Add weekly report and Refactor flask web code

@@ -2,7 +2,7 @@ import sys
 import os
 
 from flask.helpers import url_for
-from face_emotion_recognition import face_recognition, video2
+from face_emotion_recognition import face_recognition, video4
 from flask import Flask, render_template
 from flask.globals import request
 from werkzeug.utils import redirect, secure_filename
@@ -39,7 +39,7 @@ def index():
 @app.route('/goTest', methods=('GET', 'POST'))  # 접속하는 url
 def test():
     if request.method == 'GET':
-        return render_template('test.html', face_imgs=find_face_imgs())
+        return render_template('test.html')
 
 
 @app.route('/uploadFace', methods=('GET', 'POST'))
@@ -61,11 +61,19 @@ def delete_face(face_name):
     return redirect(url_for('index'))
 
 
-@app.route('/uploadVideo')
+@app.route('/uploadVideo', methods=('GET', 'POST'))
 def upload_video():
-    f = request.files.get('video')
-    f.save("./static/video/" + secure_filename(f.filename))
-    return 'video uploaded successfully'
+    if request.method == 'POST':
+        f = request.files.get('video')
+        f.save("./static/video/" + secure_filename(f.filename))
+        return redirect(url_for('test'))
+
+
+@app.route('/faceEmotinoRecognition')
+def faceEmotinoRecognition():
+    face_emotion_dict = video4.videoDetector(3, 'record0')
+    print(face_emotion_dict)
+    return render_template('result.html', face_emotion_dict=face_emotion_dict, face_imgs=find_face_imgs())
 
 
 if __name__ == "__main__":

@@ -23,13 +23,6 @@ import time
 # model = load_model(
 #     'checkpoint/er-best-mobilenet1-bt32-model-classweight-adam.h5')
 
-
-def get_key(val):
-    for key, value in labels_dict_.items():
-        if(value == val):
-            return key
-
-
 def convertMillis(millis):
     seconds = (millis/1000) % 60
     minutes = (millis/(1000*60)) % 60
@@ -51,7 +44,7 @@ def videoDetector(input_fps, video_name):
     detector = dlib.get_frontal_face_detector()
 
     # face & emotion detection time dict
-    descs = np.load('../static/img/descs.npy', allow_pickle=True)[()]
+    descs = np.load('static/img/descs.npy', allow_pickle=True)[()]
     labels_dict_ = {0: 'angry', 1: 'fear',  2: 'happy',
                     3: 'neutral', 4:  'sad', 5: 'surprise'}
     face_emotion_dict = {}
@@ -129,3 +122,5 @@ def videoDetector(input_fps, video_name):
     for i in range(1, 5):
         cv2.destroyAllWindows()
         cv2.waitKey(1)
+
+    return face_emotion_dict

@@ -9,114 +9,99 @@ import pathlib
 import time
 import pandas as pd
 import tensorflow as tf
-from tensorflow.keras.preprocessing.image import ImageDataGenerator,load_img
+from tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img
 from tensorflow.keras.models import load_model
 from tensorflow.keras import regularizers
 from tensorflow import keras
 import time
 
 
-start = time.time()
-detector = dlib.get_frontal_face_detector()
-predictor = dlib.shape_predictor('./models/shape_predictor_68_face_landmarks.dat')
-facerec = dlib.face_recognition_model_v1('./models/dlib_face_recognition_resnet_model_v1.dat')
-model = load_model('../checkpoint/er-best-mobilenet1-bt32-model-classweight-adam.h5')
-
-
-def get_key(val):
-    for key, value in labels_dict_.items():
-        if(value == val):
-            return key
-
-
 def convertMillis(millis):
-    seconds=(millis/1000)%60
-    minutes=(millis/(1000*60))%60
-    hours=(millis/(1000*60*60))%24
+    seconds = (millis/1000) % 60
+    minutes = (millis/(1000*60)) % 60
+    hours = (millis/(1000*60*60)) % 24
     return seconds, int(minutes), int(hours)
 
 
-def videoDetector(input_fps, video_name):
+def videoDetector(second, video_name):
+
+    # face & emotion detection model load
+    detector = dlib.get_frontal_face_detector()
+    predictor = dlib.shape_predictor(
+        'face_emotion_recognition/models/shape_predictor_68_face_landmarks.dat')
+    facerec = dlib.face_recognition_model_v1(
+        'face_emotion_recognition/models/dlib_face_recognition_resnet_model_v1.dat')
+    model = load_model(
+        'checkpoint/er-best-mobilenet1-bt32-model-classweight-adam.h5')
 
     # face & emotion detection time dict
-    descs = np.load('./img/descs.npy', allow_pickle=True)[()]
-    labels_dict_ = {0 : 'angry', 1 : 'fear' ,  2: 'happy', 3: 'neutral', 4:  'sad', 5: 'surprise'}
+    descs = np.load('static/img/descs.npy', allow_pickle=True)[()]
+    labels_dict_ = {0: 'angry', 1: 'fear',  2: 'happy',
+                    3: 'neutral', 4:  'sad', 5: 'surprise'}
     face_emotion_dict = {}
     for name, saved_desc in descs.items():
-        face_emotion_dict[name] = {'angry': [], 'fear': [], 'happy': [], 'neutral': [], 'sad': [], 'surprise': []}
-
+        face_emotion_dict[name] = {'angry': [], 'fear': [
+        ], 'happy': [], 'neutral': [], 'sad': [], 'surprise': []}
 
     # video 정보 불러오기
-    video_path = './data/' + video_name + '.mp4'
-    cap=cv2.VideoCapture(video_path)
+    video_path = 'static/video/' + video_name + '.mp4'
+    cap = cv2.VideoCapture(video_path)
 
     # 동영상 크기(frame정보)를 읽어옴
-    frameWidth = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-    frameHeight = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-    frame_size = (frameWidth, frameHeight)
-    fps = cap.get((cv2.CAP_PROP_FPS))
-    print(fps)
-
+    fps = cap.get(cv2.CAP_PROP_FPS)
+    multiplier = fps * second
 
-    _, img_bgr = cap.read() # (800, 1920, 3)
-    padding_size = 0
-    resized_width = 1920
-    video_size = (resized_width, int(img_bgr.shape[0] * resized_width // img_bgr.shape[1]))
-    timestamps = [cap.get(cv2.CAP_PROP_POS_MSEC)]
-    prev_time = 0
+    frameCount = 0
+    ret = 1
 
-    fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
-    while True:
-        retval, frameBGR = cap.read()	# 영상을 한 frame씩 읽어오기
-        current_time = time.time() - prev_time
+    while ret:
+        frameId = int(round(cap.get(1)))  # 현재 프레임 번호 가져오기
+        ret, frameBGR = cap.read()  # 영상을 한 frame씩 읽어오기
 
         if(type(frameBGR) == type(None)):
             pass
         else:
-            frameBGR = cv2.resize(frameBGR, video_size)
             frame = cv2.cvtColor(frameBGR, cv2.COLOR_BGR2RGB)
 
-            if (retval is True) and (current_time > 1.5) :
-                prev_time = time.time()
+            if (ret is True) and (frameId % multiplier < 1):
                 faces = detector(frame, 1)
 
                 for (i, face) in enumerate(faces):
-                    shape = predictor(frame, face)
-                    face_descriptor = facerec.compute_face_descriptor(frame, shape)
-
-                    img = cv2.resize(frame[face.top():face.bottom(), face.left():face.right()], dsize=(224, 224), interpolation = cv2.INTER_CUBIC)
-                    imgarr = np.array(img).reshape(1, 224, 224, 3) /255
-                    emotion = labels_dict_[model.predict(imgarr).argmax(axis=-1)[0]]
-
-                    last_found = {'name': 'unknown', 'dist': 0.6, 'color': (0,0,255)}
-
-                    for name, saved_desc in descs.items():
-                        dist = np.linalg.norm([face_descriptor] - saved_desc, axis=1)
-                        if dist < last_found['dist']:
-                            last_found = {'name': name, 'dist': dist, 'color': (255,255,255)}
-
-                    cv2.rectangle(frameBGR, pt1=(face.left(), face.top()), pt2=(face.right(), face.bottom()), color=last_found['color'], thickness=2)
-                    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)
-
-                    con_sec, con_min, con_hour = convertMillis(cap.get(cv2.CAP_PROP_POS_MSEC))
-                    face_emotion_dict[last_found['name']][emotion].append("{0}:{1}:{2}".format(con_hour, con_min, round(con_sec, 3)))
-                    print("{0}:{1}:{2} {3}".format(con_hour, con_min, round(con_sec, 3), emotion))
-
-            cv2.imshow('frame', frameBGR)
-
-        key = cv2.waitKey(25)
-        if key == 27 :
-            break
-
+                    try:
+                        shape = predictor(frame, face)
+                        face_descriptor = facerec.compute_face_descriptor(
+                            frame, shape)
+
+                        img = cv2.resize(frame[face.top():face.bottom(), face.left(
+                        ):face.right()], dsize=(224, 224), interpolation=cv2.INTER_CUBIC)
+                        imgarr = np.array(img).reshape(1, 224, 224, 3) / 255
+                        emotion = labels_dict_[
+                            model.predict(imgarr).argmax(axis=-1)[0]]
+
+                        last_found = {'name': 'unknown',
+                                      'dist': 0.6, 'color': (0, 0, 255)}
+
+                        for name, saved_desc in descs.items():
+                            dist = np.linalg.norm(
+                                [face_descriptor] - saved_desc, axis=1)
+                            if dist < last_found['dist']:
+                                last_found = {
+                                    'name': name, 'dist': dist, 'color': (255, 255, 255)}
+
+                        cv2.rectangle(frameBGR, pt1=(face.left(), face.top()), pt2=(
+                            face.right(), face.bottom()), color=last_found['color'], thickness=2)
+                        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)
+
+                        con_sec, con_min, con_hour = convertMillis(
+                            cap.get(cv2.CAP_PROP_POS_MSEC))
+                        face_emotion_dict[last_found['name']][emotion].append(
+                            "{0}:{1}:{2}".format(con_hour, con_min, round(con_sec, 3)))
+                        print("{0}:{1}:{2} {3}".format(
+                            con_hour, con_min, round(con_sec, 3), emotion))
+                    except Exception as e:
+                        print(str(e))
+
+                    frameCount += 1
     print(face_emotion_dict)
-    print("총 시간 : ", time.time() - start)
-    if cap.isOpened():
-        cap.release()
-
-    for i in range(1,5):
-        cv2.destroyAllWindows()
-        cv2.waitKey(1)
-
-
-if __name__ == '__main__':
-    videoDetector(3, 'zoom_1')
\ No newline at end of file
+    return face_emotion_dict

+<!doctype html>
+<html lang="en">
+
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport"
+        content="width=device-width, user-scalable=no, initial-scale=1.0, maximum-scale=1.0, minimum-scale=1.0">
+    <meta http-equiv="X-UA-Compatible" content="ie=edge">
+    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0/css/bootstrap.min.css"
+        integrity="sha384-Gn5384xqQ1aoWXA+058RXPxPg6fy4IWvTNh0E263XmFcJlSAwiGgFAW/dAiS6JXm" crossorigin="anonymous">
+    <title>Flask Face Emotion Recognition App</title>
+</head>
+
+<body>
+
+    <div class="container" style="margin-top: 100px">
+        <h3>Face Emotion Recognition Platform</h3>
+        <hr>
+
+        <form action="http://localhost:5000/uploadFace" method="POST" enctype="multipart/form-data">
+            <div class="form-group">
+                <label for="title" class="text-uppercase">Video Upload</label>
+                <input type="file" name="file">
+                <button type="submit" class="btn btn-outline-primary">Add</button>
+            </div>
+        </form>
+
+        <video autoplay width="320" height="240" controls>
+            <source src={{ url_for('static', filename="video/zoom_1.mp4") }} type="video/mp4">
+        </video>
+
+        <a href="/faceEmotinoRecognition" class="btn btn-outline-primary">얼굴 감정 인식 분석하기</a>
+
+
+        <table class="table">
+            <thead>
+                <tr>
+                    <th scope="col ">name</th>
+                    <th scope="col">happy</th>
+                    <th scope="col">sad</th>
+                    <th scope="col">fear</th>
+                    <th scope="col">angry</th>
+                    <th scope="col">neutral</th>
+                    <th scope="col">surprise</th>
+                </tr>
+            </thead>
+            <tbody>
+
+                {% for face_img in face_imgs %}
+                <tr>
+                    <td scope="row">{{ face_img.name }}</td>
+                    {% if face_emotion_dict[face_img.name].happy %}
+                    <td>
+                    {% for time in face_emotion_dict[face_img.name].happy %}
+                        <span>{{time}}</span>
+                    {% endfor %}
+                    </td>
+                    {% else %}
+                    <td> X </td>
+                    {% endif %}
+                   
+                    {% if face_emotion_dict[face_img.name].sad %}
+                    <td>
+                    {% for time in face_emotion_dict[face_img.name].sad %}
+                    <span>{{time}}</span>
+                    {% endfor %}
+                    </td> 
+                    {% else %}
+                    <td> X </td>
+                    {% endif %}
+                    
+                    {% if face_emotion_dict[face_img.name].fear %}
+                    <td>
+                    {% for time in face_emotion_dict[face_img.name].fear %}
+                    <span>{{time}}</span>
+                    {% endfor %}
+                    </td>
+                    {% else %}
+                    <td> X </td>
+                    {% endif %}
+    
+                    {% if face_emotion_dict[face_img.name].angry %}
+                    <td>
+                    {% for time in face_emotion_dict[face_img.name].angry %}
+                        <span>{{time}}</span>
+                    {% endfor %}
+                    </td>
+                    {% else %}
+                    <td> X </td>
+                    {% endif %}
+    
+                    {% if face_emotion_dict[face_img.name].neutral %}
+                    <td>
+                    {% for time in face_emotion_dict[face_img.name].neutral %}
+                        <span>{{time}}</span>
+                    {% endfor %}
+                    </td>
+                    {% else %}
+                    <td> X </td>
+                    {% endif %}
+    
+                    {% if face_emotion_dict[face_img.name].surprise %}
+                    <td>
+                    {% for time in face_emotion_dict[face_img.name].surprise %}
+                        <span>{{time}}</span>
+                    {% endfor %}
+                    </td>  
+                    {% else %}
+                    <td> X </td>
+                    {% endif %}
+            
+                </tr>
+                {% endfor %}
+            </tbody>
+        </table>
+        <hr/>
+    </div>
+
+
+    <script src="https://code.jquery.com/jquery-3.2.1.slim.min.js"
+        integrity="sha384-KJ3o2DKtIkvYIK3UENzmM7KCkRr/rE9/Qpg6aAZGJwFDMVNA/GpGFF93hXpG5KkN"
+        crossorigin="anonymous"></script>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.12.9/umd/popper.min.js"
+        integrity="sha384-ApNbgh9B+Y1QKtv3Rn7W3mgPxhU9K/ScQsAP7hUibX39j7fakFPskvXusvfa0b4Q"
+        crossorigin="anonymous"></script>
+    <script src="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0/js/bootstrap.min.js"
+        integrity="sha384-JZR6Spejh4U02d8jOt6vLEHfe/JQGiRRSQQxSfFWpi1MquVdAyjUar5+76PVCmYl"
+        crossorigin="anonymous"></script>
+</body>
+
+</html>
\ No newline at end of file

@@ -17,13 +17,21 @@
         <h3>Face Emotion Recognition Platform</h3>
         <hr>
 
-        <form action="http://localhost:5000/uploadFace" method="POST" enctype="multipart/form-data">
+        <form action="http://localhost:5000/uploadVideo" method="POST" enctype="multipart/form-data">
             <div class="form-group">
                 <label for="title" class="text-uppercase">Video Upload</label>
-                <input type="file" name="file">
+                <input type="file" name="video">
                 <button type="submit" class="btn btn-outline-primary">Add</button>
             </div>
         </form>
+
+        <video autoplay width="320" height="240" controls>
+            <source src={{ url_for('static', filename="video/zoom_1.mp4") }} type="video/mp4">
+        </video>
+
+        <a href="/faceEmotinoRecognition" class="btn btn-outline-primary">얼굴 감정 인식 분석하기</a>
+
+        <hr/>
     </div>