Merge branch 'QA_RELEASE' into db_and_monitoring_IT17097284

AttendanceApp/api.py

@@ -10,6 +10,7 @@ from rest_framework.views import APIView
 from rest_framework.parsers import MultiPartParser, FormParser
 
 from . import record
+from . import test as t
 
 from rest_framework.views import *
 
@@ -171,3 +172,29 @@ class InitiateLecture(APIView):
         return Response({
             "response": "success"
         })
+
+class stopRecording(APIView):
+    def get(self, request):
+        t.isStop = 1
+        return Response({
+            "response": "stopped"
+        })
+    def post(self, request):
+        pass
+
+# test method (delete later)
+class TestAPI(APIView):
+
+    def get(self, request):
+        t.isStop = 0
+        param = request.query_params.get('param')
+
+        # t.test()
+        t.IPWebcamTest()
+
+        return Response({
+            "response": "started"
+        })
+
+    def post(self, request):
+        pass
\ No newline at end of file

AttendanceApp/camera.py

@@ -23,15 +23,12 @@ maskNet = load_model(os.path.join(settings.BASE_DIR,'face_detector/mask_detector
 
 class IPWebCam(object):
 	def __init__(self):
-		self.url = "http://192.168.8.100:8080/shot.jpg"
-		self._count = 0
+		self.url = "http://192.168.8.103:8080/shot.jpg"
 
 	def __del__(self):
 		cv2.destroyAllWindows()
 
 	def get_frame(self):
-		fourcc = cv2.VideoWriter_fourcc(*'XVID')
-		out = cv2.VideoWriter('output.avi', fourcc, 20.0, (640, 480))
 		imgResp = urllib.request.urlopen(self.url)
 		imgNp = np.array(bytearray(imgResp.read()),dtype=np.uint8)
 		img= cv2.imdecode(imgNp,-1)
@@ -46,9 +43,6 @@ class IPWebCam(object):
 		frame_flip = cv2.flip(resize,1)
 		ret, jpeg = cv2.imencode('.jpg', frame_flip)
 
-		# capture frame and save on a given time in order to run the face recognition
-		sleep(3); cv2.imwrite("%d.jpg" % self._count, img)
-		self._count =+1
 		return jpeg.tobytes()
 
 

AttendanceApp/templates/AttendanceApp/Initiate_Lecture.html

@@ -43,6 +43,50 @@ function toggleLectureLive() {
     y.style.display = "none";
   }
 }
+var timer = false;
+//this is a test function
+function testAPI() {
+    timer = true
+    startTimer()
+    let param = 'sachith';
+    //call the API
+    fetch('http://127.0.0.1:8000/attendance/test-api/?param=' + param)
+    .then((res) => res.json())
+    .then((out) => {})
+    .catch((err) => alert('error: ' + err));
+
+}
+var time = 'time';
+function f() {
+    let param = 'sachith';
+    //call the API
+    fetch('http://127.0.0.1:8000/attendance/stop-api/?param=' + param)
+    .then((res) => res.json())
+    .then((out) => {
+        timer = false
+        startTimer();
+    })
+    .catch((err) => alert('error: ' + err));
+}
+
+function startTimer() {
+    var min = 0;
+    var seconds = 0;
+    if (timer) {
+        var sec = 0;
+        function pad ( val ) { return val > 9 ? val : "0" + val; }
+        setInterval( function(){
+            min = pad(parseInt(sec/60,10));
+            seconds = pad(++sec%60)
+            document.getElementById("seconds").innerHTML=pad(++sec%60);
+            document.getElementById("minutes").innerHTML=pad(parseInt(sec/60,10));
+        }, 1000);
+    } else {
+            document.getElementById("secondsStop").innerHTML=seconds;
+            document.getElementById("minutesStop").innerHTML=min;
+    }
+}
+
 </script>
 
 {% endblock %}
@@ -60,13 +104,21 @@ function toggleLectureLive() {
 
                     <div class="card-body">
                         <button type="button" class="btn btn-success" id="initiate_btn" onclick="toggleLectureLive()">Show Live Stream</button>
+{#                        <button type="button" class="btn btn-success" id="test_btn" onclick="testAPI()">Test</button>#}
                     </div>
+                    <span id="minutes"></span>:<span id="seconds"></span>
+                    <span id="minutesStop"></span>:<span id="secondsStop"></span>
                     <div style="vertical-align: middle; border-style: none; background-color: #055270; height: 500px; width: 100%">
                         <div class="row justify-content-center">
                             <img id="liveStreamLecture" style="display: none; height: inherit; margin-bottom: -25px;" src="{% url 'webcam_feed' %}">
                         </div>
                         <div class="row justify-content-center">
-                            <button style="display: none; width: 70px; height: 70px;" id="liveStreamLectureStartButton" class="btn btn-warning btn-circle"><i class="fas fa-video"></i></button>
+                            <div class="col">
+                                <button style="display: none; width: 70px; height: 70px;" id="liveStreamLectureStartButton" class="btn btn-warning btn-circle" onclick="testAPI()"><i class="fas fa-video"></i></button>
+                            </div>
+                            <div class="col">
+                                <button style="display: block; width: 70px; height: 70px;" id="liveStreamLectureStartButton" class="btn btn-warning btn-circle" onclick="f()"><i class="fas fa-square"></i></button>
+                            </div>
                         </div>
                     </div>
                 </div>

AttendanceApp/test.py

+
+import urllib3
+import urllib.request as req
+import cv2
+import numpy as np
+import time
+
+isStop = 0
+
+
+def IPWebcamTest():
+
+    # Replace the URL with your own IPwebcam shot.jpg IP:port
+    # url = 'http://192.168.2.35:8080/shot.jpg'
+    url = 'http://192.168.8.103:8080/shot.jpg'
+    # url = 'http://192.168.1.11:8080/startvideo?force=1&tag=rec'
+    # url = 'http://192.168.1.11:8080/stopvideo?force=1'
+
+    size = (600, 600)
+
+    vid_cod = cv2.VideoWriter_fourcc(*'XVID')
+    # vid_cod = cv2.VideoWriter_fourcc('M', 'J', 'P', 'G')
+    # output = cv2.VideoWriter("cam_video.avi", vid_cod, 20.0, (640, 480))
+    # output = cv2.VideoWriter("cam_video.mp4", vid_cod, 20.0, size)
+    output = cv2.VideoWriter("cam_video.mp4", vid_cod, 10.0, size)
+
+    no_of_frames = 0
+
+    while True:
+        # Use urllib to get the image from the IP camera
+        imgResp = req.urlopen(url)
+        # imgResp = urllib3.respon
+
+        # Numpy to convert into a array
+        imgNp = np.array(bytearray(imgResp.read()), dtype=np.uint8)
+
+        # Finally decode the array to OpenCV usable format ;)
+        img = cv2.imdecode(imgNp, -1)
+        # resize the image
+        img = cv2.resize(img, (600, 600))
+
+        # put the image on screen
+        # cv2.imshow('IPWebcam', img)
+
+        # write to the output writer
+        output.write(img)
+
+        # To give the processor some less stress
+        # time.sleep(0.1)
+        # time.sleep(1)
+
+        no_of_frames += 1
+        if isStop == 1:
+            break
+
+    # imgResp.release()
+    # cv2.destroyAllWindows()
+    print('no of frames: ', no_of_frames)
\ No newline at end of file

AttendanceApp/urls.py

@@ -2,7 +2,7 @@ from django.urls import path
 from .api import student_list, student_detail, subject_list, subject_detail, attendance_list, StudentAPIView, \
     StudentDetails
 from django.conf.urls import url
-from .api import FileView, InitiateLecture
+from .api import *
 from . import views
 
 urlpatterns = [
@@ -19,5 +19,10 @@ urlpatterns = [
     url(r'^upload/$', FileView.as_view(), name='file-upload'),
     path('webcam_feed', views.webcam_feed, name='webcam_feed'),
     # this url will initiate the lecture
-    url(r'^process-initiate-lecture/$', InitiateLecture.as_view())
+    url(r'^process-initiate-lecture/$', InitiateLecture.as_view()),
+
+    # this url will be used for testing
+    url(r'^test-api/$', TestAPI.as_view()),
+    url(r'^stop-api/$', stopRecording.as_view())
+
 ]

AttendanceApp/views.py

 from django.shortcuts import render
 from django.http.response import StreamingHttpResponse
 from AttendanceApp.camera import IPWebCam
+from FirstApp.MongoModels import LectureVideo
+from FirstApp.serializers import LectureVideoSerializer
 
 
 def initiate_lecture(request):
 
+    lecture_video = LectureVideo.objects.all()
+    lecture_video_ser = LectureVideoSerializer(lecture_video, many=True)
+
+    print('lecture video data: ', lecture_video_ser.data)
+
     return render(request, "AttendanceApp/Initiate_lecture.html")
 
+
 def gen(camera):
     while True:
         frame = camera.get_frame()
         yield (b'--frame\r\n'
                b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n\r\n')
 
+
 def webcam_feed(request):
     return StreamingHttpResponse(gen(IPWebCam()),
                                  content_type='multipart/x-mixed-replace; boundary=frame')

FirstApp/admin.py

@@ -15,3 +15,9 @@ admin.site.register(LectureActivity)
 admin.site.register(LectureGazeEstimation)
 admin.site.register(Admin)
 admin.site.register(AdminCredentialDetails)
+admin.site.register(LectureActivityFrameRecognitions)
+admin.site.register(LectureActivityFrameGroupings)
+admin.site.register(LectureEmotionFrameRecognitions)
+admin.site.register(LectureEmotionFrameGroupings)
+admin.site.register(LectureGazeFrameRecognitions)
+admin.site.register(LectureGazeFrameGroupings)
\ No newline at end of file

FirstApp/emotion_detector.py

@@ -52,6 +52,8 @@ def emotion_recognition(classifier, face_classifier, image):
         roi_gray = gray[y:y + h, x:x + w]
         roi_gray = cv2.resize(roi_gray, (48, 48), interpolation=cv2.INTER_AREA)
         # rect,face,image = face_detector(frame)
+        # draw a rectangle
+        cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
 
         if np.sum([roi_gray]) != 0:
             roi = roi_gray.astype('float') / 255.0
@@ -63,6 +65,9 @@ def emotion_recognition(classifier, face_classifier, image):
             preds = classifier.predict(roi)[0]
             label = class_labels[preds.argmax()]
 
+            # put the emotion label
+            cv2.putText(image, label, (x, y), cv2.FONT_HERSHEY_COMPLEX, 2, (0, 255, 0), 3)
+
     return label
 
 
@@ -79,6 +84,7 @@ def detect_emotion(video):
     face_classifier = cv2.CascadeClassifier(os.path.join(BASE_DIR, 'FirstApp\classifiers\haarcascade_frontalface_default.xml'))
     classifier_path = os.path.join(BASE_DIR, 'FirstApp\classifiers\Emotion_little_vgg.h5')
     classifier = load_model(classifier_path)
+    EMOTION_DIR = os.path.join(BASE_DIR, "static\\FirstApp\\emotion")
     meta_data = VideoMeta()
 
     class_labels = ['Angry', 'Happy', 'Neutral', 'Sad', 'Surprise']
@@ -99,6 +105,20 @@ def detect_emotion(video):
     # for testing purposes
     print('starting the emotion recognition process')
 
+    # get width and height of the video frames
+    frame_width = int(cap.get(3))
+    frame_height = int(cap.get(4))
+
+    # get the video frame size
+    size = (frame_width, frame_height)
+
+    # this is the annotated video path
+    ANNOTATED_VIDEO_PATH = os.path.join(EMOTION_DIR, video)
+
+    # initiailizing the video writer
+    vid_cod = cv2.VideoWriter_fourcc(*'XVID')
+    output = cv2.VideoWriter(ANNOTATED_VIDEO_PATH, vid_cod, 30.0, size)
+
     while (count_frames < frame_count):
         # Grab a single frame of video
         ret, frame = cap.read()
@@ -135,6 +155,9 @@ def detect_emotion(video):
         # for testing purposes
         print('emotion frame count: ', count_frames)
 
+        # write the video frame to the video writer
+        output.write(frame)
+
         count_frames += 1
 
     # setting up the counted values
@@ -146,8 +169,13 @@ def detect_emotion(video):
     meta_data.surprise_count = count_surprise
 
     cap.release()
+    output.release()
     cv2.destroyAllWindows()
 
+    # after saving the video, save the changes to static content
+    p = os.popen("python manage.py collectstatic", "w")
+    p.write("yes")
+
     # for testing purposes
     print('ending the emotion recognition process')
 
@@ -198,6 +226,8 @@ def get_frame_emotion_recognition(video_name):
     # for testing purposes
     print('starting the emotion frame recognition process')
 
+
+
     # looping through the frames
     while (frame_count < no_of_frames):
 
@@ -216,18 +246,19 @@ def get_frame_emotion_recognition(video_name):
         surprise_count = 0
 
         # get the detections
-        detections = ar.person_detection(image, net)
+        detections, persons = ar.person_detection(image, net)
 
         # to count the extracted detections for a frame
         detection_count = 0
 
         # if there are detections
         if (len(detections) > 0):
+
             # loop through the detections
-            for detection in detections:
+            for person in persons:
 
 
-                label = emotion_recognition(classifier, face_classifier, detection)
+                label = emotion_recognition(classifier, face_classifier, person)
 
                 # checking for the label
                 if label == class_labels[0]:
@@ -422,17 +453,17 @@ def emotion_frame_groupings(video_name, frame_landmarks, frame_group_dict):
         neutral_count = 0
         detection_count = 0
 
-        detections = ar.person_detection(image, net)
+        detections, persons = ar.person_detection(image, net)
 
         # if there are detections
         if (len(detections) > 0):
 
             # looping through the detections in each frame
-            for detection in detections:
+            for person in persons:
 
 
                 # run the model and get the emotion label
-                label = emotion_recognition(classifier, face_classifier, detection)
+                label = emotion_recognition(classifier, face_classifier, person)
 
                 # increment the count based on the label
                 if label == class_labels[0]:
@@ -639,10 +670,14 @@ def get_emotion_correlations(individual_lec_emotions, lec_recorded_activity_data
     # this variable will be used to store the correlations
     correlations = []
 
-    limit = 10
+    # limit = 10
+    limit = len(individual_lec_emotions)
 
     data_index = ['lecture-{}'.format(i + 1) for i in range(len(individual_lec_emotions))]
 
+    # declare the correlation data dictionary
+    corr_data = {}
+
     # student activity labels
     student_emotion_labels = ['Happy', 'Sad', 'Angry', 'Surprise', 'Neutral']
     lecturer_activity_labels = ['seated', 'standing', 'walking']
@@ -662,31 +697,72 @@ def get_emotion_correlations(individual_lec_emotions, lec_recorded_activity_data
 
     # loop through the lecturer recorded data (lecturer)
     for data in lec_recorded_activity_data:
+        value = int(data['seated_count'])
+        value1 = int(data['standing_count'])
+        value2 = int(data['walking_count'])
+
+        if value != 0:
             sitting_perct_list.append(int(data['seated_count']))
+        if value1 != 0:
             standing_perct_list.append(int(data['standing_count']))
+        if value2 != 0:
             walking_perct_list.append(int(data['walking_count']))
 
     # loop through the lecturer recorded data (student)
     for data in individual_lec_emotions:
+        value = int(data['happy_perct'])
+        value1 = int(data['sad_perct'])
+        value2 = int(data['angry_perct'])
+        value3 = int(data['surprise_perct'])
+        value4 = int(data['neutral_perct'])
+
+        if value != 0:
             happy_perct_list.append(int(data['happy_perct']))
+        if value1 != 0:
             sad_perct_list.append(int(data['sad_perct']))
+        if value2 != 0:
             angry_perct_list.append(int(data['angry_perct']))
+        if value3 != 0:
             surprise_perct_list.append(int(data['surprise_perct']))
+        if value4 != 0:
             neutral_perct_list.append(int(data['neutral_perct']))
 
 
-    corr_data = {'Happy': happy_perct_list, 'Sad': sad_perct_list, 'Angry': angry_perct_list, 'Surprise': surprise_perct_list, 'Neutral': neutral_perct_list,
-                 'seated': sitting_perct_list, 'standing': standing_perct_list, 'walking': walking_perct_list}
+    if len(happy_perct_list) == len(individual_lec_emotions):
+        corr_data[student_emotion_labels[0]] = happy_perct_list
+    if len(sad_perct_list) == len(individual_lec_emotions):
+        corr_data[student_emotion_labels[1]] = sad_perct_list
+    if len(angry_perct_list) == len(individual_lec_emotions):
+        corr_data[student_emotion_labels[2]] = angry_perct_list
+    if len(surprise_perct_list) == len(individual_lec_emotions):
+        corr_data[student_emotion_labels[3]] = surprise_perct_list
+    if len(neutral_perct_list) == len(individual_lec_emotions):
+        corr_data[student_emotion_labels[4]] = neutral_perct_list
+    if (len(sitting_perct_list)) == len(individual_lec_emotions):
+        corr_data[lecturer_activity_labels[0]] = sitting_perct_list
+    if (len(standing_perct_list)) == len(individual_lec_emotions):
+        corr_data[lecturer_activity_labels[1]] = standing_perct_list
+    if (len(walking_perct_list)) == len(individual_lec_emotions):
+        corr_data[lecturer_activity_labels[2]] = walking_perct_list
+
+
+    # corr_data = {'Happy': happy_perct_list, 'Sad': sad_perct_list, 'Angry': angry_perct_list, 'Surprise': surprise_perct_list, 'Neutral': neutral_perct_list,
+    #              'seated': sitting_perct_list, 'standing': standing_perct_list, 'walking': walking_perct_list}
 
     # create the dataframe
     df = pd.DataFrame(corr_data, index=data_index)
 
+    print(df)
+
     # calculate the correlation
     pd_series = ut.get_top_abs_correlations(df, limit)
 
     print('====correlated variables=====')
     print(pd_series)
 
+    # assign a new value to the 'limit' variable
+    limit = len(pd_series) if len(pd_series) < limit else limit
+
     for i in range(limit):
         # this dictionary will get the pandas.Series object's  indices and values separately
         corr_dict = {}

FirstApp/logic/batch_process.py

+import requests
+
+
+def batch_process(video_id, video_name):
+
+
+    # call the activity process
+    activity_resp = requests.get('http://127.0.0.1:8000/process-lecture-activity/?lecture_video_name=' + video_name + '&lecture_video_id=' + video_id)
+
+    # call the emotion process
+    emotion_resp = requests.get('http://127.0.0.1:8000/process-lecture-emotion/?lecture_video_name=' + video_name + '&lecture_video_id=' + video_id)
+
+    # call the gaze process
+    gaze_resp = requests.get('http://127.0.0.1:8000/process-lecture-gaze-estimation/?lecture_video_name=' + video_name + '&lecture_video_id=' + video_id)
+
+
+    pass
+
+
+# this method will save the lecture video
+def save_student_lecture_video(student_video):
+
+    # call the API
+    student_video_save_resp = requests.post('http://127.0.0.1:8000/lecture-video', student_video)
\ No newline at end of file

FirstApp/logic/head_gaze_estimation.py

@@ -171,6 +171,7 @@ def process_gaze_estimation(video_path):
 
     ret, img = cap.read()
     size = img.shape
+    font = cv2.FONT_HERSHEY_SIMPLEX
     # 3D model points.
     model_points = np.array([
         (0.0, 0.0, 0.0),  # Nose tip
@@ -211,6 +212,18 @@ def process_gaze_estimation(video_path):
     # for testing purposes
     print('starting the gaze estimation process')
 
+    # get the frame sizes
+    frame_width = int(cap.get(3))
+    frame_height = int(cap.get(4))
+
+    frame_size = (frame_width, frame_height)
+    # this is the annotated video path
+    ANNOTATED_VIDEO_PATH = os.path.join(GAZE_DIR, video_path)
+
+    # initiailizing the video writer
+    vid_cod = cv2.VideoWriter_fourcc(*'XVID')
+    output = cv2.VideoWriter(ANNOTATED_VIDEO_PATH, vid_cod, 30.0, frame_size)
+
     # iterate the video frames
     while True:
         ret, img = cap.read()
@@ -285,14 +298,19 @@ def process_gaze_estimation(video_path):
 
                 # checking for vertical and horizontal directions
                 if isLookingDown & isLookingRight:
+                    cv2.putText(img, 'looking down and right', (facebox[0], facebox[1]), font, 2, (255, 255, 128), 3)
                     head_down_right_count += 1
                 elif isLookingDown & isLookingLeft:
+                    cv2.putText(img, 'looking down and left', (facebox[0], facebox[1]), font, 2, (255, 255, 128), 3)
                     head_down_left_count += 1
                 elif isLookingUp & isLookingRight:
+                    cv2.putText(img, 'looking up and right', (facebox[0], facebox[1]), font, 2, (255, 255, 128), 3)
                     head_up_right_count += 1
                 elif isLookingUp & isLookingLeft:
+                    cv2.putText(img, 'looking up and left', (facebox[0], facebox[1]), font, 2, (255, 255, 128), 3)
                     head_up_left_count += 1
                 elif isLookingFront:
+                    cv2.putText(img, 'looking front', (facebox[0], facebox[1]), font, 2, (255, 255, 128), 3)
                     head_front_count += 1
 
 
@@ -304,6 +322,9 @@ def process_gaze_estimation(video_path):
             # for testing purposes
             print('gaze estimation count: ', frame_count)
 
+            # write to the video writer
+            output.write(img)
+
             # increment the frame count
             frame_count += 1
 
@@ -330,6 +351,12 @@ def process_gaze_estimation(video_path):
 
     cv2.destroyAllWindows()
     cap.release()
+    output.release()
+
+
+    # after saving the video, save the changes to static content
+    p = os.popen("python manage.py collectstatic", "w")
+    p.write("yes")
 
     # for testing purposes
     print('ending the gaze estimation process')
@@ -538,6 +565,7 @@ def get_lecture_gaze_estimation_for_frames(video_name):
 
 
 
+
     # for testing purposes
     print('ending the gaze estimation for frames process')
 
@@ -979,10 +1007,15 @@ def get_gaze_correlations(individual_lec_gaze, lec_recorded_activity_data):
     # this variable will be used to store the correlations
     correlations = []
 
-    limit = 10
+
+    # limit = 10
+    limit = len(individual_lec_gaze)
 
     data_index = ['lecture-{}'.format(i + 1) for i in range(len(individual_lec_gaze))]
 
+    # declare the correlation data dictionary
+    corr_data = {}
+
     # student gaze labels
     student_gaze_labels = ['Up and Right', 'Up and Left', 'Down and Right', 'Down and Left', 'Front']
     lecturer_activity_labels = ['seated', 'standing', 'walking']
@@ -1001,28 +1034,72 @@ def get_gaze_correlations(individual_lec_gaze, lec_recorded_activity_data):
 
     # loop through the lecturer recorded data (lecturer)
     for data in lec_recorded_activity_data:
+        value = int(data['seated_count'])
+        value1 = int(data['standing_count'])
+        value2 = int(data['walking_count'])
+
+        if value != 0:
             sitting_perct_list.append(int(data['seated_count']))
+        if value1 != 0:
             standing_perct_list.append(int(data['standing_count']))
+        if value2 != 0:
             walking_perct_list.append(int(data['walking_count']))
 
     # loop through the lecturer recorded data (student)
     for data in individual_lec_gaze:
+        value = int(data['looking_up_and_right_perct'])
+        value1 = int(data['looking_up_and_left_perct'])
+        value2 = int(data['looking_down_and_right_perct'])
+        value3 = int(data['looking_down_and_left_perct'])
+        value4 = int(data['looking_front_perct'])
+
+        if value != 0:
             upright_perct_list.append(int(data['looking_up_and_right_perct']))
+        if value1 != 0:
             upleft_perct_list.append(int(data['looking_up_and_left_perct']))
+        if value2 != 0:
             downright_perct_list.append(int(data['looking_down_and_right_perct']))
+        if value3 != 0:
             downleft_perct_list.append(int(data['looking_down_and_left_perct']))
+        if value4 != 0:
             front_perct_list.append(int(data['looking_front_perct']))
 
-    corr_data = {'Up and Right': upright_perct_list, 'Up and Left': upleft_perct_list, 'Down and Right': downright_perct_list,
-                 'Down and Left': downleft_perct_list, 'Front': front_perct_list,
-                 'seated': sitting_perct_list, 'standing': standing_perct_list, 'walking': walking_perct_list}
+
+    if (len(upright_perct_list)) == len(individual_lec_gaze):
+        corr_data[student_gaze_labels[0]] = upright_perct_list
+    if (len(upleft_perct_list)) == len(individual_lec_gaze):
+        corr_data[student_gaze_labels[1]] = upleft_perct_list
+    if (len(downright_perct_list)) == len(individual_lec_gaze):
+        corr_data[student_gaze_labels[2]] = downright_perct_list
+    if (len(downleft_perct_list)) == len(individual_lec_gaze):
+        corr_data[student_gaze_labels[3]] = downleft_perct_list
+    if (len(front_perct_list)) == len(individual_lec_gaze):
+        corr_data[student_gaze_labels[4]] = front_perct_list
+    if (len(sitting_perct_list)) == len(individual_lec_gaze):
+        corr_data[lecturer_activity_labels[0]] = sitting_perct_list
+    if (len(standing_perct_list)) == len(individual_lec_gaze):
+        corr_data[lecturer_activity_labels[1]] = standing_perct_list
+    if (len(walking_perct_list)) == len(individual_lec_gaze):
+        corr_data[lecturer_activity_labels[2]] = walking_perct_list
+
+
+    # corr_data = {'Up and Right': upright_perct_list, 'Up and Left': upleft_perct_list, 'Down and Right': downright_perct_list,
+    #              'Down and Left': downleft_perct_list, 'Front': front_perct_list,
+    #              'seated': sitting_perct_list, 'standing': standing_perct_list, 'walking': walking_perct_list}
 
     # create the dataframe
     df = pd.DataFrame(corr_data, index=data_index)
 
+    print(df)
+
     # calculate the correlation
     pd_series = ut.get_top_abs_correlations(df, limit)
 
+    print('====correlated variables=====')
+    print(pd_series)
+
+    # assign a new value to the 'limit' variable
+    limit = len(pd_series) if len(pd_series) < limit else limit
 
     for i in range(limit):
         # this dictionary will get the pandas.Series object's  indices and values separately

FirstApp/logic/utilities.py

 
+# this method will remove the redundant pairs in pandas dataframe
 def get_redundant_pairs(df):
     '''Get diagonal and lower triangular pairs of correlation matrix'''
     pairs_to_drop = set()
@@ -8,6 +9,7 @@ def get_redundant_pairs(df):
             pairs_to_drop.add((cols[i], cols[j]))
     return pairs_to_drop
 
+# this method will return the top specified correlations
 def get_top_abs_correlations(df, n):
     au_corr = df.corr().abs().unstack()
     labels_to_drop = get_redundant_pairs(df)

FirstApp/logic/video_extraction.py

@@ -307,3 +307,8 @@ def get_frame_landmarks(video_name):
 
     # now return the frame landmarks
     return frame_landmarks
+
+
+# this method will save leture video (student)
+def save_lecture_student_video():
+    pass
\ No newline at end of file

FirstApp/serializers.py

@@ -18,8 +18,12 @@ there are two fields inside "Meta" class, as follows.
 
 
 from rest_framework import serializers
+from djongo import models
 from .MongoModels import *
 from . models import VideoMeta
+from .logic import id_generator as ig
+# from datetime import datetime as dt
+import datetime
 
 
 # lecture serializer
@@ -190,6 +194,110 @@ class LectureVideoSerializer(serializers.ModelSerializer):
         model = LectureVideo
         fields = '__all__'
 
+    # this method will validate the input data
+    def to_internal_value(self, data):
+
+        lecturer = None
+        subject = None
+
+        lecturer_data = data.get('lecturer')
+        subject_data = data.get('subject')
+
+        # serialize the lecturer data
+        lecturer = Lecturer.objects.filter(id=lecturer_data)
+        subject = Subject.objects.filter(id=subject_data)
+
+        lecturer_ser_data = LecturerSerializer(lecturer, many=True).data[0]
+        subject_ser_data = SubjectSerializer(subject, many=True).data[0]
+
+        # retrieve the last lecture video details
+        last_lec_video = LectureVideo.objects.order_by('lecture_video_id').last()
+        # create the next lecture video id
+        new_lecture_video_id = ig.generate_new_id(last_lec_video.lecture_video_id)
+
+        # if both subject and lecturer details are available
+        if len(lecturer) == 1 & len(subject) == 1:
+            str_video_length = data.get('video_length')
+            video_length_parts = str_video_length.split(':')
+            video_length = datetime.timedelta(minutes=int(video_length_parts[0]), seconds=int(video_length_parts[1]),
+                                              milliseconds=int(video_length_parts[2]))
+
+        # this data will be passed as validated data
+        validated_data = {
+            'lecture_video_id': new_lecture_video_id,
+            'lecturer': lecturer_ser_data,
+            'subject': subject_ser_data,
+            'date': data.get('date'),
+            'video_name': data.get('video_name'),
+            'video_length': video_length
+        }
+
+        return super(LectureVideoSerializer, self).to_internal_value(validated_data)
+
+
+
+
+    # this method will override the 'create' method
+    def create(self, validated_data):
+
+        lecturer = None
+        subject = None
+
+
+
+        lecturer_data = validated_data.pop('lecturer')
+        subject_data = validated_data.pop('subject')
+
+        # serialize the lecturer data
+        lecturer = Lecturer.objects.filter(id=lecturer_data)
+        subject = Subject.objects.filter(id=subject_data)
+
+
+        # retrieve the last lecture video details
+        last_lec_video = LectureVideo.objects.order_by('lecture_video_id').last()
+        # create the next lecture video id
+        new_lecture_video_id = ig.generate_new_id(last_lec_video.lecture_video_id)
+
+
+        # if both subject and lecturer details are available
+        if len(lecturer) == 1 & len(subject) == 1:
+
+            str_video_length = validated_data.pop('video_length')
+            video_length_parts = str_video_length.split(':')
+            video_length = datetime.timedelta(minutes=int(video_length_parts[0]), seconds=int(video_length_parts[1]), milliseconds=int(video_length_parts[2]))
+
+            lecture_video, created = LectureVideo.objects.update_or_create(
+                lecture_video_id=new_lecture_video_id,
+                lecturer=lecturer[0],
+                subject=subject[0],
+                date=validated_data.pop('date'),
+                video_name=validated_data.pop('video_name'),
+                video_length=video_length
+            )
+
+        # faculty_data = validated_data.pop('faculty')
+        # serialized_faculty = FacultySerializer(data=faculty_data)
+        #
+        # if (serialized_faculty.is_valid()):
+        #     # faculty, faculty_created = Faculty.objects.get_or_create(defaults={}, faculty_id=serialized_faculty.data['faculty_id'])
+        #     faculty = Faculty.objects.filter(faculty_id=serialized_faculty.data['faculty_id'])
+        #
+        #     if (len(faculty) == 1):
+        #         lecturer, created = Lecturer.objects.update_or_create(
+        #             faculty=faculty[0],
+        #             lecturer_id=validated_data.pop('lecturer_id'),
+        #             fname=validated_data.pop('fname'),
+        #             lname=validated_data.pop('lname'),
+        #             email=validated_data.pop('email'),
+        #             telephone=validated_data('telephone')
+        #         )
+        #
+        #         return lecturer
+        #
+            return lecture_video
+
+        return None
+
 
 # lecture video time landmarks serializer
 class LectureVideoTimeLandmarksSerializer(serializers.ModelSerializer):

FirstApp/templates/FirstApp/activity.html

@@ -241,7 +241,13 @@
             //to handle the 'integrate' modal
             $('#integrate_activity').click(function () {
                 //define the student video src
+                {#global_video_name = "Video_test_9.mp4";#}
+                {#global_video_name = "Video_test_9_annotated.mp4";#}
                 let video_src = "{% static '' %}FirstApp/videos/" + global_video_name;
+                {#let video_src = "{% static '' %}FirstApp/video/" + global_video_name;#}
+                {#let video_src = "{% static '' %}/FirstApp/activity/" + global_video_name;#}
+                {#let video_src = "{% static '' %}FirstApp/emotion/" + global_video_name;#}
+
 
                 //assign the video src
                 $('#student_video').attr('src', video_src);
@@ -1078,6 +1084,11 @@
                                                 type="video/mp4">
                                         Your browser does not support the video tag.
                                     </video>
+{#                                    <video width="500" height="300" id="student_video" controls>#}
+{#                                        <source src="{% static 'FirstApp/videos/Video_test_2.mp4' %}"#}
+{#                                                type="video/mp4">#}
+{#                                        Your browser does not support the video tag.#}
+{#                                    </video>#}
                                 </div>
                                 <!--end of student video section -->
 

FirstApp/templates/FirstApp/emotion.html

@@ -74,6 +74,8 @@
                 real_class = '.' + real_class;
 
                 let date = e.target.parentNode.parentNode.firstChild.innerHTML;
+                //assign the date
+                global_lecture_date = date;
 
                 fetch('http://127.0.0.1:8000/get-lecture-emotion-availability/?lecturer=' + global_lecturer + '&date=' + date + '&index=' + global_lecturer_subject_index)
                     .then((res) => res.json())
@@ -143,7 +145,8 @@
                 $('#video_name').text(video.video_name);
                 $('#video_duration').text(video.video_length);
                 $('#video_date').text(video.date);
-                global_lecture_video_id = video.lecture_video_id;
+                {#global_lecture_video_id = video.lecture_video_id;#}
+                global_lecture_video_id = video.id;
                 global_video_name = video.video_name;
 
 
@@ -241,32 +244,67 @@
                 //define the student video src
                 let video_src = "{% static '' %}FirstApp/videos/" + global_video_name;
 
+                //assign the video src
+                $('#student_video').attr('src', video_src);
+
+
+                {#fetch('http://127.0.0.1:8000/get-random-number')#}
+                {#.then((res) => res.json())#}
+                {#.then((out) => alert(out.response))#}
+                {#.catch((err) => alert('err: ' + err));#}
+
+                //fetch the lecture recorded video name
+                fetch('http://127.0.0.1:8000/get-lecture-recorded-video-name/?lecturer=' + global_lecturer + '&subject=' + global_subject + '&date=' + global_lecture_date)
+                .then((res) => res.json())
+                .then((out) => assignLecturerRecordedVideoName(out))
+                .catch((err) => alert('error: ' + err));
+
                 {#global_lecturer_video_name = "Test_1.mp4";#}
                 {#global_lecturer_video_name = "Test_2.mp4";#}
-                global_lecturer_video_name = "Test_3.mp4";
+                {#global_lecturer_video_name = "Test_3.mp4";#}
+                {#global_lecturer_video_name = "Lecturer_Video_4.mp4";#}
+                {##}
+                {#//define the lecturer video src#}
+                {#let lecturer_video_src = "{% static '' %}FirstApp/lecturer_videos/" + global_lecturer_video_name;#}
+                {##}
+                {##}
+                {##}
+                {#//assign the video src#}
+                {#$('#lecturer_video').attr('src', lecturer_video_src);#}
+                {##}
+                {#$('#integrate_modal').modal();#}
+
+
+
+                {#//fetch data from the API#}
+                {#fetch('http://127.0.0.1:8000/get-lecture-emotion-for-frame?video_name=' + global_video_name)#}
+                {#    .then((res) => res.json())#}
+                {#    .then((out) => displayEmotionRecognitionForFrame(out.response))#}
+                {#    .catch((err) => alert('error: ' + err));#}
+
+
+            });
+
+            //assign the lecturer recorded video name
+            function assignLecturerRecordedVideoName(res) {
+
+                global_lecturer_video_name = res.video_name;
 
                 //define the lecturer video src
                 let lecturer_video_src = "{% static '' %}FirstApp/lecturer_videos/" + global_lecturer_video_name;
 
 
-                //assign the video src
-                $('#student_video').attr('src', video_src);
-
                 //assign the video src
                 $('#lecturer_video').attr('src', lecturer_video_src);
 
                 $('#integrate_modal').modal();
 
-
                 //fetch data from the API
-
                 fetch('http://127.0.0.1:8000/get-lecture-emotion-for-frame?video_name=' + global_video_name)
                     .then((res) => res.json())
                     .then((out) => displayEmotionRecognitionForFrame(out.response))
                     .catch((err) => alert('error: ' + err));
-
-
-            });
+            }
 
 
             //this function will display the emotion percentages for each frame
@@ -338,7 +376,7 @@
                 fetch('http://127.0.0.1:8000/lecturer/get-lecturer-video-frame-recognitions/?video_name=' + global_lecturer_video_name)
                     .then((res) => res.json())
                     .then((out) => displayLecturerEmotionRecognitionForFrame(out))
-                    .catch((err) => alert('error: ' + err))
+                    .catch((err) => alert('error: ' + err));
 
 
             }

FirstApp/templates/FirstApp/gaze.html

@@ -74,6 +74,8 @@
                 real_class = '.' + real_class;
 
                 let date = e.target.parentNode.parentNode.firstChild.innerHTML;
+                //assign the date
+                global_lecture_date = date;
 
                 fetch('http://127.0.0.1:8000/get-lecture-video-gaze-estimation-availability/?lecturer=' + global_lecturer + '&date=' + date + '&index=' + global_lecturer_subject_index)
                     .then((res) => res.json())
@@ -142,7 +144,8 @@
                 $('#video_name').text(video.video_name);
                 $('#video_duration').text(video.video_length);
                 $('#video_date').text(video.date);
-                global_lecture_video_id = video.lecture_video_id;
+                {#global_lecture_video_id = video.lecture_video_id;#}
+                global_lecture_video_id = video.id;
                 global_video_name = video.video_name;
 
 
@@ -239,21 +242,28 @@
                 //define the student video src
                 let video_src = "{% static '' %}FirstApp/videos/" + global_video_name;
 
-                {#global_lecturer_video_name = "Test_1.mp4";#}
-                {#global_lecturer_video_name = "Test_2.mp4";#}
-                global_lecturer_video_name = "Test_3.mp4";
-
-                //define the lecturer video src
-                let lecturer_video_src = "{% static '' %}FirstApp/lecturer_videos/" + global_lecturer_video_name;
-
-
                 //assign the video src
                 $('#student_video').attr('src', video_src);
 
-                //assign the video src
-                $('#lecturer_video').attr('src', lecturer_video_src);
+                //fetch the lecture recorded video name
+                fetch('http://127.0.0.1:8000/get-lecture-recorded-video-name/?lecturer=' + global_lecturer + '&subject=' + global_subject + '&date=' + global_lecture_date)
+                .then((res) => res.json())
+                .then((out) => assignLecturerRecordedVideoName(out))
+                .catch((err) => alert('error: ' + err));
+
+                {#global_lecturer_video_name = "Test_1.mp4";#}
+                {#global_lecturer_video_name = "Test_2.mp4";#}
+                {#global_lecturer_video_name = "Test_3.mp4";#}
+                {#global_lecturer_video_name = "Lecturer_Video_4.mp4";#}
+                {##}
+                {#//define the lecturer video src#}
+                {#let lecturer_video_src = "{% static '' %}FirstApp/lecturer_videos/" + global_lecturer_video_name;#}
+                {##}
+                {#//assign the video src#}
+                {#$('#lecturer_video').attr('src', lecturer_video_src);#}
+                {##}
+                {#$('#integrate_modal').modal();#}
 
-                $('#integrate_modal').modal();
 
                 //fetch data from the API
                 fetch('http://127.0.0.1:8000/get-lecture-gaze-estimation-for-frame/?video_name=' + global_video_name)
@@ -264,6 +274,23 @@
             });
 
 
+            //assign the lecturer recorded video name
+            function assignLecturerRecordedVideoName(res) {
+
+                global_lecturer_video_name = res.video_name;
+
+                //define the lecturer video src
+                let lecturer_video_src = "{% static '' %}FirstApp/lecturer_videos/" + global_lecturer_video_name;
+
+                alert('hello');
+
+                //assign the video src
+                $('#lecturer_video').attr('src', lecturer_video_src);
+
+                $('#integrate_modal').modal();
+            }
+
+
             //this function will load the activity recognition for frames
             function displayGazeEstimationForFrame(response) {
 

FirstApp/urls.py

@@ -151,21 +151,30 @@ urlpatterns = [
     # retrieves lecture activity summary
     url(r'^get-lecture-activity-summary/$', api.GetLectureActivitySummary.as_view()),
 
-    # retrieves lecture activity summary
+    # retrieves lecture emotion summary
     url(r'^get-lecture-emotion-summary/$', api.GetLectureEmotionSummary.as_view()),
 
-    # retrieves lecture activity summary
+    # retrieves lecture gaze estimation summary
     url(r'^get-lecture-gaze-summary/$', api.GetLectureGazeSummary.as_view()),
 
-    # retrieves lecture activity summary
+    # retrieves student activity correlations with lecturer activity
     url(r'^get-activity-correlations/$', api.GetLectureActivityCorrelations.as_view()),
 
-    # retrieves lecture activity summary
+    # retrieves student emotion correlations with lecturer activity
     url(r'^get-emotion-correlations/$', api.GetLectureEmotionCorrelations.as_view()),
 
-    # retrieves lecture activity summary
+    # retrieves student gaze estimation correlations with lecturer activity
     url(r'^get-gaze-correlations/$', api.GetLectureGazeCorrelations.as_view()),
 
+    # retrieves student activity-emotion correlations
+    url(r'^get-student-activity-emotion-correlations/$', api.GetStudentActivityEmotionCorrelations.as_view()),
+
+    # retrieves student activity-gaze correlations
+    url(r'^get-student-activity-gaze-correlations/$', api.GetStudentActivityGazeCorrelations.as_view()),
+
+    # retrieves student emotion-gaze correlations
+    url(r'^get-student-emotion-gaze-correlations/$', api.GetStudentEmotionGazeCorrelations.as_view()),
+
 
     ##### OTHERS #####
 
@@ -173,6 +182,19 @@ urlpatterns = [
     url(r'^get-lecture-recorded-video-name/$', api.GetLecturerRecordedVideo.as_view()),
 
 
+    ##### BATCH PROCESS #####
+    # perform batch process for student behavior
+    url(r'^student-behavior-batch-process/$', api.BatchProcess.as_view()),
+
+    # check availability for student behavior components
+    url(r'^check-availability/$', api.CheckStudentBehaviorAvailability.as_view()),
+
+    # perform random task (delete later)
+    url(r'^get-random-number/$', api.TestRandom.as_view()),
+
+
+
+
     # routers
     # path('', include(router.urls)),
     path('api-auth/', include('rest_framework.urls', namespace='rest_framework'))

FirstApp/views.py

@@ -189,6 +189,8 @@ def video_result(request):
 
         data = serializer.data
 
+        print('data length: ', len(data))
+
         # iterate through the existing lecture videos for the lecturer
         for video in data:
             video_id = video['id']
@@ -197,6 +199,8 @@ def video_result(request):
             # check whether the video id exist in the Activity Recognition table
             lec_activity = LectureActivity.objects.filter(lecture_video_id_id=video_id).exists()
 
+            print('lecture activity existence: ', lec_activity)
+
             if lec_activity == False:
                 to_do_lecture_list.append({
                     "lecturer": lecturer,
@@ -227,11 +231,15 @@ def video_result(request):
                 # loop through the to-do lecture list
                 for item in to_do_lecture_list:
                     isDate = item['date'] == str(day_timetable['date'])
+
+                    print('item date: ', item['date'])
+                    print('timetable date: ', str(day_timetable['date']))
                     # isLecturer = item['lecturer'] ==
                     # check for the particular lecture on the day
                     if isDate:
                         slots = day_timetable['time_slots']
 
+
                         # loop through the slots
                         for slot in slots:
                             # check for the lecturer and subject
@@ -260,6 +268,8 @@ def video_result(request):
         print('what is wrong?: ', exc)
         return redirect('/500')
 
+    print('due lectures: ', due_lecture_list)
+
     return render(request, "FirstApp/video_results.html",
                   {"lecturer": lecturer, "due_lectures": due_lecture_list})
 

integrated_slpes/settings.py

@@ -15,7 +15,6 @@ import os
 # Build paths inside the project like this: os.path.join(BASE_DIR, ...)
 BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 
-
 # Quick-start development settings - unsuitable for production
 # See https://docs.djangoproject.com/en/2.2/howto/deployment/checklist/
 
@@ -28,7 +27,6 @@ DEBUG = True
 
 ALLOWED_HOSTS = []
 
-
 # Application definition
 
 INSTALLED_APPS = [
@@ -36,6 +34,7 @@ INSTALLED_APPS = [
     'AttendanceApp.apps.AttendanceappConfig',
     'MonitorLecturerApp.apps.MonitorlecturerappConfig',
     'LectureSummarizingApp.apps.LectureSummarizingAppConfig',
+    'corsheaders',
     'django.contrib.admin',
     'django.contrib.auth',
     'django.contrib.contenttypes',
@@ -48,6 +47,7 @@ INSTALLED_APPS = [
 ]
 
 MIDDLEWARE = [
+    'corsheaders.middleware.CorsMiddleware',
     'django.middleware.security.SecurityMiddleware',
     'django.contrib.sessions.middleware.SessionMiddleware',
     'django.middleware.common.CommonMiddleware',
@@ -59,6 +59,9 @@ MIDDLEWARE = [
 
 ROOT_URLCONF = 'integrated_slpes.urls'
 
+# adding the CORS attributes
+CORS_ALLOW_ALL_ORIGINS = True
+
 TEMPLATES = [
     {
         'BACKEND': 'django.template.backends.django.DjangoTemplates',
@@ -78,7 +81,6 @@ TEMPLATES = [
 
 WSGI_APPLICATION = 'integrated_slpes.wsgi.application'
 
-
 # Database
 # https://docs.djangoproject.com/en/2.2/ref/settings/#databases
 
@@ -93,7 +95,6 @@ DATABASES = {
     }
 }
 
-
 # Password validation
 # https://docs.djangoproject.com/en/2.2/ref/settings/#auth-password-validators
 
@@ -112,7 +113,6 @@ AUTH_PASSWORD_VALIDATORS = [
     },
 ]
 
-
 # Internationalization
 # https://docs.djangoproject.com/en/2.2/topics/i18n/
 
@@ -126,7 +126,6 @@ USE_L10N = True
 
 USE_TZ = True
 
-
 # Static files (CSS, JavaScript, Images)
 # https://docs.djangoproject.com/en/2.2/howto/static-files/
 
@@ -137,7 +136,6 @@ STATICFILES_DIRS = [
 ]
 STATIC_ROOT = os.path.join(BASE_DIR, 'assets')
 
-
 # media files
 
 MEDIA_URL = '/media/'
@@ -145,7 +143,9 @@ MEDIA_ROOT = os.path.join(BASE_DIR, 'media')
 
 # REST FRAMEWORK
 REST_FRAMEWORK = {
-    'DEFAULT_PERMISSION_CLASSES': [
-        'rest_framework.permissions.IsAuthenticated',
-    ]
+    # 'DEFAULT_PERMISSION_CLASSES': [
+    #     'rest_framework.permissions.IsAuthenticated',
+    # ]
+    'DEFAULT_AUTHENTICATION_CLASSES': [],
+    'DEFAULT_PERMISSION_CLASSES': []
 }