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data.py
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from __future__ import print_function
from keras.preprocessing.image import ImageDataGenerator
import numpy as np
import os
import glob
import skimage
import math
from skimage.util import img_as_ubyte
import random
import skimage.io as io
import skimage.transform as trans
import shutil
from natsort import natsorted
Sky = [128,128,128]
Building = [128,0,0]
Pole = [192,192,128]
Road = [128,64,128]
Pavement = [60,40,222]
Tree = [128,128,0]
SignSymbol = [192,128,128]
Fence = [64,64,128]
Car = [64,0,128]
Pedestrian = [64,64,0]
Bicyclist = [0,128,192]
Unlabelled = [0,0,0]
COLOR_DICT = np.array([Sky, Building, Pole, Road, Pavement,
Tree, SignSymbol, Fence, Car, Pedestrian, Bicyclist, Unlabelled])
def adjustData(img,mask,flag_multi_class,num_class):
if(flag_multi_class):
img = img / 255
mask = mask[:,:,:,0] if(len(mask.shape) == 4) else mask[:,:,0]
new_mask = np.zeros(mask.shape + (num_class,))
for i in range(num_class):
#for one pixel in the image, find the class in mask and convert it into one-hot vector
#index = np.where(mask == i)
#index_mask = (index[0],index[1],index[2],np.zeros(len(index[0]),dtype = np.int64) + i) if (len(mask.shape) == 4) else (index[0],index[1],np.zeros(len(index[0]),dtype = np.int64) + i)
#new_mask[index_mask] = 1
new_mask[mask == i,i] = 1
new_mask = np.reshape(new_mask,(new_mask.shape[0],new_mask.shape[1]*new_mask.shape[2],new_mask.shape[3])) if flag_multi_class else np.reshape(new_mask,(new_mask.shape[0]*new_mask.shape[1],new_mask.shape[2]))
mask = new_mask
elif(np.max(img) > 1):
img = img / 255
mask = mask /255
mask[mask > 0.5] = 1
mask[mask <= 0.5] = 0
return (img,mask)
def trainGenerator(batch_size,train_path,image_folder,mask_folder,save_to_dir,aug_dict,image_color_mode = "rgb",
mask_color_mode = "grayscale",image_save_prefix = "image",mask_save_prefix = "mask",
flag_multi_class = False,num_class = 2,target_size = (256,256),seed = 0):
'''
can generate image and mask at the same time
use the same seed for image_datagen and mask_datagen to ensure the transformation for image and mask is the same
if you want to visualize the results of generator, set save_to_dir = "your path"
'''
image_datagen = ImageDataGenerator(**aug_dict)
mask_datagen = ImageDataGenerator(**aug_dict)
image_generator = image_datagen.flow_from_directory(
train_path,
classes = [image_folder],
class_mode = None,
color_mode = image_color_mode,
target_size = target_size,
batch_size = batch_size,
save_to_dir = save_to_dir,
save_prefix = image_save_prefix,
seed = seed)
mask_generator = mask_datagen.flow_from_directory(
train_path,
classes = [mask_folder],
class_mode = None,
color_mode = mask_color_mode,
target_size = target_size,
batch_size = batch_size,
save_to_dir = save_to_dir,
save_prefix = mask_save_prefix,
seed = seed)
train_generator = zip(image_generator, mask_generator)
for (img,mask) in train_generator:
img,mask = adjustData(img,mask,flag_multi_class,num_class)
yield (img,mask)
def testGenerator(test_path,num_image,target_size = (256,256),flag_multi_class = False,as_gray = False):
for i in range(num_image):
img = io.imread(os.path.join(test_path,"%d.tif"%i),as_gray = as_gray)
#img = img / 255
img = trans.resize(img,target_size)
#img = np.reshape(img,img.shape+(1,)) if (not flag_multi_class) else img
img = np.reshape(img,(1,)+img.shape)
yield img
def resultGenerator(test_path, num_image = 30, target_size = (256,256), flag_multi_class = False, as_gray = False):
for i in range(num_image):
img = io.imread(os.path.join(test_path,"%d.tif"%i),as_gray = as_gray)
#img = img / 255
img = trans.resize(img,target_size)
#img = np.reshape(img,img.shape+(1,)) if (not flag_multi_class) else img
img = np.reshape(img,(1,)+img.shape)
yield img
def geneTrainNpy(image_path,mask_path,flag_multi_class = False,num_class = 2,image_prefix = "image",mask_prefix = "mask",image_as_gray = True,mask_as_gray = True):
image_name_arr = glob.glob(os.path.join(image_path,"%s*.bmp"%image_prefix))
image_arr = []
mask_arr = []
for index,item in enumerate(image_name_arr):
img = io.imread(item,as_gray = image_as_gray)
img = np.reshape(img,img.shape + (1,)) if image_as_gray else img
mask = io.imread(item.replace(image_path,mask_path).replace(image_prefix,mask_prefix),as_gray = mask_as_gray)
mask = np.reshape(mask,mask.shape + (1,)) if mask_as_gray else mask
img,mask = adjustData(img,mask,flag_multi_class,num_class)
image_arr.append(img)
mask_arr.append(mask)
image_arr = np.array(image_arr)
mask_arr = np.array(mask_arr)
return image_arr,mask_arr
def labelVisualize(num_class,color_dict,img):
img = img[:,:,0] if len(img.shape) == 3 else img
img_out = np.zeros(img.shape + (3,))
for i in range(num_class):
img_out[img == i,:] = color_dict[i]
return img_out / 255
def kfolderGenerator(k_value,image_path,label_path):
count = 0
count2 = 0
wkdir = ''
# Iterate directory
for path in os.listdir(image_path):
# check if current path is a file
if os.path.isfile(os.path.join(image_path, path)):
count += 1
for path in os.listdir(label_path):
if os.path.isfile(os.path.join(label_path, path)):
count2 += 1
print('The image folder has:', count)
print('The label folder has:', count2)
if count == count2:
item_n = math.floor(count/k_value)
os.mkdir('Kfolder')
wkdir = os.path.join(wkdir,'Kfolder')
print('Temporary folder: Kfolder created')
imagelis = os.listdir(image_path)
for i in range(k_value):
os.mkdir(os.path.join(wkdir,str(i)))
temp_dir = os.path.join(wkdir,str(i))
train_dir = os.path.join(temp_dir,'train')
test_dir = os.path.join(temp_dir,'test')
os.mkdir(train_dir)
os.mkdir(test_dir)
os.mkdir(os.path.join(test_dir,'correctans'))
os.mkdir(os.path.join(train_dir,'image'))
os.mkdir(os.path.join(train_dir,'labels'))
os.mkdir(os.path.join(train_dir,'aug'))
if i == k_value-1:
list_piece = imagelis[i*item_n:]
else:
list_piece = imagelis[i*item_n:i*item_n+item_n]
diff = set(imagelis).difference(set(list_piece))
for item in list_piece:
shutil.copy(os.path.join(image_path,item),test_dir)
shutil.copy(os.path.join(label_path,item),os.path.join(test_dir,'correctans'))
for count, filename in enumerate(natsorted(os.listdir(test_dir))):
if filename != 'correctans':
dst = f"{count}{'.tif'}"
src1 =f"{test_dir}/{filename}"
src2 =f"{test_dir}/{'correctans'}/{filename}"
dst1 =f"{test_dir}/{dst}"
dst2 =f"{test_dir}/{'correctans'}/{dst}"
os.rename(src1, dst1)
os.rename(src2, dst2)
for item in diff:
shutil.copy(os.path.join(image_path,item),os.path.join(train_dir,'image'))
shutil.copy(os.path.join(label_path,item),os.path.join(train_dir,'labels'))
for count, filename in enumerate(natsorted(os.listdir(os.path.join(train_dir,'labels')))):
if filename != 'correctans':
dst = f"{count}{'.tif'}"
src1 =f"{train_dir}/{'image'}/{filename}"
src2 =f"{train_dir}/{'labels'}/{filename}"
dst1 =f"{train_dir}/{'image'}/{dst}"
dst2 =f"{train_dir}/{'labels'}/{dst}"
os.rename(src1, dst1)
os.rename(src2, dst2)
os.mkdir(os.path.join(wkdir,'total'))
os.mkdir(os.path.join(wkdir,'total','ans'))
os.mkdir(os.path.join(wkdir,'total','image'))
os.mkdir(os.path.join(wkdir,'total','labels'))
#print(sample)
else:
print('Unmatched number of files.')
def saveResult(current_kfold,save_path,npyfile,flag_multi_class = False,num_class = 2):
wkpath = os.path.join('Kfolder',str(current_kfold))
for i,item in enumerate(npyfile):
img = labelVisualize(num_class,COLOR_DICT,item) if flag_multi_class else item[:,:,0]
io.imsave(os.path.join(save_path,"%d_predict.png"%i),img)
for item in os.listdir(os.path.join(wkpath,'test')):
if item != 'correctans':
shutil.copy(os.path.join(wkpath,'test',item),os.path.join('Kfolder','total',str(current_kfold)+'_'+item))
if item[-4:] == '.tif':
shutil.copy(os.path.join(wkpath,'test','correctans',item),os.path.join('Kfolder','total','ans',str(current_kfold)+'_'+item))