change luminance calculation to future approach

external function calculates contrast value and returns Image with
contrast values instead of channel value (3>1). This is calcualted with
np.histogram

itten/contrasts.py

@@ -123,15 +123,17 @@ class VHistStack(View):
         for frm_nr in range(self._frames.start, self._frames.end, self._frame_step):
             pwd = self._frames.folder + self._frames.prefix + str(frm_nr) + '.png'
 
-            img = cv2.imread(pwd)
+            img = cv2.imread(pwd)  # BGR
 
-            if self._contrast == 2:
-                _img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-                luminances = luminance(_img)
-                hist_value, _ = np.histogram(luminances, bins=self._bins, range=(0, 255))
+            # TODO Dictionary um Parameter auf Kontrast Funktion zu mappen
 
+            # so wie hier für 2 das ist die Referznimplementierung
+            # luminances gibt ein Bild mit den luminanz werten in der
+            # 3. Dimension zurück
+            if self._contrast == 2:
+                luminances = luminance(img)
+                hist_value, _ = np.histogram(luminances.flatten(), bins=self._bins, range=(0, 256))
             else:
-
                 img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV_FULL)
                 hist_value = cv2.calcHist([img_hsv], [self._contrast], None, [16], [0, 256])
 

itten/helpers.py

@@ -9,18 +9,19 @@ def timelabels(val, pos):
     timelabel = "{0}:{1:02d}".format(min, sec)
     return timelabel
 
-
-# In[6]:
-
 def luminance(img):
+    """Creates an array of luminance value for each pixel of an image
+
+       The array has the same dimensions as the image. However the third
+       does only have the size 1 which contains the luminance value
+    """
     # Luminance Faktoren nach http://introcs.cs.princeton.edu/python/31datatype/luminance.py.html
     luminance_factors = np.array([.299, .587, .114])
 
     # Erzeugung eines eindimensionalen Arrays für die effizientere Berechnung
-    _img = img.reshape((img.shape[0]*img.shape[1]), img.shape[2])
-    _img = np.multiply(_img, luminance_factors)
+    img = np.multiply(img, luminance_factors)
 
     # addiert alle Werte auf einer bestimmten Achse
-    luminances = np.sum(_img, axis=1)
+    luminances = np.sum(img, axis=2)
 
     return luminances