Dump the detection results to a COCO style json file. There are 3 types of results: proposals, bbox predictions, mask predictions, an

Split side-aware features aligned with orders of bucketing targets.

Sort box vertices in clockwise order from left-top first. Args: points_x (list[float]): x of four vertices. points_y (list[float]

Crop box area from image using opencv warpPerspective w/o box jitter. Args: src_img (np.array): Image before cropping. box (list[

Convert ``xyxy`` style bounding boxes to ``xywh`` style for COCO evaluation. Args: bbox (numpy.ndarray): The bounding box

Add shared or separable branch. convs -> avg pool (optional) -> fcs

Box head forward function used in both training and testing. Returns all regression, classification results and a intermediate feature.

Decode init_proposal_bboxes according to the size of images and expand dimension of init_proposal_features to batch_size. Args:

Gather feature according to index. Args: feat (Tensor): Target feature map. ind (Tensor): Target coord index.

Initialize classification conv layers of the head.

Initialize bbox regression conv layers of the head.

Mask head forward function used in both training and testing.

Run forward function and calculate loss for mask head in training.

Parse bbox and mask annotation. Args: annotations (dict): Annotations of one image. Returns: dict: A dict con

Private function to convert masks represented with polygon to bitmaps. Args: mask_ann (list | dict): Polygon mask annotat

Get anchor centers from anchors. Args: anchors (Tensor): Anchor list with shape (N, 4), "xyxy" format. Returns:

Assign w.r.t. the overlaps of bboxes with gts. Args: overlaps (Tensor): Overlaps between k gt_bboxes and n bboxes,

Async inference image(s) with the detector. Args: model (nn.Module): The loaded detector. img (str | ndarray): Either image files

Test det bboxes with test time augmentation. Args: feats (list[Tensor]): the outer list indicates test-time augme

Decode bounding box based on distances. Args: points (Tensor): Shape (n, 2), [x, y]. bbox (Tensor): Shape (n, 4), "xyxy" format

The key correspondence from bboxes to labels, masks and segmentations.

Convert bbox coordinates from (x1, y1, x2, y2) to (cx, cy, w, h). Args: bbox (Tensor): Shape (n, 4) for bboxes. Returns: Ten

Convert Bitmasks to tensor. Args: bitmasks (list[BitmapMasks]): The BitmapMasks list. Each item is for one img.

Convert Bitmasks to tensor. Args: bitmasks (list[BitMasks]): The BitMasks list. Each item is for one img.

Builder of IoU calculator.

Classification-Aware Regression Loss (CARL). Args: cls_score (Tensor): Predicted classification scores. labels (Tensor): Targets

Check if norm layer is in correct train state.

Produce N identical layers

Async context manager that waits for work to complete on given CUDA streams.

Construct text instance boundaries from clustered text components. This code was partially adapted from https://github.com/GXYM/DRRG licensed unde

Convert the annotation into coco style. Args: image_infos(list): The list of image information dicts out_json_name(str): The outp

Convert the torch forward outputs containing tuple or list to a list only containing torch.Tensor. Args: output (list(Tensor) | tuple

see :func:`BaseInstanceMasks.crop`

See :func:`BaseInstanceMasks.crop_and_resize`.

Draw `result`(boxes and texts) on `img`. Args: img (str or np.ndarray): The image to be displayed. end2end_res (dict): Text detect

Draw boxes and texts on empty image, especially for Chinese. Args: img (np.ndarray): The original image. texts (list[str]): Recog

Map from level to values.

Calculate recalls. Args: gts (list[ndarray]): a list of arrays of shape (n, 4) proposals (list[ndarray]): a list of arrays of sha

Evaluate the dataset. Args: results (list): Testing results of the dataset. metric (str | list[str]): Metrics to be e

Directly extract features from the backbone+neck.

Directly extract features from the backbone.

Directly extract features from the backbone.

Find the head edge and tail edge of a text polygon. Args: points (ndarray): The points composing a text polygon. orie

Format the results to json (standard format for COCO evaluation). Args: results (list[tuple | numpy.ndarray]): Testing results of

Args: img (Tensor): Input images of shape (N, C, H, W). Typically these should be mean centered and std scaled.

Generate BGR color schemes.

Get category ids of repeat dataset by index. Args: idx (int): Index of data. Returns: list[int]: All categor

A wrapper for computing ATSS and FCOS targets for points in multiple images. Args: cls_scores (list[Tensor]): Box iou-awa

Initialize the weights in detector. Args: pretrained (str, optional): Path to pre-trained weights. Defaults to No

Check whether string contains Chinese or not. Args: check_str (str): String to be checked. Return True if contains Chinese, else Fal

Check if is one of the norms.

Check img_file is not png image. Args: img_file(str): The input image file name Returns: The bool flag indicating whether it

Importance-based Sample Reweighting (ISR_P), positive part. Args: cls_score (Tensor): Predicted classification scores. bbox_pred

Map from level to values based on max_value.

nn.Module: normalization layer after the first convolution layer

Normalize adjacent matrix for GCN. This code was partially adapted from https://github.com/GXYM/DRRG licensed under the MIT license. Args:

Convert k points to 1 polygon. Args: points (ndarray or list): A ndarray or a list of shape (2k) that indicates k points.

Calculate the intersection area between two polygon. Args: poly_det (Polygon): A polygon predicted by detector. poly_gt (Polygon)

Get training data and annotations after pipeline. Args: idx (int): Index of data. Returns: dict: Training da

Get training data and annotations from pipeline. Args: index (int): Index of data. Returns: dict: Training d

Randomly negate value based on random_negative_prob.

Get the respective points composing head edge, tail edge, top sideline and bottom sideline. Args: points (ndarray): The p

Resample two sidelines to be of the same points number according to step size. Args: sideline1 (ndarray): The points comp

RetinanNet Head Config.

Sample positive and negative bboxes. This is a simple implementation of bbox sampling given candidates, assigning results and ground

Expand an array of boxes by a given scale. Args: bboxes (Tensor): Shape (m, 4) scale (float): The scale factor of bboxes Ret

Set random seed. Args: seed (int): Seed to be used. deterministic (bool): Whether to set the deterministic option for

Visualize the detection results on the image. Args: model (nn.Module): The loaded detector. img (str or np.ndarray): Image filena

Test only det bboxes without augmentation. Args: x (tuple[Tensor]): Feature maps of all scale level. img_metas (list[

Simple test for mask head without augmentation.

Convert text-string to ctc-loss input tensor. Args: strings (list[str]): ['hello', 'world']. Returns: dict (s