#!/usr/bin/env python3
# This file is covered by the LICENSE file in the root of this project.

import argparse
import os
import yaml
import sys
import numpy as np
import torch
import __init__ as booger

from tasks.semantic.modules.ioueval import iouEval
from common.laserscan import SemLaserScan

# possible splits
splits = ['train','valid','test']
def save_to_log(logdir,logfile,message):
    f = open(logdir+'/'+logfile, "a")
    f.write(message+'\n')
    f.close()
    return

def eval(test_sequences,splits,pred):
    # get scan paths
    scan_names = []
    for sequence in test_sequences:
        sequence = '{0:02d}'.format(int(sequence))
        scan_paths = os.path.join(FLAGS.dataset, "sequences",
                                  str(sequence), "snow_velodyne")
        # populate the scan names
        seq_scan_names = [os.path.join(dp, f) for dp, dn, fn in os.walk(
            os.path.expanduser(scan_paths)) for f in fn if ".bin" in f]
        seq_scan_names.sort()
        scan_names.extend(seq_scan_names)
    # print(scan_names)

    # get label paths
    label_names = []
    for sequence in test_sequences:
        sequence = '{0:02d}'.format(int(sequence))
        label_paths = os.path.join(FLAGS.dataset, "sequences",
                                   str(sequence), "snow_labels")
        # populate the label names
        seq_label_names = [os.path.join(dp, f) for dp, dn, fn in os.walk(
            os.path.expanduser(label_paths)) for f in fn if ".label" in f]
        seq_label_names.sort()
        label_names.extend(seq_label_names)
    # print(label_names)

    # get predictions paths
    pred_names = []
    for sequence in test_sequences:
        sequence = '{0:02d}'.format(int(sequence))
        pred_paths = os.path.join(FLAGS.predictions, "sequences",
                                  sequence, "predictions")
        # populate the label names
        seq_pred_names = [os.path.join(dp, f) for dp, dn, fn in os.walk(
            os.path.expanduser(pred_paths)) for f in fn if ".label" in f]
        seq_pred_names.sort()
        pred_names.extend(seq_pred_names)
    # print(pred_names)

    # check that I have the same number of files
    # print("labels: ", len(label_names))
    # print("predictions: ", len(pred_names))
    assert (len(label_names) == len(scan_names) and
            len(label_names) == len(pred_names))

    print("Evaluating sequences: ")
    # open each file, get the tensor, and make the iou comparison
    for scan_file, label_file, pred_file in zip(scan_names, label_names, pred_names):
        print("evaluating label ", label_file, "with", pred_file)
        # open label
        label = SemLaserScan(project=False)
        label.open_scan(scan_file)
        label.open_label(label_file)
        u_label_sem = remap_lut[label.sem_label]  # remap to xentropy format
        if FLAGS.limit is not None:
            u_label_sem = u_label_sem[:FLAGS.limit]

        # open prediction
        pred = SemLaserScan(project=False)
        pred.open_scan(scan_file)
        pred.open_label(pred_file)
        u_pred_sem = remap_lut[pred.sem_label]  # remap to xentropy format
        if FLAGS.limit is not None:
            u_pred_sem = u_pred_sem[:FLAGS.limit]

        # add single scan to evaluation
        evaluator.addBatch(u_pred_sem, u_label_sem)

    # when I am done, print the evaluation
    m_accuracy = evaluator.getacc()
    m_jaccard, class_jaccard = evaluator.getIoU()

    print('{split} set:\n'
          'Acc avg {m_accuracy:.3f}\n'
          'IoU avg {m_jaccard:.3f}'.format(split=splits,
                                           m_accuracy=m_accuracy,
                                           m_jaccard=m_jaccard))

    save_to_log(FLAGS.predictions,'pred.txt','{split} set:\n'
          'Acc avg {m_accuracy:.3f}\n'
          'IoU avg {m_jaccard:.3f}'.format(split=splits,
                                           m_accuracy=m_accuracy,
                                           m_jaccard=m_jaccard))
    # print also classwise
    for i, jacc in enumerate(class_jaccard):
        if i not in ignore:
            print('IoU class {i:} [{class_str:}] = {jacc:.3f}'.format(
                i=i, class_str=class_strings[class_inv_remap[i]], jacc=jacc))
            save_to_log(FLAGS.predictions, 'pred.txt', 'IoU class {i:} [{class_str:}] = {jacc:.3f}'.format(
                i=i, class_str=class_strings[class_inv_remap[i]], jacc=jacc))

    # print for spreadsheet
    print("*" * 80)
    print("below can be copied straight for paper table")
    for i, jacc in enumerate(class_jaccard):
        if i not in ignore:
            sys.stdout.write('{jacc:.3f}'.format(jacc=jacc.item()))
            sys.stdout.write(",")
    sys.stdout.write('{jacc:.3f}'.format(jacc=m_jaccard.item()))
    sys.stdout.write(",")
    sys.stdout.write('{acc:.3f}'.format(acc=m_accuracy.item()))
    sys.stdout.write('\n')
    sys.stdout.flush()

if __name__ == '__main__':
    parser = argparse.ArgumentParser("./evaluate_iou.py")
    parser.add_argument(
        '--dataset', '-d',
        type=str,
        required=True,
        help='Dataset dir. No Default',
    )
    parser.add_argument(
        '--predictions', '-p',
        type=str,
        required=None,
        help='Prediction dir. Same organization as dataset, but predictions in'
             'each sequences "prediction" directory. No Default. If no option is set'
             ' we look for the labels in the same directory as dataset'
    )
    parser.add_argument(
        '--split', '-s',
        type=str,
        required=False,
        choices=["train", "valid", "test"],
        default=None,
        help='Split to evaluate on. One of ' +
             str(splits) + '. Defaults to %(default)s',
    )
    parser.add_argument(
        '--data_cfg', '-dc',
        type=str,
        required=False,
        default="config/labels/semantic-kitti.yaml",
        help='Dataset config file. Defaults to %(default)s',
    )
    parser.add_argument(
        '--limit', '-l',
        type=int,
        required=False,
        default=None,
        help='Limit to the first "--limit" points of each scan. Useful for'
             ' evaluating single scan from aggregated pointcloud.'
             ' Defaults to %(default)s',
    )

    FLAGS, unparsed = parser.parse_known_args()

    # fill in real predictions dir
    if FLAGS.predictions is None:
        FLAGS.predictions = FLAGS.dataset

    # print summary of what we will do
    print("*" * 80)
    print("INTERFACE:")
    print("Data: ", FLAGS.dataset)
    print("Predictions: ", FLAGS.predictions)
    print("Split: ", FLAGS.split)
    print("Config: ", FLAGS.data_cfg)
    print("Limit: ", FLAGS.limit)
    print("*" * 80)

    # assert split
    assert (FLAGS.split in splits)

    # open data config file
    try:
        print("Opening data config file %s" % FLAGS.data_cfg)
        DATA = yaml.safe_load(open(FLAGS.data_cfg, 'r'))
    except Exception as e:
        print(e)
        print("Error opening data yaml file.")
        quit()

    # get number of interest classes, and the label mappings
    class_strings = DATA["labels"]
    class_remap = DATA["learning_map"]
    class_inv_remap = DATA["learning_map_inv"]
    class_ignore = DATA["learning_ignore"]
    nr_classes = len(class_inv_remap)

    # make lookup table for mapping
    maxkey = 0
    for key, data in class_remap.items():
        if key > maxkey:
            maxkey = key
    # +100 hack making lut bigger just in case there are unknown labels
    remap_lut = np.zeros((maxkey + 100), dtype=np.int32)
    for key, data in class_remap.items():
        try:
            remap_lut[key] = data
        except IndexError:
            print("Wrong key ", key)
    # print(remap_lut)

    # create evaluator
    ignore = []
    for cl, ign in class_ignore.items():
        if ign:
            x_cl = int(cl)
            ignore.append(x_cl)
            print("Ignoring xentropy class ", x_cl, " in IoU evaluation")

    # create evaluator
    device = torch.device("cpu")
    evaluator = iouEval(nr_classes, device, ignore)
    evaluator.reset()

    # get test set
    if FLAGS.split is None:
        for splits in ('train','valid'):
            eval((DATA["split"][splits]),splits,FLAGS.predictions)
    else:
        eval(DATA["split"][FLAGS.split],splits,FLAGS.predictions)




