diff options
| -rw-r--r-- | Readme.md | 2 | ||||
| -rwxr-xr-x | build/build-install.sh | 4 | ||||
| -rw-r--r-- | demos/data/SinoInpaint.mat (renamed from docs/data/SinoInpaint.mat) | bin | 3335061 -> 3335061 bytes | |||
| -rw-r--r-- | demos/data/lena_gray_512.tif (renamed from docs/data/lena_gray_512.tif) | bin | 262598 -> 262598 bytes | |||
| -rw-r--r-- | demos/demoMatlab_3Ddenoise.m (renamed from docs/demos/demoMatlab_3Ddenoise.m) | 6 | ||||
| -rw-r--r-- | demos/demoMatlab_denoise.m (renamed from docs/demos/demoMatlab_denoise.m) | 6 | ||||
| -rw-r--r-- | demos/demoMatlab_inpaint.m (renamed from docs/demos/demoMatlab_inpaint.m) | 4 | ||||
| -rw-r--r-- | demos/demo_cpu_inpainters.py (renamed from docs/demos/demo_cpu_inpainters.py) | 2 | ||||
| -rw-r--r-- | demos/demo_cpu_regularisers.py (renamed from docs/demos/demo_cpu_regularisers.py) | 2 | ||||
| -rw-r--r-- | demos/demo_cpu_regularisers3D.py (renamed from docs/demos/demo_cpu_regularisers3D.py) | 2 | ||||
| -rw-r--r-- | demos/demo_cpu_vs_gpu_regularisers.py (renamed from docs/demos/demo_cpu_vs_gpu_regularisers.py) | 2 | ||||
| -rw-r--r-- | demos/demo_gpu_regularisers.py (renamed from docs/demos/demo_gpu_regularisers.py) | 2 | ||||
| -rw-r--r-- | demos/demo_gpu_regularisers3D.py (renamed from docs/demos/demo_gpu_regularisers3D.py) | 4 | ||||
| -rw-r--r-- | demos/images/TV_vs_NLTV.jpg (renamed from docs/images/TV_vs_NLTV.jpg) | bin | 111273 -> 111273 bytes | |||
| -rw-r--r-- | demos/images/probl.pdf (renamed from docs/images/probl.pdf) | bin | 62326 -> 62326 bytes | |||
| -rw-r--r-- | demos/images/probl.png (renamed from docs/images/probl.png) | bin | 38161 -> 38161 bytes | |||
| -rw-r--r-- | demos/images/reg_penalties.jpg (renamed from docs/images/reg_penalties.jpg) | bin | 237455 -> 237455 bytes | |||
| -rw-r--r-- | demos/qualitymetrics.py (renamed from docs/demos/qualitymetrics.py) | 0 | ||||
| -rw-r--r-- | docs/installation.txt | 11 | ||||
| -rw-r--r-- | test/test_CPU_regularisers.py | 91 | ||||
| -rw-r--r-- | test/test_FGP_TV.py | 152 | ||||
| -rw-r--r-- | test/test_ROF_TV.py | 3 |
22 files changed, 263 insertions, 30 deletions
@@ -65,7 +65,7 @@ cd CCPi-Regularisation-Toolkit export CCPI_BUILD_ARGS="--numpy 1.12 --python 3.6" build/jenkins-build.sh ``` -this will install `conda build` environment and compiles C/C++ and Python wrappers and performs basic tests. +this will install `conda build` environment and compiles C/C++ and Python wrappers and performs basic tests for environment with python 3.6 and numpy 1.12. ### CMake If you want to build directly using cmake, install CMake (v.>=3) to configure it. Additionally you will need a C compiler, `make` (on linux) and CUDA SDK where available. The toolkit may be used directly from C/C++ as it is compiled as a shared library (check-out the include files in `Core` for this) diff --git a/build/build-install.sh b/build/build-install.sh new file mode 100755 index 0000000..def66a4 --- /dev/null +++ b/build/build-install.sh @@ -0,0 +1,4 @@ +#!/usr/bin/env bash +export CCPI_BUILD_ARGS="--numpy 1.12 --python 3.6" +bash <(curl -L https://raw.githubusercontent.com/vais-ral/CCPi-VirtualMachine/master/scripts/jenkins-build.sh) +conda install -y ccpi-regulariser --use-local --force
\ No newline at end of file diff --git a/docs/data/SinoInpaint.mat b/demos/data/SinoInpaint.mat Binary files differindex d748fb4..d748fb4 100644 --- a/docs/data/SinoInpaint.mat +++ b/demos/data/SinoInpaint.mat diff --git a/docs/data/lena_gray_512.tif b/demos/data/lena_gray_512.tif Binary files differindex f80cafc..f80cafc 100644 --- a/docs/data/lena_gray_512.tif +++ b/demos/data/lena_gray_512.tif diff --git a/docs/demos/demoMatlab_3Ddenoise.m b/demos/demoMatlab_3Ddenoise.m index 0c331a4..cdd3117 100644 --- a/docs/demos/demoMatlab_3Ddenoise.m +++ b/demos/demoMatlab_3Ddenoise.m @@ -1,8 +1,8 @@ % Volume (3D) denoising demo using CCPi-RGL clear; close all -Path1 = sprintf(['..' filesep 'mex_compile' filesep 'installed'], 1i); -Path2 = sprintf(['..' filesep '..' filesep '..' filesep 'data' filesep], 1i); -Path3 = sprintf(['..' filesep 'supp'], 1i); +Path1 = sprintf(['..' filesep 'src' filesep 'Matlab' filesep 'mex_compile' filesep 'installed'], 1i); +Path2 = sprintf(['data' filesep], 1i); +Path3 = sprintf(['..' filesep 'src' filesep 'Matlab' filesep 'supp'], 1i); addpath(Path1); addpath(Path2); addpath(Path3); diff --git a/docs/demos/demoMatlab_denoise.m b/demos/demoMatlab_denoise.m index 14d3096..2031853 100644 --- a/docs/demos/demoMatlab_denoise.m +++ b/demos/demoMatlab_denoise.m @@ -2,9 +2,9 @@ clear; close all fsep = '/'; -Path1 = sprintf(['..' fsep 'mex_compile' fsep 'installed'], 1i); -Path2 = sprintf(['..' fsep '..' fsep '..' fsep 'data' fsep], 1i); -Path3 = sprintf(['..' fsep 'supp'], 1i); +Path1 = sprintf(['..' fsep 'src' fsep 'Matlab' fsep 'mex_compile' fsep 'installed'], 1i); +Path2 = sprintf([ data' fsep], 1i); +Path3 = sprintf(['..' filesep 'src' filesep 'Matlab' filesep 'supp'], 1i); addpath(Path1); addpath(Path2); addpath(Path3); Im = double(imread('lena_gray_512.tif'))/255; % loading image diff --git a/docs/demos/demoMatlab_inpaint.m b/demos/demoMatlab_inpaint.m index 66f9c15..a85f2b9 100644 --- a/docs/demos/demoMatlab_inpaint.m +++ b/demos/demoMatlab_inpaint.m @@ -1,7 +1,7 @@ % Image (2D) inpainting demo using CCPi-RGL clear; close all -Path1 = sprintf(['..' filesep 'mex_compile' filesep 'installed'], 1i); -Path2 = sprintf(['..' filesep '..' filesep '..' filesep 'data' filesep], 1i); +Path1 = sprintf(['..' filesep 'src' filesep 'Matlab' filesep 'mex_compile' filesep 'installed'], 1i); +Path2 = sprintf(['data' filesep], 1i); addpath(Path1); addpath(Path2); diff --git a/docs/demos/demo_cpu_inpainters.py b/demos/demo_cpu_inpainters.py index 3b4191b..d07e74a 100644 --- a/docs/demos/demo_cpu_inpainters.py +++ b/demos/demo_cpu_inpainters.py @@ -29,7 +29,7 @@ def printParametersToString(pars): ############################################################################### # read sinogram and the mask -filename = os.path.join(".." , ".." , ".." , "data" ,"SinoInpaint.mat") +filename = os.path.join("data" ,"SinoInpaint.mat") sino = io.loadmat(filename) sino_full = sino.get('Sinogram') Mask = sino.get('Mask') diff --git a/docs/demos/demo_cpu_regularisers.py b/demos/demo_cpu_regularisers.py index e6befa9..373502b 100644 --- a/docs/demos/demo_cpu_regularisers.py +++ b/demos/demo_cpu_regularisers.py @@ -31,7 +31,7 @@ def printParametersToString(pars): return txt ############################################################################### #%% -filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif") +filename = os.path.join( "data" ,"lena_gray_512.tif") # read image Im = plt.imread(filename) diff --git a/docs/demos/demo_cpu_regularisers3D.py b/demos/demo_cpu_regularisers3D.py index 2d2fc22..56baf13 100644 --- a/docs/demos/demo_cpu_regularisers3D.py +++ b/demos/demo_cpu_regularisers3D.py @@ -30,7 +30,7 @@ def printParametersToString(pars): return txt ############################################################################### #%% -filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif") +filename = os.path.join( "data" ,"lena_gray_512.tif") # read image Im = plt.imread(filename) diff --git a/docs/demos/demo_cpu_vs_gpu_regularisers.py b/demos/demo_cpu_vs_gpu_regularisers.py index 230a761..5ce8da4 100644 --- a/docs/demos/demo_cpu_vs_gpu_regularisers.py +++ b/demos/demo_cpu_vs_gpu_regularisers.py @@ -31,7 +31,7 @@ def printParametersToString(pars): return txt ############################################################################### -filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif") +filename = os.path.join("data" ,"lena_gray_512.tif") # read image Im = plt.imread(filename) diff --git a/docs/demos/demo_gpu_regularisers.py b/demos/demo_gpu_regularisers.py index e1c6575..bc9baf2 100644 --- a/docs/demos/demo_gpu_regularisers.py +++ b/demos/demo_gpu_regularisers.py @@ -31,7 +31,7 @@ def printParametersToString(pars): return txt ############################################################################### #%% -filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif") +filename = os.path.join( "data" ,"lena_gray_512.tif") # read image Im = plt.imread(filename) diff --git a/docs/demos/demo_gpu_regularisers3D.py b/demos/demo_gpu_regularisers3D.py index b6058d2..2f49cb9 100644 --- a/docs/demos/demo_gpu_regularisers3D.py +++ b/demos/demo_gpu_regularisers3D.py @@ -30,7 +30,7 @@ def printParametersToString(pars): return txt ############################################################################### #%% -filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif") +filename = os.path.join( "data" ,"lena_gray_512.tif") # read image Im = plt.imread(filename) @@ -70,7 +70,7 @@ del Im2 slices = 20 -filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif") +filename = os.path.join( "data" ,"lena_gray_512.tif") Im = plt.imread(filename) Im = np.asarray(Im, dtype='float32') diff --git a/docs/images/TV_vs_NLTV.jpg b/demos/images/TV_vs_NLTV.jpg Binary files differindex e976512..e976512 100644 --- a/docs/images/TV_vs_NLTV.jpg +++ b/demos/images/TV_vs_NLTV.jpg diff --git a/docs/images/probl.pdf b/demos/images/probl.pdf Binary files differindex 6a06021..6a06021 100644 --- a/docs/images/probl.pdf +++ b/demos/images/probl.pdf diff --git a/docs/images/probl.png b/demos/images/probl.png Binary files differindex af0e852..af0e852 100644 --- a/docs/images/probl.png +++ b/demos/images/probl.png diff --git a/docs/images/reg_penalties.jpg b/demos/images/reg_penalties.jpg Binary files differindex 923d5c4..923d5c4 100644 --- a/docs/images/reg_penalties.jpg +++ b/demos/images/reg_penalties.jpg diff --git a/docs/demos/qualitymetrics.py b/demos/qualitymetrics.py index 850829e..850829e 100644 --- a/docs/demos/qualitymetrics.py +++ b/demos/qualitymetrics.py diff --git a/docs/installation.txt b/docs/installation.txt deleted file mode 100644 index f6db38c..0000000 --- a/docs/installation.txt +++ /dev/null @@ -1,11 +0,0 @@ -One can install CCPi-RGL toolkit using cmake: - - -cmake ../CCPi-Regularisation-Toolkit/ -DBUILD_MATLAB_WRAPPERS=ON -DBUILD_PYTHON_WRAPPERS=OFF -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=install -DMatlab_ROOT_DIR=<Matlab directory> -DBUILD_CUDA=OFF - -make - -make install - -Running Matlab from Linux do: -PATH="/path/to/mex/:$PATH" LD_LIBRARY_PATH="/path/to/library:$LD_LIBRARY_PATH" ./matlab -nosplash & diff --git a/test/test_CPU_regularisers.py b/test/test_CPU_regularisers.py new file mode 100644 index 0000000..42e4735 --- /dev/null +++ b/test/test_CPU_regularisers.py @@ -0,0 +1,91 @@ +import unittest +import math +import os +import timeit +from ccpi.filters.regularisers import FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, Diff4th, ROF_TV +from testroutines import * + +############################################################################### + +class TestRegularisers(unittest.TestCase): + + def getPars(self,alg,noi=1200): + filename = os.path.join("lena_gray_512.tif") + plt = TiffReader() + # read image + Im = plt.imread(filename) + Im = np.asarray(Im, dtype='float32') + Im = Im / 255 + perc = 0.05 + u0 = Im + np.random.normal(loc=0, + scale=perc * Im, + size=np.shape(Im)) + u_ref = Im + np.random.normal(loc=0, + scale=0.01 * Im, + size=np.shape(Im)) + # map the u0 u0->u0>0 + # f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1) + u0 = u0.astype('float32') + u_ref = u_ref.astype('float32') + # set parameters + pars = {'algorithm': alg, \ + 'input': u0, \ + 'regularisation_parameter': 0.04, \ + 'number_of_iterations': noi, \ + 'tolerance_constant': 0.00001, \ + 'methodTV': 0, \ + 'nonneg': 0, \ + 'printingOut': 0, \ + 'time_marching_parameter': 0.00002 + } + return Im, pars + + + def test_FGP_TV_CPU(self): + Im, pars = self.getPars(FGP_TV) + + fgp_cpu = FGP_TV(pars['input'], + pars['regularisation_parameter'], + pars['number_of_iterations'], + pars['tolerance_constant'], + pars['methodTV'], + pars['nonneg'], + pars['printingOut'], 'cpu') + + rms = rmse(Im, fgp_cpu) + pars['rmse'] = rms + self.assertAlmostEqual(rms,0.02,delta=0.01) + + def test_TV_ROF_CPU(self): + # set parameters + Im, pars = self.getPars(ROF_TV) + # call routine + fgp_cpu = ROF_TV(pars['input'], + pars['regularisation_parameter'], + pars['number_of_iterations'], + pars['time_marching_parameter'], 'cpu') + + rms = rmse(Im, fgp_cpu) + pars['rmse'] = rms + + #txtstr = printParametersToString(pars) + #print(txtstr) + # now test that it generates some expected output + self.assertAlmostEqual(rms,0.02,delta=0.01) + + def test_SB_TV_CPU(self): + # set parameters + Im, pars = self.getPars(SB_TV) + # call routine + fgp_cpu = SB_TV(pars['input'], + pars['regularisation_parameter'], + pars['number_of_iterations'], + pars['time_marching_parameter'], 'cpu') + + rms = rmse(Im, fgp_cpu) + pars['rmse'] = rms + + #txtstr = printParametersToString(pars) + #print(txtstr) + # now test that it generates some expected output + self.assertAlmostEqual(rms,0.02,delta=0.01) diff --git a/test/test_FGP_TV.py b/test/test_FGP_TV.py new file mode 100644 index 0000000..f0dc540 --- /dev/null +++ b/test/test_FGP_TV.py @@ -0,0 +1,152 @@ +import unittest +import math +import os +import timeit +from ccpi.filters.regularisers import FGP_TV +#, FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, Diff4th +from testroutines import * + +############################################################################### + +class TestRegularisers(unittest.TestCase): + + def test_FGP_TV_CPU(self): + print(__name__) + filename = os.path.join("lena_gray_512.tif") + plt = TiffReader() + # read image + Im = plt.imread(filename) + Im = np.asarray(Im, dtype='float32') + + Im = Im / 255 + perc = 0.05 + u0 = Im + np.random.normal(loc=0, + scale=perc * Im, + size=np.shape(Im)) + u_ref = Im + np.random.normal(loc=0, + scale=0.01 * Im, + size=np.shape(Im)) + + # map the u0 u0->u0>0 + # f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1) + u0 = u0.astype('float32') + u_ref = u_ref.astype('float32') + + print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") + print("____________FGP-TV bench___________________") + print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") + + # set parameters + pars = {'algorithm': FGP_TV, \ + 'input': u0, \ + 'regularisation_parameter': 0.04, \ + 'number_of_iterations': 1200, \ + 'tolerance_constant': 0.00001, \ + 'methodTV': 0, \ + 'nonneg': 0, \ + 'printingOut': 0 + } + + print("#############FGP TV CPU####################") + start_time = timeit.default_timer() + fgp_cpu = FGP_TV(pars['input'], + pars['regularisation_parameter'], + pars['number_of_iterations'], + pars['tolerance_constant'], + pars['methodTV'], + pars['nonneg'], + pars['printingOut'], 'cpu') + + rms = rmse(Im, fgp_cpu) + pars['rmse'] = rms + + txtstr = printParametersToString(pars) + txtstr += "%s = %.3fs" % ('elapsed time', timeit.default_timer() - start_time) + print(txtstr) + self.assertTrue(math.isclose(rms,0.02,rel_tol=1e-1)) + + def test_FGP_TV_CPU_vs_GPU(self): + print(__name__) + filename = os.path.join("lena_gray_512.tif") + plt = TiffReader() + # read image + Im = plt.imread(filename) + Im = np.asarray(Im, dtype='float32') + + Im = Im / 255 + perc = 0.05 + u0 = Im + np.random.normal(loc=0, + scale=perc * Im, + size=np.shape(Im)) + u_ref = Im + np.random.normal(loc=0, + scale=0.01 * Im, + size=np.shape(Im)) + + # map the u0 u0->u0>0 + # f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1) + u0 = u0.astype('float32') + u_ref = u_ref.astype('float32') + + print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") + print("____________FGP-TV bench___________________") + print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") + + # set parameters + pars = {'algorithm': FGP_TV, \ + 'input': u0, \ + 'regularisation_parameter': 0.04, \ + 'number_of_iterations': 1200, \ + 'tolerance_constant': 0.00001, \ + 'methodTV': 0, \ + 'nonneg': 0, \ + 'printingOut': 0 + } + + print("#############FGP TV CPU####################") + start_time = timeit.default_timer() + fgp_cpu = FGP_TV(pars['input'], + pars['regularisation_parameter'], + pars['number_of_iterations'], + pars['tolerance_constant'], + pars['methodTV'], + pars['nonneg'], + pars['printingOut'], 'cpu') + + rms = rmse(Im, fgp_cpu) + pars['rmse'] = rms + + txtstr = printParametersToString(pars) + txtstr += "%s = %.3fs" % ('elapsed time', timeit.default_timer() - start_time) + print(txtstr) + + print("##############FGP TV GPU##################") + start_time = timeit.default_timer() + try: + fgp_gpu = FGP_TV(pars['input'], + pars['regularisation_parameter'], + pars['number_of_iterations'], + pars['tolerance_constant'], + pars['methodTV'], + pars['nonneg'], + pars['printingOut'], 'gpu') + + except ValueError as ve: + self.skipTest("Results not comparable. GPU computing error.") + + rms = rmse(Im, fgp_gpu) + pars['rmse'] = rms + pars['algorithm'] = FGP_TV + txtstr = printParametersToString(pars) + txtstr += "%s = %.3fs" % ('elapsed time', timeit.default_timer() - start_time) + print(txtstr) + + print("--------Compare the results--------") + tolerance = 1e-05 + diff_im = np.zeros(np.shape(fgp_cpu)) + diff_im = abs(fgp_cpu - fgp_gpu) + diff_im[diff_im > tolerance] = 1 + + self.assertLessEqual(diff_im.sum(), 1) + +if __name__ == '__main__': + unittest.main() diff --git a/test/test_ROF_TV.py b/test/test_ROF_TV.py index dda38b7..fa35680 100644 --- a/test/test_ROF_TV.py +++ b/test/test_ROF_TV.py @@ -53,9 +53,6 @@ class TestRegularisers(unittest.TestCase): def test_ROF_TV_CPU_vs_GPU(self): - # print ("tomas debug test function") - print(__name__) - self.fail("testfail2") filename = os.path.join("lena_gray_512.tif") plt = TiffReader() # read image |
