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@ -26,6 +26,7 @@ static const char* params = |
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"{ model | | model weights }" |
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"{ camera_device | 0 | camera device number}" |
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"{ source | | video or image for detection}" |
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"{ style | box | box or line style draw }" |
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"{ min_confidence | 0.24 | min confidence }" |
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"{ class_names | | File with class names, [PATH-TO-DARKNET]/data/coco.names }"; |
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@ -78,7 +79,7 @@ int main(int argc, char** argv) |
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} |
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} |
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vector<string> classNamesVec; |
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vector<String> classNamesVec; |
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ifstream classNamesFile(parser.get<String>("class_names").c_str()); |
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if (classNamesFile.is_open()) |
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{ |
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@ -87,6 +88,8 @@ int main(int argc, char** argv) |
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classNamesVec.push_back(className); |
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} |
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String object_roi_style = parser.get<String>("style"); |
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for(;;) |
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{ |
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Mat frame; |
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@ -114,11 +117,10 @@ int main(int argc, char** argv) |
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//! [Make forward pass]
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vector<double> layersTimings; |
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double freq = getTickFrequency() / 1000; |
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double time = net.getPerfProfile(layersTimings) / freq; |
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ostringstream ss; |
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ss << "FPS: " << 1000/time << " ; time: " << time << " ms"; |
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putText(frame, ss.str(), Point(20,20), 0, 0.5, Scalar(0,0,255)); |
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double tick_freq = getTickFrequency(); |
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double time_ms = net.getPerfProfile(layersTimings) / tick_freq * 1000; |
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putText(frame, format("FPS: %.2f ; time: %.2f ms", 1000.f / time_ms, time_ms), |
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Point(20, 20), 0, 0.5, Scalar(0, 0, 255)); |
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float confidenceThreshold = parser.get<float>("min_confidence"); |
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for (int i = 0; i < detectionMat.rows; i++) |
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@ -132,44 +134,34 @@ int main(int argc, char** argv) |
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if (confidence > confidenceThreshold) |
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{ |
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float x = detectionMat.at<float>(i, 0); |
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float y = detectionMat.at<float>(i, 1); |
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float width = detectionMat.at<float>(i, 2); |
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float height = detectionMat.at<float>(i, 3); |
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int xLeftBottom = static_cast<int>((x - width / 2) * frame.cols); |
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int yLeftBottom = static_cast<int>((y - height / 2) * frame.rows); |
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int xRightTop = static_cast<int>((x + width / 2) * frame.cols); |
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int yRightTop = static_cast<int>((y + height / 2) * frame.rows); |
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Rect object(xLeftBottom, yLeftBottom, |
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xRightTop - xLeftBottom, |
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yRightTop - yLeftBottom); |
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rectangle(frame, object, Scalar(0, 255, 0)); |
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if (objectClass < classNamesVec.size()) |
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float x_center = detectionMat.at<float>(i, 0) * frame.cols; |
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float y_center = detectionMat.at<float>(i, 1) * frame.rows; |
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float width = detectionMat.at<float>(i, 2) * frame.cols; |
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float height = detectionMat.at<float>(i, 3) * frame.rows; |
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Point p1(cvRound(x_center - width / 2), cvRound(y_center - height / 2)); |
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Point p2(cvRound(x_center + width / 2), cvRound(y_center + height / 2)); |
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Rect object(p1, p2); |
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Scalar object_roi_color(0, 255, 0); |
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if (object_roi_style == "box") |
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{ |
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ss.str(""); |
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ss << confidence; |
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String conf(ss.str()); |
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String label = String(classNamesVec[objectClass]) + ": " + conf; |
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int baseLine = 0; |
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Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine); |
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rectangle(frame, Rect(Point(xLeftBottom, yLeftBottom ), |
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Size(labelSize.width, labelSize.height + baseLine)), |
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Scalar(255, 255, 255), CV_FILLED); |
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putText(frame, label, Point(xLeftBottom, yLeftBottom+labelSize.height), |
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FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0,0,0)); |
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rectangle(frame, object, object_roi_color); |
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} |
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else |
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{ |
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cout << "Class: " << objectClass << endl; |
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cout << "Confidence: " << confidence << endl; |
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cout << " " << xLeftBottom |
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<< " " << yLeftBottom |
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<< " " << xRightTop |
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<< " " << yRightTop << endl; |
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Point p_center(cvRound(x_center), cvRound(y_center)); |
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line(frame, object.tl(), p_center, object_roi_color, 1); |
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} |
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String className = objectClass < classNamesVec.size() ? classNamesVec[objectClass] : cv::format("unknown(%d)", objectClass); |
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String label = format("%s: %.2f", className.c_str(), confidence); |
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int baseLine = 0; |
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Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine); |
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rectangle(frame, Rect(p1, Size(labelSize.width, labelSize.height + baseLine)), |
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object_roi_color, CV_FILLED); |
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putText(frame, label, p1 + Point(0, labelSize.height), |
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FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0,0,0)); |
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} |
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} |
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