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 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 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)
""" ================ RAG Thresholding ================ This example constructs a Region Adjacency Graph (RAG) and merges regions which are similar in color. We construct a RAG and define edges as the difference in mean color. We then join regions with similar mean color. """ 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) labels2 = graph.cut_threshold(labels1, g, 29) out2 = color.label2rgb(labels2, img, kind='avg') plt.figure() io.imshow(out1) plt.figure() io.imshow(out2) io.show()
""" ================ RAG Thresholding ================ This example constructs a Region Adjacency Graph (RAG) and merges regions which are similar in color. We construct a RAG and define edges as the difference in mean color. We then join regions with similar mean color. """ 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) labels2 = graph.cut_threshold(labels1, g, 30) out2 = color.label2rgb(labels2, img, kind='avg') plt.figure() io.imshow(out1) plt.figure() io.imshow(out2) io.show()
