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

import _init_paths

import os
import json
import cv2
import numpy as np
import time
from progress.bar import Bar
import torch

from external.nms import soft_nms
from opts import opts
from logger import Logger
from utils.utils import AverageMeter
from datasets.dataset_factory import dataset_factory
from detectors.detector_factory import detector_factory

class PrefetchDataset(torch.utils.data.Dataset):
 def __init__(self, opt, dataset, pre_process_func):
 self.images = dataset.images
 self.load_image_func = dataset.coco.loadImgs
 self.img_dir = dataset.img_dir
 self.pre_process_func = pre_process_func
 self.opt = opt
 
 def __getitem__(self, index):
 img_id = self.images[index]
 img_info = self.load_image_func(ids=[img_id])[0]
 img_path = os.path.join(self.img_dir, img_info['file_name'])
 image = cv2.imread(img_path)
 images, meta = {}, {}
 for scale in opt.test_scales:
 if opt.task == 'ddd':
 images[scale], meta[scale] = self.pre_process_func(
 image, scale, img_info['calib'])
 else:
 images[scale], meta[scale] = self.pre_process_func(image, scale)
 return img_id, {'images': images, 'image': image, 'meta': meta}

 def __len__(self):
 return len(self.images)

def prefetch_test(opt):
 os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpus_str

 Dataset = dataset_factory[opt.dataset]
 opt = opts().update_dataset_info_and_set_heads(opt, Dataset)
 print(opt)
 Logger(opt)
 Detector = detector_factory[opt.task]
 
 split = 'val' if not opt.trainval else 'test'
 dataset = Dataset(opt, split)
 detector = Detector(opt)
 
 data_loader = torch.utils.data.DataLoader(
 PrefetchDataset(opt, dataset, detector.pre_process), 
 batch_size=1, shuffle=False, num_workers=1, pin_memory=True)

 results = {}
 num_iters = len(dataset)
 bar = Bar('{}'.format(opt.exp_id), max=num_iters)
 time_stats = ['tot', 'load', 'pre', 'net', 'dec', 'post', 'merge']
 avg_time_stats = {t: AverageMeter() for t in time_stats}
 for ind, (img_id, pre_processed_images) in enumerate(data_loader):
 ret = detector.run(pre_processed_images)
 results[img_id.numpy().astype(np.int32)[0]] = ret['results']
 Bar.suffix = '[{0}/{1}]|Tot: {total:} |ETA: {eta:} '.format(
 ind, num_iters, total=bar.elapsed_td, eta=bar.eta_td)
 for t in avg_time_stats:
 avg_time_stats[t].update(ret[t])
 Bar.suffix = Bar.suffix + '|{} {tm.val:.3f}s ({tm.avg:.3f}s) '.format(
 t, tm = avg_time_stats[t])
 bar.next()
 bar.finish()
 dataset.run_eval(results, opt.save_dir)

def test(opt):
 os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpus_str

 Dataset = dataset_factory[opt.dataset]
 opt = opts().update_dataset_info_and_set_heads(opt, Dataset)
 print(opt)
 Logger(opt)
 Detector = detector_factory[opt.task]
 
 split = 'val' if not opt.trainval else 'test'
 dataset = Dataset(opt, split)
 detector = Detector(opt)

 results = {}
 num_iters = len(dataset)
 bar = Bar('{}'.format(opt.exp_id), max=num_iters)
 time_stats = ['tot', 'load', 'pre', 'net', 'dec', 'post', 'merge']
 avg_time_stats = {t: AverageMeter() for t in time_stats}
 for ind in range(num_iters):
 img_id = dataset.images[ind]
 img_info = dataset.coco.loadImgs(ids=[img_id])[0]
 img_path = os.path.join(dataset.img_dir, img_info['file_name'])

 if opt.task == 'ddd':
 ret = detector.run(img_path, img_info['calib'])
 else:
 ret = detector.run(img_path)
 
 results[img_id] = ret['results']

 Bar.suffix = '[{0}/{1}]|Tot: {total:} |ETA: {eta:} '.format(
 ind, num_iters, total=bar.elapsed_td, eta=bar.eta_td)
 for t in avg_time_stats:
 avg_time_stats[t].update(ret[t])
 Bar.suffix = Bar.suffix + '|{} {:.3f} '.format(t, avg_time_stats[t].avg)
 bar.next()
 bar.finish()
 dataset.run_eval(results, opt.save_dir)

if __name__ == '__main__':
 opt = opts().parse()
 if opt.not_prefetch_test:
 test(opt)
 else:
 prefetch_test(opt)