#!/usr/bin/env python

# --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------

"""Test a Fast R-CNN network on an image database."""
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import _init_paths
from fast_rcnn.test import _get_blobs, _get_image_blob, _get_rois_blob
from fast_rcnn.config import cfg, cfg_from_file, cfg_from_list, get_output_dir
from datasets.factory import get_imdb
import caffe
import argparse
import pprint
import time, os, sys
from utils.timer import Timer
import cv2
import numpy as np
from fast_rcnn.bbox_transform import clip_boxes, bbox_transform_inv
from fast_rcnn.nms_wrapper import nms
import cPickle
from utils.blob import im_list_to_blob


def im_detect(net, im, boxes=None):
    """Detect object classes in an image given object proposals.

    Arguments:
        net (caffe.Net): Fast R-CNN network to use
        im (ndarray): color image to test (in BGR order)
        boxes (ndarray): R x 4 array of object proposals or None (for RPN)

    Returns:
        scores (ndarray): R x K array of object class scores (K includes
            background as object category 0)
        boxes (ndarray): R x (4*K) array of predicted bounding boxes
    """
    blobs, im_scales = _get_blobs(im, boxes)

    # When mapping from image ROIs to feature map ROIs, there's some aliasing
    # (some distinct image ROIs get mapped to the same feature ROI).
    # Here, we identify duplicate feature ROIs, so we only compute features
    # on the unique subset.
    if cfg.DEDUP_BOXES > 0 and not cfg.TEST.HAS_RPN:
        v = np.array([1, 1e3, 1e6, 1e9, 1e12])
        hashes = np.round(blobs['rois'] * cfg.DEDUP_BOXES).dot(v)
        _, index, inv_index = np.unique(hashes, return_index=True,
                                        return_inverse=True)
        blobs['rois'] = blobs['rois'][index, :]
        boxes = boxes[index, :]

    if cfg.TEST.HAS_RPN:
        im_blob = blobs['data']
        blobs['im_info'] = np.array(
            [[im_blob.shape[2], im_blob.shape[3], im_scales[0]]],
            dtype=np.float32)

    # reshape network inputs
    net.blobs['data'].reshape(*(blobs['data'].shape))
    if cfg.TEST.HAS_RPN:
        net.blobs['im_info'].reshape(*(blobs['im_info'].shape))
    else:
        net.blobs['rois'].reshape(*(blobs['rois'].shape))

    # do forward
    forward_kwargs = {'data': blobs['data'].astype(np.float32, copy=False)}
    if cfg.TEST.HAS_RPN:
        forward_kwargs['im_info'] = blobs['im_info'].astype(np.float32, copy=False)
    else:
        forward_kwargs['rois'] = blobs['rois'].astype(np.float32, copy=False)
    blobs_out = net.forward(**forward_kwargs)

    if cfg.TEST.HAS_RPN:
        assert len(im_scales) == 1, "Only single-image batch implemented"
        rois = net.blobs['rois'].data.copy()
        # unscale back to raw image space
        boxes = rois[:, 1:5] / im_scales[0]

    if cfg.TEST.SVM:
        # use the raw scores before softmax under the assumption they
        # were trained as linear SVMs
        scores = net.blobs['cls_score'].data
    else:
        # use softmax estimated probabilities
        scores = blobs_out['cls_prob']

    if cfg.TEST.BBOX_REG:
        # Apply bounding-box regression deltas
        box_deltas = blobs_out['bbox_pred']
        pred_boxes = bbox_transform_inv(boxes, box_deltas)
        pred_boxes = clip_boxes(pred_boxes, im.shape)
    else:
        # Simply repeat the boxes, once for each class
        pred_boxes = np.tile(boxes, (1, scores.shape[1]))

    if cfg.DEDUP_BOXES > 0 and not cfg.TEST.HAS_RPN:
        # Map scores and predictions back to the original set of boxes
        scores = scores[inv_index, :]
        pred_boxes = pred_boxes[inv_index, :]

    return scores, pred_boxes


def get_im_save_path(output_dir, im_save_name, class_name):
    save_path = os.path.join(output_dir, 'bboximages', class_name)
    if not os.path.exists(save_path):
        os.makedirs(save_path)
    save_path = os.path.join(save_path, im_save_name)
    print "save to:{}".format(save_path)
    return save_path


def vis_detections(im, class_name, dets, output_dir, im_save_name, thresh=0.3):
    """Visual debugging of detections."""
    """
    import matplotlib
    matplotlib.use('Agg')
    import matplotlib.pyplot as plt
    """
    im = im[:, :, (2, 1, 0)]
    for i in xrange(np.minimum(10, dets.shape[0])):
        bbox = dets[i, :4]
        score = dets[i, -1]
        if score > thresh:
            plt.cla()
            plt.imshow(im)
            plt.gca().add_patch(
                plt.Rectangle((bbox[0], bbox[1]),
                              bbox[2] - bbox[0],
                              bbox[3] - bbox[1], fill=False,
                              edgecolor='g', linewidth=3)
                )
            plt.title('{}  {:.3f}'.format(class_name, score))
            #plt.show()
            plt.savefig(get_im_save_path(output_dir, im_save_name, class_name))


def test_net(net, imdb, max_per_image=100, thresh=0.05, vis=False):
    """Test a Fast R-CNN network on an image database."""
    num_images = len(imdb.image_index)
    # all detections are collected into:
    #    all_boxes[cls][image] = N x 5 array of detections in
    #    (x1, y1, x2, y2, score)
    all_boxes = [[[] for _ in xrange(num_images)]
                 for _ in xrange(imdb.num_classes)]

    output_dir = get_output_dir(imdb, net)

    # timers
    _t = {'im_detect' : Timer(), 'misc' : Timer()}

    if not cfg.TEST.HAS_RPN:
        roidb = imdb.roidb

    for i in xrange(num_images):
        # filter out any ground truth boxes
        if cfg.TEST.HAS_RPN:
            box_proposals = None
        else:
            # The roidb may contain ground-truth rois (for example, if the roidb
            # comes from the training or val split). We only want to evaluate
            # detection on the *non*-ground-truth rois. We select those the rois
            # that have the gt_classes field set to 0, which means there's no
            # ground truth.
            box_proposals = roidb[i]['boxes'][roidb[i]['gt_classes'] == 0]

        im_path = imdb.image_path_at(i)
        im = cv2.imread(im_path)
        _t['im_detect'].tic()
        scores, boxes = im_detect(net, im, box_proposals)
        _t['im_detect'].toc()

        _t['misc'].tic()
        # skip j = 0, because it's the background class
        for j in xrange(1, imdb.num_classes):
            inds = np.where(scores[:, j] > thresh)[0]
            cls_scores = scores[inds, j]
            cls_boxes = boxes[inds, j*4:(j+1)*4]
            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:
                vis_detections(im, imdb._class_to_name[imdb._classes[j]], cls_dets, output_dir, os.path.basename(im_path))
            all_boxes[j][i] = cls_dets

        # Limit to max_per_image detections *over all classes*
        if max_per_image > 0:
            image_scores = np.hstack([all_boxes[j][i][:, -1]
                                      for j in xrange(1, imdb.num_classes)])
            if len(image_scores) > max_per_image:
                image_thresh = np.sort(image_scores)[-max_per_image]
                for j in xrange(1, imdb.num_classes):
                    keep = np.where(all_boxes[j][i][:, -1] >= image_thresh)[0]
                    all_boxes[j][i] = all_boxes[j][i][keep, :]
        _t['misc'].toc()

        print 'im_detect: {:d}/{:d} {:.3f}s {:.3f}s' \
              .format(i + 1, num_images, _t['im_detect'].average_time,
                      _t['misc'].average_time)

    det_file = os.path.join(output_dir, 'detections.pkl')
    with open(det_file, 'wb') as f:
        cPickle.dump(all_boxes, f, cPickle.HIGHEST_PROTOCOL)

    print 'Evaluating detections'
    imdb.evaluate_detections(all_boxes, output_dir)


def parse_args():
    """
    Parse input arguments
    """
    parser = argparse.ArgumentParser(description='Test a Fast R-CNN network')
    parser.add_argument('--gpu', dest='gpu_id', help='GPU id to use',
                        default=0, type=int)
    parser.add_argument('--def', dest='prototxt',
                        help='prototxt file defining the network',
                        default=None, type=str)
    parser.add_argument('--net', dest='caffemodel',
                        help='model to test',
                        default=None, type=str)
    parser.add_argument('--cfg', dest='cfg_file',
                        help='optional config file', default=None, type=str)
    parser.add_argument('--wait', dest='wait',
                        help='wait until net file exists',
                        default=True, type=bool)
    parser.add_argument('--imdb', dest='imdb_name',
                        help='dataset to test',
                        default='voc_2007_test', type=str)
    parser.add_argument('--comp', dest='comp_mode', help='competition mode',
                        action='store_true')
    parser.add_argument('--set', dest='set_cfgs',
                        help='set config keys', default=None,
                        nargs=argparse.REMAINDER)
    parser.add_argument('--vis', dest='vis', help='visualize detections',
                        action='store_true')
    parser.add_argument('--num_dets', dest='max_per_image',
                        help='max number of detections per image',
                        default=100, type=int)

    if len(sys.argv) == 1:
        parser.print_help()
        sys.exit(1)

    args = parser.parse_args()
    return args

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)

    cfg.GPU_ID = args.gpu_id

    print('Using config:')
    pprint.pprint(cfg)

    while not os.path.exists(args.caffemodel) and args.wait:
        print('Waiting for {} to exist...'.format(args.caffemodel))
        time.sleep(10)

    caffe.set_mode_gpu()
    caffe.set_device(args.gpu_id)
    net = caffe.Net(args.prototxt, args.caffemodel, caffe.TEST)
    net.name = os.path.splitext(os.path.basename(args.caffemodel))[0]

    imdb = get_imdb(args.imdb_name)
    imdb.competition_mode(args.comp_mode)
    if not cfg.TEST.HAS_RPN:
        imdb.set_proposal_method(cfg.TEST.PROPOSAL_METHOD)

    test_net(net, imdb, max_per_image=args.max_per_image, vis=args.vis)
