def read_corr(self, sid1, sid2):
'''
Output:
corr_2to1: (h,w,2) correspondence from image 2 to image 1. corr_2to1[y,x] = [u,v], means image2[y,x] -> image1[v,u]
mask_2: (h,w) visibility mask of image 2.
0: human pixel with correspondence
1: human pixel without correspondece
2: background pixel
'''
fn = os.path.join(self.corr_dir, '%s_%s.corr'%(sid2, sid1))
corr_2to1, vis_2 = flow_util.read_corr(fn)
vis_2 = vis_2[...,np.newaxis]
if self.opt.vis_smooth_rate > 0:
vis_2b = cv2.medianBlur(vis_2, self.opt.vis_smooth_rate)[...,np.newaxis]
m = (vis_2<2).astype(np.uint8)
vis_2 = vis_2b*m + vis_2*(1-m)
return corr_2to1, vis_2
def read_corr(self, sid1, sid2):
'''
Output:
corr_2to1: (h, w, 2)
vis_2: (h, w)
'''
try:
fn = os.path.join(self.corr_dir, '%s_%s.corr' % (sid2, sid1))
corr_2to1, vis_2 = flow_util.read_corr(fn)
vis_2 = vis_2[..., np.newaxis]
if self.opt.vis_smooth_rate > 0:
vis_2b = cv2.medianBlur(vis_2,
self.opt.vis_smooth_rate)[...,
np.newaxis]
m = (vis_2 < 2).astype(np.uint8)
vis_2 = vis_2b * m + vis_2 * (1 - m)
return corr_2to1, vis_2
except:
h, w = self.opt.image_size
return np.zeros((h, w, 2), dtype=np.float32), np.ones(
(h, w, 1), dtype=np.float32) * 2