import os
import numpy as np
import random
import shutil
from keras.models import Sequential
from keras.layers import Conv2D, Flatten, MaxPooling2D, Dense
from keras.preprocessing import image

#dataset from https://www.kaggle.com/asdasdasasdas/garbage-classification

'''#sepperating the file into training and testing data, creation of folders by hand removal of 75 images from papers for a more even distribution
def sepperate(type):
    for i in type:
        folder = "Garbage classification\\Garbage classification\\" + i
        destination = "Garbage classification\\testset\\" + i
        howmany = len(os.listdir(folder))
        for j in range(int(howmany*0.2)):
            move1 = random.choice(os.listdir(folder))
            source = "Garbage classification\\Garbage classification\\" + i + "\\" + move1
            d = shutil.move(source, destination, copy_function = shutil.copytree)


types = ["cardboard", "glass", "metal", "paper", "plastic"]
sepperate(types)
os.rename("Garbage classification\\Garbage classification", "Garbage classification\\trainset")
'''


classifier = Sequential()
classifier.add(Conv2D(32, (3, 3), input_shape=(110, 110, 3), activation = "relu"))
classifier.add(MaxPooling2D(pool_size = (2, 2)))
classifier.add(Conv2D(64, (3, 3), activation = "relu"))
classifier.add(MaxPooling2D(pool_size=(2, 2)))
# this layer in ver 4
classifier.add(Conv2D(32, (3, 3), activation = "relu"))
classifier.add(MaxPooling2D(pool_size=(2, 2)))
# -----------------
classifier.add(Flatten())
classifier.add(Dense(activation = "relu", units = 64 ))
classifier.add(Dense(activation = "softmax", units = 5))
classifier.compile(optimizer = "adam", loss = "binary_crossentropy", metrics = ["accuracy"])


from keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(
    rescale=1./255,
    shear_range=0.1,
    zoom_range=0.1,
    width_shift_range=0.1,
    height_shift_range=0.1,
    horizontal_flip=True,
    vertical_flip=True,
)

test_datagen = ImageDataGenerator(
    rescale=1./255,
    validation_split=0.1
)

train_generator = train_datagen.flow_from_directory(
    "Garbage classification\\trainset",
    target_size=(110, 110),
    batch_size=16,
    class_mode='categorical',
    #seed=0
)

test_generator = test_datagen.flow_from_directory(
    "Garbage classification\\testset",
    target_size=(110, 110),
    batch_size=16,
    class_mode='categorical',
)


#Teaching the classifier
'''classifier.fit_generator( train_generator, steps_per_epoch = 150, epochs = 25, validation_data = test_generator )
classifier.save_weights('model_ver_4.h5')'''

import matplotlib.pyplot as plt
labels = (train_generator.class_indices)
labels = dict((value,key) for key,value in labels.items())


classifier.load_weights("model_ver_4.h5")
import random


def getTrashPhoto(x, type):
    for i in range(x):
        kind = random.choice(type)
        path = "Garbage classification\\testset\\" + kind
        file = random.choice(os.listdir(path))
        path = "Garbage classification\\testset\\" + kind + "\\" + file
        gz = image.load_img(path, target_size = (110,110))
        ti = image.img_to_array(gz)
        ti=np.array(ti)/255.0
        ti = np.expand_dims(ti, axis = 0)
        prediction = classifier.predict(ti)
        plt.subplot(1, 3, i+1)
        plt.imshow(gz)
        plt.title("AI thinks:%s \nReality:\n %s" % (labels[np.argmax(prediction)], file))
    plt.show()


types = ["cardboard", "glass", "metal", "paper", "plastic"]
type = ["metal"]

getTrashPhoto(3, types)


plt.show()