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# Copyright (c) OpenMMLab. All rights reserved.
from itertools import product
from typing import Tuple, Union
import numpy as np
def generate_gaussian_heatmaps(
heatmap_size: Tuple[int, int],
keypoints: np.ndarray,
keypoints_visible: np.ndarray,
sigma: Union[float, Tuple[float], np.ndarray],
) -> Tuple[np.ndarray, np.ndarray]:
"""Generate gaussian heatmaps of keypoints.
Args:
heatmap_size (Tuple[int, int]): Heatmap size in [W, H]
keypoints (np.ndarray): Keypoint coordinates in shape (N, K, D)
keypoints_visible (np.ndarray): Keypoint visibilities in shape
(N, K)
sigma (float or List[float]): A list of sigma values of the Gaussian
heatmap for each instance. If sigma is given as a single float
value, it will be expanded into a tuple
Returns:
tuple:
- heatmaps (np.ndarray): The generated heatmap in shape
(K, H, W) where [W, H] is the `heatmap_size`
- keypoint_weights (np.ndarray): The target weights in shape
(N, K)
"""
N, K, _ = keypoints.shape
W, H = heatmap_size
heatmaps = np.zeros((K, H, W), dtype=np.float32)
keypoint_weights = keypoints_visible.copy()
if isinstance(sigma, (int, float)):
sigma = (sigma, ) * N
for n in range(N):
# 3-sigma rule
radius = sigma[n] * 3
# xy grid
gaussian_size = 2 * radius + 1
x = np.arange(0, gaussian_size, 1, dtype=np.float32)
y = x[:, None]
x0 = y0 = gaussian_size // 2
for k in range(K):
# skip unlabled keypoints
if keypoints_visible[n, k] < 0.5:
continue
# get gaussian center coordinates
mu = (keypoints[n, k] + 0.5).astype(np.int64)
# check that the gaussian has in-bounds part
left, top = (mu - radius).astype(np.int64)
right, bottom = (mu + radius + 1).astype(np.int64)
if left >= W or top >= H or right < 0 or bottom < 0:
keypoint_weights[n, k] = 0
continue
# The gaussian is not normalized,
# we want the center value to equal 1
gaussian = np.exp(-((x - x0)**2 + (y - y0)**2) / (2 * sigma[n]**2))
# valid range in gaussian
g_x1 = max(0, -left)
g_x2 = min(W, right) - left
g_y1 = max(0, -top)
g_y2 = min(H, bottom) - top
# valid range in heatmap
h_x1 = max(0, left)
h_x2 = min(W, right)
h_y1 = max(0, top)
h_y2 = min(H, bottom)
heatmap_region = heatmaps[k, h_y1:h_y2, h_x1:h_x2]
gaussian_regsion = gaussian[g_y1:g_y2, g_x1:g_x2]
_ = np.maximum(
heatmap_region, gaussian_regsion, out=heatmap_region)
return heatmaps, keypoint_weights
def generate_unbiased_gaussian_heatmaps(
heatmap_size: Tuple[int, int],
keypoints: np.ndarray,
keypoints_visible: np.ndarray,
sigma: float,
) -> Tuple[np.ndarray, np.ndarray]:
"""Generate gaussian heatmaps of keypoints using `Dark Pose`_.
Args:
heatmap_size (Tuple[int, int]): Heatmap size in [W, H]
keypoints (np.ndarray): Keypoint coordinates in shape (N, K, D)
keypoints_visible (np.ndarray): Keypoint visibilities in shape
(N, K)
Returns:
tuple:
- heatmaps (np.ndarray): The generated heatmap in shape
(K, H, W) where [W, H] is the `heatmap_size`
- keypoint_weights (np.ndarray): The target weights in shape
(N, K)
.. _`Dark Pose`: https://arxiv.org/abs/1910.06278
"""
N, K, _ = keypoints.shape
W, H = heatmap_size
heatmaps = np.zeros((K, H, W), dtype=np.float32)
keypoint_weights = keypoints_visible.copy()
# 3-sigma rule
radius = sigma * 3
# xy grid
x = np.arange(0, W, 1, dtype=np.float32)
y = np.arange(0, H, 1, dtype=np.float32)[:, None]
for n, k in product(range(N), range(K)):
# skip unlabled keypoints
if keypoints_visible[n, k] < 0.5:
continue
mu = keypoints[n, k]
# check that the gaussian has in-bounds part
left, top = mu - radius
right, bottom = mu + radius + 1
if left >= W or top >= H or right < 0 or bottom < 0:
keypoint_weights[n, k] = 0
continue
gaussian = np.exp(-((x - mu[0])**2 + (y - mu[1])**2) / (2 * sigma**2))
_ = np.maximum(gaussian, heatmaps[k], out=heatmaps[k])
return heatmaps, keypoint_weights
def generate_udp_gaussian_heatmaps(
heatmap_size: Tuple[int, int],
keypoints: np.ndarray,
keypoints_visible: np.ndarray,
sigma: float,
) -> Tuple[np.ndarray, np.ndarray]:
"""Generate gaussian heatmaps of keypoints using `UDP`_.
Args:
heatmap_size (Tuple[int, int]): Heatmap size in [W, H]
keypoints (np.ndarray): Keypoint coordinates in shape (N, K, D)
keypoints_visible (np.ndarray): Keypoint visibilities in shape
(N, K)
sigma (float): The sigma value of the Gaussian heatmap
Returns:
tuple:
- heatmaps (np.ndarray): The generated heatmap in shape
(K, H, W) where [W, H] is the `heatmap_size`
- keypoint_weights (np.ndarray): The target weights in shape
(N, K)
.. _`UDP`: https://arxiv.org/abs/1911.07524
"""
N, K, _ = keypoints.shape
W, H = heatmap_size
heatmaps = np.zeros((K, H, W), dtype=np.float32)
keypoint_weights = keypoints_visible.copy()
# 3-sigma rule
radius = sigma * 3
# xy grid
gaussian_size = 2 * radius + 1
x = np.arange(0, gaussian_size, 1, dtype=np.float32)
y = x[:, None]
for n, k in product(range(N), range(K)):
# skip unlabled keypoints
if keypoints_visible[n, k] < 0.5:
continue
mu = (keypoints[n, k] + 0.5).astype(np.int64)
# check that the gaussian has in-bounds part
left, top = (mu - radius).astype(np.int64)
right, bottom = (mu + radius + 1).astype(np.int64)
if left >= W or top >= H or right < 0 or bottom < 0:
keypoint_weights[n, k] = 0
continue
mu_ac = keypoints[n, k]
x0 = y0 = gaussian_size // 2
x0 += mu_ac[0] - mu[0]
y0 += mu_ac[1] - mu[1]
gaussian = np.exp(-((x - x0)**2 + (y - y0)**2) / (2 * sigma**2))
# valid range in gaussian
g_x1 = max(0, -left)
g_x2 = min(W, right) - left
g_y1 = max(0, -top)
g_y2 = min(H, bottom) - top
# valid range in heatmap
h_x1 = max(0, left)
h_x2 = min(W, right)
h_y1 = max(0, top)
h_y2 = min(H, bottom)
heatmap_region = heatmaps[k, h_y1:h_y2, h_x1:h_x2]
gaussian_regsion = gaussian[g_y1:g_y2, g_x1:g_x2]
_ = np.maximum(heatmap_region, gaussian_regsion, out=heatmap_region)
return heatmaps, keypoint_weights
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