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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() |