feat: 切换后端至PaddleOCR-NCNN，切换工程为CMake
1.项目后端整体迁移至PaddleOCR-NCNN算法，已通过基本的兼容性测试 2.工程改为使用CMake组织，后续为了更好地兼容第三方库，不再提供QMake工程 3.重整权利声明文件，重整代码工程，确保最小化侵权风险 Log: 切换后端至PaddleOCR-NCNN，切换工程为CMake Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c
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wangzhengyang
283
3rdparty/opencv-4.5.4/samples/dnn/custom_layers.hpp
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283
3rdparty/opencv-4.5.4/samples/dnn/custom_layers.hpp
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#ifndef __OPENCV_SAMPLES_DNN_CUSTOM_LAYERS__
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#define __OPENCV_SAMPLES_DNN_CUSTOM_LAYERS__
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#include <opencv2/dnn.hpp>
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#include <opencv2/dnn/shape_utils.hpp> // getPlane
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//! [InterpLayer]
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class InterpLayer : public cv::dnn::Layer
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{
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public:
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InterpLayer(const cv::dnn::LayerParams ¶ms) : Layer(params)
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{
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outWidth = params.get<int>("width", 0);
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outHeight = params.get<int>("height", 0);
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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<cv::dnn::Layer>(new InterpLayer(params));
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}
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virtual bool getMemoryShapes(const std::vector<std::vector<int> > &inputs,
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const int requiredOutputs,
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std::vector<std::vector<int> > &outputs,
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std::vector<std::vector<int> > &internals) const CV_OVERRIDE
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{
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CV_UNUSED(requiredOutputs); CV_UNUSED(internals);
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std::vector<int> outShape(4);
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outShape[0] = inputs[0][0]; // batch size
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outShape[1] = inputs[0][1]; // number of channels
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outShape[2] = outHeight;
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outShape[3] = outWidth;
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outputs.assign(1, outShape);
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return false;
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}
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// Implementation of this custom layer is based on https://github.com/cdmh/deeplab-public/blob/master/src/caffe/layers/interp_layer.cpp
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virtual void forward(cv::InputArrayOfArrays inputs_arr,
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cv::OutputArrayOfArrays outputs_arr,
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cv::OutputArrayOfArrays internals_arr) CV_OVERRIDE
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{
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if (inputs_arr.depth() == CV_16S)
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{
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// In case of DNN_TARGET_OPENCL_FP16 target the following method
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// converts data from FP16 to FP32 and calls this forward again.
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forward_fallback(inputs_arr, outputs_arr, internals_arr);
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return;
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}
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std::vector<cv::Mat> inputs, outputs;
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inputs_arr.getMatVector(inputs);
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outputs_arr.getMatVector(outputs);
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cv::Mat& inp = inputs[0];
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cv::Mat& out = outputs[0];
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const float* inpData = (float*)inp.data;
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float* outData = (float*)out.data;
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const int batchSize = inp.size[0];
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const int numChannels = inp.size[1];
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const int inpHeight = inp.size[2];
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const int inpWidth = inp.size[3];
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const float rheight = (outHeight > 1) ? static_cast<float>(inpHeight - 1) / (outHeight - 1) : 0.f;
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const float rwidth = (outWidth > 1) ? static_cast<float>(inpWidth - 1) / (outWidth - 1) : 0.f;
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for (int h2 = 0; h2 < outHeight; ++h2)
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{
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const float h1r = rheight * h2;
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const int h1 = static_cast<int>(h1r);
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const int h1p = (h1 < inpHeight - 1) ? 1 : 0;
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const float h1lambda = h1r - h1;
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const float h0lambda = 1.f - h1lambda;
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for (int w2 = 0; w2 < outWidth; ++w2)
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{
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const float w1r = rwidth * w2;
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const int w1 = static_cast<int>(w1r);
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const int w1p = (w1 < inpWidth - 1) ? 1 : 0;
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const float w1lambda = w1r - w1;
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const float w0lambda = 1.f - w1lambda;
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const float* pos1 = inpData + h1 * inpWidth + w1;
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float* pos2 = outData + h2 * outWidth + w2;
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for (int c = 0; c < batchSize * numChannels; ++c)
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{
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pos2[0] =
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h0lambda * (w0lambda * pos1[0] + w1lambda * pos1[w1p]) +
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h1lambda * (w0lambda * pos1[h1p * inpWidth] + w1lambda * pos1[h1p * inpWidth + w1p]);
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pos1 += inpWidth * inpHeight;
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pos2 += outWidth * outHeight;
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}
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}
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}
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}
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private:
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int outWidth, outHeight;
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};
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//! [InterpLayer]
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//! [ResizeBilinearLayer]
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class ResizeBilinearLayer CV_FINAL : public cv::dnn::Layer
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{
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public:
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ResizeBilinearLayer(const cv::dnn::LayerParams ¶ms) : Layer(params)
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{
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CV_Assert(!params.get<bool>("align_corners", false));
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CV_Assert(!blobs.empty());
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for (size_t i = 0; i < blobs.size(); ++i)
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CV_Assert(blobs[i].type() == CV_32SC1);
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// There are two cases of input blob: a single blob which contains output
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// shape and two blobs with scaling factors.
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if (blobs.size() == 1)
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{
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CV_Assert(blobs[0].total() == 2);
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outHeight = blobs[0].at<int>(0, 0);
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outWidth = blobs[0].at<int>(0, 1);
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factorHeight = factorWidth = 0;
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}
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else
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{
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CV_Assert(blobs.size() == 2); CV_Assert(blobs[0].total() == 1); CV_Assert(blobs[1].total() == 1);
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factorHeight = blobs[0].at<int>(0, 0);
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factorWidth = blobs[1].at<int>(0, 0);
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outHeight = outWidth = 0;
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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<cv::dnn::Layer>(new ResizeBilinearLayer(params));
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}
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virtual bool getMemoryShapes(const std::vector<std::vector<int> > &inputs,
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const int,
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std::vector<std::vector<int> > &outputs,
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std::vector<std::vector<int> > &) const CV_OVERRIDE
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{
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std::vector<int> outShape(4);
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outShape[0] = inputs[0][0]; // batch size
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outShape[1] = inputs[0][1]; // number of channels
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outShape[2] = outHeight != 0 ? outHeight : (inputs[0][2] * factorHeight);
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outShape[3] = outWidth != 0 ? outWidth : (inputs[0][3] * factorWidth);
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outputs.assign(1, outShape);
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return false;
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}
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virtual void finalize(cv::InputArrayOfArrays, cv::OutputArrayOfArrays outputs_arr) CV_OVERRIDE
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{
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std::vector<cv::Mat> outputs;
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outputs_arr.getMatVector(outputs);
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if (!outWidth && !outHeight)
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{
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outHeight = outputs[0].size[2];
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outWidth = outputs[0].size[3];
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}
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}
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// This implementation is based on a reference implementation from
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// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
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virtual void forward(cv::InputArrayOfArrays inputs_arr,
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cv::OutputArrayOfArrays outputs_arr,
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cv::OutputArrayOfArrays internals_arr) CV_OVERRIDE
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{
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if (inputs_arr.depth() == CV_16S)
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{
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// In case of DNN_TARGET_OPENCL_FP16 target the following method
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// converts data from FP16 to FP32 and calls this forward again.
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forward_fallback(inputs_arr, outputs_arr, internals_arr);
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return;
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}
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std::vector<cv::Mat> inputs, outputs;
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inputs_arr.getMatVector(inputs);
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outputs_arr.getMatVector(outputs);
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cv::Mat& inp = inputs[0];
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cv::Mat& out = outputs[0];
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const float* inpData = (float*)inp.data;
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float* outData = (float*)out.data;
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const int batchSize = inp.size[0];
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const int numChannels = inp.size[1];
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const int inpHeight = inp.size[2];
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const int inpWidth = inp.size[3];
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float heightScale = static_cast<float>(inpHeight) / outHeight;
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float widthScale = static_cast<float>(inpWidth) / outWidth;
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for (int b = 0; b < batchSize; ++b)
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{
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for (int y = 0; y < outHeight; ++y)
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{
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float input_y = y * heightScale;
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int y0 = static_cast<int>(std::floor(input_y));
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int y1 = std::min(y0 + 1, inpHeight - 1);
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for (int x = 0; x < outWidth; ++x)
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{
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float input_x = x * widthScale;
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int x0 = static_cast<int>(std::floor(input_x));
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int x1 = std::min(x0 + 1, inpWidth - 1);
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for (int c = 0; c < numChannels; ++c)
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{
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float interpolation =
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inpData[offset(inp.size, c, x0, y0, b)] * (1 - (input_y - y0)) * (1 - (input_x - x0)) +
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inpData[offset(inp.size, c, x0, y1, b)] * (input_y - y0) * (1 - (input_x - x0)) +
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inpData[offset(inp.size, c, x1, y0, b)] * (1 - (input_y - y0)) * (input_x - x0) +
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inpData[offset(inp.size, c, x1, y1, b)] * (input_y - y0) * (input_x - x0);
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outData[offset(out.size, c, x, y, b)] = interpolation;
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}
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}
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}
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}
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}
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private:
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static inline int offset(const cv::MatSize& size, int c, int x, int y, int b)
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{
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return x + size[3] * (y + size[2] * (c + size[1] * b));
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}
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int outWidth, outHeight, factorWidth, factorHeight;
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};
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//! [ResizeBilinearLayer]
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//
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// The following code is used only to generate tutorials documentation.
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//
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//! [A custom layer interface]
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class MyLayer : public cv::dnn::Layer
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{
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public:
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//! [MyLayer::MyLayer]
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MyLayer(const cv::dnn::LayerParams ¶ms);
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//! [MyLayer::MyLayer]
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//! [MyLayer::create]
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static cv::Ptr<cv::dnn::Layer> create(cv::dnn::LayerParams& params);
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//! [MyLayer::create]
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//! [MyLayer::getMemoryShapes]
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virtual bool getMemoryShapes(const std::vector<std::vector<int> > &inputs,
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const int requiredOutputs,
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std::vector<std::vector<int> > &outputs,
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std::vector<std::vector<int> > &internals) const CV_OVERRIDE;
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//! [MyLayer::getMemoryShapes]
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//! [MyLayer::forward]
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virtual void forward(cv::InputArrayOfArrays inputs,
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cv::OutputArrayOfArrays outputs,
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cv::OutputArrayOfArrays internals) CV_OVERRIDE;
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//! [MyLayer::forward]
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//! [MyLayer::finalize]
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virtual void finalize(cv::InputArrayOfArrays inputs,
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cv::OutputArrayOfArrays outputs) CV_OVERRIDE;
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//! [MyLayer::finalize]
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};
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//! [A custom layer interface]
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//! [Register a custom layer]
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#include <opencv2/dnn/layer.details.hpp> // CV_DNN_REGISTER_LAYER_CLASS
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static inline void loadNet()
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{
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CV_DNN_REGISTER_LAYER_CLASS(Interp, InterpLayer);
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// ...
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//! [Register a custom layer]
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//! [Register InterpLayer]
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CV_DNN_REGISTER_LAYER_CLASS(Interp, InterpLayer);
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cv::dnn::Net caffeNet = cv::dnn::readNet("/path/to/config.prototxt", "/path/to/weights.caffemodel");
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//! [Register InterpLayer]
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//! [Register ResizeBilinearLayer]
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CV_DNN_REGISTER_LAYER_CLASS(ResizeBilinear, ResizeBilinearLayer);
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cv::dnn::Net tfNet = cv::dnn::readNet("/path/to/graph.pb");
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//! [Register ResizeBilinearLayer]
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if (false) loadNet(); // To prevent unused function warning.
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}
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#endif // __OPENCV_SAMPLES_DNN_CUSTOM_LAYERS__
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