GPU regularizers fixed, the demo runs smoothly

2 files changed, 48 insertions, 44 deletions

diff --git a/Wrappers/Python/src/gpu_regularizers.pyx b/Wrappers/Python/src/gpu_regularizers.pyx
index dc625c3..84fc30a 100644
--- a/Wrappers/Python/src/gpu_regularizers.pyx
+++ b/Wrappers/Python/src/gpu_regularizers.pyx
@@ -31,19 +31,22 @@ cdef extern float pad_crop(float *A, float *Ap,
                            int padXY, int switchpad_crop);
 
 def Diff4thHajiaboli(inputData, 
-                     regularization_parameter, 
+                     edge_preserv_parameter, 
                      iterations, 
-                     edge_preserving_parameter):
+                     time_marching_parameter,
+                     regularization_parameter):
     if inputData.ndim == 2:
         return Diff4thHajiaboli2D(inputData,  
-                     regularization_parameter, 
+                     edge_preserv_parameter, 
                      iterations, 
-                     edge_preserving_parameter)
+                     time_marching_parameter,
+                     regularization_parameter)
     elif inputData.ndim == 3:
         return Diff4thHajiaboli3D(inputData,  
-                     regularization_parameter, 
+                     edge_preserv_parameter, 
                      iterations, 
-                     edge_preserving_parameter)
+                     time_marching_parameter,
+                     regularization_parameter)
         
 def NML(inputData, 
                      SearchW_real, 
@@ -64,9 +67,10 @@ def NML(inputData,
                      lambdaf)
                     
 def Diff4thHajiaboli2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
-                     float regularization_parameter, 
+                     float edge_preserv_parameter, 
                      int iterations, 
-                     float edge_preserving_parameter):
+                     float time_marching_parameter,
+                     float regularization_parameter):
     
     cdef long dims[2]
     dims[0] = inputData.shape[0]
@@ -74,9 +78,6 @@ def Diff4thHajiaboli2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
     N = dims[0] + 2;
     M = dims[1] + 2;
     
-    #time step is sufficiently small for an explicit methods
-    tau = 0.01
-    
     #A_L = (float*)mxGetData(mxCreateNumericMatrix(N, M, mxSINGLE_CLASS, mxREAL));
     #B_L = (float*)mxGetData(mxCreateNumericMatrix(N, M, mxSINGLE_CLASS, mxREAL));
     cdef np.ndarray[np.float32_t, ndim=2, mode="c"] A_L = \
@@ -102,9 +103,9 @@ def Diff4thHajiaboli2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
             #<float*> A_L.data, <float*> B_L.data,
             &A_L[0,0], &B_L[0,0], 
                        N, M, 0, 
-                       edge_preserving_parameter,
+                       edge_preserv_parameter,
                        iterations , 
-                       tau, 
+                       time_marching_parameter, 
                        regularization_parameter);
     # copy the processed B_L to a smaller B
     for i in range(N):
@@ -120,9 +121,10 @@ def Diff4thHajiaboli2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
     return B
 
 def Diff4thHajiaboli3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, 
-                     float regularization_parameter, 
+                     float edge_preserv_parameter, 
                      int iterations, 
-                     float edge_preserving_parameter):
+                     float time_marching_parameter,
+                     float regularization_parameter):
     
     cdef long dims[3]
     dims[0] = inputData.shape[0]
@@ -132,8 +134,6 @@ def Diff4thHajiaboli3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
     M = dims[1] + 2
     Z = dims[2] + 2
     
-    # Time Step is small for an explicit methods
-    tau = 0.0007;
     
     #A_L = (float*)mxGetData(mxCreateNumericMatrix(N, M, mxSINGLE_CLASS, mxREAL));
     #B_L = (float*)mxGetData(mxCreateNumericMatrix(N, M, mxSINGLE_CLASS, mxREAL));
@@ -162,9 +162,9 @@ def Diff4thHajiaboli3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
             #<float*> A_L.data, <float*> B_L.data,
             &A_L[0,0,0], &B_L[0,0,0], 
                        N, M, Z, 
-                       edge_preserving_parameter,
+                       edge_preserv_parameter,
                        iterations , 
-                       tau, 
+                       time_marching_parameter, 
                        regularization_parameter);
     # copy the processed B_L to a smaller B
     for i in range(N):
diff --git a/Wrappers/Python/test/test_gpu_regularizers.py b/Wrappers/Python/test/test_gpu_regularizers.py
index 18fbdd3..735a25d 100644
--- a/Wrappers/Python/test/test_gpu_regularizers.py
+++ b/Wrappers/Python/test/test_gpu_regularizers.py
@@ -40,7 +40,8 @@ filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif")
 Im = plt.imread(filename)                     
 Im = np.asarray(Im, dtype='float32')
 
-perc = 0.15
+Im = Im/255
+perc = 0.075
 u0 = Im + np.random.normal(loc = Im ,
                                   scale = perc * Im , 
                                   size = np.shape(Im))
@@ -53,49 +54,52 @@ fig = plt.figure()
 
 a=fig.add_subplot(2,3,1)
 a.set_title('noise')
-imgplot = plt.imshow(u0#,cmap="gray"
-                     )
+imgplot = plt.imshow(u0,cmap="gray")
 
 
 ## Diff4thHajiaboli
 start_time = timeit.default_timer()
 pars = {'algorithm' : Diff4thHajiaboli , \
         'input' : u0,
-        'regularization_parameter':0.02 , \
-'number_of_iterations' :150 ,\
-'edge_preserving_parameter':0.001 
+        'edge_preserv_parameter':0.1 , \
+'number_of_iterations' :250 ,\
+'time_marching_parameter':0.03 ,\
+'regularization_parameter':0.7
 }
+
+
 d4h = Diff4thHajiaboli(pars['input'], 
-                     pars['regularization_parameter'], 
+                     pars['edge_preserv_parameter'], 
                      pars['number_of_iterations'], 
-                     pars['edge_preserving_parameter'])
+                     pars['time_marching_parameter'],
+                     pars['regularization_parameter'])
+rms = rmse(Im, d4h)
+pars['rmse'] = rms
 txtstr = printParametersToString(pars)
 txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
 print (txtstr)
 a=fig.add_subplot(2,3,2)
 
 # these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
 # place a text box in upper left in axes coords
-a.text(0.05, 0.95, txtstr, transform=a.transAxes, fontsize=14,
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=12,
          verticalalignment='top', bbox=props)
-imgplot = plt.imshow(d4h #, cmap="gray"
-                     )
+imgplot = plt.imshow(d4h, cmap="gray")
 
 a=fig.add_subplot(2,3,5)
 
 # these are matplotlib.patch.Patch properties
 props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
 # place a text box in upper left in axes coords
-a.text(0.05, 0.95, 'd4h - u0', transform=a.transAxes, fontsize=14,
+a.text(0.05, 0.95, 'd4h - u0', transform=a.transAxes, fontsize=12,
          verticalalignment='top', bbox=props)
-imgplot = plt.imshow(d4h - u0 #, cmap="gray"
-                     )
+imgplot = plt.imshow((d4h - u0)**2, cmap="gray")
 
 
 ## Patch Based Regul NML
 start_time = timeit.default_timer()
-
+"""
 pars = {'algorithm' : NML , \
         'input' : u0,
         'SearchW_real':3 , \
@@ -103,13 +107,15 @@ pars = {'algorithm' : NML , \
 'h':0.05 ,#
 'lambda' : 0.08
 }
+"""
 pars = {
         'input' : u0,
-        'regularization_parameter': 0.05,\
+        'regularization_parameter': 0.01,\
         'searching_window_ratio':3, \
         'similarity_window_ratio':1,\
-        'PB_filtering_parameter': 0.06
+        'PB_filtering_parameter': 0.2
 }
+
 nml = NML(pars['input'], 
                      pars['searching_window_ratio'], 
                      pars['similarity_window_ratio'], 
@@ -123,12 +129,11 @@ print (txtstr)
 a=fig.add_subplot(2,3,3)
 
 # these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
 # place a text box in upper left in axes coords
-a.text(0.05, 0.95, txtstr, transform=a.transAxes, fontsize=14,
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=12,
          verticalalignment='top', bbox=props)
-imgplot = plt.imshow(nml #, cmap="gray"
-                     )
+imgplot = plt.imshow(nml, cmap="gray")
 
 a=fig.add_subplot(2,3,6)
 
@@ -137,8 +142,7 @@ props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
 # place a text box in upper left in axes coords
 a.text(0.05, 0.95, 'nml - u0', transform=a.transAxes, fontsize=14,
          verticalalignment='top', bbox=props)
-imgplot = plt.imshow(nml - u0 #, cmap="gray"
-                     )
+imgplot = plt.imshow((nml - u0)**2, cmap="gray")
 
 plt.show()
-        
\ No newline at end of file
+