__author__ = 'chunk'
"""
Yun Q. Shi, et al - A Markov Process Based Approach to Effective Attacking JPEG Steganography
"""

import time
import math
import numpy as np

from .. import *
from ...mjpeg import Jpeg,colorMap
from ...common import *

import csv
import json
import pickle
import cv2
from sklearn import svm

base_dir = '/home/hadoop/data/HeadShoulder/'


class MPB(StegBase):
    """
    Markov Process Based Steganalyasis Algo.
    """

    def __init__(self):
        StegBase.__init__(self, sample_key)
        self.model = None
        self.svm = None

    def _get_trans_prob_mat_orig(self, ciq, T=4):
        """
        Original!
        Calculate Transition Probability Matrix.

        :param ciq: jpeg DCT coeff matrix, 2-D numpy array of int16 (pre-abs)
        :param T: signed integer, usually 1~7
        :return: TPM - 3-D tensor, numpy array of size (2*T+1, 2*T+1, 4)
        """
        ciq = np.absolute(ciq).clip(0, T)
        TPM = np.zeros((2 * T + 1, 2 * T + 1, 4), np.float64)
        # Fh = np.diff(ciq, axis=-1)
        # Fv = np.diff(ciq, axis=0)
        Fh = ciq[:-1, :-1] - ciq[:-1, 1:]
        Fv = ciq[:-1, :-1] - ciq[1:, :-1]
        Fd = ciq[:-1, :-1] - ciq[1:, 1:]
        Fm = ciq[:-1, 1:] - ciq[1:, :-1]

        Fh1 = Fh[:-1, :-1]
        Fh2 = Fh[:-1, 1:]

        Fv1 = Fv[:-1, :-1]
        Fv2 = Fv[1:, :-1]

        Fd1 = Fd[:-1, :-1]
        Fd2 = Fd[1:, 1:]

        Fm1 = Fm[:-1, 1:]
        Fm2 = Fm[1:, :-1]

        # original:(very slow!)
        for n in range(-T, T + 1):
            for m in range(-T, T + 1):
                dh = np.sum(Fh1 == m) * 1.0
                dv = np.sum(Fv1 == m) * 1.0
                dd = np.sum(Fd1 == m) * 1.0
                dm = np.sum(Fm1 == m) * 1.0

                if dh != 0:
                    TPM[m, n, 0] = np.sum(np.logical_and(Fh1 == m, Fh2 == n)) / dh

                if dv != 0:
                    TPM[m, n, 1] = np.sum(np.logical_and(Fv1 == m, Fv2 == n)) / dv

                if dd != 0:
                    TPM[m, n, 2] = np.sum(np.logical_and(Fd1 == m, Fd2 == n)) / dd

                if dm != 0:
                    TPM[m, n, 3] = np.sum(np.logical_and(Fm1 == m, Fm2 == n)) / dm

        # 1.422729s
        return TPM


    def get_trans_prob_mat(self, ciq, T=4):
        """
        Calculate Transition Probability Matrix.

        :param ciq: jpeg DCT coeff matrix, 2-D numpy array of int16 (pre-abs)
        :param T: signed integer, usually 1~7
        :return: TPM - 3-D tensor, numpy array of size (2*T+1, 2*T+1, 4)
        """

        return self._get_trans_prob_mat_orig(ciq, T)


        # timer = Timer()
        ciq = np.absolute(ciq).clip(0, T)
        TPM = np.zeros((2 * T + 1, 2 * T + 1, 4), np.float64)
        # Fh = np.diff(ciq, axis=-1)
        # Fv = np.diff(ciq, axis=0)
        Fh = ciq[:-1, :-1] - ciq[:-1, 1:]
        Fv = ciq[:-1, :-1] - ciq[1:, :-1]
        Fd = ciq[:-1, :-1] - ciq[1:, 1:]
        Fm = ciq[:-1, 1:] - ciq[1:, :-1]

        Fh1 = Fh[:-1, :-1].ravel()
        Fh2 = Fh[:-1, 1:].ravel()

        Fv1 = Fv[:-1, :-1].ravel()
        Fv2 = Fv[1:, :-1].ravel()

        Fd1 = Fd[:-1, :-1].ravel()
        Fd2 = Fd[1:, 1:].ravel()

        Fm1 = Fm[:-1, 1:].ravel()
        Fm2 = Fm[1:, :-1].ravel()



        # 0.089754s
        # timer.mark()
        # TPM[Fh1.ravel(), Fh2.ravel(), 0] += 1
        # TPM[Fv1.ravel(), Fv2.ravel(), 1] += 1
        # TPM[Fd1.ravel(), Fd2.ravel(), 2] += 1
        # TPM[Fm1.ravel(), Fm2.ravel(), 3] += 1
        # timer.report()

        # 1.459668s
        # timer.mark()
        # for i in range(len(Fh1)):
        #     TPM[Fh1[i], Fh2[i], 0] += 1
        # for i in range(len(Fv1)):
        #     TPM[Fv1[i], Fv2[i], 1] += 1
        # for i in range(len(Fd1)):
        #     TPM[Fd1[i], Fd2[i], 2] += 1
        # for i in range(len(Fm1)):
        #     TPM[Fm1[i], Fm2[i], 3] += 1
        # timer.report()

        # 1.463982s
        # timer.mark()
        for m, n in zip(Fh1.ravel(), Fh2.ravel()):
            TPM[m, n, 0] += 1

        for m, n in zip(Fv1.ravel(), Fv2.ravel()):
            TPM[m, n, 1] += 1

        for m, n in zip(Fd1.ravel(), Fd2.ravel()):
            TPM[m, n, 2] += 1

        for m, n in zip(Fm1.ravel(), Fm2.ravel()):
            TPM[m, n, 3] += 1
        # timer.report()

        # 0.057505s
        # timer.mark()
        for m in range(-T, T + 1):
            dh = np.sum(Fh1 == m) * 1.0
            dv = np.sum(Fv1 == m) * 1.0
            dd = np.sum(Fd1 == m) * 1.0
            dm = np.sum(Fm1 == m) * 1.0

            if dh != 0:
                TPM[m, :, 0] /= dh

            if dv != 0:
                TPM[m, :, 1] /= dv

            if dd != 0:
                TPM[m, :, 2] /= dd

            if dm != 0:
                TPM[m, :, 3] /= dm
        # timer.report()

        return TPM

    def load_dataset(self, mode, file):
        if mode == 'local':
            return self._load_dataset_from_local(file)
        elif mode == 'remote' or mode == 'hbase':
            return self._load_dataset_from_hbase(file)
        else:
            raise Exception("Unknown mode!")

    def _load_dataset_from_local(self, list_file='images_map_Train.tsv'):
        """
        load jpeg dataset according to a file of file-list.

        :param list_file: a tsv file with each line for a jpeg file path
        :return:(X,Y) for SVM
        """
        list_file = base_dir + list_file

        X = []
        Y = []
        dict_tagbuf = {}
        dict_dataset = {}

        with open(list_file, 'rb') as tsvfile:
            tsvfile = csv.reader(tsvfile, delimiter='\t')
            for line in tsvfile:
                imgname = line[0] + '.jpg'
                dict_tagbuf[imgname] = line[1]

        dir = base_dir + 'Feat/'
        for path, subdirs, files in os.walk(dir + 'Train/'):
            for name in files:
                featpath = os.path.join(path, name)
                # print featpath
                with open(featpath, 'rb') as featfile:
                    imgname = path.split('/')[-1] + name.replace('.mpb', '.jpg')
                    dict_dataset[imgname] = json.loads(featfile.read())

        for imgname, tag in dict_tagbuf.items():
            tag = 1 if tag == 'True' else 0
            X.append(dict_dataset[imgname])
            Y.append(tag)

        return X, Y


    def _load_dataset_from_hbase(self, table='ImgCV'):
        pass


    def _model_svm_train_sk(self, X, Y):
        timer = Timer()
        timer.mark()
        lin_clf = svm.LinearSVC()
        lin_clf.fit(X, Y)
        with open('res/tmp.model', 'wb') as modelfile:
            model = pickle.dump(lin_clf, modelfile)

        timer.report()

        self.svm = 'sk'
        self.model = lin_clf

        return lin_clf

    def _model_svm_predict_sk(self, image, clf=None):
        if clf is None:
            if self.svm == 'sk' and self.model != None:
                clf = self.model
            else:
                with open('res/tmp.model', 'rb') as modelfile:
                    clf = pickle.load(modelfile)

        im = mjpeg.Jpeg(image, key=sample_key)
        ciq = im.coef_arrays[mjpeg.colorMap['Y']]
        tpm = self.get_trans_prob_mat(ciq)

        return clf.predict(tpm)


    def _model_svm_train_cv(self, X, Y):
        svm_params = dict(kernel_type=cv2.SVM_LINEAR,
                          svm_type=cv2.SVM_C_SVC,
                          C=2.67, gamma=5.383)

        timer = Timer()
        timer.mark()
        svm = cv2.SVM()
        svm.train(X, Y, params=svm_params)
        svm.save('res/svm_data.model')

        self.svm = 'cv'
        self.model = svm

        return svm

    def _model_svm_predict_cv(self, image, svm=None):
        if svm is None:
            if self.svm == 'cv' and self.model != None:
                clf = self.model
            else:
                svm = cv2.SVM()
                svm.load('res/svm_data.model')

        im = mjpeg.Jpeg(image, key=sample_key)
        ciq = im.coef_arrays[mjpeg.colorMap['Y']]
        tpm = self.get_trans_prob_mat(ciq)

        return svm.predict(tpm)

    def train_svm(self):
        X, Y = self.load_dataset('local', 'images_map_Train.tsv')
        return self._model_svm_train_sk(X, Y)

    def predict_svm(self, image):
        return self._model_svm_predict_sk(image)