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-rw-r--r--src/Python/ccpi/filters/regularisers.py45
-rw-r--r--src/Python/src/cpu_regularisers.pyx76
2 files changed, 46 insertions, 75 deletions
diff --git a/src/Python/ccpi/filters/regularisers.py b/src/Python/ccpi/filters/regularisers.py
index 610907d..2fee8b3 100644
--- a/src/Python/ccpi/filters/regularisers.py
+++ b/src/Python/ccpi/filters/regularisers.py
@@ -2,7 +2,7 @@
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, NDF_MASK_CPU, Diff4th_CPU, TGV_CPU, LLT_ROF_CPU, PATCHSEL_CPU, NLTV_CPU
+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, MASK_CORR_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
@@ -127,37 +127,6 @@ def NDF(inputData, regularisation_parameter, edge_parameter, iterations,
raise ValueError ('GPU is not available')
raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
.format(device))
-def NDF_MASK(inputData, maskdata, select_classes, total_classesNum, diffuswindow, regularisation_parameter, edge_parameter, iterations,
- time_marching_parameter, penalty_type, tolerance_param, device='cpu'):
- if device == 'cpu':
- return NDF_MASK_CPU(inputData,
- maskdata,
- select_classes,
- total_classesNum,
- diffuswindow,
- regularisation_parameter,
- edge_parameter,
- iterations,
- time_marching_parameter,
- penalty_type,
- tolerance_param)
- elif device == 'gpu' and gpu_enabled:
- return NDF_MASK_CPU(inputData,
- maskdata,
- select_classes,
- total_classesNum,
- diffuswindow,
- regularisation_parameter,
- edge_parameter,
- iterations,
- time_marching_parameter,
- penalty_type,
- tolerance_param)
- 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, tolerance_param, device='cpu'):
if device == 'cpu':
@@ -243,3 +212,15 @@ def NDF_INP(inputData, maskData, regularisation_parameter, edge_parameter, itera
def NVM_INP(inputData, maskData, SW_increment, iterations):
return NVM_INPAINT_CPU(inputData, maskData, SW_increment, iterations)
+
+def MASK_CORR(maskdata, select_classes, total_classesNum, CorrectionWindow, device='cpu'):
+ if device == 'cpu':
+ return MASK_CORR_CPU(maskdata, select_classes, total_classesNum, CorrectionWindow)
+ elif device == 'gpu' and gpu_enabled:
+ return MASK_CORR_CPU(maskdata, select_classes, total_classesNum, CorrectionWindow)
+ 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))
+
diff --git a/src/Python/src/cpu_regularisers.pyx b/src/Python/src/cpu_regularisers.pyx
index 78c46e2..a63ecfa 100644
--- a/src/Python/src/cpu_regularisers.pyx
+++ b/src/Python/src/cpu_regularisers.pyx
@@ -24,7 +24,7 @@ cdef extern float SB_TV_CPU_main(float *Input, float *Output, float *infovector,
cdef extern float LLT_ROF_CPU_main(float *Input, float *Output, float *infovector, float lambdaROF, float lambdaLLT, int iterationsNumb, float tau, float epsil, int dimX, int dimY, int dimZ);
cdef extern float TGV_main(float *Input, float *Output, float *infovector, float lambdaPar, float alpha1, float alpha0, int iterationsNumb, float L2, float epsil, int dimX, int dimY, int dimZ);
cdef extern float Diffusion_CPU_main(float *Input, float *Output, float *infovector, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, float epsil, int dimX, int dimY, int dimZ);
-cdef extern float DiffusionMASK_CPU_main(float *Input, unsigned char *MASK, unsigned char *MASK_upd, unsigned char *SelClassesList, int SelClassesList_length, float *Output, float *infovector, int classesNumb, int DiffusWindow, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, float epsil, int dimX, int dimY, int dimZ);
+cdef extern float Mask_merge_main(unsigned char *MASK, unsigned char *MASK_upd, unsigned char *CORRECTEDRegions, unsigned char *SelClassesList, int SelClassesList_length, int classesNumb, int CorrectionWindow, int dimX, int dimY, int dimZ);
cdef extern float Diffus4th_CPU_main(float *Input, float *Output, float *infovector, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, float epsil, int dimX, int dimY, int dimZ);
cdef extern float dTV_FGP_CPU_main(float *Input, float *InputRef, float *Output, float *infovector, float lambdaPar, int iterationsNumb, float epsil, float eta, int methodTV, int nonneg, int dimX, int dimY, int dimZ);
cdef extern float TNV_CPU_main(float *Input, float *u, float lambdaPar, int maxIter, float tol, int dimX, int dimY, int dimZ);
@@ -382,48 +382,6 @@ def NDF_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
return (outputData,infovec)
#****************************************************************#
-#********Constrained Nonlinear(Isotropic) Diffusion**************#
-#****************************************************************#
-def NDF_MASK_CPU(inputData, maskData, select_classes, total_classesNum, diffuswindow, regularisation_parameter, edge_parameter, iterationsNumb,time_marching_parameter, penalty_type, tolerance_param):
- if inputData.ndim == 2:
- return NDF_MASK_2D(inputData, maskData, select_classes, total_classesNum, diffuswindow, regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, tolerance_param)
- elif inputData.ndim == 3:
- return 0
-
-def NDF_MASK_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
- np.ndarray[np.uint8_t, ndim=2, mode="c"] maskData,
- np.ndarray[np.uint8_t, ndim=1, mode="c"] select_classes,
- int total_classesNum,
- int diffuswindow,
- float regularisation_parameter,
- float edge_parameter,
- int iterationsNumb,
- float time_marching_parameter,
- int penalty_type,
- float tolerance_param):
- cdef long dims[2]
- dims[0] = inputData.shape[0]
- dims[1] = inputData.shape[1]
-
- select_classes_length = select_classes.shape[0]
-
- cdef np.ndarray[np.uint8_t, ndim=2, mode="c"] mask_upd = \
- np.zeros([dims[0],dims[1]], dtype='uint8')
- cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \
- np.zeros([dims[0],dims[1]], dtype='float32')
- cdef np.ndarray[np.float32_t, ndim=1, mode="c"] infovec = \
- np.zeros([2], dtype='float32')
-
-
- # Run constrained nonlinear diffusion iterations for 2D data
- DiffusionMASK_CPU_main(&inputData[0,0], &maskData[0,0], &mask_upd[0,0], &select_classes[0], select_classes_length, &outputData[0,0], &infovec[0],
- total_classesNum, diffuswindow, regularisation_parameter, edge_parameter, iterationsNumb,
- time_marching_parameter, penalty_type,
- tolerance_param,
- dims[1], dims[0], 1)
- return (mask_upd,outputData,infovec)
-
-#****************************************************************#
#*************Anisotropic Fourth-Order diffusion*****************#
#****************************************************************#
def Diff4th_CPU(inputData, regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter,tolerance_param):
@@ -736,6 +694,38 @@ def NVM_INP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
return (outputData, maskData_upd)
+##############################################################################
+#****************************************************************#
+#********Mask (segmented image) correction module **************#
+#****************************************************************#
+def MASK_CORR_CPU(maskData, select_classes, total_classesNum, CorrectionWindow):
+ if maskData.ndim == 2:
+ return MASK_CORR_CPU_2D(maskData, select_classes, total_classesNum, CorrectionWindow)
+ elif maskData.ndim == 3:
+ return 0
+
+def MASK_CORR_CPU_2D(np.ndarray[np.uint8_t, ndim=2, mode="c"] maskData,
+ np.ndarray[np.uint8_t, ndim=1, mode="c"] select_classes,
+ int total_classesNum,
+ int CorrectionWindow):
+
+ cdef long dims[2]
+ dims[0] = maskData.shape[0]
+ dims[1] = maskData.shape[1]
+
+ select_classes_length = select_classes.shape[0]
+
+ cdef np.ndarray[np.uint8_t, ndim=2, mode="c"] mask_upd = \
+ np.zeros([dims[0],dims[1]], dtype='uint8')
+ cdef np.ndarray[np.uint8_t, ndim=2, mode="c"] corr_regions = \
+ np.zeros([dims[0],dims[1]], dtype='uint8')
+
+ # Run the function to process given MASK
+ Mask_merge_main(&maskData[0,0], &mask_upd[0,0], &corr_regions[0,0], &select_classes[0], select_classes_length,
+ total_classesNum, CorrectionWindow, dims[1], dims[0], 1)
+ return (mask_upd,corr_regions)
+
+##############################################################################
#****************************************************************#
#***************Calculation of TV-energy functional**************#
generated by cgit v1.2.3 (git 2.25.1) at 2025-11-04 06:39:01 +0000