answer_rotation.py

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

np.random.seed(0)

num_classes = 2
img_height, img_width = 64, 64

CLS = ['akahara', 'madara']

# get train data
def data_load(path, hf=False, vf=False, rot=False):
    xs = []
    ts = []
    paths = []
    
    for dir_path in glob(path + '/*'):
        for path in glob(dir_path + '/*'):
            x = cv2.imread(path)
            x = cv2.resize(x, (img_width, img_height)).astype(np.float32)
            x /= 255.
            x = x[..., ::-1]
            xs.append(x)

            for i, cls in enumerate(CLS):
                if cls in path:
                    t = i
            
            ts.append(t)

            paths.append(path)

            if hf:
                xs.append(x[:, ::-1])
                ts.append(t)
                paths.append(path)

            if vf:
                xs.append(x[::-1])
                ts.append(t)
                paths.append(path)

            if hf and vf:
                xs.append(x[::-1, ::-1])
                ts.append(t)
                paths.append(path)

            if rot != False:
                angle = rot
                scale = 1

                # show
                a_num = 360 // rot
                w_num = np.ceil(np.sqrt(a_num))
                h_num = np.ceil(a_num / w_num)
                count = 1
                plt.subplot(h_num, w_num, count)
                plt.axis('off')
                plt.imshow(x)
                plt.title("angle=0")
                
                while angle < 360:
                    _h, _w, _c = x.shape
                    max_side = max(_h, _w)
                    tmp = np.zeros((max_side, max_side, _c))
                    tx = int((max_side - _w) / 2)
                    ty = int((max_side - _h) / 2)
                    tmp[ty: ty+_h, tx: tx+_w] = x.copy()
                    M = cv2.getRotationMatrix2D((max_side/2, max_side/2), angle, scale)
                    _x = cv2.warpAffine(tmp, M, (max_side, max_side))
                    _x = _x[tx:tx+_w, ty:ty+_h]
                    xs.append(x)
                    ts.append(t)
                    paths.append(path)

                    # show
                    count += 1
                    plt.subplot(h_num, w_num, count)
                    plt.imshow(_x)
                    plt.axis('off')
                    plt.title("angle={}".format(angle))

                    angle += rot
                plt.show()


    xs = np.array(xs, dtype=np.float32)
    ts = np.array(ts, dtype=np.int)
    
    xs = xs.transpose(0,3,1,2)

    return xs, ts, paths


xs, ts, paths = data_load("../Dataset/train/images/", hf=True, vf=True, rot=1)

mb = 3
mbi = 0
train_ind = np.arange(len(xs))
np.random.seed(0)
np.random.shuffle(train_ind)

for i in range(10):
    if mbi + mb > len(xs):
        mb_ind = train_ind[mbi:]
        np.random.shuffle(train_ind)
        mb_ind = np.hstack((mb_ind, train_ind[:(mb-(len(xs)-mbi))]))
        mbi = mb - (len(xs) - mbi)
    else:
        mb_ind = train_ind[mbi: mbi+mb]
        mbi += mb

    print(mb_ind)