def __init__(self,
source_map_size, world_size_px,
world_size, img_w, img_h,
embed_size, map_channels, gnd_channels, res_channels=32,
lang_filter=False, img_dbg=False):
super(FPVToEgoMap, self).__init__(source_map_size, world_size_px)
self.image_debug = img_dbg
self.use_lang_filter = lang_filter
# Process images using a resnet to get a feature map
if self.image_debug:
self.img_to_features = nn.MaxPool2d(8)
else:
# Provide enough padding so that the map is scaled down by powers of 2.
self.img_to_features = ImgToFeatures(res_channels, map_channels)
if self.use_lang_filter:
self.lang_filter = MapLangSemanticFilter(embed_size, map_channels, gnd_channels)
# Project feature maps to the global frame
self.map_projection = PinholeCameraProjectionModule(
source_map_size, world_size_px, world_size, source_map_size / 2, img_w, img_h)
self.grid_sampler = GridSampler()
self.prof = SimpleProfiler(torch_sync=PROFILE, print=PROFILE)
self.actual_images = None
def __init__(self,
img_w,
img_h,
res_channels,
map_channels,
img_dbg=False):
super(FPVToFPVMap, self).__init__()
self.image_debug = img_dbg
# Provide enough padding so that the map is scaled down by powers of 2.
self.img_to_features = ImgToFeatures(res_channels, map_channels, img_w,
img_h)
self.prof = SimpleProfiler(torch_sync=PROFILE, print=PROFILE)
self.actual_images = None
def __init__(self,
source_map_size,
world_size_px,
world_size_m,
img_w,
img_h,
res_channels,
map_channels,
cam_h_fov,
domain,
img_dbg=False):
super(FPVToGlobalMap, self).__init__(source_map_size, world_size_px,
world_size_m)
self.image_debug = img_dbg
self.use_lang_filter = False
# Process images using a resnet to get a feature map
if self.image_debug:
self.img_to_features = nn.MaxPool2d(8)
else:
# Provide enough padding so that the map is scaled down by powers of 2.
self.img_to_features = ImgToFeatures(res_channels, map_channels,
img_w, img_h)
# Project feature maps to the global frame
self.map_projection = PinholeCameraProjectionModuleGlobal(
source_map_size, world_size_px, world_size_m, img_w, img_h,
cam_h_fov, domain)
self.grid_sampler = GridSampler()
self.prof = SimpleProfiler(torch_sync=PROFILE, print=PROFILE)
self.actual_images = None