input_face = camera_capture
gray, detface = smilerecognition.detect_face(input_face)
# detface jaa tyhjaksi () jos ei tunnista kasvoja
if len(detface)==1:
#face_index = 0
for face in detface:
(x, y, w, h) = face
if w > 100: #w pienenee kun etaisyys kamerasta kasvaa
extracted_face = smilerecognition.extract_face_features(gray, face, (0.1, 0.05)) # (horiz,vert)
# (0.1, 0.05) => toimii!
# (0.15, 0.2) => antaa aina smile
# (0.03, 0.05) (0.075, 0.05)
# kts. extract_test.py kalibroimisesta
prediction_result = smilerecognition.predict_face_is_smiling(extracted_face)
#cv2.rectangle(input_face, (x, y), (x+w, y+h), (0, 255, 0), 2)
#face_index += 1
if prediction_result == 1:
print "smile"
#cv2.imshow("Smile", input_face)
else:
print "no smile"
#cv2.imshow("No smile", input_face)
#cv2.waitKey(0)
else:
print "Error: no face detected"
retval, im = camera.read()
return im
for i in xrange(ramp_frames):
temp = get_image()
print("Taking image %s" % frameCount)
camera_capture = get_image()
del(camera)
input_face = camera_capture
gray, detface = smilerecognition.detect_face(input_face)
if len(detface)==1:
#face_index = 0
for face in detface:
(x, y, w, h) = face
if w > 100: #w pienenee kun etaisyys kamerasta kasvaa
extracted_face = smilerecognition.extract_face_features(gray, face, (0.1, 0.05))
list.append(prediction_result, smilerecognition.predict_face_is_smiling(extracted_face))
#face_index += 1
if prediction_result[frameCount] == 1:
print("Smile")
else:
print("No smile")
else:
list.append(prediction_result,[2])
print("Error: no face detected")
frameCount += 1
time.sleep(1)
