"""
Visiualize segmentation contours on original grayscale image.
"""
from skimage import data, segmentation, filter, color
import matplotlib.pyplot as plt
coins = data.coins()
mask = coins > filter.threshold_otsu(coins)
clean_border = segmentation.clear_border(mask)
coins_edges = segmentation.visualize_boundaries(color.gray2rgb(coins),
clean_border)
plt.figure(figsize=(8, 3.5))
plt.subplot(121)
plt.imshow(clean_border, cmap='gray')
plt.axis('off')
plt.subplot(122)
plt.imshow(coins_edges)
plt.axis('off')
plt.tight_layout()
plt.show()
# -*- coding: cp936 -*-
"""
Created on Tue Dec 09 16:14:53 2014
@author: shuaiyi
"""
import skimage.segmentation as seg
import skimage.io as io
import matplotlib.pyplot as plt
img = io.imread("test.png")
ax = plt.figure()
segments_slic = seg.slic(img, n_segments=1000, compactness=10, sigma=1)
plt.imshow(seg.visualize_boundaries(img, segments_slic))
# for fun
# def f(n):
# if n == 1:
# return 1000*1.047
# return (f(n-1)+1000)*1.047
# -*- coding: cp936 -*-
"""
Created on Tue Dec 09 16:14:53 2014
@author: shuaiyi
"""
import skimage.segmentation as seg
import skimage.io as io
import matplotlib.pyplot as plt
img = io.imread('test.png')
ax = plt.figure()
segments_slic = seg.slic(img, n_segments=1000, compactness=10, sigma=1)
plt.imshow(seg.visualize_boundaries(img, segments_slic))
#for fun
#def f(n):
# if n == 1:
# return 1000*1.047
# return (f(n-1)+1000)*1.047
import numpy as np
from skimage.data import lena
from skimage.segmentation import felzenszwalb, \
visualize_boundaries, slic, quickshift
from skimage.util import img_as_float
img = img_as_float(lena()[::2, ::2])
segments_fz = felzenszwalb(img, scale=100, sigma=0.5, min_size=50)
segments_slic = slic(img, ratio=10, n_segments=250, sigma=1)
segments_quick = quickshift(img, kernel_size=3, max_dist=6, ratio=0.5)
print("Felzenszwalb's number of segments: %d" % len(np.unique(segments_fz)))
print("Slic number of segments: %d" % len(np.unique(segments_slic)))
print("Quickshift number of segments: %d" % len(np.unique(segments_quick)))
fig, ax = plt.subplots(1, 3)
fig.set_size_inches(8, 3, forward=True)
plt.subplots_adjust(0.05, 0.05, 0.95, 0.95, 0.05, 0.05)
ax[0].imshow(visualize_boundaries(img, segments_fz))
ax[0].set_title("Felzenszwalbs's method")
ax[1].imshow(visualize_boundaries(img, segments_slic))
ax[1].set_title("SLIC")
ax[2].imshow(visualize_boundaries(img, segments_quick))
ax[2].set_title("Quickshift")
for a in ax:
a.set_xticks(())
a.set_yticks(())
plt.show()
k["Y"] = Y
del k['Centroid']
fieldnames = ['Label', 'MeanIntensity', 'Y', 'X', 'Area', 'Perimeter','MajorAxisLength']
output_results = dir_output+seg+ws+str(i)+'_results.txt'
test_file = open(output_results,'wb')
csvwriter = csv.DictWriter(test_file, delimiter='\t', fieldnames=fieldnames)
csvwriter.writerow(dict((fn,fn) for fn in fieldnames))
for row in props_final:
csvwriter.writerow(row)
test_file.close()
# find outline of objects for plotting
boundaries = find_boundaries(labelled_image)
img_rgb = gray2rgb(image)
overlay = np.flipud(visualize_boundaries(img_rgb,boundaries))
# plot overlay between original picture and identified objects
# coordinates are delivered as tuples with first height and then width, opposite to plotting first x (width) and then y (height)
coords_rev = [(b, a) for a, b in coords]
rcParams['savefig.dpi'] = 700
fig = figure(figsize=(9.36, 6.24))
canvas = FigureCanvas(fig)
plot = fig.add_subplot(111)
for label, xy in zip(object_label, coords_rev):
plot.annotate(label,xy=xy,xytext=None,ha='center',va='center',color='red',fontsize=1)
plot.imshow(np.flipud(overlay))
height, width = image.shape
plot.set_xlim(0, width)
plot.set_ylim(0, height)
plot.get_xaxis().set_visible(False)
