# --------------------------------------------------------
# Tensorflow Faster R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Jiasen Lu, Jianwei Yang, based on code from Ross Girshick
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import _init_paths
import os
import sys
import numpy as np
import argparse
import pprint
import pdb
import time
import cv2
import cPickle
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.optim as optim

import torchvision.transforms as transforms
import torchvision.datasets as dset
from PIL import Image
from roi_data_layer.roidb import combined_roidb
from roi_data_layer.roibatchLoader import roibatchLoader
from model.utils.config import cfg, cfg_from_file, cfg_from_list, get_output_dir
from model.faster_rcnn.faster_rcnn_cascade import _fasterRCNN
from model.rpn.bbox_transform import clip_boxes
from model.nms.nms_wrapper import nms
from model.rpn.bbox_transform import bbox_transform_inv
from model.utils.net_utils import save_net, load_net, vis_detections
from model.utils.blob import im_list_to_blob
import pdb

def parse_args():
  """
  Parse input arguments
  """
  parser = argparse.ArgumentParser(description='Train a Fast R-CNN network')
  parser.add_argument('--cfg', dest='cfg_file',
                      help='optional config file',
                      default='cfgs/vgg16.yml', type=str)
  parser.add_argument('--imdb', dest='imdb_name',
                      help='dataset to train on',
                      default='voc_2007_trainval', type=str)
  parser.add_argument('--imdbval', dest='imdbval_name',
                      help='dataset to validate on',
                      default='voc_2007_test', type=str)
  parser.add_argument('--net', dest='net',
                      help='vgg16, res50, res101, res152',
                      default='vgg16', type=str)
  parser.add_argument('--set', dest='set_cfgs',
                      help='set config keys', default=None,
                      nargs=argparse.REMAINDER)
  parser.add_argument('--load_dir', dest='load_dir',
                      help='directory to load models', default="/srv/share/jyang375/models",
                      nargs=argparse.REMAINDER)
  parser.add_argument('--image_dir', dest='image_dir',
                      help='directory to load images', default="data/images",
                      type=str)  
  parser.add_argument('--ngpu', dest='ngpu',
                      help='number of gpu',
                      default=1, type=int)
  parser.add_argument('--checksession', dest='checksession',
                      help='checksession to load model',
                      default=4, type=int)
  parser.add_argument('--checkepoch', dest='checkepoch',
                      help='checkepoch to load network',
                      default=6, type=int)
  parser.add_argument('--checkpoint', dest='checkpoint',
                      help='checkpoint to load network',
                      default=10000, type=int)

  args = parser.parse_args()
  return args

lr = cfg.TRAIN.LEARNING_RATE
momentum = cfg.TRAIN.MOMENTUM
weight_decay = cfg.TRAIN.WEIGHT_DECAY

def _get_image_blob(im):
  """Converts an image into a network input.
  Arguments:
    im (ndarray): a color image in BGR order
  Returns:
    blob (ndarray): a data blob holding an image pyramid
    im_scale_factors (list): list of image scales (relative to im) used
      in the image pyramid
  """
  im_orig = im.astype(np.float32, copy=True)
  im_orig -= cfg.PIXEL_MEANS

  im_shape = im_orig.shape
  im_size_min = np.min(im_shape[0:2])
  im_size_max = np.max(im_shape[0:2])

  processed_ims = []
  im_scale_factors = []

  for target_size in cfg.TEST.SCALES:
    im_scale = float(target_size) / float(im_size_min)
    # Prevent the biggest axis from being more than MAX_SIZE
    if np.round(im_scale * im_size_max) > cfg.TEST.MAX_SIZE:
      im_scale = float(cfg.TEST.MAX_SIZE) / float(im_size_max)
    im = cv2.resize(im_orig, None, None, fx=im_scale, fy=im_scale,
            interpolation=cv2.INTER_LINEAR)
    im_scale_factors.append(im_scale)
    processed_ims.append(im)

  # Create a blob to hold the input images
  blob = im_list_to_blob(processed_ims)

  return blob, np.array(im_scale_factors)

if __name__ == '__main__':

  args = parse_args()

  print('Called with args:')
  print(args)

  if args.cfg_file is not None:
    cfg_from_file(args.cfg_file)
  if args.set_cfgs is not None:
    cfg_from_list(args.set_cfgs)

  print('Using config:')
  pprint.pprint(cfg)
  np.random.seed(cfg.RNG_SEED)

  # train set
  # -- Note: Use validation set and disable the flipped to enable faster loading.

  input_dir = args.load_dir + "/" + args.net
  if not os.path.exists(input_dir):
    raise Exception('There is no input directory for loading network')
  load_name = os.path.join(input_dir,
    'faster_rcnn_{}_{}_{}.pth'.format(args.checksession, args.checkepoch, args.checkpoint))


  classes = np.asarray(['__background__',
                       'aeroplane', 'bicycle', 'bird', 'boat',
                       'bottle', 'bus', 'car', 'cat', 'chair',
                       'cow', 'diningtable', 'dog', 'horse',
                       'motorbike', 'person', 'pottedplant',
                       'sheep', 'sofa', 'train', 'tvmonitor'])

  fasterRCNN = _fasterRCNN(args.net, classes)
  checkpoint = torch.load(load_name)
  fasterRCNN.load_state_dict(checkpoint['model'])
  print('load model successfully!')

  # pdb.set_trace()

  print("load checkpoint %s" % (load_name))

  # initilize the tensor holder here.
  im_data = torch.FloatTensor(1)
  im_info = torch.FloatTensor(1)
  num_boxes = torch.LongTensor(1)
  gt_boxes = torch.FloatTensor(1)

  # ship to cuda
  if args.ngpu > 0:
    im_data = im_data.cuda()
    im_info = im_info.cuda()
    num_boxes = num_boxes.cuda()
    gt_boxes = gt_boxes.cuda()

  # make variable
  im_data = Variable(im_data, volatile=True)
  im_info = Variable(im_info, volatile=True)
  num_boxes = Variable(num_boxes, volatile=True)
  gt_boxes = Variable(gt_boxes, volatile=True)

  if args.ngpu > 0:
    cfg.CUDA = True

  if args.ngpu > 0:
    fasterRCNN.cuda()

  fasterRCNN.eval()

  start = time.time()
  max_per_image = 100
  thresh = 0.05
  vis = True

  imglist = os.listdir(args.image_dir)
  num_images = len(imglist)

  print('Loaded Photo: {} images.'.format(num_images))


  for i in range(num_images):

      # Load the demo image
      im_file = os.path.join(args.image_dir, imglist[i])
      # im = cv2.imread(im_file)
      im = np.array(Image.open(im_file))
      if len(im.shape) == 2:
        im = im[:,:,np.newaxis]
        im = np.concatenate((im,im,im), axis=2)

      blobs, im_scales = _get_image_blob(im)
      assert len(im_scales) == 1, "Only single-image batch implemented"
      im_blob = blobs
      im_info_np = np.array([[im_blob.shape[1], im_blob.shape[2], im_scales[0]]], dtype=np.float32)

      im_data_pt = torch.from_numpy(im_blob)
      im_data_pt = im_data_pt.permute(0, 3, 1, 2)
      im_info_pt = torch.from_numpy(im_info_np)

      im_data.data.resize_(im_data_pt.size()).copy_(im_data_pt)
      im_info.data.resize_(im_info_pt.size()).copy_(im_info_pt)
      gt_boxes.data.resize_(1, 1, 5).zero_()
      num_boxes.data.resize_(1).zero_()

      # pdb.set_trace()


      det_tic = time.time()
      rois, cls_prob, bbox_pred, rpn_loss, rcnn_loss = \
          fasterRCNN(im_data, im_info, gt_boxes, num_boxes)

      scores = cls_prob.data
      boxes = rois[:, :, 1:5] / im_scales[0]

      if cfg.TEST.BBOX_REG:
          # Apply bounding-box regression deltas
          box_deltas = bbox_pred.data
          if cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED:
          # Optionally normalize targets by a precomputed mean and stdev
                box_deltas = box_deltas.view(-1, 4) * torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_STDS).cuda() \
                           + torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_MEANS).cuda()
                box_deltas = box_deltas.view(1, -1, 4)
          pred_boxes = bbox_transform_inv(boxes, box_deltas, 1)
          pred_boxes = clip_boxes(pred_boxes, im_info.data, 1)
      else:
          # Simply repeat the boxes, once for each class
          pred_boxes = np.tile(boxes, (1, scores.shape[1]))

      scores = scores.squeeze().cpu().numpy()
      pred_boxes = pred_boxes.squeeze().cpu().numpy()
      # _t['im_detect'].tic()
      det_toc = time.time()
      detect_time = det_toc - det_tic

      misc_tic = time.time()

      if vis:
          im2show = np.copy(im)

      for j in xrange(1, 21):
          inds = np.where(scores[:, j] > thresh)[0]
          cls_scores = scores[inds, j]
          cls_boxes = pred_boxes[inds, :]
          cls_dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])) \
              .astype(np.float32, copy=False)
          keep = nms(cls_dets, cfg.TEST.NMS)
          cls_dets = cls_dets[keep, :]
          if vis:
              im2show = vis_detections(im2show, classes[j], cls_dets)

      misc_toc = time.time()
      nms_time = misc_toc - misc_tic

      sys.stdout.write('im_detect: {:d}/{:d} {:.3f}s {:.3f}s   \r' \
          .format(i + 1, num_images, detect_time, nms_time))
      sys.stdout.flush()

      if vis:
          # cv2.imshow('test', im2show)
          # cv2.waitKey(0)
          result_path = os.path.join(args.image_dir, imglist[i][:-4] + "_det.jpg")
          cv2.imwrite(result_path, im2show)