def color_extract(img, clusters):
image = cv2.imread(img)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
fig, (ax1, ax2) = plt.subplots(2, 1)
im1 = ax1.imshow(image)
plt.axis("off")
# show our image
# plt.figure()
# plt.axis("off")
# plt.imshow(image)
# reshape into pixel matrix
image = image.reshape((image.shape[0] * image.shape[1], 3))
# cluster using kmeans package
clt = KMeans(n_clusters = (clusters+1))
clt.fit(image)
zippy = utils.remove_bg(clt)
# zippy = utils.centroid_histogram(clt)
bar = utils.plot_colors(zippy)
im2 = ax2.imshow(bar)
plt.axis("off")
# plt.show()
zippy = sorted(zippy, reverse = True)
zippy = [i for j in zippy for i in j]
zippy = [i for j in zippy for i in j.flatten('F')]
zippy.insert(0, img[2:len(img)-4])
return zippy
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()
help = "# of clusters")
args = vars(ap.parse_args())
# load the image and convert it from BGR to RGB so that
# we can dispaly it with matplotlib
image = cv2.imread(args["image"])
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 = args["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)
bar = utils.plot_colors(hist, clt.cluster_centers_)
# show our color bart
plt.figure()
plt.axis("off")
plt.imshow(bar)
plt.show()
cv2.waitKey(0);
# reshape image to metrix N x 3
imageReshaped = image.reshape((-1, 3))
# change data in array from uint8 to float32
imageReshaped = np.float32(imageReshaped)
# rule the iteration termination
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
# number of cluster
K = 8
ret, label, center = cv2.kmeans(imageReshaped, K, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)
# find mean color range and percent of color in image
hist = utils.centroid_histogram(center, label)
bar = utils.plot_colors(hist, center)
# convert back to uint8 and reshape image
center = np.uint8(center)
res = center[label.flatten()]
res2 = res.reshape((image.shape))
# print color and percent in image
for(percent, color) in zip(hist, center):
print "color(", color, ") = ", percent*100, "%"
plt.figure()
plt.axis("off")
plt.imshow(bar)
plt.show()