def FatigueDetect(img, pos):
# print 'Detect...'
print pos
faces = detector(img, 0)
#face_shape = dlib.rectangle(left = pos[0],top = pos[1],bottom = pos[0]+pos[2],right = pos[0]+pos[3])
shape = landmark_predictor(img, faces[0])
#img,shape= img_process.getRotImg(img,shape)
left_eye, t = img_process.getLeftEye(img, shape)
right_eye, t = img_process.getRightEye(img, shape)
mouth, t = img_process.getMouth(img, shape)
left_eye = cv2.resize(left_eye, (64, 32))
right_eye = cv2.resize(right_eye, (64, 32))
mouth = cv2.resize(mouth, (64, 64))
cv2.imshow('LeftEye', left_eye)
cv2.imshow('RightEye', right_eye)
cv2.imshow('Mouth', mouth)
for i in range(68):
cv2.circle(img, (shape.part(i).x, shape.part(i).y), 2, (255, 255, 255),
2)
#print 'getLbp...'
left_lbp = img_process.getLBPFeatures(left_eye, CELL_SIZE)
right_lbp = img_process.getLBPFeatures(right_eye, CELL_SIZE)
mouth_lbp = img_process.getLBPFeatures(mouth, CELL_SIZE)
feature = np.concatenate((left_lbp, right_lbp, mouth_lbp))
#feature = feature.reshape(-1,1)
#print 'get...End'
#print 'Detect...End'
return svm.predict(feature.reshape(1, -1))
def preData():
pbr = ProgressBar(maxval = 660)
global All_block,X_train,Y_train
now = time.time()
print 'Data process...'
for i in range(440):
All_block.append(block(i,1))
for i in range(220):
All_block.append(block(i+440,0))
random.shuffle(All_block)
pbr.start()
for i in range(660):
pbr.update(i)
id = All_block[i].id
label = All_block[i].label
Y_train.append(label)
path = AWAKE_PATH
if label == 0:
id -= 440
path = SLEEP_PATH
left_eye = cv2.imread(path+'LeftEyes/'+str(id)+'.jpeg',0)
right_eye = cv2.imread(path + 'RightEyes/' + str(id) + '.jpeg',0)
mouth = cv2.imread(path + 'Mouth/' + str(id) + '.jpeg',0)
left_lbp = img_process.getLBPFeatures(left_eye,CELL_SIZE)
right_lbp = img_process.getLBPFeatures(right_eye,CELL_SIZE)
mouth_lbp = img_process.getLBPFeatures(mouth,CELL_SIZE)
feature = np.concatenate((left_lbp,right_lbp,mouth_lbp))
X_train.append(feature)
pbr.finish()
X_train = np.array(X_train)
print X_train.shape
print 'End\nCost Time:' + str(time.time() - now)
def see():
open_eye = cv2.imread(AWAKE_PATH + 'LeftEyes/' + str(400) + '.jpeg', 0)
close_eye = cv2.imread(SLEEP_PATH + 'LeftEyes/' + str(180) + '.jpeg', 0)
close_mouth = cv2.imread(AWAKE_PATH + 'Mouth/' + str(0) + '.jpeg', 0)
open_mouth = cv2.imread(SLEEP_PATH + 'Mouth/' + str(19) + '.jpeg', 0)
plt.figure(1)
plt.subplot(2, 2, 1)
plt.gray()
plt.imshow(LBP(open_eye))
plt.subplot(2, 2, 2)
plt.imshow(LBP(close_eye))
plt.subplot(2, 2, 3)
plt.xlim(0, 256 * 2)
plt.ylim(0, 0.20)
plt.bar(range(256 * 2), img_process.getLBPFeatures(open_eye, CELL_SIZE))
plt.gray()
plt.grid()
plt.subplot(2, 2, 4)
plt.xlim(0, 256 * 2)
plt.ylim(0, 0.20)
plt.bar(range(256 * 2), img_process.getLBPFeatures(close_eye, CELL_SIZE))
plt.gray()
plt.grid()
plt.figure(2)
plt.subplot(2, 2, 1)
plt.gray()
plt.imshow(LBP(open_mouth))
plt.subplot(2, 2, 2)
plt.gray()
plt.imshow(LBP(close_mouth))
plt.subplot(2, 2, 3)
plt.gray()
plt.grid()
plt.xlim(0, 256 * 4)
plt.ylim(0, 0.4)
plt.bar(range(256 * 4), img_process.getLBPFeatures(open_mouth, CELL_SIZE))
plt.subplot(2, 2, 4)
plt.gray()
plt.grid()
plt.xlim(0, 256 * 4)
plt.ylim(0, 0.4)
plt.bar(range(256 * 4), img_process.getLBPFeatures(close_mouth, CELL_SIZE))
plt.show()
def preTest():
global X_test,Y_test
pbr = ProgressBar(maxval = 30)
pbr.start()
for i in range(1,31):
pbr.update(i)
id = i
label = 1
path = TEST_PATH + 'Awake/'
if i > 23:
path = TEST_PATH + 'Sleep/'
id -= 23
label = 0
img = cv2.imread(path+str(id)+'.jpeg',0)
img = cv2.resize(img,(320,320))
shape = landmark_predictor(img,face)
img,shape= img_process.getRotImg(img,shape)
left_eye,t= img_process.getLeftEye(img,shape)
right_eye,t = img_process.getRightEye(img,shape)
mouth,t = img_process.getMouth(img,shape)
left_eye = cv2.resize(left_eye,(64,32))
right_eye = cv2.resize(right_eye,(64,32))
mouth = cv2.resize(mouth,(64,64))
#cv2.imshow('GG1',left_eye)
#cv2.imshow('GG2',right_eye)
#cv2.imshow('GG3',mouth)
#key = cv2.waitKey(0)
#if key == ord('q'):
# break
left_lbp = img_process.getLBPFeatures(left_eye,CELL_SIZE)
right_lbp = img_process.getLBPFeatures(right_eye,CELL_SIZE)
mouth_lbp = img_process.getLBPFeatures(mouth,CELL_SIZE)
feature = np.concatenate((left_lbp,right_lbp,mouth_lbp))
X_test.append(feature)
Y_test.append(label)
pbr.finish()
X_test = np.array(X_test)
Y_test = np.array(Y_test)
print X_test,Y_test
def preTest():
global X_test, Y_test
for i in range(1, 31):
id = i
label = 1
path = TEST_PATH + 'Awake/'
if i > 23:
path = TEST_PATH + 'Sleep/'
id -= 23
label = 0
img = cv2.imread(path + str(id) + '.jpeg', 0)
img = cv2.resize(img, (320, 320))
shape = landmark_predictor(img, face)
img, shape = img_process.getRotImg(img, shape)
left_eye, t = img_process.getLeftEye(img, shape)
right_eye, t = img_process.getRightEye(img, shape)
mouth, t = img_process.getMouth(img, shape)
left_eye = cv2.resize(left_eye, (64, 32))
right_eye = cv2.resize(right_eye, (64, 32))
mouth = cv2.resize(mouth, (64, 64))
"""
cv2.imshow('img',img)
cv2.imshow('GG1',left_eye)
cv2.imshow('GG2',right_eye)
cv2.imshow('GG3',mouth)
key = cv2.waitKey(0)
if key == ord('q'):
break
"""
left_lbp = img_process.getLBPFeatures(left_eye, CELL_SIZE)
right_lbp = img_process.getLBPFeatures(right_eye, CELL_SIZE)
mouth_lbp = img_process.getLBPFeatures(mouth, CELL_SIZE)
feature = np.concatenate((left_lbp, right_lbp, mouth_lbp))
X_test.append(feature)
Y_test.append(label)
X_test = np.array(X_test)
Y_test = np.array(Y_test)