fixed false usage of np.uint8 for contrasts

with higher values

itten/contrasts.py

@@ -26,6 +26,7 @@ class LightDark(object):
 
         # addiert alle Werte auf einer bestimmten Achse
         luminances = np.sum(self._img, axis=2)
+        luminances = luminances.astype(np.uint8, copy=True)
 
         return luminances
 

itten/views.py

@@ -52,9 +52,9 @@ class View(np.ndarray):
         """
         obj = input_array
         if type(obj) == np.ndarray:
-            obj = np.asarray(input_array, dtype=np.uint8).view(cls).copy()
+            obj = np.asarray(input_array, dtype=np.uint32).view(cls).copy()
         else:
-            input_array = np.empty((0, 3), dtype=np.uint8)
+            input_array = np.empty((0, 3), dtype=np.uint32)
             obj = np.asarray(input_array).view(cls).copy()
         obj._frames = frames
         obj._contrast = 2
@@ -141,7 +141,7 @@ class MultivariateSequence(View):
         # selef._frames.start = start
         # self._frames.end = end
 
-        contrast_points = np.empty((0, 3), dtype=np.uint8)
+        contrast_points = np.empty((0, 3), dtype=np.uint32)
 
         # pwd list sollte in Frames sein und hier nur durchlaufen werden
         for frm_nr in range(self._frames.start, self._frames.end, self._frame_step):
@@ -152,16 +152,19 @@ class MultivariateSequence(View):
             ctrst_cls = self._get_ctrst_cls_name(self._contrast)
             ctrst_img = ctrst_cls(img).ctrst
 
-            hist_value, _ = np.histogram(ctrst_img.flatten(), bins=self._bins,
+            shape = ctrst_img.shape
+            ctrst_img = np.reshape(ctrst_img, (shape[0] * shape[1]))
+
+            hist_value, _ = np.histogram(ctrst_img, bins=self._bins,
                                          range=(0, 256))
 
             for bin_index, point in enumerate(hist_value):
-                if point > self._threshold:
-                    entry = np.array([[frm_nr, bin_index, int(point)]], dtype=np.uint8)
+                if int(point) > self._threshold:
+                    entry = np.array([[frm_nr, bin_index, point]], dtype=np.uint32)
                     contrast_points = np.vstack((contrast_points, entry))
 
         # irgendwie prüfen, ob ich contrast_points insgesamt durch self ersetzen kann
-        contrast_points = np.asarray(contrast_points, np.uint8)
+        contrast_points = np.asarray(contrast_points, np.uint32)
         shape = contrast_points.shape
         self.resize(shape, refcheck=False)
         self[:, :] = contrast_points
@@ -213,7 +216,7 @@ class BivariateSequence(View):
         # self._frames.start = start
         # self._frames.end = end
 
-        contrast_points = np.empty((0, 2), dtype=np.uint8)
+        contrast_points = np.empty((0, 2), dtype=np.uint32)
 
         # sofern kein oder nur ein Peak gefunden wird, man könnte dann auch
         # noch einen Durchlauf mit geringeren thres und min_dist versuceh
@@ -304,7 +307,7 @@ class BivariateSequence(View):
                     contrast_points = np.vstack((contrast_points,
                                                 [lastmin, lastmax]))
 
-        contrast_points = np.asarray(contrast_points, np.uint8)
+        contrast_points = np.asarray(contrast_points, np.uint32)
         shape = contrast_points.shape
         self.resize(shape, refcheck=False)
         self[:, :] = contrast_points
@@ -367,7 +370,7 @@ class UnivariateSequence(View):
         # self._frames.start = start
         # self._frames.end = end
 
-        contrast_points = np.empty((0, 4), dtype=np.uint8)
+        contrast_points = np.empty((0, 2), dtype=np.uint32)
 
         # pwd list sollte in Frames sein und hier nur durchlaufen werden
         for frm_nr in range(self._frames.start, self._frames.end,
@@ -382,18 +385,18 @@ class UnivariateSequence(View):
                                          bins=self._bins, range=(0, 256))
 
             hist_value = hist_value.flatten()
-            hist_value = hist_value.astype(np.uint8, copy=False)
+            hist_value = hist_value.astype(np.uint32, copy=False)
 
             # eigenes mean und devi durch numpy ersetzt
             # TODO Ergebnisse der unterschiedlichen Verfahrenstimmt nicht überein
-            contrast = hist_value.mean()
-            deviation = hist_value.std()
-            _contrast, _deviation = UnivariateSequence.meanmad(hist_value)
+            # contrast = hist_value.mean()
+            # deviation = hist_value.std()
+            contrast, deviation = UnivariateSequence.meanmad(hist_value)
 
             contrast_points = np.vstack((contrast_points,
-                                         [contrast, deviation, _contrast, _deviation]))
+                                         [contrast, deviation]))
 
-        contrast_points = np.asarray(contrast_points, np.uint8)
+        contrast_points = np.asarray(contrast_points, np.uint32)
         shape = contrast_points.shape
         self.resize(shape, refcheck=False)
         self[:, :] = contrast_points