Committing the partial implementation of the workflow for lecture student behavior.

FirstApp/emotion_detector.py

@@ -5,11 +5,17 @@ from keras.preprocessing import image
 import cv2
 import os
 import numpy as np
+
+from .MongoModels import *
 from . models import VideoMeta
 from . logic import custom_sorter as cs
+from .logic import id_generator as ig
 
 
 # emotion recognition method
+from .serializers import LectureEmotionSerializer
+
+
 def emotion_recognition(classifier, face_classifier, image):
     label = ""
     class_labels = ['Angry', 'Happy', 'Neutral', 'Sad', 'Surprise']
@@ -548,3 +554,82 @@ def emotion_frame_groupings(video_name, frame_landmarks, frame_group_dict):
 
     # return the dictionary
     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/head_gaze_estimation.py

@@ -16,6 +16,10 @@ import os
 import shutil
 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):
     """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
 
     # return the dictionary
     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

@@ -3,6 +3,11 @@ import cv2
 import shutil
 import datetime
 
+from FirstApp.MongoModels import *
+from FirstApp.serializers import *
+from . import id_generator as ig
+
+
 def VideoExtractor(request):
 
     BASE_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
@@ -193,3 +198,99 @@ def getFrameLandmarks(video_name, category):
                                       'front_count': 0, 'detection_count': 0}
 
     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