turn populate into multiple single functions for UnivariateSequence

itten/views.py

@@ -349,7 +349,7 @@ class UnivariateSequence(View):
         return mean, mad
 
     # TODO: Kurve muß unbeding von cuts bereinigt werde und interpolation funktioniert da nicht
-    def populate(self, variant=['mean', 'dev'], ctrst='light_dark',
+    def seqmean(self, variant=['mean', 'dev'], ctrst='light_dark',
                  method='luminance', frm_stp=10, bins=256):
         """Creates a scatterplot for the dynamic of contrast across movie frames
 
@@ -370,7 +370,7 @@ class UnivariateSequence(View):
         # self._frames.start = start
         # self._frames.end = end
 
-        contrast_points = np.empty((0, 2), dtype=np.uint32)
+        contrast_points = np.empty((0), 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,
@@ -391,13 +391,67 @@ class UnivariateSequence(View):
             # TODO Ergebnisse der unterschiedlichen Verfahrenstimmt nicht überein
             # contrast = hist_value.mean()
             # deviation = hist_value.std()
-            contrast, deviation = UnivariateSequence.meanmad(hist_value)
+            contrast, _ = UnivariateSequence.meanmad(hist_value)
 
-            contrast_points = np.vstack((contrast_points,
-                                         [contrast, deviation]))
+            contrast_points = np.hstack((contrast_points,
+                                         contrast))
 
         contrast_points = np.asarray(contrast_points, np.uint32)
         shape = contrast_points.shape
         self.resize(shape, refcheck=False)
-        self[:, :] = contrast_points
+        self[:] = contrast_points
+        contrast_points = None
+
+    # TODO: Kurve muß unbeding von cuts bereinigt werde und interpolation funktioniert da nicht
+    def seqmad(self, variant=['mean', 'dev'], ctrst='light_dark',
+                 method='luminance', frm_stp=10, bins=256):
+        """Creates a scatterplot for the dynamic of contrast across movie frames
+
+           frm_fld: path to folder with movie images
+           frm_pref: file nave in fron of the count value
+           frm_step: take every x frame
+           channel: channel in the HSV color space
+           save: save plot also to disk
+        """
+        # set class properties
+        self._variant = variant
+        self._contrast = ctrst
+        self._method = method
+        self._frame_step = frm_stp
+        self._bins = bins
+        # TODO um start und end in der Methode zu parametrisieren müssen erst
+        # getters und setters in movie.frames definiert werden
+        # self._frames.start = start
+        # self._frames.end = end
+
+        contrast_points = np.empty((0), 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):
+            pwd = self._frames.folder + self._frames.prefix + str(frm_nr) + '.png'
+            img = cv2.imread(pwd)
+
+            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, range=(0, 256))
+
+            hist_value = hist_value.flatten()
+            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()
+            _, deviation = UnivariateSequence.meanmad(hist_value)
+
+            contrast_points = np.hstack((contrast_points,
+                                         deviation))
+
+        contrast_points = np.asarray(contrast_points, np.uint32)
+        shape = contrast_points.shape
+        self.resize(shape, refcheck=False)
+        self[:] = contrast_points
         contrast_points = None

testing/bootstrap.py

 from itten.movie import Movie
-from itten.views import MultivariateSequence
+from itten.views import UnivariateSequence
 from itten.visuals import MultivariatePlot
 
 movie = Movie(prefix='rec_', folder='../DHd-2017/Data/Frames/Rec/')
-cont = MultivariateSequence(movie._frames)
-cont.populate(frm_stp=50) 
-viz = MultivariatePlot()
-fig, ax = viz.plot(cont)
+cont = UnivariateSequence(movie._frames)
+# cont.populate(frm_stp=50) 
+# viz = MultivariatePlot()
+# fig, ax = viz.plot(cont)