import os import lmdb import io import re from PIL import Image import torch from torch.utils.data import Dataset, DataLoader from torchvision import transforms import unittest from tqdm import tqdm torch.multiprocessing.set_sharing_strategy('file_system') class LMDBImageDataset(Dataset): def __init__(self, lmdb_path, transform=None, limit=None): """ Args: lmdb_path (str): Path to the LMDB directory. transform (callable, optional): Optional transform to be applied on an image. limit (int or float, optional): If a float between 0 and 1, keeps that fraction of keys. If an int, keeps that many keys. """ # Open the LMDB environment in read-only mode. self.env = lmdb.open(lmdb_path, readonly=True, lock=False, readahead=False) self.transform = transform # Retrieve all keys from the LMDB database. with self.env.begin() as txn: keys = [key.decode('utf-8') for key, _ in txn.cursor()] # Define a sort key function that extracts frame number and cow id from the filename. def sort_key(filename): # Expected pattern: "pmfeed_4_3_16_frame_10000_cow_1.jpg" match = re.search(r'frame_(\d+)_cow_(\d+)', filename) if match: frame = int(match.group(1)) cow = int(match.group(2)) return (frame, cow) return (float('inf'), float('inf')) # Sort the keys using the defined sort key function. keys = sorted(keys, key=sort_key) # Apply the limit if provided. if limit is not None: if isinstance(limit, float): if 0 <= limit <= 1: cutoff = int(len(keys) * limit) keys = keys[:cutoff] else: raise ValueError("If limit is a float, it must be between 0 and 1.") elif isinstance(limit, int): keys = keys[:limit] else: raise TypeError("limit must be either a float or an integer.") self.keys = keys def __getitem__(self, index): # Get the key and image data key_str = self.keys[index] key = key_str.encode('utf-8') with self.env.begin() as txn: image_bytes = txn.get(key) # Convert binary image data to a PIL Image. image = Image.open(io.BytesIO(image_bytes)).convert('RGB') if self.transform: image = self.transform(image) # Extract the cow id from the filename. match = re.search(r'frame_(\d+)_cow_(\d+)', key_str) if match: cow_id = int(match.group(2)) else: cow_id = -1 # Use -1 or any default value if not found return image, cow_id def __len__(self): return len(self.keys) class TestLMDBImageDataset(unittest.TestCase): def test_dataset_length(self): # Example transform: resize and convert to tensor. transform = transforms.Compose([ transforms.Resize((256, 256)), transforms.ToTensor(), ]) # Path to your LMDB directory. lmdb_path = '../lmdb_all_crops_pmfeed_4_3_16' dataset = LMDBImageDataset(lmdb_path=lmdb_path, transform=transform, limit=20) self.assertEqual(len(dataset), 20) self.assertEqual(dataset.keys, ['pmfeed_4_3_16_frame_1_cow_1.jpg', 'pmfeed_4_3_16_frame_1_cow_2.jpg', 'pmfeed_4_3_16_frame_1_cow_3.jpg', 'pmfeed_4_3_16_frame_1_cow_4.jpg', 'pmfeed_4_3_16_frame_1_cow_5.jpg', 'pmfeed_4_3_16_frame_1_cow_6.jpg', 'pmfeed_4_3_16_frame_1_cow_7.jpg', 'pmfeed_4_3_16_frame_1_cow_8.jpg', 'pmfeed_4_3_16_frame_2_cow_1.jpg', 'pmfeed_4_3_16_frame_2_cow_2.jpg', 'pmfeed_4_3_16_frame_2_cow_3.jpg', 'pmfeed_4_3_16_frame_2_cow_4.jpg', 'pmfeed_4_3_16_frame_2_cow_5.jpg', 'pmfeed_4_3_16_frame_2_cow_6.jpg', 'pmfeed_4_3_16_frame_2_cow_7.jpg', 'pmfeed_4_3_16_frame_2_cow_8.jpg', 'pmfeed_4_3_16_frame_3_cow_1.jpg', 'pmfeed_4_3_16_frame_3_cow_2.jpg', 'pmfeed_4_3_16_frame_3_cow_3.jpg', 'pmfeed_4_3_16_frame_3_cow_4.jpg']) dataset = LMDBImageDataset(lmdb_path=lmdb_path, transform=transform, limit=100) self.assertEqual(len(dataset), 100) self.assertEqual(dataset.keys[-10:], ['pmfeed_4_3_16_frame_12_cow_3.jpg', 'pmfeed_4_3_16_frame_12_cow_4.jpg', 'pmfeed_4_3_16_frame_12_cow_5.jpg', 'pmfeed_4_3_16_frame_12_cow_6.jpg', 'pmfeed_4_3_16_frame_12_cow_7.jpg', 'pmfeed_4_3_16_frame_12_cow_8.jpg', 'pmfeed_4_3_16_frame_13_cow_1.jpg', 'pmfeed_4_3_16_frame_13_cow_2.jpg', 'pmfeed_4_3_16_frame_13_cow_3.jpg', 'pmfeed_4_3_16_frame_13_cow_4.jpg']) dataset = LMDBImageDataset(lmdb_path=lmdb_path, transform=transform) self.assertEqual(len(dataset), 537908) dataset = LMDBImageDataset(lmdb_path=lmdb_path, transform=transform, limit=0.5) self.assertEqual(len(dataset), 268954) dataset = LMDBImageDataset(lmdb_path=lmdb_path, transform=transform, limit=0.3) self.assertEqual(len(dataset), 161372) def test_data_loading(self): # Example transform: resize and convert to tensor. transform = transforms.Compose([ transforms.Resize((256, 256)), transforms.ToTensor(), ]) # Path to your LMDB directory. lmdb_path = '../lmdb_all_crops_pmfeed_4_3_16' # Create the dataset: # For example, if you want to keep the first 20 keys: dataset = LMDBImageDataset(lmdb_path=lmdb_path, transform=transform) # Or, if you want to keep the first 50% of the keys: # Create a DataLoader. dataloader = DataLoader( dataset, batch_size=256, shuffle=False, num_workers=8, ) # Example: Iterate over one batch. ground_truths = [] for images, cow_ids in tqdm(dataloader, unit='batch'): # print(images.shape) # e.g., torch.Size([32, 3, 256, 256]) # print(cow_ids) # Tensor of cow IDs corresponding to each image. ground_truths.append(cow_ids) ground_truths = torch.cat(ground_truths, dim=0) self.assertEqual(len(ground_truths), 537908) self.assertEqual(set(ground_truths.tolist()), {1, 2, 3, 4, 5, 6, 7, 8}) if __name__ == "__main__": unittest.main()