def __init__(self):
self.already_marked_dir = 'Datasets/legacy_jsons/agro'
self.already_marked_list = os.listdir(self.already_marked_dir)
super(WindowClassificationPicture, self).__init__()
self.setWindowTitle("Plant Disease Recognizer")
# TODO some dev stuff
# with open(self.choose_json(content_title='config data')) as config_fp:
with open('config_create_json.json') as config_fp:
config_dict = json.load(config_fp)
# self.img_path = self.choose_picture()
# self.img_name = os.path.splitext(self.img_path)[0]
self.window_shape = config_dict['window_shape']
self.classes = config_dict['classes']
self.default_label_size = config_dict['qt_label_size']
self.img_thumb = config_dict['img_thumb']
self._init_hbox_control()
self._init_main_menu()
self.main_layout = MyGridWidget(hbox_control=self.hbox_control)
self.setCentralWidget(self.main_layout)
self.showFullScreen()
self.choose_and_render_image()
# for offset calculation
self.last_x = 0
self.last_y = 0
def __init__(self):
super(WindowPredictOnImage, self).__init__()
with open(os.path.abspath('config_full_system.json')) as config_fp:
self.config_dict = json.load(config_fp)
# load layers
self.clusterer = get_clusterer_by_name(
self.config_dict['clusterer']['name'],
self.config_dict['clusterer']['args'])
if self.config_dict['preprocessor']['use']:
self.segmentator = get_preprocessor_by_name(
self.config_dict['preprocessor']['name'],
self.config_dict['preprocessor']['args'])
self.classifier = get_classifier_by_name(
self.config_dict['classifier']['name'],
self.config_dict['classifier']['args'])
self.bad_key = self.config_dict['classifier']['bad_key']
self._define_max_key_len()
self._parse_image()
self.main_layout = MyGridWidget(hbox_control=self.hbox_control)
self.pbar = QProgressBar(self)
self.main_layout.layout.addWidget(self.pbar)
self.right_text_labels = []
self.setCentralWidget(self.main_layout)
self.showFullScreen()
self.update_main_layout()
def __init__(self, x_data, y_data, y_predicted):
super(WindowShowUnetFitting, self).__init__()
with open('config_full_system.json') as config_fp:
self.label_size = json.load(config_fp)['gui']['qt_label_size']
self.main_layout = MyGridWidget(hbox_control=self.hbox_control)
self.setCentralWidget(self.main_layout)
self.showFullScreen()
self.update_main_layout(x_data, y_data, y_predicted)
class WindowShowUnetFitting(WindowInterface):
##############################################################
# ---------------- init stuff --------------------------------
##############################################################
def __init__(self, x_data, y_data, y_predicted):
super(WindowShowUnetFitting, self).__init__()
with open('config_full_system.json') as config_fp:
self.label_size = json.load(config_fp)['gui']['qt_label_size']
self.main_layout = MyGridWidget(hbox_control=self.hbox_control)
self.setCentralWidget(self.main_layout)
self.showFullScreen()
self.update_main_layout(x_data, y_data, y_predicted)
def _init_hbox_control(self):
self.hbox_control = QtWidgets.QHBoxLayout()
self.hbox_control.addStretch(1)
self.hbox_control.addWidget(
ControlButton("Update", self.update_main_layout))
self.hbox_control.addWidget(ControlButton("Quit", self.quit_default))
##############################################################
# ---------------- gui logic stuff ---------------------------
##############################################################
def clear(self):
self.main_layout.clear()
def update_main_layout(self, x_data, y_data, y_predicted):
self.clear()
label_list = []
for x, y, y_answer in zip(x_data, y_data, y_predicted):
label_list.append(
ImageTextLabel(x=x, text='input', label_size=self.label_size))
label_list.append(
ImageTextLabel(x=y, text='output', label_size=self.label_size))
label_list.append(
ImageTextLabel(x=y_answer,
text='U-Net answer',
label_size=self.label_size))
x_len = 3
self.main_layout.update_grid(
windows_width=self.frameGeometry().width(),
window_height=self.frameGeometry().height(),
x_len=x_len,
y_len=int(x_data.shape[0] * 3 / x_len),
label_list=label_list)
def __init__(self, x_data, y_data, y_predicted, classes):
super(WindowShowPredictions, self).__init__()
config_dict = self.load_dict_from_json_with_keys(
key_list=['qt_label_size'])
self.label_size = config_dict['qt_label_size']
self.x_data = x_data
self.y_data = y_data
self.y_predicted = y_predicted
self.classes = classes
self._define_max_key_len()
self.main_layout = MyGridWidget(hbox_control=self.hbox_control)
self.setCentralWidget(self.main_layout)
self.showFullScreen()
self.update_main_layout()
class WindowClassificationPicture(WindowInterface):
##############################################################
# ---------------- init stuff --------------------------------
##############################################################
def __init__(self):
self.already_marked_dir = 'Datasets/legacy_jsons/agro'
self.already_marked_list = os.listdir(self.already_marked_dir)
super(WindowClassificationPicture, self).__init__()
self.setWindowTitle("Plant Disease Recognizer")
# TODO some dev stuff
# with open(self.choose_json(content_title='config data')) as config_fp:
with open('config_create_json.json') as config_fp:
config_dict = json.load(config_fp)
# self.img_path = self.choose_picture()
# self.img_name = os.path.splitext(self.img_path)[0]
self.window_shape = config_dict['window_shape']
self.classes = config_dict['classes']
self.default_label_size = config_dict['qt_label_size']
self.img_thumb = config_dict['img_thumb']
self._init_hbox_control()
self._init_main_menu()
self.main_layout = MyGridWidget(hbox_control=self.hbox_control)
self.setCentralWidget(self.main_layout)
self.showFullScreen()
self.choose_and_render_image()
# for offset calculation
self.last_x = 0
self.last_y = 0
def choose_and_render_image(self):
self.img_path = self.choose_picture()
if self.img_path != '':
self.clear()
self.img_name = os.path.splitext(self.img_path)[0]
self._init_images()
self._init_label_list()
self.update_main_layout()
def _init_hbox_control(self):
self.hbox_control = QtWidgets.QHBoxLayout()
self.zoom_list = [0.25, 0.5, 0.75, 1]
self.zoom = self.zoom_list[0]
self.zoom_no = 0
self.hbox_control.addWidget(
ControlButton("Okay",
self.okay_pressed,
styleSheet='background-color: #0cdb3c'))
self.hbox_control.addWidget(
ControlButton("Choose image",
self.choose_and_render_image,
styleSheet='background-color: #ffbe25'))
self.hbox_control.addWidget(
ControlButton("Quit",
self.quit_default,
styleSheet='background-color: #e84a1a'))
def _init_main_menu(self):
mainMenu = self.menuBar()
zoomMenu = mainMenu.addMenu('Zoom')
def add_zoom_to_menu(new_zoom):
newAct = QAction('Zoom %d %%' % (zoom * 100), self)
newAct.triggered.connect(lambda: self.change_zoom(new_zoom))
zoomMenu.addAction(newAct)
for zoom in self.zoom_list:
add_zoom_to_menu(zoom)
####################################################################################
# ------------------------ MOUSE DRAGGING PART -------------------------------------
####################################################################################
def mousePressEvent(self, event):
# TODO to fix
if hasattr(self, 'img_name'):
rect = list(
map(lambda x: x * self.zoom_list[self.zoom_no],
self.full_img.size))
self.first_x = max(0, min(int(rect[0]), event.x() + self.last_x))
self.first_y = max(0, min(int(rect[1]), event.y() + self.last_y))
print("event ", event.x(), event.y())
print("last ", self.last_x, self.last_y)
print("press offset ", self.first_x, self.first_y)
def mouseMoveEvent(self, event):
if hasattr(self, 'img_name'):
self.v_bar = self.main_layout.left_scroll_area.verticalScrollBar()
self.h_bar = self.main_layout.left_scroll_area.horizontalScrollBar(
)
rect = list(
map(lambda x: x * self.zoom_list[self.zoom_no],
self.full_img.size))
if self.main_layout.width() < rect[0]:
x = self.first_x - event.x()
y = self.first_y - event.y()
self.last_x = x
self.last_y = y
print("\n\nDRAG OFFSET:", x, y)
self.main_layout.set_offset(x, y)
####################################################################################
# ------------------------ WHEEL PART ----------------------------------------------
####################################################################################
# mouse wheel event scrollо
def wheelEvent(self, event):
if hasattr(self, 'img_name'):
modifiers = QApplication.keyboardModifiers()
if modifiers == QtCore.Qt.ControlModifier:
if event.angleDelta().y() > 0:
if self.zoom_no < len(self.zoom_list) - 1:
self.zoom_no += 1
else:
if self.zoom_no > 0: self.zoom_no -= 1
self.change_zoom(self.zoom_list[self.zoom_no])
self.move_by_cursor()
def move_by_cursor(self):
cursor_x = QtGui.QCursor.pos().x()
cursor_y = QtGui.QCursor.pos().y()
window_width = self.main_layout.width()
window_height = self.main_layout.height()
rect = list(
map(lambda x: x * self.zoom_list[self.zoom_no],
self.full_img.size))
real_image_width = int(rect[0])
real_image_height = int(rect[1])
print("x coor ", cursor_x, window_width, real_image_width)
if (real_image_width < window_width | real_image_height <
window_height):
print("nothing to move")
else:
koef_x = (cursor_x) / real_image_width
koef_y = (cursor_y) / real_image_height
offset_x = (real_image_width - window_width) * koef_x
offset_y = (real_image_height - window_height) * koef_y
# TODO famous math constant 4 and 2
x = int(offset_x * 4)
y = int(offset_y * 2)
print("\n\nZOOM OFFSET:", x, y)
self.main_layout.set_offset(x, y)
# TODO maybe someday zoom will work
# self.last_x = x
# self.last_y = y
####################################################################################
# ------------------------ ZOOM PART -----------------------------------------------
####################################################################################
def change_zoom(self, new_zoom):
self.zoom = new_zoom
print('new zoom = %d' % self.zoom)
self.update_main_layout()
def _init_images(self):
def get_marked_indexes():
marked_indexes = []
pref_path = self.img_path.split('.')[0].split('/')[-1]
if pref_path in list(
map(lambda repeat_file: repeat_file.split('.')[0],
self.already_marked_list)):
with open("%s/%s.json" %
(self.already_marked_dir, pref_path)) as repeat_fp:
x_repeated = np.array(json.load(repeat_fp)['x_data'],
dtype='uint8')
mae_threshold = 1.2 # experimental
for i, x in enumerate(self.x_data_full['x_data']):
for rep_i, rep_x in enumerate(x_repeated):
mae = abs((x - rep_x).astype('int8')).mean()
if mae < mae_threshold:
print('%d->%d:mae = %.3f' % (rep_i, i, mae))
marked_indexes.append(i)
break
if len(marked_indexes) != len(x_repeated):
raise Exception('bad threshold')
return marked_indexes
self.pre_rendered_img_dict = {}
QApplication.setOverrideCursor(QtGui.QCursor(QtCore.Qt.WaitCursor))
print("rendering image...")
self.x_data_full, self.full_img = \
dmk.get_x_from_croped_img(
path_to_img=self.img_path,
window_shape=self.window_shape,
step=1.0,
verbose=True
)
print("ok")
self.marked_indexes = get_marked_indexes()
QApplication.setOverrideCursor(QtGui.QCursor(QtCore.Qt.ArrowCursor))
print("ok")
def _init_label_list(self):
self.label_list = []
for i, x in enumerate(self.x_data_full['x_data']):
if i in self.marked_indexes:
img_data = x * 10
else:
img_data = x
self.label_list.append(
MergedTrainExLabel(x_data=img_data,
classes=self.classes,
label_size=self.default_label_size))
##############################################################
# ---------------- gui logic stuff ---------------------------
##############################################################
def clear(self):
self.main_layout.clear()
def update_main_layout(self):
self.clear()
for label in self.label_list:
label.updateImage(label_size=list(
map(lambda x: x * self.zoom, self.default_label_size)))
self.main_layout.update_grid(
windows_width=self.main_layout.max_width,
window_height=self.main_layout.max_height,
x_len=int(self.full_img.size[0] /
self.x_data_full["x_data"].shape[1]),
y_len=int(self.full_img.size[1] /
self.x_data_full["x_data"].shape[2]),
label_list=self.label_list)
print("img_size = %s\nex_num = %d\n" %
(str(self.full_img.size), len(self.x_data_full['x_data'])))
def okay_pressed(self):
y_data = np.empty(0)
for class_name in self.classes.keys():
for sub_class_name in self.classes[class_name]:
self.classes[class_name][sub_class_name]['num'] = 0
# Now we're going to store only examples of diseased plants
x_data_full = {
'x_data': np.empty(0, dtype='uint8'),
'x_coord': [],
'longitudes': [],
'latitudes': []
}
ex_num = 0
for x, label in zip(self.x_data_full['x_data'],
self.main_layout.label_list):
if label.class_name is not None:
x_data_full['x_data'] = np.append(x_data_full['x_data'], x)
y_data = np.append(
y_data, self.classes[label.class_name][
label.sub_class_name]['value'])
self.classes[label.class_name][
label.sub_class_name]['num'] += 1
ex_num += 1
x_data_full['x_data'].shape = (ex_num, *self.window_shape)
y_data.shape = (len(x_data_full['x_data']), 1)
dmk.json_train_create(path="%s.json" % self.img_name,
x_data_full=x_data_full,
y_data=y_data,
img_shape=self.full_img.size,
classes=self.classes)
print("OKAY")
self.choose_and_render_image()
class WindowShowPredictions(WindowInterface):
##############################################################
# ---------------- init stuff --------------------------------
##############################################################
def __init__(self, x_data, y_data, y_predicted, classes):
super(WindowShowPredictions, self).__init__()
config_dict = self.load_dict_from_json_with_keys(
key_list=['qt_label_size'])
self.label_size = config_dict['qt_label_size']
self.x_data = x_data
self.y_data = y_data
self.y_predicted = y_predicted
self.classes = classes
self._define_max_key_len()
self.main_layout = MyGridWidget(hbox_control=self.hbox_control)
self.setCentralWidget(self.main_layout)
self.showFullScreen()
self.update_main_layout()
def _init_hbox_control(self):
self.hbox_control = QtWidgets.QHBoxLayout()
self.hbox_control.addStretch(1)
self.hbox_control.addWidget(
ControlButton("Update", self.update_main_layout))
self.hbox_control.addWidget(ControlButton("Quit", self.quit_default))
def _define_max_key_len(self):
self.max_key_len = 0
for key, value in self.classes.items():
if len(key) > self.max_key_len:
self.max_key_len = len(key)
##############################################################
# ---------------- gui logic stuff ---------------------------
##############################################################
def clear(self):
self.main_layout.clear()
def update_main_layout(self):
self.clear()
def get_key_by_answer(pos_code):
answer = {'mae': 9999, 'key': None, 'value': 0}
for key in self.classes.keys():
mae = np.average(abs((self.classes[key]['value'] - pos_code)))
if mae < answer['mae']:
answer['mae'] = mae
answer['key'] = key
answer['value'] = max(pos_code)
return answer
def add_spaces(word, new_size): # TODO fix gui label alignment
while len(word) < new_size:
word += '_'
return word
label_list = []
for x, y, y_answer in zip(self.x_data, self.y_data, self.y_predicted):
answer = get_key_by_answer(pos_code=y_answer)
right_ans = get_key_by_answer(pos_code=y)
is_right_ans = True if answer['key'] == right_ans['key'] else False
answer['key'] = add_spaces(answer['key'],
new_size=self.max_key_len)
label_list.append(
ImageTextLabel(x=x,
text='%s %.2f' %
(answer['key'], answer['value']),
label_size=self.label_size))
if is_right_ans:
label_list[-1].text_label.setStyleSheet(
'background-color: green')
else:
label_list[-1].text_label.setStyleSheet(
'background-color: red')
rect_len = int(np.sqrt(len(self.x_data)))
self.main_layout.update_grid(
windows_width=self.frameGeometry().width(),
window_height=self.frameGeometry().height(),
x_len=rect_len,
y_len=rect_len,
label_list=label_list)
class WindowPredictOnImage(WindowInterface):
##############################################################
# ---------------- init stuff --------------------------------
##############################################################
def __init__(self):
super(WindowPredictOnImage, self).__init__()
with open(os.path.abspath('config_full_system.json')) as config_fp:
self.config_dict = json.load(config_fp)
# load layers
self.clusterer = get_clusterer_by_name(
self.config_dict['clusterer']['name'],
self.config_dict['clusterer']['args'])
if self.config_dict['preprocessor']['use']:
self.segmentator = get_preprocessor_by_name(
self.config_dict['preprocessor']['name'],
self.config_dict['preprocessor']['args'])
self.classifier = get_classifier_by_name(
self.config_dict['classifier']['name'],
self.config_dict['classifier']['args'])
self.bad_key = self.config_dict['classifier']['bad_key']
self._define_max_key_len()
self._parse_image()
self.main_layout = MyGridWidget(hbox_control=self.hbox_control)
self.pbar = QProgressBar(self)
self.main_layout.layout.addWidget(self.pbar)
self.right_text_labels = []
self.setCentralWidget(self.main_layout)
self.showFullScreen()
self.update_main_layout()
def _init_hbox_control(self):
self.hbox_control = QtWidgets.QHBoxLayout()
self.hbox_control.addStretch(1)
self.hbox_control.addWidget(
ControlButton("Predict", self.update_main_layout))
self.hbox_control.addWidget(
ControlButton("Choose model", self.choose_NN))
self.hbox_control.addWidget(
ControlButton("Choose image", self._parse_image))
self.hbox_control.addWidget(ControlButton("Quit", self.quit_default))
def _parse_image(self):
self.picture_path = self.choose_picture()
x_cropped = self.clusterer.cluster(self.picture_path)
self.x_data = x_cropped['x_data']
def _define_max_key_len(self):
self.max_key_len = 0
for key, value in self.classifier.classes.items():
if len(key) > self.max_key_len:
self.max_key_len = len(key)
def clear(self):
self.main_layout.clear()
for text_label in self.right_text_labels:
text_label.setParent(None)
def update_main_layout(self):
self.clear()
self.predict_thread = PredictThread(self)
fake_timer = FakeTimer(self)
fake_timer.valueChanged.connect(self.predict_thread.valueChanged.emit)
self.predict_thread.valueChanged.connect(self.pbar.setValue)
self.predict_thread.canDraw.connect(self.draw_result)
self.predict_thread.fakeTimerToStop.connect(fake_timer.terminate)
self.main_layout.update_scroll(
windows_width=self.frameGeometry().width(),
window_height=self.frameGeometry().height(),
)
# self.x_data = self.x_data[:4]
self.predict_thread.start()
fake_timer.start()
def draw_result(self):
def get_key_by_answer(pos_code, bad_key):
answer = {'mae': 9999, 'key': bad_key, 'value': 0}
if sum(pos_code) > 0:
for key in self.classifier.classes.keys():
mae = np.average(
abs((self.classifier.classes[key]['value'] -
pos_code)))
if mae < answer['mae']:
answer['mae'] = mae
answer['key'] = key
answer['value'] = max(pos_code)
return answer
label_list = []
classes_dict = self.classifier.classes.copy()
classes_dict[self.bad_key] = {}
for key in [*classes_dict.keys(), self.bad_key]:
classes_dict[key]['num'] = 0
classes_dict[key]['indexes'] = []
for i, (x, y_answer) in enumerate(
zip(self.x_data, self.predict_thread.y_answer)):
answer = get_key_by_answer(pos_code=y_answer, bad_key=self.bad_key)
classes_dict[answer['key']]['num'] += 1
classes_dict[answer['key']]['indexes'].append(i)
answer['key'] += (self.max_key_len - len(answer['key'])) * " "
answer['key'] = "%d: %s" % (i, answer['key'])
label_list.append(
ImageTextLabel(
x=x,
text='%s %.2f' % (answer['key'], answer['value']),
label_size=self.config_dict['gui']['qt_label_size']))
self.right_text_labels = []
for key in classes_dict:
text_label = QLabel()
text_label.setText("%s: %d" % (key, classes_dict[key]['num']))
text_label.setAlignment(QtCore.Qt.AlignLeft)
print("%s: %s" % (key, str(classes_dict[key]['indexes'])))
self.right_text_labels.append(text_label)
self.main_layout.right_layout.addWidget(text_label)
rect_len = int(np.sqrt(len(self.x_data)))
self.main_layout.update_grid(
windows_width=self.frameGeometry().width(),
window_height=self.frameGeometry().height(),
x_len=rect_len,
y_len=rect_len,
label_list=label_list)
def choose_NN(self):
self.weights_path = str(
QtWidgets.QFileDialog.getOpenFileName(self,
"Open *.h5 with NN weights",
"models", "*.h5 *.H5")[0])
self.structure_path = str(
QtWidgets.QFileDialog.getOpenFileName(
self, "Open *.json with NN structure", "models",
"*.json *.JSON")[0])
if os.path.isfile(self.weights_path) and os.path.isfile(
self.structure_path):
self.classifier = get_full_model(json_path=self.structure_path,
h5_path=self.weights_path)
else:
print(
"Files with model weights and model structure does't choosed")
def __init__(self, json_list):
super(WindowMultipleExamples, self).__init__()
self.postfix = 'joined'
self.label_size = (224, 224)
# TODO maybe will be restored someday
# with open(self.choose_json(content_title='config augmentation data')) as aug_config_fp:
with open('config_data_augmentation.json') as aug_config_fp:
aug_config_dict = json.load(aug_config_fp)
alghs_dict = aug_config_dict['algorithms']
self.max_aug_part = aug_config_dict['aug_part']
self.augment_all = aug_config_dict['augment_all']
self.output_json = aug_config_dict['output_json']
self.save_data_binary = aug_config_dict['save_data_to_binary']
self.save_data_dir = aug_config_dict['save_data_to_dir']
for key, value in alghs_dict.items():
if value['use'] and len(
value['val_list']) != self.max_aug_part:
raise Exception('bad val_list size for %s' % key)
self.arg_dict = {
'use_noise': alghs_dict['noise']['use'],
'intensity_noise_list': alghs_dict['noise']['val_list'],
'use_deform': alghs_dict['deform']['use'],
'k_deform_list': alghs_dict['deform']['val_list'],
'use_blur': alghs_dict['blur']['use'],
'rad_list': alghs_dict['blur']['val_list'],
'use_affine': alghs_dict['affine']['use'],
'affine_list': alghs_dict['affine']['val_list'],
'use_contrast': alghs_dict['contrast']['use'],
'contrast_list': alghs_dict['contrast']['val_list']
}
if json_list == None:
raise Exception('No passed json')
self.json_name = os.path.splitext(json_list[0])[0]
if len(json_list) == 1:
print('Parsing preprocessed json')
self.classes, img_shape, self.x_data, self.y_data = \
dmk.json_big_load(json_list[0])
self.x_data = np.array(self.x_data, dtype='uint8')
self.y_data = np.array(self.y_data, dtype='uint8')
else:
print('Parsing json list')
self.classes, self.x_data, self.y_data = dmk.get_data_from_json_list(
json_list=json_list,
# remove_classes=None,
# TODO some dev stuff
remove_classes=[
'альтернариоз', 'прочие инфекции', 'морщинистая мозаика',
'полосатая мозаика'
])
self.init_size = len(self.x_data)
self.img_shape = self.x_data.shape[1:]
# TODO some dev stuff
# self.classes['здоровый куст'] = self.classes['марь белая']
# del self.classes['марь белая']
# self.classes['мозаика'] = self.classes['прочие мозаики']
# del self.classes['прочие мозаики']
# self.classes['сорняк'] = self.classes['прочие сорняки']
# del self.classes['прочие сорняки']
self._define_max_class()
self.main_layout = MyGridWidget(hbox_control=self.hbox_control)
self.setCentralWidget(self.main_layout)
self.showFullScreen()
self.show_full()
print("---------------------------------")
print('classes = %s' % str(self.classes))
print('max_classes = %s' % str(self.max_aug_for_classes))
print('ex_num = %d' %
sum(map(lambda x: x['num'], self.classes.values())))
print("---------------------------------")
self.show_histogram(labels=list(self.classes.keys()),
values=list(
map(lambda val: val['num'],
self.classes.values())))
class WindowMultipleExamples(WindowInterface):
##############################################################
# ---------------- init stuff --------------------------------
##############################################################
def __init__(self, json_list):
super(WindowMultipleExamples, self).__init__()
self.postfix = 'joined'
self.label_size = (224, 224)
# TODO maybe will be restored someday
# with open(self.choose_json(content_title='config augmentation data')) as aug_config_fp:
with open('config_data_augmentation.json') as aug_config_fp:
aug_config_dict = json.load(aug_config_fp)
alghs_dict = aug_config_dict['algorithms']
self.max_aug_part = aug_config_dict['aug_part']
self.augment_all = aug_config_dict['augment_all']
self.output_json = aug_config_dict['output_json']
self.save_data_binary = aug_config_dict['save_data_to_binary']
self.save_data_dir = aug_config_dict['save_data_to_dir']
for key, value in alghs_dict.items():
if value['use'] and len(
value['val_list']) != self.max_aug_part:
raise Exception('bad val_list size for %s' % key)
self.arg_dict = {
'use_noise': alghs_dict['noise']['use'],
'intensity_noise_list': alghs_dict['noise']['val_list'],
'use_deform': alghs_dict['deform']['use'],
'k_deform_list': alghs_dict['deform']['val_list'],
'use_blur': alghs_dict['blur']['use'],
'rad_list': alghs_dict['blur']['val_list'],
'use_affine': alghs_dict['affine']['use'],
'affine_list': alghs_dict['affine']['val_list'],
'use_contrast': alghs_dict['contrast']['use'],
'contrast_list': alghs_dict['contrast']['val_list']
}
if json_list == None:
raise Exception('No passed json')
self.json_name = os.path.splitext(json_list[0])[0]
if len(json_list) == 1:
print('Parsing preprocessed json')
self.classes, img_shape, self.x_data, self.y_data = \
dmk.json_big_load(json_list[0])
self.x_data = np.array(self.x_data, dtype='uint8')
self.y_data = np.array(self.y_data, dtype='uint8')
else:
print('Parsing json list')
self.classes, self.x_data, self.y_data = dmk.get_data_from_json_list(
json_list=json_list,
# remove_classes=None,
# TODO some dev stuff
remove_classes=[
'альтернариоз', 'прочие инфекции', 'морщинистая мозаика',
'полосатая мозаика'
])
self.init_size = len(self.x_data)
self.img_shape = self.x_data.shape[1:]
# TODO some dev stuff
# self.classes['здоровый куст'] = self.classes['марь белая']
# del self.classes['марь белая']
# self.classes['мозаика'] = self.classes['прочие мозаики']
# del self.classes['прочие мозаики']
# self.classes['сорняк'] = self.classes['прочие сорняки']
# del self.classes['прочие сорняки']
self._define_max_class()
self.main_layout = MyGridWidget(hbox_control=self.hbox_control)
self.setCentralWidget(self.main_layout)
self.showFullScreen()
self.show_full()
print("---------------------------------")
print('classes = %s' % str(self.classes))
print('max_classes = %s' % str(self.max_aug_for_classes))
print('ex_num = %d' %
sum(map(lambda x: x['num'], self.classes.values())))
print("---------------------------------")
self.show_histogram(labels=list(self.classes.keys()),
values=list(
map(lambda val: val['num'],
self.classes.values())))
def _init_hbox_control(self):
self.hbox_control = QtWidgets.QHBoxLayout()
self.hbox_control.addStretch(1)
self.hbox_control.addWidget(ControlButton("Okay", self.okay_pressed))
self.hbox_control.addWidget(ControlButton("Show Full", self.show_full))
self.hbox_control.addWidget(
ControlButton("Multiple", self.multiple_pressed))
self.hbox_control.addWidget(ControlButton("Quit", self.quit_default))
def _define_max_class(self):
self.max_class = {'name': None, 'num': 0, 'value': None}
self.max_key_len = 0
self.max_aug_for_classes = {}
for key, value in self.classes.items():
if self.classes[key]['num'] > self.max_class['num']:
self.max_class['name'] = key
self.max_class['num'] = self.classes[key]['num']
self.max_class['value'] = self.classes[key]['value']
if len(key) > self.max_key_len:
self.max_key_len = len(key)
self.max_aug_for_classes[key] = self.classes[key]['num'] \
+ int(self.classes[key]['num'] * self.max_aug_part)
##############################################################
# ---------------- gui logic stuff ---------------------------
##############################################################
def show_histogram(self, labels, values, title='Diseases distribution'):
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(len(labels)) # the label locations
width = 0.35 # the width of the bars
fig, ax = plt.subplots()
rects1 = ax.bar(x - width / 2, values, width, label='Examples num')
# Add some text for labels, title and custom x-axis tick labels, etc.
ax.set_ylabel('Num')
ax.set_title(title)
ax.set_xticks(x)
ax.set_xticklabels(labels)
ax.legend()
def autolabel(rects):
"""Attach a text label above each bar in *rects*, displaying its height."""
for rect in rects:
height = rect.get_height()
ax.annotate(
'{}'.format(height),
xy=(rect.get_x() + rect.get_width() / 2, height),
xytext=(0, 3), # 3 points vertical offset
textcoords="offset points",
ha='center',
va='bottom')
autolabel(rects1)
fig.tight_layout()
plt.xticks(rotation=45)
plt.show()
def clear(self):
self.main_layout.clear()
def show_full(self):
self.clear()
def get_key_by_value(value):
for key in self.classes.keys():
if (self.classes[key]['value'] == value).all():
return key
raise Exception('No value == %s' % str(value))
label_list = []
for x, y in zip(self.x_data, self.y_data):
label_text = get_key_by_value(value=y)
label_text += (self.max_key_len - len(label_text)) * " "
label_list.append(
ImageTextLabel(x=x,
text=label_text,
label_size=self.label_size))
rect_len = int(np.sqrt(len(self.x_data)))
self.main_layout.update_grid(windows_width=self.main_layout.max_width,
window_height=self.main_layout.max_height,
x_len=rect_len,
y_len=rect_len,
label_list=label_list)
def okay_pressed(self):
print("Save to %s" % self.output_json)
for key in self.classes.keys():
self.classes[key]['value'] = list(
self.classes[key]['value'].flatten())
if self.save_data_binary:
###################################################################################
# ------------------------ be aware of SIGKILL ------------------------------------
###################################################################################
binary_path = dir_path = "/".join(self.output_json.split('/')[:-1]) + "/" \
+ self.output_json.split('/')[-1][:-5] + ".hd5f"
dmk.json_big_create(json_path=self.output_json,
h5_path=binary_path,
x_data=self.x_data,
y_data=self.y_data,
longitudes=None,
latitudes=None,
img_shape=None,
classes=self.classes)
elif self.save_data_dir:
###################################################################################
# we can not pass array with size 10_000 * 256 * 256 * 3 to function, shout SIGKILL
###################################################################################
dir_path = "/".join(self.output_json.split('/')[:-1]) \
+ "/" + self.output_json.split('/')[-1][:-5]
id = []
label = []
if not dir_path:
raise Exception("Data directory %s need to be existed" %
dir_path)
for key in self.classes.keys():
self.classes[key]['value_num'] = dmk.get_num_from_pos(
self.classes[key]['value'])
self.classes[key]['saved_num'] = 0
for i, (x, y) in enumerate(zip(self.x_data, self.y_data)):
for key in self.classes.keys():
if (self.classes[key]['value'] == y).all():
file_path = "%s/%d.JPG" % (dir_path, i + 1)
Image.fromarray(x).save(file_path)
print("%s saved" % file_path)
id.append(file_path)
label.append(key)
with open(self.output_json, "w") as fp:
json.dump(
{
"classes": self.classes,
"img_shape": None,
"dir_path": dir_path,
"longitudes": None,
"latitudes": None,
"dataframe": {
"id": id,
"label": label
}
}, fp)
fp.close()
self.quit_default()
def multiple_pressed(self):
if self.augment_all:
if sum(map(lambda x: x['num'], self.classes.values())
) < self.init_size * self.max_aug_part:
# ----------------------------------- augment all -----------------------------------------------------
old_class_size = len(self.x_data)
x_data_new, y_data_new = dmk.multiple_class_examples(
x_train=self.x_data[:self.init_size],
y_train=self.y_data[:self.init_size],
**self.arg_dict,
max_class_num=self.init_size * self.max_aug_part,
mode='all')
self.x_data = np.append(self.x_data, x_data_new)
self.y_data = np.append(self.y_data, y_data_new)
ex_num = int(self.y_data.shape[0] / len(self.classes))
self.x_data.shape = (ex_num, *self.img_shape)
self.y_data.shape = (ex_num, len(self.classes))
for key, value in self.classes.items():
new_ex_num = len(self.x_data) - old_class_size
print('%s : generated %d new examples' % (key, new_ex_num))
self.classes[key]['num'] = 0
for y in self.y_data:
if ((y.__eq__(self.classes[key]['value'])).all()):
self.classes[key]['num'] += 1
else:
print('ex_num = max_num')
else:
# ----------------------------------- augment by classes ---------------------------------------------
for key, value in self.classes.items():
if self.classes[key]['num'] < self.max_aug_for_classes[key]:
max_class_num = self.max_aug_for_classes[key]
old_class_size = len(self.x_data)
x_data_new, y_data_new = dmk.multiple_class_examples(
x_train=self.x_data[:self.init_size],
y_train=self.y_data[:self.init_size],
class_for_mult=self.classes[key]['value'],
**self.arg_dict,
max_class_num=max_class_num)
self.x_data = np.append(self.x_data, x_data_new)
self.y_data = np.append(self.y_data, y_data_new)
ex_num = int(self.y_data.shape[0] / len(self.classes))
self.x_data.shape = (ex_num, *self.img_shape)
self.y_data.shape = (ex_num, len(self.classes))
new_ex_num = len(self.x_data) - old_class_size
print('%s : generated %d new examples' % (key, new_ex_num))
self.classes[key]['num'] = 0
for y in self.y_data:
if ((y.__eq__(self.classes[key]['value'])).all()):
self.classes[key]['num'] += 1
else:
print(
'%s : generated %d new examples (class_size == max_size)'
% (key, 0))
print("---------------------------------")
print('classes = %s' % str(self.classes))
print('ex_num = %d' %
sum(map(lambda x: x['num'], self.classes.values())))
print("---------------------------------")
self.show_histogram(labels=list(self.classes.keys()),
values=list(
map(lambda val: val['num'],
self.classes.values())),
title='Diseases distribution after augmentation')
self.show_augmentation()
def show_augmentation(self):
self.clear()
label_list = []
for i in [0, 1, 2]:
label_list.append(
ImageTextLabel(x=self.x_data[i],
text='original',
label_size=self.label_size))
for j in range(1, int(self.max_aug_part) + 1):
label_text = 'aug %.2f' % self.arg_dict['contrast_list'][j - 1]
label_list.append(
ImageTextLabel(x=self.x_data[i + j * self.init_size],
text=label_text,
label_size=self.label_size), )
x_len = j + 1
y_len = int(len(label_list) / x_len)
self.main_layout.update_grid(windows_width=self.main_layout.max_width,
window_height=self.main_layout.max_height,
x_len=x_len,
y_len=y_len,
label_list=label_list)