def kmeans_image(image_path, clt):
image = cv2.imread(image_path)
# TODO CHECK IF OK TO REMOVE? Matlab needs rbg. but order might be different
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = image.reshape((image.shape[0] * image.shape[1], 3))
# cluster the pixel intensities
clt.fit(image)
# build a histogram of clusters and then create a figure
# representing the number of pixels labeled to each color
hist = utils.centroid_histogram(clt)
percentage_color_list = utils.get_colors_and_percentages(hist, clt.cluster_centers_)
return percentage_color_list
def kmeans_image_show(image_path, clusters=3):
pass
# construct the argument parser and parse the arguments
# ap = argparse.ArgumentParser()
# ap.add_argument("-i", "--image", required = True, help = "Path to the image")
# ap.add_argument("-c", "--clusters", required = True, type = int, help = "# of clusters")
# args = vars(ap.parse_args())
#
# load the image and convert it from BGR to RGB so that
# we can display it with matplotlib
image = cv2.imread(image_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# show our image
plt.figure()
plt.axis("off")
plt.imshow(image)
# reshape the image to be a list of pixels
image = image.reshape((image.shape[0] * image.shape[1], 3))
# cluster the pixel intensities
clt = KMeans(n_clusters = clusters)
clt.fit(image)
# build a histogram of clusters and then create a figure
# representing the number of pixels labeled to each color
hist = utils.centroid_histogram(clt)
percentage_color_list = utils.get_colors_and_percentages(hist, clt.cluster_centers_)
print percentage_color_list
print percentage_color_list[0][1][0]
bar = utils.plot_colors(hist, clt.cluster_centers_)
# show our color bart
plt.figure()
plt.axis("off")
plt.imshow(bar)
plt.show()
