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sdb_gui.py
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sdb_gui.py
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'''
MIT License
Copyright (c) 2020-present Rifqi Muhammad Harrys
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
'''
###############################################################################
################################# Main Imports ################################
from glob import iglob
from numpy.core.fromnumeric import shape
from sklearn import metrics
from sklearn.neighbors import KNeighborsRegressor
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor
from sklearn.svm import SVR
from sklearn.model_selection import train_test_split
from joblib import parallel_backend
from scipy import ndimage
import pandas as pd
import numpy as np
import geopandas as gpd
import rasterio as rio
from pathlib import Path
import sys, os
import datetime
import webbrowser
from PyQt5.QtCore import (Qt, QThread, pyqtSignal)
from PyQt5.QtWidgets import(QApplication, QWidget, QTextBrowser, QProgressBar, QFileDialog, QDialog,
QGridLayout, QPushButton, QVBoxLayout, QComboBox, QLabel, QCheckBox,
QDoubleSpinBox, QSpinBox, QTableWidgetItem, QTableWidget, QScrollArea,
QErrorMessage)
from PyQt5.QtGui import QIcon
###############################################################################
#################### For Auto PY to EXE or PyInstaller Use ####################
import sklearn.utils._weight_vector
import fiona._shim
import fiona.schema
import rasterio._features
import rasterio._shim
import rasterio.control
import rasterio.crs
import rasterio.sample
import rasterio.vrt
###############################################################################
###############################################################################
SDB_GUI_VERSION = '3.3.1'
def resource_path(relative_path):
'''Get the absolute path to the resource, works for dev and for PyInstaller'''
try:
# PyInstaller creates a temp folder and stores path in _MEIPASS
base_path = sys._MEIPASS
except Exception:
base_path = os.path.abspath(".")
return os.path.join(base_path, relative_path)
class SDBWidget(QWidget):
'''
PyQt5 widget of SDB GUI
'''
widget_signal = pyqtSignal(dict)
def __init__(self):
'''
Initialize widget and default values
'''
super(SDBWidget, self).__init__()
####### Default Values #######
self.method_dict = {
'K-Nearest Neighbors': self.knnOptionWindow,
'Multiple Linear Regression': self.mlrOptionWindow,
'Random Forest': self.rfOptionWindow,
'Support Vector Machines': self.svmOptionWindow
}
self.dir_path = os.path.abspath(Path.home())
global proc_op_dict
proc_op_dict = {
'backend': 'threading',
'n_jobs': -2,
'random_state': 0,
'auto_negative': True,
'exclude_outside': True
}
global knn_op_dict
knn_op_dict = {
'n_neighbors': 5,
'weights': 'distance',
'algorithm': 'auto',
'leaf_size': 30
}
global mlr_op_dict
mlr_op_dict = {
'fit_intercept': True,
'normalize': False,
'copy_x': True
}
global rf_op_dict
rf_op_dict = {
'n_estimators': 300,
'criterion': 'mse',
'bootstrap': True,
'random_state': 0
}
global svm_op_dict
svm_op_dict = {
'kernel': 'rbf',
'gamma': .1,
'c': 1000.0,
'degree': 3
}
global val_if_nan
val_if_nan = -999.0
global progress_step
progress_step = 7
####### Default Values #######
self.initUI()
def initUI(self):
'''
Initialize User Interface for SDB GUI Widget
'''
self.setGeometry(300, 100, 480, 640)
self.setWindowTitle('Satellite Derived Bathymetry (v%s)' %SDB_GUI_VERSION)
self.setWindowIcon(QIcon(resource_path('icons/satellite.png')))
loadImageButton = QPushButton('Load Image')
loadImageButton.clicked.connect(self.loadImageWindow)
self.loadImageLabel = QLabel()
self.loadImageLabel.setText('No Image Loaded')
self.loadImageLabel.setAlignment(Qt.AlignCenter)
loadSampleButton = QPushButton('Load Sample')
loadSampleButton.clicked.connect(self.loadSampleWindow)
self.loadSampleLabel = QLabel()
self.loadSampleLabel.setText('No Sample Loaded')
self.loadSampleLabel.setAlignment(Qt.AlignCenter)
depthHeaderLabel = QLabel('Depth Header:')
self.depthHeaderCB = QComboBox()
self.table = QTableWidget()
scroll = QScrollArea()
scroll.setWidget(self.table)
limitLabel = QLabel('Depth Limit Window:')
limitALabel = QLabel('Upper Limit:')
self.limitADSB = QDoubleSpinBox()
self.limitADSB.setRange(-100, 100)
self.limitADSB.setDecimals(1)
self.limitADSB.setValue(0)
self.limitADSB.setSuffix(' m')
self.limitADSB.setAlignment(Qt.AlignRight)
limitBLabel = QLabel('Bottom Limit:')
self.limitBDSB = QDoubleSpinBox()
self.limitBDSB.setRange(-100, 100)
self.limitBDSB.setDecimals(1)
self.limitBDSB.setValue(-30)
self.limitBDSB.setSuffix(' m')
self.limitBDSB.setAlignment(Qt.AlignRight)
self.limitCheckBox = QCheckBox('Disable Depth Limitation')
self.limitCheckBox.setChecked(False)
method_list = list(self.method_dict)
methodLabel = QLabel('Regression Method:')
self.methodCB = QComboBox()
self.methodCB.addItems(method_list)
self.methodCB.activated.connect(
lambda: self.methodSelection(
option=self.method_dict[self.methodCB.currentText()]
)
)
trainPercentLabel = QLabel('Train Data (Percent):')
self.trainPercentDSB = QDoubleSpinBox()
self.trainPercentDSB.setRange(10.0, 90.0)
self.trainPercentDSB.setDecimals(2)
self.trainPercentDSB.setValue(75.0)
self.trainPercentDSB.setSuffix(' %')
self.trainPercentDSB.setAlignment(Qt.AlignRight)
self.optionsButton = QPushButton('Method Options')
self.optionsButton.clicked.connect(self.knnOptionWindow)
makePredictionButton = QPushButton('Make Prediction')
makePredictionButton.clicked.connect(self.predict)
saveFileButton = QPushButton('Save Into File')
saveFileButton.clicked.connect(self.saveOptionWindow)
processingOptionsButton = QPushButton('Processing Options')
processingOptionsButton.clicked.connect(self.processingOptionWindow)
resultInfo = QLabel('Result Information')
self.resultText = QTextBrowser()
self.resultText.setAlignment(Qt.AlignRight)
self.progressBar = QProgressBar()
self.progressBar.setFormat('%p%')
self.progressBar.setMinimum(0)
self.progressBar.setMaximum(progress_step)
releaseButton = QPushButton('Releases')
releaseButton.clicked.connect(lambda: webbrowser.open(
'https://github.com/rifqiharrys/sdb_gui/releases'
))
licensesButton = QPushButton('Licenses')
licensesButton.clicked.connect(self.licensesDialog)
readmeButton = QPushButton('Readme')
readmeButton.clicked.connect(lambda: webbrowser.open(
'https://github.com/rifqiharrys/sdb_gui/blob/main/README.md'
))
grid = QGridLayout()
vbox = QVBoxLayout()
grid.addWidget(loadImageButton, 1, 1, 1, 2)
grid.addWidget(self.loadImageLabel, 1, 3, 1, 2)
grid.addWidget(loadSampleButton, 2, 1, 1, 2)
grid.addWidget(self.loadSampleLabel, 2, 3, 1, 2)
grid.addWidget(depthHeaderLabel, 3, 1, 1, 1)
grid.addWidget(self.depthHeaderCB, 3, 2, 1, 3)
grid.addWidget(self.table, 5, 1, 5, 4)
grid.addWidget(limitLabel, 10, 1, 1, 2)
grid.addWidget(self.limitCheckBox, 11, 1, 1, 2)
grid.addWidget(limitALabel, 10, 3, 1, 1)
grid.addWidget(self.limitADSB, 10, 4, 1, 1)
grid.addWidget(limitBLabel, 11, 3, 1, 1)
grid.addWidget(self.limitBDSB, 11, 4, 1, 1)
grid.addWidget(methodLabel, 12, 1, 1, 1)
grid.addWidget(self.methodCB, 12, 2, 1, 1)
grid.addWidget(self.optionsButton, 12, 3, 1, 2)
grid.addWidget(trainPercentLabel, 13, 1, 1, 1)
grid.addWidget(self.trainPercentDSB, 13, 2, 1, 1)
grid.addWidget(processingOptionsButton, 13, 3, 1, 2)
grid.addWidget(makePredictionButton, 14, 1, 1, 2)
grid.addWidget(saveFileButton, 14, 3, 1, 2)
grid.addWidget(resultInfo, 15, 1, 1, 2)
grid.addWidget(self.resultText, 16, 1, 1, 4)
vbox.addStretch(1)
grid.addLayout(vbox, 21, 1)
grid.addWidget(self.progressBar, 22, 1, 1, 4)
grid.addWidget(releaseButton, 23, 1, 1, 1)
grid.addWidget(licensesButton, 23, 2, 1, 2)
grid.addWidget(readmeButton, 23, 4, 1, 1)
self.setLayout(grid)
def str2bool(self, v):
'''
Transform string True or False to boolean type
'''
return v in ('True')
def fileDialog(self, command, window_text, file_type, text_browser):
'''
Showing file dialog, whether opening file or saving.
'''
fileFilter = 'All Files (*.*) ;; ' + file_type
selectedFilter = file_type
fname = command(self, window_text, self.dir_path, fileFilter, selectedFilter)
text_browser.setText(fname[0])
self.dir_path = os.path.splitext(fname[0])[0]
def methodSelection(self, option):
'''
Method selection connection from option button
to each methods' option window
'''
self.optionsButton.clicked.disconnect()
self.optionsButton.clicked.connect(option)
def loadImageWindow(self):
'''
Image loading User Interface
'''
self.loadImageDialog = QDialog()
self.loadImageDialog.setWindowTitle('Load Image')
self.loadImageDialog.setWindowIcon(QIcon(resource_path('icons/load-pngrepo-com.png')))
openFilesButton = QPushButton('Open File')
openFilesButton.clicked.connect(
lambda: self.fileDialog(
command=QFileDialog.getOpenFileName,
window_text='Open Image File',
file_type='GeoTIFF (*.tif)',
text_browser=self.imglocList
)
)
locLabel = QLabel('Location:')
self.imglocList = QTextBrowser()
cancelButton = QPushButton('Cancel')
cancelButton.clicked.connect(self.loadImageDialog.close)
loadButton = QPushButton('Load')
loadButton.clicked.connect(self.loadImageAction)
loadButton.clicked.connect(self.loadImageDialog.close)
grid = QGridLayout()
grid.addWidget(openFilesButton, 1, 1, 1, 4)
grid.addWidget(locLabel, 4, 1, 1, 1)
grid.addWidget(self.imglocList, 5, 1, 10, 4)
grid.addWidget(loadButton, 15, 3, 1, 1)
grid.addWidget(cancelButton, 15, 4, 1, 1)
self.loadImageDialog.setLayout(grid)
self.loadImageDialog.exec_()
def loadImageAction(self):
'''
Loading selected image and retrieve some metadata such as file size,
band quantity, array size, pixel size, etc. Then, recreate image 3D
array into a simple column and row array.
'''
try:
global img_size
img_size = os.path.getsize(self.imglocList.toPlainText())
global image_raw
image_raw = rio.open(self.imglocList.toPlainText())
nbands = len(image_raw.indexes)
ndata = image_raw.read(1).size
bands_dummy = np.empty((nbands, ndata))
for i in image_raw.indexes:
bands_dummy[i - 1, :] = np.ravel(image_raw.read(i))
global bands_array
bands_array = bands_dummy.T
# Change missing value (if any) to val_if_nan variable
nan_values = np.isnan(bands_array)
bands_array[nan_values] = val_if_nan
self.loadImageLabel.setText(
os.path.split(self.imglocList.toPlainText())[1]
)
print(image_raw.crs)
except:
self.loadImageDialog.close()
self.warningWithClear(
'No data loaded. Please load your data!'
)
self.loadImageWindow()
def loadSampleWindow(self):
'''
Sample loading User Interface
'''
self.loadSampleDialog = QDialog()
self.loadSampleDialog.setWindowTitle('Load Sample')
self.loadSampleDialog.setWindowIcon(QIcon(resource_path('icons/load-pngrepo-com.png')))
openFilesButton = QPushButton('Open File')
openFilesButton.clicked.connect(
lambda: self.fileDialog(
command=QFileDialog.getOpenFileName,
window_text='Open Depth Sample File',
file_type='ESRI Shapefile (*.shp)',
text_browser=self.samplelocList
)
)
locLabel = QLabel('Location:')
self.samplelocList = QTextBrowser()
self.showCheckBox = QCheckBox('Show All Data to Table')
self.showCheckBox.setChecked(False)
cancelButton = QPushButton('Cancel')
cancelButton.clicked.connect(self.loadSampleDialog.close)
loadButton = QPushButton('Load')
loadButton.clicked.connect(self.loadSampleAction)
loadButton.clicked.connect(self.loadSampleDialog.close)
grid = QGridLayout()
grid.addWidget(openFilesButton, 1, 1, 1, 4)
grid.addWidget(locLabel, 4, 1, 1, 1)
grid.addWidget(self.samplelocList, 5, 1, 10, 4)
grid.addWidget(self.showCheckBox, 15, 1, 1, 2)
grid.addWidget(loadButton, 15, 3, 1, 1)
grid.addWidget(cancelButton, 15, 4, 1, 1)
self.loadSampleDialog.setLayout(grid)
self.loadSampleDialog.exec_()
def loadSampleAction(self):
'''
Loading selected sample and retrieve file size.
Then, some or all data on selected sample to the widget.
'''
try:
global sample_size
sample_size = os.path.getsize(self.samplelocList.toPlainText())
global sample_raw
sample_raw = gpd.read_file(self.samplelocList.toPlainText())
self.loadSampleLabel.setText(os.path.split(
self.samplelocList.toPlainText())[1]
)
if (sample_raw.geom_type != 'Point').any():
del sample_raw
self.loadSampleLabel.setText('Sample Retracted')
self.depthHeaderCB.clear()
self.table.clearContents()
self.loadSampleDialog.close()
self.warningWithoutClear(
'Your data is not Point type. Please load another data!'
)
self.loadSampleWindow()
else:
raw = sample_raw.copy()
if self.showCheckBox.isChecked() == True:
data = raw
else:
data = raw.head(100)
self.depthHeaderCB.clear()
self.depthHeaderCB.addItems(data.columns)
self.table.setColumnCount(len(data.columns))
self.table.setRowCount(len(data.index))
for h in range(len(data.columns)):
self.table.setHorizontalHeaderItem(h, QTableWidgetItem(data.columns[h]))
for i in range(len(data.index)):
for j in range(len(data.columns)):
self.table.setItem(i, j, QTableWidgetItem(str(data.iloc[i, j])))
self.table.resizeColumnsToContents()
self.table.resizeRowsToContents()
print(sample_raw.crs)
except:
self.loadSampleDialog.close()
self.warningWithClear(
'No data loaded. Please load your data!'
)
self.loadSampleWindow()
def knnOptionWindow(self):
'''
K-Nearest Neighbor option User Interface
'''
optionDialog = QDialog()
optionDialog.setWindowTitle('Options (K Neighbors)')
optionDialog.setWindowIcon(QIcon(resource_path('icons/setting-tool-pngrepo-com.png')))
nneighborLabel = QLabel('Number of Neighbors:')
self.nneighborSB = QSpinBox()
self.nneighborSB.setRange(1, 1000)
self.nneighborSB.setValue(knn_op_dict['n_neighbors'])
self.nneighborSB.setAlignment(Qt.AlignRight)
weightsLabel = QLabel('Weights:')
self.weightsCB = QComboBox()
self.weightsCB.addItems(['uniform', 'distance'])
self.weightsCB.setCurrentText(knn_op_dict['weights'])
algorithmLabel = QLabel('Algorithm:')
self.algorithmCB = QComboBox()
self.algorithmCB.addItems(['auto', 'ball_tree', 'kd_tree', 'brute'])
self.algorithmCB.setCurrentText(knn_op_dict['algorithm'])
leafSizeLabel = QLabel('Leaf Size:')
self.leafSizeSB = QSpinBox()
self.leafSizeSB.setRange(1, 1000)
self.leafSizeSB.setValue(knn_op_dict['leaf_size'])
self.leafSizeSB.setAlignment(Qt.AlignRight)
cancelButton = QPushButton('Cancel')
cancelButton.clicked.connect(optionDialog.close)
loadButton = QPushButton('Load')
loadButton.clicked.connect(self.loadKNNOptionAction)
loadButton.clicked.connect(optionDialog.close)
grid = QGridLayout()
grid.addWidget(nneighborLabel, 1, 1, 1, 2)
grid.addWidget(self.nneighborSB, 1, 3, 1, 2)
grid.addWidget(weightsLabel, 2, 1, 1, 2)
grid.addWidget(self.weightsCB, 2, 3, 1, 2)
grid.addWidget(algorithmLabel, 3, 1, 1, 2)
grid.addWidget(self.algorithmCB, 3, 3, 1, 2)
grid.addWidget(leafSizeLabel, 4, 1, 1, 2)
grid.addWidget(self.leafSizeSB, 4, 3, 1, 2)
grid.addWidget(loadButton, 5, 3, 1, 1)
grid.addWidget(cancelButton, 5, 4, 1, 1)
optionDialog.setLayout(grid)
optionDialog.exec_()
def loadKNNOptionAction(self):
'''
Loading defined KNN option input
'''
knn_op_dict['n_neighbors'] = self.nneighborSB.value()
knn_op_dict['weights'] = self.weightsCB.currentText()
knn_op_dict['algorithm'] = self.algorithmCB.currentText()
knn_op_dict['leaf_size'] = self.leafSizeSB.value()
def mlrOptionWindow(self):
'''
Multi Linear Regression option User Interface
'''
optionDialog = QDialog()
optionDialog.setWindowTitle('Options (MLR)')
optionDialog.setWindowIcon(QIcon(resource_path('icons/setting-tool-pngrepo-com.png')))
fitInterceptLabel = QLabel('Fit Intercept:')
self.fitInterceptCB = QComboBox()
self.fitInterceptCB.addItems(['True', 'False'])
self.fitInterceptCB.setCurrentText(str(mlr_op_dict['fit_intercept']))
normalizeLabel = QLabel('Normalize:')
self.normalizeCB = QComboBox()
self.normalizeCB.addItems(['True', 'False'])
self.normalizeCB.setCurrentText(str(mlr_op_dict['normalize']))
copyXLabel = QLabel('Copy X:')
self.copyXCB = QComboBox()
self.copyXCB.addItems(['True', 'False'])
self.copyXCB.setCurrentText(str(mlr_op_dict['copy_x']))
cancelButton = QPushButton('Cancel')
cancelButton.clicked.connect(optionDialog.close)
loadButton = QPushButton('Load')
loadButton.clicked.connect(self.loadMLROptionAction)
loadButton.clicked.connect(optionDialog.close)
grid = QGridLayout()
grid.addWidget(fitInterceptLabel, 1, 1, 1, 2)
grid.addWidget(self.fitInterceptCB, 1, 3, 1, 2)
grid.addWidget(normalizeLabel, 2, 1, 1, 2)
grid.addWidget(self.normalizeCB, 2, 3, 1, 2)
grid.addWidget(copyXLabel, 3, 1, 1, 2)
grid.addWidget(self.copyXCB, 3, 3, 1, 2)
grid.addWidget(loadButton, 4, 3, 1, 1)
grid.addWidget(cancelButton, 4, 4, 1, 1)
optionDialog.setLayout(grid)
optionDialog.exec_()
def loadMLROptionAction(self):
'''
Loading defined MLR option input
'''
mlr_op_dict['fit_intercept'] = self.str2bool(self.fitInterceptCB.currentText())
mlr_op_dict['normalize'] = self.str2bool(self.normalizeCB.currentText())
mlr_op_dict['copy_x'] = self.str2bool(self.copyXCB.currentText())
def rfOptionWindow(self):
'''
Random Forest option User Interface
'''
optionDialog = QDialog()
optionDialog.setWindowTitle('Options (Random Forest)')
optionDialog.setWindowIcon(QIcon(resource_path('icons/setting-tool-pngrepo-com.png')))
ntreeLabel = QLabel('Number of Trees:')
self.ntreeSB = QSpinBox()
self.ntreeSB.setRange(1, 10000)
self.ntreeSB.setValue(rf_op_dict['n_estimators'])
self.ntreeSB.setAlignment(Qt.AlignRight)
criterionLabel = QLabel('Criterion:')
self.criterionCB = QComboBox()
self.criterionCB.addItems(['mse', 'mae'])
self.criterionCB.setCurrentText(rf_op_dict['criterion'])
bootstrapLabel = QLabel('Bootstrap:')
self.bootstrapCB = QComboBox()
self.bootstrapCB.addItems(['True', 'False'])
self.bootstrapCB.setCurrentText(str(rf_op_dict['bootstrap']))
randomStateLabel = QLabel('Random State:')
self.randomStateRFSB = QSpinBox()
self.randomStateRFSB.setRange(0, 1000)
self.randomStateRFSB.setValue(rf_op_dict['random_state'])
self.randomStateRFSB.setAlignment(Qt.AlignRight)
cancelButton = QPushButton('Cancel')
cancelButton.clicked.connect(optionDialog.close)
loadButton = QPushButton('Load')
loadButton.clicked.connect(self.loadRFOptionAction)
loadButton.clicked.connect(optionDialog.close)
grid = QGridLayout()
grid.addWidget(ntreeLabel, 1, 1, 1, 2)
grid.addWidget(self.ntreeSB, 1, 3, 1, 2)
grid.addWidget(criterionLabel, 2, 1, 1, 2)
grid.addWidget(self.criterionCB, 2, 3, 1, 2)
grid.addWidget(bootstrapLabel, 3, 1, 1, 2)
grid.addWidget(self.bootstrapCB, 3, 3, 1, 2)
grid.addWidget(randomStateLabel, 4, 1, 1, 2)
grid.addWidget(self.randomStateRFSB, 4, 3, 1, 2)
grid.addWidget(loadButton, 5, 3, 1, 1)
grid.addWidget(cancelButton, 5, 4, 1, 1)
optionDialog.setLayout(grid)
optionDialog.exec_()
def loadRFOptionAction(self):
'''
Loading defined RF option input
'''
rf_op_dict['n_estimators'] = self.ntreeSB.value()
rf_op_dict['criterion'] = self.criterionCB.currentText()
rf_op_dict['bootstrap'] = self.str2bool(self.bootstrapCB.currentText())
rf_op_dict['random_state'] = self.randomStateRFSB.value()
def svmOptionWindow(self):
'''
Support Vector Machine option User Interface
'''
optionDialog = QDialog()
optionDialog.setWindowTitle('Options (SVM)')
optionDialog.setWindowIcon(QIcon(resource_path('icons/setting-tool-pngrepo-com.png')))
kernelLabel = QLabel('Kernel:')
self.kernelCB = QComboBox()
self.kernelCB.addItems(['linear', 'poly', 'rbf', 'sigmoid'])
self.kernelCB.setCurrentText(svm_op_dict['kernel'])
gammaLabel = QLabel('Gamma:')
self.gammaDSB = QDoubleSpinBox()
self.gammaDSB.setRange(0, 10)
self.gammaDSB.setDecimals(3)
self.gammaDSB.setValue(svm_op_dict['gamma'])
self.gammaDSB.setAlignment(Qt.AlignRight)
cLabel = QLabel('C:')
self.cDSB = QDoubleSpinBox()
self.cDSB.setRange(.001, 10000)
self.cDSB.setDecimals(3)
self.cDSB.setValue(svm_op_dict['c'])
self.cDSB.setAlignment(Qt.AlignRight)
degreeLabel = QLabel('degree (poly):')
self.degreeSB = QSpinBox()
self.degreeSB.setRange(2, 20)
self.degreeSB.setValue(svm_op_dict['degree'])
self.degreeSB.setAlignment(Qt.AlignRight)
cancelButton = QPushButton('Cancel')
cancelButton.clicked.connect(optionDialog.close)
loadButton = QPushButton('Load')
loadButton.clicked.connect(self.loadSVMOptionAction)
loadButton.clicked.connect(optionDialog.close)
grid = QGridLayout()
grid.addWidget(kernelLabel, 1, 1, 1, 2)
grid.addWidget(self.kernelCB, 1, 3, 1, 2)
grid.addWidget(gammaLabel, 2, 1, 1, 2)
grid.addWidget(self.gammaDSB, 2, 3, 1, 2)
grid.addWidget(cLabel, 3, 1, 1, 2)
grid.addWidget(self.cDSB, 3, 3, 1, 2)
grid.addWidget(degreeLabel, 4, 1, 1, 2)
grid.addWidget(self.degreeSB, 4, 3, 1, 2)
grid.addWidget(loadButton, 5, 3, 1, 1)
grid.addWidget(cancelButton, 5, 4, 1, 1)
optionDialog.setLayout(grid)
optionDialog.exec_()
def loadSVMOptionAction(self):
'''
Loading defined SVM option input
'''
svm_op_dict['kernel'] = self.kernelCB.currentText()
svm_op_dict['gamma'] = self.gammaDSB.value()
svm_op_dict['c'] = self.cDSB.value()
svm_op_dict['degree'] = self.degreeSB.value()
def processingOptionWindow(self):
'''
Processing option User Interface
'''
self.processingOptionDialog = QDialog()
self.processingOptionDialog.setWindowTitle('Processing Options')
self.processingOptionDialog.setWindowIcon(QIcon(resource_path('icons/setting-tool-pngrepo-com.png')))
backendLabel = QLabel('Parallel Backend:')
self.backendCB = QComboBox()
self.backendCB.addItems(['loky', 'threading', 'multiprocessing'])
self.backendCB.setCurrentText(proc_op_dict['backend'])
njobsLabel = QLabel('Processing Cores:')
self.njobsSB = QSpinBox()
self.njobsSB.setRange(-100, 100)
self.njobsSB.setValue(proc_op_dict['n_jobs'])
self.njobsSB.setAlignment(Qt.AlignRight)
randomStateLabel = QLabel('Random State:')
self.randomStateProcSB = QSpinBox()
self.randomStateProcSB.setRange(0, 1000)
self.randomStateProcSB.setValue(proc_op_dict['random_state'])
self.randomStateProcSB.setAlignment(Qt.AlignRight)
self.autoNegativeCB = QCheckBox('Auto negative sign')
self.autoNegativeCB.setChecked(proc_op_dict['auto_negative'])
self.excludeOutsideCB = QCheckBox('Exclude points which out of image boundary')
self.excludeOutsideCB.setChecked(proc_op_dict['exclude_outside'])
cancelButton = QPushButton('Cancel')
cancelButton.clicked.connect(self.processingOptionDialog.close)
loadButton = QPushButton('Load')
loadButton.clicked.connect(self.loadProcessingOptionAction)
loadButton.clicked.connect(self.processingOptionDialog.close)
grid = QGridLayout()
grid.addWidget(backendLabel, 1, 1, 1, 2)
grid.addWidget(self.backendCB, 1, 3, 1, 2)
grid.addWidget(njobsLabel, 2, 1, 1, 2)
grid.addWidget(self.njobsSB, 2, 3, 1, 2)
grid.addWidget(randomStateLabel, 3, 1, 1, 2)
grid.addWidget(self.randomStateProcSB, 3, 3, 1, 2)
grid.addWidget(self.autoNegativeCB, 4, 1, 1, 4)
grid.addWidget(self.excludeOutsideCB, 5, 1, 1, 4)
grid.addWidget(loadButton, 6, 3, 1, 1)
grid.addWidget(cancelButton, 6, 4, 1, 1)
self.processingOptionDialog.setLayout(grid)
self.processingOptionDialog.exec_()
def loadProcessingOptionAction(self):
'''
Loading defined processing option input
'''
if self.njobsSB.value() == 0:
self.processingOptionDialog.close()
self.warningWithoutClear(
'Do not insert zero on Processing Cores!'
)
self.processingOptionWindow()
else:
proc_op_dict['backend'] = self.backendCB.currentText()
proc_op_dict['n_jobs'] = self.njobsSB.value()
proc_op_dict['random_state'] = self.randomStateProcSB.value()
proc_op_dict['auto_negative'] = self.autoNegativeCB.isChecked()
proc_op_dict['exclude_outside'] = self.excludeOutsideCB.isChecked()
def predict(self):
'''
Sending parameters and inputs from widget to Process Class
'''
print('widget predict')
self.resultText.clear()
self.progressBar.setValue(0)
if self.limitADSB.value() < self.limitBDSB.value():
a = self.limitADSB.value()
b = self.limitBDSB.value()
self.limitADSB.setValue(b)
self.limitBDSB.setValue(a)
global time_list
time_list = []
init_input = {
'depth_label': self.depthHeaderCB.currentText(),
'train_size': self.trainPercentDSB.value() / 100,
'limit_state': self.limitCheckBox.isChecked(),
'limit_a': self.limitADSB.value(),
'limit_b': self.limitBDSB.value(),
'method': self.methodCB.currentText()
}
try:
if sample_raw[self.depthHeaderCB.currentText()].dtype == 'float':
self.sdbProcess = Process()
self.widget_signal.connect(self.sdbProcess.inputs)
self.widget_signal.emit(init_input)
self.sdbProcess.start()
self.sdbProcess.time_signal.connect(self.timeCounting)
self.sdbProcess.thread_signal.connect(self.results)
self.sdbProcess.warning_with_clear.connect(self.warningWithClear)
else:
self.warningWithClear(
'Please select headers correctly!'
)
except NameError:
self.warningWithClear(
'No depth sample loaded. Please load your depth sample!'
)
def timeCounting(self, time_text):
'''
Receive time value on every step and its corresponding processing
text to show in result text browser and increase progress bar.
'''
time_list.append(time_text[0])
self.resultText.append(time_text[1])
self.progressBar.setValue(self.progressBar.value() + 1)
if self.progressBar.value() == self.progressBar.maximum():
self.completeDialog()
def results(self, result_dict):
'''
Recieve processing results and filter the predicted value to depth
limit window (if enabled).
Counting runtimes using saved time values and printing result info.
'''
global z_predict
z_predict = result_dict['z_predict']
rmse, mae, r2 = result_dict['rmse'], result_dict['mae'], result_dict['r2']
global train_data_df, test_data_df
train_data_df, test_data_df = result_dict['train'], result_dict['test']
global sample_geodataframe, sample_dataframe
sample_geodataframe, sample_dataframe = result_dict['sample_edit'], result_dict['sample_df']
if self.limitCheckBox.isChecked() == False:
print('checking prediction')
z_predict[z_predict < self.limitBDSB.value()] = np.nan
z_predict[z_predict > self.limitADSB.value()] = np.nan
print_limit = (
'Depth Limit:\t\tfrom ' + str(self.limitADSB.value()) + ' m ' +
'to ' + str(self.limitBDSB.value()) + ' m'
)
else:
print_limit = (
'Depth Limit:\t\tDisabled'
)
if proc_op_dict['auto_negative'] == True:
auto_negative = 'Enabled'
elif proc_op_dict['auto_negative'] == False:
auto_negative = 'Disabled'
time_array = np.array(time_list)
time_diff = time_array[1:] - time_array[:-1]
runtime = np.append(time_diff, time_list[-1] - time_list[0])
coord1 = np.array(image_raw.transform * (0, 0))
coord2 = np.array(image_raw.transform * (1, 1))
pixel_size = abs(coord2 - coord1)
global print_result_info
print_result_info = (
'Software Version:\t' + SDB_GUI_VERSION + '\n\n' +
'Image Input:\t\t' + self.imglocList.toPlainText() + ' (' +
str(round(img_size / 2**20, 2)) + ' MB)\n' +
'Sample Data:\t\t' + self.samplelocList.toPlainText() + ' (' +
str(round(sample_size / 2**20, 2)) + ' MB)\n\n' +
print_limit + '\n' +
'Used Sample:\t\t' + str(sample_dataframe.shape[0]) + ' points (' +
str(round(sample_dataframe.shape[0] / sample_raw.shape[0] * 100, 2)) +
'% of all sample)\n' +
'Train Data:\t\t' + str(train_data_df.shape[0]) + ' points (' +
str(self.trainPercentDSB.value()) + ' % of used sample)\n' +
'Test Data:\t\t' + str(test_data_df.shape[0]) + ' points (' +
str(100 - self.trainPercentDSB.value()) + ' % of used sample)\n\n' +
'Method:\t\t' + self.methodCB.currentText() + '\n' +