CenterNet:Objects as Points代码解析:通过高斯函数画热点图
Posted 萌萌滴太阳
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原理参考https://zhuanlan.zhihu.com/p/96856635
确定高斯半径
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)
通过def draw_umich_gaussian画出我们想要的heatmap.
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]
#防止所画的高斯区域超过heatmap
left, right = min(x, radius), min(width - x, radius + 1)
top, bottom = min(y, radius), min(height - y, radius + 1)
#masked_heatmap(标记的热点图):高斯圆的外切矩形,因为前面heatmap用0初始化,所以masked_heatmap中的值为0
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: # TODO debug
#因为masked_heatmap中的值为0,所画出的高斯图masked_gaussian又大于0,所以这里选两者最大的,就是将高斯图masked_gaussian画在heatmap上。
np.maximum(masked_heatmap, masked_gaussian * k, out=masked_heatmap)
return heatmap
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目标检测anchor-free框架:CenterNet :Objects as Points