def update(self):
if not hasattr(self, 'cameras'):
self.cameras = [getattr(self._robot, c) for c in self._names]
self._markers = sum([detect_markers(c.frame) for c in self.cameras],
[])
self.sensors = [Marker(m) for m in self._markers]
def capture(self):
rospy.loginfo("[%s] Start capturing." % (self.node_name))
for frame in self.camera.capture_continuous(self.stream,
format="bgr",
use_video_port=True):
stamp = rospy.Time.now()
img = cv2.remap(frame.array, self.map1, self.map2,
cv2.INTER_LINEAR)
grey = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
_, grey = cv2.threshold(grey, 127, 255, cv2.THRESH_BINARY)
markers = detect_markers(grey)
tag = Tag()
tag.header.stamp = stamp
for m in markers:
tag.id = m.id
tag.point = self.image2ground(m.center)
self.pub_tags.publish(tag)
self.stream.truncate(0)
if rospy.is_shutdown():
break
self.camera.close()
rospy.loginfo("[%s] Capture ended." % (self.node_name))
def markers(args, must_print=True):
global nao_motion
data.IP = args['--ip']
data.PORT = int(args['--port'])
next_img_func = get_nao_image
try:
if args['--no-robot']:
next_img_func = get_webcam_image
except KeyError:
pass
end_reached = False
nao_motion = MotionController()
nao_motion.stand()
while not end_reached:
img = next_img_func(int(args['--cam-no']), res=2)
detected_markers = detect_markers(img)
for m in detected_markers:
m.draw_contour(img)
cv2.putText(img, str(m.id), tuple(int(p) for p in m.center),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 3)
if must_print:
print "ID: ", str(m.id), "\n", "Contours: ", str(m.contours.ravel().reshape(-1, 2).tolist())
print
else:
end_reached = True
print
if not args['--no-image']:
cv2.imshow('Markers', img)
if cv2.waitKey(1) == 27:
print "Esc pressed : exit"
close_camera()
return 0
def __init__(self, robot, name, cameras, freq, multiprocess=True):
SensorsController.__init__(self, None, [], freq)
self.name = name
self._robot = robot
self._names = cameras
self.detect = (lambda img: self._bg_detection(img)
if multiprocess else detect_markers(img))
def getUpperHoleCoordinatesUsingMarkers(self, index, camera_position, camera_matrix, camera_dist,
tries=1, debug=False, res=640):
"""
:param res: The resolution length
:param index: the index of the hole
:type index: int
:param camera_position: the 6D position of the camera used for the detection (the bottom one),
from the robot torso
:type camera_position: tuple
:param camera_matrix: the camera distortion matrix
:type camera_matrix: np.array
:param camera_dist: the camera distortion coefficients vector
:type camera_dist: np.array
:param debug: if True, draw the detected markers
:type debug: bool
:param tries: the number of times the detection will be run. If one try fails,
the whole detection is considered as failed
:type tries: int
Get an upper hole's coordinates using the Hamming markers on the Connect 4.
This method assumes that the Hamming codes are visible on the image it will
acquire using its "next_img_func". Otherwise, the detection fails
"""
max_nb_of_markers = 0
rvec = None
tvec = None
if res == 640:
i_res = 2
else:
i_res = 1
for i in range(tries):
if self.cam_no == -1:
img = self.next_img_func(1, res=i_res) # We get the image from the bottom camera
else:
img = self.next_img_func(self.cam_no, res=i_res)
min_nb_of_codes = 2
markers = detect_markers(img)
if markers is not None and len(markers) >= min_nb_of_codes:
if debug:
for m in markers:
m.draw_contour(img)
cv2.putText(img, str(m.id), tuple(int(p) for p in m.center),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 3)
self.upper_hole_detector._hamcodes = markers
self.upper_hole_detector.runDetection([], markers)
if len(markers) > max_nb_of_markers:
max_nb_of_markers = len(markers)
rvec, tvec = self.upper_hole_detector.match3DModel(camera_matrix, camera_dist)
else:
if debug:
cv2.imshow("Debug", img)
if cv2.waitKey(100) == 27:
raise NotEnoughLandmarksException("The detection was interrupted")
raise NotEnoughLandmarksException("The model needs at least " + str(min_nb_of_codes) + " detected codes")
return self._getUpperHoleCoordinates(rvec, tvec, index, camera_position)
def on_image(self, msg):
img = self.bridge.imgmsg_to_cv2(msg, "bgr8")
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
_, img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
markers = detect_markers(img)
tag = Tag()
tag.header.stamp = msg.header.stamp
for m in markers:
tag.id = m.id
tag.point = self.image2ground(m.center)
self.pub_tags.publish(tag)
def detectMarker(self, current_pitch, current_yaw):
"""
:param current_pitch: current pitch of NAO's head to stock as data
:param current_yaw: current yaw of NAO's head to stock as data
Detect the marker of the hole in the picture
"""
marker_found = False, None
tries = 0
while not marker_found[0] and tries < 10:
img = self.nao_video.getImageFromCamera()
markers = detect_markers(img)
tries += 1
for marker in markers:
if marker.id == (self.selected_hole + 1) * 1000:
marker_found = True, marker
if marker_found[0]:
self.record(marker_found[1], current_pitch, current_yaw)
def update(self):
if not hasattr(self, 'cameras'):
self.cameras = [getattr(self._robot, c) for c in self._names]
self._markers = sum([detect_markers(c.frame) for c in self.cameras], [])
self.sensors = [Marker(m) for m in self._markers]
with closing(PoppyErgoJr()) as jr:
jr.rest_posture.start()
jr.rest_posture.wait_to_stop()
traj_rec = PositionWatcher(jr, 25., jr.motors)
jr.dance.start()
traj_rec.start()
t0 = time.time()
while time.time() - t0 < D:
cpu.append(psutil.cpu_percent())
img = jr.camera.frame
markers = detect_markers(img)
for m in markers:
m.draw_contour(img)
time.sleep(.1)
jr.dance.stop()
traj_rec.stop()
print('CPU M={}% (STD={})'.format(mean(cpu) * 4, std(cpu) * 4))
fig = pylab.figure()
ax = pylab.axes()
traj_rec.plot(ax)
pylab.show()
fig.savefig('bench.png')
def capture_data(self):
# Get Frame
okay, self.img = self.cap.read()
if self.img is None:
# Bad Image, do nothing
return
# convert image to grayscale then back so it still has
# 3 color dimensions
gray_img = cv2.cvtColor(self.img, cv2.COLOR_BGR2GRAY)
gray_img = cv2.cvtColor(gray_img, cv2.COLOR_GRAY2BGR)
# threshold the image to either pure white or pure black
# thresh is scaled so that images is thresholded at % out of 255
self.thresh_img[:, :, :] = 255.0*np.round(gray_img[:, :, :]/(510.0*self.thresh))
# blur the rounded image
#blurred_image = cv2.GaussianBlur(self.thresh_img,(5,5),0)
# find any valid AR tags in the image
markers = detect_markers(self.thresh_img)
# Assume no objects are available until proven otherwise
ducky_unavailable = True
duckybot_unavailable = True
obstacle_unavailable = True
# for each valid tag, get the pose
for m in markers:
# Draw the marker outline on the image
m.draw_contour(self.img)
# Label the tag ID on the image
if cv2.__version__[0] == "2":
# Latest Stable Version
cv2.putText(self.img, str(m.id), tuple(int(p) for p in m.center), cv2.cv.CV_FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 3)
else:
# version 3.1.0 (Dylans Version)
cv2.putText(self.img, str(m.id), tuple(int(p) for p in m.center), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 3)
# Get ducky pose if ducky AR tag was detected
if (m.id == self.ducky_tag[0]):
self.Ducky_Pose = self.get_object_pose(m, self.ducky_tag)
ducky_unavailable = False
# Get duckybot pose if duckybot AR tag was detected
elif (m.id == self.duckybot_tag[0]):
self.Duckybot_Pose = self.get_object_pose(m, self.duckybot_tag)
duckybot_unavailable = False
# Get obstacle pose if obstacle AR tag was detected
elif (m.id == self.obstacle_tag[0]):
self.Obstacle_Pose = self.get_object_pose(m, self.obstacle_tag)
obstacle_unavailable = False
elif (self.obstacle_tag[0] == -100):
# Obstacle tag is in return any mode, get this unknown tag and set it to obstacle pose
self.Obstacle_Pose = self.get_object_pose(m, self.obstacle_tag)
obstacle_unavailable = False
# set poses for objects not found
if ducky_unavailable:
self.Ducky_Pose = [None, None]
if duckybot_unavailable:
self.Duckybot_Pose = [None, None]
if obstacle_unavailable:
self.Obstacle_Pose = [None, None]
# Finished capturing available data, return
return
def _detect(self, q, img):
q.put(detect_markers(img))
camera.exposure_mode = 'sports'
rawCapture = PiRGBArray(camera, size=(1280, 720))
#camera warm up
time.sleep(2.5)
# capture frames from the camera
for frame in camera.capture_continuous(rawCapture,
format="bgr",
use_video_port=True):
# grab the raw NumPy array representing the image, then initialize the timestamp
# and occupied/unoccupied text
image = frame.array
# Detect Markers
markers = detect_markers(image)
os.system('clear')
# Draw contour and log positions
for m in markers:
print 'Found marker {} at {}'.format(m.id, m.center)
m.draw_contour(image)
# show the frame
cv2.imshow("Markers", image)
key = cv2.waitKey(1) & 0xFF
# clear the stream in preparation for the next frame
rawCapture.truncate(0)
def capture_data(self):
# Get Frame
self.c.startCapture()
try:
image = self.c.retrieveBuffer()
except PyCapture2.Fc2error as fc2Err:
print "Error retrieving buffer : ", fc2Err
self.cap = image.convert(PyCapture2.PIXEL_FORMAT.BGR)
# newimg.save("fc2TestImage.png", PyCapture2.IMAGE_FILE_FORMAT.PNG)
self.c.stopCapture()
self.img = np.array(self.cap.getData(), dtype="uint8").reshape(
(self.cap.getRows(), self.cap.getCols(), 3))
if self.img is None:
# Bad Image, do nothing
return
self.img = cv2.cvtColor(self.img, cv2.COLOR_BGR2RGB)
markers = detect_markers(self.img)
#print markers
# Assume no objects are available until proven otherwise
A_unavailable = True
B_unavailable = True
C_unavailable = True
# for each valid tag, get the pose
for m in markers:
# Draw the marker outline on the image
m.draw_contour(self.img)
# Label the tag ID on the image
if cv2.__version__[0] == "2":
# Latest Stable Version
cv2.putText(self.img, str(m.id),
tuple(int(p) for p in m.center),
cv2.cv.CV_FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 3)
else:
# version 3.1.0 (Dylans Version)
cv2.putText(self.img, str(m.id),
tuple(int(p) for p in m.center),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 3)
# Get A pose if AR tag was detected
if (m.id == self.A_tag[0]):
self.A_Pose = self.get_object_pose(m, self.A_tag)
A_unavailable = False
# Get B pose if AR tag was detected
elif (m.id == self.B_tag[0]):
self.B_Pose = self.get_object_pose(m, self.B_tag)
B_unavailable = False
# Get C pose if AR tag was detected
elif (m.id == self.C_tag[0]):
self.C_Pose = self.get_object_pose(m, self.C_tag)
C_unavailable = False
elif (self.C_tag[0] == -100):
# C tag is in return any mode, get this unknown tag and set it to C pose
self.C_Pose = self.get_object_pose(m, self.C_tag)
C_unavailable = False
# set poses for objects not found
if A_unavailable:
self.A_Pose = [None, None]
if B_unavailable:
self.B_Pose = [None, None]
if C_unavailable:
self.C_Pose = [None, None]
# Finished capturing available data, return
return
def read_tag_ar(self, image):
markers = detect_markers(image)
if markers is None or len(markers) == 0:
return None
output = zxing.BarCode("")
output.id = markers[0].id
