Committing the correlation of student behavior and lecturer evaluation...

Committing the correlation of student behavior and lecturer evaluation components in the lecturer home page.

FirstApp/api.py

 from rest_framework.permissions import IsAuthenticated, IsAdminUser
 from rest_framework.authentication import SessionAuthentication, BasicAuthentication
 
+from MonitorLecturerApp.models import LectureRecordedVideo, LecturerVideoMetaData
+from MonitorLecturerApp.serializers import LectureRecordedVideoSerializer, LecturerVideoMetaDataSerializer
 from .MongoModels import *
 from rest_framework.views import *
 from .ImageOperations import saveImage
@@ -1271,3 +1273,178 @@ class GetLectureGazeSummary(APIView):
                 "frame_group_percentages": frame_group_percentages,
                 "gaze_labels": gaze_labels
             })
+
+
+# =====OTHERS=====
+class GetLecturerRecordedVideo(APIView):
+
+    def get(self, request):
+        lecturer = request.query_params.get('lecturer')
+        subject = request.query_params.get('subject')
+        date = request.query_params.get('date')
+
+        # retrieve data
+        lec_recorded_video = LectureRecordedVideo.objects.filter(lecturer_id=lecturer, subject__subject_code=subject, lecturer_date=date)
+        lec_recorded_video_ser = LectureRecordedVideoSerializer(lec_recorded_video, many=True)
+        lec_recorded_video_data = lec_recorded_video_ser.data[0]
+
+        video_name = lec_recorded_video_data['lecture_video_name']
+
+        print('lecturer recorded video name: ', video_name)
+
+        return Response({
+            "video_name": video_name
+        })
+
+
+# this API will get lecture activity correlations
+class GetLectureActivityCorrelations(APIView):
+
+    def get(self, request):
+        option = request.query_params.get('option')
+        lecturer = request.query_params.get('lecturer')
+        int_option = int(option)
+
+        current_date = datetime.datetime.now().date()
+        option_date = datetime.timedelta(days=int_option)
+
+        previous_date = current_date - option_date
+
+        individual_lec_activities = []
+        activity_correlations = []
+
+        # retrieving lecture activities
+        lec_activity = LectureActivity.objects.filter(
+            lecture_video_id__date__gte=previous_date,
+            lecture_video_id__date__lte=current_date,
+            lecture_video_id__lecturer=lecturer
+        )
+
+        if len(lec_activity) > 0:
+            isRecordFound = True
+            activity_serializer = LectureActivitySerializer(lec_activity, many=True)
+            activity_data = activity_serializer.data
+            _, individual_lec_activities, _ = ar.get_student_activity_summary_for_period(activity_data)
+
+
+        # retrieving lecturer recorded activities
+        lec_recorded_activity = LecturerVideoMetaData.objects.filter(
+            lecturer_video_id__lecturer_date__gte=previous_date,
+            lecturer_video_id__lecturer_date__lte=current_date,
+            lecturer_video_id__lecturer=lecturer
+        )
+
+        if len(lec_recorded_activity) > 0:
+            lec_recorded_activity_ser = LecturerVideoMetaDataSerializer(lec_recorded_activity, many=True)
+            lec_recorded_activity_data = lec_recorded_activity_ser.data
+
+
+            activity_correlations = ar.get_activity_correlations(individual_lec_activities, lec_recorded_activity_data)
+
+
+        print('activity correlations: ', activity_correlations)
+
+        return Response({
+            "correlations": activity_correlations
+        })
+
+
+# this API will get lecture emotion correlations
+class GetLectureEmotionCorrelations(APIView):
+
+    def get(self, request):
+        option = request.query_params.get('option')
+        lecturer = request.query_params.get('lecturer')
+        int_option = int(option)
+
+        current_date = datetime.datetime.now().date()
+        option_date = datetime.timedelta(days=int_option)
+
+        previous_date = current_date - option_date
+
+        individual_lec_emotions = []
+        emotion_correlations = []
+
+        # retrieving lecture activities
+        lec_emotion = LectureEmotionReport.objects.filter(
+            lecture_video_id__date__gte=previous_date,
+            lecture_video_id__date__lte=current_date,
+            lecture_video_id__lecturer=lecturer
+        )
+
+        # if there are lecture emotions
+        if len(lec_emotion) > 0:
+            emotion_serializer = LectureEmotionSerializer(lec_emotion, many=True)
+            emotion_data = emotion_serializer.data
+            _, individual_lec_emotions, _ = ed.get_student_emotion_summary_for_period(emotion_data)
+
+
+        # retrieving lecturer recorded activities
+        lec_recorded_activity = LecturerVideoMetaData.objects.filter(
+            lecturer_video_id__lecturer_date__gte=previous_date,
+            lecturer_video_id__lecturer_date__lte=current_date,
+            lecturer_video_id__lecturer=lecturer
+        )
+
+        # if there are any recorded lectures
+        if len(lec_recorded_activity) > 0:
+            lec_recorded_activity_ser = LecturerVideoMetaDataSerializer(lec_recorded_activity, many=True)
+            lec_recorded_activity_data = lec_recorded_activity_ser.data
+
+            emotion_correlations = ed.get_emotion_correlations(individual_lec_emotions, lec_recorded_activity_data)
+
+
+        return Response({
+            "correlations": emotion_correlations
+        })
+
+
+# this API will get lecture gaze correlations
+class GetLectureGazeCorrelations(APIView):
+
+    def get(self, request):
+        option = request.query_params.get('option')
+        lecturer = request.query_params.get('lecturer')
+        int_option = int(option)
+
+
+        current_date = datetime.datetime.now().date()
+        option_date = datetime.timedelta(days=int_option)
+
+        previous_date = current_date - option_date
+
+        individual_lec_gaze = []
+        gaze_correlations = []
+
+        # retrieving lecture activities
+        lec_gaze = LectureGazeEstimation.objects.filter(
+            lecture_video_id__date__gte=previous_date,
+            lecture_video_id__date__lte=current_date,
+            lecture_video_id__lecturer=lecturer
+        )
+
+        # if there are gaze estimations
+        if len(lec_gaze) > 0:
+            gaze_serializer = LectureGazeEstimationSerializer(lec_gaze, many=True)
+            gaze_data = gaze_serializer.data
+            _, individual_lec_gaze, _ = hge.get_student_gaze_estimation_summary_for_period(gaze_data)
+
+        # retrieving lecturer recorded activities
+        lec_recorded_activity = LecturerVideoMetaData.objects.filter(
+            lecturer_video_id__lecturer_date__gte=previous_date,
+            lecturer_video_id__lecturer_date__lte=current_date,
+            lecturer_video_id__lecturer=lecturer
+        )
+
+        # if there are any recorded lectures
+        if len(lec_recorded_activity) > 0:
+            lec_recorded_activity_ser = LecturerVideoMetaDataSerializer(lec_recorded_activity, many=True)
+            lec_recorded_activity_data = lec_recorded_activity_ser.data
+
+            # find the correlations between lecture gaze estimations and recorded lecture
+            gaze_correlations = hge.get_gaze_correlations(individual_lec_gaze, lec_recorded_activity_data)
+
+
+        return Response({
+            "correlations": gaze_correlations
+        })

FirstApp/emotion_detector.py

@@ -11,12 +11,12 @@ from . models import VideoMeta
 from . logic import custom_sorter as cs
 from .logic import id_generator as ig
 from .logic import activity_recognition as ar
-
-
-# emotion recognition method
+from .logic import utilities as ut
 from .serializers import LectureEmotionSerializer
 
+import pandas as pd
 
+# emotion recognition method
 def emotion_recognition(classifier, face_classifier, image):
     label = ""
     class_labels = ['Angry', 'Happy', 'Neutral', 'Sad', 'Surprise']
@@ -686,3 +686,79 @@ def save_frame_groupings(video_name, frame_landmarks, frame_group_dict):
 
     # save
     new_lec_emotion_frame_groupings.save()
+
+
+# this method will get emotion correlations
+def get_emotion_correlations(individual_lec_emotions, lec_recorded_activity_data):
+    # this variable will be used to store the correlations
+    correlations = []
+
+    limit = 10
+
+    data_index = ['lecture-{}'.format(i + 1) for i in range(len(individual_lec_emotions))]
+
+    # student activity labels
+    student_emotion_labels = ['Happy', 'Sad', 'Angry', 'Surprise', 'Neutral']
+    lecturer_activity_labels = ['seated', 'standing', 'walking']
+
+    # lecturer recorded data list (lecturer)
+    sitting_perct_list = []
+    standing_perct_list = []
+    walking_perct_list = []
+
+    # lecture activity data list (student)
+    happy_perct_list = []
+    sad_perct_list = []
+    angry_perct_list = []
+    surprise_perct_list = []
+    neutral_perct_list = []
+
+
+    # loop through the lecturer recorded data (lecturer)
+    for data in lec_recorded_activity_data:
+        sitting_perct_list.append(int(data['seated_count']))
+        standing_perct_list.append(int(data['standing_count']))
+        walking_perct_list.append(int(data['walking_count']))
+
+    # loop through the lecturer recorded data (student)
+    for data in individual_lec_emotions:
+        happy_perct_list.append(int(data['happy_perct']))
+        sad_perct_list.append(int(data['sad_perct']))
+        angry_perct_list.append(int(data['angry_perct']))
+        surprise_perct_list.append(int(data['surprise_perct']))
+        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}
+
+    # create the dataframe
+    df = pd.DataFrame(corr_data, index=data_index)
+
+    # calculate the correlation
+    pd_series = ut.get_top_abs_correlations(df, limit)
+
+    print('====correlated variables=====')
+    print(pd_series)
+
+    for i in range(limit):
+        # this dictionary will get the pandas.Series object's  indices and values separately
+        corr_dict = {}
+
+        index = pd_series.index[i]
+
+        # check whether the first index is a student activity
+        isStudentEmotion = index[0] in student_emotion_labels
+        # check whether the second index is a lecturer activity
+        isLecturerAct = index[1] in lecturer_activity_labels
+
+        # if both are student and lecturer activities, add to the dictionary
+        if isStudentEmotion & isLecturerAct:
+            corr_dict['index'] = index
+            corr_dict['value'] = pd_series.values[i]
+
+            # append the dictionary to the 'correlations' list
+            correlations.append(corr_dict)
+
+    # return the list
+    return correlations

FirstApp/logic/activity_recognition.py

@@ -9,6 +9,9 @@ from .custom_sorter import *
 from ..MongoModels import *
 from ..serializers import *
 from . import id_generator as ig
+from . import utilities as ut
+
+import pandas as pd
 
 
 def activity_recognition(video_path):
@@ -849,3 +852,77 @@ def save_frame_groupings(video_name, frame_landmarks, frame_group_dict):
 
     # save
     new_lec_activity_frame_groupings.save()
+
+
+# this method will get activity correlations
+def get_activity_correlations(individual_lec_activities, lec_recorded_activity_data):
+
+    # this variable will be used to store the correlations
+    correlations = []
+
+    limit = 10
+
+    data_index = ['lecture-{}'.format(i+1)  for i in range(len(individual_lec_activities))]
+
+    # student activity labels
+    student_activity_labels = ['phone checking', 'listening', 'note taking']
+    lecturer_activity_labels = ['seated', 'standing', 'walking']
+
+    # lecturer recorded data list (lecturer)
+    sitting_perct_list = []
+    standing_perct_list = []
+    walking_perct_list = []
+
+    # lecture activity data list (student)
+    phone_perct_list = []
+    listen_perct_list = []
+    note_perct_list = []
+
+    # loop through the lecturer recorded data (lecturer)
+    for data in lec_recorded_activity_data:
+        sitting_perct_list.append(int(data['seated_count']))
+        standing_perct_list.append(int(data['standing_count']))
+        walking_perct_list.append(int(data['walking_count']))
+
+   # loop through the lecturer recorded data (student)
+    for data in individual_lec_activities:
+        phone_perct_list.append(int(data['phone_perct']))
+        listen_perct_list.append(int(data['listening_perct']))
+        note_perct_list.append(int(data['writing_perct']))
+
+
+    corr_data = {'phone checking': phone_perct_list, 'listening': listen_perct_list, 'note taking': note_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)
+
+    # calculate the correlation
+    pd_series = ut.get_top_abs_correlations(df, limit)
+
+    print('====correlated variables=====')
+    print(pd_series)
+
+    for i in range(limit):
+        # this dictionary will get the pandas.Series object's  indices and values separately
+        corr_dict = {}
+
+        index = pd_series.index[i]
+
+        # check whether the first index is a student activity
+        isStudentAct = index[0] in student_activity_labels
+        # check whether the second index is a lecturer activity
+        isLecturerAct = index[1] in lecturer_activity_labels
+
+        # if both are student and lecturer activities, add to the doctionary
+        if isStudentAct & isLecturerAct:
+            corr_dict['index'] = index
+            corr_dict['value'] = pd_series.values[i]
+
+            # append the dictionary to the 'correlations' list
+            correlations.append(corr_dict)
+
+
+    # return the list
+    return correlations
+

FirstApp/logic/head_gaze_estimation.py

@@ -15,10 +15,12 @@ from . face_landmarks import get_landmark_model, detect_marks
 import os
 import shutil
 import math
+import pandas as pd
 
 from ..MongoModels import *
 from ..serializers import *
 from . import id_generator as ig
+from . import utilities as ut
 
 
 def get_2d_points(img, rotation_vector, translation_vector, camera_matrix, val):
@@ -964,3 +966,77 @@ def save_frame_groupings(video_name, frame_landmarks, frame_group_dict):
     # save
     new_lec_gaze_frame_groupings.save()
 
+
+# this method will get gaze estimation correlations
+def get_gaze_correlations(individual_lec_gaze, lec_recorded_activity_data):
+    # this variable will be used to store the correlations
+    correlations = []
+
+    limit = 10
+
+    data_index = ['lecture-{}'.format(i + 1) for i in range(len(individual_lec_gaze))]
+
+    # 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']
+
+    # lecturer recorded data list (lecturer)
+    sitting_perct_list = []
+    standing_perct_list = []
+    walking_perct_list = []
+
+    # lecture activity data list (student)
+    upright_perct_list = []
+    upleft_perct_list = []
+    downright_perct_list = []
+    downleft_perct_list = []
+    front_perct_list = []
+
+    # loop through the lecturer recorded data (lecturer)
+    for data in lec_recorded_activity_data:
+        sitting_perct_list.append(int(data['seated_count']))
+        standing_perct_list.append(int(data['standing_count']))
+        walking_perct_list.append(int(data['walking_count']))
+
+    # loop through the lecturer recorded data (student)
+    for data in individual_lec_gaze:
+        upright_perct_list.append(int(data['looking_up_and_right_perct']))
+        upleft_perct_list.append(int(data['looking_up_and_left_perct']))
+        downright_perct_list.append(int(data['looking_down_and_right_perct']))
+        downleft_perct_list.append(int(data['looking_down_and_left_perct']))
+        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}
+
+    # create the dataframe
+    df = pd.DataFrame(corr_data, index=data_index)
+
+    # calculate the correlation
+    pd_series = ut.get_top_abs_correlations(df, limit)
+
+    print('====correlated variables=====')
+    print(pd_series)
+
+    for i in range(limit):
+        # this dictionary will get the pandas.Series object's  indices and values separately
+        corr_dict = {}
+
+        index = pd_series.index[i]
+
+        # check whether the first index is a student activity
+        isStudentGaze = index[0] in student_gaze_labels
+        # check whether the second index is a lecturer activity
+        isLecturerAct = index[1] in lecturer_activity_labels
+
+        # if both are student and lecturer activities, add to the dictionary
+        if isStudentGaze & isLecturerAct:
+            corr_dict['index'] = index
+            corr_dict['value'] = pd_series.values[i]
+
+            # append the dictionary to the 'correlations' list
+            correlations.append(corr_dict)
+
+    # return the list
+    return correlations

FirstApp/logic/utilities.py

+
+def get_redundant_pairs(df):
+    '''Get diagonal and lower triangular pairs of correlation matrix'''
+    pairs_to_drop = set()
+    cols = df.columns
+    for i in range(0, df.shape[1]):
+        for j in range(0, i+1):
+            pairs_to_drop.add((cols[i], cols[j]))
+    return pairs_to_drop
+
+def get_top_abs_correlations(df, n):
+    au_corr = df.corr().abs().unstack()
+    labels_to_drop = get_redundant_pairs(df)
+    au_corr = au_corr.drop(labels=labels_to_drop).sort_values(ascending=False)
+    return au_corr[0:n]

FirstApp/templates/FirstApp/activity.html

@@ -243,21 +243,19 @@
                 //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";#}
 
-                $('#integrate_modal').modal();
 
 
                 //fetch data from the API
@@ -268,6 +266,21 @@
 
             });
 
+            //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
+                $('#lecturer_video').attr('src', lecturer_video_src);
+
+                $('#integrate_modal').modal();
+            }
+
             //this function will load the activity recognition for frames
             function displayActivityRecognitionForFrame(response) {
                 //hide the loader
@@ -527,11 +540,11 @@
                     {% load static %}
 
                     <!-- Page Heading -->
-                    {#                    <div class="d-sm-flex align-items-center justify-content-between mb-4">#}
-                    {#                        <h1 class="h3 mb-0 text-gray-800">Student Activity Recognition</h1>#}
+                    <div class="d-sm-flex align-items-center justify-content-between mb-4">
+                        <h1 class="h3 mb-0 text-gray-800">Student Activity Recognition</h1>
                         {#                        <button type="button" data-target="#generateReportModal" data-toggle="modal" class="d-none d-sm-inline-block btn btn-sm btn-primary shadow-sm" id="generate_report_before" disabled><i#}
                         {#                                class="fas fa-download fa-sm text-white-50"></i> Generate Report</button>#}
-                    {#                    </div>#}
+                    </div>
 
 
                     <!--first row -->

FirstApp/urls.py

@@ -203,6 +203,21 @@ urlpatterns = [
     # retrieves lecture activity summary
     url(r'^get-lecture-gaze-summary/$', api.GetLectureGazeSummary.as_view()),
 
+    # retrieves lecture activity summary
+    url(r'^get-activity-correlations/$', api.GetLectureActivityCorrelations.as_view()),
+
+    # retrieves lecture activity summary
+    url(r'^get-emotion-correlations/$', api.GetLectureEmotionCorrelations.as_view()),
+
+    # retrieves lecture activity summary
+    url(r'^get-gaze-correlations/$', api.GetLectureGazeCorrelations.as_view()),
+
+
+    ##### OTHERS #####
+
+    # retrieves lecture recorded video name
+    url(r'^get-lecture-recorded-video-name/$', api.GetLecturerRecordedVideo.as_view()),
+
 
     # routers
     # path('', include(router.urls)),