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BSD500_gray.py
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from os import listdir
import matplotlib.pyplot as plt
from PIL import Image
import numpy as np
import tensorflow as tf
import random
import pywt
PIXEL_MAX = 255.0
_20_div_Log10 = 8.6859
#########################
## [ batch, imgY, imgX, channel ]
class imgSet:
def __init__(self, l_dir_train, l_dir_test, noise_stdev=0.5):
self.l_dir_train = l_dir_train
self.l_dir_test = l_dir_test
self.stdev = noise_stdev
self.jpgList_train = []
for a_file in listdir(self.l_dir_train):
self.jpgList_train.append(a_file)
self.jpgList_test = []
for a_file in listdir(self.l_dir_test):
self.jpgList_test.append(a_file)
self.train_N = int(len(self.jpgList_train))
self.test_N = int(len(self.jpgList_test))
self.total_N = self.train_N + self.test_N
self.train_id = np.arange(self.train_N)
self.test_id = np.arange(self.test_N)
def getABatch(self, isTrain, batchStart, aug=1):
if isTrain:
#training set
ids = self.train_id
jpgList = self.jpgList_train
l_dir = self.l_dir_train
else:
#test set
ids = self.test_id
jpgList = self.jpgList_test
l_dir = self.l_dir_test
aug=0
fname = l_dir+jpgList[ids[batchStart]]
labels = np.asarray(Image.open(fname,'r'))
if aug==1:
labels = self.getAug(labels)
data = labels + np.random.normal(0, self.stdev, labels.shape)
data[data<0.0] = 0.0
data[data>255.0] = 255.0
sz = data.shape
return data.reshape(1,sz[0],sz[1],1), labels.reshape(1,sz[0],sz[1],1)
def getPatch(self, img1, img2, patchSize):
sz = img1.shape
y = int(np.random.randint(sz[1]-patchSize[0]))
x = int(np.random.randint(sz[2]-patchSize[1]))
return img1[:,y:y+patchSize[0],x:x+patchSize[1],:], img2[:,y:y+patchSize[0],x:x+patchSize[1],:]
def getAug(self, img, idx1 = int(np.random.randint(2, size=1)), idx2 = int(np.random.randint(4, size=1))):
return np.rot90(np.flip(img, idx1+2), idx2+1, axes=(1,2))
def shuffleTrain_id(self):
random.shuffle(self.train_id)
def getTotalN(self):
return self.total_N
def getTrainN(self):
return len(self.train_id)
def getTestN(self):
return len(self.test_id)
def getDimForNet(self):
return self.sz
# imgSet 클래스를 상속 받아서 Wavelet 관련 기능 추가.
class imgSet_wv(imgSet):
def __init__(self, l_dir_train, l_dir_test, noise_stdev=0.5, wv_type='haar'):
imgSet.__init__(self, l_dir_train, l_dir_test, noise_stdev)
self.wv_type = wv_type
self.sz_wv = self.img2wv(Image.open(self.l_dir_train+self.jpgList_train[0],'r'), wv_dims=(0,1), concat_dim=2).shape
#family = 'bior' #'db' #sym coif haar
#wnames = pywt.wavelist(family)
#print(wnames)
def img2wv(self, img, wv_dims=(1,2), concat_dim=3):
LL, (LH, HL, HH) = pywt.dwt2(img, self.wv_type, axes = wv_dims)
if len(LL.shape)==2:
return np.stack((LL,LH,HL,HH), axis=concat_dim)
else:
return np.concatenate((LL,LH,HL,HH), axis=concat_dim)
def wv2img(self, wv, wv_dims=(1,2), split_axis=3 ):
LL, LH, HL, HH = np.split(wv,axis=split_axis, indices_or_sections=4)
return pywt.idwt2( (LL, (LH, HL, HH)), self.wv_type, axes=wv_dims)
def getABatch(self, isTrain, batchStart):
data_img, labels_img = imgSet.getABatch(self, isTrain, batchStart)
return self.img2wv(self.doPad(self.checkPad(data_img))), self.img2wv(self.doPad(self.checkPad(labels_img)))
## get batch with patch extraction on wavelet domain
def getPWBatch(self, isTrain, batchStart, batchEnd, patchSize, Aug=1):
if isTrain:
#training set
ids = self.train_id
jpgList = self.jpgList_train
l_dir = self.l_dir_train
else:
#test set
ids = self.test_id
jpgList = self.jpgList_test
l_dir = self.l_dir_test
aug=0
batchEnd = min(batchEnd, len(ids))
cur_idset = ids[batchStart:batchEnd]
batch_num = batchEnd-batchStart
labels = np.empty([batch_num, patchSize[0], patchSize[1], self.sz_wv[2]])
data = np.empty([batch_num, patchSize[0], patchSize[1], self.sz_wv[2]])
##################################################
for i in range(batch_num):
fname = l_dir+jpgList[cur_idset[i]]
tmp = np.asarray(Image.open(fname,'r'))
sz = tmp.shape
labels_i = self.checkPad(tmp.reshape(1,sz[0],sz[1],1))
if Aug==1:
labels_i = imgSet.getAug(self, img=labels_i)
data_i = labels_i + np.random.normal(0, self.stdev, labels_i.shape)
data_i[data_i<0.0] =0.0
data_i[data_i>255.0]=255.0
labels_wv = self.img2wv(labels_i)
data_wv = self.img2wv(data_i)
data_wv_p, labels_wv_p = imgSet.getPatch(self, data_wv, labels_wv,patchSize)
labels[i,:,:,:] = labels_wv_p
data[i,:,:,:] = data_wv_p
return data, labels
def checkPad(self, img):
sz = img.shape
if sz[1]%2==1:
img = np.pad(img, pad_width=((0,0),(0,1),(0,0),(0,0)), mode='edge')
if sz[2]%2==1:
img = np.pad(img, pad_width=((0,0),(0,0),(0,1),(0,0)), mode='edge')
return img
def doPad(self, img, top=3, bottom=3,left=3, right=3):
img = np.pad(img, pad_width=((0,0),(top,bottom),(left,right),(0,0)), mode='edge')
return img
def doInvPad(self, img, top=3, bottom=3,left=3, right=3, axis=1):
sz = img.shape
if len(sz)==4:
return img[:, top:-bottom, left:-right, :]
else:
return img[top:-bottom, left:-right, :]
def getDimForNet(self):
return self.sz_wv
def np2img_save(self, inp_img, rec_img, lbl_img, log_dir, save_str='test1'):
tmp_inp = self.wv2img(inp_img, wv_dims=(1,2), split_axis=3 )
tmp_img = self.wv2img(rec_img, wv_dims=(1,2), split_axis=3 )
tmp_lbl = self.wv2img(lbl_img, wv_dims=(1,2), split_axis=3 )
tmp_inp = self.doInvPad(tmp_inp)
tmp_img = self.doInvPad(tmp_img)
tmp_lbl = self.doInvPad(tmp_lbl)
PSNR = np.log(PIXEL_MAX/np.sqrt(np.mean((tmp_img-tmp_lbl)**2)))*_20_div_Log10
print(" -- %.4f" % PSNR)
tmp_inp[tmp_inp<0.0] = 0.0
tmp_inp[tmp_inp>255.0] = 255.0
tmp_img[tmp_img<0.0] = 0.0
tmp_img[tmp_img>255.0] = 255.0
tmp_lbl[tmp_lbl<0.0] = 0.0
tmp_lbl[tmp_lbl>255.0] = 255.0
error_ = np.absolute(tmp_img-tmp_lbl)
error_[error_>255.0]=255.0
im = Image.fromarray(np.uint8(tmp_inp[0,:,:,0]),'L')
im.save(log_dir+'/'+save_str+'-inp.jpg')
im = Image.fromarray(np.uint8(tmp_img[0,:,:,0]),'L')
im.save(log_dir+'/'+save_str+'-rec.jpg')
im = Image.fromarray(np.uint8(tmp_lbl[0,:,:,0]),'L')
im.save(log_dir+'/'+save_str+'-lbl.jpg')
im = Image.fromarray(np.uint8(error_[0,:,:,0]),'L')
im.save(log_dir+'/'+save_str+'-err.jpg')