@ -5,6 +5,8 @@ from shapely import geometry
from . rotate import rotate_image , rotation_image_new
from . rotate import rotate_image , rotation_image_new
from multiprocessing import Process , Queue , cpu_count
from multiprocessing import Process , Queue , cpu_count
from multiprocessing import Pool
from multiprocessing import Pool
def contours_in_same_horizon ( cy_main_hor ) :
def contours_in_same_horizon ( cy_main_hor ) :
X1 = np . zeros ( ( len ( cy_main_hor ) , len ( cy_main_hor ) ) )
X1 = np . zeros ( ( len ( cy_main_hor ) , len ( cy_main_hor ) ) )
X2 = np . zeros ( ( len ( cy_main_hor ) , len ( cy_main_hor ) ) )
X2 = np . zeros ( ( len ( cy_main_hor ) , len ( cy_main_hor ) ) )
@ -22,6 +24,7 @@ def contours_in_same_horizon(cy_main_hor):
all_args . append ( list ( set ( list_h ) ) )
all_args . append ( list ( set ( list_h ) ) )
return np . unique ( np . array ( all_args , dtype = object ) )
return np . unique ( np . array ( all_args , dtype = object ) )
def find_contours_mean_y_diff ( contours_main ) :
def find_contours_mean_y_diff ( contours_main ) :
M_main = [ cv2 . moments ( contours_main [ j ] ) for j in range ( len ( contours_main ) ) ]
M_main = [ cv2 . moments ( contours_main [ j ] ) for j in range ( len ( contours_main ) ) ]
cy_main = [ ( M_main [ j ] [ " m01 " ] / ( 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 ) ) ]
@ -29,7 +32,6 @@ def find_contours_mean_y_diff(contours_main):
def get_text_region_boxes_by_given_contours ( contours ) :
def get_text_region_boxes_by_given_contours ( contours ) :
kernel = np . ones ( ( 5 , 5 ) , np . uint8 )
kernel = np . ones ( ( 5 , 5 ) , np . uint8 )
boxes = [ ]
boxes = [ ]
contours_new = [ ]
contours_new = [ ]
@ -42,39 +44,43 @@ def get_text_region_boxes_by_given_contours(contours):
del contours
del contours
return boxes , contours_new
return boxes , contours_new
def filter_contours_area_of_image ( image , contours , hierarchy , max_area , min_area ) :
def filter_contours_area_of_image ( image , contours , hierarchy , max_area , min_area ) :
found_polygons_early = list ( )
found_polygons_early = list ( )
for jv , c in enumerate ( contours ) :
for jv , c in enumerate ( contours ) :
if len ( c ) < 3 : # A polygon cannot have less than 3 points
if len ( c ) < 3 : # A polygon cannot have less than 3 points
continue
continue
polygon = geometry . Polygon ( [ point [ 0 ] for point in c ] )
polygon = geometry . Polygon ( [ point [ 0 ] for point in c ] )
area = polygon . area
area = polygon . area
if area > = min_area * np . prod ( image . shape [ : 2 ] ) and area < = max_area * np . prod ( image . shape [ : 2 ] ) and hierarchy [ 0 ] [ jv ] [ 3 ] == - 1 : # and hierarchy[0][jv][3]==-1 :
if area > = min_area * np . prod ( image . shape [ : 2 ] ) and area < = max_area * np . prod ( image . shape [ : 2 ] ) and \
hierarchy [ 0 ] [ jv ] [ 3 ] == - 1 : # and hierarchy[0][jv][3]==-1 :
found_polygons_early . append ( np . array ( [ [ point ] for point in polygon . exterior . coords ] , dtype = np . uint ) )
found_polygons_early . append ( np . array ( [ [ point ] for point in polygon . exterior . coords ] , dtype = np . uint ) )
return found_polygons_early
return found_polygons_early
def filter_contours_area_of_image_tables ( image , contours , hierarchy , max_area , min_area ) :
def filter_contours_area_of_image_tables ( image , contours , hierarchy , max_area , min_area ) :
found_polygons_early = list ( )
found_polygons_early = list ( )
for jv , c in enumerate ( contours ) :
for jv , c in enumerate ( contours ) :
if len ( c ) < 3 : # A polygon cannot have less than 3 points
if len ( c ) < 3 : # A polygon cannot have less than 3 points
continue
continue
polygon = geometry . Polygon ( [ point [ 0 ] for point in c ] )
polygon = geometry . Polygon ( [ point [ 0 ] for point in c ] )
# area = cv2.contourArea(c)
# area = cv2.contourArea(c)
area = polygon . area
area = polygon . area
# # print(np.prod(thresh.shape[:2]))
# print(np.prod(thresh.shape[:2]))
# Check that polygon has area greater than minimal area
# Check that polygon has area greater than minimal area
# print(hierarchy[0][jv][3],hierarchy )
# print(hierarchy[0][jv][3],hierarchy )
if area > = min_area * np . prod ( image . shape [ : 2 ] ) and area < = max_area * np . prod ( image . shape [ : 2 ] ) : # and hierarchy[0][jv][3]==-1 :
if area > = min_area * np . prod ( image . shape [ : 2 ] ) and area < = max_area * np . prod (
image . shape [ : 2 ] ) : # and hierarchy[0][jv][3]==-1 :
# print(c[0][0][1])
# print(c[0][0][1])
found_polygons_early . append ( np . array ( [ [ point ] for point in polygon . exterior . coords ] , dtype = np . int32 ) )
found_polygons_early . append ( np . array ( [ [ point ] for point in polygon . exterior . coords ] , dtype = np . int32 ) )
return found_polygons_early
return found_polygons_early
def find_new_features_of_contours ( contours_main ) :
def find_new_features_of_contours ( contours_main ) :
areas_main = np . array ( [ cv2 . contourArea ( contours_main [ j ] ) for j in range ( len ( 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 ) ) ]
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 ) ) ]
cx_main = [ ( M_main [ j ] [ " m10 " ] / ( M_main [ j ] [ " m00 " ] + 1e-32 ) ) for j in range ( len ( M_main ) ) ]
@ -85,7 +91,8 @@ def find_new_features_of_contours(contours_main):
argmin_x_main = np . array ( [ np . argmin ( contours_main [ j ] [ : , 0 , 0 ] ) for j in range ( len ( contours_main ) ) ] )
argmin_x_main = np . array ( [ np . argmin ( contours_main [ j ] [ : , 0 , 0 ] ) for j in range ( len ( contours_main ) ) ] )
x_min_from_argmin = np . array ( [ contours_main [ j ] [ argmin_x_main [ j ] , 0 , 0 ] for j in range ( len ( contours_main ) ) ] )
x_min_from_argmin = np . array ( [ contours_main [ j ] [ argmin_x_main [ j ] , 0 , 0 ] for j in range ( len ( contours_main ) ) ] )
y_corr_x_min_from_argmin = np . array ( [ contours_main [ j ] [ argmin_x_main [ j ] , 0 , 1 ] for j in range ( len ( contours_main ) ) ] )
y_corr_x_min_from_argmin = np . array (
[ contours_main [ j ] [ argmin_x_main [ j ] , 0 , 1 ] 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 ) ) ] )
x_max_main = np . array ( [ np . max ( contours_main [ j ] [ : , 0 , 0 ] ) for j in range ( len ( contours_main ) ) ] )
@ -107,27 +114,28 @@ def find_new_features_of_contours(contours_main):
# dis_x=np.abs(x_max_main-x_min_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
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 ) ) ] )
def find_features_of_contours ( contours_main ) :
y_max_main = np . array ( [ np . max ( contours_main [ j ] [ : , 0 , 1 ] ) for j in range ( len ( 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
return y_min_main , y_max_main
def return_parent_contours ( contours , hierarchy ) :
def return_parent_contours ( contours , hierarchy ) :
contours_parent = [ contours [ i ] for i in range ( len ( contours ) ) if hierarchy [ 0 ] [ i ] [ 3 ] == - 1 ]
contours_parent = [ contours [ i ] for i in range ( len ( contours ) ) if hierarchy [ 0 ] [ i ] [ 3 ] == - 1 ]
return contours_parent
return contours_parent
def return_contours_of_interested_region ( region_pre_p , pixel , min_area = 0.0002 ) :
def return_contours_of_interested_region ( region_pre_p , pixel , min_area = 0.0002 ) :
# pixels of images are identified by 5
# pixels of images are identified by 5
if len ( region_pre_p . shape ) == 3 :
if len ( region_pre_p . shape ) == 3 :
cnts_images = ( region_pre_p [ : , : , 0 ] == pixel ) * 1
cnts_images = ( region_pre_p [ : , : , 0 ] == pixel ) * 1
@ -145,6 +153,7 @@ def return_contours_of_interested_region(region_pre_p, pixel, min_area=0.0002):
return contours_imgs
return contours_imgs
def do_work_of_contours_in_image ( queue_of_all_params , contours_per_process , indexes_r_con_per_pro , img , slope_first ) :
def do_work_of_contours_in_image ( queue_of_all_params , contours_per_process , indexes_r_con_per_pro , img , slope_first ) :
cnts_org_per_each_subprocess = [ ]
cnts_org_per_each_subprocess = [ ]
index_by_text_region_contours = [ ]
index_by_text_region_contours = [ ]
@ -165,14 +174,12 @@ def do_work_of_contours_in_image(queue_of_all_params, contours_per_process, inde
cont_int [ 0 ] [ : , 0 , 0 ] = cont_int [ 0 ] [ : , 0 , 0 ] + np . abs ( img_copy . shape [ 1 ] - img . shape [ 1 ] )
cont_int [ 0 ] [ : , 0 , 0 ] = cont_int [ 0 ] [ : , 0 , 0 ] + np . abs ( img_copy . shape [ 1 ] - img . shape [ 1 ] )
cont_int [ 0 ] [ : , 0 , 1 ] = cont_int [ 0 ] [ : , 0 , 1 ] + np . abs ( img_copy . shape [ 0 ] - img . shape [ 0 ] )
cont_int [ 0 ] [ : , 0 , 1 ] = cont_int [ 0 ] [ : , 0 , 1 ] + np . abs ( img_copy . shape [ 0 ] - img . shape [ 0 ] )
cnts_org_per_each_subprocess . append ( cont_int [ 0 ] )
cnts_org_per_each_subprocess . append ( cont_int [ 0 ] )
queue_of_all_params . put ( [ cnts_org_per_each_subprocess , index_by_text_region_contours ] )
queue_of_all_params . put ( [ cnts_org_per_each_subprocess , index_by_text_region_contours ] )
def get_textregion_contours_in_org_image_multi ( cnts , img , slope_first ) :
def get_textregion_contours_in_org_image_multi ( cnts , img , slope_first ) :
num_cores = cpu_count ( )
num_cores = cpu_count ( )
queue_of_all_params = Queue ( )
queue_of_all_params = Queue ( )
@ -180,10 +187,10 @@ def get_textregion_contours_in_org_image_multi(cnts, img, slope_first):
nh = np . linspace ( 0 , len ( cnts ) , num_cores + 1 )
nh = np . linspace ( 0 , len ( cnts ) , num_cores + 1 )
indexes_by_text_con = np . array ( range ( len ( cnts ) ) )
indexes_by_text_con = np . array ( range ( len ( cnts ) ) )
for i in range ( num_cores ) :
for i in range ( num_cores ) :
contours_per_process = cnts [ int ( nh [ i ] ) : int ( nh [ i + 1 ] ) ]
contours_per_process = cnts [ int ( nh [ i ] ) : int ( nh [ i + 1 ] ) ]
indexes_text_con_per_process = indexes_by_text_con [ int ( nh [ i ] ) : int ( nh [ i + 1 ] ) ]
indexes_text_con_per_process = indexes_by_text_con [ int ( nh [ i ] ) : int ( nh [ i + 1 ] ) ]
processes . append ( Process ( target = do_work_of_contours_in_image , args = ( queue_of_all_params , contours_per_process , indexes_text_con_per_process , img , slope_first ) ) )
processes . append ( Process ( target = do_work_of_contours_in_image , args = ( queue_of_all_params , contours_per_process , indexes_text_con_per_process , img , slope_first ) ) )
for i in range ( num_cores ) :
for i in range ( num_cores ) :
processes [ i ] . start ( )
processes [ i ] . start ( )
cnts_org = [ ]
cnts_org = [ ]
@ -200,7 +207,9 @@ def get_textregion_contours_in_org_image_multi(cnts, img, slope_first):
print ( all_index_text_con )
print ( all_index_text_con )
return cnts_org
return cnts_org
def loop_contour_image ( index_l , cnts , img , slope_first ) :
def loop_contour_image ( index_l , cnts , img , slope_first ) :
img_copy = np . zeros ( img . shape )
img_copy = np . zeros ( img . shape )
img_copy = cv2 . fillPoly ( img_copy , pts = [ cnts [ index_l ] ] , color = ( 1 , 1 , 1 ) )
img_copy = cv2 . fillPoly ( img_copy , pts = [ cnts [ index_l ] ] , color = ( 1 , 1 , 1 ) )
@ -209,7 +218,7 @@ def loop_contour_image(index_l, cnts,img, slope_first):
# print(img.shape,'img')
# print(img.shape,'img')
img_copy = rotation_image_new ( img_copy , - slope_first )
img_copy = rotation_image_new ( img_copy , - slope_first )
# # print(img_copy.shape,'img_copy')
# print(img_copy.shape,'img_copy')
# plt.imshow(img_copy)
# plt.imshow(img_copy)
# plt.show()
# plt.show()
@ -224,17 +233,17 @@ def loop_contour_image(index_l, cnts,img, slope_first):
# print(np.shape(cont_int[0]))
# print(np.shape(cont_int[0]))
return cont_int [ 0 ]
return cont_int [ 0 ]
def get_textregion_contours_in_org_image_multi2 ( cnts , img , slope_first ) :
def get_textregion_contours_in_org_image_multi2 ( cnts , img , slope_first ) :
cnts_org = [ ]
cnts_org = [ ]
# print(cnts,'cnts')
# print(cnts,'cnts')
with Pool ( cpu_count ( ) ) as p :
with Pool ( cpu_count ( ) ) as p :
cnts_org = p . starmap ( loop_contour_image , [ ( index_l , cnts , img , slope_first ) for index_l in range ( len ( cnts ) ) ] )
cnts_org = p . starmap ( loop_contour_image , [ ( index_l , cnts , img , slope_first ) for index_l in range ( len ( cnts ) ) ] )
return cnts_org
return cnts_org
def get_textregion_contours_in_org_image ( cnts , img , slope_first ) :
def get_textregion_contours_in_org_image ( cnts , img , slope_first ) :
cnts_org = [ ]
cnts_org = [ ]
# print(cnts,'cnts')
# print(cnts,'cnts')
for i in range ( len ( cnts ) ) :
for i in range ( len ( cnts ) ) :
@ -246,7 +255,7 @@ def get_textregion_contours_in_org_image(cnts, img, slope_first):
# print(img.shape,'img')
# print(img.shape,'img')
img_copy = rotation_image_new ( img_copy , - slope_first )
img_copy = rotation_image_new ( img_copy , - slope_first )
# # print(img_copy.shape,'img_copy')
# print(img_copy.shape,'img_copy')
# plt.imshow(img_copy)
# plt.imshow(img_copy)
# plt.show()
# plt.show()
@ -263,17 +272,17 @@ def get_textregion_contours_in_org_image(cnts, img, slope_first):
return cnts_org
return cnts_org
def get_textregion_contours_in_org_image_light ( cnts , img , slope_first ) :
def get_textregion_contours_in_org_image_light ( cnts , img , slope_first ) :
h_o = img . shape [ 0 ]
h_o = img . shape [ 0 ]
w_o = img . shape [ 1 ]
w_o = img . shape [ 1 ]
img = cv2 . resize ( img , ( int ( img . shape [ 1 ] / 3. ) , int ( img . shape [ 0 ] / 3. ) ) , interpolation = cv2 . INTER_NEAREST )
img = cv2 . resize ( img , ( int ( img . shape [ 1 ] / 3. ) , int ( img . shape [ 0 ] / 3. ) ) , interpolation = cv2 . INTER_NEAREST )
# # cnts = list( (np.array(cnts)/2).astype(np.int16) )
# cnts = list( (np.array(cnts)/2).astype(np.int16) )
# cnts = cnts/2
# cnts = cnts/2
cnts = [ ( i / 3 ) . astype ( np . int32 ) for i in cnts ]
cnts = [ ( i / 3 ) . astype ( np . int32 ) for i in cnts ]
cnts_org = [ ]
cnts_org = [ ]
# print(cnts,'cnts')
# print(cnts,'cnts')
for i in range ( len ( cnts ) ) :
for i in range ( len ( cnts ) ) :
img_copy = np . zeros ( img . shape )
img_copy = np . zeros ( img . shape )
img_copy = cv2 . fillPoly ( img_copy , pts = [ cnts [ i ] ] , color = ( 1 , 1 , 1 ) )
img_copy = cv2 . fillPoly ( img_copy , pts = [ cnts [ i ] ] , color = ( 1 , 1 , 1 ) )
@ -283,7 +292,7 @@ def get_textregion_contours_in_org_image_light(cnts, img, slope_first):
# print(img.shape,'img')
# print(img.shape,'img')
img_copy = rotation_image_new ( img_copy , - slope_first )
img_copy = rotation_image_new ( img_copy , - slope_first )
# # print(img_copy.shape,'img_copy')
# print(img_copy.shape,'img_copy')
# plt.imshow(img_copy)
# plt.imshow(img_copy)
# plt.show()
# plt.show()
@ -296,12 +305,12 @@ def get_textregion_contours_in_org_image_light(cnts, img, slope_first):
cont_int [ 0 ] [ : , 0 , 0 ] = cont_int [ 0 ] [ : , 0 , 0 ] + np . abs ( img_copy . shape [ 1 ] - img . shape [ 1 ] )
cont_int [ 0 ] [ : , 0 , 0 ] = cont_int [ 0 ] [ : , 0 , 0 ] + np . abs ( img_copy . shape [ 1 ] - img . shape [ 1 ] )
cont_int [ 0 ] [ : , 0 , 1 ] = cont_int [ 0 ] [ : , 0 , 1 ] + np . abs ( img_copy . shape [ 0 ] - img . shape [ 0 ] )
cont_int [ 0 ] [ : , 0 , 1 ] = cont_int [ 0 ] [ : , 0 , 1 ] + np . abs ( img_copy . shape [ 0 ] - img . shape [ 0 ] )
# print(np.shape(cont_int[0]))
# print(np.shape(cont_int[0]))
cnts_org . append ( cont_int [ 0 ] * 3 )
cnts_org . append ( cont_int [ 0 ] * 3 )
return cnts_org
return cnts_org
def return_contours_of_interested_textline ( region_pre_p , pixel ) :
def return_contours_of_interested_textline ( region_pre_p , pixel ) :
# pixels of images are identified by 5
# pixels of images are identified by 5
if len ( region_pre_p . shape ) == 3 :
if len ( region_pre_p . shape ) == 3 :
cnts_images = ( region_pre_p [ : , : , 0 ] == pixel ) * 1
cnts_images = ( region_pre_p [ : , : , 0 ] == pixel ) * 1
@ -317,8 +326,8 @@ def return_contours_of_interested_textline(region_pre_p, pixel):
contours_imgs = filter_contours_area_of_image_tables ( thresh , contours_imgs , hierarchy , max_area = 1 , min_area = 0.000000003 )
contours_imgs = filter_contours_area_of_image_tables ( thresh , contours_imgs , hierarchy , max_area = 1 , min_area = 0.000000003 )
return contours_imgs
return contours_imgs
def return_contours_of_image ( image ) :
def return_contours_of_image ( image ) :
if len ( image . shape ) == 2 :
if len ( image . shape ) == 2 :
image = np . repeat ( image [ : , : , np . newaxis ] , 3 , axis = 2 )
image = np . repeat ( image [ : , : , np . newaxis ] , 3 , axis = 2 )
image = image . astype ( np . uint8 )
image = image . astype ( np . uint8 )
@ -329,8 +338,8 @@ def return_contours_of_image(image):
contours , hierarchy = cv2 . findContours ( thresh , cv2 . RETR_TREE , cv2 . CHAIN_APPROX_SIMPLE )
contours , hierarchy = cv2 . findContours ( thresh , cv2 . RETR_TREE , cv2 . CHAIN_APPROX_SIMPLE )
return contours , hierarchy
return contours , hierarchy
def return_contours_of_interested_region_by_min_size ( region_pre_p , pixel , min_size = 0.00003 ) :
def return_contours_of_interested_region_by_min_size ( region_pre_p , pixel , min_size = 0.00003 ) :
# pixels of images are identified by 5
# pixels of images are identified by 5
if len ( region_pre_p . shape ) == 3 :
if len ( region_pre_p . shape ) == 3 :
cnts_images = ( region_pre_p [ : , : , 0 ] == pixel ) * 1
cnts_images = ( region_pre_p [ : , : , 0 ] == pixel ) * 1
@ -348,8 +357,8 @@ def return_contours_of_interested_region_by_min_size(region_pre_p, pixel, min_si
return contours_imgs
return contours_imgs
def return_contours_of_interested_region_by_size ( region_pre_p , pixel , min_area , max_area ) :
def return_contours_of_interested_region_by_size ( region_pre_p , pixel , min_area , max_area ) :
# pixels of images are identified by 5
# pixels of images are identified by 5
if len ( region_pre_p . shape ) == 3 :
if len ( region_pre_p . shape ) == 3 :
cnts_images = ( region_pre_p [ : , : , 0 ] == pixel ) * 1
cnts_images = ( region_pre_p [ : , : , 0 ] == pixel ) * 1
@ -367,4 +376,3 @@ def return_contours_of_interested_region_by_size(region_pre_p, pixel, min_area,
img_ret = np . zeros ( ( region_pre_p . shape [ 0 ] , region_pre_p . shape [ 1 ] , 3 ) )
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 ) )
img_ret = cv2 . fillPoly ( img_ret , pts = contours_imgs , color = ( 1 , 1 , 1 ) )
return img_ret [ : , : , 0 ]
return img_ret [ : , : , 0 ]