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#include <opencv2/dnn.hpp>
#include <opencv2/dnn/all_layers.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
using namespace cv;
using namespace dnn;
const char* keys =
"{ help h | | print help message }"
"{ proto p | | path to .prototxt }"
"{ model m | | path to .caffemodel }"
"{ image i | | path to input image }"
"{ conf c | 0.8 | minimal confidence }";
const char* classNames[] = {
"__background__",
"aeroplane", "bicycle", "bird", "boat",
"bottle", "bus", "car", "cat", "chair",
"cow", "diningtable", "dog", "horse",
"motorbike", "person", "pottedplant",
"sheep", "sofa", "train", "tvmonitor"
};
static const int kInpWidth = 800;
static const int kInpHeight = 600;
int main(int argc, char** argv)
{
// Parse command line arguments.
CommandLineParser parser(argc, argv, keys);
parser.about("This sample is used to run Faster-RCNN and R-FCN object detection "
"models with OpenCV. You can get required models from "
"https://github.com/rbgirshick/py-faster-rcnn (Faster-RCNN) and from "
"https://github.com/YuwenXiong/py-R-FCN (R-FCN). Corresponding .prototxt "
"files may be found at https://github.com/opencv/opencv_extra/tree/master/testdata/dnn.");
if (argc == 1 || parser.has("help"))
{
parser.printMessage();
return 0;
}
String protoPath = parser.get<String>("proto");
String modelPath = parser.get<String>("model");
String imagePath = parser.get<String>("image");
float confThreshold = parser.get<float>("conf");
CV_Assert(!protoPath.empty(), !modelPath.empty(), !imagePath.empty());
// Load a model.
Net net = readNetFromCaffe(protoPath, modelPath);
Mat img = imread(imagePath);
resize(img, img, Size(kInpWidth, kInpHeight));
Mat blob = blobFromImage(img, 1.0, Size(), Scalar(102.9801, 115.9465, 122.7717), false, false);
Mat imInfo = (Mat_<float>(1, 3) << img.rows, img.cols, 1.6f);
net.setInput(blob, "data");
net.setInput(imInfo, "im_info");
// Draw detections.
Mat detections = net.forward();
const float* data = (float*)detections.data;
for (size_t i = 0; i < detections.total(); i += 7)
{
// An every detection is a vector [id, classId, confidence, left, top, right, bottom]
float confidence = data[i + 2];
if (confidence > confThreshold)
{
int classId = (int)data[i + 1];
int left = max(0, min((int)data[i + 3], img.cols - 1));
int top = max(0, min((int)data[i + 4], img.rows - 1));
int right = max(0, min((int)data[i + 5], img.cols - 1));
int bottom = max(0, min((int)data[i + 6], img.rows - 1));
// Draw a bounding box.
rectangle(img, Point(left, top), Point(right, bottom), Scalar(0, 255, 0));
// Put a label with a class name and confidence.
String label = cv::format("%s, %.3f", classNames[classId], confidence);
int baseLine;
Size labelSize = cv::getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
top = max(top, labelSize.height);
rectangle(img, Point(left, top - labelSize.height),
Point(left + labelSize.width, top + baseLine),
Scalar(255, 255, 255), FILLED);
putText(img, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 0, 0));
}
}
imshow("frame", img);
waitKey();
return 0;
}