From 61bfe1f57fbda958e24e227e567676fafd7f6d3e Mon Sep 17 00:00:00 2001
From: Tomas Kulhanek <tomas.kulhanek@stfc.ac.uk>
Date: Thu, 21 Feb 2019 02:11:13 -0500
Subject: restructured sources
---
src/Python/ccpi/__init__.py | 0
src/Python/ccpi/filters/__init__.py | 0
src/Python/ccpi/filters/regularisers.py | 214 ++++++++++++++++++++++++++++++++
3 files changed, 214 insertions(+)
create mode 100644 src/Python/ccpi/__init__.py
create mode 100644 src/Python/ccpi/filters/__init__.py
create mode 100644 src/Python/ccpi/filters/regularisers.py
(limited to 'src/Python/ccpi')
diff --git a/src/Python/ccpi/__init__.py b/src/Python/ccpi/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/Python/ccpi/filters/__init__.py b/src/Python/ccpi/filters/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/Python/ccpi/filters/regularisers.py b/src/Python/ccpi/filters/regularisers.py
new file mode 100644
index 0000000..588ea32
--- /dev/null
+++ b/src/Python/ccpi/filters/regularisers.py
@@ -0,0 +1,214 @@
+"""
+script which assigns a proper device core function based on a flag ('cpu' or 'gpu')
+"""
+
+from ccpi.filters.cpu_regularisers import TV_ROF_CPU, TV_FGP_CPU, TV_SB_CPU, dTV_FGP_CPU, TNV_CPU, NDF_CPU, Diff4th_CPU, TGV_CPU, LLT_ROF_CPU, PATCHSEL_CPU, NLTV_CPU
+try:
+ from ccpi.filters.gpu_regularisers import TV_ROF_GPU, TV_FGP_GPU, TV_SB_GPU, dTV_FGP_GPU, NDF_GPU, Diff4th_GPU, TGV_GPU, LLT_ROF_GPU, PATCHSEL_GPU
+ gpu_enabled = True
+except ImportError:
+ gpu_enabled = False
+from ccpi.filters.cpu_regularisers import NDF_INPAINT_CPU, NVM_INPAINT_CPU
+
+def ROF_TV(inputData, regularisation_parameter, iterations,
+ time_marching_parameter,device='cpu'):
+ if device == 'cpu':
+ return TV_ROF_CPU(inputData,
+ regularisation_parameter,
+ iterations,
+ time_marching_parameter)
+ elif device == 'gpu' and gpu_enabled:
+ return TV_ROF_GPU(inputData,
+ regularisation_parameter,
+ iterations,
+ time_marching_parameter)
+ else:
+ if not gpu_enabled and device == 'gpu':
+ raise ValueError ('GPU is not available')
+ raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
+ .format(device))
+
+def FGP_TV(inputData, regularisation_parameter,iterations,
+ tolerance_param, methodTV, nonneg, printM, device='cpu'):
+ if device == 'cpu':
+ return TV_FGP_CPU(inputData,
+ regularisation_parameter,
+ iterations,
+ tolerance_param,
+ methodTV,
+ nonneg,
+ printM)
+ elif device == 'gpu' and gpu_enabled:
+ return TV_FGP_GPU(inputData,
+ regularisation_parameter,
+ iterations,
+ tolerance_param,
+ methodTV,
+ nonneg,
+ printM)
+ else:
+ if not gpu_enabled and device == 'gpu':
+ raise ValueError ('GPU is not available')
+ raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
+ .format(device))
+def SB_TV(inputData, regularisation_parameter, iterations,
+ tolerance_param, methodTV, printM, device='cpu'):
+ if device == 'cpu':
+ return TV_SB_CPU(inputData,
+ regularisation_parameter,
+ iterations,
+ tolerance_param,
+ methodTV,
+ printM)
+ elif device == 'gpu' and gpu_enabled:
+ return TV_SB_GPU(inputData,
+ regularisation_parameter,
+ iterations,
+ tolerance_param,
+ methodTV,
+ printM)
+ else:
+ if not gpu_enabled and device == 'gpu':
+ raise ValueError ('GPU is not available')
+ raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
+ .format(device))
+def FGP_dTV(inputData, refdata, regularisation_parameter, iterations,
+ tolerance_param, eta_const, methodTV, nonneg, printM, device='cpu'):
+ if device == 'cpu':
+ return dTV_FGP_CPU(inputData,
+ refdata,
+ regularisation_parameter,
+ iterations,
+ tolerance_param,
+ eta_const,
+ methodTV,
+ nonneg,
+ printM)
+ elif device == 'gpu' and gpu_enabled:
+ return dTV_FGP_GPU(inputData,
+ refdata,
+ regularisation_parameter,
+ iterations,
+ tolerance_param,
+ eta_const,
+ methodTV,
+ nonneg,
+ printM)
+ else:
+ if not gpu_enabled and device == 'gpu':
+ raise ValueError ('GPU is not available')
+ raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
+ .format(device))
+def TNV(inputData, regularisation_parameter, iterations, tolerance_param):
+ return TNV_CPU(inputData,
+ regularisation_parameter,
+ iterations,
+ tolerance_param)
+def NDF(inputData, regularisation_parameter, edge_parameter, iterations,
+ time_marching_parameter, penalty_type, device='cpu'):
+ if device == 'cpu':
+ return NDF_CPU(inputData,
+ regularisation_parameter,
+ edge_parameter,
+ iterations,
+ time_marching_parameter,
+ penalty_type)
+ elif device == 'gpu' and gpu_enabled:
+ return NDF_GPU(inputData,
+ regularisation_parameter,
+ edge_parameter,
+ iterations,
+ time_marching_parameter,
+ penalty_type)
+ else:
+ if not gpu_enabled and device == 'gpu':
+ raise ValueError ('GPU is not available')
+ raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
+ .format(device))
+def Diff4th(inputData, regularisation_parameter, edge_parameter, iterations,
+ time_marching_parameter, device='cpu'):
+ if device == 'cpu':
+ return Diff4th_CPU(inputData,
+ regularisation_parameter,
+ edge_parameter,
+ iterations,
+ time_marching_parameter)
+ elif device == 'gpu' and gpu_enabled:
+ return Diff4th_GPU(inputData,
+ regularisation_parameter,
+ edge_parameter,
+ iterations,
+ time_marching_parameter)
+ else:
+ if not gpu_enabled and device == 'gpu':
+ raise ValueError ('GPU is not available')
+ raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
+ .format(device))
+
+def PatchSelect(inputData, searchwindow, patchwindow, neighbours, edge_parameter, device='cpu'):
+ if device == 'cpu':
+ return PATCHSEL_CPU(inputData,
+ searchwindow,
+ patchwindow,
+ neighbours,
+ edge_parameter)
+ elif device == 'gpu' and gpu_enabled:
+ return PATCHSEL_GPU(inputData,
+ searchwindow,
+ patchwindow,
+ neighbours,
+ edge_parameter)
+ else:
+ if not gpu_enabled and device == 'gpu':
+ raise ValueError ('GPU is not available')
+ raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
+ .format(device))
+
+def NLTV(inputData, H_i, H_j, H_k, Weights, regularisation_parameter, iterations):
+ return NLTV_CPU(inputData,
+ H_i,
+ H_j,
+ H_k,
+ Weights,
+ regularisation_parameter,
+ iterations)
+
+def TGV(inputData, regularisation_parameter, alpha1, alpha0, iterations,
+ LipshitzConst, device='cpu'):
+ if device == 'cpu':
+ return TGV_CPU(inputData,
+ regularisation_parameter,
+ alpha1,
+ alpha0,
+ iterations,
+ LipshitzConst)
+ elif device == 'gpu' and gpu_enabled:
+ return TGV_GPU(inputData,
+ regularisation_parameter,
+ alpha1,
+ alpha0,
+ iterations,
+ LipshitzConst)
+ else:
+ if not gpu_enabled and device == 'gpu':
+ raise ValueError ('GPU is not available')
+ raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
+ .format(device))
+def LLT_ROF(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations,
+ time_marching_parameter, device='cpu'):
+ if device == 'cpu':
+ return LLT_ROF_CPU(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter)
+ elif device == 'gpu' and gpu_enabled:
+ return LLT_ROF_GPU(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter)
+ else:
+ if not gpu_enabled and device == 'gpu':
+ raise ValueError ('GPU is not available')
+ raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
+ .format(device))
+def NDF_INP(inputData, maskData, regularisation_parameter, edge_parameter, iterations,
+ time_marching_parameter, penalty_type):
+ return NDF_INPAINT_CPU(inputData, maskData, regularisation_parameter,
+ edge_parameter, iterations, time_marching_parameter, penalty_type)
+
+def NVM_INP(inputData, maskData, SW_increment, iterations):
+ return NVM_INPAINT_CPU(inputData, maskData, SW_increment, iterations)
--
cgit v1.2.3
From 047d9e2a7dda92e13414b980a93c3f1724665241 Mon Sep 17 00:00:00 2001
From: Tomas Kulhanek <tomas.kulhanek@stfc.ac.uk>
Date: Mon, 25 Feb 2019 03:35:50 -0500
Subject: MOVE: Wrappers/Python/supp to src/Python/ccpi/supp
---
src/Python/ccpi/supp/__init__.py | 0
src/Python/ccpi/supp/qualitymetrics.py | 65 ++++++++++++++++++++++++++++++++++
2 files changed, 65 insertions(+)
create mode 100644 src/Python/ccpi/supp/__init__.py
create mode 100644 src/Python/ccpi/supp/qualitymetrics.py
(limited to 'src/Python/ccpi')
diff --git a/src/Python/ccpi/supp/__init__.py b/src/Python/ccpi/supp/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/Python/ccpi/supp/qualitymetrics.py b/src/Python/ccpi/supp/qualitymetrics.py
new file mode 100644
index 0000000..f44d832
--- /dev/null
+++ b/src/Python/ccpi/supp/qualitymetrics.py
@@ -0,0 +1,65 @@
+#!/usr/bin/env python2
+# -*- coding: utf-8 -*-
+"""
+A class for some standard image quality metrics
+"""
+import numpy as np
+
+class QualityTools:
+ def __init__(self, im1, im2):
+ if im1.size != im2.size:
+ print ('Error: Sizes of images/volumes are different')
+ raise SystemExit
+ self.im1 = im1 # image or volume - 1
+ self.im2 = im2 # image or volume - 2
+ def nrmse(self):
+ """ Normalised Root Mean Square Error """
+ rmse = np.sqrt(np.sum((self.im2 - self.im1) ** 2) / float(self.im1.size))
+ max_val = max(np.max(self.im1), np.max(self.im2))
+ min_val = min(np.min(self.im1), np.min(self.im2))
+ return 1 - (rmse / (max_val - min_val))
+ def rmse(self):
+ """ Root Mean Square Error """
+ rmse = np.sqrt(np.sum((self.im1 - self.im2) ** 2) / float(self.im1.size))
+ return rmse
+ def ssim(self, window, k=(0.01, 0.03), l=255):
+ from scipy.signal import fftconvolve
+ """See https://ece.uwaterloo.ca/~z70wang/research/ssim/"""
+ # Check if the window is smaller than the images.
+ for a, b in zip(window.shape, self.im1.shape):
+ if a > b:
+ return None, None
+ # Values in k must be positive according to the base implementation.
+ for ki in k:
+ if ki < 0:
+ return None, None
+
+ c1 = (k[0] * l) ** 2
+ c2 = (k[1] * l) ** 2
+ window = window/np.sum(window)
+
+ mu1 = fftconvolve(self.im1, window, mode='valid')
+ mu2 = fftconvolve(self.im2, window, mode='valid')
+ mu1_sq = mu1 * mu1
+ mu2_sq = mu2 * mu2
+ mu1_mu2 = mu1 * mu2
+ sigma1_sq = fftconvolve(self.im1 * self.im1, window, mode='valid') - mu1_sq
+ sigma2_sq = fftconvolve(self.im2 * self.im2, window, mode='valid') - mu2_sq
+ sigma12 = fftconvolve(self.im1 * self.im2, window, mode='valid') - mu1_mu2
+
+ if c1 > 0 and c2 > 0:
+ num = (2 * mu1_mu2 + c1) * (2 * sigma12 + c2)
+ den = (mu1_sq + mu2_sq + c1) * (sigma1_sq + sigma2_sq + c2)
+ ssim_map = num / den
+ else:
+ num1 = 2 * mu1_mu2 + c1
+ num2 = 2 * sigma12 + c2
+ den1 = mu1_sq + mu2_sq + c1
+ den2 = sigma1_sq + sigma2_sq + c2
+ ssim_map = np.ones(np.shape(mu1))
+ index = (den1 * den2) > 0
+ ssim_map[index] = (num1[index] * num2[index]) / (den1[index] * den2[index])
+ index = (den1 != 0) & (den2 == 0)
+ ssim_map[index] = num1[index] / den1[index]
+ mssim = ssim_map.mean()
+ return mssim, ssim_map
--
cgit v1.2.3