Committing the modifications of student-lecturer correlations and rectifying...

Committing the modifications of student-lecturer correlations and rectifying an error in gaze and emotion front pages.
.

FirstApp/emotion_detector.py

@@ -670,10 +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']
@@ -693,31 +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/activity_recognition.py

@@ -752,10 +752,14 @@ def get_activity_correlations(individual_lec_activities, lec_recorded_activity_d
     # this variable will be used to store the correlations
     correlations = []
 
-    limit = 10
+    # limit = 10
+    limit = len(individual_lec_activities)
 
     data_index = ['lecture-{}'.format(i+1)  for i in range(len(individual_lec_activities))]
 
+    # declare the correlation data dictionary
+    corr_data = {}
+
     # student activity labels
     student_activity_labels = ['phone checking', 'listening', 'note taking']
     lecturer_activity_labels = ['seated', 'standing', 'walking']
@@ -772,29 +776,63 @@ def get_activity_correlations(individual_lec_activities, lec_recorded_activity_d
 
     # 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_activities:
+        value = int(data['phone_perct'])
+        value1 = int(data['listening_perct'])
+        value2 = int(data['writing_perct'])
+
+        if value != 0:
             phone_perct_list.append(int(data['phone_perct']))
+        if value1 != 0:
             listen_perct_list.append(int(data['listening_perct']))
+        if value2 != 0:
             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}
+    if (len(phone_perct_list)) == len(individual_lec_activities):
+        corr_data[student_activity_labels[0]] = phone_perct_list
+    if (len(listen_perct_list)) == len(individual_lec_activities):
+        corr_data[student_activity_labels[1]] = listen_perct_list
+    if (len(note_perct_list)) == len(individual_lec_activities):
+        corr_data[student_activity_labels[2]] = note_perct_list
+    if (len(sitting_perct_list)) == len(individual_lec_activities):
+        corr_data[lecturer_activity_labels[0]] = sitting_perct_list
+    if (len(standing_perct_list)) == len(individual_lec_activities):
+        corr_data[lecturer_activity_labels[1]] = standing_perct_list
+    if (len(walking_perct_list)) == len(individual_lec_activities):
+        corr_data[lecturer_activity_labels[2]] = walking_perct_list
+
+
+
+    # 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)
 
+    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

@@ -15,3 +15,10 @@ def batch_process(video_id, video_name):
 
 
     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

@@ -1007,10 +1007,14 @@ 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']
@@ -1029,28 +1033,72 @@ 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/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())
@@ -246,6 +248,11 @@
                 $('#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())
@@ -255,6 +262,7 @@
                 {#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;#}
@@ -268,11 +276,11 @@
 
 
 
-                //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));
+                {#//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));#}
 
 
             });
@@ -290,6 +298,12 @@
                 $('#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));
             }
 
 
@@ -362,7 +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())
@@ -249,6 +251,18 @@
                 .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();#}
 
 
                 //fetch data from the API

FirstApp/templates/FirstApp/video_results.html

@@ -301,6 +301,7 @@
             /*
             let interval = setInterval(() => {
 
+
             {#let url = 'http://127.0.0.1:8000/get-random_number';#}
                 let url = 'http://127.0.0.1:8000/check-availability';
 
@@ -401,7 +402,7 @@
                                 <div class="card-body">
 
                                     <!--loading gif -->
-                                    {% if due_lectures.count == 0 %}
+                                    {% if due_lectures|length == 0 %}
                                         <div class="text-center" id="no_subject_selected">
                                             <span class="font-italic">No lecture is to be processed</span>
                                         </div>
@@ -414,6 +415,10 @@
                                     <div class="text-center" id="no_timetable_content" hidden>
                                         <span class="font-italic">Not included in the timetable</span>
                                     </div>
+
+                                    <!-- if there are due lectures, display the table -->
+                                    {% if due_lectures %}
+
                                         <!--displaying the timetable -->
                                         <table class="table table-striped" id="timetable">
                                             {#                                    <caption id="timetable_caption"></caption>#}
@@ -446,6 +451,7 @@
                                             {% endfor %}
                                             </tbody>
                                         </table>
+                                    {% endif %}
 
 
                                 </div>
@@ -528,7 +534,6 @@
                                         <!-- end of progress row -->
 
 
-
                                     </div>
                                     <!-- end of container -->
 

FirstApp/urls.py

@@ -190,7 +190,7 @@ urlpatterns = [
     url(r'^check-availability/$', api.CheckStudentBehaviorAvailability.as_view()),
 
     # perform random task (delete later)
-    url(r'^get-random_number/$', api.TestRandom.as_view()),
+    url(r'^get-random-number/$', api.TestRandom.as_view()),