def __init__(self):
tensorflow_path = rospy.get_param(
"tensorflow_path",
os.path.expanduser('~') +
"/.opt/tensorflow_venv/lib/python2.7/site-packages")
use_gpu = rospy.get_param('use_gpu', 0)
network_input_height = float(
rospy.get_param('network_input_height', '240'))
network_input_width = float(
rospy.get_param('network_input_width', '320'))
clip = bool(rospy.get_param('~clip', 'False'))
self.saliency = Saliency(tensorflow_path, use_gpu,
network_input_height, network_input_width,
clip)
image_sub = rospy.Subscriber("/rgb/image_raw",
Image,
self.image_callback,
queue_size=1,
buff_size=2**24)
self.saliency_map_pub = rospy.Publisher("/saliency_map",
Float32MultiArray,
queue_size=1)
self.saliency_map_image_pub = rospy.Publisher("/saliency_map_image",
Image,
queue_size=1)
self.cv_bridge = CvBridge()
class SaliencyNode():
def __init__(self):
tensorflow_path = rospy.get_param(
"tensorflow_path",
os.path.expanduser('~') +
"/.opt/tensorflow_venv/lib/python2.7/site-packages")
use_gpu = rospy.get_param('use_gpu', 0)
network_input_height = float(
rospy.get_param('network_input_height', '240'))
network_input_width = float(
rospy.get_param('network_input_width', '320'))
clip = bool(rospy.get_param('~clip', 'False'))
self.saliency = Saliency(tensorflow_path, use_gpu,
network_input_height, network_input_width,
clip)
image_sub = rospy.Subscriber("/rgb/image_raw",
Image,
self.image_callback,
queue_size=1,
buff_size=2**24)
self.saliency_map_pub = rospy.Publisher("/saliency_map",
Float32MultiArray,
queue_size=1)
self.saliency_map_image_pub = rospy.Publisher("/saliency_map_image",
Image,
queue_size=1)
self.cv_bridge = CvBridge()
def __del__(self):
self.sess.close()
def image_callback(self, image):
try:
image = self.cv_bridge.imgmsg_to_cv2(image, "bgr8")
except CvBridgeError as e:
print e
saliency_map = self.saliency.compute_saliency_map(image)
height = MultiArrayDimension(size=len(saliency_map))
width = MultiArrayDimension(size=len(saliency_map[0]))
lo = MultiArrayLayout([height, width], 0)
self.saliency_map_pub.publish(
Float32MultiArray(layout=lo, data=saliency_map.flatten()))
try:
saliency_map_image = self.cv_bridge.cv2_to_imgmsg(
np.uint8(saliency_map * 255.), "mono8")
except CvBridgeError as e:
print e
self.saliency_map_image_pub.publish(saliency_map_image)
help='number of SALICON image used to compute the fitness')
parser.add_argument('--n-iter',
type=int,
default=50,
help='number of CMAES iterations')
parser.add_argument('--seed', type=int, default=10, help='random seed')
args = parser.parse_args()
try:
os.makedirs(args.out)
except OSError as e:
print(e)
model_file = args.model
saliency = Saliency(model_file=model_file)
fixation = sio.loadmat(args.fixations)
all_user_fixations = [
len(fixation['gaze'][i][0][2]) for i in range(len(fixation['gaze']))
]
time_limit = fixation["gaze"][0][0][1][-1][0]
gt_mean_rate = np.mean(all_user_fixations) * 1000. / time_limit
path_to_image = os.path.join(args.images, fixation['image'][0] + '.jpg')
print(
"Ground truth image {} has mean saccade rate: {}, std: {} ({} participants)"
.format(path_to_image, gt_mean_rate, np.std(all_user_fixations),
len(fixation['gaze'])))
image = misc.imread(path_to_image)
saliency_map = saliency.compute_saliency_map(image)
dt = 5.
from cv_bridge import CvBridge
from attention import Saliency
from std_msgs.msg import Float32MultiArray
from image_geometry import StereoCameraModel
from embodied_attention.srv import Transform
tensorflow_path = rospy.get_param(
"tensorflow_path", "/opt/tensorflow_venv/lib/python2.7/site-packages")
model_file = rospy.get_param('saliency_file', '/tmp/model.ckpt')
network_input_height = float(rospy.get_param('network_input_height', '192'))
network_input_width = float(rospy.get_param('network_input_width', '256'))
@nrp.MapVariable("saliency",
initial_value=Saliency(tensorflow_path, model_file,
network_input_height,
network_input_width, False))
@nrp.MapVariable("saliency_pub",
initial_value=rospy.Publisher("/saliency_map",
Float32MultiArray,
queue_size=1))
@nrp.MapVariable("saliency_image_pub",
initial_value=rospy.Publisher("/saliency_map_image",
Image,
queue_size=1))
@nrp.MapVariable("bridge", initial_value=CvBridge())
@nrp.MapVariable("points", initial_value=[], scope=nrp.GLOBAL)
@nrp.MapVariable("camera_model", initial_value=StereoCameraModel())
@nrp.MapVariable("camera_info_left", initial_value=None, scope=nrp.GLOBAL)
@nrp.MapVariable("camera_info_right", initial_value=None, scope=nrp.GLOBAL)
@nrp.MapVariable("disparity_image", initial_value=None, scope=nrp.GLOBAL)
import rospy
from sensor_msgs.msg import Image
from geometry_msgs.msg import PointStamped
from cv_bridge import CvBridge
from attention import Saliency
from std_msgs.msg import Float32MultiArray
from image_geometry import PinholeCameraModel
from embodied_attention.srv import Transform
tensorflow_path = rospy.get_param("tensorflow_path", "/opt/tensorflow_venv/lib/python2.7/site-packages")
model_file = rospy.get_param('saliency_file', '/tmp/model.ckpt')
network_input_height = float(rospy.get_param('network_input_height', '192'))
network_input_width = float(rospy.get_param('network_input_width', '256'))
@nrp.MapVariable("saliency", initial_value = Saliency(tensorflow_path, model_file, network_input_height, network_input_width, False))
@nrp.MapVariable("saliency_pub", initial_value = rospy.Publisher("/saliency_map", Float32MultiArray, queue_size=1))
@nrp.MapVariable("saliency_image_pub", initial_value = rospy.Publisher("/saliency_map_image", Image, queue_size=1))
@nrp.MapVariable("bridge", initial_value=CvBridge())
@nrp.MapVariable("points", initial_value=[], scope=nrp.GLOBAL)
@nrp.MapVariable("camera_model", initial_value=PinholeCameraModel())
@nrp.MapVariable("camera_info_left", initial_value=None, scope=nrp.GLOBAL)
@nrp.MapVariable("last_time", initial_value = None)
@nrp.MapVariable("elapsed", initial_value = 0)
@nrp.MapVariable("pan", initial_value = 0, scope=nrp.GLOBAL)
@nrp.MapVariable("tilt", initial_value = 0, scope=nrp.GLOBAL)
@nrp.MapVariable("saliency_map", initial_value = None)
@nrp.MapRobotSubscriber("image", Topic('/icub_model/left_eye_camera/image_raw', sensor_msgs.msg.Image)
def image_to_saliency(t, image, bridge, saliency, saliency_pub, saliency_image_pub, points, camera_model, camera_info_left, last_time, elapsed, pan, tilt, saliency_map):
if image.value is None or camera_info_left.value is None:
return
help='path to the input image')
parser.add_argument('--rf_modulation_type', type=str, default='none',
help='type of receptive field modulation')
parser.add_argument('--seed', type=int, default=10,
help='random seed')
args = parser.parse_args()
np.random.seed(args.seed)
try:
os.makedirs(args.out)
except OSError as e:
print(e)
# demonstration of saliency model
saliency = Saliency(use_gpu=args.gpu)
image = cv.imread(args.image, 1)
saliency_map = saliency.compute_saliency_map(image)
# plt.imsave(path.join(args.out, 'saliency.png'), saliency_map, cmap=plt.get_cmap('gray'))
# demonstration of saccade model
def plot_targets(all_targets, all_times, saliency_map, out):
fig = plt.figure()
ax = fig.add_subplot(111)
ax.imshow(saliency_map,
vmin=0, vmax=1.,
aspect='equal',
interpolation='none',
cmap=plt.get_cmap('gray'),
origin='upper')
ax.set_axis_off()
parser.add_argument(
'--rf-radius-score',
type=int,
default=5,
help=
'radius of circle taken into account to compute score in saliency pixels')
parser.add_argument('--seed', type=int, default=10, help='random seed')
args = parser.parse_args()
try:
os.makedirs(args.out)
except OSError as e:
print(e)
saliency = Saliency(use_gpu=args.gpu)
images = [cv.imread(im, 1) for im in args.image]
saliency_maps = [saliency.compute_saliency_map(im) for im in images]
for (i, sal) in enumerate(saliency_maps):
plt.imsave(path.join(args.out, 'saliency_{}.png'.format(i)),
sal,
cmap=plt.get_cmap('gray'))
dt = 5.
# ordered list of params to optimize
saccade_params = OrderedDict([('sig_lat', .1), ('sig_rf', 0.267),
('sig_IoR', .1), ('amp_lat', .001),
('amp_rf', 0.008), ('amp_IoR', 1.5),
('amp_noise', .09), ('k', .017), ('g', .33),
('theta', 6.), ('tau', 50.)])