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)