SponselMa71420/Projekt_Dyschromasie
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Erste Tests mit Slider und Umwandlungsfunktion

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Erste Funktionen in den Klassen implementiert; Erste Test gemacht. Ergebnisse noch nicht zufriedenstellend. Auch Implementierung einer Scrollbar nötig!!
This commit is contained in:
Max Sponsel 2020-08-25 10:45:12 +02:00
parent bbc3aafeb3
commit 62b16b4a96
1 changed files with 35 additions and 0 deletions
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35
Code/Dyschromasie-Applikation.py
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@ -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