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import json
import os
from pathlib import Path
import datasets
from PIL import Image
import jsonlines
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@misc{chen2024paligemma,
title={PaliGemma Multitask Dataset},
author={Chen, Xingqiang},
year={2024},
publisher={Hugging Face}
}
"""
_DESCRIPTION = """\
This dataset contains images and annotations for defect detection and analysis,
designed for training and evaluating the PaliGemma multitask model.
The dataset includes both basic defect detection samples and a larger set of
874 annotated images from real-world structural inspections.
"""
_HOMEPAGE = "https://huggingface.co/datasets/xingqiang/paligemma-multitask-dataset"
class PaligemmaDataset(datasets.GeneratorBasedBuilder):
"""PaliGemma Multitask Dataset for defect detection and analysis."""
VERSION = datasets.Version("1.1.0")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features({
"image": datasets.Image(),
"boxes": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), length=4)),
"labels": datasets.Sequence(datasets.ClassLabel(names=["void", "crack"])),
"caption": datasets.Value("string"),
"source": datasets.Value("string"),
}),
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"split": "val",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"split": "test",
},
),
]
def _generate_examples(self, split):
"""生成示例。"""
# 优先加载统一格式的注释
unified_path = os.path.join("annotations", f"{split}_unified.json")
if os.path.exists(unified_path):
logger.info(f"使用统一格式的注释文件: {unified_path}")
with open(unified_path, encoding="utf-8") as f:
annotations = json.load(f)
for idx, ann in enumerate(annotations):
image_path = os.path.join("images", split, ann["image_filename"])
try:
yield f"unified_{idx}", {
"image": image_path,
"boxes": ann["boxes"],
"labels": ann["labels"],
"caption": ann["caption"],
"source": ann.get("source", "unified")
}
except Exception as e:
logger.warning(f"跳过无效图像 {image_path}: {e}")
continue
return # 如果使用了统一格式，不再处理其他格式
# 如果没有统一格式，则回退到原始格式
# 加载原始 JSON 注释
json_path = os.path.join("annotations", f"{split}.json")
if os.path.exists(json_path):
with open(json_path, encoding="utf-8") as f:
annotations = json.load(f)
for idx, ann in enumerate(annotations):
image_path = os.path.join("images", split, ann["image_filename"])
try:
# 不要尝试在这里打开图像，只返回路径
yield f"orig_{idx}", {
"image": image_path,
"boxes": ann["boxes"],
"labels": ann["labels"],
"caption": ann["caption"],
"source": "original"
}
except Exception as e:
logger.warning(f"跳过无效图像 {image_path}: {e}")
continue
# 加载 JSONL 注释
jsonl_path = os.path.join("annotations", f"_annotations.{split}.jsonl")
if os.path.exists(jsonl_path):
with jsonlines.open(jsonl_path) as reader:
for idx, ann in enumerate(reader):
# 确保使用正确的图像文件名
image_filename = ann.get("image", "")
image_path = os.path.join("images", split, image_filename)
try:
# 不要尝试在这里打开图像，只返回路径
# 转换注释为我们的格式
if "annotations" in ann:
# 处理新格式
boxes = [[b["x"], b["y"], b["width"], b["height"]] for b in ann["annotations"]]
labels = [0 if b["class"] == "void" else 1 for b in ann["annotations"]]
caption = f"Image contains {len(boxes)} defects: " + \
", ".join([b["class"] for b in ann["annotations"]])
else:
# 处理旧格式 (prefix/suffix)
# 这里需要解析 suffix 中的位置信息
boxes = []
labels = []
if "suffix" in ann:
parts = ann["suffix"].split(";")
for part in parts:
part = part.strip()
if "<loc" in part:
# 解析位置和标签
loc_parts = part.split()
if len(loc_parts) >= 2:
# 提取坐标
coords = []
for loc in loc_parts[0].split("><"):
if loc.startswith("<loc"):
coords.append(int(loc[4:-1]) / 1024) # 归一化坐标
if len(coords) == 4:
boxes.append(coords)
label = 0 if "void" in loc_parts[1] else 1
labels.append(label)
caption = ann.get("prefix", "")
# 检查图像是否存在
image_exists = False
# 检查images/datasets目录
if os.path.exists(f"images/datasets/{image_filename}"):
image_exists = True
# 检查原始路径
if not image_exists:
for img_split in ["train", "val", "test"]:
if os.path.exists(f"images/{img_split}/{image_filename}"):
image_exists = True
break
if not image_exists:
print(f"警告: 图像文件不存在: {image_filename}")
continue
yield f"p1v1_{idx}", {
"image": image_path,
"boxes": boxes,
"labels": labels,
"caption": caption,
"source": "p1v1"
}
except Exception as e:
logger.warning(f"跳过无效注释 {image_path}: {e}")
continue
def convert_annotations_to_unified_format():
"""将所有注释转换为统一格式"""
print("开始转换注释为统一格式...")
# 确保annotations目录存在
os.makedirs("annotations", exist_ok=True)
# 增加对valid分割的处理（有些文件使用valid而不是val）
for split in ["train", "val", "valid", "test"]:
print(f"处理 {split} 分割...")
unified_annotations = []
# 处理 JSON 注释
json_path = f"annotations/{split}.json"
print(f"检查 JSON 文件: {json_path}")
if os.path.exists(json_path):
print(f"找到 JSON 文件: {json_path}")
with open(json_path, encoding="utf-8") as f:
try:
annotations = json.load(f)
print(f"从 {json_path} 加载了 {len(annotations)} 条注释")
for ann in annotations:
unified_annotations.append({
"image_filename": ann["image_filename"],
"boxes": ann["boxes"],
"labels": ann["labels"],
"caption": ann["caption"],
"source": "original"
})
except json.JSONDecodeError:
print(f"错误: {json_path} 不是有效的 JSON 文件")
else:
print(f"未找到 JSON 文件: {json_path}")
# 处理 JSONL 注释，包括目录p-1.v1i.paligemma下的文件
jsonl_variants = [
f"_annotations.{split}.jsonl",
f"_annotations.{split}1.jsonl",
f"p-1.v1i.paligemma/_annotations.{split}.jsonl",
f"p-1.v1i.paligemma/_annotations.{split}1.jsonl"
]
for jsonl_variant in jsonl_variants:
jsonl_path = f"annotations/{jsonl_variant}"
print(f"检查 JSONL 文件: {jsonl_path}")
if os.path.exists(jsonl_path):
print(f"找到 JSONL 文件: {jsonl_path}")
annotation_count = 0
with open(jsonl_path, encoding="utf-8") as f:
for line_num, line in enumerate(f, 1):
try:
line = line.strip()
if not line: # 跳过空行
print(f"跳过第 {line_num} 行: 空行")
continue
ann = json.loads(line)
image_filename = ann.get("image", "")
if not image_filename:
print(f"跳过第 {line_num} 行: 没有图像文件名")
continue
# 检查图像是否存在
image_exists = False
# 检查images/datasets目录
if os.path.exists(f"images/datasets/{image_filename}"):
image_exists = True
# 检查原始路径
if not image_exists:
for img_split in ["train", "val", "test"]:
if os.path.exists(f"images/{img_split}/{image_filename}"):
image_exists = True
break
if not image_exists:
print(f"警告: 图像文件不存在: {image_filename}")
continue
# 转换为统一格式
if "annotations" in ann:
# 处理新格式
boxes = [[b["x"], b["y"], b["width"], b["height"]] for b in ann["annotations"]]
labels = [0 if b["class"] == "void" else 1 for b in ann["annotations"]]
caption = f"Image contains {len(boxes)} defects: " + \
", ".join([b["class"] for b in ann["annotations"]])
else:
# 处理旧格式 (prefix/suffix)
boxes = []
labels = []
caption = ann.get("prefix", "")
if "suffix" in ann:
parts = ann["suffix"].split(";")
for part in parts:
part = part.strip()
if "<loc" in part:
# 解析位置和标签
loc_parts = part.split()
if len(loc_parts) >= 2:
# 提取坐标
coords = []
for loc in loc_parts[0].split("><"):
if loc.startswith("<loc"):
try:
coords.append(int(loc[4:-1]) / 1024) # 归一化坐标
except ValueError:
continue
if len(coords) == 4:
boxes.append(coords)
label = 0 if "void" in loc_parts[1] else 1
labels.append(label)
unified_annotations.append({
"image_filename": image_filename,
"boxes": boxes,
"labels": labels,
"caption": caption,
"source": "p1v1"
})
annotation_count += 1
except json.JSONDecodeError as e:
print(f"警告: {jsonl_path} 第 {line_num} 行不是有效的 JSON: {e}")
continue
print(f"从 {jsonl_path} 加载了 {annotation_count} 条注释")
else:
print(f"未找到 JSONL 文件: {jsonl_path}")
# 如果是valid分割，与val合并
if split == "valid" and os.path.exists(f"annotations/val_unified.json"):
try:
with open(f"annotations/val_unified.json", "r", encoding="utf-8") as f:
val_annotations = json.load(f)
unified_annotations.extend(val_annotations)
print(f"将valid分割与val分割合并，共 {len(unified_annotations)} 条记录")
except Exception as e:
print(f"合并valid和val分割时出错: {e}")
# 保存统一格式的注释
if unified_annotations:
# 对于valid分割，保存为val_unified.json
save_split = "val" if split == "valid" else split
print(f"为 {save_split} 创建统一格式注释，共 {len(unified_annotations)} 条记录")
unified_path = f"annotations/{save_split}_unified.json"
with open(unified_path, "w", encoding="utf-8") as f:
json.dump(unified_annotations, f, ensure_ascii=False, indent=2)
print(f"已保存统一格式注释到: {unified_path}")
else:
print(f"警告: {split} 没有有效的注释，跳过创建统一格式文件") |