Delete app.py

app.py

@@ -1,156 +0,0 @@
-import streamlit as st
-from PIL import Image
-import os
-import TDTSR
-import pytesseract
-from pytesseract import Output
-import pandas as pd
-import matplotlib.pyplot as plt
-import cv2
-import numpy as np
-from cv2 import dnn_superres
-
-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")
-
-c1, c2, c3 = st.columns((1,1,1))
-
-
-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))
-
-def pytess(cell_pil_img):
-    return ' '.join(pytesseract.image_to_data(cell_pil_img, output_type=Output.DICT, config='preserve_interword_spaces')['text']).strip()
-
-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 sharpen_image(pil_img):
-
-    img = PIL_to_cv(pil_img)
-    sharpen_kernel = np.array([[-1,-1,-1], [-1,9,-1], [-1,-1,-1]])
-    # sharpen_kernel = np.array([[0, -1, 0],
-    #                [-1, 5,-1],
-    #                [0, -1, 0]])
-    sharpen = cv2.filter2D(img, -1, sharpen_kernel)
-    pil_img = cv_to_PIL(sharpen)
-    return pil_img
-
-
-def preprocess_magic(pil_img):
-
-    cv_img = PIL_to_cv(pil_img)
-    grayscale_image = cv2.cvtColor(cv_img, cv2.COLOR_BGR2GRAY)
-    _, binary_image = cv2.threshold(grayscale_image, 0, 255, cv2.THRESH_OTSU)
-
-    count_white = np.sum(binary_image > 0)
-    count_black = np.sum(binary_image == 0)
-
-    if count_black > count_white:
-        binary_image = 255 - binary_image
-
-    black_text_white_background_image = binary_image
-
-    return cv_to_PIL(black_text_white_background_image)
-
-
-### main code:
-for td_sample in os.listdir('D:/Jupyter/Multi-Type-TD-TSR/TD_samples/'):
-
-    image = Image.open("D:/Jupyter/Multi-Type-TD-TSR/TD_samples/"+td_sample).convert("RGB")
-    model, image, probas, bboxes_scaled = TDTSR.table_detector(image, THRESHOLD_PROBA=0.6)
-    TDTSR.plot_results_detection(c1, model, image, probas, bboxes_scaled)
-    cropped_img_list = TDTSR.plot_table_detection(c2, model, image, probas, bboxes_scaled)
-
-    for unpadded_table in cropped_img_list:
-        # table : pil_img
-        table = TDTSR.add_margin(unpadded_table)
-        model, image, probas, bboxes_scaled = TDTSR.table_struct_recog(table, THRESHOLD_PROBA=0.6)
-
-        # The try, except block of code below plots table header row and simple rows
-        try:
-            rows, cols = TDTSR.plot_structure(c3, model, image, probas, bboxes_scaled, class_to_show=0)
-            rows, cols = TDTSR.sort_table_featuresv2(rows, cols)
-            # headers, rows, cols are ordered dictionaries with 5th element value of tuple being pil_img
-            rows, cols = TDTSR.individual_table_featuresv2(table, rows, cols)
-            # TDTSR.plot_table_features(c1, header, row_header, rows, cols)
-        except Exception as printableException: 
-            st.write(td_sample, ' terminated with exception:', printableException)
-
-        # master_row = TDTSR.master_row_set(header, row_header, rows, cols)
-        master_row = rows
-
-#         cells_img = TDTSR.object_to_cells(master_row, cols)
-        cells_img = TDTSR.object_to_cellsv2(master_row, cols)
-
-        headers = []
-        cells_list = []
-        # st.write(cells_img)
-        for n, kv in enumerate(cells_img.items()):
-            k, row_images = kv
-            if n == 0:
-                for idx, header in enumerate(row_images):
-                    # plt.imshow(header)
-                    # c2.pyplot()
-                    # c2.write(pytess(header))
-                    ############################
-                    SR_img = super_res(header)
-                    # # w, h = SR_img.size
-                    # # SR_img = SR_img.crop((0 ,0 ,w, h-60))
-                    # plt.imshow(SR_img)
-                    # c3.pyplot()
-                    # c3.write(pytess(SR_img))
-                    header_text = pytess(SR_img)
-                    if header_text == '':
-                        header_text = 'empty_col'+str(idx)
-                    headers.append(header_text)
-
-
-            else:
-                for cells in row_images:                    
-                    # plt.imshow(cells)
-                    # c2.pyplot()
-                    # c2.write(pytess(cells))
-                    ##############################
-                    SR_img = super_res(cells)
-                    # # w, h = SR_img.size
-                    # # SR_img = SR_img.crop((0 ,0 ,w, h-60))
-                    # plt.imshow(SR_img)
-                    # c3.pyplot()
-                    # c3.write(pytess(SR_img))
-                    cells_list.append(pytess(SR_img))
-
-
-
-        df = pd.DataFrame("", index=range(0, len(master_row)), columns=headers)
-
-        cell_idx = 0
-
-        for nrows in range(len(master_row)-1):
-            for ncols in range(len(cols)):
- 
-                df.iat[nrows, ncols] = cells_list[cell_idx]
-                cell_idx += 1
-
-        c3.dataframe(df)
-    # break
-