Datasets:

Voxel51
/

SLAKE

import os
import json
import fiftyone as fo
from PIL import Image
from pathlib import Path

def load_sample_files(subdir):
    """
    Load all required files for a single sample.
    
    Args:
        subdir (Path): Path to the sample subdirectory
        
    Returns:
        tuple: (detections_data, questions_data, mask_file_path, source_file_path, img_dimensions)
        Returns None if any required files are missing.
    """
    subdir_name = subdir.name
    
    # Define file paths
    detection_file = subdir / "detection.json"
    question_file = subdir / "question.json"
    mask_file = subdir / f"mask_{subdir_name}.png"
    source_file = subdir / f"source_{subdir_name}.jpg"
    
    # Check all files exist
    if not all(f.exists() for f in [detection_file, question_file, mask_file, source_file]):
        return None
    
    # Load JSON data
    with open(detection_file, 'r') as f:
        detections_data = json.load(f)
    
    with open(question_file, 'r') as f:
        questions_data = json.load(f)
    
    # Get image dimensions
    with Image.open(source_file) as img:
        img_dimensions = img.size
    
    return detections_data, questions_data, mask_file, source_file, img_dimensions

def convert_detections_to_relative(detections_data, img_width, img_height):
    """
    Convert absolute bounding boxes to relative coordinates for FiftyOne.
    
    Args:
        detections_data (list): List of detection dictionaries
        img_width (int): Image width in pixels
        img_height (int): Image height in pixels
        
    Returns:
        fo.Detections: FiftyOne Detections object
    """
    detections = []
    
    for detection_dict in detections_data:
        for label, bbox in detection_dict.items():
            x, y, width, height = bbox
            # Convert to relative coordinates
            rel_x = x / img_width
            rel_y = y / img_height
            rel_width = width / img_width
            rel_height = height / img_height
            
            detection = fo.Detection(
                label=label,
                bounding_box=[rel_x, rel_y, rel_width, rel_height]
            )
            detections.append(detection)
    
    return fo.Detections(detections=detections)

def add_sample_metadata(sample, english_questions):
    """
    Add sample-level metadata from questions data.
    
    Args:
        sample (fo.Sample): FiftyOne sample to modify
        english_questions (list): List of English question dictionaries
    """
    if not english_questions:
        return
    
    # Sample-level metadata (same for all questions in a sample)
    first_question = english_questions[0]
    sample['location'] = fo.Classification(label=first_question['location'])
    sample['modality'] = fo.Classification(label=first_question['modality'])
    sample['base_type'] = fo.Classification(label=first_question['base_type'])
    sample['answer_type'] = fo.Classification(label=first_question['answer_type'])

def add_questions_and_answers(sample, english_questions):
    """
    Add individual questions and answers to the sample.
    
    Args:
        sample (fo.Sample): FiftyOne sample to modify
        english_questions (list): List of English question dictionaries
    """
    for i, q_data in enumerate(english_questions):
        sample[f'question_{i}'] = q_data['question']
        sample[f'answer_{i}'] = fo.Classification(label=q_data['answer'])

def process_single_sample(subdir):
    """
    Process a single sample directory into a FiftyOne sample.
    
    Args:
        subdir (Path): Path to the sample subdirectory
        
    Returns:
        fo.Sample or None: FiftyOne sample, or None if processing failed
    """
    subdir_name = subdir.name
    
    # Load all files for this sample
    file_data = load_sample_files(subdir)
    if file_data is None:
        print(f"Warning: Missing files in {subdir_name}, skipping...")
        return None
    
    detections_data, questions_data, mask_file, source_file, (img_width, img_height) = file_data
    
    # Create FiftyOne sample
    sample = fo.Sample(filepath=str(source_file.absolute()))
    
    # Add detections
    sample['detections'] = convert_detections_to_relative(detections_data, img_width, img_height)
    
    # Add segmentation mask
    sample['segmentation'] = fo.Segmentation(mask_path=str(mask_file.absolute()))
    
    # Filter to English questions only and preserve order
    english_questions = [q for q in questions_data if q.get('q_lang') == 'en']
    
    # Add sample-level metadata
    add_sample_metadata(sample, english_questions)
    
    # Add individual questions and answers
    add_questions_and_answers(sample, english_questions)
    
    return sample

def parse_slake_dataset(data_root="SLAKE/imgs", dataset_name="SLAKE"):
    """
    Parse SLAKE dataset into FiftyOne format.
    
    Args:
        data_root (str): Path to the SLAKE/imgs directory
        dataset_name (str): Name for the FiftyOne dataset
    
    Returns:
        fo.Dataset: FiftyOne dataset with parsed samples
    """
    dataset = fo.Dataset(dataset_name, overwrite=True)
    
    data_root = Path(data_root)
    samples = []
    
    # Process each subdirectory
    for subdir in data_root.iterdir():
        if not subdir.is_dir():
            continue
            
        print(f"Processing {subdir.name}...")
        sample = process_single_sample(subdir)
        
        if sample is not None:
            samples.append(sample)
    
    # Add all samples to dataset efficiently
    dataset.add_samples(samples)
    dataset.compute_metadata()
    
    return dataset

import fiftyone as fo
from pathlib import Path

def load_mask_targets_from_file(mask_targets_file):
    """
    Load mask targets mapping from file.
    
    Args:
        mask_targets_file (str): Path to the mask targets file
        
    Returns:
        dict: Mapping of pixel values to organ labels
    """
    mask_targets = {}
    
    with open(mask_targets_file, 'r') as f:
        for line in f:
            line = line.strip()
            if ':' in line:
                pixel_value, label = line.split(':', 1)
                mask_targets[int(pixel_value)] = label
    
    return mask_targets

def set_dataset_mask_targets(dataset_name, mask_targets_file, segmentation_field="segmentation"):
    """
    Set mask targets for an existing FiftyOne dataset.
    
    Args:
        dataset_name (str): Name of the FiftyOne dataset
        mask_targets_file (str): Path to the mask targets mapping file
        segmentation_field (str): Name of the segmentation field (default: "segmentation")
    """
    # Load dataset
    dataset = fo.load_dataset(dataset_name)
    
    # Load mask targets from file
    mask_targets = load_mask_targets_from_file(mask_targets_file)
    
    # Set mask targets
    dataset.mask_targets = {segmentation_field: mask_targets}
    dataset.save()  # Must save after setting mask targets

    for i, (pixel_val, label) in enumerate(list(mask_targets.items())[:5]):
        print(f"  {pixel_val}: {label}")
    if len(mask_targets) > 5:
        print(f"  ... and {len(mask_targets) - 5} more")



dataset = parse_slake_dataset("SLAKE/imgs", "SLAKE")

set_dataset_mask_targets(
    dataset_name="SLAKE",  # Your dataset name
    mask_targets_file="SLAKE/mask.txt",  # Your mapping file
    segmentation_field="segmentation"
)

	import os
	import json
	import fiftyone as fo
	from PIL import Image
	from pathlib import Path

	def load_sample_files(subdir):
	"""
	Load all required files for a single sample.

	Args:
	subdir (Path): Path to the sample subdirectory

	Returns:
	tuple: (detections_data, questions_data, mask_file_path, source_file_path, img_dimensions)
	Returns None if any required files are missing.
	"""
	subdir_name = subdir.name

	# Define file paths
	detection_file = subdir / "detection.json"
	question_file = subdir / "question.json"
	mask_file = subdir / f"mask_{subdir_name}.png"
	source_file = subdir / f"source_{subdir_name}.jpg"

	# Check all files exist
	if not all(f.exists() for f in [detection_file, question_file, mask_file, source_file]):
	return None

	# Load JSON data
	with open(detection_file, 'r') as f:
	detections_data = json.load(f)

	with open(question_file, 'r') as f:
	questions_data = json.load(f)

	# Get image dimensions
	with Image.open(source_file) as img:
	img_dimensions = img.size

	return detections_data, questions_data, mask_file, source_file, img_dimensions

	def convert_detections_to_relative(detections_data, img_width, img_height):
	"""
	Convert absolute bounding boxes to relative coordinates for FiftyOne.

	Args:
	detections_data (list): List of detection dictionaries
	img_width (int): Image width in pixels
	img_height (int): Image height in pixels

	Returns:
	fo.Detections: FiftyOne Detections object
	"""
	detections = []

	for detection_dict in detections_data:
	for label, bbox in detection_dict.items():
	x, y, width, height = bbox
	# Convert to relative coordinates
	rel_x = x / img_width
	rel_y = y / img_height
	rel_width = width / img_width
	rel_height = height / img_height

	detection = fo.Detection(
	label=label,
	bounding_box=[rel_x, rel_y, rel_width, rel_height]
	)
	detections.append(detection)

	return fo.Detections(detections=detections)

	def add_sample_metadata(sample, english_questions):
	"""
	Add sample-level metadata from questions data.

	Args:
	sample (fo.Sample): FiftyOne sample to modify
	english_questions (list): List of English question dictionaries
	"""
	if not english_questions:
	return

	# Sample-level metadata (same for all questions in a sample)
	first_question = english_questions[0]
	sample['location'] = fo.Classification(label=first_question['location'])
	sample['modality'] = fo.Classification(label=first_question['modality'])
	sample['base_type'] = fo.Classification(label=first_question['base_type'])
	sample['answer_type'] = fo.Classification(label=first_question['answer_type'])

	def add_questions_and_answers(sample, english_questions):
	"""
	Add individual questions and answers to the sample.

	Args:
	sample (fo.Sample): FiftyOne sample to modify
	english_questions (list): List of English question dictionaries
	"""
	for i, q_data in enumerate(english_questions):
	sample[f'question_{i}'] = q_data['question']
	sample[f'answer_{i}'] = fo.Classification(label=q_data['answer'])

	def process_single_sample(subdir):
	"""
	Process a single sample directory into a FiftyOne sample.

	Args:
	subdir (Path): Path to the sample subdirectory

	Returns:
	fo.Sample or None: FiftyOne sample, or None if processing failed
	"""
	subdir_name = subdir.name

	# Load all files for this sample
	file_data = load_sample_files(subdir)
	if file_data is None:
	print(f"Warning: Missing files in {subdir_name}, skipping...")
	return None

	detections_data, questions_data, mask_file, source_file, (img_width, img_height) = file_data

	# Create FiftyOne sample
	sample = fo.Sample(filepath=str(source_file.absolute()))

	# Add detections
	sample['detections'] = convert_detections_to_relative(detections_data, img_width, img_height)

	# Add segmentation mask
	sample['segmentation'] = fo.Segmentation(mask_path=str(mask_file.absolute()))

	# Filter to English questions only and preserve order
	english_questions = [q for q in questions_data if q.get('q_lang') == 'en']

	# Add sample-level metadata
	add_sample_metadata(sample, english_questions)

	# Add individual questions and answers
	add_questions_and_answers(sample, english_questions)

	return sample

	def parse_slake_dataset(data_root="SLAKE/imgs", dataset_name="SLAKE"):
	"""
	Parse SLAKE dataset into FiftyOne format.

	Args:
	data_root (str): Path to the SLAKE/imgs directory
	dataset_name (str): Name for the FiftyOne dataset

	Returns:
	fo.Dataset: FiftyOne dataset with parsed samples
	"""
	dataset = fo.Dataset(dataset_name, overwrite=True)

	data_root = Path(data_root)
	samples = []

	# Process each subdirectory
	for subdir in data_root.iterdir():
	if not subdir.is_dir():
	continue

	print(f"Processing {subdir.name}...")
	sample = process_single_sample(subdir)

	if sample is not None:
	samples.append(sample)

	# Add all samples to dataset efficiently
	dataset.add_samples(samples)
	dataset.compute_metadata()

	return dataset

	import fiftyone as fo
	from pathlib import Path

	def load_mask_targets_from_file(mask_targets_file):
	"""
	Load mask targets mapping from file.

	Args:
	mask_targets_file (str): Path to the mask targets file

	Returns:
	dict: Mapping of pixel values to organ labels
	"""
	mask_targets = {}

	with open(mask_targets_file, 'r') as f:
	for line in f:
	line = line.strip()
	if ':' in line:
	pixel_value, label = line.split(':', 1)
	mask_targets[int(pixel_value)] = label

	return mask_targets

	def set_dataset_mask_targets(dataset_name, mask_targets_file, segmentation_field="segmentation"):
	"""
	Set mask targets for an existing FiftyOne dataset.

	Args:
	dataset_name (str): Name of the FiftyOne dataset
	mask_targets_file (str): Path to the mask targets mapping file
	segmentation_field (str): Name of the segmentation field (default: "segmentation")
	"""
	# Load dataset
	dataset = fo.load_dataset(dataset_name)

	# Load mask targets from file
	mask_targets = load_mask_targets_from_file(mask_targets_file)

	# Set mask targets
	dataset.mask_targets = {segmentation_field: mask_targets}
	dataset.save() # Must save after setting mask targets

	for i, (pixel_val, label) in enumerate(list(mask_targets.items())[:5]):
	print(f" {pixel_val}: {label}")
	if len(mask_targets) > 5:
	print(f" ... and {len(mask_targets) - 5} more")



	dataset = parse_slake_dataset("SLAKE/imgs", "SLAKE")

	set_dataset_mask_targets(
	dataset_name="SLAKE", # Your dataset name
	mask_targets_file="SLAKE/mask.txt", # Your mapping file
	segmentation_field="segmentation"
	)