final version done.

Author: dembasowfr

bin/Convolution.o

@@ -28,6 +28,6 @@
 
         result = Image.fromarray(im.astype(np.uint8))
 
-        result.save(sys.argv[2] + "/" + str(idx) + ".bmp")
+        result.save(sys.argv[2] + "/" + "result"+str(idx) + ".bmp")
 
         f.readline()    # empty line

bin/Filter.o

@@ -6,20 +6,22 @@
 #include "Filter.hpp"
 
 class Convolution {
-  int in_width, in_height, in_depth,
-      n_filters, window, stride, padding,
-      out_width, out_height, out_depth;
+  
+  int in_width, in_height, in_depth, n_filters, window, stride, padding, out_width, out_height, out_depth;
 
 public:
-  Convolution(int _width, int _height, int _depth, int _window, int _stride = 1, int _padding = 0, int n_filters = 1);
 
-  ~Convolution();
+  // Constructor
+  Convolution(int _width, int _height, int _depth, int _window, int _stride = 1, int _padding = 0, int n_filters = 1);
 
   // Applies convolutional filters in filters to input volume x
   // x treated as in_width * in_height * in_depth 
   // assumed n_filters elements in filters vector
   // returns shape of the output volume and pointer to the memory block
   std::tuple<int, int, int, double ***> conv2d(double ***x, const std::vector<Filter*> &filters);
+
+  // Destructor
+  ~Convolution();
 };
 
 #endif

helpers/load_img.py

@@ -15,18 +15,21 @@ class Filter {
 
     // Constructors
     Filter(int _window, int _depth);
-    Filter(double ***_w, int _window, int _depth, int _b = 0); // constructor with kernel matrix and bias term
 
-    // Destructor
-    ~Filter();
+    // constructor with kernel matrix and bias term
+    Filter(double ***_w, int _window, int _depth, int _b = 0);
+
 
     // normalize the tensor
     void normalize();
 
 
-    // normalize kernel matrix
-    void normalize_kernel();
+    // Destructor
+    ~Filter();
+
 
+
+
 };
 
 #endif