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eynollah
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simplify constructs, remove print-debugging stmts

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pull/19/head
Konstantin Baierer 4 years ago
parent 0eda4a174a
commit 4c81fa2e46
1 changed files with 9 additions and 43 deletions
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sbb_newspapers_org_image/eynollah.py
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@ -1392,11 +1392,11 @@ class eynollah:
con_inter_box = [] con_inter_box = []
con_inter_box_h = [] con_inter_box_h = []
for i in range(len(args_contours_box)): for box in args_contours_box:
con_inter_box.append(contours_only_text_parent[args_contours_box[i]]) con_inter_box.append(contours_only_text_parent[box])
for i in range(len(args_contours_box_h)): for box in args_contours_box_h:
con_inter_box_h.append(contours_only_text_parent_h[args_contours_box_h[i]]) con_inter_box_h.append(contours_only_text_parent_h[box])
indexes_sorted, matrix_of_orders, kind_of_texts_sorted, index_by_kind_sorted = order_of_regions(textline_mask_tot[int(boxes[iij][2]) : int(boxes[iij][3]), int(boxes[iij][0]) : int(boxes[iij][1])], con_inter_box, con_inter_box_h, boxes[iij][2]) indexes_sorted, matrix_of_orders, kind_of_texts_sorted, index_by_kind_sorted = order_of_regions(textline_mask_tot[int(boxes[iij][2]) : int(boxes[iij][3]), int(boxes[iij][0]) : int(boxes[iij][1])], con_inter_box, con_inter_box_h, boxes[iij][2])
@ -1431,8 +1431,7 @@ class eynollah:
order_text_new = [] order_text_new = []
for iii in range(len(order_of_texts_tot)): for iii in range(len(order_of_texts_tot)):
tartib_new = np.where(np.array(order_of_texts_tot) == iii)[0][0] order_text_new.append(np.where(np.array(order_of_texts_tot) == iii)[0][0])
order_text_new.append(tartib_new)
except Exception as why: except Exception as why:
self.logger.error(why) self.logger.error(why)
@ -1506,8 +1505,7 @@ class eynollah:
order_text_new = [] order_text_new = []
for iii in range(len(order_of_texts_tot)): for iii in range(len(order_of_texts_tot)):
tartib_new = np.where(np.array(order_of_texts_tot) == iii)[0][0] order_text_new.append(np.where(np.array(order_of_texts_tot) == iii)[0][0])
order_text_new.append(tartib_new)
return order_text_new, id_of_texts_tot return order_text_new, id_of_texts_tot
def do_order_of_regions_no_full_layout(self, contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot): def do_order_of_regions_no_full_layout(self, contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot):
@ -1557,8 +1555,7 @@ class eynollah:
order_text_new = [] order_text_new = []
for iii in range(len(order_of_texts_tot)): for iii in range(len(order_of_texts_tot)):
tartib_new = np.where(np.array(order_of_texts_tot) == iii)[0][0] order_text_new.append(np.where(np.array(order_of_texts_tot) == iii)[0][0])
order_text_new.append(tartib_new)
except Exception as why: except Exception as why:
self.logger.error(why) self.logger.error(why)
@ -1590,7 +1587,7 @@ class eynollah:
indexes_sorted_main = np.array(indexes_sorted)[np.array(kind_of_texts_sorted) == 1] indexes_sorted_main = np.array(indexes_sorted)[np.array(kind_of_texts_sorted) == 1]
indexes_by_type_main = np.array(index_by_kind_sorted)[np.array(kind_of_texts_sorted) == 1] indexes_by_type_main = np.array(index_by_kind_sorted)[np.array(kind_of_texts_sorted) == 1]
for zahler, mtv in enumerate(args_contours_box): for zahler, _ in enumerate(args_contours_box):
arg_order_v = indexes_sorted_main[zahler] arg_order_v = indexes_sorted_main[zahler]
tartib = np.where(indexes_sorted == arg_order_v)[0][0] tartib = np.where(indexes_sorted == arg_order_v)[0][0]
order_by_con_main[args_contours_box[indexes_by_type_main[zahler]]] = tartib + ref_point order_by_con_main[args_contours_box[indexes_by_type_main[zahler]]] = tartib + ref_point
@ -1606,8 +1603,7 @@ class eynollah:
order_text_new = [] order_text_new = []
for iii in range(len(order_of_texts_tot)): for iii in range(len(order_of_texts_tot)):
tartib_new = np.where(np.array(order_of_texts_tot) == iii)[0][0] order_text_new.append(np.where(np.array(order_of_texts_tot) == iii)[0][0])
order_text_new.append(tartib_new)
return order_text_new, id_of_texts_tot return order_text_new, id_of_texts_tot
@ -1675,11 +1671,7 @@ class eynollah:
scaler_h_textline = 1 # 1.2#1.2 scaler_h_textline = 1 # 1.2#1.2
scaler_w_textline = 1 # 0.9#1 scaler_w_textline = 1 # 0.9#1
textline_mask_tot_ea, _ = self.textline_contours(image_page, True, scaler_h_textline, scaler_w_textline) textline_mask_tot_ea, _ = self.textline_contours(image_page, True, scaler_h_textline, scaler_w_textline)
K.clear_session() K.clear_session()
#print(np.unique(textline_mask_tot_ea[:, :]), "textline")
# plt.imshow(textline_mask_tot_ea)
# plt.show()
if self.plotter: if self.plotter:
self.plotter.save_plot_of_textlines(textline_mask_tot_ea, image_page) self.plotter.save_plot_of_textlines(textline_mask_tot_ea, image_page)
return textline_mask_tot_ea return textline_mask_tot_ea
@ -1878,21 +1870,9 @@ class eynollah:
# plt.imshow(img_revised_tab) # plt.imshow(img_revised_tab)
# plt.show() # plt.show()
# print(img_revised_tab.shape,text_regions_p_1_n.shape)
# text_regions_p_1_n=resize_image(text_regions_p_1_n,img_revised_tab.shape[0],img_revised_tab.shape[1])
# 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) >= SLOPE_THRESHOLD: 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
# print(text_only.shape,text_only_d.shape)
# plt.imshow(text_only)
# plt.show()
# plt.imshow(text_only_d)
# plt.show()
min_con_area = 0.000005 min_con_area = 0.000005
if np.abs(slope_deskew) >= SLOPE_THRESHOLD: if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
@ -1943,26 +1923,12 @@ class eynollah:
x_diff = p_big[0] - cx_bigest_d_big x_diff = p_big[0] - cx_bigest_d_big
y_diff = p_big[1] - cy_biggest_d_big y_diff = p_big[1] - cy_biggest_d_big
# print(p_big)
# print(cx_bigest_d_big,cy_biggest_d_big)
# print(x_diff,y_diff)
contours_only_text_parent_d_ordered = [] contours_only_text_parent_d_ordered = []
for i in range(len(contours_only_text_parent)): for i in range(len(contours_only_text_parent)):
# img1=np.zeros((text_only.shape[0],text_only.shape[1],3))
# img1=cv2.fillPoly(img1,pts=[contours_only_text_parent[i]] ,color=(1,1,1))
# plt.imshow(img1[:,:,0])
# plt.show()
p = np.dot(M_22, [cx_bigest[i], cy_biggest[i]]) p = np.dot(M_22, [cx_bigest[i], cy_biggest[i]])
# print(p)
p[0] = p[0] - x_diff[0] p[0] = p[0] - x_diff[0]
p[1] = p[1] - y_diff[0] p[1] = p[1] - y_diff[0]
# print(p)
# print(cx_bigest_d)
# print(cy_biggest_d)
dists = [math.sqrt((p[0] - cx_bigest_d[j]) ** 2 + (p[1] - cy_biggest_d[j]) ** 2) for j in range(len(cx_bigest_d))] dists = [math.sqrt((p[0] - cx_bigest_d[j]) ** 2 + (p[1] - cy_biggest_d[j]) ** 2) for j in range(len(cx_bigest_d))]
# print(np.argmin(dists))
contours_only_text_parent_d_ordered.append(contours_only_text_parent_d[np.argmin(dists)]) contours_only_text_parent_d_ordered.append(contours_only_text_parent_d[np.argmin(dists)])
# img2=np.zeros((text_only.shape[0],text_only.shape[1],3)) # img2=np.zeros((text_only.shape[0],text_only.shape[1],3))
# img2=cv2.fillPoly(img2,pts=[contours_only_text_parent_d[np.argmin(dists)]] ,color=(1,1,1)) # img2=cv2.fillPoly(img2,pts=[contours_only_text_parent_d[np.argmin(dists)]] ,color=(1,1,1))

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