Python datasets.factory.get_dataset() Examples
The following are 18
code examples of datasets.factory.get_dataset().
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Example #1
| Source File: script_preprocess_annoations_S3DIS.py From object_detection_kitti with Apache License 2.0 | 6 votes |
|
def _write_map_files(b_in, b_out, transform):
cats = get_categories()
env = utils.Foo(padding=10, resolution=5, num_point_threshold=2,
valid_min=-10, valid_max=200, n_samples_per_face=200)
robot = utils.Foo(radius=15, base=10, height=140, sensor_height=120,
camera_elevation_degree=-15)
building_loader = factory.get_dataset('sbpd')
for flip in [False, True]:
b = nav_env.Building(b_out, robot, env, flip=flip,
building_loader=building_loader)
logging.info("building_in: %s, building_out: %s, transform: %d", b_in,
b_out, transform)
maps = _get_semantic_maps(b_in, transform, b.map, flip, cats)
maps = np.transpose(np.array(maps), axes=[1,2,0])
# Load file from the cache.
file_name = '{:s}_{:d}_{:d}_{:d}_{:d}_{:d}_{:d}.pkl'
file_name = file_name.format(b.building_name, b.map.size[0], b.map.size[1],
b.map.origin[0], b.map.origin[1],
b.map.resolution, flip)
out_file = os.path.join(DATA_DIR, 'processing', 'class-maps', file_name)
logging.info('Writing semantic maps to %s.', out_file)
save_variables(out_file, [maps, cats], ['maps', 'cats'], overwrite=True)
Example #2
| Source File: script_preprocess_annoations_S3DIS.py From multilabel-image-classification-tensorflow with MIT License | 6 votes |
|
def _write_map_files(b_in, b_out, transform):
cats = get_categories()
env = utils.Foo(padding=10, resolution=5, num_point_threshold=2,
valid_min=-10, valid_max=200, n_samples_per_face=200)
robot = utils.Foo(radius=15, base=10, height=140, sensor_height=120,
camera_elevation_degree=-15)
building_loader = factory.get_dataset('sbpd')
for flip in [False, True]:
b = nav_env.Building(b_out, robot, env, flip=flip,
building_loader=building_loader)
logging.info("building_in: %s, building_out: %s, transform: %d", b_in,
b_out, transform)
maps = _get_semantic_maps(b_in, transform, b.map, flip, cats)
maps = np.transpose(np.array(maps), axes=[1,2,0])
# Load file from the cache.
file_name = '{:s}_{:d}_{:d}_{:d}_{:d}_{:d}_{:d}.pkl'
file_name = file_name.format(b.building_name, b.map.size[0], b.map.size[1],
b.map.origin[0], b.map.origin[1],
b.map.resolution, flip)
out_file = os.path.join(DATA_DIR, 'processing', 'class-maps', file_name)
logging.info('Writing semantic maps to %s.', out_file)
save_variables(out_file, [maps, cats], ['maps', 'cats'], overwrite=True)
Example #3
| Source File: script_env_vis.py From multilabel-image-classification-tensorflow with MIT License | 6 votes |
|
def load_building(dataset_name, building_name):
dataset = factory.get_dataset(dataset_name)
navtask = get_args()
cp = navtask.camera_param
rgb_shader, d_shader = renderer.get_shaders(cp.modalities)
r_obj = SwiftshaderRenderer()
r_obj.init_display(width=cp.width, height=cp.height,
fov=cp.fov, z_near=cp.z_near, z_far=cp.z_far,
rgb_shader=rgb_shader, d_shader=d_shader)
r_obj.clear_scene()
b = VisualNavigationEnv(robot=navtask.robot, env=navtask.env,
task_params=navtask.task_params,
building_name=building_name, flip=False,
logdir=None, building_loader=dataset,
r_obj=r_obj)
b.load_building_into_scene()
b.set_building_visibility(False)
return b
Example #4
| Source File: script_preprocess_annoations_S3DIS.py From models with Apache License 2.0 | 6 votes |
|
def _write_map_files(b_in, b_out, transform):
cats = get_categories()
env = utils.Foo(padding=10, resolution=5, num_point_threshold=2,
valid_min=-10, valid_max=200, n_samples_per_face=200)
robot = utils.Foo(radius=15, base=10, height=140, sensor_height=120,
camera_elevation_degree=-15)
building_loader = factory.get_dataset('sbpd')
for flip in [False, True]:
b = nav_env.Building(b_out, robot, env, flip=flip,
building_loader=building_loader)
logging.info("building_in: %s, building_out: %s, transform: %d", b_in,
b_out, transform)
maps = _get_semantic_maps(b_in, transform, b.map, flip, cats)
maps = np.transpose(np.array(maps), axes=[1,2,0])
# Load file from the cache.
file_name = '{:s}_{:d}_{:d}_{:d}_{:d}_{:d}_{:d}.pkl'
file_name = file_name.format(b.building_name, b.map.size[0], b.map.size[1],
b.map.origin[0], b.map.origin[1],
b.map.resolution, flip)
out_file = os.path.join(DATA_DIR, 'processing', 'class-maps', file_name)
logging.info('Writing semantic maps to %s.', out_file)
save_variables(out_file, [maps, cats], ['maps', 'cats'], overwrite=True)
Example #5
| Source File: script_env_vis.py From models with Apache License 2.0 | 6 votes |
|
def load_building(dataset_name, building_name):
dataset = factory.get_dataset(dataset_name)
navtask = get_args()
cp = navtask.camera_param
rgb_shader, d_shader = renderer.get_shaders(cp.modalities)
r_obj = SwiftshaderRenderer()
r_obj.init_display(width=cp.width, height=cp.height,
fov=cp.fov, z_near=cp.z_near, z_far=cp.z_far,
rgb_shader=rgb_shader, d_shader=d_shader)
r_obj.clear_scene()
b = VisualNavigationEnv(robot=navtask.robot, env=navtask.env,
task_params=navtask.task_params,
building_name=building_name, flip=False,
logdir=None, building_loader=dataset,
r_obj=r_obj)
b.load_building_into_scene()
b.set_building_visibility(False)
return b
Example #6
| Source File: script_preprocess_annoations_S3DIS.py From g-tensorflow-models with Apache License 2.0 | 6 votes |
|
def _write_map_files(b_in, b_out, transform):
cats = get_categories()
env = utils.Foo(padding=10, resolution=5, num_point_threshold=2,
valid_min=-10, valid_max=200, n_samples_per_face=200)
robot = utils.Foo(radius=15, base=10, height=140, sensor_height=120,
camera_elevation_degree=-15)
building_loader = factory.get_dataset('sbpd')
for flip in [False, True]:
b = nav_env.Building(b_out, robot, env, flip=flip,
building_loader=building_loader)
logging.info("building_in: %s, building_out: %s, transform: %d", b_in,
b_out, transform)
maps = _get_semantic_maps(b_in, transform, b.map, flip, cats)
maps = np.transpose(np.array(maps), axes=[1,2,0])
# Load file from the cache.
file_name = '{:s}_{:d}_{:d}_{:d}_{:d}_{:d}_{:d}.pkl'
file_name = file_name.format(b.building_name, b.map.size[0], b.map.size[1],
b.map.origin[0], b.map.origin[1],
b.map.resolution, flip)
out_file = os.path.join(DATA_DIR, 'processing', 'class-maps', file_name)
logging.info('Writing semantic maps to %s.', out_file)
save_variables(out_file, [maps, cats], ['maps', 'cats'], overwrite=True)
Example #7
| Source File: script_env_vis.py From g-tensorflow-models with Apache License 2.0 | 6 votes |
|
def load_building(dataset_name, building_name):
dataset = factory.get_dataset(dataset_name)
navtask = get_args()
cp = navtask.camera_param
rgb_shader, d_shader = renderer.get_shaders(cp.modalities)
r_obj = SwiftshaderRenderer()
r_obj.init_display(width=cp.width, height=cp.height,
fov=cp.fov, z_near=cp.z_near, z_far=cp.z_far,
rgb_shader=rgb_shader, d_shader=d_shader)
r_obj.clear_scene()
b = VisualNavigationEnv(robot=navtask.robot, env=navtask.env,
task_params=navtask.task_params,
building_name=building_name, flip=False,
logdir=None, building_loader=dataset,
r_obj=r_obj)
b.load_building_into_scene()
b.set_building_visibility(False)
return b
Example #8
| Source File: script_preprocess_annoations_S3DIS.py From object_detection_with_tensorflow with MIT License | 6 votes |
|
def _write_map_files(b_in, b_out, transform):
cats = get_categories()
env = utils.Foo(padding=10, resolution=5, num_point_threshold=2,
valid_min=-10, valid_max=200, n_samples_per_face=200)
robot = utils.Foo(radius=15, base=10, height=140, sensor_height=120,
camera_elevation_degree=-15)
building_loader = factory.get_dataset('sbpd')
for flip in [False, True]:
b = nav_env.Building(b_out, robot, env, flip=flip,
building_loader=building_loader)
logging.info("building_in: %s, building_out: %s, transform: %d", b_in,
b_out, transform)
maps = _get_semantic_maps(b_in, transform, b.map, flip, cats)
maps = np.transpose(np.array(maps), axes=[1,2,0])
# Load file from the cache.
file_name = '{:s}_{:d}_{:d}_{:d}_{:d}_{:d}_{:d}.pkl'
file_name = file_name.format(b.building_name, b.map.size[0], b.map.size[1],
b.map.origin[0], b.map.origin[1],
b.map.resolution, flip)
out_file = os.path.join(DATA_DIR, 'processing', 'class-maps', file_name)
logging.info('Writing semantic maps to %s.', out_file)
save_variables(out_file, [maps, cats], ['maps', 'cats'], overwrite=True)
Example #9
| Source File: script_env_vis.py From object_detection_with_tensorflow with MIT License | 6 votes |
|
def load_building(dataset_name, building_name):
dataset = factory.get_dataset(dataset_name)
navtask = get_args()
cp = navtask.camera_param
rgb_shader, d_shader = renderer.get_shaders(cp.modalities)
r_obj = SwiftshaderRenderer()
r_obj.init_display(width=cp.width, height=cp.height,
fov=cp.fov, z_near=cp.z_near, z_far=cp.z_far,
rgb_shader=rgb_shader, d_shader=d_shader)
r_obj.clear_scene()
b = VisualNavigationEnv(robot=navtask.robot, env=navtask.env,
task_params=navtask.task_params,
building_name=building_name, flip=False,
logdir=None, building_loader=dataset,
r_obj=r_obj)
b.load_building_into_scene()
b.set_building_visibility(False)
return b
Example #10
| Source File: script_env_vis.py From DOTA_models with Apache License 2.0 | 6 votes |
|
def load_building(dataset_name, building_name):
dataset = factory.get_dataset(dataset_name)
navtask = get_args()
cp = navtask.camera_param
rgb_shader, d_shader = renderer.get_shaders(cp.modalities)
r_obj = SwiftshaderRenderer()
r_obj.init_display(width=cp.width, height=cp.height,
fov=cp.fov, z_near=cp.z_near, z_far=cp.z_far,
rgb_shader=rgb_shader, d_shader=d_shader)
r_obj.clear_scene()
b = VisualNavigationEnv(robot=navtask.robot, env=navtask.env,
task_params=navtask.task_params,
building_name=building_name, flip=False,
logdir=None, building_loader=dataset,
r_obj=r_obj)
b.load_building_into_scene()
b.set_building_visibility(False)
return b
Example #11
| Source File: script_env_vis.py From object_detection_kitti with Apache License 2.0 | 6 votes |
|
def load_building(dataset_name, building_name):
dataset = factory.get_dataset(dataset_name)
navtask = get_args()
cp = navtask.camera_param
rgb_shader, d_shader = renderer.get_shaders(cp.modalities)
r_obj = SwiftshaderRenderer()
r_obj.init_display(width=cp.width, height=cp.height,
fov=cp.fov, z_near=cp.z_near, z_far=cp.z_far,
rgb_shader=rgb_shader, d_shader=d_shader)
r_obj.clear_scene()
b = VisualNavigationEnv(robot=navtask.robot, env=navtask.env,
task_params=navtask.task_params,
building_name=building_name, flip=False,
logdir=None, building_loader=dataset,
r_obj=r_obj)
b.load_building_into_scene()
b.set_building_visibility(False)
return b
Example #12
| Source File: script_preprocess_annoations_S3DIS.py From hands-detection with MIT License | 6 votes |
|
def _write_map_files(b_in, b_out, transform):
cats = get_categories()
env = utils.Foo(padding=10, resolution=5, num_point_threshold=2,
valid_min=-10, valid_max=200, n_samples_per_face=200)
robot = utils.Foo(radius=15, base=10, height=140, sensor_height=120,
camera_elevation_degree=-15)
building_loader = factory.get_dataset('sbpd')
for flip in [False, True]:
b = nav_env.Building(b_out, robot, env, flip=flip,
building_loader=building_loader)
logging.info("building_in: %s, building_out: %s, transform: %d", b_in,
b_out, transform)
maps = _get_semantic_maps(b_in, transform, b.map, flip, cats)
maps = np.transpose(np.array(maps), axes=[1,2,0])
# Load file from the cache.
file_name = '{:s}_{:d}_{:d}_{:d}_{:d}_{:d}_{:d}.pkl'
file_name = file_name.format(b.building_name, b.map.size[0], b.map.size[1],
b.map.origin[0], b.map.origin[1],
b.map.resolution, flip)
out_file = os.path.join(DATA_DIR, 'processing', 'class-maps', file_name)
logging.info('Writing semantic maps to %s.', out_file)
save_variables(out_file, [maps, cats], ['maps', 'cats'], overwrite=True)
Example #13
| Source File: script_env_vis.py From hands-detection with MIT License | 6 votes |
|
def load_building(dataset_name, building_name):
dataset = factory.get_dataset(dataset_name)
navtask = get_args()
cp = navtask.camera_param
rgb_shader, d_shader = renderer.get_shaders(cp.modalities)
r_obj = SwiftshaderRenderer()
r_obj.init_display(width=cp.width, height=cp.height,
fov=cp.fov, z_near=cp.z_near, z_far=cp.z_far,
rgb_shader=rgb_shader, d_shader=d_shader)
r_obj.clear_scene()
b = VisualNavigationEnv(robot=navtask.robot, env=navtask.env,
task_params=navtask.task_params,
building_name=building_name, flip=False,
logdir=None, building_loader=dataset,
r_obj=r_obj)
b.load_building_into_scene()
b.set_building_visibility(False)
return b
Example #14
| Source File: script_preprocess_annoations_S3DIS.py From Gun-Detector with Apache License 2.0 | 6 votes |
|
def _write_map_files(b_in, b_out, transform):
cats = get_categories()
env = utils.Foo(padding=10, resolution=5, num_point_threshold=2,
valid_min=-10, valid_max=200, n_samples_per_face=200)
robot = utils.Foo(radius=15, base=10, height=140, sensor_height=120,
camera_elevation_degree=-15)
building_loader = factory.get_dataset('sbpd')
for flip in [False, True]:
b = nav_env.Building(b_out, robot, env, flip=flip,
building_loader=building_loader)
logging.info("building_in: %s, building_out: %s, transform: %d", b_in,
b_out, transform)
maps = _get_semantic_maps(b_in, transform, b.map, flip, cats)
maps = np.transpose(np.array(maps), axes=[1,2,0])
# Load file from the cache.
file_name = '{:s}_{:d}_{:d}_{:d}_{:d}_{:d}_{:d}.pkl'
file_name = file_name.format(b.building_name, b.map.size[0], b.map.size[1],
b.map.origin[0], b.map.origin[1],
b.map.resolution, flip)
out_file = os.path.join(DATA_DIR, 'processing', 'class-maps', file_name)
logging.info('Writing semantic maps to %s.', out_file)
save_variables(out_file, [maps, cats], ['maps', 'cats'], overwrite=True)
Example #15
| Source File: script_env_vis.py From Gun-Detector with Apache License 2.0 | 6 votes |
|
def load_building(dataset_name, building_name):
dataset = factory.get_dataset(dataset_name)
navtask = get_args()
cp = navtask.camera_param
rgb_shader, d_shader = renderer.get_shaders(cp.modalities)
r_obj = SwiftshaderRenderer()
r_obj.init_display(width=cp.width, height=cp.height,
fov=cp.fov, z_near=cp.z_near, z_far=cp.z_far,
rgb_shader=rgb_shader, d_shader=d_shader)
r_obj.clear_scene()
b = VisualNavigationEnv(robot=navtask.robot, env=navtask.env,
task_params=navtask.task_params,
building_name=building_name, flip=False,
logdir=None, building_loader=dataset,
r_obj=r_obj)
b.load_building_into_scene()
b.set_building_visibility(False)
return b
Example #16
| Source File: script_preprocess_annoations_S3DIS.py From yolo_v2 with Apache License 2.0 | 6 votes |
|
def _write_map_files(b_in, b_out, transform):
cats = get_categories()
env = utils.Foo(padding=10, resolution=5, num_point_threshold=2,
valid_min=-10, valid_max=200, n_samples_per_face=200)
robot = utils.Foo(radius=15, base=10, height=140, sensor_height=120,
camera_elevation_degree=-15)
building_loader = factory.get_dataset('sbpd')
for flip in [False, True]:
b = nav_env.Building(b_out, robot, env, flip=flip,
building_loader=building_loader)
logging.info("building_in: %s, building_out: %s, transform: %d", b_in,
b_out, transform)
maps = _get_semantic_maps(b_in, transform, b.map, flip, cats)
maps = np.transpose(np.array(maps), axes=[1,2,0])
# Load file from the cache.
file_name = '{:s}_{:d}_{:d}_{:d}_{:d}_{:d}_{:d}.pkl'
file_name = file_name.format(b.building_name, b.map.size[0], b.map.size[1],
b.map.origin[0], b.map.origin[1],
b.map.resolution, flip)
out_file = os.path.join(DATA_DIR, 'processing', 'class-maps', file_name)
logging.info('Writing semantic maps to %s.', out_file)
save_variables(out_file, [maps, cats], ['maps', 'cats'], overwrite=True)
Example #17
| Source File: script_env_vis.py From yolo_v2 with Apache License 2.0 | 6 votes |
|
def load_building(dataset_name, building_name):
dataset = factory.get_dataset(dataset_name)
navtask = get_args()
cp = navtask.camera_param
rgb_shader, d_shader = renderer.get_shaders(cp.modalities)
r_obj = SwiftshaderRenderer()
r_obj.init_display(width=cp.width, height=cp.height,
fov=cp.fov, z_near=cp.z_near, z_far=cp.z_far,
rgb_shader=rgb_shader, d_shader=d_shader)
r_obj.clear_scene()
b = VisualNavigationEnv(robot=navtask.robot, env=navtask.env,
task_params=navtask.task_params,
building_name=building_name, flip=False,
logdir=None, building_loader=dataset,
r_obj=r_obj)
b.load_building_into_scene()
b.set_building_visibility(False)
return b
Example #18
| Source File: script_preprocess_annoations_S3DIS.py From DOTA_models with Apache License 2.0 | 6 votes |
|
def _write_map_files(b_in, b_out, transform):
cats = get_categories()
env = utils.Foo(padding=10, resolution=5, num_point_threshold=2,
valid_min=-10, valid_max=200, n_samples_per_face=200)
robot = utils.Foo(radius=15, base=10, height=140, sensor_height=120,
camera_elevation_degree=-15)
building_loader = factory.get_dataset('sbpd')
for flip in [False, True]:
b = nav_env.Building(b_out, robot, env, flip=flip,
building_loader=building_loader)
logging.info("building_in: %s, building_out: %s, transform: %d", b_in,
b_out, transform)
maps = _get_semantic_maps(b_in, transform, b.map, flip, cats)
maps = np.transpose(np.array(maps), axes=[1,2,0])
# Load file from the cache.
file_name = '{:s}_{:d}_{:d}_{:d}_{:d}_{:d}_{:d}.pkl'
file_name = file_name.format(b.building_name, b.map.size[0], b.map.size[1],
b.map.origin[0], b.map.origin[1],
b.map.resolution, flip)
out_file = os.path.join(DATA_DIR, 'processing', 'class-maps', file_name)
logging.info('Writing semantic maps to %s.', out_file)
save_variables(out_file, [maps, cats], ['maps', 'cats'], overwrite=True)
