Daltonization in die Klasse eingefügt

Funktion der Daltonization in die Klasse Dyschromasie eingefügt.

Code/Farbaenderung.py

@@ -2,12 +2,13 @@ import numpy as np
 import cv2
 import sys
 
+
 def createGammaLookup():
     return np.array([removeGammaCorrection(i) for i in np.arange(0, 256)]).astype("float64")
 
 
 def createReverseGammaLookup():
-    return np.array([applyGammaCorrection(i/255) for i in np.arange(0.0, 256.0)]).astype("uint8")
+    return np.array([applyGammaCorrection(i / 255) for i in np.arange(0.0, 256.0)]).astype("uint8")
 
 
 def removeGammaCorrection(v):
@@ -86,52 +87,52 @@ class Dyschromasie:
 
         return self.sim_image
 
+    def daltonize(self):
 
-script_dir = sys.path[0]
-path = script_dir[:-4] + r'Beispielbilder\Fall_trees.jpg'
-image = cv2.cvtColor(cv2.imread(path), cv2.COLOR_BGR2RGB)
-
-rows, cols, kanaele = image.shape
+        err2mod = np.array([[0, 0, 0], [0.7, 1, 0], [0.7, 0, 1]])
+        simulated_image = self.Simulate()
 
-p = Dyschromasie(image, rows, cols, kanaele, 1, 'p')
+        E = np.copy(simulated_image).astype('int32')
+        for i in range(self.rows):
+            for j in range(self.cols):
+                for x in range(3):
+                    E[i, j, x] = abs(int(self.img_mat[i, j, x]) - int(simulated_image[i, j, x]))
 
-simulated_image = p.Simulate()
+        ERR = np.zeros_like(image).astype('int32')
 
-E = np.copy(simulated_image).astype('int64')
-for i in range(rows):
-    for j in range(cols):
-        for x in range(3):
-            E[i, j, x] = abs(int(image[i, j, x]) - int(simulated_image[i, j, x]))
+        for i in range(self.rows):
+            for j in range(self.cols):
+                err = E[i, j, :3]
+                ERR[i, j, :3] = np.dot(err2mod, err)
 
-ERR = np.zeros_like(image).astype('int64')
+        dtpn = np.copy(image).astype('int32')
 
-err2mod = np.array([[0,0,0],[0.7,1,0],[0.7,0,1]])
+        for i in range(self.rows):
+            for j in range(self.cols):
+                 for x in range(3):
+                     dtpn[i, j, x] = abs(int(ERR[i, j, x]) + int(image[i, j, x]))
 
-for i in range(rows):
-    for j in range(cols):
-        err = E[i,j,:3]
-        ERR[i,j,:3] = np.dot(err2mod, err)
+        for i in range(rows):
+               for j in range(cols):
+                    dtpn[i, j, 0] = max(0, dtpn[i, j, 0])
+                    dtpn[i, j, 0] = min(255, dtpn[i, j, 0])
+                    dtpn[i, j, 1] = max(0, dtpn[i, j, 1])
+                    dtpn[i, j, 1] = min(255, dtpn[i, j, 1])
+                    dtpn[i, j, 2] = max(0, dtpn[i, j, 2])
+                    dtpn[i, j, 2] = min(255, dtpn[i, j, 2])
 
-dtpn = np.copy(image).astype('int64')
+        result = dtpn.astype('uint8')
 
-for i in range(rows):
-    for j in range(cols):
-        for x in range(3):
-            dtpn[i, j, x] = abs(int(ERR[i, j, x]) + int(image[i, j, x]))
+        dalt = Dyschromasie(result, self.rows, self.cols, self.kanaele, self.sim_faktor, self.sim_kind)
+        return dalt.Simulate()
 
-for i in range(rows):
-    for j in range(cols):
-        dtpn[i, j, 0] = max(0, dtpn[i, j, 0])
-        dtpn[i, j, 0] = min(255, dtpn[i, j, 0])
-        dtpn[i, j, 1] = max(0, dtpn[i, j, 1])
-        dtpn[i, j, 1] = min(255, dtpn[i, j, 1])
-        dtpn[i,  j, 2] = max(0, dtpn[i, j, 2])
-        dtpn[i, j, 2] = min(255, dtpn[i, j, 2])
 
-result = dtpn.astype('uint8')
+script_dir = sys.path[0]
+path = script_dir[:-4] + r'Beispielbilder\rot-gruen-schwaeche-test-bild.jpg'
+image = cv2.cvtColor(cv2.imread(path), cv2.COLOR_BGR2RGB)
 
-dalt = Dyschromasie(result,rows,cols, kanaele, 1, 'p')
-dalt_p = dalt.Simulate()
+rows, cols, kanaele = image.shape
 
-cv2.imshow('Dalt_Img', cv2.cvtColor(dalt_p, cv2.COLOR_RGB2BGR))
+dalt = Dyschromasie(image, rows, cols, kanaele, 1, 'p')
+cv2.imshow('Dalt_Img', cv2.cvtColor(dalt.daltonize(), cv2.COLOR_RGB2BGR))
 cv2.waitKey(0)