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2020-101
Commit f7621cbd authored by I.K Seneviratne's avatar I.K Seneviratne
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Merge branch 'monitoring_student_behavior_IT17138000' into 'QA_RELEASE' 

Monitoring student behavior it17138000

See merge request !34
parents a48f6270 c465c70a
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Showing with 1144 additions and 647 deletions
FirstApp/api.py
View file @ f7621cbd
......@@ -761,6 +761,9 @@ class GetLectureActivitySummary(APIView):
def get(self, request):
video_name = request.query_params.get('video_name')
phone_perct = request.query_params.get('phone_perct')
listen_perct = request.query_params.get('listen_perct')
note_perct = request.query_params.get('note_perct')
# checking the existence of lecture activity frame grouping records in the db
isExist = LectureActivityFrameGroupings.objects.filter(lecture_activity_id__lecture_video_id__video_name=video_name).exists()
......@@ -796,10 +799,14 @@ class GetLectureActivitySummary(APIView):
class_labels = ['phone_perct', 'listen_perct', 'note_perct']
# get the comments list
comments = sbp.generate_student_behavior_comments("Activity", phone_perct=phone_perct, listen_perct=listen_perct, note_perct=note_perct)
return Response({
"frame_landmarks": frame_landmarks,
"frame_group_percentages": frame_group_percentages,
"activity_labels": class_labels
"activity_labels": class_labels,
"comments": comments
})
# else:
......@@ -1167,6 +1174,7 @@ class GetLectureActivityCorrelations(APIView):
activity_correlations = ar.get_activity_correlations(individual_lec_activities, lec_recorded_activity_data)
return Response({
"correlations": activity_correlations
})
......
FirstApp/emotion_detector.py
View file @ f7621cbd
......@@ -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
View file @ f7621cbd
......@@ -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
View file @ f7621cbd
......@@ -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
View file @ f7621cbd
......@@ -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/student_behavior_process.py
View file @ f7621cbd
This diff is collapsed.
FirstApp/logic/utilities.py
View file @ f7621cbd
# 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
View file @ f7621cbd
......@@ -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/templates/FirstApp/Home.html
View file @ f7621cbd
......@@ -257,8 +257,13 @@
//change the innerHTML of the clicked button
e.target.innerHTML = "<span class='font-italic'>Processing</span>";
let phone_perct = $('#phone_perct').text().split("%")[0];
let listen_perct = $('#listening_perct').text().split("%")[0];
let note_perct = $('#writing_perct').text().split("%")[0];
//fetch the activity summary details
fetch('http://127.0.0.1:8000/get-lecture-activity-summary/?video_name=' + global_video_name)
fetch('http://127.0.0.1:8000/get-lecture-activity-summary/?video_name=' + global_video_name + '&phone_perct=' + phone_perct + '&note_perct=' + note_perct + '&listen_perct=' + listen_perct)
.then((res) => res.json())
.then((out) => activityFrameGroupPercentages(out, e))
.catch((err) => alert('error: ' + err));
......@@ -295,16 +300,30 @@
//this function will handle the retrieved activity frame group percentages
function activityFrameGroupPercentages(response, e) {
//remove the previous comments
$('#student_activity_comments').empty();
lecture_activity_frame_group_percentages = response.frame_group_percentages;
let frame_landmarks = response.frame_landmarks;
{#let frame_group_dict = response.frame_group_dict;#}
let activity_labels = response.activity_labels;
let comment_list = response.comments;
//define a html string
let htmlString = "";
for (let i = 0; i < comment_list.length; i++) {
htmlString += "<p class='font-italic font-weight-bold'>";
htmlString += comment_list[i];
htmlString += "</p>";
}
//change the button back to original
e.target.innerHTML = "Summary";
//append the html string to the comments list
$('#student_activity_comments').append(htmlString);
//open the modal
$('#ActivitySummaryModal').modal();
......@@ -1240,7 +1259,7 @@
function displayActivityEmotionCorrelations(correlations) {
let htmlString = "";
if (correlations.length !== 0) {
//create the html content for the activity correlation table
for (let i = 0; i < correlations.length; i++) {
let corr = correlations[i];
......@@ -1275,6 +1294,14 @@
htmlString += "</tr>";
}
} else {
htmlString += "<tr>";
htmlString += "<td colspan='3'>";
htmlString += "<span class='font-italic'>No correlations were found</span>";
htmlString += "</td>";
htmlString += "</tr>";
}
//append to the <tbody>
$('#student_activity_emotion_corr_tbody').append(htmlString);
......@@ -1293,6 +1320,7 @@
let htmlString = "";
if (correlations.length !== 0) {
//create the html content for the activity correlation table
for (let i = 0; i < correlations.length; i++) {
let corr = correlations[i];
......@@ -1327,6 +1355,13 @@
htmlString += "</tr>";
}
} else {
htmlString += "<tr>";
htmlString += "<td colspan='3'>";
htmlString += "<span class='font-italic'>No correlations were found</span>";
htmlString += "</td>";
htmlString += "</tr>";
}
//append to the <tbody>
$('#student_activity_gaze_corr_tbody').append(htmlString);
......@@ -1346,6 +1381,7 @@
let htmlString = "";
if (correlations.length !== 0) {
//create the html content for the activity correlation table
for (let i = 0; i < correlations.length; i++) {
let corr = correlations[i];
......@@ -1380,6 +1416,14 @@
htmlString += "</tr>";
}
} else {
htmlString += "<tr>";
htmlString += "<td colspan='3'>";
htmlString += "<span class='font-italic'>No correlations were found</span>";
htmlString += "</td>";
htmlString += "</tr>";
}
//append to the <tbody>
$('#student_emotion_gaze_corr_tbody').append(htmlString);
......@@ -2253,6 +2297,21 @@
</div>
<div class="modal-body">
<div id="ActivityChartContainer" style="height: 370px; max-width: 920px; margin: 0px auto;"></div>
<!-- Notes header -->
<div class="modal-header mt-4">
<h3>Notes</h3>
</div>
<!-- End of Notes header -->
<!-- Comments row -->
<div class="row mt-3">
<div class="col-lg-6" id="student_activity_comments">
</div>
</div>
<!-- End of Comments row -->
</div>
<div class="modal-footer">
<button class="btn btn-secondary" type="button" data-dismiss="modal">Cancel</button>
......
FirstApp/templates/FirstApp/activity.html
View file @ f7621cbd
......@@ -241,8 +241,13 @@
//to handle the 'integrate' modal
$('#integrate_activity').click(function () {
//define the student video src
{#let video_src = "{% static '' %}FirstApp/videos/" + global_video_name;#}
let video_src = "{% static '' %}FirstApp/activity/" + global_video_name;
{#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);
......@@ -1079,6 +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
View file @ f7621cbd
......@@ -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())
......@@ -242,32 +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
......@@ -339,7 +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
View file @ f7621cbd
......@@ -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())
......@@ -240,21 +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)
......@@ -265,6 +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);
$('#integrate_modal').modal();
}
//this function will load the activity recognition for frames
function displayGazeEstimationForFrame(response) {
......
FirstApp/templates/FirstApp/template.html
View file @ f7621cbd
This diff is collapsed.
FirstApp/templates/FirstApp/video_results.html
View file @ f7621cbd
......@@ -301,6 +301,7 @@
/*
let interval = setInterval(() => {
{#let url = 'http://127.0.0.1:8000/get-random_number';#}
let url = 'http://127.0.0.1:8000/check-availability';
......@@ -401,7 +402,7 @@
<div class="card-body">
<!--loading gif -->
{% if due_lectures.count == 0 %}
{% if due_lectures|length == 0 %}
<div class="text-center" id="no_subject_selected">
<span class="font-italic">No lecture is to be processed</span>
</div>
......@@ -414,6 +415,10 @@
<div class="text-center" id="no_timetable_content" hidden>
<span class="font-italic">Not included in the timetable</span>
</div>
<!-- if there are due lectures, display the table -->
{% if due_lectures %}
<!--displaying the timetable -->
<table class="table table-striped" id="timetable">
{# <caption id="timetable_caption"></caption>#}
......@@ -446,6 +451,7 @@
{% endfor %}
</tbody>
</table>
{% endif %}
</div>
......@@ -528,7 +534,6 @@
<!-- end of progress row -->
</div>
<!-- end of container -->
......
FirstApp/urls.py
View file @ f7621cbd
......@@ -190,7 +190,7 @@ urlpatterns = [
url(r'^check-availability/$', 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()),
......
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