def __init__(self, config, tracking_camera, scene_type="building", gpu_count=0):
BaseEnv.__init__(self, config, scene_type, tracking_camera)
self.camera_x = 0
self.walk_target_x = 1e3 # kilometer away
self.walk_target_y = 0
self.k = 5
self.robot_tracking_id = -1
self.scale_up = 4
self.dataset = ViewDataSet3D(
transform = np.array,
mist_transform = np.array,
seqlen = 2,
off_3d = False,
train = False,
overwrite_fofn=True, env = self)
self.ground_ids = None
if self.gui:
assert(self.tracking_camera is not None)
self.gpu_count = gpu_count
self.nframe = 0
self.eps_reward = 0
self.reward = 0
self.eps_count = 0
self._robot_introduced = False
self._scene_introduced = False
def __init__(self, config, scene_type="building", gpu_count=0):
BaseEnv.__init__(self, config, scene_type)
## The following properties are already instantiated inside xxx_env.py:
# @self.timestep
# @self.frame_skip
self.camera_x = 0
self.walk_target_x = 1e3 # kilometer away
self.walk_target_y = 0
self.temp_target_x = 0 # added randomness
self.temp_target_y = 0 # added randomness
self.k = 5
self.robot_tracking_id = -1
self.scale_up = 4
self.dataset = ViewDataSet3D(transform=np.array,
mist_transform=np.array,
seqlen=2,
off_3d=False,
train=False,
overwrite_fofn=True,
env=self)
self.ground_ids = None
if self.gui:
assert (self.tracking_camera is not None)
self.gpu_count = gpu_count
self.nframe = 0
self.eps_reward = 0
self.reward = 0
self.eps_count = 0
self._robot_introduced = False
self._scene_introduced = False
def setup_camera_pc(self):
## Camera specific
assert(self._require_camera_input)
if self.scene_type == "building":
self.dataset = ViewDataSet3D(
transform = np.array,
mist_transform = np.array,
seqlen = 2,
off_3d = False,
train = False,
overwrite_fofn=True, env = self, only_load = self.config["model_id"])
scene_dict = dict(zip(self.dataset.scenes, range(len(self.dataset.scenes))))
## Todo: (hzyjerry) more error handling
if not self.model_id in scene_dict.keys():
raise error.Error("Dataset not found: model {} cannot be loaded".format(self.model_id))
else:
scene_id = scene_dict[self.model_id]
uuids, rts = self.dataset.get_scene_info(scene_id)
targets, sources, source_depths, poses = [], [], [], []
source_semantics = []
if not self.multiprocessing or self.config["envname"] == "TestEnv":
all_data = self.dataset.get_multi_index([v for k, v in uuids])
for i, data in enumerate(all_data):
target, target_depth = data[1], data[3]
if not self._require_rgb:
continue
ww = target.shape[0] // 8 + 2
target[:ww, :, :] = target[ww, :, :]
target[-ww:, :, :] = target[-ww, :, :]
if self.scale_up !=1:
target = cv2.resize(
target,None,
fx=1.0/self.scale_up,
fy=1.0/self.scale_up,
interpolation = cv2.INTER_CUBIC)
target_depth = cv2.resize(
target_depth, None,
fx=1.0/self.scale_up,
fy=1.0/self.scale_up,
interpolation = cv2.INTER_CUBIC)
pose = data[-1][0].numpy()
targets.append(target)
poses.append(pose)
sources.append(target)
source_depths.append(target_depth)
else:
all_data = self.dataset.get_multi_index([v for k, v in uuids])
for i, data in enumerate(all_data):
target, target_depth = data[1], data[3]
if not self._require_rgb:
continue
ww = target.shape[0] // 8 + 2
target[:ww, :, :] = target[ww, :, :]
target[-ww:, :, :] = target[-ww, :, :]
if self.scale_up !=1:
target = cv2.resize(
target,None,
fx=1.0/self.scale_up,
fy=1.0/self.scale_up,
interpolation = cv2.INTER_CUBIC)
target_depth = cv2.resize(
target_depth, None,
fx=1.0/self.scale_up,
fy=1.0/self.scale_up,
interpolation = cv2.INTER_CUBIC)
pose = data[-1][0].numpy()
targets.append(target)
poses.append(pose)
sources.append(target)
source_depths.append(target_depth)
self.r_camera_rgb = PCRenderer(self.port_rgb, sources, source_depths, target, rts,
scale_up=self.scale_up,
semantics=source_semantics,
gui=self.gui,
use_filler=self._use_filler,
gpu_idx=self.gpu_idx,
windowsz=self.windowsz,
env = self)
show, _ = render(sources, source_depths, poses[0], poses, target_depth)
print(show.shape)
Image.fromarray(show).save('%s/show%d.png' % (outf, idx))
Image.fromarray(target).save('%s/target%d.png' % (outf, idx))
np.savez(file = filename, source = show, depth = target_depth, target = target)
return
parser = argparse.ArgumentParser()
parser.add_argument('--debug' , action='store_true', help='debug mode')
parser.add_argument('--dataroot' , required = True, help='dataset path')
parser.add_argument('--outf' , type = str, default = '', help='path of output folder')
opt = parser.parse_args()
d = ViewDataSet3D(root=opt.dataroot, transform = np.array, mist_transform = np.array, seqlen = 5, off_3d = False, train = False)
print(len(d))
p = Pool(10)
p.map(generate_data, [(idx, d, opt.outf) for idx in range(len(d))])
#for i in range(len(d)):
# filename = "%s/data_%d.npz" % (opt.outf, i)
# print(filename)
# if not os.path.isfile(filename):
# generate_data([i, d, opt.outf])
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--debug', action='store_true', help='debug mode')
parser.add_argument('--datapath', required=True, help='dataset path')
parser.add_argument('--model_id', type=str, default=0, help='models id')
parser.add_argument('--models',
type=str,
default='',
help='path of models')
opt = parser.parse_args()
d = ViewDataSet3D(root=opt.datapath,
transform=np.array,
mist_transform=np.array,
seqlen=2,
off_3d=False,
train=True)
scene_dict = dict(zip(d.scenes, range(len(d.scenes))))
if not opt.model_id in scene_dict.keys():
print("models not found")
else:
scene_id = scene_dict[opt.model_id]
uuids, rts = d.get_scene_info(scene_id)
print(uuids, rts)
targets = []
sources = []
source_depths = []