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import os
import csv
from PIL import Image
import datasets
# Define configurations for each flavor.
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="sound_baseline",
description="Physical dataset: baseline variant",
data_dir="./physicsgen/urban_sound_25k_baseline"
),
datasets.BuilderConfig(
name="sound_reflection",
description="Physical dataset: reflection variant",
data_dir="./physicsgen/urban_sound_25k_reflection"
),
datasets.BuilderConfig(
name="sound_diffraction",
description="Physical dataset: reflection variant",
data_dir="./physicsgen/urban_sound_25k_diffraction"
),
datasets.BuilderConfig(
name="sound_combined",
description="Physical dataset: reflection variant",
data_dir="./physicsgen/urban_sound_25k_combined"
),
datasets.BuilderConfig(
name="lens_p1",
description="Distortion dataset variant",
data_dir="./physicsgen/lens_distortion_p1"
),
datasets.BuilderConfig(
name="lens_p2",
description="Distortion dataset variant",
data_dir="./physicsgen/lens_distortion_p2"
),
datasets.BuilderConfig(
name="ball_roll",
description="Double image dataset variant",
data_dir="./physicsgen/ball_roll"
),
datasets.BuilderConfig(
name="ball_bounce",
description="Double image dataset variant",
data_dir="./physicsgen/ball_bounce"
),
]
class MyPhysicalDataset(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = BUILDER_CONFIGS
VERSION = datasets.Version("1.0.2")
def _info(self):
if self.config.name in ["sound_baseline", "sound_reflection", "sound_diffraction", "sound_combined"]:
features = datasets.Features({
"lat": datasets.Value("float"),
"long": datasets.Value("float"),
"db": datasets.Value("string"),
"soundmap": datasets.Image(),
"osm": datasets.Image(),
"temperature": datasets.Value("int32"),
"humidity": datasets.Value("int32"),
"yaw": datasets.Value("float"),
"sample_id": datasets.Value("int32"),
"soundmap_512": datasets.Image(),
})
elif self.config.name in ["lens_p1", "lens_p2"]:
features = datasets.Features({
"label_path": datasets.Value("string"),
"fx": datasets.Value("float"),
"k1": datasets.Value("float"),
"k2": datasets.Value("float"),
"k3": datasets.Value("float"),
"p1": datasets.Value("float"),
"p2": datasets.Value("float"),
"cx": datasets.Value("float"),
"distortion_path": datasets.Value("string"),
})
elif self.config.name in ["ball_roll", "ball_bounce"]:
features = datasets.Features({
"ImgName": datasets.Value("string"),
"StartHeight": datasets.Value("int32"),
"GroundIncli": datasets.Value("float"),
"InputTime": datasets.Value("int32"),
"TargetTime": datasets.Value("int32"),
"input_image": datasets.Image(),
"target_image": datasets.Image(),
})
else:
raise ValueError(f"Unknown config name: {self.config.name}")
return datasets.DatasetInfo(
description="Multiple variant physical tasks dataset.",
features=features,
)
def _split_generators(self, dl_manager):
data_dir = self.config.data_dir
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"split_dir": os.path.join(data_dir, "train")},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"split_dir": os.path.join(data_dir, "test")},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"split_dir": os.path.join(data_dir, "eval")},
),
]
def _generate_examples(self, split_dir):
if self.config.name in ["sound_baseline", "sound_reflection", "sound_diffraction", "sound_combined"]:
csv_path = os.path.join(split_dir, "meta_data.csv")
with open(csv_path, encoding="utf-8") as f:
reader = csv.DictReader(f)
for idx, row in enumerate(reader):
row["soundmap"] = os.path.join(split_dir, row["soundmap"])
row["osm"] = os.path.join(split_dir, row["osm"])
row["soundmap_512"] = os.path.join(split_dir, row["soundmap_512"])
row["lat"] = float(row["lat"])
row["long"] = float(row["long"])
row["temperature"] = int(row["temperature"])
row["humidity"] = int(row["humidity"])
row["sample_id"] = int(row["sample_id"])
row["yaw"] = float(row["yaw"]) if row["yaw"] else 0.0
yield idx, row
elif self.config.name in ["lens_p1", "lens_p2"]:
csv_path = os.path.join(split_dir, "meta_data.csv")
with open(csv_path, encoding="utf-8") as f:
reader = csv.DictReader(f)
for idx, row in enumerate(reader):
row["label_path"] = str(row["label_path"])
row["distortion_path"] = str(row["distortion_path"])
row["fx"] = float(row["fx"])
row["k1"] = float(row["k1"])
row["k2"] = float(row["k2"])
row["k3"] = float(row["k3"])
row["p1"] = float(row["p1"])
row["p2"] = float(row["p2"])
row["cx"] = float(row["cx"])
yield idx, row
elif self.config.name in ["ball_roll", "ball_bounce"]:
csv_path = os.path.join(split_dir, "meta_data.csv")
with open(csv_path, encoding="utf-8") as f:
reader = csv.DictReader(f)
for idx, row in enumerate(reader):
# Construct image path from ImgName, e.g., "DoubleImg_0.jpg"
image_filename = "DoubleImg_" + row["ImgName"] + ".jpg"
input_image_path = os.path.join(split_dir, "x", image_filename)
target_image_path = os.path.join(split_dir, "y", image_filename)
row["input_image"] = input_image_path
row["target_image"] = target_image_path
row["ImgName"] = row["ImgName"]
row["StartHeight"] = int(row["StartHeight"])
row["GroundIncli"] = float(row["GroundIncli"])
row["InputTime"] = int(row["InputTime"])
row["TargetTime"] = int(row["TargetTime"])
yield idx, row
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