Datasets:

prasatee
/

lines-dataset

license: mit
task_categories:
  - image-segmentation
  - object-detection
tags:
  - P&ID
  - lines
  - pipelines
  - engineering
  - diagrams
  - line-detection
size_categories:
  - 1K<n<10K

P&ID Line Detection Dataset

This dataset contains cropped images from P&ID (Piping and Instrumentation Diagrams) with line segment annotations for line detection and segmentation tasks.

Dataset Description

Total source images: 500
Total cropped samples: 10,000
Total line segments: 87,908
Crops per image: 20
Image sizes: Various sizes under 1000px (e.g., 300x500, 512x768, 900x900)

Dataset Splits

The dataset is split by source image to prevent data leakage:

Split	Source Images	Samples	Line Segments
Train	400 (80%)	8,000	69,483
Validation	50 (10%)	1,000	9,485
Test	50 (10%)	1,000	8,940

Dataset Structure

lines_dataset/
├── train/
│   ├── metadata.jsonl
│   └── *.jpg (8,000 images)
├── validation/
│   ├── metadata.jsonl
│   └── *.jpg (1,000 images)
├── test/
│   ├── metadata.jsonl
│   └── *.jpg (1,000 images)
└── visualizer/
    └── (visualization app)

Each sample contains:

file_name: Image filename
source_image_idx: Index of the original P&ID image
crop_idx: Index of this crop from the source image
width: Crop width in pixels
height: Crop height in pixels
lines: Dictionary with:
- segments: List of line segments as [x1, y1, x2, y2] (start and end points)
- line_types: List of line types ("solid" or "dashed")
- pipelines: List of pipeline names for each line

Usage

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("imagefolder", data_dir="path/to/lines_dataset")

# Access splits
train_data = dataset["train"]
val_data = dataset["validation"]
test_data = dataset["test"]

# Access a sample
sample = train_data[0]
image = sample["image"]
lines = sample["lines"]
segments = lines["segments"]      # [[x1, y1, x2, y2], ...]
line_types = lines["line_types"]  # ["solid", "dashed", ...]
pipelines = lines["pipelines"]    # ["5\"-EK-2648", ...]

# Draw lines on image
from PIL import ImageDraw
draw = ImageDraw.Draw(image)
for seg in segments:
    x1, y1, x2, y2 = seg
    draw.line([(x1, y1), (x2, y2)], fill="blue", width=3)
image.show()

Visualizer

A built-in visualizer is included to explore the dataset:

cd lines_dataset/visualizer
pip install flask
python app.py

Then open http://localhost:5051 in your browser.

Line Segment Format

Each line segment is represented as [x1, y1, x2, y2] where:

(x1, y1) is the start point
(x2, y2) is the end point
Coordinates are in pixels, relative to the cropped image
Lines are clipped to crop boundaries - partial lines that extend beyond the crop are included with endpoints adjusted to the crop edges

Line Types

solid: Continuous pipeline lines
dashed: Dashed lines (often representing signal/instrument lines)

License

MIT License