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Zero
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#
# Authors: Wouter Van Gansbeke & Simon Vandenhende
# Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
import numpy as np
import pydensecrf.densecrf as dcrf
import pydensecrf.utils as utils
import torch
import torch.nn.functional as F
import torchvision.transforms.functional as VF
MAX_ITER = 10
POS_W = 3
POS_XY_STD = 0.3 # 1
Bi_W = 4
Bi_XY_STD = 20 # 67
Bi_RGB_STD = 3
def dense_crf(image_tensor: torch.FloatTensor, output_logits: torch.FloatTensor):
image = np.array(VF.to_pil_image(image_tensor))[:, :, ::-1]
H, W = image.shape[:2]
image = np.ascontiguousarray(image)
output_logits = F.interpolate(output_logits.unsqueeze(0), size=(H, W), mode="bilinear",
align_corners=False).squeeze()
output_probs = F.softmax(output_logits, dim=0).cpu().numpy()
c = output_probs.shape[0]
h = output_probs.shape[1]
w = output_probs.shape[2]
U = utils.unary_from_softmax(output_probs)
U = np.ascontiguousarray(U)
d = dcrf.DenseCRF2D(w, h, c)
d.setUnaryEnergy(U)
d.addPairwiseGaussian(sxy=POS_XY_STD, compat=POS_W)
d.addPairwiseBilateral(sxy=Bi_XY_STD, srgb=Bi_RGB_STD, rgbim=image, compat=Bi_W)
Q = d.inference(MAX_ITER)
Q = np.array(Q).reshape((c, h, w))
return Q
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