#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright (c) BaseDetection, Inc. and its affiliates. All Rights Reserved
import copy
import json
import logging
import os
import os.path as osp
import numpy as np
import torch
from cvpods.structures import BoxMode
from cvpods.utils import PathManager, Timer
from ..base_dataset import BaseDataset
from ..detection_utils import (
annotations_to_instances,
check_image_size,
create_keypoint_hflip_indices,
filter_empty_instances,
read_image
)
from ..registry import DATASETS
from .paths_route import _PREDEFINED_SPLITS_CROWDHUMAN
"""
This file contains functions to parse COCO-format annotations into dicts in "cvpods format".
"""
logger = logging.getLogger(__name__)
[docs]@DATASETS.register()
class CrowdHumanDataset(BaseDataset):
def __init__(self, cfg, dataset_name, transforms=[], is_train=True):
super(CrowdHumanDataset, self).__init__(cfg, dataset_name, transforms, is_train)
self.dataset_key = "_".join(self.name.split('_')[:-1])
image_root, json_file = _PREDEFINED_SPLITS_CROWDHUMAN[self.dataset_key][self.name]
self.json_file = osp.join(self.data_root, json_file) \
if "://" not in image_root else osp.join(image_root, json_file)
self.image_root = osp.join(self.data_root, image_root) \
if "://" not in image_root else image_root
self.meta = self._get_metadata()
self.dataset_dicts = self._load_annotations(
self.json_file,
self.image_root)
# fmt: off
self.data_format = cfg.INPUT.FORMAT
self.mask_on = cfg.MODEL.MASK_ON
self.mask_format = cfg.INPUT.MASK_FORMAT
self.filter_empty = cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS
self.keypoint_on = cfg.MODEL.KEYPOINT_ON
self.load_proposals = cfg.MODEL.LOAD_PROPOSALS
self.proposal_files = cfg.DATASETS.PROPOSAL_FILES_TRAIN
# fmt: on
if is_train:
self.dataset_dicts = self._filter_annotations(
filter_empty=self.filter_empty,
min_keypoints=cfg.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE
if self.keypoint_on else 0,
proposal_files=self.proposal_files if self.load_proposals else None,
)
self._set_group_flag()
self.eval_with_gt = cfg.TEST.get("WITH_GT", False)
if self.keypoint_on:
# Flip only makes sense in training
self.keypoint_hflip_indices = create_keypoint_hflip_indices(
cfg.DATASETS.TRAIN)
else:
self.keypoint_hflip_indices = None
[docs] def __getitem__(self, index):
"""Load data, apply transforms, converto to Instances.
"""
dataset_dict = copy.deepcopy(self.dataset_dicts[index])
# read image
image = read_image(dataset_dict["file_name"], format=self.data_format)
check_image_size(dataset_dict, image)
if "annotations" in dataset_dict:
annotations = dataset_dict.pop("annotations")
annotations = [
ann for ann in annotations if ann.get("iscrowd", 0) == 0]
else:
annotations = None
# apply transfrom
image, annotations = self._apply_transforms(
image, annotations)
if annotations is not None:
image_shape = image.shape[:2] # h, w
instances = annotations_to_instances(
annotations, image_shape, mask_format=self.mask_format
)
# # Create a tight bounding box from masks, useful when image is cropped
# if self.crop_gen and instances.has("gt_masks"):
# instances.gt_boxes = instances.gt_masks.get_bounding_boxes()
dataset_dict["instances"] = filter_empty_instances(instances)
# convert to Instance type
# Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,
# but not efficient on large generic data structures due to the use of pickle & mp.Queue.
# Therefore it's important to use torch.Tensor.
# h, w, c -> c, h, w
dataset_dict["image"] = torch.as_tensor(
np.ascontiguousarray(image.transpose(2, 0, 1)))
return dataset_dict
def __reset__(self):
raise NotImplementedError
def __len__(self):
return len(self.dataset_dicts)
def _load_annotations(self, json_file, image_root):
"""
Load a json file with CrowdHuman's instances annotation format.
Currently supports instance detection, instance segmentation,
and person keypoints annotations.
Args:
json_file (str): full path to the json file in CrowdHuman instances annotation format.
image_root (str): the directory where the images in this json file exists.
Returns:
list[dict]: a list of dicts in cvpods standard format. (See
`Using Custom Datasets </tutorials/datasets.html>`_ )
Notes:
1. This function does not read the image files.
The results do not have the "image" field.
"""
timer = Timer()
json_file = PathManager.get_local_path(json_file)
with open(json_file, 'r') as file:
gt_records = file.readlines()
if timer.seconds() > 1:
logger.info("Loading {} takes {:.2f} seconds.".format(
json_file, timer.seconds()))
logger.info("Loaded {} images in CrowdHuman format from {}".format(
len(gt_records), json_file))
dataset_dicts = []
ann_keys = ["tag", "hbox", "vbox", "head_attr", "extra"]
for anno_str in gt_records:
anno_dict = json.loads(anno_str)
record = {}
record["file_name"] = os.path.join(image_root, "{}.jpg".format(anno_dict["ID"]))
record["image_id"] = anno_dict["ID"]
objs = []
for anno in anno_dict['gtboxes']:
# Check that the image_id in this annotation is the same as
# the image_id we're looking at.
# This fails only when the data parsing logic or the annotation file is buggy.
# The original COCO valminusminival2014 & minival2014 annotation files
# actually contains bugs that, together with certain ways of using COCO API,
# can trigger this assertion.
obj = {key: anno[key] for key in ann_keys if key in anno}
obj["bbox"] = anno["fbox"]
obj["category_id"] = 0
if 'extra' in anno and 'ignore' in anno['extra'] and anno['extra']['ignore'] != 0:
obj["category_id"] = -1
obj["bbox_mode"] = BoxMode.XYWH_ABS
objs.append(obj)
record["annotations"] = objs
dataset_dicts.append(record)
return dataset_dicts
def _get_metadata(self):
meta = {}
meta["image_root"] = self.image_root
meta["json_file"] = self.json_file
meta["evaluator_type"] = _PREDEFINED_SPLITS_CROWDHUMAN["evaluator_type"][self.dataset_key]
meta["thing_classes"] = ['person']
return meta
[docs] def evaluate(self, predictions):
"""Dataset must provide a evaluation function to evaluate model."""
raise NotImplementedError
@property
def ground_truth_annotations(self):
return self.dataset_dicts