""" 

Copyright (c) 2016, Jose Dolz .All rights reserved.

Redistribution and use in source and binary forms, with or without modification,

are permitted provided that the following conditions are met:

    1. Redistributions of source code must retain the above copyright notice,

       this list of conditions and the following disclaimer.

    2. Redistributions in binary form must reproduce the above copyright notice,

       this list of conditions and the following disclaimer in the documentation

       and/or other materials provided with the distribution.

    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,

    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES

    OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND

    NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT

    HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,

    WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING

    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR

    OTHER DEALINGS IN THE SOFTWARE.

Jose Dolz. Dec, 2016.

email: jose.dolz.upv@gmail.com

LIVIA Department, ETS, Montreal.

"""

import numpy as np

import pdb

# If you are not using nifti files you can comment this line

import nibabel as nib

import scipy.io as sio

from ImgOperations.imgOp import applyPadding

# ----- Loader for nifti files ------ #

def load_nii (imageFileName, printFileNames) :

    if printFileNames == True:

        print (" ... Loading file: {}".format(imageFileName))

    img_proxy = nib.load(imageFileName)

    imageData = img_proxy.get_data()

    return (imageData,img_proxy)

def release_nii_proxy(img_proxy) :

    img_proxy.uncache()

# ----- Loader for matlab format ------- #

# Very important: All the volumes should have been saved as 'vol'.

# Otherwise, change its name here

def load_matlab (imageFileName, printFileNames) :

    if printFileNames == True:

        print (" ... Loading file: {}".format(imageFileName))

    mat_contents = sio.loadmat(imageFileName)

    imageData = mat_contents['vol']

    return (imageData)

""" It loads the images (CT/MRI + Ground Truth + ROI) for the patient image Idx"""

def load_imagesSinglePatient(imageIdx, 

                             imageNames, 

                             groundTruthNames, 

                             roiNames,

                             applyPaddingBool,

                             receptiveField, 

                             sampleSizes,

                             imageType

                             ):

    if imageIdx >= len(imageNames) :

        print (" ERROR!!!!! : The image index specified is greater than images array size....)")

        exit(1)

    # --- Load image data (CT/MRI/...) ---

    printFileNames = False # Get this from config.ini

    imageFileName = imageNames[imageIdx]

    if imageType == 0:

        [imageData,img_proxy] = load_nii(imageFileName, printFileNames)

    else:

        imageData = load_matlab(imageFileName, printFileNames)

    if applyPaddingBool == True : 

        [imageData, paddingValues] = applyPadding(imageData, sampleSizes, receptiveField)

    else:

        paddingValues = ((0,0),(0,0),(0,0))

    if len(imageData.shape) > 3 :

         imageData = imageData[:,:,:,0]

    if imageType == 0:

        release_nii_proxy(img_proxy)

    # --- Load ground truth (i.e. labels) ---

    if len(groundTruthNames) > 0 : 

        GTFileName = groundTruthNames[imageIdx]

        if imageType == 0:

            [gtLabelsData, gt_proxy] = load_nii (GTFileName, printFileNames)

        else:

            gtLabelsData = load_matlab(GTFileName, printFileNames)

        # Convert ground truth to int type

        if np.issubdtype( gtLabelsData.dtype, np.int ) : 

            gtLabelsData = gtLabelsData 

        else: 

            np.rint(gtLabelsData).astype("int32")

        imageGtLabels = gtLabelsData

        if imageType == 0:

            # Release data

            release_nii_proxy(gt_proxy)

        if applyPaddingBool == True : 

            [imageGtLabels, paddingValues] = applyPadding(imageGtLabels,  sampleSizes, receptiveField)

    else : 

        imageGtLabels = np.empty(0)

    # --- Load roi ---

    if len(roiNames)> 0 :

        roiFileName = roiNames[imageIdx]

        if imageType == 0:

            [roiMaskData, roi_proxy] = load_nii (roiFileName, printFileNames)

        else:

            roiMaskData = load_matlab(roiFileName, printFileNames)

        roiMask = roiMaskData

        if imageType == 0:

            # Release data

            release_nii_proxy(roi_proxy)

        if applyPaddingBool == True : 

            [roiMask, paddingValues] = applyPadding(roiMask, sampleSizes, receptiveField)

    else :

        roiMask = np.ones(imageGtLabels.shape)

    return [imageData, imageGtLabels, roiMask, paddingValues]

# -------------------------------------------------------- #

def getRandIndexes(total, maxNumberIdx) :

    # Generate a shuffle array of a vector containing "total" elements

    idxs = range(total)

    np.random.shuffle(idxs)

    rand_idxs = idxs[0:maxNumberIdx]

    return rand_idxs