#!/usr/bin/env python
# coding: utf-8

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import tensorflow as tf
import os
from keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras import layers
from tensorflow import keras


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np.random.seed(42)
tf.random.set_seed(42)


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# 데이터셋 불러오기

base = 'C:/Users/LJH/Documents/코드스테이츠/section4/project/train'

ghost_dir = os.path.join(base, 'ghost')
encanto_dir = os.path.join(base, 'encanto')
mother_dir = os.path.join(base, 'mother')
sing_dir = os.path.join(base, 'sing')


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datagen = ImageDataGenerator(rescale=1./255, validation_split=0.2)

train_generator = datagen.flow_from_directory(
        base,  
        target_size=(126, 300),
        batch_size=1364,
        class_mode='binary',
        subset='training')

test_generator = datagen.flow_from_directory(
        base,  
        target_size=(126, 300),
        batch_size=2139,
        class_mode='binary',
        subset='validation')


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train_generator.class_indices


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# 라벨 분리

X_train, y_train = next(train_generator)
X_test, y_test = next(test_generator)


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# 모델 생성

from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D, Flatten

model = Sequential()
model.add(Conv2D(32, (3,3), padding='same', activation='relu', input_shape=(126, 300, 3)))
model.add(MaxPooling2D(2,2))
model.add(Conv2D(64, (3,3), padding='same', activation='relu', input_shape=(126, 300, 3)))
model.add(MaxPooling2D(2,2))
model.add(Conv2D(64, (3,3), padding='same', activation='relu', input_shape=(126, 300, 3)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(128, activation='relu'))
model.add(Dense(4, activation='softmax'))


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model.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])


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# 모델 학습
from tensorflow.python.keras.callbacks import EarlyStopping
early_stopping = EarlyStopping(monitor='val_loss', patience=10)
model.fit(X_train, y_train, batch_size=32, epochs=100, validation_data=(X_test,y_test),callbacks=[early_stopping])


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model.evaluate(X_test, y_test)


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import matplotlib.pyplot as plt
import numpy as np


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# 스크린샷으로 성능 확인

encanto1 = plt.imread('C:/Users/LJH/Documents/코드스테이츠/section4/project/cap/AKR20211119095800005_03_i_P4.jpg')
plt.imshow(encanto1)


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encanto1_scale = encanto1/255
encanto1_re = encanto1_scale.reshape(1,126,300,3)


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y_prob1 = model.predict(encanto1_re, verbose=0)
predicted1 = y_prob1.argmax(axis=-1)
predicted1


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ghost1 = plt.imread('C:/Users/LJH/Documents/코드스테이츠/section4/project/cap/고스 (1).mkv_001477309.jpg')
plt.imshow(ghost1)


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ghost1_scale = ghost1/255
ghost1_re = ghost1_scale.reshape(1,126,300,3)


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y_prob2 = model.predict(ghost1_re, verbose=0)
predicted2 = y_prob2.argmax(axis=-1)
predicted2


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mother1 = plt.imread('C:/Users/LJH/Documents/코드스테이츠/section4/project/cap/5d0c22066389ccc248239be88fc16cd7.jpg')
plt.imshow(mother1)


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mother1_scale = mother1/255
mother1_re = mother1_scale.reshape(1,126,300,3)


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y_prob3 = model.predict(mother1_re, verbose=0)
predicted3 = y_prob3.argmax(axis=-1)
predicted3


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sing1 = plt.imread('C:/Users/LJH/Documents/코드스테이츠/section4/project/cap/08c3d11e1d57e4c0f4500b3961aa8d829bb89067.jpg')
plt.imshow(sing1)


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sing1_scale = sing1/255
sing1_re = sing1_scale.reshape(1,126,300,3)


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y_prob4 = model.predict(sing1_re, verbose=0)
predicted4 = y_prob4.argmax(axis=-1)
predicted4


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