import os
from time import time
import argparse
import logging as log

import numpy as np
from keras.layers import Conv2D,Dropout,Dense,Flatten,MaxPooling2D,BatchNormalization,GlobalAveragePooling2D,Reshape
from keras.models import Sequential,load_model
from keras.callbacks import TensorBoard,ModelCheckpoint
import keras.losses
import keras.metrics

import config as cfg
from k_util import averageDistance

keras.losses.averageDistance=averageDistance
keras.metrics.averageDistance=averageDistance

parser=argparse.ArgumentParser()
parser.add_argument("data")
parser.add_argument("--load_model")
parser.add_argument("--save_model",default="/tmp/gogo-{0:03}.h5")
parser.add_argument("--epochs",type=int,default=100)
parser.add_argument("--initial_epoch",type=int,default=0)
parser.add_argument("--log_dir",default="/tmp/tflogs")
args=parser.parse_args()


def createFullyConnected():
	model=Sequential([
		Flatten(input_shape=(224,224)),
		Dense(128, activation="relu"),
		Dropout(0.1),
		Dense(64, activation="relu"),
		Dense(8)
	])

	model.compile(
		optimizer='adam',
		loss='mse',
		metrics=['mae','accuracy']
	)
	return model

def createCNN():
	model=Sequential()

	model.add(BatchNormalization(input_shape=(224,224,1)))

	model.add(Conv2D(24,(5,5),padding="same",kernel_initializer="he_normal",activation="relu",input_shape=(224,224,1),data_format="channels_last"))
	model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),padding="valid"))

	model.add(Conv2D(36,(5,5),activation="relu"))
	model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),padding="valid"))

	model.add(Conv2D(48,(5,5),activation="relu"))
	model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),padding="valid"))

	model.add(Conv2D(64,(3,3),activation="relu"))
	model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),padding="valid"))

	model.add(Conv2D(64,(3,3),activation="relu"))

	model.add(GlobalAveragePooling2D())

	model.add(Dense(500,activation="relu"))
	model.add(Dense(90,activation="relu"))
	model.add(Dense(8))
	model.add(Reshape((4,2)))

	model.compile(optimizer="rmsprop",loss=averageDistance,metrics=["mae","accuracy"])
	return model


model=createCNN()
if args.load_model:
	model=load_model(args.load_model)

log.info("loading data...")
with np.load(args.data) as data:
	trainImages=data["trainImages"]
	trainLabels=data["trainLabels"]
	testImages=data["testImages"]
	testLabels=data["testLabels"]
log.info("done")

tensorboard=TensorBoard(log_dir=os.path.join(args.log_dir,"{}".format(time())))
checkpoint=ModelCheckpoint(args.save_model,monitor="val_loss",period=10)

model.fit(
	trainImages.reshape((-1,224,224,1)),
	trainLabels.reshape((-1,4,2)),
	epochs=args.epochs,
	initial_epoch=args.initial_epoch,
	batch_size=20,
	validation_split=0.2,
	callbacks=[tensorboard,checkpoint]
)

log.info(model.evaluate(testImages.reshape((-1,224,224,1)),testLabels.reshape((-1,4,2))))