Merge remote-tracking branch 'origin/QA_RELEASE' into db_and_monitoring_IT17097284

AttendanceApp/camera.py

+from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
+from tensorflow.keras.preprocessing.image import img_to_array
+from tensorflow.keras.models import load_model
+from imutils.video import VideoStream
+import imutils
+import cv2,os,urllib.request
+import numpy as np
+from django.conf import settings
+from time import sleep
+import random
+
+face_detection_videocam = cv2.CascadeClassifier(os.path.join(
+			settings.BASE_DIR,'opencv_haarcascade_data/haarcascade_frontalface_default.xml'))
+face_detection_webcam = cv2.CascadeClassifier(os.path.join(
+			settings.BASE_DIR,'opencv_haarcascade_data/haarcascade_frontalface_default.xml'))
+
+# load our serialized face detector model from disk
+prototxtPath = os.path.sep.join([settings.BASE_DIR, "face_detector/deploy.prototxt"])
+weightsPath = os.path.sep.join([settings.BASE_DIR,"face_detector/res10_300x300_ssd_iter_140000.caffemodel"])
+faceNet = cv2.dnn.readNet(prototxtPath, weightsPath)
+maskNet = load_model(os.path.join(settings.BASE_DIR,'face_detector/mask_detector.model'))
+
+
+class IPWebCam(object):
+	def __init__(self):
+		self.url = "http://192.168.8.100:8080/shot.jpg"
+		self._count = 0
+
+	def __del__(self):
+		cv2.destroyAllWindows()
+
+	def get_frame(self):
+		fourcc = cv2.VideoWriter_fourcc(*'XVID')
+		out = cv2.VideoWriter('output.avi', fourcc, 20.0, (640, 480))
+		imgResp = urllib.request.urlopen(self.url)
+		imgNp = np.array(bytearray(imgResp.read()),dtype=np.uint8)
+		img= cv2.imdecode(imgNp,-1)
+		# We are using Motion JPEG, but OpenCV defaults to capture raw images,
+		# so we must encode it into JPEG in order to correctly display the
+		# video stream
+		gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+		faces_detected = face_detection_webcam.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=5)
+		for (x, y, w, h) in faces_detected:
+			cv2.rectangle(img, pt1=(x, y), pt2=(x + w, y + h), color=(255, 0, 0), thickness=2)
+		resize = cv2.resize(img, (640, 480), interpolation = cv2.INTER_LINEAR) 
+		frame_flip = cv2.flip(resize,1)
+		ret, jpeg = cv2.imencode('.jpg', frame_flip)
+
+		# capture frame and save on a given time in order to run the face recognition
+		sleep(3); cv2.imwrite("%d.jpg" % self._count, img)
+		self._count =+1
+		return jpeg.tobytes()
+
+
+class MaskDetect(object):
+	def __init__(self):
+		self.vs = VideoStream(src=0).start()
+
+	def __del__(self):
+		cv2.destroyAllWindows()
+
+	def detect_and_predict_mask(self,frame, faceNet, maskNet):
+		# grab the dimensions of the frame and then construct a blob
+		# from it
+		(h, w) = frame.shape[:2]
+		blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300),
+									 (104.0, 177.0, 123.0))
+
+		# pass the blob through the network and obtain the face detections
+		faceNet.setInput(blob)
+		detections = faceNet.forward()
+
+		# initialize our list of faces, their corresponding locations,
+		# and the list of predictions from our face mask network
+		faces = []
+		locs = []
+		preds = []
+
+		# loop over the detections
+		for i in range(0, detections.shape[2]):
+			# extract the confidence (i.e., probability) associated with
+			# the detection
+			confidence = detections[0, 0, i, 2]
+
+			# filter out weak detections by ensuring the confidence is
+			# greater than the minimum confidence
+			if confidence > 0.5:
+				# compute the (x, y)-coordinates of the bounding box for
+				# the object
+				box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
+				(startX, startY, endX, endY) = box.astype("int")
+
+				# ensure the bounding boxes fall within the dimensions of
+				# the frame
+				(startX, startY) = (max(0, startX), max(0, startY))
+				(endX, endY) = (min(w - 1, endX), min(h - 1, endY))
+
+				# extract the face ROI, convert it from BGR to RGB channel
+				# ordering, resize it to 224x224, and preprocess it
+				face = frame[startY:endY, startX:endX]
+				face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
+				face = cv2.resize(face, (224, 224))
+				face = img_to_array(face)
+				face = preprocess_input(face)
+
+				# add the face and bounding boxes to their respective
+				# lists
+				faces.append(face)
+				locs.append((startX, startY, endX, endY))
+
+		# only make a predictions if at least one face was detected
+		if len(faces) > 0:
+			# for faster inference we'll make batch predictions on *all*
+			# faces at the same time rather than one-by-one predictions
+			# in the above `for` loop
+			faces = np.array(faces, dtype="float32")
+			preds = maskNet.predict(faces, batch_size=32)
+
+		# return a 2-tuple of the face locations and their corresponding
+		# locations
+		return (locs, preds)
+
+	def get_frame(self):
+		frame = self.vs.read()
+		frame = imutils.resize(frame, width=650)
+		frame = cv2.flip(frame, 1)
+		# detect faces in the frame and determine if they are wearing a
+		# face mask or not
+		(locs, preds) = self.detect_and_predict_mask(frame, faceNet, maskNet)
+
+		# loop over the detected face locations and their corresponding
+		# locations
+		for (box, pred) in zip(locs, preds):
+			# unpack the bounding box and predictions
+			(startX, startY, endX, endY) = box
+			(mask, withoutMask) = pred
+
+			# determine the class label and color we'll use to draw
+			# the bounding box and text
+			label = "Mask" if mask > withoutMask else "No Mask"
+			color = (0, 255, 0) if label == "Mask" else (0, 0, 255)
+
+			# include the probability in the label
+			label = "{}: {:.2f}%".format(label, max(mask, withoutMask) * 100)
+
+			# display the label and bounding box rectangle on the output
+			# frame
+			cv2.putText(frame, label, (startX, startY - 10),
+						cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
+			cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)
+		ret, jpeg = cv2.imencode('.jpg', frame)
+		return jpeg.tobytes()

AttendanceApp/faceRecognition.py

+import cv2
+import os
+import numpy as np
+
+#This module contains all common functions that are called in tester.py file
+
+
+#Given an image below function returns rectangle for face detected alongwith gray scale imagess
+def faceDetection(test_img):
+    gray_img=cv2.cvtColor(test_img,cv2.COLOR_BGR2GRAY)#convert color image to grayscale
+    gray_img=cv2.normalize(gray_img, None, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F)
+    gray_img=(255*gray_img).astype(np.uint8)
+    gray_img=cv2.fastNlMeansDenoising(gray_img);
+
+    face_haar_cascade=cv2.CascadeClassifier('HaarCascade/haarcascade_frontalface_default.xml')#Load haar classifier
+    faces=face_haar_cascade.detectMultiScale(gray_img,scaleFactor=1.32,minNeighbors=5)#detectMultiScale returns rectangles
+
+    return faces,gray_img
+
+#Given a directory below function returns part of gray_img which is face alongwith its label/ID
+def labels_for_training_data(directory):
+    faces=[]
+    faceID=[]
+
+    for path,subdirnames,filenames in os.walk(directory):
+        for filename in filenames:
+            if filename.startswith("."):
+                print("Skipping system file")#Skipping files that startwith .
+                continue
+
+            id=os.path.basename(path)#fetching subdirectory names
+            img_path=os.path.join(path,filename)#fetching image path
+            print("img_path:",img_path)
+            print("id:",id)
+            test_img=cv2.imread(img_path)#loading each image one by one
+            if test_img is None:
+                print("Image not loaded properly")
+                continue
+            faces_rect,gray_img=faceDetection(test_img)#Calling faceDetection function to return faces detected in particular image
+            if len(faces_rect)!=1:
+               continue #Since we are assuming only single person images are being fed to classifier
+            (x,y,w,h)=faces_rect[0]
+            roi_gray=gray_img[y:y+w,x:x+h]#cropping region of interest i.e. face area from grayscale image
+            faces.append(roi_gray)
+            faceID.append(int(id))
+    return faces,faceID
+
+def train_classifier(faces,faceID):
+    face_recognizer=cv2.face.LBPHFaceRecognizer_create()
+    face_recognizer.train(faces,np.array(faceID))
+    return face_recognizer
+
+#Below function draws bounding boxes around detected face in image
+def draw_rect(test_img,face):
+    (x,y,w,h)=face
+    cv2.rectangle(test_img,(x,y),(x+w,y+h),(255,0,0),thickness=4)
+
+#Below function writes name of person for detected label
+def put_text(test_img,text,x,y):
+    cv2.putText(test_img,text,(x,y),cv2.FONT_HERSHEY_DUPLEX,1,(255,0,0),3)
+
+#Save video frames
+def extractAndSaveFrames():
+    vidcap = cv2.VideoCapture('IT17098960.mp4')
+    success, image = vidcap.read()
+    count = 0
+    while success:
+        cv2.imwrite("frame%d.jpg" % count, image)  # save frame as JPEG file
+        success, image = vidcap.read()
+        print('Read a new frame: ', success)
+        count += 1
+
+
+
+
+
+
+
+
+
+

AttendanceApp/templates/AttendanceApp/Initiate_Lecture.html

@@ -31,18 +31,18 @@
 </script>
 
 
-<script type="text/javascript">
-
-    $(document).ready(function() {
-        $('#initiate_btn').click(function() {
-
-            fetch('http://127.0.0.1:8000/attendance/process-initiate-lecture')
-            .then((res) => res.json())
-            .then((out) => alert(out.response))
-            .catch((err) => alert('error: ' + err))
-        });
-    })
-
+<script>
+function toggleLectureLive() {
+  var x = document.getElementById("liveStreamLecture");
+  var y = document.getElementById("liveStreamLectureStartButton");
+  if (x.style.display === "none") {
+    x.style.display = "block";
+    y.style.display = "block";
+  } else {
+    x.style.display = "none";
+    y.style.display = "none";
+  }
+}
 </script>
 
 {% endblock %}
@@ -55,11 +55,19 @@
             <div class="text-center">
                 <div class="card">
                     <div class="card-header">
-                        <h4 class="card-title">Starting the lecture....</h4>
+                        <h4 class="card-title">Lecture Live</h4>
                     </div>
 
                     <div class="card-body">
-                        <button type="button" class="btn btn-success" id="initiate_btn">Initiate Lecture</button>
+                        <button type="button" class="btn btn-success" id="initiate_btn" onclick="toggleLectureLive()">Show Live Stream</button>
+                    </div>
+                    <div style="vertical-align: middle; border-style: none; background-color: #055270; height: 500px; width: 100%">
+                        <div class="row justify-content-center">
+                            <img id="liveStreamLecture" style="display: none; height: inherit; margin-bottom: -25px;" src="{% url 'webcam_feed' %}">
+                        </div>
+                        <div class="row justify-content-center">
+                            <button style="display: none; width: 70px; height: 70px;" id="liveStreamLectureStartButton" class="btn btn-warning btn-circle"><i class="fas fa-video"></i></button>
+                        </div>
                     </div>
                 </div>
             </div>

AttendanceApp/tester.py

+import cv2
+from .faceRecognition import faceDetection, train_classifier, labels_for_training_data, draw_rect, put_text
+
+
+#This module takes images  stored in diskand performs face recognition
+test_img=cv2.imread('TestImages/frame0.jpg')#test_img path
+faces_detected,gray_img=faceDetection(test_img)
+print("faces_detected:",faces_detected)
+
+
+#Comment belows lines when running this program second time.Since it saves training.yml file in directory
+faces,faceID=labels_for_training_data('trainingImages')
+face_recognizer=train_classifier(faces,faceID)
+face_recognizer.write('trainingData.yml')
+
+
+# For subsequent runs
+face_recognizer=cv2.face.LBPHFaceRecognizer_create()
+face_recognizer.read('trainingData.yml')#use this to load training data for subsequent runs
+
+name={0: "IT17138000", 1: "IT17100908", 2: "IT17098960"}#creating dictionary containing names for each label
+file = open("attendance.txt", "w")
+
+for face in faces_detected:
+    (x,y,w,h)=face
+    roi_gray=gray_img[y:y+h,x:x+h]
+    label,confidence=face_recognizer.predict(roi_gray)#predicting the label of given image
+    print("confidence:",confidence)
+    print("label:",label)
+    draw_rect(test_img,face)
+    predicted_name=name[label]
+    if(confidence>90):#If confidence more than 37 then don't print predicted face text on screen
+        continue
+    file.write(predicted_name)
+    put_text(test_img,predicted_name,x,y)
+file.close()
+resized_img=cv2.resize(test_img,(700,700))
+cv2.imshow("face dtecetion tutorial",resized_img)
+cv2.waitKey(0)#Waits indefinitely until a key is pressed
+cv2.destroyAllWindows
+
+
+
+
+

AttendanceApp/urls.py

@@ -17,7 +17,7 @@ urlpatterns = [
     path('student/', StudentAPIView.as_view()),
     path('student/<str:pk>', StudentDetails.as_view()),
     url(r'^upload/$', FileView.as_view(), name='file-upload'),
-
+    path('webcam_feed', views.webcam_feed, name='webcam_feed'),
     # this url will initiate the lecture
     url(r'^process-initiate-lecture/$', InitiateLecture.as_view())
 ]

AttendanceApp/views.py

 from django.shortcuts import render
+from django.http.response import StreamingHttpResponse
+from AttendanceApp.camera import IPWebCam
 
 
 def initiate_lecture(request):
 
     return render(request, "AttendanceApp/Initiate_lecture.html")
+
+def gen(camera):
+	while True:
+		frame = camera.get_frame()
+		yield (b'--frame\r\n'
+				b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n\r\n')
+
+def webcam_feed(request):
+	return StreamingHttpResponse(gen(IPWebCam()),
+					content_type='multipart/x-mixed-replace; boundary=frame')
\ No newline at end of file

FirstApp/ImageOperations.py

-import cv2
-import os
-import re
-import base64
-import shutil
-
-
-def saveImage(response):
-
-
-    dataUrlPattern = re.compile('data:image/(png|jpeg);base64,(.*)$')
-    base_path = os.path.join(os.path.abspath(__file__))
-    root_dir = os.path.dirname(os.path.dirname(base_path))
-    new_dir_name = "static\\FirstApp\\images\\{}".format(response["imageName"])
-    new_dir = os.path.join(root_dir, new_dir_name)
-
-    if (os.path.isdir(new_dir)):
-        # delete the previous directory
-        shutil.rmtree(new_dir)
-
-    # create the new directory
-    os.mkdir(new_dir)
-
-
-    count = 0
-
-    for url in response["ImageURLS"]:
-        url = dataUrlPattern.match(url).group(2)
-        encoded = url.encode()
-        image = base64.b64decode(encoded)
-        imageName = response["imageName"] + '_img_' + format(count) + '.png'
-
-        new_file = os.path.join(new_dir, imageName)
-
-        count += 1
-
-        # saving the images (method 1)
-        with open(new_file, "wb") as f:
-            f.write(image)
-
-    # respond 'yes' to the command line prompt
-    p = os.popen('python manage.py collectstatic', "w")
-    p.write("yes")
-
-
-
-

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_pose_estimation.py

-
-import os
-import cv2
-import numpy as np
-import shutil
-from .facial_landmarks import get2DPoints
-from .classes import pose
-# Read Image
-
-
-def estimatePose(request):
-
-
-    directory = request['directory']
-    images = request['images']
-
-    BASE_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-    IMAGE_DIR = os.path.join(BASE_DIR, "assets\\FirstApp\\images")
-    SPEC_DIR = os.path.join(IMAGE_DIR, "{}".format(directory))
-
-    # new directory will be created to store pose estimations
-    new_dir_name = "static\\FirstApp\\poses\\{}".format(directory)
-    new_dir = os.path.join(BASE_DIR, new_dir_name)
-    face_count_response = 0
-    pose_response_list = []
-
-    if (os.path.isdir(new_dir)):
-        # delete the previous directory
-        shutil.rmtree(new_dir)
-
-    # create the new directory
-    os.mkdir(new_dir)
-
-    for im in images:
-
-        IMAGE_PATH = os.path.join(SPEC_DIR, "{}".format(im))
-        image = cv2.imread(IMAGE_PATH)
-        size = image.shape
-
-        left_corner, right_corner, nose_tip, right_mouth, left_mouth, chin, face_center_top, face_center_bottom, face_count = get2DPoints(image)
-
-        # if faces are found
-        if left_corner is not None:
-
-
-            # 3D model points.
-            model_points = np.array([
-                (0.0, 0.0, 0.0),  # Nose tip
-                (0.0, -330.0, -65.0),  # Chin
-                (-225.0, 170.0, -135.0),  # Left eye left corner
-                (225.0, 170.0, -135.0),  # Right eye right corne
-                (-150.0, -150.0, -125.0),  # Left Mouth corner
-                (150.0, -150.0, -125.0)  # Right mouth corner
-
-            ])
-
-            # Camera internals
-
-            focal_length = size[1]
-            center = (size[1] / 2, size[0] / 2)
-            camera_matrix = np.array(
-                [[focal_length, 0, center[0]],
-                 [0, focal_length, center[1]],
-                 [0, 0, 1]], dtype="double"
-            )
-
-            # print("Camera Matrix :\n {0}".format(camera_matrix))
-
-            for i in range (face_count):
-
-                text = ''
-                # 2D image points. If you change the image, you need to change vector
-                image_points = np.array([
-                    nose_tip[i],
-                    chin[i],
-                    left_corner[i],
-                    right_corner[i],
-                    left_mouth[i],
-                    right_mouth[i]
-                ], dtype="double")
-
-
-                dist_coeffs = np.zeros((4, 1))  # Assuming no lens distortion
-                (success, rotation_vector, translation_vector) = cv2.solvePnP(model_points, image_points, camera_matrix, dist_coeffs,
-                                                                              flags=cv2.SOLVEPNP_ITERATIVE)
-
-
-                # Project a 3D point (0, 0, 1000.0) onto the image plane.
-                # We use this to draw a line sticking out of the nose
-
-
-                (nose_end_point2D, jacobian) = cv2.projectPoints(np.array([(0.0, 0.0, 1000.0)]), rotation_vector, translation_vector,
-                                                                 camera_matrix, dist_coeffs)
-
-                # for p in image_points:
-                #     cv2.circle(im, (int(p[0]), int(p[1])), 3, (0, 0, 255), -1)
-
-                p1 = (int(image_points[0][0]), int(image_points[0][1]))
-                p2 = (int(nose_end_point2D[0][0][0]), int(nose_end_point2D[0][0][1]))
-
-                if (p2[0] < face_center_top[i][0]):
-                    text = 'RIGHT'
-                else:
-                    text = 'LEFT'
-
-                cv2.putText(image, text, p2, cv2.FONT_HERSHEY_COMPLEX, 0.8, (0, 0, 255), 2)
-                cv2.line(image, p1, p2, (255, 0, 0), 2)
-
-                # saving the image
-                new_file = os.path.join(new_dir, im)
-
-                cv2.imwrite(new_file, image)
-
-                face_count_response += 1
-
-                # create a response object for the image
-                pose_response = {}
-                pose_response["directory"] = directory
-                pose_response["image"] = im
-                pose_response["label"] = text
-
-                pose_response_list.append(pose_response)
-
-
-
-
-
-        else:
-            print('No faces found')
-
-
-    # respond 'yes' to the command line prompt
-    p = os.popen('python manage.py collectstatic', "w")
-    p.write("yes")
-
-    # returning the static path
-    STATIC_POSE = os.path.join(BASE_DIR, "assets\\FirstApp\\pose")
-    STATIC_SPEC = os.path.join(STATIC_POSE, "{}".format(directory))
-
-    # if no images were created
-    if (face_count_response < 1):
-        shutil.rmtree(new_dir)
-        return "No faces were found"
-
-    return pose_response_list
\ No newline at end of file

FirstApp/logic/posenet_calculation.py

-import tensorflow as tf
-import tensorflow.keras
-from PIL import Image, ImageOps
-import numpy as np
-import cv2
-import os
-import math
-import shutil
-from . import custom_sorter as cs
-
-
-def get_pose_estimations(video_name):
-    BASE_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-    # face_classifier = cv2.CascadeClassifier(
-    #     os.path.join(BASE_DIR, 'FirstApp\classifiers\haarcascade_frontalface_default.xml'))
-    # classifier_path = os.path.join(BASE_DIR, 'FirstApp\classifiers\Emotion_little_vgg.h5')
-    # classifier = load_model(classifier_path)
-    EXTRACTED_DIR = os.path.join(BASE_DIR, "assets\\FirstApp\\activity\\{}".format(video_name))
-
-    class_labels = ['Angry', 'Happy', 'Neutral', 'Sad', 'Surprise']
-
-    detections = []
-    frames = []
-
-    for frame_folder in os.listdir(EXTRACTED_DIR):
-
-        FRAME_DIR = os.path.join(EXTRACTED_DIR, frame_folder)
-        frame_details = {}
-        frame_details['frame'] = frame_folder
-
-        # for each detection in the frame directory
-        detected_images = []
-        for detection in os.listdir(FRAME_DIR):
-
-            if "frame" not in detection:
-                DETECTION_PATH = os.path.join(FRAME_DIR, detection)
-
-                image = cv2.imread(DETECTION_PATH)
-
-                # label = emotion_recognition(classifier, face_classifier, image)
-
-                detected_images.append(detection)
-                detections.append(detection)
-
-        frame_details['detections'] = detected_images
-        frames.append(frame_details)
-
-    sorted_frames = cs.custom_object_sorter(frames)
-    set_detections = set(detections)
-    list_set_detections = list(set_detections)
-
-    sorted_list_set_detections = cs.custom_sort(list_set_detections)
-
-    return sorted_frames, sorted_list_set_detections
-
-
-# calculate pose estimations for a student
-def calculate_pose_estimation_for_student(video_name, student, poses):
-
-    BASE_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-    VIDEO_DIR = os.path.join(BASE_DIR, "assets\\FirstApp\\activity\\{}".format(video_name))
-    POSE_DIR = os.path.join(BASE_DIR, "assets\\FirstApp\\poses")
-    POSE_VIDEO_DIR = os.path.join(POSE_DIR, video_name)
-    pose_count = 0
-
-    # checking whether the pose directory
-    if os.path.isdir(POSE_VIDEO_DIR) == False:
-        # create the pose directory
-        os.mkdir(POSE_VIDEO_DIR)
-
-
-    # loop through each frame of the directory
-    for frame in os.listdir(VIDEO_DIR):
-
-        FRAME_FOLDER = os.path.join(VIDEO_DIR, frame)
-
-        for detection in os.listdir(FRAME_FOLDER):
-
-            DETECTION_PATH = os.path.join(FRAME_FOLDER, detection)
-            # detection image
-            detection_img = cv2.imread(DETECTION_PATH)
-
-            # checking for the given student
-            if detection == student:
-
-                # select the correct pose detection
-                pose = poses[pose_count]
-
-                # extract the coordinates
-                x1 = int(pose['keypoints'][5]['position']['x'])
-                y1 = int(pose['keypoints'][5]['position']['y'])
-                x2 = int(pose['keypoints'][6]['position']['x'])
-                y2 = int(pose['keypoints'][6]['position']['y'])
-
-                # extract the head positions
-                x_diff = x1 - x2
-                y_diff = y1 - y2
-                x_pow = math.pow(x_diff, 2)
-                y_pow = math.pow(y_diff, 2)
-                summation = x_pow + y_pow
-
-                distance = int(math.sqrt(summation))
-
-                # defining the hyperparameter
-                param = 0.6
-
-                fraction = int(math.floor(distance * param)) if int(math.floor(distance * param)) > 0 else 1
-
-                middle_x = x2 + fraction
-
-                # middle_y = y2 - 20
-                middle_y = y2
-
-                head_x = middle_x
-
-                head_y = 0 if (middle_y - fraction) < 0 else (middle_y - fraction)
-
-                left_upper_x = 0 if (middle_x - fraction) < 0 else (middle_x - fraction)
-
-
-                # extract the new image
-                new_img = detection_img[head_y:head_y+fraction, left_upper_x:left_upper_x+distance]
-
-                # new directory name
-                # new_img_dir = os.path.join(POSE_VIDEO_DIR, frame)
-                new_img_dir = os.path.join(POSE_VIDEO_DIR, detection)
-
-                # check if the directory exists
-                if os.path.isdir(new_img_dir) == False:
-                    # create the new directory
-                    os.mkdir(new_img_dir)
-
-                # create new image name
-                frame_name = frame + ".png"
-
-                new_img_path = os.path.join(new_img_dir, frame_name)
-
-                # saving the new image
-                cv2.imwrite(new_img_path, new_img)
-
-                # increment the count
-                pose_count += 1
-
-                print('saving the image')
-
-

FirstApp/logic/utilities.py

+
+def get_redundant_pairs(df):
+    '''Get diagonal and lower triangular pairs of correlation matrix'''
+    pairs_to_drop = set()
+    cols = df.columns
+    for i in range(0, df.shape[1]):
+        for j in range(0, i+1):
+            pairs_to_drop.add((cols[i], cols[j]))
+    return pairs_to_drop
+
+def get_top_abs_correlations(df, n):
+    au_corr = df.corr().abs().unstack()
+    labels_to_drop = get_redundant_pairs(df)
+    au_corr = au_corr.drop(labels=labels_to_drop).sort_values(ascending=False)
+    return au_corr[0:n]

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

FirstApp/templates/FirstApp/base.html

-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <link rel="stylesheet" href="style.css">
-    <title>{% block title %}My amazing site{% endblock %}</title>
-</head>
-
-<body>
-    <div id="sidebar">
-        {% block sidebar %}
-        <ul>
-            <li><a href="/">Home</a></li>
-            <li><a href="/blog/">Blog</a></li>
-        </ul>
-        {% endblock %}
-    </div>
-
-    <div id="content">
-        {% block content %}{% endblock %}
-    </div>
-</body>
-</html>
\ No newline at end of file

FirstApp/templates/FirstApp/child.html

-{% extends "FirstApp/base.html" %}
-
-{% block title %}My amazing blog{% endblock %}
-
-{% block content %}
-{% for entry in blog_entries %}
-    <h2>{{ entry.title }}</h2>
-    <p>{{ entry.body }}</p>
-{% endfor %}
-{% endblock %}
\ No newline at end of file

FirstApp/templates/FirstApp/extractor.html

-{% extends 'FirstApp/template.html' %}
-<!DOCTYPE html>
-<html lang="en">
-<body id="page-top">
-<!-- Page Wrapper -->
-
-{% block javascript %}
-        {% load static %}
-
-        <!-- Bootstrap core JavaScript-->
-      <script src="{% static 'FirstApp/vendor/jquery/jquery.min.js' %}"></script>
-      <script src="{% static 'FirstApp/vendor/bootstrap/js/bootstrap.bundle.min.js' %}"></script>
-
-      <!-- Core plugin JavaScript-->
-      <script src="{% static 'FirstApp/vendor/jquery-easing/jquery.easing.min.js' %}"></script>
-
-
-    <script type="text/javascript">
-        const cur_date = new Date().toDateString();
-        let video_name = '';
-        $(document).ready(function () {
-            $('.video_row').click(function () {
-                video_name = $(this).attr('id');
-                let video_duration = $(this).attr('data-duration');
-                let src = "{% static '' %}FirstApp/videos/" + video_name;
-
-                //assigning the src
-                $("video").attr('src', src);
-
-                //setting the video details
-                $('#video_name').text(video_name);
-                $('#video_duration').text(video_duration);
-                $('#video_date').text(cur_date);
-
-            });
-
-            //to handle video extraction button
-
-            $('#extractBtn').click(function () {
-                //run the api
-                fetch('http://127.0.0.1:8000/videoExtract', {
-                   method: 'POST',
-                   headers: {
-                       "Content-type": "application/json"
-                   },
-                   body: JSON.stringify({
-                       "video_name": video_name
-                   })
-                })
-                .then((res) => res.json())
-                .then((out) => alert(out.response))
-                .catch((error) => alert('this is an error'));
-            });
-        });
-    </script>
-
-
-{% endblock %}
-
-<div id="wrapper">
-    <!-- Sidebar -->
-
-    <!-- Content Wrapper -->
-    <div id="content-wrapper" class="d-flex flex-column">
-
-        <!-- Main Content -->
-        <div id="content">
-
-            <!-- Begin Page Content -->
-            {% block 'container-fluid' %}
-                <div class="container-fluid">
-                    {% load static %}
-
-                    <!-- Page Heading -->
-                    <div class="d-sm-flex align-items-center justify-content-between mb-4">
-                        <h1 class="h3 mb-0 text-gray-800">Video Extractor</h1>
-                    </div>
-
-                    <!--1st row -->
-                    <div class="row p-2">
-
-                        <!--first column -->
-                        <div class="col-lg-6">
-
-                            <!-- Main Video Context -->
-                            <div class="card shadow mb-4">
-                                <div class="card-header py-3">
-                                    <h6 class="m-0 font-weight-bold text-primary">Main Video</h6>
-                                </div>
-                                <div class="card-body">
-                                    <video width="500" height="300" id="first_video" controls>
-                                        <source src="{% static '' %}FirstApp/videos/{{ firstVideo.name }}"
-                                                type="video/mp4">
-                                        Your browser does not support the video tag.
-                                    </video>
-
-                                    <table class="table table-borderless table-striped m-1">
-                                        <tbody>
-                                            <tr>
-                                                <td class="font-weight-bold">Name</td>
-                                                <td id="video_name">{{ firstVideo.name }}</td>
-                                            </tr>
-                                            <tr>
-                                                <td class="font-weight-bold">Duration</td>
-                                                <td id="video_duration">{{ firstVideo.duration }}</td>
-                                            </tr>
-                                            <tr>
-                                                <td class="font-weight-bold">Date</td>
-                                                <td id="video_date"></td>
-                                            </tr>
-                                        </tbody>
-                                    </table>
-
-                                    <div class="col-lg-3 p-3">
-                                        <button type="button" class="btn btn-outline-primary" id="extractBtn">Extract</button>
-                                    </div>
-
-                                </div>
-                            </div>
-                        </div>
-
-
-                        <!--second column -->
-                        <div class="col-lg-6">
-
-                            <!-- Main Video Context -->
-                            <div class="card shadow mb-4">
-                                <div class="card-header py-3">
-                                    <h6 class="m-0 font-weight-bold text-primary">List of Lectures</h6>
-                                </div>
-                                <div class="card-body">
-                                    <table class="table table-bordered">
-                                        <thead>
-                                        <tr>
-                                            <th>Video Name</th>
-                                            <th>Length</th>
-                                        </tr>
-                                        </thead>
-                                        <tbody>
-                                        {% for video in Videos %}
-                                            <tr class="video_row" id="{{ video.name }}" data-duration="{{ video.duration }}">
-                                                <td>
-                                                    {{ video.name }}
-                                                </td>
-                                                <td>{{ video.duration }}</td>
-                                            </tr>
-                                        {% endfor %}
-                                        </tbody>
-                                    </table>
-                                </div>
-                            </div>
-                        </div>
-                    </div>
-
-
-
-
-                </div>
-            {% endblock %}
-            <!-- End of container-fluid -->
-
-        </div>
-        <!-- End of Main Content -->
-
-    </div>
-    <!-- End of Content Wrapper -->
-
-</div>
-
-{% block 'modal' %}
-    <div class="modal fade" id="gif-body" role="dialog" aria-labelledby="gif-body">
-        <div class="modal-dialog modal-lg" style="max-width: 1600px; max-height: 800px">
-            <div class="modal-content">
-                <div class="modal-header">
-                    <h2 class="modal-title">Processing....</h2>
-                    <button class="close" type="button" data-dismiss="modal" aria-label="Close">
-                        <span aria-hidden="true">×</span>
-                    </button>
-                </div>
-
-                <div class="modal-body text-center">
-                    <img src="{% static 'FirstApp/images/ajax-loader.gif' %}" width="200" height="500"
-                         alt="This is a GIF">
-                    <h5>This might take few seconds...</h5>
-                </div>
-            </div>
-        </div>
-    </div>
-{% endblock %}
-
-</body>
-</html>
\ No newline at end of file

FirstApp/templates/FirstApp/loggedIn.html

-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <title>Title</title>
-</head>
-<body>
-    <p>This is my username{{ username }}</p>
-</body>
-</html>
\ No newline at end of file

LectureSummarizingApp/Summary01.txt

+What is OOP OBJECT ORIENTED PROGRAMMING is a programming concept that works on the principles of abstraction, encapsulation, inheritance, and polymorphism. The basic concept of OOPs is to create objects, re-use them throughout the program, and manipulate these objects to get results. Object Oriented Programming popularly known as OOP, is used in a modern programming language like Java OBJECT ORIENTED PROGRAMMING is a programming concept that works on the principles of abstraction, encapsulation, inheritance and polymorphism the basic concept of OOPs is to create objects re-use them in the program and manipulate these objects to get results Object Oriented Programming popularly known as OOP, is used in programming language like Java. Core OOPS concepts are as follow. While the methods may be performed with these cars are driving, reverse, braking etc. next is Object An object can be defined as an instance of a class, and there can be multiple instances of a class in a program.
\ No newline at end of file

LectureSummarizingApp/api.py

@@ -9,7 +9,7 @@ from LectureSummarizingApp.serializer import LectureAudioSerializer, LectureAudi
 from . import speech_to_text as stt
 
 
-# this API will retrieve lecture audio details
+# APIs used in Lecture Summarizing Component
 class LectureAudioAPI(APIView):
 
     def get(self, request):