def main():
try:
idx = sys.argv.index('--in')
path = sys.argv[idx + 1]
except ValueError:
path = None
cam = Camera(path)
if '--lr' in sys.argv:
from lib import LRTracker
ln = LRTracker(cam)
else:
from lib import ContourTracker
ln = ContourTracker(cam)
if '--preview' in sys.argv:
ln.preview(True)
autoplay = '--manual' not in sys.argv
while True:
print(ln.track_line())
if autoplay:
cv2.waitKey(1)
time.sleep(0.1)
else:
cv2.waitKey(0)
cv2.closeAllWindows()
def main():
cam = Camera()
if '--lr' in sys.argv:
from lib import LRTracker
ln = LRTracker(cam)
else:
from lib import ContourTracker
ln = ContourTracker(cam)
if '--preview' in sys.argv:
ln.preview(True)
while True:
print(ln.track_line())
cv2.waitKey(1)
cv2.closeAllWindows()
def ConvertImageToGrayscale(self):
"""
Converts Image into a Grayscale Image for Enhanced Image Reading
Args:
No Args needed
"""
for ImageName in self.ImagesList:
GrayImage = cv2.imread(ImageName, cv2.IMREAD_GRAYSCALE)
ImageName = "Grayscale_" + ImageName
os.chdir(".\Cambire")
cv2.imwrite(ImageName, GrayImage)
cv2.imshow(ImageName, GrayImage)
cv2.waitKey(0)
cv2.closeAllWindows()
os.chdir("..")
import cv2
face_cascade=cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
img=cv2.imread("mypic.jpg",1)
#Face detection works better with grayscale images, for converting bgr to grayscale
grey_img=cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces=face_cascade.detectMultiScale(grey_img, scaleFactor=1.1, minNeighbors=5)
print(faces)
#plotting coordinates on image
for x,y,w,h in faces:
img=cv2.rectangle(img, (x,y), (x+w, y+h), (0,0,255), 2)
resized_img=cv2.resize(img,(int(img.shape[1]/2), int(img.shape[0]/2)) )
cv2.imshow("My pic",resized_img)
cv2.waitKey(0)
cv2.closeAllWindows()
def main():
# GETTING KNOWN ENCODINGS AND NAMES
home = str(os.path.dirname(os.path.abspath(__file__))) + "/../../"
known_encodings_file_path = home + "/data/known_encodings_file.csv"
people_file_path = home + "/data/people_file.csv"
# For storing the encoding of a face
known_encodings_file = Path(known_encodings_file_path)
if known_encodings_file.is_file():
known_encodings = np.genfromtxt(known_encodings_file, delimiter=',')
else:
known_encodings = []
# #For Storing the name corresponding to the encoding
people_file = Path(people_file_path)
if people_file.is_file():
people = np.genfromtxt(people_file, dtype='U',delimiter=',')
else:
people = []
# MAIN WORK
#Capture Video indefinitely
video_capture = cv2.VideoCapture(0)
# time.delay(2)
# TODO: GET FROM DATABASE
# known encodings of persons in database.
# known_encodings = []
# people = []
#Some important variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True
#Eat the Meat, Hmm process the image
while True:
#
# 1.) Capture the frame from the video.
# 2.) Compress it to its 1/4th size for faster speed.
# 3.) If this frame has to be processed, find face_location, face_encodings.
# 4.) Match with the known_encodings and set the name for each face else Unknown
# 5.) Add a border around face.
# if RED:
# unverified or not authenticated
# elif GREEN:
# everything OK ;)
# 6.) Show the frame
#
ret, frame = video_capture.read()
#smaller frame 1/4th of original size
small_frame = cv2.resize(frame, (0,0), fx=.25, fy=.25)
if process_this_frame:
#Find the face locations
face_locations = face_recognition.face_locations(small_frame)
#Find the face encodings 128 Dimensional!!
face_encodings = face_recognition.face_encodings(small_frame, face_locations)
face_names=[]
other = 0 #Count of un-authorised people
for face_encoding in face_encodings:
match = face_recognition.compare_faces(known_encodings, face_encoding)
name = "Unknown"
#Find if this person is in the present people array
for i in range(len(match)):
if match[i]:
name = people[i]
break
if "Unknown" in name:
other += 1
name += str(other)
face_names.append(name)
# Send the names of the people to the parent process
# os.write(3,b'{"dt" : "This is a test"}')
print(face_names, flush=True)
process_this_frame = not process_this_frame
#Display the border
for (top, right, bottom, left),name in zip(face_locations, face_names):
#Scale up the coordinates by 4 to get face
top *= 4
right *= 4
bottom *= 4
left *= 4
#Assuming person in authenticated
color = (0,255,0) #GREEN
if not authorised(name):
#Unauthenticated person
color = (0,0,255) #RED
#print so that parent process in Node.js can use it
print(name,flush=True)
#Display border
cv2.rectangle(frame, (left,top), (right,bottom), color, 2)
# Draw a label with name
cv2.rectangle(frame, (left,bottom-35), (right, bottom), color, cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(frame, name,(left+6, bottom-6), font, 1.0, (255,255,255), 1)
# Display the resulting image with borders and names
cv2.imshow('Video', frame)
# Hit 'q' on keyboard to quit
if cv2.waitKey(100) == 27:
break
#Release handle to the webcam
video_capture.release()
cv2.closeAllWindows()
eyes_cascade.load(cv.samples.findFile('eyeclassifier.xml'))
camInput = cv.VideoCapture(0)
while True:
booleanCheck, frame = camInput.read()
frame_gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
frame_gray = cv.equalizeHist(frame_gray)
faces = face_cascade.detectMultiScale(frame_gray)
for (x, y, w, h) in faces:
center = (x + w // 2, y + h // 2)
frame = cv.ellipse(frame, center, (w // 2, h // 2), 0, 0, 360,
(255, 0, 255), 4)
faceROI = frame_gray[y:y + h, x:x + w]
eyes = eyes_cascade.detectMultiScale(faceROI)
for (x2, y2, w2, h2) in eyes:
eye_center = (x + x2 + w2 // 2, y + y2 + h2 // 2)
radius = int(round((w2 + h2) * 0.25))
frame = cv.circle(frame, eye_center, radius, (255, 0, 0), 4)
cv.imshow('Capture - Face detection', frame)
keyPressed = cv.waitKey(1)
if keyPressed == ord('s'):
break
camInput.release()
cv.closeAllWindows()
def main():
# GETTING KNOWN ENCODINGS AND NAMES
home = str(os.path.dirname(os.path.abspath(__file__))) + "/../../"
known_encodings_file_path = home + "/data/known_encodings_file.csv"
people_file_path = home + "/data/people_file.csv"
# For storing the encoding of a face
known_encodings_file = Path(known_encodings_file_path)
if known_encodings_file.is_file():
known_encodings = np.genfromtxt(known_encodings_file, delimiter=',')
else:
known_encodings = []
# #For Storing the name corresponding to the encoding
people_file = Path(people_file_path)
if people_file.is_file():
people = np.genfromtxt(people_file, dtype='U', delimiter=',')
else:
people = []
# Capture Video indefinitely
video_capture = cv2.VideoCapture(0)
original_width = video_capture.get(cv2.CAP_PROP_FRAME_WIDTH)
original_height = video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT)
# Some important variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True
while True:
#
# 1.) Capture the frame from the video.
# 2.) Compress it to its 1/4th size for faster speed.
# 3.) If this frame has to be processed, find face_location, face_encodings.
# 4.) Match with the known_encodings and set the name for each face else Unknown
# 5.) Add a border around face.
# if RED:
# Criminal
# elif Pink:
# Missing person
# elif Pink:
# unverified or not authenticated
# elif GREEN:
# everything OK ;)
# 6.) Show the frame
#
# Due to QR Code scanning, video element changes the size of video capture,
# which also affected this process(don't know why) so to convert it to original size
if video_capture.get(cv2.CAP_PROP_FRAME_WIDTH
) != original_width or video_capture.get(
cv2.CAP_PROP_FRAME_HEIGHT) != original_height:
video_capture.set(cv2.CAP_PROP_FRAME_WIDTH, original_width)
video_capture.set(cv2.CAP_PROP_FRAME_HEIGHT, original_height)
ret, frame = video_capture.read()
# Don't proceed further until camera is able to capture pics
if not ret:
continue
#smaller frame 1/4th of original size
small_frame = cv2.resize(frame, (0, 0), fx=.25, fy=.25)
if process_this_frame:
#Find the face locations
face_locations = face_recognition.face_locations(small_frame)
#Find the face encodings 128 Dimensional!!
face_encodings = face_recognition.face_encodings(
small_frame, face_locations)
face_names = []
face_type = []
face_number = []
other = 0 #Count of un-authorised people
for face_encoding in face_encodings:
match = face_recognition.compare_faces(known_encodings,
face_encoding,
tolerance=0.5)
name = "Unknown"
vtype = ""
vnumber = ""
#Find if this person is in the present people array
for i in range(len(match)):
if match[i]:
name = people[i][0]
vtype = people[i][1]
vnumber = people[i][2]
break
# Change it, run the loop to find no. of Unknown
if "Unknown" in name:
other += 1
name += str(other)
face_names.append(name)
face_type.append(vtype)
face_number.append(vnumber)
print(face_names, flush=True)
process_this_frame = not process_this_frame
# Display the border
for (top, right, bottom,
left), name, vtype, vnumber in zip(face_locations, face_names,
face_type, face_number):
# Scale up the coordinates by 4 to get face
top *= 4
right *= 4
bottom *= 4
left *= 4
# Assuming person in authenticated
color = (0, 255, 0) #GREEN
if not authorised(name):
# Unauthenticated person
color = (255, 0, 0) #Blue
directory = "./public/photos/"
filename = directory + time.strftime("%Y%m%d-%H%M%S") + ".jpeg"
if not os.path.exists(directory + time.strftime("%Y%m%d-%H")):
os.makedirs(directory + time.strftime("%Y%m%d-%H"))
crop_img = frame[(top):(bottom), (left):(right)]
cv2.imwrite(filename, crop_img)
txt = "Hi, Identified at " + time.strftime(
"%d-%m-%Y %H:%M:%S"
) + ". Use the link to verify or report http://localhost:3000/load"
#send_email(filename,txt)
else:
if (vtype):
if vtype == "Missing":
color = (170, 80, 255) #Pink
Missing_txt = "Missing person found, ID :" + vnumber
print("Missing_txt: ", Missing_txt)
#send_email(filename,Missing_txt)
else:
color = (0, 0, 255) #RED
# Display border
cv2.rectangle(frame, (left, top), (right, bottom), color, 2)
# Draw a label with name
cv2.rectangle(frame, (left, bottom - 35), (right, bottom), color,
cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0,
(255, 255, 255), 1)
# Display the resulting image with borders and names
cv2.imshow('Video', frame)
# Hit 'q' on keyboard to quit
if cv2.waitKey(100) == 27:
break
#Release handle to the webcam
video_capture.release()
cv2.closeAllWindows()
def main():
# GETTING KNOWN ENCODINGS AND NAMES
home = str(os.path.dirname(os.path.abspath(__file__))) + "/../../"
known_encodings_file_path = home + "/data/known_encodings_file.csv"
people_file_path = home + "/data/people_file.csv"
# For storing the encoding of a face
known_encodings_file = Path(known_encodings_file_path)
if known_encodings_file.is_file():
known_encodings = np.genfromtxt(known_encodings_file, delimiter=',')
else:
known_encodings = []
# #For Storing the name corresponding to the encoding
people_file = Path(people_file_path)
if people_file.is_file():
people = np.genfromtxt(people_file, dtype='U', delimiter=',')
else:
people = []
# MAIN WORK
#Capture Video indefinitely
parser = argparse.ArgumentParser()
parser.add_argument('--dlibFacePredictor',
type=str,
help="Path to dlib's face predictor.",
default=os.path.join(
dlibModelDir,
"shape_predictor_68_face_landmarks.dat"))
parser.add_argument('--networkModel',
type=str,
help="Path to Torch network model.",
default='nn4.small2.3d.v1.t7')
# Download the 3D model from:
# https://storage.cmusatyalab.org/openface-models/nn4.small2.3d.v1.t7
parser.add_argument('--imgDim',
type=int,
help="Default image dimension.",
default=96)
parser.add_argument(
'--captureDevice',
type=int,
default=0,
help='Capture device. 0 for latop webcam and 1 for usb webcam')
# parser.add_argument('--width', type=int, default=640)
# parser.add_argument('--height', type=int, default=480)
parser.add_argument('--width', type=int, default=1280)
parser.add_argument('--height', type=int, default=800)
parser.add_argument('--scale', type=int, default=0.25)
parser.add_argument('--threshold', type=float, default=0.5)
parser.add_argument('--cuda', action='store_true')
parser.add_argument('--verbose', action='store_true')
args = parser.parse_args()
align = openface.AlignDlib(args.dlibFacePredictor)
net = openface.TorchNeuralNet(args.networkModel,
imgDim=args.imgDim,
cuda=args.cuda)
video_capture = cv2.VideoCapture(args.captureDevice)
original_width = video_capture.get(cv2.CAP_PROP_FRAME_WIDTH)
original_height = video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT)
# TODO: GET FROM DATABASE
# known encodings of persons in database.
# known_encodings = []
# people = []
#Some important variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True
#Eat the Meat, Hmm process the image
while True:
#
# 1.) Capture the frame from the video.
# 2.) Compress it to its 1/4th size for faster speed.
# 3.) If this frame has to be processed, find face_location, face_encodings.
# 4.) Match with the known_encodings and set the name for each face else Unknown
# 5.) Add a border around face.
# if RED:
# unverified or not authenticated
# elif GREEN:
# everything OK ;)
# 6.) Show the frame
#
# Due to QR Code scanning, video element changes the size of video capture,
# which also affected this process(don't know why) so to convert it to original size
if video_capture.get(cv2.CAP_PROP_FRAME_WIDTH
) != original_width or video_capture.get(
cv2.CAP_PROP_FRAME_HEIGHT) != original_height:
video_capture.set(cv2.CAP_PROP_FRAME_WIDTH, original_width)
video_capture.set(cv2.CAP_PROP_FRAME_HEIGHT, original_height)
ret, frame = video_capture.read()
# Don't proceed further until camera is able to capture pics
if not ret:
continue
#smaller frame 1/4th of original size
small_frame = cv2.resize(frame, (0, 0), fx=.25, fy=.25)
if process_this_frame:
#Find the face locations
face_locations = face_recognition.face_locations(small_frame)
#Find the face encodings 128 Dimensional!!
face_encodings = face_recognition.face_encodings(
small_frame, face_locations)
face_names = []
other = 0 #Count of un-authorised people
for face_encoding in face_encodings:
match = face_recognition.compare_faces(known_encodings,
face_encoding)
name = "Unknown"
#Find if this person is in the present people array
for i in range(len(match)):
if match[i]:
name = people[i]
break
#Change it, run the loop to find no. of Unknown
if "Unknown" in name:
other += 1
name += str(other)
face_names.append(name)
print(face_names)
process_this_frame = not process_this_frame
#Display the border
for (top, right, bottom, left), name in zip(face_locations,
face_names):
#Scale up the coordinates by 4 to get face
top *= 4
right *= 4
bottom *= 4
left *= 4
#Assuming person in authenticated
color = (0, 255, 0) #GREEN
if not authorised(name):
#Unauthenticated person
color = (0, 0, 255) #RED
#print so that parent process in Node.js can use it
# print(name,flush=True)
#Display border
cv2.rectangle(frame, (left, top), (right, bottom), color, 2)
# Draw a label with name
cv2.rectangle(frame, (left, bottom - 35), (right, bottom), color,
cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0,
(255, 255, 255), 1)
# Display the resulting image with borders and names
cv2.imshow('Video', frame)
# Hit 'q' on keyboard to quit
if cv2.waitKey(100) == 27:
break
#Release handle to the webcam
video_capture.release()
cv2.closeAllWindows()