Initial commit to the monitoring_student_behavior branch

FirstApp/MongoModels.py

@@ -128,7 +128,7 @@ class LectureActivity(models.Model):
 # Lecture emotion report
 class LectureEmotionReport(models.Model):
     lecture_emotion_id = models.CharField(max_length=10)
-    lecture_video_id = models.ForeignKey(LectureVideo, on_delete=models.CASCADE)
+    lecture_video_id = models.ForeignKey(LectureVideo, on_delete=models.CASCADE, default=0)
     happy_perct = models.DecimalField(default=0.0, max_digits=3, decimal_places=1)
     sad_perct = models.DecimalField(default=0.0, max_digits=3, decimal_places=1)
     angry_perct = models.DecimalField(default=0.0, max_digits=3, decimal_places=1)
@@ -143,6 +143,14 @@ class LectureEmotionReport(models.Model):
 
 # POSE section
 # lecture pose estimation
-class LecturePoseEstimation(models.Model):
-    lecture_pose_id = models.CharField(max_length=10)
-    lecture_video_id = models.ForeignKey(LectureVideo, on_delete=models.CASCADE)
\ No newline at end of file
+class LectureGazeEstimation(models.Model):
+    lecture_gaze_id = models.CharField(max_length=10)
+    lecture_video_id = models.ForeignKey(LectureVideo, on_delete=models.CASCADE)
+    looking_up_and_right_perct = models.DecimalField(default=0.0, max_digits=3, decimal_places=1)
+    looking_up_and_left_perct = models.DecimalField(default=0.0, max_digits=3, decimal_places=1)
+    looking_down_and_right_perct = models.DecimalField(default=0.0, max_digits=3, decimal_places=1)
+    looking_down_and_left_perct = models.DecimalField(default=0.0, max_digits=3, decimal_places=1)
+    looking_front_perct = models.DecimalField(default=0.0, max_digits=3, decimal_places=1)
+
+    def __str__(self):
+        return self.lecture_gaze_id
\ No newline at end of file

FirstApp/emotion_detector.py

@@ -66,7 +66,6 @@ def detect_emotion(video):
         gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
         faces = face_classifier.detectMultiScale(gray,1.3,5)
 
-        print('number of faces: ', len(faces))
 
         for (x, y, w, h) in faces:
             cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
@@ -104,13 +103,9 @@ def detect_emotion(video):
 
                 elif (label == 'Sad'):
                     count_sad += 1
-                    # path = os.path.join(BASE_DIR, 'static\\images\\Sad')
-                    # cv2.imwrite(os.path.join(path, 'Sad-{0}.jpg'.format(count)), frame)
 
                 elif (label == 'Surprise'):
                     count_surprise += 1
-                    # path = os.path.join(BASE_DIR, 'static\\images\\Surprise')
-                    # cv2.imwrite(os.path.join(path, 'Surprise-{0}.jpg'.format(count)), frame)
 
                 label_position = (x, y)
                 # cv2.putText(frame, label, label_position, cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 255, 0), 3)
@@ -118,13 +113,9 @@ def detect_emotion(video):
             else:
                 cv2.putText(frame, 'No Face Found', (20, 60), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 255, 0), 3)
 
-        # cv2.imshow('Emotion Detector',frame)
         count_frames += 1
-        # if cv2.waitKey(1) & 0xFF == ord('q'):
-        #     break
 
     # setting up the counted values
-
     meta_data.frame_count = count_frames
     meta_data.happy_count = count_happy
     meta_data.sad_count = count_sad
@@ -328,11 +319,11 @@ def get_frame_emotion_recognition(video_name):
 
 
         # calculating the percentages for the frame
-        happy_perct = float(happy_count / detection_count) * 100
-        sad_perct = float(sad_count / detection_count) * 100
-        angry_perct = float(angry_count / detection_count) * 100
-        neutral_perct = float(neutral_count / detection_count) * 100
-        surprise_perct = float(surprise_count / detection_count) * 100
+        happy_perct = float(happy_count / detection_count) * 100 if detection_count > 0 else 0
+        sad_perct = float(sad_count / detection_count) * 100 if detection_count > 0 else 0
+        angry_perct = float(angry_count / detection_count) * 100 if detection_count > 0 else 0
+        neutral_perct = float(neutral_count / detection_count) * 100 if detection_count > 0 else 0
+        surprise_perct = float(surprise_count / detection_count) * 100 if detection_count > 0 else 0
 
         # this dictionary will be returned
         frame_details['happy_perct'] = happy_perct
@@ -348,4 +339,63 @@ def get_frame_emotion_recognition(video_name):
     sorted_activity_frame_recognitions = cs.custom_object_sorter(frame_emotion_recognitions)
 
     # return the detected frame percentages
-    return sorted_activity_frame_recognitions
\ No newline at end of file
+    return sorted_activity_frame_recognitions
+
+
+# this method will retrieve student activity summary for given time period
+def get_student_emotion_summary_for_period(emotions):
+
+    # declare variables to add percentage values
+    happy_perct_combined = 0.0
+    sad_perct_combined = 0.0
+    angry_perct_combined = 0.0
+    disgust_perct_combined = 0.0
+    surprise_perct_combined = 0.0
+    neutral_perct_combined = 0.0
+
+    # get the number of activties to calculate average
+    no_of_emotions = len(emotions)
+
+    individual_lec_emotions = []
+
+    emotion_labels = ["happy_perct", "sad_perct", "angry_perct", "disgust_perct", "surprise_perct", "neutral_perct"]
+
+    # iterate through the activities
+    for emotion in emotions:
+
+        individual_emotion = {}
+        individual_emotion["happy_perct"] = float(emotion['happy_perct'])
+        individual_emotion["sad_perct"] = float(emotion['sad_perct'])
+        individual_emotion["angry_perct"] = float(emotion['angry_perct'])
+        individual_emotion["disgust_perct"] = float(emotion['disgust_perct'])
+        individual_emotion["surprise_perct"] = float(emotion['surprise_perct'])
+        individual_emotion["neutral_perct"] = float(emotion['neutral_perct'])
+
+        happy_perct_combined += float(emotion['happy_perct'])
+        sad_perct_combined += float(emotion['sad_perct'])
+        angry_perct_combined += float(emotion['angry_perct'])
+        disgust_perct_combined += float(emotion['disgust_perct'])
+        surprise_perct_combined += float(emotion['surprise_perct'])
+        neutral_perct_combined += float(emotion['neutral_perct'])
+
+        # append to the list
+        individual_lec_emotions.append(individual_emotion)
+
+
+    # calculate the average percentages
+    happy_average_perct = round((happy_perct_combined / no_of_emotions), 1)
+    sad_average_perct = round((sad_perct_combined / no_of_emotions), 1)
+    angry_average_perct = round((angry_perct_combined / no_of_emotions), 1)
+    disgust_average_perct = round((disgust_perct_combined / no_of_emotions), 1)
+    surprise_average_perct = round((surprise_perct_combined / no_of_emotions), 1)
+    neutral_average_perct = round((neutral_perct_combined / no_of_emotions), 1)
+
+    percentages = {}
+    percentages["happy_perct"] = happy_average_perct
+    percentages["sad_perct"] = sad_average_perct
+    percentages["angry_perct"] = angry_average_perct
+    percentages["disgust_perct"] = disgust_average_perct
+    percentages["surprise_perct"] = surprise_average_perct
+    percentages["neutral_perct"] = neutral_average_perct
+
+    return percentages, individual_lec_emotions, emotion_labels
\ No newline at end of file

FirstApp/logic/activity_recognition.py

@@ -51,7 +51,7 @@ def activity_recognition(video_path):
     note_taking_count = 0
     listening_count = 0
 
-    # video activity didrectory
+    # video activity directory
     VIDEO_ACTIVITY_DIR = os.path.join(ACTIVITY_DIR, video_path)
 
     # creating the directory for the video
@@ -75,7 +75,6 @@ def activity_recognition(video_path):
         os.mkdir(FRAME_DIR)
 
         image = cv2.resize(image, size)
-        # image = ImageOps.fit(image, size, Image.ANTIALIAS)
         detections = person_detection(image, net)
 
         image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
@@ -130,10 +129,10 @@ def activity_recognition(video_path):
     p.write("yes")
 
     # calculating the percentages for each label
-    phone_perct = float(phone_checking_count / total_detections) * 100
-    talking_perct = float(talking_count / total_detections) * 100
-    note_perct = float(note_taking_count / total_detections) * 100
-    listening_perct = float(listening_count / total_detections) * 100
+    phone_perct = float(phone_checking_count / total_detections) * 100 if total_detections > 0 else 0
+    talking_perct = float(talking_count / total_detections) * 100 if total_detections > 0 else 0
+    note_perct = float(note_taking_count / total_detections) * 100 if total_detections > 0 else 0
+    listening_perct = float(listening_count / total_detections) * 100 if total_detections > 0 else 0
 
     # assigning the percentages to the dictionary
     percentages["phone_perct"] = phone_perct
@@ -571,4 +570,48 @@ def get_individual_student_evaluation(video_name, student_name):
     percentages['writing_perct'] = writing_perct
     percentages['listening_perct'] = listening_perct
 
-    return percentages
\ No newline at end of file
+    return percentages
+
+
+# this method will retrieve student activity summary for given time period
+def get_student_activity_summary_for_period(activities):
+
+    # declare variables to add percentage values
+    phone_checking_perct_combined = 0.0
+    listening_perct_combined = 0.0
+    note_taking_perct_combined = 0.0
+
+    # get the number of activties to calculate average
+    no_of_activities = len(activities)
+
+    individual_lec_activities = []
+
+    activity_labels = ["phone_perct", "listening_perct", "writing_perct"]
+
+    # iterate through the activities
+    for activity in activities:
+
+        individual_activity = {}
+        individual_activity["phone_perct"] = float(activity['phone_perct'])
+        individual_activity["listening_perct"] = float(activity['listening_perct'])
+        individual_activity["writing_perct"] = float(activity['writing_perct'])
+
+        phone_checking_perct_combined += float(activity['phone_perct'])
+        listening_perct_combined += float(activity['listening_perct'])
+        note_taking_perct_combined += float(activity['writing_perct'])
+
+        # append to the list
+        individual_lec_activities.append(individual_activity)
+
+
+    # calculate the average percentages
+    phone_checking_average_perct = round((phone_checking_perct_combined / no_of_activities), 1)
+    listening_average_perct = round((listening_perct_combined / no_of_activities), 1)
+    note_taking_average_perct = round((note_taking_perct_combined / no_of_activities), 1)
+
+    percentages = {}
+    percentages["phone_perct"] = phone_checking_average_perct
+    percentages["listening_perct"] = listening_average_perct
+    percentages["writing_perct"] = note_taking_average_perct
+
+    return percentages, individual_lec_activities, activity_labels
\ No newline at end of file

FirstApp/logic/face_detector.py

+# -*- coding: utf-8 -*-
+"""
+Created on Wed Jul 29 17:52:00 2020
+
+@author: hp
+"""
+
+import cv2
+import numpy as np
+import os
+
+def get_face_detector(modelFile = "models/res10_300x300_ssd_iter_140000.caffemodel",
+                      configFile = "models/deploy.prototxt"):
+    """
+    Get the face detection caffe model of OpenCV's DNN module
+    
+    Parameters
+    ----------
+    modelFile : string, optional
+        Path to model file. The default is "models/res10_300x300_ssd_iter_140000.caffemodel".
+    configFile : string, optional
+        Path to config file. The default is "models/deploy.prototxt".
+
+    Returns
+    -------
+    model : dnn_Net
+
+    """
+
+    BASE_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+    CLASSIFIER_DIR = os.path.join(BASE_DIR, "FirstApp\\classifiers")
+
+    modelFile = os.path.join(CLASSIFIER_DIR, "res10_300x300_ssd_iter_140000.caffemodel")
+    configFile = os.path.join(CLASSIFIER_DIR, "deploy.prototxt")
+    model = cv2.dnn.readNetFromCaffe(configFile, modelFile)
+
+    return model
+
+def find_faces(img, model):
+    """
+    Find the faces in an image
+    
+    Parameters
+    ----------
+    img : np.uint8
+        Image to find faces from
+    model : dnn_Net
+        Face detection model
+
+    Returns
+    -------
+    faces : list
+        List of coordinates of the faces detected in the image
+
+    """
+    h, w = img.shape[:2]
+    blob = cv2.dnn.blobFromImage(cv2.resize(img, (300, 300)), 1.0,
+	(300, 300), (104.0, 177.0, 123.0))
+    model.setInput(blob)
+    res = model.forward()
+    faces = []
+    for i in range(res.shape[2]):
+        confidence = res[0, 0, i, 2]
+        if confidence > 0.5:
+            box = res[0, 0, i, 3:7] * np.array([w, h, w, h])
+            (x, y, x1, y1) = box.astype("int")
+            faces.append([x, y, x1, y1])
+    return faces
+
+def draw_faces(img, faces):
+    """
+    Draw faces on image
+
+    Parameters
+    ----------
+    img : np.uint8
+        Image to draw faces on
+    faces : List of face coordinates
+        Coordinates of faces to draw
+
+    Returns
+    -------
+    None.
+
+    """
+    for x, y, x1, y1 in faces:
+        cv2.rectangle(img, (x, y), (x1, y1), (0, 0, 255), 3)
+        
\ No newline at end of file

FirstApp/logic/face_landmarks.py

+# -*- coding: utf-8 -*-
+"""
+Created on Wed Jul 29 19:47:08 2020
+
+@author: hp
+"""
+
+import cv2
+import numpy as np
+import tensorflow as tf
+from tensorflow import keras
+import os
+
+
+def get_landmark_model():
+    """
+    Get the facial landmark model. 
+    Original repository: https://github.com/yinguobing/cnn-facial-landmark
+
+    Parameters
+    ----------
+    saved_model : string, optional
+        Path to facial landmarks model. The default is 'models/pose_model'.
+
+    Returns
+    -------
+    model : Tensorflow model
+        Facial landmarks model
+
+    """
+    # define the location of the pose model
+    BASE_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+    saved_model = os.path.join(BASE_DIR, "FirstApp\\classifiers\\pose_model")
+
+    model = keras.models.load_model(saved_model)
+    return model
+
+def get_square_box(box):
+    """Get a square box out of the given box, by expanding it."""
+    left_x = box[0]
+    top_y = box[1]
+    right_x = box[2]
+    bottom_y = box[3]
+
+    box_width = right_x - left_x
+    box_height = bottom_y - top_y
+
+    # Check if box is already a square. If not, make it a square.
+    diff = box_height - box_width
+    delta = int(abs(diff) / 2)
+
+    if diff == 0:                   # Already a square.
+        return box
+    elif diff > 0:                  # Height > width, a slim box.
+        left_x -= delta
+        right_x += delta
+        if diff % 2 == 1:
+            right_x += 1
+    else:                           # Width > height, a short box.
+        top_y -= delta
+        bottom_y += delta
+        if diff % 2 == 1:
+            bottom_y += 1
+
+    # Make sure box is always square.
+    assert ((right_x - left_x) == (bottom_y - top_y)), 'Box is not square.'
+
+    return [left_x, top_y, right_x, bottom_y]
+
+def move_box(box, offset):
+        """Move the box to direction specified by vector offset"""
+        left_x = box[0] + offset[0]
+        top_y = box[1] + offset[1]
+        right_x = box[2] + offset[0]
+        bottom_y = box[3] + offset[1]
+        return [left_x, top_y, right_x, bottom_y]
+
+def detect_marks(img, model, face):
+    """
+    Find the facial landmarks in an image from the faces
+
+    Parameters
+    ----------
+    img : np.uint8
+        The image in which landmarks are to be found
+    model : Tensorflow model
+        Loaded facial landmark model
+    face : list
+        Face coordinates (x, y, x1, y1) in which the landmarks are to be found
+
+    Returns
+    -------
+    marks : numpy array
+        facial landmark points
+
+    """
+
+    offset_y = int(abs((face[3] - face[1]) * 0.1))
+    box_moved = move_box(face, [0, offset_y])
+    facebox = get_square_box(box_moved)
+
+    # reassigning the facebox values
+    facebox[0] = facebox[0] if facebox[0] > 0 else 0
+    facebox[1] = facebox[1] if facebox[1] > 0 else 0
+    facebox[2] = facebox[2] if facebox[2] > 0 else 0
+    facebox[3] = facebox[3] if facebox[3] > 0 else 0
+
+    # draw a bounding box
+    cv2.rectangle(img, (facebox[0], facebox[1]), (facebox[2], facebox[3]), (0, 255, 0), 2)
+
+    face_img = img[facebox[1]: facebox[3],
+                     facebox[0]: facebox[2]]
+
+    marks = np.zeros((68, 2))
+
+    # if the list length is more than 0
+    if len(face_img) > 0:
+        face_img = cv2.resize(face_img, (128, 128))
+        face_img = cv2.cvtColor(face_img, cv2.COLOR_BGR2RGB)
+
+        # # Actual detection.
+        predictions = model.signatures["predict"](
+            tf.constant([face_img], dtype=tf.uint8))
+
+        # Convert predictions to landmarks.
+        marks = np.array(predictions['output']).flatten()[:136]
+        marks = np.reshape(marks, (-1, 2))
+
+        marks *= (facebox[2] - facebox[0])
+        marks[:, 0] += facebox[0]
+        marks[:, 1] += facebox[1]
+        marks = marks.astype(np.uint)
+
+
+    # return marks
+
+    # return detected facial marks and face coordinates
+    return marks, facebox
+    
+
+
+def draw_marks(image, marks, color=(0, 255, 0)):
+    """
+    Draw the facial landmarks on an image
+
+    Parameters
+    ----------
+    image : np.uint8
+        Image on which landmarks are to be drawn.
+    marks : list or numpy array
+        Facial landmark points
+    color : tuple, optional
+        Color to which landmarks are to be drawn with. The default is (0, 255, 0).
+
+    Returns
+    -------
+    None.
+
+    """
+    for mark in marks:
+        cv2.circle(image, (mark[0], mark[1]), 2, color, -1, cv2.LINE_AA)
\ No newline at end of file

FirstApp/logic/pdf_file_generator.py

+import jinja2 as ji
+import pdfkit
+import os
+# from DemoProject import jinja2
+from integrated_slpes import jinja2
+
+def generate_pdf_file(object):
+
+    # templateLoader = jinja2.FileSystemLoader(searchpath="../")
+    BASE_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+    TEMPLATE_DIR = os.path.join(BASE_DIR, "FirstApp\\templates\\FirstApp")
+    HTML_PATH = os.path.join(TEMPLATE_DIR, "pdf_template_1.html")
+    PDF_DIRECTORY = os.path.join(BASE_DIR, "FirstApp\\files")
+    templateLoader = ji.FileSystemLoader(TEMPLATE_DIR)
+    new_env = jinja2.environment()
+    templateEnv = jinja2.Environment(loader=templateLoader)
+    TEMPLATE_FILE = "pdf_template.html"
+    template = templateEnv.get_template(TEMPLATE_FILE)
+
+    print('variables: ', templateEnv.globals['dict'])
+
+
+    # render the template
+    outputText = template.render(lecturer_name=object['lecturer_name'], subject=object['subject_name'], date=object['date'], static=new_env.globals['static'])
+    html_file = open(HTML_PATH, "w")
+    html_file.write(outputText)
+    html_file.close()
+
+    # create a new pdf file path
+    NEW_PDF_PATH = os.path.join(PDF_DIRECTORY, "activity.pdf")
+
+    asset_path = os.path.join('D:/SLIIT/Year 4/CDAP/project/2020-101/assets/FirstApp/css/sb-admin-2.min.css')
+    network_path = "file:/" + asset_path
+
+    # options = {'enable-local-file-access': network_path}
+    options = {'enable-local-file-access': asset_path, 'load-error-handling': 'ignore'}
+
+    # create a new pdf file
+    pdfkit.from_file(HTML_PATH, NEW_PDF_PATH, options=options)
+

FirstApp/logic/posenet_calculation.py

@@ -117,10 +117,6 @@ def calculate_pose_estimation_for_student(video_name, student, poses):
 
                 left_upper_x = 0 if (middle_x - fraction) < 0 else (middle_x - fraction)
 
-                print('head_y: ', head_y)
-                print('fraction: ', fraction)
-                print('distance: ', distance)
-                print('left_upper_x: ', left_upper_x)
 
                 # extract the new image
                 new_img = detection_img[head_y:head_y+fraction, left_upper_x:left_upper_x+distance]

FirstApp/migrations/0007_auto_20200825_1658.py

+# Generated by Django 2.2.11 on 2020-08-25 11:28
+
+import FirstApp.MongoModels
+from django.db import migrations, models
+import django.db.models.deletion
+import djongo.models.fields
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('FirstApp', '0006_lecturercredentials'),
+    ]
+
+    operations = [
+        migrations.RenameField(
+            model_name='lectureemotionreport',
+            old_name='lecture_id',
+            new_name='lecture_emotion_id',
+        ),
+        migrations.AlterField(
+            model_name='lectureemotionreport',
+            name='angry_perct',
+            field=models.DecimalField(decimal_places=1, default=0.0, max_digits=3),
+        ),
+        migrations.AlterField(
+            model_name='lectureemotionreport',
+            name='disgust_perct',
+            field=models.DecimalField(decimal_places=1, default=0.0, max_digits=3),
+        ),
+        migrations.AlterField(
+            model_name='lectureemotionreport',
+            name='happy_perct',
+            field=models.DecimalField(decimal_places=1, default=0.0, max_digits=3),
+        ),
+        migrations.AlterField(
+            model_name='lectureemotionreport',
+            name='neutral_perct',
+            field=models.DecimalField(decimal_places=1, default=0.0, max_digits=3),
+        ),
+        migrations.AlterField(
+            model_name='lectureemotionreport',
+            name='sad_perct',
+            field=models.DecimalField(decimal_places=1, default=0.0, max_digits=3),
+        ),
+        migrations.AlterField(
+            model_name='lectureemotionreport',
+            name='surprise_perct',
+            field=models.DecimalField(decimal_places=1, default=0.0, max_digits=3),
+        ),
+        migrations.CreateModel(
+            name='LectureVideo',
+            fields=[
+                ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
+                ('lecture_video_id', models.CharField(max_length=10)),
+                ('date', models.DateField()),
+                ('video_name', models.CharField(max_length=50)),
+                ('video_length', models.DurationField()),
+                ('lecturer', models.ForeignKey(default=0, on_delete=django.db.models.deletion.CASCADE, to='FirstApp.Lecturer')),
+                ('subject', models.ForeignKey(default=0, on_delete=django.db.models.deletion.CASCADE, to='FirstApp.Subject')),
+            ],
+        ),
+        migrations.CreateModel(
+            name='LectureGazeEstimation',
+            fields=[
+                ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
+                ('lecture_gaze_id', models.CharField(max_length=10)),
+                ('lecture_video_id',
+                 models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='FirstApp.LectureVideo')),
+            ],
+        ),
+        migrations.CreateModel(
+            name='LectureActivity',
+            fields=[
+                ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
+                ('lecture_activity_id', models.CharField(max_length=10)),
+                ('talking_perct', models.DecimalField(decimal_places=1, default=0.0, max_digits=3)),
+                ('listening_perct', models.DecimalField(decimal_places=1, default=0.0, max_digits=3)),
+                ('writing_perct', models.DecimalField(decimal_places=1, default=0.0, max_digits=3)),
+                ('phone_perct', models.DecimalField(decimal_places=1, default=0.0, max_digits=3)),
+                ('lecture_video_id', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='FirstApp.LectureVideo')),
+            ],
+        ),
+        migrations.CreateModel(
+            name='FacultyTimetable',
+            fields=[
+                ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
+                ('timetable_id', models.CharField(max_length=10)),
+                ('timetable', djongo.models.fields.ArrayField(model_container=FirstApp.MongoModels.DateTimeTable)),
+                ('faculty', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='FirstApp.Faculty')),
+            ],
+        ),
+        migrations.AddField(
+            model_name='lectureemotionreport',
+            name='lecture_video_id',
+            field=models.ForeignKey(default=0, on_delete=django.db.models.deletion.CASCADE, to='FirstApp.LectureVideo'),
+        ),
+    ]

FirstApp/migrations/0008_auto_20200825_1821.py

+# Generated by Django 2.2.11 on 2020-08-25 12:51
+
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('FirstApp', '0007_auto_20200825_1658'),
+    ]
+
+    operations = [
+        migrations.AddField(
+            model_name='lecturegazeestimation',
+            name='looking_down_and_left_perct',
+            field=models.DecimalField(decimal_places=1, default=0.0, max_digits=3),
+        ),
+        migrations.AddField(
+            model_name='lecturegazeestimation',
+            name='looking_down_and_right_perct',
+            field=models.DecimalField(decimal_places=1, default=0.0, max_digits=3),
+        ),
+        migrations.AddField(
+            model_name='lecturegazeestimation',
+            name='looking_front_perct',
+            field=models.DecimalField(decimal_places=1, default=0.0, max_digits=3),
+        ),
+        migrations.AddField(
+            model_name='lecturegazeestimation',
+            name='looking_up_and_left_perct',
+            field=models.DecimalField(decimal_places=1, default=0.0, max_digits=3),
+        ),
+        migrations.AddField(
+            model_name='lecturegazeestimation',
+            name='looking_up_and_right_perct',
+            field=models.DecimalField(decimal_places=1, default=0.0, max_digits=3),
+        ),
+    ]

FirstApp/serializers.py

@@ -226,3 +226,12 @@ class VideoMetaSerializer(serializers.ModelSerializer):
     class Meta:
         model = VideoMeta
         fields = '__all__'
+
+# lecture gaze serializer
+class LectureGazeEstimationSerializer(serializers.ModelSerializer):
+
+    lecture_video_id = LectureVideoSerializer()
+
+    class Meta:
+        model = LectureGazeEstimation
+        fields = '__all__'
\ No newline at end of file

FirstApp/templates/FirstApp/charts.html

@@ -3,6 +3,8 @@
 
 <head>
 
+  {% load static %}
+
   <meta charset="utf-8">
   <meta http-equiv="X-UA-Compatible" content="IE=edge">
   <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
@@ -12,11 +14,11 @@
   <title>SB Admin 2 - Charts</title>
 
   <!-- Custom fonts for this template-->
-  <link href="vendor/fontawesome-free/css/all.min.css" rel="stylesheet" type="text/css">
+  <link href="{% static 'FirstApp/vendor/fontawesome-free/css/all.min.css' %}" rel="stylesheet" type="text/css">
   <link href="https://fonts.googleapis.com/css?family=Nunito:200,200i,300,300i,400,400i,600,600i,700,700i,800,800i,900,900i" rel="stylesheet">
 
   <!-- Custom styles for this template-->
-  <link href="css/sb-admin-2.min.css" rel="stylesheet">
+  <link href="{% static 'FirstApp/css/sb-admin-2.min.css' %}" rel="stylesheet">
 
 </head>