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Merge pull request #9941 from catree:improve_dnn_samples

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pull/9961/head
Vadim Pisarevsky 7 years ago
parent 712689e81a 48e07437f0
commit 639b800787
3 changed files with 285 additions and 119 deletions
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  1. 76
      samples/dnn/ssd_mobilenet_object_detection.cpp
  2. 142
      samples/dnn/ssd_object_detection.cpp
  3. 186
      samples/dnn/yolo_object_detection.cpp

76
samples/dnn/ssd_mobilenet_object_detection.cpp
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@ -23,23 +23,25 @@ const char* classNames[] = {"background",
"motorbike", "person", "pottedplant",
"sheep", "sofa", "train", "tvmonitor"};
const char* about = "This sample uses Single-Shot Detector "
"(https://arxiv.org/abs/1512.02325)"
"to detect objects on image.\n"
".caffemodel model's file is avaliable here: "
"https://github.com/chuanqi305/MobileNet-SSD\n";
const char* about = "This sample uses MobileNet Single-Shot Detector "
"(https://arxiv.org/abs/1704.04861) "
"to detect objects on camera/video/image.\n"
".caffemodel model's file is available here: "
"https://github.com/chuanqi305/MobileNet-SSD\n"
"Default network is 300x300 and 20-classes VOC.\n";
const char* params
= "{ help | false | print usage }"
"{ proto | MobileNetSSD_deploy.prototxt | model configuration }"
"{ model | MobileNetSSD_deploy.caffemodel | model weights }"
"{ video | | video for detection }"
"{ camera_device | 0 | camera device number }"
"{ video | | video or image for detection}"
"{ out | | path to output video file}"
"{ min_confidence | 0.2 | min confidence }";
int main(int argc, char** argv)
{
cv::CommandLineParser parser(argc, argv, params);
CommandLineParser parser(argc, argv, params);
if (parser.get<bool>("help"))
{
@ -55,19 +57,40 @@ int main(int argc, char** argv)
dnn::Net net = readNetFromCaffe(modelConfiguration, modelBinary);
//! [Initialize network]
VideoCapture cap(parser.get<String>("video"));
if(!cap.isOpened()) // check if we succeeded
if (net.empty())
{
cap = VideoCapture(0);
cerr << "Can't load network by using the following files: " << endl;
cerr << "prototxt: " << modelConfiguration << endl;
cerr << "caffemodel: " << modelBinary << endl;
cerr << "Models can be downloaded here:" << endl;
cerr << "https://github.com/chuanqi305/MobileNet-SSD" << endl;
exit(-1);
}
VideoCapture cap;
if (parser.get<String>("video").empty())
{
int cameraDevice = parser.get<int>("camera_device");
cap = VideoCapture(cameraDevice);
if(!cap.isOpened())
{
cout << "Couldn't find camera: " << cameraDevice << endl;
return -1;
}
}
else
{
cap.open(parser.get<String>("video"));
if(!cap.isOpened())
{
cout << "Couldn't find camera" << endl;
cout << "Couldn't open image or video: " << parser.get<String>("video") << endl;
return -1;
}
}
Size inVideoSize = Size((int) cap.get(CV_CAP_PROP_FRAME_WIDTH), //Acquire input size
(int) cap.get(CV_CAP_PROP_FRAME_HEIGHT));
Size inVideoSize;
inVideoSize = Size((int) cap.get(CV_CAP_PROP_FRAME_WIDTH), //Acquire input size
(int) cap.get(CV_CAP_PROP_FRAME_HEIGHT));
Size cropSize;
if (inVideoSize.width / (float)inVideoSize.height > WHRatio)
@ -93,9 +116,18 @@ int main(int argc, char** argv)
for(;;)
{
Mat frame;
cap >> frame; // get a new frame from camera
//! [Prepare blob]
cap >> frame; // get a new frame from camera/video or read image
if (frame.empty())
{
waitKey();
break;
}
if (frame.channels() == 4)
cvtColor(frame, frame, COLOR_BGRA2BGR);
//! [Prepare blob]
Mat inputBlob = blobFromImage(frame, inScaleFactor,
Size(inWidth, inHeight), meanVal, false); //Convert Mat to batch of images
//! [Prepare blob]
@ -108,15 +140,23 @@ int main(int argc, char** argv)
Mat detection = net.forward("detection_out"); //compute output
//! [Make forward pass]
std::vector<double> layersTimings;
vector<double> layersTimings;
double freq = getTickFrequency() / 1000;
double time = net.getPerfProfile(layersTimings) / freq;
cout << "Inference time, ms: " << time << endl;
Mat detectionMat(detection.size[2], detection.size[3], CV_32F, detection.ptr<float>());
frame = frame(crop);
ostringstream ss;
if (!outputVideo.isOpened())
{
ss << "FPS: " << 1000/time << " ; time: " << time << " ms";
putText(frame, ss.str(), Point(20,20), 0, 0.5, Scalar(0,0,255));
}
else
cout << "Inference time, ms: " << time << endl;
float confidenceThreshold = parser.get<float>("min_confidence");
for(int i = 0; i < detectionMat.rows; i++)
{
@ -131,7 +171,7 @@ int main(int argc, char** argv)
int xRightTop = static_cast<int>(detectionMat.at<float>(i, 5) * frame.cols);
int yRightTop = static_cast<int>(detectionMat.at<float>(i, 6) * frame.rows);
ostringstream ss;
ss.str("");
ss << confidence;
String conf(ss.str());

142
samples/dnn/ssd_object_detection.cpp
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@ -40,15 +40,26 @@ static Mat preprocess(const Mat& frame)
return preprocessed;
}
const char* classNames[] = {"background",
"aeroplane", "bicycle", "bird", "boat",
"bottle", "bus", "car", "cat", "chair",
"cow", "diningtable", "dog", "horse",
"motorbike", "person", "pottedplant",
"sheep", "sofa", "train", "tvmonitor"};
const char* about = "This sample uses Single-Shot Detector "
"(https://arxiv.org/abs/1512.02325)"
"to detect objects on image\n"; // TODO: link
"(https://arxiv.org/abs/1512.02325) "
"to detect objects on camera/video/image.\n"
".caffemodel model's file is available here: "
"https://github.com/weiliu89/caffe/tree/ssd#models\n"
"Default network is 300x300 and 20-classes VOC.\n";
const char* params
= "{ help | false | print usage }"
"{ proto | | model configuration }"
"{ model | | model weights }"
"{ image | | image for detection }"
"{ camera_device | 0 | camera device number}"
"{ video | | video or image for detection}"
"{ min_confidence | 0.5 | min confidence }";
int main(int argc, char** argv)
@ -57,7 +68,7 @@ int main(int argc, char** argv)
if (parser.get<bool>("help"))
{
std::cout << about << std::endl;
cout << about << endl;
parser.printMessage();
return 0;
}
@ -79,58 +90,101 @@ int main(int argc, char** argv)
exit(-1);
}
cv::Mat frame = cv::imread(parser.get<string>("image"), -1);
if (frame.channels() == 4)
cvtColor(frame, frame, COLOR_BGRA2BGR);
//! [Prepare blob]
Mat preprocessedFrame = preprocess(frame);
Mat inputBlob = blobFromImage(preprocessedFrame, 1.0f, Size(), Scalar(), false); //Convert Mat to batch of images
//! [Prepare blob]
VideoCapture cap;
if (parser.get<String>("video").empty())
{
int cameraDevice = parser.get<int>("camera_device");
cap = VideoCapture(cameraDevice);
if(!cap.isOpened())
{
cout << "Couldn't find camera: " << cameraDevice << endl;
return -1;
}
}
else
{
cap.open(parser.get<String>("video"));
if(!cap.isOpened())
{
cout << "Couldn't open image or video: " << parser.get<String>("video") << endl;
return -1;
}
}
//! [Set input blob]
net.setInput(inputBlob, "data"); //set the network input
//! [Set input blob]
for (;;)
{
cv::Mat frame;
cap >> frame; // get a new frame from camera/video or read image
//! [Make forward pass]
Mat detection = net.forward("detection_out"); //compute output
//! [Make forward pass]
if (frame.empty())
{
waitKey();
break;
}
Mat detectionMat(detection.size[2], detection.size[3], CV_32F, detection.ptr<float>());
if (frame.channels() == 4)
cvtColor(frame, frame, COLOR_BGRA2BGR);
float confidenceThreshold = parser.get<float>("min_confidence");
for(int i = 0; i < detectionMat.rows; i++)
{
float confidence = detectionMat.at<float>(i, 2);
//! [Prepare blob]
Mat preprocessedFrame = preprocess(frame);
if(confidence > confidenceThreshold)
{
size_t objectClass = (size_t)(detectionMat.at<float>(i, 1));
Mat inputBlob = blobFromImage(preprocessedFrame, 1.0f, Size(), Scalar(), false); //Convert Mat to batch of images
//! [Prepare blob]
float xLeftBottom = detectionMat.at<float>(i, 3) * frame.cols;
float yLeftBottom = detectionMat.at<float>(i, 4) * frame.rows;
float xRightTop = detectionMat.at<float>(i, 5) * frame.cols;
float yRightTop = detectionMat.at<float>(i, 6) * frame.rows;
//! [Set input blob]
net.setInput(inputBlob, "data"); //set the network input
//! [Set input blob]
std::cout << "Class: " << objectClass << std::endl;
std::cout << "Confidence: " << confidence << std::endl;
//! [Make forward pass]
Mat detection = net.forward("detection_out"); //compute output
//! [Make forward pass]
std::cout << " " << xLeftBottom
<< " " << yLeftBottom
<< " " << xRightTop
<< " " << yRightTop << std::endl;
vector<double> layersTimings;
double freq = getTickFrequency() / 1000;
double time = net.getPerfProfile(layersTimings) / freq;
ostringstream ss;
ss << "FPS: " << 1000/time << " ; time: " << time << " ms";
putText(frame, ss.str(), Point(20,20), 0, 0.5, Scalar(0,0,255));
Rect object((int)xLeftBottom, (int)yLeftBottom,
(int)(xRightTop - xLeftBottom),
(int)(yRightTop - yLeftBottom));
Mat detectionMat(detection.size[2], detection.size[3], CV_32F, detection.ptr<float>());
rectangle(frame, object, Scalar(0, 255, 0));
float confidenceThreshold = parser.get<float>("min_confidence");
for(int i = 0; i < detectionMat.rows; i++)
{
float confidence = detectionMat.at<float>(i, 2);
if(confidence > confidenceThreshold)
{
size_t objectClass = (size_t)(detectionMat.at<float>(i, 1));
int xLeftBottom = static_cast<int>(detectionMat.at<float>(i, 3) * frame.cols);
int yLeftBottom = static_cast<int>(detectionMat.at<float>(i, 4) * frame.rows);
int xRightTop = static_cast<int>(detectionMat.at<float>(i, 5) * frame.cols);
int yRightTop = static_cast<int>(detectionMat.at<float>(i, 6) * frame.rows);
ss.str("");
ss << confidence;
String conf(ss.str());
Rect object(xLeftBottom, yLeftBottom,
xRightTop - xLeftBottom,
yRightTop - yLeftBottom);
rectangle(frame, object, Scalar(0, 255, 0));
String label = String(classNames[objectClass]) + ": " + conf;
int baseLine = 0;
Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
rectangle(frame, Rect(Point(xLeftBottom, yLeftBottom - labelSize.height),
Size(labelSize.width, labelSize.height + baseLine)),
Scalar(255, 255, 255), CV_FILLED);
putText(frame, label, Point(xLeftBottom, yLeftBottom),
FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0,0,0));
}
}
}
imshow("detections", frame);
waitKey();
imshow("detections", frame);
if (waitKey(1) >= 0) break;
}
return 0;
} // main

186
samples/dnn/yolo_object_detection.cpp
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@ -15,29 +15,36 @@ const size_t network_width = 416;
const size_t network_height = 416;
const char* about = "This sample uses You only look once (YOLO)-Detector "
"(https://arxiv.org/abs/1612.08242)"
"to detect objects on image\n"; // TODO: link
"(https://arxiv.org/abs/1612.08242) "
"to detect objects on camera/video/image.\n"
"Models can be downloaded here: "
"https://pjreddie.com/darknet/yolo/\n"
"Default network is 416x416.\n"
"Class names can be downloaded here: "
"https://github.com/pjreddie/darknet/tree/master/data\n";
const char* params
= "{ help | false | print usage }"
"{ cfg | | model configuration }"
"{ model | | model weights }"
"{ image | | image for detection }"
"{ min_confidence | 0.24 | min confidence }";
"{ camera_device | 0 | camera device number}"
"{ video | | video or image for detection}"
"{ min_confidence | 0.24 | min confidence }"
"{ class_names | | class names }";
int main(int argc, char** argv)
{
cv::CommandLineParser parser(argc, argv, params);
CommandLineParser parser(argc, argv, params);
if (parser.get<bool>("help"))
{
std::cout << about << std::endl;
cout << about << endl;
parser.printMessage();
return 0;
}
String modelConfiguration = parser.get<string>("cfg");
String modelBinary = parser.get<string>("model");
String modelConfiguration = parser.get<String>("cfg");
String modelBinary = parser.get<String>("model");
//! [Initialize network]
dnn::Net net = readNetFromDarknet(modelConfiguration, modelBinary);
@ -53,65 +60,130 @@ int main(int argc, char** argv)
exit(-1);
}
cv::Mat frame = cv::imread(parser.get<string>("image"));
VideoCapture cap;
if (parser.get<String>("video").empty())
{
int cameraDevice = parser.get<int>("camera_device");
cap = VideoCapture(cameraDevice);
if(!cap.isOpened())
{
cout << "Couldn't find camera: " << cameraDevice << endl;
return -1;
}
}
else
{
cap.open(parser.get<String>("video"));
if(!cap.isOpened())
{
cout << "Couldn't open image or video: " << parser.get<String>("video") << endl;
return -1;
}
}
vector<string> classNamesVec;
ifstream classNamesFile(parser.get<String>("class_names").c_str());
if (classNamesFile.is_open())
{
string className = "";
while (classNamesFile >> className)
classNamesVec.push_back(className);
}
//! [Resizing without keeping aspect ratio]
cv::Mat resized;
cv::resize(frame, resized, cv::Size(network_width, network_height));
//! [Resizing without keeping aspect ratio]
for(;;)
{
Mat frame;
cap >> frame; // get a new frame from camera/video or read image
//! [Prepare blob]
Mat inputBlob = blobFromImage(resized, 1 / 255.F); //Convert Mat to batch of images
//! [Prepare blob]
if (frame.empty())
{
waitKey();
break;
}
//! [Set input blob]
net.setInput(inputBlob, "data"); //set the network input
//! [Set input blob]
if (frame.channels() == 4)
cvtColor(frame, frame, COLOR_BGRA2BGR);
//! [Make forward pass]
cv::Mat detectionMat = net.forward("detection_out"); //compute output
//! [Make forward pass]
//! [Resizing without keeping aspect ratio]
Mat resized;
resize(frame, resized, Size(network_width, network_height));
//! [Resizing without keeping aspect ratio]
//! [Prepare blob]
Mat inputBlob = blobFromImage(resized, 1 / 255.F); //Convert Mat to batch of images
//! [Prepare blob]
float confidenceThreshold = parser.get<float>("min_confidence");
for (int i = 0; i < detectionMat.rows; i++)
{
const int probability_index = 5;
const int probability_size = detectionMat.cols - probability_index;
float *prob_array_ptr = &detectionMat.at<float>(i, probability_index);
//! [Set input blob]
net.setInput(inputBlob, "data"); //set the network input
//! [Set input blob]
//! [Make forward pass]
Mat detectionMat = net.forward("detection_out"); //compute output
//! [Make forward pass]
size_t objectClass = std::max_element(prob_array_ptr, prob_array_ptr + probability_size) - prob_array_ptr;
float confidence = detectionMat.at<float>(i, (int)objectClass + probability_index);
vector<double> layersTimings;
double freq = getTickFrequency() / 1000;
double time = net.getPerfProfile(layersTimings) / freq;
ostringstream ss;
ss << "FPS: " << 1000/time << " ; time: " << time << " ms";
putText(frame, ss.str(), Point(20,20), 0, 0.5, Scalar(0,0,255));
if (confidence > confidenceThreshold)
float confidenceThreshold = parser.get<float>("min_confidence");
for (int i = 0; i < detectionMat.rows; i++)
{
float x = detectionMat.at<float>(i, 0);
float y = detectionMat.at<float>(i, 1);
float width = detectionMat.at<float>(i, 2);
float height = detectionMat.at<float>(i, 3);
float xLeftBottom = (x - width / 2) * frame.cols;
float yLeftBottom = (y - height / 2) * frame.rows;
float xRightTop = (x + width / 2) * frame.cols;
float yRightTop = (y + height / 2) * frame.rows;
std::cout << "Class: " << objectClass << std::endl;
std::cout << "Confidence: " << confidence << std::endl;
std::cout << " " << xLeftBottom
<< " " << yLeftBottom
<< " " << xRightTop
<< " " << yRightTop << std::endl;
Rect object((int)xLeftBottom, (int)yLeftBottom,
(int)(xRightTop - xLeftBottom),
(int)(yRightTop - yLeftBottom));
rectangle(frame, object, Scalar(0, 255, 0));
const int probability_index = 5;
const int probability_size = detectionMat.cols - probability_index;
float *prob_array_ptr = &detectionMat.at<float>(i, probability_index);
size_t objectClass = max_element(prob_array_ptr, prob_array_ptr + probability_size) - prob_array_ptr;
float confidence = detectionMat.at<float>(i, (int)objectClass + probability_index);
if (confidence > confidenceThreshold)
{
float x = detectionMat.at<float>(i, 0);
float y = detectionMat.at<float>(i, 1);
float width = detectionMat.at<float>(i, 2);
float height = detectionMat.at<float>(i, 3);
int xLeftBottom = static_cast<int>((x - width / 2) * frame.cols);
int yLeftBottom = static_cast<int>((y - height / 2) * frame.rows);
int xRightTop = static_cast<int>((x + width / 2) * frame.cols);
int yRightTop = static_cast<int>((y + height / 2) * frame.rows);
Rect object(xLeftBottom, yLeftBottom,
xRightTop - xLeftBottom,
yRightTop - yLeftBottom);
rectangle(frame, object, Scalar(0, 255, 0));
if (objectClass < classNamesVec.size())
{
ss.str("");
ss << confidence;
String conf(ss.str());
String label = String(classNamesVec[objectClass]) + ": " + conf;
int baseLine = 0;
Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
rectangle(frame, Rect(Point(xLeftBottom, yLeftBottom - labelSize.height),
Size(labelSize.width, labelSize.height + baseLine)),
Scalar(255, 255, 255), CV_FILLED);
putText(frame, label, Point(xLeftBottom, yLeftBottom),
FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0,0,0));
}
else
{
cout << "Class: " << objectClass << endl;
cout << "Confidence: " << confidence << endl;
cout << " " << xLeftBottom
<< " " << yLeftBottom
<< " " << xRightTop
<< " " << yRightTop << endl;
}
}
}
}
imshow("detections", frame);
waitKey();
imshow("detections", frame);
if (waitKey(1) >= 0) break;
}
return 0;
} // main
} // main

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