Source code for detectron2.structures.keypoints

# Copyright (c) Facebook, Inc. and its affiliates.
import numpy as np
from typing import Any, List, Tuple, Union
import torch
from torch.nn import functional as F

class Keypoints:
    Stores keypoint **annotation** data. GT Instances have a `gt_keypoints` property
    containing the x,y location and visibility flag of each keypoint. This tensor has shape
    (N, K, 3) where N is the number of instances and K is the number of keypoints per instance.

    The visibility flag follows the COCO format and must be one of three integers:

    * v=0: not labeled (in which case x=y=0)
    * v=1: labeled but not visible
    * v=2: labeled and visible

    def __init__(self, keypoints: Union[torch.Tensor, np.ndarray, List[List[float]]]):
            keypoints: A Tensor, numpy array, or list of the x, y, and visibility of each keypoint.
                The shape should be (N, K, 3) where N is the number of
                instances, and K is the number of keypoints per instance.
        device = keypoints.device if isinstance(keypoints, torch.Tensor) else torch.device("cpu")
        keypoints = torch.as_tensor(keypoints, dtype=torch.float32, device=device)
        assert keypoints.dim() == 3 and keypoints.shape[2] == 3, keypoints.shape
        self.tensor = keypoints

    def __len__(self) -> int:
        return self.tensor.size(0)

    def to(self, *args: Any, **kwargs: Any) -> "Keypoints":
        return type(self)(*args, **kwargs))

    def device(self) -> torch.device:
        return self.tensor.device

    def to_heatmap(self, boxes: torch.Tensor, heatmap_size: int) -> torch.Tensor:
        Convert keypoint annotations to a heatmap of one-hot labels for training,
        as described in :paper:`Mask R-CNN`.

            boxes: Nx4 tensor, the boxes to draw the keypoints to

                A tensor of shape (N, K), each element is integer spatial label
                in the range [0, heatmap_size**2 - 1] for each keypoint in the input.
                A tensor of shape (N, K) containing whether each keypoint is in the roi or not.
        return _keypoints_to_heatmap(self.tensor, boxes, heatmap_size)

    def __getitem__(self, item: Union[int, slice, torch.BoolTensor]) -> "Keypoints":
        Create a new `Keypoints` by indexing on this `Keypoints`.

        The following usage are allowed:

        1. `new_kpts = kpts[3]`: return a `Keypoints` which contains only one instance.
        2. `new_kpts = kpts[2:10]`: return a slice of key points.
        3. `new_kpts = kpts[vector]`, where vector is a torch.ByteTensor
           with `length = len(kpts)`. Nonzero elements in the vector will be selected.

        Note that the returned Keypoints might share storage with this Keypoints,
        subject to Pytorch's indexing semantics.
        if isinstance(item, int):
            return Keypoints([self.tensor[item]])
        return Keypoints(self.tensor[item])

    def __repr__(self) -> str:
        s = self.__class__.__name__ + "("
        s += "num_instances={})".format(len(self.tensor))
        return s

[docs] @staticmethod def cat(keypoints_list: List["Keypoints"]) -> "Keypoints": """ Concatenates a list of Keypoints into a single Keypoints Arguments: keypoints_list (list[Keypoints]) Returns: Keypoints: the concatenated Keypoints """ assert isinstance(keypoints_list, (list, tuple)) assert len(keypoints_list) > 0 assert all(isinstance(keypoints, Keypoints) for keypoints in keypoints_list) cat_kpts = type(keypoints_list[0])([kpts.tensor for kpts in keypoints_list], dim=0) ) return cat_kpts
# TODO make this nicer, this is a direct translation from C2 (but removing the inner loop) def _keypoints_to_heatmap( keypoints: torch.Tensor, rois: torch.Tensor, heatmap_size: int ) -> Tuple[torch.Tensor, torch.Tensor]: """ Encode keypoint locations into a target heatmap for use in SoftmaxWithLoss across space. Maps keypoints from the half-open interval [x1, x2) on continuous image coordinates to the closed interval [0, heatmap_size - 1] on discrete image coordinates. We use the continuous-discrete conversion from Heckbert 1990 ("What is the coordinate of a pixel?"): d = floor(c) and c = d + 0.5, where d is a discrete coordinate and c is a continuous coordinate. Arguments: keypoints: tensor of keypoint locations in of shape (N, K, 3). rois: Nx4 tensor of rois in xyxy format heatmap_size: integer side length of square heatmap. Returns: heatmaps: A tensor of shape (N, K) containing an integer spatial label in the range [0, heatmap_size**2 - 1] for each keypoint in the input. valid: A tensor of shape (N, K) containing whether each keypoint is in the roi or not. """ if rois.numel() == 0: return, offset_x = rois[:, 0] offset_y = rois[:, 1] scale_x = heatmap_size / (rois[:, 2] - rois[:, 0]) scale_y = heatmap_size / (rois[:, 3] - rois[:, 1]) offset_x = offset_x[:, None] offset_y = offset_y[:, None] scale_x = scale_x[:, None] scale_y = scale_y[:, None] x = keypoints[..., 0] y = keypoints[..., 1] x_boundary_inds = x == rois[:, 2][:, None] y_boundary_inds = y == rois[:, 3][:, None] x = (x - offset_x) * scale_x x = x.floor().long() y = (y - offset_y) * scale_y y = y.floor().long() x[x_boundary_inds] = heatmap_size - 1 y[y_boundary_inds] = heatmap_size - 1 valid_loc = (x >= 0) & (y >= 0) & (x < heatmap_size) & (y < heatmap_size) vis = keypoints[..., 2] > 0 valid = (valid_loc & vis).long() lin_ind = y * heatmap_size + x heatmaps = lin_ind * valid return heatmaps, valid @torch.jit.script_if_tracing def heatmaps_to_keypoints(maps: torch.Tensor, rois: torch.Tensor) -> torch.Tensor: """ Extract predicted keypoint locations from heatmaps. Args: maps (Tensor): (#ROIs, #keypoints, POOL_H, POOL_W). The predicted heatmap of logits for each ROI and each keypoint. rois (Tensor): (#ROIs, 4). The box of each ROI. Returns: Tensor of shape (#ROIs, #keypoints, 4) with the last dimension corresponding to (x, y, logit, score) for each keypoint. When converting discrete pixel indices in an NxN image to a continuous keypoint coordinate, we maintain consistency with :meth:`Keypoints.to_heatmap` by using the conversion from Heckbert 1990: c = d + 0.5, where d is a discrete coordinate and c is a continuous coordinate. """ offset_x = rois[:, 0] offset_y = rois[:, 1] widths = (rois[:, 2] - rois[:, 0]).clamp(min=1) heights = (rois[:, 3] - rois[:, 1]).clamp(min=1) widths_ceil = widths.ceil() heights_ceil = heights.ceil() num_rois, num_keypoints = maps.shape[:2] xy_preds = maps.new_zeros(rois.shape[0], num_keypoints, 4) width_corrections = widths / widths_ceil height_corrections = heights / heights_ceil keypoints_idx = torch.arange(num_keypoints, device=maps.device) for i in range(num_rois): outsize = (int(heights_ceil[i]), int(widths_ceil[i])) roi_map = F.interpolate(maps[[i]], size=outsize, mode="bicubic", align_corners=False) # Although semantically equivalent, `reshape` is used instead of `squeeze` due # to limitation during ONNX export of `squeeze` in scripting mode roi_map = roi_map.reshape(roi_map.shape[1:]) # keypoints x H x W # softmax over the spatial region max_score, _ = roi_map.view(num_keypoints, -1).max(1) max_score = max_score.view(num_keypoints, 1, 1) tmp_full_resolution = (roi_map - max_score).exp_() tmp_pool_resolution = (maps[i] - max_score).exp_() # Produce scores over the region H x W, but normalize with POOL_H x POOL_W, # so that the scores of objects of different absolute sizes will be more comparable roi_map_scores = tmp_full_resolution / tmp_pool_resolution.sum((1, 2), keepdim=True) w = roi_map.shape[2] pos = roi_map.view(num_keypoints, -1).argmax(1) x_int = pos % w y_int = (pos - x_int) // w assert ( roi_map_scores[keypoints_idx, y_int, x_int] == roi_map_scores.view(num_keypoints, -1).max(1)[0] ).all() x = (x_int.float() + 0.5) * width_corrections[i] y = (y_int.float() + 0.5) * height_corrections[i] xy_preds[i, :, 0] = x + offset_x[i] xy_preds[i, :, 1] = y + offset_y[i] xy_preds[i, :, 2] = roi_map[keypoints_idx, y_int, x_int] xy_preds[i, :, 3] = roi_map_scores[keypoints_idx, y_int, x_int] return xy_preds