2021-1-capstone-design2 / 2017103972

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5b171df1 1 parent aa5c0036

Add weekly report and Refactor flask web code

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Showing 24 changed files with 216 additions and 2451 deletions
...@@ -2,7 +2,7 @@ import sys ...@@ -2,7 +2,7 @@ import sys
2 import os 2 import os
3 3
4 from flask.helpers import url_for 4 from flask.helpers import url_for
5 -from face_emotion_recognition import face_recognition, video2 5 +from face_emotion_recognition import face_recognition, video4
6 from flask import Flask, render_template 6 from flask import Flask, render_template
7 from flask.globals import request 7 from flask.globals import request
8 from werkzeug.utils import redirect, secure_filename 8 from werkzeug.utils import redirect, secure_filename
...@@ -39,7 +39,7 @@ def index(): ...@@ -39,7 +39,7 @@ def index():
39 @app.route('/goTest', methods=('GET', 'POST')) # 접속하는 url 39 @app.route('/goTest', methods=('GET', 'POST')) # 접속하는 url
40 def test(): 40 def test():
41 if request.method == 'GET': 41 if request.method == 'GET':
42 - return render_template('test.html', face_imgs=find_face_imgs()) 42 + return render_template('test.html')
43 43
44 44
45 @app.route('/uploadFace', methods=('GET', 'POST')) 45 @app.route('/uploadFace', methods=('GET', 'POST'))
...@@ -61,11 +61,19 @@ def delete_face(face_name): ...@@ -61,11 +61,19 @@ def delete_face(face_name):
61 return redirect(url_for('index')) 61 return redirect(url_for('index'))
62 62
63 63
64 -@app.route('/uploadVideo') 64 +@app.route('/uploadVideo', methods=('GET', 'POST'))
65 def upload_video(): 65 def upload_video():
66 + if request.method == 'POST':
66 f = request.files.get('video') 67 f = request.files.get('video')
67 f.save("./static/video/" + secure_filename(f.filename)) 68 f.save("./static/video/" + secure_filename(f.filename))
68 - return 'video uploaded successfully' 69 + return redirect(url_for('test'))
70 +
71 +
72 +@app.route('/faceEmotinoRecognition')
73 +def faceEmotinoRecognition():
74 + face_emotion_dict = video4.videoDetector(3, 'record0')
75 + print(face_emotion_dict)
76 + return render_template('result.html', face_emotion_dict=face_emotion_dict, face_imgs=find_face_imgs())
69 77
70 78
71 if __name__ == "__main__": 79 if __name__ == "__main__":
......
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782 - op: "BiasAdd"
783 - input: "layer_512_1_scale1/Mul"
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...@@ -23,13 +23,6 @@ import time ...@@ -23,13 +23,6 @@ import time
23 # model = load_model( 23 # model = load_model(
24 # 'checkpoint/er-best-mobilenet1-bt32-model-classweight-adam.h5') 24 # 'checkpoint/er-best-mobilenet1-bt32-model-classweight-adam.h5')
25 25
26 -
27 -def get_key(val):
28 - for key, value in labels_dict_.items():
29 - if(value == val):
30 - return key
31 -
32 -
33 def convertMillis(millis): 26 def convertMillis(millis):
34 seconds = (millis/1000) % 60 27 seconds = (millis/1000) % 60
35 minutes = (millis/(1000*60)) % 60 28 minutes = (millis/(1000*60)) % 60
...@@ -51,7 +44,7 @@ def videoDetector(input_fps, video_name): ...@@ -51,7 +44,7 @@ def videoDetector(input_fps, video_name):
51 detector = dlib.get_frontal_face_detector() 44 detector = dlib.get_frontal_face_detector()
52 45
53 # face & emotion detection time dict 46 # face & emotion detection time dict
54 - descs = np.load('../static/img/descs.npy', allow_pickle=True)[()] 47 + descs = np.load('static/img/descs.npy', allow_pickle=True)[()]
55 labels_dict_ = {0: 'angry', 1: 'fear', 2: 'happy', 48 labels_dict_ = {0: 'angry', 1: 'fear', 2: 'happy',
56 3: 'neutral', 4: 'sad', 5: 'surprise'} 49 3: 'neutral', 4: 'sad', 5: 'surprise'}
57 face_emotion_dict = {} 50 face_emotion_dict = {}
...@@ -129,3 +122,5 @@ def videoDetector(input_fps, video_name): ...@@ -129,3 +122,5 @@ def videoDetector(input_fps, video_name):
129 for i in range(1, 5): 122 for i in range(1, 5):
130 cv2.destroyAllWindows() 123 cv2.destroyAllWindows()
131 cv2.waitKey(1) 124 cv2.waitKey(1)
125 +
126 + return face_emotion_dict
......
...@@ -9,114 +9,99 @@ import pathlib ...@@ -9,114 +9,99 @@ import pathlib
9 import time 9 import time
10 import pandas as pd 10 import pandas as pd
11 import tensorflow as tf 11 import tensorflow as tf
12 -from tensorflow.keras.preprocessing.image import ImageDataGenerator,load_img 12 +from tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img
13 from tensorflow.keras.models import load_model 13 from tensorflow.keras.models import load_model
14 from tensorflow.keras import regularizers 14 from tensorflow.keras import regularizers
15 from tensorflow import keras 15 from tensorflow import keras
16 import time 16 import time
17 17
18 18
19 -start = time.time()
20 -detector = dlib.get_frontal_face_detector()
21 -predictor = dlib.shape_predictor('./models/shape_predictor_68_face_landmarks.dat')
22 -facerec = dlib.face_recognition_model_v1('./models/dlib_face_recognition_resnet_model_v1.dat')
23 -model = load_model('../checkpoint/er-best-mobilenet1-bt32-model-classweight-adam.h5')
24 -
25 -
26 -def get_key(val):
27 - for key, value in labels_dict_.items():
28 - if(value == val):
29 - return key
30 -
31 -
32 def convertMillis(millis): 19 def convertMillis(millis):
33 - seconds=(millis/1000)%60 20 + seconds = (millis/1000) % 60
34 - minutes=(millis/(1000*60))%60 21 + minutes = (millis/(1000*60)) % 60
35 - hours=(millis/(1000*60*60))%24 22 + hours = (millis/(1000*60*60)) % 24
36 return seconds, int(minutes), int(hours) 23 return seconds, int(minutes), int(hours)
37 24
38 25
39 -def videoDetector(input_fps, video_name): 26 +def videoDetector(second, video_name):
27 +
28 + # face & emotion detection model load
29 + detector = dlib.get_frontal_face_detector()
30 + predictor = dlib.shape_predictor(
31 + 'face_emotion_recognition/models/shape_predictor_68_face_landmarks.dat')
32 + facerec = dlib.face_recognition_model_v1(
33 + 'face_emotion_recognition/models/dlib_face_recognition_resnet_model_v1.dat')
34 + model = load_model(
35 + 'checkpoint/er-best-mobilenet1-bt32-model-classweight-adam.h5')
40 36
41 # face & emotion detection time dict 37 # face & emotion detection time dict
42 - descs = np.load('./img/descs.npy', allow_pickle=True)[()] 38 + descs = np.load('static/img/descs.npy', allow_pickle=True)[()]
43 - labels_dict_ = {0 : 'angry', 1 : 'fear' , 2: 'happy', 3: 'neutral', 4: 'sad', 5: 'surprise'} 39 + labels_dict_ = {0: 'angry', 1: 'fear', 2: 'happy',
40 + 3: 'neutral', 4: 'sad', 5: 'surprise'}
44 face_emotion_dict = {} 41 face_emotion_dict = {}
45 for name, saved_desc in descs.items(): 42 for name, saved_desc in descs.items():
46 - face_emotion_dict[name] = {'angry': [], 'fear': [], 'happy': [], 'neutral': [], 'sad': [], 'surprise': []} 43 + face_emotion_dict[name] = {'angry': [], 'fear': [
47 - 44 + ], 'happy': [], 'neutral': [], 'sad': [], 'surprise': []}
48 45
49 # video 정보 불러오기 46 # video 정보 불러오기
50 - video_path = './data/' + video_name + '.mp4' 47 + video_path = 'static/video/' + video_name + '.mp4'
51 - cap=cv2.VideoCapture(video_path) 48 + cap = cv2.VideoCapture(video_path)
52 49
53 # 동영상 크기(frame정보)를 읽어옴 50 # 동영상 크기(frame정보)를 읽어옴
54 - frameWidth = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) 51 + fps = cap.get(cv2.CAP_PROP_FPS)
55 - frameHeight = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) 52 + multiplier = fps * second
56 - frame_size = (frameWidth, frameHeight)
57 - fps = cap.get((cv2.CAP_PROP_FPS))
58 - print(fps)
59 53
54 + frameCount = 0
55 + ret = 1
60 56
61 - _, img_bgr = cap.read() # (800, 1920, 3) 57 + while ret:
62 - padding_size = 0 58 + frameId = int(round(cap.get(1))) # 현재 프레임 번호 가져오기
63 - resized_width = 1920 59 + ret, frameBGR = cap.read() # 영상을 한 frame씩 읽어오기
64 - video_size = (resized_width, int(img_bgr.shape[0] * resized_width // img_bgr.shape[1]))
65 - timestamps = [cap.get(cv2.CAP_PROP_POS_MSEC)]
66 - prev_time = 0
67 -
68 - fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
69 - while True:
70 - retval, frameBGR = cap.read() # 영상을 한 frame씩 읽어오기
71 - current_time = time.time() - prev_time
72 60
73 if(type(frameBGR) == type(None)): 61 if(type(frameBGR) == type(None)):
74 pass 62 pass
75 else: 63 else:
76 - frameBGR = cv2.resize(frameBGR, video_size)
77 frame = cv2.cvtColor(frameBGR, cv2.COLOR_BGR2RGB) 64 frame = cv2.cvtColor(frameBGR, cv2.COLOR_BGR2RGB)
78 65
79 - if (retval is True) and (current_time > 1.5) : 66 + if (ret is True) and (frameId % multiplier < 1):
80 - prev_time = time.time()
81 faces = detector(frame, 1) 67 faces = detector(frame, 1)
82 68
83 for (i, face) in enumerate(faces): 69 for (i, face) in enumerate(faces):
70 + try:
84 shape = predictor(frame, face) 71 shape = predictor(frame, face)
85 - face_descriptor = facerec.compute_face_descriptor(frame, shape) 72 + face_descriptor = facerec.compute_face_descriptor(
73 + frame, shape)
86 74
87 - img = cv2.resize(frame[face.top():face.bottom(), face.left():face.right()], dsize=(224, 224), interpolation = cv2.INTER_CUBIC) 75 + img = cv2.resize(frame[face.top():face.bottom(), face.left(
88 - imgarr = np.array(img).reshape(1, 224, 224, 3) /255 76 + ):face.right()], dsize=(224, 224), interpolation=cv2.INTER_CUBIC)
89 - emotion = labels_dict_[model.predict(imgarr).argmax(axis=-1)[0]] 77 + imgarr = np.array(img).reshape(1, 224, 224, 3) / 255
78 + emotion = labels_dict_[
79 + model.predict(imgarr).argmax(axis=-1)[0]]
90 80
91 - last_found = {'name': 'unknown', 'dist': 0.6, 'color': (0,0,255)} 81 + last_found = {'name': 'unknown',
82 + 'dist': 0.6, 'color': (0, 0, 255)}
92 83
93 for name, saved_desc in descs.items(): 84 for name, saved_desc in descs.items():
94 - dist = np.linalg.norm([face_descriptor] - saved_desc, axis=1) 85 + dist = np.linalg.norm(
86 + [face_descriptor] - saved_desc, axis=1)
95 if dist < last_found['dist']: 87 if dist < last_found['dist']:
96 - last_found = {'name': name, 'dist': dist, 'color': (255,255,255)} 88 + last_found = {
97 - 89 + 'name': name, 'dist': dist, 'color': (255, 255, 255)}
98 - cv2.rectangle(frameBGR, pt1=(face.left(), face.top()), pt2=(face.right(), face.bottom()), color=last_found['color'], thickness=2) 90 +
99 - cv2.putText(frameBGR, last_found['name'] + ',' + emotion , org=(face.left(), face.top()), fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=1, color=last_found['color'], thickness=2) 91 + cv2.rectangle(frameBGR, pt1=(face.left(), face.top()), pt2=(
100 - 92 + face.right(), face.bottom()), color=last_found['color'], thickness=2)
101 - con_sec, con_min, con_hour = convertMillis(cap.get(cv2.CAP_PROP_POS_MSEC)) 93 + cv2.putText(frameBGR, last_found['name'] + ',' + emotion, org=(face.left(), face.top(
102 - face_emotion_dict[last_found['name']][emotion].append("{0}:{1}:{2}".format(con_hour, con_min, round(con_sec, 3))) 94 + )), fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=1, color=last_found['color'], thickness=2)
103 - print("{0}:{1}:{2} {3}".format(con_hour, con_min, round(con_sec, 3), emotion)) 95 +
104 - 96 + con_sec, con_min, con_hour = convertMillis(
105 - cv2.imshow('frame', frameBGR) 97 + cap.get(cv2.CAP_PROP_POS_MSEC))
106 - 98 + face_emotion_dict[last_found['name']][emotion].append(
107 - key = cv2.waitKey(25) 99 + "{0}:{1}:{2}".format(con_hour, con_min, round(con_sec, 3)))
108 - if key == 27 : 100 + print("{0}:{1}:{2} {3}".format(
109 - break 101 + con_hour, con_min, round(con_sec, 3), emotion))
110 - 102 + except Exception as e:
103 + print(str(e))
104 +
105 + frameCount += 1
111 print(face_emotion_dict) 106 print(face_emotion_dict)
112 - print("총 시간 : ", time.time() - start)
113 - if cap.isOpened():
114 - cap.release()
115 -
116 - for i in range(1,5):
117 - cv2.destroyAllWindows()
118 - cv2.waitKey(1)
119 -
120 -
121 -if __name__ == '__main__':
122 - videoDetector(3, 'zoom_1')
...\ No newline at end of file ...\ No newline at end of file
107 + return face_emotion_dict
......
1 +<!doctype html>
2 +<html lang="en">
3 +
4 +<head>
5 + <meta charset="UTF-8">
6 + <meta name="viewport"
7 + content="width=device-width, user-scalable=no, initial-scale=1.0, maximum-scale=1.0, minimum-scale=1.0">
8 + <meta http-equiv="X-UA-Compatible" content="ie=edge">
9 + <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0/css/bootstrap.min.css"
10 + integrity="sha384-Gn5384xqQ1aoWXA+058RXPxPg6fy4IWvTNh0E263XmFcJlSAwiGgFAW/dAiS6JXm" crossorigin="anonymous">
11 + <title>Flask Face Emotion Recognition App</title>
12 +</head>
13 +
14 +<body>
15 +
16 + <div class="container" style="margin-top: 100px">
17 + <h3>Face Emotion Recognition Platform</h3>
18 + <hr>
19 +
20 + <form action="http://localhost:5000/uploadFace" method="POST" enctype="multipart/form-data">
21 + <div class="form-group">
22 + <label for="title" class="text-uppercase">Video Upload</label>
23 + <input type="file" name="file">
24 + <button type="submit" class="btn btn-outline-primary">Add</button>
25 + </div>
26 + </form>
27 +
28 + <video autoplay width="320" height="240" controls>
29 + <source src={{ url_for('static', filename="video/zoom_1.mp4") }} type="video/mp4">
30 + </video>
31 +
32 + <a href="/faceEmotinoRecognition" class="btn btn-outline-primary">얼굴 감정 인식 분석하기</a>
33 +
34 +
35 + <table class="table">
36 + <thead>
37 + <tr>
38 + <th scope="col ">name</th>
39 + <th scope="col">happy</th>
40 + <th scope="col">sad</th>
41 + <th scope="col">fear</th>
42 + <th scope="col">angry</th>
43 + <th scope="col">neutral</th>
44 + <th scope="col">surprise</th>
45 + </tr>
46 + </thead>
47 + <tbody>
48 +
49 + {% for face_img in face_imgs %}
50 + <tr>
51 + <td scope="row">{{ face_img.name }}</td>
52 + {% if face_emotion_dict[face_img.name].happy %}
53 + <td>
54 + {% for time in face_emotion_dict[face_img.name].happy %}
55 + <span>{{time}}</span>
56 + {% endfor %}
57 + </td>
58 + {% else %}
59 + <td> X </td>
60 + {% endif %}
61 +
62 + {% if face_emotion_dict[face_img.name].sad %}
63 + <td>
64 + {% for time in face_emotion_dict[face_img.name].sad %}
65 + <span>{{time}}</span>
66 + {% endfor %}
67 + </td>
68 + {% else %}
69 + <td> X </td>
70 + {% endif %}
71 +
72 + {% if face_emotion_dict[face_img.name].fear %}
73 + <td>
74 + {% for time in face_emotion_dict[face_img.name].fear %}
75 + <span>{{time}}</span>
76 + {% endfor %}
77 + </td>
78 + {% else %}
79 + <td> X </td>
80 + {% endif %}
81 +
82 + {% if face_emotion_dict[face_img.name].angry %}
83 + <td>
84 + {% for time in face_emotion_dict[face_img.name].angry %}
85 + <span>{{time}}</span>
86 + {% endfor %}
87 + </td>
88 + {% else %}
89 + <td> X </td>
90 + {% endif %}
91 +
92 + {% if face_emotion_dict[face_img.name].neutral %}
93 + <td>
94 + {% for time in face_emotion_dict[face_img.name].neutral %}
95 + <span>{{time}}</span>
96 + {% endfor %}
97 + </td>
98 + {% else %}
99 + <td> X </td>
100 + {% endif %}
101 +
102 + {% if face_emotion_dict[face_img.name].surprise %}
103 + <td>
104 + {% for time in face_emotion_dict[face_img.name].surprise %}
105 + <span>{{time}}</span>
106 + {% endfor %}
107 + </td>
108 + {% else %}
109 + <td> X </td>
110 + {% endif %}
111 +
112 + </tr>
113 + {% endfor %}
114 + </tbody>
115 + </table>
116 + <hr/>
117 + </div>
118 +
119 +
120 + <script src="https://code.jquery.com/jquery-3.2.1.slim.min.js"
121 + integrity="sha384-KJ3o2DKtIkvYIK3UENzmM7KCkRr/rE9/Qpg6aAZGJwFDMVNA/GpGFF93hXpG5KkN"
122 + crossorigin="anonymous"></script>
123 + <script src="https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.12.9/umd/popper.min.js"
124 + integrity="sha384-ApNbgh9B+Y1QKtv3Rn7W3mgPxhU9K/ScQsAP7hUibX39j7fakFPskvXusvfa0b4Q"
125 + crossorigin="anonymous"></script>
126 + <script src="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0/js/bootstrap.min.js"
127 + integrity="sha384-JZR6Spejh4U02d8jOt6vLEHfe/JQGiRRSQQxSfFWpi1MquVdAyjUar5+76PVCmYl"
128 + crossorigin="anonymous"></script>
129 +</body>
130 +
131 +</html>
...\ No newline at end of file ...\ No newline at end of file
...@@ -17,13 +17,21 @@ ...@@ -17,13 +17,21 @@
17 <h3>Face Emotion Recognition Platform</h3> 17 <h3>Face Emotion Recognition Platform</h3>
18 <hr> 18 <hr>
19 19
20 - <form action="http://localhost:5000/uploadFace" method="POST" enctype="multipart/form-data"> 20 + <form action="http://localhost:5000/uploadVideo" method="POST" enctype="multipart/form-data">
21 <div class="form-group"> 21 <div class="form-group">
22 <label for="title" class="text-uppercase">Video Upload</label> 22 <label for="title" class="text-uppercase">Video Upload</label>
23 - <input type="file" name="file"> 23 + <input type="file" name="video">
24 <button type="submit" class="btn btn-outline-primary">Add</button> 24 <button type="submit" class="btn btn-outline-primary">Add</button>
25 </div> 25 </div>
26 </form> 26 </form>
27 +
28 + <video autoplay width="320" height="240" controls>
29 + <source src={{ url_for('static', filename="video/zoom_1.mp4") }} type="video/mp4">
30 + </video>
31 +
32 + <a href="/faceEmotinoRecognition" class="btn btn-outline-primary">얼굴 감정 인식 분석하기</a>
33 +
34 + <hr/>
27 </div> 35 </div>
28 36
29 37
......