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D1_D6.py
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D1_D6.py
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from PyQt5.QtWidgets import * # type: ignore
from PyQt5.QtCore import pyqtSlot
import numpy as np
from A9_C2 import A9_C2
class D1_D6(A9_C2):
def __init__(self):
super(D1_D6, self).__init__()
self.kvMenu: QMenu
self.kvMenu.triggered.connect(self.kvTrigger) # type: ignore
def konvolusi(self, kernel: np.ndarray, image=None, show=True):
if image is None:
image = self.imageOriginal
tinggi_citra, lebar_citra = image.shape[:2]
tinggi_kernel, lebar_kernel = kernel.shape[:2]
H = tinggi_kernel // 2
W = lebar_kernel // 2
out = np.zeros_like(image)
for i in range(H, tinggi_citra - H):
for j in range(W, lebar_citra - W):
sum = 0
for k in range(-H, H + 1):
for l in range(-W, W + 1):
a = image[i + k, j + l]
w = kernel[H + k, W + l]
sum += w * a
out[i, j] = np.clip(sum, 0, 255)
if show:
self.imageResult = out
self.displayImage(2)
return out
@pyqtSlot(QAction)
def kvTrigger(self, action):
if not hasattr(self, 'imageOriginal'):
return self.showMessage('Error', 'Citra masih kosong!')
menuText = action.text()
mapped = {
'Mean Filter': self.__mean,
'Gaussian Filter': self.__gaussian,
'Median Filter': self.__median,
'Max Filter': self.__max
}
sharpening = ['i', 'ii', 'iii', 'iv', 'v', 'vi']
if menuText in sharpening:
self.__sharpening(menuText)
return True
try:
mapped.get(menuText)() # type: ignore
return True
except Exception as error:
if isinstance(error, TypeError):
return False
raise error
def __mean(self):
kernel = np.full((3, 3), 1/9)
self.konvolusi(kernel)
def __gaussian(self):
kernel = (1.0 / 345) * np.array([[1, 5, 7, 5, 1],
[5, 20, 33, 20, 5],
[7, 33, 55, 33, 7],
[5, 20, 33, 20, 5],
[1, 5, 7, 5, 1]
])
self.konvolusi(kernel)
def __sharpening(self, filter):
mapped = {
'i': [[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]],
'ii': [[-1, -1, -1], [-1, 9, -1], [-1, -1, -1]],
'iii': [[0, -1, 0], [-1, 5, -1], [0, -1, 0]],
'iv': [[1, -2, 1], [-2, 5, -2], [1, -2, 1]],
'v': [[1, -2, 1], [-2, 4, -2], [1, -2, 1]],
'vi': [[0, 1, 0], [1, -4, 1], [0, 1, 0]],
}
kernel = 1.0 * np.array(mapped.get(filter))
self.konvolusi(kernel)
def __median(self):
H, W = self.imageOriginal.shape[:2]
out = self.imageOriginal.copy()
for i in range(3, H - 3):
for j in range(3, W - 3):
neighbors = []
for k in range(-3, 4):
for l in range(-3, 4):
a = self.imageOriginal[i + k, j + l]
neighbors.append(a)
neighbors.sort(key=lambda x: sum(x))
median = neighbors[24]
out[i, j] = median
self.imageResult = out
self.displayImage(2)
def __max(self):
H, W = self.imageOriginal.shape[:2]
out = self.imageOriginal.copy()
for i in range(3, H - 3):
for j in range(3, W - 3):
pixel = self.imageOriginal[i, j]
max_val = 0
for k in range(-3, 4):
for l in range(-3, 4):
pixel_value = self.imageOriginal[i + k, j + l]
intensity = np.mean(pixel_value)
if intensity > max_val:
max_val = intensity
pixel = pixel_value
out[i, j] = pixel
self.imageResult = out
self.displayImage(2)
if __name__ == '__main__':
__import__('aplikasi').main()