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Unverified Commit 8398af33 authored by Xiang Xu's avatar Xiang Xu Committed by GitHub
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[Doc] Update docstring in visualizer. (#2115) 

* fix some vis bug and update doc

* add typehint and fix bug

* Update local_visualizer.py

* update doc

* update docs

* fix doc

* update docs and fix some vis bug

* update

* update docs
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mmdet3d/visualization/local_visualizer.py
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# Copyright (c) OpenMMLab. All rights reserved.
import copy
from typing import Dict, List, Optional, Tuple, Union
from typing import List, Optional, Tuple, Union
import matplotlib.pyplot as plt
import mmcv
......@@ -11,15 +11,16 @@ from matplotlib.path import Path
from mmdet.visualization import DetLocalVisualizer
from mmengine.dist import master_only
from mmengine.structures import InstanceData
from mmengine.visualization import Visualizer as MMENGINE_Visualizer
from mmengine.visualization.utils import check_type, tensor2ndarray
from torch import Tensor
from mmdet3d.registry import VISUALIZERS
from mmdet3d.structures import (BaseInstance3DBoxes, CameraInstance3DBoxes,
Coord3DMode, DepthInstance3DBoxes,
Det3DDataSample, LiDARInstance3DBoxes,
PointData, points_cam2img)
from mmdet3d.structures.bbox_3d.box_3d_mode import Box3DMode
from mmdet3d.structures import (BaseInstance3DBoxes, Box3DMode,
CameraInstance3DBoxes, Coord3DMode,
DepthInstance3DBoxes, Det3DDataSample,
LiDARInstance3DBoxes, PointData,
points_cam2img)
from .vis_utils import (proj_camera_bbox3d_to_img, proj_depth_bbox3d_to_img,
proj_lidar_bbox3d_to_img, to_depth_mode)
......@@ -43,51 +44,62 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
Args:
name (str): Name of the instance. Defaults to 'visualizer'.
points (numpy.array, shape=[N, 3+C]): points to visualize.
image (np.ndarray, optional): the origin image to draw. The format
points (np.ndarray, optional): Points to visualize with shape (N, 3+C).
Defaults to None.
image (np.ndarray, optional): The origin image to draw. The format
should be RGB. Defaults to None.
pcd_mode (int): The point cloud mode (coordinates):
0 represents LiDAR, 1 represents CAMERA, 2
represents Depth. Defaults to 0.
vis_backends (list, optional): Visual backend config list.
pcd_mode (int): The point cloud mode (coordinates): 0 represents LiDAR,
1 represents CAMERA, 2 represents Depth. Defaults to 0.
vis_backends (List[dict], optional): Visual backend config list.
Defaults to None.
save_dir (str, optional): Save file dir for all storage backends.
If it is None, the backend storage will not save any data.
bbox_color (str, tuple(int), optional): Color of bbox lines.
The tuple of color should be in BGR order. Defaults to None.
text_color (str, tuple(int), optional): Color of texts.
The tuple of color should be in BGR order.
Defaults to (200, 200, 200).
mask_color (str, tuple(int), optional): Color of masks.
The tuple of color should be in BGR order.
Defaults to None.
line_width (int, float): The linewidth of lines.
Defaults to 3.
bbox_color (str or Tuple[int], optional): Color of bbox lines.
The tuple of color should be in BGR order. Defaults to None.
text_color (str or Tuple[int]): Color of texts. The tuple of color
should be in BGR order. Defaults to (200, 200, 200).
mask_color (str or Tuple[int], optional): Color of masks. The tuple of
color should be in BGR order. Defaults to None.
line_width (int or float): The linewidth of lines. Defaults to 3.
frame_cfg (dict): The coordinate frame config while Open3D
visualization initialization.
Defaults to dict(size=1, origin=[0, 0, 0]).
alpha (int, float): The transparency of bboxes or mask.
alpha (int or float): The transparency of bboxes or mask.
Defaults to 0.8.
Examples:
>>> import numpy as np
>>> import torch
>>> from mmengine.structures import InstanceData
>>> from mmdet3d.structures import Det3DDataSample
>>> from mmdet3d.structures import (DepthInstance3DBoxes
... Det3DDataSample)
>>> from mmdet3d.visualization import Det3DLocalVisualizer
>>> det3d_local_visualizer = Det3DLocalVisualizer()
>>> image = np.random.randint(0, 256,
... size=(10, 12, 3)).astype('uint8')
>>> points = np.random.rand((1000, ))
>>> image = np.random.randint(0, 256, size=(10, 12, 3)).astype('uint8')
>>> points = np.random.rand(1000, 3)
>>> gt_instances_3d = InstanceData()
>>> gt_instances_3d.bboxes_3d = BaseInstance3DBoxes(torch.rand((5, 7)))
>>> gt_instances_3d.bboxes_3d = DepthInstance3DBoxes(
... torch.rand((5, 7)))
>>> gt_instances_3d.labels_3d = torch.randint(0, 2, (5,))
>>> gt_det3d_data_sample = Det3DDataSample()
>>> gt_det3d_data_sample.gt_instances_3d = gt_instances_3d
>>> data_input = dict(img=image, points=points)
>>> det3d_local_visualizer.add_datasample('3D Scene', data_input,
... gt_det3d_data_sample)
>>> from mmdet3d.structures import PointData
>>> det3d_local_visualizer = Det3DLocalVisualizer()
>>> points = np.random.rand(1000, 3)
>>> gt_pts_seg = PointData()
>>> gt_pts_seg.pts_semantic_mask = torch.randint(0, 10, (1000, ))
>>> gt_det3d_data_sample = Det3DDataSample()
>>> gt_det3d_data_sample.gt_pts_seg = gt_pts_seg
>>> data_input = dict(points=points)
>>> det3d_local_visualizer.add_datasample('3D Scene', data_input,
... gt_det3d_data_sample,
... vis_task='lidar_seg')
"""
def __init__(self,
......@@ -95,15 +107,14 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
points: Optional[np.ndarray] = None,
image: Optional[np.ndarray] = None,
pcd_mode: int = 0,
vis_backends: Optional[Dict] = None,
vis_backends: Optional[List[dict]] = None,
save_dir: Optional[str] = None,
bbox_color: Optional[Union[str, Tuple[int]]] = None,
text_color: Optional[Union[str,
Tuple[int]]] = (200, 200, 200),
text_color: Union[str, Tuple[int]] = (200, 200, 200),
mask_color: Optional[Union[str, Tuple[int]]] = None,
line_width: Union[int, float] = 3,
frame_cfg: dict = dict(size=1, origin=[0, 0, 0]),
alpha: float = 0.8):
alpha: Union[int, float] = 0.8) -> None:
super().__init__(
name=name,
image=image,
......@@ -126,12 +137,12 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
del self.pcd
del self.points_colors
def _initialize_o3d_vis(self, frame_cfg) -> Visualizer:
def _initialize_o3d_vis(self, frame_cfg: dict) -> Visualizer:
"""Initialize open3d vis according to frame_cfg.
Args:
frame_cfg (dict): The config to create coordinate frame
in open3d vis.
frame_cfg (dict): The config to create coordinate frame in open3d
vis.
Returns:
:obj:`o3d.visualization.Visualizer`: Created open3d vis.
......@@ -152,32 +163,31 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
pcd_mode: int = 0,
vis_mode: str = 'replace',
frame_cfg: dict = dict(size=1, origin=[0, 0, 0]),
points_color: Tuple = (0.5, 0.5, 0.5),
points_color: Tuple[float] = (0.5, 0.5, 0.5),
points_size: int = 2,
mode: str = 'xyz') -> None:
"""Set the points to draw.
"""Set the point cloud to draw.
Args:
points (numpy.array, shape=[N, 3+C]):
points to visualize.
pcd_mode (int): The point cloud mode (coordinates):
0 represents LiDAR, 1 represents CAMERA, 2
represents Depth. Defaults to 0.
points (np.ndarray): Points to visualize with shape (N, 3+C).
pcd_mode (int): The point cloud mode (coordinates): 0 represents
LiDAR, 1 represents CAMERA, 2 represents Depth. Defaults to 0.
vis_mode (str): The visualization mode in Open3D:
'replace': Replace the existing point cloud with
input point cloud.
'add': Add input point cloud into existing point
- 'replace': Replace the existing point cloud with input point
cloud.
- 'add': Add input point cloud into existing point cloud.
Defaults to 'replace'.
frame_cfg (dict): The coordinate frame config while Open3D
frame_cfg (dict): The coordinate frame config for Open3D
visualization initialization.
Defaults to dict(size=1, origin=[0, 0, 0]).
point_color (tuple[float], optional): the color of points.
Default: (0.5, 0.5, 0.5).
points_size (int, optional): the size of points to show
on visualizer. Default: 2.
mode (str, optional): indicate type of the input points,
available mode ['xyz', 'xyzrgb']. Default: 'xyz'.
points_color (Tuple[float]): The color of points.
Defaults to (0.5, 0.5, 0.5).
points_size (int): The size of points to show on visualizer.
Defaults to 2.
mode (str): Indicate type of the input points, available mode
['xyz', 'xyzrgb']. Defaults to 'xyz'.
"""
assert points is not None
assert vis_mode in ('replace', 'add')
......@@ -221,28 +231,27 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
# for better detection performance comparison
def draw_bboxes_3d(self,
bboxes_3d: BaseInstance3DBoxes,
bbox_color=(0, 1, 0),
points_in_box_color=(1, 0, 0),
rot_axis=2,
center_mode='lidar_bottom',
mode='xyz'):
bbox_color: Tuple[float] = (0, 1, 0),
points_in_box_color: Tuple[float] = (1, 0, 0),
rot_axis: int = 2,
center_mode: str = 'lidar_bottom',
mode: str = 'xyz') -> None:
"""Draw bbox on visualizer and change the color of points inside
bbox3d.
Args:
bboxes_3d (:obj:`BaseInstance3DBoxes`, shape=[M, 7]):
3d bbox (x, y, z, x_size, y_size, z_size, yaw) to visualize.
bbox_color (tuple[float], optional): the color of 3D bboxes.
Default: (0, 1, 0).
points_in_box_color (tuple[float], optional):
the color of points inside 3D bboxes. Default: (1, 0, 0).
rot_axis (int, optional): rotation axis of 3D bboxes.
Default: 2.
center_mode (bool, optional): Indicates the center of bbox is
bottom center or gravity center. available mode
['lidar_bottom', 'camera_bottom']. Default: 'lidar_bottom'.
mode (str, optional): Indicates type of input points,
available mode ['xyz', 'xyzrgb']. Default: 'xyz'.
bboxes_3d (:obj:`BaseInstance3DBoxes`): 3D bbox
(x, y, z, x_size, y_size, z_size, yaw) to visualize.
bbox_color (Tuple[float]): The color of 3D bboxes.
Defaults to (0, 1, 0).
points_in_box_color (Tuple[float]): The color of points inside 3D
bboxes. Defaults to (1, 0, 0).
rot_axis (int): Rotation axis of 3D bboxes. Defaults to 2.
center_mode (str): Indicates the center of bbox is bottom center or
gravity center. Available mode
['lidar_bottom', 'camera_bottom']. Defaults to 'lidar_bottom'.
mode (str): Indicates the type of input points, available mode
['xyz', 'xyzrgb']. Defaults to 'xyz'.
"""
# Before visualizing the 3D Boxes in point cloud scene
# we need to convert the boxes to Depth mode
......@@ -290,7 +299,7 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
def set_bev_image(self,
bev_image: Optional[np.ndarray] = None,
bev_shape: Optional[int] = 900) -> None:
bev_shape: int = 900) -> None:
"""Set the bev image to draw.
Args:
......@@ -337,43 +346,41 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
def draw_bev_bboxes(self,
bboxes_3d: BaseInstance3DBoxes,
scale: int = 15,
edge_colors: Union[str, tuple, List[str],
List[tuple]] = 'o',
edge_colors: Union[str, Tuple[int],
List[Union[str, Tuple[int]]]] = 'o',
line_styles: Union[str, List[str]] = '-',
line_widths: Union[Union[int, float],
List[Union[int, float]]] = 1,
face_colors: Union[str, tuple, List[str],
List[tuple]] = 'none',
alpha: Union[int, float] = 1):
line_widths: Union[int, float, List[Union[int,
float]]] = 1,
face_colors: Union[str, Tuple[int],
List[Union[str,
Tuple[int]]]] = 'none',
alpha: Union[int, float] = 1) -> MMENGINE_Visualizer:
"""Draw projected 3D boxes on the image.
Args:
bboxes_3d (:obj:`BaseInstance3DBoxes`, shape=[M, 7]):
3d bbox (x, y, z, x_size, y_size, z_size, yaw) to visualize.
bboxes_3d (:obj:`BaseInstance3DBoxes`): 3D bbox
(x, y, z, x_size, y_size, z_size, yaw) to visualize.
scale (dict): Value to scale the bev bboxes for better
visualization. Defaults to 15.
edge_colors (Union[str, tuple, List[str], List[tuple]]): The
colors of bboxes. ``colors`` can have the same length with
edge_colors (str or Tuple[int] or List[str or Tuple[int]]):
The colors of bboxes. ``colors`` can have the same length with
lines or just single value. If ``colors`` is single value, all
the lines will have the same colors. Refer to `matplotlib.
colors` for full list of formats that are accepted.
Defaults to 'o'.
line_styles (Union[str, List[str]]): The linestyle
of lines. ``line_styles`` can have the same length with
texts or just single value. If ``line_styles`` is single
value, all the lines will have the same linestyle.
Reference to
line_styles (str or List[str]): The linestyle of lines.
``line_styles`` can have the same length with texts or just
single value. If ``line_styles`` is single value, all the lines
will have the same linestyle. Reference to
https://matplotlib.org/stable/api/collections_api.html?highlight=collection#matplotlib.collections.AsteriskPolygonCollection.set_linestyle
for more details. Defaults to '-'.
line_widths (Union[Union[int, float], List[Union[int, float]]]):
The linewidth of lines. ``line_widths`` can have
the same length with lines or just single value.
If ``line_widths`` is single value, all the lines will
have the same linewidth. Defaults to 2.
face_colors (Union[str, tuple, List[str], List[tuple]]):
The face colors. Default to 'none'.
alpha (Union[int, float]): The transparency of bboxes.
Defaults to 1.
line_widths (int or float or List[int or float]): The linewidth of
lines. ``line_widths`` can have the same length with lines or
just single value. If ``line_widths`` is single value, all the
lines will have the same linewidth. Defaults to 2.
face_colors (str or Tuple[int] or List[str or Tuple[int]]):
The face colors. Defaults to 'none'.
alpha (int or float): The transparency of bboxes. Defaults to 1.
"""
check_type('bboxes', bboxes_3d, BaseInstance3DBoxes)
......@@ -401,19 +408,17 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
face_colors=face_colors)
@master_only
def draw_points_on_image(
self,
def draw_points_on_image(self,
points: Union[np.ndarray, Tensor],
pts2img: np.ndarray,
sizes: Optional[Union[np.ndarray, Tensor, int]] = 10) -> None:
sizes: Union[np.ndarray, int] = 10) -> None:
"""Draw projected points on the image.
Args:
positions (Union[np.ndarray, torch.Tensor]): Positions to draw.
pts2imgs (np,ndarray): The transformatino matrix from the
coordinate of point cloud to image plane.
sizes (Optional[Union[np.ndarray, torch.Tensor, int]]): The
marker size. Default to 10.
points (np.ndarray or Tensor): Points to draw.
pts2img (np.ndarray): The transformation matrix from the coordinate
of point cloud to image plane.
sizes (np.ndarray or int): The marker size. Defaults to 10.
"""
check_type('points', points, (np.ndarray, Tensor))
points = tensor2ndarray(points)
......@@ -434,45 +439,42 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
# TODO: set bbox color according to palette
@master_only
def draw_proj_bboxes_3d(self,
def draw_proj_bboxes_3d(
self,
bboxes_3d: BaseInstance3DBoxes,
input_meta: dict,
edge_colors: Union[str, tuple, List[str],
List[tuple]] = 'royalblue',
edge_colors: Union[str, Tuple[int],
List[Union[str, Tuple[int]]]] = 'royalblue',
line_styles: Union[str, List[str]] = '-',
line_widths: Union[Union[int, float],
List[Union[int, float]]] = 2,
face_colors: Union[str, tuple, List[str],
List[tuple]] = 'royalblue',
line_widths: Union[int, float, List[Union[int, float]]] = 2,
face_colors: Union[str, Tuple[int],
List[Union[str, Tuple[int]]]] = 'royalblue',
alpha: Union[int, float] = 0.4):
"""Draw projected 3D boxes on the image.
Args:
bbox3d (:obj:`BaseInstance3DBoxes`, shape=[M, 7]):
3d bbox (x, y, z, x_size, y_size, z_size, yaw) to visualize.
bboxes_3d (:obj:`BaseInstance3DBoxes`): 3D bbox
(x, y, z, x_size, y_size, z_size, yaw) to visualize.
input_meta (dict): Input meta information.
edge_colors (Union[str, tuple, List[str], List[tuple]]): The
colors of bboxes. ``colors`` can have the same length with
edge_colors (str or Tuple[int] or List[str or Tuple[int]]):
The colors of bboxes. ``colors`` can have the same length with
lines or just single value. If ``colors`` is single value, all
the lines will have the same colors. Refer to `matplotlib.
colors` for full list of formats that are accepted.
Defaults to 'royalblue'.
line_styles (Union[str, List[str]]): The linestyle
of lines. ``line_styles`` can have the same length with
texts or just single value. If ``line_styles`` is single
value, all the lines will have the same linestyle.
Reference to
line_styles (str or List[str]): The linestyle of lines.
``line_styles`` can have the same length with texts or just
single value. If ``line_styles`` is single value, all the lines
will have the same linestyle. Reference to
https://matplotlib.org/stable/api/collections_api.html?highlight=collection#matplotlib.collections.AsteriskPolygonCollection.set_linestyle
for more details. Defaults to '-'.
line_widths (Union[Union[int, float], List[Union[int, float]]]):
The linewidth of lines. ``line_widths`` can have
the same length with lines or just single value.
If ``line_widths`` is single value, all the lines will
have the same linewidth. Defaults to 2.
face_colors (Union[str, tuple, List[str], List[tuple]]):
The face colors. Default to 'royalblue'.
alpha (Union[int, float]): The transparency of bboxes.
Defaults to 0.4.
line_widths (int or float or List[int or float]): The linewidth of
lines. ``line_widths`` can have the same length with lines or
just single value. If ``line_widths`` is single value, all the
lines will have the same linewidth. Defaults to 2.
face_colors (str or Tuple[int] or List[str or Tuple[int]]):
The face colors. Defaults to 'royalblue'.
alpha (int or float): The transparency of bboxes. Defaults to 0.4.
"""
check_type('bboxes', bboxes_3d, BaseInstance3DBoxes)
......@@ -519,13 +521,13 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
face_colors=face_colors)
@master_only
def draw_seg_mask(self, seg_mask_colors: np.array):
def draw_seg_mask(self, seg_mask_colors: np.ndarray) -> None:
"""Add segmentation mask to visualizer via per-point colorization.
Args:
seg_mask_colors (numpy.array, shape=[N, 6]):
The segmentation mask whose first 3 dims are point coordinates
and last 3 dims are converted colors.
seg_mask_colors (np.ndarray): The segmentation mask with shape
(N, 6), whose first 3 dims are point coordinates and last 3
dims are converted colors.
"""
# we can't draw the colors on existing points
# in case gt and pred mask would overlap
......@@ -540,21 +542,26 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
seg_points[:, 0] += offset
self.set_points(seg_points, pcd_mode=2, vis_mode='add', mode='xyzrgb')
def _draw_instances_3d(self, data_input: dict, instances: InstanceData,
input_meta: dict, vis_task: str,
palette: Optional[List[tuple]]):
def _draw_instances_3d(self,
data_input: dict,
instances: InstanceData,
input_meta: dict,
vis_task: str,
palette: Optional[List[tuple]] = None) -> dict:
"""Draw 3D instances of GT or prediction.
Args:
data_input (dict): The input dict to draw.
instances (:obj:`InstanceData`): Data structure for
instance-level annotations or predictions.
metainfo (dict): Meta information.
vis_task (str): Visualiztion task, it includes:
'lidar_det', 'multi-modality_det', 'mono_det'.
instances (:obj:`InstanceData`): Data structure for instance-level
annotations or predictions.
input_meta (dict): Meta information.
vis_task (str): Visualization task, it includes: 'lidar_det',
'multi-modality_det', 'mono_det'.
palette (List[tuple], optional): Palette information corresponding
to the category. Defaults to None.
Returns:
dict: the drawn point cloud and image which channel is RGB.
dict: The drawn point cloud and image whose channel is RGB.
"""
# Only visualize when there is at least one instance
......@@ -602,21 +609,16 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
points: Union[Tensor, np.ndarray],
pts_seg: PointData,
palette: Optional[List[tuple]] = None,
ignore_index: Optional[int] = None):
ignore_index: Optional[int] = None) -> None:
"""Draw 3D semantic mask of GT or prediction.
Args:
points (Tensor | np.ndarray): The input point
cloud to draw.
pts_seg (:obj:`PointData`): Data structure for
pixel-level annotations or predictions.
palette (List[tuple], optional): Palette information
corresponding to the category. Defaults to None.
ignore_index (int, optional): Ignore category.
Defaults to None.
Returns:
dict: the drawn points with color.
points (Tensor or np.ndarray): The input point cloud to draw.
pts_seg (:obj:`PointData`): Data structure for pixel-level
annotations or predictions.
palette (List[tuple], optional): Palette information corresponding
to the category. Defaults to None.
ignore_index (int, optional): Ignore category. Defaults to None.
"""
check_type('points', points, (np.ndarray, Tensor))
......@@ -641,20 +643,22 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
drawn_img: Optional[np.ndarray] = None,
win_name: str = 'image',
wait_time: int = 0,
continue_key=' ') -> None:
continue_key: str = ' ') -> None:
"""Show the drawn point cloud/image.
Args:
save_path (str, optional): Path to save open3d visualized results.
Default: None.
Defaults to None.
drawn_img_3d (np.ndarray, optional): The image to show. If
drawn_img_3d is not None, it will show the image got by
Visualizer. Defaults to None.
drawn_img (np.ndarray, optional): The image to show. If drawn_img
is None, it will show the image got by Visualizer. Defaults
to None.
is not None, it will show the image got by Visualizer.
Defaults to None.
win_name (str): The image title. Defaults to 'image'.
wait_time (int): Delay in milliseconds. 0 is the special
value that means "forever". Defaults to 0.
continue_key (str): The key for users to continue. Defaults to
the space key.
wait_time (int): Delay in milliseconds. 0 is the special value that
means "forever". Defaults to 0.
continue_key (str): The key for users to continue. Defaults to ' '.
"""
if hasattr(self, 'o3d_vis'):
self.o3d_vis.run()
......@@ -664,8 +668,9 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
self._clear_o3d_vis()
if hasattr(self, '_image'):
if drawn_img_3d is None:
if drawn_img_3d is not None:
super().show(drawn_img_3d, win_name, wait_time, continue_key)
if drawn_img is not None:
super().show(drawn_img, win_name, wait_time, continue_key)
# TODO: Support Visualize the 3D results from image and point cloud
......@@ -674,7 +679,7 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
def add_datasample(self,
name: str,
data_input: dict,
data_sample: Optional['Det3DDataSample'] = None,
data_sample: Optional[Det3DDataSample] = None,
draw_gt: bool = True,
draw_pred: bool = True,
show: bool = False,
......@@ -686,11 +691,11 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
step: int = 0) -> None:
"""Draw datasample and save to all backends.
- If GT and prediction are plotted at the same time, they are
displayed in a stitched image where the left image is the
ground truth and the right image is the prediction.
- If ``show`` is True, all storage backends are ignored, and
the images will be displayed in a local window.
- If GT and prediction are plotted at the same time, they are displayed
in a stitched image where the left image is the ground truth and the
right image is the prediction.
- If ``show`` is True, all storage backends are ignored, and the images
will be displayed in a local window.
- If ``out_file`` is specified, the drawn image will be saved to
``out_file``. It is usually used when the display is not available.
......@@ -701,23 +706,26 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
data_sample (:obj:`Det3DDataSample`, optional): Prediction
Det3DDataSample. Defaults to None.
draw_gt (bool): Whether to draw GT Det3DDataSample.
Default to True.
Defaults to True.
draw_pred (bool): Whether to draw Prediction Det3DDataSample.
Defaults to True.
show (bool): Whether to display the drawn point clouds and
image. Default to False.
show (bool): Whether to display the drawn point clouds and image.
Defaults to False.
wait_time (float): The interval of show (s). Defaults to 0.
out_file (str): Path to output file. Defaults to None.
out_file (str, optional): Path to output file. Defaults to None.
o3d_save_path (str, optional): Path to save open3d visualized
results Default: None.
vis-task (str): Visualization task. Defaults to 'mono_det'.
results. Defaults to None.
vis_task (str): Visualization task. Defaults to 'mono_det'.
pred_score_thr (float): The threshold to visualize the bboxes
and masks. Defaults to 0.3.
step (int): Global step value to record. Defaults to 0.
"""
classes = self.dataset_meta.get('CLASSES', None)
# For object detection datasets, no PALETTE is saved
palette = self.dataset_meta.get('PALETTE', None)
assert vis_task in (
'mono_det', 'multi-view_det', 'lidar_det', 'lidar_seg',
'multi-modality_det'), f'got unexpected vis_task {vis_task}.'
classes = self.dataset_meta.get('classes', None)
# For object detection datasets, no palette is saved
palette = self.dataset_meta.get('palette', None)
ignore_index = self.dataset_meta.get('ignore_index', None)
gt_data_3d = None
......@@ -739,20 +747,20 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
img = img[..., [2, 1, 0]] # bgr to rgb
gt_img_data = self._draw_instances(
img, data_sample.gt_instances, classes, palette)
if 'gt_pts_seg' in data_sample and vis_task == 'seg':
if 'gt_pts_seg' in data_sample and vis_task == 'lidar_seg':
assert classes is not None, 'class information is ' \
'not provided when ' \
'visualizing panoptic ' \
'visualizing semantic ' \
'segmentation results.'
assert 'points' in data_input
self._draw_pts_sem_seg(data_input['points'],
data_sample.pred_pts_seg, palette,
data_sample.gt_pts_seg, palette,
ignore_index)
if draw_pred and data_sample is not None:
if 'pred_instances_3d' in data_sample:
pred_instances_3d = data_sample.pred_instances_3d
# .cpu can not be used for BaseInstancesBoxes3D
# .cpu can not be used for BaseInstance3DBoxes
# so we need to use .to('cpu')
pred_instances_3d = pred_instances_3d[
pred_instances_3d.scores_3d > pred_score_thr].to('cpu')
......@@ -763,7 +771,7 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
if 'pred_instances' in data_sample:
if 'img' in data_input and len(data_sample.pred_instances) > 0:
pred_instances = data_sample.pred_instances
pred_instances = pred_instances_3d[
pred_instances = pred_instances[
pred_instances.scores > pred_score_thr].cpu()
img = data_input['img']
if isinstance(data_input['img'], Tensor):
......@@ -774,7 +782,7 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
if 'pred_pts_seg' in data_sample and vis_task == 'lidar_seg':
assert classes is not None, 'class information is ' \
'not provided when ' \
'visualizing panoptic ' \
'visualizing semantic ' \
'segmentation results.'
assert 'points' in data_input
self._draw_pts_sem_seg(data_input['points'],
......
mmdet3d/visualization/vis_utils.py
View file @ 8398af33
# Copyright (c) OpenMMLab. All rights reserved.
import copy
from typing import Tuple
import numpy as np
import torch
import trimesh
from mmdet3d.structures import (Box3DMode, CameraInstance3DBoxes, Coord3DMode,
from mmdet3d.structures import (BaseInstance3DBoxes, Box3DMode,
CameraInstance3DBoxes, Coord3DMode,
DepthInstance3DBoxes, LiDARInstance3DBoxes)
def write_obj(points, out_filename):
def write_obj(points: np.ndarray, out_filename: str) -> None:
"""Write points into ``obj`` format for meshlab visualization.
Args:
......@@ -31,18 +33,18 @@ def write_obj(points, out_filename):
fout.close()
def write_oriented_bbox(scene_bbox, out_filename):
def write_oriented_bbox(scene_bbox: np.ndarray, out_filename: str) -> None:
"""Export oriented (around Z axis) scene bbox to meshes.
Args:
scene_bbox(list[ndarray] or ndarray): xyz pos of center and
3 lengths (x_size, y_size, z_size) and heading angle around Z axis.
Y forward, X right, Z upward. heading angle of positive X is 0,
scene_bbox (np.ndarray): xyz pos of center and 3 lengths
(x_size, y_size, z_size) and heading angle around Z axis.
Y forward, X right, Z upward, heading angle of positive X is 0,
heading angle of positive Y is 90 degrees.
out_filename(str): Filename.
out_filename (str): Filename.
"""
def heading2rotmat(heading_angle):
def heading2rotmat(heading_angle: float) -> np.ndarray:
rotmat = np.zeros((3, 3))
rotmat[2, 2] = 1
cosval = np.cos(heading_angle)
......@@ -50,7 +52,8 @@ def write_oriented_bbox(scene_bbox, out_filename):
rotmat[0:2, 0:2] = np.array([[cosval, -sinval], [sinval, cosval]])
return rotmat
def convert_oriented_box_to_trimesh_fmt(box):
def convert_oriented_box_to_trimesh_fmt(
box: np.ndarray) -> trimesh.base.Trimesh:
ctr = box[:3]
lengths = box[3:6]
trns = np.eye(4)
......@@ -70,10 +73,10 @@ def write_oriented_bbox(scene_bbox, out_filename):
# save to obj file
trimesh.io.export.export_mesh(mesh_list, out_filename, file_type='obj')
return
def to_depth_mode(points, bboxes):
def to_depth_mode(
points: np.ndarray,
bboxes: BaseInstance3DBoxes) -> Tuple[np.ndarray, BaseInstance3DBoxes]:
"""Convert points and bboxes to Depth Coord and Depth Box mode."""
if points is not None:
points = Coord3DMode.convert_point(points.copy(), Coord3DMode.LIDAR,
......@@ -86,16 +89,15 @@ def to_depth_mode(points, bboxes):
# TODO: refactor lidar2img to img_meta
def proj_lidar_bbox3d_to_img(bboxes_3d: LiDARInstance3DBoxes,
input_meta: dict) -> np.array:
input_meta: dict) -> np.ndarray:
"""Project the 3D bbox on 2D plane.
Args:
bboxes_3d (:obj:`LiDARInstance3DBoxes`):
3d bbox in lidar coordinate system to visualize.
lidar2img (numpy.array, shape=[4, 4]): The projection matrix
according to the camera intrinsic parameters.
bboxes_3d (:obj:`LiDARInstance3DBoxes`): 3D bbox in lidar coordinate
system to visualize.
input_meta (dict): Meta information.
"""
corners_3d = bboxes_3d.corners
corners_3d = bboxes_3d.corners.cpu().numpy()
num_bbox = corners_3d.shape[0]
pts_4d = np.concatenate(
[corners_3d.reshape(-1, 3),
......@@ -115,13 +117,13 @@ def proj_lidar_bbox3d_to_img(bboxes_3d: LiDARInstance3DBoxes,
# TODO: remove third parameter in all functions here in favour of img_metas
def proj_depth_bbox3d_to_img(bboxes_3d: DepthInstance3DBoxes,
input_meta: dict) -> np.array:
input_meta: dict) -> np.ndarray:
"""Project the 3D bbox on 2D plane and draw on input image.
Args:
bboxes_3d (:obj:`DepthInstance3DBoxes`, shape=[M, 7]):
3d bbox in depth coordinate system to visualize.
input_meta (dict): Used in coordinates transformation.
bboxes_3d (:obj:`DepthInstance3DBoxes`): 3D bbox in depth coordinate
system to visualize.
input_meta (dict): Meta information.
"""
from mmdet3d.models import apply_3d_transformation
from mmdet3d.structures import points_cam2img
......@@ -146,14 +148,13 @@ def proj_depth_bbox3d_to_img(bboxes_3d: DepthInstance3DBoxes,
# project the camera bboxes 3d to image
def proj_camera_bbox3d_to_img(bboxes_3d: CameraInstance3DBoxes,
input_meta: dict) -> np.array:
input_meta: dict) -> np.ndarray:
"""Project the 3D bbox on 2D plane and draw on input image.
Args:
bboxes_3d (:obj:`CameraInstance3DBoxes`, shape=[M, 7]):
3d bbox in camera coordinate system to visualize.
cam2img (np.array)): Camera intrinsic matrix,
denoted as `K` in depth bbox coordinate system.
bboxes_3d (:obj:`CameraInstance3DBoxes`): 3D bbox in camera coordinate
system to visualize.
input_meta (dict): Meta information.
"""
from mmdet3d.structures import points_cam2img
......
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