Datasets:
Tasks:
Image Segmentation
Modalities:
Image
Formats:
parquet
Sub-tasks:
semantic-segmentation
Languages:
English
Size:
10K - 100K
License:
File size: 3,822 Bytes
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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") |