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" )