[docs]classCityscapes(VisionDataset):"""`Cityscapes <http://www.cityscapes-dataset.com/>`_ Dataset. Args: root (str or ``pathlib.Path``): Root directory of dataset where directory ``leftImg8bit`` and ``gtFine`` or ``gtCoarse`` are located. split (string, optional): The image split to use, ``train``, ``test`` or ``val`` if mode="fine" otherwise ``train``, ``train_extra`` or ``val`` mode (string, optional): The quality mode to use, ``fine`` or ``coarse`` target_type (string or list, optional): Type of target to use, ``instance``, ``semantic``, ``polygon`` or ``color``. Can also be a list to output a tuple with all specified target types. transform (callable, optional): A function/transform that takes in a PIL image and returns a transformed version. E.g, ``transforms.RandomCrop`` target_transform (callable, optional): A function/transform that takes in the target and transforms it. transforms (callable, optional): A function/transform that takes input sample and its target as entry and returns a transformed version. Examples: Get semantic segmentation target .. code-block:: python dataset = Cityscapes('./data/cityscapes', split='train', mode='fine', target_type='semantic') img, smnt = dataset[0] Get multiple targets .. code-block:: python dataset = Cityscapes('./data/cityscapes', split='train', mode='fine', target_type=['instance', 'color', 'polygon']) img, (inst, col, poly) = dataset[0] Validate on the "coarse" set .. code-block:: python dataset = Cityscapes('./data/cityscapes', split='val', mode='coarse', target_type='semantic') img, smnt = dataset[0] """# Based on https://github.com/mcordts/cityscapesScriptsCityscapesClass=namedtuple("CityscapesClass",["name","id","train_id","category","category_id","has_instances","ignore_in_eval","color"],)classes=[CityscapesClass("unlabeled",0,255,"void",0,False,True,(0,0,0)),CityscapesClass("ego vehicle",1,255,"void",0,False,True,(0,0,0)),CityscapesClass("rectification border",2,255,"void",0,False,True,(0,0,0)),CityscapesClass("out of roi",3,255,"void",0,False,True,(0,0,0)),CityscapesClass("static",4,255,"void",0,False,True,(0,0,0)),CityscapesClass("dynamic",5,255,"void",0,False,True,(111,74,0)),CityscapesClass("ground",6,255,"void",0,False,True,(81,0,81)),CityscapesClass("road",7,0,"flat",1,False,False,(128,64,128)),CityscapesClass("sidewalk",8,1,"flat",1,False,False,(244,35,232)),CityscapesClass("parking",9,255,"flat",1,False,True,(250,170,160)),CityscapesClass("rail track",10,255,"flat",1,False,True,(230,150,140)),CityscapesClass("building",11,2,"construction",2,False,False,(70,70,70)),CityscapesClass("wall",12,3,"construction",2,False,False,(102,102,156)),CityscapesClass("fence",13,4,"construction",2,False,False,(190,153,153)),CityscapesClass("guard rail",14,255,"construction",2,False,True,(180,165,180)),CityscapesClass("bridge",15,255,"construction",2,False,True,(150,100,100)),CityscapesClass("tunnel",16,255,"construction",2,False,True,(150,120,90)),CityscapesClass("pole",17,5,"object",3,False,False,(153,153,153)),CityscapesClass("polegroup",18,255,"object",3,False,True,(153,153,153)),CityscapesClass("traffic light",19,6,"object",3,False,False,(250,170,30)),CityscapesClass("traffic sign",20,7,"object",3,False,False,(220,220,0)),CityscapesClass("vegetation",21,8,"nature",4,False,False,(107,142,35)),CityscapesClass("terrain",22,9,"nature",4,False,False,(152,251,152)),CityscapesClass("sky",23,10,"sky",5,False,False,(70,130,180)),CityscapesClass("person",24,11,"human",6,True,False,(220,20,60)),CityscapesClass("rider",25,12,"human",6,True,False,(255,0,0)),CityscapesClass("car",26,13,"vehicle",7,True,False,(0,0,142)),CityscapesClass("truck",27,14,"vehicle",7,True,False,(0,0,70)),CityscapesClass("bus",28,15,"vehicle",7,True,False,(0,60,100)),CityscapesClass("caravan",29,255,"vehicle",7,True,True,(0,0,90)),CityscapesClass("trailer",30,255,"vehicle",7,True,True,(0,0,110)),CityscapesClass("train",31,16,"vehicle",7,True,False,(0,80,100)),CityscapesClass("motorcycle",32,17,"vehicle",7,True,False,(0,0,230)),CityscapesClass("bicycle",33,18,"vehicle",7,True,False,(119,11,32)),CityscapesClass("license plate",-1,-1,"vehicle",7,False,True,(0,0,142)),]def__init__(self,root:Union[str,Path],split:str="train",mode:str="fine",target_type:Union[List[str],str]="instance",transform:Optional[Callable]=None,target_transform:Optional[Callable]=None,transforms:Optional[Callable]=None,)->None:super().__init__(root,transforms,transform,target_transform)self.mode="gtFine"ifmode=="fine"else"gtCoarse"self.images_dir=os.path.join(self.root,"leftImg8bit",split)self.targets_dir=os.path.join(self.root,self.mode,split)self.target_type=target_typeself.split=splitself.images=[]self.targets=[]verify_str_arg(mode,"mode",("fine","coarse"))ifmode=="fine":valid_modes=("train","test","val")else:valid_modes=("train","train_extra","val")msg="Unknown value '{}' for argument split if mode is '{}'. Valid values are {{{}}}."msg=msg.format(split,mode,iterable_to_str(valid_modes))verify_str_arg(split,"split",valid_modes,msg)ifnotisinstance(target_type,list):self.target_type=[target_type][verify_str_arg(value,"target_type",("instance","semantic","polygon","color"))forvalueinself.target_type]ifnotos.path.isdir(self.images_dir)ornotos.path.isdir(self.targets_dir):ifsplit=="train_extra":image_dir_zip=os.path.join(self.root,"leftImg8bit_trainextra.zip")else:image_dir_zip=os.path.join(self.root,"leftImg8bit_trainvaltest.zip")ifself.mode=="gtFine":target_dir_zip=os.path.join(self.root,f"{self.mode}_trainvaltest.zip")elifself.mode=="gtCoarse":target_dir_zip=os.path.join(self.root,f"{self.mode}.zip")ifos.path.isfile(image_dir_zip)andos.path.isfile(target_dir_zip):extract_archive(from_path=image_dir_zip,to_path=self.root)extract_archive(from_path=target_dir_zip,to_path=self.root)else:raiseRuntimeError("Dataset not found or incomplete. Please make sure all required folders for the"' specified "split" and "mode" are inside the "root" directory')forcityinos.listdir(self.images_dir):img_dir=os.path.join(self.images_dir,city)target_dir=os.path.join(self.targets_dir,city)forfile_nameinos.listdir(img_dir):target_types=[]fortinself.target_type:target_name="{}_{}".format(file_name.split("_leftImg8bit")[0],self._get_target_suffix(self.mode,t))target_types.append(os.path.join(target_dir,target_name))self.images.append(os.path.join(img_dir,file_name))self.targets.append(target_types)
[docs]def__getitem__(self,index:int)->Tuple[Any,Any]:""" Args: index (int): Index Returns: tuple: (image, target) where target is a tuple of all target types if target_type is a list with more than one item. Otherwise, target is a json object if target_type="polygon", else the image segmentation. """image=Image.open(self.images[index]).convert("RGB")targets:Any=[]fori,tinenumerate(self.target_type):ift=="polygon":target=self._load_json(self.targets[index][i])else:target=Image.open(self.targets[index][i])# type: ignore[assignment]targets.append(target)target=tuple(targets)iflen(targets)>1elsetargets[0]# type: ignore[assignment]ifself.transformsisnotNone:image,target=self.transforms(image,target)returnimage,target
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