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import numpy as np
import time
import torch
import os
from PIL import Image
from torchvision import transforms
from torch.utils.data import Dataset
from collections import namedtuple
from datasets.kitti_360.labels import trainId2label
Label = namedtuple(
"Label",
[
"name",
"id",
"trainId",
"category",
"categoryId",
"hasInstances",
"ignoreInEval",
"color",
"to_cs27",
],
)
BDD_LABEL = [
Label("unlabeled", 0, 255, "void", 0, False, True, (0, 0, 0), 255),
Label("dynamic", 1, 255, "void", 0, False, True, (111, 74, 0), 255),
Label("ego vehicle", 2, 255, "void", 0, False, True, (0, 0, 0), 255),
Label("ground", 3, 255, "void", 0, False, True, (81, 0, 81), 255),
Label("static", 4, 255, "void", 0, False, True, (0, 0, 0), 255),
Label("parking", 5, 255, "flat", 1, False, True, (250, 170, 160), 2),
Label("rail track", 6, 255, "flat", 1, False, True, (230, 150, 140), 3),
Label("road", 7, 0, "flat", 1, False, False, (128, 64, 128), 0),
Label("sidewalk", 8, 1, "flat", 1, False, False, (244, 35, 232), 1),
Label("bridge", 9, 255, "construction", 2, False, True, (150, 100, 100), 8),
Label("building", 10, 2, "construction", 2, False, False, (70, 70, 70), 4),
Label("fence", 11, 4, "construction", 2, False, False, (190, 153, 153), 6),
Label("garage", 12, 255, "construction", 2, False, True, (180, 100, 180), 255),
Label("guard rail", 13, 255, "construction", 2, False, True, (180, 165, 180), 7),
Label("tunnel", 14, 255, "construction", 2, False, True, (150, 120, 90), 9),
Label("wall", 15, 3, "construction", 2, False, False, (102, 102, 156), 5),
Label("banner", 16, 255, "object", 3, False, True, (250, 170, 100), 255),
Label("billboard", 17, 255, "object", 3, False, True, (220, 220, 250), 255),
Label("lane divider", 18, 255, "object", 3, False, True, (255, 165, 0), 255),
Label("parking sign", 19, 255, "object", 3, False, False, (220, 20, 60), 255),
Label("pole", 20, 5, "object", 3, False, False, (153, 153, 153), 10),
Label("polegroup", 21, 255, "object", 3, False, True, (153, 153, 153), 11),
Label("street light", 22, 255, "object", 3, False, True, (220, 220, 100), 255),
Label("traffic cone", 23, 255, "object", 3, False, True, (255, 70, 0), 255),
Label("traffic device", 24, 255, "object", 3, False, True, (220, 220, 220), 255),
Label("traffic light", 25, 6, "object", 3, False, False, (250, 170, 30), 12),
Label("traffic sign", 26, 7, "object", 3, False, False, (220, 220, 0), 13),
Label("traffic sign frame", 27, 255, "object", 3, False, True, (250, 170, 250), 255),
Label("terrain", 28, 9, "nature", 4, False, False, (152, 251, 152), 15),
Label("vegetation", 29, 8, "nature", 4, False, False, (107, 142, 35), 14),
Label("sky", 30, 10, "sky", 5, False, False, (70, 130, 180), 16),
Label("person", 31, 11, "human", 6, True, False, (220, 20, 60), 17),
Label("rider", 32, 12, "human", 6, True, False, (255, 0, 0), 18),
Label("bicycle", 33, 18, "vehicle", 7, True, False, (119, 11, 32), 26),
Label("bus", 34, 15, "vehicle", 7, True, False, (0, 60, 100), 21),
Label("car", 35, 13, "vehicle", 7, True, False, (0, 0, 142), 19),
Label("caravan", 36, 255, "vehicle", 7, True, True, (0, 0, 90), 22),
Label("motorcycle", 37, 17, "vehicle", 7, True, False, (0, 0, 230), 25),
Label("trailer", 38, 255, "vehicle", 7, True, True, (0, 0, 110), 23),
Label("train", 39, 16, "vehicle", 7, True, False, (0, 80, 100), 24),
Label("truck", 40, 14, "vehicle", 7, True, False, (0, 0, 70), 20),
]
def resize_with_padding(img, target_size, padding_value, interpolation):
target_h, target_w = target_size
width, height = img.size
aspect = width / height
if aspect > (target_w / target_h):
new_w = target_w
new_h = int(target_w / aspect)
else:
new_h = target_h
new_w = int(target_h * aspect)
img = transforms.functional.resize(img, (new_h, new_w), interpolation)
pad_h = target_h - new_h
pad_w = target_w - new_w
padding = (pad_w // 2, pad_h // 2, pad_w - pad_w // 2, pad_h - pad_h // 2)
return transforms.functional.pad(img, padding, fill=padding_value)
class BDDSeg(Dataset):
def __init__(self, root, image_set, image_size=(192, 640)):
super(BDDSeg, self).__init__()
self.split = image_set
self.root = root
self.image_transform = transforms.Compose([
#transforms.Lambda(lambda img: resize_with_padding(img, image_size, padding_value=0, interpolation=transforms.InterpolationMode.BILINEAR)),
transforms.Resize((320, 640), interpolation=transforms.InterpolationMode.BILINEAR),
transforms.CenterCrop(image_size),
transforms.ToTensor(),
])
self.target_transform = transforms.Compose([
#transforms.Lambda(lambda img: resize_with_padding(img, image_size, padding_value=-1, interpolation=transforms.InterpolationMode.NEAREST)),
transforms.Resize((320, 640), interpolation=transforms.InterpolationMode.NEAREST),
transforms.CenterCrop(image_size),
transforms.PILToTensor(),
transforms.Lambda(lambda x: x.long()),
])
self.images, self.targets = [], []
image_dir = os.path.join(self.root, "images/10k", self.split)
target_dir = os.path.join(self.root, "labels/pan_seg/bitmasks", self.split)
for file_name in os.listdir(image_dir):
image_path = os.path.join(image_dir, file_name)
target_filename = os.path.splitext(file_name)[0] + ".png"
target_path = os.path.join(target_dir, target_filename)
assert os.path.isfile(target_path)
self.images.append(image_path)
self.targets.append(target_path)
self.class_mapping = torch.Tensor([trainId2label[c.trainId].id for c in BDD_LABEL]).int()
def __getitem__(self, index):
_start_time = time.time()
image = Image.open(self.images[index]).convert("RGB")
target = Image.open(self.targets[index])
image = self.image_transform(image)
target = self.target_transform(target)
image = 2.0 * image - 1.0
poses = torch.eye(4) # (4, 4)
projs = torch.eye(3) # (3, 3)
target = target[0] # ("instance", "semantic", "polygon", "color")
target = self.class_mapping[target]
_proc_time = time.time() - _start_time
data = {
"imgs": [image.numpy()],
"poses": [poses.numpy()],
"projs": [projs.numpy()],
"segs": [target.numpy()],
"t__get_item__": np.array([_proc_time]),
"index": [np.array([index])],
}
return data
def __len__(self):
return len(self.images)
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