COMP9517 lab3 image segementation
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import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from scipy import ndimage as ndi
from skimage.morphology import watershed
from skimage.feature import peak_local_max
from sklearn.cluster import MeanShift
from PIL import Image
size = 100, 100
img_names = ["shapes.png", "strawberry.png"]
ext_names = ["coins.png", "kiwi.png"]
images = [r‘C:UsersharriDesktop2020T29517lab3COMP9517_20T2_Lab3_Images\‘+i for i in img_names]
ext_images = [r‘C:UsersharriDesktop2020T29517lab3COMP9517_20T2_Lab3_Images\‘+i for i in ext_names]
def plot_three_images(figure_title, image1, label1,
image2, label2, image3, label3):
fig = plt.figure()
fig.suptitle(figure_title)
# Display the first image
fig.add_subplot(1, 3, 1)
plt.imshow(image1)
plt.axis(‘off‘)
plt.title(label1)
# Display the second image
fig.add_subplot(1, 3, 2)
plt.imshow(image2)
plt.axis(‘off‘)
plt.title(label2)
# Display the third image
fig.add_subplot(1, 3, 3)
plt.imshow(image3)
plt.axis(‘off‘)
plt.title(label3)
plt.show()
for img_path in ext_images:
img = Image.open(img_path)
img.thumbnail(size)
# TODO: perform meanshift on image
meanShift_img = np.array(img)[:,:,:3]
R = meanShift_img[:,:,0:1].flatten()[:,np.newaxis]
G = meanShift_img[:,:,1:2].flatten()[:,np.newaxis]
B = meanShift_img[:,:,2:3].flatten()[:,np.newaxis]
colour_samples = np.concatenate((R,G,B),axis=1)
MS = MeanShift(bin_seeding=True)
ms_labels = MS.fit_predict(colour_samples) # CHANGE THIS
ms_labels = ms_labels.reshape(meanShift_img.shape[0],meanShift_img.shape[1])
# TODO: perform an optimisation and then watershed on image
ws_img = img.convert(‘L‘)
gray_ws_image = np.array(ws_img)
distance = ndi.distance_transform_edt(gray_ws_image)
local_max = peak_local_max(distance, indices=False, footprint=np.ones((3, 3)),
labels=gray_ws_image)
markers = ndi.label(local_max)[0]
ws_labels = watershed(-distance,markers,mask = gray_ws_image) # CHANGE THIS
# Display the results
plot_three_images(img_path, img, "Original Image", ms_labels, "MeanShift Labels",
ws_labels, "Watershed Labels")
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