def main():
SCENE_SPLITS['mini-val'] = SCENE_SPLITS['val']
if not os.path.exists(OUT_PATH):
os.mkdir(OUT_PATH)
for split in SPLITS:
data_path = DATA_PATH + '{}/'.format(SPLITS[split])
nusc = NuScenes(
version=SPLITS[split], dataroot=data_path, verbose=True)
out_path = OUT_PATH + '{}.json'.format(split)
categories_info = [{'name': CATS[i], 'id': i + 1} for i in range(len(CATS))]
ret = {'images': [], 'annotations': [], 'categories': categories_info,
'videos': [], 'attributes': ATTRIBUTE_TO_ID}
num_images = 0
num_anns = 0
num_videos = 0
# A "sample" in nuScenes refers to a timestamp with 6 cameras and 1 LIDAR.
for sample in nusc.sample:
scene_name = nusc.get('scene', sample['scene_token'])['name']
if not (split in ['mini', 'test']) and \
not (scene_name in SCENE_SPLITS[split]):
continue
if sample['prev'] == '':
print('scene_name', scene_name)
num_videos += 1
ret['videos'].append({'id': num_videos, 'file_name': scene_name})
frame_ids = {k: 0 for k in sample['data']}
track_ids = {}
# We decompose a sample into 6 images in our case.
for sensor_name in sample['data']:
if sensor_name in USED_SENSOR:
image_token = sample['data'][sensor_name]
image_data = nusc.get('sample_data', image_token)
num_images += 1
# Complex coordinate transform. This will take time to understand.
sd_record = nusc.get('sample_data', image_token)
cs_record = nusc.get(
'calibrated_sensor', sd_record['calibrated_sensor_token'])
pose_record = nusc.get('ego_pose', sd_record['ego_pose_token'])
global_from_car = transform_matrix(pose_record['translation'],
Quaternion(pose_record['rotation']), inverse=False)
car_from_sensor = transform_matrix(
cs_record['translation'], Quaternion(cs_record['rotation']),
inverse=False)
trans_matrix = np.dot(global_from_car, car_from_sensor)
_, boxes, camera_intrinsic = nusc.get_sample_data(
image_token, box_vis_level=BoxVisibility.ANY)
calib = np.eye(4, dtype=np.float32)
calib[:3, :3] = camera_intrinsic
calib = calib[:3]
frame_ids[sensor_name] += 1
# image information in COCO format
image_info = {'id': num_images,
'file_name': image_data['filename'],
'calib': calib.tolist(),
'video_id': num_videos,
'frame_id': frame_ids[sensor_name],
'sensor_id': SENSOR_ID[sensor_name],
'sample_token': sample['token'],
'trans_matrix': trans_matrix.tolist(),
'width': sd_record['width'],
'height': sd_record['height'],
'pose_record_trans': pose_record['translation'],
'pose_record_rot': pose_record['rotation'],
'cs_record_trans': cs_record['translation'],
'cs_record_rot': cs_record['rotation']}
ret['images'].append(image_info)
anns = []
for box in boxes:
det_name = category_to_detection_name(box.name)
if det_name is None:
continue
num_anns += 1
v = np.dot(box.rotation_matrix, np.array([1, 0, 0]))
yaw = -np.arctan2(v[2], v[0])
box.translate(np.array([0, box.wlh[2] / 2, 0]))
category_id = CAT_IDS[det_name]
amodel_center = project_to_image(
np.array([box.center[0], box.center[1] - box.wlh[2] / 2, box.center[2]],
np.float32).reshape(1, 3), calib)[0].tolist()
sample_ann = nusc.get(
'sample_annotation', box.token)
instance_token = sample_ann['instance_token']
if not (instance_token in track_ids):
track_ids[instance_token] = len(track_ids) + 1
attribute_tokens = sample_ann['attribute_tokens']
attributes = [nusc.get('attribute', att_token)['name'] \
for att_token in attribute_tokens]
att = '' if len(attributes) == 0 else attributes[0]
if len(attributes) > 1:
print(attributes)
import pdb; pdb.set_trace()
track_id = track_ids[instance_token]
vel = nusc.box_velocity(box.token) # global frame
vel = np.dot(np.linalg.inv(trans_matrix),
np.array([vel[0], vel[1], vel[2], 0], np.float32)).tolist()
# instance information in COCO format
ann = {
'id': num_anns,
'image_id': num_images,
'category_id': category_id,
'dim': [box.wlh[2], box.wlh[0], box.wlh[1]],
'location': [box.center[0], box.center[1], box.center[2]],
'depth': box.center[2],
'occluded': 0,
'truncated': 0,
'rotation_y': yaw,
'amodel_center': amodel_center,
'iscrowd': 0,
'track_id': track_id,
'attributes': ATTRIBUTE_TO_ID[att],
'velocity': vel
}
bbox = KittiDB.project_kitti_box_to_image(
copy.deepcopy(box), camera_intrinsic, imsize=(1600, 900))
alpha = _rot_y2alpha(yaw, (bbox[0] + bbox[2]) / 2,
camera_intrinsic[0, 2], camera_intrinsic[0, 0])
ann['bbox'] = [bbox[0], bbox[1], bbox[2] - bbox[0], bbox[3] - bbox[1]]
ann['area'] = (bbox[2] - bbox[0]) * (bbox[3] - bbox[1])
ann['alpha'] = alpha
anns.append(ann)
# Filter out bounding boxes outside the image
visable_anns = []
for i in range(len(anns)):
vis = True
for j in range(len(anns)):
if anns[i]['depth'] - min(anns[i]['dim']) / 2 > \
anns[j]['depth'] + max(anns[j]['dim']) / 2 and \
_bbox_inside(anns[i]['bbox'], anns[j]['bbox']):
vis = False
break
if vis:
visable_anns.append(anns[i])
else:
pass
for ann in visable_anns:
ret['annotations'].append(ann)
if DEBUG:
img_path = data_path + image_info['file_name']
img = cv2.imread(img_path)
img_3d = img.copy()
for ann in visable_anns:
bbox = ann['bbox']
cv2.rectangle(img, (int(bbox[0]), int(bbox[1])),
(int(bbox[2] + bbox[0]), int(bbox[3] + bbox[1])),
(0, 0, 255), 3, lineType=cv2.LINE_AA)
box_3d = compute_box_3d(ann['dim'], ann['location'], ann['rotation_y'])
box_2d = project_to_image(box_3d, calib)
img_3d = draw_box_3d(img_3d, box_2d)
pt_3d = unproject_2d_to_3d(ann['amodel_center'], ann['depth'], calib)
pt_3d[1] += ann['dim'][0] / 2
print('location', ann['location'])
print('loc model', pt_3d)
pt_2d = np.array([(bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2],
dtype=np.float32)
pt_3d = unproject_2d_to_3d(pt_2d, ann['depth'], calib)
pt_3d[1] += ann['dim'][0] / 2
print('loc ', pt_3d)
cv2.imshow('img', img)
cv2.imshow('img_3d', img_3d)
cv2.waitKey()
nusc.render_sample_data(image_token)
plt.show()
print('reordering images')
images = ret['images']
video_sensor_to_images = {}
for image_info in images:
tmp_seq_id = image_info['video_id'] * 20 + image_info['sensor_id']
if tmp_seq_id in video_sensor_to_images:
video_sensor_to_images[tmp_seq_id].append(image_info)
else:
video_sensor_to_images[tmp_seq_id] = [image_info]
ret['images'] = []
for tmp_seq_id in sorted(video_sensor_to_images):
ret['images'] = ret['images'] + video_sensor_to_images[tmp_seq_id]
print('{} {} images {} boxes'.format(
split, len(ret['images']), len(ret['annotations'])))
print('out_path', out_path)
json.dump(ret, open(out_path, 'w'))
def main():
SCENE_SPLITS["mini-val"] = SCENE_SPLITS["val"]
if not os.path.exists(DATA_PATH):
os.mkdir(DATA_PATH)
if not os.path.exists(OUT_PATH):
os.mkdir(OUT_PATH)
for split in SPLITS:
data_path = DATA_PATH # + '{}/'.format(SPLITS[split])
nusc = NuScenes(version=SPLITS[split],
dataroot=data_path,
verbose=True)
out_path = OUT_PATH + "{}.json".format(split)
categories_info = [{
"name": CATS[i],
"id": i + 1
} for i in range(len(CATS))]
ret = {
"images": [],
"annotations": [],
"categories": categories_info,
"videos": [],
"attributes": ATTRIBUTE_TO_ID,
}
num_images = 0
num_anns = 0
num_videos = 0
# A "sample" in nuScenes refers to a timestamp with 6 cameras and 1 LIDAR.
for sample in nusc.sample:
scene_name = nusc.get("scene", sample["scene_token"])["name"]
if not (split in ["mini", "test"
]) and not (scene_name in SCENE_SPLITS[split]):
continue
if sample["prev"] == "":
print("scene_name", scene_name)
num_videos += 1
ret["videos"].append({
"id": num_videos,
"file_name": scene_name
})
frame_ids = {k: 0 for k in sample["data"]}
track_ids = {}
# We decompose a sample into 6 images in our case.
for sensor_name in sample["data"]:
if sensor_name in USED_SENSOR:
image_token = sample["data"][sensor_name]
image_data = nusc.get("sample_data", image_token)
num_images += 1
# Complex coordinate transform. This will take time to understand.
sd_record = nusc.get("sample_data", image_token)
cs_record = nusc.get("calibrated_sensor",
sd_record["calibrated_sensor_token"])
pose_record = nusc.get("ego_pose",
sd_record["ego_pose_token"])
global_from_car = transform_matrix(
pose_record["translation"],
Quaternion(pose_record["rotation"]),
inverse=False,
)
car_from_sensor = transform_matrix(
cs_record["translation"],
Quaternion(cs_record["rotation"]),
inverse=False,
)
trans_matrix = np.dot(global_from_car, car_from_sensor)
_, boxes, camera_intrinsic = nusc.get_sample_data(
image_token, box_vis_level=BoxVisibility.ANY)
calib = np.eye(4, dtype=np.float32)
calib[:3, :3] = camera_intrinsic
calib = calib[:3]
frame_ids[sensor_name] += 1
# image information in COCO format
image_info = {
"id": num_images,
"file_name": image_data["filename"],
"calib": calib.tolist(),
"video_id": num_videos,
"frame_id": frame_ids[sensor_name],
"sensor_id": SENSOR_ID[sensor_name],
"sample_token": sample["token"],
"trans_matrix": trans_matrix.tolist(),
"width": sd_record["width"],
"height": sd_record["height"],
"pose_record_trans": pose_record["translation"],
"pose_record_rot": pose_record["rotation"],
"cs_record_trans": cs_record["translation"],
"cs_record_rot": cs_record["rotation"],
}
ret["images"].append(image_info)
anns = []
for box in boxes:
det_name = category_to_detection_name(box.name)
if det_name is None:
continue
num_anns += 1
v = np.dot(box.rotation_matrix, np.array([1, 0, 0]))
yaw = -np.arctan2(v[2], v[0])
box.translate(np.array([0, box.wlh[2] / 2, 0]))
category_id = CAT_IDS[det_name]
amodel_center = project_to_image(
np.array(
[
box.center[0],
box.center[1] - box.wlh[2] / 2,
box.center[2],
],
np.float32,
).reshape(1, 3),
calib,
)[0].tolist()
sample_ann = nusc.get("sample_annotation", box.token)
instance_token = sample_ann["instance_token"]
if not (instance_token in track_ids):
track_ids[instance_token] = len(track_ids) + 1
attribute_tokens = sample_ann["attribute_tokens"]
attributes = [
nusc.get("attribute", att_token)["name"]
for att_token in attribute_tokens
]
att = "" if len(attributes) == 0 else attributes[0]
if len(attributes) > 1:
print(attributes)
import pdb
pdb.set_trace()
track_id = track_ids[instance_token]
vel = nusc.box_velocity(box.token) # global frame
vel = np.dot(
np.linalg.inv(trans_matrix),
np.array([vel[0], vel[1], vel[2], 0], np.float32),
).tolist()
# instance information in COCO format
ann = {
"id":
num_anns,
"image_id":
num_images,
"category_id":
category_id,
"dim": [box.wlh[2], box.wlh[0], box.wlh[1]],
"location":
[box.center[0], box.center[1], box.center[2]],
"depth":
box.center[2],
"occluded":
0,
"truncated":
0,
"rotation_y":
yaw,
"amodel_center":
amodel_center,
"iscrowd":
0,
"track_id":
track_id,
"attributes":
ATTRIBUTE_TO_ID[att],
"velocity":
vel,
}
bbox = KittiDB.project_kitti_box_to_image(
copy.deepcopy(box),
camera_intrinsic,
imsize=(1600, 900))
alpha = _rot_y2alpha(
yaw,
(bbox[0] + bbox[2]) / 2,
camera_intrinsic[0, 2],
camera_intrinsic[0, 0],
)
ann["bbox"] = [
bbox[0],
bbox[1],
bbox[2] - bbox[0],
bbox[3] - bbox[1],
]
ann["area"] = (bbox[2] - bbox[0]) * (bbox[3] - bbox[1])
ann["alpha"] = alpha
anns.append(ann)
# Filter out bounding boxes outside the image
visable_anns = []
for i in range(len(anns)):
vis = True
for j in range(len(anns)):
if anns[i]["depth"] - min(anns[i][
"dim"]) / 2 > anns[j]["depth"] + max(
anns[j]["dim"]) / 2 and _bbox_inside(
anns[i]["bbox"], anns[j]["bbox"]):
vis = False
break
if vis:
visable_anns.append(anns[i])
else:
pass
for ann in visable_anns:
ret["annotations"].append(ann)
if DEBUG:
img_path = data_path + image_info["file_name"]
img = cv2.imread(img_path)
img_3d = img.copy()
for ann in visable_anns:
bbox = ann["bbox"]
cv2.rectangle(
img,
(int(bbox[0]), int(bbox[1])),
(int(bbox[2] + bbox[0]),
int(bbox[3] + bbox[1])),
(0, 0, 255),
3,
lineType=cv2.LINE_AA,
)
box_3d = compute_box_3d(ann["dim"],
ann["location"],
ann["rotation_y"])
box_2d = project_to_image(box_3d, calib)
img_3d = draw_box_3d(img_3d, box_2d)
pt_3d = unproject_2d_to_3d(ann["amodel_center"],
ann["depth"], calib)
pt_3d[1] += ann["dim"][0] / 2
print("location", ann["location"])
print("loc model", pt_3d)
pt_2d = np.array(
[(bbox[0] + bbox[2]) / 2,
(bbox[1] + bbox[3]) / 2],
dtype=np.float32,
)
pt_3d = unproject_2d_to_3d(pt_2d, ann["depth"],
calib)
pt_3d[1] += ann["dim"][0] / 2
print("loc ", pt_3d)
cv2.imshow("img", img)
cv2.imshow("img_3d", img_3d)
cv2.waitKey()
nusc.render_sample_data(image_token)
plt.show()
print("reordering images")
images = ret["images"]
video_sensor_to_images = {}
for image_info in images:
tmp_seq_id = image_info["video_id"] * 20 + image_info["sensor_id"]
if tmp_seq_id in video_sensor_to_images:
video_sensor_to_images[tmp_seq_id].append(image_info)
else:
video_sensor_to_images[tmp_seq_id] = [image_info]
ret["images"] = []
for tmp_seq_id in sorted(video_sensor_to_images):
ret["images"] = ret["images"] + video_sensor_to_images[tmp_seq_id]
print("{} {} images {} boxes".format(split, len(ret["images"]),
len(ret["annotations"])))
print("out_path", out_path)
json.dump(ret, open(out_path, "w"))