if ((int(old_cent[0]) - a) <= cent[0] <= (int(old_cent[0]) + a)) and (
(int(old_cent[1]) - a) <= cent[1] <= (int(old_cent[1]) + a)) and (
(int(old_cent[2]) - a) <= cent[2] <= (int(old_cent[2]) + a)):
continue
else:
return False
return True
'''
file_path = '/Users/Rozen_mac/code/mining/K_means/sample.jpg'
img = R.Read_JPG(file_path)
for i in range(2, 6):
px = img.load()
k = Kmeans(img, k=i)
reslut = k.fit()
k.drawWindows(reslut, str(i))
'''
file_path = '/Users/Rozen_mac/code/mining/K_means/sample2.jpg'
img = R.Read_JPG(file_path)
px = img.load()
#for i in range(2, 6):
px = img.load()
k = Kmeans(img, k=5)
reslut = k.fit()
k.drawWindows(reslut, str(4))
self.r = rgb[0]
self.g = rgb[1]
self.b = rgb[2]
self.x = x
self.y = y
self.visited = False
self.isnoise = False
def show(self):
return self.r, self.g, self.b
if __name__ == '__main__':
# read an image
i = R.Read_JPG('sample.jpg')
width, height = i.size
cpixels = []
all_pixels = [] # list of tuples
for x in range(width):
for y in range(height):
cpixel = i.getpixel((x, y))
cpixels.append(cpixel)
all_pixels.append(DataSet(cpixel, x, y))
# Create object of DBSCAN class
dbScan = DBSCAN()
# Initialise dataSet
dbScan.DB = all_pixels
# build clusters
