import streamlit as st from PIL import Image, ImageEnhance import statistics import os import string from collections import Counter from itertools import tee, count # import TDTSR import pytesseract from pytesseract import Output import json import pandas as pd import matplotlib.pyplot as plt import cv2 import numpy as np # from transformers import TrOCRProcessor, VisionEncoderDecoderModel # from cv2 import dnn_superres from transformers import DetrFeatureExtractor from transformers import DetrForObjectDetection import torch import asyncio # pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe' st.set_option('deprecation.showPyplotGlobalUse', False) st.set_page_config(layout='wide') st.title("Table Detection and Table Structure Recognition") st.write("Implemented by MSFT team: https://github.com/microsoft/table-transformer") def PIL_to_cv(pil_img): return cv2.cvtColor(np.array(pil_img), cv2.COLOR_RGB2BGR) def cv_to_PIL(cv_img): return Image.fromarray(cv2.cvtColor(cv_img, cv2.COLOR_BGR2RGB)) async def pytess(cell_pil_img): pytess_output=' '.join(pytesseract.image_to_data(cell_pil_img, output_type=Output.DICT, config='-c tessedit_char_blacklist=œ˜â€œï¬â™Ã©œ¢!|”?«“¥ --psm 6 preserve_interword_spaces')['text']).strip() print("pytess_output#####################################################") print(pytess_output) print("pytess_output@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@") return pytess_output # def super_res(pil_img): # ''' # Useful for low-res docs # ''' # requires opencv-contrib-python installed without the opencv-python # sr = dnn_superres.DnnSuperResImpl_create() # image = PIL_to_cv(pil_img) # model_path = "/data/Salman/TRD/code/table-transformer/transformers/LapSRN_x2.pb" # model_name = 'lapsrn' # model_scale = 2 # sr.readModel(model_path) # sr.setModel(model_name, model_scale) # final_img = sr.upsample(image) # final_img = cv_to_PIL(final_img) # return final_img def sharpen_image(pil_img): img = PIL_to_cv(pil_img) sharpen_kernel = np.array([[-1, -1, -1], [-1, 9, -1], [-1, -1, -1]]) sharpen = cv2.filter2D(img, -1, sharpen_kernel) pil_img = cv_to_PIL(sharpen) return pil_img def uniquify(seq, suffs = count(1)): """Make all the items unique by adding a suffix (1, 2, etc). Credit: https://stackoverflow.com/questions/30650474/python-rename-duplicates-in-list-with-progressive-numbers-without-sorting-list `seq` is mutable sequence of strings. `suffs` is an optional alternative suffix iterable. """ not_unique = [k for k,v in Counter(seq).items() if v>1] suff_gens = dict(zip(not_unique, tee(suffs, len(not_unique)))) for idx,s in enumerate(seq): try: suffix = str(next(suff_gens[s])) except KeyError: continue else: seq[idx] += suffix return seq def binarizeBlur_image(pil_img): image = PIL_to_cv(pil_img) thresh = cv2.threshold(image, 150, 255, cv2.THRESH_BINARY_INV)[1] result = cv2.GaussianBlur(thresh, (5,5), 0) result = 255 - result return cv_to_PIL(result) def td_postprocess(pil_img): ''' Removes gray background from tables ''' img = PIL_to_cv(pil_img) hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) mask = cv2.inRange(hsv, (0, 0, 100), (255, 5, 255)) # (0, 0, 100), (255, 5, 255) nzmask = cv2.inRange(hsv, (0, 0, 5), (255, 255, 255)) # (0, 0, 5), (255, 255, 255)) nzmask = cv2.erode(nzmask, np.ones((3,3))) # (3,3) mask = mask & nzmask new_img = img.copy() new_img[np.where(mask)] = 255 return cv_to_PIL(new_img) # def super_res(pil_img): # # requires opencv-contrib-python installed without the opencv-python # sr = dnn_superres.DnnSuperResImpl_create() # image = PIL_to_cv(pil_img) # model_path = "./LapSRN_x8.pb" # model_name = model_path.split('/')[1].split('_')[0].lower() # model_scale = int(model_path.split('/')[1].split('_')[1].split('.')[0][1]) # sr.readModel(model_path) # sr.setModel(model_name, model_scale) # final_img = sr.upsample(image) # final_img = cv_to_PIL(final_img) # return final_img def table_detector(image, THRESHOLD_PROBA): ''' Table detection using DEtect-object TRansformer pre-trained on 1 million tables ''' feature_extractor = DetrFeatureExtractor(do_resize=True, size=800, max_size=800) encoding = feature_extractor(image, return_tensors="pt") model = DetrForObjectDetection.from_pretrained("microsoft/table-transformer-detection") with torch.no_grad(): outputs = model(**encoding) probas = outputs.logits.softmax(-1)[0, :, :-1] keep = probas.max(-1).values > THRESHOLD_PROBA target_sizes = torch.tensor(image.size[::-1]).unsqueeze(0) postprocessed_outputs = feature_extractor.post_process(outputs, target_sizes) bboxes_scaled = postprocessed_outputs[0]['boxes'][keep] return (model, probas[keep], bboxes_scaled) def table_struct_recog(image, THRESHOLD_PROBA): ''' Table structure recognition using DEtect-object TRansformer pre-trained on 1 million tables ''' feature_extractor = DetrFeatureExtractor(do_resize=True, size=1000, max_size=1000) encoding = feature_extractor(image, return_tensors="pt") model = DetrForObjectDetection.from_pretrained("microsoft/table-transformer-structure-recognition") with torch.no_grad(): outputs = model(**encoding) probas = outputs.logits.softmax(-1)[0, :, :-1] keep = probas.max(-1).values > THRESHOLD_PROBA target_sizes = torch.tensor(image.size[::-1]).unsqueeze(0) postprocessed_outputs = feature_extractor.post_process(outputs, target_sizes) bboxes_scaled = postprocessed_outputs[0]['boxes'][keep] return (model, probas[keep], bboxes_scaled) class TableExtractionPipeline(): colors = ["red", "blue", "green", "yellow", "orange", "violet"] # colors = ["red", "blue", "green", "red", "red", "red"] def add_padding(self, pil_img, top, right, bottom, left, color=(255,255,255)): ''' Image padding as part of TSR pre-processing to prevent missing table edges ''' width, height = pil_img.size new_width = width + right + left new_height = height + top + bottom result = Image.new(pil_img.mode, (new_width, new_height), color) result.paste(pil_img, (left, top)) return result def plot_results_detection(self, c1, model, pil_img, prob, boxes, delta_xmin, delta_ymin, delta_xmax, delta_ymax): ''' crop_tables and plot_results_detection must have same co-ord shifts because 1 only plots the other one updates co-ordinates ''' # st.write('img_obj') # st.write(pil_img) plt.imshow(pil_img) ax = plt.gca() for p, (xmin, ymin, xmax, ymax) in zip(prob, boxes.tolist()): cl = p.argmax() xmin, ymin, xmax, ymax = xmin-delta_xmin, ymin-delta_ymin, xmax+delta_xmax, ymax+delta_ymax ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin,fill=False, color='red', linewidth=3)) text = f'{model.config.id2label[cl.item()]}: {p[cl]:0.2f}' ax.text(xmin-20, ymin-50, text, fontsize=10,bbox=dict(facecolor='yellow', alpha=0.5)) plt.axis('off') c1.pyplot() def crop_tables(self, pil_img, prob, boxes, delta_xmin, delta_ymin, delta_xmax, delta_ymax): ''' crop_tables and plot_results_detection must have same co-ord shifts because 1 only plots the other one updates co-ordinates ''' cropped_img_list = [] for p, (xmin, ymin, xmax, ymax) in zip(prob, boxes.tolist()): xmin, ymin, xmax, ymax = xmin-delta_xmin, ymin-delta_ymin, xmax+delta_xmax, ymax+delta_ymax cropped_img = pil_img.crop((xmin, ymin, xmax, ymax)) cropped_img_list.append(cropped_img) return cropped_img_list def generate_structure(self, c2, model, pil_img, prob, boxes, expand_rowcol_bbox_top, expand_rowcol_bbox_bottom): ''' Co-ordinates are adjusted here by 3 'pixels' To plot table pillow image and the TSR bounding boxes on the table ''' # st.write('img_obj') # st.write(pil_img) plt.figure(figsize=(32,20)) plt.imshow(pil_img) ax = plt.gca() rows = {} cols = {} idx = 0 for p, (xmin, ymin, xmax, ymax) in zip(prob, boxes.tolist()): xmin, ymin, xmax, ymax = xmin, ymin, xmax, ymax cl = p.argmax() class_text = model.config.id2label[cl.item()] text = f'{class_text}: {p[cl]:0.2f}' # or (class_text == 'table column') if (class_text == 'table row') or (class_text =='table projected row header') or (class_text == 'table column'): ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin,fill=False, color=self.colors[cl.item()], linewidth=2)) ax.text(xmin-10, ymin-10, text, fontsize=5, bbox=dict(facecolor='yellow', alpha=0.5)) if class_text == 'table row': rows['table row.'+str(idx)] = (xmin, ymin-expand_rowcol_bbox_top, xmax, ymax+expand_rowcol_bbox_bottom) if class_text == 'table column': cols['table column.'+str(idx)] = (xmin, ymin-expand_rowcol_bbox_top, xmax, ymax+expand_rowcol_bbox_bottom) idx += 1 plt.axis('on') c2.pyplot() return rows, cols def sort_table_featuresv2(self, rows:dict, cols:dict): # Sometimes the header and first row overlap, and we need the header bbox not to have first row's bbox inside the headers bbox rows_ = {table_feature : (xmin, ymin, xmax, ymax) for table_feature, (xmin, ymin, xmax, ymax) in sorted(rows.items(), key=lambda tup: tup[1][1])} cols_ = {table_feature : (xmin, ymin, xmax, ymax) for table_feature, (xmin, ymin, xmax, ymax) in sorted(cols.items(), key=lambda tup: tup[1][0])} return rows_, cols_ def individual_table_featuresv2(self, pil_img, rows:dict, cols:dict): for k, v in rows.items(): xmin, ymin, xmax, ymax = v cropped_img = pil_img.crop((xmin, ymin, xmax, ymax)) rows[k] = xmin, ymin, xmax, ymax, cropped_img for k, v in cols.items(): xmin, ymin, xmax, ymax = v cropped_img = pil_img.crop((xmin, ymin, xmax, ymax)) cols[k] = xmin, ymin, xmax, ymax, cropped_img return rows, cols def object_to_cellsv2(self, master_row:dict, cols:dict, expand_rowcol_bbox_top, expand_rowcol_bbox_bottom, padd_left): '''Removes redundant bbox for rows&columns and divides each row into cells from columns Args: Returns: ''' cells_img = {} header_idx = 0 row_idx = 0 previous_xmax_col = 0 new_cols = {} new_master_row = {} previous_ymin_row = 0 new_cols = cols new_master_row = master_row ## Below 2 for loops remove redundant bounding boxes ### # for k_col, v_col in cols.items(): # xmin_col, _, xmax_col, _, col_img = v_col # if (np.isclose(previous_xmax_col, xmax_col, atol=5)) or (xmin_col >= xmax_col): # print('Found a column with double bbox') # continue # previous_xmax_col = xmax_col # new_cols[k_col] = v_col # for k_row, v_row in master_row.items(): # _, ymin_row, _, ymax_row, row_img = v_row # if (np.isclose(previous_ymin_row, ymin_row, atol=5)) or (ymin_row >= ymax_row): # print('Found a row with double bbox') # continue # previous_ymin_row = ymin_row # new_master_row[k_row] = v_row ###################################################### for k_row, v_row in new_master_row.items(): _, _, _, _, row_img = v_row xmax, ymax = row_img.size xa, ya, xb, yb = 0, 0, 0, ymax row_img_list = [] # plt.imshow(row_img) # st.pyplot() for idx, kv in enumerate(new_cols.items()): k_col, v_col = kv xmin_col, _, xmax_col, _, col_img = v_col xmin_col, xmax_col = xmin_col - padd_left - 10, xmax_col - padd_left # plt.imshow(col_img) # st.pyplot() # xa + 3 : to remove borders on the left side of the cropped cell # yb = 3: to remove row information from the above row of the cropped cell # xb - 3: to remove borders on the right side of the cropped cell xa = xmin_col xb = xmax_col if idx == 0: xa = 0 if idx == len(new_cols)-1: xb = xmax xa, ya, xb, yb = xa, ya, xb, yb row_img_cropped = row_img.crop((xa, ya, xb, yb)) row_img_list.append(row_img_cropped) cells_img[k_row+'.'+str(row_idx)] = row_img_list row_idx += 1 return cells_img, len(new_cols), len(new_master_row)-1 def clean_dataframe(self, df): ''' Remove irrelevant symbols that appear with tesseractOCR ''' # df.columns = [col.replace('|', '') for col in df.columns] for col in df.columns: df[col]=df[col].str.replace("'", '', regex=True) df[col]=df[col].str.replace('"', '', regex=True) df[col]=df[col].str.replace(']', '', regex=True) df[col]=df[col].str.replace('[', '', regex=True) df[col]=df[col].str.replace('{', '', regex=True) df[col]=df[col].str.replace('}', '', regex=True) return df @st.cache def convert_df(self, df): return df.to_csv().encode('utf-8') def create_dataframe(self, c3, cells_pytess_result:list, max_cols:int, max_rows:int): '''Create dataframe using list of cell values of the table, also checks for valid header of dataframe Args: cells_pytess_result: list of strings, each element representing a cell in a table max_cols, max_rows: number of columns and rows Returns: dataframe : final dataframe after all pre-processing ''' headers = cells_pytess_result[:max_cols] new_headers = uniquify(headers, (f' {x!s}' for x in string.ascii_lowercase)) counter = 0 cells_list = cells_pytess_result[max_cols:] df = pd.DataFrame("", index=range(0, max_rows), columns=new_headers) cell_idx = 0 for nrows in range(max_rows): for ncols in range(max_cols): df.iat[nrows, ncols] = str(cells_list[cell_idx]) cell_idx += 1 ## To check if there are duplicate headers if result of uniquify+col == col ## This check removes headers when all headers are empty or if median of header word count is less than 6 for x, col in zip(string.ascii_lowercase, new_headers): if f' {x!s}' == col: counter += 1 header_char_count = [len(col) for col in new_headers] # if (counter == len(new_headers)) or (statistics.median(header_char_count) < 6): # st.write('woooot') # df.columns = uniquify(df.iloc[0], (f' {x!s}' for x in string.ascii_lowercase)) # df = df.iloc[1:,:] df = self.clean_dataframe(df) c3.dataframe(df) csv = self.convert_df(df) c3.download_button("Download table", csv, "file.csv", "text/csv", key='download-csv') return df async def start_process(self, image_path:str, TD_THRESHOLD, TSR_THRESHOLD, padd_top, padd_left, padd_bottom, padd_right, delta_xmin, delta_ymin, delta_xmax, delta_ymax, expand_rowcol_bbox_top, expand_rowcol_bbox_bottom): ''' Initiates process of generating pandas dataframes from raw pdf-page images ''' image = Image.open(image_path).convert("RGB") model, probas, bboxes_scaled = table_detector(image, THRESHOLD_PROBA=TD_THRESHOLD) st.text(bboxes_scaled) st.image(image, caption='input') st.text(bboxes_scaled.nelement()) if bboxes_scaled.nelement() == 0: print('No table found in the pdf-page image'+image_path.split('/')[-1]) return '' # try: # st.write('Document: '+image_path.split('/')[-1]) c1, c2, c3 = st.columns((1,1,1)) self.plot_results_detection(c1, model, image, probas, bboxes_scaled, delta_xmin, delta_ymin, delta_xmax, delta_ymax) cropped_img_list = self.crop_tables(image, probas, bboxes_scaled, delta_xmin, delta_ymin, delta_xmax, delta_ymax) for unpadded_table in cropped_img_list: table = self.add_padding(unpadded_table, padd_top, padd_right, padd_bottom, padd_left) # table = super_res(table) # table = binarizeBlur_image(table) # table = sharpen_image(table) # Test sharpen image next # table = td_postprocess(table) model, probas, bboxes_scaled = table_struct_recog(table, THRESHOLD_PROBA=TSR_THRESHOLD) rows, cols = self.generate_structure(c2, model, table, probas, bboxes_scaled, expand_rowcol_bbox_top, expand_rowcol_bbox_bottom) # st.write(len(rows), len(cols)) rows, cols = self.sort_table_featuresv2(rows, cols) master_row, cols = self.individual_table_featuresv2(table, rows, cols) cells_img, max_cols, max_rows = self.object_to_cellsv2(master_row, cols, expand_rowcol_bbox_top, expand_rowcol_bbox_bottom, padd_left) sequential_cell_img_list = [] for k, img_list in cells_img.items(): for img in img_list: # img = super_res(img) # img = sharpen_image(img) # Test sharpen image next # img = binarizeBlur_image(img) # img = self.add_padding(img, 10,10,10,10) # plt.imshow(img) # c3.pyplot() sequential_cell_img_list.append(pytess(img)) cells_pytess_result = await asyncio.gather(*sequential_cell_img_list) self.create_dataframe(c3, cells_pytess_result, max_cols, max_rows) st.write('Errors in OCR is due to either quality of the image or performance of the OCR') # except: # st.write('Either incorrectly identified table or no table, to debug remove try/except') # break # break if __name__ == "__main__": img_name = st.file_uploader("Upload an image with table(s)") padd_top = st.slider('Padding top', 0, 200, 20) padd_left = st.slider('Padding left', 0, 200, 20) padd_right = st.slider('Padding right', 0, 200, 20) padd_bottom = st.slider('Padding bottom', 0, 200, 20) te = TableExtractionPipeline() # for img in image_list: if img_name is not None: asyncio.run(te.start_process(img_name, TD_THRESHOLD=0.6, TSR_THRESHOLD=0.8, padd_top=padd_top, padd_left=padd_left, padd_bottom=padd_bottom, padd_right=padd_right, delta_xmin=0, delta_ymin=0, delta_xmax=0, delta_ymax=0, expand_rowcol_bbox_top=0, expand_rowcol_bbox_bottom=0))