[VietOCR] Add VietOCR

app.py

@@ -1,9 +1,563 @@
-import gradio as gr
+import asyncio
+import json
+import string
+from collections import Counter
+from itertools import count, tee
 
+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import streamlit as st
+import torch
+from PIL import Image
+from transformers import (DetrImageProcessor,
+                          TableTransformerForObjectDetection)
+from vietocr.tool.config import Cfg
+from vietocr.tool.predictor import Predictor
 
-def greet(name):
-    return "Hello " + name + "!!"
+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")
 
+config = Cfg.load_config_from_name('vgg_transformer')
+config['cnn']['pretrained'] = False
+config['device'] = 'cpu'
+config['predictor']['beamsearch'] = True
+detector = Predictor(config)
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+table_detection_model = TableTransformerForObjectDetection.from_pretrained(
+    "microsoft/table-transformer-detection")
+
+table_recognition_model = TableTransformerForObjectDetection.from_pretrained(
+    "microsoft/table-transformer-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):
+    text = detector.predict(cell_pil_img)
+    return text.strip()
+
+
+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 = DetrImageProcessor(do_resize=True,
+                                           size=800,
+                                           max_size=800)
+    encoding = feature_extractor(image, return_tensors="pt")
+
+    with torch.no_grad():
+        outputs = table_detection_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 (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 = DetrImageProcessor(do_resize=True,
+                                           size=1000,
+                                           max_size=1000)
+    encoding = feature_extractor(image, return_tensors="pt")
+
+    with torch.no_grad():
+        outputs = table_recognition_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 (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
+                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, cell_ocr_res: 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:
+            cell_ocr_res: 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 = cell_ocr_res[:max_cols]
+        new_headers = uniquify(headers,
+                               (f' {x!s}' for x in string.ascii_lowercase))
+        counter = 0
+
+        cells_list = cell_ocr_res[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")
+        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, table_detection_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)
+
+            probas, bboxes_scaled = table_struct_recog(
+                table, THRESHOLD_PROBA=TSR_THRESHOLD)
+            rows, cols = self.generate_structure(c2, table_recognition_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))
+
+            cell_ocr_res = await asyncio.gather(*sequential_cell_img_list)
+
+            self.create_dataframe(c3, cell_ocr_res, 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, 0.8)
+    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, 40)
+    padd_left = st2.slider('Padding left', 0, 200, 40)
+    padd_right = st3.slider('Padding right', 0, 200, 40)
+    padd_bottom = st4.slider('Padding bottom', 0, 200, 40)
+
+    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))

requirements.txt

@@ -1,2 +1,3 @@
 dynaconf==3.1.12
-pytorch-lightning==2.0.0
+pytorch-lightning==2.0.0
+vietocr==0.36