def normcut_segmentations(img):
#labels1 = segmentation.slic(img, compactness=3, n_segments=50)
labels1 = segmentation.slic(img,compactness=3,n_segments=20)
out1 = color.label2rgb(labels1, img)#, kind='avg')
#return labels1
g = graph.rag_mean_color(img, labels1, mode='similarity')
labels2 = graph.cut_normalized(labels1, g)
out2 = color.label2rgb(labels2, img,image_alpha=0.2)#, kind='avg')
return (labels1,labels2)
def test_threshold_cut():
img = np.zeros((100, 100, 3), dtype='uint8')
img[:50, :50] = 255, 255, 255
img[:50, 50:] = 254, 254, 254
img[50:, :50] = 2, 2, 2
img[50:, 50:] = 1, 1, 1
labels = np.zeros((100, 100), dtype='uint8')
labels[:50, :50] = 0
labels[:50, 50:] = 1
labels[50:, :50] = 2
labels[50:, 50:] = 3
rag = graph.rag_mean_color(img, labels)
new_labels = graph.cut_threshold(labels, rag, 10)
# Two labels
assert new_labels.max() == 1
def test_threshold_cut():
img = np.zeros((100, 100, 3), dtype='uint8')
img[:50, :50] = 255, 255, 255
img[:50, 50:] = 254, 254, 254
img[50:, :50] = 2, 2, 2
img[50:, 50:] = 1, 1, 1
labels = np.zeros((100, 100), dtype='uint8')
labels[:50, :50] = 0
labels[:50, 50:] = 1
labels[50:, :50] = 2
labels[50:, 50:] = 3
rag = graph.rag_mean_color(img, labels)
new_labels = graph.cut_threshold(labels, rag, 10, in_place=False)
# Two labels
assert new_labels.max() == 1
new_labels = graph.cut_threshold(labels, rag, 10)
# Two labels
assert new_labels.max() == 1
def paint(img):
"""
Turn a standard image into a painting.
:param numpy.ndarray img:
An RGB image in numpy format.
"""
# scale the image
img = cv2.convertScaleAbs(img)
# perform SLIC segmentation
regions_slic = slic(img, compactness=SLIC_COMPACTNESS, n_segments=N_SLIC_REGIONS)
# cluster all SLIC regions
rag = rag_mean_color(img, regions_slic)
regions_rag = cut_threshold(regions_slic, rag, RAG_THRESHOLD)
# final painting
result = label2rgb(regions_rag, img, kind="avg")
return result
def test_cut_normalized():
img = np.zeros((100, 100, 3), dtype='uint8')
img[:50, :50] = 255, 255, 255
img[:50, 50:] = 254, 254, 254
img[50:, :50] = 2, 2, 2
img[50:, 50:] = 1, 1, 1
labels = np.zeros((100, 100), dtype='uint8')
labels[:50, :50] = 0
labels[:50, 50:] = 1
labels[50:, :50] = 2
labels[50:, 50:] = 3
rag = graph.rag_mean_color(img, labels, mode='similarity')
new_labels = graph.cut_normalized(labels, rag, in_place=False)
new_labels, _, _ = segmentation.relabel_sequential(new_labels)
# Two labels
assert new_labels.max() == 1
new_labels = graph.cut_normalized(labels, rag)
new_labels, _, _ = segmentation.relabel_sequential(new_labels)
assert new_labels.max() == 1
def test_cut_normalized():
img = np.zeros((100, 100, 3), dtype='uint8')
img[:50, :50] = 255, 255, 255
img[:50, 50:] = 254, 254, 254
img[50:, :50] = 2, 2, 2
img[50:, 50:] = 1, 1, 1
labels = np.zeros((100, 100), dtype='uint8')
labels[:50, :50] = 0
labels[:50, 50:] = 1
labels[50:, :50] = 2
labels[50:, 50:] = 3
rag = graph.rag_mean_color(img, labels, mode='similarity')
new_labels = graph.cut_normalized(labels, rag, in_place=False)
new_labels, _, _ = segmentation.relabel_sequential(new_labels)
# Two labels
assert new_labels.max() == 1
new_labels = graph.cut_normalized(labels, rag)
new_labels, _, _ = segmentation.relabel_sequential(new_labels)
assert new_labels.max() == 1
def paint(img):
"""
Turn a standard image into a painting.
:param numpy.ndarray img:
An RGB image in numpy format.
"""
# scale the image
img = cv2.convertScaleAbs(img)
# perform SLIC segmentation
regions_slic = slic(img,
compactness=SLIC_COMPACTNESS,
n_segments=N_SLIC_REGIONS)
# cluster all SLIC regions
rag = rag_mean_color(img, regions_slic)
regions_rag = cut_threshold(regions_slic, rag, RAG_THRESHOLD)
# final painting
result = label2rgb(regions_rag, img, kind="avg")
return result
f = plt.figure(figsize=(width, height))
plt.imshow(img)
img = data.coffee()
show_img(img)
labels = segmentation.slic(img, compactness=30, n_segments=400)
labels = labels + 1 # So that no labelled region is 0 and ignored by regionprops
regions = regionprops(labels)
label_rgb = color.label2rgb(labels, img, kind='avg')
show_img(label_rgb)
label_rgb = segmentation.mark_boundaries(label_rgb, labels, (0, 0, 0))
show_img(label_rgb)
rag = graph.rag_mean_color(img, labels)
for region in regions:
rag.node[region['label']]['centroid'] = region['centroid']
edges_drawn_all = display_edges(label_rgb, rag, np.inf )
show_img(edges_drawn_all)
edges_drawn_29 = display_edges(label_rgb, rag, 29 )
show_img(edges_drawn_29)
final_labels = graph.cut_threshold(labels, rag, 29)
final_label_rgb = color.label2rgb(final_labels, img, kind='avg')
show_img(final_label_rgb)
import numpy as np
from matplotlib import colors
def show_image(img):
width = img.shape[1] / 50.0
height = img.shape[0] * width/img.shape[1]
f = plt.figure(figsize=(width, height))
plt.imshow(img)
image = io.imread('C:\Users\sxun\Desktop\j.jpg')
show_image(image)
labels = segmentation.slic(image, compactness=30, n_segments=400)
border_image = segmentation.mark_boundaries(image, labels, (0, 0, 0))
show_image(border_image)
rag = graph.rag_mean_color(image, labels)
out = graph.draw_rag(labels, rag, border_image)
show_image(out)
out_labels=graph.cut_threshold(labels,rag,29)
out = graph.draw_rag(labels, rag, border_image)
show_image(out)
final_label_rgb = color.label2rgb(out_labels, image, kind='avg')
show_image(final_label_rgb)
"""
=====================================
Drawing Region Adjacency Graphs (RAGs)
======================================
This example constructs a Region Adjacency Graph (RAG) and draws it with
the `rag_draw` method.
"""
from skimage import graph, data, segmentation
from matplotlib import pyplot as plt, colors
img = data.coffee()
labels = segmentation.slic(img, compactness=30, n_segments=400)
g = graph.rag_mean_color(img, labels)
out = graph.draw_rag(labels, g, img)
plt.figure()
plt.title("RAG with all edges shown in green.")
plt.imshow(out)
# The color palette used was taken from
# http://www.colorcombos.com/color-schemes/2/ColorCombo2.html
cmap = colors.ListedColormap(['#6599FF', '#ff9900'])
out = graph.draw_rag(labels, g, img, node_color="#ffde00", colormap=cmap,
thresh=30, desaturate=True)
plt.figure()
plt.title("RAG with edge weights less than 30, color "
"mapped between blue and orange.")
plt.imshow(out)
Normalized Cut
==============
This example constructs a Region Adjacency Graph (RAG) and recursively performs
a Normalized Cut on it.
References
----------
.. [1] Shi, J.; Malik, J., "Normalized cuts and image segmentation",
Pattern Analysis and Machine Intelligence,
IEEE Transactions on, vol. 22, no. 8, pp. 888-905, August 2000.
"""
from skimage import graph, data, io, segmentation, color
from matplotlib import pyplot as plt
img = data.coffee()
labels1 = segmentation.slic(img, compactness=30, n_segments=400)
out1 = color.label2rgb(labels1, img, kind='avg')
g = graph.rag_mean_color(img, labels1, mode='similarity')
labels2 = graph.cut_normalized(labels1, g)
out2 = color.label2rgb(labels2, img, kind='avg')
plt.figure()
io.imshow(out1)
plt.figure()
io.imshow(out2)
io.show()
import numpy as np
from matplotlib import colors
def show_image(img):
width = img.shape[1] / 50.0
height = img.shape[0] * width/img.shape[1]
f = plt.figure(figsize=(width, height))
plt.imshow(img)
image = io.imread('C:\Users\sxun\Desktop\j.jpg')
show_image(image)
labels = segmentation.slic(image, compactness=30, n_segments=400)
border_image = segmentation.mark_boundaries(image, labels, (0, 0, 0))
show_image(border_image)
rag = graph.rag_mean_color(image, labels,connectivity=100)
out = graph.draw_rag(labels, rag, border_image)
show_image(out)
out_labels=graph.cut_threshold(labels,rag,29)
out = graph.draw_rag(labels, rag, border_image)
show_image(out)
final_label_rgb = color.label2rgb(out_labels, image, kind='avg')
show_image(final_label_rgb)
