def testSVM(testDataSetpath,clf):
for image in testDataSetpath:
if label(image)==clf.predict(extract_feature(image)):
ok+=1
else:
ko+=1
print "accuracy:" + float( ok)/(ok+ko)
def read_data(path):
X=[]
Y=[]
empty_path = path + "/empty"
white_path = path + "/white"
black_path = path + "/black"
paths = [empty_path, white_path, black_path]
for i,path in enumerate(paths):
onlyfiles = [ f for f in listdir(path) if isfile(join(path,f)) ]
for image in onlyfiles:
image_array = cv2.imread(path + "/" + image)
X.append(extract_feature(image_array))
Y.append(i)
X,Y = data_shuffle(X,Y)
return (np.asarray(X),np.asarray(Y))
