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"""Procedural, paired-render RefSeg-CA scenes with exact visible-instance GT.

These are benchmark inputs, not generated illustrations or TikZ drawings.
Geometry, visibility and query targets are shared between flat/rich images.
"""
from pathlib import Path
import argparse,json,math,hashlib,collections
import numpy as np
from PIL import Image,ImageDraw,ImageFilter
ROOT=Path(__file__).resolve().parents[1]
S=384;AA=2
COLORS={'red':(196,38,56),'blue':(38,87,176),'green':(33,139,91),'yellow':(232,188,35),'purple':(135,64,166),'orange':(223,111,38),'pink':(216,99,150),'brown':(127,83,53),'cyan':(27,160,186),'olive':(117,129,45)}
SHAPES=['circle','square','triangle','star','cross','ellipse','capsule','heart','diamond','ring','hexagon']
PLURAL={'ellipse':'ellipses','cross':'crosses','hexagon':'hexagons'}
def plural(x):return PLURAL.get(x,x+'s')

def object_mask(shape,size,angle):
    n=128*AA;m=Image.new('L',(n,n));d=ImageDraw.Draw(m);c=n/2
    x0,y0,x1,y1=[v*AA for v in (28,28,100,100)]
    if shape=='circle':d.ellipse((x0,y0,x1,y1),fill=255)
    elif shape=='ellipse':d.ellipse((20*AA,37*AA,108*AA,91*AA),fill=255)
    elif shape=='square':d.rectangle((x0,y0,x1,y1),fill=255)
    elif shape=='capsule':d.rounded_rectangle((19*AA,40*AA,109*AA,88*AA),radius=24*AA,fill=255)
    elif shape=='ring':
        d.ellipse((x0,y0,x1,y1),fill=255);d.ellipse((45*AA,45*AA,83*AA,83*AA),fill=0)
    elif shape=='cross':
        d.rectangle((49*AA,24*AA,79*AA,104*AA),fill=255);d.rectangle((24*AA,49*AA,104*AA,79*AA),fill=255)
    elif shape=='heart':
        pts=[]
        for t in np.linspace(0,2*math.pi,200):
            x=16*math.sin(t)**3;y=13*math.cos(t)-5*math.cos(2*t)-2*math.cos(3*t)-math.cos(4*t)
            pts.append((c+x*2.4*AA,c-y*2.4*AA))
        d.polygon(pts,fill=255)
    else:
        count={'triangle':3,'star':10,'diamond':4,'hexagon':6}[shape]
        pts=[]
        for j in range(count):
            theta=-math.pi/2+j*2*math.pi/count;r=(43 if shape!='star' or j%2==0 else 20)*AA
            pts.append((c+r*math.cos(theta),c+r*math.sin(theta)))
        d.polygon(pts,fill=255)
    m=m.rotate(angle,resample=Image.Resampling.BICUBIC)
    bbox=m.getbbox();m=m.crop(bbox);width,height=size
    return m.resize((int(width*AA),int(height*AA)),Image.Resampling.LANCZOS)

def make_scene(seed,index):
    rng=np.random.default_rng(seed*100000+index)
    for attempt in range(100):
        shape=SHAPES[(index+seed)%len(SHAPES)];color=str(rng.choice(list(COLORS)));other=str(rng.choice([c for c in COLORS if c!=color]))
        ashape=str(rng.choice([s for s in SHAPES if s!=shape]));acolor=str(rng.choice([c for c in COLORS if c not in (color,other)]))
        identities=[(color,shape)]*3+[(other,shape),(acolor,ashape)]
        count=int(rng.integers(9,14))
        while len(identities)<count:
            c=str(rng.choice(list(COLORS)));s=str(rng.choice(SHAPES))
            if (c,s) not in ((color,shape),(other,shape),(acolor,ashape)):identities.append((c,s))
        specs=[];masks=[];ok=True
        for j,(c,s) in enumerate(identities):
            width=int(rng.integers(46,77));height=int(width*rng.uniform(.85,1.18));angle=float(rng.uniform(-165,165))
            mask=object_mask(s,(width,height),angle)
            for trial in range(150):
                x=int(rng.integers(15,S-width-15));y=int(rng.integers(15,S-height-15))
                canvas=Image.new('L',(S*AA,S*AA));canvas.paste(mask,(x*AA,y*AA))
                b=np.asarray(canvas.resize((S,S),Image.Resampling.LANCZOS))>=128
                if all(np.logical_and(b,p).sum()<.22*min(b.sum(),p.sum()) for p in masks):break
            else:ok=False;break
            masks.append(b);specs.append(dict(id=j+1,color=c,shape=s,x=x,y=y,width=width,height=height,angle=angle,texture=str(rng.choice(['solid','stripes','dots','grid','checker','satin'])),texture_phase=int(rng.integers(0,12))))
        if not ok:continue
        owner=np.zeros((S,S),np.uint8)
        for j,b in enumerate(masks):owner[b]=j+1
        fractions=[(owner==j+1).sum()/b.sum() for j,b in enumerate(masks)]
        if min(fractions)<.7:continue
        centroids=[]
        for j in range(count):
            yy,xx=np.nonzero(owner==j+1);centroids.append((float(xx.mean()/S),float(yy.mean()/S)))
        axes=[]
        for ax in [0,1]:
            ds=[centroids[j][ax]-centroids[4][ax] for j in range(3)]
            if min(ds)<-.09 and max(ds)>.09 and min(abs(v) for v in ds)>.045:axes.append(ax)
        if not axes:continue
        axis=axes[index%len(axes)];first,second=('left','right') if axis==0 else ('above','below')
        left=[j+1 for j in range(3) if centroids[j][axis]<centroids[4][axis]];right=[j+1 for j in range(3) if centroids[j][axis]>centroids[4][axis]]
        target=f'{color} {plural(shape)}';atarget=f'{other} {plural(shape)}';anchor=f'{acolor} {ashape}'
        relation=lambda rel:f'segment all {target} '+(f'{rel} of the {anchor}' if axis==0 else f'{rel} the {anchor}')
        missing=next(c for c in COLORS if all((o['color'],o['shape'])!=(c,shape) for o in specs))
        extreme=min(range(3),key=lambda j:centroids[j][0])+1
        pair_specs=[('attribute',[f'segment all {target}',f'segment all {atarget}'],[[1,2,3],[4]]),('relational',[relation(first),relation(second)],[left,right]),('multi_target',[f'segment the leftmost {color} {shape}',f'segment all {target}'],[[extreme],[1,2,3]]),('negative_action',[f'segment all {target}',f'do not segment all {target}'],[[1,2,3],[]]),('empty_target',[f'segment all {target}',f'segment all {missing} {plural(shape)}'],[[1,2,3],[]]),('paraphrase',[f'segment all {target}',f'show every {color} {shape}'],[[1,2,3],[1,2,3]])]
        meta=dict(seed=seed,index=index,objects=specs,visible_fraction=fractions,centroids=centroids,occupancy=float((owner>0).mean()),relation_axis=axis,pairs=pair_specs)
        return meta,owner
    raise RuntimeError((seed,index,'placement_failed'))

def render(meta,rich):
    rng=np.random.default_rng(meta['seed']*100000+meta['index']+923471)
    n=S*AA;yy,xx=np.mgrid[:n,:n];base=np.array([(251,249,244),(245,249,248),(249,245,250),(246,248,253)][meta['index']%4],float)
    if rich:
        noise=rng.normal(0,1.15,(n,n));shade=(xx/n-.5)*1.3+(yy/n-.5)*1.8
        arr=np.clip(base[None,None,:]+(noise+shade)[...,None],0,255).astype('uint8')
    else:arr=np.broadcast_to(base.astype('uint8'),(n,n,3)).copy()
    image=Image.fromarray(arr).convert('RGBA')
    if rich and meta['index']%3==0:
        d=ImageDraw.Draw(image)
        for x in range(0,n,20*AA):d.line((x,0,x,n),fill=(192,202,202,20),width=1)
        for y in range(0,n,20*AA):d.line((0,y,n,y),fill=(192,202,202,20),width=1)
    for o in meta['objects']:
        mask=object_mask(o['shape'],(o['width'],o['height']),o['angle']);w,h=mask.size
        if rich:
            shadow=Image.new('RGBA',(n,n));sm=Image.new('L',(n,n));sm.paste(mask,(o['x']*AA+3*AA,o['y']*AA+4*AA));sm=sm.filter(ImageFilter.GaussianBlur(2.2*AA)).point(lambda p:int(p*.24));shadow.putalpha(sm);image=Image.alpha_composite(image,shadow)
        y,x=np.mgrid[:h,:w];rgb=np.array(COLORS[o['color']],float);factor=np.zeros((h,w))
        if rich:
            phase=o['texture_phase']*AA;pattern=o['texture']
            if pattern=='stripes':factor=np.where((x+y+phase)%(12*AA)<3*AA,.28,-.015)
            elif pattern=='dots':factor=np.where(((x+phase)%(15*AA)-7*AA)**2+((y+phase)%(15*AA)-7*AA)**2<(2.4*AA)**2,.42,0.)
            elif pattern=='grid':factor=np.where(((x+phase)%(13*AA)<2*AA)|((y+phase)%(13*AA)<2*AA),-.20,.06)
            elif pattern=='checker':factor=np.where(((x//(11*AA)+y//(11*AA))%2)==0,.20,-.13)
            elif pattern=='satin':factor=.22*np.cos((x+y)/(11*AA))
            factor+=.08*(1-y/max(h,1))-.025+rng.normal(0,.012,(h,w))
        pixels=np.where(factor[...,None]>=0,rgb+(255-rgb)*factor[...,None],rgb*(1+factor[...,None]))
        tile=Image.fromarray(np.clip(pixels,0,255).astype('uint8')).convert('RGBA')
        # A thin inset contour increases legibility without becoming GT area.
        inner=mask.filter(ImageFilter.MinFilter(3));edge=(np.asarray(mask,dtype=float)-np.asarray(inner,dtype=float))/255
        a=np.asarray(tile).copy();a[...,:3]=(a[...,:3]*(1-.22*edge[...,None])).astype('uint8');tile=Image.fromarray(a);tile.putalpha(mask)
        image.alpha_composite(tile,(o['x']*AA,o['y']*AA))
    return image.convert('RGB').resize((S,S),Image.Resampling.LANCZOS)

def main():
    ap=argparse.ArgumentParser();ap.add_argument('--per-seed',type=int,default=240);ap.add_argument('--seeds',type=int,nargs='+',default=[11,23,37,53,71]);a=ap.parse_args()
    dest=ROOT/'data/rich';dest.mkdir(parents=True,exist_ok=True);rows=[];scenes=[]
    for seed in a.seeds:
        for index in range(a.per_seed):
            meta,owner=make_scene(seed,index);sid=f'r{seed}_{index:04d}';meta['scene_id']=sid;scenes.append(meta)
            sd=dest/sid;sd.mkdir(exist_ok=True);Image.fromarray(owner).save(sd/'instances.png')
            for style in ['flat','rich']:
                ip=sd/f'{style}.png';render(meta,style=='rich').save(ip)
                for family,queries,targets in meta['pairs']:
                    for endpoint,(query,ids) in enumerate(zip(queries,targets)):
                        gp=sd/f'{family}_{endpoint}.png';gt=np.isin(owner,ids)
                        if not gp.exists():Image.fromarray(gt.astype('uint8')*255).save(gp)
                        rows.append(dict(id=f'{sid}_{style}_{family}_{endpoint}',scene_id=sid,domain='rich_synthetic',split='test',mode=family,pair_id=f'{sid}_{style}_{family}',endpoint=endpoint,target_count=len(ids),query=query,image_path=str(ip.relative_to(ROOT)),gt_path=str(gp.relative_to(ROOT)),seed=seed,corruption=style,template='scene_graph',render=style,object_count=len(meta['objects']),occupancy=meta['occupancy'],target_instance_ids=ids))
            if index%40==0:print('SCENE',sid,'records',len(rows),flush=True)
    p=ROOT/'data/rich_synthetic.jsonl';p.write_text(''.join(json.dumps(r)+'\n' for r in rows));(ROOT/'data/rich_scenes.jsonl').write_text(''.join(json.dumps(r)+'\n' for r in scenes))
    meta=dict(scenes=len(scenes),images=2*len(scenes),records=len(rows),args=vars(a),manifest_sha256=hashlib.sha256(p.read_bytes()).hexdigest(),mean_objects=float(np.mean([len(s['objects']) for s in scenes])),min_visible_fraction=min(min(s['visible_fraction']) for s in scenes),mean_occupancy=float(np.mean([s['occupancy'] for s in scenes])),renderer='Pillow procedural raster; no image generation model; common instance masks across render styles')
    (ROOT/'protocol/rich_manifest.json').write_text(json.dumps(meta,indent=2));print(json.dumps(meta,indent=2),flush=True)
if __name__=='__main__':main()