def apply_bbox(self, bbox, s):
'''
Resize bbox with random scale and position
Args:
bbox (numpy.ndarray, shape 1x4): bbox to be resized
s (dict):
ratio (tuple), scale of width and height recorded in function, apply_image
Return:
bbox (numpy.ndarray, shape 1x4): resized bbox
'''
assert 'c' in s
assert 's' in s
out_w, out_h = np.array(self.size)
trans_output = get_affine_transform(s['c'], s['s'], 0, [out_w, out_h])
bbox[:2] = affine_transform(bbox[:2], trans_output)
bbox[2:4] = affine_transform(bbox[2:4], trans_output)
bbox[[0, 2]] = np.clip(bbox[[0, 2]], 0, out_w - 1)
bbox[[1, 3]] = np.clip(bbox[[1, 3]], 0, out_h - 1)
return bbox
def apply_pts(self, cid, pts, s):
'''
Resize keypoints with random scale and position
Args:
cid (int): the class for keypoints
pts (numpy.ndarray, shape Nx2): keypoints to be resized
s (dict):
ratio (tuple), scale of width and height recorded in function, apply_image
Return:
pts (numpy.ndarray, shape Nx2): resized keypoints
'''
assert 'c' in s
assert 's' in s
out_h, out_w = (np.array(self.size) // self.stride).astype(int)
trans_output = get_affine_transform(s['c'], s['s'], 0, [out_w, out_h])
for i in range(pts.shape[0]):
pts[i] = affine_transform(pts[i], trans_output)
return pts
def apply_pts(self, cid, pts, s):
'''
Resize keypoints
Args:
cid (int): the class for keypoints
pts (numpy.ndarray, shape Nx2): keypoints to be resized
s (dict):
ratio (tuple), scale of width and height recorded in function, apply_image
Return:
pts (numpy.ndarray, shape Nx2): resized keypoints
'''
assert 'c' in s
assert 's' in s
out_w, out_h = np.array(self.size)
trans_output = get_affine_transform(s['c'], s['s'], 0, [out_w, out_h])
for i in range(pts.shape[0]):
pts[i,:2] = affine_transform(pts[i,:2], trans_output)
if ((pts[i, :2] < 0).sum() + (pts[i, :2] > (out_w, out_h)).sum()) > 0:
pts[i, 2] = 0.0
return pts