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0000000000000000000000000000000000000000..6957bc5128c97030220c9b7a85289fed863af4ee --- /dev/null +++ b/csv2json.py @@ -0,0 +1,48 @@ +import csv +import json +import os + + +def recursive_file_function(path, function): + for file in os.listdir(path): + if os.path.isdir(os.path.join(path, file)): + recursive_file_function(os.path.join(path, file), function) + else: + function(os.path.join(path, file)) + + +def csv2json(file): + if file[-4:] != '.csv': + return + + csv_file = open(file, 'r') + + csv_reader = csv.reader(csv_file) + + json_file = {} + for row in csv_reader: + if row[0] not in json_file: + json_file[row[0]] = { + "boxes": [], + "size": { + "width": int(float(row[6])), + "height": int(float(row[7])), + }, + } + box = { + "xmin": float(row[1]), + "ymin": float(row[2]), + "xmax": float(row[3]), + "ymax": float(row[4]), + "label": row[5], + } + json_file[row[0]]["boxes"].append(box) + + csv_file.close() + + json_path = file[:-4] + '.json' + with open(json_path, 'w') as f: + 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0000000000000000000000000000000000000000..44b4cc3df6fb73f9f1df380e1ef79c3191b68d18 --- /dev/null +++ b/ornithoscope.py @@ -0,0 +1,176 @@ +import json +import os + +import datasets +from datasets.tasks import ImageClassification + + +logger = datasets.logging.get_logger(__name__) + + +_CITATION = """""" + +_DESCRIPTION = """\ +Ornithoscope dataset is the dataset used to train the model for the Ornithoscope project. +""" + +_HOMEPAGE = "" + +_FOLDERS = [ + 'iNatv1', + 'iNatv2', + 'PhotoFeederv1/task_05-01-2021', +] + + +class OrnithoscopeConfig(datasets.BuilderConfig): + """BuilderConfig for Ornithoscope.""" + + def __init__( + self, + classes: list[str], + train_json: str, + validation_json: str, + test_json: str, + **kwargs + ): + """BuilderConfig for Ornithoscope. + + Args: + classes: list of classes. + train_json: path to the json file containing the train annotations. + validation_json: path to the json file containing the validation annotations. + test_json: path to the json file containing the test annotations. + **kwargs: keyword arguments forwarded to super. + """ + super().__init__(version=datasets.Version("1.0.0"), **kwargs) + self.classes = classes + + +class Ornithoscope(datasets.GeneratorBasedBuilder): + + VERSION = datasets.Version("1.0.0") + BUILDER_CONFIGS = [ + OrnithoscopeConfig( + name="DS3", + description="The main dataset.", + classes=[], # TODO + train_json="sets/DS3_train.json", + validation_json="sets/DS3_val.json", + test_json="sets/DS3_test.json", + ), + ] + + def _info(self) -> datasets.DatasetInfo: + return datasets.DatasetInfo( + description=_DESCRIPTION, + features=datasets.Features( + { + "image": datasets.Image(), + "label": datasets.ClassLabel(names=self.config.classes), + } + ), + homepage=_HOMEPAGE, + citation=_CITATION, + task_templates=[ImageClassification( + image_column="image", + label_column="label", + )], + ) + + def _split_generators(self, dl_manager: datasets.DownloadManager) -> list[datasets.SplitGenerator]: + """Returns SplitGenerators.""" + archives = self._get_archives(dl_manager) + + # Get train paths. + train_json = json.load( + open(dl_manager.download_and_extract(self.config.train_json), 'r')) + train_vals = [] + for id_path, value in train_json.items(): + root, file = os.path.split(id_path) + path = os.path.join(archives[root], file) + val = { + "id_path": id_path, + "path": path, + "boxes": value['boxes'], + "size": value['size'], + } + train_vals.append(val) + + # Get validation paths. + validation_json = json.load( + open(dl_manager.download_and_extract(self.config.validation_json), 'r')) + validation_vals = [] + for id_path, value in validation_json.items(): + root, file = os.path.split(id_path) + path = os.path.join(archives[root], file) + val = { + "id_path": id_path, + "path": path, + "boxes": value['boxes'], + "size": value['size'], + } + validation_vals.append(val) + + # Get test paths. + test_json = json.load( + open(dl_manager.download_and_extract(self.config.test_json), 'r')) + test_vals = [] + for id_path, value in test_json.item(): + root, file = os.path.split(id_path) + path = os.path.join(archives[root], file) + val = { + "id_path": id_path, + "path": path, + "boxes": value['boxes'], + "size": value['size'], + } + + return [ + datasets.SplitGenerator( + name=datasets.Split.TRAIN, + gen_kwargs={ + "values": train_vals, + }, + ), + datasets.SplitGenerator( + name=datasets.Split.VALIDATION, + gen_kwargs={ + "values": validation_vals, + }, + ), + datasets.SplitGenerator( + name=datasets.Split.TEST, + gen_kwargs={ + "values": test_vals, + }, + ), + ] + + def _generate_examples(self, values: list) -> tuple: + """Yields examples.""" + idx = 0 + for val in values: + example = { + "id_path": val["id_path"], + "path": val["path"], + "boxes": val["boxes"], + "size": val["size"], + } + yield idx, example + idx += 1 + + def _get_archives(self, dl_manager: datasets.DownloadManager) -> dict: + """Get the archives containing the images.""" + archives = {} + for folder in _FOLDERS: + i = 0 + while True: + try: + archives[folder] = dl_manager.download_and_extract( + f'data/{folder}.tar' + ) + i += 1 + except: + break + return archives diff --git a/sets/DS1_test.json b/sets/DS1_test.json new file mode 100644 index 0000000000000000000000000000000000000000..102e792912a98e9f472240576adde1485144a7c3 --- /dev/null +++ b/sets/DS1_test.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d50d27218640bffd765c6b5d57182f59c344f954d7afbafc7a970fbe6b79a76a +size 1367416 diff --git a/sets/DS1_train.json b/sets/DS1_train.json new file mode 100644 index 0000000000000000000000000000000000000000..ae2a094c9d628b5ccf44516553c3efde51b59934 --- /dev/null +++ b/sets/DS1_train.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e408e4a3c77bd965028bde519b549e33b529231fc1e659f8ef0bd81d3d7590e8 +size 5881271 diff --git a/sets/DS1_val.json b/sets/DS1_val.json new file mode 100644 index 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