Weiter CodeCleanup

Code/Dyschromasie-Applikation.py

@@ -4,69 +4,12 @@ import tkinter as tk
 from tkinter import filedialog, messagebox
 import cv2
 import numpy as np
-from Farbaenderung import gammaCorrection, reverseGammaCorrection
+from Farbaenderung import gammaCorrection, reverseGammaCorrection, Dyschromasie
 from Scrollbar import ScrollFrame
 
 root = tk.Tk()
 simGrad = tk.IntVar(root)
 
-class Dyschromasie:
-    cb_image = np.array([]).astype('float64')
-    sim_image = np.array([]).astype('uint8')
-
-    def __init__(self, img_mat=np.array([]), rows=0, cols=0, kanaele=0,sim_faktor=0, sim_kind='d'):
-        self.rows = rows
-        self.cols = cols
-        self.kanaele = kanaele
-        self.img_mat = img_mat
-        self.sim_faktor = sim_faktor
-        self.sim_kind = sim_kind
-
-    T = np.array([[0.31399022, 0.63951294, 0.04649755],
-                  [0.15537241, 0.75789446, 0.08670142],
-                  [0.01775239, 0.10944209, 0.87256922]])
-
-    T_reversed = np.array([[5.47221206, -4.6419601, 0.16963708],
-                           [-1.1252419, 2.29317094, -0.1678952],
-                           [0.02980165, -0.19318073, 1.16364789]])
-
-    def Simulate(self):
-        if self.sim_kind == 'p':
-            sim_mat = np.array([[(1 - self.sim_faktor), 1.05118294 * self.sim_faktor, -0.05116099 * self.sim_faktor],
-                                [0, 1, 0],
-                                [0, 0, 1]])
-        elif self.sim_kind == 'd':
-            sim_mat = np.array([[1, 0, 0],
-                                [0.9513092 * self.sim_faktor, (1 - self.sim_faktor), 0.04866992 * self.sim_faktor],
-                                [0, 0, 1]])
-        else:
-            sim_mat = np.array([[1, 0, 0],
-                                [0, 1, 0],
-                                [-0.86744736 * self.sim_faktor, 1.86727089 * self.sim_faktor, (1 - self.sim_faktor)]])
-
-        # Gammakorrektur durchfuehren
-        self.cb_image = np.copy(self.img_mat).astype('float64')
-
-        for i in range(self.rows):
-            for j in range(self.cols):
-                for x in range(3):
-                    self.cb_image[i, j, x] = gammaCorrection(self.img_mat[i, j, x])
-
-        # Einzelne Pixelwertberechnung
-        for i in range(self.rows):
-            for j in range(self.cols):
-                self.cb_image[i, j] = self.T_reversed.dot(sim_mat).dot(self.T).dot(self.cb_image[i, j])
-
-        self.sim_image = np.copy(self.cb_image)
-        self.sim_image = self.sim_image.astype('uint8')
-        # Rücktransformation der Gammawerte
-        for i in range(self.rows):
-            for j in range(self.cols):
-                for x in range(3):
-                    self.sim_image[i, j, x] = reverseGammaCorrection(self.cb_image[i, j, x])
-
-        return self.sim_image
-
 root.title("Projekt Dyschromasie")
 
 SB = ScrollFrame(root)

Code/Farbaenderung.py

@@ -1,3 +1,6 @@
+import numpy as np
+import cv2
+
 def gammaCorrection(v):
     if v <= 0.04045 * 255:
         return (v / 255) / 12.92
@@ -12,6 +15,61 @@ def reverseGammaCorrection(v_reverse):
         return round(255 * (1.055 * v_reverse ** 0.41666 - 0.055))
 
 
+class Dyschromasie:
+    cb_image = np.array([]).astype('float64')
+    sim_image = np.array([]).astype('uint8')
+
+    def __init__(self, img_mat=np.array([]), rows=0, cols=0, kanaele=0,sim_faktor=0, sim_kind='d'):
+        self.rows = rows
+        self.cols = cols
+        self.kanaele = kanaele
+        self.img_mat = img_mat
+        self.sim_faktor = sim_faktor
+        self.sim_kind = sim_kind
+
+    T = np.array([[0.31399022, 0.63951294, 0.04649755],
+                  [0.15537241, 0.75789446, 0.08670142],
+                  [0.01775239, 0.10944209, 0.87256922]])
+
+    T_reversed = np.array([[5.47221206, -4.6419601, 0.16963708],
+                           [-1.1252419, 2.29317094, -0.1678952],
+                           [0.02980165, -0.19318073, 1.16364789]])
+
+    def Simulate(self):
+        if self.sim_kind == 'p':
+            sim_mat = np.array([[(1 - self.sim_faktor), 1.05118294 * self.sim_faktor, -0.05116099 * self.sim_faktor],
+                                [0, 1, 0],
+                                [0, 0, 1]])
+        elif self.sim_kind == 'd':
+            sim_mat = np.array([[1, 0, 0],
+                                [0.9513092 * self.sim_faktor, (1 - self.sim_faktor), 0.04866992 * self.sim_faktor],
+                                [0, 0, 1]])
+        else:
+            sim_mat = np.array([[1, 0, 0],
+                                [0, 1, 0],
+                                [-0.86744736 * self.sim_faktor, 1.86727089 * self.sim_faktor, (1 - self.sim_faktor)]])
+
+        # Gammakorrektur durchfuehren
+        self.cb_image = np.copy(self.img_mat).astype('float64')
+
+        for i in range(self.rows):
+            for j in range(self.cols):
+                for x in range(3):
+                    self.cb_image[i, j, x] = gammaCorrection(self.img_mat[i, j, x])
+
+        # Einzelne Pixelwertberechnung
+        for i in range(self.rows):
+            for j in range(self.cols):
+                self.cb_image[i, j] = self.T_reversed.dot(sim_mat).dot(self.T).dot(self.cb_image[i, j])
+
+        self.sim_image = np.copy(self.cb_image)
+        self.sim_image = self.sim_image.astype('uint8')
+        # Rücktransformation der Gammawerte
+        for i in range(self.rows):
+            for j in range(self.cols):
+                for x in range(3):
+                    self.sim_image[i, j, x] = reverseGammaCorrection(self.cb_image[i, j, x])
 
+        return self.sim_image