Initial Dansori character workspace

_tools/reactions_layout_render.py

@@ -0,0 +1,96 @@
+#!/usr/bin/env python
+"""
+reactions_layout_render.py — 반응 시퀀서용 레이아웃 사전계산 + 오프라인 합성 검증(캐릭터 무관).
+사용: python reactions_layout_render.py <Profile_dir> <char_prefix>
+출력: <Profile>/06_Reactions/_layout.json, _reaction_preview.png
+브라우저 런타임(reactions.html)이 _layout.json 을 로드해 동일 합성.
+
+스케일 기준: 바디는 '상단 25% 최대너비(=어깨/소매 span) -> TARGET_SHOULDER'(캐릭터별 네이티브 크기 차 흡수),
+            목 부착점(상단 opaque 중앙)을 무대 NECK에 고정. 머리는 'bbox 너비 -> HEAD_TARGET_W', 목(하단중앙)을 NECK에 정합·회전.
+"""
+import sys, os, json, math
+import numpy as np
+from PIL import Image
+
+TH = 32
+NECK_X, NECK_Y = 260, 250
+TARGET_SHOULDER = 230   # 바디 상단 25% 최대너비를 이 값으로
+HEAD_TARGET_W = 150     # 머리 bbox 너비를 이 값으로
+OVERLAP = 6
+
+def bbox(a):
+    ys, xs = np.nonzero(a > TH); return xs.min(), ys.min(), xs.max(), ys.max()
+
+def main(profile, cpfx):
+    lib = os.path.join(profile, "03_Assets", "Library"); rdir = os.path.join(profile, "06_Reactions")
+    STAGE_W, STAGE_H = 520, 900
+    layout = {"stage": {"w": STAGE_W, "h": STAGE_H}, "neck": [NECK_X, NECK_Y], "overlap": OVERLAP,
+              "headTargetW": HEAD_TARGET_W, "bodies": {}, "heads": {}}
+
+    for root, _, files in os.walk(os.path.join(lib, "BakedPoses")):
+        for fn in files:
+            if not fn.endswith(".png"): continue
+            a = np.asarray(Image.open(os.path.join(root, fn)).convert("RGBA"))[:, :, 3]
+            x0, y0, x1, y1 = bbox(a); bh = y1 - y0 + 1
+            row0 = np.nonzero(a[y0] > TH)[0]; tcx = float((row0.min() + row0.max()) / 2)
+            shoulderW = 0                                    # 상단 25% 최대 행너비 = 어깨/소매 span
+            for yy in range(y0, y0 + max(3, int(bh * 0.25))):
+                cols = np.nonzero(a[yy] > TH)[0]
+                if len(cols): shoulderW = max(shoulderW, int(cols.max() - cols.min() + 1))
+            if shoulderW == 0: continue
+            scale = TARGET_SHOULDER / shoulderW
+            if not (0.1 <= scale <= 3.0): continue
+            layout["bodies"][fn[:-4]] = {"scale": round(scale, 4),
+                "ox": round(NECK_X - tcx * scale, 1), "oy": round(NECK_Y - y0 * scale, 1)}
+
+    for fn in os.listdir(os.path.join(lib, "Heads")):
+        if not fn.endswith(".png"): continue
+        im = Image.open(os.path.join(lib, "Heads", fn)).convert("RGBA"); a = np.asarray(im)[:, :, 3]
+        x0, y0, x1, y1 = bbox(a)
+        layout["heads"][fn[:-4]] = {"w": int(x1 - x0 + 1),
+            "neckNorm": [round(((x0 + x1) / 2) / im.width, 4), round(y1 / im.height, 4)]}
+
+    os.makedirs(rdir, exist_ok=True)
+    json.dump(layout, open(os.path.join(rdir, "_layout.json"), "w"), ensure_ascii=False, indent=1)
+    print(f"layout: {len(layout['bodies'])} bodies, {len(layout['heads'])} heads -> _layout.json")
+
+    # ---- offline verify (character-agnostic: pick by suffix) ----
+    T = lambda x, y: np.array([[1,0,x],[0,1,y],[0,0,1]], float)
+    def R(d):
+        r=math.radians(d);c,s=math.cos(r),math.sin(r);return np.array([[c,-s,0],[s,c,0],[0,0,1]],float)
+    Sc = lambda k: np.array([[k,0,0],[0,k,0],[0,0,1]], float)
+    def draw(base, img, M):
+        Mi=np.linalg.inv(M); a,c,e=Mi[0]; b,d,f=Mi[1]
+        return Image.alpha_composite(base, img.transform((STAGE_W,STAGE_H),Image.AFFINE,(a,c,e,b,d,f),resample=Image.BICUBIC))
+    def pick_body(suf):
+        for k in layout["bodies"]:
+            if k.endswith("_"+suf): return k
+        return None
+    def pick_head(expr):
+        for k in layout["heads"]:
+            if k.endswith("_"+expr): return k
+        return None
+    def bpath(k):
+        for root,_,files in os.walk(os.path.join(lib,"BakedPoses")):
+            if k+".png" in files: return os.path.join(root, k+".png")
+    def compose(bk, hk, rot):
+        bl=layout["bodies"][bk]; hl=layout["heads"][hk]
+        body=Image.open(bpath(bk)).convert("RGBA"); head=Image.open(os.path.join(lib,"Heads",hk+".png")).convert("RGBA")
+        fr=Image.new("RGBA",(STAGE_W,STAGE_H),(0,0,0,0))
+        fr=draw(fr, body, T(bl["ox"],bl["oy"]) @ Sc(bl["scale"]))
+        hs=HEAD_TARGET_W/hl["w"]; ax=hl["neckNorm"][0]*head.width; ay=hl["neckNorm"][1]*head.height
+        fr=draw(fr, head, T(NECK_X,NECK_Y+OVERLAP) @ R(rot) @ Sc(hs) @ T(-ax,-ay))
+        return fr
+    reactions=[("no","armscross","negative",7),("heart","heart","love",-4),("focus","listen","sleepy",4)]
+    sc=0.46; fw,fh=int(STAGE_W*sc),int(STAGE_H*sc)
+    mont=Image.new("RGBA",(fw*len(reactions),fh),(28,32,40,255))
+    for i,(nm,bsuf,expr,rot) in enumerate(reactions):
+        bk=pick_body(bsuf); hk=pick_head(expr)
+        if not bk or not hk: print(f"  skip {nm}: body={bk} head={hk}"); continue
+        try:
+            mont.alpha_composite(compose(bk,hk,rot).resize((fw,fh),Image.BICUBIC),(fw*i,0)); print(f"  {nm}: {bk} + {hk} rot={rot}")
+        except Exception as ex: print(f"  FAIL {nm}: {ex}")
+    out=os.path.join(rdir,"_reaction_preview.png"); mont.convert("RGB").save(out); print("saved:", out)
+
+if __name__ == "__main__":
+    main(sys.argv[1], sys.argv[2])

_tools/rig_pivots_render.py

@@ -0,0 +1,98 @@
+#!/usr/bin/env python
+"""
+rig_pivots_render.py — 풀캔버스 리그 파츠 검증 + 관절 피벗 자동 산출 + 배경춤 프리뷰 렌더.
+
+사용법:
+  python rig_pivots_render.py <Profile_dir> <prefix>
+예:
+  python rig_pivots_render.py "D:\\...\\Noeul_Profile" noeul_part_
+
+하는 일:
+  1) <Profile>/03_Assets/Parts/Images/<prefix><bone>.png (마스터+16파츠) 검증
+     - 전부 520x900 / 32-bit alpha / 16파츠 스택 == 마스터(alpha union) missed/extra 출력
+  2) 관절 피벗 자동 산출: pivot(bone) = centroid(opaque(bone) & opaque(parent))
+  3) <Profile>/05_Animation/dance_idle.json 로 배경춤 프레임 렌더(풀캔버스 FK)
+  출력: <Profile>/04_Rig/_pivots.json (rig.json에 반영), <Profile>/04_Rig/_dance_preview.png
+
+주의: PowerShell은 대소문자 무시 변수 충돌이 잦으니 리그 계산은 이 Python 도구를 쓸 것.
+"""
+import sys, os, json, math
+import numpy as np
+from PIL import Image
+
+CW, CH, TH = 520, 900, 32
+BONES = [  # (name, parent, z)
+ ("pelvis",None,6),("chest","pelvis",8),("neck","chest",9),("head","neck",10),
+ ("upperarm_r","chest",5),("forearm_r","upperarm_r",5),("hand_r","forearm_r",5),
+ ("upperarm_l","chest",12),("forearm_l","upperarm_l",12),("hand_l","forearm_l",13),
+ ("thigh_r","pelvis",4),("shin_r","thigh_r",3),("foot_r","shin_r",2),
+ ("thigh_l","pelvis",4),("shin_l","thigh_l",3),("foot_l","shin_l",2)]
+
+def main(profile, prefix):
+    pdir = os.path.join(profile, "03_Assets", "Parts", "Images")
+    anim_path = os.path.join(profile, "05_Animation", "dance_idle.json")
+    rigdir = os.path.join(profile, "04_Rig")
+    names = ["master_apose"] + [n for n,_,_ in BONES]
+    for nm in names:
+        p = os.path.join(pdir, f"{prefix}{nm}.png")
+        assert os.path.exists(p), f"MISSING {p}"
+        assert Image.open(p).size == (CW, CH), f"size!=520x900: {nm}"
+    print("all 17 present, all 520x900: OK")
+
+    img = {n: Image.open(os.path.join(pdir, f"{prefix}{n}.png")).convert("RGBA") for n,_,_ in BONES}
+    alpha = {n: (np.asarray(img[n])[:,:,3] > TH) for n,_,_ in BONES}
+    master = np.asarray(Image.open(os.path.join(pdir, f"{prefix}master_apose.png")).convert("RGBA"))[:,:,3] > TH
+
+    pivot = {}
+    for n,p,_ in BONES:
+        par = p if p else "chest"
+        ys,xs = np.nonzero(alpha[n] & alpha[par])
+        if len(xs): pivot[n] = (round(float(xs.mean()),1), round(float(ys.mean()),1))
+        else:
+            ys2,xs2 = np.nonzero(alpha[n]); pivot[n] = (round(float(xs2.mean()),1), round(float(ys2.min()),1))
+    print("== pivots (paste into rig.json) ==")
+    for n,_,_ in BONES: print(f"  {n:12s} {pivot[n]}")
+
+    union = np.zeros_like(master)
+    for n,_,_ in BONES: union |= alpha[n]
+    mo = int(master.sum())
+    print(f"stack union vs master: opaque={mo} missed={int((master & ~union).sum())} extra={int((union & ~master).sum())}")
+
+    os.makedirs(rigdir, exist_ok=True)
+    json.dump({n:list(pivot[n]) for n,_,_ in BONES}, open(os.path.join(rigdir,"_pivots.json"),"w"), ensure_ascii=False, indent=2)
+
+    anim = json.load(open(anim_path, encoding="utf-8"))
+    ease = lambda u: -(math.cos(math.pi*u)-1)/2
+    def samp(keys,t):
+        if not keys: return 0.0
+        if t<=keys[0]["t"]: return float(keys[0]["v"])
+        if t>=keys[-1]["t"]: return float(keys[-1]["v"])
+        for i in range(len(keys)-1):
+            a,b=keys[i],keys[i+1]
+            if a["t"]<=t<=b["t"]:
+                sp=b["t"]-a["t"]
+                return float(a["v"]) if sp<=0 else float(a["v"])+(float(b["v"])-float(a["v"]))*ease((t-a["t"])/sp)
+        return 0.0
+    T=lambda x,y: np.array([[1,0,x],[0,1,y],[0,0,1]],float)
+    def R(d):
+        r=math.radians(d);c,s=math.cos(r),math.sin(r);return np.array([[c,-s,0],[s,c,0],[0,0,1]],float)
+    def render(t):
+        W={}
+        for n,p,_ in BONES:
+            tr=anim["tracks"].get(n,{}); rot=samp(tr.get("rot"),t);tx=samp(tr.get("tx"),t);ty=samp(tr.get("ty"),t)
+            jx,jy=pivot[n]; ml=T(tx,ty)@T(jx,jy)@R(rot)@T(-jx,-jy); W[n]=(W[p]@ml) if p else ml
+        fr=Image.new("RGBA",(CW,CH),(0,0,0,0))
+        for n,_,_ in sorted(BONES,key=lambda b:b[2]):
+            Mi=np.linalg.inv(W[n]); a,c,e=Mi[0]; b,d,f=Mi[1]
+            fr=Image.alpha_composite(fr,img[n].transform((CW,CH),Image.AFFINE,(a,c,e,b,d,f),resample=Image.BICUBIC))
+        return fr
+    times=[0.0,0.5,1.0,1.5]; sc=0.42; fw,fh=int(CW*sc),int(CH*sc)
+    mont=Image.new("RGBA",(fw*len(times),fh),(28,32,40,255))
+    for i,t in enumerate(times): mont.alpha_composite(render(t).resize((fw,fh),Image.BICUBIC),(fw*i,0))
+    out=os.path.join(rigdir,"_dance_preview.png"); mont.convert("RGB").save(out)
+    print("saved preview:", out)
+
+if __name__ == "__main__":
+    if len(sys.argv) != 3:
+        print(__doc__); sys.exit(1)
+    main(sys.argv[1], sys.argv[2])