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Faster-RCNN object detection models from TensorFlow
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,108 @@ | ||
| #include "../precomp.hpp" | ||
| #include "layers_common.hpp" | ||
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| namespace cv { namespace dnn { | ||
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| class CropAndResizeLayerImpl CV_FINAL : public CropAndResizeLayer | ||
| { | ||
| public: | ||
| CropAndResizeLayerImpl(const LayerParams& params) | ||
| { | ||
| CV_Assert(params.has("width"), params.has("height")); | ||
| outWidth = params.get<float>("width"); | ||
| outHeight = params.get<float>("height"); | ||
| } | ||
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| bool getMemoryShapes(const std::vector<MatShape> &inputs, | ||
| const int requiredOutputs, | ||
| std::vector<MatShape> &outputs, | ||
| std::vector<MatShape> &internals) const CV_OVERRIDE | ||
| { | ||
| CV_Assert(inputs.size() == 2, inputs[0].size() == 4); | ||
| if (inputs[0][0] != 1) | ||
| CV_Error(Error::StsNotImplemented, ""); | ||
| outputs.resize(1, MatShape(4)); | ||
| outputs[0][0] = inputs[1][2]; // Number of bounding boxes. | ||
| outputs[0][1] = inputs[0][1]; // Number of channels. | ||
| outputs[0][2] = outHeight; | ||
| outputs[0][3] = outWidth; | ||
| return false; | ||
| } | ||
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| void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE | ||
| { | ||
| CV_TRACE_FUNCTION(); | ||
| CV_TRACE_ARG_VALUE(name, "name", name.c_str()); | ||
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| Layer::forward_fallback(inputs_arr, outputs_arr, internals_arr); | ||
| } | ||
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| void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals) CV_OVERRIDE | ||
| { | ||
| CV_TRACE_FUNCTION(); | ||
| CV_TRACE_ARG_VALUE(name, "name", name.c_str()); | ||
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| Mat& inp = *inputs[0]; | ||
| Mat& out = outputs[0]; | ||
| Mat boxes = inputs[1]->reshape(1, inputs[1]->total() / 7); | ||
| const int numChannels = inp.size[1]; | ||
| const int inpHeight = inp.size[2]; | ||
| const int inpWidth = inp.size[3]; | ||
| const int inpSpatialSize = inpHeight * inpWidth; | ||
| const int outSpatialSize = outHeight * outWidth; | ||
| CV_Assert(inp.isContinuous(), out.isContinuous()); | ||
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| for (int b = 0; b < boxes.rows; ++b) | ||
| { | ||
| float* outDataBox = out.ptr<float>(b); | ||
| float left = boxes.at<float>(b, 3); | ||
| float top = boxes.at<float>(b, 4); | ||
| float right = boxes.at<float>(b, 5); | ||
| float bottom = boxes.at<float>(b, 6); | ||
| float boxWidth = right - left; | ||
| float boxHeight = bottom - top; | ||
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| float heightScale = boxHeight * static_cast<float>(inpHeight - 1) / (outHeight - 1); | ||
| float widthScale = boxWidth * static_cast<float>(inpWidth - 1) / (outWidth - 1); | ||
| for (int y = 0; y < outHeight; ++y) | ||
| { | ||
| float input_y = top * (inpHeight - 1) + y * heightScale; | ||
| int y0 = static_cast<int>(input_y); | ||
| const float* inpData_row0 = (float*)inp.data + y0 * inpWidth; | ||
| const float* inpData_row1 = (y0 + 1 < inpHeight) ? (inpData_row0 + inpWidth) : inpData_row0; | ||
| for (int x = 0; x < outWidth; ++x) | ||
| { | ||
| float input_x = left * (inpWidth - 1) + x * widthScale; | ||
| int x0 = static_cast<int>(input_x); | ||
| int x1 = std::min(x0 + 1, inpWidth - 1); | ||
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| float* outData = outDataBox + y * outWidth + x; | ||
| const float* inpData_row0_c = inpData_row0; | ||
| const float* inpData_row1_c = inpData_row1; | ||
| for (int c = 0; c < numChannels; ++c) | ||
| { | ||
| *outData = inpData_row0_c[x0] + | ||
| (input_y - y0) * (inpData_row1_c[x0] - inpData_row0_c[x0]) + | ||
| (input_x - x0) * (inpData_row0_c[x1] - inpData_row0_c[x0] + | ||
| (input_y - y0) * (inpData_row1_c[x1] - inpData_row0_c[x1] - inpData_row1_c[x0] + inpData_row0_c[x0])); | ||
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| inpData_row0_c += inpSpatialSize; | ||
| inpData_row1_c += inpSpatialSize; | ||
| outData += outSpatialSize; | ||
| } | ||
| } | ||
| } | ||
| } | ||
| } | ||
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| private: | ||
| int outWidth, outHeight; | ||
| }; | ||
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| Ptr<Layer> CropAndResizeLayer::create(const LayerParams& params) | ||
| { | ||
| return Ptr<CropAndResizeLayer>(new CropAndResizeLayerImpl(params)); | ||
| } | ||
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| } // namespace dnn | ||
| } // namespace cv |
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