Converts a sequence of tokens into ids using the vocab.

Extent The extent of a timeline is the segment of minimum duration that contains every segments of the timeline. It is unique, by def

resize to (W,H)

Timeline support The support of a timeline is the timeline with the minimum number of segments with exactly the same time span as the

Adjust the size of the frame from the webcam to the ailia input shape. Parameters ---------- frame: numpy array det_shape: tuple

converts to PIL image

Closest frame to timestamp. Parameters ---------- t : float Timestamp, in seconds. Returns -----

(1) add_new_node(G, node) : add "node" to graph "G" (2) add_new_edge(G, node_a, node_b) : add edge "node_a--node_b" to graph "G" (3) exce

Compute the function using arguments from each of the iterables Returns a tuple if provided `specifications` is a tuple, otherwise returns th

Parameters ---------- rlabel : Reference label hlabel : Hypothesis label Returns

!!! 所有的处理都需要正确的输入 !!! 可以添加新的处理，只需要添加正则表达式和对应的处理函数即可

Iterate over pairs of intersecting segments >>> timeline1 = Timeline([Segment(0, 2), Segment(1, 2), Segment(3, 4)]) >>> timeline2 = T

Returns True if this track is confirmed.

Rename labels Parameters ---------- mapping : dict, optional {old_name: new_name} mapping dictionary. gen

Create new timeline made of union of segments Parameters ---------- timeline : Timeline Timeline whose segments a

Query labels by segment Parameters ---------- segment : Segment Query unique : bool, optional

docstring

Initialize all ailia models

Match incoming detection results with embedding and 3D infos Args: bboxes: (N, 5), [x1, y1, x2, y2, conf] labels: (N,

Normalize points based on weights. Args: pts (tensor): points weights (tensor): estimated weights Returns:

ensure z is a (dim_z, 1) shaped vector

Select state with relative ids in the main beam search. Args: state: Decoder state for prefix tokens i (int): Index

Update a matched track :type new_track: STrack :type frame_id: int :type update_feature: bool :return:

(2) clear_node_feat(G, *fts) : Clear all the node feature on graph G. (6) get_cross_nes(x, y) : Get the four cross neighbors of pixel(x, y).

Adjust the size of the frame from the webcam to the ailia input shape. Parameters ---------- frame: numpy array height: int

Iterate over pairs of intersecting tracks Parameters ---------- other : Annotation Second annotation Ret

fit image in w,h keeping aspect ratio TW,TH int/None target width,height pad_to_target bool pad remain area