import numpy as np path = "lena.bmp" #Read and process image IP = ImageProcessor() img = IP.getImg(path)/256. imgSize = IP.getSize(path) imgSpace = IP.process(path) #Segment image S = Segmenter() K = 300 #number of clusters errorThreshold = 0.01 clusters = S.segment(K, imgSpace, imgSize, errorThreshold) #Get back clusters into image shape width, height = imgSize[0],imgSize[1] clusters = np.flipud(clusters.reshape((width,height))) #Plot image #http://stackoverflow.com/questions/15822934/matplotlib-imshow-how-to-plot-points-instead-of-image # Reduce the data by a factor of 4 (so that we can see the points) im = img # generate coordinates for the image. Note that the image is "top down", so the y coordinate goes from high to low. ys, xs = np.mgrid[im.shape[0]:0:-1, 0:im.shape[1]] # Scatter plots take 1d arrays of xs and ys, and the colour takes a 2d array, # with the second dimension being RGB plt.scatter(xs.flatten(), ys.flatten(), s=4, c=im.flatten().reshape(-1, 3), edgecolor='face')
import matplotlib.pyplot as plt
import numpy as np
path = "lena.bmp"
#Read and process image
IP = ImageProcessor()
img = IP.getImg(path) / 256.
imgSize = IP.getSize(path)
imgSpace = IP.process(path)
#Segment image
S = Segmenter()
K = 300 #number of clusters
errorThreshold = 0.01
clusters = S.segment(K, imgSpace, imgSize, errorThreshold)
#Get back clusters into image shape
width, height = imgSize[0], imgSize[1]
clusters = np.flipud(clusters.reshape((width, height)))
#Plot image
#http://stackoverflow.com/questions/15822934/matplotlib-imshow-how-to-plot-points-instead-of-image
# Reduce the data by a factor of 4 (so that we can see the points)
im = img
# generate coordinates for the image. Note that the image is "top down", so the y coordinate goes from high to low.
ys, xs = np.mgrid[im.shape[0]:0:-1, 0:im.shape[1]]
# Scatter plots take 1d arrays of xs and ys, and the colour takes a 2d array,
# with the second dimension being RGB
plt.scatter(xs.flatten(),
ys.flatten(),
