"""Helper for evaluation on the Labeled Faces in the Wild dataset 
"""

# MIT License
# 
# Copyright (c) 2016 David Sandberg
# 
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# 
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# 
# 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.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import argparse
import sys
import numpy as np
from scipy import misc
from sklearn.model_selection import KFold
from scipy import interpolate
import sklearn
import datetime
import pickle
from sklearn.decomposition import PCA
import mxnet as mx
from mxnet import ndarray as nd
sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'common'))
import face_image



def calculate_roc(thresholds, embeddings1, embeddings2, actual_issame, nrof_folds=10, pca = 0):
    assert(embeddings1.shape[0] == embeddings2.shape[0])
    assert(embeddings1.shape[1] == embeddings2.shape[1])
    nrof_pairs = min(len(actual_issame), embeddings1.shape[0])
    nrof_thresholds = len(thresholds)
    k_fold = KFold(n_splits=nrof_folds, shuffle=False)
    
    tprs = np.zeros((nrof_folds,nrof_thresholds))
    fprs = np.zeros((nrof_folds,nrof_thresholds))
    accuracy = np.zeros((nrof_folds))
    indices = np.arange(nrof_pairs)
    #print('pca', pca)
    
    if pca==0:
      diff = np.subtract(embeddings1, embeddings2)
      dist = np.sum(np.square(diff),1)
    
    for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):
        #print('train_set', train_set)
        #print('test_set', test_set)
        if pca>0:
          print('doing pca on', fold_idx)
          embed1_train = embeddings1[train_set]
          embed2_train = embeddings2[train_set]
          _embed_train = np.concatenate( (embed1_train, embed2_train), axis=0 )
          #print(_embed_train.shape)
          pca_model = PCA(n_components=pca)
          pca_model.fit(_embed_train)
          embed1 = pca_model.transform(embeddings1)
          embed2 = pca_model.transform(embeddings2)
          embed1 = sklearn.preprocessing.normalize(embed1)
          embed2 = sklearn.preprocessing.normalize(embed2)
          #print(embed1.shape, embed2.shape)
          diff = np.subtract(embed1, embed2)
          dist = np.sum(np.square(diff),1)
        
        # Find the best threshold for the fold
        acc_train = np.zeros((nrof_thresholds))
        for threshold_idx, threshold in enumerate(thresholds):
            _, _, acc_train[threshold_idx] = calculate_accuracy(threshold, dist[train_set], actual_issame[train_set])
        best_threshold_index = np.argmax(acc_train)
        for threshold_idx, threshold in enumerate(thresholds):
            tprs[fold_idx,threshold_idx], fprs[fold_idx,threshold_idx], _ = calculate_accuracy(threshold, dist[test_set], actual_issame[test_set])
        _, _, accuracy[fold_idx] = calculate_accuracy(thresholds[best_threshold_index], dist[test_set], actual_issame[test_set])
          
    tpr = np.mean(tprs,0)
    fpr = np.mean(fprs,0)
    return tpr, fpr, accuracy

def calculate_accuracy(threshold, dist, actual_issame):
    predict_issame = np.less(dist, threshold)
    tp = np.sum(np.logical_and(predict_issame, actual_issame))
    fp = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame)))
    tn = np.sum(np.logical_and(np.logical_not(predict_issame), np.logical_not(actual_issame)))
    fn = np.sum(np.logical_and(np.logical_not(predict_issame), actual_issame))
  
    tpr = 0 if (tp+fn==0) else float(tp) / float(tp+fn)
    fpr = 0 if (fp+tn==0) else float(fp) / float(fp+tn)
    acc = float(tp+tn)/dist.size
    return tpr, fpr, acc


  
def calculate_val(thresholds, embeddings1, embeddings2, actual_issame, far_target, nrof_folds=10):
    assert(embeddings1.shape[0] == embeddings2.shape[0])
    assert(embeddings1.shape[1] == embeddings2.shape[1])
    nrof_pairs = min(len(actual_issame), embeddings1.shape[0])
    nrof_thresholds = len(thresholds)
    k_fold = KFold(n_splits=nrof_folds, shuffle=False)
    
    val = np.zeros(nrof_folds)
    far = np.zeros(nrof_folds)
    
    diff = np.subtract(embeddings1, embeddings2)
    dist = np.sum(np.square(diff),1)
    indices = np.arange(nrof_pairs)
    
    for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):
      
        # Find the threshold that gives FAR = far_target
        far_train = np.zeros(nrof_thresholds)
        for threshold_idx, threshold in enumerate(thresholds):
            _, far_train[threshold_idx] = calculate_val_far(threshold, dist[train_set], actual_issame[train_set])
        if np.max(far_train)>=far_target:
            f = interpolate.interp1d(far_train, thresholds, kind='slinear')
            threshold = f(far_target)
        else:
            threshold = 0.0
    
        val[fold_idx], far[fold_idx] = calculate_val_far(threshold, dist[test_set], actual_issame[test_set])
  
    val_mean = np.mean(val)
    far_mean = np.mean(far)
    val_std = np.std(val)
    return val_mean, val_std, far_mean


def calculate_val_far(threshold, dist, actual_issame):
    predict_issame = np.less(dist, threshold)
    true_accept = np.sum(np.logical_and(predict_issame, actual_issame))
    false_accept = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame)))
    n_same = np.sum(actual_issame)
    n_diff = np.sum(np.logical_not(actual_issame))
    val = float(true_accept) / float(n_same)
    far = float(false_accept) / float(n_diff)
    return val, far

def evaluate(embeddings, actual_issame, nrof_folds=10, pca = 0):
    # Calculate evaluation metrics
    thresholds = np.arange(0, 4, 0.01)
    embeddings1 = embeddings[0::2]
    embeddings2 = embeddings[1::2]
    tpr, fpr, accuracy = calculate_roc(thresholds, embeddings1, embeddings2,
        np.asarray(actual_issame), nrof_folds=nrof_folds, pca = pca)
    thresholds = np.arange(0, 4, 0.001)
    val, val_std, far = calculate_val(thresholds, embeddings1, embeddings2,
        np.asarray(actual_issame), 1e-3, nrof_folds=nrof_folds)
    return tpr, fpr, accuracy, val, val_std, far

def load_bin(path, image_size):
  bins, issame_list = pickle.load(open(path, 'rb'))
  data_list = []
  for flip in [0,1]:
    data = nd.empty((len(issame_list)*2, 3, image_size[0], image_size[1]))
    data_list.append(data)
  i = 0
  for i in xrange(len(issame_list)*2):
    _bin = bins[i]
    img = mx.image.imdecode(_bin)
    img = nd.transpose(img, axes=(2, 0, 1))
    for flip in [0,1]:
      if flip==1:
        img = mx.ndarray.flip(data=img, axis=2)
      data_list[flip][i][:] = img
    i+=1
    if i%1000==0:
      print('loading bin', i)
  print(data_list[0].shape)
  return (data_list, issame_list)

def test(data_set, mx_model, batch_size, data_extra = None):
  print('testing verification..')
  data_list = data_set[0]
  issame_list = data_set[1]
  model = mx_model
  embeddings_list = []
  if data_extra is not None:
    _data_extra = nd.array(data_extra)
  time_consumed = 0.0
  for i in xrange( len(data_list) ):
    data = data_list[i]
    embeddings = None
    ba = 0
    while ba<data.shape[0]:
      bb = min(ba+batch_size, data.shape[0])
      count = bb-ba
      _data = nd.slice_axis(data, axis=0, begin=bb-batch_size, end=bb)
      _label = nd.ones( (batch_size,) )
      #print(_data.shape, _label.shape)
      time0 = datetime.datetime.now()
      if data_extra is None:
        db = mx.io.DataBatch(data=(_data,), label=(_label,))
      else:
        db = mx.io.DataBatch(data=(_data,_data_extra), label=(_label,))
      model.forward(db, is_train=False)
      net_out = model.get_outputs()
      #_arg, _aux = model.get_params()
      #__arg = {}
      #for k,v in _arg.iteritems():
      #  __arg[k] = v.as_in_context(_ctx)
      #_arg = __arg
      #_arg["data"] = _data.as_in_context(_ctx)
      #_arg["softmax_label"] = _label.as_in_context(_ctx)
      #for k,v in _arg.iteritems():
      #  print(k,v.context)
      #exe = sym.bind(_ctx, _arg ,args_grad=None, grad_req="null", aux_states=_aux)
      #exe.forward(is_train=False)
      #net_out = exe.outputs
      _embeddings = net_out[0].asnumpy()
      time_now = datetime.datetime.now()
      diff = time_now - time0
      time_consumed+=diff.total_seconds()
      #print(_embeddings.shape)
      if embeddings is None:
        embeddings = np.zeros( (data.shape[0], _embeddings.shape[1]) )
      embeddings[ba:bb,:] = _embeddings[(batch_size-count):,:]
      ba = bb
    embeddings_list.append(embeddings)

  _xnorm = 0.0
  _xnorm_cnt = 0
  for embed in embeddings_list:
    for i in xrange(embed.shape[0]):
      _em = embed[i]
      _norm=np.linalg.norm(_em)
      #print(_em.shape, _norm)
      _xnorm+=_norm
      _xnorm_cnt+=1
  _xnorm /= _xnorm_cnt

  embeddings = embeddings_list[0].copy()
  embeddings = sklearn.preprocessing.normalize(embeddings)
  acc1 = 0.0
  std1 = 0.0
  #_, _, accuracy, val, val_std, far = evaluate(embeddings, issame_list, nrof_folds=10)
  #acc1, std1 = np.mean(accuracy), np.std(accuracy)

  #print('Validation rate: %2.5f+-%2.5f @ FAR=%2.5f' % (val, val_std, far))
  #embeddings = np.concatenate(embeddings_list, axis=1)
  embeddings = embeddings_list[0] + embeddings_list[1]
  embeddings = sklearn.preprocessing.normalize(embeddings)
  print(embeddings.shape)
  print('infer time', time_consumed)
  _, _, accuracy, val, val_std, far = evaluate(embeddings, issame_list, nrof_folds=10)
  acc2, std2 = np.mean(accuracy), np.std(accuracy)
  return acc1, std1, acc2, std2, _xnorm, embeddings_list


if __name__ == '__main__':

  parser = argparse.ArgumentParser(description='do verification')
  # general
  parser.add_argument('--data-dir', default='', help='')
  parser.add_argument('--model', default='../model/softmax,50', help='path to load model.')
  parser.add_argument('--target', default='lfw,cfp_ff,cfp_fp,agedb_30', help='test targets.')
  parser.add_argument('--gpu', default=0, type=int, help='gpu id')
  parser.add_argument('--batch-size', default=32, type=int, help='')
  parser.add_argument('--max', default='', type=str, help='')
  args = parser.parse_args()

  prop = face_image.load_property(args.data_dir)
  image_size = prop.image_size
  print('image_size', image_size)
  ctx = mx.gpu(args.gpu)
  nets = []
  vec = args.model.split(',')
  prefix = args.model.split(',')[0]
  epochs = []
  if len(vec)==1:
    pdir = os.path.dirname(prefix)
    for fname in os.listdir(pdir):
      if not fname.endswith('.params'):
        continue
      _file = os.path.join(pdir, fname)
      if _file.startswith(prefix):
        epoch = int(fname.split('.')[0].split('-')[1])
        epochs.append(epoch)
    epochs = sorted(epochs, reverse=True)
    if len(args.max)>0:
      _max = [int(x) for x in args.max.split(',')]
      assert len(_max)==2
      if len(epochs)>_max[1]:
        epochs = epochs[_max[0]:_max[1]]

  else:
    epochs = [int(x) for x in vec[1].split('|')]
  print('model number', len(epochs))
  time0 = datetime.datetime.now()
  for epoch in epochs:
    print('loading',prefix, epoch)
    model = mx.mod.Module.load(prefix, epoch, context = ctx)
    model.bind(data_shapes=[('data', (args.batch_size, 3, image_size[0], image_size[1]))], label_shapes=[('softmax_label', (args.batch_size,))])
    #model.init_params()
    nets.append(model)
  time_now = datetime.datetime.now()
  diff = time_now - time0
  print('model loading time', diff.total_seconds())

  ver_list = []
  ver_name_list = []
  for name in args.target.split(','):
    path = os.path.join(args.data_dir,name+".bin")
    if os.path.exists(path):
      print('loading.. ', name)
      data_set = load_bin(path, image_size)
      ver_list.append(data_set)
      ver_name_list.append(name)

  for i in xrange(len(ver_list)):
    results = []
    for model in nets:
      acc1, std1, acc2, std2, xnorm, embeddings_list = test(ver_list[i], model, args.batch_size)
      print('[%s]XNorm: %f' % (ver_name_list[i], xnorm))
      print('[%s]Accuracy: %1.5f+-%1.5f' % (ver_name_list[i], acc1, std1))
      print('[%s]Accuracy-Flip: %1.5f+-%1.5f' % (ver_name_list[i], acc2, std2))
      results.append(acc2)
    print('Max of [%s] is %1.5f' % (ver_name_list[i], np.max(results)))