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import numpy as np
def is_bb_overlap(rmin, rmax, cmin, cmax,
crmin, crmax, ccmin, ccmax):
is_y_overlap = (rmax > crmin) and (crmax > rmin)
is_x_overlap = (cmax > ccmin) and (ccmax > cmin)
return is_x_overlap and is_y_overlap
def get_bb_position(mask):
mask = mask > 0.5
rows = np.any(mask, axis=1)
cols = np.any(mask, axis=0)
rmin, rmax = np.where(rows)[0][[0, -1]]
cmin, cmax = np.where(cols)[0][[0, -1]]
# y_min, y_max, x_min, x_max
return rmin, rmax, cmin, cmax
def scale_bb_by(rmin, rmax, cmin, cmax, im_height, im_width, h_scale, w_scale):
height = rmax - rmin
width = cmax - cmin
rmin -= h_scale * height / 2
rmax += h_scale * height / 2
cmin -= w_scale * width / 2
cmax += w_scale * width / 2
rmin = int(max(0, rmin))
rmax = int(min(im_height-1, rmax))
cmin = int(max(0, cmin))
cmax = int(min(im_width-1, cmax))
# Prevent negative width/height
rmax = max(rmin, rmax)
cmax = max(cmin, cmax)
return rmin, rmax, cmin, cmax
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