import cv2
import numpy as np
import matplotlib.pyplot as plt
from glob import glob

# Dicrease color
def dic_color(img):
    img //= 63
    img = img * 64 + 32
    return img


# Database
def get_DB():
    # get training image path
    train = glob("dataset/train_*")
    train.sort()

    # prepare database
    db = np.zeros((len(train), 13), dtype=np.int32)
    pdb = []

    # each train
    for i, path in enumerate(train):
        # read image
        img = dic_color(cv2.imread(path))
        # histogram
        for j in range(4):
            db[i, j] = len(np.where(img[..., 0] == (64 * j + 32))[0])
            db[i, j+4] = len(np.where(img[..., 1] == (64 * j + 32))[0])
            db[i, j+8] = len(np.where(img[..., 2] == (64 * j + 32))[0])

        # get class
        if 'akahara' in path:
            cls = 0
        elif 'madara' in path:
            cls = 1

        # store class label
        db[i, -1] = cls

        # add image path
        pdb.append(path)

    return db, pdb

# k-Means
def k_means(db, pdb, Class=2, th=0.5):
    # copy database
    feats = db.copy()

    # initiate random seed
    np.random.seed(4)

    # assign random class 
    for i in range(len(feats)):
        if np.random.random() < th:
            feats[i, -1] = 0
        else:
            feats[i, -1] = 1

    while True:
        # prepare greavity
        gs = np.zeros((Class, 12), dtype=np.float32)
        change_count = 0

        # compute gravity
        for i in range(Class):
            gs[i] = np.mean(feats[np.where(feats[..., -1] == i)[0], :12], axis=0)

        # re-labeling
        for i in range(len(feats)):
            # get distance each nearest graviry
            dis = np.sqrt(np.sum(np.square(np.abs(gs - feats[i, :12])), axis=1))

            # get new label
            pred = np.argmin(dis, axis=0)

            # if label is difference from old label
            if int(feats[i, -1]) != pred:
                change_count += 1
                feats[i, -1] = pred

        if change_count < 1:
            break

    for i in range(db.shape[0]):
        print(pdb[i], " Pred:", feats[i, -1])


db, pdb = get_DB()
k_means(db, pdb, th=0.3)
pythonで、K-meansを用いて*train_@@@.jpg*の10枚を完璧にクラスタリングせよ。

ここでは、np.random.seed()の値やラベルを割り当てる閾値 np.random.random() < th　のthを変更して、K-meansでクラスを完璧に予測せよ。
train_@@@.jpgはQ.89より画像数が２倍以上になっているので、クラスタリングしやすくなっている。

これは試行錯誤するしかない。