def sort_images(self):
if self.folder is None or self.sort_path is None:
error = QErrorMessage()
error.setWindowTitle('Error')
error.showMessage('One or more paths have not been set')
error.exec_()
elif os.path.isfile(self.model):
self.progress.setValue(0)
self.identifier.set_params(self.face_model, '128D', self.jitters,
self.upsample)
self.identifier.set_folder(self.folder)
image_list = self.identifier.get_image_list()
image_list.sort()
results = list()
if self.detect_objects is True:
self.status.setText('Filtering images...')
utils = IU()
image_list = utils.detect_objects(image_list,
bar=self.progress,
classes=self.classes,
conf=self.confidence)
if self.sort_state is True:
increment = float(100.00 / float(len(image_list)))
self.progress.setValue(0)
done = 0
self.status.setText('Sorting images...')
for image in image_list:
result = self.identifier.predict(image_path=image,
threshold=self.threshold)
if result is True:
results.append(image)
done += increment
self.progress.setValue(done)
self.progress.setValue(100)
else:
results = image_list
if not os.path.exists(self.sort_path):
os.makedirs(self.sort_path)
self.progress.setValue(0)
done = 0
self.status.setText('Copying results to folder...')
increment = float(len(results) / 100.0)
for image in results:
copy(image, self.sort_path)
done += increment
self.progress.setValue(done)
self.progress.setValue(100)
self.status.setText('Done!')
else:
error = QErrorMessage()
error.setWindowTitle('Error')
error.showMessage("There was an error. That's all we know.")
error.exec_()
def sort_images(self):
if self.folder is None or self.sort_path is None:
error = QErrorMessage()
error.showMessage('One or more paths have not been set')
error.exec_()
elif os.path.isfile(self.model):
if os.path.isfile('models/training_data.clf'):
with open('models/training_data.clf', 'rb') as file:
data = pickle.load(file)
if 512 in data[0].shape and self.encoding_model == '128D':
error = QErrorMessage()
error.showMessage(
'The training data is 512D but model is 128D; they are incompatible'
)
error.exec_()
elif 128 in data[0].shape and self.encoding_model == '512D':
error = QErrorMessage()
error.showMessage(
'The training data is 128D but model is 512D; they are incompatible'
)
error.exec_()
else:
self.progress.setValue(0)
self.identifier.set_params(self.face_model,
self.encoding_model,
self.jitters, self.upsample)
self.identifier.set_folder(self.folder)
image_list = self.identifier.get_image_list()
image_list.sort()
results = list()
if self.detect_objects is True:
self.status.setText('Filtering images...')
utils = IU()
image_list = utils.detect_objects(image_list,
bar=self.progress,
classes=self.classes,
conf=self.confidence)
if self.sort_state is True:
increment = float(100.00 / float(len(image_list)))
self.progress.setValue(0)
done = 0
self.status.setText('Sorting images...')
for image in image_list:
result = self.identifier.predict(
image_path=image, threshold=self.threshold)
if self.algorithm == 'Euclidean Distance':
if result is True:
results.append(image)
elif self.algorithm == 'k-Nearest Neighbors':
for name, loc in result:
if name.lower() == 'search_face':
results.append(image)
break
elif self.algorithm == 'Support Vector Machine(SVM)':
if result is True:
results.append(image)
done += increment
self.progress.setValue(done)
self.progress.setValue(100)
else:
results = image_list
if not os.path.exists(self.sort_path):
os.makedirs(self.sort_path)
self.progress.setValue(0)
done = 0
self.status.setText('Copying results to folder...')
increment = float(len(results) / 100.0)
for image in results:
copy(image, self.sort_path)
done += increment
self.progress.setValue(done)
self.progress.setValue(100)
self.status.setText('Done!')
else:
error = QErrorMessage()
error.showMessage("There was an error. That's all we know.")
error.exec_()