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Sachith Fernando
2020-101
Commits
765ece4e
Commit
765ece4e
authored
Oct 18, 2020
by
I.K Seneviratne
Browse files
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Committing the partial implementation of the workflow for lecture student behavior.
parent
033e0193
Changes
5
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Showing
5 changed files
with
432 additions
and
161 deletions
+432
-161
FirstApp/api.py
FirstApp/api.py
+53
-113
FirstApp/emotion_detector.py
FirstApp/emotion_detector.py
+86
-1
FirstApp/logic/activity_recognition.py
FirstApp/logic/activity_recognition.py
+106
-46
FirstApp/logic/head_gaze_estimation.py
FirstApp/logic/head_gaze_estimation.py
+85
-0
FirstApp/logic/video_extraction.py
FirstApp/logic/video_extraction.py
+102
-1
No files found.
FirstApp/api.py
View file @
765ece4e
...
@@ -279,7 +279,7 @@ class LectureActivityProcess(APIView):
...
@@ -279,7 +279,7 @@ class LectureActivityProcess(APIView):
def
get
(
self
,
request
):
def
get
(
self
,
request
):
video_name
=
request
.
query_params
.
get
(
'lecture_video_name'
)
video_name
=
request
.
query_params
.
get
(
'lecture_video_name'
)
video_id
=
request
.
query_params
.
get
(
'lecture_video_id'
)
video_id
=
int
(
request
.
query_params
.
get
(
'lecture_video_id'
)
)
percentages
=
ar
.
activity_recognition
(
video_name
)
percentages
=
ar
.
activity_recognition
(
video_name
)
self
.
activity
(
video_id
,
percentages
)
self
.
activity
(
video_id
,
percentages
)
return
Response
({
"response"
:
True
})
return
Response
({
"response"
:
True
})
...
@@ -288,9 +288,10 @@ class LectureActivityProcess(APIView):
...
@@ -288,9 +288,10 @@ class LectureActivityProcess(APIView):
pass
pass
def
activity
(
self
,
lec_video_id
,
percentages
):
def
activity
(
self
,
lec_video_id
,
percentages
):
lec_video
=
LectureVideo
.
objects
.
get
(
lecture_video_id
=
lec_video_id
)
lec_video
=
LectureVideo
.
objects
.
filter
(
lecture_video_id
=
lec_video_id
)
last_lec_activity
=
LectureActivity
.
objects
.
order_by
(
'lecture_activity_id'
)
.
last
()
last_lec_activity
=
LectureActivity
.
objects
.
order_by
(
'lecture_activity_id'
)
.
last
()
lec_video_serializer
=
LectureVideoSerializer
(
lec_video
,
many
=
True
)
lec_video_serializer
=
LectureVideoSerializer
(
lec_video
,
many
=
True
)
lec_video_data
=
lec_video_serializer
.
data
[
0
]
new_lecture_activity_id
=
ig
.
generate_new_id
(
last_lec_activity
.
lecture_activity_id
)
new_lecture_activity_id
=
ig
.
generate_new_id
(
last_lec_activity
.
lecture_activity_id
)
# creating a new lecture activity
# creating a new lecture activity
...
@@ -303,6 +304,21 @@ class LectureActivityProcess(APIView):
...
@@ -303,6 +304,21 @@ class LectureActivityProcess(APIView):
writing_perct
=
percentages
[
'writing_perct'
]
writing_perct
=
percentages
[
'writing_perct'
]
)
.
save
()
)
.
save
()
# get the video name
video_name
=
lec_video_data
[
'video_name'
]
# then save the frame recognitions to the database
_
=
ar
.
save_frame_recognition
(
video_name
)
# save the time landmarks and frame landmarks
ve
.
save_time_landmarks
(
video_name
)
frame_landmarks
,
frame_group_dict
=
ve
.
save_frame_landmarks
(
video_name
)
# then save the activity frame groupings
ar
.
save_frame_groupings
(
video_name
,
frame_landmarks
,
frame_group_dict
)
class
GetLectureActivityDetections
(
APIView
):
class
GetLectureActivityDetections
(
APIView
):
...
@@ -378,41 +394,8 @@ class GetLectureActivityRecognitionsForFrames(APIView):
...
@@ -378,41 +394,8 @@ class GetLectureActivityRecognitionsForFrames(APIView):
else
:
else
:
# retrieve the lecture activity id
# perform the action of saving frame recognitions to database
lec_activity
=
LectureActivity
.
objects
.
filter
(
lecture_video_id__video_name
=
video_name
)
frame_detections
=
ar
.
save_frame_recognition
(
video_name
)
lec_activity_ser
=
LectureActivitySerializer
(
lec_activity
,
many
=
True
)
lec_activity_data
=
lec_activity_ser
.
data
[
0
]
lec_activity_id
=
lec_activity_data
[
'id'
]
# create a new lecture activity frame detections id
last_lec_activity_frame_recognitions
=
LectureActivityFrameRecognitions
.
objects
.
order_by
(
'lecture_activity_frame_recognition_id'
)
.
last
()
new_lecture_activity_frame_recognitions_id
=
"LAFR00001"
if
(
last_lec_activity_frame_recognitions
is
None
)
else
\
ig
.
generate_new_id
(
last_lec_activity_frame_recognitions
.
lecture_activity_frame_recognition_id
)
# calculate the frame detections
frame_detections
=
ar
.
get_frame_activity_recognition
(
video_name
)
frame_recognition_details
=
[]
# save the new lecture activity frame recognitions
for
detection
in
frame_detections
:
lec_activity_frame_recognition_details
=
LectureActivityFrameRecognitionDetails
()
lec_activity_frame_recognition_details
.
frame_name
=
detection
[
'frame_name'
]
lec_activity_frame_recognition_details
.
phone_perct
=
detection
[
'phone_perct'
]
lec_activity_frame_recognition_details
.
listen_perct
=
detection
[
'listening_perct'
]
lec_activity_frame_recognition_details
.
note_perct
=
detection
[
'note_perct'
]
frame_recognition_details
.
append
(
lec_activity_frame_recognition_details
)
lec_activity_frame_recognitions
=
LectureActivityFrameRecognitions
()
lec_activity_frame_recognitions
.
lecture_activity_frame_recognition_id
=
new_lecture_activity_frame_recognitions_id
lec_activity_frame_recognitions
.
lecture_activity_id_id
=
lec_activity_id
lec_activity_frame_recognitions
.
frame_recognition_details
=
frame_recognition_details
lec_activity_frame_recognitions
.
save
()
return
Response
({
return
Response
({
"response"
:
frame_detections
"response"
:
frame_detections
...
@@ -490,6 +473,7 @@ class LectureEmotionProcess(APIView):
...
@@ -490,6 +473,7 @@ class LectureEmotionProcess(APIView):
def
save_emotion_report
(
self
,
lec_video_id
,
percentages
):
def
save_emotion_report
(
self
,
lec_video_id
,
percentages
):
lec_video
=
LectureVideo
.
objects
.
get
(
lecture_video_id
=
lec_video_id
)
lec_video
=
LectureVideo
.
objects
.
get
(
lecture_video_id
=
lec_video_id
)
lec_video_serializer
=
LectureVideoSerializer
(
lec_video
,
many
=
True
)
lec_video_serializer
=
LectureVideoSerializer
(
lec_video
,
many
=
True
)
lec_video_data
=
lec_video_serializer
.
data
[
0
]
last_lec_emotion
=
LectureEmotionReport
.
objects
.
order_by
(
'lecture_emotion_id'
)
.
last
()
last_lec_emotion
=
LectureEmotionReport
.
objects
.
order_by
(
'lecture_emotion_id'
)
.
last
()
new_lecture_emotion_id
=
ig
.
generate_new_id
(
last_lec_emotion
.
lecture_emotion_id
)
new_lecture_emotion_id
=
ig
.
generate_new_id
(
last_lec_emotion
.
lecture_emotion_id
)
...
@@ -504,6 +488,20 @@ class LectureEmotionProcess(APIView):
...
@@ -504,6 +488,20 @@ class LectureEmotionProcess(APIView):
surprise_perct
=
percentages
.
surprise_perct
surprise_perct
=
percentages
.
surprise_perct
)
.
save
()
)
.
save
()
# get the video name
video_name
=
lec_video_data
[
'video_name'
]
# then save the frame recognition details to the database
_
=
ed
.
save_frame_recognitions
(
video_name
)
# retrieve the frame landmarks and frame group dictionary
frame_landmarks
,
frame_group_dict
=
ve
.
getFrameLandmarks
(
video_name
,
"Emotion"
)
# then save emotion frame groupings
ed
.
save_frame_groupings
(
video_name
,
frame_landmarks
,
frame_group_dict
)
# to get a lecture emotion report
# to get a lecture emotion report
class
GetLectureEmotionReportViewSet
(
APIView
):
class
GetLectureEmotionReportViewSet
(
APIView
):
...
@@ -576,45 +574,8 @@ class GetLectureEmotionRecognitionsForFrames(APIView):
...
@@ -576,45 +574,8 @@ class GetLectureEmotionRecognitionsForFrames(APIView):
})
})
else
:
else
:
# save the frame recognitions into the database
# retrieve the lecture emotion id
frame_detections
=
ed
.
save_frame_recognitions
(
video_name
)
lec_emotion
=
LectureEmotionReport
.
objects
.
filter
(
lecture_video_id__video_name
=
video_name
)
lec_emotion_ser
=
LectureEmotionSerializer
(
lec_emotion
,
many
=
True
)
lec_emotion_data
=
lec_emotion_ser
.
data
[
0
]
lec_emotion_id
=
lec_emotion_data
[
'id'
]
# create a new lecture activity frame detections id
last_lec_emotion_frame_recognitions
=
LectureEmotionFrameRecognitions
.
objects
.
order_by
(
'lecture_emotion_frame_recognition_id'
)
.
last
()
new_lecture_emotion_frame_recognitions_id
=
"LEFR00001"
if
(
last_lec_emotion_frame_recognitions
is
None
)
else
\
ig
.
generate_new_id
(
last_lec_emotion_frame_recognitions
.
lecture_emotion_frame_recognition_id
)
# calculate the frame detections
frame_detections
=
ed
.
get_frame_emotion_recognition
(
video_name
)
frame_recognition_details
=
[]
# save the new lecture activity frame recognitions
for
detection
in
frame_detections
:
lec_emotion_frame_recognition_details
=
LectureEmotionFrameRecognitionDetails
()
lec_emotion_frame_recognition_details
.
frame_name
=
detection
[
'frame_name'
]
lec_emotion_frame_recognition_details
.
happy_perct
=
detection
[
'happy_perct'
]
lec_emotion_frame_recognition_details
.
sad_perct
=
detection
[
'sad_perct'
]
lec_emotion_frame_recognition_details
.
angry_perct
=
detection
[
'angry_perct'
]
lec_emotion_frame_recognition_details
.
surprise_perct
=
detection
[
'surprise_perct'
]
lec_emotion_frame_recognition_details
.
neutral_perct
=
detection
[
'neutral_perct'
]
frame_recognition_details
.
append
(
lec_emotion_frame_recognition_details
)
lec_emotion_frame_recognitions
=
LectureEmotionFrameRecognitions
()
lec_emotion_frame_recognitions
.
lecture_emotion_frame_recognition_id
=
new_lecture_emotion_frame_recognitions_id
lec_emotion_frame_recognitions
.
lecture_emotion_id_id
=
lec_emotion_id
lec_emotion_frame_recognitions
.
frame_recognition_details
=
frame_recognition_details
lec_emotion_frame_recognitions
.
save
()
return
Response
({
return
Response
({
"response"
:
frame_detections
"response"
:
frame_detections
...
@@ -725,6 +686,7 @@ class ProcessLectureGazeEstimation(APIView):
...
@@ -725,6 +686,7 @@ class ProcessLectureGazeEstimation(APIView):
lec_video
=
LectureVideo
.
objects
.
get
(
lecture_video_id
=
lec_video_id
)
lec_video
=
LectureVideo
.
objects
.
get
(
lecture_video_id
=
lec_video_id
)
last_lec_gaze
=
LectureGazeEstimation
.
objects
.
order_by
(
'lecture_gaze_id'
)
.
last
()
last_lec_gaze
=
LectureGazeEstimation
.
objects
.
order_by
(
'lecture_gaze_id'
)
.
last
()
lec_video_serializer
=
LectureVideoSerializer
(
lec_video
,
many
=
True
)
lec_video_serializer
=
LectureVideoSerializer
(
lec_video
,
many
=
True
)
lec_video_data
=
lec_video_serializer
.
data
[
0
]
new_lecture_gaze_id
=
"LG000001"
if
(
last_lec_gaze
is
None
)
else
ig
.
generate_new_id
(
new_lecture_gaze_id
=
"LG000001"
if
(
last_lec_gaze
is
None
)
else
ig
.
generate_new_id
(
last_lec_gaze
.
lecture_gaze_id
)
last_lec_gaze
.
lecture_gaze_id
)
...
@@ -739,6 +701,18 @@ class ProcessLectureGazeEstimation(APIView):
...
@@ -739,6 +701,18 @@ class ProcessLectureGazeEstimation(APIView):
looking_front_perct
=
percentages
[
'head_front_perct'
]
looking_front_perct
=
percentages
[
'head_front_perct'
]
)
.
save
()
)
.
save
()
# get the video name
video_name
=
lec_video_data
[
'video_name'
]
# then save the frame recognitions to the database
_
=
hge
.
save_frame_detections
(
video_name
)
# get the frame landmarks and frame group dictionary
frame_landmarks
,
frame_group_dict
=
ve
.
getFrameLandmarks
(
video_name
,
"Gaze"
)
# then save the gaze frame groupings to the database
hge
.
save_frame_groupings
(
video_name
,
frame_landmarks
,
frame_group_dict
)
# the API to retrieve lecture gaze estimation
# the API to retrieve lecture gaze estimation
class
GetLectureGazeEstimationViewSet
(
APIView
):
class
GetLectureGazeEstimationViewSet
(
APIView
):
...
@@ -765,7 +739,7 @@ class GetLectureGazeEstimationForFrames(APIView):
...
@@ -765,7 +739,7 @@ class GetLectureGazeEstimationForFrames(APIView):
def
get
(
self
,
request
):
def
get
(
self
,
request
):
video_name
=
request
.
query_params
.
get
(
'video_name'
)
video_name
=
request
.
query_params
.
get
(
'video_name'
)
# finding the existence of Lecture
activity
frame recognition record
# finding the existence of Lecture
gaze
frame recognition record
isExist
=
LectureGazeFrameRecognitions
.
objects
.
filter
(
isExist
=
LectureGazeFrameRecognitions
.
objects
.
filter
(
lecture_gaze_id__lecture_video_id__video_name
=
video_name
)
.
exists
()
lecture_gaze_id__lecture_video_id__video_name
=
video_name
)
.
exists
()
...
@@ -784,42 +758,8 @@ class GetLectureGazeEstimationForFrames(APIView):
...
@@ -784,42 +758,8 @@ class GetLectureGazeEstimationForFrames(APIView):
else
:
else
:
# retrieve the lecture emotion id
# save recognition details into the database
lec_gaze
=
LectureGazeEstimation
.
objects
.
filter
(
lecture_video_id__video_name
=
video_name
)
frame_detections
=
hge
.
save_frame_detections
(
video_name
)
lec_gaze_ser
=
LectureGazeEstimationSerializer
(
lec_gaze
,
many
=
True
)
lec_gaze_data
=
lec_gaze_ser
.
data
[
0
]
lec_gaze_id
=
lec_gaze_data
[
'id'
]
# create a new lecture activity frame detections id
last_lec_gaze_frame_recognitions
=
LectureGazeFrameRecognitions
.
objects
.
order_by
(
'lecture_gaze_frame_recognition_id'
)
.
last
()
new_lecture_gaze_frame_recognitions_id
=
"LGFR00001"
if
(
last_lec_gaze_frame_recognitions
is
None
)
else
\
ig
.
generate_new_id
(
last_lec_gaze_frame_recognitions
.
lecture_gaze_frame_recognition_id
)
# calculate the frame detections
frame_detections
,
frame_rate
=
hge
.
get_lecture_gaze_esrimation_for_frames
(
video_name
)
frame_recognition_details
=
[]
# save the new lecture activity frame recognitions
for
detection
in
frame_detections
:
lec_gaze_frame_recognition_details
=
LectureGazeFrameRecognitionDetails
()
lec_gaze_frame_recognition_details
.
frame_name
=
detection
[
'frame_name'
]
lec_gaze_frame_recognition_details
.
upright_perct
=
detection
[
'upright_perct'
]
lec_gaze_frame_recognition_details
.
upleft_perct
=
detection
[
'upleft_perct'
]
lec_gaze_frame_recognition_details
.
downright_perct
=
detection
[
'downright_perct'
]
lec_gaze_frame_recognition_details
.
downleft_perct
=
detection
[
'downleft_perct'
]
lec_gaze_frame_recognition_details
.
front_perct
=
detection
[
'front_perct'
]
frame_recognition_details
.
append
(
lec_gaze_frame_recognition_details
)
lec_gaze_frame_recognitions
=
LectureGazeFrameRecognitions
()
lec_gaze_frame_recognitions
.
lecture_gaze_frame_recognition_id
=
new_lecture_gaze_frame_recognitions_id
lec_gaze_frame_recognitions
.
lecture_gaze_id_id
=
lec_gaze_id
lec_gaze_frame_recognitions
.
frame_recognition_details
=
frame_recognition_details
lec_gaze_frame_recognitions
.
save
()
return
Response
({
return
Response
({
"response"
:
frame_detections
"response"
:
frame_detections
...
...
FirstApp/emotion_detector.py
View file @
765ece4e
...
@@ -5,11 +5,17 @@ from keras.preprocessing import image
...
@@ -5,11 +5,17 @@ from keras.preprocessing import image
import
cv2
import
cv2
import
os
import
os
import
numpy
as
np
import
numpy
as
np
from
.MongoModels
import
*
from
.
models
import
VideoMeta
from
.
models
import
VideoMeta
from
.
logic
import
custom_sorter
as
cs
from
.
logic
import
custom_sorter
as
cs
from
.logic
import
id_generator
as
ig
# emotion recognition method
# emotion recognition method
from
.serializers
import
LectureEmotionSerializer
def
emotion_recognition
(
classifier
,
face_classifier
,
image
):
def
emotion_recognition
(
classifier
,
face_classifier
,
image
):
label
=
""
label
=
""
class_labels
=
[
'Angry'
,
'Happy'
,
'Neutral'
,
'Sad'
,
'Surprise'
]
class_labels
=
[
'Angry'
,
'Happy'
,
'Neutral'
,
'Sad'
,
'Surprise'
]
...
@@ -548,3 +554,82 @@ def emotion_frame_groupings(video_name, frame_landmarks, frame_group_dict):
...
@@ -548,3 +554,82 @@ def emotion_frame_groupings(video_name, frame_landmarks, frame_group_dict):
# return the dictionary
# return the dictionary
return
frame_group_dict
,
emotion_labels
return
frame_group_dict
,
emotion_labels
# this section will handle some database operations
def
save_frame_recognitions
(
video_name
):
# retrieve the lecture emotion id
lec_emotion
=
LectureEmotionReport
.
objects
.
filter
(
lecture_video_id__video_name
=
video_name
)
lec_emotion_ser
=
LectureEmotionSerializer
(
lec_emotion
,
many
=
True
)
lec_emotion_data
=
lec_emotion_ser
.
data
[
0
]
lec_emotion_id
=
lec_emotion_data
[
'id'
]
# create a new lecture activity frame detections id
last_lec_emotion_frame_recognitions
=
LectureEmotionFrameRecognitions
.
objects
.
order_by
(
'lecture_emotion_frame_recognition_id'
)
.
last
()
new_lecture_emotion_frame_recognitions_id
=
"LEFR00001"
if
(
last_lec_emotion_frame_recognitions
is
None
)
else
\
ig
.
generate_new_id
(
last_lec_emotion_frame_recognitions
.
lecture_emotion_frame_recognition_id
)
# calculate the frame detections
frame_detections
=
get_frame_emotion_recognition
(
video_name
)
frame_recognition_details
=
[]
# save the new lecture activity frame recognitions
for
detection
in
frame_detections
:
lec_emotion_frame_recognition_details
=
LectureEmotionFrameRecognitionDetails
()
lec_emotion_frame_recognition_details
.
frame_name
=
detection
[
'frame_name'
]
lec_emotion_frame_recognition_details
.
happy_perct
=
detection
[
'happy_perct'
]
lec_emotion_frame_recognition_details
.
sad_perct
=
detection
[
'sad_perct'
]
lec_emotion_frame_recognition_details
.
angry_perct
=
detection
[
'angry_perct'
]
lec_emotion_frame_recognition_details
.
surprise_perct
=
detection
[
'surprise_perct'
]
lec_emotion_frame_recognition_details
.
neutral_perct
=
detection
[
'neutral_perct'
]
frame_recognition_details
.
append
(
lec_emotion_frame_recognition_details
)
lec_emotion_frame_recognitions
=
LectureEmotionFrameRecognitions
()
lec_emotion_frame_recognitions
.
lecture_emotion_frame_recognition_id
=
new_lecture_emotion_frame_recognitions_id
lec_emotion_frame_recognitions
.
lecture_emotion_id_id
=
lec_emotion_id
lec_emotion_frame_recognitions
.
frame_recognition_details
=
frame_recognition_details
lec_emotion_frame_recognitions
.
save
()
# now return the frame recognitions
return
frame_detections
# this method will save the emotion frame groupings to the database
def
save_frame_groupings
(
video_name
,
frame_landmarks
,
frame_group_dict
):
frame_group_percentages
,
emotion_labels
=
emotion_frame_groupings
(
video_name
,
frame_landmarks
,
frame_group_dict
)
# save the frame group details into db
last_lec_emotion_frame_grouping
=
LectureEmotionFrameGroupings
.
objects
.
order_by
(
'lecture_emotion_frame_groupings_id'
)
.
last
()
new_lecture_emotion_frame_grouping_id
=
"LEFG00001"
if
(
last_lec_emotion_frame_grouping
is
None
)
else
\
ig
.
generate_new_id
(
last_lec_emotion_frame_grouping
.
lecture_emotion_frame_groupings_id
)
# retrieve the lecture emotion id
lec_emotion
=
LectureEmotionReport
.
objects
.
filter
(
lecture_video_id__video_name
=
video_name
)
lec_emotion_ser
=
LectureEmotionSerializer
(
lec_emotion
,
many
=
True
)
lec_emotion_id
=
lec_emotion_ser
.
data
[
0
][
'id'
]
# create the frame group details
frame_group_details
=
[]
for
key
in
frame_group_percentages
.
keys
():
# create an object of type 'LectureActivityFrameGroupDetails'
lec_emotion_frame_group_details
=
LectureEmotionFrameGroupDetails
()
lec_emotion_frame_group_details
.
frame_group
=
key
lec_emotion_frame_group_details
.
frame_group_percentages
=
frame_group_percentages
[
key
]
frame_group_details
.
append
(
lec_emotion_frame_group_details
)
new_lec_emotion_frame_groupings
=
LectureEmotionFrameGroupings
()
new_lec_emotion_frame_groupings
.
lecture_emotion_frame_groupings_id
=
new_lecture_emotion_frame_grouping_id
new_lec_emotion_frame_groupings
.
lecture_emotion_id_id
=
lec_emotion_id
new_lec_emotion_frame_groupings
.
frame_group_details
=
frame_group_details
# save
new_lec_emotion_frame_groupings
.
save
()
FirstApp/logic/activity_recognition.py
View file @
765ece4e
...
@@ -5,7 +5,10 @@ import numpy as np
...
@@ -5,7 +5,10 @@ import numpy as np
import
cv2
import
cv2
import
os
import
os
import
shutil
import
shutil
from
.
custom_sorter
import
*
from
.custom_sorter
import
*
from
..MongoModels
import
*
from
..serializers
import
*
from
.
import
id_generator
as
ig
def
activity_recognition
(
video_path
):
def
activity_recognition
(
video_path
):
...
@@ -55,30 +58,30 @@ def activity_recognition(video_path):
...
@@ -55,30 +58,30 @@ def activity_recognition(video_path):
VIDEO_ACTIVITY_DIR
=
os
.
path
.
join
(
ACTIVITY_DIR
,
video_path
)
VIDEO_ACTIVITY_DIR
=
os
.
path
.
join
(
ACTIVITY_DIR
,
video_path
)
# creating the directory for the video
# creating the directory for the video
if
(
os
.
path
.
isdir
(
VIDEO_ACTIVITY_DIR
)):
#
if (os.path.isdir(VIDEO_ACTIVITY_DIR)):
shutil
.
rmtree
(
VIDEO_ACTIVITY_DIR
)
#
shutil.rmtree(VIDEO_ACTIVITY_DIR)
#
# create the video directory
#
#
create the video directory
os
.
mkdir
(
VIDEO_ACTIVITY_DIR
)
#
os.mkdir(VIDEO_ACTIVITY_DIR)
while
(
frame_count
<
no_of_frames
):
while
(
frame_count
<
no_of_frames
):
ret
,
image
=
video
.
read
()
ret
,
image
=
video
.
read
()
FRAME_DIR
=
os
.
path
.
join
(
VIDEO_ACTIVITY_DIR
,
"frame-{}"
.
format
(
frame_count
))
FRAME_DIR
=
os
.
path
.
join
(
VIDEO_ACTIVITY_DIR
,
"frame-{}"
.
format
(
frame_count
))
frame_name
=
"frame-{}.png"
.
format
(
frame_count
)
#
frame_name = "frame-{}.png".format(frame_count)
#
FRAME_IMG
=
os
.
path
.
join
(
FRAME_DIR
,
frame_name
)
#
FRAME_IMG = os.path.join(FRAME_DIR, frame_name)
#
if
(
os
.
path
.
isdir
(
FRAME_DIR
)):
#
if (os.path.isdir(FRAME_DIR)):
shutil
.
rmtree
(
FRAME_DIR
)
#
shutil.rmtree(FRAME_DIR)
# create the new frame directory
# create the new frame directory
os
.
mkdir
(
FRAME_DIR
)
#
os.mkdir(FRAME_DIR)
image
=
cv2
.
resize
(
image
,
size
)
image
=
cv2
.
resize
(
image
,
size
)
detections
=
person_detection
(
image
,
net
)
detections
=
person_detection
(
image
,
net
)
image
=
cv2
.
cvtColor
(
image
,
cv2
.
COLOR_BGR2GRAY
)
image
=
cv2
.
cvtColor
(
image
,
cv2
.
COLOR_BGR2GRAY
)
cv2
.
imwrite
(
FRAME_IMG
,
image
)
#
cv2.imwrite(FRAME_IMG, image)
# if there are any person detections
# if there are any person detections
if
(
len
(
detections
)
>
0
):
if
(
len
(
detections
)
>
0
):
...
@@ -111,22 +114,21 @@ def activity_recognition(video_path):
...
@@ -111,22 +114,21 @@ def activity_recognition(video_path):
note_taking_count
+=
1
note_taking_count
+=
1
# saving the detection for the particular frame
# saving the detection for the particular frame
detection_name
=
"detection-{}.png"
.
format
(
detection_count
)
#
detection_name = "detection-{}.png".format(detection_count)
detection_image_path
=
os
.
path
.
join
(
FRAME_DIR
,
detection_name
)
#
detection_image_path = os.path.join(FRAME_DIR, detection_name)
#
# converting detected image into grey-scale
#
#
converting detected image into grey-scale
detection
=
cv2
.
cvtColor
(
detection
,
cv2
.
COLOR_BGR2GRAY
)
#
detection = cv2.cvtColor(detection, cv2.COLOR_BGR2GRAY)
#
cv2
.
imwrite
(
detection_image_path
,
detection
)
#
cv2.imwrite(detection_image_path, detection)
detection_count
+=
1
detection_count
+=
1
frame_count
+=
1
frame_count
+=
1
# after extracting the frames, save the changes to static content
# after extracting the frames, save the changes to static content
p
=
os
.
popen
(
"python manage.py collectstatic"
,
"w"
)
#
p = os.popen("python manage.py collectstatic", "w")
p
.
write
(
"yes"
)
#
p.write("yes")
# calculating the percentages for each label
# calculating the percentages for each label
phone_perct
=
float
(
phone_checking_count
/
total_detections
)
*
100
if
total_detections
>
0
else
0
phone_perct
=
float
(
phone_checking_count
/
total_detections
)
*
100
if
total_detections
>
0
else
0
...
@@ -140,7 +142,6 @@ def activity_recognition(video_path):
...
@@ -140,7 +142,6 @@ def activity_recognition(video_path):
percentages
[
"writing_perct"
]
=
note_perct
percentages
[
"writing_perct"
]
=
note_perct
percentages
[
"listening_perct"
]
=
listening_perct
percentages
[
"listening_perct"
]
=
listening_perct
return
percentages
return
percentages
...
@@ -162,8 +163,6 @@ def person_detection(image, net):
...
@@ -162,8 +163,6 @@ def person_detection(image, net):
person_count
=
0
person_count
=
0
# load the input image and construct an input blob for the image
# load the input image and construct an input blob for the image
# by resizing to a fixed 300x300 pixels and then normalizing it
# by resizing to a fixed 300x300 pixels and then normalizing it
# (note: normalization is done via the authors of the MobileNet SSD
# (note: normalization is done via the authors of the MobileNet SSD
...
@@ -212,10 +211,8 @@ def person_detection(image, net):
...
@@ -212,10 +211,8 @@ def person_detection(image, net):
# retrieving the extracted frames and detections for a given video
# retrieving the extracted frames and detections for a given video
def
getExtractedFrames
(
folder_name
):
def
getExtractedFrames
(
folder_name
):
image_list
=
[]
image_list
=
[]
BASE_DIR
=
os
.
path
.
dirname
(
os
.
path
.
dirname
(
os
.
path
.
dirname
(
os
.
path
.
abspath
(
__file__
))))
BASE_DIR
=
os
.
path
.
dirname
(
os
.
path
.
dirname
(
os
.
path
.
dirname
(
os
.
path
.
abspath
(
__file__
))))
EXTRACTED_DIR
=
os
.
path
.
join
(
BASE_DIR
,
"assets
\\
FirstApp
\\
activity
\\
{}"
.
format
(
folder_name
))
EXTRACTED_DIR
=
os
.
path
.
join
(
BASE_DIR
,
"assets
\\
FirstApp
\\
activity
\\
{}"
.
format
(
folder_name
))
...
@@ -240,9 +237,9 @@ def getExtractedFrames(folder_name):
...
@@ -240,9 +237,9 @@ def getExtractedFrames(folder_name):
else
:
else
:
return
"No extracted frames were found"
return
"No extracted frames were found"
# get detections for a given frame name
# get detections for a given frame name
def
get_detections
(
video_name
,
frame_name
):
def
get_detections
(
video_name
,
frame_name
):
BASE_DIR
=
os
.
path
.
dirname
(
os
.
path
.
dirname
(
os
.
path
.
dirname
(
os
.
path
.
abspath
(
__file__
))))
BASE_DIR
=
os
.
path
.
dirname
(
os
.
path
.
dirname
(
os
.
path
.
dirname
(
os
.
path
.
abspath
(
__file__
))))
EXTRACTED_DIR
=
os
.
path
.
join
(
BASE_DIR
,
"assets
\\
FirstApp
\\
activity
\\
{}"
.
format
(
video_name
))
EXTRACTED_DIR
=
os
.
path
.
join
(
BASE_DIR
,
"assets
\\
FirstApp
\\
activity
\\
{}"
.
format
(
video_name
))
FRAME_DIR
=
os
.
path
.
join
(
EXTRACTED_DIR
,
frame_name
)
FRAME_DIR
=
os
.
path
.
join
(
EXTRACTED_DIR
,
frame_name
)
...
@@ -257,7 +254,6 @@ def get_detections(video_name, frame_name):
...
@@ -257,7 +254,6 @@ def get_detections(video_name, frame_name):
# get detections for a given class name
# get detections for a given class name
def
get_detections_for_label
(
video_name
,
label_index
):
def
get_detections_for_label
(
video_name
,
label_index
):
BASE_DIR
=
os
.
path
.
dirname
(
os
.
path
.
dirname
(
os
.
path
.
dirname
(
os
.
path
.
abspath
(
__file__
))))
BASE_DIR
=
os
.
path
.
dirname
(
os
.
path
.
dirname
(
os
.
path
.
dirname
(
os
.
path
.
abspath
(
__file__
))))
EXTRACTED_DIR
=
os
.
path
.
join
(
BASE_DIR
,
"assets
\\
FirstApp
\\
activity
\\
{}"
.
format
(
video_name
))
EXTRACTED_DIR
=
os
.
path
.
join
(
BASE_DIR
,
"assets
\\
FirstApp
\\
activity
\\
{}"
.
format
(
video_name
))
CLASSIFIER_DIR
=
os
.
path
.
join
(
BASE_DIR
,
"FirstApp
\\
classifiers
\\
student_activity_version_02.h5"
)
CLASSIFIER_DIR
=
os
.
path
.
join
(
BASE_DIR
,
"FirstApp
\\
classifiers
\\
student_activity_version_02.h5"
)
...
@@ -328,7 +324,6 @@ def get_detections_for_label(video_name, label_index):
...
@@ -328,7 +324,6 @@ def get_detections_for_label(video_name, label_index):
# to get the student evaluations
# to get the student evaluations
def
get_student_activity_evaluation
(
video_name
):
def
get_student_activity_evaluation
(
video_name
):
BASE_DIR
=
os
.
path
.
dirname
(
os
.
path
.
dirname
(
os
.
path
.
dirname
(
os
.
path
.
abspath
(
__file__
))))
BASE_DIR
=
os
.
path
.
dirname
(
os
.
path
.
dirname
(
os
.
path
.
dirname
(
os
.
path
.
abspath
(
__file__
))))
EXTRACTED_DIR
=
os
.
path
.
join
(
BASE_DIR
,
"assets
\\
FirstApp
\\
activity
\\
{}"
.
format
(
video_name
))
EXTRACTED_DIR
=
os
.
path
.
join
(
BASE_DIR
,
"assets
\\
FirstApp
\\
activity
\\
{}"
.
format
(
video_name
))
# CLASSIFIER_DIR = os.path.join(BASE_DIR, "FirstApp\\classifiers\\student_activity_version_02.h5")
# CLASSIFIER_DIR = os.path.join(BASE_DIR, "FirstApp\\classifiers\\student_activity_version_02.h5")
...
@@ -401,7 +396,6 @@ def get_frame_activity_recognition(video_name):
...
@@ -401,7 +396,6 @@ def get_frame_activity_recognition(video_name):
# CLASSIFIER_DIR = os.path.join(BASE_DIR, "FirstApp\\classifiers\\student_activity_version_02.h5")
# CLASSIFIER_DIR = os.path.join(BASE_DIR, "FirstApp\\classifiers\\student_activity_version_02.h5")
CLASSIFIER_DIR
=
os
.
path
.
join
(
BASE_DIR
,
"FirstApp
\\
classifiers
\\
student_activity_version_04.h5"
)
CLASSIFIER_DIR
=
os
.
path
.
join
(
BASE_DIR
,
"FirstApp
\\
classifiers
\\
student_activity_version_04.h5"
)
np
.
set_printoptions
(
suppress
=
True
)
np
.
set_printoptions
(
suppress
=
True
)
# load the model
# load the model
...
@@ -473,7 +467,6 @@ def get_frame_activity_recognition(video_name):
...
@@ -473,7 +467,6 @@ def get_frame_activity_recognition(video_name):
# increment the detection count
# increment the detection count
detection_count
+=
1
detection_count
+=
1
# calculating the percentages for the frame
# calculating the percentages for the frame
phone_checking_perct
=
float
(
phone_checking_count
/
detection_count
)
*
100
if
detection_count
>
0
else
0
phone_checking_perct
=
float
(
phone_checking_count
/
detection_count
)
*
100
if
detection_count
>
0
else
0
listening_perct
=
float
(
listening_count
/
detection_count
)
*
100
if
detection_count
>
0
else
0
listening_perct
=
float
(
listening_count
/
detection_count
)
*
100
if
detection_count
>
0
else
0
...
@@ -575,7 +568,6 @@ def get_individual_student_evaluation(video_name, student_name):
...
@@ -575,7 +568,6 @@ def get_individual_student_evaluation(video_name, student_name):
# this method will retrieve student activity summary for given time period
# this method will retrieve student activity summary for given time period
def
get_student_activity_summary_for_period
(
activities
):
def
get_student_activity_summary_for_period
(
activities
):
# declare variables to add percentage values
# declare variables to add percentage values
phone_checking_perct_combined
=
0.0
phone_checking_perct_combined
=
0.0
listening_perct_combined
=
0.0
listening_perct_combined
=
0.0
...
@@ -590,7 +582,6 @@ def get_student_activity_summary_for_period(activities):
...
@@ -590,7 +582,6 @@ def get_student_activity_summary_for_period(activities):
# iterate through the activities
# iterate through the activities
for
activity
in
activities
:
for
activity
in
activities
:
individual_activity
=
{}
individual_activity
=
{}
individual_activity
[
"phone_perct"
]
=
float
(
activity
[
'phone_perct'
])
individual_activity
[
"phone_perct"
]
=
float
(
activity
[
'phone_perct'
])
individual_activity
[
"listening_perct"
]
=
float
(
activity
[
'listening_perct'
])
individual_activity
[
"listening_perct"
]
=
float
(
activity
[
'listening_perct'
])
...
@@ -603,7 +594,6 @@ def get_student_activity_summary_for_period(activities):
...
@@ -603,7 +594,6 @@ def get_student_activity_summary_for_period(activities):
# append to the list
# append to the list
individual_lec_activities
.
append
(
individual_activity
)
individual_lec_activities
.
append
(
individual_activity
)
# calculate the average percentages
# calculate the average percentages
phone_checking_average_perct
=
round
((
phone_checking_perct_combined
/
no_of_activities
),
1
)
phone_checking_average_perct
=
round
((
phone_checking_perct_combined
/
no_of_activities
),
1
)
listening_average_perct
=
round
((
listening_perct_combined
/
no_of_activities
),
1
)
listening_average_perct
=
round
((
listening_perct_combined
/
no_of_activities
),
1
)
...
@@ -656,7 +646,6 @@ def activity_frame_groupings(video_name, frame_landmarks, frame_group_dict):
...
@@ -656,7 +646,6 @@ def activity_frame_groupings(video_name, frame_landmarks, frame_group_dict):
# assign the difference
# assign the difference
frame_group_diff
[
key
]
=
diff
if
diff
>
0
else
1
frame_group_diff
[
key
]
=
diff
if
diff
>
0
else
1
# looping through the frames
# looping through the frames
for
frame
in
os
.
listdir
(
EXTRACTED_DIR
):
for
frame
in
os
.
listdir
(
EXTRACTED_DIR
):
# getting the frame folder
# getting the frame folder
...
@@ -682,7 +671,6 @@ def activity_frame_groupings(video_name, frame_landmarks, frame_group_dict):
...
@@ -682,7 +671,6 @@ def activity_frame_groupings(video_name, frame_landmarks, frame_group_dict):
image_array
=
np
.
asarray
(
image
)
image_array
=
np
.
asarray
(
image
)
normalized_image_array
=
(
image_array
.
astype
(
np
.
float32
)
/
127.0
)
-
1
normalized_image_array
=
(
image_array
.
astype
(
np
.
float32
)
/
127.0
)
-
1
# Load the image into the array
# Load the image into the array
data
[
0
]
=
normalized_image_array
data
[
0
]
=
normalized_image_array
...
@@ -700,11 +688,9 @@ def activity_frame_groupings(video_name, frame_landmarks, frame_group_dict):
...
@@ -700,11 +688,9 @@ def activity_frame_groupings(video_name, frame_landmarks, frame_group_dict):
elif
label
==
class_labels
[
2
]:
elif
label
==
class_labels
[
2
]:
note_count
+=
1
note_count
+=
1
# increment the detection count
# increment the detection count
detection_count
+=
1
detection_count
+=
1
# finding the time landmark that the current frame is in
# finding the time landmark that the current frame is in
for
i
in
frame_landmarks
:
for
i
in
frame_landmarks
:
index
=
frame_landmarks
.
index
(
i
)
index
=
frame_landmarks
.
index
(
i
)
...
@@ -718,14 +704,11 @@ def activity_frame_groupings(video_name, frame_landmarks, frame_group_dict):
...
@@ -718,14 +704,11 @@ def activity_frame_groupings(video_name, frame_landmarks, frame_group_dict):
if
(
frame_count
>=
i
)
&
(
frame_count
<
next_value
):
if
(
frame_count
>=
i
)
&
(
frame_count
<
next_value
):
frame_name
=
"{}-{}"
.
format
(
i
,
next_value
)
frame_name
=
"{}-{}"
.
format
(
i
,
next_value
)
frame_group_dict
[
frame_name
][
'phone_count'
]
+=
phone_count
frame_group_dict
[
frame_name
][
'phone_count'
]
+=
phone_count
frame_group_dict
[
frame_name
][
'listen_count'
]
+=
listen_count
frame_group_dict
[
frame_name
][
'listen_count'
]
+=
listen_count
frame_group_dict
[
frame_name
][
'note_count'
]
+=
note_count
frame_group_dict
[
frame_name
][
'note_count'
]
+=
note_count
frame_group_dict
[
frame_name
][
'detection_count'
]
+=
detection_count
frame_group_dict
[
frame_name
][
'detection_count'
]
+=
detection_count
# increment the frame count
# increment the frame count
frame_count
+=
1
frame_count
+=
1
...
@@ -764,6 +747,83 @@ def activity_frame_groupings(video_name, frame_landmarks, frame_group_dict):
...
@@ -764,6 +747,83 @@ def activity_frame_groupings(video_name, frame_landmarks, frame_group_dict):
# print('frame group dict: ', frame_group_dict)
# print('frame group dict: ', frame_group_dict)
activity_labels
=
[
'phone_perct'
,
'listen_perct'
,
'note_perct'
]
activity_labels
=
[
'phone_perct'
,
'listen_perct'
,
'note_perct'
]
# return the dictionary
# return the dictionary
return
frame_group_dict
,
activity_labels
return
frame_group_dict
,
activity_labels
# this section will handle saving activity entities to the database
def
save_frame_recognition
(
video_name
):
# retrieve the lecture activity id
lec_activity
=
LectureActivity
.
objects
.
filter
(
lecture_video_id__video_name
=
video_name
)
lec_activity_ser
=
LectureActivitySerializer
(
lec_activity
,
many
=
True
)
lec_activity_data
=
lec_activity_ser
.
data
[
0
]
lec_activity_id
=
lec_activity_data
[
'id'
]
# create a new lecture activity frame detections id
last_lec_activity_frame_recognitions
=
LectureActivityFrameRecognitions
.
objects
.
order_by
(
'lecture_activity_frame_recognition_id'
)
.
last
()
new_lecture_activity_frame_recognitions_id
=
"LAFR00001"
if
(
last_lec_activity_frame_recognitions
is
None
)
else
\
ig
.
generate_new_id
(
last_lec_activity_frame_recognitions
.
lecture_activity_frame_recognition_id
)
# calculate the frame detections
frame_detections
=
get_frame_activity_recognition
(
video_name
)
frame_recognition_details
=
[]
# save the new lecture activity frame recognitions
for
detection
in
frame_detections
:
lec_activity_frame_recognition_details
=
LectureActivityFrameRecognitionDetails
()
lec_activity_frame_recognition_details
.
frame_name
=
detection
[
'frame_name'
]
lec_activity_frame_recognition_details
.
phone_perct
=
detection
[
'phone_perct'
]
lec_activity_frame_recognition_details
.
listen_perct
=
detection
[
'listening_perct'
]
lec_activity_frame_recognition_details
.
note_perct
=
detection
[
'note_perct'
]
frame_recognition_details
.
append
(
lec_activity_frame_recognition_details
)
lec_activity_frame_recognitions
=
LectureActivityFrameRecognitions
()
lec_activity_frame_recognitions
.
lecture_activity_frame_recognition_id
=
new_lecture_activity_frame_recognitions_id
lec_activity_frame_recognitions
.
lecture_activity_id_id
=
lec_activity_id
lec_activity_frame_recognitions
.
frame_recognition_details
=
frame_recognition_details
lec_activity_frame_recognitions
.
save
()
# now return the frame detections
return
frame_detections
# this method will save the activity frame groupings to the database
def
save_frame_groupings
(
video_name
,
frame_landmarks
,
frame_group_dict
):
frame_group_percentages
,
activity_labels
=
activity_frame_groupings
(
video_name
,
frame_landmarks
,
frame_group_dict
)
# save the frame group details into db
last_lec_activity_frame_grouping
=
LectureActivityFrameGroupings
.
objects
.
order_by
(
'lecture_activity_frame_groupings_id'
)
.
last
()
new_lecture_activity_frame_grouping_id
=
"LAFG00001"
if
(
last_lec_activity_frame_grouping
is
None
)
else
\
ig
.
generate_new_id
(
last_lec_activity_frame_grouping
.
lecture_activity_frame_groupings_id
)
# retrieve the lecture activity id
lec_activity
=
LectureActivity
.
objects
.
filter
(
lecture_video_id__video_name
=
video_name
)
lec_activity_ser
=
LectureActivitySerializer
(
lec_activity
,
many
=
True
)
lec_activity_id
=
lec_activity_ser
.
data
[
0
][
'id'
]
# create the frame group details
frame_group_details
=
[]
for
key
in
frame_group_percentages
.
keys
():
# create an object of type 'LectureActivityFrameGroupDetails'
lec_activity_frame_group_details
=
LectureActivityFrameGroupDetails
()
lec_activity_frame_group_details
.
frame_group
=
key
lec_activity_frame_group_details
.
frame_group_percentages
=
frame_group_percentages
[
key
]
frame_group_details
.
append
(
lec_activity_frame_group_details
)
new_lec_activity_frame_groupings
=
LectureActivityFrameGroupings
()
new_lec_activity_frame_groupings
.
lecture_activity_frame_groupings_id
=
new_lecture_activity_frame_grouping_id
new_lec_activity_frame_groupings
.
lecture_activity_id_id
=
lec_activity_id
new_lec_activity_frame_groupings
.
frame_group_details
=
frame_group_details
# save
new_lec_activity_frame_groupings
.
save
()
FirstApp/logic/head_gaze_estimation.py
View file @
765ece4e
...
@@ -16,6 +16,10 @@ import os
...
@@ -16,6 +16,10 @@ import os
import
shutil
import
shutil
import
math
import
math
from
..MongoModels
import
*
from
..serializers
import
*
from
.
import
id_generator
as
ig
def
get_2d_points
(
img
,
rotation_vector
,
translation_vector
,
camera_matrix
,
val
):
def
get_2d_points
(
img
,
rotation_vector
,
translation_vector
,
camera_matrix
,
val
):
"""Return the 3D points present as 2D for making annotation box"""
"""Return the 3D points present as 2D for making annotation box"""
...
@@ -846,3 +850,84 @@ def gaze_estimation_frame_groupings(video_name, frame_landmarks, frame_group_dic
...
@@ -846,3 +850,84 @@ def gaze_estimation_frame_groupings(video_name, frame_landmarks, frame_group_dic
# return the dictionary
# return the dictionary
return
frame_group_dict
,
labels
return
frame_group_dict
,
labels
# this section will handle some database operations
def
save_frame_detections
(
video_name
):
# retrieve the lecture emotion id
lec_gaze
=
LectureGazeEstimation
.
objects
.
filter
(
lecture_video_id__video_name
=
video_name
)
lec_gaze_ser
=
LectureGazeEstimationSerializer
(
lec_gaze
,
many
=
True
)
lec_gaze_data
=
lec_gaze_ser
.
data
[
0
]
lec_gaze_id
=
lec_gaze_data
[
'id'
]
# create a new lecture activity frame detections id
last_lec_gaze_frame_recognitions
=
LectureGazeFrameRecognitions
.
objects
.
order_by
(
'lecture_gaze_frame_recognition_id'
)
.
last
()
new_lecture_gaze_frame_recognitions_id
=
"LGFR00001"
if
(
last_lec_gaze_frame_recognitions
is
None
)
else
\
ig
.
generate_new_id
(
last_lec_gaze_frame_recognitions
.
lecture_gaze_frame_recognition_id
)
# calculate the frame detections
frame_detections
,
frame_rate
=
get_lecture_gaze_esrimation_for_frames
(
video_name
)
# to be added to the field 'frame_recognition_details' in the Lecture Gaze Frame Recordings
frame_recognition_details
=
[]
# save the new lecture activity frame recognitions
for
detection
in
frame_detections
:
lec_gaze_frame_recognition_details
=
LectureGazeFrameRecognitionDetails
()
lec_gaze_frame_recognition_details
.
frame_name
=
detection
[
'frame_name'
]
lec_gaze_frame_recognition_details
.
upright_perct
=
detection
[
'upright_perct'
]
lec_gaze_frame_recognition_details
.
upleft_perct
=
detection
[
'upleft_perct'
]
lec_gaze_frame_recognition_details
.
downright_perct
=
detection
[
'downright_perct'
]
lec_gaze_frame_recognition_details
.
downleft_perct
=
detection
[
'downleft_perct'
]
lec_gaze_frame_recognition_details
.
front_perct
=
detection
[
'front_perct'
]
frame_recognition_details
.
append
(
lec_gaze_frame_recognition_details
)
lec_gaze_frame_recognitions
=
LectureGazeFrameRecognitions
()
lec_gaze_frame_recognitions
.
lecture_gaze_frame_recognition_id
=
new_lecture_gaze_frame_recognitions_id
lec_gaze_frame_recognitions
.
lecture_gaze_id_id
=
lec_gaze_id
lec_gaze_frame_recognitions
.
frame_recognition_details
=
frame_recognition_details
lec_gaze_frame_recognitions
.
save
()
# now return the frame recognitions
return
frame_detections
# this method will save gaze frame groupings to the database
def
save_frame_groupings
(
video_name
,
frame_landmarks
,
frame_group_dict
):
frame_group_percentages
,
gaze_labels
=
gaze_estimation_frame_groupings
(
video_name
,
frame_landmarks
,
frame_group_dict
)
# save the frame group details into db
last_lec_gaze_frame_grouping
=
LectureGazeFrameGroupings
.
objects
.
order_by
(
'lecture_gaze_frame_groupings_id'
)
.
last
()
new_lecture_gaze_frame_grouping_id
=
"LGFG00001"
if
(
last_lec_gaze_frame_grouping
is
None
)
else
\
ig
.
generate_new_id
(
last_lec_gaze_frame_grouping
.
lecture_gaze_frame_groupings_id
)
# retrieve the lecture activity id
lec_gaze
=
LectureGazeEstimation
.
objects
.
filter
(
lecture_video_id__video_name
=
video_name
)
lec_gaze_ser
=
LectureGazeEstimationSerializer
(
lec_gaze
,
many
=
True
)
lec_gaze_id
=
lec_gaze_ser
.
data
[
0
][
'id'
]
# create the frame group details
frame_group_details
=
[]
for
key
in
frame_group_percentages
.
keys
():
# create an object of type 'LectureActivityFrameGroupDetails'
lec_gaze_frame_group_details
=
LectureGazeFrameGroupDetails
()
lec_gaze_frame_group_details
.
frame_group
=
key
lec_gaze_frame_group_details
.
frame_group_percentages
=
frame_group_percentages
[
key
]
frame_group_details
.
append
(
lec_gaze_frame_group_details
)
new_lec_gaze_frame_groupings
=
LectureGazeFrameGroupings
()
new_lec_gaze_frame_groupings
.
lecture_gaze_frame_groupings_id
=
new_lecture_gaze_frame_grouping_id
new_lec_gaze_frame_groupings
.
lecture_gaze_id_id
=
lec_gaze_id
new_lec_gaze_frame_groupings
.
frame_group_details
=
frame_group_details
# save
new_lec_gaze_frame_groupings
.
save
()
FirstApp/logic/video_extraction.py
View file @
765ece4e
...
@@ -3,6 +3,11 @@ import cv2
...
@@ -3,6 +3,11 @@ import cv2
import
shutil
import
shutil
import
datetime
import
datetime
from
FirstApp.MongoModels
import
*
from
FirstApp.serializers
import
*
from
.
import
id_generator
as
ig
def
VideoExtractor
(
request
):
def
VideoExtractor
(
request
):
BASE_DIR
=
os
.
path
.
dirname
(
os
.
path
.
dirname
(
os
.
path
.
dirname
(
os
.
path
.
abspath
(
__file__
))))
BASE_DIR
=
os
.
path
.
dirname
(
os
.
path
.
dirname
(
os
.
path
.
dirname
(
os
.
path
.
abspath
(
__file__
))))
...
@@ -193,3 +198,99 @@ def getFrameLandmarks(video_name, category):
...
@@ -193,3 +198,99 @@ def getFrameLandmarks(video_name, category):
'front_count'
:
0
,
'detection_count'
:
0
}
'front_count'
:
0
,
'detection_count'
:
0
}
return
frame_landmarks
,
frame_group_dict
return
frame_landmarks
,
frame_group_dict
# this section will handle some database operations
def
save_time_landmarks
(
video_name
):
last_lec_video_time_landmarks
=
LectureVideoTimeLandmarks
.
objects
.
order_by
(
'lecture_video_time_landmarks_id'
)
.
last
()
new_lecture_video_time_landmarks_id
=
"LVTL00001"
if
(
last_lec_video_time_landmarks
is
None
)
else
\
ig
.
generate_new_id
(
last_lec_video_time_landmarks
.
lecture_video_time_landmarks_id
)
# retrieve lecture video details
lec_video
=
LectureVideo
.
objects
.
filter
(
video_name
=
video_name
)
lec_video_ser
=
LectureVideoSerializer
(
lec_video
,
many
=
True
)
lec_video_id
=
lec_video_ser
.
data
[
0
][
'id'
]
# save the landmark details in the db
time_landmarks
=
getTimeLandmarks
(
video_name
)
db_time_landmarks
=
[]
# loop through the time landmarks
for
landmark
in
time_landmarks
:
landmark_obj
=
Landmarks
()
landmark_obj
.
landmark
=
landmark
db_time_landmarks
.
append
(
landmark_obj
)
new_lec_video_time_landmarks
=
LectureVideoTimeLandmarks
()
new_lec_video_time_landmarks
.
lecture_video_time_landmarks_id
=
new_lecture_video_time_landmarks_id
new_lec_video_time_landmarks
.
lecture_video_id_id
=
lec_video_id
new_lec_video_time_landmarks
.
time_landmarks
=
db_time_landmarks
new_lec_video_time_landmarks
.
save
()
# this method will save frame landmarks to the database
def
save_frame_landmarks
(
video_name
):
# retrieve the previous lecture video frame landmarks details
last_lec_video_frame_landmarks
=
LectureVideoFrameLandmarks
.
objects
.
order_by
(
'lecture_video_frame_landmarks_id'
)
.
last
()
new_lecture_video_frame_landmarks_id
=
"LVFL00001"
if
(
last_lec_video_frame_landmarks
is
None
)
else
\
ig
.
generate_new_id
(
last_lec_video_frame_landmarks
.
lecture_video_frame_landmarks_id
)
frame_landmarks
,
frame_group_dict
=
getFrameLandmarks
(
video_name
,
"Activity"
)
# retrieve lecture video details
lec_video
=
LectureVideo
.
objects
.
filter
(
video_name
=
video_name
)
lec_video_ser
=
LectureVideoSerializer
(
lec_video
,
many
=
True
)
lec_video_id
=
lec_video_ser
.
data
[
0
][
'id'
]
# save the frame landmarks details into db
db_frame_landmarks
=
[]
for
landmark
in
frame_landmarks
:
landmark_obj
=
Landmarks
()
landmark_obj
.
landmark
=
landmark
db_frame_landmarks
.
append
(
landmark_obj
)
new_lec_video_frame_landmarks
=
LectureVideoFrameLandmarks
()
new_lec_video_frame_landmarks
.
lecture_video_frame_landmarks_id
=
new_lecture_video_frame_landmarks_id
new_lec_video_frame_landmarks
.
lecture_video_id_id
=
lec_video_id
new_lec_video_frame_landmarks
.
frame_landmarks
=
db_frame_landmarks
new_lec_video_frame_landmarks
.
save
()
# now return the frame landmarks and the frame group dictionary
return
frame_landmarks
,
frame_group_dict
# this method will retrieve the frame landmarks from the database
def
get_frame_landmarks
(
video_name
):
frame_landmarks
=
[]
# retrieve frame landmarks from db
lec_video_frame_landmarks
=
LectureVideoFrameLandmarks
.
objects
.
filter
(
lecture_video_id__video_name
=
video_name
)
lec_video_frame_landmarks_ser
=
LectureVideoFrameLandmarksSerializer
(
lec_video_frame_landmarks
,
many
=
True
)
lec_video_frame_landmarks_data
=
lec_video_frame_landmarks_ser
.
data
[
0
]
retrieved_frame_landmarks
=
lec_video_frame_landmarks_data
[
"frame_landmarks"
]
# creating a new list to display in the frontend
for
landmark
in
retrieved_frame_landmarks
:
frame_landmarks
.
append
(
landmark
[
'landmark'
])
# now return the frame landmarks
return
frame_landmarks
\ No newline at end of file
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