import os import logging import datasets import xml.etree.ElementTree as ET from PIL import Image from collections import defaultdict _CITATION = """ PASCAL_VOC """ _DESCRIPTION = """ PASCAL_VOC """ _URLS = { "voc2007": "voc2007.tar.gz", "voc2012": "voc2012.tar.gz", } # fmt: off CLASS_INFOS = [ # class name id train color ( 'aeroplane' , 0 , 0 , ( 128, 0, 0) ), ( 'bicycle' , 1 , 1 , ( 0, 128, 0) ), ( 'bird' , 2 , 2 , ( 128, 128, 0) ), ( 'boat' , 3 , 3 , ( 0, 0, 128) ), ( 'bottle' , 4 , 4 , ( 128, 0, 128) ), ( 'bus' , 5 , 5 , ( 0, 128, 128) ), ( 'car' , 6 , 6 , ( 128, 128, 128) ), ( 'cat' , 7 , 7 , ( 64, 0, 0) ), ( 'chair' , 8 , 8 , ( 192, 0, 0) ), ( 'cow' , 9 , 9 , ( 64, 128, 0) ), ( 'diningtable' , 10 , 10 , ( 192, 128, 0) ), ( 'dog' , 11 , 11 , ( 64, 0, 128) ), ( 'horse' , 12 , 12 , ( 192, 0, 128) ), ( 'motorbike' , 13 , 13 , ( 64, 128, 128) ), ( 'person' , 14 , 14 , ( 192, 128, 128) ), ( 'pottedplant' , 15 , 15 , ( 0, 64, 0) ), ( 'sheep' , 16 , 16 , ( 128, 64, 0) ), ( 'sofa' , 17 , 17 , ( 0, 192, 0) ), ( 'train' , 18 , 18 , ( 128, 192, 0) ), ( 'tvmonitor' , 19 , 19 , ( 0, 64, 128) ), ( 'background' , 20 , 20 , ( 0, 0, 0) ), ( 'borderingregion' , 255, 21 , ( 224, 224, 192) ), ] ACTION_INFOS = [ # class name id ( 'phoning' , 0 ), ( 'playinginstrument' , 1 ), ( 'reading' , 2 ), ( 'ridingbike' , 3 ), ( 'ridinghorse' , 4 ), ( 'running' , 5 ), ( 'takingphoto' , 6 ), ( 'usingcomputer' , 7 ), ( 'walking' , 8 ), ( 'jumping' , 9 ), ( 'other' , 10 ), ] LAYOUT_INFOS = [ # class name id ( 'Frontal' , 0 ), ( 'Left' , 1 ), ( 'Rear' , 2 ), ( 'Right' , 3 ), ( 'Unspecified' , 4 ), ] # fmt: on CLASS_NAMES = [CLASS_INFO[0] for CLASS_INFO in CLASS_INFOS] CLASS_NAMES_ALONE = [ CLASS_INFO[0] for CLASS_INFO in CLASS_INFOS if CLASS_INFO[0] not in ["background", "borderingregion"] ] ACTION_NAMES = [ACTION_INFO[0] for ACTION_INFO in ACTION_INFOS] LAYOUT_NAMES = [LAYOUT_INFO[0] for LAYOUT_INFO in LAYOUT_INFOS] CLASS_DICT = {CLASS_INFO[0]: CLASS_INFO[2] for CLASS_INFO in CLASS_INFOS} ACTION_DICT = {ACTION_INFO[0]: ACTION_INFO[1] for ACTION_INFO in ACTION_INFOS} LAYOUT_DICT = {LAYOUT_INFO[0]: LAYOUT_INFO[1] for LAYOUT_INFO in LAYOUT_INFOS} # datasets.Features action_features = datasets.Features( { "id": datasets.Value("int32"), "image": datasets.features.Image(), "height": datasets.Value("int32"), "width": datasets.Value("int32"), "classes": datasets.features.Sequence( datasets.features.ClassLabel(names=ACTION_NAMES) ), "objects": datasets.features.Sequence( { "bboxes": datasets.Sequence(datasets.Value("float32")), "classes": datasets.features.ClassLabel(names=ACTION_NAMES), "difficult": datasets.Value("int32"), } ), } ) layout_features = datasets.Features( { "id": datasets.Value("int32"), "image": datasets.features.Image(), "height": datasets.Value("int32"), "width": datasets.Value("int32"), "classes": datasets.features.Sequence( datasets.features.ClassLabel(names=LAYOUT_NAMES) ), "objects": datasets.features.Sequence( { "bboxes": datasets.Sequence(datasets.Value("float32")), "classes": datasets.features.ClassLabel(names=LAYOUT_NAMES), "difficult": datasets.Value("int32"), } ), } ) main_features = datasets.Features( { "id": datasets.Value("int32"), "image": datasets.features.Image(), "height": datasets.Value("int32"), "width": datasets.Value("int32"), "classes": datasets.features.Sequence( datasets.features.ClassLabel(names=CLASS_NAMES_ALONE) ), "objects": datasets.features.Sequence( { "bboxes": datasets.Sequence(datasets.Value("float32")), "classes": datasets.features.ClassLabel(names=CLASS_NAMES), "difficult": datasets.Value("int32"), } ), } ) segmentation_features = datasets.Features( { "id": datasets.Value("int32"), "image": datasets.features.Image(), "height": datasets.Value("int32"), "width": datasets.Value("int32"), "classes": datasets.features.Sequence(datasets.Value("int32")), "class_gt_image": datasets.features.Image(), "object_gt_image": datasets.features.Image(), } ) _DATASET_FEATURES = { "action": action_features, "layout": layout_features, "main": main_features, "segmentation": segmentation_features, } def get_main_classes(data_folder): class_infos = defaultdict(set) class_folder = os.path.join(data_folder, "ImageSets", "Main") for f in os.listdir(class_folder): if not f.endswith(".txt") or len(f.split("_")) != 2: continue lines = open(os.path.join(class_folder, f), "r").read().split("\n") name = f.split("_")[0] for line in lines: spans = line.strip().split(" ") spans = list(filter(lambda x: x.strip() != "", spans)) if len(spans) != 2 or int(spans[1]) != 1: continue class_infos[spans[0]].add(name) return class_infos def get_annotation(data_folder): anno_infos = dict() anno_folder = os.path.join(data_folder, "Annotations") for f in os.listdir(anno_folder): if not f.endswith(".xml"): continue anno_file = os.path.join(anno_folder, f) anno_tree = ET.parse(anno_file) objects = [] for obj in anno_tree.findall("./object"): info = { "class": obj.findall("./name")[0].text, "bbox": [ int(float(obj.findall("./bndbox/xmin")[0].text)), int(float(obj.findall("./bndbox/ymin")[0].text)), int(float(obj.findall("./bndbox/xmax")[0].text)), int(float(obj.findall("./bndbox/ymax")[0].text)), ], } if obj.findall("./pose"): info["pose"] = obj.findall("./pose")[0].text if obj.findall("./truncated"): info["truncated"] = int(obj.findall("./truncated")[0].text) if obj.findall("./difficult"): info["difficult"] = int(obj.findall("./difficult")[0].text) else: info["difficult"] = 0 if obj.findall("./occluded"): info["occluded"] = int(obj.findall("./occluded")[0].text) if obj.findall("./actions"): info["action"] = [ action.tag for action in obj.findall("./actions/") if int(action.text) == 1 ][0] objects.append(info) anno_info = { "image": anno_tree.findall("./filename")[0].text, "height": int(anno_tree.findall("./size/height")[0].text), "width": int(anno_tree.findall("./size/width")[0].text), "segmented": int(anno_tree.findall("./segmented")[0].text), "objects": objects, } stem, suffix = os.path.splitext(f) anno_infos[stem] = anno_info return anno_infos class PASCALConfig(datasets.BuilderConfig): def __init__(self, data_name, task_name, **kwargs): """ Args: **kwargs: keyword arguments forwarded to super. """ super().__init__(**kwargs) assert data_name in ["voc2007", "voc2012"] and task_name in [ "action", "layout", "main", "segmentation", ] assert not (data_name == "voc2007" and task_name == "action") self.data_name = data_name self.task_name = task_name class PASCALDataset(datasets.GeneratorBasedBuilder): BUILDER_CONFIGS = [ PASCALConfig( name="voc2007_layout", version=datasets.Version("1.0.0", ""), description="voc2007 layout dataset", data_name="voc2007", task_name="layout", ), PASCALConfig( name="voc2007_main", version=datasets.Version("1.0.0", ""), description="voc2007 main dataset", data_name="voc2007", task_name="main", ), PASCALConfig( name="voc2007_segmentation", version=datasets.Version("1.0.0", ""), description="voc2007 segmentation dataset", data_name="voc2007", task_name="segmentation", ), PASCALConfig( name="voc2012_action", version=datasets.Version("1.0.0", ""), description="voc2012 action dataset", data_name="voc2012", task_name="action", ), PASCALConfig( name="voc2012_layout", version=datasets.Version("1.0.0", ""), description="voc2012 layout dataset", data_name="voc2012", task_name="layout", ), PASCALConfig( name="voc2012_main", version=datasets.Version("1.0.0", ""), description="voc2012 main dataset", data_name="voc2012", task_name="main", ), PASCALConfig( name="voc2012_segmentation", version=datasets.Version("1.0.0", ""), description="voc2012 segmentation dataset", data_name="voc2012", task_name="segmentation", ), ] def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=_DATASET_FEATURES[self.config.task_name], # No default supervised_keys (as we have to pass both question # and context as input). supervised_keys=None, homepage="https://fuliucansheng.github.io/", citation=_CITATION, ) def _split_generators(self, dl_manager): downloaded_files = dl_manager.download_and_extract(_URLS[self.config.data_name]) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files, "split": "train"}, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files, "split": "val"}, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files, "split": "test"}, ), ] def _generate_examples(self, filepath, split): """This function returns the examples in the raw (text) form.""" logging.info("generating examples from = %s, split = %s", filepath, split) data_folder = os.path.join(filepath, os.listdir(filepath)[0]) anno_infos = get_annotation(data_folder) class_infos = get_main_classes(data_folder) data_file = os.path.join( data_folder, "ImageSets", self.config.task_name.capitalize(), f"{split}.txt" ) with open(data_file, encoding="utf-8") as f: for id_, line in enumerate(f): line = line.strip() if line.count(" ") > 0: line = line.split(" ")[0] anno_info = anno_infos.get(line) if anno_info is None: continue image = os.path.join(data_folder, "JPEGImages", anno_info["image"]) if not os.path.exists(image): continue classes = ( [CLASS_DICT[c] for c in class_infos.get(line.strip())] if line.strip() in class_infos else [] ) example = { "id": id_, "image": Image.open(os.path.abspath(image)), "height": anno_info["height"], "width": anno_info["width"], "classes": classes, } objects_info = anno_info["objects"] if self.config.task_name == "action": example["objects"] = [ { "bboxes": object_info["bbox"], "classes": object_info["action"], "difficult": object_info["difficult"], } for object_info in objects_info if "action" in object_info ] if len(example["objects"]) == 0 and split != "test": continue if self.config.task_name == "layout": example["objects"] = [ { "bboxes": object_info["bbox"], "classes": object_info["pose"], "difficult": object_info["difficult"], } for object_info in objects_info if "pose" in object_info ] if len(example["objects"]) == 0 and split != "test": continue if self.config.task_name == "main": example["objects"] = [ { "bboxes": object_info["bbox"], "classes": object_info["class"], "difficult": object_info["difficult"], } for object_info in objects_info if "class" in object_info ] if len(example["objects"]) == 0 and split != "test": continue if self.config.task_name == "segmentation": example["class_gt_image"] = Image.open( os.path.abspath( os.path.join( data_folder, "SegmentationClass", anno_info["image"].replace(".jpg", ".png"), ) ) ) example["object_gt_image"] = Image.open( os.path.abspath( os.path.join( data_folder, "SegmentationObject", anno_info["image"].replace(".jpg", ".png"), ) ) ) yield id_, example