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import os
import lmdb
import re
import multiprocessing
from tqdm import tqdm
def sort_key(filename):
# Extract frame number and cow id from filenames like:
# "pmfeed_4_3_16_frame_10000_cow_1.jpg"
match = re.search(r'frame_(\d+)_cow_(\d+)', filename)
if match:
frame_number = int(match.group(1))
cow_id = int(match.group(2))
return (frame_number, cow_id)
return (float('inf'), float('inf'))
def read_image(args):
image_folder, image_name = args
image_path = os.path.join(image_folder, image_name)
try:
with open(image_path, 'rb') as f:
image_data = f.read()
return (image_name, image_data)
except Exception as e:
print(f"Error reading {image_name}: {e}")
return None
def main():
# Define your pathscon
image_folder = 'all_crops_pmfeed_4_3_16'
lmdb_path = 'lmdb_all_crops_pmfeed_4_3_16'
# Create LMDB directory if it doesn't exist
if not os.path.exists(lmdb_path):
os.makedirs(lmdb_path)
# List and sort JPEG files
image_files = [f for f in os.listdir(image_folder) if f.endswith('.jpg')]
sorted_files = sorted(image_files, key=sort_key)
# For sanity check, take the first 20 images
sanity_files = sorted_files
# Prepare arguments for multiprocessing
args = [(image_folder, image_name) for image_name in sanity_files]
# Use multiprocessing Pool to read images concurrently
with multiprocessing.Pool(processes=multiprocessing.cpu_count()) as pool:
results = list(tqdm(pool.imap(read_image, args), total=len(args), desc="Reading images"))
# Filter out any failed reads
results = [res for res in results if res is not None]
# Open LMDB environment with an appropriate map size (e.g., 10GB)
map_size = 10 * 1024 * 1024 * 1024 # 10GB in bytes
env = lmdb.open(lmdb_path, map_size=map_size)
# Write the results into LMDB using a single write transaction
with env.begin(write=True) as txn:
for key, value in tqdm(results, desc="Writing to LMDB"):
txn.put(key.encode('utf-8'), value)
print("LMDB database creation complete for all images!")
if __name__ == '__main__':
main() |