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
class Capture:
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 start(self):
#setting started to true to set it is started in the train function.
#Because None type cannot be set
self.started = True
#Erasing stopEvent value (if any) to loop in the function
self.stopEvent1 = None
#checking if train thred is started
#if started close it
if (self.captured):
self.stopEvent2.set()
#initialize the thread
self.stopEvent1 = threading.Event()
self.thread1 = threading.Thread(target=self.videoLoop, args=())
self.thread1.start()
def close(self):
#initiallly checking if the threads have been started and then closing
#if started
if (self.started):
self.stopEvent1.set()
if (self.captured):
self.stopEvent2.set()
# self.vs.stop()
def exit(self):
if (self.started):
self.stopEvent1.set()
print "closing thread 1"
if (self.captured):
self.stopEvent2.set()
print "closing thread 2"
self.vs.stop()
VideoStream().stop()
def capture(self, emotion):
#setting the trained variable to true for same reason as started
self.captured = True
# self.emo = emotion
self.outputPath = "Dataset" + os.sep + emotion
if os.path.exists(self.outputPath):
pass
else:
os.mkdir(self.outputPath)
#Setting StopEvent to None to loop
self.stopEvent2 = None
#stopping started thread if running
if (self.started):
self.stopEvent1.set()
#initializing the train thred
self.stopEvent2 = threading.Event()
self.thread2 = threading.Thread(target=self.captureLoop, args=())
self.thread2.start()
def snapshot(self):
ts = datetime.datetime.now()
filename = "{}.jpg".format(ts.strftime("%Y-%m-%d_%H-%M-%S"))
output = os.path.sep.join((self.outputPath, filename))
cv2.imwrite(output, self.frame.copy())
self.showInfo()
def showInfo(self):
info.show()
def videoLoop(self):
try:
#loop until the stopEvent is not set
while not self.stopEvent1.is_set():
self.frame = self.vs.read()
gray = cv2.cvtColor(self.frame, cv2.COLOR_BGR2GRAY)
image = cv2.cvtColor(self.frame, cv2.COLOR_BGR2RGB)
faces = self.fa.detect(gray)
image = self.emotion.getLandmarks(image, faces)
image = Image.fromarray(image)
image = ImageTk.PhotoImage(image)
self.label.configure(image=image)
self.label.image = image
except RuntimeError, e:
print "Error"
print('Negative_feedback Count : {}'.format(len(negative_feedbacks)))
print(negative_feedbacks)
print()
print()
print()
print()
# emotion
wna = WNAffect('emotion_analyzer/wordnet-1.6/', 'emotion_analyzer/wn-domains-3.2/')
# part-of-speech tag
for f in feedbacks:
ws = f.split(" ")
for w in ws:
word = w
tokens = nltk.word_tokenize(word)
post_tag = nltk.pos_tag(tokens=tokens)
print(word)
print(post_tag[0][1])
emo = wna.get_emotion(str(word), str(post_tag[0][1]))
print("Emotion for *{}*: {}".format(word, emo))
if emo:
parent = emo.get_level(emo.level - 1)
print("Parent: {}".format(parent))
e = Emotion.emotions[str(parent)]
# print(e.nb_children())
Emotion.printTree(e)
#!/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()
