def tune_noise_classifier():
GlobalConfig.set('dataset', 'kylberg')
GlobalConfig.set('ECS', True)
GlobalConfig.set('algorithm', 'NoiseClassifier')
GlobalConfig.set('scale', 0.5)
GlobalConfig.set(
'CWD',
r'\\filestore.soton.ac.uk\users\ojvl1g17\mydocuments\COMP3200-Texture-Classification'
)
#GlobalConfig.set('CWD', os.getcwd())
GlobalConfig.set('folds', 10)
cores = psutil.cpu_count()
GlobalConfig.set('cpu_count', cores)
dataset = DatasetManager.KylbergTextures(num_classes=28, data_ratio=0.5)
images = dataset.load_data()
gc.collect()
bm3d_images = []
# Convert to BM3D images
for image in images:
new_image = BM3DELBP.BM3DELBPImage(image)
bm3d_images.append(new_image)
print("Image dataset loaded, loaded {} images".format(len(images)))
noise_classifier = NoiseClassifier()
cross_validator = dataset.get_cross_validator()
bm3d_sigma = [10, 30, 40, 50]
homomorphic_cutoff = [0.1, 0.5, 5, 10]
homomorphic_a = [0.5, 0.75, 1.0]
homomorphic_b = [0.1, 0.5, 1.0, 1.25]
settings_jobs = [] # List of all configuration tuples
for sigma_val in bm3d_sigma:
for cutoff in homomorphic_cutoff:
for a in homomorphic_a:
for b in homomorphic_b:
settings_jobs.append((sigma_val, cutoff, a, b))
results = [] # List of tuples (F1, sigma_val, cutoff, a, b)
out_csv = os.path.join(GlobalConfig.get('CWD'), 'NoiseClassifierTuning',
'Results.txt')
with Pool(processes=GlobalConfig.get('cpu_count'),
maxtasksperchild=50) as pool:
# Generate image featurevectors and replace DatasetManager.Image with BM3DELBP.BM3DELBPImage
for result in tqdm.tqdm(pool.istarmap(
do_classification,
zip(settings_jobs, repeat(noise_classifier),
repeat(cross_validator), repeat(bm3d_images))),
total=len(settings_jobs),
desc='NoiseClassifier tuning'):
f1, sigma, cutoff, a, b = result
# Log to CSV file
if os.path.isfile(out_csv):
# CSV exists, append to end of file
with open(out_csv, 'a', encoding="utf-8",
newline='') as resultsfile:
writer = csv.writer(resultsfile)
writer.writerow([f1, sigma, cutoff, a, b])
else:
# CSV does not exist. Write the headings
with open(out_csv, 'w', encoding="utf-8",
newline='') as resultsfile:
writer = csv.writer(resultsfile)
writer.writerow(['f1', 'sigma_psd', 'cutoff', 'a', 'b'])
writer.writerow([f1, sigma, cutoff, a, b])
results.append(result)
# Sort largest to smallest, by F1 score
results.sort(key=lambda tup: tup[0], reverse=True)
print("Finished tuning parameters.")
print("The top 3 results were:")
f1, sigma, cutoff, a, b = results[0]
print("F1: {}, sigma_val: {}, cutoff_freq: {}, a: {}, b: {}".format(
f1, sigma, cutoff, a, b))
f1, sigma, cutoff, a, b = results[1]
print("F1: {}, sigma_val: {}, cutoff_freq: {}, a: {}, b: {}".format(
f1, sigma, cutoff, a, b))
f1, sigma, cutoff, a, b = results[2]
print("F1: {}, sigma_val: {}, cutoff_freq: {}, a: {}, b: {}".format(
f1, sigma, cutoff, a, b))
def main():
# Parse Args.
# 'scale' allows the image_scaled scale to be set. Eg: 0.25, 0.5, 1.0
argList = sys.argv[1:]
shortArg = 'a:d:t:s:S:k:rn:i:me'
longArg = [
'algorithm=', 'dataset=', 'train-ratio=', 'scale=', 'test-scale=',
'folds=', 'rotations', 'noise=', 'noise-intensity=', 'multiprocess',
'example', 'data-ratio=', 'mrlbp-classifier=', 'noise-train', 'ecs',
'debug'
]
valid_algorithms = [
'RLBP', 'MRLBP', 'MRELBP', 'BM3DELBP', 'NoiseClassifier'
]
valid_datasets = ['kylberg']
valid_noise = ['gaussian', 'speckle', 'salt-pepper']
valid_mrlbp_classifiers = ['svm', 'knn']
try:
args, vals = getopt.getopt(argList, shortArg, longArg)
for arg, val in args:
if arg in ('-a', '--algorithm'):
if val in valid_algorithms:
print('Using algorithm:', val)
GlobalConfig.set("algorithm", val)
else:
raise ValueError(
'Invalid algorithm configured, choose one of the following:',
valid_algorithms)
elif arg in ('-d', '--dataset'):
if val in valid_datasets:
print("Using dataset:", val)
GlobalConfig.set("dataset", val)
else:
raise ValueError(
'Invalid dataset configured, choose one of the following:',
valid_datasets)
elif arg in ('-t', '--train-test'):
if 0 < float(val) <= 1.0:
print('Using train-ratio ratio of', val)
GlobalConfig.set('train_ratio', float(val))
else:
raise ValueError(
'Train-test ratio must be 0 < train-test <= 1.0')
elif arg in ('-s', '--scale'):
if 0 < float(val) <= 1.0:
print('Using training image scale:', val)
GlobalConfig.set('scale', float(val))
else:
raise ValueError('Scale must be 0 < scale <= 1.0')
elif arg in ('-S', '--test-scale'):
if 0 < float(val) <= 1.0:
print('Using testing image scale:', val)
GlobalConfig.set('test_scale', float(val))
else:
raise ValueError('Test scale must be 0 < scale <= 1.0')
elif arg in ('-k', '--folds'):
print('Doing {} folds'.format(val))
GlobalConfig.set("folds", int(val))
elif arg in ('-r', '--rotations'):
print('Using rotated image_scaled sources')
GlobalConfig.set("rotate", True)
elif arg in ('-n', '--noise'):
if val in valid_noise:
print('Applying noise:', val)
GlobalConfig.set("noise", val)
else:
raise ValueError(
'Invalid noise type, choose one of the following:',
valid_noise)
elif arg in ('-i', '--noise-intensity'):
print('Using noise intensity (sigma / ratio) of:', val)
GlobalConfig.set("noise_val", float(val))
elif arg in ('-m', '--multiprocess'):
cores = psutil.cpu_count()
print('Using {} processor cores for computing featurevectors'.
format(cores))
GlobalConfig.set('multiprocess', True)
GlobalConfig.set('cpu_count', cores)
elif arg in ('-e', '--example'):
print('Generating algorithm example image_scaled')
GlobalConfig.set('examples', True)
elif arg == '--data-ratio':
if 0 < float(val) <= 1.0:
print('Using dataset ratio:', val)
GlobalConfig.set('data_ratio', float(val))
else:
raise ValueError('Data ratio must be 0 < ratio <= 1.0')
elif arg == '--mrlbp-classifier':
if val in valid_mrlbp_classifiers:
print(
"MRLBP algorithm (if configured) will use {} classifier"
.format(val))
GlobalConfig.set('mrlbp_classifier', val)
else:
raise ValueError(
'Invalid classifier chosen for mrlbp, choose one of the following:',
valid_mrlbp_classifiers)
elif arg == '--noise-train':
print(
"Applying noise to the training dataset as well as the test dataset"
)
GlobalConfig.set('train_noise', True)
elif arg == '--ecs':
print("Loading dataset from C:\Local")
GlobalConfig.set('ECS', True)
elif arg == '--debug':
print("Running in debug mode")
GlobalConfig.set('debug', True)
else:
raise ValueError('Unhandled argument provided:', arg)
except getopt.error as err:
print(str(err))
if GlobalConfig.get('ECS'):
GlobalConfig.set(
'CWD',
r'\\filestore.soton.ac.uk\users\ojvl1g17\mydocuments\COMP3200-Texture-Classification'
)
else:
GlobalConfig.set('CWD', os.getcwd())
if GlobalConfig.get('examples'):
write_examples()
# Load configured Dataset
if GlobalConfig.get('dataset') == 'kylberg':
if GlobalConfig.get('debug'):
# To save time in debug mode, only load one class and load a smaller proportion of it (25% of samples)
kylberg = DatasetManager.KylbergTextures(
num_classes=2, data_ratio=GlobalConfig.get('data_ratio'))
else:
kylberg = DatasetManager.KylbergTextures(
num_classes=28, data_ratio=GlobalConfig.get('data_ratio'))
# Load Dataset & Cross Validator
dataset = kylberg.load_data()
cross_validator = kylberg.get_cross_validator()
print("Dataset loaded")
elif GlobalConfig.get('dataset') is None:
raise ValueError('No Dataset configured')
else:
raise ValueError('Invalid dataset')
if GlobalConfig.get('rotate'):
dataset_folder = GlobalConfig.get('dataset') + '-rotated'
else:
dataset_folder = GlobalConfig.get('dataset')
out_folder = os.path.join(GlobalConfig.get('CWD'), 'out',
GlobalConfig.get('algorithm'), dataset_folder)
# Initialise algorithm
if GlobalConfig.get('algorithm') == 'RLBP':
print("Applying RLBP algorithm")
algorithm = RLBP.RobustLBP()
elif GlobalConfig.get('algorithm') == 'MRLBP':
print("Applying MRLBP algorithm")
algorithm = RLBP.MultiresolutionLBP(p=[8, 16, 24], r=[1, 2, 3])
elif GlobalConfig.get('algorithm') == 'MRELBP':
print("Applying MRELBP algorithm")
algorithm = MRELBP.MedianRobustExtendedLBP(r1=[2, 4, 6, 8],
p=8,
w_center=3,
w_r1=[3, 5, 7, 9])
elif GlobalConfig.get('algorithm') == 'BM3DELBP':
print("Applying BM3DELBP algorithm")
algorithm = BM3DELBP.BM3DELBP()
elif GlobalConfig.get('algorithm') == 'NoiseClassifier':
# Noise Classifier is used in BM3DELBP algorithm usually, this allows for benchmarking of the classifier alone
algorithm = NoiseClassifier.NoiseClassifier()
pass
else:
raise ValueError('Invalid algorithm choice')
# Get the Training out directory (i.e. Images without scaling/rotation/noise)
train_out_dir = os.path.join(
out_folder, algorithm.get_outdir(noisy_image=False,
scaled_image=False))
# Get the Testing out directory (i.e. Images with scaling/rotation/noise)
if GlobalConfig.get('noise') is not None:
noisy_image = True
else:
noisy_image = False
if GlobalConfig.get('test_scale') is not None:
scaled_image = True
else:
scaled_image = False
test_out_dir = os.path.join(
out_folder, algorithm.get_outdir(noisy_image, scaled_image))
# Out path for noise classifier
noise_out_dir = os.path.join(
GlobalConfig.get('CWD'), 'out', 'NoiseClassifier', dataset_folder,
"scale-{}".format(int(GlobalConfig.get('scale') * 100)))
test_noise_out_dir = os.path.join(
GlobalConfig.get('CWD'), 'out', 'NoiseClassifier', dataset_folder,
algorithm.get_outdir(noisy_image, scaled_image))
print("Replacing DatasetManager.Image with BM3DELBPImages")
# Convert DatasetManager.Image into BM3DELBP.BM3DELBPImage
if GlobalConfig.get('algorithm') == 'NoiseClassifier' or GlobalConfig.get(
'algorithm') == 'BM3DELBP':
for index, img in enumerate(dataset):
dataset[index] = BM3DELBP.BM3DELBPImage(img)
# Also convert rotated images if necessary
if img.test_rotations is not None:
for index, rotated_img in enumerate(img.test_rotations):
img.test_rotations[index] = BM3DELBP.BM3DELBPImage(
rotated_img)
if GlobalConfig.get('multiprocess'):
for index, img in enumerate(dataset):
dataset[index] = (index, img)
if GlobalConfig.get('rotate'):
maxtasks = 50
else:
maxtasks = None
if GlobalConfig.get(
'algorithm') == 'NoiseClassifier' or GlobalConfig.get(
'algorithm') == 'BM3DELBP':
with Pool(processes=GlobalConfig.get('cpu_count'),
maxtasksperchild=maxtasks) as pool:
# Generate image noise featurevectors
for index, image in tqdm.tqdm(pool.istarmap(
describe_noise_pool,
zip(dataset, repeat(noise_out_dir),
repeat(test_noise_out_dir))),
total=len(dataset),
desc='Noise Featurevectors'):
dataset[index] = image
else:
with Pool(processes=GlobalConfig.get('cpu_count'),
maxtasksperchild=maxtasks) as pool:
# Generate featurevectors
for index, image in tqdm.tqdm(pool.istarmap(
describe_image_pool,
zip(repeat(algorithm), dataset, repeat(train_out_dir),
repeat(test_out_dir))),
total=len(dataset),
desc='Texture Featurevectors'):
dataset[index] = image
else:
# Process the images without using multiprocessing Pools
if GlobalConfig.get(
'algorithm') == 'NoiseClassifier' or GlobalConfig.get(
'algorithm') == 'BM3DELBP':
for index, img in enumerate(dataset):
# Generate image noise featurevectors
describe_noise(img, noise_out_dir, test_noise_out_dir)
else:
print("BEGINNING TIMER:")
start = timer()
for index, img in enumerate(dataset):
# Generate featurevetors
describe_image(algorithm, img, train_out_dir, test_out_dir)
end = timer()
print("TIME TAKEN:", end - start)
# Train models and perform predictions
if GlobalConfig.get('algorithm') == 'RLBP':
predictor = RLBP.RobustLBPPredictor(dataset, cross_validator)
elif GlobalConfig.get('algorithm') == 'MRLBP':
print("Performing MRLBP Classification")
predictor = RLBP.MultiresolutionLBPPredictor(dataset, cross_validator)
elif GlobalConfig.get('algorithm') == 'MRELBP':
print("Performing MRELBP Classification")
predictor = MRELBP.MedianRobustExtendedLBPPredictor(
dataset, cross_validator)
elif GlobalConfig.get('algorithm') == 'BM3DELBP':
print("Performing BM3DELBP Classification")
predictor = BM3DELBP.BM3DELBPPredictor(dataset, cross_validator)
elif GlobalConfig.get('algorithm') == 'NoiseClassifier':
print("Applying noise classifier")
predictor = BM3DELBP.NoiseTypePredictor(dataset, cross_validator)
else:
raise ValueError('Invalid algorithm choice')
# Get the test label & test prediction for every fold of cross validation
y_test, y_predicted = predictor.begin_cross_validation()
if GlobalConfig.get('algorithm') == 'NoiseClassifier':
if GlobalConfig.get('noise') is None:
classes = ['no-noise', 'gaussian', 'speckle', 'salt-pepper']
else:
classes = ['gaussian', 'speckle', 'salt-pepper']
else:
classes = kylberg.classes
# Display confusion matrix
ClassificationUtils.pretty_print_conf_matrix(
y_test,
y_predicted,
classes,
title='{} Confusion Matrix'.format(GlobalConfig.get('algorithm')),
out_dir=test_out_dir)
# Display classification report
ClassificationUtils.make_classification_report(y_test, y_predicted,
classes, test_out_dir)
class MainWindow(QMainWindow):
def __init__(self, *args, **kwargs):
super(MainWindow, self).__init__(*args, **kwargs)
self.setupUi()
self.initData()
def setupUi(self):
self.setWindowTitle("饥荒联机版服务器管理工具")
# 设置初始化的窗口大小
self.setFixedSize(1085, 700)
# 最开始窗口要居中显示
self.center()
# 设置整体布局 左右显示
pagelayout = QGridLayout()
'''
开始左上侧布局
'''
# 创建左上侧部件
top_left_frame = QFrame(self)
top_left_frame.setFrameShape(QFrame.StyledPanel)
# 按钮垂直布局
top_button_layout = QVBoxLayout(top_left_frame)
# 五个存档槽按钮
self.cluster_btns = {}
for b_index in range(5):
self.cluster_btns[b_index] = QPushButton(top_left_frame)
self.cluster_btns[b_index].setFixedSize(180, 30)
self.cluster_btns[b_index].setText("存档槽 " + str(b_index + 1))
# cluster_btns[b_index].setEnabled(False)
self.cluster_btns[b_index].index = b_index + 1
self.cluster_btns[b_index].clicked.connect(self.set_cluster)
top_button_layout.addWidget(self.cluster_btns[b_index])
# 删除存档按钮
delete_cluster_btn = QPushButton(top_left_frame)
delete_cluster_btn.setFixedSize(180, 30)
delete_cluster_btn.setText("删除存档")
top_button_layout.addWidget(delete_cluster_btn)
delete_cluster_btn.clicked.connect(self.deleteCluster)
# 导出远程存档按钮
export_remote_cluster_btn = QPushButton(top_left_frame)
export_remote_cluster_btn.setText("导出远程存档")
export_remote_cluster_btn.setFixedSize(180, 30)
top_button_layout.addWidget(export_remote_cluster_btn)
# 导入本地存档按钮
import_local_cluster_btn = QPushButton(top_left_frame)
import_local_cluster_btn.setText("导入本地存档")
import_local_cluster_btn.setFixedSize(180, 30)
top_button_layout.addWidget(import_local_cluster_btn)
'''
开始左下侧布局
'''
# 创建左下侧部件
bottom_left_frame = QFrame(self)
bottom_left_frame.setFrameShape(QFrame.StyledPanel)
# 按钮垂直布局
bottom_button_layout = QVBoxLayout(bottom_left_frame)
# 控制台按钮
console_btn = QPushButton(bottom_left_frame)
console_btn.setText("控制台")
console_btn.setFixedSize(180, 30)
bottom_button_layout.addWidget(console_btn)
# 软件设置按钮
settings_btn = QPushButton(bottom_left_frame)
settings_btn.setText("软件设置")
settings_btn.setFixedSize(180, 30)
settings_btn.clicked.connect(self.soft_settings)
bottom_button_layout.addWidget(settings_btn)
# 导出远程存档按钮
browser_online_server_btn = QPushButton(bottom_left_frame)
browser_online_server_btn.setText("浏览在线服务器")
browser_online_server_btn.setFixedSize(180, 30)
bottom_button_layout.addWidget(browser_online_server_btn)
# 导出远程存档按钮
help_and_about_btn = QPushButton(bottom_left_frame)
help_and_about_btn.setText("使用帮助和关于")
help_and_about_btn.setFixedSize(180, 30)
bottom_button_layout.addWidget(help_and_about_btn)
# 弹簧控件
spacerItem = QSpacerItem(20, 20, QSizePolicy.Minimum,
QSizePolicy.Expanding)
bottom_button_layout.addItem(spacerItem)
'''
开始右侧布局
'''
# 右侧开始布局 对应按钮布局
right_frame = QFrame(self)
right_frame.setFrameShape(QFrame.StyledPanel)
# 右边显示为stack布局
self.right_layout = QStackedLayout(right_frame)
self.settings_widget = SettingsWidget()
self.cluster_tab = MainTab()
self.right_layout.addWidget(self.cluster_tab)
self.right_layout.addWidget(self.settings_widget)
# 划分界面
splitterV = QSplitter(Qt.Vertical)
splitterV.addWidget(top_left_frame)
splitterV.addWidget(bottom_left_frame)
# 固定左上高度
top_left_frame.setFixedHeight(330)
splitterH = QSplitter(Qt.Horizontal)
splitterH.addWidget(splitterV)
splitterH.addWidget(right_frame)
# 固定左侧宽度
top_left_frame.setFixedWidth(200)
# 窗口部件添加布局
widget = QWidget()
pagelayout.addWidget(splitterH)
widget.setLayout(pagelayout)
self.setCentralWidget(widget)
# 按钮函数绑定
def soft_settings(self):
self.right_layout.setCurrentIndex(1)
# 存档按钮状态刷新
def refresh_cluster_btn_state(self, index):
for i in self.cluster_btns:
if index == self.cluster_btns[i].index:
self.cluster_btns[i].setStyleSheet("")
else:
self.cluster_btns[i].setStyleSheet("color:gray")
# 存档设置
def set_cluster(self):
self.current_cluster_index = self.sender().index
self.tempconfig.set("TEMP", "cluster_index",
str(self.current_cluster_index))
self.tempconfig.save(TEMP_FILE)
self.refresh_cluster_btn_state(self.current_cluster_index)
self.right_layout.setCurrentIndex(0)
self.mk_cluster_dir()
self.cluster_tab.cluster_settings_tab.current_cluster_file = os.path.join(
self.current_cluster_folder, "cluster.ini")
self.cluster_tab.cluster_settings_tab.read_cluster_data(
self.cluster_tab.cluster_settings_tab.current_cluster_file)
self.cluster_tab.cluster_settings_tab.setServerIP(
self.cluster_tab.cluster_settings_tab.masterip,
self.cluster_tab.cluster_settings_tab.getServerIP())
self.cluster_tab.shard_settings_tab.initShardTab()
def deleteCluster(self):
cindex = self.current_cluster_index
delm = QMessageBox.warning(self, "删除警告",
"你确定要删除存档槽" + str(cindex) + "?",
QMessageBox.Yes | QMessageBox.No,
QMessageBox.No)
if delm == QMessageBox.Yes:
sdir = os.path.join(CLUSTER_DIR, "Cluster_" + str(cindex))
if os.path.exists(sdir):
shutil.rmtree(sdir)
self.right_layout.setCurrentIndex(0)
self.mk_cluster_dir()
self.cluster_tab.setCurrentIndex(0)
self.cluster_tab.cluster_settings_tab.current_cluster_file = os.path.join(
self.current_cluster_folder, "cluster.ini")
self.cluster_tab.cluster_settings_tab.read_cluster_data(
self.cluster_tab.cluster_settings_tab.current_cluster_file)
self.cluster_tab.cluster_settings_tab.setServerIP(
self.cluster_tab.cluster_settings_tab.masterip,
self.cluster_tab.cluster_settings_tab.getServerIP())
self.cluster_tab.shard_settings_tab.initShardTab()
QMessageBox.information(self, "删除完毕",
"存档槽" + str(cindex) + "已删除并重置！",
QMessageBox.Yes)
def mk_cluster_dir(self):
self.current_cluster_folder = os.path.join(
CLUSTER_DIR, "Cluster_" + str(self.current_cluster_index))
if not os.path.exists(self.current_cluster_folder):
os.mkdir(self.current_cluster_folder)
def init_cluster_data(self, index):
self.mk_cluster_dir()
self.right_layout.setCurrentIndex(0)
self.refresh_cluster_btn_state(self.current_cluster_index)
self.cluster_tab.cluster_settings_tab.current_cluster_file = os.path.join(
self.current_cluster_folder, "cluster.ini")
self.cluster_tab.cluster_settings_tab.read_cluster_data(
self.cluster_tab.cluster_settings_tab.current_cluster_file)
def initDir(self):
if not os.path.exists(ROOT_DIR):
os.mkdir(ROOT_DIR)
if not os.path.exists(CLUSTER_DIR):
os.mkdir(CLUSTER_DIR)
if not os.path.exists(TEMP_FILE):
self.tempconfig = GlobalConfig(TEMP_FILE)
self.tempconfig.add_section("TEMP")
self.tempconfig.set("TEMP", "cluster_index", "1")
self.tempconfig.save(TEMP_FILE)
def initData(self):
self.initDir()
self.tempconfig = GlobalConfig(TEMP_FILE)
if os.path.exists(TEMP_FILE):
self.current_cluster_index = int(
self.tempconfig.get("TEMP", "cluster_index"))
else:
self.current_cluster_index = 1
self.init_cluster_data(self.current_cluster_index)
# 设置窗口居中
def center(self):
screen = QDesktopWidget().screenGeometry()
size = self.geometry()
self.move((screen.width() - size.width()) / 2,
(screen.height() - size.height()) / 2)
