Load a pretrained model from the Hugging Face hub. Arguments: model_id (str): The Hugging Face repository ID. **

Generates masks for the given image. Arguments: image (np.ndarray): The image to generate masks for, in HWC uint8 format.

Args: filepath (str): a string that contains the absolute path, including the file name, where the visualized ima

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

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

Prepare and flatten visual features.

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

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

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

Returns the smallest integer greater than or equal to 'number' that is divisible by 'factor'.

Args: segment: numpy array of shape Nx2, containing all the points in the polygon. color: color of the polygon. Refer

Returns the largest integer less than or equal to 'number' that is divisible by 'factor'.

Get the image feature on the input batch.

Sample a noised version of the top left and bottom right corners of a given `bbox` Inputs: - masks: [B, 1, H,W] boxes, dtype=torch.Tenso

Rescales the image so that the following conditions are met: 1. Both dimensions (height and width) are divisible by 'factor'. 2. The to

Converts a time string to seconds.

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

Depending on the brightness_factor, gives a lighter or darker color i.e. a color with less or more saturation than the original color

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

Compute the image features on a given frame.

Args: binary_mask (ndarray): numpy array of shape (H, W), where H is the image height and W is the image width. E

Args: text (str): class label position (tuple): a tuple of the x and y coordinates to place text on image.

Positionally encode points that are not normalized to [0,1].

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

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

Resets the image embeddings and other state variables.

Returns the closest integer to 'number' that is divisible by 'factor'.

`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].

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

From a segmentation, compute a binary boundary map with 1 pixel wide boundaries. The boundary pixels are offset by 1/2 pixel towards the

From a segmentation, compute a binary boundary map with 1 pixel wide boundaries. The boundary pixels are offset by 1/2 pixel towards the

Whether to use multimask output in the SAM head.

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..

Computes the stability score for a batch of masks. The stability score is the IoU between the binary masks obtained by thresholding the p

Add new points and labels to previous point inputs (add at the end).

Compute mean,recall and decay from per-frame evaluation. Arguments: per_frame_values (ndarray): per-frame evaluation Returns:

Compute mean,recall and decay from per-frame evaluation. Calculates precision/recall for boundaries between foreground_mask and gt_mask u

Compute mean,recall and decay from per-frame evaluation. Calculates precision/recall for boundaries between foreground_mask and gt_mask u

Initialize an inference state.

Encodes masks to an uncompressed RLE, in the format expected by pycoco tools.

Propagate the input points across frames to track in the entire video.