File size: 1,269 Bytes
a3d6c18 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 |
"""
Modified by Nikita Selin (OPHoperHPO)[https://github.com/OPHoperHPO].
Source url: https://github.com/MarcoForte/FBA_Matting
License: MIT License
"""
import cv2
import numpy as np
group_norm_std = [0.229, 0.224, 0.225]
group_norm_mean = [0.485, 0.456, 0.406]
def dt(a):
return cv2.distanceTransform((a * 255).astype(np.uint8), cv2.DIST_L2, 0)
def trimap_transform(trimap):
h, w = trimap.shape[0], trimap.shape[1]
clicks = np.zeros((h, w, 6))
for k in range(2):
if np.count_nonzero(trimap[:, :, k]) > 0:
dt_mask = -dt(1 - trimap[:, :, k]) ** 2
L = 320
clicks[:, :, 3 * k] = np.exp(dt_mask / (2 * ((0.02 * L) ** 2)))
clicks[:, :, 3 * k + 1] = np.exp(dt_mask / (2 * ((0.08 * L) ** 2)))
clicks[:, :, 3 * k + 2] = np.exp(dt_mask / (2 * ((0.16 * L) ** 2)))
return clicks
def groupnorm_normalise_image(img, format="nhwc"):
"""
Accept rgb in range 0,1
"""
if format == "nhwc":
for i in range(3):
img[..., i] = (img[..., i] - group_norm_mean[i]) / group_norm_std[i]
else:
for i in range(3):
img[..., i, :, :] = (
img[..., i, :, :] - group_norm_mean[i]
) / group_norm_std[i]
return img
|