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eynollah
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machine based reading order inference with a variable batch size

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pull/138/head^2
vahidrezanezhad 1 year ago
parent 5fdc6d4fa4
commit 49c93149a4
1 changed files with 41 additions and 55 deletions
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qurator/eynollah/eynollah.py
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@ -2857,32 +2857,20 @@ class Eynollah:
return model
def do_order_of_regions_with_machine(self,contours_only_text_parent, contours_only_text_parent_h, text_regions_p):
#print(text_regions_p.shape)
y_len = text_regions_p.shape[0]
x_len = text_regions_p.shape[1]
img_poly = np.zeros((y_len,x_len), dtype='uint8')
unique_pix = np.unique(text_regions_p)
#print(unique_pix, 'unique_pix')
#for pix in unique_pix:
#print(pix)
#plt.imshow((text_regions_p[:,:]==pix)*1 )
#plt.show()
img_poly[text_regions_p[:,:]==1] = 1
img_poly[text_regions_p[:,:]==2] = 2
img_poly[text_regions_p[:,:]==3] = 4
img_poly[text_regions_p[:,:]==6] = 5
#plt.imshow(text_regions_p)
#plt.show()
#plt.imshow(img_poly)
#plt.show()
model_ro_machine, _ = self.start_new_session_and_model(self.model_reading_order_machine_dir)
height1 =672#448
@ -2900,19 +2888,11 @@ class Eynollah:
img_header_and_sep = np.zeros((y_len,x_len), dtype='uint8')
for j in range(len(cy_main)):
#print(j, int(y_max_main[j]), x_min_main[j], x_max_main[j] )
img_header_and_sep[int(y_max_main[j]):int(y_max_main[j])+12,int(x_min_main[j]):int(x_max_main[j]) ] = 1
#plt.imshow(img_header_and_sep[:,:])
#plt.show()
co_text_all = contours_only_text_parent + contours_only_text_parent_h
#id_all_text = id_paragraph + id_header
#texts_corr_order_index = [index_tot_regions[tot_region_ref.index(i)] for i in id_all_text ]
#texts_corr_order_index_int = [int(x) for x in texts_corr_order_index]
#co_text_all, texts_corr_order_index_int = filter_contours_area_of_image(img_poly, co_text_all, texts_corr_order_index_int, max_area, min_area)
labels_con = np.zeros((y_len,x_len,len(co_text_all)),dtype='uint8')
for i in range(len(co_text_all)):
@ -2932,63 +2912,69 @@ class Eynollah:
img3 = img3.astype(np.uint16)
#plt.imshow(img3)
#plt.show()
order_matrix = np.zeros((labels_con.shape[2], labels_con.shape[2]))-1
inference_bs = 6
tot_counter = 1
batch_counter = 0
i_indexer = []
j_indexer =[]
input_1= np.zeros( (inference_bs, height1, width1,3))
tot_iteration = int( ( labels_con.shape[2]*(labels_con.shape[2]-1) )/2. )
full_bs_ite= tot_iteration//inference_bs
last_bs = tot_iteration % inference_bs
#print(labels_con.shape[2],"number of regions for reading order")
for i in range(labels_con.shape[2]):
for j in range(labels_con.shape[2]):
if j>i:
img1= np.repeat(labels_con[:,:,i][:, :, np.newaxis], 3, axis=2)
img2 = np.repeat(labels_con[:,:,j][:, :, np.newaxis], 3, axis=2)
#img1 = img1.astype(np.uint16)
#img2 = img2.astype(np.uint16)
img2[:,:,0][img3[:,:]==5] = 2
img2[:,:,0][img_header_and_sep[:,:]==1] = 3
img1[:,:,0][img3[:,:]==5] = 2
img1[:,:,0][img_header_and_sep[:,:]==1] = 3
#plt.imshow(labels_con[:,:,i])
#plt.show()
#plt.imshow(img2[:,:,0])
#plt.show()
#plt.imshow(img1[:,:,0])
#plt.show()
i_indexer.append(i)
j_indexer.append(j)
#sys.exit()
input_1= np.zeros( (height1, width1,3))
input_1[batch_counter,:,:,0] = img1[:,:,0]/3.
input_1[batch_counter,:,:,2] = img2[:,:,0]/3.
input_1[batch_counter,:,:,1] = img3[:,:]/5.
input_1[:,:,0] = img1[:,:,0]/3.
input_1[:,:,2] = img2[:,:,0]/3.
input_1[:,:,1] = img3[:,:]/5.
batch_counter = batch_counter+1
#y_pr=model.predict([img1.reshape(1,height1,width1,3) , img2.reshape(1,height2,width2,3),img3.reshape(1,height3,width3,3) ], verbose=2)
y_pr=model_ro_machine.predict(input_1.reshape(1,height1,width1,3) , verbose=0)
#print(y_pr)
if batch_counter==inference_bs or ( (tot_counter//inference_bs)==full_bs_ite and tot_counter%inference_bs==last_bs):
y_pr=model_ro_machine.predict(input_1 , verbose=0)
if y_pr>=0.5:
if batch_counter==inference_bs:
iteration_batches = inference_bs
else:
iteration_batches = last_bs
for jb in range(iteration_batches):
if y_pr[jb][0]>=0.5:
order_class = 1
else:
order_class = 0
order_matrix[i,j] = y_pr#order_class
order_matrix[j,i] = 1-y_pr#int( 1 - order_class)
order_matrix[i_indexer[jb],j_indexer[jb]] = y_pr[jb][0]#order_class
order_matrix[j_indexer[jb],i_indexer[jb]] = 1-y_pr[jb][0]#int( 1 - order_class)
batch_counter = 0
i_indexer = []
j_indexer = []
tot_counter = tot_counter+1
sum_mat = np.sum(order_matrix, axis=1)
index_sort = np.argsort(sum_mat)
index_sort = index_sort[::-1]
print(index_sort)
REGION_ID_TEMPLATE = 'region_%04d'
order_of_texts = []
id_of_texts = []
@ -3272,13 +3258,12 @@ class Eynollah:
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:
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)
self.logger.info("detection of reading order took %.1fs", time.time() - t_order)
pcgts = self.writer.build_pagexml_full_layout(contours_only_text_parent, contours_only_text_parent_h, page_coord, order_text_new, id_of_texts_tot, all_found_textline_polygons, all_found_textline_polygons_h, all_box_coord, all_box_coord_h, polygons_of_images, contours_tables, polygons_of_drop_capitals, polygons_of_marginals, all_found_textline_polygons_marginals, all_box_coord_marginals, slopes, slopes_h, slopes_marginals, cont_page, polygons_lines_xml)
self.logger.info("Job done in %.1fs", time.time() - t0)
##return pcgts
print(id_of_texts_tot,'id_of_texts_tot')
print(order_text_new,'order_text_new')
else:
contours_only_text_parent_h = None
@ -3291,6 +3276,7 @@ class Eynollah:
contours_only_text_parent_d_ordered = list(np.array(contours_only_text_parent_d_ordered, dtype=object)[index_by_text_par_con])
order_text_new, id_of_texts_tot = self.do_order_of_regions(contours_only_text_parent_d_ordered, contours_only_text_parent_h, boxes_d, textline_mask_tot_d)
self.logger.info("detection of reading order took %.1fs", time.time() - t_order)
pcgts = self.writer.build_pagexml_no_full_layout(txt_con_org, page_coord, order_text_new, id_of_texts_tot, all_found_textline_polygons, all_box_coord, polygons_of_images, polygons_of_marginals, all_found_textline_polygons_marginals, all_box_coord_marginals, slopes, slopes_marginals, cont_page, polygons_lines_xml, contours_tables)
self.logger.info("Job done in %.1fs", time.time() - t0)
##return pcgts

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