qurator-spk
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eynollah
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Merge remote-tracking branch 'vahidrezanezhad/main' into main

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pull/61/head
Konstantin Baierer 3 years ago
parent e7868b9851 0859d22f4c
commit 4223fed628
9 changed files with 653 additions and 111 deletions
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12
README.md
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@ -80,10 +80,18 @@ eynollah \
-o <directory to write output xml or enhanced image> \
-m <directory of models> \
-fl <if true, the tool will perform full layout analysis> \
-ae <if true, the tool will resize and enhance the image and produce the resulting image as output> \
-ae <if true, the tool will resize and enhance the image and produce the resulting image as output. The rescaled and enhanced image will be saved in output directory> \
-as <if true, the tool will check whether the document needs rescaling or not> \
-cl <if true, the tool will extract the contours of curved textlines instead of rectangle bounding boxes> \
-si <if a directory is given here, the tool will output image regions inside documents there>
-si <if a directory is given here, the tool will output image regions inside documents there> \
-sd <if a directory is given, deskewed image will be saved there> \
-sa <if a directory is given, all plots needed for documentation will be saved there> \
-tab <if true, this tool will try to detect tables> \
-ib <in general, eynollah uses RGB as input but if the input document is strongly dark, bright or for any other reason you can turn binarized input on. This option does not mean that you have to provide a binary image, otherwise this means that the tool itself will binarized the RGB input document> \
-ho <if true, this tool would ignore headers role in reading order detection> \
-sl <if a directory is given, plot of layout will be saved there> \
-ep <if true, the tool will be enabled to save desired plot. This should be true alongside with -sl, -sd, -sa , -si or -ae options>
```
The tool does accept and works better on original images (RGB format) than binarized images.

16
qurator/eynollah/cli.py
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@ -73,6 +73,12 @@ from qurator.eynollah.eynollah import Eynollah
is_flag=True,
help="if this parameter set to true, this tool will try to return all elements of layout.",
)
@click.option(
"--tables/--no-tables",
"-tab/-notab",
is_flag=True,
help="if this parameter set to true, this tool will try to detect tables.",
)
@click.option(
"--input_binary/--input-RGB",
"-ib/-irgb",
@ -109,6 +115,7 @@ def main(
allow_enhancement,
curved_line,
full_layout,
tables,
input_binary,
allow_scaling,
headers_off,
@ -117,11 +124,11 @@ def main(
if log_level:
setOverrideLogLevel(log_level)
initLogging()
if not enable_plotting and (save_layout or save_deskewed or save_all or save_images):
print("Error: You used one of -sl, -sd, -sa or -si but did not enable plotting with -ep")
if not enable_plotting and (save_layout or save_deskewed or save_all or save_images or allow_enhancement):
print("Error: You used one of -sl, -sd, -sa, -si or -ae but did not enable plotting with -ep")
sys.exit(1)
elif enable_plotting and not (save_layout or save_deskewed or save_all or save_images):
print("Error: You used -ep to enable plotting but set none of -sl, -sd, -sa or -si")
elif enable_plotting and not (save_layout or save_deskewed or save_all or save_images or allow_enhancement):
print("Error: You used -ep to enable plotting but set none of -sl, -sd, -sa, -si or -ae")
sys.exit(1)
eynollah = Eynollah(
image_filename=image,
@ -135,6 +142,7 @@ def main(
allow_enhancement=allow_enhancement,
curved_line=curved_line,
full_layout=full_layout,
tables=tables,
input_binary=input_binary,
allow_scaling=allow_scaling,
headers_off=headers_off,

577
qurator/eynollah/eynollah.py
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@ -26,12 +26,16 @@ sys.stderr = stderr
import tensorflow as tf
tf.get_logger().setLevel("ERROR")
warnings.filterwarnings("ignore")
from scipy.signal import find_peaks
import matplotlib.pyplot as plt
from scipy.ndimage import gaussian_filter1d
from .utils.contour import (
filter_contours_area_of_image,
filter_contours_area_of_image_tables,
find_contours_mean_y_diff,
find_new_features_of_contours,
find_features_of_contours,
get_text_region_boxes_by_given_contours,
get_textregion_contours_in_org_image,
return_contours_of_image,
@ -92,6 +96,7 @@ class Eynollah:
allow_enhancement=False,
curved_line=False,
full_layout=False,
tables=False,
input_binary=False,
allow_scaling=False,
headers_off=False,
@ -110,10 +115,12 @@ class Eynollah:
self.allow_enhancement = allow_enhancement
self.curved_line = curved_line
self.full_layout = full_layout
self.tables = tables
self.input_binary = input_binary
self.allow_scaling = allow_scaling
self.headers_off = headers_off
self.plotter = None if not enable_plotting else EynollahPlotter(
dir_out=self.dir_out,
dir_of_all=dir_of_all,
dir_of_deskewed=dir_of_deskewed,
dir_of_cropped_images=dir_of_cropped_images,
@ -137,6 +144,7 @@ class Eynollah:
self.model_page_dir = dir_models + "/model_page_mixed_best.h5"
self.model_region_dir_p_ens = dir_models + "/model_ensemble_s.h5"
self.model_textline_dir = dir_models + "/model_textline_newspapers.h5"
self.model_tables = dir_models + "/model_tables_ens_mixed_new_2.h5"
def _cache_images(self, image_filename=None, image_pil=None):
ret = {}
@ -1166,7 +1174,7 @@ class Eynollah:
try:
img_only_regions = cv2.erode(img_only_regions_with_sep[:,:], KERNEL, iterations=20)
_, _ = find_num_col(img_only_regions, multiplier=6.0)
_, _ = find_num_col(img_only_regions, num_col_classifier, self.tables, multiplier=6.0)
img = resize_image(img_org, int(img_org.shape[0]), int(img_org.shape[1]*(1.2 if is_image_enhanced else 1)))
@ -1612,11 +1620,325 @@ class Eynollah:
order_text_new.append(np.where(np.array(order_of_texts_tot) == iii)[0][0])
return order_text_new, id_of_texts_tot
def check_iou_of_bounding_box_and_contour_for_tables(self, layout, table_prediction_early, pixel_tabel, num_col_classifier):
layout_org = np.copy(layout)
layout_org[:,:,0][layout_org[:,:,0]==pixel_tabel] = 0
layout = (layout[:,:,0]==pixel_tabel)*1
layout =np.repeat(layout[:, :, np.newaxis], 3, axis=2)
layout = layout.astype(np.uint8)
imgray = cv2.cvtColor(layout, cv2.COLOR_BGR2GRAY )
_, thresh = cv2.threshold(imgray, 0, 255, 0)
contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cnt_size = np.array([cv2.contourArea(contours[j]) for j in range(len(contours))])
contours_new = []
for i in range(len(contours)):
x, y, w, h = cv2.boundingRect(contours[i])
iou = cnt_size[i] /float(w*h) *100
if iou<80:
layout_contour = np.zeros((layout_org.shape[0], layout_org.shape[1]))
layout_contour= cv2.fillPoly(layout_contour,pts=[contours[i]] ,color=(1,1,1))
layout_contour_sum = layout_contour.sum(axis=0)
layout_contour_sum_diff = np.diff(layout_contour_sum)
layout_contour_sum_diff= np.abs(layout_contour_sum_diff)
layout_contour_sum_diff_smoothed= gaussian_filter1d(layout_contour_sum_diff, 10)
peaks, _ = find_peaks(layout_contour_sum_diff_smoothed, height=0)
peaks= peaks[layout_contour_sum_diff_smoothed[peaks]>4]
for j in range(len(peaks)):
layout_contour[:,peaks[j]-3+1:peaks[j]+1+3] = 0
layout_contour=cv2.erode(layout_contour[:,:], KERNEL, iterations=5)
layout_contour=cv2.dilate(layout_contour[:,:], KERNEL, iterations=5)
layout_contour =np.repeat(layout_contour[:, :, np.newaxis], 3, axis=2)
layout_contour = layout_contour.astype(np.uint8)
imgray = cv2.cvtColor(layout_contour, cv2.COLOR_BGR2GRAY )
_, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_sep, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
for ji in range(len(contours_sep) ):
contours_new.append(contours_sep[ji])
if num_col_classifier>=2:
only_recent_contour_image = np.zeros((layout.shape[0],layout.shape[1]))
only_recent_contour_image= cv2.fillPoly(only_recent_contour_image,pts=[contours_sep[ji]] ,color=(1,1,1))
table_pixels_masked_from_early_pre = only_recent_contour_image[:,:]*table_prediction_early[:,:]
iou_in = table_pixels_masked_from_early_pre.sum() /float(only_recent_contour_image.sum()) *100
#print(iou_in,'iou_in_in1')
if iou_in>30:
layout_org= cv2.fillPoly(layout_org,pts=[contours_sep[ji]] ,color=(pixel_tabel,pixel_tabel,pixel_tabel))
else:
pass
else:
layout_org= cv2.fillPoly(layout_org,pts=[contours_sep[ji]] ,color=(pixel_tabel,pixel_tabel,pixel_tabel))
else:
contours_new.append(contours[i])
if num_col_classifier>=2:
only_recent_contour_image = np.zeros((layout.shape[0],layout.shape[1]))
only_recent_contour_image= cv2.fillPoly(only_recent_contour_image,pts=[contours[i]] ,color=(1,1,1))
table_pixels_masked_from_early_pre = only_recent_contour_image[:,:]*table_prediction_early[:,:]
iou_in = table_pixels_masked_from_early_pre.sum() /float(only_recent_contour_image.sum()) *100
#print(iou_in,'iou_in')
if iou_in>30:
layout_org= cv2.fillPoly(layout_org,pts=[contours[i]] ,color=(pixel_tabel,pixel_tabel,pixel_tabel))
else:
pass
else:
layout_org= cv2.fillPoly(layout_org,pts=[contours[i]] ,color=(pixel_tabel,pixel_tabel,pixel_tabel))
return layout_org, contours_new
def delete_separator_around(self,spliter_y,peaks_neg,image_by_region, pixel_line, pixel_table):
# format of subboxes: box=[x1, x2 , y1, y2]
pix_del = 100
if len(image_by_region.shape)==3:
for i in range(len(spliter_y)-1):
for j in range(1,len(peaks_neg[i])-1):
image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-pix_del:peaks_neg[i][j]+pix_del,0][image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-pix_del:peaks_neg[i][j]+pix_del,0]==pixel_line ]=0
image_by_region[spliter_y[i]:spliter_y[i+1],peaks_neg[i][j]-pix_del:peaks_neg[i][j]+pix_del,0][image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-pix_del:peaks_neg[i][j]+pix_del,1]==pixel_line ]=0
image_by_region[spliter_y[i]:spliter_y[i+1],peaks_neg[i][j]-pix_del:peaks_neg[i][j]+pix_del,0][image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-pix_del:peaks_neg[i][j]+pix_del,2]==pixel_line ]=0
image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-pix_del:peaks_neg[i][j]+pix_del,0][image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-pix_del:peaks_neg[i][j]+pix_del,0]==pixel_table ]=0
image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-pix_del:peaks_neg[i][j]+pix_del,0][image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-pix_del:peaks_neg[i][j]+pix_del,1]==pixel_table ]=0
image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-pix_del:peaks_neg[i][j]+pix_del,0][image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-pix_del:peaks_neg[i][j]+pix_del,2]==pixel_table ]=0
else:
for i in range(len(spliter_y)-1):
for j in range(1,len(peaks_neg[i])-1):
image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-pix_del:peaks_neg[i][j]+pix_del][image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-pix_del:peaks_neg[i][j]+pix_del]==pixel_line ]=0
image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-pix_del:peaks_neg[i][j]+pix_del][image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-pix_del:peaks_neg[i][j]+pix_del]==pixel_table ]=0
return image_by_region
def add_tables_heuristic_to_layout(self, image_regions_eraly_p,boxes, slope_mean_hor, spliter_y,peaks_neg_tot, image_revised, num_col_classifier, min_area, pixel_line):
pixel_table =10
image_revised_1 = self.delete_separator_around(spliter_y, peaks_neg_tot, image_revised, pixel_line, pixel_table)
try:
image_revised_1[:,:30][image_revised_1[:,:30]==pixel_line] = 0
image_revised_1[:,image_revised_1.shape[1]-30:][image_revised_1[:,image_revised_1.shape[1]-30:]==pixel_line] = 0
except:
pass
img_comm_e = np.zeros(image_revised_1.shape)
img_comm = np.repeat(img_comm_e[:, :, np.newaxis], 3, axis=2)
for indiv in np.unique(image_revised_1):
image_col=(image_revised_1==indiv)*255
img_comm_in=np.repeat(image_col[:, :, np.newaxis], 3, axis=2)
img_comm_in=img_comm_in.astype(np.uint8)
imgray = cv2.cvtColor(img_comm_in, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours,hirarchy=cv2.findContours(thresh.copy(), cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
if indiv==pixel_table:
main_contours = filter_contours_area_of_image_tables(thresh, contours, hirarchy, max_area = 1, min_area = 0.001)
else:
main_contours = filter_contours_area_of_image_tables(thresh, contours, hirarchy, max_area = 1, min_area = min_area)
img_comm = cv2.fillPoly(img_comm, pts = main_contours, color = (indiv, indiv, indiv))
img_comm = img_comm.astype(np.uint8)
if not self.isNaN(slope_mean_hor):
image_revised_last = np.zeros((image_regions_eraly_p.shape[0], image_regions_eraly_p.shape[1],3))
for i in range(len(boxes)):
image_box=img_comm[int(boxes[i][2]):int(boxes[i][3]),int(boxes[i][0]):int(boxes[i][1]),:]
try:
image_box_tabels_1=(image_box[:,:,0]==pixel_table)*1
contours_tab,_=return_contours_of_image(image_box_tabels_1)
contours_tab=filter_contours_area_of_image_tables(image_box_tabels_1,contours_tab,_,1,0.003)
image_box_tabels_1=(image_box[:,:,0]==pixel_line)*1
image_box_tabels_and_m_text=( (image_box[:,:,0]==pixel_table) | (image_box[:,:,0]==1) )*1
image_box_tabels_and_m_text=image_box_tabels_and_m_text.astype(np.uint8)
image_box_tabels_1=image_box_tabels_1.astype(np.uint8)
image_box_tabels_1 = cv2.dilate(image_box_tabels_1,KERNEL,iterations = 5)
contours_table_m_text,_=return_contours_of_image(image_box_tabels_and_m_text)
image_box_tabels=np.repeat(image_box_tabels_1[:, :, np.newaxis], 3, axis=2)
image_box_tabels=image_box_tabels.astype(np.uint8)
imgray = cv2.cvtColor(image_box_tabels, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_line,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
y_min_main_line ,y_max_main_line=find_features_of_contours(contours_line)
y_min_main_tab ,y_max_main_tab=find_features_of_contours(contours_tab)
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_contours(contours_table_m_text)
cx_tabl,cy_tabl ,x_min_tabl , x_max_tabl, y_min_tabl ,y_max_tabl,_= find_new_features_of_contours(contours_tab)
if len(y_min_main_tab )>0:
y_down_tabs=[]
y_up_tabs=[]
for i_t in range(len(y_min_main_tab )):
y_down_tab=[]
y_up_tab=[]
for i_l in range(len(y_min_main_line)):
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]:
pass
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]:
pass
elif np.abs(y_max_main_line[i_l]-y_min_main_line[i_l])<100:
pass
else:
y_up_tab.append(np.min([y_min_main_line[i_l], y_min_main_tab[i_t] ]) )
y_down_tab.append( np.max([ y_max_main_line[i_l],y_max_main_tab[i_t] ]) )
if len(y_up_tab)==0:
y_up_tabs.append(y_min_main_tab[i_t])
y_down_tabs.append(y_max_main_tab[i_t])
else:
y_up_tabs.append(np.min(y_up_tab))
y_down_tabs.append(np.max(y_down_tab))
else:
y_down_tabs=[]
y_up_tabs=[]
pass
except:
y_down_tabs=[]
y_up_tabs=[]
for ii in range(len(y_up_tabs)):
image_box[y_up_tabs[ii]:y_down_tabs[ii],:,0]=pixel_table
image_revised_last[int(boxes[i][2]):int(boxes[i][3]),int(boxes[i][0]):int(boxes[i][1]),:]=image_box[:,:,:]
else:
for i in range(len(boxes)):
image_box=img_comm[int(boxes[i][2]):int(boxes[i][3]),int(boxes[i][0]):int(boxes[i][1]),:]
image_revised_last[int(boxes[i][2]):int(boxes[i][3]),int(boxes[i][0]):int(boxes[i][1]),:]=image_box[:,:,:]
if num_col_classifier==1:
img_tables_col_1=( image_revised_last[:,:,0]==pixel_table )*1
img_tables_col_1=img_tables_col_1.astype(np.uint8)
contours_table_col1,_=return_contours_of_image(img_tables_col_1)
_,_ ,_ , _, y_min_tab_col1 ,y_max_tab_col1, _= find_new_features_of_contours(contours_table_col1)
if len(y_min_tab_col1)>0:
for ijv in range(len(y_min_tab_col1)):
image_revised_last[int(y_min_tab_col1[ijv]):int(y_max_tab_col1[ijv]),:,:]=pixel_table
return image_revised_last
def do_order_of_regions(self, *args, **kwargs):
if self.full_layout:
return self.do_order_of_regions_full_layout(*args, **kwargs)
return self.do_order_of_regions_no_full_layout(*args, **kwargs)
def get_tables_from_model(self, img, num_col_classifier):
img_org = np.copy(img)
img_height_h = img_org.shape[0]
img_width_h = img_org.shape[1]
model_region, session_region = self.start_new_session_and_model(self.model_tables)
patches = False
if num_col_classifier < 4 and num_col_classifier > 2:
prediction_table = self.do_prediction(patches, img, model_region)
pre_updown = self.do_prediction(patches, cv2.flip(img[:,:,:], -1), model_region)
pre_updown = cv2.flip(pre_updown, -1)
prediction_table[:,:,0][pre_updown[:,:,0]==1]=1
prediction_table = prediction_table.astype(np.int16)
elif num_col_classifier ==2:
height_ext = 0#int( img.shape[0]/4. )
h_start = int(height_ext/2.)
width_ext = int( img.shape[1]/8. )
w_start = int(width_ext/2.)
height_new = img.shape[0]+height_ext
width_new = img.shape[1]+width_ext
img_new =np.ones((height_new,width_new,img.shape[2])).astype(float)*0
img_new[h_start:h_start+img.shape[0] ,w_start: w_start+img.shape[1], : ] =img[:,:,:]
prediction_ext = self.do_prediction(patches, img_new, model_region)
pre_updown = self.do_prediction(patches, cv2.flip(img_new[:,:,:], -1), model_region)
pre_updown = cv2.flip(pre_updown, -1)
prediction_table = prediction_ext[h_start:h_start+img.shape[0] ,w_start: w_start+img.shape[1], : ]
prediction_table_updown = pre_updown[h_start:h_start+img.shape[0] ,w_start: w_start+img.shape[1], : ]
prediction_table[:,:,0][prediction_table_updown[:,:,0]==1]=1
prediction_table = prediction_table.astype(np.int16)
elif num_col_classifier ==1:
height_ext = 0# int( img.shape[0]/4. )
h_start = int(height_ext/2.)
width_ext = int( img.shape[1]/4. )
w_start = int(width_ext/2.)
height_new = img.shape[0]+height_ext
width_new = img.shape[1]+width_ext
img_new =np.ones((height_new,width_new,img.shape[2])).astype(float)*0
img_new[h_start:h_start+img.shape[0] ,w_start: w_start+img.shape[1], : ] =img[:,:,:]
prediction_ext = self.do_prediction(patches, img_new, model_region)
pre_updown = self.do_prediction(patches, cv2.flip(img_new[:,:,:], -1), model_region)
pre_updown = cv2.flip(pre_updown, -1)
prediction_table = prediction_ext[h_start:h_start+img.shape[0] ,w_start: w_start+img.shape[1], : ]
prediction_table_updown = pre_updown[h_start:h_start+img.shape[0] ,w_start: w_start+img.shape[1], : ]
prediction_table[:,:,0][prediction_table_updown[:,:,0]==1]=1
prediction_table = prediction_table.astype(np.int16)
else:
prediction_table = np.zeros(img.shape)
img_w_half = int(img.shape[1]/2.)
pre1 = self.do_prediction(patches, img[:,0:img_w_half,:], model_region)
pre2 = self.do_prediction(patches, img[:,img_w_half:,:], model_region)
pre_full = self.do_prediction(patches, img[:,:,:], model_region)
pre_updown = self.do_prediction(patches, cv2.flip(img[:,:,:], -1), model_region)
pre_updown = cv2.flip(pre_updown, -1)
prediction_table_full_erode = cv2.erode(pre_full[:,:,0], KERNEL, iterations=4)
prediction_table_full_erode = cv2.dilate(prediction_table_full_erode, KERNEL, iterations=4)
prediction_table_full_updown_erode = cv2.erode(pre_updown[:,:,0], KERNEL, iterations=4)
prediction_table_full_updown_erode = cv2.dilate(prediction_table_full_updown_erode, KERNEL, iterations=4)
prediction_table[:,0:img_w_half,:] = pre1[:,:,:]
prediction_table[:,img_w_half:,:] = pre2[:,:,:]
prediction_table[:,:,0][prediction_table_full_erode[:,:]==1]=1
prediction_table[:,:,0][prediction_table_full_updown_erode[:,:]==1]=1
prediction_table = prediction_table.astype(np.int16)
#prediction_table_erode = cv2.erode(prediction_table[:,:,0], self.kernel, iterations=6)
#prediction_table_erode = cv2.dilate(prediction_table_erode, self.kernel, iterations=6)
prediction_table_erode = cv2.erode(prediction_table[:,:,0], KERNEL, iterations=20)
prediction_table_erode = cv2.dilate(prediction_table_erode, KERNEL, iterations=20)
del model_region
del session_region
gc.collect()
return prediction_table_erode.astype(np.int16)
def run_graphics_and_columns(self, text_regions_p_1, num_col_classifier, num_column_is_classified, erosion_hurts):
img_g = self.imread(grayscale=True, uint8=True)
@ -1628,6 +1950,12 @@ class Eynollah:
img_g3[:, :, 2] = img_g[:, :]
image_page, page_coord, cont_page = self.extract_page()
if self.tables:
table_prediction = self.get_tables_from_model(image_page, num_col_classifier)
else:
table_prediction = (np.zeros((image_page.shape[0], image_page.shape[1]))).astype(np.int16)
if self.plotter:
self.plotter.save_page_image(image_page)
@ -1648,14 +1976,14 @@ class Eynollah:
try:
num_col, _ = find_num_col(img_only_regions, multiplier=6.0)
num_col, _ = find_num_col(img_only_regions, num_col_classifier, self.tables, multiplier=6.0)
num_col = num_col + 1
if not num_column_is_classified:
num_col_classifier = num_col + 1
except Exception as why:
self.logger.error(why)
num_col = None
return num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_lines, text_regions_p_1, cont_page
return num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_lines, text_regions_p_1, cont_page, table_prediction
def run_enhancement(self):
self.logger.info("resize and enhance image")
@ -1667,6 +1995,8 @@ class Eynollah:
if self.allow_enhancement:
img_res = img_res.astype(np.uint8)
self.get_image_and_scales(img_org, img_res, scale)
if self.plotter:
self.plotter.save_enhanced_image(img_res)
else:
self.get_image_and_scales_after_enhancing(img_org, img_res)
else:
@ -1699,7 +2029,7 @@ class Eynollah:
self.logger.info("slope_deskew: %s", slope_deskew)
return slope_deskew, slope_first
def run_marginals(self, image_page, textline_mask_tot_ea, mask_images, mask_lines, num_col_classifier, slope_deskew, text_regions_p_1):
def run_marginals(self, image_page, textline_mask_tot_ea, mask_images, mask_lines, num_col_classifier, slope_deskew, text_regions_p_1, table_prediction):
image_page_rotated, textline_mask_tot = image_page[:, :], textline_mask_tot_ea[:, :]
textline_mask_tot[mask_images[:, :] == 1] = 0
@ -1710,6 +2040,8 @@ class Eynollah:
if num_col_classifier in (1, 2):
try:
regions_without_separators = (text_regions_p[:, :] == 1) * 1
if self.tables:
regions_without_separators[table_prediction==1] = 1
regions_without_separators = regions_without_separators.astype(np.uint8)
text_regions_p = get_marginals(rotate_image(regions_without_separators, slope_deskew), text_regions_p, num_col_classifier, slope_deskew, kernel=KERNEL)
except Exception as e:
@ -1720,24 +2052,29 @@ class Eynollah:
self.plotter.save_plot_of_layout_main(text_regions_p, image_page)
return textline_mask_tot, text_regions_p, image_page_rotated
def run_boxes_no_full_layout(self, image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier, erosion_hurts):
def run_boxes_no_full_layout(self, image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier, table_prediction, erosion_hurts):
self.logger.debug('enter run_boxes_no_full_layout')
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
_, textline_mask_tot_d, text_regions_p_1_n = rotation_not_90_func(image_page, textline_mask_tot, text_regions_p, slope_deskew)
_, textline_mask_tot_d, text_regions_p_1_n, table_prediction_n = rotation_not_90_func(image_page, textline_mask_tot, text_regions_p, table_prediction, slope_deskew)
text_regions_p_1_n = resize_image(text_regions_p_1_n, text_regions_p.shape[0], text_regions_p.shape[1])
textline_mask_tot_d = resize_image(textline_mask_tot_d, text_regions_p.shape[0], text_regions_p.shape[1])
table_prediction_n = resize_image(table_prediction_n, text_regions_p.shape[0], text_regions_p.shape[1])
regions_without_separators_d = (text_regions_p_1_n[:, :] == 1) * 1
if self.tables:
regions_without_separators_d[table_prediction_n[:,:] == 1] = 1
regions_without_separators = (text_regions_p[:, :] == 1) * 1 # ( (text_regions_p[:,:]==1) | (text_regions_p[:,:]==2) )*1 #self.return_regions_without_separators_new(text_regions_p[:,:,0],img_only_regions)
if self.tables:
regions_without_separators[table_prediction ==1 ] = 1
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
text_regions_p_1_n = None
textline_mask_tot_d = None
regions_without_separators_d = None
pixel_lines = 3
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
_, _, matrix_of_lines_ch, splitter_y_new, _ = find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines)
_, _, matrix_of_lines_ch, splitter_y_new, _ = find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, self.tables, pixel_lines)
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
_, _, matrix_of_lines_ch_d, splitter_y_new_d, _ = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines)
_, _, matrix_of_lines_ch_d, splitter_y_new_d, _ = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, self.tables, pixel_lines)
K.clear_session()
self.logger.info("num_col_classifier: %s", num_col_classifier)
@ -1751,26 +2088,153 @@ class Eynollah:
regions_without_separators_d = cv2.erode(regions_without_separators_d[:, :], KERNEL, iterations=6)
t1 = time.time()
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
boxes = return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_without_separators, matrix_of_lines_ch, num_col_classifier, erosion_hurts)
boxes, peaks_neg_tot_tables = return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_without_separators, matrix_of_lines_ch, num_col_classifier, erosion_hurts, self.tables)
boxes_d = None
self.logger.debug("len(boxes): %s", len(boxes))
text_regions_p_tables = np.copy(text_regions_p)
text_regions_p_tables[:,:][(table_prediction[:,:] == 1)] = 10
pixel_line = 3
img_revised_tab2 = self.add_tables_heuristic_to_layout(text_regions_p_tables, boxes, 0, splitter_y_new, peaks_neg_tot_tables, text_regions_p_tables , num_col_classifier , 0.000005, pixel_line)
img_revised_tab2, contoures_tables = self.check_iou_of_bounding_box_and_contour_for_tables(img_revised_tab2,table_prediction, 10, num_col_classifier)
else:
boxes_d = return_boxes_of_images_by_order_of_reading_new(splitter_y_new_d, regions_without_separators_d, matrix_of_lines_ch_d, num_col_classifier, erosion_hurts)
boxes_d, peaks_neg_tot_tables_d = return_boxes_of_images_by_order_of_reading_new(splitter_y_new_d, regions_without_separators_d, matrix_of_lines_ch_d, num_col_classifier, erosion_hurts, self.tables)
boxes = None
self.logger.debug("len(boxes): %s", len(boxes_d))
text_regions_p_tables = np.copy(text_regions_p_1_n)
text_regions_p_tables =np.round(text_regions_p_tables)
text_regions_p_tables[:,:][(text_regions_p_tables[:,:] != 3) & (table_prediction_n[:,:] == 1)] = 10
pixel_line = 3
img_revised_tab2 = self.add_tables_heuristic_to_layout(text_regions_p_tables,boxes_d,0,splitter_y_new_d,peaks_neg_tot_tables_d,text_regions_p_tables, num_col_classifier, 0.000005, pixel_line)
img_revised_tab2_d,_ = self.check_iou_of_bounding_box_and_contour_for_tables(img_revised_tab2,table_prediction_n, 10, num_col_classifier)
img_revised_tab2_d_rotated = rotate_image(img_revised_tab2_d, -slope_deskew)
img_revised_tab2_d_rotated = np.round(img_revised_tab2_d_rotated)
img_revised_tab2_d_rotated = img_revised_tab2_d_rotated.astype(np.int8)
img_revised_tab2_d_rotated = resize_image(img_revised_tab2_d_rotated, text_regions_p.shape[0], text_regions_p.shape[1])
self.logger.info("detecting boxes took %ss", str(time.time() - t1))
img_revised_tab = text_regions_p[:, :]
if self.tables:
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
img_revised_tab = np.copy(img_revised_tab2[:,:,0])
img_revised_tab[:,:][(text_regions_p[:,:] == 1) & (img_revised_tab[:,:] != 10)] = 1
else:
img_revised_tab = np.copy(text_regions_p[:,:])
img_revised_tab[:,:][img_revised_tab[:,:] == 10] = 0
img_revised_tab[:,:][img_revised_tab2_d_rotated[:,:,0] == 10] = 10
text_regions_p[:,:][text_regions_p[:,:]==10] = 0
text_regions_p[:,:][img_revised_tab[:,:]==10] = 10
else:
img_revised_tab=text_regions_p[:,:]
#img_revised_tab = text_regions_p[:, :]
polygons_of_images = return_contours_of_interested_region(img_revised_tab, 2)
# plt.imshow(img_revised_tab)
# plt.show()
pixel_img = 4
min_area_mar = 0.00001
polygons_of_marginals = return_contours_of_interested_region(text_regions_p, pixel_img, min_area_mar)
pixel_img = 10
contours_tables = return_contours_of_interested_region(text_regions_p, pixel_img, min_area_mar)
K.clear_session()
self.logger.debug('exit run_boxes_no_full_layout')
return polygons_of_images, img_revised_tab, text_regions_p_1_n, textline_mask_tot_d, regions_without_separators_d, boxes, boxes_d
return polygons_of_images, img_revised_tab, text_regions_p_1_n, textline_mask_tot_d, regions_without_separators_d, boxes, boxes_d, polygons_of_marginals, contours_tables
def run_boxes_full_layout(self, image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier, img_only_regions):
def run_boxes_full_layout(self, image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier, img_only_regions, table_prediction, erosion_hurts):
self.logger.debug('enter run_boxes_full_layout')
if self.tables:
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
image_page_rotated_n,textline_mask_tot_d,text_regions_p_1_n , table_prediction_n = rotation_not_90_func(image_page, textline_mask_tot, text_regions_p, table_prediction, slope_deskew)
text_regions_p_1_n = resize_image(text_regions_p_1_n,text_regions_p.shape[0],text_regions_p.shape[1])
textline_mask_tot_d = resize_image(textline_mask_tot_d,text_regions_p.shape[0],text_regions_p.shape[1])
table_prediction_n = resize_image(table_prediction_n,text_regions_p.shape[0],text_regions_p.shape[1])
regions_without_separators_d=(text_regions_p_1_n[:,:] == 1)*1
regions_without_separators_d[table_prediction_n[:,:] == 1] = 1
else:
text_regions_p_1_n = None
textline_mask_tot_d = None
regions_without_separators_d = None
regions_without_separators = (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_separators[table_prediction == 1] = 1
pixel_lines=3
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
num_col, peaks_neg_fin, matrix_of_lines_ch, splitter_y_new, seperators_closeup_n = find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, self.tables, pixel_lines)
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
num_col_d, peaks_neg_fin_d, matrix_of_lines_ch_d, splitter_y_new_d, seperators_closeup_n_d = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2),num_col_classifier, self.tables, pixel_lines)
K.clear_session()
gc.collect()
if num_col_classifier>=3:
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
regions_without_separators = regions_without_separators.astype(np.uint8)
regions_without_separators = cv2.erode(regions_without_separators[:,:], KERNEL, iterations=6)
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
regions_without_separators_d = regions_without_separators_d.astype(np.uint8)
regions_without_separators_d = cv2.erode(regions_without_separators_d[:,:], KERNEL, iterations=6)
else:
pass
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
boxes, peaks_neg_tot_tables = return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_without_separators, matrix_of_lines_ch, num_col_classifier, erosion_hurts, self.tables)
text_regions_p_tables = np.copy(text_regions_p)
text_regions_p_tables[:,:][(table_prediction[:,:]==1)] = 10
pixel_line = 3
img_revised_tab2 = self.add_tables_heuristic_to_layout(text_regions_p_tables, boxes, 0, splitter_y_new, peaks_neg_tot_tables, text_regions_p_tables , num_col_classifier , 0.000005, pixel_line)
img_revised_tab2,contoures_tables = self.check_iou_of_bounding_box_and_contour_for_tables(img_revised_tab2, table_prediction, 10, num_col_classifier)
else:
boxes_d, peaks_neg_tot_tables_d = return_boxes_of_images_by_order_of_reading_new(splitter_y_new_d, regions_without_separators_d, matrix_of_lines_ch_d, num_col_classifier, erosion_hurts, self.tables)
text_regions_p_tables = np.copy(text_regions_p_1_n)
text_regions_p_tables = np.round(text_regions_p_tables)
text_regions_p_tables[:,:][(text_regions_p_tables[:,:]!=3) & (table_prediction_n[:,:]==1)] = 10
pixel_line = 3
img_revised_tab2 = self.add_tables_heuristic_to_layout(text_regions_p_tables,boxes_d,0,splitter_y_new_d,peaks_neg_tot_tables_d,text_regions_p_tables, num_col_classifier, 0.000005, pixel_line)
img_revised_tab2_d,_ = self.check_iou_of_bounding_box_and_contour_for_tables(img_revised_tab2, table_prediction_n, 10, num_col_classifier)
img_revised_tab2_d_rotated = rotate_image(img_revised_tab2_d, -slope_deskew)
img_revised_tab2_d_rotated = np.round(img_revised_tab2_d_rotated)
img_revised_tab2_d_rotated = img_revised_tab2_d_rotated.astype(np.int8)
img_revised_tab2_d_rotated = resize_image(img_revised_tab2_d_rotated, text_regions_p.shape[0], text_regions_p.shape[1])
if np.abs(slope_deskew) < 0.13:
img_revised_tab = np.copy(img_revised_tab2[:,:,0])
else:
img_revised_tab = np.copy(text_regions_p[:,:])
img_revised_tab[:,:][img_revised_tab[:,:] == 10] = 0
img_revised_tab[:,:][img_revised_tab2_d_rotated[:,:,0] == 10] = 10
##img_revised_tab=img_revised_tab2[:,:,0]
#img_revised_tab=text_regions_p[:,:]
text_regions_p[:,:][text_regions_p[:,:]==10] = 0
text_regions_p[:,:][img_revised_tab[:,:]==10] = 10
#img_revised_tab[img_revised_tab2[:,:,0]==10] =10
pixel_img = 4
min_area_mar = 0.00001
polygons_of_marginals = return_contours_of_interested_region(text_regions_p, pixel_img, min_area_mar)
pixel_img = 10
contours_tables = return_contours_of_interested_region(text_regions_p, pixel_img, min_area_mar)
# set first model with second model
text_regions_p[:, :][text_regions_p[:, :] == 2] = 5
text_regions_p[:, :][text_regions_p[:, :] == 3] = 6
@ -1811,26 +2275,27 @@ class Eynollah:
text_regions_p[:, :][regions_fully_np[:, :, 0] == 4] = 4
#plt.imshow(text_regions_p)
#plt.show()
####if not self.tables:
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
_, textline_mask_tot_d, text_regions_p_1_n, regions_fully_n = rotation_not_90_func_full_layout(image_page, textline_mask_tot, text_regions_p, regions_fully, slope_deskew)
text_regions_p_1_n = resize_image(text_regions_p_1_n, text_regions_p.shape[0], text_regions_p.shape[1])
textline_mask_tot_d = resize_image(textline_mask_tot_d, text_regions_p.shape[0], text_regions_p.shape[1])
regions_fully_n = resize_image(regions_fully_n, text_regions_p.shape[0], text_regions_p.shape[1])
regions_without_separators_d = (text_regions_p_1_n[:, :] == 1) * 1
if not self.tables:
regions_without_separators_d = (text_regions_p_1_n[:, :] == 1) * 1
else:
text_regions_p_1_n = None
textline_mask_tot_d = None
regions_without_separators_d = None
regions_without_separators = (text_regions_p[:, :] == 1) * 1 # ( (text_regions_p[:,:]==1) | (text_regions_p[:,:]==2) )*1 #self.return_regions_without_separators_new(text_regions_p[:,:,0],img_only_regions)
if not self.tables:
regions_without_separators = (text_regions_p[:, :] == 1) * 1
K.clear_session()
img_revised_tab = np.copy(text_regions_p[:, :])
polygons_of_images = return_contours_of_interested_region(img_revised_tab, 5)
self.logger.debug('exit run_boxes_full_layout')
return polygons_of_images, img_revised_tab, text_regions_p_1_n, textline_mask_tot_d, regions_without_separators_d, regions_fully, regions_without_separators
return polygons_of_images, img_revised_tab, text_regions_p_1_n, textline_mask_tot_d, regions_without_separators_d, regions_fully, regions_without_separators, polygons_of_marginals, contours_tables
def run(self):
"""
@ -1848,7 +2313,7 @@ class Eynollah:
self.logger.info("Textregion detection took %ss ", str(time.time() - t1))
t1 = time.time()
num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_lines, text_regions_p_1, cont_page = \
num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_lines, text_regions_p_1, cont_page, table_prediction = \
self.run_graphics_and_columns(text_regions_p_1, num_col_classifier, num_column_is_classified, erosion_hurts)
self.logger.info("Graphics detection took %ss ", str(time.time() - t1))
self.logger.info('cont_page %s', cont_page)
@ -1867,21 +2332,17 @@ class Eynollah:
slope_deskew, slope_first = self.run_deskew(textline_mask_tot_ea)
self.logger.info("deskewing took %ss", str(time.time() - t1))
t1 = time.time()
#plt.imshow(table_prediction)
#plt.show()
textline_mask_tot, text_regions_p, image_page_rotated = self.run_marginals(image_page, textline_mask_tot_ea, mask_images, mask_lines, num_col_classifier, slope_deskew, text_regions_p_1)
textline_mask_tot, text_regions_p, image_page_rotated = self.run_marginals(image_page, textline_mask_tot_ea, mask_images, mask_lines, num_col_classifier, slope_deskew, text_regions_p_1, table_prediction)
self.logger.info("detection of marginals took %ss", str(time.time() - t1))
t1 = time.time()
if not self.full_layout:
polygons_of_images, img_revised_tab, text_regions_p_1_n, textline_mask_tot_d, regions_without_separators_d, boxes, boxes_d = self.run_boxes_no_full_layout(image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier, erosion_hurts)
pixel_img = 4
min_area_mar = 0.00001
polygons_of_marginals = return_contours_of_interested_region(text_regions_p, pixel_img, min_area_mar)
polygons_of_images, img_revised_tab, text_regions_p_1_n, textline_mask_tot_d, regions_without_separators_d, boxes, boxes_d, polygons_of_marginals, contours_tables = self.run_boxes_no_full_layout(image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier, table_prediction, erosion_hurts)
if self.full_layout:
polygons_of_images, img_revised_tab, text_regions_p_1_n, textline_mask_tot_d, regions_without_separators_d, regions_fully, regions_without_separators = self.run_boxes_full_layout(image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier, img_only_regions)
polygons_of_images, img_revised_tab, text_regions_p_1_n, textline_mask_tot_d, regions_without_separators_d, regions_fully, regions_without_separators, polygons_of_marginals, contours_tables = self.run_boxes_full_layout(image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier, img_only_regions, table_prediction, erosion_hurts)
text_only = ((img_revised_tab[:, :] == 1)) * 1
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
text_only_d = ((text_regions_p_1_n[:, :] == 1)) * 1
@ -2018,24 +2479,24 @@ class Eynollah:
K.clear_session()
polygons_of_tabels = []
pixel_img = 4
polygons_of_drop_capitals = return_contours_of_interested_region_by_min_size(text_regions_p, pixel_img)
all_found_texline_polygons = adhere_drop_capital_region_into_corresponding_textline(text_regions_p, polygons_of_drop_capitals, 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, kernel=KERNEL, curved_line=self.curved_line)
# print(len(contours_only_text_parent_h),len(contours_only_text_parent_h_d_ordered),'contours_only_text_parent_h')
pixel_lines = 6
if not self.headers_off:
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
num_col, _, matrix_of_lines_ch, splitter_y_new, _ = 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, _, matrix_of_lines_ch, splitter_y_new, _ = find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, self.tables, pixel_lines, contours_only_text_parent_h)
else:
_, _, matrix_of_lines_ch_d, splitter_y_new_d, _ = 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)
_, _, matrix_of_lines_ch_d, splitter_y_new_d, _ = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, self.tables, pixel_lines, contours_only_text_parent_h_d_ordered)
elif self.headers_off:
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
num_col, _, matrix_of_lines_ch, splitter_y_new, _ = find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines)
num_col, _, matrix_of_lines_ch, splitter_y_new, _ = find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, self.tables, pixel_lines)
else:
_, _, matrix_of_lines_ch_d, splitter_y_new_d, _ = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines)
_, _, matrix_of_lines_ch_d, splitter_y_new_d, _ = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, self.tables, pixel_lines)
# print(peaks_neg_fin,peaks_neg_fin_d,'num_col2')
# print(splitter_y_new,splitter_y_new_d,'num_col_classifier')
@ -2045,22 +2506,42 @@ class Eynollah:
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
regions_without_separators = regions_without_separators.astype(np.uint8)
regions_without_separators = cv2.erode(regions_without_separators[:, :], KERNEL, iterations=6)
random_pixels_for_image = np.random.randn(regions_without_separators.shape[0], regions_without_separators.shape[1])
random_pixels_for_image[random_pixels_for_image < -0.5] = 0
random_pixels_for_image[random_pixels_for_image != 0] = 1
regions_without_separators[(random_pixels_for_image[:, :] == 1) & (text_regions_p[:, :] == 5)] = 1
#regions_without_separators_0 = regions_without_separators[:, :].sum(axis=0)
#meda_n_updown = regions_without_separators_0[len(regions_without_separators_0) :: -1]
#first_nonzero = next((i for i, x in enumerate(regions_without_separators_0) if x), 0)
#last_nonzero = next((i for i, x in enumerate(meda_n_updown) if x), 0)
#last_nonzero = len(regions_without_separators_0) - last_nonzero
#random_pixels_for_image = np.random.randn(regions_without_separators.shape[0], regions_without_separators.shape[1])
#random_pixels_for_image[random_pixels_for_image < -0.5] = 0
#random_pixels_for_image[random_pixels_for_image != 0] = 1
#regions_without_separators[(random_pixels_for_image[:, :] == 1) & (text_regions_p[:, :] == 5)] = 1
#regions_without_separators[:, 0:first_nonzero] = 0
#regions_without_separators[:, last_nonzero:] = 0
else:
regions_without_separators_d = regions_without_separators_d.astype(np.uint8)
regions_without_separators_d = cv2.erode(regions_without_separators_d[:, :], KERNEL, iterations=6)
random_pixels_for_image = np.random.randn(regions_without_separators_d.shape[0], regions_without_separators_d.shape[1])
random_pixels_for_image[random_pixels_for_image < -0.5] = 0
random_pixels_for_image[random_pixels_for_image != 0] = 1
regions_without_separators_d[(random_pixels_for_image[:, :] == 1) & (text_regions_p_1_n[:, :] == 5)] = 1
#regions_without_separators_0 = regions_without_separators_d[:, :].sum(axis=0)
#meda_n_updown = regions_without_separators_0[len(regions_without_separators_0) :: -1]
#first_nonzero = next((i for i, x in enumerate(regions_without_separators_0) if x), 0)
#last_nonzero = next((i for i, x in enumerate(meda_n_updown) if x), 0)
#last_nonzero = len(regions_without_separators_0) - last_nonzero
#random_pixels_for_image = np.random.randn(regions_without_separators_d.shape[0], regions_without_separators_d.shape[1])
#random_pixels_for_image[random_pixels_for_image < -0.5] = 0
#random_pixels_for_image[random_pixels_for_image != 0] = 1
##regions_without_separators_d[(random_pixels_for_image[:, :] == 1) & (text_regions_p_1_n[:, :] == 5)] = 1
#regions_without_separators_d[:, 0:first_nonzero] = 0
#regions_without_separators_d[:, last_nonzero:] = 0
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
boxes = return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_without_separators, matrix_of_lines_ch, num_col_classifier, erosion_hurts)
boxes, peaks_neg_tot_tables = return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_without_separators, matrix_of_lines_ch, num_col_classifier, erosion_hurts, self.tables)
else:
boxes_d = return_boxes_of_images_by_order_of_reading_new(splitter_y_new_d, regions_without_separators_d, matrix_of_lines_ch_d, num_col_classifier, erosion_hurts)
boxes_d, peaks_neg_tot_tables_d = return_boxes_of_images_by_order_of_reading_new(splitter_y_new_d, regions_without_separators_d, matrix_of_lines_ch_d, num_col_classifier, erosion_hurts, self.tables)
if self.plotter:
self.plotter.write_images_into_directory(polygons_of_images, image_page)
@ -2071,7 +2552,7 @@ class Eynollah:
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)
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_texline_polygons, all_found_texline_polygons_h, all_box_coord, all_box_coord_h, polygons_of_images, polygons_of_tabels, polygons_of_drop_capitals, polygons_of_marginals, all_found_texline_polygons_marginals, all_box_coord_marginals, slopes, slopes_h, slopes_marginals, cont_page, polygons_lines_xml)
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_texline_polygons, all_found_texline_polygons_h, all_box_coord, all_box_coord_h, polygons_of_images, contours_tables, polygons_of_drop_capitals, polygons_of_marginals, all_found_texline_polygons_marginals, all_box_coord_marginals, slopes, slopes_h, slopes_marginals, cont_page, polygons_lines_xml)
self.logger.info("Job done in %ss", str(time.time() - t0))
return pcgts
else:
@ -2081,6 +2562,6 @@ class Eynollah:
else:
contours_only_text_parent_d_ordered = list(np.array(contours_only_text_parent_d_ordered)[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)
pcgts = self.writer.build_pagexml_no_full_layout(txt_con_org, page_coord, order_text_new, id_of_texts_tot, all_found_texline_polygons, all_box_coord, polygons_of_images, polygons_of_marginals, all_found_texline_polygons_marginals, all_box_coord_marginals, slopes, slopes_marginals, cont_page, polygons_lines_xml)
pcgts = self.writer.build_pagexml_no_full_layout(txt_con_org, page_coord, order_text_new, id_of_texts_tot, all_found_texline_polygons, all_box_coord, polygons_of_images, polygons_of_marginals, all_found_texline_polygons_marginals, all_box_coord_marginals, slopes, slopes_marginals, cont_page, polygons_lines_xml, contours_tables)
self.logger.info("Job done in %ss", str(time.time() - t0))
return pcgts

6
qurator/eynollah/plot.py
Unescape Escape View File

@ -17,6 +17,7 @@ class EynollahPlotter():
def __init__(
self,
*,
dir_out,
dir_of_all,
dir_of_deskewed,
dir_of_layout,
@ -26,6 +27,7 @@ class EynollahPlotter():
scale_x=1,
scale_y=1,
):
self.dir_out = dir_out
self.dir_of_all = dir_of_all
self.dir_of_layout = dir_of_layout
self.dir_of_cropped_images = dir_of_cropped_images
@ -125,7 +127,9 @@ class EynollahPlotter():
def save_page_image(self, image_page):
if self.dir_of_all is not None:
cv2.imwrite(os.path.join(self.dir_of_all, self.image_filename_stem + "_page.png"), image_page)
def save_enhanced_image(self, img_res):
cv2.imwrite(os.path.join(self.dir_out, self.image_filename_stem + "_enhanced.png"), img_res)
def save_plot_of_textline_density(self, img_patch_org):
if self.dir_of_all is not None:
plt.figure(figsize=(80,40))

45
qurator/eynollah/utils/__init__.py
Unescape Escape View File

@ -360,7 +360,7 @@ def find_num_col_deskew(regions_without_separators, sigma_, multiplier=3.8):
return np.std(z)
def find_num_col(regions_without_separators, multiplier=3.8):
def find_num_col(regions_without_separators, num_col_classifier, tables, multiplier=3.8):
regions_without_separators_0 = regions_without_separators[:, :].sum(axis=0)
##plt.plot(regions_without_separators_0)
##plt.show()
@ -416,6 +416,19 @@ def find_num_col(regions_without_separators, multiplier=3.8):
interest_neg_fin = interest_neg[(interest_neg < grenze)]
peaks_neg_fin = peaks_neg[(interest_neg < grenze)]
# interest_neg_fin=interest_neg[(interest_neg<grenze)]
if not tables:
if ( num_col_classifier - ( (len(interest_neg_fin))+1 ) ) >= 3:
index_sort_interest_neg_fin= np.argsort(interest_neg_fin)
peaks_neg_sorted = np.array(peaks_neg)[index_sort_interest_neg_fin]
interest_neg_fin_sorted = np.array(interest_neg_fin)[index_sort_interest_neg_fin]
if len(index_sort_interest_neg_fin)>=num_col_classifier:
peaks_neg_fin = list( peaks_neg_sorted[:num_col_classifier] )
interest_neg_fin = list( interest_neg_fin_sorted[:num_col_classifier] )
else:
peaks_neg_fin = peaks_neg[:]
interest_neg_fin = interest_neg[:]
num_col = (len(interest_neg_fin)) + 1
@ -489,9 +502,9 @@ def find_num_col(regions_without_separators, multiplier=3.8):
num_col = 1
peaks_neg_true = []
diff_peaks_annormal = diff_peaks[diff_peaks < 360]
diff_peaks_abnormal = diff_peaks[diff_peaks < 360]
if len(diff_peaks_annormal) > 0:
if len(diff_peaks_abnormal) > 0:
arg_help = np.array(range(len(diff_peaks)))
arg_help_ann = arg_help[diff_peaks < 360]
@ -1248,7 +1261,7 @@ def return_points_with_boundies(peaks_neg_fin, first_point, last_point):
peaks_neg_tot.append(last_point)
return peaks_neg_tot
def find_number_of_columns_in_document(region_pre_p, num_col_classifier, pixel_lines, contours_h=None):
def find_number_of_columns_in_document(region_pre_p, num_col_classifier, tables, pixel_lines, contours_h=None):
separators_closeup=( (region_pre_p[:,:,:]==pixel_lines))*1
@ -1561,7 +1574,7 @@ def find_number_of_columns_in_document(region_pre_p, num_col_classifier, pixel_l
#regions_without_separators_tile=cv2.erode(regions_without_separators_tile,kernel,iterations = 3)
#
try:
num_col, peaks_neg_fin = find_num_col(regions_without_separators_tile,multiplier=7.0)
num_col, peaks_neg_fin = find_num_col(regions_without_separators_tile, num_col_classifier, tables, multiplier=7.0)
except:
num_col = 0
peaks_neg_fin = []
@ -1583,9 +1596,9 @@ def find_number_of_columns_in_document(region_pre_p, num_col_classifier, pixel_l
return num_col_fin, peaks_neg_fin_fin,matrix_of_lines_ch,splitter_y_new,separators_closeup_n
def return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_without_separators, matrix_of_lines_ch, num_col_classifier, erosion_hurts):
def return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_without_separators, matrix_of_lines_ch, num_col_classifier, erosion_hurts, tables):
boxes=[]
peaks_neg_tot_tables = []
for i in range(len(splitter_y_new)-1):
#print(splitter_y_new[i],splitter_y_new[i+1])
@ -1599,20 +1612,21 @@ def return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_witho
try:
if erosion_hurts:
num_col, peaks_neg_fin=find_num_col(regions_without_separators[int(splitter_y_new[i]):int(splitter_y_new[i+1]),:],multiplier=6.)
num_col, peaks_neg_fin=find_num_col(regions_without_separators[int(splitter_y_new[i]):int(splitter_y_new[i+1]),:], num_col_classifier, tables, multiplier=6.)
else:
num_col, peaks_neg_fin=find_num_col(regions_without_separators[int(splitter_y_new[i]):int(splitter_y_new[i+1]),:],multiplier=7.)
num_col, peaks_neg_fin=find_num_col(regions_without_separators[int(splitter_y_new[i]):int(splitter_y_new[i+1]),:],num_col_classifier, tables, multiplier=7.)
except:
peaks_neg_fin=[]
num_col = 0
try:
peaks_neg_fin_org=np.copy(peaks_neg_fin)
if (len(peaks_neg_fin)+1)<num_col_classifier:
if (len(peaks_neg_fin)+1)<num_col_classifier or num_col_classifier==6:
#print('burda')
if len(peaks_neg_fin)==0:
num_col, peaks_neg_fin=find_num_col(regions_without_separators[int(splitter_y_new[i]):int(splitter_y_new[i+1]),:],multiplier=3.)
num_col, peaks_neg_fin=find_num_col(regions_without_separators[int(splitter_y_new[i]):int(splitter_y_new[i+1]),:],num_col_classifier, tables, multiplier=3.)
peaks_neg_fin_early=[]
peaks_neg_fin_early.append(0)
#print(peaks_neg_fin,'peaks_neg_fin')
@ -1628,12 +1642,12 @@ def return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_witho
#plt.plot(regions_without_separators[int(splitter_y_new[i]):int(splitter_y_new[i+1]),peaks_neg_fin_early[i_n]:peaks_neg_fin_early[i_n+1]].sum(axis=0) )
#plt.show()
try:
num_col, peaks_neg_fin1=find_num_col(regions_without_separators[int(splitter_y_new[i]):int(splitter_y_new[i+1]),peaks_neg_fin_early[i_n]:peaks_neg_fin_early[i_n+1]],multiplier=7.)
num_col, peaks_neg_fin1=find_num_col(regions_without_separators[int(splitter_y_new[i]):int(splitter_y_new[i+1]),peaks_neg_fin_early[i_n]:peaks_neg_fin_early[i_n+1]],num_col_classifier,tables, multiplier=7.)
except:
peaks_neg_fin1=[]
try:
num_col, peaks_neg_fin2=find_num_col(regions_without_separators[int(splitter_y_new[i]):int(splitter_y_new[i+1]),peaks_neg_fin_early[i_n]:peaks_neg_fin_early[i_n+1]],multiplier=5.)
num_col, peaks_neg_fin2=find_num_col(regions_without_separators[int(splitter_y_new[i]):int(splitter_y_new[i+1]),peaks_neg_fin_early[i_n]:peaks_neg_fin_early[i_n+1]],num_col_classifier,tables, multiplier=5.)
except:
peaks_neg_fin2=[]
@ -1679,6 +1693,8 @@ def return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_witho
peaks_neg_tot=return_points_with_boundies(peaks_neg_fin,0, regions_without_separators[:,:].shape[1])
peaks_neg_tot_tables.append(peaks_neg_tot)
reading_order_type,x_starting,x_ending,y_type_2,y_diff_type_2,y_lines_without_mother,x_start_without_mother,x_end_without_mother,there_is_sep_with_child,y_lines_with_child_without_mother,x_start_with_child_without_mother,x_end_with_child_without_mother=return_x_start_end_mothers_childs_and_type_of_reading_order(x_min_hor_some,x_max_hor_some,cy_hor_some,peaks_neg_tot,cy_hor_diff)
@ -2236,5 +2252,4 @@ def return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_witho
#else:
#boxes.append([ 0, regions_without_separators[:,:].shape[1] ,splitter_y_new[i],splitter_y_new[i+1]])
return boxes
return boxes, peaks_neg_tot_tables

14
qurator/eynollah/utils/contour.py
Unescape Escape View File

@ -109,7 +109,21 @@ def find_new_features_of_contours(contours_main):
# dis_x=np.abs(x_max_main-x_min_main)
return cx_main, cy_main, x_min_main, x_max_main, y_min_main, y_max_main, y_corr_x_min_from_argmin
def find_features_of_contours(contours_main):
areas_main=np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))])
M_main=[cv2.moments(contours_main[j]) for j in range(len(contours_main))]
cx_main=[(M_main[j]['m10']/(M_main[j]['m00']+1e-32)) for j in range(len(M_main))]
cy_main=[(M_main[j]['m01']/(M_main[j]['m00']+1e-32)) for j in range(len(M_main))]
x_min_main=np.array([np.min(contours_main[j][:,0,0]) for j in range(len(contours_main))])
x_max_main=np.array([np.max(contours_main[j][:,0,0]) for j in range(len(contours_main))])
y_min_main=np.array([np.min(contours_main[j][:,0,1]) for j in range(len(contours_main))])
y_max_main=np.array([np.max(contours_main[j][:,0,1]) for j in range(len(contours_main))])
return y_min_main, y_max_main
def return_parent_contours(contours, hierarchy):
contours_parent = [contours[i] for i in range(len(contours)) if hierarchy[0][i][3] == -1]
return contours_parent

73
qurator/eynollah/utils/drop_capitals.py
Unescape Escape View File

@ -142,53 +142,54 @@ def adhere_drop_capital_region_into_corresponding_textline(
# areas_main=np.array([cv2.contourArea(all_found_texline_polygons[int(region_final)][0][j] ) for j in range(len(all_found_texline_polygons[int(region_final)]))])
# cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contours(all_found_texline_polygons[int(region_final)])
try:
cx_t, cy_t, _, _, _, _, _ = find_new_features_of_contours(all_found_texline_polygons[int(region_final)])
# print(all_box_coord[j_cont])
# print(cx_t)
# print(cy_t)
# print(cx_d[i_drop])
# print(cy_d[i_drop])
y_lines = np.array(cy_t) # all_box_coord[int(region_final)][0]+np.array(cy_t)
cx_t, cy_t, _, _, _, _, _ = find_new_features_of_contours(all_found_texline_polygons[int(region_final)])
# print(all_box_coord[j_cont])
# print(cx_t)
# print(cy_t)
# print(cx_d[i_drop])
# print(cy_d[i_drop])
y_lines = np.array(cy_t) # all_box_coord[int(region_final)][0]+np.array(cy_t)
y_lines[y_lines < y_min_d[i_drop]] = 0
# print(y_lines)
y_lines[y_lines < y_min_d[i_drop]] = 0
# print(y_lines)
arg_min = np.argmin(np.abs(y_lines - y_min_d[i_drop]))
# print(arg_min)
arg_min = np.argmin(np.abs(y_lines - y_min_d[i_drop]))
# print(arg_min)
cnt_nearest = np.copy(all_found_texline_polygons[int(region_final)][arg_min])
cnt_nearest[:, 0, 0] = all_found_texline_polygons[int(region_final)][arg_min][:, 0, 0] # +all_box_coord[int(region_final)][2]
cnt_nearest[:, 0, 1] = all_found_texline_polygons[int(region_final)][arg_min][:, 0, 1] # +all_box_coord[int(region_final)][0]
cnt_nearest = np.copy(all_found_texline_polygons[int(region_final)][arg_min])
cnt_nearest[:, 0, 0] = all_found_texline_polygons[int(region_final)][arg_min][:, 0, 0] # +all_box_coord[int(region_final)][2]
cnt_nearest[:, 0, 1] = all_found_texline_polygons[int(region_final)][arg_min][:, 0, 1] # +all_box_coord[int(region_final)][0]
img_textlines = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1], 3))
img_textlines = cv2.fillPoly(img_textlines, pts=[cnt_nearest], color=(255, 255, 255))
img_textlines = cv2.fillPoly(img_textlines, pts=[polygons_of_drop_capitals[i_drop]], color=(255, 255, 255))
img_textlines = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1], 3))
img_textlines = cv2.fillPoly(img_textlines, pts=[cnt_nearest], color=(255, 255, 255))
img_textlines = cv2.fillPoly(img_textlines, pts=[polygons_of_drop_capitals[i_drop]], color=(255, 255, 255))
img_textlines = img_textlines.astype(np.uint8)
img_textlines = img_textlines.astype(np.uint8)
# plt.imshow(img_textlines)
# plt.show()
imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
# plt.imshow(img_textlines)
# plt.show()
imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_combined, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours_combined, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# print(len(contours_combined),'len textlines mixed')
areas_cnt_text = np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])
# print(len(contours_combined),'len textlines mixed')
areas_cnt_text = np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])
contours_biggest = contours_combined[np.argmax(areas_cnt_text)]
contours_biggest = contours_combined[np.argmax(areas_cnt_text)]
# print(np.shape(contours_biggest))
# print(contours_biggest[:])
# contours_biggest[:,0,0]=contours_biggest[:,0,0]#-all_box_coord[int(region_final)][2]
# contours_biggest[:,0,1]=contours_biggest[:,0,1]#-all_box_coord[int(region_final)][0]
# print(np.shape(contours_biggest),'contours_biggest')
# print(np.shape(all_found_texline_polygons[int(region_final)][arg_min]))
##contours_biggest=contours_biggest.reshape(np.shape(contours_biggest)[0],np.shape(contours_biggest)[2])
all_found_texline_polygons[int(region_final)][arg_min] = contours_biggest
# print(np.shape(contours_biggest))
# print(contours_biggest[:])
# contours_biggest[:,0,0]=contours_biggest[:,0,0]#-all_box_coord[int(region_final)][2]
# contours_biggest[:,0,1]=contours_biggest[:,0,1]#-all_box_coord[int(region_final)][0]
# print(np.shape(contours_biggest),'contours_biggest')
# print(np.shape(all_found_texline_polygons[int(region_final)][arg_min]))
##contours_biggest=contours_biggest.reshape(np.shape(contours_biggest)[0],np.shape(contours_biggest)[2])
all_found_texline_polygons[int(region_final)][arg_min] = contours_biggest
except:
pass
# print(cx_t,'print')
try:
# print(all_found_texline_polygons[j_cont][0])
cx_t, cy_t, _, _, _, _, _ = find_new_features_of_contours(all_found_texline_polygons[int(region_final)])

9
qurator/eynollah/utils/rotate.py
Unescape Escape View File

@ -52,20 +52,21 @@ def rotate_image_different( img, slope):
img_rotation = cv2.warpAffine(img, rotation_matrix, (num_cols, num_rows))
return img_rotation
def rotate_max_area(image, rotated, rotated_textline, rotated_layout, angle):
def rotate_max_area(image, rotated, rotated_textline, rotated_layout, rotated_table_prediction, angle):
wr, hr = rotatedRectWithMaxArea(image.shape[1], image.shape[0], math.radians(angle))
h, w, _ = rotated.shape
y1 = h // 2 - int(hr / 2)
y2 = y1 + int(hr)
x1 = w // 2 - int(wr / 2)
x2 = x1 + int(wr)
return rotated[y1:y2, x1:x2], rotated_textline[y1:y2, x1:x2], rotated_layout[y1:y2, x1:x2]
return rotated[y1:y2, x1:x2], rotated_textline[y1:y2, x1:x2], rotated_layout[y1:y2, x1:x2], rotated_table_prediction[y1:y2, x1:x2]
def rotation_not_90_func(img, textline, text_regions_p_1, thetha):
def rotation_not_90_func(img, textline, text_regions_p_1, table_prediction, thetha):
rotated = imutils.rotate(img, thetha)
rotated_textline = imutils.rotate(textline, thetha)
rotated_layout = imutils.rotate(text_regions_p_1, thetha)
return rotate_max_area(img, rotated, rotated_textline, rotated_layout, thetha)
rotated_table_prediction = imutils.rotate(table_prediction, thetha)
return rotate_max_area(img, rotated, rotated_textline, rotated_layout, rotated_table_prediction, thetha)
def rotation_not_90_func_full_layout(img, textline, text_regions_p_1, text_regions_p_fully, thetha):
rotated = imutils.rotate(img, thetha)

12
qurator/eynollah/writer.py
Unescape Escape View File

@ -141,7 +141,7 @@ class EynollahXmlWriter():
with open(out_fname, 'w') as f:
f.write(to_xml(pcgts))
def build_pagexml_no_full_layout(self, found_polygons_text_region, page_coord, order_of_texts, id_of_texts, all_found_texline_polygons, all_box_coord, found_polygons_text_region_img, found_polygons_marginals, all_found_texline_polygons_marginals, all_box_coord_marginals, slopes, slopes_marginals, cont_page, polygons_lines_to_be_written_in_xml):
def build_pagexml_no_full_layout(self, found_polygons_text_region, page_coord, order_of_texts, id_of_texts, all_found_texline_polygons, all_box_coord, found_polygons_text_region_img, found_polygons_marginals, all_found_texline_polygons_marginals, all_box_coord_marginals, slopes, slopes_marginals, cont_page, polygons_lines_to_be_written_in_xml, found_polygons_tables):
self.logger.debug('enter build_pagexml_no_full_layout')
# create the file structure
@ -189,6 +189,16 @@ class EynollahXmlWriter():
points_co += str(int((polygons_lines_to_be_written_in_xml[mm][lmm,0,1] ) / self.scale_y))
points_co += ' '
sep_hor.get_Coords().set_points(points_co[:-1])
for mm in range(len(found_polygons_tables)):
tab_region = TableRegionType(id=counter.next_region_id, Coords=CoordsType())
page.add_TableRegion(tab_region)
points_co = ''
for lmm in range(len(found_polygons_tables[mm])):
points_co += str(int((found_polygons_tables[mm][lmm,0,0] + page_coord[2]) / self.scale_x))
points_co += ','
points_co += str(int((found_polygons_tables[mm][lmm,0,1] + page_coord[0]) / self.scale_y))
points_co += ' '
tab_region.get_Coords().set_points(points_co[:-1])
return pcgts

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