def test(num_rows, num_features, X, y, namesClasses = list()):
print "Testing"
# get the classnames from the directory structure
directory_names = list(set(glob.glob(os.path.join("test", "*"))).difference(set(glob.glob(os.path.join("test", "*.*")))))
# X is the feature vector with one row of features per image
# consisting of the pixel values and our metric
X_test = np.zeros((num_rows, num_features), dtype=float)
# y is the numeric class label
y_test = np.zeros((num_rows))
true_res = list()
files = []
parseImage.readImage(False,list(), directory_names,
X_test, y_test,files, true_res)
# Random forest build test
clf = RF(n_estimators=100, n_jobs=3)
clf.fit(X, y)
y_predict = clf.predict(X_test)
total = 0
resLen = len( true_res)
writer = open('result.txt', 'w')
for cur in range(resLen):
real_res = namesClasses[int(y_predict[cur])]
writer.write(real_res + " "+ true_res[cur] + "\n")
if true_res[cur] == real_res :
total += 1
print float(total)/float(resLen)
writer.close()
def main():
print "In Main Experiment\n"
# get the classnames from the directory structure
directory_names = list(set(glob.glob(os.path.join("train", "*"))).difference(set(glob.glob(os.path.join("train", "*.*")))))
# get the number of rows through image count
numberofImages = parseImage.gestNumberofImages(directory_names)
num_rows = numberofImages # one row for each image in the training dataset
# We'll rescale the images to be 25x25
maxPixel = 25
imageSize = maxPixel * maxPixel
num_features = imageSize + 2 + 128 # for our ratio
X = np.zeros((num_rows, num_features), dtype=float)
y = np.zeros((num_rows)) # numeric class label
files = []
namesClasses = list() #class name list
# Get the image training data
parseImage.readImage(True, namesClasses, directory_names,X, y, files)
print "Training"
# get test result
train.train(X, y, namesClasses)
print "Testing"
test.test(num_rows, num_features, X, y, namesClasses = list())