# -*- coding: utf-8 -*-
# @Time    : 18-7-25 下午2:33
# @Author  : DuanBin
# @Email   : 20092758@cqu.edu.cn
# @File    : catl_train.py
# @Software: PyCharm

# USAGE
# python catl_train.py --dataset data --model catl.model

# import the necessary packages
from keras.preprocessing.image import ImageDataGenerator
from keras.optimizers import Adam
from sklearn.model_selection import train_test_split
from keras.preprocessing.image import img_to_array
from keras.utils import to_categorical
from keras.models import Model
from keras.models import load_model
from lenet import LeNet
from imutils import paths
import matplotlib.pyplot as plt
import numpy as np
import argparse
import random
import cv2
import os

# set the matplotlib backend so figures can be saved in the background
import matplotlib
matplotlib.use("Agg")

dataPath = "data"
modelPath = "catl_5_5.model"
plotPath = "catl_plot_5_5_blog.png"

# initialize the number of epochs to train for, initia learning rate,
# and batch size
EPOCHS = 50
INIT_LR = 0.001
BS = 3
classNumber = 3
imageDepth = 1

# initialize the data and labels
print("[INFO] loading images...")
data = []
labels = []

# grab the image paths and randomly shuffle them
imagePaths = sorted(list(paths.list_images(dataPath)))  # args["dataset"])))
random.seed(42)
random.shuffle(imagePaths)

# loop over the input images
for imagePath in imagePaths:
    # load the image, pre-process it, and store it in the data list
    image = cv2.imread(imagePath, 0)
    image = cv2.resize(image, (28, 28))
    image = img_to_array(image)
    data.append(image)

    # extract the class label from the image path and update the
    # labels list
    label = imagePath.split(os.path.sep)[-2]
    if label == "dity":
        label = 0
    elif label == "tan":
        label = 1
    elif label == "valley":
        label = 2
    labels.append(label)

# scale the raw pixel intensities to the range [0, 1]
data = np.array(data, dtype="float") / 255.0
labels = np.array(labels)

# partition the data into training and testing splits using 75% of
# the data for training and the remaining 25% for testing
(trainX, testX, trainY, testY) = train_test_split(data, labels, test_size=0.3, random_state=42)
print(trainX.shape)

# convert the labels from integers to vectors
trainY = to_categorical(trainY, num_classes=classNumber)
testY = to_categorical(testY, num_classes=classNumber)
print(trainY.shape)
print(testX.shape)


# construct the image generator for data augmentation
aug = ImageDataGenerator(rotation_range=30, width_shift_range=0.1,
                         height_shift_range=0.1, shear_range=0.2, zoom_range=0.2,
                         horizontal_flip=True, fill_mode="nearest")

# # initialize the model
print("[INFO] compiling model...")
model = LeNet.build(width=28, height=28, depth=imageDepth, classes=classNumber)
opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)
model.compile(loss="categorical_crossentropy", optimizer=opt,
              metrics=["accuracy"])

model.summary()

# train the network
print("[INFO] training network...")
H = model.fit_generator(aug.flow(trainX, trainY, batch_size=BS),
                        validation_data=(testX, testY), steps_per_epoch=len(trainX) // BS,
                        epochs=EPOCHS, verbose=1)

# save the model to disk
print("[INFO] serializing network...")
model.save(modelPath)  # args["model"])
model.save_weights("catl_5_5_wight.h5")

# plot the training loss and accuracy
plt.style.use("ggplot")
plt.figure()
N = EPOCHS
plt.plot(np.arange(0, N), H.history["loss"], label="train_loss")
plt.plot(np.arange(0, N), H.history["val_loss"], label="val_loss")
plt.plot(np.arange(0, N), H.history["acc"], label="train_acc")
plt.plot(np.arange(0, N), H.history["val_acc"], label="val_acc")
plt.title("Training Loss and Accuracy")
plt.xlabel("Epoch #")
plt.ylabel("Loss/Accuracy")
plt.legend(loc="lower left")
plt.savefig(plotPath)  # args["plot"])
plt.show()

layer_outputs = [layer.output for layer in model.layers]
activation_model = Model(inputs=model.input, outputs=layer_outputs)
activations = activation_model.predict(testX[0].reshape(1, 28, 28, 1))


def display_activation(activations, col_size, row_size, act_index):
    activation = activations[act_index]
    activation_index = 0
    fig, ax = plt.subplots(row_size, col_size, figsize=(row_size * 2.5, col_size * 1.5))
    for row in range(0, row_size):
        for col in range(0, col_size):
            ax[row][col].imshow(activation[0, :, :, activation_index], cmap=\'gray\')
            activation_index += 1

    plt.show()


display_activation(activations, 4, 5, 1)

# import the necessary packages
from keras.models import Sequential
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.layers.core import Activation
from keras.layers.core import Flatten
from keras.layers.core import Dense
from keras.layers.core import Dropout
from tensorflow.keras import backend as K


class LeNet:
    @staticmethod
    def build(width, height, depth, classes):
        # initialize the model
        model = Sequential()
        inputShape = (height, width, depth)

        # if we are using "channels first", update the input shape
        if K.image_data_format() == "channels_first":
            inputShape = (depth, height, width)
        else:
            inputShape = (width, height, depth)

        # first set of CONV => RELU => POOL layers
        model.add(Conv2D(20, (3, 3), padding="same", input_shape=inputShape))
        model.add(Activation("relu"))
        model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))

        # second set of CONV => RELU => POOL layers
        model.add(Conv2D(50, (3, 3), padding="same"))
        model.add(Activation("relu"))
        model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))


        # first (and only) set of FC => RELU layers
        model.add(Flatten())
        model.add(Dense(500))
        model.add(Activation("relu"))

        # softmax classifier
        model.add(Dense(classes))
        model.add(Activation("softmax"))

        # return the constructed network architecture
        return model