Python process_image_dsift 예제들

예제 #1

    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)

예제 #2

파일: ch08_P172_dsift.py 프로젝트: CBIR-LL/pcv-book-code

# -*- 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()

예제 #3

파일: ch08_P172_dsift.py 프로젝트: professor98911/PythonComputerVision-9-Image-Content-Classification

# -*- 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()

예제 #4

# -*- 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()

예제 #5

파일: ch8_dsift.py 프로젝트: LeeC20/PCV_Python3

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()

예제 #6

파일: ch08_P173_gesture.py 프로젝트: CBIR-LL/pcv-book-code

    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)

예제 #7

# -*- 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()

예제 #8

파일: ch08_P173_Fig8-3.py 프로젝트: professor98911/PythonComputerVision-9-Image-Content-Classification

# -*- 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()

예제 #9

파일: ch8_dsift.py 프로젝트: CharlieGit/PCV

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()

예제 #10

파일: ch08_P173_Fig8-3.py 프로젝트: CBIR-LL/pcv-book-code

# -*- 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()