【Caffe代码解析】compute_image_mean

功能：
计算训练数据库的平均图像。

由于平均归一化训练图像会对结果有提升，所以Caffe里面，提供了一个可选项。

用法：
compute_image_mean [FLAGS] INPUT_DB [OUTPUT_FILE]\n”)
參数：INPUT_DB：数据库
參数（可选）：OUTPUT_FILE：输出文件名称,不提供的话，不保存平均图像blob

实现方法：

数据源：求平均图像的方法是直接从数据库（LevelDB或者LMDB）里面直接读取出来的，而不是直接用图像数据库里面求出，意味着，必须先进行图像到数据库的转换后，才干求平均图像这一步。

接下来就是遍历KV数据库的每个值while (cursor->valid()) 将每个数据值转换为Datum，datum.ParseFromString(cursor->value());

接着将Datum阶码到sum_blob 中。sum_blob 是一个num=1，channels=图像.channel,height=图像.height ,width=图像.width 的blob

累加：

sum_blob.set_data(i, sum_blob.data(i) + (uint8_t)data[i]);

最后求平均：

sum_blob.set_data(i, sum_blob.data(i) / count);

存在的问题：上述代码仅仅是先累加在处于数目求和，显然，假设须要求平均的图像的数目相当多的话，就有可能溢出（浮点溢出）。

最后，假设要求简单一点的话，也能够直接求每个通道的平均值。
源码：//2015.06.04版本号

#include <stdint.h>
#include <algorithm>
#include <string>
#include <utility>
#include <vector>

#include "boost/scoped_ptr.hpp"
#include "gflags/gflags.h"
#include "glog/logging.h"

#include "caffe/proto/caffe.pb.h"
#include "caffe/util/db.hpp"
#include "caffe/util/io.hpp"

using namespace caffe;  // NOLINT(build/namespaces)

using std::max;
using std::pair;
using boost::scoped_ptr;

DEFINE_string(backend, "lmdb",
        "The backend {leveldb, lmdb} containing the images");

int main(int argc, char** argv) {
  ::google::InitGoogleLogging(argv[0]);

#ifndef GFLAGS_GFLAGS_H_
  namespace gflags = google;
#endif

  gflags::SetUsageMessage("Compute the mean_image of a set of images given by"
        " a leveldb/lmdb\n"
        "Usage:\n"
        "    compute_image_mean [FLAGS] INPUT_DB [OUTPUT_FILE]\n");

  gflags::ParseCommandLineFlags(&argc, &argv, true);

  if (argc < 2 || argc > 3) {
    gflags::ShowUsageWithFlagsRestrict(argv[0], "tools/compute_image_mean");
    return 1;
  }

  scoped_ptr<db::DB> db(db::GetDB(FLAGS_backend));
  db->Open(argv[1], db::READ);
  scoped_ptr<db::Cursor> cursor(db->NewCursor());

  BlobProto sum_blob;
  int count = 0;
  // load first datum
  Datum datum;
  datum.ParseFromString(cursor->value());

  if (DecodeDatumNative(&datum)) {
    LOG(INFO) << "Decoding Datum";
  }

  sum_blob.set_num(1);
  sum_blob.set_channels(datum.channels());
  sum_blob.set_height(datum.height());
  sum_blob.set_width(datum.width());
  const int data_size = datum.channels() * datum.height() * datum.width();
  int size_in_datum = std::max<int>(datum.data().size(),
                                    datum.float_data_size());
  for (int i = 0; i < size_in_datum; ++i) {
    sum_blob.add_data(0.);
  }
  LOG(INFO) << "Starting Iteration";
  while (cursor->valid()) {
    Datum datum;
    datum.ParseFromString(cursor->value());
    DecodeDatumNative(&datum);

    const std::string& data = datum.data();
    size_in_datum = std::max<int>(datum.data().size(),
        datum.float_data_size());
    CHECK_EQ(size_in_datum, data_size) << "Incorrect data field size " <<
        size_in_datum;
    if (data.size() != 0) {
      CHECK_EQ(data.size(), size_in_datum);
      for (int i = 0; i < size_in_datum; ++i) {
        sum_blob.set_data(i, sum_blob.data(i) + (uint8_t)data[i]);
      }
    } else {
      CHECK_EQ(datum.float_data_size(), size_in_datum);
      for (int i = 0; i < size_in_datum; ++i) {
        sum_blob.set_data(i, sum_blob.data(i) +
            static_cast<float>(datum.float_data(i)));
      }
    }
    ++count;
    if (count % 10000 == 0) {
      LOG(INFO) << "Processed " << count << " files.";
    }
    cursor->Next();
  }

  if (count % 10000 != 0) {
    LOG(INFO) << "Processed " << count << " files.";
  }
  for (int i = 0; i < sum_blob.data_size(); ++i) {
    sum_blob.set_data(i, sum_blob.data(i) / count);
  }
  // Write to disk
  if (argc == 3) {
    LOG(INFO) << "Write to " << argv[2];
    WriteProtoToBinaryFile(sum_blob, argv[2]);
  }
  const int channels = sum_blob.channels();
  const int dim = sum_blob.height() * sum_blob.width();
  std::vector<float> mean_values(channels, 0.0);
  LOG(INFO) << "Number of channels: " << channels;
  for (int c = 0; c < channels; ++c) {
    for (int i = 0; i < dim; ++i) {
      mean_values[c] += sum_blob.data(dim * c + i);
    }
    LOG(INFO) << "mean_value channel [" << c << "]:" << mean_values[c] / dim;
  }
  return 0;
}

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【Caffe代码解析】compute_image_mean

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