Segmentation with KMeans
Numpy implementation of k-Means on image data. k-Means algorithm has been run on an intensity image, RGB image, and properly scaled image position on an intensity image.
Segmentation with KMeans¶
In [1]:
import numpy as npimport cv2 as cvimport matplotlib.pyplot as pltfrom kmeans import Kmeans%load_ext autoreload%autoreload 2KMeans on Intensity Image¶
In [2]:
np.random.seed(42)img = cv.imread('images/flower.png')gray_img = cv.cvtColor(img, cv.COLOR_BGR2GRAY)plt.imshow(gray_img, cmap="gray")plt.title("original image")fig, axs = plt.subplots(1, 3, figsize=(15,15))for i, k in enumerate([2,4,6]): centers, index = Kmeans(gray_img.reshape(-1,1), k=k) reconstructed_img = centers[index].reshape(gray_img.copy().shape) _ = axs[i].imshow(reconstructed_img/255) _ = axs[i].set_title("reconstructed_img k="+str(k))Has been completed with 4 iterations for 2 centers Has been completed with 13 iterations for 4 centers Has been completed with 14 iterations for 6 centers
KMeans on RGB Image¶
In [3]:
img = cv.imread('images/flower.png')plt.imshow(img)plt.title("original image")fig, axs = plt.subplots(1, 3, figsize=(15,15))for i,k in enumerate([2,4,6]): centers, index = Kmeans(img.reshape(-1,3), k=k) reconstructed_img = centers[index].reshape(img.copy().shape) _ = axs[i].imshow(reconstructed_img/255) _ = axs[i].set_title("reconstructed_img k="+str(k))Has been completed with 9 iterations for 2 centers Has been completed with 40 iterations for 4 centers Has been completed with 35 iterations for 6 centers
KMeans on Intensity and (properly scaled) Image Position¶
In [4]:
img = cv.imread('images/flower.png')gray_img = cv.cvtColor(img, cv.COLOR_BGR2GRAY)plt.imshow(gray_img, cmap="gray")plt.title("original image")xx, yy = np.meshgrid(np.arange(gray_img.shape[1]), np.arange(gray_img.shape[0]))xx = xx/np.max(xx)yy = yy/np.max(yy)new_img = gray_img/np.max(gray_img)data = np.stack((xx, yy, new_img), axis=2)plt.imshow(cv.cvtColor(data.astype(np.float32), cv.COLOR_BGR2RGB))plt.title("original image")fig, axs = plt.subplots(1, 3, figsize=(15,15))for i,k in enumerate([2,4,6]): centers, index = Kmeans(data.reshape(-1,3), k=k) reconstructed_img = centers[index].reshape(data.copy().shape)[:,:,:] _ = axs[i].imshow(reconstructed_img) _ = axs[i].set_title("reconstructed_img k="+str(k))Has been completed with 32 iterations for 2 centers Has been completed with 40 iterations for 4 centers Has been completed with 30 iterations for 6 centers
