122 lines
5.1 KiB
Python
122 lines
5.1 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 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 CUHKSYSU(BaseDataset):
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def __init__(self, root, transforms, split):
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self.name = "CUHK-SYSU"
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self.img_prefix = osp.join(root, "Image", "SSM")
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super(CUHKSYSU, self).__init__(root, transforms, split)
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def _load_queries(self):
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# TestG50: a test protocol, 50 gallery images per query
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protoc = loadmat(osp.join(self.root, "annotation/test/train_test/TestG50.mat"))
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protoc = protoc["TestG50"].squeeze()
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queries = []
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for item in protoc["Query"]:
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img_name = str(item["imname"][0, 0][0])
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roi = item["idlocate"][0, 0][0].astype(np.int32)
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roi[2:] += roi[:2]
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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([-100]), # dummy pid
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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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# gallery images
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gallery_imgs = loadmat(osp.join(self.root, "annotation", "pool.mat"))
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gallery_imgs = gallery_imgs["pool"].squeeze()
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gallery_imgs = [str(a[0]) for a in gallery_imgs]
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if self.split == "gallery":
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return gallery_imgs
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# all images
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all_imgs = loadmat(osp.join(self.root, "annotation", "Images.mat"))
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all_imgs = all_imgs["Img"].squeeze()
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all_imgs = [str(a[0][0]) for a in all_imgs]
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# training images = all images - gallery images
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training_imgs = sorted(list(set(all_imgs) - set(gallery_imgs)))
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return training_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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# load all images and build a dict from image to boxes
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all_imgs = loadmat(osp.join(self.root, "annotation", "Images.mat"))
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all_imgs = all_imgs["Img"].squeeze()
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name_to_boxes = {}
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name_to_pids = {}
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unlabeled_pid = 5555 # default pid for unlabeled people
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for img_name, _, boxes in all_imgs:
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img_name = str(img_name[0])
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boxes = np.asarray([b[0] for b in boxes[0]])
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boxes = boxes.reshape(boxes.shape[0], 4) # (x1, y1, w, h)
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valid_index = np.where((boxes[:, 2] > 0) & (boxes[:, 3] > 0))[0]
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assert valid_index.size > 0, "Warning: {} has no valid boxes.".format(img_name)
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boxes = boxes[valid_index]
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name_to_boxes[img_name] = boxes.astype(np.int32)
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name_to_pids[img_name] = unlabeled_pid * np.ones(boxes.shape[0], dtype=np.int32)
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def set_box_pid(boxes, box, pids, pid):
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for i in range(boxes.shape[0]):
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if np.all(boxes[i] == box):
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pids[i] = pid
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return
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# assign a unique pid from 1 to N for each identity
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if self.split == "train":
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train = loadmat(osp.join(self.root, "annotation/test/train_test/Train.mat"))
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train = train["Train"].squeeze()
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for index, item in enumerate(train):
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scenes = item[0, 0][2].squeeze()
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for img_name, box, _ in scenes:
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img_name = str(img_name[0])
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box = box.squeeze().astype(np.int32)
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set_box_pid(name_to_boxes[img_name], box, name_to_pids[img_name], index + 1)
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else:
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protoc = loadmat(osp.join(self.root, "annotation/test/train_test/TestG50.mat"))
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protoc = protoc["TestG50"].squeeze()
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for index, item in enumerate(protoc):
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# query
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im_name = str(item["Query"][0, 0][0][0])
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box = item["Query"][0, 0][1].squeeze().astype(np.int32)
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set_box_pid(name_to_boxes[im_name], box, name_to_pids[im_name], index + 1)
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# gallery
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gallery = item["Gallery"].squeeze()
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for im_name, box, _ in gallery:
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im_name = str(im_name[0])
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if box.size == 0:
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break
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box = box.squeeze().astype(np.int32)
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set_box_pid(name_to_boxes[im_name], box, name_to_pids[im_name], index + 1)
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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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boxes = name_to_boxes[img_name]
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boxes[:, 2:] += boxes[:, :2] # (x1, y1, w, h) -> (x1, y1, x2, y2)
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pids = name_to_pids[img_name]
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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": boxes,
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"pids": pids,
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}
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)
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return annotations
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