98 lines
3.3 KiB
Python
98 lines
3.3 KiB
Python
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# This file is part of COAT, and is distributed under the
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# OSI-approved BSD 3-Clause License. See top-level LICENSE file or
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# https://github.com/Kitware/COAT/blob/master/LICENSE for details.
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import os.path as osp
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import re
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import numpy as np
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from scipy.io import loadmat
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from .base import BaseDataset
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class PRW(BaseDataset):
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def __init__(self, root, transforms, split):
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self.name = "PRW"
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self.img_prefix = osp.join(root, "frames")
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super(PRW, self).__init__(root, transforms, split)
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def _get_cam_id(self, img_name):
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match = re.search(r"c\d", img_name).group().replace("c", "")
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return int(match)
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def _load_queries(self):
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query_info = osp.join(self.root, "query_info.txt")
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with open(query_info, "rb") as f:
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raw = f.readlines()
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queries = []
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for line in raw:
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linelist = str(line, "utf-8").split(" ")
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pid = int(linelist[0])
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x, y, w, h = (
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float(linelist[1]),
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float(linelist[2]),
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float(linelist[3]),
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float(linelist[4]),
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)
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roi = np.array([x, y, x + w, y + h]).astype(np.int32)
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roi = np.clip(roi, 0, None) # several coordinates are negative
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img_name = linelist[5][:-2] + ".jpg"
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queries.append(
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{
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"img_name": img_name,
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"img_path": osp.join(self.img_prefix, img_name),
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"boxes": roi[np.newaxis, :],
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"pids": np.array([pid]),
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"cam_id": self._get_cam_id(img_name),
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}
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)
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return queries
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def _load_split_img_names(self):
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"""
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Load the image names for the specific split.
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"""
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assert self.split in ("train", "gallery")
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if self.split == "train":
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imgs = loadmat(osp.join(self.root, "frame_train.mat"))["img_index_train"]
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else:
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imgs = loadmat(osp.join(self.root, "frame_test.mat"))["img_index_test"]
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return [img[0][0] + ".jpg" for img in imgs]
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def _load_annotations(self):
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if self.split == "query":
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return self._load_queries()
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annotations = []
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imgs = self._load_split_img_names()
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for img_name in imgs:
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anno_path = osp.join(self.root, "annotations", img_name)
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anno = loadmat(anno_path)
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box_key = "box_new"
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if box_key not in anno.keys():
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box_key = "anno_file"
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if box_key not in anno.keys():
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box_key = "anno_previous"
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rois = anno[box_key][:, 1:]
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ids = anno[box_key][:, 0]
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rois = np.clip(rois, 0, None) # several coordinates are negative
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assert len(rois) == len(ids)
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rois[:, 2:] += rois[:, :2]
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ids[ids == -2] = 5555 # assign pid = 5555 for unlabeled people
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annotations.append(
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{
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"img_name": img_name,
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"img_path": osp.join(self.img_prefix, img_name),
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"boxes": rois.astype(np.int32),
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# (training pids) 1, 2,..., 478, 480, 481, 482, 483, 932, 5555
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"pids": ids.astype(np.int32),
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"cam_id": self._get_cam_id(img_name),
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}
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)
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return annotations
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