|
|
|
@ -1056,14 +1056,15 @@ class textlineerkenner:
|
|
|
|
|
return ang_int
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def do_work_of_slopes(self,q,poly,box_sub,boxes_per_process,textline_mask_tot,contours_per_process):
|
|
|
|
|
def do_work_of_slopes(self,q,poly,box_sub,boxes_per_process,contours_sub,textline_mask_tot,contours_per_process):
|
|
|
|
|
slope_biggest=0
|
|
|
|
|
slopes_sub = []
|
|
|
|
|
boxes_sub_new=[]
|
|
|
|
|
poly_sub=[]
|
|
|
|
|
contours_sub_per_p=[]
|
|
|
|
|
for mv in range(len(boxes_per_process)):
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
contours_sub_per_p.append(contours_per_process[mv])
|
|
|
|
|
crop_img, _ = self.crop_image_inside_box(boxes_per_process[mv],
|
|
|
|
|
np.repeat(textline_mask_tot[:, :, np.newaxis], 3, axis=2))
|
|
|
|
|
crop_img=crop_img[:,:,0]
|
|
|
|
@ -1099,20 +1100,23 @@ class textlineerkenner:
|
|
|
|
|
|
|
|
|
|
poly_sub.append(cnt_clean_rot)
|
|
|
|
|
boxes_sub_new.append(boxes_per_process[mv] )
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
q.put(slopes_sub)
|
|
|
|
|
poly.put(poly_sub)
|
|
|
|
|
box_sub.put(boxes_sub_new )
|
|
|
|
|
contours_sub.put(contours_sub_per_p)
|
|
|
|
|
|
|
|
|
|
def get_slopes_and_deskew(self, contours,textline_mask_tot):
|
|
|
|
|
|
|
|
|
|
slope_biggest=0#self.return_deskew_slop(img_int_p,sigma_des)
|
|
|
|
|
|
|
|
|
|
num_cores = 1 # XXX cpu_count()
|
|
|
|
|
num_cores = cpu_count()
|
|
|
|
|
q = Queue()
|
|
|
|
|
poly=Queue()
|
|
|
|
|
box_sub=Queue()
|
|
|
|
|
contours_sub=Queue()
|
|
|
|
|
|
|
|
|
|
processes = []
|
|
|
|
|
nh=np.linspace(0, len(self.boxes), num_cores+1)
|
|
|
|
@ -1121,28 +1125,33 @@ class textlineerkenner:
|
|
|
|
|
for i in range(num_cores):
|
|
|
|
|
boxes_per_process=self.boxes[int(nh[i]):int(nh[i+1])]
|
|
|
|
|
contours_per_process=contours[int(nh[i]):int(nh[i+1])]
|
|
|
|
|
processes.append(Process(target=self.do_work_of_slopes, args=(q,poly,box_sub, boxes_per_process, textline_mask_tot, contours_per_process)))
|
|
|
|
|
processes.append(Process(target=self.do_work_of_slopes, args=(q,poly,box_sub, boxes_per_process, contours_sub, textline_mask_tot, contours_per_process)))
|
|
|
|
|
|
|
|
|
|
for i in range(num_cores):
|
|
|
|
|
processes[i].start()
|
|
|
|
|
|
|
|
|
|
self.slopes = []
|
|
|
|
|
self.all_found_texline_polygons=[]
|
|
|
|
|
all_found_text_regions=[]
|
|
|
|
|
self.boxes=[]
|
|
|
|
|
|
|
|
|
|
for i in range(num_cores):
|
|
|
|
|
slopes_for_sub_process=q.get(True)
|
|
|
|
|
boxes_for_sub_process=box_sub.get(True)
|
|
|
|
|
polys_for_sub_process=poly.get(True)
|
|
|
|
|
contours_for_subprocess=contours_sub.get(True)
|
|
|
|
|
|
|
|
|
|
for j in range(len(slopes_for_sub_process)):
|
|
|
|
|
self.slopes.append(slopes_for_sub_process[j])
|
|
|
|
|
self.all_found_texline_polygons.append(polys_for_sub_process[j])
|
|
|
|
|
self.boxes.append(boxes_for_sub_process[j])
|
|
|
|
|
all_found_text_regions.append(contours_for_subprocess[j])
|
|
|
|
|
|
|
|
|
|
for i in range(num_cores):
|
|
|
|
|
processes[i].join()
|
|
|
|
|
|
|
|
|
|
return all_found_text_regions
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def order_of_regions(self, textline_mask,contours_main):
|
|
|
|
|
mada_n=textline_mask.sum(axis=1)
|
|
|
|
@ -1441,33 +1450,23 @@ class textlineerkenner:
|
|
|
|
|
|
|
|
|
|
t4=time.time()
|
|
|
|
|
|
|
|
|
|
# get orders of each textregion. This method by now only works for one column documents.
|
|
|
|
|
indexes_sorted, matrix_of_orders=self.order_of_regions(textline_mask_tot,contours)
|
|
|
|
|
order_of_texts, id_of_texts=self.order_and_id_of_texts(contours ,matrix_of_orders ,indexes_sorted )
|
|
|
|
|
|
|
|
|
|
##########
|
|
|
|
|
gc.collect()
|
|
|
|
|
# calculate the slope for deskewing for each box of text region.
|
|
|
|
|
contours=self.get_slopes_and_deskew(contours,textline_mask_tot)
|
|
|
|
|
|
|
|
|
|
gc.collect()
|
|
|
|
|
t5=time.time()
|
|
|
|
|
|
|
|
|
|
# just get the textline result for each box of text regions
|
|
|
|
|
#self.get_textlines_for_each_textregions(textline_mask_tot)
|
|
|
|
|
|
|
|
|
|
##########
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# calculate the slope for deskewing for each box of text region.
|
|
|
|
|
self.get_slopes_and_deskew(contours,textline_mask_tot)
|
|
|
|
|
# get orders of each textregion. This method by now only works for one column documents.
|
|
|
|
|
indexes_sorted, matrix_of_orders=self.order_of_regions(textline_mask_tot,contours)
|
|
|
|
|
order_of_texts, id_of_texts=self.order_and_id_of_texts(contours ,matrix_of_orders ,indexes_sorted )
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
##########
|
|
|
|
|
gc.collect()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
t6=time.time()
|
|
|
|
|
|
|
|
|
|
# do deskewing for each box of text region.
|
|
|
|
|
###self.deskew_textline_patches(contours,textline_mask_tot)
|
|
|
|
|
|
|
|
|
|
self.get_all_image_patches_coordination(image_page)
|
|
|
|
|
|
|
|
|
@ -1490,8 +1489,8 @@ class textlineerkenner:
|
|
|
|
|
print( "time needed for page extraction = "+"{0:.2f}".format(t2-t1) )
|
|
|
|
|
print( "time needed for text region extraction and get contours = "+"{0:.2f}".format(t3-t2) )
|
|
|
|
|
print( "time needed for textlines = "+"{0:.2f}".format(t4-t3) )
|
|
|
|
|
print( "time needed to get order of regions = "+"{0:.2f}".format(t5-t4) )
|
|
|
|
|
print( "time needed to get slopes of regions (deskewing) = "+"{0:.2f}".format(t6-t5) )
|
|
|
|
|
print( "time needed to get slopes of regions (deskewing) = "+"{0:.2f}".format(t5-t4) )
|
|
|
|
|
print( "time needed to get order of regions = "+"{0:.2f}".format(t6-t5) )
|
|
|
|
|
print( "time needed to implement deskewing = "+"{0:.2f}".format(t7-t6) )
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|