basic.py

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from sklearn.cluster import KMeans
import cv2         
import sys

def getPixelFeatures(img):
    D = img.reshape((img.shape[0] * img.shape[1], 3))
    r = np.arange(0, img.shape[0])
    c = np.arange(0, img.shape[1])
    rc = np.array([[i,j] for i in r for j in c])
    D = np.concatenate((rc,D), axis = 1).astype(float)
    return D


# Get command line args
if len(sys.argv) < 3:
    print("USAGE: cv.py img k")

img = cv2.imread(sys.argv[1])
#  img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
#  img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (0,0), fx=0.3, fy=0.3)
k = int(sys.argv[2])

km = KMeans(n_clusters = k)
D = getPixelFeatures(img)
D_norm = D / D.max(axis=0)
print(D_norm)
km.fit(D_norm)
print(km.labels_)
colors = km.labels_.reshape((img.shape[0], img.shape[1]))
#  colors = colors * (255/np.max(colors))

fig = plt.figure()

def f(x, y):
    return np.cos(x) + np.cos(y)

x = np.linspace(0, 2 * np.pi, 120)
y = np.linspace(0, 2 * np.pi, 100).reshape(-1, 1)

im = plt.imshow(img, animated=True, cmap='gray')
0
#  grow = [0, 0, 0, 0, 0, 
growl = list(range(1,20))
growl = growl[::-1]
growc = [1,1,1]
growr = list(range(1,20))

grow = growl + growc + growr
print(grow)
temp = 0

def updatefig(*args):
    global temp
    f = args[0]
    color = colors.copy()
    #  print(color)
    #  color[color == f%k] = 255
    color[color == temp] = 255
    color[color != 255] = 0
    kernel = np.ones((5,5),np.uint8)
    color = cv2.dilate(color.astype(np.uint8), kernel, iterations = grow[f%len(grow)])
    if f % len(grow) == len(grow)-1:
        temp = temp + 1
    if temp == k:
        temp = 0
    im.set_array(color)
    #  x += np.pi / 15.
    #  y += np.pi / 20.
    #  im.set_array(f(x, y))
    return im,

ani = animation.FuncAnimation(fig, updatefig, interval=10, blit=True)
plt.show()