class YourFaceSoundsFamiliar(BaseWidget):
def __init__(self):
super(YourFaceSoundsFamiliar,self).__init__('Your Face Sounds Familiar')
#Predict Tab
self._imagepath = ControlText('Path')
self._browsebuttonpredict = ControlButton('Browse')
self._nametopred = ControlText('Name')
self._selectfile = ControlFile()
self._selectfile.changed = self.__change_path
self._predictimage = ControlImage()
self._predictbutton = ControlButton('Predict')
self._predicteddetails = ControlLabel('Details')
self._name = ControlLabel('Recognized Name: ')
self._fscore = ControlLabel('FScore: ')
self._predictbutton.value = self.__predictbAction
#Train Tab
self._pername = ControlText('Name')
self._selectdir = ControlDir()
self._selectdir.changed = self.__change_path_dir
self._imagetotrain = ControlImage()
# self._imagetotest = ControlImage()
self._totrainlist = ControlList("To Train",defaultValue=[])
self.traininglist = self._totrainlist.value
self._addtolistbutton = ControlButton('Add')
self._addtolistbutton.value = self.__addtolistbAction
self._trainbutton = ControlButton('Train')
self._trainbutton.value = self.__trainbAction
#Formsets
self._formset = [{
'Predict':['_selectfile','=','_nametopred','=','_predictimage',
'=','_predictbutton','=',
'_predicteddetails','=','_name',
'=','_fscore'],
'Train': ['_pername', '=', '_selectdir',
'=', '_imagetotrain', '=', '_addtolistbutton','=' ,
'_totrainlist', '=', '_trainbutton']
}]
self.trainingsetall = []
self.nn = self.__init_nn()
self.learned = {}
self._k = 4
self._trainingPercent = 0.8
self.learned = self.__load_learned()
self.cross_validation_set = [np.empty((0,0))]*self._k
self.cross_validation_set_y = [np.empty((0,0))]*self._k
self.test_set = np.empty((0, 0))
self.testing_y = np.empty((0, 0))
self.training_X = [np.empty((0, 900))] * self._k
self.training_y = [np.empty((0, 1))] * self._k
self.X = np.empty((0, 0))
def __load_learned(self):
try:
with open('learned.json') as learned_file:
for line in learned_file:
learned = json.loads(line)
for key in learned.keys():
self._totrainlist.__add__([key])
except IOError:
learned = {}
config = {'input_size': 30 * 30, 'hidden_size': 30 * 30, 'lambda': 1, 'num_labels': (len(learned))}
self.nn = NeuralNetwork(config=config)
return learned
def __predictbAction(self):
predictset_filename = 'predictset.csv'
np.savetxt(predictset_filename,self.predictset, delimiter=',')
prediction = np.argmax(self.nn.predict(self.predictset)) + 1
for k, v in self.learned.iteritems():
if prediction == v:
self._name.value = k
def __init_nn(self):
nn = NeuralNetwork()
return nn
def __change_path(self):
image = cv2.imread(self._selectfile.value)
self._predictimage.value = []
self._predictimage.value = FaceDetection().drawrectangle(image)
resizedimage = FaceDetection().resizeimageb(self._predictimage.value)
croppedimage = FaceDetection().cropface(resizedimage)
resizedcroppedimage = FaceDetection().resizeimagea(croppedimage)
self.predictset = np.array(resizedcroppedimage[1]).flatten()
def __change_path_dir(self):
name = self._selectdir.value
name = name.split('/')
self._pername.value = name.pop(len(name)-1)
self._imagetotrain.value = []
# self._imagetotest.value = []
listofimages = os.listdir(self._selectdir.value)
listofimages = sorted(listofimages)
listofimages = [cv2.imread(os.path.join(self._selectdir.value, filename)) for filename in listofimages]
resizedimages = [FaceDetection().resizeimageb(image) for image in listofimages]
croppedimages = [FaceDetection().cropface(image) for image in resizedimages]
resized_images = [FaceDetection().resizeimagea(image) for image in croppedimages if image is not None]
resizedcroppedimages = [image[0] for image in resized_images]
resizedcroppedimagesgray = [image[1] for image in resized_images]
trainthisImages = resizedcroppedimagesgray[0:int(len(resizedcroppedimagesgray)*self._trainingPercent)]
testthisImages = resizedcroppedimagesgray[int(len(resizedcroppedimagesgray)*self._trainingPercent):]
self.trainingsetimage = [np.array(image).flatten() for image in trainthisImages]
self.testingsetimage = [np.array(image).flatten() for image in testthisImages]
self._imagetotrain.value = trainthisImages
self.cross_validation = [np.empty((0,0))]*self._k
self.cv_size = [0]*self._k
l = 0
for j in self.trainingsetimage:
if l == self._k:
l = 0
self.cross_validation[l] = np.append(self.cross_validation[l], j)
self.cv_size[l] += 1
l += 1
self._imagetotrain.value = resizedcroppedimages
def __trainbAction(self):
config = {'input_size': 30 * 30, 'hidden_size': 30 * 30, 'lambda': 1, 'num_labels': (len(self.learned))}
self.nn = NeuralNetwork(config=config)
cost_params_fscore = []
for i in range(self._k):
cost_params_fscore.append(self.nn.train(self.training_X[i], self.training_y[i], self.cross_validation_set[i], self.test_set, self.cross_validation_set_y[i], self.testing_y))
best_model = max(cost_params_fscore, key=itemgetter(2))
print best_model[0], best_model[2]
def __addtolistbAction(self):
### TEST ###
if self._pername.value in self.learned:
return
X = np.array(self.trainingsetimage) # size: 8
X_set = np.empty((0, 0)) # this will be a k-sized set of X slices
slice_size = X.shape[0] / self._k # 2
if self._pername.value not in self.learned:
label = len(self.learned) + 1
self.learned[self._pername.value] = label
else:
return
for i in range(self._k):
sliced = np.delete(X, slice(i*2, (i*2)+2), axis=0)
self.training_X[i] = np.append(self.training_X[i], sliced, axis=0)
y = np.empty((sliced.shape[0], 1))
y.fill(label)
self.training_y[i] = np.append(self.training_y[i], y, axis=0)
print self.training_X[i].shape
self._totrainlist.__add__([self._pername.value])
with open('learned.json', 'w') as learned_file:
learned_file.write(json.dumps(self.learned))
############
for i in range(self._k):
self.cross_validation_set[i] = np.append(self.cross_validation_set[i], self.cross_validation[i])
self.cross_validation_set_y[i] = np.append(self.cross_validation_set_y[i], [label]*self.cv_size[i])
self.test_set = np.append(self.test_set, self.testingsetimage)
self.testing_y = np.append(self.testing_y, [label]*(len(self.testingsetimage)))
class EditHarvest(BaseWidget):
def __init__(self):
super().__init__('Oogstlijst Manager')
reference_list = Main.hl.reference_data
self._list_left = ControlList('Stamlijst')
self._list_left.readonly = True
self._list_right = ControlList('Oogstlijst')
self._list_right.select_entire_row = True
self._list_right.horizontal_headers = [
'Oogst',
'Hoeveelheid (kg)',
]
self._save_button = ControlButton('Verder')
self._save_button.value = self._continue
self.formset = ['_list_left', '_list_right', '_save_button']
def onclick_left(row, column):
r = self._list_left.get_currentrow_value()
self._list_right.__add__(r + [0])
# cell = self._list_right.get_cell(0, self._list_right.rows_count - 1)
# cell.setFlags(QtCore.Qt.ItemIsEditable)
# cell = self._list_right.get_cell(1, self._list_right.rows_count - 1)
# cell.setTextAlignment(QtCore.Qt.AlignCenter)
def onclick_right(row, column):
# # print(dir(self._list_right))
if column == 0:
index = self._list_right.selected_row_index
print(index)
self._list_right.__sub__(index)
# # r = self._list_right.get_currentrow_value()
# self._list_right.__sub__(self._list_right.selected_row_index())
# #cell = self._list_right.get_cell(1, self._list_right.rows_count - 1)
# #cell.setTextAlignment(QtCore.Qt.AlignCenter)
self._list_left.cell_double_clicked_event = onclick_left
self._list_right.cell_double_clicked_event = onclick_right
for r in reference_list:
self._list_left.__add__([' '.join(r[1:3])])
if Main.hl.harvest_list:
for r in Main.hl.harvest_list:
self._list_right.__add__([r[0], r[1]])
def _continue(self):
user_input = self._list_right.value
if not user_input:
return
harvest_list = []
# Combine user input with reference data
for row in user_input:
reference = [ ref for ref in Main.hl.reference_data if\
ref[1] and ref[1] in row[0] and ref[2] and ref[2] in row[0] ][0]
if not reference:
raise Exception('Reference data not found')
row += [reference[0]] # add prod nr
row += [reference[2]] # add part
row += [reference[3]] # add date
harvest_list.append(row)
Main.hl.harvest_list = Main.hl_pickle.harvest_list = harvest_list
window = Rapport()
Main.root._panel.value = window
