이미지 증식

# Day_24_01_DogsAndCats.py
import tensorflow as tf
import os
import shutil
from PIL import Image
from sklearn import preprocessing, model_selection
import numpy as np

# dogs_and_cats
# +-- train
# +-- small
#       +-- train
#           +-- cat
#           +-- dog
#       +-- validation
#           +-- cat
#           +-- dog
#       +-- test
#           +-- cat
#           +-- dog


# 이미지 개와 고양이를 각각
# small/train 1000개, small/validation 500개, small/test 500개 복사하기
def make_dataset_folders():
    def make_if_not(dst_folder):
        if not os.path.exists(dst_folder):
            os.mkdir(dst_folder)

    # 부모폴더가 없어도 만들어줌
    def make_if_not_2(dst_folder):
        if not os.path.exists(dst_folder):
            os.makedirs(dst_folder)

    # make_if_not('dogs_and_cats/small')
    #
    # make_if_not('dogs_and_cats/small/train')
    # make_if_not('dogs_and_cats/small/validation')
    # make_if_not('dogs_and_cats/small/test')
    #
    # make_if_not('dogs_and_cats/small/train/cat')
    # make_if_not('dogs_and_cats/small/train/dog')
    # make_if_not('dogs_and_cats/small/validation/cat')
    # make_if_not('dogs_and_cats/small/validation/dog')
    # make_if_not('dogs_and_cats/small/test/cat')
    # make_if_not('dogs_and_cats/small/test/dog')
    # make_if_not('dogs_and_cats/small')
    make_if_not_2('dogs_and_cats/small/train')
    make_if_not_2('dogs_and_cats/small/validation')
    make_if_not_2('dogs_and_cats/small/test')

    make_if_not_2('dogs_and_cats/small/train/cat')
    make_if_not_2('dogs_and_cats/small/train/dog')
    make_if_not_2('dogs_and_cats/small/validation/cat')
    make_if_not_2('dogs_and_cats/small/validation/dog')
    make_if_not_2('dogs_and_cats/small/test/cat')
    make_if_not_2('dogs_and_cats/small/test/dog')


def make_small_datasets():
    def copy_animals(kind, start, end, dst_folder):
        for i in range(start, end):
            filename = '{}.{}.jpg'.format(kind, i)

            src_path = os.path.join('dogs_and_cats/train', filename)
            dst_path = os.path.join(dst_folder, filename)

            shutil.copy(src_path, dst_path)

    copy_animals('cat', 0, 1000, 'dogs_and_cats/small/train/cat')
    copy_animals('dog', 0, 1000, 'dogs_and_cats/small/train/dog')
    copy_animals('cat', 1000, 1500, 'dogs_and_cats/small/validation/cat')
    copy_animals('dog', 1000, 1500, 'dogs_and_cats/small/validation/dog')
    copy_animals('cat', 1500, 2000, 'dogs_and_cats/small/test/cat')
    copy_animals('dog', 1500, 2000, 'dogs_and_cats/small/test/dog')


def generator_basic():
    # 이미지 만들어내는 함수
    # 이미지를 만들어냄 방법만 알고 있고 소스를 다시 연결해줘야함
    gen = tf.keras.preprocessing.image.ImageDataGenerator()

    # 3가지 방법 -> (x,y), pandas, 폴더에 있는것
    # 우리 폴더 안에 있는 25000개의 데이터를 무제한으로 가져옴 -> 조심해야함
    flow = gen.flow_from_directory('dogs_and_cats/small/train',
                                   batch_size=7,
                                   target_size=(224, 224),  # resize 기능을 제공해줌
                                   class_mode='binary')     # 시그모이드에 어울리는 모드로 변환
    # class_mode    "categorical", "binary", "sparse"

    for i, (x, y) in enumerate(flow):
        print(x.shape, y.shape)     # (32, 256, 256, 3) (32, 2) 위에서 batch_size 안해주면 32로
        #               32개씩 Image 가져옴, y는 자동 셔플해서 one_hot 벡터로 가져옴
        print(y[:3])

        if i >= 2:
            break
    # hi

    return


# make_dataset_folders()
# make_small_datasets()
generator_basic()

# ------------------- #
# d = {'name': 'joo', 'age': 26}
# a = [d, d, d]
# b = [{'name': 'joo', 'age': 26}] * 3 + [{'name': 'joo', 'age': 26}]
#
# print(a)
# print(b)
# ------------------- #


# return resnet_utils.Block(scope, bottleneck, [{
#       'depth': base_depth * 4,
#       'depth_bottleneck': base_depth,
#       'stride': 1
#   }] * (num_units - 1) + [{
#       'depth': base_depth * 4,
#       'depth_bottleneck': base_depth,
#       'stride': stride
#   }])