def add_extra_images(self):
"""
If --extra flag exists, adds images from extra-images dir to dataset
:return: tuple of list dataset images, dataset labels
"""
detector = Detector()
print('\nAdding extra images...')
for emotion_index, emotion in enumerate(EMOTIONS):
print('\nEmotion', emotion)
files = glob.glob('{}\\{}\\*'.format(EXTRA_DIR, emotion))
total = len(files)
for i, f in enumerate(files):
image = cv2.imread(f)
detected_faces = detector.detect_faces(image)
for face in detected_faces:
self.images.append(face)
self.emotions.append(
PrepareData._emotion_to_vec(emotion_index))
self.count.append(emotion_index)
print('Progress: {}/{} {:.2f}%'.format(i, total,
i * 100.0 / total))
class Recognizer:
"""
Class of recognizer. Recognize emotions on image using trained model
"""
def __init__(self):
self.detector = Detector()
self.model = TrainNetwork().load_trained_model()
self.cap = cv2.VideoCapture(0)
if not self.cap.isOpened():
print('Webcam is not open.')
exit()
def recognize(self, frame):
"""
Recognize emotions on frame for each face
:param frame: input frame from webcam
:return: tuple of list faces and predictions
"""
detected_faces = self.detector.detect_faces(frame)
predictions = []
for face in detected_faces:
face = face.reshape([-1, IMG_SIZE, IMG_SIZE, 1])
prediction = self.model.predict(face)
print(EMOTIONS[np.argmax(prediction[0])])
predictions.append(prediction[0])
return detected_faces, predictions
def run(self):
"""
Gets frames from webcam and push it to recognizer
Stops by CTRL+C
:return:
"""
predictions = []
times = []
try:
while self.cap.isOpened():
sleep(0.5)
_, frame = self.cap.read()
faces, preds = self.recognize(frame)
for f, p in zip(faces, preds):
predictions.append(p)
times.append(datetime.now().time())
Recognizer._save_recognized_image(frame, p, datetime.now())
except KeyboardInterrupt:
self.cap.release()
return times, predictions
@staticmethod
def _save_recognized_image(face, prediction, dt):
"""
Saves image from webcam with face(user data)
:param face: face image
:param prediction: emotion prediction
:param dt: datetime
:return:
"""
if not os.path.exists(os.path.join(os.getcwd(), USER_DATA_DIR)):
os.mkdir(USER_DATA_DIR)
emotion = EMOTIONS[np.argmax(prediction)]
class PrepareData:
"""
Class for prepare dataset
"""
def __init__(self):
self.images = []
self.emotions = []
self.count = []
self.detector = Detector()
def create_dataset_from_csv(self):
"""
Create dataset from fer2013 dataset csv file
:return: tuple of list dataset images, dataset labels
"""
print('\nCreating dataset...')
data = pd.read_csv(CSV_FILE_NAME)
total = data.shape[0]
for i, row in data.iterrows():
image = PrepareData._data_to_image(row['pixels'])
emotion = PrepareData._emotion_to_vec(row['emotion'])
detected_faces = self.detector.detect_faces(image,
fer2013_image=True)
for face in detected_faces:
self.images.append(face)
self.emotions.append(emotion)
self.count.append(row['emotion'])
print('Progress: {}/{} {:.2f}%'.format(i, total,
i * 100.0 / total))
return self.images, self.emotions, self.count
def add_extra_images(self):
"""
If --extra flag exists, adds images from extra-images dir to dataset
:return: tuple of list dataset images, dataset labels
"""
detector = Detector()
print('\nAdding extra images...')
for emotion_index, emotion in enumerate(EMOTIONS):
print('\nEmotion', emotion)
files = glob.glob('{}\\{}\\*'.format(EXTRA_DIR, emotion))
total = len(files)
for i, f in enumerate(files):
image = cv2.imread(f)
detected_faces = detector.detect_faces(image)
for face in detected_faces:
self.images.append(face)
self.emotions.append(
PrepareData._emotion_to_vec(emotion_index))
self.count.append(emotion_index)
print('Progress: {}/{} {:.2f}%'.format(i, total,
i * 100.0 / total))
@staticmethod
def _data_to_image(data):
"""
Private method. Convert csv row of pixels to image
:param data: row of pixels from csv file
:return:
"""
data_image = [int(pixel) for pixel in data.split(' ')]
image = np.asarray(data_image).reshape(IMG_SIZE, IMG_SIZE)
image = cv2.resize(image.astype('uint8'), (IMG_SIZE, IMG_SIZE))
# image = image.astype('float32') / 255.
return image
@staticmethod
def _emotion_to_vec(x):
"""
Private method. Convert num of emotion to emotions labels array([0., 0., 0. ,1., 0., 0., 0.,])
:param x: num of emotion
:return: array
"""
d = np.zeros(len(EMOTIONS))
d[x] = 1.0
return d
