SponselMa71420/Projekt_Dyschromasie
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Vereinfachung des Codes mit cv2.cvtColor

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Statt das Array doppelt mit der flipud Funktion zu spiegeln, wird es direkt im Einleseprozess von BGR(OpenCV) zu RGB(Pillow und Algorithmus) konvertiert.
This commit is contained in:
Max Sponsel 2020-09-10 11:34:04 +02:00
parent e62ea2a23e
commit 08881f47cf
1 changed files with 8 additions and 10 deletions
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18
Code/Dyschromasie-Applikation.py
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@ -49,6 +49,7 @@ class Protanopie(Dyschromasie):
# 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):
@ -57,12 +58,10 @@ class Protanopie(Dyschromasie):
# Einzelne Pixelwertberechnung
for i in range(self.rows):
for j in range(self.cols):
self.cb_image[i, j] = np.flipud(
self.T_reversed.dot(sim_mat).dot(self.T).dot(np.flipud(self.cb_image[i, j])))
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):
@ -89,8 +88,7 @@ class Deuteranopie(Dyschromasie):
# Einzelne Pixelwertberechnung
for i in range(self.rows):
for j in range(self.cols):
self.cb_image[i, j] = np.flipud(
self.T_reversed.dot(sim_mat).dot(self.T).dot(np.flipud(self.cb_image[i, j])))
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')
@ -121,8 +119,7 @@ class Tritanopie(Dyschromasie):
# Einzelne Pixelwertberechnung
for i in range(self.rows):
for j in range(self.cols):
self.cb_image[i, j] = np.flipud(
self.T_reversed.dot(sim_mat).dot(self.T).dot(np.flipud(self.cb_image[i, j])))
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')
@ -229,6 +226,7 @@ def browse():
# Einspeichern der Path-Informationen
global img, rows, cols, kanaele
img = cv2.imread(path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
rows, cols, kanaele = img.shape
@ -236,7 +234,7 @@ def simulate():
global img, rows, cols, kanaele, sim_pro, sim_deut, sim_tri
if sim_deut.get():
d = Deuteranopie(img, rows, cols, kanaele, simGrad.get()/100)
display_array_deut = cv2.cvtColor(np.copy(d.Simulate()), cv2.COLOR_BGR2RGB)
display_array_deut = d.Simulate()
T = tk.Text(SB.frame, height=1, width=15)
T.grid(columnspan=5)
@ -249,7 +247,7 @@ def simulate():
sim_pic_deut.grid(columnspan=5)
elif sim_tri.get():
t = Tritanopie(img, rows, cols, kanaele, simGrad.get()/100)
display_array_tri = cv2.cvtColor(np.copy(t.Simulate()), cv2.COLOR_BGR2RGB)
display_array_tri = t.Simulate()
T = tk.Text(SB.frame, height=1, width=15)
T.grid(columnspan=5)
@ -262,7 +260,7 @@ def simulate():
sim_pic_tri.grid(columnspan=5)
elif sim_pro.get():
p = Protanopie(img, rows, cols, kanaele, simGrad.get()/100)
display_array_pro = cv2.cvtColor(np.copy(p.Simulate()), cv2.COLOR_BGR2RGB)
display_array_pro = p.Simulate()
T = tk.Text(SB.frame, height=1, width=15)
T.grid(columnspan=5)