Reverse sign of Jacobian values

This doesn't seem to affect anything, since both the linearized
transform and the Hann window are symmetric, but this buts the Jacobian
values in the normal finite-difference form of [f(x+dx) - f(x)] / dx

reproject/adaptive/deforest.pyx

@@ -258,17 +258,17 @@ def map_coordinates(double[:,:] source, double[:,:] target, Ci, int max_samples_
         Jy = np.empty((target.shape[0] + 1, target.shape[1], 2))
         for yi in range(target.shape[0]):
             for xi in range(target.shape[1]):
-                Jx[yi, xi, 0] = pixel_source[yi+1, xi, 0] - pixel_source[yi+1, xi+1, 0]
-                Jx[yi, xi, 1] = pixel_source[yi+1, xi, 1] - pixel_source[yi+1, xi+1, 1]
-                Jy[yi, xi, 0] = pixel_source[yi, xi+1, 0] - pixel_source[yi+1, xi+1, 0]
-                Jy[yi, xi, 1] = pixel_source[yi, xi+1, 1] - pixel_source[yi+1, xi+1, 1]
+                Jx[yi, xi, 0] = -pixel_source[yi+1, xi, 0] + pixel_source[yi+1, xi+1, 0]
+                Jx[yi, xi, 1] = -pixel_source[yi+1, xi, 1] + pixel_source[yi+1, xi+1, 1]
+                Jy[yi, xi, 0] = -pixel_source[yi, xi+1, 0] + pixel_source[yi+1, xi+1, 0]
+                Jy[yi, xi, 1] = -pixel_source[yi, xi+1, 1] + pixel_source[yi+1, xi+1, 1]
             xi = target.shape[1]
-            Jx[yi, xi, 0] = pixel_source[yi+1, xi, 0] - pixel_source[yi+1, xi+1, 0]
-            Jx[yi, xi, 1] = pixel_source[yi+1, xi, 1] - pixel_source[yi+1, xi+1, 1]
+            Jx[yi, xi, 0] = -pixel_source[yi+1, xi, 0] + pixel_source[yi+1, xi+1, 0]
+            Jx[yi, xi, 1] = -pixel_source[yi+1, xi, 1] + pixel_source[yi+1, xi+1, 1]
         yi = target.shape[0]
         for xi in range(target.shape[1]):
-            Jy[yi, xi, 0] = pixel_source[yi, xi+1, 0] - pixel_source[yi+1, xi+1, 0]
-            Jy[yi, xi, 1] = pixel_source[yi, xi+1, 1] - pixel_source[yi+1, xi+1, 1]
+            Jy[yi, xi, 0] = -pixel_source[yi, xi+1, 0] + pixel_source[yi+1, xi+1, 0]
+            Jy[yi, xi, 1] = -pixel_source[yi, xi+1, 1] + pixel_source[yi+1, xi+1, 1]
 
         # Now trim the padding we added earlier. Since `delta` was used above,
         # the value at (0,0) will now truly represent (0,0) and so on. After
@@ -302,10 +302,10 @@ def map_coordinates(double[:,:] source, double[:,:] target, Ci, int max_samples_
                 if center_jacobian:
                     # Compute the Jacobian for the transformation applied to
                     # this pixel, as finite differences.
-                    Ji[0,0] = offset_source_x[yi, xi, 0] - offset_source_x[yi, xi+1, 0]
-                    Ji[1,0] = offset_source_x[yi, xi, 1] - offset_source_x[yi, xi+1, 1]
-                    Ji[0,1] = offset_source_y[yi, xi, 0] - offset_source_y[yi+1, xi, 0]
-                    Ji[1,1] = offset_source_y[yi, xi, 1] - offset_source_y[yi+1, xi, 1]
+                    Ji[0,0] = -offset_source_x[yi, xi, 0] + offset_source_x[yi, xi+1, 0]
+                    Ji[1,0] = -offset_source_x[yi, xi, 1] + offset_source_x[yi, xi+1, 1]
+                    Ji[0,1] = -offset_source_y[yi, xi, 0] + offset_source_y[yi+1, xi, 0]
+                    Ji[1,1] = -offset_source_y[yi, xi, 1] + offset_source_y[yi+1, xi, 1]
                 else:
                     # Compute the Jacobian for the transformation applied to
                     # this pixel, as a mean of the Jacobian a half-pixel