sift_extractors.py

import cv2
from sklearn.cluster import KMeans
import pickle
from scipy.spatial.distance import cdist
import os
import numpy as np


def extract_sift_features(list_image):

    image_descriptors = []
    sift = cv2.xfeatures2d.SIFT_create()
    for image in list_image:
        _, descriptor = sift.detectAndCompute(image, None)
        image_descriptors.append(descriptor)

    return image_descriptors

def kmean_bow(all_descriptors, num_cluster):
    bow_dict = []

    kmeans = KMeans(n_clusters = num_cluster)
    kmeans.fit(all_descriptors)

    bow_dict = kmeans.cluster_centers_

    if not os.path.isfile('bow_dictionary.pkl'):
        pickle.dump(bow_dict, open('bow_dictionary.pkl', 'wb'))

    return bow_dict

def create_feature_bow(image_descriptors, BoW, num_cluster):

    X_features = []

    for i in range(len(image_descriptors)):
        features = np.array([0] * num_cluster)

        if image_descriptors[i] is not None:
            distance = cdist(image_descriptors[i], BoW)

            argmin = np.argmin(distance, axis = 1)

            for j in argmin:
                features[j] += 1
        X_features.append(features)

    return X_features