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brendenlake small change to README de6e867 Oct 21, 2015
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import numpy as np
import copy
from scipy.ndimage import imread
from scipy.spatial.distance import cdist
# Parameters
nrun = 20 # number of classification runs
fname_label = 'class_labels.txt' # where class labels are stored for each run
def classification_run(folder,f_load,f_cost,ftype='cost'):
# Compute error rate for one run of one-shot classification
#
# Input
# folder : contains images for a run of one-shot classification
# f_load : itemA = f_load('file.png') should read in the image file and process it
# f_cost : f_cost(itemA,itemB) should compute similarity between two images, using output of f_load
# ftype : 'cost' if small values from f_cost mean more similar, or 'score' if large values are more similar
#
# Output
# perror : percent errors (0 to 100% error)
#
assert ((ftype=='cost') | (ftype=='score'))
# get file names
with open(folder+'/'+fname_label) as f:
content = f.read().splitlines()
pairs = [line.split() for line in content]
test_files = [pair[0] for pair in pairs]
train_files = [pair[1] for pair in pairs]
answers_files = copy.copy(train_files)
test_files.sort()
train_files.sort()
ntrain = len(train_files)
ntest = len(test_files)
# load the images (and, if needed, extract features)
train_items = [f_load(f) for f in train_files]
test_items = [f_load(f) for f in test_files ]
# compute cost matrix
costM = np.zeros((ntest,ntrain),float)
for i in range(ntest):
for c in range(ntrain):
costM[i,c] = f_cost(test_items[i],train_items[c])
if ftype == 'cost':
YHAT = np.argmin(costM,axis=1)
elif ftype == 'score':
YHAT = np.argmax(costM,axis=1)
else:
assert False
# compute the error rate
correct = 0.0
for i in range(ntest):
if train_files[YHAT[i]] == answers_files[i]:
correct += 1.0
pcorrect = 100 * correct / ntest
perror = 100 - pcorrect
return perror
def ModHausdorffDistance(itemA,itemB):
# Modified Hausdorff Distance
#
# Input
# itemA : [n x 2] coordinates of "inked" pixels
# itemB : [m x 2] coordinates of "inked" pixels
#
# M.-P. Dubuisson, A. K. Jain (1994). A modified hausdorff distance for object matching.
# International Conference on Pattern Recognition, pp. 566-568.
#
D = cdist(itemA,itemB)
mindist_A = D.min(axis=1)
mindist_B = D.min(axis=0)
mean_A = np.mean(mindist_A)
mean_B = np.mean(mindist_B)
return max(mean_A,mean_B)
def LoadImgAsPoints(fn):
# Load image file and return coordinates of 'inked' pixels in the binary image
#
# Output:
# D : [n x 2] rows are coordinates
I = imread(fn,flatten=True)
I = np.array(I,dtype=bool)
I = np.logical_not(I)
(row,col) = I.nonzero()
D = np.array([row,col])
D = np.transpose(D)
D = D.astype(float)
n = D.shape[0]
mean = np.mean(D,axis=0)
for i in range(n):
D[i,:] = D[i,:] - mean
return D
if __name__ == "__main__":
#
# Running this demo should lead to a result of 38.8 percent errors.
#
# M.-P. Dubuisson, A. K. Jain (1994). A modified hausdorff distance for object matching.
# International Conference on Pattern Recognition, pp. 566-568.
#
# ** Models should be trained on images in 'images_background' directory to avoid
# using images and alphabets used in the one-shot evaluation **
#
print 'One-shot classification demo with Modified Hausdorff Distance'
perror = np.zeros(nrun)
for r in range(1,nrun+1):
rs = str(r)
if len(rs)==1:
rs = '0' + rs
perror[r-1] = classification_run('run'+rs, LoadImgAsPoints, ModHausdorffDistance, 'cost')
print " run " + str(r) + " (error " + str( perror[r-1] ) + "%)"
total = np.mean(perror)
print " average error " + str(total) + "%"
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