/*
All modification made by Intel Corporation: © 2016 Intel Corporation

All contributions by the University of California:
Copyright (c) 2014, 2015, The Regents of the University of California (Regents)
All rights reserved.

All other contributions:
Copyright (c) 2014, 2015, the respective contributors
All rights reserved.
For the list of contributors go to https://github.com/BVLC/caffe/blob/master/CONTRIBUTORS.md


Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

    * Redistributions of source code must retain the above copyright notice,
      this list of conditions and the following disclaimer.
    * Redistributions in binary form must reproduce the above copyright
      notice, this list of conditions and the following disclaimer in the
      documentation and/or other materials provided with the distribution.
    * Neither the name of Intel Corporation nor the names of its contributors
      may be used to endorse or promote products derived from this software
      without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/

#ifdef USE_OPENCV
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>

#include <stdint.h>
#include <algorithm>
#include <csignal>
#include <map>
#include <string>
#include <vector>

#include "caffe/data_transformer.hpp"
#include "caffe/layers/video_data_layer.hpp"
#include "caffe/util/benchmark.hpp"

namespace caffe {

template <typename Dtype>
VideoDataLayer<Dtype>::VideoDataLayer(const LayerParameter& param)
  : BasePrefetchingDataLayer<Dtype>(param) {
}

template <typename Dtype>
VideoDataLayer<Dtype>::~VideoDataLayer() {
  this->StopInternalThread();
  if (cap_.isOpened()) {
    cap_.release();
  }
}

template <typename Dtype>
void VideoDataLayer<Dtype>::DataLayerSetUp(
    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
  const int batch_size = this->layer_param_.data_param().batch_size();
  const VideoDataParameter& video_data_param =
      this->layer_param_.video_data_param();
  video_type_ = video_data_param.video_type();
  skip_frames_ = video_data_param.skip_frames();
  CHECK_GE(skip_frames_, 0);

  // Read an image, and use it to initialize the top blob.
  cv::Mat cv_img;
  if (video_type_ == VideoDataParameter_VideoType_WEBCAM) {
    const int device_id = video_data_param.device_id();
    if (!cap_.open(device_id)) {
      LOG(FATAL) << "Failed to open webcam: " << device_id;
    }
    cap_ >> cv_img;
  } else if (video_type_ == VideoDataParameter_VideoType_VIDEO) {
    CHECK(video_data_param.has_video_file()) << "Must provide video file!";
    const string& video_file = video_data_param.video_file();
    if (!cap_.open(video_file)) {
      LOG(FATAL) << "Failed to open video: " << video_file;
    }
    total_frames_ = cap_.get(CV_CAP_PROP_FRAME_COUNT);
    processed_frames_ = 0;
    // Read image to infer shape.
    cap_ >> cv_img;
    // Set index back to the first frame.
    cap_.set(CV_CAP_PROP_POS_FRAMES, 0);
  } else {
    LOG(FATAL) << "Unknow video type!";
  }
  CHECK(cv_img.data) << "Could not load image!";
  // Use data_transformer to infer the expected blob shape from a cv_image.
  top_shape_ = this->data_transformer_->InferBlobShape(cv_img);
  this->transformed_data_.Reshape(top_shape_);
  top_shape_[0] = batch_size;
  top[0]->Reshape(top_shape_);
  for (int i = 0; i < this->PREFETCH_COUNT; ++i) {
    this->prefetch_[i].data_.Reshape(top_shape_);
  }
  LOG(INFO) << "output data size: " << top[0]->num() << ","
      << top[0]->channels() << "," << top[0]->height() << ","
      << top[0]->width();
  // label
  if (this->output_labels_) {
    vector<int> label_shape(1, batch_size);
    top[1]->Reshape(label_shape);
    for (int i = 0; i < this->PREFETCH_COUNT; ++i) {
      this->prefetch_[i].label_.Reshape(label_shape);
    }
  }
}

// This function is called on prefetch thread
template<typename Dtype>
void VideoDataLayer<Dtype>::load_batch(Batch<Dtype>* batch) {
  CPUTimer batch_timer;
  batch_timer.Start();
  double read_time = 0;
  double trans_time = 0;
  CPUTimer timer;
  CHECK(batch->data_.count());
  CHECK(this->transformed_data_.count());

  // Reshape according to the first anno_datum of each batch
  // on single input batches allows for inputs of varying dimension.
  const int batch_size = this->layer_param_.data_param().batch_size();
  top_shape_[0] = 1;
  this->transformed_data_.Reshape(top_shape_);
  // Reshape batch according to the batch_size.
  top_shape_[0] = batch_size;
  batch->data_.Reshape(top_shape_);

  Dtype* top_data = batch->data_.mutable_cpu_data();
  Dtype* top_label = NULL;  // suppress warnings about uninitialized variables
  if (this->output_labels_) {
    top_label = batch->label_.mutable_cpu_data();
  }

  int skip_frames = skip_frames_;
  for (int item_id = 0; item_id < batch_size; ++item_id) {
    timer.Start();
    cv::Mat cv_img;
    if (video_type_ == VideoDataParameter_VideoType_WEBCAM) {
      cap_ >> cv_img;
    } else if (video_type_ == VideoDataParameter_VideoType_VIDEO) {
      if (processed_frames_ >= total_frames_) {
        LOG(INFO) << "Finished processing video.";
        raise(SIGINT);
      }
      ++processed_frames_;
      cap_ >> cv_img;
    } else {
      LOG(FATAL) << "Unknown video type.";
    }
    CHECK(cv_img.data) << "Could not load image!";
    read_time += timer.MicroSeconds();
    if (skip_frames > 0) {
      --skip_frames;
      --item_id;
    } else {
      skip_frames = skip_frames_;
      timer.Start();
      // Apply transformations (mirror, crop...) to the image
      int offset = batch->data_.offset(item_id);
      this->transformed_data_.set_cpu_data(top_data + offset);
      this->data_transformer_->Transform(cv_img, &(this->transformed_data_));
      trans_time += timer.MicroSeconds();
    }
    CHECK(cv_img.data) << "Could not load image!";
    read_time += timer.MicroSeconds();
    timer.Start();
    // Apply transformations (mirror, crop...) to the image
    int offset = batch->data_.offset(item_id);
    this->transformed_data_.set_cpu_data(top_data + offset);
    this->data_transformer_->Transform(cv_img, &(this->transformed_data_));
    trans_time += timer.MicroSeconds();
    if (this->output_labels_) {
      top_label[item_id] = 0;
    }
  }
  timer.Stop();
  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.";
}

INSTANTIATE_CLASS(VideoDataLayer);
REGISTER_LAYER_CLASS(VideoData);

}  // namespace caffe
#endif  // USE_OPENCV
