Merge pull request StanfordVL#18 from StanfordVL/mv_publish_hw4

Prevent cascading errors

hw4_release/seam_carving.py

@@ -161,18 +161,21 @@ def reduce(image, size, axis=1, efunc=energy_function, cfunc=compute_cost, bfunc
 
     At each step, we remove the lowest energy seam from the image. We repeat the process
     until we obtain an output of desired size.
-    Use functions:
-        - efunc
-        - cfunc
-        - backtrack_seam
-        - remove_seam
+    
+    SUPER IMPORTANT: IF YOU WANT TO PREVENT CASCADING ERRORS IN THE CODE OF reduce(), USE FUNCTIONS:
+        - efunc (instead of energy_function)
+        - cfunc (instead of compute_cost)
+        - bfunc (instead of backtrack_seam)
+        - rfunc (instead of remove_seam)
 
     Args:
         image: numpy array of shape (H, W, 3)
         size: size to reduce height or width to (depending on axis)
         axis: reduce in width (axis=1) or height (axis=0)
         efunc: energy function to use
         cfunc: cost function to use
+        bfunc: backtrack seam function to use
+        rfunc: remove seam function to use
 
     Returns:
         out: numpy array of shape (size, W, 3) if axis=0, or (H, size, 3) if axis=1
@@ -228,18 +231,21 @@ def enlarge_naive(image, size, axis=1, efunc=energy_function, cfunc=compute_cost
 
     At each step, we duplicate the lowest energy seam from the image. We repeat the process
     until we obtain an output of desired size.
-    Use functions:
-        - efunc
-        - cfunc
-        - backtrack_seam
-        - duplicate_seam
+    
+    SUPER IMPORTANT: IF YOU WANT TO PREVENT CASCADING ERRORS IN THE CODE OF enlarge_naive(), USE FUNCTIONS:
+        - efunc (instead of energy_function)
+        - cfunc (instead of compute_cost)
+        - bfunc (instead of backtrack_seam)
+        - dfunc (instead of duplicate_seam)
 
     Args:
         image: numpy array of shape (H, W, C)
         size: size to increase height or width to (depending on axis)
         axis: increase in width (axis=1) or height (axis=0)
         efunc: energy function to use
         cfunc: cost function to use
+        bfunc: backtrack seam function to use
+        dfunc: duplicate seam function to use
 
     Returns:
         out: numpy array of shape (size, W, C) if axis=0, or (H, size, C) if axis=1
@@ -264,7 +270,7 @@ def enlarge_naive(image, size, axis=1, efunc=energy_function, cfunc=compute_cost
     return out
 
 
-def find_seams(image, k, axis=1, efunc=energy_function, cfunc=compute_cost):
+def find_seams(image, k, axis=1, efunc=energy_function, cfunc=compute_cost, bfunc=backtrack_seam, rfunc=remove_seam):
     """Find the top k seams (with lowest energy) in the image.
 
     We act like if we remove k seams from the image iteratively, but we need to store their
@@ -274,18 +280,20 @@ def find_seams(image, k, axis=1, efunc=energy_function, cfunc=compute_cost):
     is the output of find_seams.
     We also keep an indices array to map current pixels to their original position in the image.
 
-    Use functions:
-        - efunc
-        - cfunc
-        - backtrack_seam
-        - remove_seam
+    SUPER IMPORTANT: IF YOU WANT TO PREVENT CASCADING ERRORS IN THE CODE OF find_seams(), USE FUNCTIONS:
+        - efunc (instead of energy_function)
+        - cfunc (instead of compute_cost)
+        - bfunc (instead of backtrack_seam)
+        - rfunc (instead of remove_seam)
 
     Args:
         image: numpy array of shape (H, W, C)
         k: number of seams to find
         axis: find seams in width (axis=1) or height (axis=0)
         efunc: energy function to use
         cfunc: cost function to use
+        bfunc: backtrack seam function to use
+        rfunc: remove seam function to use
 
     Returns:
         seams: numpy array of shape (H, W)
@@ -320,10 +328,10 @@ def find_seams(image, k, axis=1, efunc=energy_function, cfunc=compute_cost):
         energy = efunc(image)
         cost, paths = cfunc(image, energy)
         end = np.argmin(cost[H - 1])
-        seam = backtrack_seam(paths, end)
+        seam = bfunc(paths, end)
 
         # Remove that seam from the image
-        image = remove_seam(image, seam)
+        image = rfunc(image, seam)
 
         # Store the new seam with value i+1 in the image
         # We can assert here that we are only writing on zeros (not overwriting existing seams)
@@ -332,30 +340,37 @@ def find_seams(image, k, axis=1, efunc=energy_function, cfunc=compute_cost):
         seams[np.arange(H), indices[np.arange(H), seam]] = i + 1
 
         # We remove the indices used by the seam, so that `indices` keep the same shape as `image`
-        indices = remove_seam(indices, seam)
+        indices = rfunc(indices, seam)
 
     if axis == 0:
         seams = np.transpose(seams, (1, 0))
 
     return seams
 
 
-def enlarge(image, size, axis=1, efunc=energy_function, cfunc=compute_cost):
+def enlarge(image, size, axis=1, efunc=energy_function, cfunc=compute_cost, dfunc=duplicate_seam, bfunc=backtrack_seam, rfunc=remove_seam):
     """Enlarges the size of the image by duplicating the low energy seams.
 
     We start by getting the k seams to duplicate through function find_seams.
     We iterate through these seams and duplicate each one iteratively.
 
-    Use functions:
+    SUPER IMPORTANT: IF YOU WANT TO PREVENT CASCADING ERRORS IN THE CODE OF enlarge(), USE FUNCTIONS:
+        - efunc (instead of energy_function)
+        - cfunc (instead of compute_cost)
+        - dfunc (instead of duplicate_seam)
+        - bfunc (instead of backtrack_seam)
+        - rfunc (instead of remove_seam)
         - find_seams
-        - duplicate_seam
 
     Args:
         image: numpy array of shape (H, W, C)
         size: size to reduce height or width to (depending on axis)
         axis: enlarge in width (axis=1) or height (axis=0)
         efunc: energy function to use
         cfunc: cost function to use
+        dfunc: duplicate seam function to use
+        bfunc: backtrack seam function to use
+        rfunc: remove seam function to use
 
     Returns:
         out: numpy array of shape (size, W, C) if axis=0, or (H, size, C) if axis=1