mirror of
https://github.com/qurator-spk/eynollah.git
synced 2025-06-09 20:29:55 +02:00
more extraction of unused functions
This commit is contained in:
Konstantin Baierer
parent
f8c153775b
commit
915e076774
2 changed files with 115 additions and 115 deletions
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@ -3320,121 +3320,6 @@ class eynollah:
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tree.write(os.path.join(dir_of_image, self.f_name) + ".xml")
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# cv2.imwrite(os.path.join(dir_of_image, self.f_name) + ".tif",self.image_org)
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def add_tables_heuristic_to_layout(self, image_regions_eraly_p, boxes, slope_mean_hor, spliter_y, peaks_neg_tot, image_revised):
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image_revised_1 = delete_seperator_around(spliter_y, peaks_neg_tot, image_revised)
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img_comm_e = np.zeros(image_revised_1.shape)
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img_comm = np.repeat(img_comm_e[:, :, np.newaxis], 3, axis=2)
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for indiv in np.unique(image_revised_1):
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# print(indiv,'indd')
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image_col = (image_revised_1 == indiv) * 255
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img_comm_in = np.repeat(image_col[:, :, np.newaxis], 3, axis=2)
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img_comm_in = img_comm_in.astype(np.uint8)
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imgray = cv2.cvtColor(img_comm_in, cv2.COLOR_BGR2GRAY)
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ret, thresh = cv2.threshold(imgray, 0, 255, 0)
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contours, hirarchy = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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main_contours = filter_contours_area_of_image_tables(thresh, contours, hirarchy, max_area=1, min_area=0.0001)
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img_comm = cv2.fillPoly(img_comm, pts=main_contours, color=(indiv, indiv, indiv))
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###img_comm_in=cv2.fillPoly(img_comm, pts =interior_contours, color=(0,0,0))
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# img_comm=np.repeat(img_comm[:, :, np.newaxis], 3, axis=2)
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img_comm = img_comm.astype(np.uint8)
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if not isNaN(slope_mean_hor):
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image_revised_last = np.zeros((image_regions_eraly_p.shape[0], image_regions_eraly_p.shape[1], 3))
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for i in range(len(boxes)):
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image_box = img_comm[int(boxes[i][2]) : int(boxes[i][3]), int(boxes[i][0]) : int(boxes[i][1]), :]
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image_box_tabels_1 = (image_box[:, :, 0] == 7) * 1
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contours_tab, _ = return_contours_of_image(image_box_tabels_1)
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contours_tab = filter_contours_area_of_image_tables(image_box_tabels_1, contours_tab, _, 1, 0.001)
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image_box_tabels_1 = (image_box[:, :, 0] == 6) * 1
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image_box_tabels_and_m_text = ((image_box[:, :, 0] == 7) | (image_box[:, :, 0] == 1)) * 1
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image_box_tabels_and_m_text = image_box_tabels_and_m_text.astype(np.uint8)
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image_box_tabels_1 = image_box_tabels_1.astype(np.uint8)
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image_box_tabels_1 = cv2.dilate(image_box_tabels_1, self.kernel, iterations=5)
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contours_table_m_text, _ = return_contours_of_image(image_box_tabels_and_m_text)
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image_box_tabels = np.repeat(image_box_tabels_1[:, :, np.newaxis], 3, axis=2)
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image_box_tabels = image_box_tabels.astype(np.uint8)
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imgray = cv2.cvtColor(image_box_tabels, cv2.COLOR_BGR2GRAY)
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ret, thresh = cv2.threshold(imgray, 0, 255, 0)
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contours_line, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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y_min_main_line, y_max_main_line, _ = find_features_of_contours(contours_line)
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# _,_,y_min_main_line ,y_max_main_line,x_min_main_line,x_max_main_line=find_new_features_of_contoures(contours_line)
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y_min_main_tab, y_max_main_tab, _ = find_features_of_contours(contours_tab)
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cx_tab_m_text, cy_tab_m_text, x_min_tab_m_text, x_max_tab_m_text, y_min_tab_m_text, y_max_tab_m_text = find_new_features_of_contoures(contours_table_m_text)
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cx_tabl, cy_tabl, x_min_tabl, x_max_tabl, y_min_tabl, y_max_tabl, _ = find_new_features_of_contoures(contours_tab)
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if len(y_min_main_tab) > 0:
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y_down_tabs = []
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y_up_tabs = []
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for i_t in range(len(y_min_main_tab)):
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y_down_tab = []
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y_up_tab = []
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for i_l in range(len(y_min_main_line)):
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if y_min_main_tab[i_t] > y_min_main_line[i_l] and y_max_main_tab[i_t] > y_min_main_line[i_l] and y_min_main_tab[i_t] > y_max_main_line[i_l] and y_max_main_tab[i_t] > y_min_main_line[i_l]:
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pass
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elif y_min_main_tab[i_t] < y_max_main_line[i_l] and y_max_main_tab[i_t] < y_max_main_line[i_l] and y_max_main_tab[i_t] < y_min_main_line[i_l] and y_min_main_tab[i_t] < y_min_main_line[i_l]:
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pass
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elif np.abs(y_max_main_line[i_l] - y_min_main_line[i_l]) < 100:
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pass
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else:
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y_up_tab.append(np.min([y_min_main_line[i_l], y_min_main_tab[i_t]]))
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y_down_tab.append(np.max([y_max_main_line[i_l], y_max_main_tab[i_t]]))
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if len(y_up_tab) == 0:
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for v_n in range(len(cx_tab_m_text)):
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if cx_tabl[i_t] <= x_max_tab_m_text[v_n] and cx_tabl[i_t] >= x_min_tab_m_text[v_n] and cy_tabl[i_t] <= y_max_tab_m_text[v_n] and cy_tabl[i_t] >= y_min_tab_m_text[v_n] and cx_tabl[i_t] != cx_tab_m_text[v_n] and cy_tabl[i_t] != cy_tab_m_text[v_n]:
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y_up_tabs.append(y_min_tab_m_text[v_n])
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y_down_tabs.append(y_max_tab_m_text[v_n])
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# y_up_tabs.append(y_min_main_tab[i_t])
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# y_down_tabs.append(y_max_main_tab[i_t])
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else:
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y_up_tabs.append(np.min(y_up_tab))
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y_down_tabs.append(np.max(y_down_tab))
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else:
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y_down_tabs = []
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y_up_tabs = []
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pass
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for ii in range(len(y_up_tabs)):
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image_box[y_up_tabs[ii] : y_down_tabs[ii], :, 0] = 7
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image_revised_last[int(boxes[i][2]) : int(boxes[i][3]), int(boxes[i][0]) : int(boxes[i][1]), :] = image_box[:, :, :]
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else:
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for i in range(len(boxes)):
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image_box = img_comm[int(boxes[i][2]) : int(boxes[i][3]), int(boxes[i][0]) : int(boxes[i][1]), :]
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image_revised_last[int(boxes[i][2]) : int(boxes[i][3]), int(boxes[i][0]) : int(boxes[i][1]), :] = image_box[:, :, :]
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##plt.figure(figsize=(20,20))
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##plt.imshow(image_box[:,:,0])
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##plt.show()
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return image_revised_last
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def return_teilwiese_deskewed_lines(self, text_regions_p, textline_rotated):
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kernel = np.ones((5, 5), np.uint8)
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@ -1491,3 +1491,118 @@ def return_boxes_of_images_by_order_of_reading_without_seperators_2cols(spliter_
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return boxes
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def add_tables_heuristic_to_layout(image_regions_eraly_p, boxes, slope_mean_hor, spliter_y, peaks_neg_tot, image_revised):
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image_revised_1 = delete_seperator_around(spliter_y, peaks_neg_tot, image_revised)
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img_comm_e = np.zeros(image_revised_1.shape)
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img_comm = np.repeat(img_comm_e[:, :, np.newaxis], 3, axis=2)
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for indiv in np.unique(image_revised_1):
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# print(indiv,'indd')
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image_col = (image_revised_1 == indiv) * 255
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img_comm_in = np.repeat(image_col[:, :, np.newaxis], 3, axis=2)
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img_comm_in = img_comm_in.astype(np.uint8)
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imgray = cv2.cvtColor(img_comm_in, cv2.COLOR_BGR2GRAY)
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ret, thresh = cv2.threshold(imgray, 0, 255, 0)
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contours, hirarchy = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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main_contours = filter_contours_area_of_image_tables(thresh, contours, hirarchy, max_area=1, min_area=0.0001)
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img_comm = cv2.fillPoly(img_comm, pts=main_contours, color=(indiv, indiv, indiv))
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###img_comm_in=cv2.fillPoly(img_comm, pts =interior_contours, color=(0,0,0))
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# img_comm=np.repeat(img_comm[:, :, np.newaxis], 3, axis=2)
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img_comm = img_comm.astype(np.uint8)
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if not isNaN(slope_mean_hor):
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image_revised_last = np.zeros((image_regions_eraly_p.shape[0], image_regions_eraly_p.shape[1], 3))
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for i in range(len(boxes)):
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image_box = img_comm[int(boxes[i][2]) : int(boxes[i][3]), int(boxes[i][0]) : int(boxes[i][1]), :]
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image_box_tabels_1 = (image_box[:, :, 0] == 7) * 1
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contours_tab, _ = return_contours_of_image(image_box_tabels_1)
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contours_tab = filter_contours_area_of_image_tables(image_box_tabels_1, contours_tab, _, 1, 0.001)
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image_box_tabels_1 = (image_box[:, :, 0] == 6) * 1
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image_box_tabels_and_m_text = ((image_box[:, :, 0] == 7) | (image_box[:, :, 0] == 1)) * 1
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image_box_tabels_and_m_text = image_box_tabels_and_m_text.astype(np.uint8)
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image_box_tabels_1 = image_box_tabels_1.astype(np.uint8)
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image_box_tabels_1 = cv2.dilate(image_box_tabels_1, self.kernel, iterations=5)
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contours_table_m_text, _ = return_contours_of_image(image_box_tabels_and_m_text)
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image_box_tabels = np.repeat(image_box_tabels_1[:, :, np.newaxis], 3, axis=2)
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image_box_tabels = image_box_tabels.astype(np.uint8)
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imgray = cv2.cvtColor(image_box_tabels, cv2.COLOR_BGR2GRAY)
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ret, thresh = cv2.threshold(imgray, 0, 255, 0)
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contours_line, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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y_min_main_line, y_max_main_line, _ = find_features_of_contours(contours_line)
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# _,_,y_min_main_line ,y_max_main_line,x_min_main_line,x_max_main_line=find_new_features_of_contoures(contours_line)
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y_min_main_tab, y_max_main_tab, _ = find_features_of_contours(contours_tab)
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cx_tab_m_text, cy_tab_m_text, x_min_tab_m_text, x_max_tab_m_text, y_min_tab_m_text, y_max_tab_m_text = find_new_features_of_contoures(contours_table_m_text)
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cx_tabl, cy_tabl, x_min_tabl, x_max_tabl, y_min_tabl, y_max_tabl, _ = find_new_features_of_contoures(contours_tab)
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if len(y_min_main_tab) > 0:
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y_down_tabs = []
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y_up_tabs = []
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for i_t in range(len(y_min_main_tab)):
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y_down_tab = []
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y_up_tab = []
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for i_l in range(len(y_min_main_line)):
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if y_min_main_tab[i_t] > y_min_main_line[i_l] and y_max_main_tab[i_t] > y_min_main_line[i_l] and y_min_main_tab[i_t] > y_max_main_line[i_l] and y_max_main_tab[i_t] > y_min_main_line[i_l]:
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pass
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elif y_min_main_tab[i_t] < y_max_main_line[i_l] and y_max_main_tab[i_t] < y_max_main_line[i_l] and y_max_main_tab[i_t] < y_min_main_line[i_l] and y_min_main_tab[i_t] < y_min_main_line[i_l]:
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pass
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elif np.abs(y_max_main_line[i_l] - y_min_main_line[i_l]) < 100:
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pass
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else:
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y_up_tab.append(np.min([y_min_main_line[i_l], y_min_main_tab[i_t]]))
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y_down_tab.append(np.max([y_max_main_line[i_l], y_max_main_tab[i_t]]))
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if len(y_up_tab) == 0:
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for v_n in range(len(cx_tab_m_text)):
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if cx_tabl[i_t] <= x_max_tab_m_text[v_n] and cx_tabl[i_t] >= x_min_tab_m_text[v_n] and cy_tabl[i_t] <= y_max_tab_m_text[v_n] and cy_tabl[i_t] >= y_min_tab_m_text[v_n] and cx_tabl[i_t] != cx_tab_m_text[v_n] and cy_tabl[i_t] != cy_tab_m_text[v_n]:
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y_up_tabs.append(y_min_tab_m_text[v_n])
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y_down_tabs.append(y_max_tab_m_text[v_n])
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# y_up_tabs.append(y_min_main_tab[i_t])
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# y_down_tabs.append(y_max_main_tab[i_t])
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else:
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y_up_tabs.append(np.min(y_up_tab))
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y_down_tabs.append(np.max(y_down_tab))
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else:
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y_down_tabs = []
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y_up_tabs = []
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pass
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for ii in range(len(y_up_tabs)):
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image_box[y_up_tabs[ii] : y_down_tabs[ii], :, 0] = 7
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image_revised_last[int(boxes[i][2]) : int(boxes[i][3]), int(boxes[i][0]) : int(boxes[i][1]), :] = image_box[:, :, :]
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else:
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for i in range(len(boxes)):
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image_box = img_comm[int(boxes[i][2]) : int(boxes[i][3]), int(boxes[i][0]) : int(boxes[i][1]), :]
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image_revised_last[int(boxes[i][2]) : int(boxes[i][3]), int(boxes[i][0]) : int(boxes[i][1]), :] = image_box[:, :, :]
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##plt.figure(figsize=(20,20))
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##plt.imshow(image_box[:,:,0])
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##plt.show()
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return image_revised_last
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