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create_db.py
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executable file
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#!/usr/bin/env python2
# Copyright (c) 2015-2017, NVIDIA CORPORATION. All rights reserved.
"""
Functions for creating temporary LMDBs
Used in test_views
"""
import argparse
import os
import random
import re
import sys
import time
import lmdb
import numpy as np
import PIL.Image
if __name__ == '__main__':
dirname = os.path.dirname(os.path.realpath(__file__))
sys.path.insert(0, os.path.join(dirname, '..', '..'))
import digits.config # noqa
from digits import utils # noqa
# Import digits.config first to set the path to Caffe
import caffe.io # noqa
import caffe_pb2 # noqa
IMAGE_SIZE = 10
TRAIN_IMAGE_COUNT = 1000
VAL_IMAGE_COUNT = 1000
TEST_IMAGE_COUNT = 10
DB_BATCH_SIZE = 100
def create_lmdbs(folder, file_list, image_count=None, db_batch_size=None):
"""
Creates LMDBs for generic inference
Returns the filename for a test image
Creates these files in "folder":
train_images/
train_labels/
val_images/
val_labels/
mean.binaryproto
test.png
"""
if image_count is None:
train_image_count = TRAIN_IMAGE_COUNT
else:
train_image_count = image_count
val_image_count = VAL_IMAGE_COUNT
if db_batch_size is None:
db_batch_size = DB_BATCH_SIZE
# read file list
images = []
f = open(file_list)
for line in f.readlines():
line = line.strip()
if not line:
continue
path = None
# might contain a numerical label at the end
match = re.match(r'(.*\S)\s+(\d+)$', line)
if match:
path = match.group(1)
ground_truth = int(match.group(2))
images.append([path, ground_truth])
print "Found %d image paths in image list" % len(images)
for phase, image_count in [
('train', train_image_count),
('val', val_image_count)]:
print "Will create %d pairs of %s images" % (image_count, phase)
# create DBs
image_db = lmdb.open(os.path.join(folder, '%s_images' % phase),
map_async=True, max_dbs=0)
label_db = lmdb.open(os.path.join(folder, '%s_labels' % phase),
map_async=True, max_dbs=0)
# add up all images to later create mean image
image_sum = None
shape = None
# save test images (one for each label)
testImagesSameClass = []
testImagesDifferentClass = []
# arrays for image and label batch writing
image_batch = []
label_batch = []
for i in xrange(image_count):
# pick up random indices from image list
index1 = random.randint(0, len(images) - 1)
index2 = random.randint(0, len(images) - 1)
# label=1 if images are from the same class otherwise label=0
label = 1 if int(images[index1][1]) == int(images[index2][1]) else 0
# load images from files
image1 = np.array(utils.image.load_image(images[index1][0]))
image2 = np.array(utils.image.load_image(images[index2][0]))
if not shape:
# initialize image sum for mean image
shape = image1.shape
image_sum = np.zeros((3, shape[0], shape[1]), 'float64')
assert(image1.shape == shape and image2.shape == shape)
# create BGR image: blue channel will contain first image,
# green channel will contain second image
image_pair = np.zeros(image_sum.shape)
image_pair[0] = image1
image_pair[1] = image2
image_sum += image_pair
# save test images on first pass
if label > 0 and len(testImagesSameClass) < TEST_IMAGE_COUNT:
testImagesSameClass.append(image_pair)
if label == 0 and len(testImagesDifferentClass) < TEST_IMAGE_COUNT:
testImagesDifferentClass.append(image_pair)
# encode into Datum object
image = image_pair.astype('uint8')
datum = caffe.io.array_to_datum(image, -1)
image_batch.append([str(i), datum])
# create label Datum
label_datum = caffe_pb2.Datum()
label_datum.channels, label_datum.height, label_datum.width = 1, 1, 1
label_datum.float_data.extend(np.array([label]).flat)
label_batch.append([str(i), label_datum])
if (i % db_batch_size == (db_batch_size - 1)) or (i == image_count - 1):
_write_batch_to_lmdb(image_db, image_batch)
_write_batch_to_lmdb(label_db, label_batch)
image_batch = []
label_batch = []
if i % (image_count / 20) == 0:
print "%d/%d" % (i, image_count)
# close databases
image_db.close()
label_db.close()
# save mean
mean_image = (image_sum / image_count).astype('uint8')
_save_mean(mean_image, os.path.join(folder, '%s_mean.binaryproto' % phase))
_save_mean(mean_image, os.path.join(folder, '%s_mean.png' % phase))
# create test images
for idx, image in enumerate(testImagesSameClass):
_save_image(image, os.path.join(folder, '%s_test_same_class_%d.png' % (phase, idx)))
for idx, image in enumerate(testImagesDifferentClass):
_save_image(image, os.path.join(folder, '%s_test_different_class_%d.png' % (phase, idx)))
return
def _write_batch_to_lmdb(db, batch):
"""
Write a batch of (key,value) to db
"""
try:
with db.begin(write=True) as lmdb_txn:
for key, datum in batch:
lmdb_txn.put(key, datum.SerializeToString())
except lmdb.MapFullError:
# double the map_size
curr_limit = db.info()['map_size']
new_limit = curr_limit * 2
try:
db.set_mapsize(new_limit) # double it
except AttributeError as e:
version = tuple(int(x) for x in lmdb.__version__.split('.'))
if version < (0, 87):
raise ImportError('py-lmdb is out of date (%s vs 0.87)' % lmdb.__version__)
else:
raise e
# try again
_write_batch_to_lmdb(db, batch)
def _save_image(image, filename):
# converting from BGR to RGB
image = image[[2, 1, 0], ...] # channel swap
# convert to (height, width, channels)
image = image.astype('uint8').transpose((1, 2, 0))
image = PIL.Image.fromarray(image)
image.save(filename)
def _save_mean(mean, filename):
"""
Saves mean to file
Arguments:
mean -- the mean as an np.ndarray
filename -- the location to save the image
"""
if filename.endswith('.binaryproto'):
blob = caffe_pb2.BlobProto()
blob.num = 1
blob.channels = mean.shape[0]
blob.height = mean.shape[1]
blob.width = mean.shape[2]
blob.data.extend(mean.astype(float).flat)
with open(filename, 'wb') as outfile:
outfile.write(blob.SerializeToString())
elif filename.endswith(('.jpg', '.jpeg', '.png')):
_save_image(mean, filename)
else:
raise ValueError('unrecognized file extension')
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Create-LMDB tool - DIGITS')
# Positional arguments
parser.add_argument('folder', help='Where to save the images')
parser.add_argument('file_list', help='File list')
# Optional arguments
parser.add_argument('-c', '--image_count', type=int, help='How many images')
args = vars(parser.parse_args())
if os.path.exists(args['folder']):
print 'ERROR: Folder already exists'
sys.exit(1)
else:
os.makedirs(args['folder'])
print 'Creating images at "%s" ...' % args['folder']
start_time = time.time()
create_lmdbs(
args['folder'],
args['file_list'],
image_count=args['image_count'],
)
print 'Done after %s seconds' % (time.time() - start_time,)