def detect(path):
input_image = io.imread(path)
gnb = joblib.load('/home/qburst/Desktop/Emotion_detection/ED_dist/Classifier/gnb.pkl')
face_count, features, faces = p.feature_extraction(input_image)
if face_count:
emotions = gnb.predict(features)
print emotions
for d, emotion in zip(faces, emotions):
print d, emotion
if emotion == 0:
cv2.rectangle(input_image, (d.left(), d.top()), (d.right(), d.bottom()), (0, 0, 0), 2)
elif emotion == 1:
cv2.rectangle(input_image, (d.left(), d.top()), (d.right(), d.bottom()), (0, 0, 255), 3)
elif emotion == 2:
cv2.rectangle(input_image, (d.left(), d.top()), (d.right(), d.bottom()), (255, 255, 0), 3)
elif emotion == 3:
cv2.rectangle(input_image, (d.left(), d.top()), (d.right(), d.bottom()), (255, 0, 0), 3)
io.imsave('Detected/emotion.jpg', input_image)
return face_count, 'Detected/emotion.jpg'
else:
return face_count, 'err'
def detect(path):
input_image = io.imread(path)
gnb = joblib.load(
'/home/qburst/Desktop/Emotion_detection/ED_dist/Classifier/gnb.pkl')
face_count, features, faces = p.feature_extraction(input_image)
if face_count:
emotions = gnb.predict(features)
print emotions
for d, emotion in zip(faces, emotions):
print d, emotion
if emotion == 0:
cv2.rectangle(input_image, (d.left(), d.top()),
(d.right(), d.bottom()), (0, 0, 0), 2)
elif emotion == 1:
cv2.rectangle(input_image, (d.left(), d.top()),
(d.right(), d.bottom()), (0, 0, 255), 3)
elif emotion == 2:
cv2.rectangle(input_image, (d.left(), d.top()),
(d.right(), d.bottom()), (255, 255, 0), 3)
elif emotion == 3:
cv2.rectangle(input_image, (d.left(), d.top()),
(d.right(), d.bottom()), (255, 0, 0), 3)
io.imsave('Detected/emotion.jpg', input_image)
return face_count, 'Detected/emotion.jpg'
else:
return face_count, 'err'
#Convert to numpy array
y = np.asarray(y, dtype = np.int32)
x = np.asarray(x)
count = 0
#Extracting features
for emotion_folder in os.listdir(train_path):
print emotion_folder
for img_file in os.listdir(train_path + '/' + emotion_folder):
count += 1
print count, img_file
img = io.imread(train_path + '/' + emotion_folder + '/' + img_file)
face_count, feature, faces = p.feature_extraction(img)
x = np.vstack((x, feature[0]))
#Assigning code to each emotion
if emotion_folder == 'sad':
y = np.append(y, 0)
elif emotion_folder == 'surprise':
y = np.append(y, 1)
elif emotion_folder == 'happy':
y = np.append(y, 2)
elif emotion_folder == 'angry':
y = np.append(y, 3)
#Deleting the 1st element in x array
import processing as p
from sklearn.naive_bayes import GaussianNB
from sklearn.externals import joblib
import numpy as np
input_image_path = '/home/qburst/Desktop/test_sad1.jpg'
input_image = io.imread(input_image_path)
##x = np.load('/home/qburst/Desktop/Emotion Classification/Numpy arrays with only distances/dist_only_x.npy')
##y = np.load('/home/qburst/Desktop/Emotion Classification/Numpy arrays with only distances/y.npy')
###initializing a NB classifier
##gnb = GaussianNB()
##gnb.fit(x, y)
##joblib.dump(gnb, '/home/qburst/Desktop/TrainedGNB.pkl')
gnb = joblib.load('/home/qburst/Desktop/Emotion_detection/ED_dist/Classifier/gnb.pkl')
face_count, features, faces = p.feature_extraction(input_image)
emotions = gnb.predict(features)
print emotions
##for count, face in enumerate(faces):
## gnb.predict(features[0])
#Convert to numpy array
y = np.asarray(y, dtype=np.int32)
x = np.asarray(x)
count = 0
#Extracting features
for emotion_folder in os.listdir(train_path):
print emotion_folder
for img_file in os.listdir(train_path + '/' + emotion_folder):
count += 1
print count, img_file
img = io.imread(train_path + '/' + emotion_folder + '/' + img_file)
face_count, feature, faces = p.feature_extraction(img)
x = np.vstack((x, feature[0]))
#Assigning code to each emotion
if emotion_folder == 'sad':
y = np.append(y, 0)
elif emotion_folder == 'surprise':
y = np.append(y, 1)
elif emotion_folder == 'happy':
y = np.append(y, 2)
#Deleting the 1st element in x array
#x = np.delete(x, 0, 0)
#np.save('/home/qburst/Desktop/dist_only_x', x)
#np.save('/home/qburst/Desktop/y', y)
from skimage import io
import processing as p
from sklearn.naive_bayes import GaussianNB
from sklearn.externals import joblib
import numpy as np
input_image_path = '/home/qburst/Desktop/Test images/testsaveopencvimg without resize.jpg'
input_image = io.imread(input_image_path)
##x = np.load('/home/qburst/Desktop/Emotion Classification/Numpy arrays with only distances/dist_only_x.npy')
##y = np.load('/home/qburst/Desktop/Emotion Classification/Numpy arrays with only distances/y.npy')
###initializing a NB classifier
##gnb = GaussianNB()
##gnb.fit(x, y)
##joblib.dump(gnb, '/home/qburst/Desktop/TrainedGNB.pkl')
gnb = joblib.load('/home/qburst/Desktop/TrainedGNB.pkl')
face_count, features, faces = p.feature_extraction(input_image)
emotions = gnb.predict(features)
print emotions
##for count, face in enumerate(faces):
## gnb.predict(features[0])