Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity

Merge pull request #24611 from Abdurrahheem:ash/add_yolov6_test

Browse Source 
Add test for YoloX Yolo v6 and Yolo v8 #24611

This PR adds test for YOLOv6 model (which was absent before)
The onnx weights for the test are located in this PR [ #1126](https://github.com/opencv/opencv_extra/pull/1126)

### 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
- [ ] 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
pull/24592/head
Abduragim Shtanchaev 12 months ago committed by GitHub
parent 06ff35c9af
commit d3dd2e463c
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 182 additions and 19 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 201
      modules/dnn/test/test_onnx_importer.cpp

201
modules/dnn/test/test_onnx_importer.cpp
Unescape View File

@ -12,6 +12,15 @@
#include <numeric>
namespace opencv_test { namespace {
void yoloPostProcessing(
std::vector<Mat>& outs,
std::vector<int>& keep_classIds,
std::vector<float>& keep_confidences,
std::vector<Rect2d>& keep_boxes,
float conf_threshold,
float iou_threshold,
const std::string& test_name);
template<typename TString>
static std::string _tf(TString filename, bool required = true)
{
@ -2606,16 +2615,15 @@ TEST_P(Test_ONNX_layers, CumSum)
}
static void testYOLO(const std::string& weightPath, const std::vector<int>& refClassIds,
const std::vector<float>& refScores, const std::vector<Rect2d>& refBoxes)
const std::vector<float>& refScores, const std::vector<Rect2d>& refBoxes,
Image2BlobParams imgParams, float conf_threshold = 0.3, float iou_threshold = 0.5,
double scores_diff = 1e-5, double boxes_iou_diff = 1e-4, const std::string test_name = "")
{
std::string imgPath = _tf("../dog_orig_size.png");
Size targetSize{640, 640};
float conf_threshold = 0.3;
float iou_threshold = 0.5;
Mat img = imread(imgPath);
Mat inp = blobFromImage(img, 1/255.0, targetSize, Scalar(0, 0, 0), true, false);
Mat inp = blobFromImageWithParams(img, imgParams);
Net net = readNet(weightPath);
@ -2623,27 +2631,55 @@ static void testYOLO(const std::string& weightPath, const std::vector<int>& refC
std::vector<Mat> outs;
net.forward(outs, net.getUnconnectedOutLayersNames());
// Retrieve
std::vector<int> keep_classIds;
std::vector<float> keep_confidences;
std::vector<Rect2d> keep_boxes;
yoloPostProcessing(outs, keep_classIds, keep_confidences, keep_boxes, conf_threshold, iou_threshold, test_name);
normAssertDetections(
refClassIds, refScores, refBoxes,
keep_classIds, keep_confidences, keep_boxes,
"", 0.0, scores_diff, boxes_iou_diff);
}
void yoloPostProcessing(
std::vector<Mat>& outs,
std::vector<int>& keep_classIds,
std::vector<float>& keep_confidences,
std::vector<Rect2d>& keep_boxes,
float conf_threshold,
float iou_threshold,
const std::string& test_name
){
// Retrieve
std::vector<int> classIds;
std::vector<float> confidences;
std::vector<Rect2d> boxes;
if (test_name == "yolov8"){
cv::transposeND(outs[0], {0, 2, 1}, outs[0]);
}
// each row is [cx, cy, w, h, conf_obj, conf_class1, ..., conf_class80]
for (auto preds : outs)
{
preds = preds.reshape(1, preds.size[1]);
for (auto preds : outs){
preds = preds.reshape(1, preds.size[1]); // [1, 8400, 85] -> [8400, 85]
for (int i = 0; i < preds.rows; ++i)
{
// filter out non objects
float obj_conf = preds.row(i).at<float>(4);
float obj_conf = (test_name != "yolov8") ? preds.at<float>(i, 4) : 1.0f;
if (obj_conf < conf_threshold)
continue;
// get class id and conf
Mat scores = preds.row(i).colRange(5, preds.cols);
Mat scores = preds.row(i).colRange((test_name != "yolov8") ? 5 : 4, preds.cols);
double conf;
Point maxLoc;
minMaxLoc(scores, 0, &conf, 0, &maxLoc);
conf *= obj_conf;
conf = (test_name != "yolov8") ? conf * obj_conf : conf;
if (conf < conf_threshold)
continue;
@ -2653,6 +2689,7 @@ static void testYOLO(const std::string& weightPath, const std::vector<int>& refC
double cy = det[1];
double w = det[2];
double h = det[3];
// [x1, y1, x2, y2]
boxes.push_back(Rect2d(cx - 0.5 * w, cy - 0.5 * h,
cx + 0.5 * w, cy + 0.5 * h));
@ -2665,17 +2702,81 @@ static void testYOLO(const std::string& weightPath, const std::vector<int>& refC
std::vector<int> keep_idx;
NMSBoxes(boxes, confidences, conf_threshold, iou_threshold, keep_idx);
std::vector<int> keep_classIds;
std::vector<float> keep_confidences;
std::vector<Rect2d> keep_boxes;
for (auto i : keep_idx)
{
keep_classIds.push_back(classIds[i]);
keep_confidences.push_back(confidences[i]);
keep_boxes.push_back(boxes[i]);
}
}
TEST_P(Test_ONNX_nets, YOLOX)
{
std::string weightPath = _tf("models/yolox_s_inf_decoder.onnx", false);
Size targetSize{640, 640};
float conf_threshold = 0.50;
float iou_threshold = 0.50;
normAssertDetections(refClassIds, refScores, refBoxes, keep_classIds, keep_confidences, keep_boxes);
std::vector<int> refClassIds{1, 16, 7};
std::vector<float> refScores{0.9649f, 0.9163f, 0.6879f};
std::vector<Rect2d> refBoxes{
Rect2d(105.5384, 179.4100, 470.6339, 428.5553),
Rect2d(111.4482, 263.4098, 258.7438, 526.1140),
Rect2d(389.1421, 143.9286, 577.9495, 222.0294)
};
Image2BlobParams imgParams(
Scalar::all(1),
targetSize,
Scalar::all(0),
true,
CV_32F,
DNN_LAYOUT_NCHW,
DNN_PMODE_LETTERBOX,
Scalar::all(114)
);
testYOLO(
weightPath, refClassIds, refScores, refBoxes,
imgParams, conf_threshold, iou_threshold,
1.0e-4, 1.0e-4);
}
TEST_P(Test_ONNX_nets, YOLOv8)
{
std::string weightPath = _tf("models/yolov8n.onnx", false);
Size targetSize{640, 640};
float conf_threshold = 0.25;
float iou_threshold = 0.50;
std::vector<int> refClassIds{16, 1, 2};
std::vector<float> refScores{0.9332f, 0.8959f, 0.6157f};
// [x1, y1, x2, y2]
std::vector<Rect2d> refBoxes{
Rect2d(108.8965, 261.9094, 257.1633, 530.3049),
Rect2d(110.4020, 192.9843, 473.4418, 429.5965),
Rect2d(389.1603, 143.2506, 577.3542, 223.0615),
};
Image2BlobParams imgParams(
Scalar::all(1/255.0),
targetSize,
Scalar::all(0),
true,
CV_32F,
DNN_LAYOUT_NCHW,
DNN_PMODE_LETTERBOX,
Scalar::all(114)
);
testYOLO(
weightPath, refClassIds, refScores, refBoxes,
imgParams, conf_threshold, iou_threshold,
1.0e-4, 1.0e-4, "yolov8");
}
// This test is mainly to test:
@ -2693,7 +2794,55 @@ TEST_P(Test_ONNX_nets, YOLOv7)
std::vector<Rect2d> refBoxes{Rect2d(105.973236f, 150.16716f, 472.59012f, 466.48834f),
Rect2d(109.97953f, 246.17862f, 259.83676f, 600.76624f),
Rect2d(385.96185f, 83.02809f, 576.07355f, 189.82793f)};
testYOLO(weightPath, refClassIds, refScores, refBoxes);
Size targetSize{640, 640};
Image2BlobParams imgParams(
Scalar::all(1/255.0),
targetSize,
Scalar::all(0),
true,
CV_32F,
DNN_LAYOUT_NCHW,
DNN_PMODE_NULL,
Scalar::all(0)
);
testYOLO(weightPath, refClassIds, refScores, refBoxes, imgParams);
}
TEST_P(Test_ONNX_nets, YOLOv6)
{
std::string weightPath = _tf("models/yolov6n.onnx", false);
Size targetSize{640, 640};
float conf_threshold = 0.30;
float iou_threshold = 0.50;
std::vector<int> refClassIds{1, 16, 7, 1};
std::vector<float> refScores{0.95031f, 0.87123f, 0.65453f, 0.34142f};
// [x1, y1, x2, y2] x 3
std::vector<Rect2d> refBoxes{Rect2d(98.84, 177.91, 473.29, 431.19),
Rect2d(109.80, 265.50, 258.86, 531.97),
Rect2d(387.79, 141.61, 576.98, 223.52),
Rect2d(105.62, 199.24, 218.37, 389.84),
};
Image2BlobParams imgParams(
Scalar::all(1/255.0),
targetSize,
Scalar::all(0),
true,
CV_32F,
DNN_LAYOUT_NCHW,
DNN_PMODE_LETTERBOX,
Scalar::all(114)
);
testYOLO(
weightPath, refClassIds, refScores, refBoxes,
imgParams, conf_threshold, iou_threshold,
1.0e-4, 1.0e-3);
}
TEST_P(Test_ONNX_nets, YOLOv5n)
@ -2707,7 +2856,21 @@ TEST_P(Test_ONNX_nets, YOLOv5n)
std::vector<Rect2d> refBoxes{Rect2d(108.088f, 239.293f, 266.196f, 607.658f),
Rect2d(392.028f, 89.9233f, 579.152f, 190.447f),
Rect2d(120.278f, 159.76, 214.481f, 241.473f)};
testYOLO(weightPath, refClassIds, refScores, refBoxes);
Size targetSize{640, 640};
Image2BlobParams imgParams(
Scalar::all(1/255.0),
targetSize,
Scalar::all(0),
true,
CV_32F,
DNN_LAYOUT_NCHW,
DNN_PMODE_NULL,
Scalar::all(0)
);
testYOLO(weightPath, refClassIds, refScores, refBoxes, imgParams);
}
TEST_P(Test_ONNX_layers, Tile)

Write Preview
Loading…
Cancel
Save