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eynollah
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rfct: create utils and extract filter_contours_area_of_image_tables

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pull/8/head
Konstantin Baierer 4 years ago
parent 62e76324be
commit ec553a2060
2 changed files with 35 additions and 32 deletions
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43
sbb_newspapers_org_image/eynollah.py
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@ -1,5 +1,3 @@
#! /usr/bin/env python3
"""
tool to extract table form data from alto xml data
"""
@ -37,6 +35,7 @@ from matplotlib import pyplot, transforms
import matplotlib.patches as mpatches
import imutils
from .utils import filter_contours_area_of_image_tables
class eynollah:
@ -76,26 +75,6 @@ class eynollah:
###self.model_region_dir_p = dir_models +'/model_layout_newspapers.h5'#'/model_ensemble_s.h5'#'/model_layout_newspapers.h5'#'/model_ensemble_s.h5'#'/model_main_home_5_soft_new.h5'#'/model_home_soft_5_all_data.h5' #'/model_main_office_long_soft.h5'#'/model_20_cat_main.h5'
self.model_textline_dir = dir_models + "/model_textline_newspapers.h5" #'/model_hor_ver_home_trextline_very_good.h5'# '/model_hor_ver_1_great.h5'#'/model_curved_office_works_great.h5'
def filter_contours_area_of_image_tables(self, image, contours, hirarchy, max_area, min_area):
found_polygons_early = list()
jv = 0
for c in contours:
if len(c) < 3: # A polygon cannot have less than 3 points
continue
polygon = geometry.Polygon([point[0] for point in c])
# area = cv2.contourArea(c)
area = polygon.area
##print(np.prod(thresh.shape[:2]))
# Check that polygon has area greater than minimal area
# print(hirarchy[0][jv][3],hirarchy )
if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(image.shape[:2]): # and hirarchy[0][jv][3]==-1 :
# print(c[0][0][1])
found_polygons_early.append(np.array([[point] for point in polygon.exterior.coords], dtype=np.int32))
jv += 1
return found_polygons_early
def find_polygons_size_filter(self, contours, median_area, scaler_up=1.2, scaler_down=0.8):
found_polygons_early = list()
@ -879,7 +858,7 @@ class eynollah:
contours_imgs, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours_imgs = self.return_parent_contours(contours_imgs, hiearchy)
contours_imgs = self.filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=1, min_area=min_area)
contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=1, min_area=min_area)
return contours_imgs
@ -898,7 +877,7 @@ class eynollah:
contours_imgs, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours_imgs = self.return_parent_contours(contours_imgs, hiearchy)
contours_imgs = self.filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=1, min_area=min_size)
contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=1, min_area=min_size)
return contours_imgs
@ -916,7 +895,7 @@ class eynollah:
contours_imgs, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours_imgs = self.return_parent_contours(contours_imgs, hiearchy)
contours_imgs = self.filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=1, min_area=0.000000003)
contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=1, min_area=0.000000003)
return contours_imgs
def find_images_contours_and_replace_table_and_graphic_pixels_by_image(self, region_pre_p):
@ -931,7 +910,7 @@ class eynollah:
contours_imgs = self.return_parent_contours(contours_imgs, hiearchy)
# print(len(contours_imgs),'contours_imgs')
contours_imgs = self.filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=1, min_area=0.0003)
contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=1, min_area=0.0003)
# print(len(contours_imgs),'contours_imgs')
@ -3131,7 +3110,7 @@ class eynollah:
contours_imgs, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours_imgs = self.return_parent_contours(contours_imgs, hiearchy)
contours_imgs = self.filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=max_area, min_area=min_area)
contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=max_area, min_area=min_area)
cont_final = []
###print(add_boxes_coor_into_textlines,'ikki')
@ -3665,7 +3644,7 @@ class eynollah:
contours, hirarchy = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
main_contours = self.filter_contours_area_of_image_tables(thresh, contours, hirarchy, max_area=1, min_area=0.003)
main_contours = filter_contours_area_of_image_tables(thresh, contours, hirarchy, max_area=1, min_area=0.003)
textline_maskt = textline_mask[:, :, 0]
textline_maskt[textline_maskt != 0] = 1
@ -7907,7 +7886,7 @@ class eynollah:
contours, hirarchy = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
main_contours = self.filter_contours_area_of_image_tables(thresh, contours, hirarchy, max_area=1, min_area=0.0001)
main_contours = filter_contours_area_of_image_tables(thresh, contours, hirarchy, max_area=1, min_area=0.0001)
img_comm = cv2.fillPoly(img_comm, pts=main_contours, color=(indiv, indiv, indiv))
###img_comm_in=cv2.fillPoly(img_comm, pts =interior_contours, color=(0,0,0))
@ -7925,7 +7904,7 @@ class eynollah:
contours_tab, _ = self.return_contours_of_image(image_box_tabels_1)
contours_tab = self.filter_contours_area_of_image_tables(image_box_tabels_1, contours_tab, _, 1, 0.001)
contours_tab = filter_contours_area_of_image_tables(image_box_tabels_1, contours_tab, _, 1, 0.001)
image_box_tabels_1 = (image_box[:, :, 0] == 6) * 1
@ -8389,7 +8368,7 @@ class eynollah:
contours_imgs, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours_imgs = self.return_parent_contours(contours_imgs, hiearchy)
contours_imgs = self.filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=1, min_area=0.0003)
contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=1, min_area=0.0003)
boxes = []
@ -8758,7 +8737,7 @@ class eynollah:
contours_imgs, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours_imgs = self.return_parent_contours(contours_imgs, hiearchy)
contours_imgs = self.filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=max_area, min_area=min_area)
contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=max_area, min_area=min_area)
img_ret = np.zeros((region_pre_p.shape[0], region_pre_p.shape[1], 3))
img_ret = cv2.fillPoly(img_ret, pts=contours_imgs, color=(1, 1, 1))

24
sbb_newspapers_org_image/utils.py
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import numpy as np
from shapely import geometry
def filter_contours_area_of_image_tables(self, image, contours, hirarchy, max_area, min_area):
found_polygons_early = list()
jv = 0
for c in contours:
if len(c) < 3: # A polygon cannot have less than 3 points
continue
polygon = geometry.Polygon([point[0] for point in c])
# area = cv2.contourArea(c)
area = polygon.area
##print(np.prod(thresh.shape[:2]))
# Check that polygon has area greater than minimal area
# print(hirarchy[0][jv][3],hirarchy )
if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(image.shape[:2]): # and hirarchy[0][jv][3]==-1 :
# print(c[0][0][1])
found_polygons_early.append(np.array([[point] for point in polygon.exterior.coords], dtype=np.int32))
jv += 1
return found_polygons_early
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