/*M/////////////////////////////////////////////////////////////////////////////////////// // // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // // By downloading, copying, installing or using the software you agree to this license. // If you do not agree to this license, do not download, install, // copy or use the software. // // // License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2000-2008, Intel Corporation, all rights reserved. // Copyright (C) 2009, Willow Garage Inc., all rights reserved. // Third party copyrights are property of their respective owners. // // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: // // * Redistribution's of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistribution's in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // // * The name of the copyright holders may not be used to endorse or promote products // derived from this software without specific prior written permission. // // This software is provided by the copyright holders and contributors "as is" and // any express or implied warranties, including, but not limited to, the implied // warranties of merchantability and fitness for a particular purpose are disclaimed. // In no event shall the Intel Corporation or contributors be liable for any direct, // indirect, incidental, special, exemplary, or consequential damages // (including, but not limited to, procurement of substitute goods or services; // loss of use, data, or profits; or business interruption) however caused // and on any theory of liability, whether in contract, strict liability, // or tort (including negligence or otherwise) arising in any way out of // the use of this software, even if advised of the possibility of such damage. // //M*/ #include "precomp.hpp" using namespace std; namespace cv { namespace detail { static const float WEIGHT_EPS = 1e-5f; Ptr Blender::createDefault(int type, bool try_gpu) { if (type == NO) return new Blender(); if (type == FEATHER) return new FeatherBlender(); if (type == MULTI_BAND) return new MultiBandBlender(try_gpu); CV_Error(CV_StsBadArg, "unsupported blending method"); return NULL; } void Blender::prepare(const vector &corners, const vector &sizes) { prepare(resultRoi(corners, sizes)); } void Blender::prepare(Rect dst_roi) { dst_.create(dst_roi.size(), CV_16SC3); dst_.setTo(Scalar::all(0)); dst_mask_.create(dst_roi.size(), CV_8U); dst_mask_.setTo(Scalar::all(0)); dst_roi_ = dst_roi; } void Blender::feed(const Mat &img, const Mat &mask, Point tl) { CV_Assert(img.type() == CV_16SC3); CV_Assert(mask.type() == CV_8U); int dx = tl.x - dst_roi_.x; int dy = tl.y - dst_roi_.y; for (int y = 0; y < img.rows; ++y) { const Point3_ *src_row = img.ptr >(y); Point3_ *dst_row = dst_.ptr >(dy + y); const uchar *mask_row = mask.ptr(y); uchar *dst_mask_row = dst_mask_.ptr(dy + y); for (int x = 0; x < img.cols; ++x) { if (mask_row[x]) dst_row[dx + x] = src_row[x]; dst_mask_row[dx + x] |= mask_row[x]; } } } void Blender::blend(Mat &dst, Mat &dst_mask) { dst_.setTo(Scalar::all(0), dst_mask_ == 0); dst = dst_; dst_mask = dst_mask_; dst_.release(); dst_mask_.release(); } void FeatherBlender::prepare(Rect dst_roi) { Blender::prepare(dst_roi); dst_weight_map_.create(dst_roi.size(), CV_32F); dst_weight_map_.setTo(0); } void FeatherBlender::feed(const Mat &img, const Mat &mask, Point tl) { CV_Assert(img.type() == CV_16SC3); CV_Assert(mask.type() == CV_8U); createWeightMap(mask, sharpness_, weight_map_); int dx = tl.x - dst_roi_.x; int dy = tl.y - dst_roi_.y; for (int y = 0; y < img.rows; ++y) { const Point3_* src_row = img.ptr >(y); Point3_* dst_row = dst_.ptr >(dy + y); const float* weight_row = weight_map_.ptr(y); float* dst_weight_row = dst_weight_map_.ptr(dy + y); for (int x = 0; x < img.cols; ++x) { dst_row[dx + x].x += static_cast(src_row[x].x * weight_row[x]); dst_row[dx + x].y += static_cast(src_row[x].y * weight_row[x]); dst_row[dx + x].z += static_cast(src_row[x].z * weight_row[x]); dst_weight_row[dx + x] += weight_row[x]; } } } void FeatherBlender::blend(Mat &dst, Mat &dst_mask) { normalizeUsingWeightMap(dst_weight_map_, dst_); dst_mask_ = dst_weight_map_ > WEIGHT_EPS; Blender::blend(dst, dst_mask); } Rect FeatherBlender::createWeightMaps(const vector &masks, const vector &corners, vector &weight_maps) { weight_maps.resize(masks.size()); for (size_t i = 0; i < masks.size(); ++i) createWeightMap(masks[i], sharpness_, weight_maps[i]); Rect dst_roi = resultRoi(corners, masks); Mat weights_sum(dst_roi.size(), CV_32F); weights_sum.setTo(0); for (size_t i = 0; i < weight_maps.size(); ++i) { Rect roi(corners[i].x - dst_roi.x, corners[i].y - dst_roi.y, weight_maps[i].cols, weight_maps[i].rows); weights_sum(roi) += weight_maps[i]; } for (size_t i = 0; i < weight_maps.size(); ++i) { Rect roi(corners[i].x - dst_roi.x, corners[i].y - dst_roi.y, weight_maps[i].cols, weight_maps[i].rows); Mat tmp = weights_sum(roi); tmp.setTo(1, tmp < numeric_limits::epsilon()); divide(weight_maps[i], tmp, weight_maps[i]); } return dst_roi; } MultiBandBlender::MultiBandBlender(int try_gpu, int num_bands, int weight_type) { setNumBands(num_bands); #ifdef HAVE_OPENCV_GPU can_use_gpu_ = try_gpu && gpu::getCudaEnabledDeviceCount(); #else (void)try_gpu; can_use_gpu_ = false; #endif CV_Assert(weight_type == CV_32F || weight_type == CV_16S); weight_type_ = weight_type; } void MultiBandBlender::prepare(Rect dst_roi) { dst_roi_final_ = dst_roi; // Crop unnecessary bands double max_len = static_cast(max(dst_roi.width, dst_roi.height)); num_bands_ = min(actual_num_bands_, static_cast(ceil(log(max_len) / log(2.0)))); // Add border to the final image, to ensure sizes are divided by (1 << num_bands_) dst_roi.width += ((1 << num_bands_) - dst_roi.width % (1 << num_bands_)) % (1 << num_bands_); dst_roi.height += ((1 << num_bands_) - dst_roi.height % (1 << num_bands_)) % (1 << num_bands_); Blender::prepare(dst_roi); dst_pyr_laplace_.resize(num_bands_ + 1); dst_pyr_laplace_[0] = dst_; dst_band_weights_.resize(num_bands_ + 1); dst_band_weights_[0].create(dst_roi.size(), weight_type_); dst_band_weights_[0].setTo(0); for (int i = 1; i <= num_bands_; ++i) { dst_pyr_laplace_[i].create((dst_pyr_laplace_[i - 1].rows + 1) / 2, (dst_pyr_laplace_[i - 1].cols + 1) / 2, CV_16SC3); dst_band_weights_[i].create((dst_band_weights_[i - 1].rows + 1) / 2, (dst_band_weights_[i - 1].cols + 1) / 2, weight_type_); dst_pyr_laplace_[i].setTo(Scalar::all(0)); dst_band_weights_[i].setTo(0); } } void MultiBandBlender::feed(const Mat &img, const Mat &mask, Point tl) { CV_Assert(img.type() == CV_16SC3 || img.type() == CV_8UC3); CV_Assert(mask.type() == CV_8U); // Keep source image in memory with small border int gap = 3 * (1 << num_bands_); Point tl_new(max(dst_roi_.x, tl.x - gap), max(dst_roi_.y, tl.y - gap)); Point br_new(min(dst_roi_.br().x, tl.x + img.cols + gap), min(dst_roi_.br().y, tl.y + img.rows + gap)); // Ensure coordinates of top-left, bottom-right corners are divided by (1 << num_bands_). // After that scale between layers is exactly 2. // // We do it to avoid interpolation problems when keeping sub-images only. There is no such problem when // image is bordered to have size equal to the final image size, but this is too memory hungry approach. tl_new.x = dst_roi_.x + (((tl_new.x - dst_roi_.x) >> num_bands_) << num_bands_); tl_new.y = dst_roi_.y + (((tl_new.y - dst_roi_.y) >> num_bands_) << num_bands_); int width = br_new.x - tl_new.x; int height = br_new.y - tl_new.y; width += ((1 << num_bands_) - width % (1 << num_bands_)) % (1 << num_bands_); height += ((1 << num_bands_) - height % (1 << num_bands_)) % (1 << num_bands_); br_new.x = tl_new.x + width; br_new.y = tl_new.y + height; int dy = max(br_new.y - dst_roi_.br().y, 0); int dx = max(br_new.x - dst_roi_.br().x, 0); tl_new.x -= dx; br_new.x -= dx; tl_new.y -= dy; br_new.y -= dy; int top = tl.y - tl_new.y; int left = tl.x - tl_new.x; int bottom = br_new.y - tl.y - img.rows; int right = br_new.x - tl.x - img.cols; // Create the source image Laplacian pyramid Mat img_with_border; copyMakeBorder(img, img_with_border, top, bottom, left, right, BORDER_REFLECT); vector src_pyr_laplace; if (can_use_gpu_ && img_with_border.depth() == CV_16S) createLaplacePyrGpu(img_with_border, num_bands_, src_pyr_laplace); else createLaplacePyr(img_with_border, num_bands_, src_pyr_laplace); // Create the weight map Gaussian pyramid Mat weight_map; vector weight_pyr_gauss(num_bands_ + 1); if(weight_type_ == CV_32F) { mask.convertTo(weight_map, CV_32F, 1./255.); } else// weight_type_ == CV_16S { mask.convertTo(weight_map, CV_16S); add(weight_map, 1, weight_map, mask != 0); } copyMakeBorder(weight_map, weight_pyr_gauss[0], top, bottom, left, right, BORDER_CONSTANT); for (int i = 0; i < num_bands_; ++i) pyrDown(weight_pyr_gauss[i], weight_pyr_gauss[i + 1]); int y_tl = tl_new.y - dst_roi_.y; int y_br = br_new.y - dst_roi_.y; int x_tl = tl_new.x - dst_roi_.x; int x_br = br_new.x - dst_roi_.x; // Add weighted layer of the source image to the final Laplacian pyramid layer if(weight_type_ == CV_32F) { for (int i = 0; i <= num_bands_; ++i) { for (int y = y_tl; y < y_br; ++y) { int y_ = y - y_tl; const Point3_* src_row = src_pyr_laplace[i].ptr >(y_); Point3_* dst_row = dst_pyr_laplace_[i].ptr >(y); const float* weight_row = weight_pyr_gauss[i].ptr(y_); float* dst_weight_row = dst_band_weights_[i].ptr(y); for (int x = x_tl; x < x_br; ++x) { int x_ = x - x_tl; dst_row[x].x += static_cast(src_row[x_].x * weight_row[x_]); dst_row[x].y += static_cast(src_row[x_].y * weight_row[x_]); dst_row[x].z += static_cast(src_row[x_].z * weight_row[x_]); dst_weight_row[x] += weight_row[x_]; } } x_tl /= 2; y_tl /= 2; x_br /= 2; y_br /= 2; } } else// weight_type_ == CV_16S { for (int i = 0; i <= num_bands_; ++i) { for (int y = y_tl; y < y_br; ++y) { int y_ = y - y_tl; const Point3_* src_row = src_pyr_laplace[i].ptr >(y_); Point3_* dst_row = dst_pyr_laplace_[i].ptr >(y); const short* weight_row = weight_pyr_gauss[i].ptr(y_); short* dst_weight_row = dst_band_weights_[i].ptr(y); for (int x = x_tl; x < x_br; ++x) { int x_ = x - x_tl; dst_row[x].x += short((src_row[x_].x * weight_row[x_]) >> 8); dst_row[x].y += short((src_row[x_].y * weight_row[x_]) >> 8); dst_row[x].z += short((src_row[x_].z * weight_row[x_]) >> 8); dst_weight_row[x] += weight_row[x_]; } } x_tl /= 2; y_tl /= 2; x_br /= 2; y_br /= 2; } } } void MultiBandBlender::blend(Mat &dst, Mat &dst_mask) { for (int i = 0; i <= num_bands_; ++i) normalizeUsingWeightMap(dst_band_weights_[i], dst_pyr_laplace_[i]); if (can_use_gpu_) restoreImageFromLaplacePyrGpu(dst_pyr_laplace_); else restoreImageFromLaplacePyr(dst_pyr_laplace_); dst_ = dst_pyr_laplace_[0]; dst_ = dst_(Range(0, dst_roi_final_.height), Range(0, dst_roi_final_.width)); dst_mask_ = dst_band_weights_[0] > WEIGHT_EPS; dst_mask_ = dst_mask_(Range(0, dst_roi_final_.height), Range(0, dst_roi_final_.width)); dst_pyr_laplace_.clear(); dst_band_weights_.clear(); Blender::blend(dst, dst_mask); } ////////////////////////////////////////////////////////////////////////////// // Auxiliary functions void normalizeUsingWeightMap(const Mat& weight, Mat& src) { #ifdef HAVE_TEGRA_OPTIMIZATION if(tegra::normalizeUsingWeightMap(weight, src)) return; #endif CV_Assert(src.type() == CV_16SC3); if(weight.type() == CV_32FC1) { for (int y = 0; y < src.rows; ++y) { Point3_ *row = src.ptr >(y); const float *weight_row = weight.ptr(y); for (int x = 0; x < src.cols; ++x) { row[x].x = static_cast(row[x].x / (weight_row[x] + WEIGHT_EPS)); row[x].y = static_cast(row[x].y / (weight_row[x] + WEIGHT_EPS)); row[x].z = static_cast(row[x].z / (weight_row[x] + WEIGHT_EPS)); } } } else { CV_Assert(weight.type() == CV_16SC1); for (int y = 0; y < src.rows; ++y) { const short *weight_row = weight.ptr(y); Point3_ *row = src.ptr >(y); for (int x = 0; x < src.cols; ++x) { int w = weight_row[x] + 1; row[x].x = static_cast((row[x].x << 8) / w); row[x].y = static_cast((row[x].y << 8) / w); row[x].z = static_cast((row[x].z << 8) / w); } } } } void createWeightMap(const Mat &mask, float sharpness, Mat &weight) { CV_Assert(mask.type() == CV_8U); distanceTransform(mask, weight, CV_DIST_L1, 3); threshold(weight * sharpness, weight, 1.f, 1.f, THRESH_TRUNC); } void createLaplacePyr(const Mat &img, int num_levels, vector &pyr) { #ifdef HAVE_TEGRA_OPTIMIZATION if(tegra::createLaplacePyr(img, num_levels, pyr)) return; #endif pyr.resize(num_levels + 1); if(img.depth() == CV_8U) { if(num_levels == 0) { img.convertTo(pyr[0], CV_16S); return; } Mat downNext; Mat current = img; pyrDown(img, downNext); for(int i = 1; i < num_levels; ++i) { Mat lvl_up; Mat lvl_down; pyrDown(downNext, lvl_down); pyrUp(downNext, lvl_up, current.size()); subtract(current, lvl_up, pyr[i-1], noArray(), CV_16S); current = downNext; downNext = lvl_down; } { Mat lvl_up; pyrUp(downNext, lvl_up, current.size()); subtract(current, lvl_up, pyr[num_levels-1], noArray(), CV_16S); downNext.convertTo(pyr[num_levels], CV_16S); } } else { pyr[0] = img; for (int i = 0; i < num_levels; ++i) pyrDown(pyr[i], pyr[i + 1]); Mat tmp; for (int i = 0; i < num_levels; ++i) { pyrUp(pyr[i + 1], tmp, pyr[i].size()); subtract(pyr[i], tmp, pyr[i]); } } } void createLaplacePyrGpu(const Mat &img, int num_levels, vector &pyr) { #ifdef HAVE_OPENCV_GPU pyr.resize(num_levels + 1); vector gpu_pyr(num_levels + 1); gpu_pyr[0].upload(img); for (int i = 0; i < num_levels; ++i) gpu::pyrDown(gpu_pyr[i], gpu_pyr[i + 1]); gpu::GpuMat tmp; for (int i = 0; i < num_levels; ++i) { gpu::pyrUp(gpu_pyr[i + 1], tmp); gpu::subtract(gpu_pyr[i], tmp, gpu_pyr[i]); gpu_pyr[i].download(pyr[i]); } gpu_pyr[num_levels].download(pyr[num_levels]); #else (void)img; (void)num_levels; (void)pyr; #endif } void restoreImageFromLaplacePyr(vector &pyr) { if (pyr.empty()) return; Mat tmp; for (size_t i = pyr.size() - 1; i > 0; --i) { pyrUp(pyr[i], tmp, pyr[i - 1].size()); add(tmp, pyr[i - 1], pyr[i - 1]); } } void restoreImageFromLaplacePyrGpu(vector &pyr) { #ifdef HAVE_OPENCV_GPU if (pyr.empty()) return; vector gpu_pyr(pyr.size()); for (size_t i = 0; i < pyr.size(); ++i) gpu_pyr[i].upload(pyr[i]); gpu::GpuMat tmp; for (size_t i = pyr.size() - 1; i > 0; --i) { gpu::pyrUp(gpu_pyr[i], tmp); gpu::add(tmp, gpu_pyr[i - 1], gpu_pyr[i - 1]); } gpu_pyr[0].download(pyr[0]); #else (void)pyr; #endif } } // namespace detail } // namespace cv