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import cv2
import numpy as np
import torch
import torch.nn.functional as F
def gaussian2D(shape, sigma=1):
m, n = [(ss - 1.) / 2. for ss in shape]
y, x = np.ogrid[-m:m+1,-n:n+1]
h = np.exp(-(x * x + y * y) / (2 * sigma * sigma))
h[h < np.finfo(h.dtype).eps * h.max()] = 0
return h
def draw_gaussian(heatmap, center, radius, k=1):
diameter = 2 * radius + 1
gaussian = gaussian2D((diameter, diameter), sigma=diameter / 6)
x, y = center
height, width = heatmap.shape[0:2]
left, right = min(x, radius), min(width - x, radius + 1)
top, bottom = min(y, radius), min(height - y, radius + 1)
masked_heatmap = heatmap[y - top:y + bottom, x - left:x + right]
masked_gaussian = gaussian[radius - top:radius + bottom, radius - left:radius + right]
np.maximum(masked_heatmap, masked_gaussian * k, out=masked_heatmap)
def gaussian_radius(det_size, min_overlap):
height, width = det_size
a1 = 1
b1 = (height + width)
c1 = width * height * (1 - min_overlap) / (1 + min_overlap)
sq1 = np.sqrt(b1 ** 2 - 4 * a1 * c1)
r1 = (b1 - sq1) / (2 * a1)
a2 = 4
b2 = 2 * (height + width)
c2 = (1 - min_overlap) * width * height
sq2 = np.sqrt(b2 ** 2 - 4 * a2 * c2)
r2 = (b2 - sq2) / (2 * a2)
a3 = 4 * min_overlap
b3 = -2 * min_overlap * (height + width)
c3 = (min_overlap - 1) * width * height
sq3 = np.sqrt(b3 ** 2 - 4 * a3 * c3)
r3 = (b3 + sq3) / (2 * a3)
return min(r1, r2, r3)
def compute_kl_divergence(src_aff, tgt_aff):
"""
Compute kl divergence of two affordance map.
See https://github.com/Tushar-N/interaction-hotspots/blob/master/utils/evaluation.py
"""
eps = 1e-12
# normalize affordance map so that it sums to 1
src_aff_norm = src_aff / (src_aff.sum(dim=-1).sum(dim=-1).unsqueeze(-1).unsqueeze(-1) + eps)
tgt_aff_norm = tgt_aff / (tgt_aff.sum(dim=-1).sum(dim=-1).unsqueeze(-1).unsqueeze(-1) + eps)
kld = F.kl_div(src_aff_norm.log(), tgt_aff_norm, reduction='none')
kld = kld.sum(dim=-1).sum(dim=-1)
# sometimes kld is inf
kld = kld[~torch.isinf(kld)]
return kld
def compute_sim(src_aff, tgt_aff):
"""
Compute histogram intersection of two affordance map.
See https://github.com/Tushar-N/interaction-hotspots/blob/master/utils/evaluation.py
"""
eps = 1e-12
# normalize affordance map so that it sums to 1
src_aff_norm = src_aff / (src_aff.sum(dim=-1).sum(dim=-1).unsqueeze(-1).unsqueeze(-1) + eps)
tgt_aff_norm = tgt_aff / (tgt_aff.sum(dim=-1).sum(dim=-1).unsqueeze(-1).unsqueeze(-1) + eps)
intersection = torch.minimum(src_aff_norm, tgt_aff_norm)
intersection = intersection.sum(dim=-1).sum(dim=-1)
return intersection |