def convert_image(self, image: carla.Image, color_converter=None):
color_converter = color_converter or self.color_converter or carla.ColorConverter.Raw
image.convert(color_converter)
array = np.frombuffer(image.raw_data, dtype=np.uint8)
array = np.reshape(array, (image.height, image.width, 4))
array = array[:, :, :3]
array = array[:, :, ::-1]
return array
def convert_rgb_from_source_to_agent(
self, source: carla.Image) -> Union[RGBData, None]:
"""Convert CARLA raw Image to a Union with RGB numpy array"""
try:
source.convert(cc.Raw)
return RGBData(data=self._to_rgb_array(source))
except:
return None
def carla_cityscapes_image_to_ndarray(image: carla.Image) -> np.ndarray: # pylint: disable=no-member
"""Returns a `NumPy` array from a `CARLA` semantic segmentation image.
Args:
image: The `CARLA` semantic segmented image.
Returns:
A `NumPy` array representation of the image.
"""
image.convert(carla.ColorConverter.CityScapesPalette) # pylint: disable=no-member
array = np.frombuffer(image.raw_data, dtype=np.dtype("uint8"))
array = array.astype(np.float32) / 255
array = np.reshape(array, (image.height, image.width, 4))
array = array[:, :, :3]
array = array[:, :, ::-1]
return array
def convert(image: carla.Image, log=False):
"""Converts the given carla.Image into grayscale in which each pixel contains the depth distance."""
image.convert(carla.ColorConverter.Raw)
# to rgb image
array = np.frombuffer(image.raw_data, dtype=np.uint8)
array = np.reshape(array, (image.height, image.width, 4))
array = array[:, :, :3]
array = array[:, :, ::-1]
# to depth
b = array[:, :, 0]
g = array[:, :, 1]
r = array[:, :, 2]
normalized = (r + g * 256 + b * 256 * 256) / (256 * 256 * 256 - 1)
in_meters = 1000 * normalized
if log:
return np.log(in_meters)
return in_meters
