1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
|
/**
* Copyright (c) 2016-present, Facebook, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
// This script converts an image dataset to leveldb.
//
// FLAGS_input_folder is the root folder that holds all the images, and
// FLAGS_list_file should be a list of files as well as their labels, in
// the format as
// subfolder1/file1.JPEG 7
// ....
#include <opencv2/opencv.hpp>
#include <fstream> // NOLINT(readability/streams)
#include <memory>
#include <random>
#include <string>
#include "caffe2/core/init.h"
#include "caffe2/proto/caffe2_pb.h"
#include "caffe2/core/logging.h"
#include "leveldb/db.h"
#include "leveldb/write_batch.h"
C10_DEFINE_string(input_db_name, "", "The input image file name.");
C10_DEFINE_string(output_db_name, "", "The output training leveldb name.");
C10_DEFINE_bool(color, true, "If set, load images in color.");
C10_DEFINE_int(
scale,
256,
"If FLAGS_raw is set, scale all the images' shorter edge to the given "
"value.");
C10_DEFINE_bool(warp, false, "If warp is set, warp the images to square.");
namespace caffe2 {
void ConvertToRawDataset(
const string& input_db_name, const string& output_db_name) {
// input leveldb
std::unique_ptr<leveldb::DB> input_db;
LOG(INFO) << "Opening input leveldb " << input_db_name;
{
leveldb::Options options;
options.create_if_missing = false;
leveldb::DB* db_temp;
leveldb::Status status = leveldb::DB::Open(
options, input_db_name, &db_temp);
CAFFE_ENFORCE(status.ok(), "Failed to open leveldb ", input_db_name, ".");
input_db.reset(db_temp);
}
// output leveldb
std::unique_ptr<leveldb::DB> output_db;
std::unique_ptr<leveldb::WriteBatch> batch;
LOG(INFO) << "Opening leveldb " << output_db_name;
{
leveldb::Options options;
options.error_if_exists = true;
options.create_if_missing = true;
options.write_buffer_size = 268435456;
leveldb::DB* db_temp;
leveldb::Status status = leveldb::DB::Open(
options, output_db_name, &db_temp);
CAFFE_ENFORCE(
status.ok(),
"Failed to open leveldb ",
output_db_name,
". Is it already existing?");
output_db.reset(db_temp);
}
batch.reset(new leveldb::WriteBatch());
TensorProtos input_protos;
TensorProtos output_protos;
TensorProto* data = output_protos.add_protos();
TensorProto* label = output_protos.add_protos();
data->set_data_type(TensorProto::BYTE);
data->add_dims(0);
data->add_dims(0);
if (FLAGS_color) {
data->add_dims(3);
}
string value;
unique_ptr<leveldb::Iterator> iter;
iter.reset(input_db->NewIterator(leveldb::ReadOptions()));
iter->SeekToFirst();
int count = 0;
for (; iter->Valid(); iter->Next()) {
CAFFE_ENFORCE(input_protos.ParseFromString(iter->value().ToString()));
label->CopyFrom(input_protos.protos(1));
const string& encoded_image = input_protos.protos(0).string_data(0);
int encoded_size = encoded_image.size();
cv::Mat img = cv::imdecode(
cv::Mat(
1, &encoded_size, CV_8UC1, const_cast<char*>(encoded_image.data())),
FLAGS_color ? cv::IMREAD_COLOR : cv::IMREAD_GRAYSCALE);
cv::Mat resized_img;
int scaled_width, scaled_height;
if (FLAGS_warp) {
scaled_width = FLAGS_scale;
scaled_height = FLAGS_scale;
} else if (img.rows > img.cols) {
scaled_width = FLAGS_scale;
scaled_height = static_cast<float>(img.rows) * FLAGS_scale / img.cols;
} else {
scaled_height = FLAGS_scale;
scaled_width = static_cast<float>(img.cols) * FLAGS_scale / img.rows;
}
cv::resize(img, resized_img, cv::Size(scaled_width, scaled_height), 0, 0,
cv::INTER_LINEAR);
data->set_dims(0, scaled_height);
data->set_dims(1, scaled_width);
DCHECK(resized_img.isContinuous());
data->set_byte_data(
resized_img.ptr(),
scaled_height * scaled_width * (FLAGS_color ? 3 : 1));
output_protos.SerializeToString(&value);
// Put in db
batch->Put(iter->key(), value);
if (++count % 1000 == 0) {
output_db->Write(leveldb::WriteOptions(), batch.get());
batch.reset(new leveldb::WriteBatch());
LOG(INFO) << "Processed " << count << " files.";
}
}
// write the last batch
if (count % 1000 != 0) {
output_db->Write(leveldb::WriteOptions(), batch.get());
}
LOG(INFO) << "Processed a total of " << count << " files.";
}
} // namespace caffe2
int main(int argc, char** argv) {
caffe2::GlobalInit(&argc, &argv);
caffe2::ConvertToRawDataset(FLAGS_input_db_name, FLAGS_output_db_name);
return 0;
}
|
