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Merge pull request #15 from vahidrezanezhad/master

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continue training, loss functions, rotation and ...
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24
config_params.json
View file

@ -1,24 +1,30 @@
{
"n_classes" : 2,
"n_classes" : 3,
"n_epochs" : 2,
"input_height" : 448,
"input_width" : 896,
"input_width" : 672,
"weight_decay" : 1e-6,
"n_batch" : 1,
"n_batch" : 2,
"learning_rate": 1e-4,
"patches" : true,
"pretraining" : true,
"augmentation" : false,
"flip_aug" : false,
"elastic_aug" : false,
"blur_aug" : false,
"scaling" : false,
"scaling" : true,
"binarization" : false,
"scaling_bluring" : false,
"scaling_binarization" : false,
"scaling_flip" : false,
"rotation": false,
"weighted_loss": true,
"dir_train": "../train",
"dir_eval": "../eval",
"dir_output": "../output"
"rotation_not_90": false,
"continue_training": false,
"index_start": 0,
"dir_of_start_model": " ",
"weighted_loss": false,
"is_loss_soft_dice": false,
"data_is_provided": false,
"dir_train": "/home/vahid/Documents/handwrittens_train/train",
"dir_eval": "/home/vahid/Documents/handwrittens_train/eval",
"dir_output": "/home/vahid/Documents/handwrittens_train/output"
}

208
train.py
View file

@ -8,7 +8,8 @@ from sacred import Experiment
from models import *
from utils import *
from metrics import *
from keras.models import load_model
from tqdm import tqdm
def configuration():
keras.backend.clear_session()
@ -47,7 +48,6 @@ def config_params():
# extraction this should be set to false since model should see all image.
augmentation=False
flip_aug=False # Flip image (augmentation).
elastic_aug=False # Elastic transformation (augmentation).
blur_aug=False # Blur patches of image (augmentation).
scaling=False # Scaling of patches (augmentation) will be imposed if this set to true.
binarization=False # Otsu thresholding. Used for augmentation in the case of binary case like textline prediction. For multicases should not be applied.
@ -55,82 +55,120 @@ def config_params():
dir_eval=None # Directory of validation dataset (sub-folders should be named images and labels).
dir_output=None # Directory of output where the model should be saved.
pretraining=False # Set true to load pretrained weights of resnet50 encoder.
weighted_loss=False # Set True if classes are unbalanced and you want to use weighted loss function.
scaling_bluring=False
rotation: False
scaling_binarization=False
scaling_flip=False
thetha=[10,-10]
blur_k=['blur','guass','median'] # Used in order to blur image. Used for augmentation.
scales=[0.9 , 1.1 ] # Scale patches with these scales. Used for augmentation.
flip_index=[0,1] # Flip image. Used for augmentation.
scales= [ 0.5, 2 ] # Scale patches with these scales. Used for augmentation.
flip_index=[0,1,-1] # Flip image. Used for augmentation.
continue_training = False # If
index_start = 0
dir_of_start_model = ''
is_loss_soft_dice = False
weighted_loss = False
data_is_provided = False
@ex.automain
def run(n_classes,n_epochs,input_height,
input_width,weight_decay,weighted_loss,
n_batch,patches,augmentation,flip_aug,blur_aug,scaling, binarization,
blur_k,scales,dir_train,
index_start,dir_of_start_model,is_loss_soft_dice,
n_batch,patches,augmentation,flip_aug
,blur_aug,scaling, binarization,
blur_k,scales,dir_train,data_is_provided,
scaling_bluring,scaling_binarization,rotation,
rotation_not_90,thetha,scaling_flip,continue_training,
flip_index,dir_eval ,dir_output,pretraining,learning_rate):
dir_img,dir_seg=get_dirs_or_files(dir_train)
dir_img_val,dir_seg_val=get_dirs_or_files(dir_eval)
# make first a directory in output for both training and evaluations in order to flow data from these directories.
dir_train_flowing=os.path.join(dir_output,'train')
dir_eval_flowing=os.path.join(dir_output,'eval')
dir_flow_train_imgs=os.path.join(dir_train_flowing,'images')
dir_flow_train_labels=os.path.join(dir_train_flowing,'labels')
dir_flow_eval_imgs=os.path.join(dir_eval_flowing,'images')
dir_flow_eval_labels=os.path.join(dir_eval_flowing,'labels')
if os.path.isdir(dir_train_flowing):
os.system('rm -rf '+dir_train_flowing)
os.makedirs(dir_train_flowing)
if data_is_provided:
dir_train_flowing=os.path.join(dir_output,'train')
dir_eval_flowing=os.path.join(dir_output,'eval')
dir_flow_train_imgs=os.path.join(dir_train_flowing,'images')
dir_flow_train_labels=os.path.join(dir_train_flowing,'labels')
dir_flow_eval_imgs=os.path.join(dir_eval_flowing,'images')
dir_flow_eval_labels=os.path.join(dir_eval_flowing,'labels')
configuration()
else:
os.makedirs(dir_train_flowing)
dir_img,dir_seg=get_dirs_or_files(dir_train)
dir_img_val,dir_seg_val=get_dirs_or_files(dir_eval)
if os.path.isdir(dir_eval_flowing):
os.system('rm -rf '+dir_eval_flowing)
os.makedirs(dir_eval_flowing)
else:
os.makedirs(dir_eval_flowing)
# make first a directory in output for both training and evaluations in order to flow data from these directories.
dir_train_flowing=os.path.join(dir_output,'train')
dir_eval_flowing=os.path.join(dir_output,'eval')
dir_flow_train_imgs=os.path.join(dir_train_flowing,'images/')
dir_flow_train_labels=os.path.join(dir_train_flowing,'labels/')
dir_flow_eval_imgs=os.path.join(dir_eval_flowing,'images/')
dir_flow_eval_labels=os.path.join(dir_eval_flowing,'labels/')
if os.path.isdir(dir_train_flowing):
os.system('rm -rf '+dir_train_flowing)
os.makedirs(dir_train_flowing)
else:
os.makedirs(dir_train_flowing)
if os.path.isdir(dir_eval_flowing):
os.system('rm -rf '+dir_eval_flowing)
os.makedirs(dir_eval_flowing)
else:
os.makedirs(dir_eval_flowing)
os.mkdir(dir_flow_train_imgs)
os.mkdir(dir_flow_train_labels)
os.mkdir(dir_flow_eval_imgs)
os.mkdir(dir_flow_eval_labels)
#set the gpu configuration
configuration()
#writing patches into a sub-folder in order to be flowed from directory.
provide_patches(dir_img,dir_seg,dir_flow_train_imgs,
dir_flow_train_labels,
input_height,input_width,blur_k,blur_aug,
flip_aug,binarization,scaling,scales,flip_index,
scaling_bluring,scaling_binarization,rotation,
rotation_not_90,thetha,scaling_flip,
augmentation=augmentation,patches=patches)
provide_patches(dir_img_val,dir_seg_val,dir_flow_eval_imgs,
dir_flow_eval_labels,
input_height,input_width,blur_k,blur_aug,
flip_aug,binarization,scaling,scales,flip_index,
scaling_bluring,scaling_binarization,rotation,
rotation_not_90,thetha,scaling_flip,
augmentation=False,patches=patches)
os.mkdir(dir_flow_train_imgs)
os.mkdir(dir_flow_train_labels)
os.mkdir(dir_flow_eval_imgs)
os.mkdir(dir_flow_eval_labels)
#set the gpu configuration
configuration()
#writing patches into a sub-folder in order to be flowed from directory.
provide_patches(dir_img,dir_seg,dir_flow_train_imgs,
dir_flow_train_labels,
input_height,input_width,blur_k,blur_aug,
flip_aug,binarization,scaling,scales,flip_index,
scaling_bluring,scaling_binarization,rotation,
augmentation=augmentation,patches=patches)
provide_patches(dir_img_val,dir_seg_val,dir_flow_eval_imgs,
dir_flow_eval_labels,
input_height,input_width,blur_k,blur_aug,
flip_aug,binarization,scaling,scales,flip_index,
scaling_bluring,scaling_binarization,rotation,
augmentation=False,patches=patches)
if weighted_loss:
weights=np.zeros(n_classes)
for obj in os.listdir(dir_seg):
label_obj=cv2.imread(dir_seg+'/'+obj)
label_obj_one_hot=get_one_hot( label_obj,label_obj.shape[0],label_obj.shape[1],n_classes)
weights+=(label_obj_one_hot.sum(axis=0)).sum(axis=0)
if data_is_provided:
for obj in os.listdir(dir_flow_train_labels):
try:
label_obj=cv2.imread(dir_flow_train_labels+'/'+obj)
label_obj_one_hot=get_one_hot( label_obj,label_obj.shape[0],label_obj.shape[1],n_classes)
weights+=(label_obj_one_hot.sum(axis=0)).sum(axis=0)
except:
pass
else:
for obj in os.listdir(dir_seg):
try:
label_obj=cv2.imread(dir_seg+'/'+obj)
label_obj_one_hot=get_one_hot( label_obj,label_obj.shape[0],label_obj.shape[1],n_classes)
weights+=(label_obj_one_hot.sum(axis=0)).sum(axis=0)
except:
pass
weights=1.00/weights
@ -138,27 +176,35 @@ def run(n_classes,n_epochs,input_height,
weights=weights/float(np.sum(weights))
weights=weights/float(np.min(weights))
weights=weights/float(np.sum(weights))
#get our model.
model = resnet50_unet(n_classes, input_height, input_width,weight_decay,pretraining)
if continue_training:
if is_loss_soft_dice:
model = load_model (dir_of_start_model, compile = True, custom_objects={'soft_dice_loss': soft_dice_loss})
if weighted_loss:
model = load_model (dir_of_start_model, compile = True, custom_objects={'loss': weighted_categorical_crossentropy(weights)})
if not is_loss_soft_dice and not weighted_loss:
model = load_model (dir_of_start_model, compile = True)
else:
#get our model.
index_start = 0
model = resnet50_unet(n_classes, input_height, input_width,weight_decay,pretraining)
#if you want to see the model structure just uncomment model summary.
#model.summary()
if not weighted_loss:
if not is_loss_soft_dice and not weighted_loss:
model.compile(loss='categorical_crossentropy',
optimizer = Adam(lr=learning_rate),metrics=['accuracy'])
if is_loss_soft_dice:
model.compile(loss=soft_dice_loss,
optimizer = Adam(lr=learning_rate),metrics=['accuracy'])
if weighted_loss:
model.compile(loss=weighted_categorical_crossentropy(weights),
optimizer = Adam(lr=learning_rate),metrics=['accuracy'])
mc = keras.callbacks.ModelCheckpoint('weights{epoch:08d}.h5',
save_weights_only=True, period=1)
#generating train and evaluation data
train_gen = data_gen(dir_flow_train_imgs,dir_flow_train_labels, batch_size = n_batch,
@ -166,20 +212,20 @@ def run(n_classes,n_epochs,input_height,
val_gen = data_gen(dir_flow_eval_imgs,dir_flow_eval_labels, batch_size = n_batch,
input_height=input_height, input_width=input_width,n_classes=n_classes )
for i in tqdm(range(index_start, n_epochs+index_start)):
model.fit_generator(
train_gen,
steps_per_epoch=int(len(os.listdir(dir_flow_train_imgs))/n_batch)-1,
validation_data=val_gen,
validation_steps=1,
epochs=1)
model.save(dir_output+'/'+'model_'+str(i)+'.h5')
model.fit_generator(
train_gen,
steps_per_epoch=int(len(os.listdir(dir_flow_train_imgs))/n_batch)-1,
validation_data=val_gen,
validation_steps=1,
epochs=n_epochs)
#os.system('rm -rf '+dir_train_flowing)
#os.system('rm -rf '+dir_eval_flowing)
os.system('rm -rf '+dir_train_flowing)
os.system('rm -rf '+dir_eval_flowing)
model.save(dir_output+'/'+'model'+'.h5')
#model.save(dir_output+'/'+'model'+'.h5')

263
utils.py
View file

@ -6,7 +6,8 @@ from scipy.ndimage.interpolation import map_coordinates
from scipy.ndimage.filters import gaussian_filter
import random
from tqdm import tqdm
import imutils
import math
@ -19,6 +20,79 @@ def bluring(img_in,kind):
img_blur=cv2.blur(img_in,(5,5))
return img_blur
def elastic_transform(image, alpha, sigma,seedj, random_state=None):
"""Elastic deformation of images as described in [Simard2003]_.
.. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
Convolutional Neural Networks applied to Visual Document Analysis", in
Proc. of the International Conference on Document Analysis and
Recognition, 2003.
"""
if random_state is None:
random_state = np.random.RandomState(seedj)
shape = image.shape
dx = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma, mode="constant", cval=0) * alpha
dy = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma, mode="constant", cval=0) * alpha
dz = np.zeros_like(dx)
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))
distored_image = map_coordinates(image, indices, order=1, mode='reflect')
return distored_image.reshape(image.shape)
def rotation_90(img):
img_rot=np.zeros((img.shape[1],img.shape[0],img.shape[2]))
img_rot[:,:,0]=img[:,:,0].T
img_rot[:,:,1]=img[:,:,1].T
img_rot[:,:,2]=img[:,:,2].T
return img_rot
def rotatedRectWithMaxArea(w, h, angle):
"""
Given a rectangle of size wxh that has been rotated by 'angle' (in
radians), computes the width and height of the largest possible
axis-aligned rectangle (maximal area) within the rotated rectangle.
"""
if w <= 0 or h <= 0:
return 0,0
width_is_longer = w >= h
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,
# 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))
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,
# the other two corners are on the mid-line parallel to the longer line
x = 0.5*side_short
wr,hr = (x/sin_a,x/cos_a) if width_is_longer else (x/cos_a,x/sin_a)
else:
# fully constrained case: crop touches all 4 sides
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
return wr,hr
def rotate_max_area(image,rotated, rotated_label,angle):
""" image: cv2 image matrix object
angle: in degree
"""
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_label[y1:y2, x1:x2]
def rotation_not_90_func(img,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):
ann_u=range(n_classes)
if len(np.shape(seg))==3:
@ -65,7 +139,7 @@ def IoU(Yi,y_predi):
return mIoU
def data_gen(img_folder, mask_folder, batch_size,input_height, input_width,n_classes):
c = 0
n = 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)
while True:
img = np.zeros((batch_size, input_height, input_width, 3)).astype('float')
@ -73,18 +147,26 @@ def data_gen(img_folder, mask_folder, batch_size,input_height, input_width,n_cla
for i in range(c, c+batch_size): #initially from 0 to 16, c = 0.
#print(img_folder+'/'+n[i])
filename=n[i].split('.')[0]
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
img[i-c] = train_img #add to array - img[0], img[1], and so on.
train_mask = cv2.imread(mask_folder+'/'+filename+'.png')
#print(mask_folder+'/'+filename+'.png')
#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 = train_mask.reshape(224, 224, 1) # Add extra dimension for parity with train_img size [512 * 512 * 3]
mask[i-c] = train_mask
try:
filename=n[i].split('.')[0]
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
img[i-c] = train_img #add to array - img[0], img[1], and so on.
train_mask = cv2.imread(mask_folder+'/'+filename+'.png')
#print(mask_folder+'/'+filename+'.png')
#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 = train_mask.reshape(224, 224, 1) # Add extra dimension for parity with train_img size [512 * 512 * 3]
mask[i-c] = train_mask
except:
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')
c+=batch_size
if(c+batch_size>=len(os.listdir(img_folder))):
@ -104,16 +186,10 @@ def otsu_copy(img):
img_r[:,:,1]=threshold1
img_r[:,:,2]=threshold1
return img_r
def rotation_90(img):
img_rot=np.zeros((img.shape[1],img.shape[0],img.shape[2]))
img_rot[:,:,0]=img[:,:,0].T
img_rot[:,:,1]=img[:,:,1].T
img_rot[:,:,2]=img[:,:,2].T
return img_rot
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]
img_w=img.shape[1]
@ -151,12 +227,39 @@ def get_patches(dir_img_f,dir_seg_f,img,label,height,width,indexer):
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
def do_padding(img,label,height,width):
height_new=img.shape[0]
width_new=img.shape[1]
h_start=0
w_start=0
if img.shape[0]<height:
h_start=int( abs(height-img.shape[0])/2. )
height_new=height
def get_patches_num_scale(dir_img_f,dir_seg_f,img,label,height,width,indexer,scaler):
if img.shape[1]<width:
w_start=int( abs(width-img.shape[1])/2. )
width_new=width
img_new=np.ones((height_new,width_new,img.shape[2])).astype(float)*255
label_new=np.zeros((height_new,width_new,label.shape[2])).astype(float)
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):
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]
@ -204,6 +307,58 @@ def get_patches_num_scale(dir_img_f,dir_seg_f,img,label,height,width,indexer,sca
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:
img,label=do_padding(img,label,height,width)
img_h=img.shape[0]
img_w=img.shape[1]
height_scale=int(height*1)
width_scale=int(width*1)
nxf=img_w/float(width_scale)
nyf=img_h/float(height_scale)
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 j in range(nyf):
index_x_d=i*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_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,:]
label_patch=label[index_y_d:index_y_u,index_x_d:index_x_u,:]
#img_patch=resize_image(img_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_seg_f+'/img_'+str(indexer)+'.png' , label_patch )
indexer+=1
return indexer
def provide_patches(dir_img,dir_seg,dir_flow_train_imgs,
@ -211,6 +366,7 @@ def provide_patches(dir_img,dir_seg,dir_flow_train_imgs,
input_height,input_width,blur_k,blur_aug,
flip_aug,binarization,scaling,scales,flip_index,
scaling_bluring,scaling_binarization,rotation,
rotation_not_90,thetha,scaling_flip,
augmentation=False,patches=False):
imgs_cv_train=np.array(os.listdir(dir_img))
@ -219,24 +375,12 @@ def provide_patches(dir_img,dir_seg,dir_flow_train_imgs,
indexer=0
for im, seg_i in tqdm(zip(imgs_cv_train,segs_cv_train)):
img_name=im.split('.')[0]
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_labels+'/img_'+str(indexer)+'.png' , resize_image(cv2.imread(dir_seg+'/'+img_name+'.png'),input_height,input_width ) )
indexer+=1
if augmentation:
if rotation:
cv2.imwrite(dir_flow_train_imgs+'/img_'+str(indexer)+'.png',
rotation_90( resize_image(cv2.imread(dir_img+'/'+im),
input_height,input_width) ) )
cv2.imwrite(dir_flow_train_labels+'/img_'+str(indexer)+'.png',
rotation_90 ( resize_image(cv2.imread(dir_seg+'/'+img_name+'.png'),
input_height,input_width) ) )
indexer+=1
if flip_aug:
for f_i in flip_index:
cv2.imwrite(dir_flow_train_imgs+'/img_'+str(indexer)+'.png',
@ -270,10 +414,10 @@ def provide_patches(dir_img,dir_seg,dir_flow_train_imgs,
if patches:
indexer=get_patches(dir_flow_train_imgs,dir_flow_train_labels,
cv2.imread(dir_img+'/'+im),cv2.imread(dir_seg+'/'+img_name+'.png'),
input_height,input_width,indexer=indexer)
cv2.imread(dir_img+'/'+im),cv2.imread(dir_seg+'/'+img_name+'.png'),
input_height,input_width,indexer=indexer)
if augmentation:
@ -284,29 +428,37 @@ def provide_patches(dir_img,dir_seg,dir_flow_train_imgs,
rotation_90( cv2.imread(dir_img+'/'+im) ),
rotation_90( cv2.imread(dir_seg+'/'+img_name+'.png') ),
input_height,input_width,indexer=indexer)
if rotation_not_90:
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)
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:
for f_i in flip_index:
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_seg+'/'+img_name+'.png') ,f_i),
input_height,input_width,indexer=indexer)
if blur_aug:
for blur_i in blur_k:
indexer=get_patches(dir_flow_train_imgs,dir_flow_train_labels,
bluring( cv2.imread(dir_img+'/'+im) , blur_i),
cv2.imread(dir_seg+'/'+img_name+'.png'),
input_height,input_width,indexer=indexer)
input_height,input_width,indexer=indexer)
if scaling:
for sc_ind in scales:
indexer=get_patches_num_scale(dir_flow_train_imgs,dir_flow_train_labels,
cv2.imread(dir_img+'/'+im) ,
cv2.imread(dir_seg+'/'+img_name+'.png'),
indexer=get_patches_num_scale_new(dir_flow_train_imgs,dir_flow_train_labels,
cv2.imread(dir_img+'/'+im) ,
cv2.imread(dir_seg+'/'+img_name+'.png'),
input_height,input_width,indexer=indexer,scaler=sc_ind)
if binarization:
indexer=get_patches(dir_flow_train_imgs,dir_flow_train_labels,
otsu_copy( cv2.imread(dir_img+'/'+im)),
cv2.imread(dir_seg+'/'+img_name+'.png'),
@ -317,17 +469,26 @@ def provide_patches(dir_img,dir_seg,dir_flow_train_imgs,
if scaling_bluring:
for sc_ind in scales:
for blur_i in blur_k:
indexer=get_patches_num_scale(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) ,
cv2.imread(dir_seg+'/'+img_name+'.png') ,
input_height,input_width,indexer=indexer,scaler=sc_ind)
if scaling_binarization:
for sc_ind in scales:
indexer=get_patches_num_scale(dir_flow_train_imgs,dir_flow_train_labels,
otsu_copy( cv2.imread(dir_img+'/'+im)) ,
cv2.imread(dir_seg+'/'+img_name+'.png'),
indexer=get_patches_num_scale_new(dir_flow_train_imgs,dir_flow_train_labels,
otsu_copy( cv2.imread(dir_img+'/'+im)) ,
cv2.imread(dir_seg+'/'+img_name+'.png'),
input_height,input_width,indexer=indexer,scaler=sc_ind)
if scaling_flip:
for sc_ind in scales:
for f_i in flip_index:
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_seg+'/'+img_name+'.png') ,f_i) ,
input_height,input_width,indexer=indexer,scaler=sc_ind)