def detect_motion(frameCount):
# grab global references to the video stream, output frame, and
# lock variables
global vs, outputFrame, lock
# initialize the motion detector and the total number of frames
# read thus far
md = SingleMotionDetector(accumWeight=0.1)
total = 0
# loop over frames from the video stream
while True:
# read the next frame from the video stream, resize it,
# convert the frame to grayscale, and blur it
frame = vs.read()
frame = imutils.resize(frame, width=1280)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (7, 7), 0)
# grab the current timestamp and draw it on the frame
# update the background model and increment the total number
# of frames read thus far
md.update(gray)
total += 1
# acquire the lock, set the output frame, and release the
# lock
with lock:
outputFrame = frame.copy()
def detect_motion(frameCount):
# grab global references to the video stream, output frame, and
# lock variables
global vs, outputFrame, lock
# initialize the motion detector and the total number of frames
# read thus far
md = SingleMotionDetector(accumWeight=0.1)
total = 0
# loop over frames from the video stream
while True:
# read the next frame from the video stream, resize it,
# convert the frame to grayscale, and blur it
frame = vs.read()
frame = imutils.resize(frame, width=400)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (7, 7), 0)
# # grab the current timestamp and draw it on the frame
# timestamp = datetime.datetime.now()
# cv2.putText(frame, timestamp.strftime(
# "%A %d %B %Y %I:%M:%S%p"), (10, frame.shape[0] - 10),
# cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0, 0, 255), 1)
# # if the total number of frames has reached a sufficient
# # number to construct a reasonable background model, then
# # continue to process the frame
# if total > frameCount:
# # detect motion in the image
# motion = md.detect(gray)
# # check to see if motion was found in the frame
# if motion is not None:
# # unpack the tuple and draw the box surrounding the
# # "motion area" on the output frame
# (thresh, (minX, minY, maxX, maxY)) = motion
# cv2.rectangle(frame, (minX, minY), (maxX, maxY),
# (0, 0, 255), 2)
face_frame = detect_faces(frame, face_cascade)
if face_frame is not None:
eyes = detect_eyes(face_frame, eye_cascade)
for eye in eyes:
if eye is not None:
# threshold = r = cv2.getTrackbarPos('threshold', 'image')
eye = cut_eyebrows(eye)
keypoints = blob_process(eye, 50, detector)
eye = cv2.drawKeypoints(
eye, keypoints, eye, (0, 0, 255),
cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
# update the background model and increment the total number
# of frames read thus far
md.update(gray)
total += 1
# acquire the lock, set the output frame, and release the
# lock
with lock:
outputFrame = frame.copy()
def detect_motion(frameCount=32):
# grab global references to the video stream, output frame, and lock variables
global vs, outputFrame, lock, count
# initialize the motion detector and the total number of frames
# read thus far
md = SingleMotionDetector(accumWeight=0.1)
total = 0
# loop over frames from the video stream
while True:
# read the next frame from the video stream, resize it,
# convert the frame to grayscale, and blur it
frame = vs.read()
#frame = imutils.resize(frame, width=720)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (7, 7), 0)
# grab the current timestamp and draw it on the frame
"""
timestamp = datetime.datetime.now()
cv2.putText(frame, timestamp.strftime(
"%A %d %B %Y %I:%M:%S%p"), (10, frame.shape[0] - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0, 0, 255), 1)
"""
# if the total number of frames has reached a sufficient number
# to construct a reasonable background model, then continue to process the frame
if total > frameCount:
# detect motion in the image
motion = md.detect(gray)
# check to see if motion was found in the frame
"""
if motion is not None:
# unpack the tuple and draw the box surrounding the
# "motion area" on the output frame
(thresh, (minX, minY, maxX, maxY)) = motion
cv2.rectangle(frame, (minX, minY), (maxX, maxY),
(255, 255, 255), 1)
"""
# update the background model and increment the total number
# of frames read thus far
md.update(gray)
total += 1
count += 1
# acquire the lock, set the output frame, and release the
# lock
with lock:
outputFrame = frame.copy()
def detect_motion(frameCount):
# Grab global references to video stream, output frame, and lock variables
global vs, outputFrame, lock
# Initialize motion detector and total number of frames read
md = SingleMotionDetector(accumWeight=0.1)
total = 0
# Start looping over frames
while True:
# Read next frame from video stream, resize, convert to grayscale, blur
frame = vs.read()
frame = imutils.resize(frame, width=400)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (7, 7), 0)
# Grab the current timestamp and draw it on the frame
timestamp = datetime.datetime.now()
cv2.putText(frame, timestamp.strftime("%A %d %B %Y %I:%M:%S%p"),
(10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.35,
(0, 0, 255), 1)
#
# If total number of frames is sufficient to construct the
# background model, then process the frame
if total > frameCount:
motion = md.detect(gray)
# Check if motion was found
if motion is not None:
# Unpack tuple and draw the box surrounding the
# motion area on output frame
(thresh, (minX, minY, maxX, maxY)) = motion
cv2.rectangle(frame, (minX, minY), (maxX, maxY), (0, 0, 255),
2)
#
#
# Udpate background model and increment total number of frames
md.update(gray)
total += 1
# Acquire lock (mutex), set the output frame, and release lock
with lock:
outputFrame = frame.copy()
def detect_motion(frameCount):
# traz variáveis globais para dentro da função
global vs, outputFrame, lock
# inicializa o detector de movimento e o número total
# de frames lidos até o momento
md = SingleMotionDetector(accumWeight=0.1)
total = 0
# laço que acessa os frames vindos da stream
while True:
# acessa o próximo frame da stream, redimensiona a imagem,
# converte para uma escala de cinza e aplica um filtro para
# redução de ruído
frame = vs.read()
frame = imutils.resize(frame, width=400)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (7, 7), 0)
# Aplica a data atual no frame
timestamp = datetime.datetime.now()
cv2.putText(frame, timestamp.strftime("%A %d %B %Y %I:%M:%S%p"),
(10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.35,
(0, 0, 255), 1)
# garante que o número mínimo de frames foram lidos para a construção
# do modelo de background
if total > frameCount:
# Detecta se houve movimento no frame
motion = md.detect(gray)
# checagem se para confirmar se houve movimento no frame
# e se houve realizar as ações
if motion is not None:
# Desenha uma borda em volta da área de movimento no frame
(thresh, (minX, minY, maxX, maxY)) = motion
cv2.rectangle(frame, (minX, minY), (maxX, maxY), (0, 0, 255),
2)
# atualiza o modelo de background e incrementa o número de frames
# lidos até o momento
md.update(gray)
total += 1
# utilizando do lock, define o frame que vai ser enviado ao cliente
with lock:
outputFrame = frame.copy()
def detect_motion(frameCount):
global vs,outputFrame,lock
md = SingleMotionDetector(accumWeight=0.1)
total = 0
while True:
frame = vs.read()
frame = imutils.resize(frame,width=400)
gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray,(7,7),0)
timestamp = datetime.datetime.now()
cv2.putText(frame,timestamp.strftime("%A %d %B %Y %I:%M:%S%p"),(10,frame.shape[0]-10),cv2.FONT_HERSHEY_SIMPLEX,0.35,(0,0,255),1)
if total > frameCount:
motion = md.detect(gray)
if motion is not None:
(thresh,(minX,minY,maxX,maxY))=motion
cv2.rectangle(frame,(minX,minY),(maxX,maxY),(0,0,255),2)
md.update(gray)
total += 1
with lock:
outputFrame = frame.copy()
def detect_motion(frameCount):
global vs, outputFrame, lock
md = SingleMotionDetector(accumWeight=0.1)
total = 0
while True:
frame = vs.read()
frame = imutils.resize(frame, width=400)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (7, 7), 0)
faces = faceCascade.detectMultiScale(gray,
scaleFactor=1.5,
minNeighbors=5,
minSize=(30, 30),
flags=cv2.CASCADE_SCALE_IMAGE)
timestamp = datetime.datetime.now()
cv2.putText(frame, timestamp.strftime("%A %d %B %Y %I:%M:%S%p"),
(10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.35,
(0, 0, 255), 1)
if len(faces) == 0:
nbr = 0
else:
nbr = str(faces.shape[0])
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.putText(frame, "Number of faces detected: " + nbr,
(0, frame.shape[0] - frame.shape[0] + 10),
cv2.FONT_HERSHEY_TRIPLEX, 0.35, (0, 255, 0), 1)
md.update(gray)
total += 1
with lock:
outputFrame = frame.copy()