from pylab import *
from PIL import Image
from PCV.localdescriptors import harris
from numpy import *
im = asarray(Image.open('1.jpeg').convert('L'))
harrisim = harris.compute_harris_response(im)
harrisim1 = 255 - harrisim
figure()
gray()
subplot(141)
imshow(harrisim1)
threshold = [0.01, 0.05, 0.1]
for i, thres in enumerate(threshold):
filtered_coords = harris.get_harris_points(harrisim, 10, thres)
subplot(1, 4, i + 2)
imshow(im)
plot([p[1] for p in filtered_coords], [p[0] for p in filtered_coords], '*')
axis('off')
show()
from pylab import *
from numpy import *
from PIL import Image
from PCV.localdescriptors import harris
"""
Example of detecting Harris corner points (Figure 2-1 in the book).
"""
# open image
im = array(Image.open('../data/empire.jpg').convert('L'))
# detect corners and plot
harrisim = harris.compute_harris_response(im)
filtered_coords = harris.get_harris_points(harrisim, 10, threshold=0.01)
harris.plot_harris_points(im, filtered_coords)
# plot only 200 strongest
harris.plot_harris_points(im, filtered_coords[:200])
from PCV.localdescriptors import harris
from PCV.tools.imtools import imresize
"""
This is the Harris point matching example in Figure 2-2.
"""
im1 = array(Image.open("../data/crans_1_small.jpg").convert("L"))
im2 = array(Image.open("../data/crans_2_small.jpg").convert("L"))
# resize to make matching faster
im1 = imresize(im1,(im1.shape[1]/2,im1.shape[0]/2))
im2 = imresize(im2,(im2.shape[1]/2,im2.shape[0]/2))
wid = 5
harrisim = harris.compute_harris_response(im1,5)
filtered_coords1 = harris.get_harris_points(harrisim,wid+1)
d1 = harris.get_descriptors(im1,filtered_coords1,wid)
harrisim = harris.compute_harris_response(im2,5)
filtered_coords2 = harris.get_harris_points(harrisim,wid+1)
d2 = harris.get_descriptors(im2,filtered_coords2,wid)
print 'starting matching'
matches = harris.match_twosided(d1,d2)
figure()
gray()
harris.plot_matches(im1,im2,filtered_coords1,filtered_coords2,matches)
show()
from PIL import Image
from PCV.localdescriptors import harris
from PCV.tools.imtools import imresize
"""
This is the Harris point matching example in Figure 2-2.
"""
im1 = array(Image.open("data/crans_1_small.jpg").convert("L"))
im2 = array(Image.open("data/crans_2_small.jpg").convert("L"))
# resize to make matching faster
im1 = imresize(im1, (im1.shape[1] / 2, im1.shape[0] / 2))
im2 = imresize(im2, (im2.shape[1] / 2, im2.shape[0] / 2))
wid = 5
harrisim = harris.compute_harris_response(im1, 5)
filtered_coords1 = harris.get_harris_points(harrisim, wid + 1)
d1 = harris.get_descriptors(im1, filtered_coords1, wid)
harrisim = harris.compute_harris_response(im2, 5)
filtered_coords2 = harris.get_harris_points(harrisim, wid + 1)
d2 = harris.get_descriptors(im2, filtered_coords2, wid)
print 'starting matching'
matches = harris.match_twosided(d1, d2)
figure()
gray()
harris.plot_matches(im1, im2, filtered_coords1, filtered_coords2, matches)
show()
from PCV.localdescriptors import harris
# 添加中文字体支持
from matplotlib.font_manager import FontProperties
font = FontProperties(fname=r"c:\windows\fonts\SimSun.ttc", size=14)
imname = '../data/empire.jpg'
im = array(Image.open(imname).convert('L'))
sift.process_image(imname, 'empire.sift')
l1, d1 = sift.read_features_from_file('empire.sift')
figure()
gray()
subplot(131)
sift.plot_features(im, l1, circle=False)
title(u'SIFT特征', fontproperties=font)
subplot(132)
sift.plot_features(im, l1, circle=True)
title(u'用圆圈表示SIFT特征尺度', fontproperties=font)
# 检测harris角点
harrisim = harris.compute_harris_response(im)
subplot(133)
filtered_coords = harris.get_harris_points(harrisim, 6, 0.1)
imshow(im)
plot([p[1] for p in filtered_coords], [p[0] for p in filtered_coords], '*')
axis('off')
title(u'Harris角点', fontproperties=font)
show()
harrisim = harris.compute_harris_response(im)
# Harris响应函数
harrisim1 = 255 - harrisim
figure()
gray()
#画出Harris响应图
subplot(141)
imshow(harrisim1)
print harrisim1.shape
axis('off')
axis('equal')
threshold = [0.01, 0.05, 0.1]
for i, thres in enumerate(threshold):
filtered_coords = harris.get_harris_points(harrisim, 6, thres)
subplot(1, 4, i+2)
imshow(im)
print im.shape
plot([p[1] for p in filtered_coords], [p[0] for p in filtered_coords], '*')
axis('off')
#原书采用的PCV中PCV harris模块
#harris.plot_harris_points(im, filtered_coords)
# plot only 200 strongest
# harris.plot_harris_points(im, filtered_coords[:200])
show()