diff --git a/mindspore/ccsrc/minddata/dataset/kernels/image/lite_cv/image_process.cc b/mindspore/ccsrc/minddata/dataset/kernels/image/lite_cv/image_process.cc index 68d4a3f8789..f1aac99f907 100644 --- a/mindspore/ccsrc/minddata/dataset/kernels/image/lite_cv/image_process.cc +++ b/mindspore/ccsrc/minddata/dataset/kernels/image/lite_cv/image_process.cc @@ -516,18 +516,10 @@ void ConvertBoxes(std::vector> &boxes, const std::vector ApplyNms(const std::vector> &all_boxes, std::vector &all_scores, float thres, int max_boxes) { int boxes_num = all_boxes.size(); - std::vector y1(boxes_num); - std::vector x1(boxes_num); - std::vector y2(boxes_num); - std::vector x2(boxes_num); std::vector areas(boxes_num); std::vector order(boxes_num); for (int i = 0; i < boxes_num; i++) { - y1[i] = all_boxes[i][0]; - x1[i] = all_boxes[i][1]; - y2[i] = all_boxes[i][2]; - x2[i] = all_boxes[i][3]; - areas[i] = (x2[i] - x1[i] + 1) * (y2[i] - y1[i] + 1); + areas[i] = (all_boxes[i][3] - all_boxes[i][1] + 1) * (all_boxes[i][2] - all_boxes[i][0] + 1); order[i] = i; } @@ -543,10 +535,10 @@ std::vector ApplyNms(const std::vector> &all_boxes, std: int len = order.size() - 1; std::vector ovr(len); for (int j = 0; j < len; j++) { - float xx1 = std::max(x1[i], x1[order[j + 1]]); - float yy1 = std::max(y1[i], y1[order[j + 1]]); - float xx2 = std::min(x2[i], x2[order[j + 1]]); - float yy2 = std::min(y2[i], y2[order[j + 1]]); + float xx1 = std::max(all_boxes[i][1], all_boxes[order[j + 1]][1]); + float yy1 = std::max(all_boxes[i][0], all_boxes[order[j + 1]][0]); + float xx2 = std::min(all_boxes[i][3], all_boxes[order[j + 1]][3]); + float yy2 = std::min(all_boxes[i][2], all_boxes[order[j + 1]][2]); float w = std::max(0.0f, xx2 - xx1 + 1); float h = std::max(0.0f, yy2 - yy1 + 1); @@ -568,14 +560,15 @@ std::vector ApplyNms(const std::vector> &all_boxes, std: return keep; } -void WarpAffine(LiteMat &src, LiteMat &out_img, double M[6], std::vector dsize, UINT8_C3 borderValue) { +template +void ImplementAffine(LiteMat &src, LiteMat &out_img, double M[6], std::vector &dsize, Pixel_Type borderValue) { double IM[6]; for (int i = 0; i < 6; i++) { IM[i] = M[i]; } double D = IM[0] * IM[4] - IM[1] * IM[3]; - D = D != 0 ? 1. / D : 0; + D = D != 0 ? 1.0f / D : 0; double A11 = IM[4] * D, A22 = IM[0] * D; IM[0] = A11; IM[1] *= -D; @@ -592,13 +585,22 @@ void WarpAffine(LiteMat &src, LiteMat &out_img, double M[6], std::vector int src_x = IM[0] * x + IM[1] * y + IM[2]; int src_y = IM[3] * x + IM[4] * y + IM[5]; if (src_x >= 0 && src_y >= 0 && src_x < src.width_ && src_y < src.height_) { - UINT8_C3 src_pixel = static_cast(src.data_ptr_)[src_y * src.width_ + src_x]; - static_cast(out_img.data_ptr_)[y * src.width_ + x] = src_pixel; + Pixel_Type src_pixel = static_cast(src.data_ptr_)[src_y * src.width_ + src_x]; + static_cast(out_img.data_ptr_)[y * src.width_ + x] = src_pixel; } else { - static_cast(out_img.data_ptr_)[y * src.width_ + x] = borderValue; + static_cast(out_img.data_ptr_)[y * src.width_ + x] = borderValue; } } } } + +void Affine(LiteMat &src, LiteMat &out_img, double M[6], std::vector dsize, UINT8_C1 borderValue) { + ImplementAffine(src, out_img, M, dsize, borderValue); +} + +void Affine(LiteMat &src, LiteMat &out_img, double M[6], std::vector dsize, UINT8_C3 borderValue) { + ImplementAffine(src, out_img, M, dsize, borderValue); +} + } // namespace dataset } // namespace mindspore diff --git a/mindspore/ccsrc/minddata/dataset/kernels/image/lite_cv/image_process.h b/mindspore/ccsrc/minddata/dataset/kernels/image/lite_cv/image_process.h index 297c24f5a46..69243999ba5 100644 --- a/mindspore/ccsrc/minddata/dataset/kernels/image/lite_cv/image_process.h +++ b/mindspore/ccsrc/minddata/dataset/kernels/image/lite_cv/image_process.h @@ -20,6 +20,7 @@ #include #include #include +#include #include "lite_cv/lite_mat.h" @@ -68,13 +69,20 @@ bool SubStractMeanNormalize(const LiteMat &src, LiteMat &dst, float *mean, float bool Padd(const LiteMat &src, LiteMat &dst, int top, int bottom, int left, int right, PaddBorderType pad_type, uint8_t fill_b_or_gray, uint8_t fill_g, uint8_t fill_r); -void WarpAffine(const LiteMat &src, LiteMat &out_img, double M[6], std::vector dsize, uint8_t borderValue[3]); +/// \brief Apply affine transformation for 1 channel image +void Affine(LiteMat &src, LiteMat &out_img, double M[6], std::vector dsize, UINT8_C1 borderValue); +/// \brief Apply affine transformation for 3 channel image +void Affine(LiteMat &src, LiteMat &out_img, double M[6], std::vector dsize, UINT8_C3 borderValue); + +/// \brief Get default anchor boxes for Faster R-CNN, SSD, YOLO etc std::vector> GetDefaultBoxes(const BoxesConfig config); +/// \brief Convert the prediction boxes to the actual boxes of (y, x, h, w) void ConvertBoxes(std::vector> &boxes, const std::vector> &default_boxes, const BoxesConfig config); +/// \brief Apply Non-Maximum Suppression std::vector ApplyNms(const std::vector> &all_boxes, std::vector &all_scores, float thres, int max_boxes); diff --git a/mindspore/ccsrc/minddata/dataset/kernels/image/lite_cv/lite_mat.h b/mindspore/ccsrc/minddata/dataset/kernels/image/lite_cv/lite_mat.h index 1e902e89c53..48250d9948f 100644 --- a/mindspore/ccsrc/minddata/dataset/kernels/image/lite_cv/lite_mat.h +++ b/mindspore/ccsrc/minddata/dataset/kernels/image/lite_cv/lite_mat.h @@ -27,17 +27,20 @@ namespace dataset { template struct Chn1 { + Chn1(T c1) : c1(c1) {} T c1; }; template struct Chn2 { + Chn2(T c1, T c2) : c1(c1), c2(c2) {} T c1; T c2; }; template struct Chn3 { + Chn3(T c1, T c2, T c3) : c1(c1), c2(c2), c3(c3) {} T c1; T c2; T c3; @@ -45,6 +48,7 @@ struct Chn3 { template struct Chn4 { + Chn4(T c1, T c2, T c3, T c4) : c1(c1), c2(c2), c3(c3), c4(c4) {} T c1; T c2; T c3; diff --git a/tests/ut/cpp/dataset/image_process_test.cc b/tests/ut/cpp/dataset/image_process_test.cc index 3f8bbd14e38..c0402b76221 100644 --- a/tests/ut/cpp/dataset/image_process_test.cc +++ b/tests/ut/cpp/dataset/image_process_test.cc @@ -21,6 +21,8 @@ #include #include "utils/log_adapter.h" +#include + using namespace mindspore::dataset; class MindDataImageProcess : public UT::Common { public: @@ -228,6 +230,7 @@ TEST_F(MindDataImageProcess, TestPadd) { } TEST_F(MindDataImageProcess, TestGetDefaultBoxes) { + std::string benchmark = "data/dataset/testLite/default_boxes.bin"; BoxesConfig config; config.img_shape = {300, 300}; config.num_default = {3, 6, 6, 6, 6, 6}; @@ -238,8 +241,25 @@ TEST_F(MindDataImageProcess, TestGetDefaultBoxes) { config.steps = {16, 32, 64, 100, 150, 300}; config.prior_scaling = {0.1, 0.2}; + int rows = 1917; + int cols = 4; + std::vector benchmark_boxes(rows * cols); + std::ifstream in(benchmark, std::ios::in | std::ios::binary); + in.read(reinterpret_cast(benchmark_boxes.data()), benchmark_boxes.size() * sizeof(double)); + in.close(); + std::vector> default_boxes = GetDefaultBoxes(config); - ASSERT_TRUE(default_boxes.size() == 1917); + EXPECT_EQ(default_boxes.size(), rows); + EXPECT_EQ(default_boxes[0].size(), cols); + + double distance = 0.0f; + for (int i = 0; i < rows; i++) { + for (int j = 0; j < cols; j++) { + distance += pow(default_boxes[i][j] - benchmark_boxes[i * cols + j], 2); + } + } + distance = sqrt(distance); + EXPECT_LT(distance, 1e-5); } TEST_F(MindDataImageProcess, TestApplyNms) { @@ -250,3 +270,67 @@ TEST_F(MindDataImageProcess, TestApplyNms) { ASSERT_TRUE(keep[1] == 0); ASSERT_TRUE(keep[2] == 1); } + +TEST_F(MindDataImageProcess, TestAffine) { + // The input matrix + // 0 0 1 0 0 + // 0 0 1 0 0 + // 2 2 3 2 2 + // 0 0 1 0 0 + // 0 0 1 0 0 + size_t rows = 5; + size_t cols = 5; + LiteMat src(rows, cols); + for (size_t i = 0; i < rows; i++) { + for (size_t j = 0; j < cols; j++) { + if (i == 2 && j == 2) { + static_cast(src.data_ptr_)[i * cols + j] = 3; + } else if (i == 2) { + static_cast(src.data_ptr_)[i * cols + j] = 2; + } else if (j == 2) { + static_cast(src.data_ptr_)[i * cols + j] = 1; + } else { + static_cast(src.data_ptr_)[i * cols + j] = 0; + } + } + } + + // Expect output matrix + // 0 0 2 0 0 + // 0 0 2 0 0 + // 1 1 3 1 1 + // 0 0 2 0 0 + // 0 0 2 0 0 + LiteMat expect(rows, cols); + for (size_t i = 0; i < rows; i++) { + for (size_t j = 0; j < cols; j++) { + if (i == 2 && j == 2) { + static_cast(expect.data_ptr_)[i * cols + j] = 3; + } else if (i == 2) { + static_cast(expect.data_ptr_)[i * cols + j] = 1; + } else if (j == 2) { + static_cast(expect.data_ptr_)[i * cols + j] = 2; + } else { + static_cast(expect.data_ptr_)[i * cols + j] = 0; + } + } + } + + double angle = 90.0f; + cv::Point2f center(rows / 2, cols / 2); + cv::Mat rotate_matrix = cv::getRotationMatrix2D(center, angle, 1.0); + double M[6]; + for (size_t i = 0; i < 6; i++) { + M[i] = rotate_matrix.at(i); + } + std::cout << std::endl; + LiteMat dst; + Affine(src, dst, M, {rows, cols}, UINT8_C1(0)); + + for (size_t i = 0; i < rows; i++) { + for (size_t j = 0; j < cols; j++) { + EXPECT_EQ(static_cast(expect.data_ptr_)[i * cols + j].c1, + static_cast(dst.data_ptr_)[i * cols + j].c1); + } + } +} 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