#!/usr/bin/env python3 """测量已标注的参考图区域，输出原始几何、局部颜色、重复网格和阈值扫描；不猜测原始 CSS。""" from __future__ import annotations import argparse import base64 import hashlib import html import json import math from pathlib import Path from statistics import median from PIL import Image def positive(value, name): if isinstance(value, bool) or not isinstance(value, (int, float)) or not math.isfinite(value) or value <= 0: raise ValueError(f'{name} 必须是有限正数') return value def integer(value, name): if isinstance(value, bool) or not isinstance(value, int): raise ValueError(f'{name} 必须是整数') return value def box_checked(box, size): if not isinstance(box, list) or len(box) != 4: raise ValueError('box 必须为 [x,y,width,height]') x, y, w, h = [integer(v, 'box 坐标') for v in box] if min(x, y) < 0 or min(w, h) <= 0 or x+w > size[0] or y+h > size[1]: raise ValueError('标注越界或面积无效；不自动裁短') return x, y, w, h def distribution(values): values = sorted(values) def quantile(p): pos = (len(values)-1)*p lo, hi = math.floor(pos), math.ceil(pos) return round(values[lo] + (values[hi]-values[lo])*(pos-lo), 3) return {'median': quantile(.5), 'p10': quantile(.1), 'p90': quantile(.9)} def measure(manifest_path, out): data = json.loads(manifest_path.read_text(encoding='utf-8')) source = (manifest_path.parent / data['reference']).resolve() with Image.open(source) as original: im = original.convert('RGBA') css_width = data.get('css_viewport_width') factor = positive(css_width, 'css_viewport_width')/im.width if css_width is not None else None regions = [] names = set() def take_name(item): name = item['name'] if not isinstance(name, str) or not name.strip() or name in names: raise ValueError('标注名称必须非空且唯一') names.add(name) return name for item in data.get('regions', []): name = take_name(item) kind = item.get('kind', 'box') if kind not in ('box', 'color'): raise ValueError('kind 只支持 box 或 color') x,y,w,h = box_checked(item['box'], im.size) box_type = item.get('box_type', 'sample' if kind == 'color' else 'element') if box_type not in ('element', 'ink', 'sample') or (kind == 'color' and box_type != 'sample'): raise ValueError('box_type 必须为 element、ink 或 sample；颜色区只能为 sample') region = {'name': name, 'kind':kind, 'box_type':box_type, 'box':[x,y,w,h], 'normalized_box':[round(x/im.width,6),round(y/im.height,6),round(w/im.width,6),round(h/im.height,6)], 'boundary_evidence':'Agent 选择的标注范围，非自动识别的真实元素边界'} if factor is not None: region['css_box'] = [round(v*factor,3) for v in [x,y,w,h]] if 'selector' in item: region['selector'] = item['selector'] region['dom_comparable'] = box_type == 'element' if 'group' in item: if box_type != 'element': raise ValueError('重复网格只接受元素框，不能混用字形框或采样框') if not isinstance(item['group'],str) or not item['group'].strip(): raise ValueError('group 必须是非空字符串') region['group'] = item['group'] if kind == 'color': inset=integer(item.get('inset',0),'inset') if inset < 0 or inset*2 >= min(w,h): raise ValueError('inset 使采样区域无效') crop = im.crop((x+inset,y+inset,x+w-inset,y+h-inset)) # 只统计完全不透明的像素，不将透明底错误统计为黑色。 pixels = [p for p in crop.get_flattened_data() if p[3] == 255] if hasattr(crop,'get_flattened_data') else [p for p in crop.getdata() if p[3] == 255] if not pixels: raise ValueError(f'{name} 没有不透明的颜色样本') channels = [distribution([p[c] for p in pixels]) for c in range(3)] rgb = [round(c['median']) for c in channels] region['color'] = {'median_hex':'#' + ''.join(f'{v:02X}' for v in rgb),'rgb_channels':channels, 'opaque_samples':len(pixels),'excluded_nonopaque':crop.width*crop.height-len(pixels), 'sample_box':[x+inset,y+inset,w-inset*2,h-inset*2], 'max_channel_p90_minus_p10':round(max(c['p90']-c['p10'] for c in channels),3), 'meaning':'局部像素统计，不证明原始色值、纯色或渐变模型'} regions.append(region) groups=[] for name in sorted({r['group'] for r in regions if 'group' in r}): members=sorted([r for r in regions if r.get('group')==name],key=lambda r:r['box'][0]) if len(members)<2: raise ValueError('重复网格至少需要两个标注') gaps=[b['box'][0]-(a['box'][0]+a['box'][2]) for a,b in zip(members,members[1:])] groups.append({'name':name,'members':[r['name'] for r in members],'x_gaps':gaps, 'median_width':median([r['box'][2] for r in members]),'median_height':median([r['box'][3] for r in members]), 'median_gap':median(gaps),'y_spread':max(r['box'][1] for r in members)-min(r['box'][1] for r in members), 'width_spread':max(r['box'][2] for r in members)-min(r['box'][2] for r in members), 'meaning':'横向重复结构的描述统计，不自动把不等宽设计改成等宽'}) scans=[] for spec in data.get('vertical_scans',[]): name=take_name(spec) x,y,_,h=box_checked([spec['x'],spec['y'],1,spec['height']],im.size) floor=integer(spec['min_channel'],'min_channel'); spread=integer(spec['max_channel_spread'],'max_channel_spread') run_min=integer(spec.get('min_run',1),'min_run') if not 0<=floor<=255 or not 0<=spread<=255 or run_min<1: raise ValueError('扫描阈值无效') runs=[];start=None for yy in range(y,y+h+1): p=im.getpixel((x,yy)) if yy=floor and max(p[:3])-min(p[:3])<=spread if match and start is None: start=yy if not match and start is not None: if yy-start>=run_min:runs.append({'y_start':start,'y_end_exclusive':yy,'height':yy-start}) start=None scans.append({'name':name,'configuration':spec,'runs':runs,'meaning':'满足给定颜色阈值的连续像素，不自动认定为区块边界'}) if not regions and not scans: raise ValueError('至少提供一个区域或扫描') result={'source':str(source),'source_sha256':hashlib.sha256(source.read_bytes()).hexdigest(),'source_size':list(im.size), 'css_mapping':{'assumed_viewport_width':css_width,'css_per_image_pixel':factor,'meaning':'实现约定，不证明参考图原始 DPR 或 CSS 视口'}, 'regions':regions,'groups':groups,'vertical_scans':scans} if 'expected' in data: result['expected'] = data['expected'] result['expected_assets'] = data.get('expected_assets', {}) result['input_manifest_sha256'] = hashlib.sha256(manifest_path.read_bytes()).hexdigest() # 所有输入检查完成后才创建输出，且拒绝复用已有轮次。 out.mkdir(parents=True,exist_ok=False) (out/'measurements.json').write_text(json.dumps(result,ensure_ascii=False,indent=2),encoding='utf-8') # PNG 编码原图用于自包含的 SVG 标注层；不缩放、去背或重建素材。 import io buffer=io.BytesIO();im.save(buffer,format='PNG') encoded=base64.b64encode(buffer.getvalue()).decode('ascii') svg=[f'',f''] for region in regions: x,y,w,h=region['box'];label=html.escape(region['name']);color='#36edcc' if region['kind']=='box' else '#f6cb46' svg.append(f'{label}') svg.append('') (out/'annotations.svg').write_text('\n'.join(svg),encoding='utf-8') return result def main(): parser=argparse.ArgumentParser(description=__doc__) parser.add_argument('--manifest',type=Path,required=True) parser.add_argument('--out-dir',type=Path,required=True) args=parser.parse_args() try: result=measure(args.manifest,args.out_dir) print(json.dumps({'status':'标注区域测量完成，设计关系仍需判断','regions':len(result['regions']),'groups':len(result['groups']),'output':str(args.out_dir)},ensure_ascii=False)) except (OSError,ValueError,KeyError,TypeError) as error: parser.exit(2,f'测量失败：{error}\n') if __name__=='__main__':main()