@ -10,18 +10,17 @@ import imutils
import math import math
def bluring ( img_in , kind ) :
def bluring ( img_in , kind ) : if kind == ' guass ' :
if kind == ' guass ' :
img_blur = cv2 . GaussianBlur ( img_in , ( 5 , 5 ) , 0 )
img_blur = cv2 . GaussianBlur ( img_in , ( 5 , 5 ) , 0 )
elif kind == " median " :
elif kind == " median " :
img_blur = cv2 . medianBlur ( img_in , 5 )
img_blur = cv2 . medianBlur ( img_in , 5 )
elif kind == ' blur ' :
elif kind == ' blur ' :
img_blur = cv2 . blur ( img_in , ( 5 , 5 ) )
img_blur = cv2 . blur ( img_in , ( 5 , 5 ) )
return img_blur
return img_blur
def elastic_transform ( image , alpha , sigma , seedj , random_state = None ) :
def elastic_transform ( image , alpha , sigma , seedj , random_state = None ) :
""" Elastic deformation of images as described in [Simard2003]_.
""" Elastic deformation of images as described in [Simard2003]_.
. . [ Simard2003 ] Simard , Steinkraus and Platt , " Best Practices for
. . [ Simard2003 ] Simard , Steinkraus and Platt , " Best Practices for
Convolutional Neural Networks applied to Visual Document Analysis " , in
Convolutional Neural Networks applied to Visual Document Analysis " , in
@ -37,461 +36,459 @@ def elastic_transform(image, alpha, sigma,seedj, random_state=None):
dz = np . zeros_like ( dx )
dz = np . zeros_like ( dx )
x , y , z = np . meshgrid ( np . arange ( shape [ 1 ] ) , np . arange ( shape [ 0 ] ) , np . arange ( shape [ 2 ] ) )
x , y , z = np . meshgrid ( np . arange ( shape [ 1 ] ) , np . arange ( shape [ 0 ] ) , np . arange ( shape [ 2 ] ) )
indices = np . reshape ( y + dy , ( - 1 , 1 ) ) , np . reshape ( x + dx , ( - 1 , 1 ) ) , np . reshape ( z , ( - 1 , 1 ) )
indices = np . reshape ( y + dy , ( - 1 , 1 ) ) , np . reshape ( x + dx , ( - 1 , 1 ) ) , np . reshape ( z , ( - 1 , 1 ) )
distored_image = map_coordinates ( image , indices , order = 1 , mode = ' reflect ' )
distored_image = map_coordinates ( image , indices , order = 1 , mode = ' reflect ' )
return distored_image . reshape ( image . shape )
return distored_image . reshape ( image . shape )
def rotation_90 ( img ) : def rotation_90 ( img ) :
img_rot = np . zeros ( ( img . shape [ 1 ] , img . shape [ 0 ] , img . shape [ 2 ] ) )
img_rot = np . zeros ( ( img . shape [ 1 ] , img . shape [ 0 ] , img . shape [ 2 ] ) )
img_rot [ : , : , 0 ] = img [ : , : , 0 ] . T
img_rot [ : , : , 0 ] = img [ : , : , 0 ] . T
img_rot [ : , : , 1 ] = img [ : , : , 1 ] . T
img_rot [ : , : , 1 ] = img [ : , : , 1 ] . T
img_rot [ : , : , 2 ] = img [ : , : , 2 ] . T
img_rot [ : , : , 2 ] = img [ : , : , 2 ] . T
return img_rot
return img_rot
def rotatedRectWithMaxArea ( w , h , angle ) : def rotatedRectWithMaxArea ( w , h , angle ) :
"""
"""
Given a rectangle of size wxh that has been rotated by ' angle ' ( in
Given a rectangle of size wxh that has been rotated by ' angle ' ( in
radians ) , computes the width and height of the largest possible
radians ) , computes the width and height of the largest possible
axis - aligned rectangle ( maximal area ) within the rotated rectangle .
axis - aligned rectangle ( maximal area ) within the rotated rectangle .
"""
"""
if w < = 0 or h < = 0 :
if w < = 0 or h < = 0 :
return 0 , 0
return 0 , 0
width_is_longer = w > = h
width_is_longer = w > = h
side_long , side_short = ( w , h ) if width_is_longer else ( h , w )
side_long , side_short = ( w , h ) if width_is_longer else ( h , w )
# since the solutions for angle, -angle and 180-angle are all the same,
# since the solutions for angle, -angle and 180-angle are all the same,
# if suffices to look at the first quadrant and the absolute values of sin,cos:
# if suffices to look at the first quadrant and the absolute values of sin,cos:
sin_a , cos_a = abs ( math . sin ( angle ) ) , abs ( math . cos ( angle ) )
sin_a , cos_a = abs ( math . sin ( angle ) ) , abs ( math . cos ( angle ) )
if side_short < = 2. * sin_a * cos_a * side_long or abs ( sin_a - cos_a ) < 1e-10 :
if side_short < = 2. * sin_a * cos_a * side_long or abs ( sin_a - cos_a ) < 1e-10 :
# half constrained case: two crop corners touch the longer side,
# half constrained case: two crop corners touch the longer side,
# the other two corners are on the mid-line parallel to the longer line
# the other two corners are on the mid-line parallel to the longer line
x = 0.5 * side_short
x = 0.5 * side_short
wr , hr = ( x / sin_a , x / cos_a ) if width_is_longer else ( x / cos_a , x / sin_a )
wr , hr = ( x / sin_a , x / cos_a ) if width_is_longer else ( x / cos_a , x / sin_a )
else :
else :
# fully constrained case: crop touches all 4 sides
# fully constrained case: crop touches all 4 sides
cos_2a = cos_a * cos_a - sin_a * sin_a
cos_2a = cos_a * cos_a - sin_a * sin_a
wr , hr = ( w * cos_a - h * sin_a ) / cos_2a , ( h * cos_a - w * sin_a ) / cos_2a
wr , hr = ( w * cos_a - h * sin_a ) / cos_2a , ( h * cos_a - w * sin_a ) / cos_2a
return wr , hr
return wr , hr
def rotate_max_area ( image , rotated , rotated_label , angle ) :
def rotate_max_area ( image , rotated , rotated_label , angle ) :
""" image: cv2 image matrix object
""" image: cv2 image matrix object
angle : in degree
angle : in degree
"""
"""
wr , hr = rotatedRectWithMaxArea ( image . shape [ 1 ] , image . shape [ 0 ] ,
wr , hr = rotatedRectWithMaxArea ( image . shape [ 1 ] , image . shape [ 0 ] ,
math . radians ( angle ) )
math . radians ( angle ) )
h , w , _ = rotated . shape
h , w , _ = rotated . shape
y1 = h / / 2 - int ( hr / 2 )
y1 = h / / 2 - int ( hr / 2 )
y2 = y1 + int ( hr )
y2 = y1 + int ( hr )
x1 = w / / 2 - int ( wr / 2 )
x1 = w / / 2 - int ( wr / 2 )
x2 = x1 + int ( wr )
x2 = x1 + int ( wr )
return rotated [ y1 : y2 , x1 : x2 ] , rotated_label [ y1 : y2 , x1 : x2 ]
return rotated [ y1 : y2 , x1 : x2 ] , rotated_label [ y1 : y2 , x1 : x2 ]
def rotation_not_90_func ( img , label , thetha ) :
rotated = imutils . rotate ( img , thetha )
rotated_label = imutils . rotate ( label , thetha )
def rotation_not_90_func ( img , label , thetha ) :
return rotate_max_area ( img , rotated , rotated_label , thetha )
rotated = imutils . rotate ( img , thetha )
rotated_label = imutils . rotate ( label , thetha )
return rotate_max_area ( img , rotated , rotated_label , thetha )
def color_images ( seg , n_classes ) : def color_images ( seg , n_classes ) :
ann_u = range ( n_classes )
ann_u = range ( n_classes )
if len ( np . shape ( seg ) ) == 3 :
if len ( np . shape ( seg ) ) == 3 :
seg = seg [ : , : , 0 ]
seg = seg [ : , : , 0 ]
seg_img = np . zeros ( ( np . shape ( seg ) [ 0 ] , np . shape ( seg ) [ 1 ] , 3 ) ) . astype ( float )
seg_img = np . zeros ( ( np . shape ( seg ) [ 0 ] , np . shape ( seg ) [ 1 ] , 3 ) ) . astype ( float )
colors = sns . color_palette ( " hls " , n_classes )
colors = sns . color_palette ( " hls " , n_classes )
for c in ann_u :
for c in ann_u :
c = int ( c )
c = int ( c )
segl = ( seg == c )
segl = ( seg == c )
seg_img [ : , : , 0 ] + = segl * ( colors [ c ] [ 0 ] )
seg_img [ : , : , 0 ] + = segl * ( colors [ c ] [ 0 ] )
seg_img [ : , : , 1 ] + = segl * ( colors [ c ] [ 1 ] )
seg_img [ : , : , 1 ] + = segl * ( colors [ c ] [ 1 ] )
seg_img [ : , : , 2 ] + = segl * ( colors [ c ] [ 2 ] )
seg_img [ : , : , 2 ] + = segl * ( colors [ c ] [ 2 ] )
return seg_img
return seg_img
def resize_image ( seg_in , input_height , input_width ) : def resize_image ( seg_in , input_height , input_width ) :
return cv2 . resize ( seg_in , ( input_width , input_height ) , interpolation = cv2 . INTER_NEAREST )
return cv2 . resize ( seg_in , ( input_width , input_height ) , interpolation = cv2 . INTER_NEAREST )
def get_one_hot ( seg , input_height , input_width , n_classes ) :
seg = seg [ : , : , 0 ]
seg_f = np . zeros ( ( input_height , input_width , n_classes ) )
def get_one_hot ( seg , input_height , input_width , n_classes ) :
seg = seg [ : , : , 0 ]
seg_f = np . zeros ( ( input_height , input_width , n_classes ) )
for j in range ( n_classes ) :
for j in range ( n_classes ) :
seg_f [ : , : , j ] = ( seg == j ) . astype ( int )
seg_f [ : , : , j ] = ( seg == j ) . astype ( int )
return seg_f
return seg_f
def IoU ( Yi , y_predi ) : def IoU ( Yi , y_predi ) :
# # mean Intersection over Union
# mean Intersection over Union
# # Mean IoU = TP/(FN + TP + FP)
# Mean IoU = TP/(FN + TP + FP)
IoUs = [ ]
IoUs = [ ]
classes_true = np . unique ( Yi )
classes_true = np . unique ( Yi )
for c in classes_true :
for c in classes_true :
TP = np . sum ( ( Yi == c ) & ( y_predi == c ) )
TP = np . sum ( ( Yi == c ) & ( y_predi == c ) )
FP = np . sum ( ( Yi != c ) & ( y_predi == c ) )
FP = np . sum ( ( Yi != c ) & ( y_predi == c ) )
FN = np . sum ( ( Yi == c ) & ( y_predi != c ) )
FN = np . sum ( ( Yi == c ) & ( y_predi != c ) )
IoU = TP / float ( TP + FP + FN )
IoU = TP / float ( TP + FP + FN )
print ( " class {:02.0f} : #TP= {:6.0f} , #FP= {:6.0f} , #FN= {:5.0f} , IoU= {:4.3f} " . format ( c , TP , FP , FN , IoU ) )
print ( " class {:02.0f} : #TP= {:6.0f} , #FP= {:6.0f} , #FN= {:5.0f} , IoU= {:4.3f} " . format ( c , TP , FP , FN , IoU ) )
IoUs . append ( IoU )
IoUs . append ( IoU )
mIoU = np . mean ( IoUs )
mIoU = np . mean ( IoUs )
print ( " _________________ " )
print ( " _________________ " )
print ( " Mean IoU: {:4.3f} " . format ( mIoU ) )
print ( " Mean IoU: {:4.3f} " . format ( mIoU ) )
return mIoU
return mIoU
def data_gen ( img_folder , mask_folder , batch_size , input_height , input_width , n_classes ) :
def data_gen ( img_folder , mask_folder , batch_size , input_height , input_width , n_classes ) :
c = 0
c = 0
n = [ f for f in os . listdir ( img_folder ) if not f . startswith ( ' . ' ) ] # os.listdir(img_folder) #List of training images
n = [ f for f in os . listdir ( img_folder ) if not f . startswith ( ' . ' ) ] # os.listdir(img_folder) #List of training images
random . shuffle ( n )
random . shuffle ( n )
while True :
while True :
img = np . zeros ( ( batch_size , input_height , input_width , 3 ) ) . astype ( ' float ' )
img = np . zeros ( ( batch_size , input_height , input_width , 3 ) ) . astype ( ' float ' )
mask = np . zeros ( ( batch_size , input_height , input_width , n_classes ) ) . astype ( ' float ' )
mask = np . zeros ( ( batch_size , input_height , input_width , n_classes ) ) . astype ( ' float ' )
for i in range ( c , c + batch_size ) : # initially from 0 to 16, c = 0.
for i in range ( c , c + batch_size ) : # initially from 0 to 16, c = 0.
# print(img_folder+'/'+n[i])
# print(img_folder+'/'+n[i])
try :
try :
filename = n [ i ] . split ( ' . ' ) [ 0 ]
filename = n [ i ] . split ( ' . ' ) [ 0 ]
train_img = cv2 . imread ( img_folder + ' / ' + n [ i ] ) / 255.
train_img = cv2 . imread ( img_folder + ' / ' + n [ i ] ) / 255.
train_img = cv2 . resize ( train_img , ( input_width , input_height ) , interpolation = cv2 . INTER_NEAREST ) # Read an image from folder and resize
train_img = cv2 . resize ( train_img , ( input_width , input_height ) ,
interpolation = cv2 . INTER_NEAREST ) # Read an image from folder and resize
img [ i - c ] = train_img #add to array - img[0], img[1], and so on.
img [ i - c ] = train_img # add to array - img[0], img[1], and so on.
train_mask = cv2 . imread ( mask_folder + ' / ' + filename + ' .png ' )
train_mask = cv2 . imread ( mask_folder + ' / ' + filename + ' .png ' )
#print(mask_folder+'/'+filename+'.png')
# print(mask_folder+'/'+filename+'.png')
#print(train_mask.shape)
# print(train_mask.shape)
train_mask = get_one_hot ( resize_image ( train_mask , input_height , input_width ) , input_height , input_width , n_classes )
train_mask = get_one_hot ( resize_image ( train_mask , input_height , input_width ) , input_height , input_width ,
#train_mask = train_mask.reshape(224, 224, 1) # Add extra dimension for parity with train_img size [512 * 512 * 3]
n_classes )
# train_mask = train_mask.reshape(224, 224, 1) # Add extra dimension for parity with train_img size [512 * 512 * 3]
mask [ i - c ] = train_mask
mask [ i - c ] = train_mask
except :
except :
img [ i - c ] = np . ones ( ( input_height , input_width , 3 ) ) . astype ( ' float ' )
img [ i - c ] = np . ones ( ( input_height , input_width , 3 ) ) . astype ( ' float ' )
mask [ i - c ] = np . zeros ( ( input_height , input_width , n_classes ) ) . astype ( ' float ' )
mask [ i - c ] = np . zeros ( ( input_height , input_width , n_classes ) ) . astype ( ' float ' )
c + = batch_size
c + = batch_size
if c + batch_size > = len ( os . listdir ( img_folder ) ) :
if ( c + batch_size > = len ( os . listdir ( img_folder ) ) ) :
c = 0
c = 0
random . shuffle ( n )
random . shuffle ( n )
yield img , mask
yield img , mask
def otsu_copy ( img ) : def otsu_copy ( img ) :
img_r = np . zeros ( img . shape )
img_r = np . zeros ( img . shape )
img1 = img [ : , : , 0 ]
img1 = img [ : , : , 0 ]
img2 = img [ : , : , 1 ]
img2 = img [ : , : , 1 ]
img3 = img [ : , : , 2 ]
img3 = img [ : , : , 2 ]
_ , threshold1 = cv2 . threshold ( img1 , 0 , 255 , cv2 . THRESH_BINARY + cv2 . THRESH_OTSU )
_ , threshold1 = cv2 . threshold ( img1 , 0 , 255 , cv2 . THRESH_BINARY + cv2 . THRESH_OTSU )
_ , threshold2 = cv2 . threshold ( img2 , 0 , 255 , cv2 . THRESH_BINARY + cv2 . THRESH_OTSU )
_ , threshold2 = cv2 . threshold ( img2 , 0 , 255 , cv2 . THRESH_BINARY + cv2 . THRESH_OTSU )
_ , threshold3 = cv2 . threshold ( img3 , 0 , 255 , cv2 . THRESH_BINARY + cv2 . THRESH_OTSU )
_ , threshold3 = cv2 . threshold ( img3 , 0 , 255 , cv2 . THRESH_BINARY + cv2 . THRESH_OTSU )
img_r [ : , : , 0 ] = threshold1
img_r [ : , : , 0 ] = threshold1
img_r [ : , : , 1 ] = threshold1
img_r [ : , : , 1 ] = threshold1
img_r [ : , : , 2 ] = threshold1
img_r [ : , : , 2 ] = threshold1
return img_r
return img_r
def get_patches ( dir_img_f , dir_seg_f , img , label , height , width , indexer ) :
if img . shape [ 0 ] < height or img . shape [ 1 ] < width :
img , label = do_padding ( img , label , height , width )
img_h = img . shape [ 0 ]
def get_patches ( dir_img_f , dir_seg_f , img , label , height , width , indexer ) :
img_w = img . shape [ 1 ]
if img . shape [ 0 ] < height or img . shape [ 1 ] < width :
img , label = do_padding ( img , label , height , width )
img_h = img . shape [ 0 ]
img_w = img . shape [ 1 ]
nxf = img_w / float ( width )
nxf = img_w / float ( width )
nyf = img_h / float ( height )
nyf = img_h / float ( height )
if nxf > int ( nxf ) :
if nxf > int ( nxf ) :
nxf = int ( nxf ) + 1
nxf = int ( nxf ) + 1
if nyf > int ( nyf ) :
if nyf > int ( nyf ) :
nyf = int ( nyf ) + 1
nyf = int ( nyf ) + 1
nxf = int ( nxf )
nxf = int ( nxf )
nyf = int ( nyf )
nyf = int ( nyf )
for i in range ( nxf ) :
for i in range ( nxf ) :
for j in range ( nyf ) :
for j in range ( nyf ) :
index_x_d = i * width
index_x_d = i * width
index_x_u = ( i + 1 ) * width
index_x_u = ( i + 1 ) * width
index_y_d = j * height
index_y_d = j * height
index_y_u = ( j + 1 ) * height
index_y_u = ( j + 1 ) * height
if index_x_u > img_w :
if index_x_u > img_w :
index_x_u = img_w
index_x_u = img_w
index_x_d = img_w - width
index_x_d = img_w - width
if index_y_u > img_h :
if index_y_u > img_h :
index_y_u = img_h
index_y_u = img_h
index_y_d = img_h - height
index_y_d = img_h - height
img_patch = img [ index_y_d : index_y_u , index_x_d : index_x_u , : ]
label_patch = label [ index_y_d : index_y_u , index_x_d : index_x_u , : ]
img_patch = img [ index_y_d : index_y_u , index_x_d : index_x_u , : ]
cv2 . imwrite ( dir_img_f + ' /img_ ' + str ( indexer ) + ' .png ' , img_patch )
label_patch = label [ index_y_d : index_y_u , index_x_d : index_x_u , : ]
cv2 . imwrite ( dir_seg_f + ' /img_ ' + str ( indexer ) + ' .png ' , label_patch )
indexer + = 1
cv2 . imwrite ( dir_img_f + ' /img_ ' + str ( indexer ) + ' .png ' , img_patch )
cv2 . imwrite ( dir_seg_f + ' /img_ ' + str ( indexer ) + ' .png ' , label_patch )
indexer + = 1
return indexer
return indexer
def do_padding ( img , label , height , width ) :
height_new = img . shape [ 0 ]
width_new = img . shape [ 1 ]
h_start = 0
def do_padding ( img , label , height , width ) :
w_start = 0
height_new = img . shape [ 0 ]
width_new = img . shape [ 1 ]
if img . shape [ 0 ] < height :
h_start = 0
h_start = int ( abs ( height - img . shape [ 0 ] ) / 2. )
w_start = 0
height_new = height
if img . shape [ 1] < width :
if img . shape [ 0] < height :
w_start= int ( abs ( width - img . shape [ 1 ] ) / 2. )
h_start = int ( abs ( height - img . shape [ 0 ] ) / 2. )
width_new= width
height_new = height
img_new = np . ones ( ( height_new , width_new , img . shape [ 2 ] ) ) . astype ( float ) * 255
if img . shape [ 1 ] < width :
label_new = np . zeros ( ( height_new , width_new , label . shape [ 2 ] ) ) . astype ( float )
w_start = int ( abs ( width - img . shape [ 1 ] ) / 2. )
width_new = width
img_new [ h_start : h_start + img . shape [ 0 ] , w_start : w_start + img . shape [ 1 ] , : ] = np . copy ( img [ : , : , : ] )
img_new = np . ones ( ( height_new , width_new , img . shape [ 2 ] ) ) . astype ( float ) * 255
label_new [ h_start : h_start + label . shape [ 0 ] , w_start : w_start + label . shape [ 1 ] , : ] = np . copy ( label [ : , : , : ] )
label_new = np . zeros ( ( height_new , width_new , label . shape [ 2 ] ) ) . astype ( float )
return img_new , label_new
img_new [ h_start : h_start + img . shape [ 0 ] , w_start : w_start + img . shape [ 1 ] , : ] = np . copy ( img [ : , : , : ] )
label_new [ h_start : h_start + label . shape [ 0 ] , w_start : w_start + label . shape [ 1 ] , : ] = np . copy ( label [ : , : , : ] )
return img_new , label_new
def get_patches_num_scale ( dir_img_f , dir_seg_f , img , label , height , width , indexer , n_patches , scaler ) :
def get_patches_num_scale ( dir_img_f , dir_seg_f , img , label , height , width , indexer , n_patches , scaler ) :
if img . shape [ 0 ] < height or img . shape [ 1 ] < width :
img , label = do_padding ( img , label , height , width )
if img . shape [ 0 ] < height or img . shape [ 1 ] < width :
img_h = img . shape [ 0 ]
img , label = do_padding ( img , label , height , width )
img_w = img . shape [ 1 ]
img_h= img . shape [ 0 ]
height_scale = int ( height * scaler )
img_w= img . shape [ 1 ]
width_scale = int ( width * scaler )
height_scale= int ( height * scaler )
nxf = img_w / float ( width_scale )
width_scale= int ( width * scaler )
nyf = img_h / float ( height_scale )
if nxf > int ( nxf ) :
nxf = int ( nxf ) + 1
if nyf > int ( nyf ) :
nyf = int ( nyf ) + 1
nxf = img_w / float ( width_scale )
nxf = int ( nxf )
nyf = img_h / float ( height_scale )
nyf = int ( nyf )
if nxf > int ( nxf ) :
nxf = int ( nxf ) + 1
if nyf > int ( nyf ) :
nyf = int ( nyf ) + 1
nxf = int ( nxf )
nyf = int ( nyf )
for i in range ( nxf ) :
for i in range ( nxf ) :
for j in range ( nyf ) :
for j in range ( nyf ) :
index_x_d = i * width_scale
index_x_d = i * width_scale
index_x_u = ( i + 1 ) * width_scale
index_x_u = ( i + 1 ) * width_scale
index_y_d = j * height_scale
index_y_u = ( j + 1 ) * height_scale
if index_x_u > img_w :
index_y_d = j * height_scale
index_x_u = img_w
index_y_u = ( j + 1 ) * height_scale
index_x_d = img_w - width_scale
if index_y_u > img_h :
index_y_u = img_h
index_y_d = img_h - height_scale
if index_x_u > img_w :
index_x_u = img_w
index_x_d = img_w - width_scale
if index_y_u > img_h :
index_y_u = img_h
index_y_d = img_h - height_scale
img_patch = img [ index_y_d : index_y_u , index_x_d : index_x_u , : ]
img_patch = img [ index_y_d : index_y_u , index_x_d : index_x_u , : ]
label_patch = label [ index_y_d : index_y_u , index_x_d : index_x_u , : ]
label_patch = label [ index_y_d : index_y_u , index_x_d : index_x_u , : ]
img_patch = resize_image ( img_patch , height , width )
img_patch = resize_image ( img_patch , height , width )
label_patch = resize_image ( label_patch , height , width )
label_patch = resize_image ( label_patch , height , width )
cv2 . imwrite ( dir_img_f + ' /img_ ' + str ( indexer ) + ' .png ' , img_patch )
cv2 . imwrite ( dir_img_f + ' /img_ ' + str ( indexer ) + ' .png ' , img_patch )
cv2 . imwrite ( dir_seg_f + ' /img_ ' + str ( indexer ) + ' .png ' , label_patch )
cv2 . imwrite ( dir_seg_f + ' /img_ ' + str ( indexer ) + ' .png ' , label_patch )
indexer + = 1
indexer + = 1
return indexer
return indexer
def get_patches_num_scale_new ( dir_img_f , dir_seg_f , img , label , height , width , indexer , scaler ) :
img = resize_image ( img , int ( img . shape [ 0 ] * scaler ) , int ( img . shape [ 1 ] * scaler ) )
label = resize_image ( label , int ( label . shape [ 0 ] * scaler ) , int ( label . shape [ 1 ] * scaler ) )
if img . shape [ 0 ] < height or img . shape [ 1 ] < width :
def get_patches_num_scale_new ( dir_img_f , dir_seg_f , img , label , height , width , indexer , scaler ) :
img , label = do_padding ( img , label , height , width )
img = resize_image ( img , int ( img . shape [ 0 ] * scaler ) , int ( img . shape [ 1 ] * scaler ) )
label = resize_image ( label , int ( label . shape [ 0 ] * scaler ) , int ( label . shape [ 1 ] * scaler ) )
img_h = img . shape [ 0 ]
if img . shape [ 0 ] < height or img . shape [ 1 ] < width :
img_w = img . shape [ 1 ]
img , label = do_padding ( img , label , height , width )
height_scale= int ( height * 1 )
img_h = img . shape [ 0 ]
width_scale= int ( width * 1 )
img_w = img . shape [ 1 ]
height_scale = int ( height * 1 )
width_scale = int ( width * 1 )
nxf = img_w / float ( width_scale )
nxf = img_w / float ( width_scale )
nyf = img_h / float ( height_scale )
nyf = img_h / float ( height_scale )
if nxf > int ( nxf ) :
if nxf > int ( nxf ) :
nxf = int ( nxf ) + 1
nxf = int ( nxf ) + 1
if nyf > int ( nyf ) :
if nyf > int ( nyf ) :
nyf = int ( nyf ) + 1
nyf = int ( nyf ) + 1
nxf = int ( nxf )
nxf = int ( nxf )
nyf = int ( nyf )
nyf = int ( nyf )
for i in range ( nxf ) :
for i in range ( nxf ) :
for j in range ( nyf ) :
for j in range ( nyf ) :
index_x_d = i * width_scale
index_x_d = i * width_scale
index_x_u = ( i + 1 ) * width_scale
index_x_u = ( i + 1 ) * width_scale
index_y_d = j * height_scale
index_y_d = j * height_scale
index_y_u = ( j + 1 ) * height_scale
index_y_u = ( j + 1 ) * height_scale
if index_x_u > img_w :
if index_x_u > img_w :
index_x_u = img_w
index_x_u = img_w
index_x_d = img_w - width_scale
index_x_d = img_w - width_scale
if index_y_u > img_h :
if index_y_u > img_h :
index_y_u = img_h
index_y_u = img_h
index_y_d = img_h - height_scale
index_y_d = img_h - height_scale
img_patch = img [ index_y_d : index_y_u , index_x_d : index_x_u , : ]
label_patch = label [ index_y_d : index_y_u , index_x_d : index_x_u , : ]
img_patch = img [ index_y_d : index_y_u , index_x_d : index_x_u , : ]
# img_patch=resize_image(img_patch,height,width)
label_patch = label [ index_y_d : index_y_u , index_x_d : index_x_u , : ]
# label_patch=resize_image(label_patch,height,width)
#img_patch=resize_image(img_patch,height,width)
cv2 . imwrite ( dir_img_f + ' /img_ ' + str ( indexer ) + ' .png ' , img_patch )
#label_patch=resize_image(label_patch,height,width)
cv2 . imwrite ( dir_seg_f + ' /img_ ' + str ( indexer ) + ' .png ' , label_patch )
indexer + = 1
cv2 . imwrite ( dir_img_f + ' /img_ ' + str ( indexer ) + ' .png ' , img_patch )
cv2 . imwrite ( dir_seg_f + ' /img_ ' + str ( indexer ) + ' .png ' , label_patch )
indexer + = 1
return indexer
return indexer
def provide_patches ( dir_img , dir_seg , dir_flow_train_imgs , def provide_patches ( dir_img , dir_seg , dir_flow_train_imgs ,
dir_flow_train_labels ,
dir_flow_train_labels ,
input_height , input_width , blur_k , blur_aug ,
input_height , input_width , blur_k , blur_aug ,
flip_aug , binarization , scaling , scales , flip_index ,
flip_aug , binarization , scaling , scales , flip_index ,
scaling_bluring , scaling_binarization , rotation ,
scaling_bluring , scaling_binarization , rotation ,
rotation_not_90 , thetha , scaling_flip ,
rotation_not_90 , thetha , scaling_flip ,
augmentation = False , patches = False ) :
augmentation = False , patches = False ) :
imgs_cv_train = np . array ( os . listdir ( dir_img ) )
imgs_cv_train = np . array ( os . listdir ( dir_img ) )
segs_cv_train = np . array ( os . listdir ( dir_seg ) )
segs_cv_train = np . array ( os . listdir ( dir_seg ) )
indexer = 0
indexer = 0
for im , seg_i in tqdm ( zip ( imgs_cv_train , segs_cv_train ) ) :
for im , seg_i in tqdm ( zip ( imgs_cv_train , segs_cv_train ) ) :
img_name = im . split ( ' . ' ) [ 0 ]
img_name = im . split ( ' . ' ) [ 0 ]
if not patches :
if not patches :
cv2 . imwrite ( dir_flow_train_imgs + ' /img_ ' + str ( indexer ) + ' .png ' , resize_image ( cv2 . imread ( dir_img + ' / ' + im ) , input_height , input_width ) )
cv2 . imwrite ( dir_flow_train_imgs + ' /img_ ' + str ( indexer ) + ' .png ' ,
cv2 . imwrite ( dir_flow_train_labels + ' /img_ ' + str ( indexer ) + ' .png ' , resize_image ( cv2 . imread ( dir_seg + ' / ' + img_name + ' .png ' ) , input_height , input_width ) )
resize_image ( cv2 . imread ( dir_img + ' / ' + im ) , input_height , input_width ) )
indexer + = 1
cv2 . imwrite ( dir_flow_train_labels + ' /img_ ' + str ( indexer ) + ' .png ' ,
resize_image ( cv2 . imread ( dir_seg + ' / ' + img_name + ' .png ' ) , input_height , input_width ) )
indexer + = 1
if augmentation :
if augmentation :
if flip_aug :
if flip_aug :
for f_i in flip_index :
for f_i in flip_index :
cv2 . imwrite ( dir_flow_train_imgs + ' /img_ ' + str ( indexer ) + ' .png ' ,
cv2 . imwrite ( dir_flow_train_imgs + ' /img_ ' + str ( indexer ) + ' .png ' ,
resize_image ( cv2 . flip ( cv2 . imread ( dir_img + ' / ' + im ) , f_i ) , input_height , input_width ) )
resize_image ( cv2 . flip ( cv2 . imread ( dir_img + ' / ' + im ) , f_i ) , input_height ,
input_width ) )
cv2 . imwrite ( dir_flow_train_labels + ' /img_ ' + str ( indexer ) + ' .png ' ,
cv2 . imwrite ( dir_flow_train_labels + ' /img_ ' + str ( indexer ) + ' .png ' ,
resize_image ( cv2 . flip ( cv2 . imread ( dir_seg + ' / ' + img_name + ' .png ' ) , f_i ) , input_height , input_width ) )
resize_image ( cv2 . flip ( cv2 . imread ( dir_seg + ' / ' + img_name + ' .png ' ) , f_i ) ,
indexer + = 1
input_height , input_width ) )
indexer + = 1
if blur_aug :
if blur_aug :
for blur_i in blur_k :
for blur_i in blur_k :
cv2 . imwrite ( dir_flow_train_imgs + ' /img_ ' + str ( indexer ) + ' .png ' ,
cv2 . imwrite ( dir_flow_train_imgs + ' /img_ ' + str ( indexer ) + ' .png ' ,
( resize_image ( bluring ( cv2 . imread ( dir_img + ' / ' + im ) , blur_i ) , input_height , input_width ) ) )
( resize_image ( bluring ( cv2 . imread ( dir_img + ' / ' + im ) , blur_i ) , input_height ,
input_width ) ) )
cv2 . imwrite ( dir_flow_train_labels + ' /img_ ' + str ( indexer ) + ' .png ' ,
resize_image ( cv2 . imread ( dir_seg + ' / ' + img_name + ' .png ' ) , input_height , input_width ) )
indexer + = 1
cv2 . imwrite ( dir_flow_train_labels + ' /img_ ' + str ( indexer ) + ' .png ' ,
resize_image ( cv2 . imread ( dir_seg + ' / ' + img_name + ' .png ' ) , input_height ,
input_width ) )
indexer + = 1
if binarization :
if binarization :
cv2 . imwrite ( dir_flow_train_imgs + ' /img_ ' + str ( indexer ) + ' .png ' ,
cv2 . imwrite ( dir_flow_train_imgs + ' /img_ ' + str ( indexer ) + ' .png ' ,
resize_image ( otsu_copy ( cv2 . imread ( dir_img + ' / ' + im ) ) , input_height , input_width ) )
resize_image ( otsu_copy ( cv2 . imread ( dir_img + ' / ' + im ) ) , input_height , input_width ) )
cv2 . imwrite ( dir_flow_train_labels + ' /img_ ' + str ( indexer ) + ' .png ' ,
resize_image ( cv2 . imread ( dir_seg + ' / ' + img_name + ' .png ' ) , input_height , input_width ) )
indexer + = 1
cv2 . imwrite ( dir_flow_train_labels + ' /img_ ' + str ( indexer ) + ' .png ' ,
resize_image ( cv2 . imread ( dir_seg + ' / ' + img_name + ' .png ' ) , input_height , input_width ) )
indexer + = 1
if patches :
if patches :
indexer = get_patches ( dir_flow_train_imgs , dir_flow_train_labels ,
indexer = get_patches ( dir_flow_train_imgs , dir_flow_train_labels ,
cv2 . imread ( dir_img + ' / ' + im ) , cv2 . imread ( dir_seg + ' / ' + img_name + ' .png ' ) ,
cv2 . imread ( dir_img + ' / ' + im ) , cv2 . imread ( dir_seg + ' / ' + img_name + ' .png ' ) ,
input_height , input_width , indexer = indexer )
input_height , input_width , indexer = indexer )
if augmentation :
if augmentation :
if rotation :
if rotation :
indexer = get_patches ( dir_flow_train_imgs , dir_flow_train_labels ,
rotation_90 ( cv2 . imread ( dir_img + ' / ' + im ) ) ,
indexer = get_patches ( dir_flow_train_imgs , dir_flow_train_labels ,
rotation_90 ( cv2 . imread ( dir_seg + ' / ' + img_name + ' .png ' ) ) ,
rotation_90 ( cv2 . imread ( dir_img + ' / ' + im ) ) ,
input_height , input_width , indexer = indexer )
rotation_90 ( cv2 . imread ( dir_seg + ' / ' + img_name + ' .png ' ) ) ,
input_height , input_width , indexer = indexer )
if rotation_not_90 :
if rotation_not_90 :
for thetha_i in thetha :
for thetha_i in thetha :
img_max_rotated , label_max_rotated = rotation_not_90_func ( cv2 . imread ( dir_img + ' / ' + im ) , cv2 . imread ( dir_seg + ' / ' + img_name + ' .png ' ) , thetha_i )
img_max_rotated , label_max_rotated = rotation_not_90_func ( cv2 . imread ( dir_img + ' / ' + im ) ,
indexer = get_patches ( dir_flow_train_imgs , dir_flow_train_labels ,
cv2 . imread (
img_max_rotated ,
dir_seg + ' / ' + img_name + ' .png ' ) ,
label_max_rotated ,
thetha_i )
input_height , input_width , indexer = indexer )
indexer = get_patches ( dir_flow_train_imgs , dir_flow_train_labels ,
img_max_rotated ,
label_max_rotated ,
input_height , input_width , indexer = indexer )
if flip_aug :
if flip_aug :
for f_i in flip_index :
for f_i in flip_index :
indexer = get_patches ( dir_flow_train_imgs , dir_flow_train_labels ,
indexer = get_patches ( dir_flow_train_imgs , dir_flow_train_labels ,
cv2 . flip ( cv2 . imread ( dir_img + ' / ' + im ) , f_i ) ,
cv2 . flip ( cv2 . imread ( dir_img + ' / ' + im ) , f_i ) ,
cv2 . flip ( cv2 . imread ( dir_seg + ' / ' + img_name + ' .png ' ) , f_i ) ,
cv2 . flip ( cv2 . imread ( dir_seg + ' / ' + img_name + ' .png ' ) , f_i ) ,
input_height , input_width , indexer = indexer )
input_height , input_width , indexer = indexer )
if blur_aug :
if blur_aug :
for blur_i in blur_k :
for blur_i in blur_k :
indexer = get_patches ( dir_flow_train_imgs , dir_flow_train_labels ,
indexer = get_patches ( dir_flow_train_imgs , dir_flow_train_labels ,
bluring ( cv2 . imread ( dir_img + ' / ' + im ) , blur_i ) ,
bluring ( cv2 . imread ( dir_img + ' / ' + im ) , blur_i ) ,
cv2 . imread ( dir_seg + ' / ' + img_name + ' .png ' ) ,
cv2 . imread ( dir_seg + ' / ' + img_name + ' .png ' ) ,
input_height , input_width , indexer = indexer )
input_height , input_width , indexer = indexer )
if scaling :
if scaling :
for sc_ind in scales :
for sc_ind in scales :
indexer = get_patches_num_scale_new ( dir_flow_train_imgs , dir_flow_train_labels ,
indexer = get_patches_num_scale_new ( dir_flow_train_imgs , dir_flow_train_labels ,
cv2 . imread ( dir_img + ' / ' + im ) ,
cv2 . imread ( dir_img + ' / ' + im ) ,
cv2 . imread ( dir_seg + ' / ' + img_name + ' .png ' ) ,
cv2 . imread ( dir_seg + ' / ' + img_name + ' .png ' ) ,
input_height , input_width , indexer = indexer , scaler = sc_ind )
input_height , input_width , indexer = indexer , scaler = sc_ind )
if binarization :
if binarization :
indexer = get_patches ( dir_flow_train_imgs , dir_flow_train_labels ,
indexer = get_patches ( dir_flow_train_imgs , dir_flow_train_labels ,
otsu_copy ( cv2 . imread ( dir_img + ' / ' + im ) ) ,
otsu_copy ( cv2 . imread ( dir_img + ' / ' + im ) ) ,
cv2 . imread ( dir_seg + ' / ' + img_name + ' .png ' ) ,
cv2 . imread ( dir_seg + ' / ' + img_name + ' .png ' ) ,
input_height , input_width , indexer = indexer )
input_height , input_width , indexer = indexer )
if scaling_bluring :
if scaling_bluring :
for sc_ind in scales :
for sc_ind in scales :
for blur_i in blur_k :
for blur_i in blur_k :
indexer = get_patches_num_scale_new ( dir_flow_train_imgs , dir_flow_train_labels ,
indexer = get_patches_num_scale_new ( dir_flow_train_imgs , dir_flow_train_labels ,
bluring ( cv2 . imread ( dir_img + ' / ' + im ) , blur_i ) ,
bluring ( cv2 . imread ( dir_img + ' / ' + im ) , blur_i ) ,
cv2 . imread ( dir_seg + ' / ' + img_name + ' .png ' ) ,
cv2 . imread ( dir_seg + ' / ' + img_name + ' .png ' ) ,
input_height , input_width , indexer = indexer , scaler = sc_ind )
input_height , input_width , indexer = indexer ,
scaler = sc_ind )
if scaling_binarization :
if scaling_binarization :
for sc_ind in scales :
for sc_ind in scales :
indexer = get_patches_num_scale_new ( dir_flow_train_imgs , dir_flow_train_labels ,
indexer = get_patches_num_scale_new ( dir_flow_train_imgs , dir_flow_train_labels ,
otsu_copy ( cv2 . imread ( dir_img + ' / ' + im ) ) ,
otsu_copy ( cv2 . imread ( dir_img + ' / ' + im ) ) ,
cv2 . imread ( dir_seg + ' / ' + img_name + ' .png ' ) ,
cv2 . imread ( dir_seg + ' / ' + img_name + ' .png ' ) ,
input_height , input_width , indexer = indexer , scaler = sc_ind )
input_height , input_width , indexer = indexer , scaler = sc_ind )
if scaling_flip :
if scaling_flip :
for sc_ind in scales :
for sc_ind in scales :
for f_i in flip_index :
for f_i in flip_index :
indexer = get_patches_num_scale_new ( dir_flow_train_imgs , dir_flow_train_labels ,
indexer = get_patches_num_scale_new ( dir_flow_train_imgs , dir_flow_train_labels ,
cv2 . flip ( cv2 . imread ( dir_img + ' / ' + im ) , f_i ) ,
cv2 . flip ( cv2 . imread ( dir_img + ' / ' + im ) , f_i ) ,
cv2 . flip ( cv2 . imread ( dir_seg + ' / ' + img_name + ' .png ' ) , f_i ) ,
cv2 . flip ( cv2 . imread ( dir_seg + ' / ' + img_name + ' .png ' ) ,
input_height , input_width , indexer = indexer , scaler = sc_ind )
f_i ) ,
input_height , input_width , indexer = indexer ,
scaler = sc_ind )
cneud
9 months ago