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# ------------------------------------------------------------------------
# Copyright (c) 2022 megvii-research. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from DETR (https://github.com/facebookresearch/detr)
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# ------------------------------------------------------------------------
"""
MOT dataset which returns image_id for evaluation.
"""
from collections import defaultdict
import json
import os
from pathlib import Path
import cv2
import numpy as np
import torch
import torch.utils.data
import os.path as osp
from PIL import Image, ImageDraw
import copy
import datasets.transforms as T
from models.structures import Instances
from random import choice, randint
def is_crowd(ann):
return 'extra' in ann and 'ignore' in ann['extra'] and ann['extra']['ignore'] == 1
class DetMOTDetection:
def __init__(self, args, data_txt_path: str, seqs_folder, transform):
self.args = args
self.transform = transform
self.num_frames_per_batch = max(args.sampler_lengths)
self.sample_mode = args.sample_mode
self.sample_interval = args.sample_interval
self.video_dict = {}
self.mot_path = args.mot_path
self.labels_full = defaultdict(lambda : defaultdict(list))
def add_mot_folder(split_dir):
print("Adding", split_dir)
for vid in os.listdir(os.path.join(self.mot_path, split_dir)):
if 'seqmap' == vid:
continue
vid = os.path.join(split_dir, vid)
if 'DPM' in vid or 'FRCNN' in vid:
print(f'filter {vid}')
continue
gt_path = os.path.join(self.mot_path, vid, 'gt', 'gt.txt')
for l in open(gt_path):
t, i, *xywh, mark, label = l.strip().split(',')[:8]
t, i, mark, label = map(int, (t, i, mark, label))
if mark == 0:
continue
if label in [3, 4, 5, 6, 9, 10, 11]: # Non-person
continue
else:
crowd = False
x, y, w, h = map(float, (xywh))
self.labels_full[vid][t].append([x, y, w, h, i, crowd])
add_mot_folder("DanceTrack/train")
vid_files = list(self.labels_full.keys())
self.indices = []
self.vid_tmax = {}
for vid in vid_files:
self.video_dict[vid] = len(self.video_dict)
t_min = min(self.labels_full[vid].keys())
t_max = max(self.labels_full[vid].keys()) + 1
self.vid_tmax[vid] = t_max - 1
for t in range(t_min, t_max - self.num_frames_per_batch):
self.indices.append((vid, t))
print(f"Found {len(vid_files)} videos, {len(self.indices)} frames")
self.sampler_steps: list = args.sampler_steps
self.lengths: list = args.sampler_lengths
print("sampler_steps={} lenghts={}".format(self.sampler_steps, self.lengths))
self.period_idx = 0
# crowdhuman
self.ch_dir = Path(args.mot_path) / 'crowdhuman'
self.ch_indices = []
if args.append_crowd:
for line in open(self.ch_dir / f"annotation_trainval.odgt"):
datum = json.loads(line)
boxes = [ann['fbox'] for ann in datum['gtboxes'] if not is_crowd(ann)]
self.ch_indices.append((datum['ID'], boxes))
# self.ch_indices = self.ch_indices + self.ch_indices
print(f"Found {len(self.ch_indices)} images")
if args.det_db:
with open(os.path.join(args.mot_path, args.det_db)) as f:
self.det_db = json.load(f)
else:
self.det_db = defaultdict(list)
def set_epoch(self, epoch):
self.current_epoch = epoch
if self.sampler_steps is None or len(self.sampler_steps) == 0:
# fixed sampling length.
return
for i in range(len(self.sampler_steps)):
if epoch >= self.sampler_steps[i]:
self.period_idx = i + 1
print("set epoch: epoch {} period_idx={}".format(epoch, self.period_idx))
self.num_frames_per_batch = self.lengths[self.period_idx]
def step_epoch(self):
# one epoch finishes.
print("Dataset: epoch {} finishes".format(self.current_epoch))
self.set_epoch(self.current_epoch + 1)
@staticmethod
def _targets_to_instances(targets: dict, img_shape) -> Instances:
gt_instances = Instances(tuple(img_shape))
n_gt = len(targets['labels'])
gt_instances.boxes = targets['boxes'][:n_gt]
gt_instances.labels = targets['labels']
gt_instances.obj_ids = targets['obj_ids']
return gt_instances
def load_crowd(self, index):
ID, boxes = self.ch_indices[index]
boxes = copy.deepcopy(boxes)
img = Image.open(self.ch_dir / 'Images' / f'{ID}.jpg')
w, h = img._size
n_gts = len(boxes)
scores = [0. for _ in range(len(boxes))]
for line in self.det_db[f'crowdhuman/train_image/{ID}.txt']:
*box, s = map(float, line.split(','))
boxes.append(box)
scores.append(s)
boxes = torch.tensor(boxes, dtype=torch.float32)
areas = boxes[..., 2:].prod(-1)
boxes[:, 2:] += boxes[:, :2]
target = {
'boxes': boxes,
'scores': torch.as_tensor(scores),
'labels': torch.zeros((n_gts, ), dtype=torch.long),
'iscrowd': torch.zeros((n_gts, ), dtype=torch.bool),
'image_id': torch.tensor([0]),
'area': areas,
'obj_ids': torch.arange(n_gts),
'size': torch.as_tensor([h, w]),
'orig_size': torch.as_tensor([h, w]),
'dataset': "CrowdHuman",
}
rs = T.FixedMotRandomShift(self.num_frames_per_batch)
return rs([img], [target])
def _pre_single_frame(self, vid, idx: int):
img_path = os.path.join(self.mot_path, vid, 'img1', f'{idx:08d}.jpg')
img = Image.open(img_path)
targets = {}
w, h = img._size
assert w > 0 and h > 0, "invalid image {} with shape {} {}".format(img_path, w, h)
obj_idx_offset = self.video_dict[vid] * 100000 # 100000 unique ids is enough for a video.
targets['dataset'] = 'MOT17'
targets['boxes'] = []
targets['iscrowd'] = []
targets['labels'] = []
targets['obj_ids'] = []
targets['scores'] = []
targets['image_id'] = torch.as_tensor(idx)
targets['size'] = torch.as_tensor([h, w])
targets['orig_size'] = torch.as_tensor([h, w])
for *xywh, id, crowd in self.labels_full[vid][idx]:
targets['boxes'].append(xywh)
assert not crowd
targets['iscrowd'].append(crowd)
targets['labels'].append(0)
targets['obj_ids'].append(id + obj_idx_offset)
targets['scores'].append(1.)
txt_key = os.path.join(vid, 'img1', f'{idx:08d}.txt')
for line in self.det_db[txt_key]:
*box, s = map(float, line.split(','))
targets['boxes'].append(box)
targets['scores'].append(s)
targets['iscrowd'] = torch.as_tensor(targets['iscrowd'])
targets['labels'] = torch.as_tensor(targets['labels'])
targets['obj_ids'] = torch.as_tensor(targets['obj_ids'], dtype=torch.float64)
targets['scores'] = torch.as_tensor(targets['scores'])
targets['boxes'] = torch.as_tensor(targets['boxes'], dtype=torch.float32).reshape(-1, 4)
targets['boxes'][:, 2:] += targets['boxes'][:, :2]
return img, targets
def _get_sample_range(self, start_idx):
# take default sampling method for normal dataset.
assert self.sample_mode in ['fixed_interval', 'random_interval'], 'invalid sample mode: {}'.format(self.sample_mode)
if self.sample_mode == 'fixed_interval':
sample_interval = self.sample_interval
elif self.sample_mode == 'random_interval':
sample_interval = np.random.randint(1, self.sample_interval + 1)
default_range = start_idx, start_idx + (self.num_frames_per_batch - 1) * sample_interval + 1, sample_interval
return default_range
def pre_continuous_frames(self, vid, indices):
return zip(*[self._pre_single_frame(vid, i) for i in indices])
def sample_indices(self, vid, f_index):
assert self.sample_mode == 'random_interval'
rate = randint(1, self.sample_interval + 1)
tmax = self.vid_tmax[vid]
ids = [f_index + rate * i for i in range(self.num_frames_per_batch)]
return [min(i, tmax) for i in ids]
def __getitem__(self, idx):
if idx < len(self.indices):
vid, f_index = self.indices[idx]
indices = self.sample_indices(vid, f_index)
images, targets = self.pre_continuous_frames(vid, indices)
else:
images, targets = self.load_crowd(idx - len(self.indices))
if self.transform is not None:
images, targets = self.transform(images, targets)
gt_instances, proposals = [], []
for img_i, targets_i in zip(images, targets):
gt_instances_i = self._targets_to_instances(targets_i, img_i.shape[1:3])
gt_instances.append(gt_instances_i)
n_gt = len(targets_i['labels'])
proposals.append(torch.cat([
targets_i['boxes'][n_gt:],
targets_i['scores'][n_gt:, None],
], dim=1))
return {
'imgs': images,
'gt_instances': gt_instances,
'proposals': proposals,
}
def __len__(self):
return len(self.indices) + len(self.ch_indices)
class DetMOTDetectionValidation(DetMOTDetection):
def __init__(self, args, seqs_folder, transform):
args.data_txt_path = args.val_data_txt_path
super().__init__(args, seqs_folder, transform)
def make_transforms_for_mot17(image_set, args=None):
normalize = T.MotCompose([
T.MotToTensor(),
T.MotNormalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
scales = [608, 640, 672, 704, 736, 768, 800, 832, 864, 896, 928, 960, 992]
if image_set == 'train':
return T.MotCompose([
T.MotRandomHorizontalFlip(),
T.MotRandomSelect(
T.MotRandomResize(scales, max_size=1536),
T.MotCompose([
T.MotRandomResize([800, 1000, 1200]),
T.FixedMotRandomCrop(800, 1200),
T.MotRandomResize(scales, max_size=1536),
])
),
T.MOTHSV(),
normalize,
])
if image_set == 'val':
return T.MotCompose([
T.MotRandomResize([800], max_size=1333),
normalize,
])
raise ValueError(f'unknown {image_set}')
def build_transform(args, image_set):
mot17_train = make_transforms_for_mot17('train', args)
mot17_test = make_transforms_for_mot17('val', args)
if image_set == 'train':
return mot17_train
elif image_set == 'val':
return mot17_test
else:
raise NotImplementedError()
def build(image_set, args):
root = Path(args.mot_path)
assert root.exists(), f'provided MOT path {root} does not exist'
transform = build_transform(args, image_set)
if image_set == 'train':
data_txt_path = args.data_txt_path_train
dataset = DetMOTDetection(args, data_txt_path=data_txt_path, seqs_folder=root, transform=transform)
if image_set == 'val':
data_txt_path = args.data_txt_path_val
dataset = DetMOTDetection(args, data_txt_path=data_txt_path, seqs_folder=root, transform=transform)
return dataset
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