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eynollah
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set up constants for "magic numbers"

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pull/8/head
Konstantin Baierer 4 years ago
parent 97bc57be35
commit 6c305de279
1 changed files with 39 additions and 36 deletions
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75
sbb_newspapers_org_image/eynollah.py
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@ -37,6 +37,9 @@ import imutils
from .utils import filter_contours_area_of_image_tables from .utils import filter_contours_area_of_image_tables
SLOPE_THRESHOLD = 0.13
VERY_LARGE_NUMBER = 1000000000000000000000
class eynollah: class eynollah:
def __init__( def __init__(
@ -3959,13 +3962,13 @@ class eynollah:
# print(rot,var_spectrum,'var_spectrum') # print(rot,var_spectrum,'var_spectrum')
res_me = np.mean(neg_peaks) res_me = np.mean(neg_peaks)
if res_me == 0: if res_me == 0:
res_me = 1000000000000000000000 res_me = VERY_LARGE_NUMBER
else: else:
pass pass
res_num = len(neg_peaks) res_num = len(neg_peaks)
except: except:
res_me = 1000000000000000000000 res_me = VERY_LARGE_NUMBER
res_num = 0 res_num = 0
var_spectrum = 0 var_spectrum = 0
if self.isNaN(res_me): if self.isNaN(res_me):
@ -4003,13 +4006,13 @@ class eynollah:
# print(indexer,'indexer') # print(indexer,'indexer')
res_me = np.mean(neg_peaks) res_me = np.mean(neg_peaks)
if res_me == 0: if res_me == 0:
res_me = 1000000000000000000000 res_me = VERY_LARGE_NUMBER
else: else:
pass pass
res_num = len(neg_peaks) res_num = len(neg_peaks)
except: except:
res_me = 1000000000000000000000 res_me = VERY_LARGE_NUMBER
res_num = 0 res_num = 0
var_spectrum = 0 var_spectrum = 0
if self.isNaN(res_me): if self.isNaN(res_me):
@ -4054,13 +4057,13 @@ class eynollah:
# print(rot,var_spectrum,'var_spectrum') # print(rot,var_spectrum,'var_spectrum')
res_me = np.mean(neg_peaks) res_me = np.mean(neg_peaks)
if res_me == 0: if res_me == 0:
res_me = 1000000000000000000000 res_me = VERY_LARGE_NUMBER
else: else:
pass pass
res_num = len(neg_peaks) res_num = len(neg_peaks)
except: except:
res_me = 1000000000000000000000 res_me = VERY_LARGE_NUMBER
res_num = 0 res_num = 0
var_spectrum = 0 var_spectrum = 0
if self.isNaN(res_me): if self.isNaN(res_me):
@ -4113,13 +4116,13 @@ class eynollah:
# print(indexer,'indexer') # print(indexer,'indexer')
res_me = np.mean(neg_peaks) res_me = np.mean(neg_peaks)
if res_me == 0: if res_me == 0:
res_me = 1000000000000000000000 res_me = VERY_LARGE_NUMBER
else: else:
pass pass
res_num = len(neg_peaks) res_num = len(neg_peaks)
except: except:
res_me = 1000000000000000000000 res_me = VERY_LARGE_NUMBER
res_num = 0 res_num = 0
var_spectrum = 0 var_spectrum = 0
if self.isNaN(res_me): if self.isNaN(res_me):
@ -4159,13 +4162,13 @@ class eynollah:
# print(indexer,'indexer') # print(indexer,'indexer')
res_me = np.mean(neg_peaks) res_me = np.mean(neg_peaks)
if res_me == 0: if res_me == 0:
res_me = 1000000000000000000000 res_me = VERY_LARGE_NUMBER
else: else:
pass pass
res_num = len(neg_peaks) res_num = len(neg_peaks)
except: except:
res_me = 1000000000000000000000 res_me = VERY_LARGE_NUMBER
res_num = 0 res_num = 0
var_spectrum = 0 var_spectrum = 0
if self.isNaN(res_me): if self.isNaN(res_me):
@ -4207,13 +4210,13 @@ class eynollah:
# print(rot,var_spectrum,'var_spectrum') # print(rot,var_spectrum,'var_spectrum')
res_me = np.mean(neg_peaks) res_me = np.mean(neg_peaks)
if res_me == 0: if res_me == 0:
res_me = 1000000000000000000000 res_me = VERY_LARGE_NUMBER
else: else:
pass pass
res_num = len(neg_peaks) res_num = len(neg_peaks)
except: except:
res_me = 1000000000000000000000 res_me = VERY_LARGE_NUMBER
res_num = 0 res_num = 0
var_spectrum = 0 var_spectrum = 0
if self.isNaN(res_me): if self.isNaN(res_me):
@ -4256,13 +4259,13 @@ class eynollah:
# print(indexer,'indexer') # print(indexer,'indexer')
res_me = np.mean(neg_peaks) res_me = np.mean(neg_peaks)
if res_me == 0: if res_me == 0:
res_me = 1000000000000000000000 res_me = VERY_LARGE_NUMBER
else: else:
pass pass
res_num = len(neg_peaks) res_num = len(neg_peaks)
except: except:
res_me = 1000000000000000000000 res_me = VERY_LARGE_NUMBER
res_num = 0 res_num = 0
var_spectrum = 0 var_spectrum = 0
if self.isNaN(res_me): if self.isNaN(res_me):
@ -4302,13 +4305,13 @@ class eynollah:
# print(indexer,'indexer') # print(indexer,'indexer')
res_me = np.mean(neg_peaks) res_me = np.mean(neg_peaks)
if res_me == 0: if res_me == 0:
res_me = 1000000000000000000000 res_me = VERY_LARGE_NUMBER
else: else:
pass pass
res_num = len(neg_peaks) res_num = len(neg_peaks)
except: except:
res_me = 1000000000000000000000 res_me = VERY_LARGE_NUMBER
res_num = 0 res_num = 0
var_spectrum = 0 var_spectrum = 0
if self.isNaN(res_me): if self.isNaN(res_me):
@ -4367,13 +4370,13 @@ class eynollah:
neg_peaks, var_spectrum = self.get_standard_deviation_of_summed_textline_patch_along_width(img_rotated, sigma_des, 20.3) neg_peaks, var_spectrum = self.get_standard_deviation_of_summed_textline_patch_along_width(img_rotated, sigma_des, 20.3)
res_me = np.mean(neg_peaks) res_me = np.mean(neg_peaks)
if res_me == 0: if res_me == 0:
res_me = 1000000000000000000000 res_me = VERY_LARGE_NUMBER
else: else:
pass pass
res_num = len(neg_peaks) res_num = len(neg_peaks)
except: except:
res_me = 1000000000000000000000 res_me = VERY_LARGE_NUMBER
res_num = 0 res_num = 0
var_spectrum = 0 var_spectrum = 0
if self.isNaN(res_me): if self.isNaN(res_me):
@ -4415,13 +4418,13 @@ class eynollah:
neg_peaks, var_spectrum = self.get_standard_deviation_of_summed_textline_patch_along_width(img_rotated, sigma_des, 20.3) neg_peaks, var_spectrum = self.get_standard_deviation_of_summed_textline_patch_along_width(img_rotated, sigma_des, 20.3)
res_me = np.mean(neg_peaks) res_me = np.mean(neg_peaks)
if res_me == 0: if res_me == 0:
res_me = 1000000000000000000000 res_me = VERY_LARGE_NUMBER
else: else:
pass pass
res_num = len(neg_peaks) res_num = len(neg_peaks)
except: except:
res_me = 1000000000000000000000 res_me = VERY_LARGE_NUMBER
res_num = 0 res_num = 0
var_spectrum = 0 var_spectrum = 0
if self.isNaN(res_me): if self.isNaN(res_me):
@ -11162,7 +11165,7 @@ class eynollah:
if not self.full_layout: if not self.full_layout:
if np.abs(slope_deskew) >= 0.13: if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
image_page_rotated_n, textline_mask_tot_d, text_regions_p_1_n = self.rotation_not_90_func(image_page, textline_mask_tot, text_regions_p, slope_deskew) image_page_rotated_n, textline_mask_tot_d, text_regions_p_1_n = self.rotation_not_90_func(image_page, textline_mask_tot, text_regions_p, slope_deskew)
text_regions_p_1_n = self.resize_image(text_regions_p_1_n, text_regions_p.shape[0], text_regions_p.shape[1]) text_regions_p_1_n = self.resize_image(text_regions_p_1_n, text_regions_p.shape[0], text_regions_p.shape[1])
@ -11173,10 +11176,10 @@ class eynollah:
regions_without_seperators = (text_regions_p[:, :] == 1) * 1 # ( (text_regions_p[:,:]==1) | (text_regions_p[:,:]==2) )*1 #self.return_regions_without_seperators_new(text_regions_p[:,:,0],img_only_regions) regions_without_seperators = (text_regions_p[:, :] == 1) * 1 # ( (text_regions_p[:,:]==1) | (text_regions_p[:,:]==2) )*1 #self.return_regions_without_seperators_new(text_regions_p[:,:,0],img_only_regions)
pixel_lines = 3 pixel_lines = 3
if np.abs(slope_deskew) < 0.13: if np.abs(slope_deskew) < SLOPE_THRESHOLD:
num_col, peaks_neg_fin, matrix_of_lines_ch, spliter_y_new, seperators_closeup_n = self.find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines) num_col, peaks_neg_fin, matrix_of_lines_ch, spliter_y_new, seperators_closeup_n = self.find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines)
if np.abs(slope_deskew) >= 0.13: if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
num_col_d, peaks_neg_fin_d, matrix_of_lines_ch_d, spliter_y_new_d, seperators_closeup_n_d = self.find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines) num_col_d, peaks_neg_fin_d, matrix_of_lines_ch_d, spliter_y_new_d, seperators_closeup_n_d = self.find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines)
K.clear_session() K.clear_session()
gc.collect() gc.collect()
@ -11186,7 +11189,7 @@ class eynollah:
print(num_col_classifier, "num_col_classifier") print(num_col_classifier, "num_col_classifier")
if num_col_classifier >= 3: if num_col_classifier >= 3:
if np.abs(slope_deskew) < 0.13: if np.abs(slope_deskew) < SLOPE_THRESHOLD:
regions_without_seperators = regions_without_seperators.astype(np.uint8) regions_without_seperators = regions_without_seperators.astype(np.uint8)
regions_without_seperators = cv2.erode(regions_without_seperators[:, :], self.kernel, iterations=6) regions_without_seperators = cv2.erode(regions_without_seperators[:, :], self.kernel, iterations=6)
@ -11196,7 +11199,7 @@ class eynollah:
regions_without_seperators[(random_pixels_for_image[:, :] == 1) & (text_regions_p[:, :] == 2)] = 1 regions_without_seperators[(random_pixels_for_image[:, :] == 1) & (text_regions_p[:, :] == 2)] = 1
if np.abs(slope_deskew) >= 0.13: if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
regions_without_seperators_d = regions_without_seperators_d.astype(np.uint8) regions_without_seperators_d = regions_without_seperators_d.astype(np.uint8)
regions_without_seperators_d = cv2.erode(regions_without_seperators_d[:, :], self.kernel, iterations=6) regions_without_seperators_d = cv2.erode(regions_without_seperators_d[:, :], self.kernel, iterations=6)
@ -11208,7 +11211,7 @@ class eynollah:
else: else:
pass pass
if np.abs(slope_deskew) < 0.13: if np.abs(slope_deskew) < SLOPE_THRESHOLD:
boxes = self.return_boxes_of_images_by_order_of_reading_new(spliter_y_new, regions_without_seperators, matrix_of_lines_ch) boxes = self.return_boxes_of_images_by_order_of_reading_new(spliter_y_new, regions_without_seperators, matrix_of_lines_ch)
else: else:
boxes_d = self.return_boxes_of_images_by_order_of_reading_new(spliter_y_new_d, regions_without_seperators_d, matrix_of_lines_ch_d) boxes_d = self.return_boxes_of_images_by_order_of_reading_new(spliter_y_new_d, regions_without_seperators_d, matrix_of_lines_ch_d)
@ -11293,7 +11296,7 @@ class eynollah:
# plt.imshow(text_regions_p) # plt.imshow(text_regions_p)
# plt.show() # plt.show()
if np.abs(slope_deskew) >= 0.13: if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
image_page_rotated_n, textline_mask_tot_d, text_regions_p_1_n, regions_fully_n = self.rotation_not_90_func_full_layout(image_page, textline_mask_tot, text_regions_p, regions_fully, slope_deskew) image_page_rotated_n, textline_mask_tot_d, text_regions_p_1_n, regions_fully_n = self.rotation_not_90_func_full_layout(image_page, textline_mask_tot, text_regions_p, regions_fully, slope_deskew)
text_regions_p_1_n = self.resize_image(text_regions_p_1_n, text_regions_p.shape[0], text_regions_p.shape[1]) text_regions_p_1_n = self.resize_image(text_regions_p_1_n, text_regions_p.shape[0], text_regions_p.shape[1])
@ -11325,7 +11328,7 @@ class eynollah:
# print(np.unique(text_regions_p_1_n),'uni') # print(np.unique(text_regions_p_1_n),'uni')
text_only = ((img_revised_tab[:, :] == 1)) * 1 text_only = ((img_revised_tab[:, :] == 1)) * 1
if np.abs(slope_deskew) >= 0.13: if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
text_only_d = ((text_regions_p_1_n[:, :] == 1)) * 1 text_only_d = ((text_regions_p_1_n[:, :] == 1)) * 1
##text_only_h=( (img_revised_tab[:,:,0]==2) )*1 ##text_only_h=( (img_revised_tab[:,:,0]==2) )*1
@ -11338,7 +11341,7 @@ class eynollah:
min_con_area = 0.000005 min_con_area = 0.000005
if np.abs(slope_deskew) >= 0.13: if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
contours_only_text, hir_on_text = self.return_contours_of_image(text_only) contours_only_text, hir_on_text = self.return_contours_of_image(text_only)
contours_only_text_parent = self.return_parent_contours(contours_only_text, hir_on_text) contours_only_text_parent = self.return_parent_contours(contours_only_text, hir_on_text)
@ -11488,7 +11491,7 @@ class eynollah:
##plt.imshow(img2[:,:,0]) ##plt.imshow(img2[:,:,0])
##plt.show() ##plt.show()
if np.abs(slope_deskew) >= 0.13: if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
contours_only_text_parent_d_ordered = list(np.array(contours_only_text_parent_d_ordered)[index_by_text_par_con]) contours_only_text_parent_d_ordered = list(np.array(contours_only_text_parent_d_ordered)[index_by_text_par_con])
text_regions_p, contours_only_text_parent, contours_only_text_parent_h, all_box_coord, all_box_coord_h, all_found_texline_polygons, all_found_texline_polygons_h, slopes, slopes_h, contours_only_text_parent_d_ordered, contours_only_text_parent_h_d_ordered = self.check_any_text_region_in_model_one_is_main_or_header(text_regions_p, regions_fully, contours_only_text_parent, all_box_coord, all_found_texline_polygons, slopes, contours_only_text_parent_d_ordered) text_regions_p, contours_only_text_parent, contours_only_text_parent_h, all_box_coord, all_box_coord_h, all_found_texline_polygons, all_found_texline_polygons_h, slopes, slopes_h, contours_only_text_parent_d_ordered, contours_only_text_parent_h_d_ordered = self.check_any_text_region_in_model_one_is_main_or_header(text_regions_p, regions_fully, contours_only_text_parent, all_box_coord, all_found_texline_polygons, slopes, contours_only_text_parent_d_ordered)
@ -11524,12 +11527,12 @@ class eynollah:
pixel_lines = 6 pixel_lines = 6
if not self.headers_off: if not self.headers_off:
if np.abs(slope_deskew) < 0.13: if np.abs(slope_deskew) < SLOPE_THRESHOLD:
num_col, peaks_neg_fin, matrix_of_lines_ch, spliter_y_new, seperators_closeup_n = self.find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines, contours_only_text_parent_h) num_col, peaks_neg_fin, matrix_of_lines_ch, spliter_y_new, seperators_closeup_n = self.find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines, contours_only_text_parent_h)
else: else:
num_col_d, peaks_neg_fin_d, matrix_of_lines_ch_d, spliter_y_new_d, seperators_closeup_n_d = self.find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines, contours_only_text_parent_h_d_ordered) num_col_d, peaks_neg_fin_d, matrix_of_lines_ch_d, spliter_y_new_d, seperators_closeup_n_d = self.find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines, contours_only_text_parent_h_d_ordered)
elif self.headers_off: elif self.headers_off:
if np.abs(slope_deskew) < 0.13: if np.abs(slope_deskew) < SLOPE_THRESHOLD:
num_col, peaks_neg_fin, matrix_of_lines_ch, spliter_y_new, seperators_closeup_n = self.find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines) num_col, peaks_neg_fin, matrix_of_lines_ch, spliter_y_new, seperators_closeup_n = self.find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines)
else: else:
num_col_d, peaks_neg_fin_d, matrix_of_lines_ch_d, spliter_y_new_d, seperators_closeup_n_d = self.find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines) num_col_d, peaks_neg_fin_d, matrix_of_lines_ch_d, spliter_y_new_d, seperators_closeup_n_d = self.find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines)
@ -11542,7 +11545,7 @@ class eynollah:
if num_col_classifier >= 3: if num_col_classifier >= 3:
if np.abs(slope_deskew) < 0.13: if np.abs(slope_deskew) < SLOPE_THRESHOLD:
regions_without_seperators = regions_without_seperators.astype(np.uint8) regions_without_seperators = regions_without_seperators.astype(np.uint8)
regions_without_seperators = cv2.erode(regions_without_seperators[:, :], self.kernel, iterations=6) regions_without_seperators = cv2.erode(regions_without_seperators[:, :], self.kernel, iterations=6)
@ -11565,7 +11568,7 @@ class eynollah:
else: else:
pass pass
if np.abs(slope_deskew) < 0.13: if np.abs(slope_deskew) < SLOPE_THRESHOLD:
boxes = self.return_boxes_of_images_by_order_of_reading_new(spliter_y_new, regions_without_seperators, matrix_of_lines_ch) boxes = self.return_boxes_of_images_by_order_of_reading_new(spliter_y_new, regions_without_seperators, matrix_of_lines_ch)
else: else:
boxes_d = self.return_boxes_of_images_by_order_of_reading_new(spliter_y_new_d, regions_without_seperators_d, matrix_of_lines_ch_d) boxes_d = self.return_boxes_of_images_by_order_of_reading_new(spliter_y_new_d, regions_without_seperators_d, matrix_of_lines_ch_d)
@ -11575,7 +11578,7 @@ class eynollah:
self.write_images_into_directory(polygons_of_images, self.dir_of_cropped_images, image_page) self.write_images_into_directory(polygons_of_images, self.dir_of_cropped_images, image_page)
if self.full_layout: if self.full_layout:
if np.abs(slope_deskew) < 0.13: if np.abs(slope_deskew) < SLOPE_THRESHOLD:
order_text_new, id_of_texts_tot = self.do_order_of_regions(contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot) order_text_new, id_of_texts_tot = self.do_order_of_regions(contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot)
else: else:
order_text_new, id_of_texts_tot = self.do_order_of_regions(contours_only_text_parent_d_ordered, contours_only_text_parent_h_d_ordered, boxes_d, textline_mask_tot_d) order_text_new, id_of_texts_tot = self.do_order_of_regions(contours_only_text_parent_d_ordered, contours_only_text_parent_h_d_ordered, boxes_d, textline_mask_tot_d)
@ -11584,7 +11587,7 @@ class eynollah:
else: else:
contours_only_text_parent_h = None contours_only_text_parent_h = None
# print('bura galmir?') # print('bura galmir?')
if np.abs(slope_deskew) < 0.13: if np.abs(slope_deskew) < SLOPE_THRESHOLD:
contours_only_text_parent = list(np.array(contours_only_text_parent)[index_by_text_par_con]) contours_only_text_parent = list(np.array(contours_only_text_parent)[index_by_text_par_con])
order_text_new, id_of_texts_tot = self.do_order_of_regions(contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot) order_text_new, id_of_texts_tot = self.do_order_of_regions(contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot)
else: else:

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