ByoTrack ViZ
We add some visualization tools. They rely on matplotlib and opencv-python.
Note
We developed these tools under linux using opencv + gtk backend. It seems that the zoom/drag/quit operations are not automatically supported on other platforms.
- byotrack.visualize.display_lifetime(tracks: Collection[Track])
Display the lifetime of tracks
Active tracks are in white. (Tracks on x-axis, Frames on y-axis)
- Parameters:
tracks (Collection[byotrack.Track]) – Tracks
- byotrack.visualize.temporal_projection(tracks_tensor: Tensor, colors: Sequence[Tuple[int, int, int]] = ((255, 255, 255),), background: ndarray | None = None, color_by_time=False) ndarray
Project all given tracks into a single image
A track is displayed as the line of its consecutive positions. Track’s undefined positions are not displayed.
- Parameters:
tracks_tensor (torch.Tensor) – Tracks tensor (See byotrack.Track.tensorize) Positions of each track at each frame. (NaN if not defined). Shape: (T, N, 2)
colors (Sequence[Tuple[int, int, int]]) – Color sequence. When we color each track independently, each track is associated with a color (ith track with ith color, it loops if more tracks than colors). For time coloring, each frame has its own color (same across all tracks). Default: ((255, 255, 255),) (Everything is white)
background (Optional[np.ndarray]) – Optional frame to display behind the tracks. If not given, we set the background as 0, with the smallest size to contain all tracks. Default: None Shape: (H, W[, C])
color_by_time (bool) – If set to True, each frame has its own color. Otherwise, each track has its own color. Color by time is much slower (cv2 do not allow complex multilines draw) Default: False
- Returns:
- Projection image
Shape: (H, W, 3), dtype: np.uint8
- Return type:
np.ndarray
- class byotrack.visualize.InteractiveVisualizer(video: Sequence[ndarray] | ndarray = (), detections_sequence: Sequence[Detections] = (), tracks: Collection[Track] = ())
Bases:
objectInteractive visualization with opencv
- Keys:
space: Pause/Unpause the video
w/x: Move backward/forward in the video (when paused)
d: Switch detections display mode (Not displayed, Mask, Segmentation) if available
t: Switch on/off the display of tracks if available
v: Switch on/off the display of the video
- video
Optional video to display. Default: () (no frames)
- Type:
Sequence[np.ndarray] | np.ndarray
- detections_sequence
Optional detections to display Default: () (no detections)
- Type:
Sequence[byotrack.Detections]
- tracks
Optional tracks to display Default: () (no tracks)
- Type:
Collection[byotrack.Tracks]
- tracks_colors
Colors to use for tracks Colors are picked circularly given the track identifier. By default we use the hsv colormaps from matplotlib
- class byotrack.visualize.InteractiveFlowVisualizer(video: Sequence[ndarray] | ndarray, optflow: OpticalFlow, grid_step=20, precompute=False)
Bases:
objectInteractive optical flow visualization using a moving grid above the video
The video is displayed with a uniform grid (control points) above that moves according to the computed flow map. If the optical flow algorithm is good, the points should moves along the video.
- Keys:
space: Pause/Unpause the video
w/x: Move backward/forward in the video (when paused)
g: Reset the control points as a grid
Note
To prevent lagging, even when precompute is False, optical flow map are saved and never computed twice. You should not use this function with too large videos. (Use slicing to reduce the size of a video)
- video
The video of interest
- Type:
Sequence[np.ndarray] | np.ndarray
- optflow
Optical flow algorithm to visualize
- Type:
byotrack.OpticalFlow
- points
Control points (grid) Shape: (N, 2), dtype: float64
- Type:
np.ndarray
- precompute
Compute all the flows first (bi directionnal) to prevent lagging Default: False
- Type:
- tracks_colors
Colors to use for tracks Colors are picked circularly given the track identifier. By default we use the hsv colormaps from matplotlib