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DataPreprocess.py
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# -*- coding: utf-8 -*-
"""
Created on Mon Apr 13 15:37:52 2020
@author: Administrator
"""
import warnings
warnings.simplefilter('ignore')
from skimage import io
import numpy as np
from skimage.util import random_noise
from skimage import img_as_ubyte
import os
from PIL import Image
import random
img_list = os.listdir("STARE/stare-images/")
patch_size = 256
######################################################################################################
#################################################TRAIN DATA###########################################
######################################################################################################
count = 0
for img in img_list:
image = Image.open("STARE/stare-images/"+img)
label = Image.open("STARE/labels-vk/"+img[0:6]+".vk.ppm")
for i in range(5):
count+=1
# Random Patch
w, h = image.size
x,y = np.random.randint(0,w-patch_size),np.random.randint(0,h-patch_size)
img1 = image.crop((x, y, x+patch_size, y+patch_size))
lab1 = label.crop((x, y, x+patch_size, y+patch_size))
img1 = img1.save("data/training/"+str(count)+"_orig.jpg")
lab1 = lab1.save("data/manual/"+str(count)+"_orig.jpg")
# Rotation
w, h = image.size
x,y = np.random.randint(0,w-patch_size),np.random.randint(0,h-patch_size)
img1 = image.crop((x, y, x+patch_size, y+patch_size))
lab1 = label.crop((x, y, x+patch_size, y+patch_size))
angle = random.choice([90,180,270])
img1 = img1.rotate(angle)
lab1 = lab1.rotate(angle)
img1 = img1.save("data/training/"+str(count)+"_rot.jpg")
lab1 = lab1.save("data/manual/"+str(count)+"_rot.jpg")
# Adding Noise with sigma=0.05
w, h = image.size
x,y = np.random.randint(0,w-patch_size),np.random.randint(0,h-patch_size)
img1 = image.crop((x, y, x+patch_size, y+patch_size))
lab1 = label.crop((x, y, x+patch_size, y+patch_size))
img1 = img1.save("data/training/"+str(count)+"_noise.jpg")
lab1 = lab1.save("data/manual/"+str(count)+"_noise.jpg")
img1 = io.imread("data/training/"+str(count)+"_noise.jpg")
noisyimg1 = random_noise(img1,var=0.05**2)
io.imsave("data/training/"+str(count)+"_noise.jpg",img_as_ubyte(noisyimg1))
######################################################################################################
#################################################VALIDATION DATA######################################
######################################################################################################
count = 0
imgs_folder = "data/validation/"
labels_folder = "data/validation_manual/"
for img in img_list:
image = Image.open("STARE/stare-images/"+img)
label = Image.open("STARE/labels-vk/"+img[0:6]+".vk.ppm")
for i in range(1):
count+=1
# Random Patch
w, h = image.size
x,y = np.random.randint(0,w-patch_size),np.random.randint(0,h-patch_size)
img1 = image.crop((x, y, x+patch_size, y+patch_size))
lab1 = label.crop((x, y, x+patch_size, y+patch_size))
img1 = img1.save(imgs_folder+str(count)+"_orig.jpg")
lab1 = lab1.save(labels_folder+str(count)+"_orig.jpg")
# Rotation
w, h = image.size
x,y = np.random.randint(0,w-patch_size),np.random.randint(0,h-patch_size)
img1 = image.crop((x, y, x+patch_size, y+patch_size))
lab1 = label.crop((x, y, x+patch_size, y+patch_size))
angle = random.choice([90,180,270])
img1 = img1.rotate(angle)
lab1 = lab1.rotate(angle)
img1 = img1.save(imgs_folder+str(count)+"_rot.jpg")
lab1 = lab1.save(labels_folder+str(count)+"_rot.jpg")
# Adding Noise with sigma=0.05
w, h = image.size
x,y = np.random.randint(0,w-patch_size),np.random.randint(0,h-patch_size)
img1 = image.crop((x, y, x+patch_size, y+patch_size))
lab1 = label.crop((x, y, x+patch_size, y+patch_size))
img1 = img1.save(imgs_folder+str(count)+"_noise.jpg")
lab1 = lab1.save(labels_folder+str(count)+"_noise.jpg")
img1 = io.imread(imgs_folder+str(count)+"_noise.jpg")
noisyimg1 = random_noise(img1,var=0.05**2)
io.imsave(imgs_folder+str(count)+"_noise.jpg",img_as_ubyte(noisyimg1))