├── .vscode
└── settings.json
├── README.md
├── actionxhand_data0524_0513.h5
├── app.py
├── sentence_data.xlsx
├── sentence_model.pkl
├── static
├── face-api.min.js
├── models
│ ├── face_expression_model-shard1
│ ├── face_expression_model-weights_manifest.json
│ ├── face_landmark_68_model-shard1
│ ├── face_landmark_68_model-weights_manifest.json
│ ├── face_landmark_68_tiny_model-shard1
│ ├── face_landmark_68_tiny_model-weights_manifest.json
│ ├── face_recognition_model-shard1
│ ├── face_recognition_model-shard2
│ ├── face_recognition_model-weights_manifest.json
│ ├── ssd_mobilenetv1_model-shard1
│ ├── ssd_mobilenetv1_model-shard2
│ ├── ssd_mobilenetv1_model-weights_manifest.json
│ ├── tiny_face_detector_model-shard1
│ └── tiny_face_detector_model-weights_manifest.json
└── style.css
└── templates
└── index.html
/.vscode/settings.json:
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1 | {
2 | "liveServer.settings.port": 5501
3 | }
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/README.md:
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1 | # Sign-Language-Translator
2 | KW-Capstone-project : Korean Sign-Language Translator
3 |
4 | ## 개요
5 | >2014년부터 2020년까지 통계청에 따르면 국내 등록 농인수는 꾸준히 증가하고 있다. 농인이 일상생활에서 느끼는 불편한 점으로 의사 소통의 어려움, 편의 시설 부족, 외출 시 동반자 부재, 주변의 시선 및 기타 등이 있었지만, 그 중 가장 불편한 점은 의사 소통의 어려움이 54%에 달했다. 그리하여 농인이 어려움 없이 의사소통 할 수 있는 수어 번역기 웹 서비스 플랫폼을 개발하였다.
6 | 딥러닝 모델로 학습된 수어 번역기에 농인이 수화를 진행하면 실시간 번역이 단어별로 진행이 되고, 번역된 결과를 한국어 문장으로 출력한다. 농인과 의사소통하는 상대방은 수화를 이해하지 못하더라도 농인이 말하고자 하는 바를 알 수 있다. 완성된 수어 번역기에서는 공공데이터 세트의 제한에 따라 장소를 식당으로 한정 지어 식당에서 농인이 요구할 수 있는 상황을 설계해 제작하였지만, 이후 장소를 식당에서 국한 시킬게 아니라 다른 곳에서도 필요에 맞게끔 영상 데이터 셋을 얻어 딥러닝 모델링을 새로 한다면 어디든 쓸 수 있고 장애인들이 비장애인처럼 일상생활에서 누릴 수 있는 서비스들을 편리하게 이용할 수 있는 Barrier Free를 구축할 수 있다.
7 | >본 프로젝트의 결과로 얻을 수 있는 기대효과 및 활용방안은 크게 3가지로 정의했다.
8 | 첫 번째, 농인의 권리보장을 위한 통역 서비스 실현 가능하다. 일반인들과 비교하면 의사소통에 어려움을 겪는 경우가 많은 농인의 알 권리 보장 및 편리성 증진으로 인한 삶의 질 향상 효과를 기대할 수 있다. 두 번째, 시 공간 제약이 없는 수어 번역 서비스 제공이다. 수어 통역사의 도움 없이도 언제 어디서든지 농인들을 위한 수어 번역 서비스를 제공이 가능할 것이다. 세 번째, 한국어 외 다양한 언어권 적용 가능성이다. 본 프로젝트에서는 언어권을 한국으로 설정하여 개발을 진행했지만, 한국어 수어가 아닌 다른 언어권의 수어 데이터를 준비한다면 한국어뿐만 아니라 다른 언어권에서도 서비스 제공이 가능하다.
9 |
10 |
11 |
12 |
13 | ## 설계 과정
14 |
15 | - AIhub 수어 영상 데이터 셋
16 | - 필요한 수어 영상 데이터의 부족으로 인해 직접 수어를 배워 수어 영상 데이터세트를 구축함
17 |
18 | 
19 |
20 | - MediaPipe의 Hollistic 솔루션을 이용하여 웹캠으로 부터 얻은 영상에서 왼손, 오른손 Keypoints를 추출
21 |
22 | 
23 |
24 | - 총 45개의 수어 단어 세트
25 |
26 | 
27 |
28 | - LSTM 모델링은 tensorflow의 keras를 통해 진행되었다. input_shape=(timestep, feature)에서 timestep은 하나의 영상을 구성하는 프레임 개수, feature에는 126개의 왼 손, 오른 손 3D*(x, y, z) keypoints로 파라미터로 전달했다. 또 예측하고자 하는 target data인 수화 단어들(actions)을 출력층에 전달해 줬다. 또, 본 프로젝트 모델링에서는 hidden layer에 Stacked LSTM을 사용하여 LSTM이 더 복잡한 task를 해결할 수 있도록 LSTM 모델의 복잡도를 높혔다.
29 |
30 | 
31 |
32 | - Flask-SocketIO를 이용하여 사용자로 부터 받은 영상을 실시간으로 처리하여 예측된 결과를 응답으로 보내줌
33 |
34 |
35 |
36 |
37 | ## 동작과정
38 |
39 | - 시작하기 버튼을 통해서 Client의 Webcam 에 접근 권한을 얻음.
40 | 번역하기 버튼을 통해서 수어 번역을 시작하며 사용자에게 올바른 위치를 알려주기 위해서 FaceDetection API를
41 | 이용해서 사용자의 위치가 규격에 맞게 들어올 경우 번역을 시작함.
42 |
43 | 
44 |
45 | - 사용자가 올바른 곳에 위치하였을 때 규격을 나타내는 상자가 붉은색으로 변하면서 번역 시작을 알림
46 |
47 | 
48 |
49 | - 사용자가 수어 동작을 수행
50 |
51 | 
52 |
53 | - 사용자로부터 받은 30개의 frame이 하나의 영상이 되어 서버에서 예측, 이후에 예측된 단어를 Client에게 Return
54 |
55 | 
56 |
57 | - 입력 받은 영상으로부터 단어를 예측할 수 없을 경우 재동작을 요청
58 |
59 | 
60 |
61 | - 예측한 단어가 잘못된 단어일 경우 단어 삭제 버튼을 이용하여 예측된 단어를 삭제 가능
62 |
63 | 
64 |
65 | - 단어 삭제 버튼을 통해서 잘못 입력된 부탁이라는 단어가 삭제된 것을 확인가능
66 |
67 | 
68 |
69 | - 접시, 주세요 단어가 입력된 것을 확인 가능
70 |
71 | 
72 |
73 | - 입력된 단어 ‘접시’, ‘주세요’를 바탕으로 예측된 ‘접시 주세요’ 문장을 출력
74 |
75 | 
76 |
77 |
78 |
79 |
80 | ## 결론
81 |
82 | >본 프로젝트에서는 클라이언트로부터 생성한 프레임을 서버로 전달하고 서버는 받은 프레임을 저장했다가 하나의 영상을 만들 수 있는 양이 되면 학습모델의 예측할 데이터로 사용했다. 그리고 이는 이벤트 핸들러를 통해 진행되었다. 그러나 클라이언트로 부터 오는 빠른 데이터 전송이 서버에게 큰 부담으로 작용했고 클라이언트에서는 딜레이가 발생했다. 딜레이를 개선하기 위해 클라이언트와 서버사이의 시간 동기를 맞추거나, 클라이언트에서 데이터를 보낼 때 약간의 지연이 발생하도록 설계 했다. 약간의 딜레이가 여전히 존재했다. 이를 해결하지 못한 채 수어 번역 서비스 플랫폼 프로젝트를 마무리 했다.
83 |
84 |
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/actionxhand_data0524_0513.h5:
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https://raw.githubusercontent.com/kimjaebeom98/Sign-Language-Translator/f8fca3fd5ff6dc67386b101bb70d41740f399672/actionxhand_data0524_0513.h5
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/app.py:
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1 | from flask import Flask, render_template, Response
2 | from flask_socketio import SocketIO, emit
3 | import time, io, os, time, sys, natsort, random, math
4 | from PIL import Image
5 | import base64,cv2
6 | import numpy as np
7 | import joblib
8 | # import library
9 | import pandas as pd
10 | import mediapipe as mp
11 | from glob import glob
12 | from PIL import Image, ImageDraw, ImageFont
13 | from tensorflow.keras.models import Sequential
14 | from tensorflow.keras.layers import LSTM, Dense
15 | from tensorflow.keras.callbacks import TensorBoard
16 | from sklearn.preprocessing import LabelEncoder
17 |
18 | app = Flask(__name__)
19 | # SocketIO는 ‘app’에 적용되고 있으며 나중에 애플리케이션을 실행할 때 앱 대신 socketio를 사용할 수 있도록 socketio 변수에 저장된다.
20 | socketio = SocketIO(app,cors_allowed_origins='*' )
21 |
22 | mp_holistic = mp.solutions.holistic
23 | mp_drawing = mp.solutions.drawing_utils
24 | mp_face_mesh = mp.solutions.face_mesh
25 |
26 | def mediapipe_detection(image, model):
27 | image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
28 | image.flags.writeable = False
29 | results = model.process(image)
30 | image.flags.writeable = True
31 | image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
32 | return image, results
33 |
34 | # 왼손, 오른손 key_point 추출
35 | def extract_keypoints(results):
36 | lh = np.array([[res.x*3, res.y*3, res.z*3] for res in results.left_hand_landmarks.landmark]).flatten() if results.left_hand_landmarks else np.zeros(21*3)
37 | rh = np.array([[res.x*3, res.y*3, res.z*3] for res in results.right_hand_landmarks.landmark]).flatten() if results.right_hand_landmarks else np.zeros(21*3)
38 | return np.concatenate([lh, rh])
39 |
40 | my_dict ={"None":0, "계산":1, "고맙다":2, "괜찮다":3, "기다리다":4, "나" :5, "네": 6,
41 | "다음":7, "달다":8, "더":9, "도착":10, "돈":11, "또":12,
42 | "맵다":13, "먼저":14, "무엇":15, "물":16, "물음":17, "부탁":18, "사람":19,
43 | "수저":20, "시간":21, "아니요":22, "어디":23, "얼마":24,"예약":25,
44 | "오다":26, "우리":27, "음식":28, "이거":29, "인기":30, "있다":31, "자리":32,
45 | "접시":33, "제일":34, "조금":35, "주문":36, "주세요":37, "짜다":38, "책":39,
46 | "추천":40, "화장실":41, "확인":42}
47 |
48 | ## 받은 5개의 단어들을 데이터 프레임으로 변환
49 | def make_word_df(word0, word1, word2, word3, word4):
50 | info = [[word0, word1, word2, word3, word4]]
51 | df = pd.DataFrame(info, columns = ['target0', 'target1', 'target2', 'target3', 'target4'])
52 | return df
53 |
54 | ## 받은 단어를 숫자로 반환
55 | def get_key(val):
56 | for key, value in my_dict.items():
57 | if val == key:
58 | return value
59 |
60 | return "There is no such Key"
61 |
62 | ## 인자로 받은 단어 5개의 데이터프레임을
63 | def make_num_df(input_1):
64 | num_oflist = []
65 | for i in input_1.columns:
66 | num_oflist.append(get_key(input_1[i].values))
67 | input2 = make_word_df(num_oflist[0], num_oflist[1], num_oflist[2], num_oflist[3], num_oflist[4])
68 | return input2
69 |
70 | log_dir = os.path.join('Logs')
71 | tb_callback = TensorBoard(log_dir = log_dir)
72 |
73 | # Actions that we try to detect
74 | actions = np.array(['None', '계산', '고맙다', '괜찮다', '기다리다', '나', '네', '다음',
75 | '달다', '더', '도착', '돈', '또', '맵다', '먼저', '무엇', '물', '물음',
76 | '부탁', '사람', '수저', '시간', '아니요', '어디', '얼마', '예약', '오다',
77 | '우리', '음식', '이거', '인기', '있다', '자리', '접시', '제일', '조금',
78 | '주문', '주세요', '짜다', '책', '추천', '화장실', '확인'])
79 |
80 | model = Sequential()
81 | model.add(LSTM(64, return_sequences=True, activation='relu', input_shape=(30, 126)))
82 | model.add(LSTM(128, return_sequences=True, activation='relu'))
83 | model.add(LSTM(64, return_sequences=False, activation='relu'))
84 | model.add(Dense(64, activation='relu'))
85 | model.add(Dense(32, activation='relu'))
86 | model.add(Dense(actions.shape[0], activation='softmax'))
87 |
88 | model.compile(optimizer='Adam', loss ='categorical_crossentropy', metrics=['categorical_accuracy'])
89 | model.load_weights("C:/Users/MASTER/Desktop/Sign-Language-Translator/actionxhand_data0524_0513.h5")
90 | rlf = joblib.load("C:/Users/MASTER/Desktop/Sign-Language-Translator/sentence_model.pkl")
91 | data = pd.read_excel("C:/Users/MASTER/Desktop/Sign-Language-Translator/sentence_data.xlsx", engine = 'openpyxl')
92 | data_x = data.drop(['sentence'], axis = 1)
93 | data_y = data['sentence']
94 | le = LabelEncoder()
95 | le.fit(data['sentence'])
96 |
97 | font = ImageFont.truetype("fonts/HMFMMUEX.TTC", 10)
98 | font2 = ImageFont.truetype("fonts/HMFMMUEX.TTC", 20)
99 | blue_color = (255,0,0)
100 |
101 | @app.route('/', methods=['POST', 'GET'])
102 | def index():
103 | return render_template('index.html')
104 |
105 |
106 | def readb64(base64_string):
107 | idx = base64_string.find('base64,')
108 | base64_string = base64_string[idx+7:]
109 |
110 | sbuf = io.BytesIO()
111 |
112 | # Take in base64 string and return PIL image
113 | sbuf.write(base64.b64decode(base64_string, ' /'))
114 | pimg = Image.open(sbuf)
115 |
116 | # convert PIL Image to an RGB image( technically a numpy array ) that's compatible with opencv
117 | return cv2.cvtColor(np.array(pimg), cv2.COLOR_RGB2BGR)
118 |
119 | def moving_average(x):
120 | return np.mean(x)
121 |
122 | # 서버가 클라이언트가 보낸 메시지를 받는 방법, 클라이언트의 메시지를 확인하는 방법
123 | # catch-frame 이벤트 핸들러 정의
124 | # catch-frame 를 트리거 할 때 response_back 이벤트로 전송함 2번째 인자 data와 같이
125 | @socketio.on('catch-frame')
126 | def catch_frame(data):
127 | emit('response_back', data)
128 |
129 | global count, sequence, sentece, predictions
130 | sequence = []
131 | sentence = []
132 | predictions = []
133 | count = 0
134 | # image 이벤트 핸들러 정의 클라이언트에서 image 이벤트 핸들러로 image data를 보냈으니 받는 것
135 | @socketio.on('image')
136 | def image(data_image):
137 | global sequence, sentence, predictions, count
138 | threshold = 0.5
139 | if(data_image == "delete"):
140 | if(len(sentence) != 0):
141 | sequence = []
142 | count = 0
143 | delete_word = sentence[-1]
144 | sentence.pop(-1)
145 | delete_word = delete_word + "가 삭제되었습니다."
146 | emit('delete_back', delete_word)
147 | else:
148 | delete_word = "번역된 단어가 없습니다."
149 | emit('delete_back', delete_word)
150 | return
151 | else:
152 | frame = (readb64(data_image))
153 | with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:
154 | count = count+1
155 | # Make detections
156 | image, results = mediapipe_detection(frame, holistic)
157 | keypoints = extract_keypoints(results)
158 | sequence.append(keypoints)
159 | print(len(sequence))
160 | if (len(sequence) % 30 == 0):
161 | sentence_len1 = len(sentence)
162 | res = model.predict(np.expand_dims(sequence, axis=0))[0]
163 | print(actions[np.argmax(res)])
164 |
165 | """
166 | predictions.append(np.argmax(res))
167 | u = np.bincount(predictions[-1:])
168 | b = u.argmax()
169 | if b == np.argmax(res):
170 |
171 | """
172 |
173 | if res[np.argmax(res)] > threshold:
174 | if len(sentence) > 0:
175 | if actions[np.argmax(res)] == 'None':
176 | sentence.append(actions[np.argmax(res)])
177 | else:
178 | if(actions[np.argmax(res)] != sentence[-1]):
179 | sentence.append(actions[np.argmax(res)])
180 | else:
181 | sentence.append(actions[np.argmax(res)])
182 |
183 |
184 | sentence_len2 = len(sentence)
185 | count = 0
186 | sequence.clear()
187 | if(sentence_len1 != sentence_len2):
188 |
189 | if(len(sentence) == 5):
190 | data_form = make_word_df(sentence[0], sentence[1], sentence[2], sentence[3], sentence[4])
191 | input_data = make_num_df(data_form)
192 | y_pred = rlf.predict(input_data)
193 | le.inverse_transform(y_pred)
194 | predict_word = np.array2string(le.inverse_transform(y_pred))
195 | sentence.clear()
196 | emit('result', predict_word)
197 | else:
198 | predict_word = sentence[-1]
199 | emit('response_back', predict_word)
200 | else:
201 | predict_word = "failed"
202 | emit('response_back', predict_word)
203 | emit('start', 'start')
204 |
205 |
206 | if __name__ == '__main__':
207 | socketio.run(app ,debug=True)
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/sentence_data.xlsx:
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https://raw.githubusercontent.com/kimjaebeom98/Sign-Language-Translator/f8fca3fd5ff6dc67386b101bb70d41740f399672/sentence_data.xlsx
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/sentence_model.pkl:
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https://raw.githubusercontent.com/kimjaebeom98/Sign-Language-Translator/f8fca3fd5ff6dc67386b101bb70d41740f399672/sentence_model.pkl
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/static/models/face_expression_model-shard1:
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https://raw.githubusercontent.com/kimjaebeom98/Sign-Language-Translator/f8fca3fd5ff6dc67386b101bb70d41740f399672/static/models/face_expression_model-shard1
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/static/models/face_expression_model-weights_manifest.json:
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42 |
43 | a.button {
44 | color: rgba(30, 22, 54, 0.6);
45 | box-shadow: rgba(30, 22, 54, 0.4) 0 0px 0px 2px inset;
46 | }
47 |
48 | a.button:hover {
49 | color: rgba(255, 255, 255, 0.85);
50 | box-shadow: rgba(30, 22, 54, 0.7) 0 0px 0px 40px inset;
51 | }
52 |
53 | a.button2 {
54 | color: rgba(30, 22, 54, 0.6);
55 | box-shadow: rgba(30, 22, 54, 0.4) 0 0px 0px 2px inset;
56 | }
57 |
58 | a.button2:hover {
59 | color: rgba(255, 255, 255, 0.85);
60 | box-shadow: rgba(30, 22, 54, 0.7) 0 0px 0px 40px inset;
61 | }
62 | a.button3 {
63 | color: rgba(30, 22, 54, 0.6);
64 | box-shadow: rgba(30, 22, 54, 0.4) 0 0px 0px 2px inset;
65 | }
66 |
67 | a.button3:hover {
68 | color: rgba(255, 255, 255, 0.85);
69 | box-shadow: rgba(30, 22, 54, 0.7) 0 0px 0px 40px inset;
70 | }
71 |
72 |
73 | a {
74 | -webkit-transition: all 200ms cubic-bezier(0.390, 0.500, 0.150, 1.360);
75 | -moz-transition: all 200ms cubic-bezier(0.390, 0.500, 0.150, 1.360);
76 | -ms-transition: all 200ms cubic-bezier(0.390, 0.500, 0.150, 1.360);
77 | -o-transition: all 200ms cubic-bezier(0.390, 0.500, 0.150, 1.360);
78 | transition: all 200ms cubic-bezier(0.390, 0.500, 0.150, 1.360);
79 | text-decoration: none;
80 | padding: 5px 20px;
81 | margin-bottom: 0px;
82 | }
83 |
84 |
85 | .outline {
86 | position : absolute;
87 | left: 240px; top: 30px;
88 | width : 130px;
89 | height : 130px;
90 | padding : 20px;
91 | border : 2px dashed white;
92 | }
93 | .inner {
94 | position : absolute;
95 | left: 255px; top:45px;
96 | width : 100px;
97 | height : 100px;
98 | padding : 20px;
99 | border : 2px dashed white;
100 |
101 | }
102 | canvas {
103 | position: absolute;
104 | }
105 | body {
106 | margin: 0;
107 | padding: 0;
108 | width: 100vw;
109 | height: 100vh;
110 | display: flex;
111 | justify-content: center;
112 | align-items: center;
113 | }
114 | textarea{
115 | font-weight:bold;
116 | font-size: 16px;
117 | text-align:center;
118 | width: 100%;
119 | height: 95px;
120 | border: solid 2px white;
121 | }
122 |
123 |
124 | #count_box{
125 | position : absolute;
126 | left: 400px; top:5px;
127 | text-align:right;
128 | font-weight: bold ;
129 | }
130 |
131 |
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/templates/index.html:
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