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)
pklist=['points_normal.pkl','points_ring.pkl']
figure()
# load 2D points using Pickle
for i, pklfile in enumerate(pklist):
with open(pklfile, 'r') as f:
class_1 = pickle.load(f)
class_2 = pickle.load(f)
labels = pickle.load(f)
# load test data using Pickle
with open(pklfile[:-4]+'_test.pkl', 'r') as f:
class_1 = pickle.load(f)
class_2 = pickle.load(f)
labels = pickle.load(f)
model = knn.KnnClassifier(labels,vstack((class_1,class_2)))
# test on the first point
print model.classify(class_1[0])
#define function for plotting
def classify(x,y,model=model):
return array([model.classify([xx,yy]) for (xx,yy) in zip(x,y)])
# lot the classification boundary
subplot(1,2,i+1)
imtools.plot_2D_boundary([-6,6,-6,6],[class_1,class_2],classify,[1,-1])
titlename=pklfile[:-4]
title(titlename)
show()