def detect_motion(frameCount):
global vs, outputFrame, lock, motion_detected
# initialize the motion detector tot frames
md = MotionDetector(accumWeight=0.1)
total = 0
while True:
# read next frame from stream, resize, 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)
# timestamp on 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 tot frames reached enough to construct background model, continue
if total > frameCount:
# detect
motion = md.detect(gray)
# check if motion was found
if motion is not None:
# draw bounding box
(thresh, (minX, minY, maxX, maxY)) = motion
cv2.rectangle(frame, (minX, minY), (maxX, maxY),
(0, 0, 255), 2)
if motion_detected is False:
res = telegram_bot.telegram_bot_sendtext("motion detected: " + timestamp.strftime(
"%A %d %B %Y %I:%M:%S%p"))
print(res)
motion_detected = True
# update background model and increment total frames
md.update(gray)
total += 1
# acquire the lock, set output frame, release
with lock:
outputFrame = frame.copy()
frame = cv2.flip(frame, 2)
#copy frame ke variabel clone
clone = frame.copy()
#mengambil tinggi dan lebar dari frame
(frameH, frameW) = frame.shape[:2]
#crop frame pada ROI untuk dideteksi
roi = frame[top:bot, right:left]
#dijadikan ke grayscale
gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
#smoothing gambar dengan gaussian blur
gray = cv2.GaussianBlur(gray, (7, 7), 0)
#frame pertama digunakan sebagai background
if numFrames < 1:
md.update(gray)
else:
#deteksi gerakan
skin = md.detect(gray)
#jika ada gerakan
if skin is not None:
(thresh, c) = skin
#gambar contour
cv2.drawContours(
clone, [c + (right, top)], -1, (0, 255, 0), 2
) #draw objek skin dengan menambahkan nilai kontur ke batasan box
#pengenalan dan menghitung jumlah jari terangkat
fingers = gd.detect(thresh, c)
if gesture is None:
gesture = [1, fingers]
# Initialize face detector
face_detector = FaceDetector()
# FPS calculation
fps = FPS().start()
print("[INFO] Start collecting face images.")
while True:
# grab frame
frame = frame_grabber.read()
frame_show = frame.copy()
frame_roi = frame[up_offsetY:down_offsetY, left_offsetX:right_offsetX]
frame_gray = cv2.cvtColor(frame_roi, cv2.COLOR_BGR2GRAY)
frame_gray = cv2.GaussianBlur(frame_gray, (21, 21), 0)
motion_locs = motion_detector.update(frame_gray)
# form a nice average before motion detection
if num_frame_read < 15:
num_frame_read += 1
continue
if len(motion_locs) > 0:
# @ZC may consider to process in every other frame to accelerate
face_locs = face_detector.detect(frame_roi, motion_locs)
if len(face_locs) > 0:
# Save image with faces detected
timestamp = datetime.datetime.now()
ts = timestamp.strftime("%Y-%m-%d_%H:%M:%S_%f")
print("[INFO] " + str(len(face_locs)) + " face found." + ts)
image_save_path = "images/" + ts + ".jpg"