feat: v4 multimodal chat input, multi-format support, and annotation detection
- Replace st.chat_input with st-multimodal-chatinput (Ctrl+V paste, drag-drop, file button) - Extract _process_uploaded_file() shared handler (eliminates ~70 duplicated lines) - Add XLSX (openpyxl), XLS (xlrd), DOC (olefile) parsers to file_parser.py - Add backend/annotation_detector.py: circle detection (HoughCircles) + arrow detection (HoughLinesP clustering) + OCR correlation + LLM context formatting - Add annotation_result field to AgentState with session persistence - Wire annotation detection into process_input and _format_ocr_context - Add 11 new tests: 7 annotation detector + 4 multi-format parser - Update all docs: CLAUDE.md, README.md, CODE_GUIDE.md, ROADMAP.md
This commit is contained in:
@@ -0,0 +1,331 @@
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"""批注检测器：识别图片上的圈选（圆）和箭头，定位用户要修改的字段。
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依赖 OpenCV (cv2)，从 PaddleOCR 传递依赖已安装。
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"""
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from __future__ import annotations
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import math
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from dataclasses import dataclass, field
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from typing import Optional
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import cv2
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import numpy as np
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@dataclass
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class Annotation:
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"""单个批注标记。"""
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type: str # "circle" | "arrow"
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bbox: dict # {"x": int, "y": int, "w": int, "h": int}
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center: tuple[int, int] # (cx, cy)
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nearby_texts: list[str] = field(default_factory=list)
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from_text: str = "" # 箭头出发点的文本
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to_text: str = "" # 箭头指向的文本
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from_pt: Optional[tuple[int, int]] = None
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to_pt: Optional[tuple[int, int]] = None
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def detect_annotations(image_path: str, ocr_elements: list[dict]) -> dict:
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"""检测图片上的手写批注（圈选 + 箭头），并与 OCR 文本关联。
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Args:
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image_path: 图片文件路径
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ocr_elements: OCR 元素列表 [{"text": str, "bbox": {x,y,w,h}, "confidence": float}]
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Returns:
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{"circles": [...], "arrows": [...], "total": int}
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"""
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img = cv2.imread(image_path)
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if img is None:
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return {"circles": [], "arrows": [], "total": 0, "error": "无法读取图片"}
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h, w = img.shape[:2]
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circles = _detect_circles(img)
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arrows = _detect_arrows(img)
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all_annotations = circles + arrows
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_correlate_with_ocr(all_annotations, ocr_elements, w, h)
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result: dict = {
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"circles": [_annotation_to_dict(a) for a in circles],
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"arrows": [_annotation_to_dict(a) for a in arrows],
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"total": len(all_annotations),
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}
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return result
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def _annotation_to_dict(a: Annotation) -> dict:
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d = {
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"type": a.type,
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"bbox": a.bbox,
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"center": list(a.center),
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"nearby_texts": a.nearby_texts,
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}
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if a.type == "arrow":
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d["from_text"] = a.from_text
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d["to_text"] = a.to_text
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if a.from_pt:
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d["from_pt"] = list(a.from_pt)
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if a.to_pt:
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d["to_pt"] = list(a.to_pt)
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return d
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# ---------------------------------------------------------------------------
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# 圆圈检测
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# ---------------------------------------------------------------------------
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def _detect_circles(img: np.ndarray) -> list[Annotation]:
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"""检测图片中可能是手绘批注的圆圈。"""
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h, w = img.shape[:2]
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b, g, r = cv2.split(img)
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red_enhanced = cv2.addWeighted(r.astype(np.float32), 1.5,
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g.astype(np.float32), -0.3, 0)
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red_enhanced = cv2.addWeighted(red_enhanced, 1.2,
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b.astype(np.float32), -0.3, 0)
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red_enhanced = np.clip(red_enhanced, 0, 255).astype(np.uint8)
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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combined = cv2.addWeighted(gray, 0.5, red_enhanced, 0.5, 0)
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blurred = cv2.GaussianBlur(combined, (9, 9), 2)
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min_radius = max(15, min(w, h) // 40)
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max_radius = min(200, max(w, h) // 8)
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circles_raw = cv2.HoughCircles(
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blurred, cv2.HOUGH_GRADIENT, dp=1.2, minDist=min_radius * 2,
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param1=50, param2=30, minRadius=min_radius, maxRadius=max_radius,
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)
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annotations: list[Annotation] = []
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if circles_raw is not None:
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for cx, cy, r in circles_raw[0]:
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bbox = {
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"x": max(0, int(cx - r)),
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"y": max(0, int(cy - r)),
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"w": int(r * 2),
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"h": int(r * 2),
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}
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annotations.append(Annotation(
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type="circle",
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bbox=bbox,
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center=(int(cx), int(cy)),
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))
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return annotations
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# ---------------------------------------------------------------------------
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# 箭头检测
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# ---------------------------------------------------------------------------
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def _detect_arrows(img: np.ndarray) -> list[Annotation]:
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"""检测图片中的手绘箭头（直线段 + 端点三角形）。"""
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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edges = cv2.Canny(gray, 50, 150, apertureSize=3)
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lines = cv2.HoughLinesP(
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edges, rho=1, theta=np.pi / 180, threshold=40,
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minLineLength=30, maxLineGap=15,
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)
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if lines is None:
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return []
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segments = [(x1, y1, x2, y2) for x1, y1, x2, y2 in lines[:, 0]]
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clusters = _cluster_segments(segments)
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annotations: list[Annotation] = []
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for segs in clusters:
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if len(segs) < 2:
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continue
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all_pts = []
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for x1, y1, x2, y2 in segs:
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all_pts.append((x1, y1))
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all_pts.append((x2, y2))
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all_pts_arr = np.array(all_pts)
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max_dist = 0
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p1 = p2 = all_pts[0]
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for i in range(len(all_pts)):
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for j in range(i + 1, len(all_pts)):
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d = (all_pts[i][0] - all_pts[j][0]) ** 2 + (all_pts[i][1] - all_pts[j][1]) ** 2
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if d > max_dist:
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max_dist = d
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p1, p2 = all_pts[i], all_pts[j]
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from_pt, to_pt = _find_arrow_direction(edges, p1, p2)
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x1, y1 = from_pt
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x2, y2 = to_pt
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bbox = {
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"x": min(x1, x2),
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"y": min(y1, y2),
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"w": abs(x2 - x1),
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"h": abs(y2 - y1),
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}
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cx = (x1 + x2) // 2
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cy = (y1 + y2) // 2
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annotations.append(Annotation(
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type="arrow",
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bbox=bbox,
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center=(cx, cy),
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from_pt=from_pt,
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to_pt=to_pt,
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))
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return annotations
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def _cluster_segments(segments: list[tuple]) -> list[list[tuple]]:
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"""将线段按方向和空间距离聚类。"""
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clusters: list[list[tuple]] = []
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used = [False] * len(segments)
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for i, (x1, y1, x2, y2) in enumerate(segments):
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if used[i]:
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continue
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cluster = [(x1, y1, x2, y2)]
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used[i] = True
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angle_i = math.atan2(y2 - y1, x2 - x1)
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for j in range(i + 1, len(segments)):
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if used[j]:
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continue
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x3, y3, x4, y4 = segments[j]
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angle_j = math.atan2(y4 - y3, x4 - x3)
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angle_diff = abs(angle_i - angle_j)
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if angle_diff > math.pi:
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angle_diff = 2 * math.pi - angle_diff
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if angle_diff < 0.35:
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d1 = math.hypot(x3 - x2, y3 - y2)
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d2 = math.hypot(x1 - x4, y1 - y4)
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d3 = math.hypot(x3 - x1, y3 - y1)
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d4 = math.hypot(x4 - x2, y4 - y2)
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if min(d1, d2, d3, d4) < 80:
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cluster.append((x3, y3, x4, y4))
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used[j] = True
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clusters.append(cluster)
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return clusters
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def _find_arrow_direction(edges: np.ndarray, p1: tuple, p2: tuple) -> tuple[tuple, tuple]:
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"""判断箭头的方向（哪端是箭头/三角形汇聚点）。"""
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r = 20
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h, w = edges.shape[:2]
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def edge_density(cx, cy):
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x1 = max(0, int(cx - r))
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y1 = max(0, int(cy - r))
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x2 = min(w, int(cx + r))
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y2 = min(h, int(cy + r))
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roi = edges[y1:y2, x1:x2]
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if roi.size == 0:
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return 0
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return float(np.count_nonzero(roi)) / roi.size
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d1 = edge_density(p1[0], p1[1])
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d2 = edge_density(p2[0], p2[1])
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if d1 > d2 * 1.3:
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return p2, p1
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if d2 > d1 * 1.3:
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return p1, p2
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return p1, p2
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# ---------------------------------------------------------------------------
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# OCR 关联
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# ---------------------------------------------------------------------------
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def _correlate_with_ocr(
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annotations: list[Annotation],
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ocr_elements: list[dict],
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img_w: int,
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img_h: int,
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) -> None:
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"""将批注与附近的 OCR 文本关联。"""
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if not ocr_elements:
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return
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for ann in annotations:
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ax = ann.center[0]
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ay = ann.center[1]
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near_texts: list[tuple[str, float]] = []
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for elem in ocr_elements:
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bbox = elem.get("bbox", {})
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ex = bbox.get("x", 0) + bbox.get("w", 0) / 2
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ey = bbox.get("y", 0) + bbox.get("h", 0) / 2
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dist = math.hypot(ax - ex, ay - ey)
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max_dist = max(img_w, img_h) * 0.15
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if dist < max_dist:
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near_texts.append((elem.get("text", ""), dist))
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near_texts.sort(key=lambda x: x[1])
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ann.nearby_texts = [t for t, _ in near_texts[:5]]
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if ann.type == "arrow" and ann.from_pt and ann.to_pt:
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ann.from_text = _closest_text(ann.from_pt, ocr_elements, img_w, img_h)
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ann.to_text = _closest_text(ann.to_pt, ocr_elements, img_w, img_h)
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def _closest_text(pt: tuple[int, int], ocr_elements: list[dict], img_w: int, img_h: int) -> str:
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"""找到离 pt 最近的 OCR 文本。"""
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best_text = ""
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best_dist = max(img_w, img_h) * 0.12
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for elem in ocr_elements:
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bbox = elem.get("bbox", {})
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ex = bbox.get("x", 0) + bbox.get("w", 0) / 2
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ey = bbox.get("y", 0) + bbox.get("h", 0) / 2
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dist = math.hypot(pt[0] - ex, pt[1] - ey)
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if dist < best_dist:
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best_dist = dist
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best_text = elem.get("text", "")
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return best_text
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# ---------------------------------------------------------------------------
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# LLM 上下文格式化
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# ---------------------------------------------------------------------------
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def format_annotation_context(annotation_result: dict) -> str:
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"""将批注检测结果格式化为中文 LLM 提示文本。"""
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if not annotation_result or not isinstance(annotation_result, dict):
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return ""
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circles = annotation_result.get("circles", [])
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arrows = annotation_result.get("arrows", [])
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total = annotation_result.get("total", len(circles) + len(arrows))
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if total == 0:
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return ""
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parts = ["[图片批注检测结果]"]
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if circles:
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parts.append(f"\n检测到 {len(circles)} 个圈选标记:")
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for i, c in enumerate(circles):
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center = c.get("center", [0, 0])
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near = c.get("nearby_texts", [])
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parts.append(
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f" 圈{i+1}. 位置 ({center[0]},{center[1]})"
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f" — 圈选内容: {', '.join(near) if near else '(附近无文字)'}"
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)
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if arrows:
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parts.append(f"\n检测到 {len(arrows)} 个箭头标记:")
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for i, a in enumerate(arrows):
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ft = a.get("from_text", "")
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tt = a.get("to_text", "")
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parts.append(f" 箭头{i+1}. 从「{ft}」→ 指向「{tt}」")
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parts.append("\n请根据上述圈选/箭头定位用户要修改的报表字段。")
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return "\n".join(parts)
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+108
-20
@@ -51,6 +51,9 @@ def parse_file(file_path: str, file_type: str = "") -> dict:
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".webp": _parse_image,
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".pdf": _parse_pdf,
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".docx": _parse_docx,
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".xlsx": _parse_xlsx,
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".xls": _parse_xls,
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".doc": _parse_doc,
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}
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parser = parsers.get(suffix)
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@@ -72,26 +75,7 @@ def _parse_image(path: Path) -> dict:
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except Exception:
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info = "[图片: 无法读取元数据]"
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# 优先 EasyOCR（Windows 兼容性更好）
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try:
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import easyocr
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import numpy as np
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reader = easyocr.Reader(["ch_sim", "en"], gpu=False, verbose=False)
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result = reader.readtext(np.array(img))
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lines = [text.strip() for (_, text, _) in result if text.strip()]
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if lines:
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return {
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"text": f"{info}\n识别文本:\n" + "\n".join(lines),
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"file_type": "image",
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"method": "easyocr",
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"error": None,
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}
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except ImportError:
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pass
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except Exception:
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pass
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# 回退 PaddleOCR
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# 优先 PaddleOCR（精确识别）
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try:
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from paddleocr import PaddleOCR
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ocr = PaddleOCR(lang="ch")
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@@ -114,6 +98,25 @@ def _parse_image(path: Path) -> dict:
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except Exception:
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pass
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# 回退 EasyOCR
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try:
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import easyocr
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import numpy as np
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reader = easyocr.Reader(["ch_sim", "en"], gpu=False, verbose=False)
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result = reader.readtext(np.array(img))
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lines = [text.strip() for (_, text, _) in result if text.strip()]
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if lines:
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return {
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"text": f"{info}\n识别文本:\n" + "\n".join(lines),
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"file_type": "image",
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"method": "easyocr",
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"error": None,
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}
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except ImportError:
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pass
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except Exception:
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pass
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# OCR 不可用 → 返回图片元信息 + 安装提示
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return {
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"text": f"{info}\n(如需 OCR 文字识别，请安装: pip install easyocr)",
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@@ -195,6 +198,91 @@ def _parse_docx(path: Path) -> dict:
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"error": "DOCX 解析需要安装 python-docx"}
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def _parse_xlsx(path: Path) -> dict:
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"""提取 Excel .xlsx 文件中的文本。"""
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try:
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from openpyxl import load_workbook
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wb = load_workbook(path, read_only=True, data_only=True)
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parts = []
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for name in wb.sheetnames:
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ws = wb[name]
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rows = []
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for row in ws.iter_rows(values_only=True):
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cells = [str(c) if c is not None else "" for c in row]
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if any(c for c in cells):
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rows.append("\t".join(cells))
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if rows:
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parts.append(f"[Sheet: {name}]\n" + "\n".join(rows))
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wb.close()
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text = "\n\n".join(parts)
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return {"text": text, "file_type": "xlsx", "method": "openpyxl", "error": None}
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except ImportError:
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pass
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except Exception as e:
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return {"text": "", "file_type": "xlsx", "method": "none",
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"error": f"XLSX 解析失败: {e}"}
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return {"text": "", "file_type": "xlsx", "method": "none",
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"error": "XLSX 解析需要安装 openpyxl"}
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def _parse_xls(path: Path) -> dict:
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"""提取旧版 Excel .xls 文件中的文本。"""
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try:
|
||||
import xlrd
|
||||
wb = xlrd.open_workbook(path)
|
||||
parts = []
|
||||
for name in wb.sheet_names():
|
||||
ws = wb.sheet_by_name(name)
|
||||
rows = []
|
||||
for rx in range(ws.nrows):
|
||||
cells = [str(ws.cell_value(rx, cx)) if ws.cell_value(rx, cx) != "" else ""
|
||||
for cx in range(ws.ncols)]
|
||||
if any(c for c in cells):
|
||||
rows.append("\t".join(cells))
|
||||
if rows:
|
||||
parts.append(f"[Sheet: {name}]\n" + "\n".join(rows))
|
||||
text = "\n\n".join(parts)
|
||||
return {"text": text, "file_type": "xls", "method": "xlrd", "error": None}
|
||||
except ImportError:
|
||||
pass
|
||||
except Exception as e:
|
||||
return {"text": "", "file_type": "xls", "method": "none",
|
||||
"error": f"XLS 解析失败: {e}"}
|
||||
return {"text": "", "file_type": "xls", "method": "none",
|
||||
"error": "XLS 解析需要安装 xlrd"}
|
||||
|
||||
|
||||
def _parse_doc(path: Path) -> dict:
|
||||
"""提取旧版 Word .doc 文件中的文本（尽力而为，二进制格式）。"""
|
||||
try:
|
||||
import olefile
|
||||
ole = olefile.OleFileIO(path)
|
||||
if not ole.exists("WordDocument"):
|
||||
ole.close()
|
||||
return {"text": "", "file_type": "doc", "method": "none",
|
||||
"error": "不是有效的 .doc 文件"}
|
||||
raw = ole.openstream("WordDocument").read()
|
||||
ole.close()
|
||||
# 提取可打印 UTF-16LE 字符段
|
||||
text = ""
|
||||
try:
|
||||
decoded = raw.decode("utf-16-le", errors="ignore")
|
||||
text = "".join(c for c in decoded if c.isprintable() or c in "\n\r\t")
|
||||
except Exception:
|
||||
pass
|
||||
if not text.strip():
|
||||
return {"text": "", "file_type": "doc", "method": "olefile",
|
||||
"error": "无法提取文本（.doc 为二进制格式，建议转换为 .docx）"}
|
||||
return {"text": text.strip(), "file_type": "doc", "method": "olefile", "error": None}
|
||||
except ImportError:
|
||||
pass
|
||||
except Exception as e:
|
||||
return {"text": "", "file_type": "doc", "method": "none",
|
||||
"error": f"DOC 解析失败: {e}"}
|
||||
return {"text": "", "file_type": "doc", "method": "none",
|
||||
"error": "DOC 解析需要安装 olefile"}
|
||||
|
||||
|
||||
def _parse_text(path: Path) -> dict:
|
||||
"""读取纯文本文件。"""
|
||||
try:
|
||||
|
||||
+34
-34
@@ -373,40 +373,7 @@ def _load_image(path: Path) -> Optional[PIL.Image.Image]:
|
||||
def _ocr_elements(img: PIL.Image.Image, file_path: str) -> list[dict]:
|
||||
"""OCR 提取图片中的文字元素（位置+内容）。优先 EasyOCR，回退 PaddleOCR。"""
|
||||
|
||||
# 优先 EasyOCR
|
||||
try:
|
||||
import easyocr
|
||||
import numpy as np
|
||||
|
||||
reader = easyocr.Reader(["ch_sim", "en"], gpu=False, verbose=False)
|
||||
result = reader.readtext(np.array(img))
|
||||
|
||||
elements = []
|
||||
for (bbox, text, confidence) in result:
|
||||
if not text.strip():
|
||||
continue
|
||||
xs = [p[0] for p in bbox]
|
||||
ys = [p[1] for p in bbox]
|
||||
x_min, x_max = min(xs), max(xs)
|
||||
y_min, y_max = min(ys), max(ys)
|
||||
|
||||
elements.append({
|
||||
"x": round(x_min, 1),
|
||||
"y": round(y_min, 1),
|
||||
"w": round(x_max - x_min, 1),
|
||||
"h": round(y_max - y_min, 1),
|
||||
"font_size": round(y_max - y_min, 1),
|
||||
"text": text.strip(),
|
||||
})
|
||||
|
||||
elements.sort(key=lambda e: (e["y"], e["x"]))
|
||||
return elements
|
||||
except ImportError:
|
||||
pass
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# 回退 PaddleOCR
|
||||
# 优先 PaddleOCR（精确识别）
|
||||
try:
|
||||
from paddleocr import PaddleOCR
|
||||
import numpy as np
|
||||
@@ -446,6 +413,39 @@ def _ocr_elements(img: PIL.Image.Image, file_path: str) -> list[dict]:
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# 回退 EasyOCR
|
||||
try:
|
||||
import easyocr
|
||||
import numpy as np
|
||||
|
||||
reader = easyocr.Reader(["ch_sim", "en"], gpu=False, verbose=False)
|
||||
result = reader.readtext(np.array(img))
|
||||
|
||||
elements = []
|
||||
for (bbox, text, confidence) in result:
|
||||
if not text.strip():
|
||||
continue
|
||||
xs = [p[0] for p in bbox]
|
||||
ys = [p[1] for p in bbox]
|
||||
x_min, x_max = min(xs), max(xs)
|
||||
y_min, y_max = min(ys), max(ys)
|
||||
|
||||
elements.append({
|
||||
"x": round(x_min, 1),
|
||||
"y": round(y_min, 1),
|
||||
"w": round(x_max - x_min, 1),
|
||||
"h": round(y_max - y_min, 1),
|
||||
"font_size": round(y_max - y_min, 1),
|
||||
"text": text.strip(),
|
||||
})
|
||||
|
||||
elements.sort(key=lambda e: (e["y"], e["x"]))
|
||||
return elements
|
||||
except ImportError:
|
||||
pass
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
return []
|
||||
|
||||
|
||||
|
||||
@@ -284,13 +284,13 @@ class OcrExtractor:
|
||||
try:
|
||||
import numpy as np
|
||||
|
||||
easyocr_result = self._try_easyocr(np.array(img))
|
||||
if easyocr_result:
|
||||
return easyocr_result
|
||||
|
||||
paddleocr_result = self._try_paddleocr(img, file_path)
|
||||
if paddleocr_result:
|
||||
return paddleocr_result
|
||||
|
||||
easyocr_result = self._try_easyocr(np.array(img))
|
||||
if easyocr_result:
|
||||
return easyocr_result
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
|
||||
Reference in New Issue
Block a user