# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import cv2 import numpy as np from collections import OrderedDict try: from collections.abc import Sequence except Exception: from collections import Sequence from .dataset import DetDataset, _make_dataset, _is_valid_file from ppdet.core.workspace import register, serializable from ppdet.utils.logger import setup_logger logger = setup_logger(__name__) @register @serializable class MOTDataSet(DetDataset): """ Load dataset with MOT format. Args: dataset_dir (str): root directory for dataset. image_lists (str|list): mot data image lists, muiti-source mot dataset. data_fields (list): key name of data dictionary, at least have 'image'. sample_num (int): number of samples to load, -1 means all. Notes: MOT datasets root directory following this: dataset/mot |——————image_lists | |——————caltech.train | |——————caltech.val | |——————mot16.train | |——————mot17.train | ...... |——————Caltech |——————MOT17 |——————...... All the MOT datasets have the following structure: Caltech |——————images | └——————00001.jpg | |—————— ... | └——————0000N.jpg └——————labels_with_ids └——————00001.txt |—————— ... └——————0000N.txt or MOT17 |——————images | └——————train | └——————test └——————labels_with_ids └——————train """ def __init__(self, dataset_dir=None, image_lists=[], data_fields=['image'], sample_num=-1): super(MOTDataSet, self).__init__( dataset_dir=dataset_dir, data_fields=data_fields, sample_num=sample_num) self.dataset_dir = dataset_dir self.image_lists = image_lists if isinstance(self.image_lists, str): self.image_lists = [self.image_lists] self.roidbs = None self.cname2cid = None def get_anno(self): if self.image_lists == []: return # only used to get categories and metric return os.path.join(self.dataset_dir, 'image_lists', self.image_lists[0]) def parse_dataset(self): self.img_files = OrderedDict() self.img_start_index = OrderedDict() self.label_files = OrderedDict() self.tid_num = OrderedDict() self.tid_start_index = OrderedDict() img_index = 0 for data_name in self.image_lists: # check every data image list image_lists_dir = os.path.join(self.dataset_dir, 'image_lists') assert os.path.isdir(image_lists_dir), \ "The {} is not a directory.".format(image_lists_dir) list_path = os.path.join(image_lists_dir, data_name) assert os.path.exists(list_path), \ "The list path {} does not exist.".format(list_path) # record img_files, filter out empty ones with open(list_path, 'r') as file: self.img_files[data_name] = file.readlines() self.img_files[data_name] = [ os.path.join(self.dataset_dir, x.strip()) for x in self.img_files[data_name] ] self.img_files[data_name] = list( filter(lambda x: len(x) > 0, self.img_files[data_name])) self.img_start_index[data_name] = img_index img_index += len(self.img_files[data_name]) # check data directory, images and labels_with_ids if len(self.img_files[data_name]) == 0: continue else: # self.img_files[data_name] each line following this: # {self.dataset_dir}/MOT17/images/... first_path = self.img_files[data_name][0] data_dir = first_path.replace(self.dataset_dir, '').split('/')[1] data_dir = os.path.join(self.dataset_dir, data_dir) assert os.path.exists(data_dir), \ "The data directory {} does not exist.".format(data_dir) data_dir_images = os.path.join(data_dir, 'images') assert os.path.exists(data_dir), \ "The data images directory {} does not exist.".format(data_dir_images) data_dir_labels_with_ids = os.path.join(data_dir, 'labels_with_ids') assert os.path.exists(data_dir), \ "The data labels directory {} does not exist.".format(data_dir_labels_with_ids) # record label_files self.label_files[data_name] = [ x.replace('images', 'labels_with_ids').replace( '.png', '.txt').replace('.jpg', '.txt') for x in self.img_files[data_name] ] for data_name, label_paths in self.label_files.items(): max_index = -1 for lp in label_paths: lb = np.loadtxt(lp) if len(lb) < 1: continue if len(lb.shape) < 2: img_max = lb[1] else: img_max = np.max(lb[:, 1]) if img_max > max_index: max_index = img_max self.tid_num[data_name] = int(max_index + 1) last_index = 0 for i, (k, v) in enumerate(self.tid_num.items()): self.tid_start_index[k] = last_index last_index += v self.total_identities = int(last_index + 1) self.num_imgs_each_data = [len(x) for x in self.img_files.values()] self.total_imgs = sum(self.num_imgs_each_data) logger.info('=' * 80) logger.info('MOT dataset summary: ') logger.info(self.tid_num) logger.info('total images: {}'.format(self.total_imgs)) logger.info('image start index: {}'.format(self.img_start_index)) logger.info('total identities: {}'.format(self.total_identities)) logger.info('identity start index: {}'.format(self.tid_start_index)) logger.info('=' * 80) records = [] cname2cid = mot_label() for img_index in range(self.total_imgs): for i, (k, v) in enumerate(self.img_start_index.items()): if img_index >= v: data_name = list(self.label_files.keys())[i] start_index = v img_file = self.img_files[data_name][img_index - start_index] lbl_file = self.label_files[data_name][img_index - start_index] if not os.path.exists(img_file): logger.warn('Illegal image file: {}, and it will be ignored'. format(img_file)) continue if not os.path.isfile(lbl_file): logger.warn('Illegal label file: {}, and it will be ignored'. format(lbl_file)) continue labels = np.loadtxt(lbl_file, dtype=np.float32).reshape(-1, 6) # each row in labels (N, 6) is [gt_class, gt_identity, cx, cy, w, h] cx, cy = labels[:, 2], labels[:, 3] w, h = labels[:, 4], labels[:, 5] gt_bbox = np.stack((cx, cy, w, h)).T.astype('float32') gt_class = labels[:, 0:1].astype('int32') gt_score = np.ones((len(labels), 1)).astype('float32') gt_ide = labels[:, 1:2].astype('int32') mot_rec = { 'im_file': img_file, 'im_id': img_index, } if 'image' in self.data_fields else {} gt_rec = { 'gt_class': gt_class, 'gt_score': gt_score, 'gt_bbox': gt_bbox, 'gt_ide': gt_ide, } for k, v in gt_rec.items(): if k in self.data_fields: mot_rec[k] = v records.append(mot_rec) if self.sample_num > 0 and img_index >= self.sample_num: break assert len(records) > 0, 'not found any mot record in %s' % ( self.image_lists) self.roidbs, self.cname2cid = records, cname2cid def mot_label(): labels_map = {'person': 0} return labels_map @register @serializable class MOTImageFolder(DetDataset): def __init__(self, task, dataset_dir=None, data_root=None, image_dir=None, sample_num=-1, keep_ori_im=False, **kwargs): super(MOTImageFolder, self).__init__( dataset_dir, image_dir, sample_num=sample_num) self.task = task self.data_root = data_root self.keep_ori_im = keep_ori_im self._imid2path = {} self.roidbs = None def check_or_download_dataset(self): return def parse_dataset(self, ): if not self.roidbs: self.roidbs = self._load_images() def _parse(self): image_dir = self.image_dir if not isinstance(image_dir, Sequence): image_dir = [image_dir] images = [] for im_dir in image_dir: if os.path.isdir(im_dir): im_dir = os.path.join(self.dataset_dir, im_dir) images.extend(_make_dataset(im_dir)) elif os.path.isfile(im_dir) and _is_valid_file(im_dir): images.append(im_dir) return images def _load_images(self): images = self._parse() ct = 0 records = [] for image in images: assert image != '' and os.path.isfile(image), \ "Image {} not found".format(image) if self.sample_num > 0 and ct >= self.sample_num: break rec = {'im_id': np.array([ct]), 'im_file': image} if self.keep_ori_im: rec.update({'keep_ori_im': 1}) self._imid2path[ct] = image ct += 1 records.append(rec) assert len(records) > 0, "No image file found" return records def get_imid2path(self): return self._imid2path def set_images(self, images): self.image_dir = images self.roidbs = self._load_images() def _is_valid_video(f, extensions=('.mp4', '.avi', '.mov', '.rmvb', 'flv')): return f.lower().endswith(extensions) @register @serializable class MOTVideoDataset(DetDataset): """ Load MOT dataset with MOT format from video for inference. Args: video_file (str): path of the video file dataset_dir (str): root directory for dataset. keep_ori_im (bool): whether to keep original image, default False. Set True when used during MOT model inference while saving images or video, or used in DeepSORT. """ def __init__(self, video_file='', dataset_dir=None, keep_ori_im=False, **kwargs): super(MOTVideoDataset, self).__init__(dataset_dir=dataset_dir) self.video_file = video_file self.dataset_dir = dataset_dir self.keep_ori_im = keep_ori_im self.roidbs = None def parse_dataset(self, ): if not self.roidbs: self.roidbs = self._load_video_images() def _load_video_images(self): self.cap = cv2.VideoCapture(self.video_file) self.vn = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT)) logger.info('Length of the video: {:d} frames'.format(self.vn)) res = True ct = 0 records = [] while res: res, img = self.cap.read() image = np.ascontiguousarray(img, dtype=np.float32) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) im_shape = image.shape rec = { 'im_id': np.array([ct]), 'image': image, 'h': im_shape[0], 'w': im_shape[1], 'im_shape': np.array( im_shape[:2], dtype=np.float32), 'scale_factor': np.array( [1., 1.], dtype=np.float32), } if self.keep_ori_im: rec.update({'ori_image': image}) ct += 1 records.append(rec) records = records[:-1] assert len(records) > 0, "No image file found" return records def set_video(self, video_file): self.video_file = video_file assert os.path.isfile(self.video_file) and _is_valid_video(self.video_file), \ "wrong or unsupported file format: {}".format(self.video_file) self.roidbs = self._load_video_images()