def main(model, image_path):
emotions = ['neutral', 'anger', 'disgust', 'happy', 'sadness', 'surprise']
# Read the image
image = cv2.imread(image_path, cv2.IMREAD_UNCHANGED)
#gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Draw a rectangle around the faces
for normalized_face, (x, y, w, h) in find_faces(image):
#print((x, y, w, h))
emotion = model.predict(normalized_face) # do prediction
emotion = emotion[0]
print emotions[emotion]
# try to add a emoji to a existing face
s_img = cv2.imread("./emojis/" + emotions[emotion] + ".png",
cv2.IMREAD_UNCHANGED)
s_height, s_width, s_channels = s_img.shape
l_img = image
resize_ratio = round(w * 10 / (10.0 * s_width), 5)
s_img = cv2.resize(s_img, (0, 0), fx=resize_ratio, fy=resize_ratio)
draw_with_alpha(l_img, s_img, (x, y, w, h))
cv2.imwrite("result-emoji.jpg", image)
cv2.imshow("Faces found", image)
cv2.waitKey(0)
def get_faces(path):
data = []
frame = cv2.imread(path)
normalized_faces = find_faces(frame)
print(normalized_faces)
for img, (x, y, w, h) in normalized_faces:
img = cv2.resize(img[0], (image_size, image_size))
img = np.reshape(img, (image_size * image_size))
data.append(img)
return data, normalized_faces
def show_webcam_and_run(model,
emoticons,
window_size=None,
window_name='webcam',
update_time=10):
"""
Muestra la webcam y detecta los rostros y las emociones en tiempo real para dibujar los emojis.
:param model: Modelo para reconocer emociones.
:param emoticons: emojis.
:param window_size: tamao de la ventana donde estara el stream.
:param window_name: Nombre de la ventana.
:param update_time: tiempo para actualizar la imagen.
"""
cv2.namedWindow(window_name, WINDOW_NORMAL)
if window_size:
width, height = window_size
cv2.resizeWindow(window_name, width, height)
# 选择摄像头，0为本地
vc = cv2.VideoCapture(
0
) # http://192.168.0.2:4747/mjpegfeed para camara android remota por medio de Droidcam
# 摄像头分辨率，默认为当前使用摄像头的最高分辨率
# vc.set(cv2.CAP_PROP_FRAME_WIDTH, 1920)
# vc.set(cv2.CAP_PROP_FRAME_HEIGHT, 1080)
if vc.isOpened():
read_value, webcam_image = vc.read()
else:
print("[ERROR] No se enontro camara.")
return
while read_value:
for normalized_face, (x, y, w, h) in find_faces(webcam_image):
prediction = network.predict(normalized_face) # hace la prediccion
prediction = prediction[
0] # guarda el numero de la emocion para diujar el emoji
# carga el emoji para dibujarlo
image_to_draw = emoticons[prediction.tolist().index(
max(prediction))]
# dibuja el emoji
draw_with_alpha(
webcam_image, image_to_draw,
(x, y - 100, w, h)) # image_to_draw, , webcam_image,
cv2.setWindowProperty(window_name, cv2.WND_PROP_FULLSCREEN,
cv2.WINDOW_FULLSCREEN)
cv2.imshow(window_name, webcam_image)
read_value, webcam_image = vc.read()
key = cv2.waitKey(update_time)
if key == 27: # salir con esc
break
cv2.destroyWindow(window_name)
def extract_faces(emotions):
print("Extracting faces")
for emotion in emotions:
photos = glob.glob('data/sorted_set/%s/*' % emotion)
for file_number, photo in enumerate(photos):
frame = cv2.imread(photo)
normalized_faces = find_faces(frame)
os.remove(photo)
for face in normalized_faces:
try:
cv2.imwrite(
"data/sorted_set/%s/%s.png" %
(emotion, file_number + 1), face[0])
except:
print("error in processing %s" % photo)
def show_webcam_and_run(model,
emoticons,
window_size=None,
window_name='webcam',
update_time=10):
"""
Shows webcam image, detects faces and its emotions in real time and draw emoticons over those faces.
:param model: Learnt emotion detection model.
:param emoticons: List of emotions images.
:param window_size: Size of webcam image window.
:param window_name: Name of webcam image window.
:param update_time: Image update time interval.
"""
cv2.namedWindow(window_name, WINDOW_NORMAL)
if window_size:
width, height = window_size
cv2.resizeWindow(window_name, width, height)
vc = cv2.VideoCapture(0)
if vc.isOpened():
read_value, webcam_image = vc.read()
else:
print("webcam not found")
return
while read_value:
for normalized_face, (x, y, w, h) in find_faces(webcam_image):
prediction = model.predict(normalized_face) # do prediction
if cv2.__version__ != '3.1.0':
prediction = prediction[0]
image_to_draw = emoticons[prediction]
draw_with_alpha(webcam_image, image_to_draw, (x, y, w, h))
cv2.imshow(window_name, webcam_image)
read_value, webcam_image = vc.read()
key = cv2.waitKey(update_time)
if key == 27: # exit on ESC
break
cv2.destroyWindow(window_name)
def extract_faces(emotions):
"""
Crops faces in emotions images.
:param emotions: List of emotions names.
"""
print("Extracting faces")
for emotion in emotions:
photos = glob.glob('./data/sorted_set/%s/*' % emotion)
for file_number, photo in enumerate(photos):
frame = cv2.imread(photo)
normalized_faces = find_faces(frame)
os.remove(photo)
for face in normalized_faces:
try:
cv2.imwrite("./data/sorted_set/%s/%s.png" %
(emotion, file_number + 1),
face[0]) # write image
except:
print("error in processing %s" % photo)
def show_webcam_and_run(model, emoticons, window_size=None, window_name='webcam', update_time=10):
"""
Shows webcam image, detects faces and its emotions in real time and draw emoticons over those faces.
:param model: Learnt emotion detection model.
:param emoticons: List of emotions images.
:param window_size: Size of webcam image window.
:param window_name: Name of webcam image window.
:param update_time: Image update time interval.
"""
cv2.namedWindow(window_name, WINDOW_NORMAL)
if window_size:
width, height = window_size
cv2.resizeWindow(window_name, width, height)
vc = cv2.VideoCapture(0)
if vc.isOpened():
read_value, webcam_image = vc.read()
else:
print("webcam not found")
return
while read_value:
for normalized_face, (x, y, w, h) in find_faces(webcam_image):
prediction = model.predict(normalized_face) # do prediction
if cv2.__version__ != '3.1.0':
prediction = prediction[0]
image_to_draw = emoticons[prediction]
draw_with_alpha(webcam_image, image_to_draw, (x, y, w, h))
cv2.imshow(window_name, webcam_image)
read_value, webcam_image = vc.read()
key = cv2.waitKey(update_time)
if key == 27: # exit on ESC
break
cv2.destroyWindow(window_name)
def show_webcam_and_run(model,
emoticons,
window_size=None,
window_name='webcam',
update_time=10):
"""
Shows webcam image, detects faces and its emotions in real time and draw emoticons over those faces.
model: learnt emotion detection model.
emoticons: list of emotions images.
window_size: size of webcam image window.
window_name: name of webcam image window.
update_time: image update time interval.
"""
cv2.namedWindow(window_name, WINDOW_NORMAL)
if window_size:
width, height = window_size
cv2.resizeWindow(window_name, width, height)
vc = cv2.VideoCapture(0)
if vc.isOpened():
read_value, webcam_image = vc.read()
while read_value:
for normalized_face, (x, y, w, h) in find_faces(webcam_image):
prediction = model.predict(normalized_face) # do prediction
prediction = prediction[0]
emoj_to_draw = emoticons[prediction]
draw_with_alpha(webcam_image, emoj_to_draw, (x, y, w, h))
cv2.imshow(window_name, webcam_image)
read_value, webcam_image = vc.read()
key = cv2.waitKey(update_time)
if key == 27: # exit on ESC
break
cv2.destroyWindow(window_name)
emotions = ['neutral', 'fear', 'happy', 'sadness', 'surprise']
for emotion in emotions:
paths = glob.glob("data/%s/*" % (emotion))
normalized_path = "normalized_images/%s/" % (emotion)
if not os.path.exists(normalized_path):
os.makedirs(normalized_path)
test_path = "test/%s/" % (emotion)
if not os.path.exists(test_path):
os.makedirs(test_path)
print("Removing old dataset")
filelist = glob.glob("normalized_imagest/%s/*" % emotion)
for f in filelist:
os.remove(f)
filelist = glob.glob("test/%s/*" % emotion)
for f in filelist:
os.remove(f)
number_random = int(len(paths) / 10)
random_paths = random.sample(paths, number_random)
for path in paths:
frame = cv2.imread(path)
normalized_faces = find_faces(frame)
for face in normalized_faces:
if path in random_paths:
cv2.imwrite(test_path + str(os.path.basename(path)), face[0])
else:
cv2.imwrite(normalized_path + str(os.path.basename(path)),
face[0])
def show_webcam_and_run(model,
emoticons,
window_size=None,
window_name='webcam',
update_time=10):
"""
Shows webcam image, detects faces and its emotions in real time and draw emoticons over those faces.
:param model: Learnt emotion detection model.
:param emoticons: List of emotions images.
:param window_size: Size of webcam image window.
:param window_name: Name of webcam image window.
:param update_time: Image update time interval.
"""
cv2.namedWindow(window_name, WINDOW_NORMAL)
if window_size:
width, height = window_size
cv2.resizeWindow(window_name, width, height)
vc = cv2.VideoCapture(0)
if vc.isOpened():
read_value, webcam_image = vc.read()
else:
print("webcam not found")
return
while read_value:
for normalized_face, (x, y, w, h) in find_faces(webcam_image):
prediction = model.predict(cv2.resize(normalized_face,
(350, 350))) # do prediction
mixer.init()
if prediction[0] == 0:
mixer.music.load('songs/neutral.mp3')
mixer.music.play(-1, 2.0)
time.sleep(20)
mixer.music.stop()
if prediction[0] == 1:
mixer.music.load('songs/anger.mp3')
mixer.music.play(-1, 2.0)
time.sleep(10)
mixer.music.stop()
if prediction[0] == 5:
mixer.music.load('songs/surprise.mp3')
mixer.music.play(-1, 2.0)
time.sleep(10)
mixer.music.stop()
if prediction[0] == 3:
mixer.music.load('songs/happy.mp3')
mixer.music.play(-1, 2.0)
time.sleep(10)
mixer.music.stop()
if prediction[0] == 4:
mixer.music.load('songs/sadness.mp3')
mixer.music.play(-1, 2.0)
time.sleep(10)
mixer.music.stop()
image_to_draw = emoticons[prediction[0]]
draw_with_alpha(webcam_image, image_to_draw, (x, y, w, h))
cv2.imshow(window_name, webcam_image)
read_value, webcam_image = vc.read()
key = cv2.waitKey(update_time)
if key == 27: # exit on ESC
break
cv2.destroyWindow(window_name)
def show_webcam_and_run(model,
emoticons,
window_size=None,
window_name='webcam',
update_time=10):
cv2.namedWindow(window_name, WINDOW_NORMAL)
if window_size:
width, height = window_size
cv2.resizeWindow(window_name, width, height)
vc = cv2.VideoCapture(0)
if vc.isOpened():
read_value, webcam_image = vc.read()
else:
print("webcam not found")
return
cs, ch, cn, ca = 0, 0, 0, 0
while read_value:
for normalized_face, (x, y, w, h) in find_faces(webcam_image):
prediction = model.predict(normalized_face)
print(prediction[1])
if (prediction[1] > 700 and prediction[1] < 860):
prediction = 0
cn += 1
if (cn >= 3):
pattern = "songs/neutral/*"
filename = random.choice(glob.glob(pattern))
print(filename)
mixer.init()
mixer.music.load(filename)
mixer.music.play(-1, 0.0)
time.sleep(10)
mixer.music.stop()
cn = 0
elif (prediction[1] > 420 and prediction[1] < 650):
prediction = 2
ch += 1
if (ch >= 3):
pattern = "songs/happy/*"
filename = random.choice(glob.glob(pattern))
print(filename)
mixer.init()
mixer.music.load(filename)
mixer.music.play(-1, 0.0)
time.sleep(10)
mixer.music.stop()
ch = 0
elif (prediction[1] > 860 and prediction[1] < 1300):
prediction = 3
cs += 1
if (cs >= 3):
pattern = "songs/sad/*"
filename = random.choice(glob.glob(pattern))
print(filename)
mixer.init()
mixer.music.load(filename)
mixer.music.play(-1, 0.0)
time.sleep(10)
mixer.music.stop()
cs = 0
elif (prediction[1] > 1000 and prediction[1] < 3300):
prediction = 1
ca += 1
if (ca >= 3):
pattern = "songs/anger/*"
filename = random.choice(glob.glob(pattern))
print(filename)
mixer.init()
mixer.music.load(filename)
mixer.music.play(-1, 0.0)
time.sleep(10)
mixer.music.stop()
ca = 0
else:
prediction = 0
time.sleep(2)
image_to_draw = emoticons[prediction]
font = cv2.FONT_HERSHEY_SIMPLEX
text = emotions[prediction]
webcam_image = cv2.putText(webcam_image, text, (10, 50), font, 1,
(0, 255, 255), 2, cv2.LINE_AA)
draw_with_alpha(webcam_image, image_to_draw, (x, y, w, h))
time.sleep(2)
cv2.imshow(window_name, webcam_image)
read_value, webcam_image = vc.read()
key = cv2.waitKey(update_time)
if key == 27:
break
cv2.destroyWindow(window_name)
def show_webcam_and_run(model,
emoticons,
window_size=None,
window_name='webcam',
update_time=10):
"""
Shows webcam image, detects faces and its emotions in real time and draw emoticons over those faces.
:param model: Learnt emotion detection model.
:param emoticons: List of emotions images.
:param window_size: Size of webcam image window.
:param window_name: Name of webcam image window.
:param update_time: Image update time interval.
"""
cv2.namedWindow(window_name, WINDOW_NORMAL)
if window_size:
width, height = window_size
cv2.resizeWindow(window_name, width, height)
vc = cv2.VideoCapture("testing.mp4")
if vc.isOpened():
read_value, webcam_image = vc.read()
else:
print("webcam not found")
return
while True:
for normalized_face, (x, y, w, h) in find_faces(webcam_image):
prediction = model.predict(normalized_face) # do prediction
#if cv2.__version__ != '3.1.0':
prediction = prediction[0]
image_to_draw = emoticons[prediction]
draw_with_alpha(webcam_image, image_to_draw, (x, y, w, h))
print(prediction) #target variable
cv2.imshow(window_name, webcam_image)
read_value, webcam_image = vc.read()
key = cv2.waitKey(update_time)
img = webcam_image
#----------------------
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(
gray,
scaleFactor=1.2,
minNeighbors=5,
minSize=(int(minW), int(minH)),
)
#lola=0
for (x, y, w, h) in faces:
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
id, confidence = recognizer.predict(gray[y:y + h, x:x + w])
# Check if confidence is less them 100 ==> "0" is perfect match
if (confidence < 100):
percent = 100 - confidence
if (confidence < 60):
hit = 1
id = names[id]
confidence = " {0}%".format(round(100 - confidence))
else:
id = names[id]
confidence = " {0}%".format(round(100 - confidence))
hit = 0
global lola
if lola == 2:
from twilio.rest import Client
account_sid = "AC22c8e1723b1046ac741bd563caa0995d"
# # our Auth Token from twilio.com/console
auth_token = "9422efc06f0c7ab5b637536569d2d818"
client = Client(account_sid, auth_token)
message = client.messages.create(
to="+919944333726",
from_="+12407022806",
body=
"You are travelling with an unauthorized driver. Please check!"
)
lola = 0
else:
id = "unknown"
confidence = " {0}%".format(round(100 - confidence))
hit = 0
cv2.putText(img, str(id), (x + 5, y - 5), font, 1, (255, 255, 255),
2)
cv2.putText(img, str(confidence), (x + 5, y + h - 5), font, 1,
(255, 255, 0), 1)
def face(hit):
return hit
#print(percent)
cv2.imshow(window_name, img)
#----------------------
if key == 27: # exit on ESC
break
cv2.destroyWindow(window_name)
