Delete app.py

app.py

@@ -1,495 +0,0 @@
-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")
-
-
-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):
-    return ' '.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()
-
-
-# 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("SalML/DETR-table-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("SalML/DETR-table-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')
-        st.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')
-        st.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=50)) 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=50)) 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):
-            df.columns = uniquify(df.iloc[0], (f' {x!s}' for x in string.ascii_lowercase))
-            df = df.iloc[1:,:]
-
-        df = self.clean_dataframe(df)
-        
-        st.dataframe(df)
-        csv = self.convert_df(df)
-        st.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:
-            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)
-                    
-                    # 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)
-        # 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)")
-
-    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=20, padd_left=20, padd_bottom=20, padd_right=20, delta_xmin=0, delta_ymin=0, delta_xmax=0, delta_ymax=0, expand_rowcol_bbox_top=0, expand_rowcol_bbox_bottom=0))
-
-
-