files = glob.glob(data_path)
image = np.zeros(32).reshape((1, 32))
for f1 in files:
img_vec = np.loadtxt(f1[:-4] + '.txt')
#image = kmeans.BoVW(means, img_vec)
image_row = kmeans.BoVW(means, img_vec).reshape((1, 32))
image = np.concatenate((image, image_row), axis = 0)
print(f1)
np.savetxt('image_data.txt', image)
image = np.loadtxt('image_data.txt')
"""
img = cv2.imread('31.png')
print(img)
img_obj = ImageHandler(img)
x = img_obj.ToShiftedPatches()
x = np.array(x)
np.savetxt('cell_1.txt', x)
kmeans_obj = KMeans(3, x)
kmeans_obj.fit(3, 0.002)
means = kmeans_obj.mean_vec
cov_mat_list = kmeans_obj.CovMatrix()
mixture_coeff = kmeans_obj.MixtureCoeff()
print(cov_mat_list)
"""from sklearn.cluster import KMeans
obj = KMeans(n_clusters = 3, init = 'k-means++', max_iter = 100, n_init = 10, random_state = 0)
y_Kmeans = obj.fit_predict(x)
