gis如何无缝拼接两张图_opencv图像拼接

int MyVideoStitcher::Prepare(vector<Mat> &src)
{
cv::setBreakOnError(true);
int num_images = static_cast<int>(src.size());
if (num_images < 2)
{
printf("Need more images");
return STITCH_CONFIG_ERROR;
}
int cudaStatus = testGPU();
if( is_try_gpu_ && cudaStatus != 0 )
{
//printf("GPU acceleration failed! Error code: " <<  cudaStatus << " Please ensure that you have a CUDA-capable GPU installed!");
printf("Stitching with CPU next ...");
return STITCH_CONFIG_ERROR;
}
int flag;
warp_type_ = "apap";
if(warp_type_ == "apap")
flag = PrepareAPAP(src);
else
flag = PrepareClassical(src);
if(flag == 0 && is_try_gpu_)
{
flag = initGPU(num_images);
if(flag != 0)
return flag;
C2GInitData *c2g_data = new C2GInitData[num_images];
for(int i = 0; i < num_images; i++)
{
c2g_data[i].xmap			= xmaps_[i].ptr<float>(0);
c2g_data[i].ymap			= ymaps_[i].ptr<float>(0);
c2g_data[i].ec_weight		= ec_weight_maps_[i].ptr<float>(0);
c2g_data[i].blend_weight	= blend_weight_maps_[i].ptr<float>(0);
c2g_data[i].total_weight	= total_weight_maps_[i].ptr<float>(0);
c2g_data[i].height			= src[i].rows;
c2g_data[i].width			= src[i].cols;
c2g_data[i].warped_height	= xmaps_[i].rows;
c2g_data[i].warped_width	= xmaps_[i].cols;
c2g_data[i].corner_x		= corners_[i].x - dst_roi_.x;
c2g_data[i].corner_y		= corners_[i].y - dst_roi_.y;
}
initdataCopy2GPU(c2g_data, dst_roi_.height, dst_roi_.width);
}
if(flag == STITCH_SUCCESS)
{
printf("\t~Prepare complete");
is_prepared_ = true;
}
return flag;
}
int MyVideoStitcher::PrepareAPAP(vector<Mat> &src)
{
int num_images = static_cast<int>(src.size());
this->InitMembers(num_images);
// 计算一些放缩的尺度，在特征检测和计算接缝的时候，为了提高程序效率，可以对源图像进行一些放缩
work_megapix_ = -1;	//	先不考虑放缩
seam_megapix_ = -1;	//	先不考虑放缩
this->SetScales(src);
// 特征检测
vector<ImageFeatures> features(num_images);
this->FindFeatures(src, features);
// 特征匹配，并去掉噪声图片
vector<MatchesInfo> pairwise_matches;
this->MatchImages(features, pairwise_matches);
// APAP算法
APAPWarper apap_warper;
apap_warper.buildMaps(src, features, pairwise_matches, xmaps_, ymaps_, corners_);
for(int i = 0; i < num_images; i++)
sizes_[i] = xmaps_[i].size();
dst_roi_ = resultRoi(corners_, sizes_);
// 计算接缝
vector<Mat> seamed_masks(num_images);
vector<Mat> images_warped(num_images);
vector<Mat> init_masks(num_images);
for(int i = 0; i < num_images; i++)
{
init_masks[i].create(src[i].size(), CV_8U);
init_masks[i].setTo(Scalar::all(255));
remap(src[i], images_warped[i], xmaps_[i], ymaps_[i], INTER_LINEAR);
remap(init_masks[i], final_warped_masks_[i], xmaps_[i], ymaps_[i], INTER_NEAREST, BORDER_CONSTANT);
final_warped_masks_[i].copyTo(seamed_masks[i]);
}
this->FindSeam(images_warped, seamed_masks);
printf("find seam");
// 曝光补偿
compensator_.createWeightMaps(corners_, images_warped, final_warped_masks_, ec_weight_maps_);
// 曝光补偿时，各像素权值也要resize一下
compensator_.gainMapResize(sizes_, ec_weight_maps_);
printf("compensate");
images_warped.clear();
// 计算融合时，各像素的权值
Size dst_sz = dst_roi_.size();
//cout << "dst size: " << dst_sz << endl;
float blend_width = sqrt(static_cast<float>(dst_sz.area())) * blend_strength_ / 100.f;
blender_.setSharpness(1.f / blend_width);
for(int i = 0; i < num_images; i++)
final_blend_masks_[i] = final_warped_masks_[i] & seamed_masks[i];
blender_.createWeightMaps(dst_roi_, corners_, seamed_masks, blend_weight_maps_);
return STITCH_SUCCESS;
}
int MyVideoStitcher::FindFeatures(vector<Mat> &src, vector<ImageFeatures> &features)
{
Ptr<Feature2D> finder;
if (features_type_ == "surf")
{
#ifdef HAVE_OPENCV_GPU
if (is_try_gpu_ && gpu::getCudaEnabledDeviceCount() > 0)
finder = new SurfFeaturesFinderGpu();
else
#endif
finder = xfeatures2d::SURF::create();
}
else if (features_type_ == "orb")
{
finder = ORB::create();
}
else
{
cout << "Unknown 2D features type: '" << features_type_ << "'.\n";
return STITCH_CONFIG_ERROR;
}
int num_images = static_cast<int>(src.size());
Mat full_img, img;
for (int i = 0; i < num_images; ++i)
{
full_img = src[i].clone();//
if (work_megapix_ < 0)
img = full_img;
else
resize(full_img, img, Size(), work_scale_, work_scale_);
computeImageFeatures(finder, img, features[i]);
//LOGLN("Features in image #" << i+1 << "("<<img.size()<< "): " << features[i].keypoints.size());
features[i].img_idx = i;
}
//finder->collectGarbage();
full_img.release();
img.release();
return STITCH_SUCCESS;
}
/*
* 特征匹配，然后去除噪声图片。本代码实现时，一旦出现噪声图片，就终止算法
* 返回值：
*		0	——	正常
*		-2	——	存在噪声图片
*/
int MyVideoStitcher::MatchImages(vector<ImageFeatures> &features, vector<MatchesInfo> &pairwise_matches)
{
int total_num_images = static_cast<int>(features.size());
BestOf2NearestMatcher matcher(is_try_gpu_, match_conf_);
matcher(features, pairwise_matches);
matcher.collectGarbage();
// 去除噪声图像
vector<int> indices = leaveBiggestComponent(features, pairwise_matches, conf_thresh_);
// 一旦出现噪声图片，就终止算法
int num_images = static_cast<int>(indices.size());
if (num_images != total_num_images)
{
//LOGLN(total_num_images - num_images << " videos are invaild");
return STITCH_NOISE;
}
return STITCH_SUCCESS;
}