C++ Datum::encoded方法代码示例

vector<int> DataTransformer<Dtype>::InferBlobShape(const Datum& datum) {
  if (datum.encoded()) {
#ifdef USE_OPENCV
    CHECK(!(param_.force_color() && param_.force_gray()))
        << "cannot set both force_color and force_gray";
    cv::Mat cv_img;
    if (param_.force_color() || param_.force_gray()) {
    // If force_color then decode in color otherwise decode in gray.
      cv_img = DecodeDatumToCVMat(datum, param_.force_color());
    } else {
      cv_img = DecodeDatumToCVMatNative(datum);
    }
    // InferBlobShape using the cv::image.
    return InferBlobShape(cv_img);
#else
    LOG(FATAL) << "Encoded datum requires OpenCV; compile with USE_OPENCV.";
#endif  // USE_OPENCV
  }
  const int crop_size = param_.crop_size();
  const int datum_channels = datum.channels();
  const int datum_height = datum.height();
  const int datum_width = datum.width();
  // Check dimensions.
  CHECK_GT(datum_channels, 0);
  CHECK_GE(datum_height, crop_size);
  CHECK_GE(datum_width, crop_size);
  // Build BlobShape.
  vector<int> shape(4);
  shape[0] = 1;
  shape[1] = datum_channels;
  shape[2] = (crop_size)? crop_size: datum_height;
  shape[3] = (crop_size)? crop_size: datum_width;
  return shape;
}

cv::Mat DatumToCVMat(const Datum& datum) {

    if (datum.encoded()) {
        cv::Mat cv_img;
        cv_img = DecodeDatumToCVMatNative(datum);
        return cv_img;
    }

    const string& data = datum.data();

    int datum_channels = datum.channels();
    int datum_height = datum.height();
    int datum_width = datum.width();

    CHECK(datum_channels==3);

    cv::Mat cv_img(datum_height, datum_width, CV_8UC3);

    for (int h = 0; h < datum_height; ++h) {
        for (int w = 0; w < datum_width; ++w) {
            for (int c = 0; c < datum_channels; ++c) {
                int datum_index = (c * datum_height + h) * datum_width + w;
                cv_img.at<cv::Vec3b>(h, w)[c] = static_cast<uchar>(data[datum_index]);
            }
        }
    }

    return cv_img;
}

TEST_F(IOTest, TestReadFileToDatum) {
  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
  Datum datum;
  EXPECT_TRUE(ReadFileToDatum(filename, &datum));
  EXPECT_TRUE(datum.encoded());
  EXPECT_EQ(datum.label(), -1);
  EXPECT_EQ(datum.data().size(), 140391);
}

cv::Mat DecodeDatumToCVMatNative(const Datum& datum) {
  cv::Mat cv_img;
  CHECK(datum.encoded()) << "Datum not encoded";
  const string& data = datum.data();
  std::vector<char> vec_data(data.c_str(), data.c_str() + data.size());
  cv_img = cv::imdecode(vec_data, -1);
  if (!cv_img.data) {
    LOG(ERROR) << "Could not decode datum ";
  }
  return cv_img;
}

void DataTransformer<Dtype>::Transform(const Datum& datum,
                                       Blob<Dtype>* transformed_blob) {
  
  #ifndef CAFFE_HEADLESS

  // If datum is encoded, decoded and transform the cv::image.
  if (datum.encoded()) {
    CHECK(!(param_.force_color() && param_.force_gray()))
        << "cannot set both force_color and force_gray";
    cv::Mat cv_img;
    if (param_.force_color() || param_.force_gray()) {
    // If force_color then decode in color otherwise decode in gray.
      cv_img = DecodeDatumToCVMat(datum, param_.force_color());
    } else {
      cv_img = DecodeDatumToCVMatNative(datum);
    }
    // Transform the cv::image into blob.
    return Transform(cv_img, transformed_blob);
  } else {
    if (param_.force_color() || param_.force_gray()) {
      LOG(ERROR) << "force_color and force_gray only for encoded datum";
    }
  }

  #endif

  const int crop_size = param_.crop_size();
  const int datum_channels = datum.channels();
  const int datum_height = datum.height();
  const int datum_width = datum.width();

  // Check dimensions.
  const int channels = transformed_blob->channels();
  const int height = transformed_blob->height();
  const int width = transformed_blob->width();
  const int num = transformed_blob->num();

  CHECK_EQ(channels, datum_channels);
  CHECK_LE(height, datum_height);
  CHECK_LE(width, datum_width);
  CHECK_GE(num, 1);

  if (crop_size) {
    CHECK_EQ(crop_size, height);
    CHECK_EQ(crop_size, width);
  } else {
    CHECK_EQ(datum_height, height);
    CHECK_EQ(datum_width, width);
  }

  Dtype* transformed_data = transformed_blob->mutable_cpu_data();
  Transform(datum, transformed_data);
}

cv::Mat DecodeDatumToCVMat(const Datum& datum, bool is_color) {
  cv::Mat cv_img;
  CHECK(datum.encoded()) << "Datum not encoded";
  const string& data = datum.data();
  std::vector<char> vec_data(data.c_str(), data.c_str() + data.size());
  int cv_read_flag = (is_color ? CV_LOAD_IMAGE_COLOR :
    CV_LOAD_IMAGE_GRAYSCALE);
  cv_img = cv::imdecode(vec_data, cv_read_flag);
  if (!cv_img.data) {
    LOG(ERROR) << "Could not decode datum ";
  }
  return cv_img;
}

cv::Mat DecodeDatumToCVMat(const Datum& datum,
    const int height, const int width, const bool is_color) {
  cv::Mat cv_img;
  CHECK(datum.encoded()) << "Datum not encoded";
  int cv_read_flag = (is_color ? CV_LOAD_IMAGE_COLOR :
    CV_LOAD_IMAGE_GRAYSCALE);
  const string& data = datum.data();
  std::vector<char> vec_data(data.c_str(), data.c_str() + data.size());
  if (height > 0 && width > 0) {
    cv::Mat cv_img_origin = cv::imdecode(cv::Mat(vec_data), cv_read_flag);
    cv::resize(cv_img_origin, cv_img, cv::Size(width, height));
  } else {
    cv_img = cv::imdecode(vec_data, cv_read_flag);
  }
  if (!cv_img.data) {
    LOG(ERROR) << "Could not decode datum ";
  }
  return cv_img;
}

void DataLayer<Dtype>::InternalThreadEntry() {
  CPUTimer batch_timer;
  batch_timer.Start();
  double read_time = 0;
  double trans_time = 0;
  CPUTimer timer;
  CHECK(this->prefetch_data_.count());
  CHECK(this->transformed_data_.count());

  // Reshape on single input batches for inputs of varying dimension.
  const int batch_size = this->layer_param_.data_param().batch_size();
  const int crop_size = this->layer_param_.transform_param().crop_size();
  if (batch_size == 1 && crop_size == 0) {
    Datum datum;
    datum.ParseFromString(cursor_->value());
    this->prefetch_data_.Reshape(1, datum.channels(),
        datum.height(), datum.width());
    this->transformed_data_.Reshape(1, datum.channels(),
        datum.height(), datum.width());
  }

  Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
  Dtype* top_label = NULL;  // suppress warnings about uninitialized variables

  if (this->output_labels_) {
    top_label = this->prefetch_label_.mutable_cpu_data();
  }
  bool force_color = this->layer_param_.data_param().force_encoded_color();
  for (int item_id = 0; item_id < batch_size; ++item_id) {
    timer.Start();
    // get a blob
    Datum datum;
    datum.ParseFromString(cursor_->value());

    cv::Mat cv_img;
    if (datum.encoded()) {
      if (force_color) {
        cv_img = DecodeDatumToCVMat(datum, true);
      } else {
        cv_img = DecodeDatumToCVMatNative(datum);
      }
      if (cv_img.channels() != this->transformed_data_.channels()) {
        LOG(WARNING) << "Your dataset contains encoded images with mixed "
        << "channel sizes. Consider adding a 'force_color' flag to the "
        << "model definition, or rebuild your dataset using "
        << "convert_imageset.";
      }
    }
    read_time += timer.MicroSeconds();
    timer.Start();

    // Apply data transformations (mirror, scale, crop...)
    int offset = this->prefetch_data_.offset(item_id);
    this->transformed_data_.set_cpu_data(top_data + offset);
    if (datum.encoded()) {
      this->data_transformer_->Transform(cv_img, &(this->transformed_data_));
    } else {
      this->data_transformer_->Transform(datum, &(this->transformed_data_));
    }
    if (this->output_labels_) {
      for (int label_i = 0; label_i < datum.label_size(); ++label_i){
        top_label[item_id * datum.label_size() + label_i] = datum.label(label_i);
      }
      //top_label[item_id] = datum.label();
    }
    trans_time += timer.MicroSeconds();
    // go to the next iter
    cursor_->Next();
    if (!cursor_->valid()) {
      DLOG(INFO) << "Restarting data prefetching from start.";
      cursor_->SeekToFirst();
    }
  }
  batch_timer.Stop();
  DLOG(INFO) << "Prefetch batch: " << batch_timer.MilliSeconds() << " ms.";
  DLOG(INFO) << "     Read time: " << read_time / 1000 << " ms.";
  DLOG(INFO) << "Transform time: " << trans_time / 1000 << " ms.";
}