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app.py

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+from PIL import Image, ImageEnhance, ImageOps
+import string
+from collections import Counter 
+from itertools import tee, count
+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 DetrFeatureExtractor
+from transformers import TableTransformerForObjectDetection
+import torch
+import gradio as gr
+
+def plot_results_detection(model, image, prob, bboxes_scaled,  delta_xmin, delta_ymin, delta_xmax, delta_ymax):
+  plt.imshow(image)
+  ax = plt.gca()
+
+  for p, (xmin, ymin, xmax, ymax) in zip(prob, bboxes_scaled.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')
+
+def crop_tables(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 add_padding(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 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)
+
+def generate_structure(model, pil_img, prob, boxes, expand_rowcol_bbox_top, expand_rowcol_bbox_bottom):
+    colors = ["red", "blue", "green", "yellow", "orange", "violet"]
+    '''
+    Co-ordinates are adjusted here by 3 'pixels'
+    To plot table pillow image and the TSR bounding boxes on the table
+    '''
+    # 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=colors[0], 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')
+    return rows, cols
+
+def sort_table_featuresv2(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(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(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 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 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 clean_dataframe(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)
+        df[col]=df[col].str.replace('|', '', regex=True)
+    return df
+
+def create_dataframe(cells_pytess_result:list, max_cols:int, max_rows:int,csv_path):
+    '''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 = clean_dataframe(df)
+    # df.to_csv(csv_path)
+
+    return df
+
+def process_image(image, TD_THRESHOLD, TSR_THRESHOLD, padd_top, padd_left, padd_bottom, padd_right, delta_xmin = 0, delta_ymin = 0, delta_xmax = 0, delta_ymax = 0, expand_rowcol_bbox_top = 0, expand_rowcol_bbox_bottom = 0):
+  image = image.convert('RGB')
+  model, probas, bboxes_scaled = table_detector(image, THRESHOLD_PROBA=TD_THRESHOLD)
+  # plot_results_detection(model, image, probas, bboxes_scaled,  delta_xmin, delta_ymin, delta_xmax, delta_ymax) 
+  cropped_img_list = crop_tables(image, probas, bboxes_scaled, delta_xmin, delta_ymin, delta_xmax, delta_ymax)
+
+  result = []
+  for idx, unpadded_table in enumerate(cropped_img_list):
+      table = add_padding(unpadded_table, padd_top, padd_right, padd_bottom, padd_left)
+      model, probas, bboxes_scaled = table_struct_recog(table, THRESHOLD_PROBA=TSR_THRESHOLD)
+      rows, cols = generate_structure(model, table, probas, bboxes_scaled, expand_rowcol_bbox_top, expand_rowcol_bbox_bottom)
+      rows, cols = sort_table_featuresv2(rows, cols)
+      master_row, cols = individual_table_featuresv2(table, rows, cols)
+      cells_img, max_cols, max_rows = 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:
+              sequential_cell_img_list.append(pytess(img))
+
+      csv_path = '/content/sample_data/table_' + str(idx)
+      df = create_dataframe(sequential_cell_img_list, max_cols, max_rows, csv_path)
+      result.append(df)
+  res = result[0].to_json()
+  return res
+
+
+title = "Interactive demo OCR: microsoft - table-transformer-detection + tesseract"
+description = "Demo for microsoft - table-transformer-detection + tesseract"
+article = "<p style='text-align: center'></p>"
+examples =[["image_0.png"]]
+
+iface = gr.Interface(fn=process_image, 
+                     inputs=[gr.Image(type="pil"), gr.Slider(0, 1, value=0.9), gr.Slider(0, 1, value=0.8), gr.Slider(0, 200, value=100), gr.Slider(0, 200, value=100), gr.Slider(0, 200, value=100), gr.Slider(0, 200, value=100)], 
+                     outputs="text",
+                     title=title,
+                     description=description,
+                     article=article,
+                     examples=examples)
+iface.launch(debug=True)

packages.txt

@@ -0,0 +1,6 @@
+ffmpeg 
+libsm6 
+libxext6 
+libgl1
+tesseract-ocr-eng
+python3-opencv

requirements.txt

@@ -0,0 +1,47 @@
+Cython==0.29.14
+dask==2021.3.1
+datasets==1.18.3
+Flask==2.0.1
+GitPython==3.1.26
+imutils==0.5.4
+multiprocess==0.70.12.2
+numba==0.54.1
+numexpr==2.7.3
+numpy==1.20.3
+oauthlib==3.1.0
+opencv-contrib-python==4.6.0.66
+openpyxl==3.0.7
+Pillow==9.0.1
+plotly==4.14.3
+ply==3.11
+protobuf==3.14.0
+psutil==5.8.0
+pyarrow==7.0.0
+pydantic==1.7.3
+pydeck==0.7.1
+PyDictionary==2.0.1
+pydot==1.4.2
+pymongo==4.0.2
+Pympler==1.0.1
+PyMuPDF==1.20.2
+pyperclip==1.8.2
+pyppeteer==0.2.5
+pyquery==1.4.3
+pyreadline3==3.3
+pytesseract==0.3.10
+pytz-deprecation-shim==0.1.0.post0
+PyWavelets==1.1.1
+PyYAML==5.4.1
+scipy==1.4.1
+seaborn==0.11.1
+sklearn==0.0
+streamlit==1.5.1
+timm==0.6.7
+tokenizers==0.12.1
+toml==0.10.2
+toolz==0.11.1
+torch==1.10.0
+torchvision==0.11.1
+git+https://github.com/huggingface/transformers.git
+#-e git+https://github.com/nielsrogge/transformers.git@d34f7e6ffbb911d39465173ef2b35ba147ef58a9#egg=transformers
+urllib3==1.26.7