confuse = zeros((n, n))
for i in range(len(test_labels)):
confuse[class_ind[res[i]], class_ind[test_labels[i]]] += 1
print('Confusion matrix for')
print(classnames)
print(confuse)
filelist_train = get_imagelist('../data/gesture/train')
filelist_test = get_imagelist('../data/gesture/test')
imlist = filelist_train + filelist_test
# process images at fixed size (50,50)
for filename in imlist:
featfile = filename[:-3] + 'dsift'
dsift.process_image_dsift(filename, featfile, 10, 5, resize=(50, 50))
features, labels = read_gesture_features_labels('../data/gesture/train/')
test_features, test_labels = read_gesture_features_labels('../data/gesture/test/')
classnames = unique(labels)
# test kNN
k = 1
knn_classifier = knn.KnnClassifier(labels, features)
res = array([knn_classifier.classify(test_features[i], k) for i in
range(len(test_labels))])
# accuracy
acc = sum(1.0 * (res == test_labels)) / len(test_labels)
print('Accuracy:', acc)
print_confusion(res, test_labels, classnames)
# -*- coding: utf-8 -*-
from PCV.localdescriptors import sift, dsift
from pylab import *
from PIL import Image
dsift.process_image_dsift('../data/empire.jpg','empire.dsift',90,40,True)
l,d = sift.read_features_from_file('empire.dsift')
im = array(Image.open('../data/empire.jpg'))
sift.plot_features(im,l,True)
title('dense SIFT')
show()
# -*- coding: utf-8 -*-
from PCV.localdescriptors import sift, dsift
from pylab import *
from PIL import Image
dsift.process_image_dsift('gesture/empire.jpg', 'empire.dsift', 90, 40, True)
l, d = sift.read_features_from_file('empire.dsift')
im = array(Image.open('gesture/empire.jpg'))
sift.plot_features(im, l, True)
title('dense SIFT')
show()
# -*- coding: utf-8 -*-
import os
from PCV.localdescriptors import sift, dsift
from pylab import *
from PIL import Image
imlist = [
'../data/gesture/train/A-uniform01.ppm',
'../data/gesture/train/B-uniform01.ppm',
'../data/gesture/train/C-uniform01.ppm',
'../data/gesture/train/Five-uniform01.ppm',
'../data/gesture/train/Point-uniform01.ppm',
'../data/gesture/train/V-uniform01.ppm'
]
figure()
for i, im in enumerate(imlist):
dsift.process_image_dsift(im, im[:-3] + '.dsift', 90, 40, True)
l, d = sift.read_features_from_file(im[:-3] + 'dsift')
dirpath, filename = os.path.split(im)
im = array(Image.open(im))
#显示手势含义title
titlename = filename[:-14]
subplot(2, 3, i + 1)
sift.plot_features(im, l, True)
title(titlename)
show()
from PIL import Image
from pylab import *
from numpy import *
from PCV.localdescriptors import dsift, sift
"""
This is the dense SIFT illustration, it will reproduce the plot
in Figure 8-2.
"""
dsift.process_image_dsift('../data/empire.jpg', 'empire.sift', 90, 40, True)
l, d = sift.read_features_from_file('empire.sift')
im = array(Image.open('../data/empire.jpg'))
sift.plot_features(im, l, True)
show()
class_ind = dict([(classnames[i],i) for i in range(n)])
confuse = zeros((n,n))
for i in range(len(test_labels)):
confuse[class_ind[res[i]],class_ind[test_labels[i]]] += 1
print 'Confusion matrix for'
print classnames
print confuse
filelist_train = get_imagelist('../data/gesture/train')
filelist_test = get_imagelist('../data/gesture/test')
imlist=filelist_train+filelist_test
# process images at fixed size (50,50)
for filename in imlist:
featfile = filename[:-3]+'dsift'
dsift.process_image_dsift(filename,featfile,10,5,resize=(50,50))
features,labels = read_gesture_features_labels('../data/gesture/train/')
test_features,test_labels = read_gesture_features_labels('../data/gesture/test/')
classnames = unique(labels)
# test kNN
k = 1
knn_classifier = knn.KnnClassifier(labels,features)
res = array([knn_classifier.classify(test_features[i],k) for i in
range(len(test_labels))])
# accuracy
acc = sum(1.0*(res==test_labels)) / len(test_labels)
print 'Accuracy:', acc
print_confusion(res,test_labels,classnames)
# -*- coding: utf-8 -*-
from PCV.localdescriptors import sift, dsift
from pylab import *
from PIL import Image
dsift.process_image_dsift('../data/empire.jpg', 'empire.dsift', 90, 40, True)
l, d = sift.read_features_from_file('empire.dsift')
im = array(Image.open('../data/empire.jpg'))
sift.plot_features(im, l, True)
title('dense SIFT')
show()
# -*- coding: utf-8 -*-
import os
from PCV.localdescriptors import sift, dsift
from pylab import *
from PIL import Image
imlist = [
'gesture/image2/B-uniform01.jpg', 'gesture/image2/F-uniform01.jpg',
'gesture/image2/G-uniform01.jpg', 'gesture/image2/L-uniform01.jpg',
'gesture/image2/O-uniform01.jpg', 'gesture/image2/V-uniform01.jpg'
]
figure()
for i, im in enumerate(imlist):
print(im)
dsift.process_image_dsift(im, im[:-3] + 'dsift', 10, 5, True)
l, d = sift.read_features_from_file(im[:-3] + 'dsift')
dirpath, filename = os.path.split(im)
im = array(Image.open(im))
#显示手势含义title
titlename = filename[:-14]
subplot(2, 3, i + 1)
sift.plot_features(im, l, True)
title(titlename)
show()
from PIL import Image
from pylab import *
from numpy import *
from PCV.localdescriptors import dsift, sift
"""
This is the dense SIFT illustration, it will reproduce the plot
in Figure 8-2.
"""
dsift.process_image_dsift('../data/empire.jpg', 'empire.sift', 90, 40, True)
l,d = sift.read_features_from_file('empire.sift')
im = array(Image.open('../data/empire.jpg'))
sift.plot_features(im, l, True)
show()
# -*- coding: utf-8 -*-
import os
from PCV.localdescriptors import sift, dsift
from pylab import *
from PIL import Image
imlist=['../data/gesture/train/A-uniform01.ppm','../data/gesture/train/B-uniform01.ppm',
'../data/gesture/train/C-uniform01.ppm','../data/gesture/train/Five-uniform01.ppm',
'../data/gesture/train/Point-uniform01.ppm','../data/gesture/train/V-uniform01.ppm']
figure()
for i, im in enumerate(imlist):
dsift.process_image_dsift(im,im[:-3]+'.dsift',90,40,True)
l,d = sift.read_features_from_file(im[:-3]+'dsift')
dirpath, filename=os.path.split(im)
im = array(Image.open(im))
#显示手势含义title
titlename=filename[:-14]
subplot(2,3,i+1)
sift.plot_features(im,l,True)
title(titlename)
show()
