"""阶段 2:逐审查单元取证(多源证据层 + 视觉多图)。 对每个审查单元:取结构化数据(实体/文字/标注/块/多段线)+ 视觉截图(整体 + 可选局部)。 结构化数据是权威依据,截图仅作视觉上下文。 多源设计:`collect()` 按审查单元来源分发;当前仅实现 CAD 源,PDF / 图片源预留 seam (未来在对应分支产出**同形** evidence,下游无需改动)。 """ import math import time from .. import config, evidence_model from ..mcp_client import result_images, result_json _LABEL_OVERALL = "overall" def bbox_to_window(bbox): """[minx,miny,maxx,maxy] -> window 对象。""" return { "min_x": float(bbox[0]), "min_y": float(bbox[1]), "max_x": float(bbox[2]), "max_y": float(bbox[3]), } def sub_windows(window, tiles): """把 window 等分为 tiles 个子窗口(tiles=4 → 2x2 象限)。 tiles<=1 返回 []。返回 [{"min_x",..,"max_x",..}, ...],行优先(左上→右上→左下→右下)。 """ if not tiles or tiles <= 1: return [] cols = int(math.ceil(math.sqrt(tiles))) rows = int(math.ceil(tiles / cols)) min_x, min_y = window["min_x"], window["min_y"] max_x, max_y = window["max_x"], window["max_y"] w = (max_x - min_x) / cols h = (max_y - min_y) / rows out = [] for r in range(rows): for c in range(cols): if len(out) >= tiles: break out.append( { "min_x": min_x + c * w, "min_y": max_y - (r + 1) * h, "max_x": min_x + (c + 1) * w, "max_y": max_y - r * h, } ) return out def _zoom_and_shot(ctx, window, margin=0.02): """缩放到窗口、等待渲染、截一张图,返回 (b64, mime) 或 None。""" ctx.tools.call("zoom_to_window", dict(window, margin=margin)) time.sleep(config.RENDER_WAIT) shot = ctx.tools.call("get_viewport_screenshot", {}) images = result_images(shot) if not images: return None return images[0] def capture_images(ctx, window, *, tiles=0, max_images=None): """采集视觉证据:整体一张 + 局部 tiles 张。 - 整体 label="overall",局部 label="tile-1"..;行优先。 - 结束时**复位到整体视口**,保证后续流程从整幅视图起手。 - 受 max_images(默认 config.EVIDENCE_MAX_IMAGES)截断。 """ if max_images is None: max_images = config.EVIDENCE_MAX_IMAGES images = [] def _add(shot, label): if shot and len(images) < max_images: images.append(evidence_model.make_image(shot[0], shot[1], label)) # 整体 _add(_zoom_and_shot(ctx, window), _LABEL_OVERALL) # 局部(等分象限) for i, sub in enumerate(sub_windows(window, tiles), 1): if len(images) >= max_images: break _add(_zoom_and_shot(ctx, sub), f"tile-{i}") # 复位整体视口(不截图) ctx.tools.call("zoom_to_window", dict(window, margin=0.02)) return images def collect_frame(ctx, index, frame): """采集单个图框(CAD 源)的证据;bbox 无效时返回 None。""" bbox = frame.get("bbox") if not bbox or len(bbox) != 4: return None win = bbox_to_window(bbox) limit = ctx.frame_limit # a. 结构化数据(权威;按 window 查询,不依赖缩放) entities = result_json(ctx.tools.call("query_entities", {"window": win, "limit": limit}), {}) texts = result_json(ctx.tools.call("get_text_content", {"window": win, "limit": limit}), {}) dimensions = result_json(ctx.tools.call("get_dimensions", {"window": win, "limit": limit}), {}) blocks = result_json( ctx.tools.call("get_block_references", {"window": win, "limit": limit}), {} ) polylines = result_json( ctx.tools.call("get_polylines_info", {"window": win, "limit": limit}), {} ) # b. 视觉:整体 + 可选局部(多图) images = capture_images(ctx, win, tiles=config.EVIDENCE_DETAIL_TILES) return { "source_type": evidence_model.source_type_of(frame), "index": index, "frame": frame, "window": win, "entities": entities, "texts": texts, "dimensions": dimensions, "blocks": blocks, "polylines": polylines, "images": images, } def collect(ctx, index, unit): """按审查单元来源分发取证。 当前仅实现 CAD 源;PDF / 图片源预留(未来在对应分支接入离线解析 / 直接读图, 产出与 CAD **同形**的 evidence:window=None、结构化键给空信封、images 带 label)。 """ source = evidence_model.source_type_of(unit) if source == evidence_model.SOURCE_CAD: return collect_frame(ctx, index, unit) # TODO(pdf/image): 接入离线解析(DWG/PDF)与直接读图。 return None