|
|
import numpy as np
|
|
|
|
|
|
|
|
|
def gaussian_radius(det_size, min_overlap=0.7):
|
|
|
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
|
|
|
|
|
|
a2 = 4
|
|
|
b2 = 2 * (height + width)
|
|
|
c2 = (1 - min_overlap) * width * height
|
|
|
sq2 = np.sqrt(b2 ** 2 - 4 * a2 * c2)
|
|
|
r2 = (b2 + sq2) / 2
|
|
|
|
|
|
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
|
|
|
return min(r1, r2, r3)
|
|
|
|
|
|
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_umich_gaussian(heatmap, center, radius, k=1):
|
|
|
diameter = 2 * radius + 1
|
|
|
gaussian = gaussian2D((diameter, diameter), sigma=diameter / 6)
|
|
|
|
|
|
x, y = int(center[0]), int(center[1])
|
|
|
|
|
|
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]
|
|
|
if min(masked_gaussian.shape) > 0 and min(masked_heatmap.shape) > 0:
|
|
|
np.maximum(masked_heatmap, masked_gaussian * k, out=masked_heatmap)
|
|
|
return heatmap |
