idx creation improved

.gitignore

@@ -55,4 +55,5 @@ MANIFEST
 docs
 htmlcov
 tests/in
-tests/out
+tests/out
+src/kubi/kubi_.py

CHANGELOG.rst

@@ -2,6 +2,12 @@
 Changelog
 =========
 
+Version 0.1.2
+===========
+
+- performance of idx creation improved
+- Basic tests passed (97% coverage | 194run | 0 missing | 4 excluded | 8 partial)
+
 Version 0.1.1
 ===========
 

README.md

@@ -67,8 +67,8 @@ Marzipano.ImageUrlSource.fromString("<some_path>/dstfile_{f}/{z}/{y}/{x}.jpg");
 
 | face size | 1024px | 2048px | 4096px |
 | ---| --- | --- | --- |
-| kubi                                                   | 0.9s | 1.7s | 4.9s  |
-| [py360convert](https://pypi.org/project/py360convert/) | 2.5s | 8.7s | 33.0s |
+| kubi                                                   | 0.9s | 1.6s | 4.7s  |
+| [py360convert](https://pypi.org/project/py360convert/) | 2.6s | 8.6s | 32.2s |
 | *any others ?* | - | - | - |
 
 ### single input file - tiled output 
@@ -77,8 +77,8 @@ Marzipano.ImageUrlSource.fromString("<some_path>/dstfile_{f}/{z}/{y}/{x}.jpg");
 
 | face size | 1024px | 2048px | 4096px |
 | ---| --- | --- | --- |
-| kubi                                                   | 0.9s | 1.4s | 3.5s  |
-| [panorama_windows.exe](https://github.com/blackironj/panorama) | 1.4s | 4.4s | 16.8s |
+| kubi                                                   | 0.9s | 1.3s | 3.1s  |
+| [panorama_windows.exe](https://github.com/blackironj/panorama) | 1.5s | 4.7s | 16.9s |
 | *any others ?* | - | - | - |
 
 ### multiple input files

src/kubi/kubi.py

@@ -31,7 +31,6 @@
 # `from kubi.kubi import kubi`,
 # when using this Python module as a library.
 
-
 def kubi(args):
 
     if IsWin:
@@ -70,32 +69,33 @@ def kubi(args):
         elif args.transform == "otc": # M.Zucker & Y.Higashi (2018): Cube-to-sphere Projections for Procedural Texturing and Beyond
             ls = np.tan(ls * 0.8687) / np.tan(0.8687)
 
-        xv,yv = np.meshgrid(ls, ls)
-
-        xv2 = xv ** 2
-        yv2 = yv ** 2
-
-        idx = np.stack([
-            np.arctan(xv),                      #tha0
-            np.arctan2(yv, np.sqrt(1 + xv2)),   #phi0
-            np.arctan2(xv, yv),                 #tha1
-            np.arctan2(1, np.sqrt(yv2 + xv2))   #phi1
-        ], axis=-1) 
-
-        ### end of numpy
+        xv,yv = np.meshgrid(ls, ls, copy=False)
 
-        ls = xv = yv = xv2 = yv2 = None
-
-        idx = pyvips.Image.new_from_memory(idx.reshape(size**2 * 4), size, size, 4, 'float') / (pi/2)
+        x0 = np.arctan(xv)
+        y0 = np.arctan2(yv, np.hypot(1,xv))
+        x1 = np.arctan2(xv, yv)
+        y1 = np.arctan(np.hypot(yv,xv))
+
+        ls = xv = yv = None
+
+        piot = pi/2
+
+        x0 = pyvips.Image.new_from_memory(x0.ravel(), size, size, 1, 'float') / piot
+        y0 = pyvips.Image.new_from_memory(y0.ravel(), size, size, 1, 'float') / piot
+        x1 = pyvips.Image.new_from_memory(x1.ravel(), size, size, 1, 'float') / piot
+        y1 = pyvips.Image.new_from_memory(y1.ravel(), size, size, 1, 'float') / piot
+
+        ### end of numpy
 
         idx = [
-            pyvips.Image.bandjoin(idx[0]+3,idx[1]+1),
-            pyvips.Image.bandjoin(idx[0]+1,idx[1]+1),
-            pyvips.Image.bandjoin((idx[2]-2)%4,1-idx[3]),
-            pyvips.Image.bandjoin((4-idx[2])%4,1+idx[3]),
-            pyvips.Image.bandjoin(idx[0]+2,idx[1]+1),
-            pyvips.Image.bandjoin(idx[0]%4,idx[1]+1),
+            pyvips.Image.bandjoin(x0+3,y0+1),
+            pyvips.Image.bandjoin(x0+1,y0+1),
+            pyvips.Image.bandjoin((x1-2)%4,y1),
+            pyvips.Image.bandjoin((4-x1)%4,2-y1),
+            pyvips.Image.bandjoin(x0+2,y0+1),
+            pyvips.Image.bandjoin(x0%4,y0+1),
         ]
+
 
         if args.layout is None or args.layout in ("column","row"):
             if args.inverse is not None: