def main():
n = int(input('Masukkan kapasitas barang: '))
arrId = []
arrBarang = []
arrHarga = []
while True:
print('Kapasitas barang', n)
print('Pilih Menu (Ketik 1/2/3 dan 0 untuk keluar')
print('1. Input Data')
print('2. Lihat Data')
print('3. Lihat Data Terurut')
menu = input('Ketikkan: ')
if menu == '1':
arrId, arrBarang, arrHarga = MyLib.InputData(
arrId, arrBarang, arrHarga, n)
elif menu == '2':
MyLib.LihatData(arrId, arrBarang, arrHarga, n)
elif menu == '3':
MyLib.LihatDataTerurut(arrId, arrBarang, arrHarga, n)
elif menu == '0':
break
else:
print('Menu tidak ada.')
input('Tekan sembarang untuk kembali ke menu')
def testAll():
## test all the testing samples
iniA = 0
iniUp3x3 = 0
if FLAGS.Ynormalize:
data = sio.loadmat("CAVEdata/Ynum")
Ynum = data['Ynum']
else:
Ynum = 1
Y = tf.placeholder(tf.float32, shape=(1, 512, 512, 3)) # supervised data (None,64,64,3)
Z = tf.placeholder(tf.float32, shape=(1, 512/32, 512/32, FLAGS.outDim))
outX, X1, YA, _, HY = MHFnet.HSInet(Y, Z, iniUp3x3,iniA,FLAGS.upRank,FLAGS.outDim,FLAGS.HSInetL,FLAGS.subnetL)
config = tf.ConfigProto(allow_soft_placement=True,log_device_placement=True)
config.gpu_options.allow_growth = True
saver = tf.train.Saver(max_to_keep = 5)
save_path = FLAGS.train_dir
ML.mkdir(FLAGS.test_dir)
with tf.Session(config=config) as sess:
ckpt = tf.train.latest_checkpoint(save_path)
saver.restore(sess, ckpt)
for root, dirs, files in os.walk('CAVEdata/X/'):
for i in range(32):
data = sio.loadmat("CAVEdata/Y/"+files[i])
inY = data['RGB']
inY = np.expand_dims(inY, axis = 0)*Ynum
data = sio.loadmat("CAVEdata/Z/"+files[i])
inZ = data['Zmsi']
inZ = np.expand_dims(inZ, axis = 0)
pred_X,ListX,pred_HY,pred_YA = sess.run([outX, X1, HY, YA],feed_dict={Y:inY,Z:inZ})
pred_Lr = ListX[FLAGS.HSInetL-2]
sio.savemat(FLAGS.test_dir+files[i], {'outX': pred_X,'outLR': pred_Lr,'outHY': pred_HY, 'outYA':pred_YA})
print(files[i] + ' done!')
def TD_to_1D_All_Point(pk_or_kr):
x = []
y = []
for i in range(number):
for j in range(number):
for k in range(number):
x.append(
math.sqrt((i - number / 2)**2 + (j - number / 2)**2 +
(k - number / 2)**2))
y.append(pk_or_kr[i][j][k])
xx = list(set(x))
xx.sort()
stop = ml.Fib_Search(number / 2, xx)
del xx[stop + 1:]
del yy[stop + 1:]
block = len(xx)
yy_n = [0] * block
yy = [0] * block
for i in range(len(x)):
j = ml.Fib_Search(x[i], xx)
if j <> -1:
yy_n[j] += 1
yy[j] += y[i]
for j in range(block):
yy[j] = yy[j] / yy_n[j]
return xx, yy
def PrepareDataAndiniValue(prepare='Yes'):
DataRoad = 'CAVEdata/'
if prepare != 'No':
print('Generating the training and testing data in folder CAVEdata')
Ind = [
2, 31, 25, 6, 27, 15, 19, 14, 12, 28, 26, 29, 8, 13, 22, 7, 24, 30,
10, 23, 18, 17, 21, 3, 9, 4, 20, 5, 16, 32, 11, 1
]
ML.mkdir(DataRoad + 'X/')
n = 0
for root, dirs, files in os.walk('rowData/CAVEdata/complete_ms_data/'):
for i in range(32):
n = n + 1
print('processing ' + dirs[Ind[i] - 1])
X = readImofDir('rowData/CAVEdata/complete_ms_data/' +
dirs[Ind[i] - 1] + '/' +
dirs[Ind[i] - 1]) / 255
sio.savemat(DataRoad + 'X/' + dirs[Ind[i] - 1], {'msi': X})
if n <= 20:
if n == 1:
allX = np.reshape(X, [512 * 512, 31])
else:
allX = np.vstack((allX, np.reshape(X,
[512 * 512, 31])))
break
allX = np.matrix(allX)
sio.savemat(DataRoad + 'List', {'Ind': Ind})
else:
print('Using the prepared data and initial values in folder CAVEdata')
def update() :
MyLogger.logger.info("+Lance la mise à jour.")
try:
MyLogger.logger.info("+Update des fichiers.")
MyLib.copytree(config_json["parametres"]["cheminWWW"]+config_json["parametres"]["cheminAPI"]+"deploy",config_json["parametres"]["cheminWWW"],True,config_json["noUpdate"],config_json["parametres"])
except Exception as e:
MyLogger.logger.error("Erreur pendant update : ("+format(e)+")")
sys.exit("Erreur")
def update():
MyLogger.logger.info("+Lance la mise à jour.")
try:
MyLogger.logger.info("+Update des fichiers.")
MyLib.copytree(
config_json["parametres"]["cheminWWW"] +
config_json["parametres"]["cheminAPI"] + "deploy",
config_json["parametres"]["cheminWWW"], True,
config_json["noUpdate"], config_json["parametres"])
except Exception as e:
MyLogger.logger.error("Erreur pendant update : (" + format(e) + ")")
sys.exit("Erreur")
def update() :
MyLogger.logger.info("Lance la mise à jour.")
try:
if arguments["mode"] == "full" or arguments["mode"] == "client" :
MyLogger.logger.info("Update client")
mergedListNoUpdate = config_json_project["client"]["update"]["noUpdate"] + config_json_private["client"]["update"]["noUpdate"]
MyLib.copytree(config_json_project["pathToFw"]+"/OdaClient/Deploy/",config_json_project["client"]["path"],True,mergedListNoUpdate)
MyLogger.logger.info("Update client")
mergedListNoUpdate = config_json_project["client"]["update"]["noUpdate"] + config_json_private["client"]["update"]["noUpdate"]
MyLib.copytree(config_json_project["pathToFw"]+"/OdaClient/Api/",config_json_project["client"]["path"]+"/API/",True,"")
if arguments["mode"] == "full" or arguments["mode"] == "server" :
MyLogger.logger.info("Update server")
mergedListNoUpdate = config_json_project["server"]["update"]["noUpdate"] + config_json_private["server"]["update"]["noUpdate"]
MyLib.copytree(config_json_project["pathToFw"]+"/OdaServer/Deploy/",config_json_project["server"]["path"],True,mergedListNoUpdate)
MyLogger.logger.info("Update server")
mergedListNoUpdate = config_json_project["server"]["update"]["noUpdate"] + config_json_private["server"]["update"]["noUpdate"]
MyLib.copytree(config_json_project["pathToFw"]+"/OdaServer/Api/",config_json_project["server"]["path"]+"/API/",True,"")
except Exception as e:
MyLogger.logger.error("Erreur pendant update : ("+format(e)+")")
sys.exit("Erreur")
def install() :
MyLogger.logger.info("+Lance l'installation.")
try:
MyLogger.logger.info("+Etape 1 : copie des fichiers.")
MyLib.copytree(config_json["parametres"]["cheminWWW"]+config_json["parametres"]["cheminAPI"]+"deploy",config_json["parametres"]["cheminWWW"],True,"","")
MyLogger.logger.info("+Etape 2 : personnalisation des fichiers.")
for cur_fichier in config_json["fichiers"]:
MyLogger.logger.debug("-Edition du fichier : "+cur_fichier["chemin"]+cur_fichier["nom"])
MyLib.editFile(config_json["parametres"]["cheminWWW"] + cur_fichier["chemin"] + cur_fichier["nom"], config_json["valeurs"])
except Exception as e:
MyLogger.logger.error("Erreur pendant install : ("+format(e)+")")
sys.exit("Erreur")
def getC(Y, Z, inC, sizeC=32, ratio= 32):
#用来估计RC的代码
inC = np.reshape(inC, [sizeC*sizeC,1])
v1 = np.ones([sizeC*sizeC,1])
h,w,outDim = Z.shape
# print(outDim)
Z = np.reshape(Z, [h*w,outDim])
Ypatch = ML.im2Patch(Y, sizeC, ratio)
YY = np.linalg.inv(np.matmul(Ypatch.T,Ypatch)+np.eye(sizeC*sizeC)*0.001)
vYYv = np.matmul(np.matmul(v1.T,YY),v1)
# inC = np.ones([sizeC,sizeC])/sizeC/sizeC
numMS = Y.shape[2]
for i in range(30):
CY = np.matmul(Ypatch, inC)
R = np.matmul(np.matmul(np.linalg.inv(np.matmul(Z.T,Z)), Z.T),np.reshape(CY,[h*w,numMS]))
ZR = np.matmul(Z,R)
uZR = np.reshape(ZR,[1,h*w*numMS])
inC = np.matmul((np.matmul(uZR,Ypatch)- (np.matmul(np.matmul(np.matmul(uZR,Ypatch),YY),v1) -1 )/vYYv*v1.T),YY)
inC = inC.T
# print(np.sum(inC))
inC = np.reshape(inC,[sizeC,sizeC])
inC = np.expand_dims(inC[::-1,::-1], axis = 2)
inC = np.expand_dims(inC, axis = 3)
return inC
def getC2(Y, Z, allC, sizeC=32, ratio= 32):
#用来估计RC的代码
numA = allC.shape[2]
allC = np.reshape(allC, [sizeC*sizeC, numA])
alpha = np.ones([numA,1])
# inC = np.reshape(inC, [sizeC*sizeC,1])
v1 = np.ones([numA,1])
h,w,outDim = Z.shape
# print(outDim)
Z = np.reshape(Z, [h*w,outDim])
Ypatch = ML.im2Patch(Y, sizeC, ratio)
YCall = np.matmul(Ypatch, allC)
YY = np.linalg.inv(np.matmul(YCall.T,YCall)+np.eye(numA)*0.0000001)
vYYv = np.matmul(np.matmul(v1.T,YY),v1)
# inC = np.ones([sizeC,sizeC])/sizeC/sizeC
for i in range(30):
CY = np.matmul(YCall, alpha)
R = np.matmul(np.matmul(np.linalg.inv(np.matmul(Z.T,Z)), Z.T),np.reshape(CY,[h*w,3]))
ZR = np.matmul(Z,R)
uZR = np.reshape(ZR,[1,h*w*3])
alpha = np.matmul((np.matmul(uZR,YCall)- (np.matmul(np.matmul(np.matmul(uZR,YCall),YY),v1) -1 )/vYYv*v1.T),YY)
alpha = alpha.T
# print(np.sum(inC))
inC = np.matmul(allC, alpha)
inC = np.reshape(inC,[sizeC,sizeC])
inC = np.expand_dims(inC[::-1,::-1], axis = 2)
inC = np.expand_dims(inC, axis = 3)
return inC, R
def lines2vecs(lines):
lines= [x for x in lines if len(x.strip())>0] #remove empty lines
ncol= len(lines[0].split())
TT= []
for i,line in enumerate(lines):
ww= line.split()
v= plt.zeros(ncol,'float')
for j in range(len(ww)): v[j]= ML.num(ww[j])
TT.append(v)
return TT
def lines2vecs(lines):
lines = [x for x in lines if len(x.strip()) > 0] #remove empty lines
ncol = len(lines[0].split())
TT = []
for i, line in enumerate(lines):
ww = line.split()
v = plt.zeros(ncol, 'float')
for j in range(len(ww)):
v[j] = ML.num(ww[j])
TT.append(v)
return TT
def backup(self):
existsBackups = [
int(fileName.replace(CFG_FILE_BASE_NAME, "").replace(u".bak", ""))
for fileName in MyLib.getFiles(APP_DIR)
if fileName.endswith(u".bak")
]
if existsBackups:
maxNum = max(existsBackups) + 1
else:
maxNum = 0
self.save(fileName=os.path.join(
APP_DIR, u"{0}{1}.bak".format(CFG_FILE_BASE_NAME, maxNum)))
def install():
MyLogger.logger.info("+Lance l'installation.")
try:
MyLogger.logger.info("+Etape 1 : copie des fichiers.")
MyLib.copytree(
config_json["parametres"]["cheminWWW"] +
config_json["parametres"]["cheminAPI"] + "deploy",
config_json["parametres"]["cheminWWW"], True, "", "")
MyLogger.logger.info("+Etape 2 : personnalisation des fichiers.")
for cur_fichier in config_json["fichiers"]:
MyLogger.logger.debug("-Edition du fichier : " +
cur_fichier["chemin"] + cur_fichier["nom"])
MyLib.editFile(
config_json["parametres"]["cheminWWW"] +
cur_fichier["chemin"] + cur_fichier["nom"],
config_json["valeurs"])
except Exception as e:
MyLogger.logger.error("Erreur pendant install : (" + format(e) + ")")
sys.exit("Erreur")
def test():
data = sio.loadmat(FLAGS.test_data_name)
Y = data['RGB']
Z = data['Zmsi']
X = data['msi']
if FLAGS.Ynormalize:
Ynum = 1
# Depending on the situation, this line may need to be changed as following:
# data = sio.loadmat("CAVEdata/Ynum")
# Ynum = data['Ynum']
else:
Ynum = 1
## banchsize H W C
inY = np.expand_dims(Y, axis = 0)
inY = tf.to_float(inY)*Ynum;
inZ = np.expand_dims(Z, axis = 0)
inZ = tf.to_float(inZ);
inX = np.expand_dims(X, axis = 0)
inX = tf.to_float(inX);
iniA = 0
iniUp3x3 = 0
outX, X1, YA, _, HY = MHFnet.HSInet(inY,inZ, iniUp3x3, iniA,FLAGS.upRank,FLAGS.outDim,FLAGS.HSInetL,FLAGS.subnetL)
config = tf.ConfigProto(allow_soft_placement=True,log_device_placement=True)
config.gpu_options.allow_growth = True
saver = tf.train.Saver(max_to_keep = 5)
save_path = FLAGS.train_dir
with tf.Session(config=config) as sess:
ckpt = tf.train.latest_checkpoint(save_path)
saver.restore(sess, ckpt)
pred_X,pred_YA,pred_HY,inX = sess.run([outX, YA, HY, inX])
toshow = np.hstack((ML.normalized(ML.get3band_of_tensor(pred_HY)),ML.get3band_of_tensor(pred_YA)))
toshow2 = np.hstack((ML.get3band_of_tensor(pred_X),ML.get3band_of_tensor(inX)))
toshow = np.vstack((toshow,toshow2))
print('The vasaul result of Y_hat (left upper), Y*A (right upper), fusion result (left lower) and ground truth (right lower)')
ML.imshow(toshow)
ML.imwrite(toshow)
def read(self, fileName=CFG_FILE_NAME):
workingDirs = {}
try:
f = open(fileName)
except IOError as e:
if e.errno != errno.ENOENT:
print u"{0}. Код ошибки: {1}".format(e.strerror, e.errno)
print u"Программа завершается."
sys.exit()
except:
print u"Неизвестная ошибка."
sys.exit()
else:
borderCnt = 0 # число прочитанных строк, содержащих BORDER_STR в начале
for line in f:
if line.startswith(BORDER_STR):
if borderCnt > 0:
break
borderCnt += 1
elif line.startswith(WKDIR_LINE):
wkDirName = line[2:-1].decode("utf-8")
workingDirs[wkDirName] = {
"entries": {},
"new": [],
"exists": os.path.exists(wkDirName)
}
wkDir = workingDirs[wkDirName]
elif line.startswith(ENTRY_STR):
entryName = line[1:-1].decode("utf-8")
wkDir["entries"][entryName] = {
"links": [],
"exists":
os.path.exists(os.path.join(wkDirName, entryName))
}
entryLinks = wkDir["entries"][entryName]["links"]
else:
linkName = line[:-1].decode("utf-8")
entryLinks.append(linkName)
# ищем неучтенные элементы в рабочих каталогах
for wkDirName in workingDirs:
wkDir = workingDirs[wkDirName]
if wkDir["exists"]:
entries = MyLib.getEntries(wkDirName)
for entry in entries:
if not entry in wkDir["entries"]:
wkDir["new"].append(entry)
f.close()
self.workingDirs = workingDirs
self.needToSave = False
return workingDirs
def test():
data = sio.loadmat(FLAGS.test_data_name)
Y = data['RGB']
Z = data['Zmsi']
X = data['msi']
## banchsize H W C
inY = np.expand_dims(Y, axis=0)
inY = tf.to_float(inY)
inZ = np.expand_dims(Z, axis=0)
inZ = tf.to_float(inZ)
inX = np.expand_dims(X, axis=0)
inX = tf.to_float(inX)
iniA = 0
iniUp3x3 = 0
outX, X1, YA, _, HY = MHFnet.HSInet(inY, inZ, iniUp3x3, iniA, FLAGS.upRank,
FLAGS.outDim, FLAGS.HSInetL,
FLAGS.subnetL)
config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=True)
config.gpu_options.allow_growth = True
saver = tf.train.Saver(max_to_keep=5)
save_path = FLAGS.train_dir
with tf.Session(config=config) as sess:
ckpt = tf.train.latest_checkpoint(save_path)
saver.restore(sess, ckpt)
pred_X, pred_YA, pred_HY, inX = sess.run([outX, YA, HY, inX])
toshow = np.hstack((ML.normalized(ML.get3band_of_tensor(pred_HY)),
ML.get3band_of_tensor(pred_YA)))
toshow2 = np.hstack(
(ML.get3band_of_tensor(pred_X), ML.get3band_of_tensor(inX)))
toshow = np.vstack((toshow, toshow2))
ML.imshow(toshow)
ML.imwrite(toshow)
def PrepareDataAndiniValue():
DataRoad = 'CAVEdata/'
folder = os.path.exists(DataRoad + 'iniA.mat')
if not folder:
print('Generating the training and testing data in folder CAVEdata')
Ind = [
2, 31, 25, 6, 27, 15, 19, 14, 12, 28, 26, 29, 8, 13, 22, 7, 24, 30,
10, 23, 18, 17, 21, 3, 9, 4, 20, 5, 16, 32, 11, 1
]
#random index
data = sio.loadmat('rowData/CAVEdata/response coefficient')
R = data['A']
ML.mkdir(DataRoad + 'X/')
ML.mkdir(DataRoad + 'Y/')
ML.mkdir(DataRoad + 'Z/')
n = 0
for root, dirs, files in os.walk('rowData/CAVEdata/complete_ms_data/'):
for i in range(32):
n = n + 1
Z = np.zeros([16, 16, 31])
print('processing ' + dirs[Ind[i] - 1])
# if dirs[Ind[i]-1]=='watercolors_ms':
# X = readImofDir('rowData/CAVEdata/complete_ms_data/'+dirs[Ind[i]-1])/255
# else:
X = readImofDir('rowData/CAVEdata/complete_ms_data/' +
dirs[Ind[i] - 1] + '/' +
dirs[Ind[i] - 1]) / 255
Y = np.tensordot(X, R, (2, 0))
for j in range(32):
for k in range(32):
Z = Z + X[j:512:32, k:512:32, :] / 32 / 32
sio.savemat(DataRoad + 'X/' + dirs[Ind[i] - 1], {'msi': X})
sio.savemat(DataRoad + 'Y/' + dirs[Ind[i] - 1], {'RGB': Y})
sio.savemat(DataRoad + 'Z/' + dirs[Ind[i] - 1], {'Zmsi': Z})
if n <= 20:
if n == 1:
allX = np.reshape(X, [512 * 512, 31])
allY = np.reshape(Y, [512 * 512, 3])
else:
allX = np.vstack((allX, np.reshape(X,
[512 * 512, 31])))
allY = np.vstack((allY, np.reshape(Y, [512 * 512, 3])))
break
allX = np.matrix(allX)
allY = np.matrix(allY)
iniA = (allY.T * allY).I * (allY.T * allX)
sio.savemat(DataRoad + 'iniA', {'iniA': iniA})
sio.savemat(DataRoad + 'List', {'Ind': Ind})
initemp = np.eye(31)
iniUp1 = np.tile(initemp, [3, 3, 1, 1])
sio.savemat(DataRoad + 'iniUp', {'iniUp1': iniUp1})
else:
print('Using the prepared data and initial values in folder CAVEdata')
def dilatacao(img, elem):
pad = elem.shape[0]
paddedImg = ml.paddingImage(img, padding_size=(pad // 2))
imgNew = np.zeros(img.shape)
for i in range(0, img.shape[0]):
for j in range(0, img.shape[1]):
non_zeros = np.count_nonzero(elem *
paddedImg[i:i + pad, j:j + pad])
if non_zeros == 0:
imgNew[i, j] = 0
else:
imgNew[i, j] = 255
return imgNew
def update():
MyLogger.logger.info("Lance la mise à jour.")
try:
if arguments["mode"] == "full" or arguments["mode"] == "client":
MyLogger.logger.info("Update client")
mergedListNoUpdate = config_json_project["client"]["update"][
"noUpdate"] + config_json_private["client"]["update"][
"noUpdate"]
MyLib.copytree(
config_json_project["pathToFw"] + "/OdaClient/Deploy/",
config_json_project["client"]["path"], True,
mergedListNoUpdate)
MyLogger.logger.info("Update client")
mergedListNoUpdate = config_json_project["client"]["update"][
"noUpdate"] + config_json_private["client"]["update"][
"noUpdate"]
MyLib.copytree(config_json_project["pathToFw"] + "/OdaClient/Api/",
config_json_project["client"]["path"] + "/API/",
True, "")
if arguments["mode"] == "full" or arguments["mode"] == "server":
MyLogger.logger.info("Update server")
mergedListNoUpdate = config_json_project["server"]["update"][
"noUpdate"] + config_json_private["server"]["update"][
"noUpdate"]
MyLib.copytree(
config_json_project["pathToFw"] + "/OdaServer/Deploy/",
config_json_project["server"]["path"], True,
mergedListNoUpdate)
MyLogger.logger.info("Update server")
mergedListNoUpdate = config_json_project["server"]["update"][
"noUpdate"] + config_json_private["server"]["update"][
"noUpdate"]
MyLib.copytree(config_json_project["pathToFw"] + "/OdaServer/Api/",
config_json_project["server"]["path"] + "/API/",
True, "")
except Exception as e:
MyLogger.logger.error("Erreur pendant update : (" + format(e) + ")")
sys.exit("Erreur")
def im2Patch(Y, sizeC, ratio):
k = 0
H,W,C = Y.shape
h = int(H/ratio)
w = int(W/ratio)
Ypatch = np.zeros([h*w*C, sizeC*sizeC],'f')
# print(Ypatch.shape)
# padY = mypadding(Y, (sizeC-ratio)/2)
padY = np.squeeze(ML.mypadding(np.expand_dims(Y, axis = 0), int((sizeC-ratio)/2)),axis = 0)
for i in range(sizeC):
for j in range(sizeC):
temp = padY[i:h*ratio+i:ratio,j:w*ratio+j:ratio,:]
# print(temp.shape)
Ypatch[:,k] = np.reshape(temp,[h*w*C])
k=k+1
return Ypatch
def read(self, fileName = CFG_FILE_NAME):
workingDirs = {}
try:
f = open(fileName)
except IOError as e:
if e.errno != errno.ENOENT:
print u"{0}. Код ошибки: {1}".format(e.strerror, e.errno)
print u"Программа завершается."
sys.exit()
except:
print u"Неизвестная ошибка."
sys.exit()
else:
borderCnt = 0 # число прочитанных строк, содержащих BORDER_STR в начале
for line in f:
if line.startswith(BORDER_STR):
if borderCnt > 0:
break
borderCnt += 1
elif line.startswith(WKDIR_LINE):
wkDirName = line[2:-1].decode("utf-8")
workingDirs[wkDirName] = {"entries": {}, "new": [], "exists": os.path.exists(wkDirName)}
wkDir = workingDirs[wkDirName]
elif line.startswith(ENTRY_STR):
entryName = line[1:-1].decode("utf-8")
wkDir["entries"][entryName] = {"links": [], "exists": os.path.exists(os.path.join(wkDirName, entryName))}
entryLinks = wkDir["entries"][entryName]["links"]
else:
linkName = line[:-1].decode("utf-8")
entryLinks.append(linkName)
# ищем неучтенные элементы в рабочих каталогах
for wkDirName in workingDirs:
wkDir = workingDirs[wkDirName]
if wkDir["exists"]:
entries = MyLib.getEntries(wkDirName)
for entry in entries:
if not entry in wkDir["entries"]:
wkDir["new"].append(entry)
f.close()
self.workingDirs = workingDirs
self.needToSave = False
return workingDirs
def main():
print("Element List Output")
MyLib.loopListOutput()
print("")
print("Calculate List")
MyLib.calculateList()
print("")
print("Ball Club Winning Chance")
MyLib.winningChance()
def PrepareDataAndiniValue():
DataRoad = 'CAVEdata_2/'
folder = os.path.exists(DataRoad)
if not folder:
Ind = [
2, 31, 25, 6, 27, 15, 19, 14, 12, 28, 26, 29, 8, 13, 22, 7, 24, 30,
10, 23, 18, 17, 21, 3, 9, 4, 20, 5, 16, 32, 11, 1
]
#random index
data = sio.loadmat('rowData/CAVEdata/response coefficient')
R = data['A']
ML.mkdir(DataRoad + 'X/')
ML.mkdir(DataRoad + 'Y/')
ML.mkdir(DataRoad + 'Z/')
n = 0
for root, dirs, files in os.walk('rowData/CAVEdata/complete_ms_data/'):
files.sort()
for i in range(32):
n = n + 1
if Ind[i] == 32:
X = readImofDir('rowData/CAVEdata/complete_ms_data/' +
files[Ind[i] - 1]) / 255
else:
X = readImofDir('rowData/CAVEdata/complete_ms_data/' +
files[Ind[i] - 1]) / (2**16 - 1)
Y = np.tensordot(X, R, (2, 0))
for j in range(32):
for k in range(32):
Z = X + X[:, j:512:32, k:512:32, :] / 32 / 32
sio.savemat(DataRoad + 'X/' + files[Ind[i] - 1], {'msi': X})
sio.savemat(DataRoad + 'Y/' + files[Ind[i] - 1], {'RGB': Y})
sio.savemat(DataRoad + 'Z/' + files[Ind[i] - 1], {'Zmsi': Z})
if n <= 20:
if n == 1:
allX = np.reshape(X, [512 * 512, 31])
allY = np.reshape(Y, [512 * 512, 3])
else:
allX = np.hstack((allX, np.reshape(X,
[512 * 512, 31])))
allY = np.hstack((allY, np.reshape(Y, [512 * 512, 3])))
allX = np.matrix(allX)
allY = np.matrix(allY)
iniA = (allY.T * allY).I * (allY.T * allX)
sio.savemat(DataRoad, {'iniA': iniA})
initemp = np.eye(31)
iniUp1 = np.tile(initemp, [3, 3, 1, 1])
sio.savemat(DataRoad, {'iniUp': iniUp1})
else:
print('Using the prepared data and initial values in folder CAVEdata')
def TD_to_1D(pk_or_kr):
if pk_or_kr[0] == 0: #Normal plot
partition = range(number / 2)
block = len(partition)
y = [0] * block
y_n = [0] * block
for i in range(number):
for j in range(number):
for k in range(number):
distance = math.sqrt(i**2 + j**2 + k**2)
distance = math.sqrt((i - number / 2)**2 +
(j - number / 2)**2 +
(k - number / 2)**2)
if distance < number / 2:
y[int(distance)] += pk_or_kr[1][i][j][k]
y_n[int(distance)] += 1
for i in range(block):
if y_n[i] <> 0:
y[i] = y[i] / y_n[i]
return partition, y
elif pk_or_kr[0] == 1: #Log plot
partition = ml.Create_Partition(number / 2, 0)
block = len(partition) - 1
y = [0] * block
y_n = [0] * block
for i in range(number):
for j in range(number):
for k in range(number):
distance = math.sqrt((i - number / 2)**2 +
(j - number / 2)**2 +
(k - number / 2)**2)
for l in range(block):
if distance >= partition[l] and distance < partition[
l + 1]:
y_n[l] += 1
y[l] += pk_or_kr[1][i][j][k]
del partition[-1]
for i in range(block):
if y_n[i] <> 0:
y[i] = y[i] / y_n[i]
return partition, y
def main():
# input
x = int(input("Masukkan angka pertama untuk aritmatika:"))
y = int(input("Masukkan angka kedua untuk aritmatika:"))
s = float(input("Masukkan sisi persegi (float):"))
a, t = map(float, input("Masukkan a & t segitiga (float)(float):").split())
#output dengan memanggil fungsi pada aritmatika untuk masukan int
print("Hasil Penjumlahan:", MyLib.Tambah(x, y))
print("Hasil Pengurangan:", MyLib.Kurang(x, y))
#berikut ini contoh men-assign hasil fungsi ke variable
hasilkali = MyLib.Kali(x, y)
print("Hasil Perkalian:", hasilkali)
print("Hasil Pembagian:", MyLib.Bagi(x, y))
#Luas
print("Luas persegi:", MyLib.LuasPersegi(s))
print("Luas segitiga:", MyLib.LuasSegitiga(a, t))
sys.exit("Erreur")
else:
arguments["fichier_conf"] = x
elif i == 3:
arguments["action"] = x
if x not in ["install", "update", "more"]:
MyLogger.logger.warning(
"+Votre premier argument (" + x +
") est incorrect, seul 'install','update','more' sont aurorisés."
)
sys.exit("Erreur")
else:
MyLogger.logger.info("+Mode d'action choisi : " + x + ".")
if len(arguments) == 0:
arguments["action"] = "more"
#Initialisation
config_json = MyLib.charger_config(arguments["fichier_conf"])
#Affichage
if arguments["action"] == "install":
install()
elif arguments["action"] == "update":
update()
elif arguments["action"] == "more":
more()
#Message de fin.
MyLogger.logger.info("+Fin du script.")
sys.exit(0)
if i == 2 :
arguments["jsonFile"] = x
elif i == 3 :
arguments["action"] = x
if x not in ["lunch","more"] :
MyLogger.logger.warning("Votre premier argument ("+x+") est incorrect, seul 'more','lunch' sont aurorisés.")
sys.exit("Erreur")
else :
MyLogger.logger.info("Mode d'action choisi : "+x+".")
arguments["action"] = x
if len(arguments) == 0 :
arguments["action"] = "more"
#Initialisation
config_json = MyLib.charger_config(arguments["jsonFile"])
#Message de bienvenue.
MyLogger.logger.info ("Chargement du json pour "+config_json["parameters"]["labelInstance"]+" reussi.")
#Affichage
if arguments["action"] == "lunch" :
lunch()
elif arguments["action"] == "more" :
more()
#Message de fin.
MyLogger.logger.info ("Fin du script.")
sys.exit(0)
"""
from PIL import Image
import numpy as np
import MyLib as ml
img1 = Image.open('images/ruidosa1.tif')
img1 = np.array(img1)
img2 = Image.open('images/ruidosa2.tif')
img2 = np.array(img2)
imgOrig = Image.open('images/original.tif')
imgOrig = np.array(imgOrig)
padding_size = ml.getPaddingSize(img1.shape, (11, 11))
img1Media = ml.conv2D(img1, ml.MEAN_FILTER_11, padding=padding_size, padding_type='repeat')
img1Mediana = ml.medianFilter(img1, (11, 11), padding_size, padding_type='repeat')
img1MedianaAdaptativa = ml.adaptativeMedian(img1, (17, 17))
titles1 = ["Imagem Original: ruidosa1.tif", "Filtro Média (11x11)", "Filtro Mediana (11x11)", "Filtro Mediana Adaptativa"]
images1 = [img1, img1Media, img1Mediana, img1MedianaAdaptativa]
print("PSNR da imagem: ruidosa1.tif")
print("Original: {}".format(ml.PSNR(imgOrig, img1)))
print("Media: {}".format(ml.PSNR(imgOrig, img1Media)))
print("Mediana: {}".format(ml.PSNR(imgOrig, img1Mediana)))
print("Mediana Adaptativa: {}".format(ml.PSNR(imgOrig, img1MedianaAdaptativa)))
padding_size = ml.getPaddingSize(img2.shape, (11, 11))
img2Media = ml.conv2D(img2, ml.MEAN_FILTER_11, padding=padding_size, padding_type='repeat')
import numpy as np import MyLib print(MyLib.func1(1, 2)) print(MyLib.func2()) x = 0 MyLib.func3(x) print(x) v = MyLib.Vec3(1, 2, 3) print(v[1]) print(v + np.array([1, 2, 3]))
# -*- coding: utf-8 -*-
""" Processamento Digital de Imagens
Aluno: Leonardo Santos Paulucio
Lista de Exercicios 1 - Pós-Graduação
Data: 09/05/19
"""
from PIL import Image
import numpy as np
import MyLib as ml
img = Image.open('images/Fig4.19(a).jpg')
img = np.array(img)
imgMed = ml.conv2D(img, ml.MEAN_FILTER2) # Filtro media ponderada
# histograma = ml.calculaHistograma(img) # Comentado, pois foi utilizado para escolher o threshold
# plt.plot(histograma[0], histogram[1])
# plt.show()
imgBin = ml.binarizaImg(imgMed, 200) # Binarizacao da imagem
im = ml.conv2D(imgBin, ml.LAPLACE_FILTER) # Filtro Laplaciano
images = [img, imgMed, imgBin, im]
titles = [
"Imagem Original", "Resultado após Filtro Média Ponderada",
"Imagem Binarizada", "Filtro Laplaciano"
]
ml.show_images(images, 2, titles)
# 9. Caso contrário, saímos. Os vértices em FECHADA são os vértices do ajuste poligonal dos pontos pertencentes a P.
return closeStack
imgOrig = Image.open('images/pontos.bmp')
imgOrig = np.array(imgOrig)
points = findPoints(imgOrig, 255)
sorted = sortPoints(points)
images = [imgOrig]
title = ['Imagen Original']
fnt = ImageFont.truetype('Pillow/Tests/fonts/FreeMono.ttf', 15)
for i in [1, 20, 50, 100, 300]:
img = Image.fromarray(deepcopy(imgOrig))
f = connectPoints(sorted, img, threshold=i)
title.append('Resultado com Threshold = {}'.format(i))
draw = ImageDraw.Draw(img)
draw.line(f, fill=255, width=1)
for i, p in enumerate(sorted):
draw.text(p, str(i), font=fnt, fill=(255))
img = np.array(img)
images.append(img)
ml.show_images(images, 3, title)
def getServer(self):
r = MyLib.post("messages.getLongPollServer", lp_version=3).json()
self._key = r['response']['key']
self._server = r['response']['server']
self._ts = r['response']['ts']
def install() :
MyLogger.logger.info("Lance l'installation.")
try:
if arguments["mode"] == "full" or arguments["mode"] == "client" :
MyLogger.logger.info("Install client")
MyLib.copytree(config_json_project["pathToFw"]+"/OdaClient/Deploy/",config_json_project["client"]["path"],True,"")
MyLogger.logger.info("Personnalisation des fichiers clients.")
for cur_fichier in config_json_private["client"]["install"]["fileToCusto"]:
MyLogger.logger.debug("Edition du fichier : "+cur_fichier["pathInProject"]+cur_fichier["file"])
MyLib.editFile(config_json_project["client"]["path"] + cur_fichier["pathInProject"] + cur_fichier["file"], config_json_project["client"]["install"]["values"])
MyLogger.logger.info("Install client API")
MyLib.copytree(config_json_project["pathToFw"]+"/OdaClient/Api/",config_json_project["client"]["path"]+"/API/",True,"")
if arguments["mode"] == "full" or arguments["mode"] == "server" :
MyLogger.logger.info("Install server")
MyLib.copytree(config_json_project["pathToFw"]+"/OdaServer/Deploy/",config_json_project["server"]["path"],True,"")
MyLogger.logger.info("Personnalisation des fichiers server.")
for cur_fichier in config_json_private["server"]["install"]["fileToCusto"]:
MyLogger.logger.debug("Edition du fichier : "+cur_fichier["pathInProject"]+cur_fichier["file"])
MyLib.editFile(config_json_project["server"]["path"] + cur_fichier["pathInProject"] + cur_fichier["file"], config_json_project["server"]["install"]["values"])
MyLogger.logger.info("Install server API")
MyLib.copytree(config_json_project["pathToFw"]+"/OdaServer/Api/",config_json_project["server"]["path"]+"/API/",True,"")
except Exception as e:
MyLogger.logger.error("Erreur pendant install : ("+format(e)+")")
sys.exit("Erreur")
Ok= False
if os.path.isfile("error.log"):
res= open("error.log",'r').read()
if res.strip()=="PERFECT": Ok= True
else: print "error.log="+res ; raw_input()
else:
print "error.log NOT FOUND"
return Ok
#---------------------------------------------------------------------//
i0= 0
i= 0
for sFile in files:
if os.path.isfile("error.log"): os.remove("error.log")
sCommand= ML.inn_scan_list(sFile,"TEST","COMPUTE")
sDirout= ML.inn_scan_word(sFile,"CONDITIONS","OUTPUT").replace('\\','/')
sPath= os.path.dirname(sDirout)
if not os.path.exists(sPath): os.makedirs(sPath)
print '\n=========processing '+ sFile + '==========================\n'
os.system("%s %s %s" % (sExe,sFile,Debug))
if not check_done(): continue
if "GEMPATH" in sCommand:
if 1:
s=sDirout+"_phase_grams.restab"
titr= ML.extractFileName(s)
lines= open(s,'r').readlines()
labels,tData= ML.lines2table(lines)
def backup(self):
existsBackups = [int(fileName.replace(CFG_FILE_BASE_NAME, "").replace(u".bak", "")) for fileName in MyLib.getFiles(APP_DIR) if fileName.endswith(u".bak")]
if existsBackups:
maxNum = max(existsBackups) + 1
else:
maxNum = 0
self.save(fileName = os.path.join(APP_DIR, u"{0}{1}.bak".format(CFG_FILE_BASE_NAME, maxNum)))
train_loss = sum_loss/N
train_data.append(float(train_loss))
if epoch % show_interval == 0:
print '\tepoch :'+str(epoch)
print '\ttrain mean loss='+str(train_loss)+" next save :"+str(min_loss)
print '\ttime sum : {}[sec] ave : {}[sec]'.format(sum_time,sum_time/time_count)
#%%
key = cv2.waitKey(1)
if key in [myl.KEY_ESC, ord('q')]:
break
if min_loss > train_loss and not train_loss == 0 :
network.saveModel('model/_model_pred'+str(epoch))
min_loss = train_loss*save_rate
if epoch > n_epoch:
print '\n!test clear! : '+str(train_loss)
break
#%%
print '\n---Save model\n'
network.saveModel('model/_model_pred'+str(epoch))
print '\n---Save graph\n'
g_size = (8,6)
g_legend = ["train_loss"]
g_title = "Loss of digit recognition. lasl train loss :"+str(train_loss)
g_file_name = 'graph_train_loss'+str(epoch)+'.png'
myl.showGraphData(train_data,g_size,g_legend,g_title,g_file_name)
cv2.destroyAllWindows()
MyLogger.logger.warning("+Votre second argument est ("+x+") est incorrect, un fichier de configuration .api.oda.json est attendu.")
sys.exit("Erreur")
else :
arguments["fichier_conf"] = x
elif i == 3 :
arguments["action"] = x
if x not in ["install", "update", "more"] :
MyLogger.logger.warning("+Votre premier argument ("+x+") est incorrect, seul 'install','update','more' sont aurorisés.")
sys.exit("Erreur")
else :
MyLogger.logger.info("+Mode d'action choisi : "+x+".")
if len(arguments) == 0 :
arguments["action"] = "more"
#Initialisation
config_json = MyLib.charger_config(arguments["fichier_conf"])
#Affichage
if arguments["action"] == "install" :
install()
elif arguments["action"] == "update" :
update()
elif arguments["action"] == "more" :
more()
#Message de fin.
MyLogger.logger.info ("+Fin du script.")
sys.exit(0)
graph.windowSize(1000, 1000)
graph.canvasSize(1000, 1000)
x0 = 600
y0 = 300
r = 200
graph.penColor(0, 0, 0)
graph.penSize(0.05)
graph.brushColor(155, 155, 155)
graph.circle(x0, y0, r)
indent = r / 10
MyLib.sector(x0 - r / 5, y0 + r / 5, r, (3 / 2) * pi, 2 * pi, 1, 0)
MyLib.sector(x0 - r / 5 - indent, y0 + r / 5 + indent, r, (3 / 2) * pi, 2 * pi,
1, 0)
MyLib.sector(x0 - r / 5 - 2.5 * indent, y0 + r / 5 + 2.5 * indent, r,
(3 / 2) * pi, 2 * pi, 1, 0)
MyLib.sector(x0 + r / 4, y0 + r / 2, r, pi, (3 / 2) * pi - pi / 6, 1, 0)
MyLib.sector(x0 + r / 4 + 2 * indent, y0 + r / 2 + 2 * indent, r, pi,
(3 / 2) * pi - pi / 6, 1, 0)
MyLib.sector(x0 + r / 4 + 4 * indent, y0 + r / 2 + 4 * indent, r, pi + pi / 10,
(3 / 2) * pi - pi / 6, 1, 0)
graph.penColor(255, 255, 255)
smr = r / 2
x1 = x0 - r * sin(pi / 4) - smr * sin(pi / 4)
freemanChain.append(i)
points.remove(currentPoint)
sortedPoints.append(currentPoint)
currentPoint = nextPoint
break
sortedPoints.append(currentPoint)
sortedPoints.append(startPoint)
return freemanChain, sortedPoints
img = Image.open('images/Fig11.10.jpg')
img = np.array(img)
# Getting object border
border = ml.conv2D(img, ml.LAPLACE_FILTER)
images = [deepcopy(border)]
# Getting border points
vertices = getPoints(border)
gridSize = 43
# Creating grid
grid = makeGrid(border, gridSize)
# Resampling border points to grid
points = convert2grid(vertices, grid)
# Finding Freeman chain
f, p = getFreemanChain(points, gridSize)
import MyLib print(MyLib.myAdd(1, 2))
MyLogger.logger.info("Mode d'action choisi : "+x+".")
if i == 3 :
arguments["mode"] = x
if x not in ["client", "server", "full"] :
MyLogger.logger.warning("Votre argument ("+x+") est incorrect, seul 'client','server','full' sont aurorisés.")
sys.exit("Erreur")
else :
MyLogger.logger.info("Mode d'action choisi : "+x+".")
elif i == 4 :
if ".json" not in x:
MyLogger.logger.warning("Votre argument est ("+x+") est incorrect, un fichier de configuration .json est attendu.")
sys.exit("Erreur")
else :
arguments["fichier_conf"] = x
#Initialisation
config_json_private = MyLib.charger_config(pathFile + "/" + "private.config.odafw.json")
config_json_project = MyLib.charger_config(arguments["fichier_conf"])
#Affichage
if arguments["action"] == "install" :
install()
elif arguments["action"] == "update" :
update()
elif arguments["action"] == "more" :
more()
#Message de fin.
MyLogger.logger.info ("Fin du script.")
MyLogger.logger.info ("-------------------------------------------------------")
sys.exit(0)
