len(gt_cards), "hits": img_hits, |
| "iou": round(sum(img_ious) / len(img_ious), 3) if img_ious else 0, |
| "sec": round(dt, 1), "ocr": ocr_lines[:8]}) |
| if i < 3: |
| print(f"[{name}] ocr={ocr_lines[:10]} objs={obj_names[:10]} " |
| f"({dt:.1f}s)", flush=True) |
| wall = time.time() - t_start |
| summary = { |
| "names": names[:5], "sections": sections, "scale": args.scale, |
| "card_read_acc": round(card_hits / max(card_true, 1), 3), |
| "card_bbox_recall_iou40": round(iou_hits / max(iou_true, 1), 3), |
| "pot_read_acc": round(pot_hits / max(pot_true, 1), 3), |
| "cards_total": card_true, "sec_per_image": round(wall / len(names), 1), |
| "wall_min": round(wall / 60, 1), |
| } |
| Path(args.out).write_text(json.dumps( |
| {"summary": summary, "per_image": per_image}, indent=1)) |
| print(json.dumps(summary, indent=2), flush=True) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
