debu das
Update app.py
f652a8c
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
from transformers import TableTransformerForObjectDetection
import torch
import asyncio
import paddlehub as hub
from paddleocr import PaddleOCR,draw_ocr
# 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@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@")
###paddleocr
paddle_output=' '
cell_cv_img=PIL_to_cv(cell_pil_img)
height, width, channels = cell_cv_img.shape
st.text('height:'+str(height)+'/n'+'width:'+str(width))
if height>=10 and width>=10:
ocr = PaddleOCR(use_angle_cls=True,use_space_char=True) # need to run only once to download and load model into memory
result = ocr.ocr(cell_cv_img,cls=True)
print(result)
print("___________________________________________________________")
for idx in range(len(result)):
res = result[idx]
for line in res:
print(line)
print(line[1][0])
print("____________________________@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@")
paddle_output=paddle_output+' '+line[1][0]
paddle_output=paddle_output+' '
print("paddleocr@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@")
print(paddle_output)
print("paddleocr$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$")
st.image(cell_pil_img, caption=paddle_output)
return str(paddle_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 = TableTransformerForObjectDetection.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 = TableTransformerForObjectDetection.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)
if bboxes_scaled.nelement() == 0:
st.write('No table found in the pdf-page image')
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)")
st1, st2 = st.columns((1,1))
TD_th = st1.slider('Table detection threshold', 0.0, 1.0, 0.6)
TSR_th = st2.slider('Table structure recognition threshold', 0.0, 1.0, 0.8)
st1, st2, st3, st4 = st.columns((1,1,1,1))
padd_top = st1.slider('Padding top', 0, 200, 20)
padd_left = st2.slider('Padding left', 0, 200, 20)
padd_right = st3.slider('Padding right', 0, 200, 20)
padd_bottom = st4.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=TD_th , TSR_THRESHOLD=TSR_th , 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))