Erste Tests mit Slider und Umwandlungsfunktion

Erste Funktionen in den Klassen implementiert; Erste Test gemacht. Ergebnisse noch nicht zufriedenstellend. Auch Implementierung einer Scrollbar nötig!!
Merge remote-tracking branch 'origin/master'
2020-08-25 10:45:12 +02:00 · 2020-08-25 09:40:33 +02:00 · 2020-08-25 09:40:27 +02:00
1 changed files with 35 additions and 0 deletions
@@ -7,6 +7,7 @@ import numpy as np


 class Dyschromasie:
+    global simGrad
    cb_image = np.array([]).astype('float64')
    sim_image = np.array([]).astype('uint8')

@@ -65,6 +66,16 @@ class Protanopie(Dyschromasie):
                for x in range(3):
                    self.sim_image[i, j, x] = self.reverseGammaCorrection(self.cb_image[i, j, x])

+        # Anpassung fuer den Slider
+        if(simGrad.get() != 0):
+            for i in range(rows):
+                for j in range(cols):
+                    for x in range(3):
+                        if self.sim_image[i, j, x] > img[i, j, x]:
+                            self.sim_image[i, j, x] = img[i, j, x] + abs(self.sim_image[i, j, x] - img[i, j, x])* (simGrad.get()/100)
+
+                        elif self.sim_image[i, j, x] < img[i, j, x]:
+                            self.sim_image[i, j, x] = self.sim_image[i, j, x] + abs(img[i, j, x] - self.sim_image[i, j, x])* (simGrad.get()/100)
        return self.sim_image


@@ -95,6 +106,18 @@ class Deuteranopie(Dyschromasie):
            for j in range(self.cols):
                for x in range(3):
                    self.sim_image[i, j, x] = self.reverseGammaCorrection(self.cb_image[i, j, x])
+
+        # Anpassung fuer den Slider
+        if (simGrad.get() != 0):
+            for i in range(rows):
+                for j in range(cols):
+                    for x in range(3):
+                        if self.sim_image[i, j, x] > img[i, j, x]:
+                            self.sim_image[i, j, x] = img[i, j, x] + abs(self.sim_image[i, j, x] - img[i, j, x]) * (simGrad.get() / 100)
+
+                        elif self.sim_image[i, j, x] < img[i, j, x]:
+                            self.sim_image[i, j, x] = self.sim_image[i, j, x] + abs(img[i, j, x] - self.sim_image[i, j, x]) * (simGrad.get() / 100)
+
        return self.sim_image


@@ -125,6 +148,18 @@ class Tritanopie(Dyschromasie):
            for j in range(self.cols):
                for x in range(3):
                    self.sim_image[i, j, x] = self.reverseGammaCorrection(self.cb_image[i, j, x])
+
+        # Anpassung fuer den Slider
+        if (simGrad.get() != 0):
+            for i in range(rows):
+                for j in range(cols):
+                    for x in range(3):
+                        if self.sim_image[i, j, x] > img[i, j, x]:
+                            self.sim_image[i, j, x] = img[i, j, x] + abs(self.sim_image[i, j, x] - img[i, j, x]) * (simGrad.get() / 100)
+
+                        elif self.sim_image[i, j, x] < img[i, j, x]:
+                            self.sim_image[i, j, x] = self.sim_image[i, j, x] + abs(img[i, j, x] - self.sim_image[i, j, x]) * (simGrad.get() / 100)
+
        return self.sim_image