def main():
np.random.seed(7)
t1 = time.time()
image_path = config.image_path
track_path = config.track_path
track_dic_path = config.track_dic_path
track_dict = load.load_json(track_dic_path)
intensity_mean,intensity_std = config.intensity_mean, config.intensity_std
batch_size = config.batch_size
ModelCheckpoint_file = config.ModelCheckpoint_file
look_back = config.look_back
img_rows,img_cols = config.img_rows,config.img_cols
subdir_list = []
hist_path = config.hist_path
# train_x = np.random.uniform(0,1,(17, 3, 1, 512, 512))
# train_y = np.random.uniform(0,1,(17,1))
# print (train_x)
# train_x = np.array(train_x,dtype = 'float32')
# train_y = np.array(train_y,dtype= 'float32')
# hist = model.fit(train_x, train_y, nb_epoch=1, batch_size=batch_size, verbose=2, validation_split=0.1,shuffle=False)
"""
count the number of image in each typhoon sequence
"""
image_number_dictionary={}
for subdirs, dirs, files in os.walk(image_path):
# print (subdirs)
subdir_list.append(subdirs)
for subdir in subdir_list:
count = 0
for subdirs, dirs, files in os.walk(subdir):
for file in files:
count += 1
key = subdir.split('/')[-1]
image_number_dictionary[key] = count
if count < 24:
print (key,count)
# print (image_number_dictionary)
"""
check the number of images equals the number of track data?
"""
# for subdir in subdir_list:
# for subdirs, dirs, files in os.walk(subdir):
# for file in files:
# # print (file)
# [k1, k2] = file.split("-")[:2]
# key = "".join((k1,k2))
# try:
# mark = track_dict[key]
# except KeyError:
# print (file +'do not have track value')
# for k in track_dict.keys():
# k2 = k[-6:] # typhoon number
# k1 = k[:-6]
# file = k1 +'-' + k2 +'*'
# file_path = image_path + k2 +'/' + file
# if not os.path.isfile(file_path):
# print (file_path not exists)
track_dict_number ={}
equal_track_image_list = []
not_equal_track_image_list = []
for subdir in subdir_list:
key =subdir.split('/')[-1]
if len(key) > 0 and key not in ['201620','201621','201622']:
track_file_path = track_path + key+'.itk'
with open(track_file_path,'rb') as tsv_file:
tsv_reader = csv.reader(tsv_file, delimiter='\t')
count = 0
for row in tsv_reader:
count += 1
track_dict_number[key] = count
if count != image_number_dictionary[key]:
not_equal_track_image_list.append(key)
# print (key,count,image_number_dictionary[key],'not equal')
if count == image_number_dictionary[key]:
# print (key,count,image_number_dictionary[key],' equal')
equal_track_image_list.append(key)
# print (not_equal_track_image_list,'not_equal_track_image_list')
# print (equal_track_image_list,'equal_track_image_list')
print (len(equal_track_image_list),'lenth of eqaual track image list')
# "check if track file difference is one hour, result is yes for both equal and not_eqaul_image_list "
for key in not_equal_track_image_list:
ts =[]
track_file_path = track_path + key+'.itk'
with open(track_file_path,'rb') as tsv_file:
tsv_reader = csv.reader(tsv_file, delimiter='\t')
for row in tsv_reader:
yy = row[0]
mm = row[1]
dd = row[2]
hh = row[3]
t = datetime.datetime.strptime(yy +":" + mm +":" + dd +':' +hh, '%Y:%m:%d:%H')
ts.append(t)
tmp = ts[0]
for i in range(1,len(ts)):
dif = (ts[i] - tmp).total_seconds()
# print (dif,'dif')
if dif != 3600:
print (dif,i,key)
tmp = ts[i]
# break
data_folder_path = config.data_folder_path
if not os.path.exists(data_folder_path):
equal_track_image_list = np.array(equal_track_image_list)
np.random.shuffle(equal_track_image_list)
equal_track_image_list = list(equal_track_image_list)
# equal_track_image_list = equal_track_image_list[:2]
train_folder = equal_track_image_list[:int(0.9 * len(equal_track_image_list))]
test_folder = equal_track_image_list[int(0.9* len(equal_track_image_list)):]
with open(data_folder_path,'w') as f:
json.dump({'train_folder':train_folder,'test_folder': test_folder},f)
print ('data_folder_path dumped to: ',data_folder_path)
else:
with open(data_folder_path,'r') as f:
data_folder = json.load(f)
train_folder = data_folder['train_folder']
test_folder = data_folder['test_folder']
print ('load data folder from: ' , data_folder_path)
"""
data_path = config.data_path
if not os.path.exists(data_path):
train_x =[]
train_y=[]
test_x = []
test_y = []
vgg_model = VGG_16('vgg16_weights.h5')
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
vgg_model.compile(optimizer=sgd, loss='categorical_crossentropy')
for key in test_folder:
print(key)
image_folder = image_path + key +'/'
track_file_path = track_path + key + '.itk'
dataset_image = prepare_dataset.dataset_2(image_folder)
print (dataset_image.shape)
dataset_input = get_fc2(vgg_model,dataset_image)
dataset_intensity = prepare_dataset.dataset_1(track_file_path)
dataset_intensity = prepare_dataset.normalize_intensity(dataset_intensity,intensity_mean,intensity_std)
print (dataset_image.shape,'dataset_image.shape')
print (dataset_intensity.shape,'dataset_intensity')
data_x,data_y = prepare_dataset.create_dataset_2(dataset_input, dataset_intensity,look_back = look_back)
test_x += data_x
test_y += data_y
# print test_y.shape,test_y
# train_histss =[]
# validation_histss=[]
for key in train_folder:
print(key)
image_folder = image_path + key +'/'
track_file_path = track_path + key + '.itk'
dataset_image = prepare_dataset.dataset_2(image_folder)
dataset_input = get_fc2(vgg_model,dataset_image)
dataset_intensity = prepare_dataset.dataset_1(track_file_path)
dataset_intensity = prepare_dataset.normalize_intensity(dataset_intensity,intensity_mean,intensity_std)
print (dataset_image.shape,'dataset_image.shape')
print (dataset_intensity.shape,'dataset_intensity')
data_x,data_y = prepare_dataset.create_dataset_2(dataset_input, dataset_intensity,look_back = look_back)
# print (len(data_x))
train_x += data_x
train_y += data_y
data_x = np.array(data_x)
data_y = np.array(data_y)
# print (data_x.shape,data_y.shape,'data_x,data_y')
# train_hists=[]
# validation_hists=[]
# for i in range(20):
# print('start train')
# hist = model.fit(data_x, data_y, nb_epoch=1, batch_size=batch_size, verbose=2, validation_split=0.1,shuffle=False)
# model.reset_states()
# train_hists.append(hist.history['loss'][0])
# validation_hists.append(hist.history['val_loss'][0])
# # print (hists,'hists')
# train_histss.append(train_hists)
# validation_histss.append(validation_hists)
# print (train_histss,'train_histss')
# print (validation_histss, 'validation_histss')
# print ((data_x.shape),data_y.shape)
train_x = np.array(train_x,dtype = 'float32')
train_y = np.array(train_y,dtype = 'float32')
test_x = np.array(test_x,dtype = 'float32')
test_y = np.array(test_y,dtype = 'float32')
hf = h5py.File(data_path)
hf.create_dataset('train_x',data = train_x)
hf.create_dataset('train_y',data = train_y)
hf.create_dataset('test_x', data= test_x)
hf.create_dataset('test_y', data= test_y)
hf.close()
print ('dump train test data to' ,data_path)
else:
with h5py.File(data_path,'r') as hf:
train_x = np.array(hf.get('train_x'))
train_y = np.array(hf.get('train_y'))
test_x = np.array(hf.get('test_x'))
test_y = np.array(hf.get('test_y'))
print ('loaded train test data from ', data_path)
print (train_x.shape,train_y.shape)
print (test_x.shape,test_y.shape)
"""
# get train test data from pre_built dataset
dataset_image_path = 'test_file/dataset_imageset.hdf5'
dataset_type_path = 'test_file/dataset_type.hdf5'
hf_image = h5py.File(dataset_image_path)
hf_type = h5py.File(dataset_type_path)
train_x = []
train_y = []
test_x = []
test_y = []
vgg_fc2_mean = config.vgg_fc2_mean
vgg_fc2_std = config.vgg_fc2_std
"""
dataset_imageset
0.423964 mean data
0.569374 std data
0.0 min
4.71836 max
"""
# train_folder =train_folder[:2]
# test_folder = test_folder[:2]
for key in train_folder:
print(key)
dataset_image = np.array(hf_image.get(key))
dataset_image = prepare_dataset.normalize_intensity(dataset_image,vgg_fc2_mean,vgg_fc2_std) #normalize image (the same function of normalize intensity)
dataset_type = np.array(hf_type.get(key))
if len(dataset_image) > look_back:
data_x,data_y = prepare_dataset.extend_dataset_2(dataset_image, dataset_type,look_back = look_back)
train_x += data_x
train_y += data_y
for key in test_folder:
print (key)
dataset_image = np.array(hf_image.get(key))
dataset_image = prepare_dataset.normalize_intensity(dataset_image,vgg_fc2_mean,vgg_fc2_std)
dataset_type = np.array(hf_type.get(key))
if len(dataset_image) > look_back:
data_x,data_y = prepare_dataset.extend_dataset_2(dataset_image, dataset_type,look_back = look_back)
test_x += data_x
test_y += data_y
hf_type.close()
hf_image.close()
# train = train_x + test_x
train_x = np.array(train_x,dtype = 'float32')
train_y = np.array(train_y,dtype = 'float32')
test_x = np.array(test_x,dtype = 'float32')
test_y = np.array(test_y,dtype = 'float32')
print (train_x.shape,train_y.shape)
print (test_x.shape,test_y.shape)
# nb_classes = max(len(set(train_y)), len(set(test_y)))
# print set(train_y)
# print set(test_y)
# print nb_classes,'nb_classes'
model = pretrain_model(look_back,batch_size)
if os.path.exists(ModelCheckpoint_file):
print ('load load_weights',ModelCheckpoint_file)
model.load_weights(ModelCheckpoint_file)
print(model.summary())
y_train = np_utils.to_categorical(train_y, None)
y_test = np_utils.to_categorical(test_y, None)
print y_train.shape
train_loss_hists=[]
validation_loss_hists=[]
train_acc_hists=[]
validation_acc_hists=[]
val_acc = sys.float_info.min
for i in range(1000):
print (i,'epoch')
# ModelCheckpoint_file = 'test_file/orig_weights_lstm_1.0_image_lookback_'+str(look_back)+str(i)+'_whole_equal.hdf5'
# print('start train')
hist = model.fit(train_x, y_train, nb_epoch=1, batch_size=batch_size, verbose=2, validation_split=0.1,shuffle=False)
print hist.history
model.reset_states()
train_loss_hists.append(hist.history['loss'][0])
validation_loss_hists.append(hist.history['val_loss'][0])
train_acc_hists.append(hist.history['acc'][0])
validation_acc_hists.append(hist.history['val_acc'][0])
if val_acc < hist.history['val_acc'][0]:
model.save_weights(ModelCheckpoint_file)
print(i,val_acc,'->',hist.history['val_acc'][0],'save_weights',ModelCheckpoint_file)
val_acc = hist.history['val_acc'][0]
# print (train_hists,'train_hists')
# print (validation_hists, 'validation_hists')
with open(hist_path,'w') as f:
json.dump({'train_loss':train_loss_hists,'val_loss':validation_loss_hists,'train_acc':train_acc_hists,'val_acc':validation_acc_hists},f)
# hist = model.fit(train_x, train_y, nb_epoch=2, batch_size=batch_size, verbose=2, validation_split = 0.1,shuffle=False)
# break
# with open(hist_path,'w') as j:
# json.dump(hist.history,j)
# validation_hists_least_index = validation_hists.index(min(validation_hists))
# print ('ModelCheckpoint_file','test_file/orig_weights_lstm_1.0_image_lookback_'+str(look_back)+str(validation_hists_least_index)+'_whole_equal.hdf5')
# model.load_weights('test_file/orig_weights_lstm_1.0_image_lookback_'+str(look_back)+str(validation_hists_least_index)+'_whole_equal.hdf5')
print('load_weights',ModelCheckpoint_file)
model.load_weights(ModelCheckpoint_file)
trainPredict = model.predict(train_x, batch_size=batch_size)
model.reset_states()
testPredict = model.predict(test_x, batch_size=batch_size)
# # invert predictions
# # calculate root mean squared error
train_predictions = np.argmax(trainPredict, 1)
train_labels = np.argmax(y_train, 1)
test_predictions = np.argmax(testPredict, 1)
test_labels = np.argmax(y_test, 1)
print(look_back,'look_back')
train_accuracy, train_cm = get_accuracy(train_predictions, train_labels, True)
test_accuracy, test_cm = get_accuracy(test_predictions, test_labels, True)
print (train_accuracy,'train accuracy')
print(train_cm,'train_cm')
print (test_accuracy,'test accuracy')
print(test_cm,'test_cm')
train_cm = train_cm.tolist()
train_confusion_matrix_path = 'test_file/confusion_matrix_train_extend_normalize_'+ str( look_back) +'.json'
with open(train_confusion_matrix_path, 'w') as f:
json.dump(train_cm,f)
test_cm = test_cm.tolist()
test_confusion_matrix_path = 'test_file/confusion_matrix_test_extend_normalize_'+ str(look_back) +'.json'
with open(test_confusion_matrix_path, 'w') as f:
json.dump(test_cm,f)
t2 = time.time()
print ("using %s seconds" % (t2-t1))
def main():
np.random.seed(7)
# trackDictPath = config.track_dic_path
# track_dict = load.load_json(trackDictPath)
track_path = config.track_path
suspicious_file_list_path = config.suspicious_file_list_path
suspicious_file_list = load.load_json(suspicious_file_list_path)
train_validation_test_subdirs_split = config.train_validation_test_subdirs_split
intensity_mean, intensity_std = config.intensity_mean, config.intensity_std
batch_size = config.batch_size
ModelCheckpoint_file = 'test_file/orig_weights_lstm_1.0_lookback_24.hdf5'
print('ModelCheckpoint_file',
ModelCheckpoint_file) #config.ModelCheckpoint_file
look_back = 1
batch_size = 1
print(look_back, 'look_back')
file_list = []
model = lstm_model_1(batch_size, look_back)
# model.load_weights(ModelCheckpoint_file)
for subdir, dirs, files in os.walk(track_path):
for file in files:
file_path = os.path.join(subdir, file)
file_list.append(file_path)
file_list = np.array(file_list)
np.random.shuffle(file_list)
file_list = list(file_list)
# file_list = file_list[:10]
# print (file_list)
# for file in file_list:
# if len(file) <=2:
# print (file)
# print (file_list.index(file))
file_list = file_list[:10]
train_file_list = file_list[:int(0.9 * len(file_list))]
# validation_file_list = file_list[int(0.85*len(file_list)):int(0.9*len(file_list))]
test_file_list = file_list[int(0.9 * len(file_list)):]
print(len(train_file_list))
# print (len(validation_file_list))
print(len(test_file_list))
testX = []
testY = []
# dataset_count = 0
train_histss = []
validation_histss = []
train_file_list_copy = train_file_list
# trainXS=np.array([]).reshape(0,look_back)
# print (trainXS.shape,'trainxs shape')
# trainYS = np.array([]).reshape(0,1)
trainXS = []
trainYS = []
for i in np.arange(0, len(train_file_list_copy),
12): #len(train_file_list_copy)
trainX = []
trainY = []
train_hists = []
validation_hists = []
print(i, 'i')
train_file_list = train_file_list_copy[i:i + 12]
# print len(train_file_list)
for file in train_file_list:
# print file
# try:
data = prepare_dataset.dataset_1(file)
data = prepare_dataset.normalize_intensity(data, intensity_mean,
intensity_std)
# data = list(data)
trainXx, trainYy = prepare_dataset.create_dataset(data, look_back)
trainX += trainXx
trainY += trainYy
# print (trainX,'trainX')
# print (trainY,'trainY')
# break
# dataset_count += data.shape[0]
# except:
# print(file,'error')
trainX = np.array(trainX, dtype='float32')
trainY = np.array(trainY, dtype='float32')
# print (trainX.shape)
# print(trainY.shape,'trainY SHAPE')
trainX = np.reshape(trainX, (trainX.shape[0], trainX.shape[1], 1))
# trainXS = np.vstack((trainXS, trainX))
# trainYS = np.vstack((trainYS, trainY))
# print (trainXS.shape,'trainxs shape')
# break
# return
trainXS.append(trainX)
trainYS.append(trainY)
"""
training
"""
for i in range(100):
hist = model.fit(trainX,
trainY,
nb_epoch=1,
batch_size=batch_size,
verbose=2,
validation_split=0.1,
shuffle=False)
model.reset_states()
train_hists.append(hist.history['loss'][0])
validation_hists.append(hist.history['val_loss'][0])
# print (hists,'hists')
train_histss.append(train_hists)
validation_histss.append(validation_hists)
print(train_histss, 'train_histss')
print(validation_histss, 'validation_histss')
"""
def main():
np.random.seed(7)
# trackDictPath = config.track_dic_path
# track_dict = load.load_json(trackDictPath)
track_path = config.track_path
suspicious_file_list_path = config.suspicious_file_list_path
suspicious_file_list = load.load_json(suspicious_file_list_path)
train_validation_test_subdirs_split = config.train_validation_test_subdirs_split
intensity_mean, intensity_std = config.intensity_mean, config.intensity_std
batch_size = config.batch_size
ModelCheckpoint_file = config.ModelCheckpoint_file
train_predict_image = config.train_predict_image
test_predict_image = config.test_predict_image
look_back = 3
file_list = []
for subdir, dirs, files in os.walk(track_path):
for file in files:
file_path = os.path.join(subdir, file)
file_list.append(file_path)
file_list = np.array(file_list)
np.random.shuffle(file_list)
file_list = list(file_list)
file_list = file_list[:10]
# print (file_list)
# for file in file_list:
# if len(file) <=2:
# print (file)
# print (file_list.index(file))
# file_list = file_list[:10]
train_file_list = file_list[:int(0.9 * len(file_list))]
test_file_list = file_list[int(0.9 * len(file_list)):]
# print(train_file_list)
trainX = []
trainY = []
testX = []
testY = []
dataset_count = 0
for file in train_file_list:
try:
data = prepare_dataset.dataset_1(file)
data = prepare_dataset.normalize_intensity(data, intensity_mean,
intensity_std)
# data = list(data)
trainXx, trainYy = prepare_dataset.create_dataset(data, look_back)
trainX += trainXx
trainY += trainYy
dataset_count += data.shape[0]
except:
print(file)
for file in test_file_list:
try:
data = prepare_dataset.dataset_1(file)
data = prepare_dataset.normalize_intensity(data, intensity_mean,
intensity_std)
# data = list(data)
testXx, testYy = prepare_dataset.create_dataset(data, look_back)
testX += testXx
testY += testYy
dataset_count += data.shape[0]
except:
print(file)
trainX = np.array(trainX, dtype='float32')
trainY = np.array(trainY, dtype='float32')
testX = np.array(testX, dtype='float32')
testY = np.array(testY, dtype='float32')
print(trainX.shape)
print(testX.shape)
trainX = np.reshape(trainX, (trainX.shape[0], trainX.shape[1], 1))
testX = np.reshape(testX, (testX.shape[0], testX.shape[1], 1))
batch_size = 1
model = Sequential()
model.add(
LSTM(4, batch_input_shape=(batch_size, look_back, 1), stateful=True))
model.add(Dense(3))
model.compile(loss='mean_squared_error', optimizer='adam')
# checkpointer = ModelCheckpoint(filepath=ModelCheckpoint_file, verbose=2, save_best_only=True)
hists = []
for i in range(10):
hist = model.fit(trainX,
trainY,
nb_epoch=1,
batch_size=batch_size,
verbose=2,
shuffle=False)
model.reset_states()
hists.append(hist.history['loss'][0])
print(hists, 'hists')
# model.save_weights(ModelCheckpoint_file)
# make predictions
trainPredict = model.predict(trainX, batch_size=batch_size)
model.reset_states()
testPredict = model.predict(testX, batch_size=batch_size)
# invert predictions
trainPredict = prepare_dataset.reverse_normalize_intensity(
trainPredict, intensity_mean, intensity_std)
trainY = prepare_dataset.reverse_normalize_intensity(
trainY, intensity_mean, intensity_std)
testPredict = prepare_dataset.reverse_normalize_intensity(
testPredict, intensity_mean, intensity_std)
testY = prepare_dataset.reverse_normalize_intensity(
testY, intensity_mean, intensity_std)
# calculate root mean squared error
# print (trainPredict[:,0], 'trainPredict')
# print (trainPredict.shape,'len_train_predict')
# print(trainY[0],'trainY')
trainScore = math.sqrt(mean_squared_error(trainY, trainPredict[:, 0]))
print('Train Score: %.2f RMSE' % (trainScore))
testScore = math.sqrt(mean_squared_error(testY, testPredict[:, 0]))
print('Test Score: %.2f RMSE' % (testScore))
dataset = np.zeros((dataset_count, 1), dtype='float32')
# trainPredictPlot = np.empty_like(dataset)
# trainPredictPlot[:, :] = np.nan
# trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict
# # shift test predictions for plotting
# testPredictPlot = np.empty_like(dataset)
# testPredictPlot[:, :] = np.nan
# testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict
# # plt.plot(dataset))
fig = plt.figure()
plt.title('train_predicts_look_back')
plt.plot(list(trainPredict[:, 0]), 'r--', label='train_predict')
plt.plot(list(trainY), 'g--', label='train')
plt.legend(loc='upper left', shadow=True)
plt.xlabel('typhoon_image')
plt.ylael('typhoon intensity')
plt.savefig(train_predict_image)
plt.close(fig)
fig = plt.figure()
plt.title('test_predicts_look_back')
plt.plot(list(testPredict[:, 0]), 'r--', label='test_predict')
plt.plot(list(testY), 'g--', label='test')
plt.xlabel('typhoon_image')
plt.ylael('typhoon intensity')
plt.legend(loc='upper left', shadow=True)
plt.savefig(test_predict_image)
plt.close(fig)
def main():
np.random.seed(7)
t1 = time.time()
image_path = config.image_path
track_path = config.track_path
track_dic_path = config.track_dic_path
track_dict = load.load_json(track_dic_path)
intensity_mean, intensity_std = config.intensity_mean, config.intensity_std
batch_size = config.batch_size
ModelCheckpoint_file = config.ModelCheckpoint_file
look_back = config.look_back
img_rows, img_cols = config.img_rows, config.img_cols
subdir_list = []
hist_path = config.hist_path
mean_v, std_v = config.mean_v, config.std_v
intensity_mean, intensity_std = config.intensity_mean, config.intensity_std
model = pretrain_model(look_back, batch_size)
if os.path.exists(ModelCheckpoint_file):
print('load load_weights', ModelCheckpoint_file)
model.load_weights(ModelCheckpoint_file)
print(model.summary())
# train_x = np.random.uniform(0,1,(17, 3, 1, 512, 512))
# train_y = np.random.uniform(0,1,(17,1))
# print (train_x)
# train_x = np.array(train_x,dtype = 'float32')
# train_y = np.array(train_y,dtype= 'float32')
# hist = model.fit(train_x, train_y, nb_epoch=1, batch_size=batch_size, verbose=2, validation_split=0.1,shuffle=False)
"""
count the number of image in each typhoon sequence
"""
image_number_dictionary = {}
for subdirs, dirs, files in os.walk(image_path):
# print (subdirs)
subdir_list.append(subdirs)
for subdir in subdir_list:
count = 0
for subdirs, dirs, files in os.walk(subdir):
for file in files:
count += 1
key = subdir.split('/')[-1]
image_number_dictionary[key] = count
if count < 24:
print(key, count)
# print (image_number_dictionary)
"""
check the number of images equals the number of track data?
"""
# for subdir in subdir_list:
# for subdirs, dirs, files in os.walk(subdir):
# for file in files:
# # print (file)
# [k1, k2] = file.split("-")[:2]
# key = "".join((k1,k2))
# try:
# mark = track_dict[key]
# except KeyError:
# print (file +'do not have track value')
# for k in track_dict.keys():
# k2 = k[-6:] # typhoon number
# k1 = k[:-6]
# file = k1 +'-' + k2 +'*'
# file_path = image_path + k2 +'/' + file
# if not os.path.isfile(file_path):
# print (file_path not exists)
track_dict_number = {}
equal_track_image_list = []
not_equal_track_image_list = []
for subdir in subdir_list:
key = subdir.split('/')[-1]
if len(key) > 0 and key not in ['201620', '201621', '201622']:
track_file_path = track_path + key + '.itk'
with open(track_file_path, 'rb') as tsv_file:
tsv_reader = csv.reader(tsv_file, delimiter='\t')
count = 0
for row in tsv_reader:
count += 1
track_dict_number[key] = count
if count != image_number_dictionary[key]:
not_equal_track_image_list.append(key)
# print (key,count,image_number_dictionary[key],'not equal')
if count == image_number_dictionary[key]:
# print (key,count,image_number_dictionary[key],' equal')
equal_track_image_list.append(key)
# print (not_equal_track_image_list,'not_equal_track_image_list')
# print (equal_track_image_list,'equal_track_image_list')
print(len(equal_track_image_list), 'lenth of eqaual track image list')
# "check if track file difference is one hour, result is yes for both equal and not_eqaul_image_list "
for key in not_equal_track_image_list:
ts = []
track_file_path = track_path + key + '.itk'
with open(track_file_path, 'rb') as tsv_file:
tsv_reader = csv.reader(tsv_file, delimiter='\t')
for row in tsv_reader:
yy = row[0]
mm = row[1]
dd = row[2]
hh = row[3]
t = datetime.datetime.strptime(
yy + ":" + mm + ":" + dd + ':' + hh, '%Y:%m:%d:%H')
ts.append(t)
tmp = ts[0]
for i in range(1, len(ts)):
dif = (ts[i] - tmp).total_seconds()
# print (dif,'dif')
if dif != 3600:
print(dif, i, key)
tmp = ts[i]
# break
dataset_imageset_path = 'test_file/dataset_image_unequal.hdf5'
dataset_intensity_path = 'test_file/dataset_intensity_unequal.hdf5'
hf_image = h5py.File(dataset_imageset_path)
hf_intensity = h5py.File(dataset_intensity_path)
vgg_model = VGG_16('vgg16_weights.h5')
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
vgg_model.compile(optimizer=sgd, loss='categorical_crossentropy')
for key in not_equal_track_image_list:
# # for key in equal_track_image_list:
image_folder = image_path + key + '/'
# dataset_x,dataset_y = prepare_dataset.dataset_1_2(image_folder,track_dict)
# print dataset_x.shape
# print dataset_y.shape
# break
file_path_list = []
# print key
dataset_image = []
dataset_intensity = []
for subdirs, dirs, files in os.walk(image_folder):
for file in files:
file_path = os.path.join(subdirs, file)
file_path_list.append(file_path)
sorted_file_list = sorted(
file_path_list, key=lambda x: int(x.split('/')[-1].split('-')[-4]))
# print (len(sorted_file_list),'len of sorted_file_list')
ts = []
intensities = []
for file_path in sorted_file_list:
yymmddhh = file_path.split('/')[-1].split('-')[-4]
track_key = yymmddhh + key
intensities.append(float(track_dict[track_key][-2]))
t = datetime.datetime.strptime(yymmddhh, '%Y%m%d%H')
ts.append(t)
# print len(ts),'len ts'
tmp = ts[0]
orig_image = load.get_x(sorted_file_list, img_rows, img_cols, mean_v,
std_v)
tmp_image = orig_image[0]
# dataset_input = get_fc2(vgg_model,dataset_image)
# dataset_input = np.array(dataset_input)
dataset_image.append(orig_image[0])
dataset_intensity.append(intensities[0])
for i in range(1, len(ts)):
dif = (ts[i] - tmp).total_seconds()
# print (dif,'dif')
if dif != 3600:
print(dif / 3600.0, i, key, ts[i])
for j in range(1, int(dif / 3600.0)):
t2 = tmp + datetime.timedelta(seconds=3600)
yy = t2.year
mm = str(t2.month).zfill(2)
dd = str(t2.day).zfill(2)
hh = str(t2.hour).zfill(2)
yymmddhh = str(yy) + mm + dd + hh
track_key = yymmddhh + key
intensity = float(track_dict[track_key][-2])
image = (1 - (float(j) / (dif / 3600.0))) * tmp_image + (
float(j) / (dif / 3600.0)) * orig_image[i]
dataset_image.append(image)
dataset_intensity.append(intensity)
dataset_image.append(orig_image[i])
dataset_intensity.append(intensities[i])
tmp = ts[i]
tmp_image = orig_image[i]
dataset_image = np.array(dataset_image)
dataset_input = get_fc2(vgg_model, dataset_image)
dataset_intensity = np.array(dataset_intensity)
dataset_intensity = prepare_dataset.normalize_intensity(
dataset_intensity, intensity_mean, intensity_std)
hf_image.create_dataset(key, data=dataset_input)
hf_intensity.create_dataset(key, data=dataset_intensity)
hf_image.close()
hf_intensity.close()
t2 = time.time()
print("using %s seconds" % (t2 - t1))