from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from simplepreprocessor import SimplePreprocessor
from simpledatasetloader import SimpleDatasetLoader
from imutils import paths
import argparse

if __name__ == '__main__':
    ap = argparse.ArgumentParser()
    ap.add_argument("-d", "--dataset", required=True, help="path to input dataset")
    ap.add_argument("-k", "--neighbors", type=int, default=1, help="of nearest neighbors for classification")
    ap.add_argument("-j", "--jobs", type=int, help="of jobs for K-NN distance (-1 uses all variables cores)")
    args = vars(ap.parse_args())

    print("[INFO] loading images...")
    imagePaths = list(paths.list_images(args["dataset"]))

    sp = SimplePreprocessor(32, 32)
    sdl = SimpleDatasetLoader(preprocessors=[sp])
    (data, labels) = sdl.load(imagePaths, verbose=100)
    data = data.reshape((data.shape[0], 3072))

    print("[INFO] features matrix:{:.1f}MB".format(data.nbytes / (1024 *1000.0)))

    le = LabelEncoder()
    labels = le.fit_transform(labels)

    (trainX, testX, trainY, testY) = train_test_split(data, labels, test_size=0.25, random_state=42)

    print("[INFO] evaluating K-NN classifier...")

    model =KNeighborsClassifier(n_neighbors=3)
    model.fit(trainX, trainY)
    print(classification_report(testY, model.predict(testX), target_names=le.classes_))

