class MainView(ZApplicationView):
# MainView Constructor
def __init__(self, title, x, y, width, height):
super(MainView, self).__init__(title, x, y, width, height)
self.font = QFont("Arial", 10)
self.setFont(self.font)
# 1 Add a labeled combobox to top dock area
self.add_datasets_combobox()
# 2 Add a textedit to the left pane of the center area.
self.text_editor = QTextEdit()
self.text_editor.setFont(self.font)
self.text_editor.setLineWrapColumnOrWidth(600)
self.text_editor.setLineWrapMode(QTextEdit.FixedPixelWidth)
self.description = QTextEdit()
self.description.setFont(self.font)
self.description.setLineWrapColumnOrWidth(600)
self.description.setLineWrapMode(QTextEdit.FixedPixelWidth)
# 3 Add a tabbled_window to the right pane of the center area.
self.tabbed_window = ZTabbedWindow(self, 0, 0, width/2, height)
# 4 Add a figure_view to the tabbed_window
self.figure_view = ZScalableScrolledFigureView(self, 0, 0, width/2, height)
self.add(self.text_editor)
self.add(self.tabbed_window)
self.tabbed_window.add("Description", self.description)
self.tabbed_window.add("Importances", self.figure_view)
self.figure_view.hide()
self.show()
def add_datasets_combobox(self):
self.dataset_id = Boston
self.datasets_combobox = ZLabeledComboBox(self, "Datasets", Qt.Horizontal)
# We use the following datasets of sklearn to test RandomForestRegressor.
self.datasets = {"Boston": Boston, "Diabetes": Diabetes}
title = self.get_title()
self.setWindowTitle( "Boston" + " - " + title)
self.datasets_combobox.add_items(self.datasets.keys())
self.datasets_combobox.add_activated_callback(self.datasets_activated)
self.datasets_combobox.set_current_text(self.dataset_id)
self.start_button = ZPushButton("Start", self)
self.clear_button = ZPushButton("Clear", self)
self.start_button.add_activated_callback(self.start_button_activated)
self.clear_button.add_activated_callback(self.clear_button_activated)
self.datasets_combobox.add(self.start_button)
self.datasets_combobox.add(self.clear_button)
self.set_top_dock(self.datasets_combobox)
def write(self, text):
self.text_editor.append(text)
self.text_editor.repaint()
def datasets_activated(self, text):
self.dataset_id = self.datasets[text]
title = self.get_title()
self.setWindowTitle(text + " - " + title)
def start_button_activated(self, text):
self.model = RandomForestRegressorModel(self.dataset_id, self)
self.start_button.setEnabled(False)
self.clear_button.setEnabled(False)
try:
self.model.run()
except:
pass
self.start_button.setEnabled(True)
self.clear_button.setEnabled(True)
def file_new(self):
self.text_editor.setText("")
self.description.setText("")
self.figure_view.hide()
plt.close()
def clear_button_activated(self, text):
self.file_new()
def visualize(self, importances):
self.figure_view.show()
plt.close()
plt.title("Importances in Regression Model")
importances.plot(kind = "barh")
self.figure_view.set_figure( plt)
class MainView(ZApplicationView):
# Class variables
# ClassifierView Constructor
def __init__(self, title, x, y, width, height):
super(MainView, self).__init__(title, x, y, width, height)
self.font = QFont("Arial", 10)
self.setFont(self.font)
# 1 Add a labeled combobox to top dock area
self.add_datasets_combobox()
# 2 Add a textedit to the left pane of the center area.
self.text_editor = QTextEdit()
self.text_editor.setLineWrapColumnOrWidth(600)
self.text_editor.setLineWrapMode(QTextEdit.FixedPixelWidth)
# 3 Add a description to display dataset.DESCR.
self.description = QTextEdit()
self.description.setLineWrapColumnOrWidth(600)
self.description.setLineWrapMode(QTextEdit.FixedPixelWidth)
# 4 Add a tabbed_window to the tabbed_window.
self.tabbed_window = ZTabbedWindow(self, 0, 0, width / 2, height)
# 5 Add a figure_view to the tabbed_window.
self.figure_view = ZScalableScrolledFigureView(self, 0, 0, width / 2,
height)
self.add(self.text_editor)
self.add(self.tabbed_window)
self.tabbed_window.add("Description", self.description)
self.tabbed_window.add("ConfusionMatrix", self.figure_view)
self.figure_view.hide()
self.show()
def add_datasets_combobox(self):
self.dataset_id = Iris
self.datasets_combobox = ZLabeledComboBox(self, "Datasets",
Qt.Horizontal)
# We use the following datasets of sklearn to test XGBClassifier.
self.datasets = {
"Iris": Iris,
"Digits": Digits,
"Wine": Wine,
"BreastCancer": BreastCancer
}
title = self.get_title()
self.setWindowTitle("Iris" + " - " + title)
self.datasets_combobox.add_items(self.datasets.keys())
self.datasets_combobox.add_activated_callback(self.datasets_activated)
self.datasets_combobox.set_current_text(self.dataset_id)
self.start_button = ZPushButton("Start", self)
self.clear_button = ZPushButton("Clear", self)
self.start_button.add_activated_callback(self.start_button_activated)
self.clear_button.add_activated_callback(self.clear_button_activated)
self.datasets_combobox.add(self.start_button)
self.datasets_combobox.add(self.clear_button)
self.set_top_dock(self.datasets_combobox)
def write(self, text):
self.text_editor.append(text)
self.text_editor.repaint()
def datasets_activated(self, text):
self.dataset_id = self.datasets[text]
title = self.get_title()
self.setWindowTitle(text + " - " + title)
def start_button_activated(self, text):
self.model = LightGBMClassifierModel(self.dataset_id, self)
self.start_button.setEnabled(False)
self.clear_button.setEnabled(False)
try:
self.model.run()
except:
pass
self.start_button.setEnabled(True)
self.clear_button.setEnabled(True)
def clear_button_activated(self, text):
self.text_editor.setText("")
self.description.setText("")
self.figure_view.hide()
plt.close()
def visualize(self, cmatrix):
self.figure_view.show()
plt.close()
sns.set()
df = pd.DataFrame(cmatrix)
sns.heatmap(df, annot=True, fmt="d")
# Set a new figure to the figure_view.
self.figure_view.set_figure(plt)
class ZImageClassifierView(ZApplicationView):
# Class variables
# ClassifierView Constructor
def __init__(self, title, x, y, width, height, datasets={"ImageModel": 0}):
super(ZImageClassifierView, self).__init__(title, x, y, width, height)
self.font = QFont("Arial", 10)
self.setFont(self.font)
self.datasets = datasets
self.model_loaded = False
self.class_names_set = [None, None]
# Image filename to be classified
self.filename = None
# Target image to be classified
self.image = None
# 1 Add a labeled combobox to top dock area
self.add_datasets_combobox()
# 2 Add a textedit to the left pane of the center area.
self.text_editor = QTextEdit()
self.text_editor.setLineWrapColumnOrWidth(600)
self.text_editor.setLineWrapMode(QTextEdit.FixedPixelWidth)
self.text_editor.setGeometry(0, 0, width/2, height)
# 3 Add a tabbed_window the rigth pane of the center area.
self.tabbed_window = ZTabbedWindow(self, 0, 0, width/2, height)
# 4 Add a imageview to the tabbed_window.
self.image_view = ZScalableScrolledImageView(self, 0, 0, width/3, height/3)
self.tabbed_window.add("SourceImage", self.image_view)
# 5 Add a test_imageview to the right pane of the center area.
self.test_image_view = ZScalableScrolledImageView(self, 0, 0, width/3, height/3)
self.tabbed_window.add("TestImage", self.test_image_view)
self.add(self.text_editor)
self.add(self.tabbed_window)
def add_datasets_combobox(self):
datasetkey = list(self.datasets.keys())[0]
self.dataset_id = self.datasets[datasetkey]
print("Current combobox item {} {}".format(datasetkey, self.dataset_id))
self.datasets_combobox = ZLabeledComboBox(self, "Datasets", Qt.Horizontal)
self.datasets_combobox.setFont(self.font)
title = self.get_title()
self.setWindowTitle(self.__class__.__name__ + " - " + title)
self.datasets_combobox.add_items(self.datasets.keys())
self.datasets_combobox.add_activated_callback(self.datasets_activated)
self.datasets_combobox.set_current_text(self.dataset_id)
self.classifier_button = ZPushButton("Classify", self)
self.classifier_button.setEnabled(False)
self.clear_button = ZPushButton("Clear", self)
self.classifier_button.add_activated_callback(self.classifier_button_activated)
self.clear_button.add_activated_callback(self.clear_button_activated)
self.datasets_combobox.add(self.classifier_button)
self.datasets_combobox.add(self.clear_button)
self.set_top_dock(self.datasets_combobox)
def write(self, text):
self.text_editor.append(text)
self.text_editor.repaint()
def datasets_activated(self, text):
pass
# Show FileOpenDialog and select an image file.
def file_open(self):
if self.model_loaded:
options = QFileDialog.Options()
filename, _ = QFileDialog.getOpenFileName(self,"FileOpenDialog", "",
"All Files (*);;Image Files (*.png;*jpg;*.jpeg)", options=options)
if filename:
self.filename = filename
self.load_file(filename)
self.classifier_button.setEnabled(True)
else:
QMessageBox.warning(self, "ImageClassifier: Weight File Missing",
"Please run: python RoadSignsModel.py " + str(self.dataset_id))
def remove_alpha_channel(self, array):
shape = array.shape
if len(shape) ==3:
w, h, c = shape
if c == 4:
#(w, h, 4) -> (w, h, 3)
#print("Remove the alpha channel of array")
array = array[:,:,:3]
return array
def load_file(self, filename):
from keras.preprocessing.image import load_img, img_to_array
try:
image_cropper = ZPILImageCropper(filename)
# 1 Crop larget_central square region from an image of filename.
cropped_image = image_cropper.crop_largest_central_square_region()
# 2 Load an image from the cropped_fle.
self.image_view.set_image(img_to_array(cropped_image))
self.image_view.update_scale()
self.set_filenamed_title(filename)
# 3 Resize the cropped_image
self.image = cropped_image.resize(self.image_size)
# 4 Convert the self.image to numpy ndarray and remove alpha channel.
self.image = self.remove_alpha_channel(img_to_array(self.image))
# 5 Set self.nadarryy to the test_image_view.
self.test_image_view.set_image(self.image)
# 6 Convert self.image in range[0-1.0]
self.image = self.image.astype('float32')/255.0
# 7 Expand the dimension of the self.image
self.image = np.expand_dims(self.image, axis=0)
#print(self.image.shape)
except:
self.write(formatted_traceback())
def classifier_button_activated(self, text):
self.classifier_button.setEnabled(False)
self.clear_button.setEnabled(False)
try:
self.classify()
except:
self.write(formatted_traceback())
self.classifier_button.setEnabled(True)
self.clear_button.setEnabled(True)
def get_top_five(self, predictions, classes, K=5):
pred = predictions[0]
indices = np.argpartition(pred, -K)[-K:]
results = []
for i in indices:
results.append([pred[i], classes[i]])
results = sorted(results, reverse=True)
return results
def get_class_names(self, path="./class_names.txt"):
classes = []
with open(path, "r") as file:
classes = [s.strip() for s in file.readlines()]
return classes
def classify(self):
self.write("------------------------------------------------------------")
self.write("classify start")
self.write(self.filename)
prediction = self.model.predict(self.image)
pred = np.argmax(prediction, axis=1)
#self.write("Prediction: index " + str(pred))
class_names = self.model.classes
if pred >0 or pred <len(class_names):
self.write("Prediction:" + class_names[int(pred)])
self.write("classify end")
def clear_button_activated(self, text):
self.text_editor.setText("")
pass
