Merge branch 'monitoring_student_behavior_IT17138000' into 'QA_RELEASE'

Monitoring student behavior it17138000

See merge request !24

FirstApp/emotion_detector.py

@@ -15,9 +15,7 @@ main methods include
 
 
 from tensorflow.keras.models import load_model
-from time import sleep
 from keras.preprocessing.image import img_to_array
-from keras.preprocessing import image
 import cv2
 import os
 import numpy as np

FirstApp/logic/activity_recognition.py

@@ -15,13 +15,10 @@ main methods include
 
 import tensorflow as tf
 import tensorflow.keras
-from PIL import Image, ImageOps
 import numpy as np
 import cv2
 import os
-import shutil
 from .custom_sorter import *
-from ..MongoModels import *
 from ..serializers import *
 from . import id_generator as ig
 from . import utilities as ut

FirstApp/logic/classes/pose.py

-
-class PoseResponse:
-    # directory = ''
-    # image_name = ''
-    # label = ''
-
-    def __init__(self, directory, image_name, label):
-        self.directory = directory
-        self.image_name = image_name
-        self.labels = label
-

FirstApp/logic/facial_landmarks.py

-from imutils import face_utils
-import os
-import cv2
-import dlib
-import numpy as np
-import imutils
-
-
-def get2DPoints(image):
-
-    BASE_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-    CLASSIFIER_DIR = os.path.join(BASE_DIR, "FirstApp\\classifiers")
-
-    detector_path = os.path.join(CLASSIFIER_DIR, "shape_predictor_68_face_landmarks.dat")
-    detector = dlib.get_frontal_face_detector()
-    predictor = dlib.shape_predictor(detector_path)
-
-    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-
-    # detect faces in the grayscale image
-    rects = detector(gray, 1)
-
-    left_corner_arr = None
-    right_corner_arr = None
-    nose_tip_arr = None
-    right_mouth_arr = None
-    left_mouth_arr = None
-    chin_arr = None
-    face_center_arr = None
-    face_center_top_arr = None
-    face_center_bottom_arr = None
-    count = 0
-
-    print('no of faces: ', len(rects))
-
-    if (len(rects)):
-        left_corner_arr = np.zeros((len(rects), 2))
-        right_corner_arr = np.zeros((len(rects), 2))
-        nose_tip_arr = np.zeros((len(rects), 2))
-        right_mouth_arr = np.zeros((len(rects), 2))
-        left_mouth_arr = np.zeros((len(rects), 2))
-        chin_arr = np.zeros((len(rects), 2))
-        face_center_top_arr = np.zeros((len(rects), 2))
-        face_center_bottom_arr = np.zeros((len(rects), 2))
-
-
-        for (i, rect) in enumerate(rects):
-
-            left_corner = None
-            right_corner = None
-            nose_tip = None
-            right_mouth = None
-            left_mouth = None
-            chin = None
-
-
-            (fx, fy, fw, fh) = face_utils.rect_to_bb(rect)
-            cv2.rectangle(image, (fx, fy), (fx+fw, fy+fh), (0, 255, 0), 2)
-            face_center_top = [int(fx + fw/2), int(fy)]
-            face_center_bottom = [int(fx + fw/2), int(fy + fh)]
-            cv2.line(image, (int(fx + fw/2), int(fy)), (int(fx + fw/2), int(fy + fh)), (0, 255, 0), 2)
-
-            shape = predictor(gray, rect)
-            shape = face_utils.shape_to_np(shape)
-
-            # looping through each facial landmark category
-            for (name, (i, j)) in face_utils.FACIAL_LANDMARKS_IDXS.items():
-                # clone the original image so we can draw on it, then
-                # display the name of the face part on the image
-                clone = image
-
-                # loop over the subset of facial landmarks, drawing the
-                # specific face part
-
-                for (x, y) in shape[i:j]:
-                    if (name == 'left_eye'):
-                        # maxArr = np.amax(shape[i:j], axis=0)
-                        # minArr = np.amin(shape[i:j], axis=0)
-                        left_corner = np.amax(shape[i:j], axis=0)
-
-                    elif (name == 'right_eye'):
-                        # maxArr = np.amax(shape[i:j], axis=0)
-                        # minArr = np.amin(shape[i:j], axis=0)
-                        right_corner = np.amin(shape[i:j], axis=0)
-
-                    elif (name == 'jaw'):
-                        minArr = np.array(shape[i:j][8], dtype=int)
-                        chin = np.array(shape[i:j][8], dtype=int)
-
-                        # cv2.putText(clone, "Chin", (int(minArr[0]), int(minArr[1])), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
-                        cv2.circle(image, (int(minArr[0]), int(minArr[1])), 3, (255, 0, 255), -1)
-                        # cv2.circle(clone, (int(minArr[0]), int(minArr[1])), 3, (0, 255, 255), -1)
-
-                    elif (name == 'nose'):
-                        # nose_tip = np.array(shape[i:j][3], dtype=int)
-                        nose_tip = np.array(shape[i:j][3], dtype=int)
-                        # cv2.putText(clone, "Nose tip", (int(nose_tip[0]), int(nose_tip[1])), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255),
-                        #             2)
-                        # cv2.circle(clone, (int(nose_tip[0]), int(nose_tip[1])), 3, (255, 0, 255), -1)
-
-                    elif (name == 'inner_mouth'):
-                        # maxArr = np.amax(shape[i:j], axis=0)
-                        # minArr = np.amin(shape[i:j], axis=0)
-                        right_mouth = np.amin(shape[i:j], axis=0)
-                        left_mouth = np.amax(shape[i:j], axis=0)
-                        # cv2.circle(clone, (maxArr[0], maxArr[1]), 3, (127, 0, 255), -1)
-                        # cv2.circle(clone, (minArr[0], minArr[1]), 3, (127, 0, 255), -1)
-
-                    # else:
-                    # cv2.circle(image, (x, y), 3, (255, 0, 255), -1)
-
-            left_corner_arr[count] = left_corner
-            right_corner_arr[count] = right_corner
-            nose_tip_arr[count] = nose_tip
-            right_mouth_arr[count] = right_mouth
-            left_mouth_arr[count] = left_mouth
-            chin_arr[count] = chin
-            face_center_top_arr[count] = face_center_top
-            face_center_bottom_arr[count] = face_center_bottom
-
-            count += 1
-
-
-
-    return left_corner_arr, right_corner_arr, nose_tip_arr, right_mouth_arr, left_mouth_arr, chin_arr, face_center_top_arr, face_center_bottom_arr, count
-
-            # extract the ROI of the face region as a separate image
-        #     (x, y, w, h) = cv2.boundingRect(np.array([shape[i:j]]))
-        #     roi = image[y:y + h, x:x + w]
-        #     roi = imutils.resize(roi, width=250, inter=cv2.INTER_CUBIC)
-        #
-        #     # show the particular face part
-        #     cv2.imshow("ROI", roi)
-        #     cv2.imshow("Image", clone)
-        #     cv2.waitKey(0)
-        #
-        #     # visualize all facial landmarks with a transparent overlay
-        # output = face_utils.visualize_facial_landmarks(image, shape)
-        # cv2.imshow("Image", output)
-        # cv2.waitKey(0)
\ No newline at end of file

FirstApp/logic/head_gaze_estimation.py

@@ -13,19 +13,14 @@ main methods include
 
 """
 from decimal import Decimal
-from . custom_sorter import *
-
 import cv2
 import numpy as np
-import math
 from . face_detector import get_face_detector, find_faces
 from . face_landmarks import get_landmark_model, detect_marks
 import os
-import shutil
 import math
 import pandas as pd
 
-from ..MongoModels import *
 from ..serializers import *
 from . import id_generator as ig
 from . import utilities as ut

FirstApp/serializers.py

-from rest_framework import serializers
-from .models import Teachers, RegisterUser
-from .MongoModels import *
-from .logic import classes
-from . models import VideoMeta
+"""
 
-class TeachersSerializer(serializers.ModelSerializer):
+This file is responsible for implementing the serializer classes for each model classes
 
-    class Meta:
-        model = Teachers
-        fields = ('firstName', 'lastName')
-# fields = __all__
+each serializer class extended by the djangorestframework's serializers class
 
-class RegisterUserSerializer(serializers.ModelSerializer):
+there should be an inner class named "Meta" that needs to implemented inside each serializer class
 
-    class Meta:
-        model = RegisterUser
-        fields = ('firstName', 'lastName', 'email', 'password')
+there are two fields inside "Meta" class, as follows.
+    model: the relevant model class that needs to be serialized
+    fields: fields that need to be displayed when serializing the model class
+        ('__all__' indicates that all the fields are required to be displayed)
 
+"""
 
-# image serializer
-class ImageSerializer(serializers.Serializer):
 
-    metaData = serializers.CharField()
 
 
-# image serializer
-class PoseSerializer(serializers.Serializer):
 
-    directory = serializers.CharField()
-    image_name = serializers.CharField()
-    text = serializers.CharField()
+from rest_framework import serializers
+from .MongoModels import *
+from . models import VideoMeta
 
 
 # lecture serializer