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https://github.com/lltcggie/waifu2x-caffe.git
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テスト中
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lltcggie
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ea6af02321
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a63b2510c6
@ -280,8 +280,11 @@ public:
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}
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};
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void reg();
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int main(int argc, char** argv) {
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CV_DNN_REGISTER_LAYER_CLASS(CropCenter, CropCenterLayer);
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//CV_DNN_REGISTER_LAYER_CLASS(CropCenter, CropCenterLayer);
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reg();
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// ImageNet Caffeリファレンスモデル
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string protoFile = "models/upresnet10/noise0_scale2.0x_model.prototxt";
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@ -298,7 +301,7 @@ int main(int argc, char** argv) {
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//net.setPreferableBackend(cv::dnn::DNN_BACKEND_CUDA);
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//net.setPreferableTarget(cv::dnn::DNN_TARGET_CUDA);
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//net.setPreferableBackend(cv::dnn::DNN_BACKEND_OPENCV);
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net.setPreferableBackend(cv::dnn::DNN_BACKEND_OPENCV);
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net.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);
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}
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catch (const cv::Exception& e) {
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@ -326,8 +329,8 @@ int main(int argc, char** argv) {
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const auto probMat = net.forward();
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std::vector<cv::Mat> outImgs;
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//cv::dnn::imagesFromBlob(probMat, outImgs);
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cv::dnn::imagesFromBlob(inputBlob, outImgs);
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cv::dnn::imagesFromBlob(probMat, outImgs);
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//cv::dnn::imagesFromBlob(inputBlob, outImgs);
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auto outImg = outImgs[0];
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// 値を0～1にクリッピング
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@ -23,14 +23,11 @@
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using namespace cv::dnn::cuda4dnn;
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#endif
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namespace cv
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namespace
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{
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namespace dnn
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cv::Range normalizeRange(const cv::Range& input_range, int n)
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{
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Range normalizeRange(const Range& input_range, int n)
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{
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Range range = input_range;
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cv::Range range = input_range;
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if (range.start != n) {
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range.start = std::min(std::max(range.start, -n), n - 1);
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@ -50,7 +47,7 @@ namespace cv
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}
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// TODO: support cv::Range with steps and negative steps to get rid of this transformation
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void tranformForNegSteps(const MatShape& inpShape, std::vector<std::vector<Range> >& sliceRanges, std::vector<std::vector<int> >& sliceSteps)
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void tranformForNegSteps(const cv::dnn::MatShape& inpShape, std::vector<std::vector<cv::Range> >& sliceRanges, std::vector<std::vector<int> >& sliceSteps)
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{
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// in case of negative steps,
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// x of shape [5, 10], x[5:0:-1, 10:1:-3] <=> np.flip(x[1:5:1, 2:10:3], aixs=(0, 1))
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@ -77,7 +74,7 @@ namespace cv
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}
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}
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std::vector<std::vector<cv::Range> > finalizeSliceRange(const MatShape& inpShape, int& axis,
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std::vector<std::vector<cv::Range> > finalizeSliceRange(const cv::dnn::MatShape& inpShape, int& axis,
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const std::vector<std::vector<cv::Range> >& inputSliceRanges)
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{
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std::vector<std::vector<cv::Range> > sliceRanges = inputSliceRanges;
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@ -86,10 +83,10 @@ namespace cv
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axis = (axis + static_cast<int>(inpShape.size())) % inpShape.size();
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for (size_t i = 0; i < sliceRanges.size(); ++i) {
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std::vector<Range>& ranges = sliceRanges[i];
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std::vector<cv::Range>& ranges = sliceRanges[i];
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if (axisNeg)
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{
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ranges.insert(ranges.begin(), axis, Range::all());
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ranges.insert(ranges.begin(), axis, cv::Range::all());
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}
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for (size_t j = 0; j < ranges.size(); ++j)
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@ -106,6 +103,14 @@ namespace cv
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return sliceRanges;
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}
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}
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namespace cv
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{
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namespace dnn
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{
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class SliceLayerImpl : public SliceLayer
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{
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@ -826,10 +831,10 @@ namespace cv
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bool hasSteps;
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};
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class CropLayerImpl CV_FINAL : public SliceLayerImpl
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class CropCenterLayerImpl CV_FINAL : public SliceLayerImpl
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{
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public:
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CropLayerImpl(const LayerParams& params) : SliceLayerImpl(LayerParams())
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CropCenterLayerImpl(const LayerParams& params) : SliceLayerImpl(LayerParams())
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{
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setParamsFrom(params);
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axis = params.get<int>("axis", 2);
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@ -840,6 +845,21 @@ namespace cv
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for (int i = 0; i < paramOffset->size(); i++)
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offset.push_back(paramOffset->get<int>(i));
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}
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if (params.has("crop_size"))
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{
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const auto& paramCropSize = params.get("crop_size");
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const auto& str = paramCropSize.getStringValue();
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const int s = atoi(str.c_str());
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offset.resize(4);
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offset[0] = 0;
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offset[1] = 0;
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offset[2] = s;
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offset[3] = s;
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axis = 0;
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}
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}
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bool getMemoryShapes(const std::vector<MatShape>& inputs,
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@ -847,26 +867,75 @@ namespace cv
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std::vector<MatShape>& outputs,
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std::vector<MatShape>& internals) const CV_OVERRIDE
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{
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CV_Assert(inputs.size() == 2);
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//CV_Assert(inputs.size() == 2);
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//MatShape dstShape = inputs[0];
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//int start = normalize_axis(axis, dstShape);
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//for (int i = start; i < dstShape.size(); i++)
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//{
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// dstShape[i] = inputs[1][i];
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//}
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//outputs.resize(1, dstShape);
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MatShape dstShape = inputs[0];
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int start = normalize_axis(axis, dstShape);
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for (int i = start; i < dstShape.size(); i++)
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{
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dstShape[i] = inputs[1][i];
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dstShape[i] = dstShape[i] - offset[i] * 2;
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}
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outputs.resize(1, dstShape);
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return false;
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}
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void finalize(InputArrayOfArrays inputs_arr, OutputArrayOfArrays) CV_OVERRIDE
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{
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//std::vector<Mat> inputs;
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//inputs_arr.getMatVector(inputs);
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//CV_Assert(2 == inputs.size());
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//const Mat& inpBlob = inputs[0];
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//const Mat& inpSzBlob = inputs[1];
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//int dims = inpBlob.dims;
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//int start_axis = normalize_axis(axis, dims);
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//std::vector<int> offset_final(dims, 0);
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//if (offset.size() == 1)
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//{
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// for (int i = start_axis; i < dims; i++)
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// offset_final[i] = offset[0];
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//}
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//else if (offset.size() > 1)
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//{
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// if ((int)offset.size() != dims - start_axis)
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// CV_Error(Error::StsBadArg, "number of offset values specified must be "
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// "equal to the number of dimensions following axis.");
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// for (int i = start_axis; i < dims; i++)
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// offset_final[i] = offset[i - start_axis];
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//}
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//finalSliceRanges.resize(1);
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//finalSliceRanges[0].resize(dims);
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//for (int i = 0; i < start_axis; i++)
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//{
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// finalSliceRanges[0][i] = Range(0, inpBlob.size[i]);
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//}
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//for (int i = start_axis; i < dims; i++)
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//{
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// if (offset_final[i] < 0 || offset_final[i] + inpSzBlob.size[i] > inpBlob.size[i])
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// CV_Error(Error::StsBadArg, "invalid crop parameters or blob sizes");
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// finalSliceRanges[0][i] = Range(offset_final[i], offset_final[i] + inpSzBlob.size[i]);
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//}
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std::vector<Mat> inputs;
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inputs_arr.getMatVector(inputs);
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CV_Assert(2 == inputs.size());
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CV_Assert(1 == inputs.size());
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const Mat& inpBlob = inputs[0];
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const Mat& inpSzBlob = inputs[1];
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int dims = inpBlob.dims;
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int start_axis = normalize_axis(axis, dims);
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@ -895,25 +964,29 @@ namespace cv
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}
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for (int i = start_axis; i < dims; i++)
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{
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if (offset_final[i] < 0 || offset_final[i] + inpSzBlob.size[i] > inpBlob.size[i])
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int w = inpBlob.size[i] - offset_final[i] * 2;
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if (offset_final[i] < 0 || w < 0)
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CV_Error(Error::StsBadArg, "invalid crop parameters or blob sizes");
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finalSliceRanges[0][i] = Range(offset_final[i], offset_final[i] + inpSzBlob.size[i]);
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finalSliceRanges[0][i] = Range(offset_final[i], offset_final[i] + w);
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}
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}
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static cv::Ptr<cv::dnn::Layer> create(cv::dnn::LayerParams& params)
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{
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return cv::Ptr<CropCenterLayerImpl>(new CropCenterLayerImpl(params));
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}
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private:
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std::vector<int> offset;
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};
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Ptr<SliceLayer> SliceLayer::create(const LayerParams& params)
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{
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return Ptr<SliceLayer>(new SliceLayerImpl(params));
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}
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Ptr<Layer> CropLayer::create(const LayerParams& params)
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{
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return Ptr<Layer>(new CropLayerImpl(params));
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}
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}
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}
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# include <opencv2/dnn/layer.details.hpp>
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void reg()
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{
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CV_DNN_REGISTER_LAYER_CLASS(CropCenter, cv::dnn::CropCenterLayerImpl);
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}
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