def add_subject(self, dto):
"""
Call validate on dto(), convert dto to Subject, call validate() on it and add it to the repo.
dto should contain "name,teacher_name". Missing fields are initialised with None.
Validate and repo exceptions are raised.
"""
dto.validate(["name", "teacher_name"])
subject = Subject(None, *dto.split())
subject.validate()
self._subject_repository.add_entity(subject)
def get_subject_by_teacher(self, dto):
"""
Call validate on dto(), convert dto to Subject and return a list of matching subjects as ordered dicts.
dto should contain "teacher_name". Missing fields are initialised with None.
"""
dto.validate(["teacher_name"])
subject = Subject(None, None, *dto.split())
return self._subject_repository.get_entity(subject)
def del_subject(self, dto):
"""
Call validate on dto(), convert dto to Subject and delete it from the repo.
dto should contain "id". Missing fields are initialised with None.
Repo exceptions are raised.
"""
dto.validate(["id"])
subject = Subject(*dto.split(), None, None)
self._subject_repository.del_entity(subject)
def createSubject():
"""
post endpoint
---
tags:
- subjectController
parameters:
- name: body
in: body
required: true
schema:
required:
- subjectName
properties:
subjectName:
type: string
description: The subject's name.
subjectId:
type: string
description: The subject's id.
default: ""
responses:
200:
description: The response from subject_controller
schema:
"""
subjectName = request.json['subjectName']
subjectId = request.json['subjectId']
adminId = request.json['adminId']
if userRep.findById(adminId) is None:
return "Not this user", status.HTTP_404_NOT_FOUND
if subjectId is "":
subjectId = ig.generateId('subject')
else:
subject = subjectRep.findById(subjectId)
if subject is not None:
return '', 226
subject = Subject(id=subjectId,
name=subjectName,
createTime=tg.getNowAsMilli(),
updateTime=tg.getNowAsMilli())
subject = subjectRep.save(subject)
subjectUser = SubjectUser(id=ig.generateId('subjectUser'),
userId=adminId,
subjectId=subjectId,
role=1,
createTime=tg.getNowAsMilli(),
updateTime=tg.getNowAsMilli())
subjectUserRep.save(subjectUser)
return json_util.dumps({'subject': subject.__dict__
}), status.HTTP_200_OK, ContentType.json
def save(subject: Subject):
subject._id = mongo.db.subject.insert_one(subject.__dict__).inserted_id
return subject
def main(argv):
# parameters for loading data and images
detection_model_path = '../trained_models/detection_models/haarcascade_frontalface_default.xml'
emotion_model_path = '../trained_models/emotion_models/fer2013_mini_XCEPTION.102-0.66.hdf5'
gender_model_path = '../trained_models/gender_models/simple_CNN.81-0.96.hdf5'
emotion_labels = get_labels('fer2013')
gender_labels = get_labels('imdb')
font = cv2.FONT_HERSHEY_SIMPLEX
# hyper-parameters for bounding boxes shape
frame_window = 10
gender_offsets = (30, 60)
emotion_offsets = (20, 40)
# loading models
face_detection = load_detection_model(detection_model_path)
emotion_classifier = load_model(emotion_model_path, compile=False)
gender_classifier = load_model(gender_model_path, compile=False)
# getting input model shapes for inference
emotion_target_size = emotion_classifier.input_shape[1:3]
gender_target_size = gender_classifier.input_shape[1:3]
# starting lists for calculating modes
gender_window = []
emotion_window = []
# starting video streaming
cv2.namedWindow('window_frame')
video_capture = cv2.VideoCapture(0)
try:
opts, args = getopt.getopt(argv, "hs:", ["subject="])
except getopt.GetoptError:
print("emotion.py -s <subject>")
sys.exit(2)
subject = Subject()
for opt, arg in opts:
if opt == '-h':
print("emotion.py - s <subject>")
sys.exit()
elif opt in ("-s", "--subject"):
subject.name = arg
print(subject.name)
subject.start()
while True:
bgr_image = video_capture.read()[1]
gray_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2GRAY)
rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB)
faces = detect_faces(face_detection, gray_image)
for face_coordinates in faces:
x1, x2, y1, y2 = apply_offsets(face_coordinates, gender_offsets)
rgb_face = rgb_image[y1:y2, x1:x2]
x1, x2, y1, y2 = apply_offsets(face_coordinates, emotion_offsets)
gray_face = gray_image[y1:y2, x1:x2]
try:
rgb_face = cv2.resize(rgb_face, (gender_target_size))
gray_face = cv2.resize(gray_face, (emotion_target_size))
except:
continue
gray_face = preprocess_input(gray_face, False)
gray_face = np.expand_dims(gray_face, 0)
gray_face = np.expand_dims(gray_face, -1)
emotion_label_arg = np.argmax(
emotion_classifier.predict(gray_face))
emotion_text = emotion_labels[emotion_label_arg]
emotion_window.append(emotion_text)
rgb_face = np.expand_dims(rgb_face, 0)
rgb_face = preprocess_input(rgb_face, False)
gender_prediction = gender_classifier.predict(rgb_face)
gender_label_arg = np.argmax(gender_prediction)
gender_text = gender_labels[gender_label_arg]
gender_window.append(gender_text)
subject.addMood(emotion_text, gender_text)
if len(gender_window) > frame_window:
emotion_window.pop(0)
gender_window.pop(0)
try:
emotion_mode = mode(emotion_window)
gender_mode = mode(gender_window)
except:
continue
if gender_text == gender_labels[0]:
color = (0, 0, 255)
else:
color = (255, 0, 0)
draw_bounding_box(face_coordinates, rgb_image, color)
draw_text(face_coordinates, rgb_image, gender_mode, color, 0, -20,
1, 1)
draw_text(face_coordinates, rgb_image, emotion_mode, color, 0, -45,
1, 1)
bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)
cv2.imshow('window_frame', bgr_image)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
subject.stop()
def get_all(self):
"""
Return a list of all subjects as ordered dicts.
"""
return self._subject_repository.get_entity(Subject(None, None, None))