def make_player(canvas, engine="gstreamer"):
def stop(player):
player.file = ""
def play(player, media):
stop(player)
player.file = join_path(DATA_DIR, media)
player.play = True
def replay(player):
fname = player.file
stop(player)
play(player, fname)
sfx_player = Emotion(canvas, module_filename=engine)
bgm_player = Emotion(canvas, module_filename=engine)
bgm_player.on_playback_finished_add(replay)
add_hook('play:sfx', play, sfx_player)
add_hook('game:end', stop, sfx_player)
add_hook('play:bgm', play, bgm_player)
add_hook('game:over', stop, bgm_player)
add_hook('game:end', stop, bgm_player)
def _load_emotions(self, wn_domains_dir):
"""Loads the hierarchy of emotions from the WordNet-Affect xml."""
tree = ET.parse("{0}/a-hierarchy.xml".format(wn_domains_dir))
root = tree.getroot()
for elem in root.findall("categ"):
name = elem.get("name")
if name == "root":
Emotion.emotions["root"] = Emotion("root")
else:
Emotion.emotions[name] = Emotion(name, elem.get("isa"))
def __init__(self, label, clf):
self.frame = None
self.thread1 = None
self.thread2 = None
self.stopEvent1 = None
self.stopEvent2 = None
self.started = None
self.captured = None
self.label = label
self.vs = VideoStream().start()
self.fa = Face()
self.emotion = Emotion(clf)
self.outputPath = None
def main(path, friend, limit):
print('Loading sentences...')
sentences = load_sentences(path=path, friend=friend, limit=limit)
print('Inferring emotions...')
labels = Emotion().predict(sentences)
print('Grouping sentences and labels...')
emotion_dict = group_sentences_and_labels(sentences, labels)
print('Creating wordclouds...')
wordclouds = create_wordclouds(emotion_dict)
print('Plotting wordclouds...')
plot_wordclouds(wordclouds)
def compute_emotions(self, emo_labels, fps=4):
#import video
video = Video([], self.duration, path=self.front_video, name=self.ID, fps=fps)
if video.successful_loading:
#Detect and crop the face of the user
video.face_detect(mode='crop', model='both')
#Predict emotions with the CNN with the cropped face as input
video.emotions_prediction()
#7 predicitons outputed from the NN for each timestamp (4 fps) along the video
#contenated with the winning emotion for each timestamp
preds7 = np.asarray(video.emotions.preds7)
best_guess7 = np.asarray(np.expand_dims(video.emotions.best_guess7, axis=1))
concat = np.concatenate((preds7, best_guess7), axis=1)
df = pd.concat([pd.Series(x) for x in concat], axis=1).T
#Create an emotion class for further processing
self.emotion = Emotion(df, emo_labels, fps=fps)
return 1
else:
print('Front video of clip {} not found.' .format(self.ID))
return 0
#!/usr/bin/env python
# Playing a radio stream with EFL (Emotion)
import ecore
import ecore.evas
from emotion import Emotion
DSIZE = 320, 240
FILENAME = 'http://relay5.slayradio.org:8000/'
ee = ecore.evas.new()
ee.size = DSIZE
ee.show()
canvas = ee.evas
em = Emotion(canvas, module_filename='generic')
em.file = FILENAME
em.size = DSIZE
em.play = True
em.show()
if __name__ == '__main__':
m = ecore.MainLoop()
m.begin()
