【Caffe代码解析】compute_image_mean

功能：
计算训练数据库的平均图像。
因为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。

使用方法：
compute_image_mean [FLAGS] INPUT_DB [OUTPUT_FILE]\n”)
参数：INPUT_DB： 数据库
参数（可选）：OUTPUT_FILE： 输出文件名,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，才能求平均图像这一步。

接下来就是遍历KV数据库的每一个值while (cursor->valid()) 将每一个数据值转换为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：上述代码只是先累加在处于数目求和，显然，如果需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，如果要求简单一点的话，也可以直接求每个通道的平均值。
源代码：//2015.06.04版本

#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;}