dTV some bugs in cython

8 files changed, 27 insertions, 22 deletions

diff --git a/Wrappers/Python/ccpi/filters/regularisers.py b/Wrappers/Python/ccpi/filters/regularisers.py
index c6723fa..376cc9c 100644
--- a/Wrappers/Python/ccpi/filters/regularisers.py
+++ b/Wrappers/Python/ccpi/filters/regularisers.py
@@ -2,8 +2,8 @@
 script which assigns a proper device core function based on a flag ('cpu' or 'gpu')
 """
 
-from ccpi.filters.cpu_regularisers_cython import TV_ROF_CPU, TV_FGP_CPU dTV_FGP_CPU
-from ccpi.filters.gpu_regularisers import TV_ROF_GPU, TV_FGP_GPU dTV_FGP_GPU
+from ccpi.filters.cpu_regularisers_cython import TV_ROF_CPU, TV_FGP_CPU, dTV_FGP_CPU
+from ccpi.filters.gpu_regularisers import TV_ROF_GPU, TV_FGP_GPU, dTV_FGP_GPU
 
 def ROF_TV(inputData, regularisation_parameter, iterations,
                      time_marching_parameter,device='cpu'):
diff --git a/Wrappers/Python/conda-recipe/run_test.py b/Wrappers/Python/conda-recipe/run_test.py.in
index 04bbd40..9a6f4de 100644
--- a/Wrappers/Python/conda-recipe/run_test.py
+++ b/Wrappers/Python/conda-recipe/run_test.py.in
@@ -1,8 +1,6 @@
 import unittest
 import numpy as np
-import os
 from ccpi.filters.regularisers import ROF_TV, FGP_TV
-import matplotlib.pyplot as plt
 
 def rmse(im1, im2):
     rmse = np.sqrt(np.sum((im1 - im2) ** 2) / float(im1.size))
@@ -14,13 +12,16 @@ class TestRegularisers(unittest.TestCase):
         pass
 
     def test_cpu_regularisers(self):
-        filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif")
+        #filename = os.path.join(".." , ".." , ".." , "data" ,"testLena.npy")
         
+        Im = np.load('testLena.npy');
+        """
         # read noiseless image
         Im = plt.imread(filename)
         Im = np.asarray(Im, dtype='float32')
 
         Im = Im/255
+        """
         tolerance = 1e-05
         rms_rof_exp = 0.006812507 #expected value for ROF model
         rms_fgp_exp = 0.019152347 #expected value for FGP model
@@ -80,13 +81,11 @@ class TestRegularisers(unittest.TestCase):
         """
         self.assertTrue(res)
     def test_gpu_regularisers(self):
-        filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif")
+        #filename = os.path.join(".." , ".." , ".." , "data" ,"testLena.npy")
         
-        # read noiseless image
-        Im = plt.imread(filename)
-        Im = np.asarray(Im, dtype='float32')
+        Im = np.load('testLena.npy');
 
-        Im = Im/255
+        #Im = Im/255
         tolerance = 1e-05
         rms_rof_exp = 0.006812507 #expected value for ROF model
         rms_fgp_exp = 0.019152347 #expected value for FGP model
@@ -146,4 +145,4 @@ class TestRegularisers(unittest.TestCase):
         """
         self.assertTrue(res)
 if __name__ == '__main__':
-    unittest.main()
\ No newline at end of file
+    unittest.main()
diff --git a/Wrappers/Python/conda-recipe/testLena.npy b/Wrappers/Python/conda-recipe/testLena.npy
new file mode 100644
index 0000000..14bc0e3
--- /dev/null
+++ b/Wrappers/Python/conda-recipe/testLena.npy
diff --git a/Wrappers/Python/demos/demo_cpu_regularisers.py b/Wrappers/Python/demos/demo_cpu_regularisers.py
index fd3050c..00beb0b 100644
--- a/Wrappers/Python/demos/demo_cpu_regularisers.py
+++ b/Wrappers/Python/demos/demo_cpu_regularisers.py
@@ -22,6 +22,8 @@ def printParametersToString(pars):
                 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'
@@ -196,7 +198,7 @@ plt.title('{}'.format('CPU results'))
 
 # Uncomment to test 3D regularisation performance 
 #%%
-
+"""
 N = 512
 slices = 20
 
@@ -318,8 +320,8 @@ a.set_title('Noisy Image')
 imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
 
 # set parameters
-pars = {'algorithm' : FGP_dTV, \
-        'input' : noisyVol,\ 
+pars = {'algorithm' : FGP_dTV,\
+        'input' : noisyVol,\
         'refdata' : noisyRef,\
         'regularisation_parameter':0.04, \
         'number_of_iterations' :300 ,\
@@ -358,4 +360,5 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
          verticalalignment='top', bbox=props)
 imgplot = plt.imshow(fgp_dTV_cpu3D[10,:,:], cmap="gray")
 plt.title('{}'.format('Recovered volume on the CPU using FGP-dTV'))
+"""
 #%%
diff --git a/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py b/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py
index aa1f865..310cf75 100644
--- a/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py
+++ b/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py
@@ -22,6 +22,8 @@ def printParametersToString(pars):
                 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'
diff --git a/Wrappers/Python/demos/demo_gpu_regularisers.py b/Wrappers/Python/demos/demo_gpu_regularisers.py
index 4759cc3..24a3c88 100644
--- a/Wrappers/Python/demos/demo_gpu_regularisers.py
+++ b/Wrappers/Python/demos/demo_gpu_regularisers.py
@@ -22,6 +22,8 @@ def printParametersToString(pars):
                 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'
@@ -192,7 +194,7 @@ plt.title('{}'.format('GPU results'))
 
 # Uncomment to test 3D regularisation performance 
 #%%
-
+"""
 N = 512
 slices = 20
 
@@ -314,7 +316,7 @@ imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
 
 # set parameters
 pars = {'algorithm' : FGP_dTV, \
-        'input' : noisyVol,\ 
+        'input' : noisyVol,\
         'refdata' : noisyRef,\
         'regularisation_parameter':0.04, \
         'number_of_iterations' :300 ,\
@@ -352,5 +354,5 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
          verticalalignment='top', bbox=props)
 imgplot = plt.imshow(fgp_dTV_gpu3D[10,:,:], cmap="gray")
 plt.title('{}'.format('Recovered volume on the GPU using FGP-dTV'))
-
+"""
 #%%
diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx
index 8f9185a..1661375 100644
--- a/Wrappers/Python/src/cpu_regularisers.pyx
+++ b/Wrappers/Python/src/cpu_regularisers.pyx
@@ -156,8 +156,8 @@ def dTV_FGP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
     dTV_FGP_CPU_main(&inputData[0,0], &refdata[0,0], &outputData[0,0], regularisation_parameter, 
                        iterationsNumb, 
                        tolerance_param,
-                       methodTV,
                        eta_const,
+                       methodTV,                       
                        nonneg,
                        printM,
                        dims[0], dims[1], 1)
diff --git a/Wrappers/Python/src/gpu_regularisers.pyx b/Wrappers/Python/src/gpu_regularisers.pyx
index 4a14f69..18efdcd 100644
--- a/Wrappers/Python/src/gpu_regularisers.pyx
+++ b/Wrappers/Python/src/gpu_regularisers.pyx
@@ -20,7 +20,7 @@ cimport numpy as np
 
 cdef extern void TV_ROF_GPU_main(float* Input, float* Output, float lambdaPar, int iter, float tau, int N, int M, int Z);
 cdef extern void TV_FGP_GPU_main(float *Input, float *Output, float lambdaPar, int iter, float epsil, int methodTV, int nonneg, int printM, int N, int M, int Z);
-cdef extern void dTV_FGP_CPU_main(float *Input, float *InputRef, float *Output, float lambdaPar, int iterationsNumb, float epsil, float eta, int methodTV, int nonneg, int printM, int N, int M, int Z);
+cdef extern void dTV_FGP_GPU_main(float *Input, float *InputRef, float *Output, float lambdaPar, int iterationsNumb, float epsil, float eta, int methodTV, int nonneg, int printM, int N, int M, int Z);
 
 # Total-variation Rudin-Osher-Fatemi (ROF)
 def TV_ROF_GPU(inputData,
@@ -187,8 +187,7 @@ def FGPTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
 		    np.zeros([dims[0],dims[1],dims[2]], dtype='float32')
           
     # Running CUDA code here    
-    TV_FGP_GPU_main(            
-            &inputData[0,0,0], &outputData[0,0,0], 
+    TV_FGP_GPU_main(&inputData[0,0,0], &outputData[0,0,0], 
                        regularisation_parameter , 
                        iterations, 
                        tolerance_param,
@@ -204,7 +203,7 @@ def FGPTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
 #****************************************************************#
 #******** Directional TV Fast-Gradient-Projection (FGP)*********#
 def FGPdTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
-             np.ndarray[np.float32_t, ndim=3, mode="c"] refdata,
+             np.ndarray[np.float32_t, ndim=2, mode="c"] refdata,
                      float regularisation_parameter,
                      int iterations, 
                      float tolerance_param,