Consolidate the per-object temporary outputs in `temp_output_dict_per_obj` on a frame into a single output for all objects, including

Compute the image features on a given frame.

Resize the object scores to the original video resolution (video_res_masks) and apply non-overlapping constraints for final output.

Run tracking on a single frame based on current inputs and previous memory.

Get the total number of unique object ids received so far in this session.

Prepare and flatten visual features.

Get the image feature on the input batch.

Split a multi-object output into per-object output slices and add them into `output_dict_per_obj`. The resulting slices share the sam

Remove the non-conditioning memory around the input frame. When users provide correction clicks, the surrounding frames' non-conditio

Encode the current image and its prediction into a memory feature.

Map client-side object id to model-side object index.

Predict masks for the given input prompts, using the currently set image. Input prompts are batched torch tensors and are expected to

Dice coeff for batches

Dice coeff for batches

Returns the positional encoding used to encode point prompts, applied to a dense set of points the shape of the image encoding.

Expects a torch tensor with length 2 in the last dimension. The coordinates can be in absolute image or normalized coordinates, If th

Apply non-overlapping constraints to the object scores in pred_masks. Here we keep only the highest scoring object at each spatial lo

Get the connected components (8-connectivity) of binary masks of shape (N, 1, H, W). Inputs: - mask: A binary mask tensor of shape (N, 1

Resets the image embeddings and other state variables.

Add new points to a frame.

Forward SAM prompt encoders and mask heads. Inputs: - backbone_features: image features of [B, C, H, W] shape - poin

`maskmem_pos_enc` is the same across frames and objects, so we cache it as a constant in the inference session to reduce session stor

Positionally encode points that are normalized to [0,1].

Calculates boxes in XYXY format around masks. Return [0,0,0,0] for an empty mask. For input shape C1xC2x...xHxW, the output shape is C1xC2x..