diff options
| -rw-r--r-- | Wrappers/Python/conda-recipe/lena_gray_512.tif | bin | 0 -> 262598 bytes | |||
| -rw-r--r-- | Wrappers/Python/conda-recipe/meta.yaml | 7 | ||||
| -rwxr-xr-x | Wrappers/Python/conda-recipe/run_test.py | 795 | ||||
| -rw-r--r-- | Wrappers/Python/conda-recipe/run_test.py.in | 148 | ||||
| -rw-r--r-- | Wrappers/Python/conda-recipe/testLena.npy | bin | 1048656 -> 0 bytes |
5 files changed, 801 insertions, 149 deletions
diff --git a/Wrappers/Python/conda-recipe/lena_gray_512.tif b/Wrappers/Python/conda-recipe/lena_gray_512.tif Binary files differnew file mode 100644 index 0000000..f80cafc --- /dev/null +++ b/Wrappers/Python/conda-recipe/lena_gray_512.tif diff --git a/Wrappers/Python/conda-recipe/meta.yaml b/Wrappers/Python/conda-recipe/meta.yaml index 2d79984..4774563 100644 --- a/Wrappers/Python/conda-recipe/meta.yaml +++ b/Wrappers/Python/conda-recipe/meta.yaml @@ -9,6 +9,12 @@ build: - CIL_VERSION # number: 0 +test: + files: + - lena_gray_512.tif + requires: + - pillow + requirements: build: - python @@ -28,7 +34,6 @@ requirements: - vc 14 # [win and py36] - vc 14 # [win and py35] - vc 9 # [win and py27] - about: home: http://www.ccpi.ac.uk diff --git a/Wrappers/Python/conda-recipe/run_test.py b/Wrappers/Python/conda-recipe/run_test.py new file mode 100755 index 0000000..99ef239 --- /dev/null +++ b/Wrappers/Python/conda-recipe/run_test.py @@ -0,0 +1,795 @@ +import unittest
+import sys
+import numpy as np
+import os
+import timeit
+from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, DIFF4th
+from PIL import Image
+
+class TiffReader(object):
+ def imread(self, filename):
+ return np.asarray(Image.open(filename))
+###############################################################################
+def printParametersToString(pars):
+ txt = r''
+ for key, value in pars.items():
+ if key== 'algorithm' :
+ txt += "{0} = {1}".format(key, value.__name__)
+ elif key == 'input':
+ txt += "{0} = {1}".format(key, np.shape(value))
+ elif key == 'refdata':
+ txt += "{0} = {1}".format(key, np.shape(value))
+ else:
+ txt += "{0} = {1}".format(key, value)
+ txt += '\n'
+ return txt
+def nrmse(im1, im2):
+ rmse = np.sqrt(np.sum((im2 - im1) ** 2) / float(im1.size))
+ max_val = max(np.max(im1), np.max(im2))
+ min_val = min(np.min(im1), np.min(im2))
+ return 1 - (rmse / (max_val - min_val))
+
+def rmse(im1, im2):
+ rmse = np.sqrt(np.sum((im1 - im2) ** 2) / float(im1.size))
+ return rmse
+###############################################################################
+
+class TestRegularisers(unittest.TestCase):
+
+
+ def test_ROF_TV_CPU_vs_GPU(self):
+ 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 ("____________ROF-TV bench___________________")
+ print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+ # set parameters
+ pars = {'algorithm': ROF_TV, \
+ 'input' : u0,\
+ 'regularisation_parameter':0.04,\
+ 'number_of_iterations': 1200,\
+ 'time_marching_parameter': 0.0025
+ }
+ print ("#############ROF TV CPU####################")
+ start_time = timeit.default_timer()
+ rof_cpu = ROF_TV(pars['input'],
+ pars['regularisation_parameter'],
+ pars['number_of_iterations'],
+ pars['time_marching_parameter'],'cpu')
+ rms = rmse(Im, rof_cpu)
+ pars['rmse'] = rms
+
+ txtstr = printParametersToString(pars)
+ txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+ print (txtstr)
+ print ("##############ROF TV GPU##################")
+ start_time = timeit.default_timer()
+ rof_gpu = ROF_TV(pars['input'],
+ pars['regularisation_parameter'],
+ pars['number_of_iterations'],
+ pars['time_marching_parameter'],'gpu')
+
+ rms = rmse(Im, rof_gpu)
+ pars['rmse'] = rms
+ pars['algorithm'] = ROF_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(rof_cpu))
+ diff_im = abs(rof_cpu - rof_gpu)
+ diff_im[diff_im > tolerance] = 1
+
+ self.assertLessEqual(diff_im.sum() , 1)
+
+ def test_FGP_TV_CPU_vs_GPU(self):
+ 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()
+ fgp_gpu = FGP_TV(pars['input'],
+ pars['regularisation_parameter'],
+ pars['number_of_iterations'],
+ pars['tolerance_constant'],
+ pars['methodTV'],
+ pars['nonneg'],
+ pars['printingOut'],'gpu')
+
+ 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)
+
+ def test_SB_TV_CPU_vs_GPU(self):
+ 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 ("____________SB-TV bench___________________")
+ print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+
+ # set parameters
+ pars = {'algorithm' : SB_TV, \
+ 'input' : u0,\
+ 'regularisation_parameter':0.04, \
+ 'number_of_iterations' :150 ,\
+ 'tolerance_constant':1e-05,\
+ 'methodTV': 0 ,\
+ 'printingOut': 0
+ }
+
+ print ("#############SB-TV CPU####################")
+ start_time = timeit.default_timer()
+ sb_cpu = SB_TV(pars['input'],
+ pars['regularisation_parameter'],
+ pars['number_of_iterations'],
+ pars['tolerance_constant'],
+ pars['methodTV'],
+ pars['printingOut'],'cpu')
+
+
+ rms = rmse(Im, sb_cpu)
+ pars['rmse'] = rms
+
+ txtstr = printParametersToString(pars)
+ txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+ print (txtstr)
+
+ print ("##############SB TV GPU##################")
+ start_time = timeit.default_timer()
+ sb_gpu = SB_TV(pars['input'],
+ pars['regularisation_parameter'],
+ pars['number_of_iterations'],
+ pars['tolerance_constant'],
+ pars['methodTV'],
+ pars['printingOut'],'gpu')
+
+ rms = rmse(Im, sb_gpu)
+ pars['rmse'] = rms
+ pars['algorithm'] = SB_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(sb_cpu))
+ diff_im = abs(sb_cpu - sb_gpu)
+ diff_im[diff_im > tolerance] = 1
+ self.assertLessEqual(diff_im.sum(), 1)
+
+ def test_TGV_CPU_vs_GPU(self):
+ 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 ("____________TGV bench___________________")
+ print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+
+ # set parameters
+ pars = {'algorithm' : TGV, \
+ 'input' : u0,\
+ 'regularisation_parameter':0.04, \
+ 'alpha1':1.0,\
+ 'alpha0':0.7,\
+ 'number_of_iterations' :250 ,\
+ 'LipshitzConstant' :12 ,\
+ }
+
+ print ("#############TGV CPU####################")
+ start_time = timeit.default_timer()
+ tgv_cpu = TGV(pars['input'],
+ pars['regularisation_parameter'],
+ pars['alpha1'],
+ pars['alpha0'],
+ pars['number_of_iterations'],
+ pars['LipshitzConstant'],'cpu')
+
+ rms = rmse(Im, tgv_cpu)
+ pars['rmse'] = rms
+
+ txtstr = printParametersToString(pars)
+ txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+ print (txtstr)
+
+ print ("##############TGV GPU##################")
+ start_time = timeit.default_timer()
+ tgv_gpu = TGV(pars['input'],
+ pars['regularisation_parameter'],
+ pars['alpha1'],
+ pars['alpha0'],
+ pars['number_of_iterations'],
+ pars['LipshitzConstant'],'gpu')
+
+ rms = rmse(Im, tgv_gpu)
+ pars['rmse'] = rms
+ pars['algorithm'] = TGV
+ 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(tgv_gpu))
+ diff_im = abs(tgv_cpu - tgv_gpu)
+ diff_im[diff_im > tolerance] = 1
+ self.assertLessEqual(diff_im.sum() , 1)
+
+ def test_LLT_ROF_CPU_vs_GPU(self):
+ 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 ("____________LLT-ROF bench___________________")
+ print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+
+ # set parameters
+ pars = {'algorithm' : LLT_ROF, \
+ 'input' : u0,\
+ 'regularisation_parameterROF':0.04, \
+ 'regularisation_parameterLLT':0.01, \
+ 'number_of_iterations' :500 ,\
+ 'time_marching_parameter' :0.0025 ,\
+ }
+
+ print ("#############LLT- ROF CPU####################")
+ start_time = timeit.default_timer()
+ lltrof_cpu = LLT_ROF(pars['input'],
+ pars['regularisation_parameterROF'],
+ pars['regularisation_parameterLLT'],
+ pars['number_of_iterations'],
+ pars['time_marching_parameter'],'cpu')
+
+ rms = rmse(Im, lltrof_cpu)
+ pars['rmse'] = rms
+
+ txtstr = printParametersToString(pars)
+ txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+ print (txtstr)
+ print ("#############LLT- ROF GPU####################")
+ start_time = timeit.default_timer()
+ lltrof_gpu = LLT_ROF(pars['input'],
+ pars['regularisation_parameterROF'],
+ pars['regularisation_parameterLLT'],
+ pars['number_of_iterations'],
+ pars['time_marching_parameter'],'gpu')
+
+ rms = rmse(Im, lltrof_gpu)
+ pars['rmse'] = rms
+ pars['algorithm'] = LLT_ROF
+ 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(lltrof_gpu))
+ diff_im = abs(lltrof_cpu - lltrof_gpu)
+ diff_im[diff_im > tolerance] = 1
+ self.assertLessEqual(diff_im.sum(), 1)
+
+ def test_Diff4th_CPU_vs_GPU(self):
+ 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 ("_______________NDF bench___________________")
+ print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+
+ # set parameters
+ pars = {'algorithm' : NDF, \
+ 'input' : u0,\
+ 'regularisation_parameter':0.06, \
+ 'edge_parameter':0.04,\
+ 'number_of_iterations' :1000 ,\
+ 'time_marching_parameter':0.025,\
+ 'penalty_type': 1
+ }
+
+ print ("#############NDF CPU####################")
+ start_time = timeit.default_timer()
+ ndf_cpu = NDF(pars['input'],
+ pars['regularisation_parameter'],
+ pars['edge_parameter'],
+ pars['number_of_iterations'],
+ pars['time_marching_parameter'],
+ pars['penalty_type'],'cpu')
+
+ rms = rmse(Im, ndf_cpu)
+ pars['rmse'] = rms
+
+ txtstr = printParametersToString(pars)
+ txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+ print (txtstr)
+
+ print ("##############NDF GPU##################")
+ start_time = timeit.default_timer()
+ ndf_gpu = NDF(pars['input'],
+ pars['regularisation_parameter'],
+ pars['edge_parameter'],
+ pars['number_of_iterations'],
+ pars['time_marching_parameter'],
+ pars['penalty_type'],'gpu')
+
+ rms = rmse(Im, ndf_gpu)
+ pars['rmse'] = rms
+ pars['algorithm'] = NDF
+ 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(ndf_cpu))
+ diff_im = abs(ndf_cpu - ndf_gpu)
+ diff_im[diff_im > tolerance] = 1
+ self.assertLessEqual(diff_im.sum(), 1)
+
+
+ def test_Diff4th_CPU_vs_GPU(self):
+ 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 ("___Anisotropic Diffusion 4th Order (2D)____")
+ print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+ # set parameters
+ pars = {'algorithm' : DIFF4th, \
+ 'input' : u0,\
+ 'regularisation_parameter':3.5, \
+ 'edge_parameter':0.02,\
+ 'number_of_iterations' :500 ,\
+ 'time_marching_parameter':0.001
+ }
+
+ print ("#############Diff4th CPU####################")
+ start_time = timeit.default_timer()
+ diff4th_cpu = DIFF4th(pars['input'],
+ pars['regularisation_parameter'],
+ pars['edge_parameter'],
+ pars['number_of_iterations'],
+ pars['time_marching_parameter'],'cpu')
+
+ rms = rmse(Im, diff4th_cpu)
+ pars['rmse'] = rms
+
+ txtstr = printParametersToString(pars)
+ txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+ print (txtstr)
+ print ("##############Diff4th GPU##################")
+ start_time = timeit.default_timer()
+ diff4th_gpu = DIFF4th(pars['input'],
+ pars['regularisation_parameter'],
+ pars['edge_parameter'],
+ pars['number_of_iterations'],
+ pars['time_marching_parameter'], 'gpu')
+
+ rms = rmse(Im, diff4th_gpu)
+ pars['rmse'] = rms
+ pars['algorithm'] = DIFF4th
+ 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(diff4th_cpu))
+ diff_im = abs(diff4th_cpu - diff4th_gpu)
+ diff_im[diff_im > tolerance] = 1
+ self.assertLessEqual(diff_im.sum() , 1)
+ #%%
+ def test_FDGdTV_CPU_vs_GPU(self):
+ 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-dTV bench___________________")
+ print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+ # set parameters
+ pars = {'algorithm' : FGP_dTV, \
+ 'input' : u0,\
+ 'refdata' : u_ref,\
+ 'regularisation_parameter':0.04, \
+ 'number_of_iterations' :2000 ,\
+ 'tolerance_constant':1e-06,\
+ 'eta_const':0.2,\
+ 'methodTV': 0 ,\
+ 'nonneg': 0 ,\
+ 'printingOut': 0
+ }
+
+ print ("#############FGP dTV CPU####################")
+ start_time = timeit.default_timer()
+ fgp_dtv_cpu = FGP_dTV(pars['input'],
+ pars['refdata'],
+ pars['regularisation_parameter'],
+ pars['number_of_iterations'],
+ pars['tolerance_constant'],
+ pars['eta_const'],
+ pars['methodTV'],
+ pars['nonneg'],
+ pars['printingOut'],'cpu')
+
+
+ rms = rmse(Im, fgp_dtv_cpu)
+ pars['rmse'] = rms
+
+ txtstr = printParametersToString(pars)
+ txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+ print (txtstr)
+ print ("##############FGP dTV GPU##################")
+ start_time = timeit.default_timer()
+ fgp_dtv_gpu = FGP_dTV(pars['input'],
+ pars['refdata'],
+ pars['regularisation_parameter'],
+ pars['number_of_iterations'],
+ pars['tolerance_constant'],
+ pars['eta_const'],
+ pars['methodTV'],
+ pars['nonneg'],
+ pars['printingOut'],'gpu')
+ rms = rmse(Im, fgp_dtv_gpu)
+ pars['rmse'] = rms
+ pars['algorithm'] = FGP_dTV
+ 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_dtv_cpu))
+ diff_im = abs(fgp_dtv_cpu - fgp_dtv_gpu)
+ diff_im[diff_im > tolerance] = 1
+ self.assertLessEqual(diff_im.sum(), 1)
+ #%%
+
+ def test_cpu_ROF_TV(self):
+ #filename = os.path.join(".." , ".." , ".." , "data" ,"testLena.npy")
+
+ filename = os.path.join("lena_gray_512.tif")
+
+ plt = TiffReader()
+ # read image
+ Im = plt.imread(filename)
+ Im = np.asarray(Im, dtype='float32')
+
+ """
+ # read noiseless image
+ Im = plt.imread(filename)
+ Im = np.asarray(Im, dtype='float32')
+
+ Im = Im/255
+ """
+ tolerance = 1e-05
+ rms_rof_exp = 0.006812507 #expected value for ROF model
+ rms_fgp_exp = 0.019152347 #expected value for FGP model
+
+ # set parameters for ROF-TV
+ pars_rof_tv = {'algorithm': ROF_TV, \
+ 'input' : Im,\
+ 'regularisation_parameter':0.04,\
+ 'number_of_iterations': 50,\
+ 'time_marching_parameter': 0.0025
+ }
+ print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+ print ("_________testing ROF-TV (2D, CPU)__________")
+ print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+ res = True
+ rof_cpu = ROF_TV(pars_rof_tv['input'],
+ pars_rof_tv['regularisation_parameter'],
+ pars_rof_tv['number_of_iterations'],
+ pars_rof_tv['time_marching_parameter'],'cpu')
+ rms_rof = rmse(Im, rof_cpu)
+ # now compare obtained rms with the expected value
+ self.assertLess(abs(rms_rof-rms_rof_exp) , tolerance)
+ def test_cpu_FGP_TV(self):
+ #filename = os.path.join(".." , ".." , ".." , "data" ,"testLena.npy")
+
+ filename = os.path.join("lena_gray_512.tif")
+
+ plt = TiffReader()
+ # read image
+ Im = plt.imread(filename)
+ Im = np.asarray(Im, dtype='float32')
+
+ """
+ # read noiseless image
+ Im = plt.imread(filename)
+ Im = np.asarray(Im, dtype='float32')
+
+ Im = Im/255
+ """
+ tolerance = 1e-05
+ rms_rof_exp = 0.006812507 #expected value for ROF model
+ rms_fgp_exp = 0.019152347 #expected value for FGP model
+
+ pars_fgp_tv = {'algorithm' : FGP_TV, \
+ 'input' : Im,\
+ 'regularisation_parameter':0.04, \
+ 'number_of_iterations' :50 ,\
+ 'tolerance_constant':1e-08,\
+ 'methodTV': 0 ,\
+ 'nonneg': 0 ,\
+ 'printingOut': 0
+ }
+ print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+ print ("_________testing FGP-TV (2D, CPU)__________")
+ print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+ fgp_cpu = FGP_TV(pars_fgp_tv['input'],
+ pars_fgp_tv['regularisation_parameter'],
+ pars_fgp_tv['number_of_iterations'],
+ pars_fgp_tv['tolerance_constant'],
+ pars_fgp_tv['methodTV'],
+ pars_fgp_tv['nonneg'],
+ pars_fgp_tv['printingOut'],'cpu')
+ rms_fgp = rmse(Im, fgp_cpu)
+ # now compare obtained rms with the expected value
+ self.assertLess(abs(rms_fgp-rms_fgp_exp) , tolerance)
+
+ def test_gpu_ROF(self):
+ #filename = os.path.join(".." , ".." , ".." , "data" ,"testLena.npy")
+ 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
+ tolerance = 1e-05
+ rms_rof_exp = 0.006812507 #expected value for ROF model
+ rms_fgp_exp = 0.019152347 #expected value for FGP model
+
+ # set parameters for ROF-TV
+ pars_rof_tv = {'algorithm': ROF_TV, \
+ 'input' : Im,\
+ 'regularisation_parameter':0.04,\
+ 'number_of_iterations': 50,\
+ 'time_marching_parameter': 0.0025
+ }
+ print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+ print ("_________testing ROF-TV (2D, GPU)__________")
+ print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+ res = True
+ rof_gpu = ROF_TV(pars_rof_tv['input'],
+ pars_rof_tv['regularisation_parameter'],
+ pars_rof_tv['number_of_iterations'],
+ pars_rof_tv['time_marching_parameter'],'gpu')
+ rms_rof = rmse(Im, rof_gpu)
+ # now compare obtained rms with the expected value
+ self.assertLess(abs(rms_rof-rms_rof_exp) , tolerance)
+ def test_gpu_FGP(self):
+ #filename = os.path.join(".." , ".." , ".." , "data" ,"testLena.npy")
+ 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
+ tolerance = 1e-05
+ rms_rof_exp = 0.006812507 #expected value for ROF model
+ rms_fgp_exp = 0.019152347 #expected value for FGP model
+
+ # set parameters for FGP-TV
+ pars_fgp_tv = {'algorithm' : FGP_TV, \
+ 'input' : Im,\
+ 'regularisation_parameter':0.04, \
+ 'number_of_iterations' :50 ,\
+ 'tolerance_constant':1e-08,\
+ 'methodTV': 0 ,\
+ 'nonneg': 0 ,\
+ 'printingOut': 0
+ }
+ print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+ print ("_________testing FGP-TV (2D, GPU)__________")
+ print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+ fgp_gpu = FGP_TV(pars_fgp_tv['input'],
+ pars_fgp_tv['regularisation_parameter'],
+ pars_fgp_tv['number_of_iterations'],
+ pars_fgp_tv['tolerance_constant'],
+ pars_fgp_tv['methodTV'],
+ pars_fgp_tv['nonneg'],
+ pars_fgp_tv['printingOut'],'gpu')
+ rms_fgp = rmse(Im, fgp_gpu)
+ # now compare obtained rms with the expected value
+ self.assertLess(abs(rms_fgp-rms_fgp_exp) , tolerance)
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/Wrappers/Python/conda-recipe/run_test.py.in b/Wrappers/Python/conda-recipe/run_test.py.in deleted file mode 100644 index 9a6f4de..0000000 --- a/Wrappers/Python/conda-recipe/run_test.py.in +++ /dev/null @@ -1,148 +0,0 @@ -import unittest -import numpy as np -from ccpi.filters.regularisers import ROF_TV, FGP_TV - -def rmse(im1, im2): - rmse = np.sqrt(np.sum((im1 - im2) ** 2) / float(im1.size)) - return rmse - -class TestRegularisers(unittest.TestCase): - - def setUp(self): - pass - - def test_cpu_regularisers(self): - #filename = os.path.join(".." , ".." , ".." , "data" ,"testLena.npy") - - Im = np.load('testLena.npy'); - """ - # read noiseless image - Im = plt.imread(filename) - Im = np.asarray(Im, dtype='float32') - - Im = Im/255 - """ - tolerance = 1e-05 - rms_rof_exp = 0.006812507 #expected value for ROF model - rms_fgp_exp = 0.019152347 #expected value for FGP model - - # set parameters for ROF-TV - pars_rof_tv = {'algorithm': ROF_TV, \ - 'input' : Im,\ - 'regularisation_parameter':0.04,\ - 'number_of_iterations': 50,\ - 'time_marching_parameter': 0.0025 - } - # set parameters for FGP-TV - pars_fgp_tv = {'algorithm' : FGP_TV, \ - 'input' : Im,\ - 'regularisation_parameter':0.04, \ - 'number_of_iterations' :50 ,\ - 'tolerance_constant':1e-08,\ - 'methodTV': 0 ,\ - 'nonneg': 0 ,\ - 'printingOut': 0 - } - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - print ("_________testing ROF-TV (2D, CPU)__________") - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - res = True - rof_cpu = ROF_TV(pars_rof_tv['input'], - pars_rof_tv['regularisation_parameter'], - pars_rof_tv['number_of_iterations'], - pars_rof_tv['time_marching_parameter'],'cpu') - rms_rof = rmse(Im, rof_cpu) - # now compare obtained rms with the expected value - self.assertLess(abs(rms_rof-rms_rof_exp) , tolerance) - """ - if abs(rms_rof-self.rms_rof_exp) > self.tolerance: - raise TypeError('ROF-TV (2D, CPU) test FAILED') - else: - print ("test PASSED") - """ - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - print ("_________testing FGP-TV (2D, CPU)__________") - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - fgp_cpu = FGP_TV(pars_fgp_tv['input'], - pars_fgp_tv['regularisation_parameter'], - pars_fgp_tv['number_of_iterations'], - pars_fgp_tv['tolerance_constant'], - pars_fgp_tv['methodTV'], - pars_fgp_tv['nonneg'], - pars_fgp_tv['printingOut'],'cpu') - rms_fgp = rmse(Im, fgp_cpu) - # now compare obtained rms with the expected value - self.assertLess(abs(rms_fgp-rms_fgp_exp) , tolerance) - """ - if abs(rms_fgp-self.rms_fgp_exp) > self.tolerance: - raise TypeError('FGP-TV (2D, CPU) test FAILED') - else: - print ("test PASSED") - """ - self.assertTrue(res) - def test_gpu_regularisers(self): - #filename = os.path.join(".." , ".." , ".." , "data" ,"testLena.npy") - - Im = np.load('testLena.npy'); - - #Im = Im/255 - tolerance = 1e-05 - rms_rof_exp = 0.006812507 #expected value for ROF model - rms_fgp_exp = 0.019152347 #expected value for FGP model - - # set parameters for ROF-TV - pars_rof_tv = {'algorithm': ROF_TV, \ - 'input' : Im,\ - 'regularisation_parameter':0.04,\ - 'number_of_iterations': 50,\ - 'time_marching_parameter': 0.0025 - } - # set parameters for FGP-TV - pars_fgp_tv = {'algorithm' : FGP_TV, \ - 'input' : Im,\ - 'regularisation_parameter':0.04, \ - 'number_of_iterations' :50 ,\ - 'tolerance_constant':1e-08,\ - 'methodTV': 0 ,\ - 'nonneg': 0 ,\ - 'printingOut': 0 - } - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - print ("_________testing ROF-TV (2D, GPU)__________") - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - res = True - rof_gpu = ROF_TV(pars_rof_tv['input'], - pars_rof_tv['regularisation_parameter'], - pars_rof_tv['number_of_iterations'], - pars_rof_tv['time_marching_parameter'],'gpu') - rms_rof = rmse(Im, rof_gpu) - # now compare obtained rms with the expected value - self.assertLess(abs(rms_rof-rms_rof_exp) , tolerance) - """ - if abs(rms_rof-self.rms_rof_exp) > self.tolerance: - raise TypeError('ROF-TV (2D, GPU) test FAILED') - else: - print ("test PASSED") - """ - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - print ("_________testing FGP-TV (2D, GPU)__________") - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - fgp_gpu = FGP_TV(pars_fgp_tv['input'], - pars_fgp_tv['regularisation_parameter'], - pars_fgp_tv['number_of_iterations'], - pars_fgp_tv['tolerance_constant'], - pars_fgp_tv['methodTV'], - pars_fgp_tv['nonneg'], - pars_fgp_tv['printingOut'],'gpu') - rms_fgp = rmse(Im, fgp_gpu) - # now compare obtained rms with the expected value - self.assertLess(abs(rms_fgp-rms_fgp_exp) , tolerance) - """ - if abs(rms_fgp-self.rms_fgp_exp) > self.tolerance: - raise TypeError('FGP-TV (2D, GPU) test FAILED') - else: - print ("test PASSED") - """ - self.assertTrue(res) -if __name__ == '__main__': - unittest.main() diff --git a/Wrappers/Python/conda-recipe/testLena.npy b/Wrappers/Python/conda-recipe/testLena.npy Binary files differdeleted file mode 100644 index 14bc0e3..0000000 --- a/Wrappers/Python/conda-recipe/testLena.npy +++ /dev/null |