def getColor(self):
vs = VideoStream(usePiCamera=True).start()
time.sleep(0.5)
count = 3
colors = {'r': 0, 'b': 0, 'g': 0, 'y': 0}
while count:
count -= 1
frame = vs.read()
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
mask_red = cv2.inRange(hsv, self.lower_red, self.upper_red)
mask_blue = cv2.inRange(hsv, self.lower_blue, self.upper_blue)
mask_green = cv2.inRange(hsv, self.lower_green, self.upper_green)
mask_yellow = cv2.inRange(hsv, self.lower_yellow,
self.upper_yellow)
colors['r'] = cv2.countNonZero(mask_red)
colors['b'] = cv2.countNonZero(mask_blue)
colors['g'] = cv2.countNonZero(mask_green)
colors['y'] = cv2.countNonZero(mask_yellow)
#res = cv2.bitwise_and(frame,frame,mask = mask_yellow)
#cv2.imshow("Res",res)
#cv2.waitKey(10)
vs.stop()
print(colors)
for color in colors:
if colors[color] > 5000:
return color
return 'x'
def getArucoID(self):
vs = VideoStream(usePiCamera=True).start()
time.sleep(0.5)
ids = []
while len(ids) < 4:
ID = 0 #stores the detected ID
frame = vs.read()
aruco_list = self.detectAruco(frame)
if len(aruco_list):
foundID = True
ID = list(aruco_list.keys())
ID = ID[0]
#check that the detected ID is not repeated and add to the list of ids
if ID > 0 and ID not in ids:
ids.append(ID)
#self.IDs.append(bin(ID)[2:]) #store ID in binary format
print("ID Detected: {}".format(ID))
vs.stop()
import imutils
from imutils.video.videostream import VideoStream
import cv2
from imutils.video import FPS
import datetime
import time
import numpy as np
# if a video path was not supplied, grab the reference to the webcam
camera = VideoStream(src=0).start()
fps = FPS().start()
current_fps = 0
# keep looping
while True:
frame = camera.read()
if fps._numFrames % 2 == 0:
# 帧率计数
start_time = datetime.datetime.now()
frame = imutils.resize(frame, width=400)
# 生成插入帧
if fps._numFrames % 2 == 0:
insert_frame = frame
if fps._numFrames % 2 == 1:
# 帧率计数
end_time = datetime.datetime.now()
def gen_frames_detection(): # generate frame by frame from camera
# camera = cv2.VideoCapture(0)
# Capture frame-by-frame
EYE_AR_THRESH = 0.3
EYE_AR_CONSEC_FRAMES = 3
# initialize the frame counters and the total number of blinks
COUNTER = 0
TOTAL = 0
# initialize dlib's face detector (HOG-based) and then create
# the facial landmark predictor
print("\n[INFO] loading facial landmark predictor...")
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(
"shape_predictor_68_face_landmarks.dat")
# grab the indexes of the facial landmarks for the left and
# right eye, respectively
(lStart, lEnd) = face_utils.FACIAL_LANDMARKS_IDXS["left_eye"]
(rStart, rEnd) = face_utils.FACIAL_LANDMARKS_IDXS["right_eye"]
# start the video stream thread
print("\n[INFO] starting video stream thread...")
# vs = FileVideoStream(args["video"]).start()
# fileStream = True
# vs = VideoStream(src=0).start()
# vs = VideoStream(usePiCamera=True).start() #Use for Raspberry Pi
vs = VideoStream().start()
fileStream = False
time.sleep(1.0)
j = 0
result = False
# for cctv camera use rtsp://username:password@ip_address:554/user=username_password='******'_channel=channel_number_stream=0.sdp' instead of camera
# for local webcam use cv2.VideoCapture(0)
user_name = ""
key = cv2. waitKey(1)
while True and result == False:
try:
# loop over frames from the video stream
while True and result == False:
try:
j = j+1
# if this is a file video stream, then we need to check if
# there any more frames left in the buffer to process
if fileStream and not vs.more():
break
# grab the frame from the threaded video file stream, resize
# it, and convert it to grayscale
# channels)
frame = vs.read()
frame = imutils.resize(frame, width=800)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# detect faces in the grayscale frame
rects = detector(gray, 0)
# loop over the face detections
for rect in rects:
# determine the facial landmarks for the face region, then
# convert the facial landmark (x, y)-coordinates to a NumPy
# array
shape = predictor(gray, rect)
shape = face_utils.shape_to_np(shape)
# extract the left and right eye coordinates, then use the
# coordinates to compute the eye aspect ratio for both eyes
leftEye = shape[lStart:lEnd]
rightEye = shape[rStart:rEnd]
leftEAR = eye_aspect_ratio(leftEye)
rightEAR = eye_aspect_ratio(rightEye)
# average the eye aspect ratio together for both eyes
ear = (leftEAR + rightEAR) / 2.0
# compute the convex hull for the left and right eye, then
# visualize each of the eyes
leftEyeHull = cv2.convexHull(leftEye)
rightEyeHull = cv2.convexHull(rightEye)
cv2.drawContours(
frame, [leftEyeHull], -1, (0, 255, 0), 1)
cv2.drawContours(
frame, [rightEyeHull], -1, (0, 255, 0), 1)
# check to see if the eye aspect ratio is below the blink
# threshold, and if so, increment the blink frame counter
if ear < EYE_AR_THRESH:
COUNTER += 1
# otherwise, the eye aspect ratio is not below the blink
# threshold
else:
# if the eyes were closed for a sufficient number of
# then increment the total number of blinks
if COUNTER >= EYE_AR_CONSEC_FRAMES:
TOTAL += 1
# reset the eye frame counter
COUNTER = 0
# draw the total number of blinks on the frame along with
# the computed eye aspect ratio for the frame
cv2.putText(frame, "Blinks: {}".format(TOTAL), (10, 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
cv2.putText(frame, "EAR: {:.2f}".format(ear), (200, 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
if TOTAL >= 3 or j > 101: # 3 is how many times the face must blink
result = True
if TOTAL >= 3:
cv2.putText(frame, "Real face, comparing now ...", (500, 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2)
check_extract_faces(frame)
decision, user_name = final_compare_blink(result)
cv2.putText(frame, "Welcome {}".format(user_name), (500, 55),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2)
else: # not real face, maybe photo
cv2.putText(frame, "Eyes blinks not detectec!", (500, 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 0, 255), 2)
else:
if j % 10 == 0: # tictac
cv2.putText(frame, "Unrecognized face", (10, 55),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
else:
cv2.putText(frame, "# Unrecognized face", (10, 55),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
print(j)
# return the frame
ret, buffer = cv2.imencode('.jpg', frame)
frame = buffer.tobytes()
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')
except(KeyboardInterrupt):
vs.stop()
break
except(KeyboardInterrupt):
vs.stop()
break
vs.stop()
stop_image = np.zeros((500, 500, 3), np.uint8)
if user_name == "":
cv2.putText(stop_image, "Unrecognized face", (150, 250),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
else:
cv2.putText(stop_image, "Welcome {}".format(user_name), (150, 250),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2)
ret, buffer = cv2.imencode('.jpg', stop_image)
frame = buffer.tobytes()
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')
default=0.5,
help="minimum probability to filter weak detections")
args = vars(ap.parse_args())
# face detector model을 불러옴
print("[INFO] loading face detector model...")
faceNet = cv2.dnn.readNet('models/deploy.prototxt',
'models/res10_300x300_ssd_iter_140000.caffemodel')
# load the face mask detector model from disk
print("[INFO] loading face mask detector model...")
maskNet = load_model(args["model"])
# initialize the video stream and allow the camera sensor to warm up
print("[INFO] starting video stream...")
vs = VideoStream(src=0).start()
time.sleep(2.0)
while True:
# 400픽셀로 resize
frame = vs.read()
if np.shape(frame) != ():
# grab the frame dimensions and convert it to a blob
(h, w) = frame.shape[:2]
#frame = imutils.resize(frame, width=400)
# 프레임에서 마스크 착용 유무 판별