Add augmentation

.vscode/settings.json

@@ -0,0 +1,3 @@
+{
+    "python.pythonPath": "C:\\Users\\fayer\\anaconda3\\envs\\cv-env\\python.exe"
+}

Facial_Image_Augmentation.py

@@ -0,0 +1,57 @@
+import sys
+import cv2
+import os
+import numpy as np
+import math
+import random
+
+
+def facial_recognition_augmentation(label):
+    aug = 5
+    image_path = sys.path[0] + '/Facial_images/face_rec/train/' + label + '/'
+    imagelist = os.listdir(image_path)
+
+    for file_name in imagelist:
+        (nameWithoutExtention, extention) = os.path.splitext(os.path.basename(file_name))
+
+        if (extention==".jpg"):
+
+            #print(image_path+file_name)
+            inputImage = cv2.imread(image_path+file_name)
+        
+            for i in range(aug):
+                rotate = random.randint(-8,8)
+                brightness = random.randint(-30, +30)
+                clahe = random.uniform(0.0, 1.0)
+
+                choice = random.randint(0, 10)
+
+                (h, w) = inputImage.shape[:2] #10
+                center = (w // 2, h // 2) #11
+                
+                M = cv2.getRotationMatrix2D(center, rotate, 1.0) #12
+                inputImage = cv2.warpAffine(inputImage, M, (w, h)) #13
+                
+                if (choice%3 == 1):
+                    clahe = cv2.createCLAHE(clipLimit=clahe, tileGridSize=(8,8))
+                    inputImage = cv2.cvtColor(inputImage, cv2.COLOR_BGR2LAB)  # convert from BGR to LAB color space
+                    l, a, b = cv2.split(inputImage)  # split on 3 different channels
+                    l2 = clahe.apply(l)  # apply CLAHE to the L-channel
+                    inputImage = cv2.merge((l2,a,b))  # merge channels
+                    inputImage = cv2.cvtColor(inputImage, cv2.COLOR_LAB2BGR)  # convert from LAB to BGR
+                elif(choice%3 == 2):
+                    fI = inputImage/255.0
+                    gamma = random.uniform(0.2, 0.4)
+                    #inputImage = np.power(fI, gamma)
+                else:
+                    cv2.addWeighted( inputImage, 0.5, inputImage, 0.5, brightness, inputImage)
+
+                choice_1 = random.randint(0, 10)
+                if (choice_1%2 == 1):
+                    gauss = random.randint(1, 2)*2-1
+
+                    inputImage = cv2.GaussianBlur(inputImage, (gauss,gauss), 0)
+                
+                cv2.imwrite(image_path+nameWithoutExtention+"_aug"+str(i)+".jpg", inputImage)
+
+

Facial_Recognition_Registration.py

@@ -3,10 +3,11 @@ import os
 import math
 import cv2
 import Facial_Recognition_Enrollment
+import Facial_Image_Augmentation
 
 
 def register_your_face(label):
-    num_cap = 50
+    num_cap = 10
 
     path = sys.path[0] + '/Facial_images/face_rec/train/' + label
 
@@ -30,14 +31,4 @@ def register_your_face(label):
     cap.release()
     cv2.destroyAllWindows()
 
-
-if __name__ == "__main__":
-    print("Face registration start...")
-    print()
-    label = input('Pleas enter your Name/Label: ')
-    register_your_face(label)
-    print("Data saved! Starting enrollment...")
-    print()
-    Facial_Recognition_Enrollment.enroll_face_dataset()  # Need discuss and modify after intergrate with database.
-    print("Face registration completed!")
-    print()
+    Facial_Image_Augmentation.facial_recognition_augmentation(label)

Facial_Recognition_Render(abandoned).py

@@ -1,298 +0,0 @@
-import cv2
-import dlib
-import numpy as np
-import math
-
-# Returns 8 points on the boundary of a rectangle
-def getEightBoundaryPoints(h, w):
-  boundaryPts = []
-  boundaryPts.append((0,0))
-  boundaryPts.append((w/2, 0))
-  boundaryPts.append((w-1,0))
-  boundaryPts.append((w-1, h/2))
-  boundaryPts.append((w-1, h-1))
-  boundaryPts.append((w/2, h-1))
-  boundaryPts.append((0, h-1))
-  boundaryPts.append((0, h/2))
-  return np.array(boundaryPts, dtype=np.float)
-
-
-# Constrains points to be inside boundary
-def constrainPoint(p, w, h):
-  p = (min(max(p[0], 0), w - 1), min(max(p[1], 0), h - 1))
-  return p
-
-# convert Dlib shape detector object to list of tuples
-def dlibLandmarksToPoints(shape):
-  points = []
-  for p in shape.parts():
-    pt = (p.x, p.y)
-    points.append(pt)
-  return points
-
-# Compute similarity transform given two sets of two points.
-# OpenCV requires 3 pairs of corresponding points.
-# We are faking the third one.
-def similarityTransform(inPoints, outPoints):
-  s60 = math.sin(60*math.pi/180)
-  c60 = math.cos(60*math.pi/180)
-
-  inPts = np.copy(inPoints).tolist()
-  outPts = np.copy(outPoints).tolist()
-
-  # The third point is calculated so that the three points make an equilateral triangle
-  xin = c60*(inPts[0][0] - inPts[1][0]) - s60*(inPts[0][1] - inPts[1][1]) + inPts[1][0]
-  yin = s60*(inPts[0][0] - inPts[1][0]) + c60*(inPts[0][1] - inPts[1][1]) + inPts[1][1]
-
-  inPts.append([np.int(xin), np.int(yin)])
-
-  xout = c60*(outPts[0][0] - outPts[1][0]) - s60*(outPts[0][1] - outPts[1][1]) + outPts[1][0]
-  yout = s60*(outPts[0][0] - outPts[1][0]) + c60*(outPts[0][1] - outPts[1][1]) + outPts[1][1]
-
-  outPts.append([np.int(xout), np.int(yout)])
-  # Now we can use estimateRigidTransform for calculating the similarity transform.
-  tform = cv2.estimateAffinePartial2D(np.array([inPts]), np.array([outPts]))
-  return tform[0]
-
-# Normalizes a facial image to a standard size given by outSize.
-# Normalization is done based on Dlib's landmark points passed as pointsIn
-# After normalization, left corner of the left eye is at (0.3 * w, h/3 )
-# and right corner of the right eye is at ( 0.7 * w, h / 3) where w and h
-# are the width and height of outSize.
-def normalizeImagesAndLandmarks(outSize, imIn, pointsIn):
-  h, w = outSize
-
-  # Corners of the eye in input image
-  if len(pointsIn) == 68:
-    eyecornerSrc = [pointsIn[36], pointsIn[45]]
-  elif len(pointsIn) == 5:
-    eyecornerSrc = [pointsIn[2], pointsIn[0]]
-
-  # Corners of the eye in normalized image
-  eyecornerDst = [(np.int(0.3 * w), np.int(h/3)),
-                  (np.int(0.7 * w), np.int(h/3))]
-
-  # Calculate similarity transform
-  tform = similarityTransform(eyecornerSrc, eyecornerDst)
-  imOut = np.zeros(imIn.shape, dtype=imIn.dtype)
-
-  # Apply similarity transform to input image
-  imOut = cv2.warpAffine(imIn, tform, (w, h))
-
-  # reshape pointsIn from numLandmarks x 2 to numLandmarks x 1 x 2
-  points2 = np.reshape(pointsIn, (pointsIn.shape[0], 1, pointsIn.shape[1]))
-
-  # Apply similarity transform to landmarks
-  pointsOut = cv2.transform(points2, tform)
-
-  # reshape pointsOut to numLandmarks x 2
-  pointsOut = np.reshape(pointsOut, (pointsIn.shape[0], pointsIn.shape[1]))
-
-  return imOut, pointsOut
-
-def alignFace(imIn, faceRect, landmarkDetector, outSize):
-    # Corners of the eye in input image
-    (w, h) = outSize
-    landmarks = landmarkDetector(cv2.cvtColor(imIn, cv2.COLOR_BGR2RGB), faceRect)
-    pointsIn = np.array(dlibLandmarksToPoints(landmarks))
-
-    eyecornerSrc = [pointsIn[2], pointsIn[0]]
-
-    # Corners of the eye in normalized image
-    eyecornerDst = [(np.int(0.2 * w), np.int(h/3)),
-                  (np.int(0.8 * w), np.int(h/3))]
-
-    # Calculate similarity transform
-    tform = similarityTransform(eyecornerSrc, eyecornerDst)
-
-    imIn = np.float32(imIn)/255.0
-    imOut = np.zeros(imIn.shape, dtype=imIn.dtype)
-
-    # Apply similarity transform to input image
-    imOut = cv2.warpAffine(imIn, tform, outSize)
-
-    imOut = np.uint8(imOut*255)
-
-    return imOut
-
-
-# find the point closest to an array of points
-# pointsArray is a Nx2 and point is 1x2 ndarray
-def findIndex(pointsArray, point):
-  dist = np.linalg.norm(pointsArray-point, axis=1)
-  minIndex = np.argmin(dist)
-  return minIndex
-
-
-# Check if a point is inside a rectangle
-def rectContains(rect, point):
-  if point[0] < rect[0]:
-    return False
-  elif point[1] < rect[1]:
-    return False
-  elif point[0] > rect[2]:
-    return False
-  elif point[1] > rect[3]:
-    return False
-  return True
-
-
-# Calculate Delaunay triangles for set of points
-# Returns the vector of indices of 3 points for each triangle
-def calculateDelaunayTriangles(rect, points):
-
-  # Create an instance of Subdiv2D
-  subdiv = cv2.Subdiv2D(rect)
-
-  # Insert points into subdiv
-  for p in points:
-    subdiv.insert((p[0], p[1]))
-
-  # Get Delaunay triangulation
-  triangleList = subdiv.getTriangleList()
-
-  # Find the indices of triangles in the points array
-  delaunayTri = []
-
-  for t in triangleList:
-    # The triangle returned by getTriangleList is
-    # a list of 6 coordinates of the 3 points in
-    # x1, y1, x2, y2, x3, y3 format.
-    # Store triangle as a list of three points
-    pt = []
-    pt.append((t[0], t[1]))
-    pt.append((t[2], t[3]))
-    pt.append((t[4], t[5]))
-
-    pt1 = (t[0], t[1])
-    pt2 = (t[2], t[3])
-    pt3 = (t[4], t[5])
-
-    if rectContains(rect, pt1) and rectContains(rect, pt2) and rectContains(rect, pt3):
-      # Variable to store a triangle as indices from list of points
-      ind = []
-      # Find the index of each vertex in the points list
-      for j in range(0, 3):
-        for k in range(0, len(points)):
-          if(abs(pt[j][0] - points[k][0]) < 1.0 and abs(pt[j][1] - points[k][1]) < 1.0):
-            ind.append(k)
-        # Store triangulation as a list of indices
-      if len(ind) == 3:
-        delaunayTri.append((ind[0], ind[1], ind[2]))
-
-  return delaunayTri
-
-# Apply affine transform calculated using srcTri and dstTri to src and
-# output an image of size.
-def applyAffineTransform(src, srcTri, dstTri, size):
-
-  # Given a pair of triangles, find the affine transform.
-  warpMat = cv2.getAffineTransform(np.float32(srcTri), np.float32(dstTri))
-
-  # Apply the Affine Transform just found to the src image
-  dst = cv2.warpAffine(src, warpMat, (size[0], size[1]), None,
-             flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT_101)
-
-  return dst
-
-# Warps and alpha blends triangular regions from img1 and img2 to img
-def warpTriangle(img1, img2, t1, t2):
-  # Find bounding rectangle for each triangle
-  r1 = cv2.boundingRect(np.float32([t1]))
-  r2 = cv2.boundingRect(np.float32([t2]))
-
-  # Offset points by left top corner of the respective rectangles
-  t1Rect = []
-  t2Rect = []
-  t2RectInt = []
-
-  for i in range(0, 3):
-    t1Rect.append(((t1[i][0] - r1[0]), (t1[i][1] - r1[1])))
-    t2Rect.append(((t2[i][0] - r2[0]), (t2[i][1] - r2[1])))
-    t2RectInt.append(((t2[i][0] - r2[0]), (t2[i][1] - r2[1])))
-
-  # Get mask by filling triangle
-  mask = np.zeros((r2[3], r2[2], 3), dtype=np.float32)
-  cv2.fillConvexPoly(mask, np.int32(t2RectInt), (1.0, 1.0, 1.0), 16, 0)
-
-  # Apply warpImage to small rectangular patches
-  img1Rect = img1[r1[1]:r1[1] + r1[3], r1[0]:r1[0] + r1[2]]
-
-  size = (r2[2], r2[3])
-
-  img2Rect = applyAffineTransform(img1Rect, t1Rect, t2Rect, size)
-
-  img2Rect = img2Rect * mask
-
-  # Copy triangular region of the rectangular patch to the output image
-  img2[r2[1]:r2[1]+r2[3], r2[0]:r2[0]+r2[2]] = img2[r2[1]:r2[1]+r2[3], r2[0]:r2[0]+r2[2]] * ((1.0, 1.0, 1.0) - mask)
-  img2[r2[1]:r2[1]+r2[3], r2[0]:r2[0]+r2[2]] = img2[r2[1]:r2[1]+r2[3], r2[0]:r2[0]+r2[2]] + img2Rect
-
-# detect facial landmarks in image
-def getLandmarks(faceDetector, landmarkDetector, im, FACE_DOWNSAMPLE_RATIO = 1):
-  points = []
-  imSmall = cv2.resize(im,None,
-                       fx=1.0/FACE_DOWNSAMPLE_RATIO,
-                       fy=1.0/FACE_DOWNSAMPLE_RATIO,
-                       interpolation = cv2.INTER_LINEAR)
-
-  faceRects = faceDetector(imSmall, 0)
-
-  if len(faceRects) > 0:
-    maxArea = 0
-    maxRect = None
-    # TODO: test on images with multiple faces
-    for face in faceRects:
-      if face.area() > maxArea:
-        maxArea = face.area()
-        maxRect = [face.left(),
-                   face.top(),
-                   face.right(),
-                   face.bottom()
-                  ]
-
-    rect = dlib.rectangle(*maxRect)
-    scaledRect = dlib.rectangle(int(rect.left()*FACE_DOWNSAMPLE_RATIO),
-                             int(rect.top()*FACE_DOWNSAMPLE_RATIO),
-                             int(rect.right()*FACE_DOWNSAMPLE_RATIO),
-                             int(rect.bottom()*FACE_DOWNSAMPLE_RATIO))
-
-    landmarks = landmarkDetector(im, scaledRect)
-    points = dlibLandmarksToPoints(landmarks)
-  return points
-
-# Warps an image in a piecewise affine manner.
-# The warp is defined by the movement of landmark points specified by pointsIn
-# to a new location specified by pointsOut. The triangulation beween points is specified
-# by their indices in delaunayTri.
-def warpImage(imIn, pointsIn, pointsOut, delaunayTri):
-  h, w, ch = imIn.shape
-  # Output image
-  imOut = np.zeros(imIn.shape, dtype=imIn.dtype)
-
-  # Warp each input triangle to output triangle.
-  # The triangulation is specified by delaunayTri
-  for j in range(0, len(delaunayTri)):
-    # Input and output points corresponding to jth triangle
-    tin = []
-    tout = []
-
-    for k in range(0, 3):
-      # Extract a vertex of input triangle
-      pIn = pointsIn[delaunayTri[j][k]]
-      # Make sure the vertex is inside the image.
-      pIn = constrainPoint(pIn, w, h)
-
-      # Extract a vertex of the output triangle
-      pOut = pointsOut[delaunayTri[j][k]]
-      # Make sure the vertex is inside the image.
-      pOut = constrainPoint(pOut, w, h)
-
-      # Push the input vertex into input triangle
-      tin.append(pIn)
-      # Push the output vertex into output triangle
-      tout.append(pOut)
-
-    # Warp pixels inside input triangle to output triangle.
-    warpTriangle(imIn, imOut, tin, tout)
-  return imOut

Facial_Recognition_Wrapper(abandoned).py

@@ -1,290 +0,0 @@
-import sys
-import os
-import math
-import cv2
-import dlib
-import numpy as np
-import Facial_Recognition_Render as fr
-import _pickle as cPickle
-import glob
-import DBHelper
-
-faceWidth = 320
-faceHeight = 320
-SKIP_FRAMES = 1
-
-
-def alignFace(imFace, landmarks):
-    l_x = landmarks[39][0]
-    l_y = landmarks[39][1]
-    r_x = landmarks[42][0]
-    r_y = landmarks[42][1]
-    dy = r_y - l_y
-    dx = r_x - l_x
-    # Convert from radians to degrees
-    angle = math.atan2(dy, dx) * 180.0 / math.pi
-
-    eyesCenter = ((l_x + r_x) * 0.5, (l_y + r_y) * 0.5)
-    rotMatrix = cv2.getRotationMatrix2D(eyesCenter, angle, 1)
-    alignedImFace = np.zeros(imFace.shape, dtype=np.uint8)
-    alignedImFace = cv2.warpAffine(imFace, rotMatrix, (imFace.shape[1], imFace.shape[0]))
-    return alignedImFace
-
-
-def face_detector_haarcascade(image):
-    grey = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-
-    resize_fx = 1
-    resize_fy = 1
-    grey = cv2.resize(grey, dsize=None, fx=resize_fx, fy=resize_fy, interpolation=cv2.INTER_AREA)
-
-    pwd = sys.path[0]
-    classfier = cv2.CascadeClassifier(pwd + "/Facial_models/haarcascade_frontalface_alt2.xml")
-
-    faceRects = classfier.detectMultiScale(grey, scaleFactor=1.2, minNeighbors=1, minSize=(16, 16))
-
-    if len(faceRects) > 0:
-        for faceRect in faceRects:
-            x, y, w, h = faceRect
-            x = int(x / resize_fx)
-            y = int(y / resize_fy)
-            w = int(w / resize_fx)
-            h = int(h / resize_fy)
-            cv2.rectangle(image, (x - 10, y - 10), (x + w + 10, y + h + 10), (0, 255, 0), 5)
-
-    return image
-
-
-def face_detector_ssd(image):
-    pwd = sys.path[0]
-    net = cv2.dnn.readNetFromCaffe(pwd + "/Facial_models/deploy.prototxt",
-                                   pwd + "/Facial_models/res10_300x300_ssd_iter_140000_fp16.caffemodel")
-
-    resize = (300, 300)
-    confidence_thres = 0.65
-
-    blob = cv2.dnn.blobFromImage(cv2.resize(image, dsize=resize), 1.0, resize, (104.0, 177.0, 123.0))
-    # blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 1.0, (300, 300), (104.0, 177.0, 123.0))
-
-    net.setInput(blob)
-
-    detections = net.forward()
-
-    h, w, c = image.shape
-
-    for i in range(0, detections.shape[2]):
-        confidence = detections[0, 0, i, 2]
-        if confidence > confidence_thres:
-            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
-            (startX, startY, endX, endY) = box.astype("int")
-            text = "{:.2f}%".format(confidence * 100)
-            y = startY - 10 if startY - 10 > 10 else startY + 10
-            cv2.rectangle(image, (startX, startY), (endX, endY), (0, 255, 0), 5)
-            cv2.putText(image, text, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 1.00, (0, 255, 0), 3)
-
-    return image
-
-
-def training_data_loader():
-    imagesFolder = sys.path[0] + "/Facial_images/face_rec/train/"
-    subfolders = []
-
-    for x in os.listdir(imagesFolder):
-        xpath = os.path.join(imagesFolder, x)
-        if os.path.isdir(xpath):
-            subfolders.append(xpath)
-
-    imagePaths = []
-    labels = []
-    labelsMap = {}
-    labelsMap[-1] = "unknown"
-
-    for i, subfolder in enumerate(subfolders):
-        labelsMap[i] = DBHelper.get_firstname(os.path.basename(subfolder)) + "_" + DBHelper.get_lastname(
-            os.path.basename(subfolder))
-        for x in os.listdir(subfolder):
-            xpath = os.path.join(subfolder, x)
-            if x.endswith('jpg') or x.endswith('pgm'):
-                imagePaths.append(xpath)
-                labels.append(i)
-
-    imagesFaceTrain = []
-    labelsFaceTrain = []
-
-    faceDetector = dlib.get_frontal_face_detector()
-    landmarkDetector = dlib.shape_predictor(sys.path[0] + "/Facial_models/shape_predictor_68_face_landmarks.dat")
-
-    for j, imagePath in enumerate(imagePaths):
-        im = cv2.imread(imagePath, 0)
-        imHeight, imWidth = im.shape[:2]
-
-        landmarks = fr.getLandmarks(faceDetector, landmarkDetector, im)
-
-        landmarks = np.array(landmarks)
-
-        if len(landmarks) == 68:
-            x1Limit = landmarks[0][0] - (landmarks[36][0] - landmarks[0][0])
-            x2Limit = landmarks[16][0] + (landmarks[16][0] - landmarks[45][0])
-            y1Limit = landmarks[27][1] - 3 * (landmarks[30][1] - landmarks[27][1])
-            y2Limit = landmarks[8][1] + (landmarks[30][1] - landmarks[29][1])
-
-            x1 = max(x1Limit, 0)
-            x2 = min(x2Limit, imWidth)
-            y1 = max(y1Limit, 0)
-            y2 = min(y2Limit, imHeight)
-            imFace = im[y1:y2, x1:x2]
-
-            alignedFace = alignFace(imFace, landmarks)
-            alignedFace = cv2.resize(alignedFace, (faceHeight, faceWidth))
-
-            imagesFaceTrain.append(np.float32(alignedFace) / 255.0)
-            labelsFaceTrain.append(labels[j])
-
-    return imagesFaceTrain, labelsFaceTrain, labelsMap
-
-
-def training_recognizer(rec_type):
-    imagesFaceTrain, labelsFaceTrain, labelsMap = training_data_loader()
-
-    if (rec_type == 'LBPH'):
-        faceRecognizer = cv2.face.LBPHFaceRecognizer_create()
-        print("Training using LBPH Faces")
-    elif (rec_type == 'Eigen'):
-        faceRecognizer = cv2.face.EigenFaceRecognizer_create()
-        print("Training using Eigen Faces")
-    elif (rec_type == 'Fisher'):
-        faceRecognizer = cv2.face.FisherFaceRecognizer_create()
-        print("Training using Fisher Faces")
-
-    faceRecognizer.train(imagesFaceTrain, np.array(labelsFaceTrain))
-    faceRecognizer.write(sys.path[0] + '/Facial_models/face_rec_model.yml')
-
-    with open(sys.path[0] + '/Facial_models/labels_map.pkl', 'wb') as f:
-        cPickle.dump(labelsMap, f)
-
-
-def face_recognition_inference(rec_type):
-    # testFiles = glob.glob(sys.path[0]+'/Facial_test_images/face_rec/test/*.jpg')
-    # testFiles.sort()
-    i = 0
-    correct = 0
-    error = 0
-    faceDetector = dlib.get_frontal_face_detector()
-    print(sys.path[0])
-    landmarkDetector = dlib.shape_predictor(sys.path[0] + '/Facial_models/shape_predictor_68_face_landmarks.dat')
-
-    if rec_type == 'LBPH':
-        faceRecognizer = cv2.face.LBPHFaceRecognizer_create()
-        print("Test using LBPH Faces")
-    elif rec_type == 'Eigen':
-        faceRecognizer = cv2.face.EigenFaceRecognizer_create()
-        print("Test using Eigen Faces")
-    elif rec_type == 'Fisher':
-        faceRecognizer = cv2.face.FisherFaceRecognizer_create()
-        print("Test using Fisher Faces")
-
-    faceRecognizer.read(sys.path[0] + '/Facial_models/face_rec_model.yml')
-    labelsMap = np.load(sys.path[0] + '/Facial_models/labels_map.pkl', allow_pickle=True)
-
-    cam = cv2.VideoCapture(0)
-
-    while DBHelper.get_power() == "on":
-        # imagePath = testFiles[i]
-        success, original = cam.read()
-        im = cv2.resize(original, (640, 480))
-        i += 1
-
-        im = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
-        imHeight, imWidth = im.shape[:2]
-
-        landmarks = fr.getLandmarks(faceDetector, landmarkDetector, im)
-        landmarks = np.array(landmarks)
-
-        cond = False
-        cond2 = False
-
-        if len(landmarks) == 68:
-            x1Limit = landmarks[0][0] - (landmarks[36][0] - landmarks[0][0])
-            x2Limit = landmarks[16][0] + (landmarks[16][0] - landmarks[45][0])
-            y1Limit = landmarks[27][1] - 3 * (landmarks[30][1] - landmarks[27][1])
-            y2Limit = landmarks[8][1] + (landmarks[30][1] - landmarks[29][1])
-
-            x1 = max(x1Limit, 0)
-            x2 = min(x2Limit, imWidth)
-            y1 = max(y1Limit, 0)
-            y2 = min(y2Limit, imHeight)
-            imFace = im[y1:y2, x1:x2]
-
-            alignedFace = alignFace(imFace, landmarks)
-            alignedFace = cv2.resize(alignedFace, (faceHeight, faceWidth))
-            imFaceFloat = np.float32(alignedFace) / 255.0
-
-            predictedLabel = -1
-            predictedLabel, score = faceRecognizer.predict(imFaceFloat)
-            center = (int((x1 + x2) / 2), int((y1 + y2) / 2))
-            radius = int((y2 - y1) / 2.0)
-            text = '{} {}%'.format(labelsMap[predictedLabel], round(score, 5))
-            cv2.rectangle(original, (x1, y1), (x2, y2), (0, 255, 0), 5)
-            cv2.putText(original, text, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 3)
-            cond = True
-
-        if cond:
-            DBHelper.set_motor("on")
-            DBHelper.set_alarm("off")
-        elif not cond:
-            DBHelper.set_motor("off")
-            DBHelper.set_alarm("on")
-
-        cv2.imshow('Face Recognition Demo', original)
-
-        k = cv2.waitKey(10)
-
-    DBHelper.set_alarm("off")
-    DBHelper.set_motor("off")
-    cam.release()
-    cv2.destroyAllWindows()
-
-
-if __name__ == "__main__":
-    mode = 'test'
-    rec_type = 'LBPH'  # 'LBPH' 'Fisher' 'Eigen'
-
-    if mode == 'train':
-        training_recognizer(rec_type)
-    elif mode == 'test':
-        face_recognition_inference(rec_type)
-
-    # video process (keep it in case if needed)
-    '''
-    cameraCapture = cv2.VideoCapture(1)
-    success, frame = cameraCapture.read()
-
-    while success and cv2.waitKey(1) == -1:
-        success, frame = cameraCapture.read()
-        face_detector_ssd(frame)
-        cv2.imshow("video", frame)
-
-    cameraCapture.release()
-    cv2.destroyAllWindows() 
-    '''
-
-    # image process (keep it in case if needed)
-    '''
-    image_name = "8.jpg"
-    split_name = image_name.split(".")
-
-    image_read_path = sys.path[0]+"/Facial_test_images/"+image_name
-    image_save_path = sys.path[0]+"/Facial_test_images/output/"+split_name[0]+"_result."+split_name[1]
-
-    image = cv2.imread(image_read_path)
-
-    image = face_detector_ssd(image)
-    image = face_detector_haarcascade(image)
-
-    print(image_save_path)
-
-    cv2.imwrite(image_save_path, image)
-    cv2.imshow("result", image)
-    cv2.waitKey()
-    cv2.destroyAllWindows()
-    '''