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# coding=utf-8
# Copyright 2024 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""DenseCRF."""
import numpy as np
from pydensecrf import densecrf as dcrf
from pydensecrf import utils
import torch
import torch.nn.functional as F
class DenseCRF(object):
"""DenseCRF class."""
def __init__(self, iter_max, pos_w, pos_xy_std, bi_w, bi_xy_std, bi_rgb_std):
self.iter_max = iter_max
self.pos_w = pos_w
self.pos_xy_std = pos_xy_std
self.bi_w = bi_w
self.bi_xy_std = bi_xy_std
self.bi_rgb_std = bi_rgb_std
def __call__(self, image, probmap):
c, h, w = probmap.shape
u = utils.unary_from_softmax(probmap)
u = np.ascontiguousarray(u)
image = np.ascontiguousarray(image)
d = dcrf.DenseCRF2D(w, h, c)
d.setUnaryEnergy(u)
d.addPairwiseGaussian(sxy=self.pos_xy_std, compat=self.pos_w)
d.addPairwiseBilateral(
sxy=self.bi_xy_std,
srgb=self.bi_rgb_std,
rgbim=image,
compat=self.bi_w,
)
q = d.inference(self.iter_max)
q = np.array(q).reshape((c, h, w))
return q
class PostProcess:
"""Post processing with dense CRF."""
def __init__(self, device):
self.device = device
self.postprocessor = DenseCRF(
iter_max=10,
pos_xy_std=1,
pos_w=3,
bi_xy_std=67,
bi_rgb_std=3,
bi_w=4,
)
def apply_crf(self, image, cams, bg_factor=1.0):
"""Apply dense CRF."""
bg_score = np.power(1 - np.max(cams, axis=0, keepdims=True), bg_factor)
cams = np.concatenate((bg_score, cams), axis=0)
prob = cams
image = image.astype(np.uint8).transpose(1, 2, 0)
prob = self.postprocessor(image, prob)
label = np.argmax(prob, axis=0)
label_tensor = torch.from_numpy(label).long()
refined_mask = F.one_hot(label_tensor).to(device=self.device)
refined_mask = refined_mask.permute(2, 0, 1)
refined_mask = refined_mask[1:].float()
return refined_mask
def __call__(self, image, cams, separate=False, bg_factor=1.0):
mean_bgr = (104.008, 116.669, 122.675)
# covert Image to numpy array
image = np.array(image).astype(np.float32)
# RGB -> BGR
image = image[:, :, ::-1]
# Mean subtraction
image -= mean_bgr
# HWC -> CHW
image = image.transpose(2, 0, 1)
if isinstance(cams, torch.Tensor):
cams = cams.cpu().detach().numpy()
if separate:
refined_mask = [
self.apply_crf(image, cam[None], bg_factor) for cam in cams
]
refined_mask = torch.cat(refined_mask, dim=0)
else:
refined_mask = self.apply_crf(image, cams, bg_factor)
return refined_mask
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