diff options
Diffstat (limited to 'test')
| -rw-r--r-- | test/lena_gray_512.tif | bin | 0 -> 262598 bytes | |||
| -rw-r--r-- | test/test_ROF_TV.py | 127 | ||||
| -rw-r--r-- | test/testroutines.py | 37 |
3 files changed, 164 insertions, 0 deletions
diff --git a/test/lena_gray_512.tif b/test/lena_gray_512.tif Binary files differnew file mode 100644 index 0000000..f80cafc --- /dev/null +++ b/test/lena_gray_512.tif diff --git a/test/test_ROF_TV.py b/test/test_ROF_TV.py new file mode 100644 index 0000000..dda38b7 --- /dev/null +++ b/test/test_ROF_TV.py @@ -0,0 +1,127 @@ +import unittest +import math +import os +import timeit +from ccpi.filters.regularisers import ROF_TV +#, FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, Diff4th +from testroutines import * + +class TestRegularisers(unittest.TestCase): + + def test_ROF_TV_CPU(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') + + + # set parameters + pars = {'algorithm': ROF_TV, \ + 'input': u0, \ + 'regularisation_parameter': 0.04, \ + 'number_of_iterations': 2500, \ + 'time_marching_parameter': 0.00002 + } + 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) + + self.assertTrue(math.isclose(rms,0.02067839,rel_tol=1e-2)) + + + 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 + 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': 2500, \ + 'time_marching_parameter': 0.00002 + } + print("##############ROF TV GPU##################") + start_time = timeit.default_timer() + try: + rof_gpu = ROF_TV(pars['input'], + pars['regularisation_parameter'], + pars['number_of_iterations'], + pars['time_marching_parameter'], 'gpu') + except ValueError as ve: + self.skipTest("Results not comparable. GPU computing error.") + + 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("#############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("--------Compare the results--------") + tolerance = 1e-04 + 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) + +if __name__ == '__main__': + unittest.main() diff --git a/test/testroutines.py b/test/testroutines.py new file mode 100644 index 0000000..8da5c5e --- /dev/null +++ b/test/testroutines.py @@ -0,0 +1,37 @@ +import numpy as np +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 + + +############################################################################### |