Segmentasi Citra

 

a. Penggunaan mahotas.thresholding

[in]	import numpy as np import mahotas import mahotas.demos   from mahotas.thresholding import soft_threshold from matplotlib import pyplot as plt from os import path f = mahotas.demos.load('luispedro', as_grey=True) f = f[:256,:256] plt.gray() # Show the data: print("Fraction of zeros in original image: {0}".format(np.mean(f==0))) plt.imshow(f) plt.show()
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b. Penggunaan mahotas.otsu

[in]	import mahotas import mahotas.demos import numpy as np from pylab import imshow, gray, show from os import path   photo = mahotas.demos.load('luispedro', as_grey=True) photo = photo.astype(np.uint8)   gray() imshow(photo) show()
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[in]	T_otsu = mahotas.otsu(photo) print(T_otsu) imshow(photo > T_otsu) show()
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[in]	import mahotas import mahotas.demos import numpy as np from pylab import imshow, gray, show from os import path   photo = mahotas.demos.load('luispedro', as_grey=True) photo = photo.astype(np.uint8)   T_otsu = mahotas.otsu(photo) print(T_otsu) gray() imshow(photo > T_otsu) show()
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[in]	T_rc = mahotas.rc(photo) print(T_rc) imshow(photo > T_rc) show()
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C. Segmentasi Chan Vase

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import matplotlib.pyplot as plt from skimage import data, img_as_float from skimage.segmentation import chan_vese   image = img_as_float(data.camera()) # Feel free to play around with the parameters to see how they impact the result cv = chan_vese(image, mu=0.25, lambda1=1, lambda2=1, tol=1e-3, max_iter=200,                dt=0.5, init_level_set="checkerboard", extended_output=True)   fig, axes = plt.subplots(2, 2, figsize=(8, 8)) ax = axes.flatten()   ax[0].imshow(image, cmap="gray") ax[0].set_axis_off() ax[0].set_title("Original Image", fontsize=12)   ax[1].imshow(cv[0], cmap="gray") ax[1].set_axis_off() title = "Chan-Vese segmentation - {} iterations".format(len(cv[2])) ax[1].set_title(title, fontsize=12)   ax[2].imshow(cv[1], cmap="gray") ax[2].set_axis_off() ax[2].set_title("Final Level Set", fontsize=12)   ax[3].plot(cv[2]) ax[3].set_title("Evolution of energy over iterations", fontsize=12)   fig.tight_layout() plt.show()

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 Deteksi Wajah

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import io import zipfile import requests import numpy as np import cv2 import matplotlib.pyplot as plt %matplotlib inline   img = cv2.imread('keluarga.jpg')   gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)   path = 'data/haarcascade_frontalface_default.xml' face_cascade = cv2.CascadeClassifier(path)   for x, y, w, h in face_cascade.detectMultiScale(         gray, 1.3):     cv2.rectangle(         gray, (x, y), (x + w, y + h), (255, 0, 0), 2) fig, ax = plt.subplots(1, 1, figsize=(8, 6)) ax.imshow(gray, cmap=plt.cm.gray) ax.set_axis_off()

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