Integrated Facial Software to Database and Main Program.
This commit is contained in:
parent
9f7adf2762
commit
e1f6e307fa
6 changed files with 117 additions and 110 deletions
|
@ -5,74 +5,71 @@ import sys
|
||||||
import numpy as np
|
import numpy as np
|
||||||
|
|
||||||
try:
|
try:
|
||||||
import cPickle # Python2.
|
import cPickle # Python2.
|
||||||
except ImportError:
|
except ImportError:
|
||||||
import _pickle as cPickle # Python3.
|
import _pickle as cPickle # Python3.
|
||||||
|
|
||||||
|
|
||||||
def enroll_face_dataset():
|
def enroll_face_dataset():
|
||||||
pwd = sys.path[0]
|
pwd = sys.path[0]
|
||||||
PREDICTOR_PATH = pwd + '/Facial_models/shape_predictor_68_face_landmarks.dat'
|
PREDICTOR_PATH = pwd + '/Facial_models/shape_predictor_68_face_landmarks.dat'
|
||||||
FACE_RECOGNITION_MODEL_PATH = pwd + '/Facial_models/dlib_face_recognition_resnet_model_v1.dat'
|
FACE_RECOGNITION_MODEL_PATH = pwd + '/Facial_models/dlib_face_recognition_resnet_model_v1.dat'
|
||||||
|
|
||||||
faceDetector = dlib.get_frontal_face_detector()
|
faceDetector = dlib.get_frontal_face_detector()
|
||||||
shapePredictor = dlib.shape_predictor(PREDICTOR_PATH)
|
shapePredictor = dlib.shape_predictor(PREDICTOR_PATH)
|
||||||
faceRecognizer = dlib.face_recognition_model_v1(FACE_RECOGNITION_MODEL_PATH)
|
faceRecognizer = dlib.face_recognition_model_v1(FACE_RECOGNITION_MODEL_PATH)
|
||||||
|
|
||||||
|
faceDatasetFolder = pwd + '/Facial_images/face_rec/train/'
|
||||||
|
|
||||||
faceDatasetFolder = pwd + '/Facial_images/face_rec/train/'
|
subfolders = []
|
||||||
|
for x in os.listdir(faceDatasetFolder):
|
||||||
|
xpath = os.path.join(faceDatasetFolder, x)
|
||||||
|
if os.path.isdir(xpath):
|
||||||
|
subfolders.append(xpath)
|
||||||
|
|
||||||
subfolders = []
|
nameLabelMap = {}
|
||||||
for x in os.listdir(faceDatasetFolder):
|
labels = []
|
||||||
xpath = os.path.join(faceDatasetFolder, x)
|
imagePaths = []
|
||||||
if os.path.isdir(xpath):
|
for i, subfolder in enumerate(subfolders):
|
||||||
subfolders.append(xpath)
|
for x in os.listdir(subfolder):
|
||||||
|
xpath = os.path.join(subfolder, x)
|
||||||
|
if x.endswith('jpg'):
|
||||||
|
imagePaths.append(xpath)
|
||||||
|
labels.append(i)
|
||||||
|
nameLabelMap[xpath] = subfolder.split('/')[-1]
|
||||||
|
|
||||||
|
index = {}
|
||||||
|
i = 0
|
||||||
|
faceDescriptors = None
|
||||||
|
for imagePath in imagePaths:
|
||||||
|
# print("processing: {}".format(imagePath))
|
||||||
|
img = cv2.imread(imagePath)
|
||||||
|
|
||||||
nameLabelMap = {}
|
faces = faceDetector(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
|
||||||
labels = []
|
|
||||||
imagePaths = []
|
|
||||||
for i, subfolder in enumerate(subfolders):
|
|
||||||
for x in os.listdir(subfolder):
|
|
||||||
xpath = os.path.join(subfolder, x)
|
|
||||||
if x.endswith('jpg'):
|
|
||||||
imagePaths.append(xpath)
|
|
||||||
labels.append(i)
|
|
||||||
nameLabelMap[xpath] = subfolder.split('/')[-1]
|
|
||||||
|
|
||||||
index = {}
|
# print("{} Face(s) found".format(len(faces)))
|
||||||
i = 0
|
|
||||||
faceDescriptors = None
|
|
||||||
for imagePath in imagePaths:
|
|
||||||
#print("processing: {}".format(imagePath))
|
|
||||||
img = cv2.imread(imagePath)
|
|
||||||
|
|
||||||
faces = faceDetector(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
|
for k, face in enumerate(faces):
|
||||||
|
|
||||||
#print("{} Face(s) found".format(len(faces)))
|
shape = shapePredictor(cv2.cvtColor(img, cv2.COLOR_BGR2RGB), face)
|
||||||
|
|
||||||
for k, face in enumerate(faces):
|
landmarks = [(p.x, p.y) for p in shape.parts()]
|
||||||
|
|
||||||
shape = shapePredictor(cv2.cvtColor(img, cv2.COLOR_BGR2RGB), face)
|
faceDescriptor = faceRecognizer.compute_face_descriptor(img, shape)
|
||||||
|
|
||||||
landmarks = [(p.x, p.y) for p in shape.parts()]
|
faceDescriptorList = [x for x in faceDescriptor]
|
||||||
|
faceDescriptorNdarray = np.asarray(faceDescriptorList, dtype=np.float64)
|
||||||
|
faceDescriptorNdarray = faceDescriptorNdarray[np.newaxis, :]
|
||||||
|
|
||||||
faceDescriptor = faceRecognizer.compute_face_descriptor(img, shape)
|
if faceDescriptors is None:
|
||||||
|
faceDescriptors = faceDescriptorNdarray
|
||||||
|
else:
|
||||||
|
faceDescriptors = np.concatenate((faceDescriptors, faceDescriptorNdarray), axis=0)
|
||||||
|
|
||||||
|
index[i] = nameLabelMap[imagePath]
|
||||||
|
i += 1
|
||||||
|
|
||||||
faceDescriptorList = [x for x in faceDescriptor]
|
# Write descriors and index to disk
|
||||||
faceDescriptorNdarray = np.asarray(faceDescriptorList, dtype=np.float64)
|
np.save(pwd + '/Facial_models/descriptors.npy', faceDescriptors)
|
||||||
faceDescriptorNdarray = faceDescriptorNdarray[np.newaxis, :]
|
with open(pwd + '/Facial_models/index.pkl', 'wb') as f:
|
||||||
|
cPickle.dump(index, f)
|
||||||
|
|
||||||
if faceDescriptors is None:
|
|
||||||
faceDescriptors = faceDescriptorNdarray
|
|
||||||
else:
|
|
||||||
faceDescriptors = np.concatenate((faceDescriptors, faceDescriptorNdarray), axis=0)
|
|
||||||
|
|
||||||
index[i] = nameLabelMap[imagePath]
|
|
||||||
i += 1
|
|
||||||
|
|
||||||
# Write descriors and index to disk
|
|
||||||
np.save(pwd+'/Facial_models/descriptors.npy', faceDescriptors)
|
|
||||||
with open(pwd+'/Facial_models/index.pkl', 'wb') as f:
|
|
||||||
cPickle.dump(index, f)
|
|
||||||
|
|
|
@ -1,13 +1,13 @@
|
||||||
import os,sys,time
|
import os, sys, time
|
||||||
import dlib
|
import dlib
|
||||||
import cv2
|
import cv2
|
||||||
import numpy as np
|
import numpy as np
|
||||||
|
import DBHelper
|
||||||
|
|
||||||
try:
|
try:
|
||||||
import cPickle # Python 2
|
import cPickle # Python 2
|
||||||
except ImportError:
|
except ImportError:
|
||||||
import _pickle as cPickle # Python 3
|
import _pickle as cPickle # Python 3
|
||||||
|
|
||||||
|
|
||||||
pwd = sys.path[0]
|
pwd = sys.path[0]
|
||||||
PREDICTOR_PATH = pwd + '/Facial_models/shape_predictor_68_face_landmarks.dat'
|
PREDICTOR_PATH = pwd + '/Facial_models/shape_predictor_68_face_landmarks.dat'
|
||||||
|
@ -20,75 +20,83 @@ faceDetector = dlib.get_frontal_face_detector()
|
||||||
shapePredictor = dlib.shape_predictor(PREDICTOR_PATH)
|
shapePredictor = dlib.shape_predictor(PREDICTOR_PATH)
|
||||||
faceRecognizer = dlib.face_recognition_model_v1(FACE_RECOGNITION_MODEL_PATH)
|
faceRecognizer = dlib.face_recognition_model_v1(FACE_RECOGNITION_MODEL_PATH)
|
||||||
|
|
||||||
index = np.load(pwd+'/Facial_models/index.pkl', allow_pickle=True)
|
index = np.load(pwd + '/Facial_models/index.pkl', allow_pickle=True)
|
||||||
faceDescriptorsEnrolled = np.load(pwd+'/Facial_models/descriptors.npy')
|
faceDescriptorsEnrolled = np.load(pwd + '/Facial_models/descriptors.npy')
|
||||||
|
|
||||||
|
cam = cv2.VideoCapture(0)
|
||||||
cam = cv2.VideoCapture(1)
|
|
||||||
count = 0
|
count = 0
|
||||||
|
|
||||||
x1 = x2 = y1 = y2 = 0
|
x1 = x2 = y1 = y2 = 0
|
||||||
|
|
||||||
while True:
|
cond = False
|
||||||
t = time.time()
|
|
||||||
success, im = cam.read()
|
|
||||||
|
|
||||||
if not success:
|
while DBHelper.get_power() == "on":
|
||||||
print('cannot capture input from camera')
|
t = time.time()
|
||||||
break
|
success, im = cam.read()
|
||||||
|
|
||||||
|
if not success:
|
||||||
|
print('cannot capture input from camera')
|
||||||
|
break
|
||||||
|
|
||||||
if (count % SKIP_FRAMES) == 0:
|
if (count % SKIP_FRAMES) == 0:
|
||||||
|
|
||||||
img = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
|
img = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
|
||||||
faces = faceDetector(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
|
faces = faceDetector(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
|
||||||
|
|
||||||
for face in faces:
|
for face in faces:
|
||||||
|
|
||||||
shape = shapePredictor(cv2.cvtColor(img, cv2.COLOR_BGR2RGB), face)
|
shape = shapePredictor(cv2.cvtColor(img, cv2.COLOR_BGR2RGB), face)
|
||||||
|
|
||||||
x1 = face.left()
|
x1 = face.left()
|
||||||
y1 = face.top()
|
y1 = face.top()
|
||||||
x2 = face.right()
|
x2 = face.right()
|
||||||
y2 = face.bottom()
|
y2 = face.bottom()
|
||||||
|
|
||||||
faceDescriptor = faceRecognizer.compute_face_descriptor(img, shape)
|
faceDescriptor = faceRecognizer.compute_face_descriptor(img, shape)
|
||||||
|
|
||||||
# dlib format to list
|
# dlib format to list
|
||||||
faceDescriptorList = [m for m in faceDescriptor]
|
faceDescriptorList = [m for m in faceDescriptor]
|
||||||
# to numpy array
|
# to numpy array
|
||||||
faceDescriptorNdarray = np.asarray(faceDescriptorList, dtype=np.float64)
|
faceDescriptorNdarray = np.asarray(faceDescriptorList, dtype=np.float64)
|
||||||
faceDescriptorNdarray = faceDescriptorNdarray[np.newaxis, :]
|
faceDescriptorNdarray = faceDescriptorNdarray[np.newaxis, :]
|
||||||
|
|
||||||
# Euclidean distances
|
# Euclidean distances
|
||||||
distances = np.linalg.norm(faceDescriptorsEnrolled - faceDescriptorNdarray, axis=1)
|
distances = np.linalg.norm(faceDescriptorsEnrolled - faceDescriptorNdarray, axis=1)
|
||||||
|
|
||||||
# Calculate minimum distance and index of face
|
# Calculate minimum distance and index of face
|
||||||
argmin = np.argmin(distances) # index
|
argmin = np.argmin(distances) # index
|
||||||
minDistance = distances[argmin] # minimum distance
|
minDistance = distances[argmin] # minimum distance
|
||||||
|
|
||||||
|
if minDistance <= THRESHOLD:
|
||||||
|
label = DBHelper.get_firstname(index[argmin]) + "_" + DBHelper.get_lastname(index[argmin])
|
||||||
|
cond = True
|
||||||
|
else:
|
||||||
|
label = 'unknown'
|
||||||
|
cond = False
|
||||||
|
|
||||||
if minDistance <= THRESHOLD:
|
# print("time taken = {:.3f} seconds".format(time.time() - t))
|
||||||
label = index[argmin]
|
|
||||||
else:
|
|
||||||
label = 'unknown'
|
|
||||||
|
|
||||||
#print("time taken = {:.3f} seconds".format(time.time() - t))
|
cv2.rectangle(im, (x1, y1), (x2, y2), (0, 255, 0), 2)
|
||||||
|
font_face = cv2.FONT_HERSHEY_SIMPLEX
|
||||||
|
font_scale = 0.8
|
||||||
|
text_color = (0, 255, 0)
|
||||||
|
printLabel = '{} {:0.4f}'.format(label, minDistance)
|
||||||
|
cv2.putText(im, printLabel, (int(x1), int(y1)), font_face, font_scale, text_color, thickness=2)
|
||||||
|
|
||||||
|
cv2.imshow('img', im)
|
||||||
|
|
||||||
cv2.rectangle(im, (x1, y1), (x2, y2), (0, 255, 0), 2)
|
k = cv2.waitKey(1) & 0xff
|
||||||
font_face = cv2.FONT_HERSHEY_SIMPLEX
|
if k == 27:
|
||||||
font_scale = 0.8
|
break
|
||||||
text_color = (0, 255, 0)
|
|
||||||
printLabel = '{} {:0.4f}'.format(label, minDistance)
|
|
||||||
cv2.putText(im, printLabel, (int(x1), int(y1)) , font_face, font_scale, text_color, thickness=2)
|
|
||||||
|
|
||||||
|
count += 1
|
||||||
|
if cond:
|
||||||
|
DBHelper.set_motor("on")
|
||||||
|
DBHelper.set_alarm("off")
|
||||||
|
elif not cond:
|
||||||
|
DBHelper.set_motor("off")
|
||||||
|
DBHelper.set_alarm("on")
|
||||||
|
|
||||||
cv2.imshow('img', im)
|
DBHelper.set_alarm("off")
|
||||||
|
DBHelper.set_motor("off")
|
||||||
k = cv2.waitKey(1) & 0xff
|
|
||||||
if k == 27:
|
|
||||||
break
|
|
||||||
|
|
||||||
count += 1
|
|
||||||
cv2.destroyAllWindows()
|
cv2.destroyAllWindows()
|
||||||
|
|
|
@ -4,6 +4,7 @@ import math
|
||||||
import cv2
|
import cv2
|
||||||
import Facial_Recognition_Enrollment
|
import Facial_Recognition_Enrollment
|
||||||
|
|
||||||
|
|
||||||
def register_your_face(label):
|
def register_your_face(label):
|
||||||
num_cap = 50
|
num_cap = 50
|
||||||
|
|
||||||
|
@ -37,6 +38,6 @@ if __name__ == "__main__":
|
||||||
register_your_face(label)
|
register_your_face(label)
|
||||||
print("Data saved! Starting enrollment...")
|
print("Data saved! Starting enrollment...")
|
||||||
print()
|
print()
|
||||||
Facial_Recognition_Enrollment.enroll_face_dataset() #Need discuss and modify after intergrate with database.
|
Facial_Recognition_Enrollment.enroll_face_dataset() # Need discuss and modify after intergrate with database.
|
||||||
print("Face registration completed!")
|
print("Face registration completed!")
|
||||||
print()
|
print()
|
||||||
|
|
|
@ -1,5 +1,6 @@
|
||||||
import DBHelper
|
import DBHelper
|
||||||
import Facial_Recognition_Registration
|
import Facial_Recognition_Registration
|
||||||
|
import Facial_Recognition_Enrollment
|
||||||
|
|
||||||
|
|
||||||
def upload_your_face(firstname, lastname, email, phone):
|
def upload_your_face(firstname, lastname, email, phone):
|
||||||
|
@ -13,6 +14,7 @@ def upload_your_face(firstname, lastname, email, phone):
|
||||||
count += 1
|
count += 1
|
||||||
DBHelper.upload_data("User_" + str(count), firstname, lastname, email, phone)
|
DBHelper.upload_data("User_" + str(count), firstname, lastname, email, phone)
|
||||||
Facial_Recognition_Registration.register_your_face("User_" + str(count))
|
Facial_Recognition_Registration.register_your_face("User_" + str(count))
|
||||||
|
Facial_Recognition_Enrollment.enroll_face_dataset()
|
||||||
for i in range(20):
|
for i in range(20):
|
||||||
DBHelper.upload_user_photo("User_" + str(count) + "/" + str(i) + ".jpg")
|
DBHelper.upload_user_photo("User_" + str(count) + "/" + str(i) + ".jpg")
|
||||||
except:
|
except:
|
||||||
|
|
Binary file not shown.
|
@ -1,6 +1,6 @@
|
||||||
import os
|
import os
|
||||||
import DBHelper
|
import DBHelper
|
||||||
import Facial_Recognition_Wrapper
|
import Facial_Recognition_Inference
|
||||||
|
|
||||||
|
|
||||||
def start():
|
def start():
|
||||||
|
@ -32,8 +32,7 @@ def start():
|
||||||
print("Success.")
|
print("Success.")
|
||||||
except:
|
except:
|
||||||
print("No Thieves are registered.")
|
print("No Thieves are registered.")
|
||||||
Facial_Recognition_Wrapper.training_recognizer("LBPH")
|
Facial_Recognition_Inference
|
||||||
Facial_Recognition_Wrapper.face_recognition_inference("LBPH")
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
if __name__ == "__main__":
|
||||||
|
|
Loading…
Reference in a new issue