Three Kingdoms: Warlord's Fate — complete playable game
- Stylized 3D ink-painting map of China (12 provinces, 32 cities, 17 factions) - Custom warlord creation (8 origins, banner, starting city) or historical factions - City management: 7 buildings, 5 dev tiers, recruitment from levies - Character system: stats, traits, loyalty, relationships, wounds, capture, death, succession - Turn-based tactical battles with formations, stances, hero skills, cinematic 3D replay - Sieges: assault, starvation, bribery, infiltration - Diplomacy with trust memory, alliances, NAPs, trade, marriage, espionage, betrayal - Scripted diverging history (Dong Zhuo, Guandu, Red Cliffs...) + world crises + court events - AI factions with distinct personalities; prisoners (execute/release/recruit/ransom) - Procedural guqin/taiko WebAudio score; save/load; victory + dynasty chronicle screens - View-relative camera controls; headless test suites (smoke, stress, map validator)
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deepseek
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#!/usr/bin/env python3
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"""Programmatic visual review of screenshots: OCR geometry, contrast, palette, composition."""
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import sys, subprocess, csv, io, math
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from PIL import Image
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import numpy as np
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SHOTS = sys.argv[1:]
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LANG = "eng+chi_sim"
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def rel_lum(arr):
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a = arr.astype(np.float64) / 255.0
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def f(c): return np.where(c <= 0.03928, c / 12.92, ((c + 0.055) / 1.055) ** 2.4)
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return 0.2126 * f(a[..., 0]) + 0.7152 * f(a[..., 1]) + 0.0722 * f(a[..., 2])
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def cr(l1, l2):
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hi, lo = max(l1, l2), min(l1, l2)
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return (hi + 0.05) / (lo + 0.05)
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def hexc(rgb):
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return "#%02x%02x%02x" % tuple(int(x) for x in rgb)
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def ocr_lines(img_path):
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"""Return list of (text, x,y,w,h, conf) grouped per line."""
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p = subprocess.run(
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["tesseract", img_path, "stdout", "-l", LANG, "--psm", "11", "tsv"],
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capture_output=True, text=True)
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rows = list(csv.DictReader(io.StringIO(p.stdout), delimiter="\t"))
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lines = {}
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for r in rows:
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try:
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conf = float(r["conf"])
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except Exception:
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continue
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if conf < 35 or not r["text"].strip():
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continue
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key = (r["block_num"], r["par_num"], r["line_num"])
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e = lines.setdefault(key, {"words": [], "x0": 10**9, "y0": 10**9, "x1": -1, "y1": -1, "confs": []})
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x, y, w, h = int(r["left"]), int(r["top"]), int(r["width"]), int(r["height"])
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e["words"].append((r["text"], conf))
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e["confs"].append(conf)
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e["x0"] = min(e["x0"], x); e["y0"] = min(e["y0"], y)
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e["x1"] = max(e["x1"], x + w); e["y1"] = max(e["y1"], y + h)
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out = []
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for k in sorted(lines, key=lambda k: (lines[k]["y0"], lines[k]["x0"])):
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e = lines[k]
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out.append({"text": " ".join(w for w, _ in e["words"]),
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"bbox": (e["x0"], e["y0"], e["x1"] - e["x0"], e["y1"] - e["y0"]),
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"conf": sum(e["confs"]) / len(e["confs"])})
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return out
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def analyze(path):
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im = Image.open(path).convert("RGB")
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W, H = im.size
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arr = np.asarray(im)
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L = rel_lum(arr)
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gray = np.asarray(im.convert("L"))
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print(f"\n{'='*78}\n{path} ({W}x{H})\n{'='*78}")
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# Global tone
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print(f"luminance mean={L.mean():.3f} p5={np.percentile(L,5):.3f} p50={np.percentile(L,50):.3f} "
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f"p95={np.percentile(L,95):.3f} | dark(<0.15)={100*(L<0.15).mean():.0f}% bright(>0.85)={100*(L>0.85).mean():.0f}%")
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# Palette
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q = im.quantize(colors=8, method=Image.Quantize.MEDIANCUT)
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pal = q.getpalette()
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counts = sorted(q.getcolors(W * H), reverse=True)
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tot = W * H
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tops = []
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for cnt, idx in counts[:8]:
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rgb = pal[idx*3:idx*3+3]
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tops.append(f"{hexc(rgb)} {100*cnt/tot:.0f}%")
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print("palette: " + " ".join(tops))
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# Composition grid: 16 cols x 10 rows -> mean luminance (0-9) and dominant hue class letter
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GX, GY = 16, 10
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print("composition grid (mean lum 0-9; hue: R=red/warm Y=yellow/tan G=green C=cyan B=blue M=magenta K=neutral-dark N=neutral-mid W=white-ish):")
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hsv = np.asarray(im.convert("HSV")).astype(int)
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hh, ss, vv = hsv[..., 0], hsv[..., 1], hsv[..., 2]
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for gy in range(GY):
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row = ""
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for gx in range(GX):
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cell = L[gy*H//GY:(gy+1)*H//GY, gx*W//GX:(gx+1)*W//GX]
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m = cell.mean()
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# dominant hue of saturated pixels
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ch = hh[gy*H//GY:(gy+1)*H//GY, gx*W//GX:(gx+1)*W//GX]
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cs = ss[gy*H//GY:(gy+1)*H//GY, gx*W//GX:(gx+1)*W//GX]
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sat = cs > 60
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if m > 0.85: c = "W"
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elif sat.mean() > 0.12:
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ang = np.median(ch[sat]) * 360 / 255
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c = "R" if ang < 25 or ang >= 330 else "Y" if ang < 70 else "G" if ang < 160 else "C" if ang < 200 else "B" if ang < 270 else "M"
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elif m > 0.45: c = "N"
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else: c = "K"
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row += str(min(9, int(m * 10))) + c
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print(" " + row)
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# Low-variance (flat) regions — candidate dead space
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flat = []
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for gy in range(GY):
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for gx in range(GX):
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cell = gray[gy*H//GY:(gy+1)*H//GY, gx*W//GX:(gx+1)*W//GX]
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if cell.std() < 6:
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flat.append((gx, gy, round(float(cell.mean()))))
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print(f"flat cells (std<6): {len(flat)}/{GX*GY}" + (f" at {flat[:12]}" if flat else ""))
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# OCR line analysis
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lines = ocr_lines(path)
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print(f"OCR: {len(lines)} lines (conf>=35)")
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low_contrast, overlaps, tiny = [], [], []
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boxes = []
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for ln in lines:
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x, y, w, h = ln["bbox"]
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if w <= 1 or h <= 1 or x < 0 or y < 0 or x + w > W or y + h > H:
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continue
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pad = 2
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x0, y0 = max(0, x - pad), max(0, y - pad)
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x1, y1 = min(W, x + w + pad), min(H, y + h + pad)
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reg = L[y0:y1, x0:x1]
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lo, hi = np.percentile(reg, 5), np.percentile(reg, 95)
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ratio = cr(hi, lo)
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boxes.append((x, y, w, h, ln["text"]))
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if h < 9 and ln["conf"] > 50:
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tiny.append(ln)
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if ratio < 4.0 and len(ln["text"].strip()) > 1:
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low_contrast.append((ln["text"][:40], (x, y, w, h), round(ratio, 2)))
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# overlap between different lines (>25% of smaller area)
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for i in range(len(boxes)):
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for j in range(i + 1, len(boxes)):
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ax, ay, aw, ah, at = boxes[i]; bx, by, bw, bh, bt = boxes[j]
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ox = max(0, min(ax+aw, bx+bw) - max(ax, bx)); oy = max(0, min(ay+ah, by+bh) - max(ay, by))
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inter = ox * oy
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small = min(aw*ah, bw*bh)
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if small and inter / small > 0.30 and at != bt:
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overlaps.append((at[:28], bt[:28], round(100*inter/small)))
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if low_contrast:
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print("LOW CONTRAST (<4:1):")
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for t, b, r in low_contrast[:14]:
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print(f" {r:>5}:1 @({b[0]},{b[1]},{b[2]}x{b[3]}) '{t}'")
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if len(low_contrast) > 14: print(f" ... +{len(low_contrast)-14} more")
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else:
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print("LOW CONTRAST: none flagged")
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if overlaps:
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print("OVERLAPPING TEXT BOXES:")
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for a, b, pct in overlaps[:10]:
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print(f" {pct}% '{a}' vs '{b}'")
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if tiny:
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print(f"VERY SMALL TEXT (<9px tall): {len(tiny)} e.g. " + "; ".join(t['text'][:18] for t in tiny[:5]))
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# Print all OCR lines with geometry so reviewer can reconstruct layout
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print("--- OCR transcript (x,y w h | conf | text) ---")
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for ln in lines:
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x, y, w, h = ln["bbox"]
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print(f" ({x:>4},{y:>4} {w:>4}x{h:<3}|{ln['conf']:.0f}) {ln['text'][:90]}")
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for p in SHOTS:
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try:
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analyze(p)
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except Exception as e:
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print(f"\nFAILED {p}: {type(e).__name__}: {e}")
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