Integrated Facial Software to Database and Main Program.
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6 changed files with 117 additions and 110 deletions
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@ -5,74 +5,71 @@ import sys
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import numpy as np
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try:
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import cPickle # Python2.
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import cPickle # Python2.
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except ImportError:
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import _pickle as cPickle # Python3.
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import _pickle as cPickle # Python3.
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def enroll_face_dataset():
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pwd = sys.path[0]
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PREDICTOR_PATH = pwd + '/Facial_models/shape_predictor_68_face_landmarks.dat'
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FACE_RECOGNITION_MODEL_PATH = pwd + '/Facial_models/dlib_face_recognition_resnet_model_v1.dat'
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pwd = sys.path[0]
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PREDICTOR_PATH = pwd + '/Facial_models/shape_predictor_68_face_landmarks.dat'
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FACE_RECOGNITION_MODEL_PATH = pwd + '/Facial_models/dlib_face_recognition_resnet_model_v1.dat'
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faceDetector = dlib.get_frontal_face_detector()
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shapePredictor = dlib.shape_predictor(PREDICTOR_PATH)
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faceRecognizer = dlib.face_recognition_model_v1(FACE_RECOGNITION_MODEL_PATH)
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faceDetector = dlib.get_frontal_face_detector()
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shapePredictor = dlib.shape_predictor(PREDICTOR_PATH)
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faceRecognizer = dlib.face_recognition_model_v1(FACE_RECOGNITION_MODEL_PATH)
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faceDatasetFolder = pwd + '/Facial_images/face_rec/train/'
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faceDatasetFolder = pwd + '/Facial_images/face_rec/train/'
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subfolders = []
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for x in os.listdir(faceDatasetFolder):
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xpath = os.path.join(faceDatasetFolder, x)
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if os.path.isdir(xpath):
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subfolders.append(xpath)
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subfolders = []
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for x in os.listdir(faceDatasetFolder):
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xpath = os.path.join(faceDatasetFolder, x)
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if os.path.isdir(xpath):
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subfolders.append(xpath)
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nameLabelMap = {}
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labels = []
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imagePaths = []
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for i, subfolder in enumerate(subfolders):
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for x in os.listdir(subfolder):
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xpath = os.path.join(subfolder, x)
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if x.endswith('jpg'):
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imagePaths.append(xpath)
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labels.append(i)
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nameLabelMap[xpath] = subfolder.split('/')[-1]
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index = {}
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i = 0
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faceDescriptors = None
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for imagePath in imagePaths:
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# print("processing: {}".format(imagePath))
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img = cv2.imread(imagePath)
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nameLabelMap = {}
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labels = []
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imagePaths = []
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for i, subfolder in enumerate(subfolders):
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for x in os.listdir(subfolder):
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xpath = os.path.join(subfolder, x)
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if x.endswith('jpg'):
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imagePaths.append(xpath)
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labels.append(i)
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nameLabelMap[xpath] = subfolder.split('/')[-1]
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faces = faceDetector(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
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index = {}
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i = 0
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faceDescriptors = None
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for imagePath in imagePaths:
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#print("processing: {}".format(imagePath))
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img = cv2.imread(imagePath)
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# print("{} Face(s) found".format(len(faces)))
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faces = faceDetector(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
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for k, face in enumerate(faces):
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#print("{} Face(s) found".format(len(faces)))
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shape = shapePredictor(cv2.cvtColor(img, cv2.COLOR_BGR2RGB), face)
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for k, face in enumerate(faces):
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landmarks = [(p.x, p.y) for p in shape.parts()]
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shape = shapePredictor(cv2.cvtColor(img, cv2.COLOR_BGR2RGB), face)
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faceDescriptor = faceRecognizer.compute_face_descriptor(img, shape)
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landmarks = [(p.x, p.y) for p in shape.parts()]
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faceDescriptorList = [x for x in faceDescriptor]
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faceDescriptorNdarray = np.asarray(faceDescriptorList, dtype=np.float64)
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faceDescriptorNdarray = faceDescriptorNdarray[np.newaxis, :]
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faceDescriptor = faceRecognizer.compute_face_descriptor(img, shape)
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if faceDescriptors is None:
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faceDescriptors = faceDescriptorNdarray
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else:
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faceDescriptors = np.concatenate((faceDescriptors, faceDescriptorNdarray), axis=0)
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faceDescriptorList = [x for x in faceDescriptor]
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faceDescriptorNdarray = np.asarray(faceDescriptorList, dtype=np.float64)
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faceDescriptorNdarray = faceDescriptorNdarray[np.newaxis, :]
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index[i] = nameLabelMap[imagePath]
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i += 1
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if faceDescriptors is None:
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faceDescriptors = faceDescriptorNdarray
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else:
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faceDescriptors = np.concatenate((faceDescriptors, faceDescriptorNdarray), axis=0)
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index[i] = nameLabelMap[imagePath]
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i += 1
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# Write descriors and index to disk
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np.save(pwd+'/Facial_models/descriptors.npy', faceDescriptors)
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with open(pwd+'/Facial_models/index.pkl', 'wb') as f:
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cPickle.dump(index, f)
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# Write descriors and index to disk
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np.save(pwd + '/Facial_models/descriptors.npy', faceDescriptors)
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with open(pwd + '/Facial_models/index.pkl', 'wb') as f:
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cPickle.dump(index, f)
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@ -1,13 +1,13 @@
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import os,sys,time
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import os, sys, time
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import dlib
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import cv2
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import numpy as np
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import DBHelper
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try:
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import cPickle # Python 2
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import cPickle # Python 2
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except ImportError:
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import _pickle as cPickle # Python 3
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import _pickle as cPickle # Python 3
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pwd = sys.path[0]
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PREDICTOR_PATH = pwd + '/Facial_models/shape_predictor_68_face_landmarks.dat'
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@ -20,75 +20,83 @@ faceDetector = dlib.get_frontal_face_detector()
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shapePredictor = dlib.shape_predictor(PREDICTOR_PATH)
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faceRecognizer = dlib.face_recognition_model_v1(FACE_RECOGNITION_MODEL_PATH)
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index = np.load(pwd+'/Facial_models/index.pkl', allow_pickle=True)
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faceDescriptorsEnrolled = np.load(pwd+'/Facial_models/descriptors.npy')
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index = np.load(pwd + '/Facial_models/index.pkl', allow_pickle=True)
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faceDescriptorsEnrolled = np.load(pwd + '/Facial_models/descriptors.npy')
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cam = cv2.VideoCapture(1)
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cam = cv2.VideoCapture(0)
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count = 0
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x1 = x2 = y1 = y2 = 0
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while True:
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t = time.time()
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success, im = cam.read()
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cond = False
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if not success:
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print('cannot capture input from camera')
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break
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while DBHelper.get_power() == "on":
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t = time.time()
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success, im = cam.read()
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if not success:
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print('cannot capture input from camera')
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break
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if (count % SKIP_FRAMES) == 0:
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if (count % SKIP_FRAMES) == 0:
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img = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
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faces = faceDetector(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
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img = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
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faces = faceDetector(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
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for face in faces:
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for face in faces:
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shape = shapePredictor(cv2.cvtColor(img, cv2.COLOR_BGR2RGB), face)
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shape = shapePredictor(cv2.cvtColor(img, cv2.COLOR_BGR2RGB), face)
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x1 = face.left()
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y1 = face.top()
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x2 = face.right()
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y2 = face.bottom()
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x1 = face.left()
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y1 = face.top()
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x2 = face.right()
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y2 = face.bottom()
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faceDescriptor = faceRecognizer.compute_face_descriptor(img, shape)
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faceDescriptor = faceRecognizer.compute_face_descriptor(img, shape)
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# dlib format to list
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faceDescriptorList = [m for m in faceDescriptor]
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# to numpy array
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faceDescriptorNdarray = np.asarray(faceDescriptorList, dtype=np.float64)
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faceDescriptorNdarray = faceDescriptorNdarray[np.newaxis, :]
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# dlib format to list
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faceDescriptorList = [m for m in faceDescriptor]
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# to numpy array
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faceDescriptorNdarray = np.asarray(faceDescriptorList, dtype=np.float64)
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faceDescriptorNdarray = faceDescriptorNdarray[np.newaxis, :]
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# Euclidean distances
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distances = np.linalg.norm(faceDescriptorsEnrolled - faceDescriptorNdarray, axis=1)
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# Calculate minimum distance and index of face
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argmin = np.argmin(distances) # index
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minDistance = distances[argmin] # minimum distance
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# Euclidean distances
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distances = np.linalg.norm(faceDescriptorsEnrolled - faceDescriptorNdarray, axis=1)
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if minDistance <= THRESHOLD:
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label = index[argmin]
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else:
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label = 'unknown'
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# Calculate minimum distance and index of face
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argmin = np.argmin(distances) # index
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minDistance = distances[argmin] # minimum distance
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#print("time taken = {:.3f} seconds".format(time.time() - t))
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if minDistance <= THRESHOLD:
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label = DBHelper.get_firstname(index[argmin]) + "_" + DBHelper.get_lastname(index[argmin])
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cond = True
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else:
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label = 'unknown'
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cond = False
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# print("time taken = {:.3f} seconds".format(time.time() - t))
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cv2.rectangle(im, (x1, y1), (x2, y2), (0, 255, 0), 2)
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font_face = cv2.FONT_HERSHEY_SIMPLEX
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font_scale = 0.8
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text_color = (0, 255, 0)
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printLabel = '{} {:0.4f}'.format(label, minDistance)
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cv2.putText(im, printLabel, (int(x1), int(y1)) , font_face, font_scale, text_color, thickness=2)
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cv2.rectangle(im, (x1, y1), (x2, y2), (0, 255, 0), 2)
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font_face = cv2.FONT_HERSHEY_SIMPLEX
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font_scale = 0.8
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text_color = (0, 255, 0)
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printLabel = '{} {:0.4f}'.format(label, minDistance)
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cv2.putText(im, printLabel, (int(x1), int(y1)), font_face, font_scale, text_color, thickness=2)
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cv2.imshow('img', im)
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cv2.imshow('img', im)
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k = cv2.waitKey(1) & 0xff
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if k == 27:
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break
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k = cv2.waitKey(1) & 0xff
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if k == 27:
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break
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count += 1
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count += 1
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if cond:
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DBHelper.set_motor("on")
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DBHelper.set_alarm("off")
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elif not cond:
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DBHelper.set_motor("off")
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DBHelper.set_alarm("on")
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DBHelper.set_alarm("off")
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DBHelper.set_motor("off")
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cv2.destroyAllWindows()
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@ -4,6 +4,7 @@ import math
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import cv2
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import Facial_Recognition_Enrollment
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def register_your_face(label):
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num_cap = 50
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@ -37,6 +38,6 @@ if __name__ == "__main__":
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register_your_face(label)
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print("Data saved! Starting enrollment...")
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print()
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Facial_Recognition_Enrollment.enroll_face_dataset() #Need discuss and modify after intergrate with database.
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Facial_Recognition_Enrollment.enroll_face_dataset() # Need discuss and modify after intergrate with database.
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print("Face registration completed!")
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print()
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@ -1,5 +1,6 @@
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import DBHelper
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import Facial_Recognition_Registration
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import Facial_Recognition_Enrollment
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def upload_your_face(firstname, lastname, email, phone):
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@ -13,6 +14,7 @@ def upload_your_face(firstname, lastname, email, phone):
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count += 1
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DBHelper.upload_data("User_" + str(count), firstname, lastname, email, phone)
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Facial_Recognition_Registration.register_your_face("User_" + str(count))
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Facial_Recognition_Enrollment.enroll_face_dataset()
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for i in range(20):
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DBHelper.upload_user_photo("User_" + str(count) + "/" + str(i) + ".jpg")
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except:
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Binary file not shown.
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@ -1,6 +1,6 @@
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import os
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import DBHelper
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import Facial_Recognition_Wrapper
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import Facial_Recognition_Inference
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def start():
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@ -32,8 +32,7 @@ def start():
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print("Success.")
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except:
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print("No Thieves are registered.")
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Facial_Recognition_Wrapper.training_recognizer("LBPH")
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Facial_Recognition_Wrapper.face_recognition_inference("LBPH")
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Facial_Recognition_Inference
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if __name__ == "__main__":
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