Skip to content

2
2020-101
Commit 0c116b06 authored by I.K Seneviratne's avatar I.K Seneviratne
Browse files
Options

Merge branch 'monitoring_student_behavior_IT17138000' into 'QA_RELEASE' 

Monitoring student behavior it17138000

See merge request !24
parents 8b6b583d 5c1c3cca
Hide whitespace changes
Inline Side-by-side
Showing with 12 additions and 183 deletions
FirstApp/emotion_detector.py
View file @ 0c116b06
......@@ -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
View file @ 0c116b06
......@@ -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 deleted 100644 → 0
View file @ 8b6b583d
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 deleted 100644 → 0
View file @ 8b6b583d
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
View file @ 0c116b06
......@@ -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
View file @ 0c116b06
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
......
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment