def testsift(image1, image2, maxd=1000, distthresh=0.4):
im1 = imread(image1)
print im1[:, :]
im2 = imread(image2)
print im2.shape
kdt = [[], [], []]
d1 = [[], [], []]
d2 = [[], [], []]
C = 3
for channel in range(C):
if C == 1:
_, d1[channel] = sift.descriptors(im1, maxd)
else:
_, d1[channel] = sift.descriptors(im1[:, :, channel], maxd)
print channel, d1[channel].shape
if C == 1:
_, d2[channel] = sift.descriptors(im2, maxd)
else:
_, d2[channel] = sift.descriptors(im2[:, :, channel], maxd)
print channel, d2[channel].shape
kdt[channel] = KDTree(d1[channel])
for desc in d2[channel]:
dist, index = kdt[channel].query(desc)
if distthresh >= dist:
print dist, index
def imagedesc(filename, imgsize, maxd, crop, datamode):
if datamode == 1:
im = imread(filename, flatten=True)
if crop > 0:
hpix = int(im.shape[0] * crop)
wpix = int(im.shape[1] * crop)
im = im[hpix : im.shape[0] - hpix, wpix : im.shape[1] - wpix]
im = imresize(im, float(imgsize) / max(im.shape))
# im = (exposure.equalize_hist(im)*256).astype(np.float32)
_, d = sift.descriptors(im, maxd)
return d
if datamode == 2:
im = imread(filename)
if crop > 0:
hpix = int(im.shape[0] * crop)
wpix = int(im.shape[1] * crop)
im = im[hpix : im.shape[0] - hpix, wpix : im.shape[1] - wpix, :]
im = imresize(im, float(imgsize) / max(im.shape))
d = [None, None, None]
for channel in range(3):
# im = (exposure.equalize_hist(im)*256).astype(np.float32)
_, d[channel] = sift.descriptors(im[:, :, channel], maxd)
return d
def imagedescriptors(image_filename, object_id):
ndimage = imread(path_images + image_filename)
if len(ndimage.shape) == 2:
print "Warning: image %s is an grayscale image" % image_filename
return None
size = max(ndimage[:, :, 0].shape)
ndimage = imresize(ndimage, 360.0 / size)
print ndimage.shape
result = []
for channel in range(3):
print object_id, image_filename
keypoints, descriptors = sift.descriptors(ndimage[:, :, channel], max_descriptors)
nd = descriptors.shape[0]
descriptors = descriptors.reshape(nd, 128)
result.append(descriptors)
return result
import sift
import numpy as np
from scipy.misc import imread,imshow,imresize
import matplotlib
from time import time
im=imread("test2.jpg")
#im = matplotlib.colors.rgb_to_hsv(im)
R=im[:,:,0].astype(np.float32)
G=im[:,:,1].astype(np.float32)
B=im[:,:,2].astype(np.float32)
for channel in [R,G,B]:
t = time()
keypoints, descriptors = sift.descriptors(channel, 30)
print time() - t
print descriptors.shape
for point in keypoints:
channel[point[1],point[0]] = 0
channel[point[1],point[0]+1] = 255
imshow(channel)