general_detection_op.cpp

// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
//
// 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.

#include "core/general-server/op/general_detection_op.h"
#include <algorithm>
#include <iostream>
#include <memory>
#include <sstream>
#include "core/predictor/framework/infer.h"
#include "core/predictor/framework/memory.h"
#include "core/predictor/framework/resource.h"
#include "core/util/include/timer.h"


/*
#include "opencv2/imgcodecs/legacy/constants_c.h"
#include "opencv2/imgproc/types_c.h"
*/

namespace baidu {
namespace paddle_serving {
namespace serving {

using baidu::paddle_serving::Timer;
using baidu::paddle_serving::predictor::MempoolWrapper;
using baidu::paddle_serving::predictor::general_model::Tensor;
using baidu::paddle_serving::predictor::general_model::Response;
using baidu::paddle_serving::predictor::general_model::Request;
using baidu::paddle_serving::predictor::general_model::FetchInst;
using baidu::paddle_serving::predictor::InferManager;
using baidu::paddle_serving::predictor::PaddleGeneralModelConfig;

int GeneralDetectionOp::inference() {
  VLOG(2) << "Going to run inference";
  const std::vector<std::string> pre_node_names = pre_names();
  if (pre_node_names.size() != 1) {
    LOG(ERROR) << "This op(" << op_name()
               << ") can only have one predecessor op, but received "
               << pre_node_names.size();
    return -1;
  }
  const std::string pre_name = pre_node_names[0];

  const GeneralBlob *input_blob = get_depend_argument<GeneralBlob>(pre_name);
  if (!input_blob) {
    LOG(ERROR) << "input_blob is nullptr,error";
      return -1;
  }
  uint64_t log_id = input_blob->GetLogId();
  VLOG(2) << "(logid=" << log_id << ") Get precedent op name: " << pre_name;

  GeneralBlob *output_blob = mutable_data<GeneralBlob>();
  if (!output_blob) {
    LOG(ERROR) << "output_blob is nullptr,error";
      return -1;
  }
  output_blob->SetLogId(log_id);

  if (!input_blob) {
    LOG(ERROR) << "(logid=" << log_id
               << ") Failed mutable depended argument, op:" << pre_name;
    return -1;
  }

  const TensorVector *in = &input_blob->tensor_vector;
  TensorVector* out = &output_blob->tensor_vector;

  int batch_size = input_blob->_batch_size;
  VLOG(2) << "(logid=" << log_id << ") input batch size: " << batch_size;

  output_blob->_batch_size = batch_size;

  VLOG(2) << "(logid=" << log_id << ") infer batch size: " << batch_size;

  std::vector<int> input_shape;
  int in_num =0;
  void* databuf_data = NULL;
  char* databuf_char = NULL;
  size_t databuf_size = 0;

  std::string* input_ptr = static_cast<std::string*>(in->at(0).data.data());
  std::string base64str = input_ptr[0];
  float ratio_h{};
  float ratio_w{};

  
  cv::Mat img = Base2Mat(base64str);
  cv::Mat srcimg;
  cv::Mat resize_img;
  
  cv::Mat resize_img_rec;
  cv::Mat crop_img;
  img.copyTo(srcimg);

  this->resize_op_.Run(img, resize_img, this->max_side_len_, ratio_h, ratio_w,
                       this->use_tensorrt_);

  this->normalize_op_.Run(&resize_img, this->mean_det, this->scale_det,
                          this->is_scale_);

  std::vector<float> input(1 * 3 * resize_img.rows * resize_img.cols, 0.0f);
  this->permute_op_.Run(&resize_img, input.data());


  TensorVector* real_in = new TensorVector();
  if (!real_in) {
    LOG(ERROR) << "real_in is nullptr,error";
    return -1;
  }

  for (int i = 0; i < in->size(); ++i) {
    input_shape = {1, 3, resize_img.rows, resize_img.cols};
    in_num = std::accumulate(input_shape.begin(), input_shape.end(), 1, std::multiplies<int>());
    databuf_size = in_num*sizeof(float);
    databuf_data = MempoolWrapper::instance().malloc(databuf_size);
    if (!databuf_data) {
        LOG(ERROR) << "Malloc failed, size: " << databuf_size;
        return -1;
    }
    memcpy(databuf_data,input.data(),databuf_size);
    databuf_char = reinterpret_cast<char*>(databuf_data);
    paddle::PaddleBuf paddleBuf(databuf_char, databuf_size);
    paddle::PaddleTensor tensor_in;
    tensor_in.name = in->at(i).name;
    tensor_in.dtype = paddle::PaddleDType::FLOAT32;
    tensor_in.shape = {1, 3, resize_img.rows, resize_img.cols};
    tensor_in.lod = in->at(i).lod;
    tensor_in.data = paddleBuf;
    real_in->push_back(tensor_in);
  }

  Timer timeline;
  int64_t start = timeline.TimeStampUS();
  timeline.Start();
  
  if (InferManager::instance().infer(
          engine_name().c_str(), real_in, out, batch_size)) {
    LOG(ERROR) << "(logid=" << log_id
               << ") Failed do infer in fluid model: " << engine_name().c_str();
    return -1;
  }
  std::vector<int> output_shape;
  int out_num =0;
  void* databuf_data_out = NULL;
  char* databuf_char_out = NULL;
  size_t databuf_size_out = 0;
  //this is special add for PaddleOCR postprecess
  int infer_outnum =  out->size();
  for (int k = 0;k <infer_outnum; ++k) {
    int n2 = out->at(k).shape[2];
    int n3 = out->at(k).shape[3];
    int n = n2 * n3;

    float* out_data = static_cast<float*>(out->at(k).data.data());
    std::vector<float> pred(n, 0.0);
    std::vector<unsigned char> cbuf(n, ' ');

    for (int i = 0; i < n; i++) {
      pred[i] = float(out_data[i]);
      cbuf[i] = (unsigned char)((out_data[i]) * 255);
    }

    cv::Mat cbuf_map(n2, n3, CV_8UC1, (unsigned char *)cbuf.data());
    cv::Mat pred_map(n2, n3, CV_32F, (float *)pred.data());

    const double threshold = this->det_db_thresh_ * 255;
    const double maxvalue = 255;
    cv::Mat bit_map;
    cv::threshold(cbuf_map, bit_map, threshold, maxvalue, cv::THRESH_BINARY);
    cv::Mat dilation_map;
    cv::Mat dila_ele = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(2, 2));
    cv::dilate(bit_map, dilation_map, dila_ele);
    boxes = post_processor_.BoxesFromBitmap(pred_map, dilation_map,
                                            this->det_db_box_thresh_,
                                            this->det_db_unclip_ratio_);

    boxes = post_processor_.FilterTagDetRes(boxes, ratio_h, ratio_w, srcimg);

    for (int i = boxes.size() - 1; i >= 0; i--) {
      crop_img = GetRotateCropImage(img, boxes[i]);

      float wh_ratio = float(crop_img.cols) / float(crop_img.rows);

      this->resize_op_rec.Run(crop_img, resize_img_rec, wh_ratio, this->use_tensorrt_);

      this->normalize_op_.Run(&resize_img_rec, this->mean_rec, this->scale_rec,
                              this->is_scale_);

      std::vector<float> output_rec(1 * 3 * resize_img_rec.rows * resize_img_rec.cols, 0.0f);

      this->permute_op_.Run(&resize_img_rec, output_rec.data());

      // Inference.
      output_shape = {1, 3, resize_img_rec.rows, resize_img_rec.cols};
      out_num = std::accumulate(output_shape.begin(), output_shape.end(), 1, std::multiplies<int>());
      databuf_size_out = out_num*sizeof(float);
      databuf_data_out = MempoolWrapper::instance().malloc(databuf_size_out);
      if (!databuf_data_out) {
          LOG(ERROR) << "Malloc failed, size: " << databuf_size_out;
          return -1;
      }
      memcpy(databuf_data_out,output_rec.data(),databuf_size_out);
      databuf_char_out = reinterpret_cast<char*>(databuf_data_out);
      paddle::PaddleBuf paddleBuf(databuf_char_out, databuf_size_out);
      paddle::PaddleTensor tensor_out;
      tensor_out.name = "image";
      tensor_out.dtype = paddle::PaddleDType::FLOAT32;
      tensor_out.shape = {1, 3, resize_img_rec.rows, resize_img_rec.cols};
      tensor_out.data = paddleBuf;
      out->push_back(tensor_out);
    }
  }
  out->erase(out->begin(),out->begin()+infer_outnum);

  
  int64_t end = timeline.TimeStampUS();
  CopyBlobInfo(input_blob, output_blob);
  AddBlobInfo(output_blob, start);
  AddBlobInfo(output_blob, end);
  return 0;
}

cv::Mat GeneralDetectionOp::Base2Mat(std::string &base64_data)
{
	cv::Mat img;
	std::string s_mat;
	s_mat = base64Decode(base64_data.data(), base64_data.size());
	std::vector<char> base64_img(s_mat.begin(), s_mat.end());
	img = cv::imdecode(base64_img, cv::IMREAD_COLOR);//CV_LOAD_IMAGE_COLOR
	return img;
}

std::string GeneralDetectionOp::base64Decode(const char* Data, int DataByte)
{

	const char DecodeTable[] =
	{
		0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
		0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
		62, // '+'
		0, 0, 0,
		63, // '/'
		52, 53, 54, 55, 56, 57, 58, 59, 60, 61, // '0'-'9'
		0, 0, 0, 0, 0, 0, 0,
		0, 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, // 'A'-'Z'
		0, 0, 0, 0, 0, 0,
		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, // 'a'-'z'
	};

	std::string strDecode;
	int nValue;
	int i = 0;
	while (i < DataByte)
	{
		if (*Data != '\r' && *Data != '\n')
		{
			nValue = DecodeTable[*Data++] << 18;
			nValue += DecodeTable[*Data++] << 12;
			strDecode += (nValue & 0x00FF0000) >> 16;
			if (*Data != '=')
			{
				nValue += DecodeTable[*Data++] << 6;
				strDecode += (nValue & 0x0000FF00) >> 8;
				if (*Data != '=')
				{
					nValue += DecodeTable[*Data++];
					strDecode += nValue & 0x000000FF;
				}
			}
			i += 4;
		}
		else// 回车换行,跳过
		{
			Data++;
			i++;
		}
	}
	return strDecode;
}

cv::Mat GeneralDetectionOp::GetRotateCropImage(const cv::Mat &srcimage,
                                           std::vector<std::vector<int>> box) {
  cv::Mat image;
  srcimage.copyTo(image);
  std::vector<std::vector<int>> points = box;

  int x_collect[4] = {box[0][0], box[1][0], box[2][0], box[3][0]};
  int y_collect[4] = {box[0][1], box[1][1], box[2][1], box[3][1]};
  int left = int(*std::min_element(x_collect, x_collect + 4));
  int right = int(*std::max_element(x_collect, x_collect + 4));
  int top = int(*std::min_element(y_collect, y_collect + 4));
  int bottom = int(*std::max_element(y_collect, y_collect + 4));

  cv::Mat img_crop;
  image(cv::Rect(left, top, right - left, bottom - top)).copyTo(img_crop);

  for (int i = 0; i < points.size(); i++) {
    points[i][0] -= left;
    points[i][1] -= top;
  }

  int img_crop_width = int(sqrt(pow(points[0][0] - points[1][0], 2) +
                                pow(points[0][1] - points[1][1], 2)));
  int img_crop_height = int(sqrt(pow(points[0][0] - points[3][0], 2) +
                                 pow(points[0][1] - points[3][1], 2)));

  cv::Point2f pts_std[4];
  pts_std[0] = cv::Point2f(0., 0.);
  pts_std[1] = cv::Point2f(img_crop_width, 0.);
  pts_std[2] = cv::Point2f(img_crop_width, img_crop_height);
  pts_std[3] = cv::Point2f(0.f, img_crop_height);

  cv::Point2f pointsf[4];
  pointsf[0] = cv::Point2f(points[0][0], points[0][1]);
  pointsf[1] = cv::Point2f(points[1][0], points[1][1]);
  pointsf[2] = cv::Point2f(points[2][0], points[2][1]);
  pointsf[3] = cv::Point2f(points[3][0], points[3][1]);

  cv::Mat M = cv::getPerspectiveTransform(pointsf, pts_std);

  cv::Mat dst_img;
  cv::warpPerspective(img_crop, dst_img, M,
                      cv::Size(img_crop_width, img_crop_height),
                      cv::BORDER_REPLICATE);

  if (float(dst_img.rows) >= float(dst_img.cols) * 1.5) {
    cv::Mat srcCopy = cv::Mat(dst_img.rows, dst_img.cols, dst_img.depth());
    cv::transpose(dst_img, srcCopy);
    cv::flip(srcCopy, srcCopy, 0);
    return srcCopy;
  } else {
    return dst_img;
  }
}

DEFINE_OP(GeneralDetectionOp);

}  // namespace serving
}  // namespace paddle_serving
}  // namespace baidu