SponselMa71420/Projekt_Dyschromasie
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Daltonization in die Klasse eingefügt

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Funktion der Daltonization in die Klasse Dyschromasie eingefügt.
This commit is contained in:
Max Sponsel 2020-09-18 10:58:28 +02:00
parent 638fadeb28
commit 8498500c68
1 changed files with 44 additions and 43 deletions
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87
Code/Farbaenderung.py
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@ -2,12 +2,13 @@ import numpy as np
import cv2 import cv2
import sys import sys
def createGammaLookup(): def createGammaLookup():
return np.array([removeGammaCorrection(i) for i in np.arange(0, 256)]).astype("float64") return np.array([removeGammaCorrection(i) for i in np.arange(0, 256)]).astype("float64")
def createReverseGammaLookup(): 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): def removeGammaCorrection(v):
@ -86,52 +87,52 @@ class Dyschromasie:
return self.sim_image return self.sim_image
def daltonize(self):
err2mod = np.array([[0, 0, 0], [0.7, 1, 0], [0.7, 0, 1]])
simulated_image = self.Simulate()
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]))
ERR = np.zeros_like(image).astype('int32')
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)
dtpn = np.copy(image).astype('int32')
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):
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')
dalt = Dyschromasie(result, self.rows, self.cols, self.kanaele, self.sim_faktor, self.sim_kind)
return dalt.Simulate()
script_dir = sys.path[0] script_dir = sys.path[0]
path = script_dir[:-4] + r'Beispielbilder\Fall_trees.jpg' path = script_dir[:-4] + r'Beispielbilder\rot-gruen-schwaeche-test-bild.jpg'
image = cv2.cvtColor(cv2.imread(path), cv2.COLOR_BGR2RGB) image = cv2.cvtColor(cv2.imread(path), cv2.COLOR_BGR2RGB)
rows, cols, kanaele = image.shape rows, cols, kanaele = image.shape
p = Dyschromasie(image, rows, cols, kanaele, 1, 'p') dalt = Dyschromasie(image, rows, cols, kanaele, 1, 'p')
cv2.imshow('Dalt_Img', cv2.cvtColor(dalt.daltonize(), cv2.COLOR_RGB2BGR))
simulated_image = p.Simulate()
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]))
ERR = np.zeros_like(image).astype('int64')
err2mod = np.array([[0,0,0],[0.7,1,0],[0.7,0,1]])
for i in range(rows):
for j in range(cols):
err = E[i,j,:3]
ERR[i,j,:3] = np.dot(err2mod, err)
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] = abs(int(ERR[i, j, x]) + int(image[i, j, x]))
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')
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) cv2.waitKey(0)