corrections to dimens issues

4 files changed, 49 insertions, 12 deletions

diff --git a/Wrappers/Python/demos/demo_cpu_inpainters.py b/Wrappers/Python/demos/demo_cpu_inpainters.py
index 9a677c4..348d235 100644
--- a/Wrappers/Python/demos/demo_cpu_inpainters.py
+++ b/Wrappers/Python/demos/demo_cpu_inpainters.py
@@ -37,12 +37,14 @@ Mask = sino.get('Mask')
 sino_full = sino_full/np.max(sino_full)
 #apply mask to sinogram
 sino_cut = sino_full*(1-Mask)
-sino_cut_new = np.zeros((angles_dim,detectors_dim),'float32')
+#sino_cut_new = np.zeros((angles_dim,detectors_dim),'float32')
 #sino_cut_new = sino_cut.copy(order='c')
-sino_cut_new[:] = sino_cut[:]
-mask = np.zeros((angles_dim,detectors_dim),'uint8')
+#sino_cut_new[:] = sino_cut[:]
+sino_cut_new = np.ascontiguousarray(sino_cut, dtype=np.float32);
+#mask = np.zeros((angles_dim,detectors_dim),'uint8')
 #mask =Mask.copy(order='c')
-mask[:] = Mask[:]
+#mask[:] = Mask[:]
+mask = np.ascontiguousarray(Mask, dtype=np.uint8);
 
 plt.figure(1)
 plt.subplot(121)
@@ -68,11 +70,11 @@ imgplot = plt.imshow(sino_cut_new,cmap="gray")
 pars = {'algorithm' : NDF_INP, \
         'input' : sino_cut_new,\
         'maskData' : mask,\
-        'regularisation_parameter':1000,\
-        'edge_parameter':0.0,\
+        'regularisation_parameter':5000,\
+        'edge_parameter':0,\
         'number_of_iterations' :1000 ,\
         'time_marching_parameter':0.000075,\
-        'penalty_type':1
+        'penalty_type':0
         }
         
 start_time = timeit.default_timer()
@@ -163,7 +165,7 @@ pars = {'algorithm' : NVM_INP, \
         'input' : sino_cut_new,\
         'maskData' : mask,\
         'SW_increment': 1,\
-        'number_of_iterations' :20
+        'number_of_iterations' :0
         }
         
 start_time = timeit.default_timer()
diff --git a/Wrappers/Python/demos/demo_cpu_regularisers.py b/Wrappers/Python/demos/demo_cpu_regularisers.py
index 3567f91..f803870 100644
--- a/Wrappers/Python/demos/demo_cpu_regularisers.py
+++ b/Wrappers/Python/demos/demo_cpu_regularisers.py
@@ -50,7 +50,24 @@ u_ref = Im + np.random.normal(loc = 0 ,
 u0 = u0.astype('float32')
 u_ref = u_ref.astype('float32')
 
+# change dims to check that modules work with non-squared images
+(N,M) = np.shape(u0)
+u_ref2 = np.zeros([N,M-100],dtype='float32')
+u_ref2[:,0:M-100] = u_ref[:,0:M-100]
+u_ref = u_ref2
+del u_ref2
+
+u02 = np.zeros([N,M-100],dtype='float32')
+u02[:,0:M-100] = u0[:,0:M-100]
+u0 = u02
+del u02
+
+Im2 = np.zeros([N,M-100],dtype='float32')
+Im2[:,0:M-100] = Im[:,0:M-100]
+Im = Im2
+del Im2
 
+#%%
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 print ("_______________ROF-TV (2D)_________________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
diff --git a/Wrappers/Python/demos/demo_gpu_regularisers.py b/Wrappers/Python/demos/demo_gpu_regularisers.py
index b873700..dfdceee 100644
--- a/Wrappers/Python/demos/demo_gpu_regularisers.py
+++ b/Wrappers/Python/demos/demo_gpu_regularisers.py
@@ -49,6 +49,22 @@ u_ref = Im + np.random.normal(loc = 0 ,
 u0 = u0.astype('float32')
 u_ref = u_ref.astype('float32')
 
+(N,M) = np.shape(u0)
+u_ref2 = np.zeros([N,M-100],dtype='float32')
+u_ref2[:,0:M-100] = u_ref[:,0:M-100]
+u_ref = u_ref2
+del u_ref2
+
+u02 = np.zeros([N,M-100],dtype='float32')
+u02[:,0:M-100] = u0[:,0:M-100]
+u0 = u02
+del u02
+
+Im2 = np.zeros([N,M-100],dtype='float32')
+Im2[:,0:M-100] = Im[:,0:M-100]
+Im = Im2
+del Im2
+
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 print ("____________ROF-TV regulariser_____________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx
index 52befd7..21a1a00 100644
--- a/Wrappers/Python/src/cpu_regularisers.pyx
+++ b/Wrappers/Python/src/cpu_regularisers.pyx
@@ -49,7 +49,7 @@ def TV_ROF_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
             np.zeros([dims[0],dims[1]], dtype='float32')
                    
     # Run ROF iterations for 2D data 
-    TV_ROF_CPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, iterationsNumb, marching_step_parameter, dims[0], dims[1], 1)
+    TV_ROF_CPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, iterationsNumb, marching_step_parameter, dims[1], dims[0], 1)
     
     return outputData
             
@@ -333,18 +333,20 @@ def NDF_INPAINT_CPU(inputData, maskData, regularisation_parameter, edge_paramete
         return NDF_INP_3D(inputData, maskData, regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type)
 
 def NDF_INP_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=2, mode="c"] maskData,
                      float regularisation_parameter,
                      float edge_parameter,
                      int iterationsNumb,
                      float time_marching_parameter,
                      int penalty_type):
+
     cdef long dims[2]
     dims[0] = inputData.shape[0]
     dims[1] = inputData.shape[1]
-    
+
+
     cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \
-            np.zeros([dims[0],dims[1]], dtype='float32')   
+            np.zeros([dims[0],dims[1]], dtype='float32')
     
     # Run Inpaiting by Diffusion iterations for 2D data 
     Diffusion_Inpaint_CPU_main(&inputData[0,0], &maskData[0,0], &outputData[0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[0], dims[1], 1)