- 新增 evidence_model.py:统一证据模型(source_type + images 列表),访问器兼容旧 image_b64/image_mime 单图字段 - phases/evidence.py:新增 collect() 多源分发 seam(仅 CAD)、capture_images() 整体+等分象限多图(结束复位视口)、sub_windows() - phases/review.py:多图逐张下采样喂 LLM,payload 增加 source_type - config.py:EVIDENCE_DETAIL_TILES=4 / EVIDENCE_MAX_IMAGES=5(默认开启多图) - 新增 tests/test_evidence_model.py,更新 evidence/review 断言与 fakes 归一
146 lines
4.9 KiB
Python
146 lines
4.9 KiB
Python
"""阶段 2:逐审查单元取证(多源证据层 + 视觉多图)。
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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)。
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结构化数据是权威依据,截图仅作视觉上下文。
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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
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(未来在对应分支产出**同形** evidence,下游无需改动)。
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"""
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import math
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import time
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from .. import config, evidence_model
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from ..mcp_client import result_images, result_json
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_LABEL_OVERALL = "overall"
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def bbox_to_window(bbox):
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"""[minx,miny,maxx,maxy] -> window 对象。"""
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return {
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"min_x": float(bbox[0]),
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"min_y": float(bbox[1]),
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"max_x": float(bbox[2]),
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"max_y": float(bbox[3]),
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}
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def sub_windows(window, tiles):
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"""把 window 等分为 tiles 个子窗口(tiles=4 → 2x2 象限)。
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tiles<=1 返回 []。返回 [{"min_x",..,"max_x",..}, ...],行优先(左上→右上→左下→右下)。
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"""
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if not tiles or tiles <= 1:
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return []
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cols = int(math.ceil(math.sqrt(tiles)))
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rows = int(math.ceil(tiles / cols))
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min_x, min_y = window["min_x"], window["min_y"]
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max_x, max_y = window["max_x"], window["max_y"]
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w = (max_x - min_x) / cols
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h = (max_y - min_y) / rows
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out = []
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for r in range(rows):
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for c in range(cols):
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if len(out) >= tiles:
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break
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out.append(
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{
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"min_x": min_x + c * w,
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"min_y": max_y - (r + 1) * h,
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"max_x": min_x + (c + 1) * w,
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"max_y": max_y - r * h,
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}
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)
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return out
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def _zoom_and_shot(ctx, window, margin=0.02):
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"""缩放到窗口、等待渲染、截一张图,返回 (b64, mime) 或 None。"""
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ctx.tools.call("zoom_to_window", dict(window, margin=margin))
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time.sleep(config.RENDER_WAIT)
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shot = ctx.tools.call("get_viewport_screenshot", {})
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images = result_images(shot)
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if not images:
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return None
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return images[0]
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def capture_images(ctx, window, *, tiles=0, max_images=None):
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"""采集视觉证据:整体一张 + 局部 tiles 张。
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- 整体 label="overall",局部 label="tile-1"..;行优先。
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- 结束时**复位到整体视口**,保证后续流程从整幅视图起手。
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- 受 max_images(默认 config.EVIDENCE_MAX_IMAGES)截断。
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"""
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if max_images is None:
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max_images = config.EVIDENCE_MAX_IMAGES
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images = []
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def _add(shot, label):
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if shot and len(images) < max_images:
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images.append(evidence_model.make_image(shot[0], shot[1], label))
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# 整体
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_add(_zoom_and_shot(ctx, window), _LABEL_OVERALL)
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# 局部(等分象限)
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for i, sub in enumerate(sub_windows(window, tiles), 1):
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if len(images) >= max_images:
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break
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_add(_zoom_and_shot(ctx, sub), f"tile-{i}")
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# 复位整体视口(不截图)
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ctx.tools.call("zoom_to_window", dict(window, margin=0.02))
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return images
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def collect_frame(ctx, index, frame):
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"""采集单个图框(CAD 源)的证据;bbox 无效时返回 None。"""
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bbox = frame.get("bbox")
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if not bbox or len(bbox) != 4:
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return None
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win = bbox_to_window(bbox)
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limit = ctx.frame_limit
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# a. 结构化数据(权威;按 window 查询,不依赖缩放)
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entities = result_json(ctx.tools.call("query_entities", {"window": win, "limit": limit}), {})
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texts = result_json(ctx.tools.call("get_text_content", {"window": win, "limit": limit}), {})
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dimensions = result_json(ctx.tools.call("get_dimensions", {"window": win, "limit": limit}), {})
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blocks = result_json(
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ctx.tools.call("get_block_references", {"window": win, "limit": limit}), {}
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)
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polylines = result_json(
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ctx.tools.call("get_polylines_info", {"window": win, "limit": limit}), {}
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)
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# b. 视觉:整体 + 可选局部(多图)
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images = capture_images(ctx, win, tiles=config.EVIDENCE_DETAIL_TILES)
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return {
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"source_type": evidence_model.source_type_of(frame),
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"index": index,
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"frame": frame,
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"window": win,
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"entities": entities,
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"texts": texts,
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"dimensions": dimensions,
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"blocks": blocks,
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"polylines": polylines,
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"images": images,
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}
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def collect(ctx, index, unit):
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"""按审查单元来源分发取证。
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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,
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产出与 CAD **同形**的 evidence:window=None、结构化键给空信封、images 带 label)。
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"""
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source = evidence_model.source_type_of(unit)
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if source == evidence_model.SOURCE_CAD:
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return collect_frame(ctx, index, unit)
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# TODO(pdf/image): 接入离线解析(DWG/PDF)与直接读图。
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return None
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