Python compute_harris_responseの例

コード例 #1

ファイル: 01.py プロジェクト: VRER1997/python_CV

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()

コード例 #2

ファイル: ch2_harris_matching.py プロジェクト: Adon-m/PCV

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()

コード例 #3

ファイル: ch2_harris_corners.py プロジェクト: Adon-m/PCV

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])

コード例 #4

ファイル: harris_match.py プロジェクト: mouthwatering/computer_vision

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()