mask_rcnn.cpp

// Copyright (C) 2018-2019, BigVision LLC (LearnOpenCV.com), All Rights Reserved. 
// Author : Sunita Nayak
// Article : https://www.learnopencv.com/deep-learning-based-object-detection-and-instance-segmentation-using-mask-r-cnn-in-opencv-python-c/
// License: BSD-3-Clause-Attribution (Please read the license file.)

// Usage example:  ./mask_rcnn.out --video=run.mp4
//                 ./mask_rcnn.out --image=bird.jpg
#include <fstream>
#include <sstream>
#include <iostream>
#include <string.h>

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

const char* keys =
"{help h usage ? | | Usage examples: \n\t\t./mask-rcnn.out --image=traffic.jpg \n\t\t./mask-rcnn.out --video=sample.mp4}"
"{image i        |<none>| input image   }"
"{video v       |<none>| input video   }"
;
using namespace cv;
using namespace dnn;
using namespace std;

// Initialize the parameters
float confThreshold = 0.5; // Confidence threshold
float maskThreshold = 0.3; // Mask threshold

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

// Draw the predicted bounding box
void drawBox(Mat& frame, int classId, float conf, Rect box, Mat& objectMask);

// Postprocess the neural network's output for each frame
void postprocess(Mat& frame, const vector<Mat>& outs);

int main(int argc, char** argv)
{
    CommandLineParser parser(argc, argv, keys);
    parser.about("Use this script to run object detection using YOLO3 in OpenCV.");
    if (parser.has("help"))
    {
        parser.printMessage();
        return 0;
    }
    // Load names of classes
    string classesFile = "mscoco_labels.names";
    ifstream ifs(classesFile.c_str());
    string line;
    while (getline(ifs, line)) classes.push_back(line);
    
    // Load the colors
    string colorsFile = "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 = "./mask_rcnn_inception_v2_coco_2018_01_28.pbtxt";
    String modelWeights = "./mask_rcnn_inception_v2_coco_2018_01_28/frozen_inference_graph.pb";

    // Load the network
    Net net = readNetFromTensorflow(modelWeights, textGraph);
    net.setPreferableBackend(DNN_BACKEND_OPENCV);
    net.setPreferableTarget(DNN_TARGET_CPU);
    
    // Open a video file or an image file or a camera stream.
    string str, outputFile;
    VideoCapture cap;
    VideoWriter video;
    Mat frame, blob;
    
    try {
        
        outputFile = "mask_rcnn_out_cpp.avi";
        if (parser.has("image"))
        {
            // Open the image file
            str = parser.get<String>("image");
            //cout << "Image file input : " << str << endl;
            ifstream ifile(str);
            if (!ifile) throw("error");
            cap.open(str);
            str.replace(str.end()-4, str.end(), "_mask_rcnn_out.jpg");
            outputFile = str;
        }
        else if (parser.has("video"))
        {
            // Open the video file
            str = parser.get<String>("video");
            ifstream ifile(str);
            if (!ifile) throw("error");
            cap.open(str);
            str.replace(str.end()-4, str.end(), "_mask_rcnn_out.avi");
            outputFile = str;
        }
        // Open the webcam
        else cap.open(parser.get<int>("device"));
        
    }
    catch(...) {
        cout << "Could not open the input image/video stream" << endl;
        return 0;
    }
    
    // Get the video writer initialized to save the output video
    if (!parser.has("image")) {
        video.open(outputFile, VideoWriter::fourcc('M','J','P','G'), 28, Size(cap.get(CAP_PROP_FRAME_WIDTH), cap.get(CAP_PROP_FRAME_HEIGHT)));
    }

    // Create a window
    static const string kWinName = "Deep learning object detection in OpenCV";
    namedWindow(kWinName, WINDOW_NORMAL);

    // Process frames.
    while (waitKey(1) < 0)
    {
        // get frame from the video
        cap >> frame;
        
        // Stop the program if reached end of video
        if (frame.empty()) {
            cout << "Done processing !!!" << endl;
            cout << "Output file is stored as " << outputFile << endl;
            waitKey(3000);
            break;
        }
        // Create a 4D blob from a frame.
         blobFromImage(frame, blob, 1.0, Size(frame.cols, frame.rows), 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);
        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("Mask-RCNN on 2.5 GHz Intel Core i7 CPU, Inference time for a frame : %0.0f ms", t);
        putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 0, 0));
        
        // Write the frame with the detection boxes
        Mat detectedFrame;
        frame.convertTo(detectedFrame, CV_8U);
        if (parser.has("image")) imwrite(outputFile, detectedFrame);
        else video.write(detectedFrame);
        
        imshow(kWinName, frame);

    }
    
    cap.release();
    if (!parser.has("image")) video.release();

    return 0;
}

// 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);
            
        }
    }
}

// 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), 3);
    
    //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(0,0,0),1);

    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 = (0.3 * 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);

}