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Merge pull request #26486 from gursimarsingh:object_detection_engine_update

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Code Fixes and changed post processing based on models.yml in Object Detection Sample #26486

## Major Changes

1. Changes to add findModel support for config file in models like yolov4, yolov4-tiny, yolov3, ssd_caffe, tiny-yolo-voc, ssd_tf and faster_rcnn_tf.
2. Added new model and config download links for ssd_caffe, as previous links were not working.
3. Switched to DNN ENGINE_CLASSIC for non-cpu convig as new engine does not support it.
4. Fixes in python sample related to yolov5 usage.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
This commit is contained in:
Gursimar Singh
2024-12-02 11:35:25 +05:30
committed by GitHub
parent b476ed6d06
commit efbe580ff3
5 changed files with 110 additions and 74 deletions
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samples/dnn/object_detection.cpp
+30 -35
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@@ -76,7 +76,7 @@ string modelName, framework;
static void preprocess(const Mat& frame, Net& net, Size inpSize);
static void postprocess(Mat& frame, const vector<Mat>& outs, Net& net, int backend, vector<int>& classIds, vector<float>& confidences, vector<Rect>& boxes, const string yolo_name);
static void postprocess(Mat& frame, const vector<Mat>& outs, Net& net, int backend, vector<int>& classIds, vector<float>& confidences, vector<Rect>& boxes, const string postprocessing);
static void drawPred(vector<int>& classIds, vector<float>& confidences, vector<Rect>& boxes, Mat& frame, FontFace& sans, int stdSize, int stdWeight, int stdImgSize, int stdThickness);
@@ -91,7 +91,7 @@ static void yoloPostProcessing(
vector<Rect2d>& keep_boxes,
float conf_threshold,
float iou_threshold,
const string& yolo_name);
const string& postprocessing);
static void printAliases(string& zooFile){
vector<string> aliases = findAliases(zooFile, "object_detection");
@@ -195,12 +195,13 @@ int main(int argc, char** argv)
inpHeight = parser.get<int>("height");
int async = parser.get<int>("async");
paddingValue = parser.get<float>("padvalue");
const string yolo_name = parser.get<String>("postprocessing");
const string postprocessing = parser.get<String>("postprocessing");
paddingMode = static_cast<ImagePaddingMode>(parser.get<int>("paddingmode"));
//![preprocess_params]
String sha1 = parser.get<String>("sha1");
String config_sha1 = parser.get<String>("config_sha1");
const string modelPath = findModel(parser.get<String>("model"), sha1);
const string configPath = findFile(parser.get<String>("config"));
const string configPath = findModel(parser.get<String>("config"), config_sha1);
framework = modelPath.substr(modelPath.rfind('.') + 1);
if (parser.has("labels"))
@@ -216,7 +217,11 @@ int main(int argc, char** argv)
}
}
//![read_net]
Net net = readNet(modelPath, configPath);
EngineType engine = ENGINE_AUTO;
if ((parser.get<String>("backend") != "default") || (parser.get<String>("target") != "cpu")){
engine = ENGINE_CLASSIC;
}
Net net = readNet(modelPath, configPath, "", engine);
int backend = getBackendID(parser.get<String>("backend"));
net.setPreferableBackend(backend);
net.setPreferableTarget(getTargetID(parser.get<String>("target")));
@@ -230,7 +235,7 @@ int main(int argc, char** argv)
// Open a video file or an image file or a camera stream.
VideoCapture cap;
bool openSuccess = parser.has("input") ? cap.open(parser.get<String>("input")) : cap.open(parser.get<int>("device"));
bool openSuccess = parser.has("input") ? cap.open(findFile(parser.get<String>("input"))) : cap.open(parser.get<int>("device"));
if (!openSuccess){
cout << "Could not open input file or camera device" << endl;
return 0;
@@ -324,7 +329,7 @@ int main(int argc, char** argv)
classIds.clear();
confidences.clear();
boxes.clear();
postprocess(frame, outs, net, backend, classIds, confidences, boxes, yolo_name);
postprocess(frame, outs, net, backend, classIds, confidences, boxes, postprocessing);
drawPred(classIds, confidences, boxes, frame, sans, stdSize, stdWeight, stdImgSize, stdThickness);
@@ -354,7 +359,7 @@ int main(int argc, char** argv)
CV_Error(Error::StsNotImplemented, "Asynchronous forward is supported only with Inference Engine backend.");
// Threading is disabled, run synchronously
Mat frame, blob;
while (waitKey(100) < 0) {
while (waitKey(1) < 0) {
cap >> frame;
if (frame.empty()) {
waitKey();
@@ -369,7 +374,7 @@ int main(int argc, char** argv)
confidences.clear();
boxes.clear();
postprocess(frame, outs, net, backend, classIds, confidences, boxes, yolo_name);
postprocess(frame, outs, net, backend, classIds, confidences, boxes, postprocessing);
drawPred(classIds, confidences, boxes, frame, sans, stdSize, stdWeight, stdImgSize, stdThickness);
@@ -379,7 +384,7 @@ int main(int argc, char** argv)
int weight = static_cast<int>((stdWeight * imgWidth) / (stdImgSize * 1.5));
double freq = getTickFrequency() / 1000;
double t = net.getPerfProfile(layersTimes) / freq;
string label = format("Inference time: %.2f ms", t);
string label = format("FPS: %.2f", 1000/t);
putText(frame, label, Point(0, size), Scalar(0, 255, 0), sans, size, weight);
imshow(kWinName, frame);
}
@@ -414,15 +419,6 @@ void preprocess(const Mat& frame, Net& net, Size inpSize)
// Set the blob as the network input
net.setInput(inp);
// Check if the model is Faster-RCNN or R-FCN
if (net.getLayer(0)->outputNameToIndex("im_info") != -1)
{
// Resize the frame and prepare imInfo
resize(frame, frame, size);
Mat imInfo = (Mat_<float>(1, 3) << size.height, size.width, 1.6f);
net.setInput(imInfo, "im_info");
}
}
void yoloPostProcessing(
@@ -432,7 +428,7 @@ void yoloPostProcessing(
vector<Rect2d>& keep_boxes,
float conf_threshold,
float iou_threshold,
const string& yolo_name)
const string& postprocessing)
{
// Retrieve
vector<int> classIds;
@@ -441,12 +437,12 @@ void yoloPostProcessing(
vector<Mat> outs_copy = outs;
if (yolo_name == "yolov8")
if (postprocessing == "yolov8")
{
transposeND(outs_copy[0], {0, 2, 1}, outs_copy[0]);
}
if (yolo_name == "yolonas")
if (postprocessing == "yolonas")
{
// outs contains 2 elements of shape [1, 8400, 80] and [1, 8400, 4]. Concat them to get [1, 8400, 84]
Mat concat_out;
@@ -467,16 +463,16 @@ void yoloPostProcessing(
for (int i = 0; i < preds.rows; ++i)
{
// filter out non-object
float obj_conf = (yolo_name == "yolov8" || yolo_name == "yolonas") ? 1.0f : preds.at<float>(i, 4);
float obj_conf = (postprocessing == "yolov8" || postprocessing == "yolonas") ? 1.0f : preds.at<float>(i, 4);
if (obj_conf < conf_threshold)
continue;
Mat scores = preds.row(i).colRange((yolo_name == "yolov8" || yolo_name == "yolonas") ? 4 : 5, preds.cols);
Mat scores = preds.row(i).colRange((postprocessing == "yolov8" || postprocessing == "yolonas") ? 4 : 5, preds.cols);
double conf;
Point maxLoc;
minMaxLoc(scores, 0, &conf, 0, &maxLoc);
conf = (yolo_name == "yolov8" || yolo_name == "yolonas") ? conf : conf * obj_conf;
conf = (postprocessing == "yolov8" || postprocessing == "yolonas") ? conf : conf * obj_conf;
if (conf < conf_threshold)
continue;
@@ -488,7 +484,7 @@ void yoloPostProcessing(
double h = det[3];
// [x1, y1, x2, y2]
if (yolo_name == "yolonas") {
if (postprocessing == "yolonas") {
boxes.push_back(Rect2d(cx, cy, w, h));
} else {
boxes.push_back(Rect2d(cx - 0.5 * w, cy - 0.5 * h,
@@ -511,12 +507,10 @@ void yoloPostProcessing(
}
}
void postprocess(Mat& frame, const vector<Mat>& outs, Net& net, int backend, vector<int>& classIds, vector<float>& confidences, vector<Rect>& boxes, const string yolo_name)
void postprocess(Mat& frame, const vector<Mat>& outs, Net& net, int backend, vector<int>& classIds, vector<float>& confidences, vector<Rect>& boxes, const string postprocessing)
{
static vector<int> outLayers = net.getUnconnectedOutLayers();
static string outLayerType = net.getLayer(outLayers[0])->type;
if (outLayerType == "DetectionOutput")
if (postprocessing == "ssd")
{
// Network produces output blob with a shape 1x1xNx7 where N is a number of
// detections and an every detection is a vector of values
@@ -552,7 +546,7 @@ void postprocess(Mat& frame, const vector<Mat>& outs, Net& net, int backend, vec
}
}
}
else if (outLayerType == "Region")
else if (postprocessing == "darknet")
{
for (size_t i = 0; i < outs.size(); ++i)
{
@@ -582,7 +576,7 @@ void postprocess(Mat& frame, const vector<Mat>& outs, Net& net, int backend, vec
}
}
}
else if (outLayerType == "Identity")
else if (postprocessing == "yolov8" || postprocessing == "yolov5")
{
//![forward_buffers]
vector<int> keep_classIds;
@@ -591,7 +585,7 @@ void postprocess(Mat& frame, const vector<Mat>& outs, Net& net, int backend, vec
//![forward_buffers]
//![postprocess]
yoloPostProcessing(outs, keep_classIds, keep_confidences, keep_boxes, confThreshold, nmsThreshold, yolo_name);
yoloPostProcessing(outs, keep_classIds, keep_confidences, keep_boxes, confThreshold, nmsThreshold, postprocessing);
//![postprocess]
for (size_t i = 0; i < keep_classIds.size(); ++i)
@@ -614,12 +608,13 @@ void postprocess(Mat& frame, const vector<Mat>& outs, Net& net, int backend, vec
}
else
{
CV_Error(Error::StsNotImplemented, "Unknown output layer type: " + outLayerType);
cout<< ("Unknown postprocessing method: " + postprocessing)<<endl;
exit(-1);
}
// NMS is used inside Region layer only on DNN_BACKEND_OPENCV for other backends we need NMS in sample
// or NMS is required if the number of outputs > 1
if (outLayers.size() > 1 || (outLayerType == "Region" && backend != DNN_BACKEND_OPENCV))
if (outLayers.size() > 1 || (postprocessing == "darknet" && backend != DNN_BACKEND_OPENCV))
{
map<int, vector<size_t> > class2indices;
for (size_t i = 0; i < classIds.size(); i++)