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import os
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import csv
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from PIL import Image
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import datasets
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(
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name="sound_baseline",
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description="Physical dataset: baseline variant",
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data_dir="./physicsgen/urban_sound_25k_baseline"
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),
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datasets.BuilderConfig(
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name="sound_reflection",
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description="Physical dataset: reflection variant",
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data_dir="./physicsgen/urban_sound_25k_reflection"
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),
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datasets.BuilderConfig(
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name="sound_diffraction",
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description="Physical dataset: reflection variant",
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data_dir="./physicsgen/urban_sound_25k_diffraction"
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),
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datasets.BuilderConfig(
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name="sound_combined",
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description="Physical dataset: reflection variant",
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data_dir="./physicsgen/urban_sound_25k_combined"
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),
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datasets.BuilderConfig(
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name="lens_p1",
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description="Distortion dataset variant",
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data_dir="./physicsgen/lens_distortion_p1"
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),
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datasets.BuilderConfig(
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name="lens_p2",
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description="Distortion dataset variant",
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data_dir="./physicsgen/lens_distortion_p2"
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),
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datasets.BuilderConfig(
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name="ball_roll",
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description="Double image dataset variant",
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data_dir="./physicsgen/ball_roll"
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),
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datasets.BuilderConfig(
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name="ball_bounce",
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description="Double image dataset variant",
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data_dir="./physicsgen/ball_bounce"
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),
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]
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class MyPhysicalDataset(datasets.GeneratorBasedBuilder):
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BUILDER_CONFIGS = BUILDER_CONFIGS
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VERSION = datasets.Version("1.0.2")
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def _info(self):
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if self.config.name in ["sound_baseline", "sound_reflection", "sound_diffraction", "sound_combined"]:
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features = datasets.Features({
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"lat": datasets.Value("float"),
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"long": datasets.Value("float"),
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"db": datasets.Value("string"),
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"soundmap": datasets.Image(),
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"osm": datasets.Image(),
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"temperature": datasets.Value("int32"),
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"humidity": datasets.Value("int32"),
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"yaw": datasets.Value("float"),
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"sample_id": datasets.Value("int32"),
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"soundmap_512": datasets.Image(),
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})
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elif self.config.name in ["lens_p1", "lens_p2"]:
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features = datasets.Features({
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"label_path": datasets.Value("string"),
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"fx": datasets.Value("float"),
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"k1": datasets.Value("float"),
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"k2": datasets.Value("float"),
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"k3": datasets.Value("float"),
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"p1": datasets.Value("float"),
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"p2": datasets.Value("float"),
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"cx": datasets.Value("float"),
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"distortion_path": datasets.Value("string"),
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})
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elif self.config.name in ["ball_roll", "ball_bounce"]:
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features = datasets.Features({
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"ImgName": datasets.Value("string"),
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"StartHeight": datasets.Value("int32"),
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"GroundIncli": datasets.Value("float"),
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"InputTime": datasets.Value("int32"),
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"TargetTime": datasets.Value("int32"),
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"input_image": datasets.Image(),
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"target_image": datasets.Image(),
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})
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else:
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raise ValueError(f"Unknown config name: {self.config.name}")
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return datasets.DatasetInfo(
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description="Multiple variant physical tasks dataset.",
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features=features,
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)
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def _split_generators(self, dl_manager):
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data_dir = self.config.data_dir
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={"split_dir": os.path.join(data_dir, "train")},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={"split_dir": os.path.join(data_dir, "test")},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={"split_dir": os.path.join(data_dir, "eval")},
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),
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]
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def _generate_examples(self, split_dir):
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if self.config.name in ["sound_baseline", "sound_reflection", "sound_diffraction", "sound_combined"]:
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csv_path = os.path.join(split_dir, "meta_data.csv")
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with open(csv_path, encoding="utf-8") as f:
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reader = csv.DictReader(f)
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for idx, row in enumerate(reader):
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row["soundmap"] = os.path.join(split_dir, row["soundmap"])
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row["osm"] = os.path.join(split_dir, row["osm"])
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row["soundmap_512"] = os.path.join(split_dir, row["soundmap_512"])
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row["lat"] = float(row["lat"])
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row["long"] = float(row["long"])
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row["temperature"] = int(row["temperature"])
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row["humidity"] = int(row["humidity"])
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row["sample_id"] = int(row["sample_id"])
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row["yaw"] = float(row["yaw"]) if row["yaw"] else 0.0
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yield idx, row
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elif self.config.name in ["lens_p1", "lens_p2"]:
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csv_path = os.path.join(split_dir, "meta_data.csv")
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with open(csv_path, encoding="utf-8") as f:
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reader = csv.DictReader(f)
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for idx, row in enumerate(reader):
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row["label_path"] = str(row["label_path"])
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row["distortion_path"] = str(row["distortion_path"])
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row["fx"] = float(row["fx"])
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row["k1"] = float(row["k1"])
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row["k2"] = float(row["k2"])
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row["k3"] = float(row["k3"])
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row["p1"] = float(row["p1"])
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row["p2"] = float(row["p2"])
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row["cx"] = float(row["cx"])
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yield idx, row
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elif self.config.name in ["ball_roll", "ball_bounce"]:
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csv_path = os.path.join(split_dir, "meta_data.csv")
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with open(csv_path, encoding="utf-8") as f:
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reader = csv.DictReader(f)
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for idx, row in enumerate(reader):
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image_filename = "DoubleImg_" + row["ImgName"] + ".jpg"
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input_image_path = os.path.join(split_dir, "x", image_filename)
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target_image_path = os.path.join(split_dir, "y", image_filename)
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row["input_image"] = input_image_path
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row["target_image"] = target_image_path
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row["ImgName"] = row["ImgName"]
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row["StartHeight"] = int(row["StartHeight"])
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row["GroundIncli"] = float(row["GroundIncli"])
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row["InputTime"] = int(row["InputTime"])
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row["TargetTime"] = int(row["TargetTime"])
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yield idx, row
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