Angepasster Daltonizationalgorithmus

https://github.com/indranilsinharoy/daltonization/blob/master/daltonize.py

Implementierung des gezeigten Algorithmus. Wichtig ist die Int64 Konvertierung , um Überläufe zu verhindern.

Code/Farbaenderung.py

 
 
 script_dir = sys.path[0]
-path = script_dir[:-4] + "Beispielbilder\Fall_trees.jpg"
+path = script_dir[:-4] + r'Beispielbilder\Fall_trees.jpg'
 image = cv2.cvtColor(cv2.imread(path), cv2.COLOR_BGR2RGB)
 
 rows, cols, kanaele = image.shape
 
 simulated_image = p.Simulate()
 
-E = np.copy(simulated_image)
+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(simulated_image[i, j, x]) - int(image[i, j, x]))
+            E[i, j, x] = abs(int(image[i, j, x]) - int(simulated_image[i, j, x]))
 
-ERR = np.zeros_like(image)
+ERR = np.zeros_like(image).astype('int64')
 
 err2mod = np.array([[0,0,0],[0.7,1,0],[0.7,0,1]])
 
         err = E[i,j,:3]
         ERR[i,j,:3] = np.dot(err2mod, err)
 
-dtpn = np.copy(image)
+dtpn = np.copy(image).astype('int64')
 
 for i in range(rows):
     for j in range(cols):
         for x in range(3):
-            dtpn[i, j, x] = (int(ERR[i, j, x]) + int(image[i, j, x]))
+            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):
         dtpn[i,  j, 2] = max(0, dtpn[i, j, 2])
         dtpn[i, j, 2] = min(255, dtpn[i, j, 2])
 
-cv2.imshow('Dalt_Img', cv2.cvtColor(dtpn, cv2.COLOR_RGB2BGR))
+result = dtpn.astype('uint8')
+
+dalt = Dyschromasie(result,rows,cols, kanaele, 1, 'p')
+dalt_p = dalt.Simulate()
+
+cv2.imshow('Dalt_Img', cv2.cvtColor(dalt_p, cv2.COLOR_RGB2BGR))
 cv2.waitKey(0)