comments in headers

include/iterator.hpp

@@ -7,6 +7,21 @@
 using namespace cv;
 using namespace std;
 
+/**
+ * This is a wrapper over MatIterator_ to make it consistent with
+ * range based for loops in C++11.
+ * The IterableMat<T> class has a begin() and an end() iterator
+ * that function similarly to MatIterator_<T> class. Also, while
+ * creating the IterableMat, a warning is issued if the type of
+ * the matrix is not the same as T
+ *
+ * A simple way to get an IterableMat<T> from a Mat is to do
+ * iterate<T>(mat). Thus, an easy range based for loop would look
+ * like:
+ *
+ * for(T elem : iterate<T>(mat)) {...}
+ */
+
 template<class Element> class IterableMat : public Mat {
     public:
         struct Iterator {
@@ -43,10 +58,28 @@ template<class Element> class IterableMat : public Mat {
 
 template<class Element>
 IterableMat<Element> iterate(Mat &x) {
-    CHECKER_ASSERT(DataType<Element>::type == x.type(), "WARNING: Data type mismatch in iterable, may lead to wrong results\n");
+    CHECKER_ASSERT(DataType<Element>::type == x.type(),
+            "WARNING: Data type mismatch in iterable, may lead to wrong results\n");
     return IterableMat<Element>(x);
 }
 
+/**
+ * This is another wrapper over MatIterator_ but it also gives the
+ * (x,y) coordinates of the current loop instance.
+ * The EnumerableMat<T> class has an EnumerationIterator
+ * that gives back an Enumeration object upon dereference. The
+ * Enumeration object has three fields: x, y, T& val, which are
+ * set according to the current iteration.
+ *
+ * A simple way to get an EnumerableMat<T> from a Mat is to do
+ * enumerate<T>(mat). Thus, an easy range based for loop would look
+ * like:
+ *
+ * for(auto en : enumerate<float>(mat)) {
+ *      printf("%d %d %f", en.x, en.y, en.val);
+ * }
+ */
+
 template<class Element> class EnumerableMat : public Mat {
     public:
         struct Enumeration {
@@ -104,7 +137,8 @@ template<class Element> class EnumerableMat : public Mat {
 
 template<class Element>
 EnumerableMat<Element> enumerate(Mat &x) {
-    CHECKER_ASSERT(DataType<Element>::type == x.type(), "WARNING: Data type mismatch in enumerable, may lead to wrong results\n");
+    CHECKER_ASSERT(DataType<Element>::type == x.type(),
+            "WARNING: Data type mismatch in enumerable, may lead to wrong results\n");
     return EnumerableMat<Element>(x);
 }
 

include/transform.hpp

@@ -7,6 +7,10 @@
 using namespace std;
 using namespace cv;
 
+/**
+ * a simple function_traits class to get argument and result types of functions
+ */
+
 template <typename T>
 struct function_traits
     : public function_traits<decltype(&T::operator())>
@@ -18,21 +22,57 @@ template <typename ClassType, typename ReturnType, typename... Args>
 struct function_traits<ReturnType(ClassType::*)(Args...) const>
 // we specialize for pointers to member function
 {
-    enum { arity = sizeof...(Args) };
-    // arity is the number of arguments.
-
     typedef ReturnType result_type;
     typedef typename std::tuple_element<0, std::tuple<Args...>>::type argument_type;
-
-    template <size_t i>
-        struct arg
-        {
-            typedef typename std::tuple_element<i, std::tuple<Args...>>::type type;
-            // the i-th argument is equivalent to the i-th tuple element of a tuple
-            // composed of those arguments.
-        };
 };
 
+
+/**
+ * This is the workhorse of per-pixel transforms and the like
+ * An IntermediateValue stores the current intermediate Mat in its val
+ * and can be freely cast to or from a Mat instance
+ *
+ * It provides two methods:
+ * pp_transform(func), which is equivalent to
+ *
+ * Mat output = Mat(val.size(), <return type of func>)
+ * for(each pixel in val) {
+ *      output[pixel] = func(input[pixel]);
+ * }
+ * return IntermediateValue(output);
+ *
+ * pp_transform can also be called with an explicit output
+ * type, such as: 
+ * pp_transform(CV_32FC3, func);
+ *
+ * One can directly obtain an IntermediateValue by calling
+ * pp_transform(image, func)
+ *
+ * Since a pp_transform returns a IntermediateValue, one can create a chain
+ * of pp_transforms such as
+ * pp_transform(image, func).
+ * pp_transform(func2).
+ * pp_transform(func3);
+ *
+ * Also, another similar method is total_transform, which simply calls
+ * func on the Mat itself, like:
+ *
+ * Mat output = func(val);
+ * return IntermediateValue(output);
+ *
+ * This method is meant to be used for OpenCV implemented transforms such
+ * as edge detection etc. An example of edge detection on the R-channel of a
+ * normalized color image is below
+ *
+ * Mat output = pp_transform(image, [](Vec3b pixel) { // get normalized R channel
+ *     float sum = pixel[0] + pixel[1] + pixel[2];
+ *     return saturate_cast<uint8_t>(pixel[2]/sum*255);
+ * }).total_transform([](Mat input) {
+ *     Mat output;
+ *     Canny(input, output, 20, 60);
+ *     return output;
+ * });
+ */
 struct IntermediateValue {
     Mat val;