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Merge pull request #24384 from Dhanwanth1803:feat-crop

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Fixes #22747. Support [crop] configuration for DarkNet #24384

Request for comments. This is my first PR. 

**Merge with extra**: https://github.com/opencv/opencv_extra/pull/1112

resolves https://github.com/opencv/opencv/issues/22747

- [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
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
This commit is contained in:
Dhanwanth1803
2023-12-22 17:25:01 +05:30
committed by GitHub
parent 4453c157fc
commit 027aee8ad4
2 changed files with 64 additions and 4 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/dnn/src/darknet/darknet_io.cpp
+59 -4
View File
@@ -309,6 +309,48 @@ namespace cv {
fused_layer_names.push_back(last_layer);
}
void setCrop(int crop_height, int crop_width, int inp_height, int inp_width, bool noadjust)
{
cv::dnn::LayerParams crop_param;
crop_param.name = "CropLayer-name";
std::vector<int> begin = {0, 0, (inp_height - crop_height) / 2, (inp_width - crop_width) / 2};
std::vector<int> sizes = {-1, -1, crop_height, crop_width};
crop_param.set("begin", DictValue::arrayInt(&begin[0], begin.size()));
crop_param.set("size", DictValue::arrayInt(&sizes[0], sizes.size()));
crop_param.type = "Slice";
darknet::LayerParameter lp;
std::string layer_name = cv::format("crop_%d", layer_id);
lp.layer_name = layer_name;
lp.layer_type = crop_param.type;
lp.layerParams = crop_param;
lp.bottom_indexes.push_back(last_layer);
last_layer = layer_name;
net->layers.push_back(lp);
layer_id++;
if (!noadjust)
{
cv::dnn::LayerParams params;
params.set("bias_term", true);
params.blobs = {
Mat(1, 1, CV_32F, Scalar(2)),
Mat(1, 1, CV_32F, Scalar(-1))
};
darknet::LayerParameter lp;
std::string layer_name = cv::format("adjust_crop_%d", layer_id);
lp.layer_name = layer_name;
lp.layer_type = "Scale";
lp.layerParams = params;
lp.bottom_indexes.push_back(last_layer);
last_layer = layer_name;
net->layers.push_back(lp);
layer_id++;
}
fused_layer_names.push_back(last_layer);
}
void setSoftmax()
{
cv::dnn::LayerParams softmax_param;
@@ -685,8 +727,8 @@ namespace cv {
MatShape tensor_shape(3);
tensor_shape[0] = net->channels;
tensor_shape[1] = net->width;
tensor_shape[2] = net->height;
tensor_shape[1] = net->height;
tensor_shape[2] = net->width;
net->out_channels_vec.resize(net->layers_cfg.size());
layers_counter = -1;
@@ -763,6 +805,19 @@ namespace cv {
tensor_shape[1] = 1;
tensor_shape[2] = 1;
}
else if (layer_type == "crop")
{
int crop_height = getParam<int>(layer_params, "crop_height", 0);
int crop_width = getParam<int>(layer_params, "crop_width", 0);
bool noadjust = getParam<int>(layer_params, "noadjust", false);
CV_CheckGT(crop_height, 0, "");
CV_CheckGT(crop_width, 0, "");
setParams.setCrop(crop_height, crop_width, tensor_shape[1], tensor_shape[2], noadjust);
tensor_shape[1] = crop_height;
tensor_shape[2] = crop_width;
}
else if (layer_type == "softmax")
{
int groups = getParam<int>(layer_params, "groups", 1);
@@ -937,8 +992,8 @@ namespace cv {
MatShape tensor_shape(3);
tensor_shape[0] = net->channels;
tensor_shape[1] = net->width;
tensor_shape[2] = net->height;
tensor_shape[1] = net->height;
tensor_shape[2] = net->width;
int cv_layers_counter = -1;
int darknet_layers_counter = -1;
modules/dnn/test/test_darknet_importer.cpp
+5
View File
@@ -1049,6 +1049,11 @@ TEST_P(Test_Darknet_layers, avgpool_softmax)
testDarknetLayer("avgpool_softmax");
}
TEST_P(Test_Darknet_layers, crop)
{
testDarknetLayer("crop");
}
TEST_P(Test_Darknet_layers, region)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)