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README.md

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+---
+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:** 10000
+- **Total line segments:** 44754
+- **Crops per image:** 20
+- **Image sizes:** Various sizes under 1000px (e.g., 300x500, 512x768, 900x900)
+
+## Dataset Structure
+
+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
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("imagefolder", data_dir="path/to/lines_dataset")
+
+# Access a sample
+sample = dataset["train"][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()
+```
+
+## 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
+- Only lines where **both endpoints** are fully inside the crop are included
+
+## Line Types
+
+- `solid`: Continuous pipeline lines
+- `dashed`: Dashed lines (often representing signal/instrument lines)
+
+## License
+
+MIT License

split_dataset.py

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+#!/usr/bin/env python3
+"""
+Script to split the lines dataset into train/validation/test sets (80/10/10)
+and transform the data format.
+"""
+
+import json
+import os
+import shutil
+import random
+from pathlib import Path
+
+# Set random seed for reproducibility
+random.seed(42)
+
+# Define paths
+BASE_DIR = Path("/Users/prasatee/Desktop/unsloth/DigitizePID_Dataset/lines_dataset")
+TRAIN_DIR = BASE_DIR / "train"
+VAL_DIR = BASE_DIR / "validation"
+TEST_DIR = BASE_DIR / "test"
+
+# Read current metadata
+metadata_path = TRAIN_DIR / "metadata.jsonl"
+data = []
+
+print("Reading metadata...")
+with open(metadata_path, "r") as f:
+    for line in f:
+        entry = json.loads(line.strip())
+        # Transform to new format (flatten the "lines" field)
+        new_entry = {
+            "file_name": entry["file_name"],
+            "source_image_idx": entry["source_image_idx"],
+            "crop_idx": entry["crop_idx"],
+            "width": entry["width"],
+            "height": entry["height"],
+            "segments": entry["lines"]["segments"],
+            "line_types": entry["lines"]["line_types"],
+            "pipelines": entry["lines"]["pipelines"],
+        }
+        data.append(new_entry)
+
+print(f"Total entries: {len(data)}")
+
+# Shuffle data
+random.shuffle(data)
+
+# Calculate split sizes
+total = len(data)
+train_size = int(0.8 * total)
+val_size = int(0.1 * total)
+test_size = total - train_size - val_size
+
+train_data = data[:train_size]
+val_data = data[train_size:train_size + val_size]
+test_data = data[train_size + val_size:]
+
+print(f"Train: {len(train_data)}, Validation: {len(val_data)}, Test: {len(test_data)}")
+
+# Create directories
+VAL_DIR.mkdir(exist_ok=True)
+TEST_DIR.mkdir(exist_ok=True)
+
+print("\nMoving files...")
+
+# Move validation files
+print("Processing validation set...")
+for entry in val_data:
+    src = TRAIN_DIR / entry["file_name"]
+    dst = VAL_DIR / entry["file_name"]
+    if src.exists():
+        shutil.move(str(src), str(dst))
+
+# Move test files
+print("Processing test set...")
+for entry in test_data:
+    src = TRAIN_DIR / entry["file_name"]
+    dst = TEST_DIR / entry["file_name"]
+    if src.exists():
+        shutil.move(str(src), str(dst))
+
+# Write new metadata files
+print("\nWriting metadata files...")
+
+# Train metadata
+train_metadata_path = TRAIN_DIR / "metadata.jsonl"
+with open(train_metadata_path, "w") as f:
+    for entry in train_data:
+        f.write(json.dumps(entry) + "\n")
+
+# Validation metadata
+val_metadata_path = VAL_DIR / "metadata.jsonl"
+with open(val_metadata_path, "w") as f:
+    for entry in val_data:
+        f.write(json.dumps(entry) + "\n")
+
+# Test metadata
+test_metadata_path = TEST_DIR / "metadata.jsonl"
+with open(test_metadata_path, "w") as f:
+    for entry in test_data:
+        f.write(json.dumps(entry) + "\n")
+
+print("\nDone!")
+print(f"Train set: {len(train_data)} samples in {TRAIN_DIR}")
+print(f"Validation set: {len(val_data)} samples in {VAL_DIR}")
+print(f"Test set: {len(test_data)} samples in {TEST_DIR}")
+
+# Verify first entry format
+print("\nSample entry format:")
+print(json.dumps(train_data[0], indent=2))
+