sapiens/pose/src/datasets/codecs/udp_heatmap.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

from abc import ABCMeta
from typing import Optional, Tuple

import cv2
import numpy as np

from .utils import (
    generate_offset_heatmap,
    generate_udp_gaussian_heatmaps,
    get_heatmap_maximum,
    refine_keypoints_dark_udp,
)


class UDPHeatmap(metaclass=ABCMeta):
    auxiliary_encode_keys = set()

    def __init__(
        self,
        input_size: Tuple[int, int],
        heatmap_size: Tuple[int, int],
        heatmap_type: str = "gaussian",
        sigma: float = 2.0,
        radius_factor: float = 0.0546875,
        blur_kernel_size: int = 11,
    ) -> None:
        super().__init__()
        self.input_size = input_size
        self.heatmap_size = heatmap_size
        self.sigma = sigma
        self.radius_factor = radius_factor
        self.heatmap_type = heatmap_type
        self.blur_kernel_size = blur_kernel_size
        self.scale_factor = (
            (np.array(input_size) - 1) / (np.array(heatmap_size) - 1)
        ).astype(np.float32)

        if self.heatmap_type not in {"gaussian", "combined"}:
            raise ValueError(
                f"{self.__class__.__name__} got invalid `heatmap_type` value"
                f"{self.heatmap_type}. Should be one of "
                '{"gaussian", "combined"}'
            )

    def encode(
        self, keypoints: np.ndarray, keypoints_visible: Optional[np.ndarray] = None
    ) -> dict:
        assert keypoints.shape[0] == 1, (
            f"{self.__class__.__name__} only support single-instance keypoint encoding"
        )

        if keypoints_visible is None:
            keypoints_visible = np.ones(keypoints.shape[:2], dtype=np.float32)

        if self.heatmap_type == "gaussian":
            heatmaps, keypoint_weights = generate_udp_gaussian_heatmaps(
                heatmap_size=self.heatmap_size,
                keypoints=keypoints / self.scale_factor,
                keypoints_visible=keypoints_visible,
                sigma=self.sigma,
            )
        elif self.heatmap_type == "combined":
            heatmaps, keypoint_weights = generate_offset_heatmap(
                heatmap_size=self.heatmap_size,
                keypoints=keypoints / self.scale_factor,
                keypoints_visible=keypoints_visible,
                radius_factor=self.radius_factor,
            )
        else:
            raise ValueError(
                f"{self.__class__.__name__} got invalid `heatmap_type` value"
                f"{self.heatmap_type}. Should be one of "
                '{"gaussian", "combined"}'
            )

        encoded = dict(heatmaps=heatmaps, keypoint_weights=keypoint_weights)

        return encoded

    def decode(self, encoded: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
        heatmaps = encoded.copy()

        if self.heatmap_type == "gaussian":
            keypoints, scores = get_heatmap_maximum(heatmaps)
            # unsqueeze the instance dimension for single-instance results
            keypoints = keypoints[None]
            scores = scores[None]

            keypoints = refine_keypoints_dark_udp(
                keypoints, heatmaps, blur_kernel_size=self.blur_kernel_size
            )

        elif self.heatmap_type == "combined":
            _K, H, W = heatmaps.shape
            K = _K // 3

            for cls_heatmap in heatmaps[::3]:
                # Apply Gaussian blur on classification maps
                ks = 2 * self.blur_kernel_size + 1
                cv2.GaussianBlur(cls_heatmap, (ks, ks), 0, cls_heatmap)

            # valid radius
            radius = self.radius_factor * max(W, H)

            x_offset = heatmaps[1::3].flatten() * radius
            y_offset = heatmaps[2::3].flatten() * radius
            keypoints, scores = get_heatmap_maximum(heatmaps=heatmaps[::3])
            index = (keypoints[..., 0] + keypoints[..., 1] * W).flatten()
            index += W * H * np.arange(0, K)
            index = index.astype(int)
            keypoints += np.stack((x_offset[index], y_offset[index]), axis=-1)
            # unsqueeze the instance dimension for single-instance results
            keypoints = keypoints[None].astype(np.float32)
            scores = scores[None]

        W, H = self.heatmap_size
        keypoints = keypoints / [W - 1, H - 1] * self.input_size

        return keypoints, scores