TextProcessor.py

import pandas as pd
import numpy as np
import fitz
import re
import cv2


def paragraphs(folderpath):
    doc = fitz.open(folderpath + '/opinion.pdf')
    df = pd.read_csv(folderpath + '/data.csv').replace({np.nan: None})
    indices = list(df.index)
    pg_indices = df['Pg Ind'].tolist()

    x1s, y1s, x2s, y2s, line_texts, line_inds, pg_inds, baselines, rights = [], [], [], [], [], [], [], {}, {}
    paras, types = [], []
    for (i, pg_ind) in enumerate(pg_indices):
        lines = eval(df[df['Pg Ind'] == i]['Lines'].tolist()[0])
        pg_x1s, pg_x2s = [], []
        for (j, n) in enumerate(lines):
            x1s.append(n[0])
            y1s.append(n[1])
            x2s.append(n[2])
            y2s.append(n[3])
            line_texts.append(n[4])
            pg_x1s.append(n[0])
            pg_x2s.append(n[2])
            pg_inds.append(i)
            line_inds.append(j)
        baselines[i] = min(pg_x1s)
        rights[i] = max(pg_x2s)

    is_inblock = False
    for (j, line_text) in enumerate(line_texts):
        if j == 0:
            para = []
            continue

        if len(line_texts[j]) > 0:
            prior_median = (baselines[pg_inds[j - 1]] + rights[pg_inds[j - 1]]) / 2
            current_median = (baselines[pg_inds[j]] + rights[pg_inds[j]]) / 2

            prior_endswith_period = re.search('[:\.]([^A-z]{0,2})$',line_texts[j - 1].strip()) is not None  # Include colon?
            prior_is_section_header = re.search('^([ABCDEIVX]+)$', line_texts[j - 1].strip()) is not None
            current_is_section_header = re.search('^([ABCDEIVX]+)$', line_texts[j].strip()) is not None
            prior_is_asterisk = re.search('^([\s\*]+)$', line_texts[j - 1].strip()) is not None
            current_is_asterisk = re.search('^([\s\*]+)$', line_texts[j].strip()) is not None
            prior_is_date = re.search('(\[[A-z\s0-9]*,\s[0-9]*]+)$', line_texts[j - 1].strip()) is not None

            current_tabbed = x1s[j] - baselines[pg_inds[j]] > 7
            prior_tabbed = x1s[j - 1] - baselines[pg_inds[j - 1]] > 7
            prior_supertabbed = x1s[j - 1] - baselines[pg_inds[j - 1]] >= 11
            current_supertabbed = x1s[j] - baselines[pg_inds[j]] >= 11
            prior_more_left = (x1s[j] - baselines[pg_inds[j]]) - (x1s[j - 1] - baselines[pg_inds[j - 1]]) > 7
            prior_right_margin = x1s[j - 1] > prior_median

            is_section_header = (prior_is_section_header or current_is_section_header or prior_is_asterisk or current_is_asterisk or prior_is_date)
            is_the_classic = (prior_endswith_period and current_tabbed and prior_more_left and not prior_supertabbed)  # Note: Supertabbing oofs stuff
            is_start_blockquote = (prior_endswith_period and current_supertabbed and prior_more_left and not is_inblock)
            is_after_blockquote = (prior_endswith_period and not current_supertabbed and is_inblock)
            is_after_disposition = (prior_right_margin and current_tabbed)
            # is_after_oneline_paragraph = (prior_tabbed and current_tabbed and not prior_supertabbed and not current_supertabbed and not is_inblock)

            if is_start_blockquote and not is_section_header:
                is_inblock = True
            if is_after_blockquote or prior_is_date: # This date hack helps ensure that slip opinion headers do not get caught
                is_inblock = False

            if is_section_header or is_the_classic or is_start_blockquote or is_after_blockquote or is_after_disposition:
                paras.append(para)
                types.append(is_inblock)
                para = []
            #     print('\n')
            # print(str([j, pg_inds[j]]) + ':\t' + str(is_the_classic) + '\t' + str(is_start_blockquote)  + '\t' + str(is_after_blockquote)+ '\t' + str(is_after_disposition) + '\t' + str(is_after_oneline_paragraph) + '\t' + line_text)
            para.append((pg_inds[j], line_inds[j], line_text))

    paras.append(para)
    types.append(is_inblock)
    paras_df = pd.DataFrame({'Lines': paras, 'Block Quote': types})
    return paras_df

def process_file(folderpath):
    paras_df = paragraphs(folderpath)
    paras_df.to_csv(folderpath + '/paragraphs.csv', index=True)
    data_df = pd.read_csv(folderpath + '/data.csv')
    paras_lines = paras_df['Lines'].tolist()
    indents = []
    for (i, para_lines) in enumerate(paras_lines):
        para = []
        para_start_pg_ind, para_start_line_ind, para_first_line = para_lines[0]
        page_df = data_df[data_df['Pg Ind'] == para_start_pg_ind]
        pg_lines = eval(page_df['Lines'].tolist()[0])
        x1, y1, x2, y2, text = pg_lines[para_start_line_ind]
        indents.append((x1, y1, x2, y2, para_first_line, para_start_pg_ind))

    for indent in indents:
        x1, y1, x2, y2, para_first_line, pg_ind = indent
        image = cv2.imread(folderpath + '/' + str(pg_ind) + '-processed.png')
        cv2.circle(image, (x1 - 15, int(0.5 * (y1 + y2))), radius=1, color=(240, 32, 160), thickness=2)
        cv2.imwrite(folderpath + '/' + str(pg_ind) + '-processed.png', image)

process_file('PDF Cases/333_178')