def start_instances_for_all_cameras():
# start all cameras
number_of_cameras = countCameras()
LOG.log("Found %s cameras" % (number_of_cameras), "SYSTEM")
for i in range(number_of_cameras):
start_instance('CAM_%s' % (i), i, 'NORMAL')
def main():
LOG.log("Starting system", "SYSTEM")
LOG.log("Start all threads", "SYSTEM")
#LOG.log("Find all cameras", "SYSTEM")
#start_instances_for_all_cameras();
start_instance('CAM_%s' % (0), 0, 'NORMAL')
def run(self):
# wait for initial detection
while not self.shared_variables.detection_done:
pass
# initiate tracker
self.create_custom_tracker()
# tracking loop
while self.shared_variables.tracking_running:
if self.shared_variables.camera_capture.isOpened():
self.start_time = datetime.datetime.now()
#ret_val, self.frame = self.shared_variables.camera_capture.read()
self.frame = self.shared_variables.frame
self.object_custom_tracking()
self.end_time = datetime.datetime.now()
if self.shared_variables.debug or self.shared_variables.debug_tracking:
LOG.log(
"Tracking time : " +
str(self.end_time - self.start_time),
self.shared_variables.name)
def run(self):
color_array = []
# Get 9 pixels on nose
x, y = self.get_facial(2)
nose_points = self.get_approx_positions(x, y, -1,
1) # list of point round nose
# left cheek
x, y = self.get_more_facial(i1=0, i2=3) # point between
left_cheek = self.get_approx_positions(x, y, -1, 1)
# right cheek
x, y = self.get_more_facial(i1=1, i2=4)
right_cheek = self.get_approx_positions(x, y, -1, 1)
# put them in an array
color_array.append(nose_points)
color_array.append(left_cheek)
color_array.append(right_cheek)
# calculate median rgb
mean_RGB = self.calculate_medium_color(color_array)
LOG.log("Face RGB: " + str(mean_RGB), "SKIN_COLOR: ")
def start_instance(instance_name,camera_id,camera_mode='NORMAL'):
LOG.log("Capturing Camera %s" % camera_id, instance_name)
# Capture camera
_camera_capture = cv2.VideoCapture(camera_id)
# initiate shared variables instance
_shared_variables = shared_variables.Shared_Variables(instance_name,
_camera_capture)
# detection Thread
_shared_variables.start_detection_thread()
# tracking Thread
_shared_variables.start_tracking_thread()
# show camera thread
_shared_variables.start_camera_thread(camera_mode)
def run(self):
with tf.Session() as sess:
'''if not self.Loaded_model:
LOG.log("Loading modell",self.shared_variables.name)
# Load model
self.pnet, self.rnet, self.onet = detect_and_align.create_mtcnn(sess, None)
model_exp = os.path.expanduser(self.model_path)
if (os.path.isfile(model_exp)):
with gfile.FastGFile(model_exp, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
tf.import_graph_def(graph_def, name='')
self.Loaded_model = True
'''
# Load model
LOG.log("Loading modell", self.shared_variables.name)
self.pnet, self.rnet, self.onet = detect_and_align.create_mtcnn(
sess, None)
LOG.log("Start detections", self.shared_variables.name)
# Start Loop
while self.shared_variables.detection_running:
if self.shared_variables.camera_capture.isOpened():
self.start_time = datetime.datetime.now()
# ret_val, frame = self.shared_variables.camera_capture.read()
frame = self.shared_variables.frame
if self.do_flipp_test:
frame = imutils.rotate(
frame, self.flipp_test_degree * self.flipp_test_nr)
# Do detection
face_patches, padded_bounding_boxes, landmarks, score = detect_and_align.align_image(
frame, self.pnet, self.rnet, self.onet)
# if found faces
if len(face_patches) > 0:
self.shared_variables.detection_score = score
self.no_face_count = 0
# Save frames
self.shared_variables.detection_frame = frame
self.shared_variables.tracking_and_detection_frame = frame
# Save landmark
self.shared_variables.landmarks = landmarks
# Convert box from Tensorflow to OpenCV
face_box = self.convert_tensorflow_box_to_openCV_box(
padded_bounding_boxes[0])
# Save boxes
self.shared_variables.face_box = face_box
self.shared_variables.detection_box = face_box
# Do flipp test on detection
if self.shared_variables.flipp_test and self.do_flipp_test:
# save flipp as success
degree = self.shared_variables.flipp_test_degree + self.flipp_test_nr * self.flipp_test_degree
degree = degree - (degree % 360) * 360
self.shared_variables.flipp_test_degree = degree
# log frame change
LOG.log(
"Flipp test successful add degree :" +
str(self.flipp_test_nr *
self.flipp_test_degree),
self.shared_variables.name)
# end flipp test
self.do_flipp_test = False
self.flipp_test_nr = 1
# Wake tracking thread
if not self.shared_variables.tracking_running:
self.sleep_time = self.SHORT_SLEEP
self.shared_variables.start_tracking_thread()
LOG.log("Start detection",
self.shared_variables.name)
else:
# No face
self.shared_variables.face_found = False
# if max face misses has been done, stop tracking and do less detections
if self.no_face_count >= self.NO_FACE_MAX and self.shared_variables.tracking_running:
# do flipp test
if self.shared_variables.flipp_test:
# doing flipp test
if self.do_flipp_test:
self.flipp_test_nr = self.flipp_test_nr + 1
# flipp test did not find anything
if self.flipp_test_nr * self.flipp_test_degree >= 360:
self.do_flipp_test = False
self.flipp_test_nr = 1
self.sleep_time = self.LONG_SLEEP
self.shared_variables.tracking_running = False
LOG.log("Initiate energy save",
self.shared_variables.name)
else:
self.do_flipp_test = True
else:
self.sleep_time = self.LONG_SLEEP
self.shared_variables.tracking_running = False
LOG.log("Initiate energy save",
self.shared_variables.name)
else:
self.no_face_count = self.no_face_count + 1
if self.no_face_count >= self.flipp_test_long_intervall and self.shared_variables.flipp_test:
self.no_face_count = 0
self.end_time = datetime.datetime.now()
# Debug detection time
if self.shared_variables.debug_detection or self.shared_variables.debug:
LOG.log(
'Detection time:' +
str(self.end_time - self.start_time),
self.shared_variables.name)
time.sleep(self.sleep_time) # sleep if wanted
def run(self):
if not self.Loaded_model:
LOG.log("Loading modell", self.shared_variables.name)
# Load model
self.face_detector = dlib.get_frontal_face_detector()
self.landmarks_predictor = dlib.shape_predictor(
self.landmarks_model_path)
#face_cascade = cv2.CascadeClassifier(face_cascade_path)
self.Loaded_model = True
LOG.log("Start detections", self.shared_variables.name)
#wait for first cam frame
while self.shared_variables.frame is None:
pass
# Start Loop
while self.shared_variables.detection_running:
if self.shared_variables.camera_capture.isOpened():
self.start_time = datetime.datetime.now()
frame = self.shared_variables.frame
if self.do_flipp_test:
frame = imutils.rotate(
frame, self.flipp_test_degree * self.flipp_test_nr)
# Do detection
success, face_box, landmarks, score = self.object_detection()
# if found faces
if success:
self.shared_variables.detection_score = score
self.no_face_count = 0
# Save frames
self.shared_variables.detection_frame = frame
self.shared_variables.tracking_and_detection_frame = frame
# Save landmark
self.shared_variables.landmarks = landmarks
# Save boxes
self.shared_variables.face_box = face_box
self.shared_variables.detection_box = face_box
# Do flipp test on detection
if self.shared_variables.flipp_test and self.do_flipp_test:
# save flipp as success
degree = self.shared_variables.flipp_test_degree + self.flipp_test_nr * self.flipp_test_degree
degree = degree - (degree % 360) * 360
self.shared_variables.flipp_test_degree = degree
# log frame change
LOG.log(
"Flipp test successful add degree :" +
str(self.flipp_test_nr * self.flipp_test_degree),
self.shared_variables.name)
# end flipp test
self.do_flipp_test = False
self.flipp_test_nr = 1
# Wake tracking thread
if not self.shared_variables.tracking_running:
self.sleep_time = self.SHORT_SLEEP
self.shared_variables.start_tracking_thread()
LOG.log("Start detection", self.shared_variables.name)
else:
# No face
self.shared_variables.face_found = False
# if max face misses has been done, stop tracking and do less detections
if self.no_face_count >= self.NO_FACE_MAX and self.shared_variables.tracking_running:
# do flipp test
if self.shared_variables.flipp_test:
# doing flipp test
if self.do_flipp_test:
self.flipp_test_nr = self.flipp_test_nr + 1
# flipp test did not find anything
if self.flipp_test_nr * self.flipp_test_degree >= 360:
self.do_flipp_test = False
self.flipp_test_nr = 1
self.sleep_time = self.LONG_SLEEP
self.shared_variables.tracking_running = False
LOG.log("Initiate energy save",
self.shared_variables.name)
else:
self.do_flipp_test = True
else:
self.sleep_time = self.LONG_SLEEP
self.shared_variables.tracking_running = False
LOG.log("Initiate energy save",
self.shared_variables.name)
else:
self.no_face_count = self.no_face_count + 1
if self.no_face_count >= self.flipp_test_long_intervall and self.shared_variables.flipp_test:
self.no_face_count = 0
self.end_time = datetime.datetime.now()
# Debug detection time
if self.shared_variables.debug_detection or self.shared_variables.debug:
LOG.log(
'Detection time:' + str(self.end_time - self.start_time),
self.shared_variables.name)
time.sleep(self.sleep_time) # sleep if wanted
def run(self):
# Load model
LOG.info("Loading OPENCV model" + str(self.index),
"SYSTEM-" + self.shared_variables.name)
face_cascade = cv2.CascadeClassifier(
'utils/haarcascade_frontalface_default.xml')
facial_features_cascade = cv2.CascadeClassifier(
'utils/haarcascade_facial_features.xml')
LOG.info("Start opencv detections " + str(self.index),
"SYSTEM-" + self.shared_variables.name)
# Start Loop
while self.shared_variables.system_running:
self.start_time = datetime.datetime.now()
frame = self.shared_variables.frame[self.index]
if self.do_flipp_test:
frame = imutils.rotate(
frame, self.flipp_test_degree * self.flipp_test_nr)
# Do detection
if frame is not None:
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
landmarksAndFaces = []
face_patches = face_cascade.detectMultiScale(gray, 1.3, 5)
# if found faces
if len(face_patches) > 0:
landmarksAndFaces.append(face_patches[0].tolist())
for (x, y, w, h) in face_patches:
roi_gray = gray[y:y + h, x:x + w]
# To dont use landmarks, instead use boxes
for (ex, ey, ew,
eh) in facial_features_cascade.detectMultiScale(
roi_gray):
landmarksAndFaces.append([x + ex, y + ey, ew, eh])
self.no_face_count = 0
self.shared_variables.face_found[self.index] = True
# Save boxes
self.shared_variables.face_box[
self.index] = landmarksAndFaces
#self.shared_variables.detection_box[self.index] = face_box
self.shared_variables.set_detection_box(
landmarksAndFaces, self.index)
# Do flipp test on detection
if self.shared_variables.flipp_test[
self.index] and self.do_flipp_test:
# save flipp as success
degree = self.shared_variables.flipp_test_degree[
self.
index] + self.flipp_test_nr * self.flipp_test_degree
degree = degree - (degree % 360) * 360
self.shared_variables.flipp_test_degree[
self.index] = degree
# log frame change
LOG.log(
"Flipp test successful add degree :" +
str(self.flipp_test_nr * self.flipp_test_degree),
self.shared_variables.name)
# end flipp test
self.do_flipp_test = False
self.flipp_test_nr = 1
else:
# No face
self.shared_variables.face_found[self.index] = False
# if max face misses has been done, stop tracking and do less detections
if self.no_face_count >= self.NO_FACE_MAX:
# do flipp test
if self.shared_variables.flipp_test:
# doing flipp test
if self.do_flipp_test:
self.flipp_test_nr = self.flipp_test_nr + 1
# flipp test did not find anything
if self.flipp_test_nr * self.flipp_test_degree >= 360:
self.do_flipp_test = False
self.flipp_test_nr = 1
self.sleep_time = self.LONG_SLEEP
else:
self.do_flipp_test = True
else:
#self.sleep_time = self.LONG_SLEEP
#self.shared_variables.tracking_running = False
#LOG.log("Initiate energy save",self.shared_variables.name)
pass
else:
self.no_face_count = self.no_face_count + 1
if self.no_face_count >= self.flipp_test_long_intervall and self.shared_variables.flipp_test[
self.index]:
self.no_face_count = 0
self.end_time = datetime.datetime.now()
# Debug detection time
if self.shared_variables.debug:
LOG.debug(
'OPENCV Detection time:' +
str(self.end_time - self.start_time),
self.shared_variables.name)
time.sleep(self.sleep_time) # sleep if wanted
LOG.info("Ending OPENCV detection " + str(self.index),
"SYSTEM-" + self.shared_variables.name)
def main():
LOG.log("Starting system", "SYSTEM")
shared_var = shared_variables.Shared_Variables(name="shared_version")
controll_thread = controller.parse_controller(shared_var)
controll_thread.start()
