#! /usr/bin/env python
# -*- coding: utf-8 -*-
#
# author: Avinash Kori
# contact: koriavinash1@gmail.com
# MIT License
# Copyright (c) 2020 Avinash Kori
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import numpy as np
from scipy.ndimage import uniform_filter, maximum_filter
import pydensecrf.densecrf as dcrf
from pydensecrf.utils import (unary_from_softmax,
create_pairwise_bilateral,
create_pairwise_gaussian)
from scipy.ndimage.measurements import label
from skimage.morphology import erosion, dilation
[docs]def densecrf(logits):
"""
applies coditional random fields on predictions
The idea is consider the nbr voxels in making
class prediction of current pixel
refer CRF and MRF papers for more theoretical idea
args
logits: Nb_classes x Height x Width x Depth
returns
tensor of size Height x Width x Depth
"""
shape = logits.shape[1:]
new_image = np.empty(shape)
d = dcrf.DenseCRF(np.prod(shape), logits.shape[0])
U = unary_from_softmax(logits)
d.setUnaryEnergy(U)
feats = create_pairwise_gaussian(sdims=(1.0, 1.0, 1.0), shape=shape)
d.addPairwiseEnergy(feats, compat=3, kernel=dcrf.DIAG_KERNEL, normalization=dcrf.NORMALIZE_SYMMETRIC)
Q = d.inference(5)
new_image = np.argmax(Q, axis=0).reshape((shape[0], shape[1],shape[2]))
return new_image
[docs]def connected_components(voxels, threshold=0.8):
"""
This clusters entire segmentations into multiple clusters
and considers significant cluster for further analysis
args
voxels: np.uint8 height x width x depth
threshold: number of pixels in cluster to
consider it as significant
returns
tensor with same size as voxels
"""
c,n = label(voxels)
nums = np.array([np.sum(c==i) for i in range(1, n+1)])
max_area = np.max(nums)
selected_components = nums >= (threshold*max_area)
mask = np.zeros_like(voxels)
for i, select in enumerate(selected_components):
if select:
mask[c == (i+1)] = 1
return mask*voxels
[docs]def class_wise_cc(pred):
"""
Applies connected components on class wise slices
args
pred dimension: width, height, depth
returns
tensor of same size as logits (uint8)
"""
classes = np.unique(pred)
return_ = np.zeros(pred.shape)
for class_ in classes:
if class_ == 0: continue
return_ += class_*connected_components(1*(pred == class_))
return_ = np.clip(return_, np.min(return_), class_)
return np.uint8(return_)