e2404d728b
Implements image extraction from Excel files with metadata. Features: - Extract embedded images from all sheets in Excel workbook - Capture image metadata: format, position (anchor cell), description, size - Base64 encode images for easy storage and transmission - Support multiple image formats (PNG, JPEG, GIF, BMP, etc.) - Handle images across multiple sheets - Include comprehensive unit tests (6 tests, all passing) Implementation: - Add extract_images() method to RAGFlowExcelParser - Use openpyxl's built-in image handling (_images property) - Convert column numbers to Excel letters (A, B, AA, etc.) - Extract alt text/descriptions when available - Return structured image data with position information Tests: - test_extract_images_from_excel: Basic extraction - test_extract_images_from_excel_without_images: Empty file handling - test_extract_images_multiple_sheets: Multi-sheet support - test_column_letter_conversion: Position calculation - test_extract_images_with_description: Metadata extraction - test_extract_images_with_size: Size information Fixes #11618
322 lines
12 KiB
Python
322 lines
12 KiB
Python
# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import logging
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import re
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import sys
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from io import BytesIO
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from typing import List, Dict, Any
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import base64
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import pandas as pd
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from openpyxl import Workbook, load_workbook
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from rag.nlp import find_codec
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# copied from `/openpyxl/cell/cell.py`
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ILLEGAL_CHARACTERS_RE = re.compile(r"[\000-\010]|[\013-\014]|[\016-\037]")
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class RAGFlowExcelParser:
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@staticmethod
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def _load_excel_to_workbook(file_like_object):
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if isinstance(file_like_object, bytes):
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file_like_object = BytesIO(file_like_object)
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# Read first 4 bytes to determine file type
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file_like_object.seek(0)
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file_head = file_like_object.read(4)
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file_like_object.seek(0)
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if not (file_head.startswith(b"PK\x03\x04") or file_head.startswith(b"\xd0\xcf\x11\xe0")):
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logging.info("Not an Excel file, converting CSV to Excel Workbook")
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try:
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file_like_object.seek(0)
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df = pd.read_csv(file_like_object)
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return RAGFlowExcelParser._dataframe_to_workbook(df)
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except Exception as e_csv:
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raise Exception(f"Failed to parse CSV and convert to Excel Workbook: {e_csv}")
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try:
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return load_workbook(file_like_object, data_only=True)
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except Exception as e:
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logging.info(f"openpyxl load error: {e}, try pandas instead")
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try:
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file_like_object.seek(0)
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try:
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dfs = pd.read_excel(file_like_object, sheet_name=None)
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return RAGFlowExcelParser._dataframe_to_workbook(dfs)
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except Exception as ex:
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logging.info(f"pandas with default engine load error: {ex}, try calamine instead")
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file_like_object.seek(0)
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df = pd.read_excel(file_like_object, engine="calamine")
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return RAGFlowExcelParser._dataframe_to_workbook(df)
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except Exception as e_pandas:
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raise Exception(f"pandas.read_excel error: {e_pandas}, original openpyxl error: {e}")
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@staticmethod
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def _clean_dataframe(df: pd.DataFrame):
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def clean_string(s):
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if isinstance(s, str):
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return ILLEGAL_CHARACTERS_RE.sub(" ", s)
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return s
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return df.apply(lambda col: col.map(clean_string))
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@staticmethod
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def _dataframe_to_workbook(df):
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# if contains multiple sheets use _dataframes_to_workbook
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if isinstance(df, dict) and len(df) > 1:
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return RAGFlowExcelParser._dataframes_to_workbook(df)
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df = RAGFlowExcelParser._clean_dataframe(df)
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wb = Workbook()
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ws = wb.active
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ws.title = "Data"
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for col_num, column_name in enumerate(df.columns, 1):
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ws.cell(row=1, column=col_num, value=column_name)
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for row_num, row in enumerate(df.values, 2):
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for col_num, value in enumerate(row, 1):
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ws.cell(row=row_num, column=col_num, value=value)
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return wb
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@staticmethod
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def _dataframes_to_workbook(dfs: dict):
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wb = Workbook()
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default_sheet = wb.active
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wb.remove(default_sheet)
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for sheet_name, df in dfs.items():
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df = RAGFlowExcelParser._clean_dataframe(df)
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ws = wb.create_sheet(title=sheet_name)
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for col_num, column_name in enumerate(df.columns, 1):
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ws.cell(row=1, column=col_num, value=column_name)
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for row_num, row in enumerate(df.values, 2):
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for col_num, value in enumerate(row, 1):
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ws.cell(row=row_num, column=col_num, value=value)
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return wb
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def html(self, fnm, chunk_rows=256):
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from html import escape
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file_like_object = BytesIO(fnm) if not isinstance(fnm, str) else fnm
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wb = RAGFlowExcelParser._load_excel_to_workbook(file_like_object)
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tb_chunks = []
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def _fmt(v):
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if v is None:
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return ""
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return str(v).strip()
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for sheetname in wb.sheetnames:
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ws = wb[sheetname]
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try:
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rows = list(ws.rows)
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except Exception as e:
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logging.warning(f"Skip sheet '{sheetname}' due to rows access error: {e}")
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continue
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if not rows:
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continue
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tb_rows_0 = "<tr>"
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for t in list(rows[0]):
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tb_rows_0 += f"<th>{escape(_fmt(t.value))}</th>"
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tb_rows_0 += "</tr>"
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for chunk_i in range((len(rows) - 1) // chunk_rows + 1):
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tb = ""
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tb += f"<table><caption>{sheetname}</caption>"
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tb += tb_rows_0
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for r in list(rows[1 + chunk_i * chunk_rows : min(1 + (chunk_i + 1) * chunk_rows, len(rows))]):
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tb += "<tr>"
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for i, c in enumerate(r):
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if c.value is None:
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tb += "<td></td>"
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else:
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tb += f"<td>{escape(_fmt(c.value))}</td>"
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tb += "</tr>"
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tb += "</table>\n"
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tb_chunks.append(tb)
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return tb_chunks
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def markdown(self, fnm):
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import pandas as pd
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file_like_object = BytesIO(fnm) if not isinstance(fnm, str) else fnm
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try:
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file_like_object.seek(0)
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df = pd.read_excel(file_like_object)
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except Exception as e:
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logging.warning(f"Parse spreadsheet error: {e}, trying to interpret as CSV file")
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file_like_object.seek(0)
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df = pd.read_csv(file_like_object)
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df = df.replace(r"^\s*$", "", regex=True)
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return df.to_markdown(index=False)
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def extract_images(self, fnm) -> List[Dict[str, Any]]:
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"""
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Extract all embedded images from Excel file.
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Args:
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fnm: File path or bytes
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Returns:
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List of dictionaries containing image information:
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{
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'image_data': base64 encoded image data,
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'format': image format (png, jpeg, etc.),
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'sheet': sheet name,
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'anchor': cell anchor position (e.g., 'A1'),
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'description': alt text/description if available,
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'size': (width, height) in pixels
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}
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"""
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file_like_object = BytesIO(fnm) if not isinstance(fnm, str) else fnm
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wb = RAGFlowExcelParser._load_excel_to_workbook(file_like_object)
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images = []
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image_index = 0
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for sheetname in wb.sheetnames:
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ws = wb[sheetname]
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# openpyxl stores images in worksheet._images
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if hasattr(ws, '_images') and ws._images:
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for img in ws._images:
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try:
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# Get image data
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img_data = img._data() if hasattr(img, '_data') else img.ref
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# Encode image to base64
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if isinstance(img_data, bytes):
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img_base64 = base64.b64encode(img_data).decode('utf-8')
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else:
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img_base64 = img_data
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# Get image format
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img_format = getattr(img, 'format', 'png').lower()
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if img_format == 'emf':
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img_format = 'png' # Convert EMF to common format indicator
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# Get anchor position
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anchor = 'Unknown'
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if hasattr(img, 'anchor') and img.anchor:
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if hasattr(img.anchor, '_from'):
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# Anchor is a cell reference
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anchor_cell = img.anchor._from
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if hasattr(anchor_cell, 'col') and hasattr(anchor_cell, 'row'):
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# Convert column number to letter
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col_letter = self._number_to_column_letter(anchor_cell.col)
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anchor = f"{col_letter}{anchor_cell.row + 1}"
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elif hasattr(img.anchor, 'col') and hasattr(img.anchor, 'row'):
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col_letter = self._number_to_column_letter(img.anchor.col)
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anchor = f"{col_letter}{img.anchor.row + 1}"
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# Get description/alt text
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description = getattr(img, 'name', '') or getattr(img, 'description', '') or f'Image_{image_index}'
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# Get size
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width = getattr(img, 'width', 0)
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height = getattr(img, 'height', 0)
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images.append({
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'image_data': img_base64,
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'format': img_format,
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'sheet': sheetname,
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'anchor': anchor,
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'description': description,
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'size': (width, height),
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'index': image_index
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})
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image_index += 1
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logging.info(f"Extracted image from sheet '{sheetname}' at {anchor}")
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except Exception as e:
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logging.warning(f"Failed to extract image from sheet '{sheetname}': {e}")
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continue
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logging.info(f"Extracted {len(images)} images from Excel file")
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return images
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@staticmethod
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def _number_to_column_letter(n):
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"""Convert column number to Excel column letter (0 -> A, 1 -> B, etc.)"""
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result = ""
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while n >= 0:
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result = chr(n % 26 + 65) + result
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n = n // 26 - 1
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if n < 0:
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break
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return result
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def __call__(self, fnm):
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file_like_object = BytesIO(fnm) if not isinstance(fnm, str) else fnm
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wb = RAGFlowExcelParser._load_excel_to_workbook(file_like_object)
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res = []
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for sheetname in wb.sheetnames:
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ws = wb[sheetname]
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try:
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rows = list(ws.rows)
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except Exception as e:
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logging.warning(f"Skip sheet '{sheetname}' due to rows access error: {e}")
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continue
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if not rows:
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continue
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ti = list(rows[0])
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for r in list(rows[1:]):
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fields = []
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for i, c in enumerate(r):
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if not c.value:
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continue
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t = str(ti[i].value) if i < len(ti) else ""
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t += ("：" if t else "") + str(c.value)
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fields.append(t)
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line = "; ".join(fields)
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if sheetname.lower().find("sheet") < 0:
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line += " ——" + sheetname
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res.append(line)
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return res
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@staticmethod
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def row_number(fnm, binary):
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if fnm.split(".")[-1].lower().find("xls") >= 0:
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wb = RAGFlowExcelParser._load_excel_to_workbook(BytesIO(binary))
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total = 0
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for sheetname in wb.sheetnames:
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try:
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ws = wb[sheetname]
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total += len(list(ws.rows))
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except Exception as e:
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logging.warning(f"Skip sheet '{sheetname}' due to rows access error: {e}")
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continue
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return total
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if fnm.split(".")[-1].lower() in ["csv", "txt"]:
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encoding = find_codec(binary)
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txt = binary.decode(encoding, errors="ignore")
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return len(txt.split("\n"))
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if __name__ == "__main__":
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psr = RAGFlowExcelParser()
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psr(sys.argv[1])
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