def __init__(self):
# Using OpenCV to capture from device 0. If you have trouble capturing
# from a webcam, comment the line below out and use a video file
# instead.
self.camera = camera.VideoCamera()
self.known_face_encodings = []
self.known_face_names = []
self.org_frame = []
dirname = 'knowns'
# Load sample pictures and learn how to recognize it.
files = os.listdir(dirname)
for filename in files:
name, ext = os.path.splitext(filename)
if ext == '.jpg':
self.known_face_names.append(name)
pathname = os.path.join(dirname, filename)
img = face_recognition.load_image_file(pathname)
face_encoding = face_recognition.face_encodings(img)[0]
self.known_face_encodings.append(face_encoding)
# Initialize some variables
self.face_locations = []
self.face_encodings = []
self.face_names = []
self.process_this_frame = True
self.face_cascade = cv2.CascadeClassifier(
'opencv_data/haarcascade_frontface.xml')
def __init__(self):
# Using OpenCV to capture from device 0. If you have trouble capturing
# from a webcam, comment the line below out and use a video file
# instead.
self.camera = camera.VideoCamera()
self.known_face_encodings = []
self.known_face_names = []
# Load sample pictures and learn how to recognize it.
dirname = 'knowns'
files = os.listdir(dirname) # return all image files from dirname
for filename in files:
name, ext = os.path.splitext(filename)
if ext == '.jpg':
self.known_face_names.append(name)
pathname = os.path.join(dirname, filename)
img = face_recognition.load_image_file(pathname)
face_encoding = face_recognition.face_encodings(img)[0] # not modified
self.known_face_encodings.append(face_encoding)
# Initialize some variables
self.face_locations = []
self.face_encodings = []
self.face_names = []
self.process_this_frame = True
def __init__(self):
print("FaceRecog execute...")
# Using OpenCV to capture from device 0. If you have trouble capturing
# from a webcam, comment the line below out and use a video file
# instead.
self.camera = camera.VideoCamera()
self.known_face_encodings = []
self.known_face_names = []
# Load sample pictures and learn how to recognize it.
dirname = 'my sample/'
files = os.listdir(dirname)
for filename in files:
name, ext = os.path.splitext(filename)
if ext == '.jpg':
self.known_face_names.append(name)
pathname = os.path.join(dirname, filename)
img = face_recognition.load_image_file(pathname)
face_encoding = face_recognition.face_encodings(img)[0]
# -> 'IndexError: list index out of range' 발생
# 'my sample' 폴더에 사진 하나만 넣는다.
self.known_face_encodings.append(face_encoding)
# Initialize some variables
self.face_locations = []
self.face_encodings = []
self.face_names = []
self.process_this_frame = True
def __init__(self):
# 모듈 선언
self.camera = camera.VideoCamera()
self.known_face_encodings = []
self.known_face_names = []
self.staticData = static.staticVar
self.etc_module = etc_module
# 해당 경로에 있는 이미지로 학습 시작
dirname = 'knowns'
files = os.listdir(dirname)
for filename in files:
name, ext = os.path.splitext(filename)
if ext == '.jpg':
self.known_face_names.append(name)
pathname = os.path.join(dirname, filename)
img = face_recognition.load_image_file(pathname)
face_encoding = face_recognition.face_encodings(img)[0]
self.known_face_encodings.append(face_encoding)
# 변수 초기화
self.face_locations = []
self.face_encodings = []
self.face_names = []
self.process_this_frame = True
def __init__(self):
# Using OpenCV to capture from device 0. If you have trouble capturing
# from a webcam, comment the line below out and use a video file
# instead.
self.camera = camera.VideoCamera()
self.known_face_encodings = []
self.known_face_names = []
# Load sample pictures and learn how to recognize it.
#knowns 디렉토리에서 사진 파일을 읽습니다. 파일 이름으로부터 사람 이름을 추출합니다
dirname = 'knowns'
files = os.listdir(dirname)
for filename in files:
name, ext = os.path.splitext(filename)
if ext == '.jpg':
self.known_face_names.append(name)
pathname = os.path.join(dirname, filename)
# 사진에서 얼굴 영역을 알아내고, face landmarks라 불리는
# 68개 얼굴 특징의 위치를 분석한 데이터를 known_face_encodings에 저장합니다.
img = face_recognition.load_image_file(pathname)
#face_encoding = face_recognition.face_encodings(img)[0]
face_encodings = face_recognition.face_encodings(img)
if len(face_encodings) > 0:
face_encoding = face_encodings[0]
self.known_face_encodings.append(face_encoding)
# Initialize some variables
self.face_locations = []
self.face_encodings = []
self.face_names = []
self.process_this_frame = True
def __init__(self):
self.camera = camera.VideoCamera()
self.known_face_encodings = []
self.known_face_names = []
self.known_face_no = []
# Load sample pictures and learn how to recognize it.
file = open("encoding.txt", 'r')
datas = file.read().strip().split("])]")
for data in datas:
if len(data) != 0:
imgdata = data.split("[array([")
user_data = imgdata[0].split("<<")
print(user_data[1] + ":" + user_data[0])
self.known_face_names.append(user_data[1])
self.known_face_no.append(user_data[0])
tmp = imgdata[1].split(',')
self.known_face_val = []
for fval in tmp:
self.known_face_val.append(float(fval))
face_encoding = self.known_face_val
self.known_face_encodings.append(face_encoding)
# Initialize some variables
self.face_locations = []
self.face_encodings = []
self.face_names = []
self.process_this_frame = True
def __init__(self):
# Using OpenCV to capture from device 0. If you have trouble capturing
# from a webcam, comment the line below out and use a video file
# instead.
self.camera = camera.VideoCamera()
self.known_face_encodings = []
self.known_face_names = []
# Load sample pictures and learn how to recognize it.
dirname = 'knowns'
files = os.listdir(dirname)
for filename in files:
name, ext = os.path.splitext(filename)
if ext == '.jpg':
self.known_face_names.append(name) ### Maybe its right moving this into if below
pathname = os.path.join(dirname, filename)
img = face_recognition.load_image_file(pathname)
face_encoding = face_recognition.face_encodings(img)
if len(face_encoding) > 0:
face_encoding = face_encoding[0]
self.known_face_encodings.append(face_encoding)
else:
print("No faces found in this image! \"", end='')
print(name, end='')
print("\"")
# Initialize some variables
self.face_locations = []
self.face_encodings = []
self.face_names = []
self.process_this_frame = True
def __init__(self):
# Using OpenCV to capture from device 0. If you have trouble capturing
# from a webcam, comment the line below out and use a video file
# instead.
self.camera = camera.VideoCamera(0)
self.currentFace = None
self.known_face_encodings = []
self.known_face_names = []
self.countDf = pd.DataFrame(data={
"user_name": [],
"count": [],
"timestamp": []
})
# # Load sample pictures and learn how to recognize it.
# Initialize some variables
self.face_locations = []
self.face_encodings = []
self.face_names = []
self.process_this_frame = True
self.face_detected = False
self.init_customer_face()
# 좋아요 계산
self.cal_like()
def __init__(self):
# 객체 생성
self.camera = camera.VideoCamera()
self.known_face_encodings = []
self.known_face_names = []
# knowns 에서 사진파일을 읽고 인식하여 특징 추출
dirname = './'
files = os.listdir(dirname)
for filename in files:
name, ext = os.path.splitext(filename)
if ext == '.jpg' and name == 'recognizeface':
self.known_face_names.append(name)
# knowns 디렉토리에서 사진 파일을 읽어와서 사람 이름을 추출
pathname = os.path.join(dirname, filename)
img = face_recognition.load_image_file(pathname)
# 특징 추출
# 얼굴 특징을 검출할 수 없을 경우 에러 발생
## face_encoding = face_recognition.face_encodings(img)[0]
# 에러 발생시 해결하는 코드
encodings = face_recognition.face_encodings(img)
if len(encodings) > 0:
face_encoding = encodings[0]
print("Face recogniezd.".format(name))
else: # 얼굴 특징을 찾을 수 없는 사진이 있을 경우 해당 사진을 출력해줌
print("Face recognize error.".format(name))
def __init__(self):
# OpenCV를 사용하여 장치 0에서 캡처. 캡처에 문제가 있는 경우
# 웹캠에서 아래 줄을 읽고 비디오 파일을 사용하십시오.
self.camera = camera.VideoCamera()
self.known_face_encodings = []
self.known_face_names = []
# 사진을 업로드 하여 인지하는 것 확인
dirname = 'knowns'
files = os.listdir(dirname)
for filename in files:
name, ext = os.path.splitext(filename)
if ext == '.jpg':
self.known_face_names.append(name)
pathname = os.path.join(dirname, filename)
img = face_recognition.load_image_file(pathname)
face_encoding = face_recognition.face_encodings(img)[0]
self.known_face_encodings.append(face_encoding)
# 변수 초기화
self.face_locations = []
self.face_encodings = []
self.face_names = []
self.process_this_frame = True
def __init__(self):
# Using OpenCV to capture from device 0. If you have trouble capturing
# from a webcam, comment the line below out and use a video file
# instead.
self.camera = camera.VideoCamera(0)
self.known_face_encodings = []
self.known_face_names = []
#
# # Load sample pictures and learn how to recognize it.
dirname = 'customers'
files = os.listdir(dirname)
for filename in files:
name, ext = os.path.splitext(filename)
name = name.split('_')[0]
if ext == '.jpg' or ext == '.jpeg' or ext == '.png':
self.known_face_names.append(name)
pathname = os.path.join(dirname, filename)
img = face_recognition.load_image_file(pathname)
if len(face_recognition.face_encodings(img)) > 0:
face_encoding = face_recognition.face_encodings(img)[0]
self.known_face_encodings.append(face_encoding)
print(self.known_face_names)
# Initialize some variables
self.face_locations = []
self.face_encodings = []
self.face_names = []
self.process_this_frame = True
self.face_detected = False
def gen(fr):
detector = ObjectDetector('ssd_mobilenet_v1_coco_2017_11_17')
#detector = ObjectDetector('mask_rcnn_inception_v2_coco_2018_01_28')
#detector = ObjectDetector('pet', label_file='data/pet_label_map.pbtxt')
cam = camera.VideoCamera()
while True:
frame = cam.get_frame()
frame = detector.detect_objects(frame)
ret, jpg = cv2.imencode('.jpg', frame)
jpg_bytes = jpg.tobytes()
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' + jpg_bytes + b'\r\n\r\n')
def __init__(self):
self.camera = camera.VideoCamera()
self.known_face_encodings = []
self.known_face_names = []
dirname = 'knowns'
files = os.listdir(dirname)
for filename in files:
name, ext = os.path.splitext(filename)
if ext == '.jpg':
self.known_face_names.append(name)
pathname = os.path.join(dirname, filename)
img = face_recognition.load_image_file(pathname)
face_encoding = face_recognition.face_encodings(img)[0]
self.known_face_encodings.append(face_encoding)
self.face_locations = []
self.face_encodings = []
self.face_names = []
self.process_this_frame = True
def __init__(self):
# 객체 생성
self.camera = camera.VideoCamera()
self.known_face_encodings = []
self.known_face_names = []
# knowns 에서 사진파일을 읽고 인식하여 특징 추출
dirname = 'knowns'
files = os.listdir(dirname)
for filename in files:
name, ext = os.path.splitext(filename)
if ext == '.jpg':
self.known_face_names.append(name)
# knowns 디렉토리에서 사진 파일을 읽어와서 사람 이름을 추출
pathname = os.path.join(dirname, filename)
img = face_recognition.load_image_file(pathname)
# 특징 추출
# 얼굴 특징을 검출할 수 없을 경우 에러 발생
## face_encoding = face_recognition.face_encodings(img)[0]
# 에러 발생시 해결하는 코드
encodings = face_recognition.face_encodings(img)
if len(encodings) > 0:
face_encoding = encodings[0]
else: # 얼굴 특징을 찾을 수 없는 사진이 있을 경우 해당 사진을 출력해줌
print("{} : No faces found in the image!".format(name))
## quit()
# 사진에서 얼굴 특징의 데이터를 분석한 데이터를 self.known_face_encodings 에 저장
self.known_face_encodings.append(face_encoding)
# 변수 초기화
self.face_locations = []
self.face_encodings = []
self.face_names = []
self.process_this_frame = True
# Exception for overlapped folder name
try:
os.mkdir(face_path)
print('Face directory is created :: ', face_path)
except FileExistsError:
print('Folder name exception :: already registered face')
# Path adequacy check
assert os.path.exists(face_path)
# Set DB
DBmanager = DBmanager.DBmanager("log.db")
DBmanager.ShowAllWorker()
# Set camera module
cam = camera.VideoCamera(camera_num)
# Camera loading assertion
assert cam is not None
# Camera loop
while True:
# Grab a single frame of video
frame = cam.get_frame()
# Frame loading assertion
assert frame is not None
copy_frame = frame.copy()
# Resize frame of video to 1/4 size for faster face recognition processing
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
rgb_small_frame = small_frame[:, :, ::-1]
def video_feed():
return Response(gen(camera.VideoCamera()),
mimetype='multipart/x-mixed-replace; boundary=frame')
import threading
import time
import hashlib
import logging
import datetime
import ssl
import cloud
app = Flask(__name__)
conf = config.Configuration()
logging.basicConfig(filename='app.log', level=logging.DEBUG)
auth = flask_httpauth.HTTPBasicAuth()
app.secret_key = os.urandom(24)
user = None
online = None
cmra = camera.VideoCamera(conf)
drop = cloud.DropObj(conf)
@auth.get_password
def get_pw(username):
global user
user = username
return conf.get('User')[username]
@auth.hash_password
def hash_pw(password):
return hashlib.sha224(password).hexdigest()
def get_jpg_bytes(self):
frame = self.get_frame()
# We are using Motion JPEG, but OpenCV defaults to capture raw images,
# so we must encode it into JPEG in order to correctly display the
# video stream.
ret, jpg = cv2.imencode('.jpg', frame)
return jpg.tobytes()
if __name__ == '__main__':
import camera
import calibration
#detector = ObjectDetector('ssd_mobilenet_v1_coco_2017_11_17')
detector = ObjectDetector('mask_rcnn_inception_v2_coco_2018_01_28')
# instead.
cam = camera.VideoCamera()
print("press `q` to quit")
while True:
frame = cam.get_frame()
frame = detector.detect_objects(frame)
# show the frame
cv2.imshow("Frame", frame)
# if the `q` key was pressed, break from the loop
key = cv2.waitKey(1) & 0xFF
if key == ord('q'):
break
# q= False
label = 'Inference time: %.2f ms' % (t * 1000.0 /
cv.getTickFrequency())
cv.putText(copy_frame, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5,
(0, 0, 255))
return copy_frame
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--path',
default=0,
help='Input test video path or web-cam number.')
args = parser.parse_args()
cam = camera.VideoCamera(args.path)
helmet_detection = helmet_detection()
# Set window
winName = 'Helmet detection'
cv.namedWindow(winName, cv.WINDOW_NORMAL)
while True:
# Get frame from Camera module
frame = cam.get_frame()
frame = cv.resize(frame, dsize=(640, 480), interpolation=cv.INTER_AREA)
frame = helmet_detection.get_detection(frame=frame, copy_frame=frame)
# show the frame
cv.imshow(winName, frame)
key = cv.waitKey(1) & 0xFF
def __init__(self):
# Using OpenCV to capture from device 0. If you have trouble capturing
# from a webcam, comment the line below out and use a video file
# instead.
self.camera = camera.VideoCamera()
self.facenet = cv2.dnn.readNet(
'models/deploy.prototxt',
'models/res10_300x300_ssd_iter_140000.caffemodel')
self.known_face_encodings = []
self.known_face_names = []
self.face = []
# Load sample pictures and learn how to recognize it.
dirname = 'knowns'
files = os.listdir(dirname)
for filename in files:
name, ext = os.path.splitext(filename)
if ext == '.jpeg':
self.known_face_names.append(name)
pathname = os.path.join(dirname, filename)
img = face_recognition.load_image_file(pathname)
test1 = cv2.imread(pathname)
# print(test1.shape[:2])
h, w = test1.shape[:2]
# print(test1.shape) # (960, 721, 3)
blob = cv2.dnn.blobFromImage(img,
scalefactor=1.,
size=(300, 300),
mean=(104., 177., 123.))
self.facenet.setInput(blob)
dets = self.facenet.forward()
for i in range(dets.shape[2]):
# print(name)
# 검출한 결과가 신뢰도
confidence = dets[0, 0, i, 2]
# 신뢰도를 0.5로 임계치 지정
if confidence < 0.5:
continue
# print('confidence :: ', confidence * 100)
# 바운딩 박스를 구함
x1 = int(dets[0, 0, i, 3] * w) # 박스 시작점 x 좌표
y1 = int(dets[0, 0, i, 4] * h) # 박스 시작점 y 좌표
x2 = int(dets[0, 0, i, 5] * w) # 박스 끝점 x 좌표
y2 = int(dets[0, 0, i, 6] * h) # 박스 끝점 y 좌표
# load DB
# dir_name = "result"
# pdb = PersonDB()
# pdb.load_db(dir_name)
# pdb.print_persons()
# 원본 이미지에서 얼굴영역 추출
face = img[y1:y2, x1:x2]
# print(type(face))
# print('face.shape :: ', face.shape)
self.face.append(face)
for i in self.face:
print(i.shape)
face_encoding = face_recognition.face_encodings(i)
if len(face_encoding) > 0:
print('ok')
self.known_face_encodings.append(face_encoding[0])
else:
print("이미지에 얼굴이 없습니다!")
# face_encoding = face_recognition.face_encodings(img)[0]
# self.known_face_encodings.append(face_encoding)
# Initialize some variables
self.face_locations = []
self.face_encodings = []
self.face_names = []
self.process_this_frame = True
def __init__(self):
self.camera = camera.VideoCamera()
self.registered_names = os.listdir('knowns/')
self.location = {'top' : 0, 'bottom' : 0, 'left' : 0, 'right' : 0}
def __init__(self, cnum):
# Using OpenCV to capture from device 0. If you have trouble capturing
# from a webcam, comment the line below out and use a video file
# instead.
self.camera = camera.VideoCamera(cnum)
self.known_face_encodings = []
self.known_face_names = []
# 맨처음 실행할때 파일 생성하기, 이미 파일이 존재하면 실행 안하기
if os.path.isfile(encoding_filename):
# 있으면 파일 열어서 읽어오기
f = open(encoding_filename, "r")
while True:
name = f.readline()
if not name:
print("txt 파일 끝까지 읽기 완료")
break
self.known_face_names.append(name)
#print(name)
# 얼굴 인코딩 데이터 읽어오기
datas = []
for i in range(0, 128):
data = f.readline().split("\n")
datas.append(float(data[0]))
face_encoding = np.array(datas)
datas.clear()
self.known_face_encodings.append(face_encoding)
#print(face_encoding)
else:
# 없으면 파일을 만들고, 파일에 인코딩 데이터 저장하기
f = open(encoding_filename, "w")
# Load sample pictures and learn how to recognize it.
dirname = 'knowns'
files = os.listdir(dirname)
for filename in files:
name, ext = os.path.splitext(filename)
if ext == '.jpg':
self.known_face_names.append(name)
pathname = os.path.join(dirname, filename)
img = face_recognition.load_image_file(pathname)
face_encoding = face_recognition.face_encodings(img)[0]
#print(face_encoding)
self.known_face_encodings.append(face_encoding)
#파일에 이름+인코딩 데이터 저장
f.write(name + "\n")
# 얼굴 인코딩 데이터 저장
np.savetxt(f, face_encoding, delimiter=", ")
# Initialize some variables
self.face_locations = []
self.face_encodings = []
self.face_names = []
self.process_this_frame = True
# 파일 닫기
f.close()
return
helmet_result = helmet.get_detection(frame=small_frame,
copy_frame=face_recog_result)
# show the frame
cv.imshow(winName, helmet_result)
key = cv.waitKey(2000) & 0xFF
# if the `q` key was pressed, break from the loop
if key == ord("q"):
break
# --image option process
elif args.video:
# Get known image from train_path
helmet = helmet.helmet_detection()
cam = camera.VideoCamera(test_path)
face_recog = face_recog.FaceRecog(train_path)
while True:
frame = cam.get_frame()
frame = cv.resize(frame,
dsize=(640, 480),
interpolation=cv.INTER_AREA)
face_recog_result = face_recog.get_frame(frame)
helmet_result = helmet.get_detection(frame, face_recog_result)
# show the frame
cv.imshow(winName, helmet_result)
key = cv.waitKey(1) & 0xFF
