# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# ---------------------------------------------------------------------
# Part of the following code in this file refs to torchvision
# BSD 3-Clause License
#
# Copyright (c) Soumith Chintala 2016,
# All rights reserved.
# ---------------------------------------------------------------------
import json
import os
import cv2
import numpy as np
from .meta_vision import VisionDataset
[文档]class Cityscapes(VisionDataset):
r"""`Cityscapes <http://www.cityscapes-dataset.com/>`_ Dataset."""
supported_order = (
"image",
"mask",
"info",
)
def __init__(self, root, image_set, mode, *, order=None):
super().__init__(root, order=order, supported_order=self.supported_order)
city_root = self.root
if not os.path.isdir(city_root):
raise RuntimeError("Dataset not found or corrupted.")
self.mode = mode
self.images_dir = os.path.join(city_root, "leftImg8bit", image_set)
self.masks_dir = os.path.join(city_root, self.mode, image_set)
self.images, self.masks = [], []
# self.target_type = ["instance", "semantic", "polygon", "color"]
# for semantic segmentation
if mode == "gtFine":
valid_modes = ("train", "test", "val")
else:
valid_modes = ("train", "train_extra", "val")
for city in os.listdir(self.images_dir):
img_dir = os.path.join(self.images_dir, city)
mask_dir = os.path.join(self.masks_dir, city)
for file_name in os.listdir(img_dir):
mask_name = "{}_{}".format(
file_name.split("_leftImg8bit")[0],
self._get_target_suffix(self.mode, "semantic"),
)
self.images.append(os.path.join(img_dir, file_name))
self.masks.append(os.path.join(mask_dir, mask_name))
def __getitem__(self, index):
target = []
for k in self.order:
if k == "image":
image = cv2.imread(self.images[index], cv2.IMREAD_COLOR)
target.append(image)
elif k == "mask":
mask = cv2.imread(self.masks[index], cv2.IMREAD_GRAYSCALE)
mask = self._trans_mask(mask)
mask = mask[:, :, np.newaxis]
target.append(mask)
elif k == "info":
if image is None:
image = cv2.imread(self.images[index], cv2.IMREAD_COLOR)
info = [image.shape[0], image.shape[1], self.images[index]]
target.append(info)
else:
raise NotImplementedError
return tuple(target)
def __len__(self):
return len(self.images)
def _trans_mask(self, mask):
trans_labels = [
7,
8,
11,
12,
13,
17,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28,
31,
32,
33,
]
label = np.ones(mask.shape) * 255
for i, tl in enumerate(trans_labels):
label[mask == tl] = i
return label.astype(np.uint8)
def _get_target_suffix(self, mode, target_type):
if target_type == "instance":
return "{}_instanceIds.png".format(mode)
elif target_type == "semantic":
return "{}_labelIds.png".format(mode)
elif target_type == "color":
return "{}_color.png".format(mode)
else:
return "{}_polygons.json".format(mode)
def _load_json(self, path):
with open(path, "r") as file:
data = json.load(file)
return data
class_names = (
"road",
"sidewalk",
"building",
"wall",
"fence",
"pole",
"traffic light",
"traffic sign",
"vegetation",
"terrain",
"sky",
"person",
"rider",
"car",
"truck",
"bus",
"train",
"motorcycle",
"bicycle",
)