Datasets:
Tasks:
Image Segmentation
Modalities:
Image
Formats:
parquet
Sub-tasks:
semantic-segmentation
Languages:
English
Size:
10K - 100K
License:
| from datasets import Dataset, DatasetDict, Features, Image, Value | |
| import os | |
| # Script for preparing the dataset from a local directory. | |
| def load_ai4mars_dataset(data_dir): | |
| # Define features | |
| features = Features({ | |
| 'image': Image(decode=True), | |
| 'label_mask': Image(decode=True), | |
| 'rover_mask': Image(decode=True), | |
| 'range_mask': Image(decode=True), | |
| 'has_masks': Value(dtype='bool'), | |
| 'has_labels': Value(dtype='bool') | |
| }) | |
| dataset_dict = {} | |
| train_data = { | |
| 'image': [], | |
| 'label_mask': [], | |
| 'rover_mask': [], | |
| 'range_mask': [], | |
| 'has_masks': [], | |
| 'has_labels': [] | |
| } | |
| # Training data paths | |
| train_img_dir = os.path.join(data_dir, 'msl/images/edr') | |
| train_label_dir = os.path.join(data_dir, 'msl/labels/train') | |
| train_mxy_dir = os.path.join(data_dir, 'msl/images/mxy') | |
| train_range_dir = os.path.join(data_dir, 'msl/images/rng-30m') | |
| without_labels = 0 | |
| without_masks = 0 | |
| for img_name in os.listdir(train_img_dir): | |
| base_name = os.path.splitext(img_name)[0] | |
| img_path = os.path.join(train_img_dir, img_name) | |
| label_path = os.path.join(train_label_dir, f"{base_name}.png") | |
| rover_path = os.path.join(train_mxy_dir, f"{base_name}.png").replace('EDR', 'MXY') | |
| range_path = os.path.join(train_range_dir, f"{base_name}.png").replace('EDR', 'RNG') | |
| # Always add the image | |
| train_data['image'].append(img_path) | |
| # Check if label files exist | |
| has_labels = os.path.exists(label_path) | |
| has_masks = os.path.exists(rover_path) and os.path.exists(range_path) | |
| without_labels += 1 if not has_labels else 0 | |
| without_masks += 1 if not has_masks else 0 | |
| train_data['has_labels'].append(has_labels) | |
| train_data['has_masks'].append(has_masks) | |
| # Add paths if they exist, None if they don't | |
| train_data['label_mask'].append(label_path if os.path.exists(label_path) else None) | |
| train_data['rover_mask'].append(rover_path if os.path.exists(rover_path) else None) | |
| train_data['range_mask'].append(range_path if os.path.exists(range_path) else None) | |
| print(f"Training data without labels: {without_labels}") | |
| print(f"Training data without masks: {without_masks}") | |
| dataset_dict['train'] = Dataset.from_dict(train_data, features=features) | |
| # Load test data for each agreement level | |
| for agreement in ['min1', 'min2', 'min3']: | |
| test_data = { | |
| 'image': [], | |
| 'label_mask': [], | |
| 'rover_mask': [], | |
| 'range_mask': [], | |
| 'has_masks': [], | |
| 'has_labels': [] | |
| } | |
| test_label_dir = os.path.join(data_dir, f'msl/labels/test/masked-gold-{agreement}-100agree') | |
| for label_name in os.listdir(test_label_dir): | |
| base_name = os.path.splitext(label_name)[0] | |
| img_path = os.path.join(data_dir, 'msl/images/edr', f"{base_name[:-len('_merged')]}.JPG") | |
| if os.path.exists(img_path): | |
| test_data['image'].append(img_path) | |
| test_data['label_mask'].append(os.path.join(test_label_dir, label_name)) | |
| test_data['rover_mask'].append(os.path.join(train_mxy_dir, f"{base_name.replace('_merged', '').replace('EDR', 'MXY')}.png")) | |
| test_data['range_mask'].append(os.path.join(train_range_dir, f"{base_name.replace('_merged', '').replace('EDR', 'RNG')}.png")) | |
| test_data['has_labels'].append(True) | |
| test_data['has_masks'].append(True) | |
| dataset_dict[f'test_{agreement}'] = Dataset.from_dict(test_data, features=features) | |
| return DatasetDict(dataset_dict) | |
| dataset = load_ai4mars_dataset("./ai4mars-dataset-merged-0.1") |