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2020-101
Commit 5c581853 authored by I.K Seneviratne's avatar I.K Seneviratne
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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.
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parent ad27aa6d
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Showing with 689 additions and 495 deletions
FirstApp/emotion_detector.py
View file @ 5c581853
......@@ -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:
sitting_perct_list.append(int(data['seated_count']))
standing_perct_list.append(int(data['standing_count']))
walking_perct_list.append(int(data['walking_count']))
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:
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}
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']))
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 @ 5c581853
......@@ -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:
sitting_perct_list.append(int(data['seated_count']))
standing_perct_list.append(int(data['standing_count']))
walking_perct_list.append(int(data['walking_count']))
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:
phone_perct_list.append(int(data['phone_perct']))
listen_perct_list.append(int(data['listening_perct']))
note_perct_list.append(int(data['writing_perct']))
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']))
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}
# 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 @ 5c581853
......@@ -14,4 +14,11 @@ def batch_process(video_id, video_name):
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
\ No newline at end of file
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 @ 5c581853
......@@ -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:
sitting_perct_list.append(int(data['seated_count']))
standing_perct_list.append(int(data['standing_count']))
walking_perct_list.append(int(data['walking_count']))
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:
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}
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']))
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
View file @ 5c581853
# 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
View file @ 5c581853
......@@ -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);
$('#integrate_modal').modal();
//fetch data from the API
fetch('http://127.0.0.1:8000/get-lecture-emotion-for-frame?video_name=' + global_video_name)
.then((res) => res.json())
.then((out) => displayEmotionRecognitionForFrame(out.response))
.catch((err) => alert('error: ' + err));
}
......@@ -362,7 +376,7 @@
fetch('http://127.0.0.1:8000/lecturer/get-lecturer-video-frame-recognitions/?video_name=' + global_lecturer_video_name)
.then((res) => res.json())
.then((out) => displayLecturerEmotionRecognitionForFrame(out))
.catch((err) => alert('error: ' + err))
.catch((err) => alert('error: ' + err));
}
......
FirstApp/templates/FirstApp/gaze.html
View file @ 5c581853
......@@ -74,6 +74,8 @@
real_class = '.' + real_class;
let date = e.target.parentNode.parentNode.firstChild.innerHTML;
//assign the date
global_lecture_date = date;
fetch('http://127.0.0.1:8000/get-lecture-video-gaze-estimation-availability/?lecturer=' + global_lecturer + '&date=' + date + '&index=' + global_lecturer_subject_index)
.then((res) => res.json())
......@@ -249,6 +251,18 @@
.then((out) => assignLecturerRecordedVideoName(out))
.catch((err) => alert('error: ' + err));
{#global_lecturer_video_name = "Test_1.mp4";#}
{#global_lecturer_video_name = "Test_2.mp4";#}
{#global_lecturer_video_name = "Test_3.mp4";#}
{#global_lecturer_video_name = "Lecturer_Video_4.mp4";#}
{##}
{#//define the lecturer video src#}
{#let lecturer_video_src = "{% static '' %}FirstApp/lecturer_videos/" + global_lecturer_video_name;#}
{##}
{#//assign the video src#}
{#$('#lecturer_video').attr('src', lecturer_video_src);#}
{##}
{#$('#integrate_modal').modal();#}
//fetch data from the API
......
FirstApp/templates/FirstApp/template.html
View file @ 5c581853
This diff is collapsed.
FirstApp/templates/FirstApp/video_results.html
View file @ 5c581853
......@@ -301,7 +301,8 @@
/*
let interval = setInterval(() => {
{#let url = 'http://127.0.0.1:8000/get-random_number';#}
{#let url = 'http://127.0.0.1:8000/get-random_number';#}
let url = 'http://127.0.0.1:8000/check-availability';
fetch(url)
......@@ -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>
......@@ -414,38 +415,43 @@
<div class="text-center" id="no_timetable_content" hidden>
<span class="font-italic">Not included in the timetable</span>
</div>
<!--displaying the timetable -->
<table class="table table-striped" id="timetable">
{# <caption id="timetable_caption"></caption>#}
<thead>
<tr>
<th>Date</th>
<th>Subject</th>
<th>start time</th>
<th>end time</th>
<th></th>
</tr>
</thead>
<tbody id="timetable_body">
{% for lecture in due_lectures %}
<!-- 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>#}
<thead>
<tr>
<td class="font-weight-bolder">{{ lecture.date }}</td>
{# <td>{{ lecture.subject }}</td>#}
<td class="font-weight-bolder">{{ lecture.subject_name }}</td>
<td class="font-weight-bolder">{{ lecture.start_time }}</td>
<td class="font-weight-bolder">{{ lecture.end_time }}</td>
<td>
<button type="button" class="btn btn-success batch_process"
data-video-id="{{ lecture.video_id }}"
data-video-name="{{ lecture.video_name }}"
id="{{ lecture.subject }}">Process
</button>
{# <span class="font-italic font-weight-bolder text-success">Processing</span>#}
</td>
<th>Date</th>
<th>Subject</th>
<th>start time</th>
<th>end time</th>
<th></th>
</tr>
{% endfor %}
</tbody>
</table>
</thead>
<tbody id="timetable_body">
{% for lecture in due_lectures %}
<tr>
<td class="font-weight-bolder">{{ lecture.date }}</td>
{# <td>{{ lecture.subject }}</td>#}
<td class="font-weight-bolder">{{ lecture.subject_name }}</td>
<td class="font-weight-bolder">{{ lecture.start_time }}</td>
<td class="font-weight-bolder">{{ lecture.end_time }}</td>
<td>
<button type="button" class="btn btn-success batch_process"
data-video-id="{{ lecture.video_id }}"
data-video-name="{{ lecture.video_name }}"
id="{{ lecture.subject }}">Process
</button>
{# <span class="font-italic font-weight-bolder text-success">Processing</span>#}
</td>
</tr>
{% endfor %}
</tbody>
</table>
{% endif %}
</div>
......@@ -528,7 +534,6 @@
<!-- end of progress row -->
</div>
<!-- end of container -->
......@@ -541,9 +546,9 @@
</div>
<!-- end of progress row -->
<!-- snackbar -->
<div id="snackbar">The lecture is completely processed..</div>
<!-- end of snackbar -->
<!-- snackbar -->
<div id="snackbar">The lecture is completely processed..</div>
<!-- end of snackbar -->
</div>
{% endblock %}
......
FirstApp/urls.py
View file @ 5c581853
......@@ -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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