File size: 1,526 Bytes
4a582ec |
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 47 48 49 50 51 52 53 54 55 56 57 58 59 |
from typing import Optional
import numpy as np
import paddle
import paddle.nn.functional as F
def reverse_transform(alpha, trans_info):
"""recover pred to origin shape"""
for item in trans_info[::-1]:
if item[0] == "resize":
h, w = item[1][0], item[1][1]
alpha = F.interpolate(alpha, [h, w], mode="bilinear")
elif item[0] == "padding":
h, w = item[1][0], item[1][1]
alpha = alpha[:, :, 0:h, 0:w]
else:
raise Exception(f"Unexpected info '{item[0]}' in im_info")
return alpha
def preprocess(img, transforms, trimap=None):
data = {}
data["img"] = img
if trimap is not None:
data["trimap"] = trimap
data["gt_fields"] = ["trimap"]
data["trans_info"] = []
data = transforms(data)
data["img"] = paddle.to_tensor(data["img"])
data["img"] = data["img"].unsqueeze(0)
if trimap is not None:
data["trimap"] = paddle.to_tensor(data["trimap"])
data["trimap"] = data["trimap"].unsqueeze((0, 1))
return data
def predict(
model,
transforms,
image: np.ndarray,
trimap: Optional[np.ndarray] = None,
):
with paddle.no_grad():
data = preprocess(img=image, transforms=transforms, trimap=None)
alpha = model(data)
alpha = reverse_transform(alpha, data["trans_info"])
alpha = alpha.numpy().squeeze()
if trimap is not None:
alpha[trimap == 0] = 0
alpha[trimap == 255] = 1.
return alpha
|