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| author | algol <dkazanc@hotmail.com> | 2018-05-04 11:58:55 +0100 |
|---|---|---|
| committer | algol <dkazanc@hotmail.com> | 2018-05-04 11:58:55 +0100 |
| commit | 50a968bab5220001c25b00926f59405faa5767bc (patch) | |
| tree | 00649907c8c109241816ede000f3c7b14b6dc0df /Wrappers/Python | |
| parent | 74ec16b72b077ea93c6e23330f8dfa4a7c3f7749 (diff) | |
| download | regularization-50a968bab5220001c25b00926f59405faa5767bc.tar.gz regularization-50a968bab5220001c25b00926f59405faa5767bc.tar.bz2 regularization-50a968bab5220001c25b00926f59405faa5767bc.tar.xz regularization-50a968bab5220001c25b00926f59405faa5767bc.zip | |
energy bug fixed
Diffstat (limited to 'Wrappers/Python')
| -rw-r--r-- | Wrappers/Python/src/cpu_regularisers.pyx | 15 |
1 files changed, 9 insertions, 6 deletions
diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx index bb55df5..bdb1eff 100644 --- a/Wrappers/Python/src/cpu_regularisers.pyx +++ b/Wrappers/Python/src/cpu_regularisers.pyx @@ -449,15 +449,17 @@ def NVM_INP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, #****************************************************************# #***************Calculation of TV-energy functional**************# #****************************************************************# -def TV_ENERGY(inputData, regularisation_parameter, typeFunctional): +def TV_ENERGY(inputData, inputData0, regularisation_parameter, typeFunctional): if inputData.ndim == 2: - return TV_ENERGY_2D(inputData, regularisation_parameter, typeFunctional) + return TV_ENERGY_2D(inputData, inputData0, regularisation_parameter, typeFunctional) elif inputData.ndim == 3: - return TV_ENERGY_3D(inputData, regularisation_parameter, typeFunctional) + return TV_ENERGY_3D(inputData, inputData0, regularisation_parameter, typeFunctional) def TV_ENERGY_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, + np.ndarray[np.float32_t, ndim=2, mode="c"] inputData0, float regularisation_parameter, int typeFunctional): + cdef long dims[2] dims[0] = inputData.shape[0] dims[1] = inputData.shape[1] @@ -466,11 +468,12 @@ def TV_ENERGY_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, np.zeros([1], dtype='float32') # run function - TV_energy2D(&inputData[0,0], &outputData[0], regularisation_parameter, typeFunctional, dims[1], dims[0]) + TV_energy2D(&inputData[0,0], &inputData0[0,0], &outputData[0], regularisation_parameter, typeFunctional, dims[1], dims[0]) return outputData -def TV_ENERGY_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, +def TV_ENERGY_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, + np.ndarray[np.float32_t, ndim=3, mode="c"] inputData0, float regularisation_parameter, int typeFunctional): @@ -483,6 +486,6 @@ def TV_ENERGY_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, np.zeros([1], dtype='float32') # Run function - TV_energy3D(&inputData[0,0,0], &outputData[0], regularisation_parameter, typeFunctional, dims[2], dims[1], dims[0]) + TV_energy3D(&inputData[0,0,0], &inputData0[0,0,0], &outputData[0], regularisation_parameter, typeFunctional, dims[2], dims[1], dims[0]) return outputData |