387 lines
14 KiB
Python
387 lines
14 KiB
Python
from __future__ import division
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import os
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import sys
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import copy
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import types
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import logging
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import copy_reg
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import subprocess
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from .imgproc import (adaptive_threshold, find_lines, find_table_contours,
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find_table_joints)
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from .table import Table
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from .utils import (scale_to_pdf, scale_to_image, segments_bbox, text_in_bbox,
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merge_close_values, get_table_index, get_score, count_empty,
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encode_list, get_text_objects, get_page_layout, remove_empty)
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__all__ = ['Lattice']
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logger = logging.getLogger("app_logger")
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def _reduce_method(m):
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if m.im_self is None:
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return getattr, (m.im_class, m.im_func.func_name)
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else:
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return getattr, (m.im_self, m.im_func.func_name)
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copy_reg.pickle(types.MethodType, _reduce_method)
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def _reduce_index(t, idx, shift_text):
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"""Reduces index of a text object if it lies within a spanning
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cell.
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Parameters
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----------
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table : object
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camelot.table.Table
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idx : list
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List of tuples of the form (r_idx, c_idx, text).
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shift_text : list
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{'l', 'r', 't', 'b'}
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Select one or more from above and pass them as a list to
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specify where the text in a spanning cell should flow.
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Returns
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-------
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indices : list
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List of tuples of the form (idx, text) where idx is the reduced
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index of row/column and text is the an lttextline substring.
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"""
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indices = []
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for r_idx, c_idx, text in idx:
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for d in shift_text:
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if d == 'l':
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if t.cells[r_idx][c_idx].spanning_h:
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while not t.cells[r_idx][c_idx].left:
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c_idx -= 1
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if d == 'r':
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if t.cells[r_idx][c_idx].spanning_h:
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while not t.cells[r_idx][c_idx].right:
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c_idx += 1
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if d == 't':
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if t.cells[r_idx][c_idx].spanning_v:
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while not t.cells[r_idx][c_idx].top:
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r_idx -= 1
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if d == 'b':
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if t.cells[r_idx][c_idx].spanning_v:
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while not t.cells[r_idx][c_idx].bottom:
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r_idx += 1
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indices.append((r_idx, c_idx, text))
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return indices
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def _fill_spanning(t, fill=None):
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"""Fills spanning cells.
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Parameters
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----------
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t : object
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camelot.table.Table
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fill : list
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{'h', 'v'}
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Specify to fill spanning cells in horizontal or vertical
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direction.
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(optional, default: None)
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Returns
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-------
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t : object
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camelot.table.Table
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"""
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for f in fill:
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if f == "h":
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for i in range(len(t.cells)):
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for j in range(len(t.cells[i])):
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if t.cells[i][j].get_text().strip() == '':
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if t.cells[i][j].spanning_h:
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t.cells[i][j].add_text(t.cells[i][j - 1].get_text())
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elif f == "v":
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for i in range(len(t.cells)):
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for j in range(len(t.cells[i])):
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if t.cells[i][j].get_text().strip() == '':
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if t.cells[i][j].spanning_v:
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t.cells[i][j].add_text(t.cells[i - 1][j].get_text())
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return t
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class Lattice:
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"""Lattice looks for lines in the pdf to form a table.
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If you want to give fill and mtol for each table when specifying
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multiple table areas, make sure that the length of fill and mtol
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is equal to the length of table_area. Mapping between them is based
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on index.
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Parameters
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----------
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table_area : list
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List of strings of the form x1,y1,x2,y2 where
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(x1, y1) -> left-top and (x2, y2) -> right-bottom in PDFMiner's
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coordinate space, denoting table areas to analyze.
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(optional, default: None)
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fill : list
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List of strings specifying directions to fill spanning cells.
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{'h', 'v'} to fill spanning cells in horizontal or vertical
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direction.
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(optional, default: None)
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mtol : list
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List of ints specifying m-tolerance parameters.
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(optional, default: [2])
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jtol : list
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List of ints specifying j-tolerance parameters.
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(optional, default: [2])
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blocksize : int
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Size of a pixel neighborhood that is used to calculate a
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threshold value for the pixel: 3, 5, 7, and so on.
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(optional, default: 15)
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threshold_constant : float
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Constant subtracted from the mean or weighted mean
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(see the details below). Normally, it is positive but may be
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zero or negative as well.
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(optional, default: -2)
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scale : int
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Used to divide the height/width of a pdf to get a structuring
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element for image processing.
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(optional, default: 15)
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iterations : int
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Number of iterations for dilation.
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(optional, default: 2)
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invert : bool
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Whether or not to invert the image. Useful when pdfs have
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tables with lines in background.
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(optional, default: False)
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margins : tuple
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PDFMiner margins. (char_margin, line_margin, word_margin)
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(optional, default: (1.0, 0.5, 0.1))
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split_text : bool
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Whether or not to split a text line if it spans across
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different cells.
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(optional, default: False)
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flag_size : bool
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Whether or not to highlight a substring using <s></s>
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if its size is different from rest of the string, useful for
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super and subscripts.
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(optional, default: True)
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shift_text : list
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{'l', 'r', 't', 'b'}
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Select one or more from above and pass them as a list to
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specify where the text in a spanning cell should flow.
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(optional, default: ['l', 't'])
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debug : string
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{'contour', 'line', 'joint', 'table'}
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Set to one of the above values to generate a matplotlib plot
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of detected contours, lines, joints and the table generated.
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(optional, default: None)
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"""
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def __init__(self, table_area=None, fill=None, mtol=[2], jtol=[2],
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blocksize=15, threshold_constant=-2, scale=15, iterations=2,
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invert=False, margins=(1.0, 0.5, 0.1), split_text=False,
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flag_size=True, shift_text=['l', 't'], debug=None):
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self.method = 'lattice'
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self.table_area = table_area
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self.fill = fill
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self.mtol = mtol
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self.jtol = jtol
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self.blocksize = blocksize
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self.threshold_constant = threshold_constant
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self.scale = scale
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self.iterations = iterations
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self.invert = invert
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self.char_margin, self.line_margin, self.word_margin = margins
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self.split_text = split_text
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self.flag_size = flag_size
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self.shift_text = shift_text
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self.debug = debug
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def get_tables(self, pdfname):
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"""Expects a single page pdf as input with rotation corrected.
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Parameters
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----------
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pdfname : string
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Path to single page pdf file.
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Returns
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-------
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page : dict
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"""
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layout, dim = get_page_layout(pdfname, char_margin=self.char_margin,
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line_margin=self.line_margin, word_margin=self.word_margin)
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lttextlh = get_text_objects(layout, ltype="lh")
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lttextlv = get_text_objects(layout, ltype="lv")
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ltchar = get_text_objects(layout, ltype="char")
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width, height = dim
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bname, __ = os.path.splitext(pdfname)
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logger.info('Parsing tables from {0}.'.format(os.path.basename(bname)))
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if not ltchar:
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logger.warning("{0}: PDF has no text. It may be an image.".format(
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os.path.basename(bname)))
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return {os.path.basename(bname): None}
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imagename = ''.join([bname, '.png'])
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gs_call = [
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"-q", "-sDEVICE=png16m", "-o", imagename, "-r600", pdfname
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]
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if "ghostscript" in subprocess.check_output(["gs", "-version"]).lower():
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gs_call.insert(0, "gs")
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else:
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gs_call.insert(0, "gsc")
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subprocess.call(gs_call, stdout=open(os.devnull, 'w'),
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stderr=subprocess.STDOUT)
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img, threshold = adaptive_threshold(imagename, invert=self.invert,
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blocksize=self.blocksize, c=self.threshold_constant)
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pdf_x = width
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pdf_y = height
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img_x = img.shape[1]
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img_y = img.shape[0]
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sc_x_image = img_x / float(pdf_x)
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sc_y_image = img_y / float(pdf_y)
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sc_x_pdf = pdf_x / float(img_x)
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sc_y_pdf = pdf_y / float(img_y)
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factors_image = (sc_x_image, sc_y_image, pdf_y)
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factors_pdf = (sc_x_pdf, sc_y_pdf, img_y)
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vmask, v_segments = find_lines(threshold, direction='vertical',
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scale=self.scale, iterations=self.iterations)
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hmask, h_segments = find_lines(threshold, direction='horizontal',
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scale=self.scale, iterations=self.iterations)
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if self.table_area is not None:
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if self.fill is not None:
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if len(self.table_area) != len(self.fill):
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raise ValueError("Length of table area and fill should be equal.")
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areas = []
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for area in self.table_area:
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x1, y1, x2, y2 = area.split(",")
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x1 = float(x1)
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y1 = float(y1)
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x2 = float(x2)
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y2 = float(y2)
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x1, y1, x2, y2 = scale_to_image((x1, y1, x2, y2), factors_image)
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areas.append((x1, y1, abs(x2 - x1), abs(y2 - y1)))
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table_bbox = find_table_joints(areas, vmask, hmask)
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else:
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contours = find_table_contours(vmask, hmask)
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table_bbox = find_table_joints(contours, vmask, hmask)
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if len(self.mtol) == 1 and self.mtol[0] == 2:
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mtolerance = copy.deepcopy(self.mtol) * len(table_bbox)
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else:
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mtolerance = copy.deepcopy(self.mtol)
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if len(self.jtol) == 1 and self.jtol[0] == 2:
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jtolerance = copy.deepcopy(self.jtol) * len(table_bbox)
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else:
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jtolerance = copy.deepcopy(self.jtol)
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if self.debug:
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self.debug_images = (img, table_bbox)
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table_bbox, v_segments, h_segments = scale_to_pdf(table_bbox, v_segments,
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h_segments, factors_pdf)
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if self.debug:
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self.debug_segments = (v_segments, h_segments)
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self.debug_tables = []
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page = {}
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tables = {}
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# sort tables based on y-coord
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for table_no, k in enumerate(sorted(table_bbox.keys(), key=lambda x: x[1], reverse=True)):
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# select elements which lie within table_bbox
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table_data = {}
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t_bbox = {}
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v_s, h_s = segments_bbox(k, v_segments, h_segments)
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t_bbox['horizontal'] = text_in_bbox(k, lttextlh)
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t_bbox['vertical'] = text_in_bbox(k, lttextlv)
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char_bbox = text_in_bbox(k, ltchar)
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table_data['text_p'] = 100 * (1 - (len(char_bbox) / len(ltchar)))
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for direction in t_bbox:
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t_bbox[direction].sort(key=lambda x: (-x.y0, x.x0))
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cols, rows = zip(*table_bbox[k])
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cols, rows = list(cols), list(rows)
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cols.extend([k[0], k[2]])
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rows.extend([k[1], k[3]])
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# sort horizontal and vertical segments
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cols = merge_close_values(sorted(cols), mtol=mtolerance[table_no])
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rows = merge_close_values(
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sorted(rows, reverse=True), mtol=mtolerance[table_no])
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# make grid using x and y coord of shortlisted rows and cols
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cols = [(cols[i], cols[i + 1])
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for i in range(0, len(cols) - 1)]
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rows = [(rows[i], rows[i + 1])
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for i in range(0, len(rows) - 1)]
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table = Table(cols, rows)
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# set table edges to True using ver+hor lines
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table = table.set_edges(v_s, h_s, jtol=jtolerance[table_no])
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nouse = table.nocont_ / (len(v_s) + len(h_s))
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table_data['line_p'] = 100 * (1 - nouse)
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# set spanning cells to True
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table = table.set_spanning()
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# set table border edges to True
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table = table.set_border_edges()
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if self.debug:
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self.debug_tables.append(table)
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assignment_errors = []
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table_data['split_text'] = []
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table_data['superscript'] = []
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for direction in ['vertical', 'horizontal']:
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for t in t_bbox[direction]:
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indices, error = get_table_index(
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table, t, direction, split_text=self.split_text,
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flag_size=self.flag_size)
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if indices[:2] != (-1, -1):
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assignment_errors.append(error)
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indices = _reduce_index(table, indices, shift_text=self.shift_text)
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if len(indices) > 1:
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table_data['split_text'].append(indices)
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for r_idx, c_idx, text in indices:
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if all(s in text for s in ['<s>', '</s>']):
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table_data['superscript'].append((r_idx, c_idx, text))
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table.cells[r_idx][c_idx].add_text(text)
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score = get_score([[100, assignment_errors]])
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table_data['score'] = score
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if self.fill is not None:
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table = _fill_spanning(table, fill=self.fill[table_no])
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ar = table.get_list()
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ar = remove_empty(ar)
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ar = encode_list(ar)
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table_data['data'] = ar
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empty_p, r_nempty_cells, c_nempty_cells = count_empty(ar)
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table_data['empty_p'] = empty_p
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table_data['r_nempty_cells'] = r_nempty_cells
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table_data['c_nempty_cells'] = c_nempty_cells
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table_data['nrows'] = len(ar)
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table_data['ncols'] = len(ar[0])
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tables['table-{0}'.format(table_no + 1)] = table_data
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page[os.path.basename(bname)] = tables
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if self.debug:
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return None
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return page |