def __init__(self, calib_file=''):
self.publisher = rospy.Publisher(IMAGE_TOPIC, Image, queue_size=1)
rospy.init_node(NODE_NAME)
width = rospy.get_param('~width', 640)
height = rospy.get_param('~height', 480)
rospy.logwarn(width)
self.calib_file = calib_file
self.camera = Camera(width=width,height=height)
def test_init_start_stop(self):
"""Test camera's thread init/start/stop methods."""
camera = Camera()
camera.start()
time.sleep(1)
image = bgr8_to_jpeg(camera.value)
camera.stop()
# Check if image changed after stopping
assert image == bgr8_to_jpeg(camera.value)
mean = torch.Tensor([0.485, 0.456, 0.406]).cuda().half()
std = torch.Tensor([0.229, 0.224, 0.225]).cuda().half()
def preprocess(image):
image = PIL.Image.fromarray(image)
image = transforms.functional.to_tensor(image).to(device).half()
image.sub_(mean[:, None, None]).div_(std[:, None, None])
return image[None, ...]
from IPython.display import display
import ipywidgets
import traitlets
from jetbot import Camera, bgr8_to_jpeg
camera = Camera()
image_widget = ipywidgets.Image()
traitlets.dlink((camera, 'value'), (image_widget, 'value'), transform=bgr8_to_jpeg)
display(image_widget)
from jetbot import Robot
robot = Robot()
speed_gain_slider = ipywidgets.FloatSlider(min=0.0, max=1.0, step=0.01, description='speed gain')
steering_gain_slider = ipywidgets.FloatSlider(min=0.0, max=1.0, step=0.01, value=0.2, description='steering gain')
steering_dgain_slider = ipywidgets.FloatSlider(min=0.0, max=0.5, step=0.001, value=0.0, description='steering kd')
steering_bias_slider = ipywidgets.FloatSlider(min=-0.3, max=0.3, step=0.01, value=0.0, description='steering bias')
def __init__(self, camera_width, camera_height):
self.camera = Camera(width=camera_width, height=camera_height)
self.image = None
