OpenCV DNN模块教程(四)Mask-RCNN实例分割

    本文为OpenCV DNN模块官方教程的扩展，介绍如何使用OpenCV加载TensorFlow Object Detection API训练的模型做实例分割，以Mask-RCNN为例来检测缺陷。TensorFlow Object Detection API的github链接地址如下：https://github.com/tensorflow/models/tree/master/research/object_detection

    本文以TensorFlow 1.x为例(TF2.x等后续稳定支持OpenCV后介绍)，介绍OpenCV DNN模块调用Mask-RCNN模型做实例分割的步骤如下：

    (1) 下载或自己训练生成 .pb 格式的模型文件。本文以自己训练好的缺陷检测模型frozen_inference_graph.pb为例：

    (2) 使用指令用.pb文件生成.pbtxt文件, Mask-RCNN使用tf_text_graph_mask_rcnn.py，指令如下：

 

主要参数三个：

    --input 输入.pb模型文件完整路径；

    --output 输出.pbtxt文件完整路径；

    --config 输入config文件完整路径

完整指令：

python tf_text_graph_mask_rcnn.py --input E:\Practice\TensorFlow\DataSet\mask_defects2\model\export\frozen_inference_graph.pb --output E:\Practice\TensorFlow\DataSet\mask_defects2\model\export\frozen_inference_graph.pbtxt --config E:\Practice\TensorFlow\DataSet\mask_defects2\model\train\mask_rcnn_inception_v2_coco.config

运行结果：

    (3) 配置OpenCV4.4，加载图片测试 ，代码如下：

#include <fstream>
#include <sstream>
#include <iostream>
#include <string.h>

#include <opencv2/dnn.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>


using namespace cv;
using namespace dnn;
using namespace std;

// Initialize the parameters
float confThreshold = 0.4; // Confidence threshold
float maskThreshold = 0.5; // Mask threshold

vector<string> classes;
vector<Scalar> colors;

// Draw the predicted bounding box, colorize and show the mask on the image
void drawBox(Mat& frame, int classId, float conf, Rect box, Mat& objectMask)
{
  //Draw a rectangle displaying the bounding box
  rectangle(frame, Point(box.x, box.y), Point(box.x + box.width, box.y + box.height), Scalar(255, 178, 50), 5);

  //Get the label for the class name and its confidence
  string label = format("%.2f", conf);
  if (!classes.empty())
  {
    CV_Assert(classId < (int)classes.size());
    label = classes[classId] + ":" + label;
  }

  //Display the label at the top of the bounding box
  int baseLine;
  Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
  box.y = max(box.y, labelSize.height);
  //rectangle(frame, Point(box.x, box.y - round(1.5*labelSize.height)), Point(box.x + round(1.5*labelSize.width), box.y + baseLine), Scalar(255, 255, 255), FILLED);
  putText(frame, label, Point(box.x, box.y), FONT_HERSHEY_SIMPLEX, 0.75, Scalar(0, 255, 0), 2);

  Scalar color = colors[classId%colors.size()];

  // Resize the mask, threshold, color and apply it on the image
  resize(objectMask, objectMask, Size(box.width, box.height));
  Mat mask = (objectMask > maskThreshold);
  Mat coloredRoi = (0.5 * color + 0.7 * frame(box));
  coloredRoi.convertTo(coloredRoi, CV_8UC3);

  // Draw the contours on the image
  vector<Mat> contours;
  Mat hierarchy;
  mask.convertTo(mask, CV_8U);
  findContours(mask, contours, hierarchy, RETR_CCOMP, CHAIN_APPROX_SIMPLE);
  drawContours(coloredRoi, contours, -1, color, 5, LINE_8, hierarchy, 100);
  coloredRoi.copyTo(frame(box), mask);

}
// For each frame, extract the bounding box and mask for each detected object

void postprocess(Mat& frame, const vector<Mat>& outs)
{
  Mat outDetections = outs[0];
  Mat outMasks = outs[1];

  // Output size of masks is NxCxHxW where
  // N - number of detected boxes
  // C - number of classes (excluding background)
  // HxW - segmentation shape
  const int numDetections = outDetections.size[2];
  const int numClasses = outMasks.size[1];

  outDetections = outDetections.reshape(1, outDetections.total() / 7);
  for (int i = 0; i < numDetections; ++i)
  {
    float score = outDetections.at<float>(i, 2);
    if (score > confThreshold)
    {
      // Extract the bounding box
      int classId = static_cast<int>(outDetections.at<float>(i, 1));
      int left = static_cast<int>(frame.cols * outDetections.at<float>(i, 3));
      int top = static_cast<int>(frame.rows * outDetections.at<float>(i, 4));
      int right = static_cast<int>(frame.cols * outDetections.at<float>(i, 5));
      int bottom = static_cast<int>(frame.rows * outDetections.at<float>(i, 6));

      left = max(0, min(left, frame.cols - 1));
      top = max(0, min(top, frame.rows - 1));
      right = max(0, min(right, frame.cols - 1));
      bottom = max(0, min(bottom, frame.rows - 1));
      Rect box = Rect(left, top, right - left + 1, bottom - top + 1);

      // Extract the mask for the object
      Mat objectMask(outMasks.size[2], outMasks.size[3], CV_32F, outMasks.ptr<float>(i, classId));

      // Draw bounding box, colorize and show the mask on the image
      drawBox(frame, classId, score, box, objectMask);

    }
  }
}


/***************Image Test****************/
int main()
{
  // Load names of classes
  string classesFile = "./model2/label.names";
  ifstream ifs(classesFile.c_str());
  string line;
  while (getline(ifs, line)) classes.push_back(line);

  // Load the colors
  string colorsFile = "./model2/colors.txt";
  ifstream colorFptr(colorsFile.c_str());
  while (getline(colorFptr, line))
  {
    char* pEnd;
    double r, g, b;
    r = strtod(line.c_str(), &pEnd);
    g = strtod(pEnd, NULL);
    b = strtod(pEnd, NULL);
    Scalar color = Scalar(r, g, b, 255.0);
    colors.push_back(Scalar(r, g, b, 255.0));
  }

  // Give the configuration and weight files for the model
  String textGraph = "./model2/defect_label.pbtxt";
  String modelWeights = "./model2/frozen_inference_graph.pb";

  // Load the network
  Net net = readNetFromTensorflow(modelWeights, textGraph);

  // Open a video file or an image file or a camera stream.
  string str, outputFile;
  Mat frame, blob;

  // Create a window
  static const string kWinName = "OpenCV DNN Mask-RCNN Demo";

  // Process frames.
  frame = imread("./imgs/4.jpg");
  // Create a 4D blob from a frame.
  //blobFromImage(frame, blob, 1.0, Size(frame.cols, frame.rows), Scalar(), true, false);
  //blobFromImage(frame, blob, 1.0, Size(1012, 800), Scalar(), true, false);
  blobFromImage(frame, blob, 1.0, Size(800, 800), Scalar(), true, false);
  //blobFromImage(frame, blob);

  //Sets the input to the network
  net.setInput(blob);

  // Runs the forward pass to get output from the output layers
  std::vector<String> outNames(2);
  cout << outNames[0] << endl;
  cout << outNames[1] << endl;
  outNames[0] = "detection_out_final";
  outNames[1] = "detection_masks";
  vector<Mat> outs;
  net.forward(outs, outNames);

  // Extract the bounding box and mask for each of the detected objects
  postprocess(frame, outs);

  // Put efficiency information. The function getPerfProfile returns the overall time for inference(t) and the timings for each of the layers(in layersTimes)
  vector<double> layersTimes;
  double freq = getTickFrequency() / 1000;
  double t = net.getPerfProfile(layersTimes) / freq;
  string label = format("test use time: %0.0f ms", t);
  putText(frame, label, Point(10, 20), FONT_HERSHEY_SIMPLEX, 0.8, Scalar(0, 0, 255), 2);

  // Write the frame with the detection boxes
  Mat detectedFrame;
  frame.convertTo(detectedFrame, CV_8U);
  imwrite("result.jpg", frame);
  //resize(frame, frame, Size(frame.cols / 3, frame.rows / 3));
  imshow(kWinName, frame);
  waitKey(0);
  return 0;
}

   测试图像：

    运行结果：

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