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209 213 241 171 265 189 341 175 380 207 404 175 440 204 393 170 421 192 382 174 398 187 313 173 326 184 329 174 338 183 374 167 389 181 +/scail/group/deeplearning/driving_data/andriluka/IMAGES/driving_data_q50_data/all_extracted/6-19-14-101-split_0_101S_d2/6-19-14-101-split_0_101S_d2_001091.jpeg 9 170 168 192 186 27 164 90 193 212 176 227 184 377 160 397 187 387 176 404 190 403 174 433 202 302 173 327 198 313 171 385 238 234 178 284 216 +/scail/group/deeplearning/driving_data/andriluka/IMAGES/driving_data_q50_data/all_extracted/4-30-14-280-split_0_280N_d2/4-30-14-280-split_0_280N_d2_001901.jpeg 4 180 166 196 177 280 171 318 202 357 177 406 209 402 174 437 201 +/scail/group/deeplearning/driving_data/andriluka/IMAGES/driving_data_q50_data/all_extracted/6-17-14-280-split_0_280n_c2/6-17-14-280-split_0_280n_c2_000131.jpeg 6 418 171 429 180 441 173 453 182 417 181 462 216 396 179 420 197 356 177 376 194 182 177 259 232 +/scail/group/deeplearning/driving_data/andriluka/IMAGES/driving_data_q50_data/all_extracted/4-2-14-monterey-split_0_280S_a2/4-2-14-monterey-split_0_280S_a2_000901.jpeg 6 293 170 314 186 395 177 448 216 316 173 342 191 309 177 370 225 222 171 236 182 153 176 227 232 From 8e25844b9ed485195ff3377afdec7a33550e4e63 Mon Sep 17 00:00:00 2001 From: Tao Wang Date: Tue, 4 Nov 2014 15:34:11 -0800 Subject: [PATCH 2/6] changed lmdb path to my home dir under /deep/ --- models/brody/train_val_brody.prototxt | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/models/brody/train_val_brody.prototxt b/models/brody/train_val_brody.prototxt index e98103aa5a7..093d3fe925c 100644 --- a/models/brody/train_val_brody.prototxt +++ b/models/brody/train_val_brody.prototxt @@ -6,7 +6,7 @@ layers { type: DATA top: "data" data_param { - source: "driving_img_train" + source: "/deep/group/driving_data/twangcat/lmdb/driving_img_train" backend: LMDB batch_size: 25 } @@ -22,7 +22,7 @@ layers { type: DATA top: "label" data_param { - source: "driving_label_train" + source: "/deep/group/driving_data/twangcat/lmdb/driving_label_train" backend: LMDB batch_size: 25 } @@ -35,7 +35,7 @@ layers { type: DATA top: "data" data_param { - source: "driving_img_test" + source: "/deep/group/driving_data/twangcat/lmdb/driving_img_test" backend: LMDB batch_size: 25 } @@ -51,7 +51,7 @@ layers { type: DATA top: "label" data_param { - source: "driving_label_test" + source: "/deep/group/driving_data/twangcat/lmdb/driving_label_test" backend: LMDB batch_size: 25 } From ddf1cdfe7cb7396f2d7793fd4d2cbbd693411b99 Mon Sep 17 00:00:00 2001 From: Tao Wang Date: Thu, 6 Nov 2014 20:43:58 -0800 Subject: [PATCH 3/6] debugging data loader --- .gitignore | 2 +- models/brody/solver.prototxt | 4 +- src/caffe/.__afs6319 | Bin 0 -> 53248 bytes src/caffe/solver.cpp | 65 ++++++++++++++++++++++++++++++ tools/convert_detection_label.cpp | 2 +- 5 files changed, 69 insertions(+), 4 deletions(-) create mode 100644 src/caffe/.__afs6319 diff --git a/.gitignore b/.gitignore index ed7e358b43b..6839a0aa24e 100644 --- a/.gitignore +++ b/.gitignore @@ -36,7 +36,7 @@ distribute/* python/caffe/proto/ # Editor temporaries -*.swp +*.sw? *~ # IPython notebook checkpoints diff --git a/models/brody/solver.prototxt b/models/brody/solver.prototxt index 347fb123911..c1501649dbc 100644 --- a/models/brody/solver.prototxt +++ b/models/brody/solver.prototxt @@ -5,9 +5,9 @@ base_lr: 0.0000001 lr_policy: "step" gamma: 0.1 stepsize: 100000 -display: 20 +display: 10 max_iter: 1450000 -momentum: 0. +momentum: 0.9 weight_decay: 0.00005 snapshot: 10000 snapshot_prefix: "models/brody/caffe_brody_train" diff --git a/src/caffe/.__afs6319 b/src/caffe/.__afs6319 new file mode 100644 index 0000000000000000000000000000000000000000..2365e5d4e5d3c6f9ae73658818832e7cb8ec7f3f GIT binary patch literal 53248 zcmeI533y~jb?+Nv!WJABKNg#ii{|sVHIiDh*uiKtg7It~Jl2dy8XGbUO{@D#YO6PT 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z0sIko7T62^6FtGZK@J=QPY0)iAE6ugG!WfE5zK*EZ~&YM{$ z+Ee7rT!oGqkImlbENh2oaE_CcnJvt5n!GX~2ROf*`I<@<}84k8c-6g5> z13;Qvg`2r$DEcjV%KT*_tLDuGRdOQrn|*5Z0bN3ygxiDLg&V7qZYlyXaZEGwOomOl zV|_($Y3Ucjq*ZhPM*fd@n^~)XE-`cs)I}^8rIUGSMUQTQ<196qj*yptL*et zHEp^dVTLHTh-VaN`h})%Em@|cW;9vp4c>w@YWM!qnSiEccStPJn@CDcs@Ym3)HD6cm1<{8=%Cf|ZA&glEi=&uMswBE+aI)zOd!vx> zOtqHYKIRSvM}Z?Qtevg|o7)oV%Ivlf?Mn_ zOFPUNO}5m8ZGjrb+ygG@B*yxP3U;h;fU3+OX4}#sQ4fXD+CZiaM&_p6HCUD{BC@22 zZYGpyt%l^@g}9ZdVD8p+f{rLqyb2YD$;+;c+UMl9Lo;W%-J@Q4nVm!vhsCeAkw814E3JSg!D=2VyjPR z$+5vyDmVGDC#YH~NL3Z6E7LNQbET9+pG29-6GrzbFLItuYCJUqtEa-RBU)C=*#^cR zw<_}=nJLs8b`mdxiO7N8)PQ4~5DUEXT1&^Qm`*ft zY1?kUNTXR88{;y=Qcun&u49S%hP5}Yp*DmT!w{WLWJuM`lD+h=vOr>or&6jh+IywG zF?<=*aYEaK2^H;qHa{)xu6t-qqcTmcVkc>%ItbR7RU5I6O)F)m8 +#include +#include +#include +#include // std::cout, std::ios +#include + + + namespace caffe { template @@ -190,6 +200,61 @@ void Solver::Solve(const char* resume_file) { if (display) { LOG(INFO) << "Iteration " << iter_ << ", loss = " << loss; const vector*>& result = net_->output_blobs(); + //added by Tao. for debugging purpose only + const vector& blob_names = net_->blob_names(); + const vector > >& blobs = net_->blobs(); + string str1("data"); + string str2("label"); + string str3("pixel-label"); + string str4("bb-label"); + for (int j = 0; j < blobs.size(); ++j) { + if(blob_names[j].compare(str1)==0) + { + LOG(INFO) << "data " << blobs[j]->num()<<" "<channels()<<" "<height()<<" "<width(); + for(int n=0; nnum(); ++n) + { + int image_id = this->iter_*5+n; + const Dtype* foo; + foo = blobs[j]->cpu_data()+n*blobs[j]->channels()*blobs[j]->height()*blobs[j]->width(); + cv::Mat curr_img = cv::Mat(blobs[j]->height(), blobs[j]->width(), CV_32FC3, cv::Scalar(0,0,255)); + double minVal, maxVal; + cv::minMaxLoc(curr_img, &minVal, &maxVal); //find minimum and maximum intensities + //std::copy ( foo, foo+blobs[j]->channels()*blobs[j]->height()*blobs[j]->width(), curr_img.data ); + for(int kk=0; kkchannels();++kk) + { + for(int yy=0; yyheight();++yy) + { + for(int xx=0; xxwidth();++xx) + { + //std::cout<<*(foo+(((n*blobs[j]->channels() + kk) * blobs[j]->height() + yy) * blobs[j]->width() + xx))<<" "; + //*(curr_img.data+((yy * blobs[j]->width() + xx) * 3 + kk))=*(foo+(((n*blobs[j]->channels() + kk) * blobs[j]->height() + yy) * blobs[j]->width() + xx)); + curr_img.at(yy,xx)[kk]=*(foo+(((n*blobs[j]->channels() + kk) * blobs[j]->height() + yy) * blobs[j]->width() + xx)); + } + } + } + //std::cout<num()<<" "<channels()<<" "<height()<<" "<width(); + } + if(blob_names[j].compare(str3)==0) + { + LOG(INFO) << "pixel-label " << blobs[j]->num()<<" "<channels()<<" "<height()<<" "<width(); + } + if(blob_names[j].compare(str4)==0) + { + LOG(INFO) << "bb-label " << blobs[j]->num()<<" "<channels()<<" "<height()<<" "<width(); + }*/ + } + //end int score_index = 0; for (int j = 0; j < result.size(); ++j) { const Dtype* result_vec = result[j]->cpu_data(); diff --git a/tools/convert_detection_label.cpp b/tools/convert_detection_label.cpp index 8d1f6b9a562..8043c4b0d25 100644 --- a/tools/convert_detection_label.cpp +++ b/tools/convert_detection_label.cpp @@ -73,7 +73,7 @@ bool ReadBoundingBoxLabelToDatum( float ymax = bbs[i + 3]; float w = xmax - xmin; float h = ymax - ymin; - + // shrink bboxes int gxmin = cvRound((xmin + w / 4) * scaling); int gxmax = cvRound((xmax - w / 4) * scaling); int gymin = cvRound((ymin + h / 4) * scaling); From ff97eab18af8e59f5e7bebe8e4794b1ff9f0cc3f Mon Sep 17 00:00:00 2001 From: Tao Wang Date: Fri, 14 Nov 2014 21:03:54 -0800 Subject: [PATCH 4/6] added video data loader. Now need to add lane label reader --- include/caffe/data_layers.hpp | 43 ++++- include/caffe/util/io.hpp | 3 + models/brody/train_val_brody.prototxt | 15 +- src/caffe/.__afs6319 | Bin 53248 -> 53248 bytes src/caffe/data_transformer.cpp | 2 +- src/caffe/layer_factory.cpp | 3 +- src/caffe/layers/video_data_layer.cpp | 232 ++++++++++++++++++++++++++ src/caffe/proto/caffe.proto | 35 +++- src/caffe/solver.cpp | 95 +++++++---- src/caffe/util/io.cpp | 10 ++ 10 files changed, 397 insertions(+), 41 deletions(-) create mode 100644 src/caffe/layers/video_data_layer.cpp diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp index 8e2637b0658..7ccb7a6e805 100644 --- a/include/caffe/data_layers.hpp +++ b/include/caffe/data_layers.hpp @@ -4,7 +4,6 @@ #include #include #include - #include "boost/scoped_ptr.hpp" #include "hdf5.h" #include "leveldb/db.h" @@ -17,6 +16,10 @@ #include "caffe/internal_thread.hpp" #include "caffe/layer.hpp" #include "caffe/proto/caffe.pb.h" +#include +#include +#include +#include namespace caffe { @@ -272,6 +275,44 @@ class ImageDataLayer : public BasePrefetchingDataLayer { int lines_id_; }; + +/** + * @brief Provides data to the Net from video files. + * + * TODO(dox): thorough documentation for Forward and proto params. + */ +template +class VideoDataLayer : public BasePrefetchingDataLayer { + public: + explicit VideoDataLayer(const LayerParameter& param) + : BasePrefetchingDataLayer(param) {} + virtual ~VideoDataLayer(); + virtual void DataLayerSetUp(const vector*>& bottom, + vector*>* top); + + virtual inline LayerParameter_LayerType type() const { + return LayerParameter_LayerType_VIDEO_DATA; + } + virtual inline int ExactNumBottomBlobs() const { return 0; } + virtual inline int ExactNumTopBlobs() const { return 1; } + + protected: + shared_ptr prefetch_rng_; + virtual void ShuffleBatches(); + virtual void InternalThreadEntry(); + bool ReadVideoFrameToDatum(const string& filename, size_t id, + size_t persp, const int height, const int width, Datum* datum); + +//inline bool ReadVideoBatchToDatum(const string& filename, std::vector frameIds, +// std::vectortrans, Datum* datum) { + // return ReadVideoBatchToDatum(filename, frameIds, trans, 0, 0, datum); +//} + + vector, std::vector > > > lines_; + int lines_id_; + cv::VideoCapture* cap; +}; + /** * @brief Provides data to the Net from memory. * diff --git a/include/caffe/util/io.hpp b/include/caffe/util/io.hpp index 7edd1a4149c..a27f2528a0f 100644 --- a/include/caffe/util/io.hpp +++ b/include/caffe/util/io.hpp @@ -105,6 +105,9 @@ inline bool ReadImageToDatum(const string& filename, const int label, return ReadImageToDatum(filename, label, 0, 0, datum); } + + + leveldb::Options GetLevelDBOptions(); template diff --git a/models/brody/train_val_brody.prototxt b/models/brody/train_val_brody.prototxt index 23c08d1206f..b70ea376e0e 100644 --- a/models/brody/train_val_brody.prototxt +++ b/models/brody/train_val_brody.prototxt @@ -3,12 +3,11 @@ name: "BrodyNet" # Training input. layers { name: "data" - type: DATA + type: VIDEO_DATA top: "data" - data_param { - source: "/deep/group/driving_data/twangcat/lmdb/driving_img_train" - backend: LMDB - batch_size: 5 + video_data_param { + source: "/scail/group/deeplearning/driving_data/twangcat/schedules/q50_multilane_planar_train_schedule1_batch20_2cam.txt" + batch_size: 20 } transform_param { mean_file: "driving_img_mean.binaryproto" @@ -24,7 +23,7 @@ layers { data_param { source: "/deep/group/driving_data/twangcat/lmdb/driving_label_train" backend: LMDB - batch_size: 5 + batch_size: 20 } include: { phase: TRAIN } } @@ -37,7 +36,7 @@ layers { data_param { source: "/deep/group/driving_data/twangcat/lmdb/driving_img_test" backend: LMDB - batch_size: 5 + batch_size: 20 } transform_param { mean_file: "driving_img_mean.binaryproto" @@ -53,7 +52,7 @@ layers { data_param { source: "/deep/group/driving_data/twangcat/lmdb/driving_label_test" backend: LMDB - batch_size: 5 + batch_size: 20 } include: { phase: TEST } } diff --git a/src/caffe/.__afs6319 b/src/caffe/.__afs6319 index 2365e5d4e5d3c6f9ae73658818832e7cb8ec7f3f..65eafff2724f3ae981df768dd0cb16a417bb590e 100644 GIT binary patch delta 39 tcmZozz}&EaSv1KY%+puFT+fIB2m}}ye3fECCT$eG#mpGG`2(}S834#s3e5lj delta 39 tcmZozz}&EaSv1KY%+puFT+fIB2m}}ynw4Tg+BS;bVrFF6{DE2E3;@X{3Zei2 diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp index 7150fd99c18..452e4c4a31a 100644 --- a/src/caffe/data_transformer.cpp +++ b/src/caffe/data_transformer.cpp @@ -108,4 +108,4 @@ unsigned int DataTransformer::Rand() { INSTANTIATE_CLASS(DataTransformer); -} // namespace caffe +} // namespace caffe \ No newline at end of file diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp index b78167f21eb..6002bd07f0e 100644 --- a/src/caffe/layer_factory.cpp +++ b/src/caffe/layer_factory.cpp @@ -1,5 +1,4 @@ #include - #include "caffe/layer.hpp" #include "caffe/proto/caffe.pb.h" #include "caffe/vision_layers.hpp" @@ -249,6 +248,8 @@ Layer* GetLayer(const LayerParameter& param) { return new SplitLayer(param); case LayerParameter_LayerType_TANH: return GetTanHLayer(name, param); + case LayerParameter_LayerType_VIDEO_DATA: + return new VideoDataLayer(param); case LayerParameter_LayerType_WINDOW_DATA: return new WindowDataLayer(param); case LayerParameter_LayerType_NONE: diff --git a/src/caffe/layers/video_data_layer.cpp b/src/caffe/layers/video_data_layer.cpp new file mode 100644 index 00000000000..ac814fbbe0f --- /dev/null +++ b/src/caffe/layers/video_data_layer.cpp @@ -0,0 +1,232 @@ +#include // NOLINT(readability/streams) +#include // NOLINT(readability/streams) +#include +#include +#include +#include +#include "caffe/data_layers.hpp" +#include "caffe/layer.hpp" +#include "caffe/util/io.hpp" +#include "caffe/util/math_functions.hpp" +#include "caffe/util/rng.hpp" +#include +namespace caffe { + +template +VideoDataLayer::~VideoDataLayer() { + this->JoinPrefetchThread(); +} + +template +bool VideoDataLayer:: ReadVideoFrameToDatum(const string& filename, size_t id, size_t persp, + const int height, const int width, Datum* datum) { + int cam_num = (int)(filename.at(filename.length()-5) - '0'); + cam_num = cam_num>2?2:cam_num; // 3rd cam is for testing only. So using cam2 distortions as dummy + //int numPersp = mTransforms.size()/2; + cv::Mat cv_img, cv_img_origin; + bool set_ok = this->cap->set(CV_CAP_PROP_POS_FRAMES, id ); + if(!set_ok) { + LOG(ERROR)<<"Failed to set video frame"; + return false; + } + bool read_ok = this->cap->read(cv_img_origin); + if(!read_ok) { + LOG(ERROR)<<"Failed to read video frame"; + return false; + } + // resize image if necessary + if (height > 0 && width > 0) { + cv::resize(cv_img_origin, cv_img, cv::Size(width, height)); + } else { + cv_img = cv_img_origin; + } + // apply perspective transform + //cv::Mat warpMatrix = mTransforms[persp+(cam_num-1)*numPersp]; + //cv::warpPerspective(cv_img, cv_img, warpMatrix, frame.size(), cv::INTER_LINEAR, cv::BORDER_REPLICATE); + // copy data to datum + int num_channels = 3; + datum->set_channels(num_channels); + datum->set_height(cv_img.rows); + datum->set_width(cv_img.cols); + datum->set_label(0); // dummy label for now. + datum->clear_data(); + datum->clear_float_data(); + string* datum_string = datum->mutable_data(); + for (int c = 0; c < num_channels; ++c) { + for (int h = 0; h < cv_img.rows; ++h) { + for (int w = 0; w < cv_img.cols; ++w) { + datum_string->push_back( + static_cast(cv_img.at(h, w)[c])); + } + } + } + return true; +} + +template +void VideoDataLayer::DataLayerSetUp(const vector*>& bottom, + vector*>* top) { + const int new_height = this->layer_param_.video_data_param().new_height(); + const int new_width = this->layer_param_.video_data_param().new_width(); + CHECK((new_height == 0 && new_width == 0) || + (new_height > 0 && new_width > 0)) << "Current implementation requires " + "new_height and new_width to be set at the same time."; + // Read the file with filenames and labels + const string& source = this->layer_param_.video_data_param().source(); + LOG(INFO) << "Opening schedule file " << source; + std::ifstream infile(source.c_str()); + + string batch_string; + + string filename; + //while (infile >> batch_string) { + while (getline (infile, batch_string)) { + if(!infile) + { + if(infile.eof()) + { + LOG(INFO) << "Reached EOF of schedule file."; + break; + } + else + LOG(FATAL)<< "Error while reading schedule file. Possibly corrupted."; + } + std::vector batch_fields; + // first split a line into fields with delimiter ",". Fields should be [filename, frame_ids, transform_ids] + boost::split(batch_fields, batch_string, boost::is_any_of(","), + boost::token_compress_on); + if(batch_fields.size()!=3) + LOG(FATAL) << "Each line must have 3 fields separated by comma, " + < frame_ids_str; + std::vector frame_ids; + boost::split(frame_ids_str, batch_fields[1], boost::is_any_of(" "), + boost::token_compress_on); + for (int f=0; f trans_ids_str; + std::vector trans_ids; + boost::split(trans_ids_str, batch_fields[2], boost::is_any_of(" "), + boost::token_compress_on); + for (int f=0; flayer_param_.video_data_param().shuffle()) { + // randomly shuffle data + LOG(INFO) << "Shuffling batches"; + const unsigned int prefetch_rng_seed = caffe_rng_rand(); + prefetch_rng_.reset(new Caffe::RNG(prefetch_rng_seed)); + ShuffleBatches(); + } + LOG(INFO) << "A total of " << lines_.size() << " batches."; + + lines_id_ = 0; + // Check if we would need to randomly skip a few data points + if (this->layer_param_.video_data_param().rand_skip()) { + unsigned int skip = caffe_rng_rand() % + this->layer_param_.video_data_param().rand_skip(); + LOG(INFO) << "Skipping first " << skip << " data points."; + CHECK_GT(lines_.size(), skip) << "Not enough points to skip"; + lines_id_ = skip; + } + // Read a data batch, and use it to initialize the top blob. + this->cap = new cv::VideoCapture(lines_[lines_id_].first); + Datum datum; + CHECK(ReadVideoFrameToDatum(lines_[lines_id_].first, lines_[lines_id_].second.first[0], + lines_[lines_id_].second.second[0], new_height, new_width, &datum)); + this->cap->release(); + // image + const int crop_size = this->layer_param_.transform_param().crop_size(); + const int batch_size = this->layer_param_.video_data_param().batch_size(); + if (crop_size > 0) { + (*top)[0]->Reshape(batch_size, datum.channels(), crop_size, crop_size); + this->prefetch_data_.Reshape(batch_size, datum.channels(), crop_size, + crop_size); + } else { + (*top)[0]->Reshape(batch_size, datum.channels(), datum.height(), + datum.width()); + this->prefetch_data_.Reshape(batch_size, datum.channels(), datum.height(), + datum.width()); + } + LOG(INFO) << "output data size: " << (*top)[0]->num() << "," + << (*top)[0]->channels() << "," << (*top)[0]->height() << "," + << (*top)[0]->width(); + // label + //(*top)[1]->Reshape(batch_size, 1, 1, 1); + this->prefetch_label_.Reshape(batch_size, 1, 1, 1); + // datum size + this->datum_channels_ = datum.channels(); + this->datum_height_ = datum.height(); + this->datum_width_ = datum.width(); + this->datum_size_ = datum.channels() * datum.height() * datum.width(); +} + +template +void VideoDataLayer::ShuffleBatches() { + caffe::rng_t* prefetch_rng = + static_cast(prefetch_rng_->generator()); + shuffle(lines_.begin(), lines_.end(), prefetch_rng); +} + + + +// This function is used to create a thread that prefetches the data. +template +void VideoDataLayer::InternalThreadEntry() { + Datum datum; + CHECK(this->prefetch_data_.count()); + Dtype* top_data = this->prefetch_data_.mutable_cpu_data(); + //Dtype* top_label = this->prefetch_label_.mutable_cpu_data(); + VideoDataParameter video_data_param = this->layer_param_.video_data_param(); + const int batch_size = video_data_param.batch_size(); + const int new_height = video_data_param.new_height(); + const int new_width = video_data_param.new_width(); + + // datum scales + const int lines_size = lines_.size(); + string filename = lines_[lines_id_].first; + std::vector frameIds = lines_[lines_id_].second.first; + std::vector trans = lines_[lines_id_].second.second; + if (batch_size!=frameIds.size() || batch_size!=trans.size()) + LOG(ERROR)<<"Frame count mismatch!"; + LOG(INFO)<<"reading video file "<cap = new cv::VideoCapture(filename); + for (int item_id = 0; item_id < batch_size; ++item_id) { + // get a blob + //CHECK_GT(lines_size, lines_id_); + if (!ReadVideoFrameToDatum(filename, frameIds[item_id], trans[item_id], + new_height, new_width, &datum)) { + LOG(ERROR)<< "Error reading frame from video!"; + continue; + } + + // Apply transformations (mirror, crop...) to the data + this->data_transformer_.Transform(item_id, datum, this->mean_, top_data); + + // go to the next iter + lines_id_++; + if (lines_id_ >= lines_size) { + // We have reached the end. Restart from the first. + DLOG(INFO) << "Restarting data prefetching from start."; + lines_id_ = 0; + if (this->layer_param_.image_data_param().shuffle()) { + ShuffleBatches(); + } + } + } + this->cap->release(); +} + +INSTANTIATE_CLASS(VideoDataLayer); + +} // namespace caffe diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index 9395c38f3e9..0e69ef54e77 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -219,7 +219,7 @@ message LayerParameter { // line above the enum. Update the next available ID when you add a new // LayerType. // - // LayerType next available ID: 38 (last added: CONTRASTIVE_LOSS) + // LayerType next available ID: 39 (last added: VIDEO_DATA) enum LayerType { // "NONE" layer type is 0th enum element so that we don't cause confusion // by defaulting to an existent LayerType (instead, should usually error if @@ -260,8 +260,9 @@ message LayerParameter { SPLIT = 22; SLICE = 33; TANH = 23; - WINDOW_DATA = 24; THRESHOLD = 31; + VIDEO_DATA=38; + WINDOW_DATA = 24; } optional LayerType type = 5; // the layer type from the enum above @@ -316,6 +317,7 @@ message LayerParameter { optional SliceParameter slice_param = 31; optional TanHParameter tanh_param = 37; optional ThresholdParameter threshold_param = 25; + optional VideoDataParameter video_data_param = 41; optional WindowDataParameter window_data_param = 20; // Parameters for data pre-processing. @@ -524,6 +526,35 @@ message ImageDataParameter { optional bool mirror = 6 [default = false]; } +// Message that stores parameters used by VideoDataLayer +message VideoDataParameter { + // Specify the data source. + optional string source = 1; + // Specify the batch size. + optional uint32 batch_size = 4; + // The rand_skip variable is for the data layer to skip a few data points + // to avoid all asynchronous sgd clients to start at the same point. The skip + // point would be set as rand_skip * rand(0,1). Note that rand_skip should not + // be larger than the number of keys in the leveldb. + optional uint32 rand_skip = 7 [default = 0]; + // Whether or not VideoDataLayer should shuffle the list of files at every epoch. + optional bool shuffle = 8 [default = false]; + // It will also resize images if new_height or new_width are not zero. + optional uint32 new_height = 9 [default = 0]; + optional uint32 new_width = 10 [default = 0]; + // DEPRECATED. See TransformationParameter. For data pre-processing, we can do + // simple scaling and subtracting the data mean, if provided. Note that the + // mean subtraction is always carried out before scaling. + optional float scale = 2 [default = 1]; + optional string mean_file = 3; + // DEPRECATED. See TransformationParameter. Specify if we would like to randomly + // crop an image. + optional uint32 crop_size = 5 [default = 0]; + // DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror + // data. + optional bool mirror = 6 [default = false]; +} + // Message that stores parameters InfogainLossLayer message InfogainLossParameter { // Specify the infogain matrix source. diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp index 26bab64b2ca..aa1dd9e137e 100644 --- a/src/caffe/solver.cpp +++ b/src/caffe/solver.cpp @@ -207,52 +207,89 @@ void Solver::Solve(const char* resume_file) { string str2("label"); string str3("pixel-label"); string str4("bb-label"); + string save_dir("/scr/twangcat/caffenet_results/train/"); + vector save_imgs; + int quad_height; + int quad_width; + int batch_size; + const Dtype* pix_start; + const Dtype* bb_start; for (int j = 0; j < blobs.size(); ++j) { - if(blob_names[j].compare(str1)==0) + if(blob_names[j].compare(str3)==0) //pixel label + { + LOG(INFO) << "pixel-label " << blobs[j]->num()<<" "<channels()<<" "<height()<<" "<width(); + pix_start = blobs[j]->cpu_data(); + quad_height = blobs[j]->height(); + quad_width = blobs[j]->width(); + batch_size = blobs[j]->num(); + } + if(blob_names[j].compare(str4)==0) // bb label + { + LOG(INFO) << "bb-label " << blobs[j]->num()<<" "<channels()<<" "<height()<<" "<width(); + bb_start = blobs[j]->cpu_data(); + } + if(blob_names[j].compare(str1)==0) // actual image { LOG(INFO) << "data " << blobs[j]->num()<<" "<channels()<<" "<height()<<" "<width(); + const Dtype* data_start = blobs[j]->cpu_data(); for(int n=0; nnum(); ++n) { - int image_id = this->iter_*5+n; - const Dtype* foo; - foo = blobs[j]->cpu_data()+n*blobs[j]->channels()*blobs[j]->height()*blobs[j]->width(); cv::Mat curr_img = cv::Mat(blobs[j]->height(), blobs[j]->width(), CV_32FC3, cv::Scalar(0,0,255)); - double minVal, maxVal; - cv::minMaxLoc(curr_img, &minVal, &maxVal); //find minimum and maximum intensities - //std::copy ( foo, foo+blobs[j]->channels()*blobs[j]->height()*blobs[j]->width(), curr_img.data ); for(int kk=0; kkchannels();++kk) { for(int yy=0; yyheight();++yy) { for(int xx=0; xxwidth();++xx) { - //std::cout<<*(foo+(((n*blobs[j]->channels() + kk) * blobs[j]->height() + yy) * blobs[j]->width() + xx))<<" "; - //*(curr_img.data+((yy * blobs[j]->width() + xx) * 3 + kk))=*(foo+(((n*blobs[j]->channels() + kk) * blobs[j]->height() + yy) * blobs[j]->width() + xx)); - curr_img.at(yy,xx)[kk]=*(foo+(((n*blobs[j]->channels() + kk) * blobs[j]->height() + yy) * blobs[j]->width() + xx)); + curr_img.at(yy,xx)[kk]=*(data_start+(((n*blobs[j]->channels() + kk) * blobs[j]->height() + yy) * blobs[j]->width() + xx)); } } } - //std::cout<num()<<" "<channels()<<" "<height()<<" "<width(); - } - if(blob_names[j].compare(str3)==0) - { - LOG(INFO) << "pixel-label " << blobs[j]->num()<<" "<channels()<<" "<height()<<" "<width(); + } + int grid_dim=4; + int label_count = 0; + int label_height = quad_height*grid_dim; + int label_width = quad_width*grid_dim; + Dtype scaling = 1.0/8; + for(int n=0; niter_*5+n; + cv::Mat save_img = save_imgs[n]; + std::ostringstream stringStream; + stringStream <(y/scaling,x/scaling) = cv::Vec3f(0,255,0); + save_img.at(y/scaling-1,x/scaling-1) = cv::Vec3f(0,255,0); + save_img.at(y/scaling+1,x/scaling-1) = cv::Vec3f(0,255,0); + save_img.at(y/scaling-1,x/scaling+1) = cv::Vec3f(0,255,0); + save_img.at(y/scaling+1,x/scaling+1) = cv::Vec3f(0,255,0); + + float x_adj = (qx*grid_dim + grid_dim / 2) / scaling; + float y_adj = (qy*grid_dim + grid_dim / 2) / scaling; + int x_min = *(bb_start+(((n*64+z)*quad_height+qy)*quad_width+qx))+x_adj; + int y_min = *(bb_start+(((n*64+z+16)*quad_height+qy)*quad_width+qx))+y_adj; + int x_max = *(bb_start+(((n*64+z+32)*quad_height+qy)*quad_width+qx))+x_adj; + int y_max = *(bb_start+(((n*64+z+48)*quad_height+qy)*quad_width+qx))+y_adj; + cv::Rect bb(x_min, y_min, x_max-x_min+1, y_max-y_min+1); + cv::rectangle(save_img, bb, cv::Scalar(100, 100, 200), 2); + } + } + } + cv::imwrite(save_name, save_img); } - if(blob_names[j].compare(str4)==0) - { - LOG(INFO) << "bb-label " << blobs[j]->num()<<" "<channels()<<" "<height()<<" "<width(); - }*/ } //end int score_index = 0; @@ -321,6 +358,7 @@ void Solver::Test(const int test_net_id) { const shared_ptr >& test_net = test_nets_[test_net_id]; Dtype loss = 0; for (int i = 0; i < param_.test_iter(test_net_id); ++i) { + LOG(INFO) << "i = " << i<<" of "<*>& result = test_net->Forward(bottom_vec, &iter_loss); @@ -350,6 +388,7 @@ void Solver::Test(const int test_net_id) { LOG(INFO) << "Test loss: " << loss; } for (int i = 0; i < test_score.size(); ++i) { + LOG(INFO) << "i2 = " << i<<" of "<output_blob_indices()[test_score_output_id[i]]; const string& output_name = test_net->blob_names()[output_blob_index]; diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp index cf4ba8cffa4..991ef91491d 100644 --- a/src/caffe/util/io.cpp +++ b/src/caffe/util/io.cpp @@ -166,6 +166,16 @@ bool ReadImageToDatum(const string& filename, const int label, return true; } +//added by Tao + + + + + +//end + + + leveldb::Options GetLevelDBOptions() { // In default, we will return the leveldb option and set the max open files // in order to avoid using up the operating system's limit. From 801de4f504194ce72641879f4f9ce6c95152df87 Mon Sep 17 00:00:00 2001 From: Tao Wang Date: Sat, 29 Nov 2014 13:05:39 -0800 Subject: [PATCH 5/6] added multilane label reading python modules, interfaced with C++ using boost numpy --- Makefile | 5 +- include/caffe/data_layers.hpp | 35 +- models/brody/train_val_brody.prototxt | 7 +- prep_opencv.sh | 6 + src/caffe/layer_factory.cpp | 2 + src/caffe/layers/multilane_label_layer.cpp | 213 ++ src/caffe/layers/video_data_layer.cpp | 67 +- src/caffe/proto/caffe.proto | 34 +- src/caffe/py_lane_label_reader/ArgParser.py | 34 + .../py_lane_label_reader/CameraParams.py | 48 + .../CameraReprojection.py | 36 + src/caffe/py_lane_label_reader/GPSReader.py | 102 + .../py_lane_label_reader/GPSReprojection.py | 285 +++ .../py_lane_label_reader/GPSTransforms.py | 109 + .../py_lane_label_reader/LidarTransforms.py | 192 ++ src/caffe/py_lane_label_reader/Q50_config.py | 9 + .../py_lane_label_reader/SetPerspDist.py | 49 + src/caffe/py_lane_label_reader/WGS84toENU.py | 66 + src/caffe/py_lane_label_reader/WarpUtils.py | 14 + src/caffe/py_lane_label_reader/__init__.py | 2 + .../multilane_label_reader.py | 404 ++++ .../parameters/__init__.py | 0 .../parameters/q50_3_7_14_params.py | 76 + .../parameters/q50_4_3_14_params.py | 147 ++ .../parameters/transformations.py | 1907 +++++++++++++++++ .../perspective_reader.py | 9 + .../py_lane_label_reader/transformations.py | 1907 +++++++++++++++++ src/caffe/solver.cpp | 25 +- 28 files changed, 5767 insertions(+), 23 deletions(-) create mode 100755 prep_opencv.sh create mode 100644 src/caffe/layers/multilane_label_layer.cpp create mode 100644 src/caffe/py_lane_label_reader/ArgParser.py create mode 100644 src/caffe/py_lane_label_reader/CameraParams.py create mode 100644 src/caffe/py_lane_label_reader/CameraReprojection.py create mode 100644 src/caffe/py_lane_label_reader/GPSReader.py create mode 100644 src/caffe/py_lane_label_reader/GPSReprojection.py create mode 100644 src/caffe/py_lane_label_reader/GPSTransforms.py create mode 100644 src/caffe/py_lane_label_reader/LidarTransforms.py create mode 100644 src/caffe/py_lane_label_reader/Q50_config.py create mode 100644 src/caffe/py_lane_label_reader/SetPerspDist.py create mode 100644 src/caffe/py_lane_label_reader/WGS84toENU.py create mode 100644 src/caffe/py_lane_label_reader/WarpUtils.py create mode 100644 src/caffe/py_lane_label_reader/__init__.py create mode 100644 src/caffe/py_lane_label_reader/multilane_label_reader.py create mode 100644 src/caffe/py_lane_label_reader/parameters/__init__.py create mode 100644 src/caffe/py_lane_label_reader/parameters/q50_3_7_14_params.py create mode 100644 src/caffe/py_lane_label_reader/parameters/q50_4_3_14_params.py create mode 100644 src/caffe/py_lane_label_reader/parameters/transformations.py create mode 100644 src/caffe/py_lane_label_reader/perspective_reader.py create mode 100644 src/caffe/py_lane_label_reader/transformations.py diff --git a/Makefile b/Makefile index e85f338ed23..bc33985be18 100644 --- a/Makefile +++ b/Makefile @@ -169,9 +169,12 @@ LIBRARIES += pthread \ glog gflags protobuf leveldb snappy \ lmdb \ boost_system \ + python2.7 \ + boost_python \ + boost_numpy \ hdf5_hl hdf5 \ opencv_core opencv_highgui opencv_imgproc -PYTHON_LIBRARIES := boost_python python2.7 +PYTHON_LIBRARIES := boost_python boost_numpy python2.7 opencv_core opencv_highgui opencv_imgproc WARNINGS := -Wall -Wno-sign-compare ############################## diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp index 7ccb7a6e805..3ff4654ac1f 100644 --- a/include/caffe/data_layers.hpp +++ b/include/caffe/data_layers.hpp @@ -20,7 +20,6 @@ #include #include #include - namespace caffe { #define HDF5_DATA_DATASET_NAME "data" @@ -302,7 +301,7 @@ class VideoDataLayer : public BasePrefetchingDataLayer { virtual void InternalThreadEntry(); bool ReadVideoFrameToDatum(const string& filename, size_t id, size_t persp, const int height, const int width, Datum* datum); - + void setPerspective(); //inline bool ReadVideoBatchToDatum(const string& filename, std::vector frameIds, // std::vectortrans, Datum* datum) { // return ReadVideoBatchToDatum(filename, frameIds, trans, 0, 0, datum); @@ -310,9 +309,41 @@ class VideoDataLayer : public BasePrefetchingDataLayer { vector, std::vector > > > lines_; int lines_id_; + std::vector mTransforms; cv::VideoCapture* cap; }; + +/** + * @brief Provides multilane labels to the Net. + * + * TODO(dox): thorough documentation for Forward and proto params. + */ +template +class MultilaneLabelLayer : public BasePrefetchingDataLayer { + public: + explicit MultilaneLabelLayer(const LayerParameter& param) + : BasePrefetchingDataLayer(param) {} + virtual ~MultilaneLabelLayer(); + virtual void DataLayerSetUp(const vector*>& bottom, + vector*>* top); + + virtual inline LayerParameter_LayerType type() const { + return LayerParameter_LayerType_VIDEO_DATA; + } + virtual inline int ExactNumBottomBlobs() const { return 0; } + virtual inline int ExactNumTopBlobs() const { return 1; } + + protected: + shared_ptr prefetch_rng_; + virtual void ShuffleBatches(); + virtual void InternalThreadEntry(); + //boost::numpy::ndarray ReadLabelBatch(const string& filename, std::vector &frame_ids, std::vector trans, const int height, const int width); + + vector, std::vector > > > lines_; + int lines_id_; +}; + /** * @brief Provides data to the Net from memory. * diff --git a/models/brody/train_val_brody.prototxt b/models/brody/train_val_brody.prototxt index b70ea376e0e..6f45e3b47d0 100644 --- a/models/brody/train_val_brody.prototxt +++ b/models/brody/train_val_brody.prototxt @@ -18,11 +18,10 @@ layers { # Bounding box label and pixel label. layers { name: "label" - type: DATA + type: MULTILANE_LABEL top: "label" - data_param { - source: "/deep/group/driving_data/twangcat/lmdb/driving_label_train" - backend: LMDB + multilane_label_param { + source: "/scail/group/deeplearning/driving_data/twangcat/schedules/q50_multilane_planar_train_schedule1_batch20_2cam.txt" batch_size: 20 } include: { phase: TRAIN } diff --git a/prep_opencv.sh b/prep_opencv.sh new file mode 100755 index 00000000000..1d3b113b340 --- /dev/null +++ b/prep_opencv.sh @@ -0,0 +1,6 @@ +#!/bin/bash +export PKG_CONFIG_PATH=/deep/group/driving_data/twangcat/env_deep6/opencv/lib/pkgconfig/ +export LD_LIBRARY_PATH=/deep/group/driving_data/twangcat/env_deep6/opencv/lib/:$LD_LIBRARY_PATH +export PATH=/deep/group/driving_data/twangcat/env_deep6/opencv/include/:$PATH +export CPLUS_INCLUDE_PATH=/deep/group/driving_data/twangcat/env_deep6/opencv/include/:$CPLUS_INCLUDE_PATH + diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp index 6002bd07f0e..a20f2c6e636 100644 --- a/src/caffe/layer_factory.cpp +++ b/src/caffe/layer_factory.cpp @@ -222,6 +222,8 @@ Layer* GetLayer(const LayerParameter& param) { return new LRNLayer(param); case LayerParameter_LayerType_MEMORY_DATA: return new MemoryDataLayer(param); + case LayerParameter_LayerType_MULTILANE_LABEL: + return new MultilaneLabelLayer(param); case LayerParameter_LayerType_MVN: return new MVNLayer(param); case LayerParameter_LayerType_MULTINOMIAL_LOGISTIC_LOSS: diff --git a/src/caffe/layers/multilane_label_layer.cpp b/src/caffe/layers/multilane_label_layer.cpp new file mode 100644 index 00000000000..27257e2b26c --- /dev/null +++ b/src/caffe/layers/multilane_label_layer.cpp @@ -0,0 +1,213 @@ +#include // NOLINT(readability/streams) +#include // NOLINT(readability/streams) +#include +#include +#include +#include +#include "caffe/data_layers.hpp" +#include "caffe/layer.hpp" +#include "caffe/util/io.hpp" +#include "caffe/util/math_functions.hpp" +#include "caffe/util/rng.hpp" +#include +#include +#include +#include + + +namespace py = boost::python; // create namespace variable for boost::python +namespace np = boost::numpy; // create namespace variable for boost::python +namespace caffe { + +//np::ndarray ReadLabelBatch(const string& filename, std::vector &frame_ids, std::vector &trans, +// const int height, const int width) { +//}; +template +MultilaneLabelLayer::~MultilaneLabelLayer() { + this->JoinPrefetchThread(); +} + + +template +void MultilaneLabelLayer::DataLayerSetUp(const vector*>& bottom, + vector*>* top) { + const int new_height = this->layer_param_.multilane_label_param().new_height(); + const int new_width = this->layer_param_.multilane_label_param().new_width(); + CHECK((new_height == 0 && new_width == 0) || + (new_height > 0 && new_width > 0)) << "Current implementation requires " + "new_height and new_width to be set at the same time."; + // Read the file with filenames and labels + const string& source = this->layer_param_.multilane_label_param().source(); + LOG(INFO) << "Opening schedule file " << source; + std::ifstream infile(source.c_str()); + + + + string batch_string; + + string filename; + //while (infile >> batch_string) { + while (getline (infile, batch_string)) { + if(!infile) + { + if(infile.eof()) + { + LOG(INFO) << "Reached EOF of schedule file."; + break; + } + else + LOG(FATAL)<< "Error while reading schedule file. Possibly corrupted."; + } + std::vector batch_fields; + // first split a line into fields with delimiter ",". Fields should be [filename, frame_ids, transform_ids] + boost::split(batch_fields, batch_string, boost::is_any_of(","), + boost::token_compress_on); + if(batch_fields.size()!=3) + LOG(FATAL) << "Each line must have 3 fields separated by comma, " + < frame_ids_str; + std::vector frame_ids; + boost::split(frame_ids_str, batch_fields[1], boost::is_any_of(" "), + boost::token_compress_on); + for (int f=0; f trans_ids_str; + std::vector trans_ids; + boost::split(trans_ids_str, batch_fields[2], boost::is_any_of(" "), + boost::token_compress_on); + for (int f=0; flayer_param_.multilane_label_param().shuffle()) { + // randomly shuffle data + LOG(INFO) << "Shuffling batches"; + const unsigned int prefetch_rng_seed = caffe_rng_rand(); + prefetch_rng_.reset(new Caffe::RNG(prefetch_rng_seed)); + ShuffleBatches(); + } + LOG(INFO) << "A total of " << lines_.size() << " batches."; + + lines_id_ = 0; + // Check if we would need to randomly skip a few data points + if (this->layer_param_.multilane_label_param().rand_skip()) { + unsigned int skip = caffe_rng_rand() % + this->layer_param_.multilane_label_param().rand_skip(); + LOG(INFO) << "Skipping first " << skip << " data points."; + CHECK_GT(lines_.size(), skip) << "Not enough points to skip"; + lines_id_ = skip; + } + const int crop_size = this->layer_param_.transform_param().crop_size(); + const int batch_size = this->layer_param_.multilane_label_param().batch_size(); + if (crop_size > 0) { + (*top)[0]->Reshape(batch_size, 80, crop_size, crop_size); + this->prefetch_data_.Reshape(batch_size, 80, crop_size, + crop_size); + } else { + (*top)[0]->Reshape(batch_size, 80, 15,20); + this->prefetch_data_.Reshape(batch_size, 80, 15,20); + } + LOG(INFO) << "output data size: " << (*top)[0]->num() << "," + << (*top)[0]->channels() << "," << (*top)[0]->height() << "," + << (*top)[0]->width(); + // label + //(*top)[1]->Reshape(batch_size, 1, 1, 1); + this->prefetch_label_.Reshape(batch_size, 1, 1, 1); + // datum size + this->datum_channels_ = 80; + this->datum_height_ = 15; + this->datum_width_ = 20; + this->datum_size_ = 80*15*20; +} + +template +void MultilaneLabelLayer::ShuffleBatches() { + caffe::rng_t* prefetch_rng = + static_cast(prefetch_rng_->generator()); + shuffle(lines_.begin(), lines_.end(), prefetch_rng); +} + + + +// This function is used to create a thread that prefetches the data. +template +void MultilaneLabelLayer::InternalThreadEntry() { + CHECK(this->prefetch_data_.count()); + Dtype* top_data = this->prefetch_data_.mutable_cpu_data(); + //Dtype* top_label = this->prefetch_label_.mutable_cpu_data(); + MultilaneLabelParameter multilane_label_param = this->layer_param_.multilane_label_param(); + const int batch_size = multilane_label_param.batch_size(); + const int new_height = multilane_label_param.new_height(); + const int new_width = multilane_label_param.new_width(); + + // datum scales + const int lines_size = lines_.size(); + string filename = lines_[lines_id_].first; + std::vector frameIds = lines_[lines_id_].second.first; + std::vector trans = lines_[lines_id_].second.second; + if (batch_size!=frameIds.size() || batch_size!=trans.size()) + LOG(ERROR)<<"Frame count mismatch!"; + LOG(INFO)<<"reading label batch "<(); + py::tuple shape = py::make_tuple(batch_size) ; + py::tuple stride = py::make_tuple(4) ; + py::object own1; + py::object own2; + int* framePtr = const_cast(&frameIds[0]); + int* transPtr = const_cast(&trans[0]); + np::ndarray frameArr = np::from_data(framePtr,dt,shape,stride,own1); + np::ndarray transArr = np::from_data(transPtr,dt,shape,stride,own2); + // call function + arr_handle = reader.attr("runLabelling")(filename, frameArr, transArr); + }catch(py::error_already_set const &){ + PyErr_Print(); + LOG(FATAL) <<"numpy label reading function failed!"; + } + np::ndarray pArray = np::from_object(arr_handle); + + int array_size = py::extract(pArray.attr("size")); + LOG(INFO)<<"numpy array size = "<= lines_size) { + // We have reached the end. Restart from the first. + DLOG(INFO) << "Restarting data prefetching from start."; + lines_id_ = 0; + if (this->layer_param_.image_data_param().shuffle()) { + ShuffleBatches(); + } + } +} + +INSTANTIATE_CLASS(MultilaneLabelLayer); + +} // namespace caffe diff --git a/src/caffe/layers/video_data_layer.cpp b/src/caffe/layers/video_data_layer.cpp index ac814fbbe0f..1563cbebdfd 100644 --- a/src/caffe/layers/video_data_layer.cpp +++ b/src/caffe/layers/video_data_layer.cpp @@ -10,6 +10,10 @@ #include "caffe/util/math_functions.hpp" #include "caffe/util/rng.hpp" #include +#include +#include +namespace py = boost::python; // create namespace variable for boost::python +namespace np = boost::numpy; // create namespace variable for boost::python namespace caffe { template @@ -41,8 +45,8 @@ bool VideoDataLayer:: ReadVideoFrameToDatum(const string& filename, size_ cv_img = cv_img_origin; } // apply perspective transform - //cv::Mat warpMatrix = mTransforms[persp+(cam_num-1)*numPersp]; - //cv::warpPerspective(cv_img, cv_img, warpMatrix, frame.size(), cv::INTER_LINEAR, cv::BORDER_REPLICATE); + cv::Mat warpMatrix = this->mTransforms[persp]; + cv::warpPerspective(cv_img, cv_img, warpMatrix, cv_img.size(), cv::INTER_LINEAR, cv::BORDER_REPLICATE); // copy data to datum int num_channels = 3; datum->set_channels(num_channels); @@ -63,6 +67,59 @@ bool VideoDataLayer:: ReadVideoFrameToDatum(const string& filename, size_ return true; } + +template +void VideoDataLayer::setPerspective() { + + /*try{ + Py_Initialize(); + PyRun_SimpleString("import sys"); + PyRun_SimpleString("sys.path.append(\"/afs/cs.stanford.edu/u/twangcat/scratch/caffenet/src/caffe/py_lane_label_reader/\")"); + np::initialize(); + }catch(py::error_already_set const &){ + PyErr_Print(); + LOG(FATAL) <<"boost python and numpy initialization failed."; + } + py::object list; + try{ + // initialize python helper class + py::object module = py::import("perspective_reader"); + py::object readerClass = module.attr("PerspectiveReader"); + py::object reader = readerClass(); + list = reader.attr("read")(); + }catch(py::error_already_set const &){ + PyErr_Print(); + LOG(FATAL) <<"numpy perspective reading function failed!"; + } + int num_persps = py::len(list); + for(int i=0;imTransforms.push_back(cv::Mat::eye(3, 3, CV_32F, persp_data)); + } +*/ + float persp_data[] = {2.00000000e+00, 4.85722573e-16, -1.98951966e-13, 1.55172414e-01, 2.20689655e+00, -9.93103448e+01, 2.35813973e-18, 1.46367293e-18, 2.00000000e+00}; + // essential to do a clone here otherwise data is not copied properly. not sure why + this->mTransforms.push_back(cv::Mat(3, 3, CV_32F, persp_data).clone()); + persp_data = {2.00000000e+00, -2.60991180e-16, -1.35814320e-13, -1.55172414e-01, 2.20689655e+00, -7.89095352e-14, -1.39242621e-19, -7.96792478e-19, 2.00000000e+00}; + this->mTransforms.push_back(cv::Mat(3, 3, CV_32F, persp_data).clone()); + persp_data = {2.22608696e+00, 3.01449275e-01, -1.44695652e+02, -1.74860126e-15, 2.00000000e+00, -9.94759830e-14, 2.00577402e-18, -4.60785923e-18, 2.00000000e+00}; + this->mTransforms.push_back(cv::Mat(3, 3, CV_32F, persp_data).clone()); + persp_data = {2.22608696e+00, -2.08695652e-01, -4.81606942e-14, -1.21601236e-15, 2.06956522e+00, -2.68140544e-14, 1.30767163e-18, 1.44927536e-04, 2.00000000e+00}; + this->mTransforms.push_back(cv::Mat(3, 3, CV_32F, persp_data).clone()); + persp_data = {1.76207983e+00, 2.42673669e-16, -1.35389096e-13, -1.65464295e-01, 2.18780567e+00, -8.38443898e-14, -6.37686030e-04, 3.91261819e-04, 2.00000000e+00}; + this->mTransforms.push_back(cv::Mat(3, 3, CV_32F, persp_data).clone()); + persp_data = {2.15126050e+00, -2.48222366e-01, 1.11700065e+01, -7.49400542e-16, 2.46153846e+00, -1.10769231e+02, 2.33103467e-18, 8.13151629e-19, 2.00000000e+00}; + this->mTransforms.push_back(cv::Mat(3, 3, CV_32F, persp_data).clone()); + persp_data = {1., 0., 0., 0., 1., 0., 0., 0., 1.}; + this->mTransforms.push_back(cv::Mat(3, 3, CV_32F, persp_data).clone()); +} + + + template void VideoDataLayer::DataLayerSetUp(const vector*>& bottom, vector*>* top) { @@ -73,11 +130,12 @@ void VideoDataLayer::DataLayerSetUp(const vector*>& bottom, "new_height and new_width to be set at the same time."; // Read the file with filenames and labels const string& source = this->layer_param_.video_data_param().source(); + setPerspective(); + + LOG(INFO) << "Opening schedule file " << source; std::ifstream infile(source.c_str()); - string batch_string; - string filename; //while (infile >> batch_string) { while (getline (infile, batch_string)) { @@ -212,7 +270,6 @@ void VideoDataLayer::InternalThreadEntry() { // Apply transformations (mirror, crop...) to the data this->data_transformer_.Transform(item_id, datum, this->mean_, top_data); - // go to the next iter lines_id_++; if (lines_id_ >= lines_size) { diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index 0e69ef54e77..3bbf33f70c0 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -219,7 +219,7 @@ message LayerParameter { // line above the enum. Update the next available ID when you add a new // LayerType. // - // LayerType next available ID: 39 (last added: VIDEO_DATA) + // LayerType next available ID: 40 (last added: MULTILANE_LABEL) enum LayerType { // "NONE" layer type is 0th enum element so that we don't cause confusion // by defaulting to an existent LayerType (instead, should usually error if @@ -247,6 +247,7 @@ message LayerParameter { INNER_PRODUCT = 14; LRN = 15; MEMORY_DATA = 29; + MULTILANE_LABEL = 39; MULTINOMIAL_LOGISTIC_LOSS = 16; MVN = 34; POOLING = 17; @@ -308,6 +309,7 @@ message LayerParameter { optional InnerProductParameter inner_product_param = 17; optional LRNParameter lrn_param = 18; optional MemoryDataParameter memory_data_param = 22; + optional MultilaneLabelParameter multilane_label_param = 42; optional MVNParameter mvn_param = 34; optional PoolingParameter pooling_param = 19; optional PowerParameter power_param = 21; @@ -526,6 +528,35 @@ message ImageDataParameter { optional bool mirror = 6 [default = false]; } +// Message that stores parameters used by MultilaneLabelLayer +message MultilaneLabelParameter { + // Specify the data source. + optional string source = 1; + // Specify the batch size. + optional uint32 batch_size = 4; + // The rand_skip variable is for the data layer to skip a few data points + // to avoid all asynchronous sgd clients to start at the same point. The skip + // point would be set as rand_skip * rand(0,1). Note that rand_skip should not + // be larger than the number of keys in the leveldb. + optional uint32 rand_skip = 7 [default = 0]; + // Whether or not MultilaneLabelLayer should shuffle the list of files at every epoch. + optional bool shuffle = 8 [default = false]; + // It will also resize images if new_height or new_width are not zero. + optional uint32 new_height = 9 [default = 0]; + optional uint32 new_width = 10 [default = 0]; + // DEPRECATED. See TransformationParameter. For data pre-processing, we can do + // simple scaling and subtracting the data mean, if provided. Note that the + // mean subtraction is always carried out before scaling. + optional float scale = 2 [default = 1]; + optional string mean_file = 3; + // DEPRECATED. See TransformationParameter. Specify if we would like to randomly + // crop an image. + optional uint32 crop_size = 5 [default = 0]; + // DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror + // data. + optional bool mirror = 6 [default = false]; +} + // Message that stores parameters used by VideoDataLayer message VideoDataParameter { // Specify the data source. @@ -554,7 +585,6 @@ message VideoDataParameter { // data. optional bool mirror = 6 [default = false]; } - // Message that stores parameters InfogainLossLayer message InfogainLossParameter { // Specify the infogain matrix source. diff --git a/src/caffe/py_lane_label_reader/ArgParser.py b/src/caffe/py_lane_label_reader/ArgParser.py new file mode 100644 index 00000000000..0db65b696b5 --- /dev/null +++ b/src/caffe/py_lane_label_reader/ArgParser.py @@ -0,0 +1,34 @@ +from Q50_config import LoadParameters + +def parse_args(folder, video_file): + """Folder is the top level folder. +video_file is the name of the camera split to use +ex: parse_args('data/280S', '280S_a2.avi') +returns a dictionary with keys: ('map', 'frames', 'radar' 'gps', 'video', 'params') +corresponding to file names. +""" + + basename = folder + '/' + video_file[:-5] + + map_file = basename + '.map' + lidar_folder = basename + '_frames/' + radar_folder = basename + '_rdr/' + gps_file = basename + '_gps.out' + gps_mark1_file = basename + '_gpsmark1.out' + gps_mark2_file = basename + '_gpsmark2.out' + + video_file_num = video_file[-5] + + video_file = folder + '/' + video_file + param_file = folder + '/params.ini' + params = open(param_file, 'r').readline().rstrip() + + return {'map': map_file, + 'frames': lidar_folder, + 'radar': radar_folder, + 'gps': gps_file, + 'gps_mark1': gps_mark1_file, + 'gps_mark2': gps_mark2_file, + 'video': video_file, + 'cam_num': int(video_file[-5]), + 'params': LoadParameters(params)} diff --git a/src/caffe/py_lane_label_reader/CameraParams.py b/src/caffe/py_lane_label_reader/CameraParams.py new file mode 100644 index 00000000000..275c437f0c5 --- /dev/null +++ b/src/caffe/py_lane_label_reader/CameraParams.py @@ -0,0 +1,48 @@ +import numpy as np +import pickle +import sys +from WGS84toENU import * + +__all__ = [ "getCameraParams" ] + + +def getCameraParams(): + cam = [{}, {}] + for i in [0, 1]: + cam[i]['R_to_c_from_i'] = np.array([[-1, 0, 0], + [0, 0, -1], + [0, -1, 0]]) + + if i == 0: + cam[i]['rot_x'] = deg2rad(-0.8) # better cam 1 + cam[i]['rot_y'] = deg2rad(-0.5) + cam[i]['rot_z'] = deg2rad(-0.005) + cam[i]['t_x'] = -0.5 + cam[i]['t_y'] = 1.1 + cam[i]['t_z'] = 0.0 + elif i == 1: + cam[i]['rot_x'] = deg2rad(-0.61) # better cam 2 + cam[i]['rot_y'] = deg2rad(0.2) + cam[i]['rot_z'] = deg2rad(0.0) + cam[i]['t_x'] = 0.5 + cam[i]['t_y'] = 1.1 + cam[i]['t_z'] = 0.0 + + cam[i]['fx'] = 2221.8 + cam[i]['fy'] = 2233.7 + cam[i]['cu'] = 623.7 + cam[i]['cv'] = 445.7 + cam[i]['KK'] = np.array([[cam[i]['fx'], 0.0, cam[i]['cu']], + [0.0, cam[i]['fy'], cam[i]['cv']], + [0.0, 0.0, 1.0]]) + cam[i]['f'] = (cam[i]['fx'] + cam[i]['fy']) / 2 + + return cam + +if __name__ == '__main__': + cam= getCameraParams() + filename = 'cam_params.pickle' + if len(sys.argv) > 1: + filename = sys.argv[1] + pickle.dump(cam, open(filename, 'wb')) + diff --git a/src/caffe/py_lane_label_reader/CameraReprojection.py b/src/caffe/py_lane_label_reader/CameraReprojection.py new file mode 100644 index 00000000000..85ab2657169 --- /dev/null +++ b/src/caffe/py_lane_label_reader/CameraReprojection.py @@ -0,0 +1,36 @@ +import numpy as np +from numpy import sin, cos +from transformations import euler_matrix +""" +pixels is Nx2 matrix of coordinates, +cam is camera structure +""" +''' +recover 3d position of points on the ground surface close to the car, assumes fixed +camera height and flat road, so won't work for points in distances. +''' +def pixelTo3d(pixels, cam): + height = 1.105 # constants based on camera setup + R_camera_pitch = euler_matrix(cam['rot_x'], cam['rot_y'], cam['rot_z'], 'sxyz')[0:3,0:3] + KK = cam['KK'] + N = pixels.shape[0] + assert(pixels.shape[1] == 2) + # compute X/Z and Y/Z using inv(KK*R)*pixels + Pos_S = np.linalg.solve(np.dot(KK,R_camera_pitch), np.concatenate((pixels,np.ones([N,1])), axis=1).transpose()).transpose() + Y = np.ones((N))*height + Z = Y/Pos_S[:,1] + X = Pos_S[:,0]*Z + return np.vstack((X,Y,Z)).transpose() + +''' +recover 3d position of points that are at fixed Z distances +''' +def recover3d(pixels, cam, Z=np.array([10,20,30,40,50,60,70,80])): + KK = cam['KK'] + N = pixels.shape[0] + assert(pixels.shape[1] == 2) + # compute X/Z and Y/Z using inv(KK*R)*pixels + Pos_S = np.linalg.solve(KK, np.concatenate((pixels,np.ones([N,1])), axis=1).transpose()).transpose() + Y = Pos_S[:,1]*Z + X = Pos_S[:,0]*Z + return np.vstack((X,Y,Z)).transpose() diff --git a/src/caffe/py_lane_label_reader/GPSReader.py b/src/caffe/py_lane_label_reader/GPSReader.py new file mode 100644 index 00000000000..6c32ce5edb6 --- /dev/null +++ b/src/caffe/py_lane_label_reader/GPSReader.py @@ -0,0 +1,102 @@ +import numpy as np +from scipy.ndimage.filters import convolve1d + +def smoothData(arr, wind=51): + filt = np.ones(wind) / wind + return convolve1d(arr, filt, mode='nearest') + +def integrateVelocity(v_arr, d0): + return d0+ np.cumsum(v_arr) + +def smoothDerivativeData(arr, wind=51): + darr = arr[1:] - arr[:-1] + darr = smoothData(darr, wind) + return integrateVelocity(darr, arr[0]) + +def holdDerivativeData(arr, clipVal=0.1): + darr = arr[1:] - arr[:-1] + darr = np.clip(darr, -clipVal, clipVal) + return integrateVelocity(darr,arr[0]) + +class GPSCovarianceReader(): + def __init__(self,filename): + from math import sqrt + f = open(filename); + self.nextLineInsCov = False + self.data = [ ] + self.token_order = ['tx','ty','tz','rx','ry','rz'] + for line in f: + if 'INSCOV' in line: + self.nextLineInsCov = True + elif self.nextLineInsCov: + record = { } + self.nextLineInsCov = False + tokens = line.split(' ') + offset = 7 + record['tx'] = sqrt(float(tokens[offset + 0])) + record['ty'] = sqrt(float(tokens[offset + 4])) + record['tz'] = sqrt(float(tokens[offset + 8])) + offset += 9 + record['rx'] = sqrt(float(tokens[offset + 0])) + record['ry'] = sqrt(float(tokens[offset + 4])) + record['rz'] = sqrt(float(tokens[offset + 8])) + self.data.append(record) + + def getNumericData(self): + arr = np.zeros([len(self.data), 6], np.float64); + for t in range(len(self.data)): + for j in range(6): + arr[t,j] = self.data[t][self.token_order[j]] + return arr + +class GPSReader(): + def __init__(self, filename): + self.data = [ ] + self.token_order = ['seconds', 'lat', 'long', 'height', + 'v_north', 'v_east', 'v_up', 'rot_y', 'rot_x', + 'azimuth', 'week']; + f = open(filename); + for line in f: + l = line.rstrip() + tokens = l.split(',') + for k in range(len(tokens)): + if 'PVA' in tokens[k]: + break + tokens = tokens[k:] + # for TAGGEDMARKxPVA logs, delete the tag, and then process as is + #print tokens + if len(tokens) == 22: + del tokens[11] # eventID token + if len(tokens) == 21: + record = { } + for idx in range(10): + record[self.token_order[idx]] = float(tokens[idx+10]) + # Parse out the gps week + record[self.token_order[10]] = long((tokens[9].split(';'))[1]) + self.data.append(record) + + def getData(self): + return self.data; + + def getNumericData(self): + arr = np.zeros([len(self.data), 11], np.float64); + for t in range(len(self.data)): + for j in range(11): + arr[t,j] = self.data[t][self.token_order[j]] + + + #arr[:, 1] = smoothData(arr[:, 1]) + #arr[:, 2] = smoothData(arr[:, 2]) + #arr[:, 3] = smoothData(arr[:, 3]) + #arr[:-1, 3] = smoothDerivativeData(arr[:, 3], wind=251) + #arr[:-1, 1] = holdDerivativeData(arr[:, 1]) + #arr[:-1, 2] = holdDerivativeData(arr[:, 2]) + #arr[0:-1, 3] = holdDerivativeData(arr[:, 3], clipVal=0.1) + #arr[:, 3] = smoothData(arr[:, 3]) + #arr[:, 7] = smoothData(arr[:, 7]) + #arr[:, 8] = smoothData(arr[:, 8]) + #arr[:, 9] = smoothData(arr[:, 9]) + + return arr; + + diff --git a/src/caffe/py_lane_label_reader/GPSReprojection.py b/src/caffe/py_lane_label_reader/GPSReprojection.py new file mode 100644 index 00000000000..e512f886411 --- /dev/null +++ b/src/caffe/py_lane_label_reader/GPSReprojection.py @@ -0,0 +1,285 @@ +from transformations import euler_matrix +import numpy as np +from WGS84toENU import * +from numpy import array, dot, zeros, around, divide, ones +from LidarTransforms import * +from Q50_config import * +from cv2 import projectPoints +# only works for new Q50 data. Maps positions wrt imu_0 to cam_t +def MapPos(map_pos, imu_transforms_t, cam, T_from_i_to_l): + pts_wrt_imu_0 = array(map_pos).transpose() + pts_wrt_imu_0 = np.vstack((pts_wrt_imu_0, np.ones((1,pts_wrt_imu_0.shape[1])))) + # transform points from imu_0 to imu_t + pts_wrt_imu_t = np.dot( np.linalg.inv(imu_transforms_t), pts_wrt_imu_0) + # transform points from imu_t to lidar_t + pts_wrt_lidar_t = np.dot(T_from_i_to_l, pts_wrt_imu_t); + # transform points from lidar_t to camera_t + pts_wrt_camera_t = pts_wrt_lidar_t.transpose()[:, 0:3] + cam['displacement_from_l_to_c_in_lidar_frame'] + pts_wrt_camera_t = dot(R_to_c_from_l(cam), pts_wrt_camera_t.transpose()) + pts_wrt_camera_t = np.vstack((pts_wrt_camera_t, + np.ones((1,pts_wrt_camera_t.shape[1])))) + pts_wrt_camera_t = dot(cam['E'], pts_wrt_camera_t) + pts_wrt_camera_t = pts_wrt_camera_t[0:3,:] + return pts_wrt_camera_t + +# only works for new Q50 data. Maps vectors wrt imu_0 to cam_t +def MapVec(map_vec, imu_transforms_t, cam, T_from_i_to_l): + vec_wrt_imu_0 = array(map_vec).transpose() + # transform vectors from imu_0 to imu_t + vec_wrt_imu_t = np.dot(imu_transforms_t[0:3,0:3].transpose(), vec_wrt_imu_0) + # transform vectors from imu_t to lidar_t + vec_wrt_lidar_t = np.dot(T_from_i_to_l[0:3,0:3], vec_wrt_imu_t); + # transform vectors from lidar_t to camera_t + vec_wrt_camera_t = dot(R_to_c_from_l(cam), vec_wrt_lidar_t) + vec_wrt_camera_t = dot(cam['E'][0:3,0:3], vec_wrt_camera_t) + return vec_wrt_camera_t + +# same as MapPos, but takes the imu transforms of the car and computes the local trajectory on ground. +def MapPosTrajectory(imu_tr, imu_transforms_t, cam, T_from_i_to_l, height): + height_array = np.zeros([3,3]) + height_array[2,0]=-height + aa = np.dot(imu_tr[:,0:3,0:3], height_array)[:,:,0] # shift down in the self frame + pts_wrt_imu_0 = (array(imu_tr[:,0:3,3])+aa).transpose() + pts_wrt_imu_0 = np.vstack((pts_wrt_imu_0, np.ones((1,pts_wrt_imu_0.shape[1])))) + # transform points from imu_0 to imu_t + pts_wrt_imu_t = np.dot( np.linalg.inv(imu_transforms_t), pts_wrt_imu_0) + # transform points from imu_t to lidar_t + pts_wrt_lidar_t = np.dot(T_from_i_to_l, pts_wrt_imu_t); + # transform points from lidar_t to camera_t + pts_wrt_camera_t = pts_wrt_lidar_t.transpose()[:, 0:3] + cam['displacement_from_l_to_c_in_lidar_frame'] + pts_wrt_camera_t = dot(R_to_c_from_l(cam), pts_wrt_camera_t.transpose()) + pts_wrt_camera_t = np.vstack((pts_wrt_camera_t, + np.ones((1,pts_wrt_camera_t.shape[1])))) + pts_wrt_camera_t = dot(cam['E'], pts_wrt_camera_t) + pts_wrt_camera_t = pts_wrt_camera_t[0:3,:] + return pts_wrt_camera_t + + + +def IMUPosTrajectory(imu_tr, imu_transforms_t, cam, height): + height_array = np.zeros([3,3]) + height_array[2,0]=-height + aa = np.dot(imu_tr[:,0:3,0:3], height_array)[:,:,0] # shift down in the self frame + pts_wrt_imu_0 = (array(imu_tr[:,0:3,3])+aa).transpose() + pts_wrt_imu_0 = np.vstack((pts_wrt_imu_0, np.ones((1,pts_wrt_imu_0.shape[1])))) + # transform points from imu_0 to imu_t + pts_wrt_imu_t = np.dot( np.linalg.inv(imu_transforms_t), pts_wrt_imu_0) + return pts_wrt_imu_t[0:3] + +def ENU2IMUQ50(world_coordinates, start_frame): + roll_start = deg2rad(start_frame[8]); + pitch_start = deg2rad(start_frame[7]); + yaw_start = -deg2rad(start_frame[9]); + + psi = pitch_start; + cp = cos(psi); + sp = sin(psi); + theta = roll_start; + ct = cos(theta); + st = sin(theta); + gamma = yaw_start; + cg = cos(gamma); + sg = sin(gamma); + + R_to_i_from_w = \ + array([[cg*cp-sg*st*sp, -sg*ct, cg*sp+sg*st*cp], + [sg*cp+cg*st*sp, cg*ct, sg*sp-cg*st*cp], + [-ct*sp, st, ct*cp]]).transpose() + pos_wrt_imu = dot(R_to_i_from_w, world_coordinates); + return pos_wrt_imu + + + +def GPSVelocities(GPSData): + return (np.apply_along_axis(np.linalg.norm, 1, GPSData[:,4:7])) + +def GPSPos(GPSData, Camera, start_frame): + roll_start = -deg2rad(start_frame[7]); + pitch_start = deg2rad(start_frame[8]); + yaw_start = -deg2rad(start_frame[9]+90); + + psi = pitch_start; + cp = cos(psi); + sp = sin(psi); + theta = roll_start; + ct = cos(theta); + st = sin(theta); + gamma = yaw_start; + cg = cos(gamma); + sg = sin(gamma); + + R_to_i_from_w = \ + array([[cg*cp-sg*st*sp, -sg*ct, cg*sp+sg*st*cp], + [sg*cp+cg*st*sp, cg*ct, sg*sp-cg*st*cp], + [-ct*sp, st, ct*cp]]).transpose() + + + pts = WGS84toENU(start_frame[1:4], GPSData[:, 1:4]) + world_coordinates = pts; + pos_wrt_imu = dot(R_to_i_from_w, world_coordinates); + R_to_c_from_i = Camera['R_to_c_from_i'] + R_camera_pitch = euler_matrix(Camera['rot_x'], Camera['rot_y'],\ + Camera['rot_z'], 'sxyz')[0:3,0:3] + R_to_c_from_i = dot(R_camera_pitch, R_to_c_from_i) + + pos_wrt_camera = dot(R_to_c_from_i, pos_wrt_imu); + + pos_wrt_camera[0,:] += Camera['t_x'] #move to left/right + pos_wrt_camera[1,:] += Camera['t_y'] #move up/down image + pos_wrt_camera[2,:] += Camera['t_z'] #move away from cam + return pos_wrt_camera + + + +def GPSPosShifted(GPSData, Camera, start_frame): + roll_start = -deg2rad(start_frame[7]); + pitch_start = deg2rad(start_frame[8]); + yaw_start = -deg2rad(start_frame[9]+90); + + psi = pitch_start; + cp = cos(psi); + sp = sin(psi); + theta = roll_start; + ct = cos(theta); + st = sin(theta); + gamma = yaw_start; + cg = cos(gamma); + sg = sin(gamma); + + R_to_i_from_w = \ + array([[cg*cp-sg*st*sp, -sg*ct, cg*sp+sg*st*cp], + [sg*cp+cg*st*sp, cg*ct, sg*sp-cg*st*cp], + [-ct*sp, st, ct*cp]]).transpose() + + + pts = WGS84toENU(start_frame[1:4], GPSData[:, 1:4]) + vel = GPSData[:,4:7] + vel[:,[0, 1]] = vel[:,[1, 0]] + sideways = np.cross(vel, np.array([0,0,1]), axisa=1) + sideways/= np.sqrt((sideways ** 2).sum(-1))[..., np.newaxis] + #pts[0,:] = pts[0,:]+np.transpose(GPSData[:,4])/40 + #pts[1,:] = pts[1,:]-np.transpose(GPSData[:,5])/40 + pts = pts+sideways.transpose() + world_coordinates = pts; + pos_wrt_imu = dot(R_to_i_from_w, world_coordinates); + R_to_c_from_i = Camera['R_to_c_from_i'] + R_camera_pitch = euler_matrix(Camera['rot_x'], Camera['rot_y'],\ + Camera['rot_z'], 'sxyz')[0:3,0:3] + R_to_c_from_i = dot(R_camera_pitch, R_to_c_from_i) + + pos_wrt_camera = dot(R_to_c_from_i, pos_wrt_imu); + + pos_wrt_camera[0,:] += Camera['t_x'] #move to left/right + pos_wrt_camera[1,:] += Camera['t_y'] #move up/down image + pos_wrt_camera[2,:] += Camera['t_z'] #move away from cam + return pos_wrt_camera + + + +def ENU2IMU(world_coordinates, start_frame): + roll_start = -deg2rad(start_frame[7]); + pitch_start = deg2rad(start_frame[8]); + yaw_start = -deg2rad(start_frame[9]+90); + + psi = pitch_start; + cp = cos(psi); + sp = sin(psi); + theta = roll_start; + ct = cos(theta); + st = sin(theta); + gamma = yaw_start; + cg = cos(gamma); + sg = sin(gamma); + + R_to_i_from_w = \ + array([[cg*cp-sg*st*sp, -sg*ct, cg*sp+sg*st*cp], + [sg*cp+cg*st*sp, cg*ct, sg*sp-cg*st*cp], + [-ct*sp, st, ct*cp]]).transpose() + pos_wrt_imu = dot(R_to_i_from_w, world_coordinates); + return pos_wrt_imu + + +def GPSPosIMU(GPSData, Camera, start_frame): + roll_start = -deg2rad(start_frame[7]); + pitch_start = deg2rad(start_frame[8]); + yaw_start = -deg2rad(start_frame[9]+90); + + psi = pitch_start; + cp = cos(psi); + sp = sin(psi); + theta = roll_start; + ct = cos(theta); + st = sin(theta); + gamma = yaw_start; + cg = cos(gamma); + sg = sin(gamma); + + R_to_i_from_w = \ + array([[cg*cp-sg*st*sp, -sg*ct, cg*sp+sg*st*cp], + [sg*cp+cg*st*sp, cg*ct, sg*sp-cg*st*cp], + [-ct*sp, st, ct*cp]]).transpose() + pts = WGS84toENU(start_frame[1:4], GPSData[:, 1:4]) + world_coordinates = pts; + pos_wrt_imu = dot(R_to_i_from_w, world_coordinates); + return pos_wrt_imu + +def GPSPosCamera(pos_wrt_imu, Camera): + R_to_c_from_i = Camera['R_to_c_from_i'] + R_camera_pitch = euler_matrix(Camera['rot_x'], Camera['rot_y'],Camera['rot_z'], 'sxyz')[0:3,0:3] + R_to_c_from_i = dot(R_camera_pitch, R_to_c_from_i) + + pos_wrt_camera = dot(R_to_c_from_i, pos_wrt_imu); + + pos_wrt_camera[0,:] += Camera['t_x'] #move to left/right + pos_wrt_camera[1,:] += Camera['t_y'] #move up/down image + pos_wrt_camera[2,:] += Camera['t_z'] #move away from cam + return pos_wrt_camera + + + + +def GPSColumns(GPSData, Camera, start_frame): + pos_wrt_camera = GPSPos(GPSData, Camera, start_frame) + return PointsMask(pos_wrt_camera[:,1:], Camera) + +def GPSShiftedColumns(GPSData, Camera, start_frame): + pos_wrt_camera = GPSPosShifted(GPSData, Camera, start_frame) + return PointsMask(pos_wrt_camera, Camera) + +def PointsMask(pos_wrt_camera, Camera): + if False:#'distort' in Camera: + (vpix, J) = projectPoints(pts_wrt_camera.transpose(), np.array([0.0,0.0,0.0]), np.array([0.0,0.0,0.0]), Camera['KK'], Camera['distort']) + else: + vpix = dot(Camera['KK'], divide(pos_wrt_camera, pos_wrt_camera[2,:])) + vpix = around(vpix).astype(np.int32) + return vpix + + +def GPSMask(GPSData, Camera, width=2): + I = 255*ones((960,1280,3), np.uint8) + vpix = GPSColumns(GPSData, Camera, GPSData[0, :]) + vpix = vpix[:,vpix[0,:] > 0 + width/2] + if vpix.size > 0: + vpix = vpix[:,vpix[1,:] > 0 + width/2] + if vpix.size > 0: + vpix = vpix[:,vpix[0,:] < 1279 - width/2] + if vpix.size > 0: + vpix = vpix[:,vpix[1,:] < 959 - width/2] + + for p in range(-width/2,width/2): + I[vpix[1,:]+p, vpix[0,:], :] = 0 + I[vpix[1,:], vpix[0,:]+p, :] = 0 + I[vpix[1,:]-p, vpix[0,:], :] = 0 + I[vpix[1,:], vpix[0,:]-p, :] = 0 + + """ + for idx in range(1,pts.shape[1]): + pix = vpix[:,idx] + if (pix[0] > 0 and pix[0] < 1280 and pix[1] > 0 and pix[1] < 960): + I[pix[1]-width+1:pix[1]+width, pix[0]-width+1:pix[0]+width, 0] = 0; + I[pix[1]-width+1:pix[1]+width, pix[0]-width+1:pix[0]+width, 1] = 0; + I[pix[1]-width+1:pix[1]+width, pix[0]-width+1:pix[0]+width, 2] = 0; + """ + + return I + diff --git a/src/caffe/py_lane_label_reader/GPSTransforms.py b/src/caffe/py_lane_label_reader/GPSTransforms.py new file mode 100644 index 00000000000..edc4504754e --- /dev/null +++ b/src/caffe/py_lane_label_reader/GPSTransforms.py @@ -0,0 +1,109 @@ +from transformations import euler_matrix +import numpy as np +from WGS84toENU import * +from numpy import array, dot, zeros, around, divide, ones +import bisect + + + +# get transformation id using time +def Idfromt(gps_times, t): + idx = bisect.bisect(gps_times, t) - 1 + return idx + + +def R_to_i_from_w(roll, pitch, yaw): + cp = cos(pitch) + sp = sin(pitch) + ct = cos(roll) + st = sin(roll) + cg = cos(yaw) + sg = sin(yaw) + + R_to_i_from_w = \ + array([[cg*cp-sg*st*sp, -sg*ct, cg*sp+sg*st*cp], + [sg*cp+cg*st*sp, cg*ct, sg*sp-cg*st*cp], + [-ct*sp, st, ct*cp]]).transpose() + + return R_to_i_from_w; + +def R_to_c_from_i(cam): + R_to_c_from_i = cam['R_to_c_from_i'] + R_camera_pitch = euler_matrix(cam['rot_x'], cam['rot_y'], cam['rot_z'], 'sxyz')[0:3,0:3] + R_to_c_from_i = dot(R_camera_pitch, R_to_c_from_i) + + return R_to_c_from_i + +def R_to_c_from_w(roll, pitch, yaw, cam): + return dot(R_to_c_from_i(cam), R_to_i_from_w(roll, pitch, yaw) ) + + +def integrateVelocity(vel, dt=1/50.0): + return np.cumsum(vel*dt, axis=0) + +def smoothCoordinates(GPSData): + tr = np.array([np.eye(4),]*GPSData.shape[0]) + + roll_start = deg2rad(GPSData[0,8]) + pitch_start = deg2rad(GPSData[0,7]) + yaw_start = -deg2rad(GPSData[0,9]) + base_R_to_i_from_w = R_to_i_from_w(roll_start, pitch_start, yaw_start) + + NEU_coords = integrateVelocity(GPSData[:,4:7]) + ENU_coords = np.array(NEU_coords) + ENU_coords[:,0] = NEU_coords[:,1] + ENU_coords[:,1] = NEU_coords[:,0] + ENU_coords[:,2] = NEU_coords[:,2] + + pos_wrt_imu = np.dot(base_R_to_i_from_w, ENU_coords.transpose()) + + tr[:,0,3] = pos_wrt_imu[0,:] + tr[:,1,3] = pos_wrt_imu[1,:] + tr[:,2,3] = pos_wrt_imu[2,:] + + for i in xrange(GPSData.shape[0]): + roll = deg2rad(GPSData[i,8]) + pitch = deg2rad(GPSData[i,7]) + yaw = -deg2rad(GPSData[i,9]) + + rot = R_to_i_from_w(roll, pitch, yaw).transpose() + tr[i, 0:3,0:3] = dot(base_R_to_i_from_w, rot) + + return tr + + +""" +returns the relative transformations of the IMU from time 0 to the end of the GPS log. +""" +def IMUTransforms(GPSData): + return smoothCoordinates(GPSData) + +def GPSTransforms(GPSData, Camera, width=2): + + tr = np.array([np.eye(4),]*GPSData.shape[0]) + + roll_start = -deg2rad(GPSData[0,7]); + pitch_start = deg2rad(GPSData[0,8]); + yaw_start = -deg2rad(GPSData[0,9]+90); + + base_R_to_c_from_w = R_to_c_from_w(roll_start, pitch_start, yaw_start, Camera) + pts = WGS84toENU(GPSData[0,1:4], GPSData[:,1:4]) + + world_coordinates = pts; + pos_wrt_camera = dot(base_R_to_c_from_w, world_coordinates); + + tr[:,0,3] = pos_wrt_camera[0,:] + tr[:,1,3] = pos_wrt_camera[1,:] + tr[:,2,3] = pos_wrt_camera[2,:] + + for i in xrange(GPSData.shape[0]): + roll = -deg2rad(GPSData[i,7]) + pitch = deg2rad(GPSData[i,8]) + yaw = -deg2rad(GPSData[i,9] + 90) + + rot = R_to_c_from_w(roll, pitch, yaw, Camera).transpose() + + pos = pos_wrt_camera[0:3,i] + tr[i, 0:3,0:3] = dot(base_R_to_c_from_w, rot) + + return tr diff --git a/src/caffe/py_lane_label_reader/LidarTransforms.py b/src/caffe/py_lane_label_reader/LidarTransforms.py new file mode 100644 index 00000000000..bd672fdfb90 --- /dev/null +++ b/src/caffe/py_lane_label_reader/LidarTransforms.py @@ -0,0 +1,192 @@ +import os +import glob +import numpy as np +import bisect +from transformations import quaternion_from_matrix, quaternion_slerp,\ + quaternion_matrix + +# PARAM +SWEEP_TIME_MICROSEC = 100000 + + +def loadLDR(ldrfile): + z = np.fromfile(ldrfile, dtype=np.float32) + z = z.reshape((z.shape[0] / 6, 6)) + return z + +def loadLDRCamMap(frame_cloud_map): + map_file = open(frame_cloud_map, 'r') + clouds = [] + frame_folder, map_name = os.path.split(frame_cloud_map) + frame_folder = frame_folder + '/' + map_name.split('.')[0] + '_frames' + + for line in map_file: + ldr_file = line.rstrip().split(' ')[1] + clouds.append(frame_folder + '/' + ldr_file) + map_file.close() + + return clouds + +def utc_from_gps_log(log): + return utc_from_gps(log[10], log[0]) + + +def utc_from_gps_log_all(log): + return utc_from_gps(log[:, 10], log[:, 0]) + + +def utc_from_gps(gps_week, seconds, leap_seconds=16): + """ Converts from gps week time to UTC time. UTC time starts from JAN 1, + 1970 and GPS time starts from JAN 6, 1980. + + http://leapsecond.com/java/gpsclock.htm + """ + + secs_in_week = 604800 + secs_gps_to_utc = 315964800 + + utc = (gps_week * secs_in_week + seconds + secs_gps_to_utc - + leap_seconds) * 1000000 + if type(gps_week) is np.ndarray: + return np.array(utc, dtype=np.int64) + else: + return long(utc) + + +class LDRLoader(object): + + def __init__(self, ldr_dir): + ldr_files = sorted(list(glob.glob(os.path.join(ldr_dir, '*.ldr')))) + file_times = np.array([int(os.path.splitext(os.path.basename(f))[0]) for f in ldr_files], dtype=np.int64) + sweep_end_times = file_times + sweep_start_times = sweep_end_times - SWEEP_TIME_MICROSEC + self.ldr_files = np.array(ldr_files) + self.sweep_start_times = sweep_start_times + self.sweep_end_times = sweep_end_times + + def loadLDRWindow(self, microsec_since_epoch, time_window_sec): + time_window = time_window_sec * 1e6 + + # Get files that may have points within the time window + mask = (self.sweep_start_times >= microsec_since_epoch - time_window / 2.0 - SWEEP_TIME_MICROSEC) &\ + (self.sweep_end_times <= microsec_since_epoch + time_window / 2.0 + SWEEP_TIME_MICROSEC) + ldr_files = self.ldr_files[mask].tolist() + #print ldr_files + sweep_end_times = self.sweep_end_times[mask].tolist() + + # Load the points from those times + all_data = None + all_times = None + for (ldr_file, sweep_end_time) in zip(ldr_files, sweep_end_times): + data = loadLDR(ldr_file) + times = -1 * np.array(data[:, 5], dtype=np.int64) + sweep_end_time + + if all_data is None: + all_data = data + all_times = times + else: + all_data = np.vstack((all_data, data)) + all_times = np.concatenate((all_times, times)) + + # Filter points within time window + + mask = (all_times >= microsec_since_epoch - time_window / 2.0) &\ + (all_times <= microsec_since_epoch + time_window / 2.0) + if all_data is None: + return None, None + return all_data[mask, :], all_times[mask] + + +def R_to_c_from_l_old(cam): + # hard coded calibration parameters for now + R_to_c_from_l = np.array([[0.0, -1.0, 0.0], + [0.0, 0.0, -1.0], + [1.0, 0.0, 0.0]]) + + return R_to_c_from_l + +def R_to_c_from_l(cam): + # hard coded calibration parameters for now + R_to_c_from_l = np.array([[0.0, -1.0, 0.0], + [0.0, 0.0, -1.0], + [1.0, 0.0, 0.0]]) + R_to_c_from_l = np.dot(cam['R_to_c_from_l_in_camera_frame'], R_to_c_from_l) + + return R_to_c_from_l + + +def interp_transforms(T1, T2, alpha): + assert alpha <= 1 + T_avg = alpha * T1 + (1 - alpha) * T2 + q1 = quaternion_from_matrix(T1) + q2 = quaternion_from_matrix(T2) + q3 = quaternion_slerp(q1, q2, alpha) + R = quaternion_matrix(q3) + T_avg[0:3, 0:3] = R[0:3, 0:3] + return T_avg + + +def interp_transforms_backward(imu_transforms, ind): + assert ind < 0, 'No need to call interp_transforms_backward' + T_interp = imu_transforms[0, :, :] -\ + (imu_transforms[-1 * ind, :, :] - imu_transforms[0, :, :]) + R = T_interp[0:3, 0:3] + R = np.linalg.qr(R, mode='complete')[0] + T_interp[0:3, 0:3] = R + return T_interp + + +def transform_points_in_sweep(pts, times, fnum, imu_transforms): + for time in set(times): + mask = times == time + + # FIXME PARAM + offset = (time / float(1e6)) / 0.05 + offset = min(5, offset) + + ind1 = int(fnum - math.ceil(offset)) + ind2 = int(fnum - math.floor(offset)) + + # FIXME Hack to interpolate before 0 + #ind1, ind2 = max(ind1, 0), max(ind2, 0) + if ind1 < 0: + T1 = interp_transforms_backward(imu_transforms, ind1) + else: + T1 = imu_transforms[ind1, :, :] + if ind2 < 0: + T2 = interp_transforms_backward(imu_transforms, ind2) + else: + T2 = imu_transforms[ind2, :, :] + + transform = interp_transforms(T1, T2, offset / 5.0) + + # transform data into imu_0 frame + pts[:, mask] = np.dot(transform, pts[:, mask]) + + +def transform_points_by_times(pts, t_pts, imu_transforms, gps_times): + for t in set(t_pts): + mask = t_pts == t + + fnum1 = bisect.bisect(gps_times, t) - 1 + fnum2 = fnum1 + 1 + try: + alpha = (1 - (t - gps_times[fnum1])) / float(gps_times[fnum2] - + gps_times[fnum1]) + except IndexError: + continue + + T1 = imu_transforms[fnum1, :, :] + T2 = imu_transforms[fnum2, :, :] + + transform = interp_transforms(T1, T2, alpha) + + # transform data into imu_0 frame + pts[:, mask] = np.dot(transform, pts[:, mask]) + + +if __name__ == '__main__': + import sys + z = loadLDRCamMap(sys.argv[1]) + for x in z: + print x diff --git a/src/caffe/py_lane_label_reader/Q50_config.py b/src/caffe/py_lane_label_reader/Q50_config.py new file mode 100644 index 00000000000..c2de9084088 --- /dev/null +++ b/src/caffe/py_lane_label_reader/Q50_config.py @@ -0,0 +1,9 @@ +def LoadParameters(name): + if name == 'q50_4_3_14_params': + from parameters.q50_4_3_14_params import GetQ50Params + return GetQ50Params() + elif name == 'q50_3_7_14_params': + from parameters.q50_3_7_14_params import GetQ50Params + return GetQ50Params() + else: + raise Exception("Parameter file " + name + " not found") diff --git a/src/caffe/py_lane_label_reader/SetPerspDist.py b/src/caffe/py_lane_label_reader/SetPerspDist.py new file mode 100644 index 00000000000..5fb058f75c3 --- /dev/null +++ b/src/caffe/py_lane_label_reader/SetPerspDist.py @@ -0,0 +1,49 @@ +import numpy as np +from transformations import euler_matrix +from transformations import translation_matrix +import pickle +# defines a set of perspective transforms that correspond to shifts/rotations in the real world + +def setPersp(scale_factor = 1, distort_location = '/afs/cs.stanford.edu/u/twangcat/scratch/sail-car-log/process/'): + distort = { } + distort['M_1'] = [np.eye(3)] # perspective distrotion in cam1 image space + distort['M_2'] = [np.eye(3)] # perspective distrotion in cam2 image space + distort['T'] = [np.eye(4)] # corresponding real-world transformation + + + distort['T'].insert(0,translation_matrix(np.array([1.0,0,0]))) + distort['T'].insert(0,translation_matrix(np.array([-1.0,0,0]))) + distort['T'].insert(0,euler_matrix(0, 0.06, 0)) + distort['T'].insert(0,euler_matrix(0, -0.06, 0)) + distort['T'].insert(0,euler_matrix(0, 0.12, 0)) + distort['T'].insert(0,euler_matrix(0, -0.12, 0)) + + + distort['M_1'].insert(0,pickle.load(open(distort_location+'M_shift1.0_cam1.pickle'))) # shift 1.0 + distort['M_1'].insert(0,pickle.load(open(distort_location+'M_shift-1.0_cam1.pickle'))) # shift -1.0 + distort['M_1'].insert(0,pickle.load(open(distort_location+'M_rot0.06_cam1.pickle','r'))) # rotate 0.06 + distort['M_1'].insert(0,pickle.load(open(distort_location+'M_rot-0.06_cam1.pickle','r'))) # rotate -0.06 + distort['M_1'].insert(0,pickle.load(open(distort_location+'M_rot0.12_cam1.pickle','r'))) # rotate 0.12 + distort['M_1'].insert(0,pickle.load(open(distort_location+'M_rot-0.12_cam1.pickle','r'))) # rotate -0.12 + + + + + distort['M_2'].insert(0,pickle.load(open(distort_location+'M_shift1.0_cam2.pickle'))) # shift 1.0 + distort['M_2'].insert(0,pickle.load(open(distort_location+'M_shift-1.0_cam2.pickle'))) # shift -1.0 + distort['M_2'].insert(0,pickle.load(open(distort_location+'M_rot0.06_cam2.pickle','r'))) # rotate 0.06 + distort['M_2'].insert(0,pickle.load(open(distort_location+'M_rot-0.06_cam2.pickle','r'))) # rotate -0.06 + distort['M_2'].insert(0,pickle.load(open(distort_location+'M_rot0.12_cam2.pickle','r'))) # rotate 0.12 + distort['M_2'].insert(0,pickle.load(open(distort_location+'M_rot-0.12_cam2.pickle','r'))) # rotate -0.12 + + assert len(distort['M_1']) == len(distort['M_2']) and len(distort['M_1'])==len(distort['T']) + if scale_factor != 1: + for i in xrange(len(distort['M_1'])-1): # last one is always identity, no need change. + distort['M_1'][i][:,0:2]/=scale_factor + distort['M_1'][i][2,:]/=scale_factor + distort['M_2'][i][:,0:2]/=scale_factor + distort['M_2'][i][2,:]/=scale_factor + + + return distort + diff --git a/src/caffe/py_lane_label_reader/WGS84toENU.py b/src/caffe/py_lane_label_reader/WGS84toENU.py new file mode 100644 index 00000000000..72bc967df9c --- /dev/null +++ b/src/caffe/py_lane_label_reader/WGS84toENU.py @@ -0,0 +1,66 @@ +# port of dllh2denu.m +from math import pi, cos, sin, sqrt +from numpy import array, power + +def WGS84toENU(llh0, llh): + ########CONSTANTS + a = 6378137; + b = 6356752.3142; + e2 = 1 - (b/a)**2; + ########Location of reference point in radians + phi = llh0[0]*pi/180; + lam = llh0[1]*pi/180; + h = llh0[2]; + ########Location of data points in radians + dphi = llh[:,0]*pi/180 - phi; + dlam = llh[:,1]*pi/180 - lam; + dh = llh[:,2] - h; + ########Some useful definitions + tmp1 = sqrt(1-e2*power(sin(phi),2.0)); + cl = cos(lam); + sl = sin(lam); + cp = cos(phi); + sp = sin(phi); + ########Transformations + de = (a/tmp1+h)*cp*dlam - (a*(1-e2)/(tmp1**3)+h)*sp*dphi*dlam +cp*dlam*dh; + dn = (a*(1-e2)/tmp1**3 + h)*dphi + 1.5*cp*sp*a*e2*power(dphi,2) + sp**2*dh*dphi \ + + 0.5*sp*cp*(a/tmp1 +h)*power(dlam,2); + du = dh - 0.5*(a-1.5*a*e2*cp**2+0.5*a*e2+h)*power(dphi,2) \ + - 0.5*cp**2*(a/tmp1 -h)*power(dlam,2); + denu = array([de, dn, du]); + return denu; + + +def WGS84toENU_slow(llh0, llh): + ########CONSTANTS + a = 6378137; + b = 6356752.3142; + e2 = 1 - (b/a)**2; + ########Location of reference point in radians + phi = llh0[0]*pi/180; + lam = llh0[1]*pi/180; + h = llh0[2]; + ########Location of data points in radians + dphi= llh[0]*pi/180 - phi; + dlam= llh[1]*pi/180 - lam; + dh = llh[2] - h; + ########Some useful definitions + tmp1 = sqrt(1-e2*sin(phi)**2); + cl = cos(lam); + sl = sin(lam); + cp = cos(phi); + sp = sin(phi); + ########Transformations + de = (a/tmp1+h)*cp*dlam - (a*(1-e2)/(tmp1**3)+h)*sp*dphi*dlam +cp*dlam*dh; + dn = (a*(1-e2)/tmp1**3 + h)*dphi + 1.5*cp*sp*a*e2*dphi**2 + sp**2*dh*dphi \ + + 0.5*sp*cp*(a/tmp1 +h)*dlam**2; + du = dh - 0.5*(a-1.5*a*e2*cp**2+0.5*a*e2+h)*dphi**2 \ + - 0.5*cp**2*(a/tmp1 -h)*dlam**2; + denu = array([de, dn, du]); + return denu; + +def deg2rad(angleInDeg): + return (pi / 180) * angleInDeg; + +def rad2deg(angleInRad): + return (180 / pi) * angleInRad; diff --git a/src/caffe/py_lane_label_reader/WarpUtils.py b/src/caffe/py_lane_label_reader/WarpUtils.py new file mode 100644 index 00000000000..c88cdf417cc --- /dev/null +++ b/src/caffe/py_lane_label_reader/WarpUtils.py @@ -0,0 +1,14 @@ +import cv, cv2 +import numpy as np + + +def warpPoints(P, pts): + """ + warpPoints takes a list of points and performs a matrix transform on them + + P is a perspective transform matrix (3x3) + pts is a 2xN matrix of (x, y) coordinates to be transformed + """ + pts = np.vstack([pts, np.ones((1, pts.shape[1]))]) + out = np.dot(P, pts) + return out[0:2] / out[2] diff --git a/src/caffe/py_lane_label_reader/__init__.py b/src/caffe/py_lane_label_reader/__init__.py new file mode 100644 index 00000000000..1227be3e9ee --- /dev/null +++ b/src/caffe/py_lane_label_reader/__init__.py @@ -0,0 +1,2 @@ +from multilane_label_reader import * +from perspective_reader import * diff --git a/src/caffe/py_lane_label_reader/multilane_label_reader.py b/src/caffe/py_lane_label_reader/multilane_label_reader.py new file mode 100644 index 00000000000..8643516c075 --- /dev/null +++ b/src/caffe/py_lane_label_reader/multilane_label_reader.py @@ -0,0 +1,404 @@ +from numpy import array, dot, zeros, around, divide, ones +import cv,cv2 +from transformations import euler_matrix +from copy import deepcopy +import collections +import os +import numpy as np +import logging +from threading import Thread +from Queue import Queue +import pickle +from GPSReader import GPSReader +from GPSTransforms import * +from GPSReprojection import * +from WarpUtils import warpPoints +from Q50_config import * +from ArgParser import * +from scipy.io import loadmat +import string +import time +from SetPerspDist import setPersp +import os +import time +__all__=['MultilaneLabelReader'] + + +blue = np.array([255,0,0]) +green = np.array([0,255,0]) +red = np.array([0,0,255]) + +def dist2color(dist, max_dist = 90.0): + # given a distance and a maximum distance, gives a color code for the distance. + # red being closest, green is mid-range, blue being furthest + alpha = (dist/max_dist) + if alpha<0.5: + color = red*(1-alpha*2)+green*alpha*2 + else: + beta = alpha-0.5 + color = green*(1-beta*2)+blue*beta*2 + return color.astype(np.int) + +def colorful_line(img, start, end, start_color, end_color, thickness): + # similar to cv.line, but draws a line with gradually (linearly) changing color. + # allows starting and ending color to be specified. + # implemented using recursion. + if ((start[0]-end[0])**2 + (start[1]-end[1])**2)**0.5<=thickness*2: + cv2.line(img, start, end ,start_color,thickness) + return img + mid = (int((start[0]+end[0])/2),int((start[1]+end[1])/2)) + mid_color = [int((start_color[0]+end_color[0])/2),int((start_color[1]+end_color[1])/2),int((start_color[2]+end_color[2]))/2] + img = colorful_line(img, start, mid, start_color, mid_color, thickness) + img = colorful_line(img, mid, end, mid_color, end_color, thickness) + return img + + + +class MultilaneLabelReader(): + def __init__(self, imdepth=3, imwidth=640, imheight=480, markingWidth=0.07, distortion_file='/scail/group/deeplearning/driving_data/perspective_transforms.pickle', pixShift=0, label_dim = [80,60], new_distort=False, predict_depth = False, readVideo=False): + self.new_distort = new_distort + if new_distort: + self.Ps = setPersp() + else: + self.Ps = pickle.load(open(distortion_file, 'rb')) + self.lane_values = dict() + self.gps_values1 = dict() + self.gps_values2 = dict() + self.count=0 + self.markingWidth = markingWidth + self.pixShift = pixShift + self.labelw = label_dim[0] + self.labelh = label_dim[1] + self.labeld = 6 if predict_depth else 4 + self.imwidth = imwidth + self.imheight = imheight + self.imdepth = imdepth + self.label_scale = None + self.img_scale = None + self.predict_depth = predict_depth + self.visualize = readVideo + self.griddim=4 + self.colors = [(255,0,0),(0,255,0),(0,0,255),(255,255,0),(255,0,255),(0,255,255),(128,128,255),(128,255,128),(255,128,128),(128,128,0),(128,0,128),(0,128,128),(0,128,255),(0,255,128),(128,0,255),(128,255,0),(255,0,128),(255,128,0)] + # arrays to adjust for rf offset + self.x_adj = (np.floor(np.arange(label_dim[0])/self.griddim)*self.griddim+self.griddim/2)*imwidth/label_dim[0] + self.y_adj = (np.floor(np.arange(label_dim[1])/self.griddim)*self.griddim+self.griddim/2)*imheight/label_dim[1] + self.y_adj = np.array([self.y_adj]).transpose() + + def zDistances(self, distances, global_frame, starting_point, meters_per_point, points_fwd): + output = [] + point_num = 1 + dist = 0 + for pt in xrange(points_fwd): + dist = pt * meters_per_point+starting_point + output.append((np.abs(distances-dist)).argmin()+global_frame) + return output + + + def dist2Id(self, dist, laneWidth=3.6576): # default lane width for US highways is 12 ft + return np.floor(np.abs(dist)/laneWidth)*2+int(dist>0) + + def setLaneIDs(self, lane, center2, sideways): + # go through each lane. Define lane id based on lateral dist at fixed longitudinal distances. + num_anchors = center2.shape[1] + #id_change=False # if the lane id changed during the course of this lane in the current frame + # define the anchor pts on this lane + anchors = np.empty([0,5]) + id=float('nan') + for n in range(num_anchors): + diff=lane-center2[:,n:n+1] + minid = np.argmin((diff**2).sum(0)) + mindist = np.dot(diff[:,minid],sideways[n,:]) # length projected to perpendicular vector + if (minid==0 or minid==lane.shape[1]-1) and np.abs(mindist/np.sqrt(np.sum(diff[:,minid]**2)))<0.95: + continue + else: + anchor_id = self.dist2Id(mindist) + temp = np.empty([1,5]) + #temp[0,0:3]=lane[:,minid] + temp[0,0:3]=center2[:,n]+mindist*sideways[n,:] + temp[0,3]=n + temp[0,4]=anchor_id + anchors = np.r_[anchors, temp] + if not np.isnan(id): + id = anchor_id + return lane,id, anchors + + + + def runBatch(self, vid_name, gps_dat, gps_times1, gps_times2, frames, start_frame, final_frame, lanes, tr1,Pid, split_num, cam_num, params): + if self.visualize: + print 'warning: reading videos in labeller...' + cap = cv2.VideoCapture(vid_name) + cam = params['cam'][cam_num-1]#self.cam[cam_num - 1] + lidar_height = params['lidar']['height'] + T_from_l_to_i = params['lidar']['T_from_l_to_i'] + T_from_i_to_l = np.linalg.inv(T_from_l_to_i) + starting_point = 4#12 + meters_per_point = 80#24#12#6 + points_fwd = 2#6#12 + starting_point2 = 15#12 + meters_per_point2 = 5#24#12#6 + points_fwd2 = 15#6#12 + scan_range = starting_point + (points_fwd-1)*meters_per_point + seconds_ahead=5 + output_num = 0 + batchSize = frames.shape[0] + labels= np.zeros([batchSize,self.labelh, self.labelw,1],dtype='f4',order='C') + reg_labels= np.zeros([batchSize, self.labelh, self.labelw,self.labeld],dtype='f4',order='C') + weight_labels= np.ones([batchSize, self.labelh, self.labelw,1],dtype='f4',order='C') + + labels_3d= [] + trajectory_3d= [] + count = 0 + #print 'reading labels... ', + labelling_time=0 + for idx in xrange(batchSize): + frame = frames[idx] + fnum2 =frame*10+split_num-1 # global video frame. if split0, *10+9; if split1, *10+0; if split 2, *10+1 .... if split9, *10+8 + #fnum2 =frame # global video frame. if split0, *10+9; if split1, *10+0; if split 2, *10+1 .... if split9, *10+8 + if cam_num>2: + fnum2 *=2 # wideview cams have half the framerate + t = gps_times2[fnum2] # time stamp for the current video frame (same as gps_mark2) + fnum1 = Idfromt(gps_times1,t) # corresponding frame in gps_mark1 + if self.new_distort: + T = self.Ps['T'][Pid[idx]] + P=self.Ps['M_'+str(cam_num)][Pid[idx]] + else: + T = np.eye(4) + P = self.Ps[Pid[idx]] + if frame < start_frame or (final_frame != -1 and frame >= final_frame): + continue + + # car trajectory in current camera frame + local_pts = MapPos(tr1[fnum1:fnum1+290,0:3,3], tr1[fnum1,:,:], cam, T_from_i_to_l) + local_pts[1,:]+=lidar_height # subtract height to get point on ground + # transform according to real-world distortion + local_pts = np.vstack((local_pts, np.ones((1,local_pts.shape[1])))) + local_pts = np.dot(T, local_pts)[0:3,:] + # pick start and end point frame ids + ids = np.where(np.logical_and(gps_times1>t-seconds_ahead*1000000, gps_times10] # make sure in front of camera + depths = lane3d[2,:] + # project into 2d image + (c, J) = cv2.projectPoints(lane3d[0:3,:].transpose(), np.array([0.0,0.0,0.0]), np.array([0.0,0.0,0.0]), cam['KK'], cam['distort']) + # need to define lane id. If necessary split current lane based on lateral distance. + lane3d,lane_id, anchors=self.setLaneIDs(lane3d, center2, sideways_curr) + Lane3d['pts'].append(lane3d) + Lane3d['id'].append(lane_id) + Lane3d['anchors'] = np.r_[Lane3d['anchors'],anchors] + c= warpPoints(P, c[:,0,:].transpose()[0:2,:]) + # scale down to the size of the label mask + labelpix = np.transpose(np.round(c*self.label_scale)) + # scale down to the size of the actual image + imgpix = c*self.img_scale + # find unique indices to be marked in the label mask + #lu = np.ascontiguousarray(labelpix).view(np.dtype((np.void, labelpix.dtype.itemsize * labelpix.shape[1]))) + #_, l_idx = np.unique(lu, return_index=True) + #l_idx = np.sort(l_idx) + labelpix = (np.transpose(labelpix)).astype('i4') + # draw labels on temp masks + if self.visualize: # if need to visualize, make the lines more colorful! + mask_color = l+1 + else: + mask_color=1 + for ii in range(1,imgpix.shape[1]-1): + ip = ii-1 + ic = ii + xp = labelpix[0,ip] + yp = labelpix[1,ip] + xc = labelpix[0,ic] + yc = labelpix[1,ic] + + if np.abs(xp-xc)>1 or np.abs(yp-yc)>1: + x1 = xp + y1 = yp + else: + x1 = xc + y1 = yc + x2 = xc + y2 = yc + if yc>-1 and yc-1 and xc0.5: + #cv2.putText(img, str(int(temp_label[ii,jj]-1)), (reg_label[ii,jj,0],reg_label[ii,jj,1]), cv2.FONT_HERSHEY_PLAIN, 1.0, self.colors[int(temp_label[ii,jj]-1)%len(self.colors)],thickness=1) + + if self.predict_depth: + cv2.line(img, (reg_label[ii,jj,0]*self.img_scale[0],reg_label[ii,jj,1]*self.img_scale[0]), (reg_label[ii,jj,2]*self.img_scale[1], reg_label[ii,jj,3]*self.img_scale[1]), dist2color((reg_label[ii,jj,4]+reg_label[ii,jj,5])/2).tolist(), thickness=2 ) + else: + cv2.line(img, (reg_label[ii,jj,0],reg_label[ii,jj,1]), (reg_label[ii,jj,2], reg_label[ii,jj,3]), self.colors[int(temp_label[ii,jj]-1)%len(self.colors)], thickness=2 ) + cv2.imwrite('/scr/twangcat/lane_detect_results/test2/label_'+str(self.count)+'.png', np.clip(img, 0,255).astype('u1')) + self.count+=1 + # push a batch of data to the data queue + #self.q.put([labels.astype(np.float32),reg_labels,weight_labels,Pid, cam, labels_3d, trajectory_3d]) + label_view = np.transpose(labels, [0,3,1,2]).reshape(batchSize, 1, self.labelh//self.griddim, self.griddim, self.labelw//self.griddim, self.griddim, order='C') + label_grid = np.transpose(label_view,[0,1,3,5,2,4]).reshape(batchSize, self.griddim*self.griddim,self.labelh//self.griddim,self.labelw//self.griddim, order='C') + reg_view = np.transpose(reg_labels, [0,3,1,2]).reshape(batchSize, self.labeld, self.labelh//self.griddim, self.griddim, self.labelw//self.griddim, self.griddim) + reg_grid = np.transpose(reg_view,[0,1,3,5,2,4]).reshape(batchSize, self.labeld*self.griddim*self.griddim,self.labelh//self.griddim,self.labelw//self.griddim) + full_label = np.empty([batchSize, 80, 15, 20], dtype='f4',order='C') + full_label[:,0:16,:,:] = label_grid + full_label[:,16:,:,:] = reg_grid + return full_label + + + + def runLabelling(self, f, frames, Pid): # filename, frame numbers, transformation ids + Pid = Pid.tolist() + print frames + print Pid + cam_num = int(f[-5]) + splitidx = string.index(f,'split_') + split_num = int(f[splitidx+6]) + if split_num==0: + split_num=10 + path, fname = os.path.split(f) + fname = fname[8:] # remove 'split_?' + args = parse_args(path, fname) + prefix = path + '/' + fname + + params = args['params'] + cam = params['cam'][cam_num-1] + self.label_scale = np.array([[float(self.labelw) / cam['width']], [float(self.labelh) / cam['height']]]) + self.img_scale = np.array([[float(self.imwidth) / cam['width']], [float(self.imheight) / cam['height']]]) + if os.path.isfile(args['gps_mark2']): + gps_key1='gps_mark1' + gps_key2='gps_mark2' + postfix_len = 13 + else: + gps_key1='gps' + gps_key2='gps' + postfix_len=8 + + # gps_mark2 + gps_filename2= args[gps_key2] + if not (gps_filename2 in self.gps_values2): # if haven't read this gps file before, cache it in dict. + gps_reader2 = GPSReader(gps_filename2) + self.gps_values2[gps_filename2] = gps_reader2.getNumericData() + gps_data2 = self.gps_values2[gps_filename2] + gps_times2 = utc_from_gps_log_all(gps_data2) + + # gps_mark1 + gps_filename1= args[gps_key1] + if not (gps_filename1 in self.gps_values1): # if haven't read this gps file before, cache it in dict. + gps_reader1 = GPSReader(gps_filename1) + self.gps_values1[gps_filename1] = gps_reader1.getNumericData() + gps_data1 = self.gps_values1[gps_filename1] + tr1 = IMUTransforms(gps_data1) + gps_times1 = utc_from_gps_log_all(gps_data1) + + prefix = gps_filename2[0:-postfix_len] + + #lane_filename = prefix+'_multilane_points_done.npz' + lane_filename = prefix+'_multilane_points_planar_done.npz' + if not (lane_filename in self.lane_values): + self.lane_values[lane_filename] = np.load(lane_filename) + lanes = self.lane_values[lane_filename] # these values are alread pre-computed and saved, now just read it from dictionary + + start_frame = 0 #frames to skip #int(sys.argv[3]) if len(sys.argv) > 3 else 0 + final_frame = -1 #int(sys.argv[4]) if len(sys.argv) > 4 else -1 + return self.runBatch(f, gps_data1, gps_times1, gps_times2, frames, start_frame, final_frame, lanes, tr1, Pid, split_num, cam_num, params) + + + +if __name__ == '__main__': + label_reader = MultilaneLabelReader(400, 0, 3, 640, 480, label_dim = [80,60], readVideo = True,predict_depth=True) + fid = open('/scail/group/deeplearning/driving_data/twangcat/schedules/q50_test_schedule_4-2-14-monterey-17S_a2.avi_96.pickle') + batches = pickle.load(fid) + fid.close() + label_reader.start() + for b in batches: + label_reader.push_batch(b) + diff --git a/src/caffe/py_lane_label_reader/parameters/__init__.py b/src/caffe/py_lane_label_reader/parameters/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/src/caffe/py_lane_label_reader/parameters/q50_3_7_14_params.py b/src/caffe/py_lane_label_reader/parameters/q50_3_7_14_params.py new file mode 100644 index 00000000000..60b4652bf23 --- /dev/null +++ b/src/caffe/py_lane_label_reader/parameters/q50_3_7_14_params.py @@ -0,0 +1,76 @@ +import numpy as np +from transformations import euler_matrix + +# defines calibration parameters for the Q50 setup. + +# IMU orientation -> x forward, y out the left door, z up +# to convert from WGS84, roll, pitch, azimuth to IMU based cartesian coordinate sysem use: + + +# Lidar orientation -> x forward, y out the left door, z up + +# camera orientation -> todo + +##### LIDAR to IMU calibration parameters ##### + +# Note: the translation vector is currently unknown +def GetQ50LidarParams(): + params = { } + params['R_from_i_to_l'] = euler_matrix(-0.005,-0.0081,-0.0375)[0:3,0:3] + params['T_from_l_to_i'] = np.eye(4) + params['T_from_l_to_i'][0:3,0:3] = params['R_from_i_to_l'].transpose() + params['height']=1.85 + return params + +##### LIDAR to Camera Calibration parameters ##### + +def GetQ50CameraParams(): + cam = [{}, {}] + for i in [1, 0]: + cam[i]['R_to_c_from_i'] = np.array([[-1, 0, 0], + [0, 0, -1], + [0, -1, 0]]) + + if i == 0: + cam[i]['R_to_c_from_l_in_camera_frame'] = cam[1]['R_to_c_from_l_in_camera_frame'] + cam[i]['displacement_from_l_to_c_in_lidar_frame'] = cam[1]['displacement_from_l_to_c_in_lidar_frame'] + + # extrinsics parameters for transforming points in right camera frame to this camera + T = np.array([-0.5873763710461054, 0.00012196510170337307, 0.08922401781210791]) + R = np.array([0.9999355343485463, -0.00932576944123699, 0.006477435558612815, 0.009223923954826548, 0.9998360945238545, 0.015578938158992275, -0.006621659456863392, -0.01551818647957998, 0.9998576596268203]) + R = R.reshape((3,3)) + T = T.reshape((3,1)) + E = np.hstack((R.transpose(), np.dot(-R.transpose(),T))) + E = np.vstack((E,np.array([0,0,0,1]))) + cam[i]['E'] = E + cam[i]['fx'] = 2254.76 + cam[i]['fy'] = 2266.30 + cam[i]['cu'] = 655.55 + cam[i]['cv'] = 488.85 + cam[i]['distort'] = np.array([-0.22146000368016028, 0.7987879799679538, -6.542034918087567e-05, 2.8680581938024014e-05, 0.0]) + + elif i == 1: + R_to_c_from_l_in_camera_frame = euler_matrix(0.046,0.0071,0.0065)[0:3,0:3] + cam[i]['R_to_c_from_l_in_camera_frame'] = R_to_c_from_l_in_camera_frame + cam[i]['displacement_from_l_to_c_in_lidar_frame'] = np.array([-0.07, 0.325, 0.16]); + cam[i]['fx'] = 2250.72 + cam[i]['fy'] = 2263.75 + cam[i]['cu'] = 648.95 + cam[i]['cv'] = 450.24 + cam[i]['distort'] = np.array([-0.16879238412882028, 0.11971166628565273, -0.0017457365846050555, 0.0001853749033525837, 0.0]) + cam[i]['E'] = np.eye(4) + + + + cam[i]['KK'] = np.array([[cam[i]['fx'], 0.0, cam[i]['cu']], + [0.0, cam[i]['fy'], cam[i]['cv']], + [0.0, 0.0, 1.0]]) + cam[i]['f'] = (cam[i]['fx'] + cam[i]['fy']) / 2 + + return cam + +def GetQ50Params(): + params = { } + params['lidar'] = GetQ50LidarParams(); + params['cam'] = GetQ50CameraParams(); + return params diff --git a/src/caffe/py_lane_label_reader/parameters/q50_4_3_14_params.py b/src/caffe/py_lane_label_reader/parameters/q50_4_3_14_params.py new file mode 100644 index 00000000000..422aff6745e --- /dev/null +++ b/src/caffe/py_lane_label_reader/parameters/q50_4_3_14_params.py @@ -0,0 +1,147 @@ +import numpy as np +from transformations import euler_matrix + +# defines calibration parameters for the Q50 setup. + +# IMU orientation -> x forward, y out the left door, z up +# to convert from WGS84, roll, pitch, azimuth to IMU based cartesian coordinate sysem use: + + +# Lidar orientation -> x forward, y out the left door, z up + +# camera orientation -> todo + +##### LIDAR to IMU calibration parameters ##### + +def GetQ50LidarParams(): + params = { } + params['R_from_i_to_l'] = euler_matrix(-0.04, -0.0146, -0.0165)[0:3,0:3] + params['T_from_l_to_i'] = np.eye(4) + params['T_from_l_to_i'][0:3,0:3] = params['R_from_i_to_l'].transpose() + params['height'] = 2.0 + return params + +##### LIDAR to Camera Calibration parameters ##### + +def GetQ50CameraParams(): + cam = [{}, {}, {}, {}, {}, {}] + for i in [1, 0, 2, 3, 4, 5]: + cam[i]['R_to_c_from_i'] = np.array([[-1, 0, 0], + [0, 0, -1], + [0, -1, 0]]) + + if i == 0: # left camera + + cam[i]['R_to_c_from_l_in_camera_frame'] = cam[1]['R_to_c_from_l_in_camera_frame'] + cam[i]['displacement_from_l_to_c_in_lidar_frame'] = cam[1]['displacement_from_l_to_c_in_lidar_frame'] + + # extrinsics parameters for transforming points in right camera frame to this camera + T = np.array([-0.5873763710461054, 0.00012196510170337307, 0.08922401781210791]) + R = np.array([0.9999355343485463, -0.00932576944123699, 0.006477435558612815, 0.009223923954826548, 0.9998360945238545, 0.015578938158992275, -0.006621659456863392, -0.01551818647957998, 0.9998576596268203]) + R = R.reshape((3,3)) + T = T.reshape((3,1)) + E = np.hstack((R.transpose(), np.dot(-R.transpose(),T))) + E = np.vstack((E,np.array([0,0,0,1]))) + cam[i]['E'] = E + cam[i]['E_t'] = T + cam[i]['E_R'] = R + cam[i]['width'] = 1280 + cam[i]['height'] = 960 + cam[i]['fx'] = 2254.76 + cam[i]['fy'] = 2266.30 + cam[i]['cu'] = 655.55 + cam[i]['cv'] = 488.85 + cam[i]['distort'] = np.array([-0.22146000368016028, 0.7987879799679538, -6.542034918087567e-05, 2.8680581938024014e-05, 0.0]) + + elif i == 1: # right camera + # R_to_c_from_l_in_camera_frame = euler_matrix(0.044,0.0291,0.0115)[0:3,0:3] + + #R_to_c_from_l_in_camera_frame = euler_matrix(0.042000,0.022000,0.015000)[0:3,0:3] + R_to_c_from_l_in_camera_frame = euler_matrix(0.04116677, 0.02722644, 0.00895531)[0:3,0:3] + cam[i]['R_to_c_from_l_in_camera_frame'] = R_to_c_from_l_in_camera_frame + cam[i]['displacement_from_l_to_c_in_lidar_frame'] = np.array([-0.5,0.31,0.34]); + cam[i]['E'] = np.eye(4) + cam[i]['width'] = 1280 + cam[i]['height'] = 960 + cam[i]['fx'] = 2250.72 + cam[i]['fy'] = 2263.75 + cam[i]['cu'] = 648.95 + cam[i]['cv'] = 450.24 + cam[i]['distort'] = np.array([-0.16879238412882028, 0.11971166628565273, -0.0017457365846050555, 0.0001853749033525837, 0.0]) + + elif i == 2: #wfov front + # FIXME + R_to_c_from_l_in_camera_frame = euler_matrix(-0.005, 0.06, 0.0)[0:3,0:3] + cam[i]['R_to_c_from_l_in_camera_frame'] = R_to_c_from_l_in_camera_frame + # FIXME + cam[i]['displacement_from_l_to_c_in_lidar_frame'] = np.array([-0.53, 0.0, 0.40]) + cam[i]['E'] = np.eye(4) + cam[i]['width'] = 2080 + cam[i]['height'] = 1040 + cam[i]['fx'] = 669.688574 + cam[i]['fy'] = 673.807170 + cam[i]['cu'] = 996.121080 + cam[i]['cv'] = 264.973916*2 + # FIXME TODO + cam[i]['distort'] = np.array([-0.006067, 0.001167, 0.000275, 0.000828, 0.000000]) + + elif i == 3: #wfov left + R_to_c_from_l_in_camera_frame = euler_matrix(1.575, 0.04, -0.01, 'syxz')[0:3,0:3] + cam[i]['R_to_c_from_l_in_camera_frame'] = R_to_c_from_l_in_camera_frame + cam[i]['displacement_from_l_to_c_in_lidar_frame'] = np.array([0.0, -0.762, 0.40]) + cam[i]['E'] = np.eye(4) + cam[i]['width'] = 2080 + cam[i]['height'] = 1040 + cam[i]['fx'] = 669.688574 + cam[i]['fy'] = 673.807170 + cam[i]['cu'] = 996.121080 + cam[i]['cv'] = 264.973916*2 + cam[i]['distort'] = np.array([-0.006067, 0.001167, 0.000275, 0.000828, 0.000000]) + + elif i == 4: #wfov right + R_to_c_from_l_in_camera_frame = euler_matrix(-1.57, 0.06, 0.01, 'syxz')[0:3,0:3] + cam[i]['R_to_c_from_l_in_camera_frame'] = R_to_c_from_l_in_camera_frame + cam[i]['displacement_from_l_to_c_in_lidar_frame'] = np.array([0.0, 0.67, 0.40]) + cam[i]['E'] = np.eye(4) + cam[i]['width'] = 2080 + cam[i]['height'] = 1040 + cam[i]['fx'] = 669.688574 + cam[i]['fy'] = 673.807170 + cam[i]['cu'] = 996.121080 + cam[i]['cv'] = 264.973916*2 + cam[i]['distort'] = np.array([-0.006067, 0.001167, 0.000275, 0.000828, 0.000000]) + + elif i == 5: #wfov back + R_to_c_from_l_in_camera_frame = euler_matrix(0.005, 3.14, 0.005)[0:3,0:3] + cam[i]['R_to_c_from_l_in_camera_frame'] = R_to_c_from_l_in_camera_frame + cam[i]['displacement_from_l_to_c_in_lidar_frame'] = np.array([0.50, 0.0, 0.40]) + cam[i]['E'] = np.eye(4) + cam[i]['width'] = 2080 + cam[i]['height'] = 1040 + cam[i]['fx'] = 669.688574 + cam[i]['fy'] = 673.807170 + cam[i]['cu'] = 996.121080 + cam[i]['cv'] = 264.973916*2 + cam[i]['distort'] = np.array([-0.006067, 0.001167, 0.000275, 0.000828, 0.000000]) + + cam[i]['KK'] = np.array([[cam[i]['fx'], 0.0, cam[i]['cu']], + [0.0, cam[i]['fy'], cam[i]['cv']], + [0.0, 0.0, 1.0]]) + cam[i]['f'] = (cam[i]['fx'] + cam[i]['fy']) / 2 + + return cam + + +def GetQ50RadarParams(): + params = { } + params['R_from_r_to_l'] = euler_matrix(0, 0, -.009)[0:3,0:3] + params['T_from_r_to_l'] = [3.17, 0.4, -1.64] + + return params + +def GetQ50Params(): + params = { } + params['lidar'] = GetQ50LidarParams(); + params['cam'] = GetQ50CameraParams(); + params['radar'] = GetQ50RadarParams(); + return params diff --git a/src/caffe/py_lane_label_reader/parameters/transformations.py b/src/caffe/py_lane_label_reader/parameters/transformations.py new file mode 100644 index 00000000000..c31b66050d8 --- /dev/null +++ b/src/caffe/py_lane_label_reader/parameters/transformations.py @@ -0,0 +1,1907 @@ +# -*- coding: utf-8 -*- +# transformations.py + +# Copyright (c) 2006-2013, Christoph Gohlke +# Copyright (c) 2006-2013, The Regents of the University of California +# Produced at the Laboratory for Fluorescence Dynamics +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# * Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. +# * Redistributions 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. +# * Neither the name of the copyright holders nor the names of any +# contributors may 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 COPYRIGHT OWNER 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. + +"""Homogeneous Transformation Matrices and Quaternions. + +A library for calculating 4x4 matrices for translating, rotating, reflecting, +scaling, shearing, projecting, orthogonalizing, and superimposing arrays of +3D homogeneous coordinates as well as for converting between rotation matrices, +Euler angles, and quaternions. Also includes an Arcball control object and +functions to decompose transformation matrices. + +:Author: + `Christoph Gohlke `_ + +:Organization: + Laboratory for Fluorescence Dynamics, University of California, Irvine + +:Version: 2013.04.16 + +Requirements +------------ +* `CPython 2.7, 3.2 or 3.3 `_ +* `Numpy 1.7 `_ +* `Transformations.c 2013.01.18 `_ + (recommended for speedup of some functions) + +Notes +----- +The API is not stable yet and is expected to change between revisions. + +This Python code is not optimized for speed. Refer to the transformations.c +module for a faster implementation of some functions. + +Documentation in HTML format can be generated with epydoc. + +Matrices (M) can be inverted using numpy.linalg.inv(M), be concatenated using +numpy.dot(M0, M1), or transform homogeneous coordinate arrays (v) using +numpy.dot(M, v) for shape (4, \*) column vectors, respectively +numpy.dot(v, M.T) for shape (\*, 4) row vectors ("array of points"). + +This module follows the "column vectors on the right" and "row major storage" +(C contiguous) conventions. The translation components are in the right column +of the transformation matrix, i.e. M[:3, 3]. +The transpose of the transformation matrices may have to be used to interface +with other graphics systems, e.g. with OpenGL's glMultMatrixd(). See also [16]. + +Calculations are carried out with numpy.float64 precision. + +Vector, point, quaternion, and matrix function arguments are expected to be +"array like", i.e. tuple, list, or numpy arrays. + +Return types are numpy arrays unless specified otherwise. + +Angles are in radians unless specified otherwise. + +Quaternions w+ix+jy+kz are represented as [w, x, y, z]. + +A triple of Euler angles can be applied/interpreted in 24 ways, which can +be specified using a 4 character string or encoded 4-tuple: + + *Axes 4-string*: e.g. 'sxyz' or 'ryxy' + + - first character : rotations are applied to 's'tatic or 'r'otating frame + - remaining characters : successive rotation axis 'x', 'y', or 'z' + + *Axes 4-tuple*: e.g. (0, 0, 0, 0) or (1, 1, 1, 1) + + - inner axis: code of axis ('x':0, 'y':1, 'z':2) of rightmost matrix. + - parity : even (0) if inner axis 'x' is followed by 'y', 'y' is followed + by 'z', or 'z' is followed by 'x'. Otherwise odd (1). + - repetition : first and last axis are same (1) or different (0). + - frame : rotations are applied to static (0) or rotating (1) frame. + +References +---------- +(1) Matrices and transformations. Ronald Goldman. + In "Graphics Gems I", pp 472-475. Morgan Kaufmann, 1990. +(2) More matrices and transformations: shear and pseudo-perspective. + Ronald Goldman. In "Graphics Gems II", pp 320-323. Morgan Kaufmann, 1991. +(3) Decomposing a matrix into simple transformations. Spencer Thomas. + In "Graphics Gems II", pp 320-323. Morgan Kaufmann, 1991. +(4) Recovering the data from the transformation matrix. Ronald Goldman. + In "Graphics Gems II", pp 324-331. Morgan Kaufmann, 1991. +(5) Euler angle conversion. Ken Shoemake. + In "Graphics Gems IV", pp 222-229. Morgan Kaufmann, 1994. +(6) Arcball rotation control. Ken Shoemake. + In "Graphics Gems IV", pp 175-192. Morgan Kaufmann, 1994. +(7) Representing attitude: Euler angles, unit quaternions, and rotation + vectors. James Diebel. 2006. +(8) A discussion of the solution for the best rotation to relate two sets + of vectors. W Kabsch. Acta Cryst. 1978. A34, 827-828. +(9) Closed-form solution of absolute orientation using unit quaternions. + BKP Horn. J Opt Soc Am A. 1987. 4(4):629-642. +(10) Quaternions. Ken Shoemake. + http://www.sfu.ca/~jwa3/cmpt461/files/quatut.pdf +(11) From quaternion to matrix and back. JMP van Waveren. 2005. + http://www.intel.com/cd/ids/developer/asmo-na/eng/293748.htm +(12) Uniform random rotations. Ken Shoemake. + In "Graphics Gems III", pp 124-132. Morgan Kaufmann, 1992. +(13) Quaternion in molecular modeling. CFF Karney. + J Mol Graph Mod, 25(5):595-604 +(14) New method for extracting the quaternion from a rotation matrix. + Itzhack Y Bar-Itzhack, J Guid Contr Dynam. 2000. 23(6): 1085-1087. +(15) Multiple View Geometry in Computer Vision. Hartley and Zissermann. + Cambridge University Press; 2nd Ed. 2004. Chapter 4, Algorithm 4.7, p 130. +(16) Column Vectors vs. Row Vectors. + http://steve.hollasch.net/cgindex/math/matrix/column-vec.html + +Examples +-------- +>>> alpha, beta, gamma = 0.123, -1.234, 2.345 +>>> origin, xaxis, yaxis, zaxis = [0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1] +>>> I = identity_matrix() +>>> Rx = rotation_matrix(alpha, xaxis) +>>> Ry = rotation_matrix(beta, yaxis) +>>> Rz = rotation_matrix(gamma, zaxis) +>>> R = concatenate_matrices(Rx, Ry, Rz) +>>> euler = euler_from_matrix(R, 'rxyz') +>>> numpy.allclose([alpha, beta, gamma], euler) +True +>>> Re = euler_matrix(alpha, beta, gamma, 'rxyz') +>>> is_same_transform(R, Re) +True +>>> al, be, ga = euler_from_matrix(Re, 'rxyz') +>>> is_same_transform(Re, euler_matrix(al, be, ga, 'rxyz')) +True +>>> qx = quaternion_about_axis(alpha, xaxis) +>>> qy = quaternion_about_axis(beta, yaxis) +>>> qz = quaternion_about_axis(gamma, zaxis) +>>> q = quaternion_multiply(qx, qy) +>>> q = quaternion_multiply(q, qz) +>>> Rq = quaternion_matrix(q) +>>> is_same_transform(R, Rq) +True +>>> S = scale_matrix(1.23, origin) +>>> T = translation_matrix([1, 2, 3]) +>>> Z = shear_matrix(beta, xaxis, origin, zaxis) +>>> R = random_rotation_matrix(numpy.random.rand(3)) +>>> M = concatenate_matrices(T, R, Z, S) +>>> scale, shear, angles, trans, persp = decompose_matrix(M) +>>> numpy.allclose(scale, 1.23) +True +>>> numpy.allclose(trans, [1, 2, 3]) +True +>>> numpy.allclose(shear, [0, math.tan(beta), 0]) +True +>>> is_same_transform(R, euler_matrix(axes='sxyz', *angles)) +True +>>> M1 = compose_matrix(scale, shear, angles, trans, persp) +>>> is_same_transform(M, M1) +True +>>> v0, v1 = random_vector(3), random_vector(3) +>>> M = rotation_matrix(angle_between_vectors(v0, v1), vector_product(v0, v1)) +>>> v2 = numpy.dot(v0, M[:3,:3].T) +>>> numpy.allclose(unit_vector(v1), unit_vector(v2)) +True + +""" + +from __future__ import division, print_function + +import math + +import numpy + +__version__ = '2013.04.16' +__docformat__ = 'restructuredtext en' +__all__ = [] + + +def identity_matrix(): + """Return 4x4 identity/unit matrix. + + >>> I = identity_matrix() + >>> numpy.allclose(I, numpy.dot(I, I)) + True + >>> numpy.sum(I), numpy.trace(I) + (4.0, 4.0) + >>> numpy.allclose(I, numpy.identity(4)) + True + + """ + return numpy.identity(4) + + +def translation_matrix(direction): + """Return matrix to translate by direction vector. + + >>> v = numpy.random.random(3) - 0.5 + >>> numpy.allclose(v, translation_matrix(v)[:3, 3]) + True + + """ + M = numpy.identity(4) + M[:3, 3] = direction[:3] + return M + + +def translation_from_matrix(matrix): + """Return translation vector from translation matrix. + + >>> v0 = numpy.random.random(3) - 0.5 + >>> v1 = translation_from_matrix(translation_matrix(v0)) + >>> numpy.allclose(v0, v1) + True + + """ + return numpy.array(matrix, copy=False)[:3, 3].copy() + + +def reflection_matrix(point, normal): + """Return matrix to mirror at plane defined by point and normal vector. + + >>> v0 = numpy.random.random(4) - 0.5 + >>> v0[3] = 1. + >>> v1 = numpy.random.random(3) - 0.5 + >>> R = reflection_matrix(v0, v1) + >>> numpy.allclose(2, numpy.trace(R)) + True + >>> numpy.allclose(v0, numpy.dot(R, v0)) + True + >>> v2 = v0.copy() + >>> v2[:3] += v1 + >>> v3 = v0.copy() + >>> v2[:3] -= v1 + >>> numpy.allclose(v2, numpy.dot(R, v3)) + True + + """ + normal = unit_vector(normal[:3]) + M = numpy.identity(4) + M[:3, :3] -= 2.0 * numpy.outer(normal, normal) + M[:3, 3] = (2.0 * numpy.dot(point[:3], normal)) * normal + return M + + +def reflection_from_matrix(matrix): + """Return mirror plane point and normal vector from reflection matrix. + + >>> v0 = numpy.random.random(3) - 0.5 + >>> v1 = numpy.random.random(3) - 0.5 + >>> M0 = reflection_matrix(v0, v1) + >>> point, normal = reflection_from_matrix(M0) + >>> M1 = reflection_matrix(point, normal) + >>> is_same_transform(M0, M1) + True + + """ + M = numpy.array(matrix, dtype=numpy.float64, copy=False) + # normal: unit eigenvector corresponding to eigenvalue -1 + w, V = numpy.linalg.eig(M[:3, :3]) + i = numpy.where(abs(numpy.real(w) + 1.0) < 1e-8)[0] + if not len(i): + raise ValueError("no unit eigenvector corresponding to eigenvalue -1") + normal = numpy.real(V[:, i[0]]).squeeze() + # point: any unit eigenvector corresponding to eigenvalue 1 + w, V = numpy.linalg.eig(M) + i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0] + if not len(i): + raise ValueError("no unit eigenvector corresponding to eigenvalue 1") + point = numpy.real(V[:, i[-1]]).squeeze() + point /= point[3] + return point, normal + + +def rotation_matrix(angle, direction, point=None): + """Return matrix to rotate about axis defined by point and direction. + + >>> R = rotation_matrix(math.pi/2, [0, 0, 1], [1, 0, 0]) + >>> numpy.allclose(numpy.dot(R, [0, 0, 0, 1]), [1, -1, 0, 1]) + True + >>> angle = (random.random() - 0.5) * (2*math.pi) + >>> direc = numpy.random.random(3) - 0.5 + >>> point = numpy.random.random(3) - 0.5 + >>> R0 = rotation_matrix(angle, direc, point) + >>> R1 = rotation_matrix(angle-2*math.pi, direc, point) + >>> is_same_transform(R0, R1) + True + >>> R0 = rotation_matrix(angle, direc, point) + >>> R1 = rotation_matrix(-angle, -direc, point) + >>> is_same_transform(R0, R1) + True + >>> I = numpy.identity(4, numpy.float64) + >>> numpy.allclose(I, rotation_matrix(math.pi*2, direc)) + True + >>> numpy.allclose(2, numpy.trace(rotation_matrix(math.pi/2, + ... direc, point))) + True + + """ + sina = math.sin(angle) + cosa = math.cos(angle) + direction = unit_vector(direction[:3]) + # rotation matrix around unit vector + R = numpy.diag([cosa, cosa, cosa]) + R += numpy.outer(direction, direction) * (1.0 - cosa) + direction *= sina + R += numpy.array([[ 0.0, -direction[2], direction[1]], + [ direction[2], 0.0, -direction[0]], + [-direction[1], direction[0], 0.0]]) + M = numpy.identity(4) + M[:3, :3] = R + if point is not None: + # rotation not around origin + point = numpy.array(point[:3], dtype=numpy.float64, copy=False) + M[:3, 3] = point - numpy.dot(R, point) + return M + + +def rotation_from_matrix(matrix): + """Return rotation angle and axis from rotation matrix. + + >>> angle = (random.random() - 0.5) * (2*math.pi) + >>> direc = numpy.random.random(3) - 0.5 + >>> point = numpy.random.random(3) - 0.5 + >>> R0 = rotation_matrix(angle, direc, point) + >>> angle, direc, point = rotation_from_matrix(R0) + >>> R1 = rotation_matrix(angle, direc, point) + >>> is_same_transform(R0, R1) + True + + """ + R = numpy.array(matrix, dtype=numpy.float64, copy=False) + R33 = R[:3, :3] + # direction: unit eigenvector of R33 corresponding to eigenvalue of 1 + w, W = numpy.linalg.eig(R33.T) + i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0] + if not len(i): + raise ValueError("no unit eigenvector corresponding to eigenvalue 1") + direction = numpy.real(W[:, i[-1]]).squeeze() + # point: unit eigenvector of R33 corresponding to eigenvalue of 1 + w, Q = numpy.linalg.eig(R) + i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0] + if not len(i): + raise ValueError("no unit eigenvector corresponding to eigenvalue 1") + point = numpy.real(Q[:, i[-1]]).squeeze() + point /= point[3] + # rotation angle depending on direction + cosa = (numpy.trace(R33) - 1.0) / 2.0 + if abs(direction[2]) > 1e-8: + sina = (R[1, 0] + (cosa-1.0)*direction[0]*direction[1]) / direction[2] + elif abs(direction[1]) > 1e-8: + sina = (R[0, 2] + (cosa-1.0)*direction[0]*direction[2]) / direction[1] + else: + sina = (R[2, 1] + (cosa-1.0)*direction[1]*direction[2]) / direction[0] + angle = math.atan2(sina, cosa) + return angle, direction, point + + +def scale_matrix(factor, origin=None, direction=None): + """Return matrix to scale by factor around origin in direction. + + Use factor -1 for point symmetry. + + >>> v = (numpy.random.rand(4, 5) - 0.5) * 20 + >>> v[3] = 1 + >>> S = scale_matrix(-1.234) + >>> numpy.allclose(numpy.dot(S, v)[:3], -1.234*v[:3]) + True + >>> factor = random.random() * 10 - 5 + >>> origin = numpy.random.random(3) - 0.5 + >>> direct = numpy.random.random(3) - 0.5 + >>> S = scale_matrix(factor, origin) + >>> S = scale_matrix(factor, origin, direct) + + """ + if direction is None: + # uniform scaling + M = numpy.diag([factor, factor, factor, 1.0]) + if origin is not None: + M[:3, 3] = origin[:3] + M[:3, 3] *= 1.0 - factor + else: + # nonuniform scaling + direction = unit_vector(direction[:3]) + factor = 1.0 - factor + M = numpy.identity(4) + M[:3, :3] -= factor * numpy.outer(direction, direction) + if origin is not None: + M[:3, 3] = (factor * numpy.dot(origin[:3], direction)) * direction + return M + + +def scale_from_matrix(matrix): + """Return scaling factor, origin and direction from scaling matrix. + + >>> factor = random.random() * 10 - 5 + >>> origin = numpy.random.random(3) - 0.5 + >>> direct = numpy.random.random(3) - 0.5 + >>> S0 = scale_matrix(factor, origin) + >>> factor, origin, direction = scale_from_matrix(S0) + >>> S1 = scale_matrix(factor, origin, direction) + >>> is_same_transform(S0, S1) + True + >>> S0 = scale_matrix(factor, origin, direct) + >>> factor, origin, direction = scale_from_matrix(S0) + >>> S1 = scale_matrix(factor, origin, direction) + >>> is_same_transform(S0, S1) + True + + """ + M = numpy.array(matrix, dtype=numpy.float64, copy=False) + M33 = M[:3, :3] + factor = numpy.trace(M33) - 2.0 + try: + # direction: unit eigenvector corresponding to eigenvalue factor + w, V = numpy.linalg.eig(M33) + i = numpy.where(abs(numpy.real(w) - factor) < 1e-8)[0][0] + direction = numpy.real(V[:, i]).squeeze() + direction /= vector_norm(direction) + except IndexError: + # uniform scaling + factor = (factor + 2.0) / 3.0 + direction = None + # origin: any eigenvector corresponding to eigenvalue 1 + w, V = numpy.linalg.eig(M) + i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0] + if not len(i): + raise ValueError("no eigenvector corresponding to eigenvalue 1") + origin = numpy.real(V[:, i[-1]]).squeeze() + origin /= origin[3] + return factor, origin, direction + + +def projection_matrix(point, normal, direction=None, + perspective=None, pseudo=False): + """Return matrix to project onto plane defined by point and normal. + + Using either perspective point, projection direction, or none of both. + + If pseudo is True, perspective projections will preserve relative depth + such that Perspective = dot(Orthogonal, PseudoPerspective). + + >>> P = projection_matrix([0, 0, 0], [1, 0, 0]) + >>> numpy.allclose(P[1:, 1:], numpy.identity(4)[1:, 1:]) + True + >>> point = numpy.random.random(3) - 0.5 + >>> normal = numpy.random.random(3) - 0.5 + >>> direct = numpy.random.random(3) - 0.5 + >>> persp = numpy.random.random(3) - 0.5 + >>> P0 = projection_matrix(point, normal) + >>> P1 = projection_matrix(point, normal, direction=direct) + >>> P2 = projection_matrix(point, normal, perspective=persp) + >>> P3 = projection_matrix(point, normal, perspective=persp, pseudo=True) + >>> is_same_transform(P2, numpy.dot(P0, P3)) + True + >>> P = projection_matrix([3, 0, 0], [1, 1, 0], [1, 0, 0]) + >>> v0 = (numpy.random.rand(4, 5) - 0.5) * 20 + >>> v0[3] = 1 + >>> v1 = numpy.dot(P, v0) + >>> numpy.allclose(v1[1], v0[1]) + True + >>> numpy.allclose(v1[0], 3-v1[1]) + True + + """ + M = numpy.identity(4) + point = numpy.array(point[:3], dtype=numpy.float64, copy=False) + normal = unit_vector(normal[:3]) + if perspective is not None: + # perspective projection + perspective = numpy.array(perspective[:3], dtype=numpy.float64, + copy=False) + M[0, 0] = M[1, 1] = M[2, 2] = numpy.dot(perspective-point, normal) + M[:3, :3] -= numpy.outer(perspective, normal) + if pseudo: + # preserve relative depth + M[:3, :3] -= numpy.outer(normal, normal) + M[:3, 3] = numpy.dot(point, normal) * (perspective+normal) + else: + M[:3, 3] = numpy.dot(point, normal) * perspective + M[3, :3] = -normal + M[3, 3] = numpy.dot(perspective, normal) + elif direction is not None: + # parallel projection + direction = numpy.array(direction[:3], dtype=numpy.float64, copy=False) + scale = numpy.dot(direction, normal) + M[:3, :3] -= numpy.outer(direction, normal) / scale + M[:3, 3] = direction * (numpy.dot(point, normal) / scale) + else: + # orthogonal projection + M[:3, :3] -= numpy.outer(normal, normal) + M[:3, 3] = numpy.dot(point, normal) * normal + return M + + +def projection_from_matrix(matrix, pseudo=False): + """Return projection plane and perspective point from projection matrix. + + Return values are same as arguments for projection_matrix function: + point, normal, direction, perspective, and pseudo. + + >>> point = numpy.random.random(3) - 0.5 + >>> normal = numpy.random.random(3) - 0.5 + >>> direct = numpy.random.random(3) - 0.5 + >>> persp = numpy.random.random(3) - 0.5 + >>> P0 = projection_matrix(point, normal) + >>> result = projection_from_matrix(P0) + >>> P1 = projection_matrix(*result) + >>> is_same_transform(P0, P1) + True + >>> P0 = projection_matrix(point, normal, direct) + >>> result = projection_from_matrix(P0) + >>> P1 = projection_matrix(*result) + >>> is_same_transform(P0, P1) + True + >>> P0 = projection_matrix(point, normal, perspective=persp, pseudo=False) + >>> result = projection_from_matrix(P0, pseudo=False) + >>> P1 = projection_matrix(*result) + >>> is_same_transform(P0, P1) + True + >>> P0 = projection_matrix(point, normal, perspective=persp, pseudo=True) + >>> result = projection_from_matrix(P0, pseudo=True) + >>> P1 = projection_matrix(*result) + >>> is_same_transform(P0, P1) + True + + """ + M = numpy.array(matrix, dtype=numpy.float64, copy=False) + M33 = M[:3, :3] + w, V = numpy.linalg.eig(M) + i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0] + if not pseudo and len(i): + # point: any eigenvector corresponding to eigenvalue 1 + point = numpy.real(V[:, i[-1]]).squeeze() + point /= point[3] + # direction: unit eigenvector corresponding to eigenvalue 0 + w, V = numpy.linalg.eig(M33) + i = numpy.where(abs(numpy.real(w)) < 1e-8)[0] + if not len(i): + raise ValueError("no eigenvector corresponding to eigenvalue 0") + direction = numpy.real(V[:, i[0]]).squeeze() + direction /= vector_norm(direction) + # normal: unit eigenvector of M33.T corresponding to eigenvalue 0 + w, V = numpy.linalg.eig(M33.T) + i = numpy.where(abs(numpy.real(w)) < 1e-8)[0] + if len(i): + # parallel projection + normal = numpy.real(V[:, i[0]]).squeeze() + normal /= vector_norm(normal) + return point, normal, direction, None, False + else: + # orthogonal projection, where normal equals direction vector + return point, direction, None, None, False + else: + # perspective projection + i = numpy.where(abs(numpy.real(w)) > 1e-8)[0] + if not len(i): + raise ValueError( + "no eigenvector not corresponding to eigenvalue 0") + point = numpy.real(V[:, i[-1]]).squeeze() + point /= point[3] + normal = - M[3, :3] + perspective = M[:3, 3] / numpy.dot(point[:3], normal) + if pseudo: + perspective -= normal + return point, normal, None, perspective, pseudo + + +def clip_matrix(left, right, bottom, top, near, far, perspective=False): + """Return matrix to obtain normalized device coordinates from frustum. + + The frustum bounds are axis-aligned along x (left, right), + y (bottom, top) and z (near, far). + + Normalized device coordinates are in range [-1, 1] if coordinates are + inside the frustum. + + If perspective is True the frustum is a truncated pyramid with the + perspective point at origin and direction along z axis, otherwise an + orthographic canonical view volume (a box). + + Homogeneous coordinates transformed by the perspective clip matrix + need to be dehomogenized (divided by w coordinate). + + >>> frustum = numpy.random.rand(6) + >>> frustum[1] += frustum[0] + >>> frustum[3] += frustum[2] + >>> frustum[5] += frustum[4] + >>> M = clip_matrix(perspective=False, *frustum) + >>> numpy.dot(M, [frustum[0], frustum[2], frustum[4], 1]) + array([-1., -1., -1., 1.]) + >>> numpy.dot(M, [frustum[1], frustum[3], frustum[5], 1]) + array([ 1., 1., 1., 1.]) + >>> M = clip_matrix(perspective=True, *frustum) + >>> v = numpy.dot(M, [frustum[0], frustum[2], frustum[4], 1]) + >>> v / v[3] + array([-1., -1., -1., 1.]) + >>> v = numpy.dot(M, [frustum[1], frustum[3], frustum[4], 1]) + >>> v / v[3] + array([ 1., 1., -1., 1.]) + + """ + if left >= right or bottom >= top or near >= far: + raise ValueError("invalid frustum") + if perspective: + if near <= _EPS: + raise ValueError("invalid frustum: near <= 0") + t = 2.0 * near + M = [[t/(left-right), 0.0, (right+left)/(right-left), 0.0], + [0.0, t/(bottom-top), (top+bottom)/(top-bottom), 0.0], + [0.0, 0.0, (far+near)/(near-far), t*far/(far-near)], + [0.0, 0.0, -1.0, 0.0]] + else: + M = [[2.0/(right-left), 0.0, 0.0, (right+left)/(left-right)], + [0.0, 2.0/(top-bottom), 0.0, (top+bottom)/(bottom-top)], + [0.0, 0.0, 2.0/(far-near), (far+near)/(near-far)], + [0.0, 0.0, 0.0, 1.0]] + return numpy.array(M) + + +def shear_matrix(angle, direction, point, normal): + """Return matrix to shear by angle along direction vector on shear plane. + + The shear plane is defined by a point and normal vector. The direction + vector must be orthogonal to the plane's normal vector. + + A point P is transformed by the shear matrix into P" such that + the vector P-P" is parallel to the direction vector and its extent is + given by the angle of P-P'-P", where P' is the orthogonal projection + of P onto the shear plane. + + >>> angle = (random.random() - 0.5) * 4*math.pi + >>> direct = numpy.random.random(3) - 0.5 + >>> point = numpy.random.random(3) - 0.5 + >>> normal = numpy.cross(direct, numpy.random.random(3)) + >>> S = shear_matrix(angle, direct, point, normal) + >>> numpy.allclose(1, numpy.linalg.det(S)) + True + + """ + normal = unit_vector(normal[:3]) + direction = unit_vector(direction[:3]) + if abs(numpy.dot(normal, direction)) > 1e-6: + raise ValueError("direction and normal vectors are not orthogonal") + angle = math.tan(angle) + M = numpy.identity(4) + M[:3, :3] += angle * numpy.outer(direction, normal) + M[:3, 3] = -angle * numpy.dot(point[:3], normal) * direction + return M + + +def shear_from_matrix(matrix): + """Return shear angle, direction and plane from shear matrix. + + >>> angle = (random.random() - 0.5) * 4*math.pi + >>> direct = numpy.random.random(3) - 0.5 + >>> point = numpy.random.random(3) - 0.5 + >>> normal = numpy.cross(direct, numpy.random.random(3)) + >>> S0 = shear_matrix(angle, direct, point, normal) + >>> angle, direct, point, normal = shear_from_matrix(S0) + >>> S1 = shear_matrix(angle, direct, point, normal) + >>> is_same_transform(S0, S1) + True + + """ + M = numpy.array(matrix, dtype=numpy.float64, copy=False) + M33 = M[:3, :3] + # normal: cross independent eigenvectors corresponding to the eigenvalue 1 + w, V = numpy.linalg.eig(M33) + i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-4)[0] + if len(i) < 2: + raise ValueError("no two linear independent eigenvectors found %s" % w) + V = numpy.real(V[:, i]).squeeze().T + lenorm = -1.0 + for i0, i1 in ((0, 1), (0, 2), (1, 2)): + n = numpy.cross(V[i0], V[i1]) + w = vector_norm(n) + if w > lenorm: + lenorm = w + normal = n + normal /= lenorm + # direction and angle + direction = numpy.dot(M33 - numpy.identity(3), normal) + angle = vector_norm(direction) + direction /= angle + angle = math.atan(angle) + # point: eigenvector corresponding to eigenvalue 1 + w, V = numpy.linalg.eig(M) + i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0] + if not len(i): + raise ValueError("no eigenvector corresponding to eigenvalue 1") + point = numpy.real(V[:, i[-1]]).squeeze() + point /= point[3] + return angle, direction, point, normal + + +def decompose_matrix(matrix): + """Return sequence of transformations from transformation matrix. + + matrix : array_like + Non-degenerative homogeneous transformation matrix + + Return tuple of: + scale : vector of 3 scaling factors + shear : list of shear factors for x-y, x-z, y-z axes + angles : list of Euler angles about static x, y, z axes + translate : translation vector along x, y, z axes + perspective : perspective partition of matrix + + Raise ValueError if matrix is of wrong type or degenerative. + + >>> T0 = translation_matrix([1, 2, 3]) + >>> scale, shear, angles, trans, persp = decompose_matrix(T0) + >>> T1 = translation_matrix(trans) + >>> numpy.allclose(T0, T1) + True + >>> S = scale_matrix(0.123) + >>> scale, shear, angles, trans, persp = decompose_matrix(S) + >>> scale[0] + 0.123 + >>> R0 = euler_matrix(1, 2, 3) + >>> scale, shear, angles, trans, persp = decompose_matrix(R0) + >>> R1 = euler_matrix(*angles) + >>> numpy.allclose(R0, R1) + True + + """ + M = numpy.array(matrix, dtype=numpy.float64, copy=True).T + if abs(M[3, 3]) < _EPS: + raise ValueError("M[3, 3] is zero") + M /= M[3, 3] + P = M.copy() + P[:, 3] = 0.0, 0.0, 0.0, 1.0 + if not numpy.linalg.det(P): + raise ValueError("matrix is singular") + + scale = numpy.zeros((3, )) + shear = [0.0, 0.0, 0.0] + angles = [0.0, 0.0, 0.0] + + if any(abs(M[:3, 3]) > _EPS): + perspective = numpy.dot(M[:, 3], numpy.linalg.inv(P.T)) + M[:, 3] = 0.0, 0.0, 0.0, 1.0 + else: + perspective = numpy.array([0.0, 0.0, 0.0, 1.0]) + + translate = M[3, :3].copy() + M[3, :3] = 0.0 + + row = M[:3, :3].copy() + scale[0] = vector_norm(row[0]) + row[0] /= scale[0] + shear[0] = numpy.dot(row[0], row[1]) + row[1] -= row[0] * shear[0] + scale[1] = vector_norm(row[1]) + row[1] /= scale[1] + shear[0] /= scale[1] + shear[1] = numpy.dot(row[0], row[2]) + row[2] -= row[0] * shear[1] + shear[2] = numpy.dot(row[1], row[2]) + row[2] -= row[1] * shear[2] + scale[2] = vector_norm(row[2]) + row[2] /= scale[2] + shear[1:] /= scale[2] + + if numpy.dot(row[0], numpy.cross(row[1], row[2])) < 0: + numpy.negative(scale, scale) + numpy.negative(row, row) + + angles[1] = math.asin(-row[0, 2]) + if math.cos(angles[1]): + angles[0] = math.atan2(row[1, 2], row[2, 2]) + angles[2] = math.atan2(row[0, 1], row[0, 0]) + else: + #angles[0] = math.atan2(row[1, 0], row[1, 1]) + angles[0] = math.atan2(-row[2, 1], row[1, 1]) + angles[2] = 0.0 + + return scale, shear, angles, translate, perspective + + +def compose_matrix(scale=None, shear=None, angles=None, translate=None, + perspective=None): + """Return transformation matrix from sequence of transformations. + + This is the inverse of the decompose_matrix function. + + Sequence of transformations: + scale : vector of 3 scaling factors + shear : list of shear factors for x-y, x-z, y-z axes + angles : list of Euler angles about static x, y, z axes + translate : translation vector along x, y, z axes + perspective : perspective partition of matrix + + >>> scale = numpy.random.random(3) - 0.5 + >>> shear = numpy.random.random(3) - 0.5 + >>> angles = (numpy.random.random(3) - 0.5) * (2*math.pi) + >>> trans = numpy.random.random(3) - 0.5 + >>> persp = numpy.random.random(4) - 0.5 + >>> M0 = compose_matrix(scale, shear, angles, trans, persp) + >>> result = decompose_matrix(M0) + >>> M1 = compose_matrix(*result) + >>> is_same_transform(M0, M1) + True + + """ + M = numpy.identity(4) + if perspective is not None: + P = numpy.identity(4) + P[3, :] = perspective[:4] + M = numpy.dot(M, P) + if translate is not None: + T = numpy.identity(4) + T[:3, 3] = translate[:3] + M = numpy.dot(M, T) + if angles is not None: + R = euler_matrix(angles[0], angles[1], angles[2], 'sxyz') + M = numpy.dot(M, R) + if shear is not None: + Z = numpy.identity(4) + Z[1, 2] = shear[2] + Z[0, 2] = shear[1] + Z[0, 1] = shear[0] + M = numpy.dot(M, Z) + if scale is not None: + S = numpy.identity(4) + S[0, 0] = scale[0] + S[1, 1] = scale[1] + S[2, 2] = scale[2] + M = numpy.dot(M, S) + M /= M[3, 3] + return M + + +def orthogonalization_matrix(lengths, angles): + """Return orthogonalization matrix for crystallographic cell coordinates. + + Angles are expected in degrees. + + The de-orthogonalization matrix is the inverse. + + >>> O = orthogonalization_matrix([10, 10, 10], [90, 90, 90]) + >>> numpy.allclose(O[:3, :3], numpy.identity(3, float) * 10) + True + >>> O = orthogonalization_matrix([9.8, 12.0, 15.5], [87.2, 80.7, 69.7]) + >>> numpy.allclose(numpy.sum(O), 43.063229) + True + + """ + a, b, c = lengths + angles = numpy.radians(angles) + sina, sinb, _ = numpy.sin(angles) + cosa, cosb, cosg = numpy.cos(angles) + co = (cosa * cosb - cosg) / (sina * sinb) + return numpy.array([ + [ a*sinb*math.sqrt(1.0-co*co), 0.0, 0.0, 0.0], + [-a*sinb*co, b*sina, 0.0, 0.0], + [ a*cosb, b*cosa, c, 0.0], + [ 0.0, 0.0, 0.0, 1.0]]) + + +def affine_matrix_from_points(v0, v1, shear=True, scale=True, usesvd=True): + """Return affine transform matrix to register two point sets. + + v0 and v1 are shape (ndims, \*) arrays of at least ndims non-homogeneous + coordinates, where ndims is the dimensionality of the coordinate space. + + If shear is False, a similarity transformation matrix is returned. + If also scale is False, a rigid/Euclidean transformation matrix + is returned. + + By default the algorithm by Hartley and Zissermann [15] is used. + If usesvd is True, similarity and Euclidean transformation matrices + are calculated by minimizing the weighted sum of squared deviations + (RMSD) according to the algorithm by Kabsch [8]. + Otherwise, and if ndims is 3, the quaternion based algorithm by Horn [9] + is used, which is slower when using this Python implementation. + + The returned matrix performs rotation, translation and uniform scaling + (if specified). + + >>> v0 = [[0, 1031, 1031, 0], [0, 0, 1600, 1600]] + >>> v1 = [[675, 826, 826, 677], [55, 52, 281, 277]] + >>> affine_matrix_from_points(v0, v1) + array([[ 0.14549, 0.00062, 675.50008], + [ 0.00048, 0.14094, 53.24971], + [ 0. , 0. , 1. ]]) + >>> T = translation_matrix(numpy.random.random(3)-0.5) + >>> R = random_rotation_matrix(numpy.random.random(3)) + >>> S = scale_matrix(random.random()) + >>> M = concatenate_matrices(T, R, S) + >>> v0 = (numpy.random.rand(4, 100) - 0.5) * 20 + >>> v0[3] = 1 + >>> v1 = numpy.dot(M, v0) + >>> v0[:3] += numpy.random.normal(0, 1e-8, 300).reshape(3, -1) + >>> M = affine_matrix_from_points(v0[:3], v1[:3]) + >>> numpy.allclose(v1, numpy.dot(M, v0)) + True + + More examples in superimposition_matrix() + + """ + v0 = numpy.array(v0, dtype=numpy.float64, copy=True) + v1 = numpy.array(v1, dtype=numpy.float64, copy=True) + + ndims = v0.shape[0] + if ndims < 2 or v0.shape[1] < ndims or v0.shape != v1.shape: + raise ValueError("input arrays are of wrong shape or type") + + # move centroids to origin + t0 = -numpy.mean(v0, axis=1) + M0 = numpy.identity(ndims+1) + M0[:ndims, ndims] = t0 + v0 += t0.reshape(ndims, 1) + t1 = -numpy.mean(v1, axis=1) + M1 = numpy.identity(ndims+1) + M1[:ndims, ndims] = t1 + v1 += t1.reshape(ndims, 1) + + if shear: + # Affine transformation + A = numpy.concatenate((v0, v1), axis=0) + u, s, vh = numpy.linalg.svd(A.T) + vh = vh[:ndims].T + B = vh[:ndims] + C = vh[ndims:2*ndims] + t = numpy.dot(C, numpy.linalg.pinv(B)) + t = numpy.concatenate((t, numpy.zeros((ndims, 1))), axis=1) + M = numpy.vstack((t, ((0.0,)*ndims) + (1.0,))) + elif usesvd or ndims != 3: + # Rigid transformation via SVD of covariance matrix + u, s, vh = numpy.linalg.svd(numpy.dot(v1, v0.T)) + # rotation matrix from SVD orthonormal bases + R = numpy.dot(u, vh) + if numpy.linalg.det(R) < 0.0: + # R does not constitute right handed system + R -= numpy.outer(u[:, ndims-1], vh[ndims-1, :]*2.0) + s[-1] *= -1.0 + # homogeneous transformation matrix + M = numpy.identity(ndims+1) + M[:ndims, :ndims] = R + else: + # Rigid transformation matrix via quaternion + # compute symmetric matrix N + xx, yy, zz = numpy.sum(v0 * v1, axis=1) + xy, yz, zx = numpy.sum(v0 * numpy.roll(v1, -1, axis=0), axis=1) + xz, yx, zy = numpy.sum(v0 * numpy.roll(v1, -2, axis=0), axis=1) + N = [[xx+yy+zz, 0.0, 0.0, 0.0], + [yz-zy, xx-yy-zz, 0.0, 0.0], + [zx-xz, xy+yx, yy-xx-zz, 0.0], + [xy-yx, zx+xz, yz+zy, zz-xx-yy]] + # quaternion: eigenvector corresponding to most positive eigenvalue + w, V = numpy.linalg.eigh(N) + q = V[:, numpy.argmax(w)] + q /= vector_norm(q) # unit quaternion + # homogeneous transformation matrix + M = quaternion_matrix(q) + + if scale and not shear: + # Affine transformation; scale is ratio of RMS deviations from centroid + v0 *= v0 + v1 *= v1 + M[:ndims, :ndims] *= math.sqrt(numpy.sum(v1) / numpy.sum(v0)) + + # move centroids back + M = numpy.dot(numpy.linalg.inv(M1), numpy.dot(M, M0)) + M /= M[ndims, ndims] + return M + + +def superimposition_matrix(v0, v1, scale=False, usesvd=True): + """Return matrix to transform given 3D point set into second point set. + + v0 and v1 are shape (3, \*) or (4, \*) arrays of at least 3 points. + + The parameters scale and usesvd are explained in the more general + affine_matrix_from_points function. + + The returned matrix is a similarity or Euclidean transformation matrix. + This function has a fast C implementation in transformations.c. + + >>> v0 = numpy.random.rand(3, 10) + >>> M = superimposition_matrix(v0, v0) + >>> numpy.allclose(M, numpy.identity(4)) + True + >>> R = random_rotation_matrix(numpy.random.random(3)) + >>> v0 = [[1,0,0], [0,1,0], [0,0,1], [1,1,1]] + >>> v1 = numpy.dot(R, v0) + >>> M = superimposition_matrix(v0, v1) + >>> numpy.allclose(v1, numpy.dot(M, v0)) + True + >>> v0 = (numpy.random.rand(4, 100) - 0.5) * 20 + >>> v0[3] = 1 + >>> v1 = numpy.dot(R, v0) + >>> M = superimposition_matrix(v0, v1) + >>> numpy.allclose(v1, numpy.dot(M, v0)) + True + >>> S = scale_matrix(random.random()) + >>> T = translation_matrix(numpy.random.random(3)-0.5) + >>> M = concatenate_matrices(T, R, S) + >>> v1 = numpy.dot(M, v0) + >>> v0[:3] += numpy.random.normal(0, 1e-9, 300).reshape(3, -1) + >>> M = superimposition_matrix(v0, v1, scale=True) + >>> numpy.allclose(v1, numpy.dot(M, v0)) + True + >>> M = superimposition_matrix(v0, v1, scale=True, usesvd=False) + >>> numpy.allclose(v1, numpy.dot(M, v0)) + True + >>> v = numpy.empty((4, 100, 3)) + >>> v[:, :, 0] = v0 + >>> M = superimposition_matrix(v0, v1, scale=True, usesvd=False) + >>> numpy.allclose(v1, numpy.dot(M, v[:, :, 0])) + True + + """ + v0 = numpy.array(v0, dtype=numpy.float64, copy=False)[:3] + v1 = numpy.array(v1, dtype=numpy.float64, copy=False)[:3] + return affine_matrix_from_points(v0, v1, shear=False, + scale=scale, usesvd=usesvd) + + +def euler_matrix(ai, aj, ak, axes='sxyz'): + """Return homogeneous rotation matrix from Euler angles and axis sequence. + + ai, aj, ak : Euler's roll, pitch and yaw angles + axes : One of 24 axis sequences as string or encoded tuple + + >>> R = euler_matrix(1, 2, 3, 'syxz') + >>> numpy.allclose(numpy.sum(R[0]), -1.34786452) + True + >>> R = euler_matrix(1, 2, 3, (0, 1, 0, 1)) + >>> numpy.allclose(numpy.sum(R[0]), -0.383436184) + True + >>> ai, aj, ak = (4*math.pi) * (numpy.random.random(3) - 0.5) + >>> for axes in _AXES2TUPLE.keys(): + ... R = euler_matrix(ai, aj, ak, axes) + >>> for axes in _TUPLE2AXES.keys(): + ... R = euler_matrix(ai, aj, ak, axes) + + """ + try: + firstaxis, parity, repetition, frame = _AXES2TUPLE[axes] + except (AttributeError, KeyError): + _TUPLE2AXES[axes] # validation + firstaxis, parity, repetition, frame = axes + + i = firstaxis + j = _NEXT_AXIS[i+parity] + k = _NEXT_AXIS[i-parity+1] + + if frame: + ai, ak = ak, ai + if parity: + ai, aj, ak = -ai, -aj, -ak + + si, sj, sk = math.sin(ai), math.sin(aj), math.sin(ak) + ci, cj, ck = math.cos(ai), math.cos(aj), math.cos(ak) + cc, cs = ci*ck, ci*sk + sc, ss = si*ck, si*sk + + M = numpy.identity(4) + if repetition: + M[i, i] = cj + M[i, j] = sj*si + M[i, k] = sj*ci + M[j, i] = sj*sk + M[j, j] = -cj*ss+cc + M[j, k] = -cj*cs-sc + M[k, i] = -sj*ck + M[k, j] = cj*sc+cs + M[k, k] = cj*cc-ss + else: + M[i, i] = cj*ck + M[i, j] = sj*sc-cs + M[i, k] = sj*cc+ss + M[j, i] = cj*sk + M[j, j] = sj*ss+cc + M[j, k] = sj*cs-sc + M[k, i] = -sj + M[k, j] = cj*si + M[k, k] = cj*ci + return M + + +def euler_from_matrix(matrix, axes='sxyz'): + """Return Euler angles from rotation matrix for specified axis sequence. + + axes : One of 24 axis sequences as string or encoded tuple + + Note that many Euler angle triplets can describe one matrix. + + >>> R0 = euler_matrix(1, 2, 3, 'syxz') + >>> al, be, ga = euler_from_matrix(R0, 'syxz') + >>> R1 = euler_matrix(al, be, ga, 'syxz') + >>> numpy.allclose(R0, R1) + True + >>> angles = (4*math.pi) * (numpy.random.random(3) - 0.5) + >>> for axes in _AXES2TUPLE.keys(): + ... R0 = euler_matrix(axes=axes, *angles) + ... R1 = euler_matrix(axes=axes, *euler_from_matrix(R0, axes)) + ... if not numpy.allclose(R0, R1): print(axes, "failed") + + """ + try: + firstaxis, parity, repetition, frame = _AXES2TUPLE[axes.lower()] + except (AttributeError, KeyError): + _TUPLE2AXES[axes] # validation + firstaxis, parity, repetition, frame = axes + + i = firstaxis + j = _NEXT_AXIS[i+parity] + k = _NEXT_AXIS[i-parity+1] + + M = numpy.array(matrix, dtype=numpy.float64, copy=False)[:3, :3] + if repetition: + sy = math.sqrt(M[i, j]*M[i, j] + M[i, k]*M[i, k]) + if sy > _EPS: + ax = math.atan2( M[i, j], M[i, k]) + ay = math.atan2( sy, M[i, i]) + az = math.atan2( M[j, i], -M[k, i]) + else: + ax = math.atan2(-M[j, k], M[j, j]) + ay = math.atan2( sy, M[i, i]) + az = 0.0 + else: + cy = math.sqrt(M[i, i]*M[i, i] + M[j, i]*M[j, i]) + if cy > _EPS: + ax = math.atan2( M[k, j], M[k, k]) + ay = math.atan2(-M[k, i], cy) + az = math.atan2( M[j, i], M[i, i]) + else: + ax = math.atan2(-M[j, k], M[j, j]) + ay = math.atan2(-M[k, i], cy) + az = 0.0 + + if parity: + ax, ay, az = -ax, -ay, -az + if frame: + ax, az = az, ax + return ax, ay, az + + +def euler_from_quaternion(quaternion, axes='sxyz'): + """Return Euler angles from quaternion for specified axis sequence. + + >>> angles = euler_from_quaternion([0.99810947, 0.06146124, 0, 0]) + >>> numpy.allclose(angles, [0.123, 0, 0]) + True + + """ + return euler_from_matrix(quaternion_matrix(quaternion), axes) + + +def quaternion_from_euler(ai, aj, ak, axes='sxyz'): + """Return quaternion from Euler angles and axis sequence. + + ai, aj, ak : Euler's roll, pitch and yaw angles + axes : One of 24 axis sequences as string or encoded tuple + + >>> q = quaternion_from_euler(1, 2, 3, 'ryxz') + >>> numpy.allclose(q, [0.435953, 0.310622, -0.718287, 0.444435]) + True + + """ + try: + firstaxis, parity, repetition, frame = _AXES2TUPLE[axes.lower()] + except (AttributeError, KeyError): + _TUPLE2AXES[axes] # validation + firstaxis, parity, repetition, frame = axes + + i = firstaxis + 1 + j = _NEXT_AXIS[i+parity-1] + 1 + k = _NEXT_AXIS[i-parity] + 1 + + if frame: + ai, ak = ak, ai + if parity: + aj = -aj + + ai /= 2.0 + aj /= 2.0 + ak /= 2.0 + ci = math.cos(ai) + si = math.sin(ai) + cj = math.cos(aj) + sj = math.sin(aj) + ck = math.cos(ak) + sk = math.sin(ak) + cc = ci*ck + cs = ci*sk + sc = si*ck + ss = si*sk + + q = numpy.empty((4, )) + if repetition: + q[0] = cj*(cc - ss) + q[i] = cj*(cs + sc) + q[j] = sj*(cc + ss) + q[k] = sj*(cs - sc) + else: + q[0] = cj*cc + sj*ss + q[i] = cj*sc - sj*cs + q[j] = cj*ss + sj*cc + q[k] = cj*cs - sj*sc + if parity: + q[j] *= -1.0 + + return q + + +def quaternion_about_axis(angle, axis): + """Return quaternion for rotation about axis. + + >>> q = quaternion_about_axis(0.123, [1, 0, 0]) + >>> numpy.allclose(q, [0.99810947, 0.06146124, 0, 0]) + True + + """ + q = numpy.array([0.0, axis[0], axis[1], axis[2]]) + qlen = vector_norm(q) + if qlen > _EPS: + q *= math.sin(angle/2.0) / qlen + q[0] = math.cos(angle/2.0) + return q + + +def quaternion_matrix(quaternion): + """Return homogeneous rotation matrix from quaternion. + + >>> M = quaternion_matrix([0.99810947, 0.06146124, 0, 0]) + >>> numpy.allclose(M, rotation_matrix(0.123, [1, 0, 0])) + True + >>> M = quaternion_matrix([1, 0, 0, 0]) + >>> numpy.allclose(M, numpy.identity(4)) + True + >>> M = quaternion_matrix([0, 1, 0, 0]) + >>> numpy.allclose(M, numpy.diag([1, -1, -1, 1])) + True + + """ + q = numpy.array(quaternion, dtype=numpy.float64, copy=True) + n = numpy.dot(q, q) + if n < _EPS: + return numpy.identity(4) + q *= math.sqrt(2.0 / n) + q = numpy.outer(q, q) + return numpy.array([ + [1.0-q[2, 2]-q[3, 3], q[1, 2]-q[3, 0], q[1, 3]+q[2, 0], 0.0], + [ q[1, 2]+q[3, 0], 1.0-q[1, 1]-q[3, 3], q[2, 3]-q[1, 0], 0.0], + [ q[1, 3]-q[2, 0], q[2, 3]+q[1, 0], 1.0-q[1, 1]-q[2, 2], 0.0], + [ 0.0, 0.0, 0.0, 1.0]]) + + +def quaternion_from_matrix(matrix, isprecise=False): + """Return quaternion from rotation matrix. + + If isprecise is True, the input matrix is assumed to be a precise rotation + matrix and a faster algorithm is used. + + >>> q = quaternion_from_matrix(numpy.identity(4), True) + >>> numpy.allclose(q, [1, 0, 0, 0]) + True + >>> q = quaternion_from_matrix(numpy.diag([1, -1, -1, 1])) + >>> numpy.allclose(q, [0, 1, 0, 0]) or numpy.allclose(q, [0, -1, 0, 0]) + True + >>> R = rotation_matrix(0.123, (1, 2, 3)) + >>> q = quaternion_from_matrix(R, True) + >>> numpy.allclose(q, [0.9981095, 0.0164262, 0.0328524, 0.0492786]) + True + >>> R = [[-0.545, 0.797, 0.260, 0], [0.733, 0.603, -0.313, 0], + ... [-0.407, 0.021, -0.913, 0], [0, 0, 0, 1]] + >>> q = quaternion_from_matrix(R) + >>> numpy.allclose(q, [0.19069, 0.43736, 0.87485, -0.083611]) + True + >>> R = [[0.395, 0.362, 0.843, 0], [-0.626, 0.796, -0.056, 0], + ... [-0.677, -0.498, 0.529, 0], [0, 0, 0, 1]] + >>> q = quaternion_from_matrix(R) + >>> numpy.allclose(q, [0.82336615, -0.13610694, 0.46344705, -0.29792603]) + True + >>> R = random_rotation_matrix() + >>> q = quaternion_from_matrix(R) + >>> is_same_transform(R, quaternion_matrix(q)) + True + + """ + M = numpy.array(matrix, dtype=numpy.float64, copy=False)[:4, :4] + if isprecise: + q = numpy.empty((4, )) + t = numpy.trace(M) + if t > M[3, 3]: + q[0] = t + q[3] = M[1, 0] - M[0, 1] + q[2] = M[0, 2] - M[2, 0] + q[1] = M[2, 1] - M[1, 2] + else: + i, j, k = 1, 2, 3 + if M[1, 1] > M[0, 0]: + i, j, k = 2, 3, 1 + if M[2, 2] > M[i, i]: + i, j, k = 3, 1, 2 + t = M[i, i] - (M[j, j] + M[k, k]) + M[3, 3] + q[i] = t + q[j] = M[i, j] + M[j, i] + q[k] = M[k, i] + M[i, k] + q[3] = M[k, j] - M[j, k] + q *= 0.5 / math.sqrt(t * M[3, 3]) + else: + m00 = M[0, 0] + m01 = M[0, 1] + m02 = M[0, 2] + m10 = M[1, 0] + m11 = M[1, 1] + m12 = M[1, 2] + m20 = M[2, 0] + m21 = M[2, 1] + m22 = M[2, 2] + # symmetric matrix K + K = numpy.array([[m00-m11-m22, 0.0, 0.0, 0.0], + [m01+m10, m11-m00-m22, 0.0, 0.0], + [m02+m20, m12+m21, m22-m00-m11, 0.0], + [m21-m12, m02-m20, m10-m01, m00+m11+m22]]) + K /= 3.0 + # quaternion is eigenvector of K that corresponds to largest eigenvalue + w, V = numpy.linalg.eigh(K) + q = V[[3, 0, 1, 2], numpy.argmax(w)] + if q[0] < 0.0: + numpy.negative(q, q) + return q + + +def quaternion_multiply(quaternion1, quaternion0): + """Return multiplication of two quaternions. + + >>> q = quaternion_multiply([4, 1, -2, 3], [8, -5, 6, 7]) + >>> numpy.allclose(q, [28, -44, -14, 48]) + True + + """ + w0, x0, y0, z0 = quaternion0 + w1, x1, y1, z1 = quaternion1 + return numpy.array([-x1*x0 - y1*y0 - z1*z0 + w1*w0, + x1*w0 + y1*z0 - z1*y0 + w1*x0, + -x1*z0 + y1*w0 + z1*x0 + w1*y0, + x1*y0 - y1*x0 + z1*w0 + w1*z0], dtype=numpy.float64) + + +def quaternion_conjugate(quaternion): + """Return conjugate of quaternion. + + >>> q0 = random_quaternion() + >>> q1 = quaternion_conjugate(q0) + >>> q1[0] == q0[0] and all(q1[1:] == -q0[1:]) + True + + """ + q = numpy.array(quaternion, dtype=numpy.float64, copy=True) + numpy.negative(q[1:], q[1:]) + return q + + +def quaternion_inverse(quaternion): + """Return inverse of quaternion. + + >>> q0 = random_quaternion() + >>> q1 = quaternion_inverse(q0) + >>> numpy.allclose(quaternion_multiply(q0, q1), [1, 0, 0, 0]) + True + + """ + q = numpy.array(quaternion, dtype=numpy.float64, copy=True) + numpy.negative(q[1:], q[1:]) + return q / numpy.dot(q, q) + + +def quaternion_real(quaternion): + """Return real part of quaternion. + + >>> quaternion_real([3, 0, 1, 2]) + 3.0 + + """ + return float(quaternion[0]) + + +def quaternion_imag(quaternion): + """Return imaginary part of quaternion. + + >>> quaternion_imag([3, 0, 1, 2]) + array([ 0., 1., 2.]) + + """ + return numpy.array(quaternion[1:4], dtype=numpy.float64, copy=True) + + +def quaternion_slerp(quat0, quat1, fraction, spin=0, shortestpath=True): + """Return spherical linear interpolation between two quaternions. + + >>> q0 = random_quaternion() + >>> q1 = random_quaternion() + >>> q = quaternion_slerp(q0, q1, 0) + >>> numpy.allclose(q, q0) + True + >>> q = quaternion_slerp(q0, q1, 1, 1) + >>> numpy.allclose(q, q1) + True + >>> q = quaternion_slerp(q0, q1, 0.5) + >>> angle = math.acos(numpy.dot(q0, q)) + >>> numpy.allclose(2, math.acos(numpy.dot(q0, q1)) / angle) or \ + numpy.allclose(2, math.acos(-numpy.dot(q0, q1)) / angle) + True + + """ + q0 = unit_vector(quat0[:4]) + q1 = unit_vector(quat1[:4]) + if fraction == 0.0: + return q0 + elif fraction == 1.0: + return q1 + d = numpy.dot(q0, q1) + if abs(abs(d) - 1.0) < _EPS: + return q0 + if shortestpath and d < 0.0: + # invert rotation + d = -d + numpy.negative(q1, q1) + angle = math.acos(d) + spin * math.pi + if abs(angle) < _EPS: + return q0 + isin = 1.0 / math.sin(angle) + q0 *= math.sin((1.0 - fraction) * angle) * isin + q1 *= math.sin(fraction * angle) * isin + q0 += q1 + return q0 + + +def random_quaternion(rand=None): + """Return uniform random unit quaternion. + + rand: array like or None + Three independent random variables that are uniformly distributed + between 0 and 1. + + >>> q = random_quaternion() + >>> numpy.allclose(1, vector_norm(q)) + True + >>> q = random_quaternion(numpy.random.random(3)) + >>> len(q.shape), q.shape[0]==4 + (1, True) + + """ + if rand is None: + rand = numpy.random.rand(3) + else: + assert len(rand) == 3 + r1 = numpy.sqrt(1.0 - rand[0]) + r2 = numpy.sqrt(rand[0]) + pi2 = math.pi * 2.0 + t1 = pi2 * rand[1] + t2 = pi2 * rand[2] + return numpy.array([numpy.cos(t2)*r2, numpy.sin(t1)*r1, + numpy.cos(t1)*r1, numpy.sin(t2)*r2]) + + +def random_rotation_matrix(rand=None): + """Return uniform random rotation matrix. + + rand: array like + Three independent random variables that are uniformly distributed + between 0 and 1 for each returned quaternion. + + >>> R = random_rotation_matrix() + >>> numpy.allclose(numpy.dot(R.T, R), numpy.identity(4)) + True + + """ + return quaternion_matrix(random_quaternion(rand)) + + +class Arcball(object): + """Virtual Trackball Control. + + >>> ball = Arcball() + >>> ball = Arcball(initial=numpy.identity(4)) + >>> ball.place([320, 320], 320) + >>> ball.down([500, 250]) + >>> ball.drag([475, 275]) + >>> R = ball.matrix() + >>> numpy.allclose(numpy.sum(R), 3.90583455) + True + >>> ball = Arcball(initial=[1, 0, 0, 0]) + >>> ball.place([320, 320], 320) + >>> ball.setaxes([1, 1, 0], [-1, 1, 0]) + >>> ball.setconstrain(True) + >>> ball.down([400, 200]) + >>> ball.drag([200, 400]) + >>> R = ball.matrix() + >>> numpy.allclose(numpy.sum(R), 0.2055924) + True + >>> ball.next() + + """ + def __init__(self, initial=None): + """Initialize virtual trackball control. + + initial : quaternion or rotation matrix + + """ + self._axis = None + self._axes = None + self._radius = 1.0 + self._center = [0.0, 0.0] + self._vdown = numpy.array([0.0, 0.0, 1.0]) + self._constrain = False + if initial is None: + self._qdown = numpy.array([1.0, 0.0, 0.0, 0.0]) + else: + initial = numpy.array(initial, dtype=numpy.float64) + if initial.shape == (4, 4): + self._qdown = quaternion_from_matrix(initial) + elif initial.shape == (4, ): + initial /= vector_norm(initial) + self._qdown = initial + else: + raise ValueError("initial not a quaternion or matrix") + self._qnow = self._qpre = self._qdown + + def place(self, center, radius): + """Place Arcball, e.g. when window size changes. + + center : sequence[2] + Window coordinates of trackball center. + radius : float + Radius of trackball in window coordinates. + + """ + self._radius = float(radius) + self._center[0] = center[0] + self._center[1] = center[1] + + def setaxes(self, *axes): + """Set axes to constrain rotations.""" + if axes is None: + self._axes = None + else: + self._axes = [unit_vector(axis) for axis in axes] + + def setconstrain(self, constrain): + """Set state of constrain to axis mode.""" + self._constrain = bool(constrain) + + def getconstrain(self): + """Return state of constrain to axis mode.""" + return self._constrain + + def down(self, point): + """Set initial cursor window coordinates and pick constrain-axis.""" + self._vdown = arcball_map_to_sphere(point, self._center, self._radius) + self._qdown = self._qpre = self._qnow + if self._constrain and self._axes is not None: + self._axis = arcball_nearest_axis(self._vdown, self._axes) + self._vdown = arcball_constrain_to_axis(self._vdown, self._axis) + else: + self._axis = None + + def drag(self, point): + """Update current cursor window coordinates.""" + vnow = arcball_map_to_sphere(point, self._center, self._radius) + if self._axis is not None: + vnow = arcball_constrain_to_axis(vnow, self._axis) + self._qpre = self._qnow + t = numpy.cross(self._vdown, vnow) + if numpy.dot(t, t) < _EPS: + self._qnow = self._qdown + else: + q = [numpy.dot(self._vdown, vnow), t[0], t[1], t[2]] + self._qnow = quaternion_multiply(q, self._qdown) + + def next(self, acceleration=0.0): + """Continue rotation in direction of last drag.""" + q = quaternion_slerp(self._qpre, self._qnow, 2.0+acceleration, False) + self._qpre, self._qnow = self._qnow, q + + def matrix(self): + """Return homogeneous rotation matrix.""" + return quaternion_matrix(self._qnow) + + +def arcball_map_to_sphere(point, center, radius): + """Return unit sphere coordinates from window coordinates.""" + v0 = (point[0] - center[0]) / radius + v1 = (center[1] - point[1]) / radius + n = v0*v0 + v1*v1 + if n > 1.0: + # position outside of sphere + n = math.sqrt(n) + return numpy.array([v0/n, v1/n, 0.0]) + else: + return numpy.array([v0, v1, math.sqrt(1.0 - n)]) + + +def arcball_constrain_to_axis(point, axis): + """Return sphere point perpendicular to axis.""" + v = numpy.array(point, dtype=numpy.float64, copy=True) + a = numpy.array(axis, dtype=numpy.float64, copy=True) + v -= a * numpy.dot(a, v) # on plane + n = vector_norm(v) + if n > _EPS: + if v[2] < 0.0: + numpy.negative(v, v) + v /= n + return v + if a[2] == 1.0: + return numpy.array([1.0, 0.0, 0.0]) + return unit_vector([-a[1], a[0], 0.0]) + + +def arcball_nearest_axis(point, axes): + """Return axis, which arc is nearest to point.""" + point = numpy.array(point, dtype=numpy.float64, copy=False) + nearest = None + mx = -1.0 + for axis in axes: + t = numpy.dot(arcball_constrain_to_axis(point, axis), point) + if t > mx: + nearest = axis + mx = t + return nearest + + +# epsilon for testing whether a number is close to zero +_EPS = numpy.finfo(float).eps * 4.0 + +# axis sequences for Euler angles +_NEXT_AXIS = [1, 2, 0, 1] + +# map axes strings to/from tuples of inner axis, parity, repetition, frame +_AXES2TUPLE = { + 'sxyz': (0, 0, 0, 0), 'sxyx': (0, 0, 1, 0), 'sxzy': (0, 1, 0, 0), + 'sxzx': (0, 1, 1, 0), 'syzx': (1, 0, 0, 0), 'syzy': (1, 0, 1, 0), + 'syxz': (1, 1, 0, 0), 'syxy': (1, 1, 1, 0), 'szxy': (2, 0, 0, 0), + 'szxz': (2, 0, 1, 0), 'szyx': (2, 1, 0, 0), 'szyz': (2, 1, 1, 0), + 'rzyx': (0, 0, 0, 1), 'rxyx': (0, 0, 1, 1), 'ryzx': (0, 1, 0, 1), + 'rxzx': (0, 1, 1, 1), 'rxzy': (1, 0, 0, 1), 'ryzy': (1, 0, 1, 1), + 'rzxy': (1, 1, 0, 1), 'ryxy': (1, 1, 1, 1), 'ryxz': (2, 0, 0, 1), + 'rzxz': (2, 0, 1, 1), 'rxyz': (2, 1, 0, 1), 'rzyz': (2, 1, 1, 1)} + +_TUPLE2AXES = dict((v, k) for k, v in _AXES2TUPLE.items()) + + +def vector_norm(data, axis=None, out=None): + """Return length, i.e. Euclidean norm, of ndarray along axis. + + >>> v = numpy.random.random(3) + >>> n = vector_norm(v) + >>> numpy.allclose(n, numpy.linalg.norm(v)) + True + >>> v = numpy.random.rand(6, 5, 3) + >>> n = vector_norm(v, axis=-1) + >>> numpy.allclose(n, numpy.sqrt(numpy.sum(v*v, axis=2))) + True + >>> n = vector_norm(v, axis=1) + >>> numpy.allclose(n, numpy.sqrt(numpy.sum(v*v, axis=1))) + True + >>> v = numpy.random.rand(5, 4, 3) + >>> n = numpy.empty((5, 3)) + >>> vector_norm(v, axis=1, out=n) + >>> numpy.allclose(n, numpy.sqrt(numpy.sum(v*v, axis=1))) + True + >>> vector_norm([]) + 0.0 + >>> vector_norm([1]) + 1.0 + + """ + data = numpy.array(data, dtype=numpy.float64, copy=True) + if out is None: + if data.ndim == 1: + return math.sqrt(numpy.dot(data, data)) + data *= data + out = numpy.atleast_1d(numpy.sum(data, axis=axis)) + numpy.sqrt(out, out) + return out + else: + data *= data + numpy.sum(data, axis=axis, out=out) + numpy.sqrt(out, out) + + +def unit_vector(data, axis=None, out=None): + """Return ndarray normalized by length, i.e. Euclidean norm, along axis. + + >>> v0 = numpy.random.random(3) + >>> v1 = unit_vector(v0) + >>> numpy.allclose(v1, v0 / numpy.linalg.norm(v0)) + True + >>> v0 = numpy.random.rand(5, 4, 3) + >>> v1 = unit_vector(v0, axis=-1) + >>> v2 = v0 / numpy.expand_dims(numpy.sqrt(numpy.sum(v0*v0, axis=2)), 2) + >>> numpy.allclose(v1, v2) + True + >>> v1 = unit_vector(v0, axis=1) + >>> v2 = v0 / numpy.expand_dims(numpy.sqrt(numpy.sum(v0*v0, axis=1)), 1) + >>> numpy.allclose(v1, v2) + True + >>> v1 = numpy.empty((5, 4, 3)) + >>> unit_vector(v0, axis=1, out=v1) + >>> numpy.allclose(v1, v2) + True + >>> list(unit_vector([])) + [] + >>> list(unit_vector([1])) + [1.0] + + """ + if out is None: + data = numpy.array(data, dtype=numpy.float64, copy=True) + if data.ndim == 1: + data /= math.sqrt(numpy.dot(data, data)) + return data + else: + if out is not data: + out[:] = numpy.array(data, copy=False) + data = out + length = numpy.atleast_1d(numpy.sum(data*data, axis)) + numpy.sqrt(length, length) + if axis is not None: + length = numpy.expand_dims(length, axis) + data /= length + if out is None: + return data + + +def random_vector(size): + """Return array of random doubles in the half-open interval [0.0, 1.0). + + >>> v = random_vector(10000) + >>> numpy.all(v >= 0) and numpy.all(v < 1) + True + >>> v0 = random_vector(10) + >>> v1 = random_vector(10) + >>> numpy.any(v0 == v1) + False + + """ + return numpy.random.random(size) + + +def vector_product(v0, v1, axis=0): + """Return vector perpendicular to vectors. + + >>> v = vector_product([2, 0, 0], [0, 3, 0]) + >>> numpy.allclose(v, [0, 0, 6]) + True + >>> v0 = [[2, 0, 0, 2], [0, 2, 0, 2], [0, 0, 2, 2]] + >>> v1 = [[3], [0], [0]] + >>> v = vector_product(v0, v1) + >>> numpy.allclose(v, [[0, 0, 0, 0], [0, 0, 6, 6], [0, -6, 0, -6]]) + True + >>> v0 = [[2, 0, 0], [2, 0, 0], [0, 2, 0], [2, 0, 0]] + >>> v1 = [[0, 3, 0], [0, 0, 3], [0, 0, 3], [3, 3, 3]] + >>> v = vector_product(v0, v1, axis=1) + >>> numpy.allclose(v, [[0, 0, 6], [0, -6, 0], [6, 0, 0], [0, -6, 6]]) + True + + """ + return numpy.cross(v0, v1, axis=axis) + + +def angle_between_vectors(v0, v1, directed=True, axis=0): + """Return angle between vectors. + + If directed is False, the input vectors are interpreted as undirected axes, + i.e. the maximum angle is pi/2. + + >>> a = angle_between_vectors([1, -2, 3], [-1, 2, -3]) + >>> numpy.allclose(a, math.pi) + True + >>> a = angle_between_vectors([1, -2, 3], [-1, 2, -3], directed=False) + >>> numpy.allclose(a, 0) + True + >>> v0 = [[2, 0, 0, 2], [0, 2, 0, 2], [0, 0, 2, 2]] + >>> v1 = [[3], [0], [0]] + >>> a = angle_between_vectors(v0, v1) + >>> numpy.allclose(a, [0, 1.5708, 1.5708, 0.95532]) + True + >>> v0 = [[2, 0, 0], [2, 0, 0], [0, 2, 0], [2, 0, 0]] + >>> v1 = [[0, 3, 0], [0, 0, 3], [0, 0, 3], [3, 3, 3]] + >>> a = angle_between_vectors(v0, v1, axis=1) + >>> numpy.allclose(a, [1.5708, 1.5708, 1.5708, 0.95532]) + True + + """ + v0 = numpy.array(v0, dtype=numpy.float64, copy=False) + v1 = numpy.array(v1, dtype=numpy.float64, copy=False) + dot = numpy.sum(v0 * v1, axis=axis) + dot /= vector_norm(v0, axis=axis) * vector_norm(v1, axis=axis) + return numpy.arccos(dot if directed else numpy.fabs(dot)) + + +def inverse_matrix(matrix): + """Return inverse of square transformation matrix. + + >>> M0 = random_rotation_matrix() + >>> M1 = inverse_matrix(M0.T) + >>> numpy.allclose(M1, numpy.linalg.inv(M0.T)) + True + >>> for size in range(1, 7): + ... M0 = numpy.random.rand(size, size) + ... M1 = inverse_matrix(M0) + ... if not numpy.allclose(M1, numpy.linalg.inv(M0)): print(size) + + """ + return numpy.linalg.inv(matrix) + + +def concatenate_matrices(*matrices): + """Return concatenation of series of transformation matrices. + + >>> M = numpy.random.rand(16).reshape((4, 4)) - 0.5 + >>> numpy.allclose(M, concatenate_matrices(M)) + True + >>> numpy.allclose(numpy.dot(M, M.T), concatenate_matrices(M, M.T)) + True + + """ + M = numpy.identity(4) + for i in matrices: + M = numpy.dot(M, i) + return M + + +def is_same_transform(matrix0, matrix1): + """Return True if two matrices perform same transformation. + + >>> is_same_transform(numpy.identity(4), numpy.identity(4)) + True + >>> is_same_transform(numpy.identity(4), random_rotation_matrix()) + False + + """ + matrix0 = numpy.array(matrix0, dtype=numpy.float64, copy=True) + matrix0 /= matrix0[3, 3] + matrix1 = numpy.array(matrix1, dtype=numpy.float64, copy=True) + matrix1 /= matrix1[3, 3] + return numpy.allclose(matrix0, matrix1) + + +def _import_module(name, package=None, warn=True, prefix='_py_', ignore='_'): + """Try import all public attributes from module into global namespace. + + Existing attributes with name clashes are renamed with prefix. + Attributes starting with underscore are ignored by default. + + Return True on successful import. + + """ + import warnings + from importlib import import_module + try: + if not package: + module = import_module(name) + else: + module = import_module('.' + name, package=package) + except ImportError: + if warn: + warnings.warn("failed to import module %s" % name) + else: + for attr in dir(module): + if ignore and attr.startswith(ignore): + continue + if prefix: + if attr in globals(): + globals()[prefix + attr] = globals()[attr] + elif warn: + warnings.warn("no Python implementation of " + attr) + globals()[attr] = getattr(module, attr) + return True + + +#_import_module('_transformations') + +if __name__ == "__main__": + import doctest + import random # used in doctests + numpy.set_printoptions(suppress=True, precision=5) + doctest.testmod() + diff --git a/src/caffe/py_lane_label_reader/perspective_reader.py b/src/caffe/py_lane_label_reader/perspective_reader.py new file mode 100644 index 00000000000..adbc837eca5 --- /dev/null +++ b/src/caffe/py_lane_label_reader/perspective_reader.py @@ -0,0 +1,9 @@ +import numpy as np +import pickle +__all__=['PerspectiveReader'] + +class PerspectiveReader(): + def __init__(self, distortion_file='/scail/group/deeplearning/driving_data/perspective_transforms.pickle'): + self.Ps = pickle.load(open(distortion_file, 'rb')) + def read(self): + return self.Ps diff --git a/src/caffe/py_lane_label_reader/transformations.py b/src/caffe/py_lane_label_reader/transformations.py new file mode 100644 index 00000000000..c31b66050d8 --- /dev/null +++ b/src/caffe/py_lane_label_reader/transformations.py @@ -0,0 +1,1907 @@ +# -*- coding: utf-8 -*- +# transformations.py + +# Copyright (c) 2006-2013, Christoph Gohlke +# Copyright (c) 2006-2013, The Regents of the University of California +# Produced at the Laboratory for Fluorescence Dynamics +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# * Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. +# * Redistributions 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. +# * Neither the name of the copyright holders nor the names of any +# contributors may 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 COPYRIGHT OWNER 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. + +"""Homogeneous Transformation Matrices and Quaternions. + +A library for calculating 4x4 matrices for translating, rotating, reflecting, +scaling, shearing, projecting, orthogonalizing, and superimposing arrays of +3D homogeneous coordinates as well as for converting between rotation matrices, +Euler angles, and quaternions. Also includes an Arcball control object and +functions to decompose transformation matrices. + +:Author: + `Christoph Gohlke `_ + +:Organization: + Laboratory for Fluorescence Dynamics, University of California, Irvine + +:Version: 2013.04.16 + +Requirements +------------ +* `CPython 2.7, 3.2 or 3.3 `_ +* `Numpy 1.7 `_ +* `Transformations.c 2013.01.18 `_ + (recommended for speedup of some functions) + +Notes +----- +The API is not stable yet and is expected to change between revisions. + +This Python code is not optimized for speed. Refer to the transformations.c +module for a faster implementation of some functions. + +Documentation in HTML format can be generated with epydoc. + +Matrices (M) can be inverted using numpy.linalg.inv(M), be concatenated using +numpy.dot(M0, M1), or transform homogeneous coordinate arrays (v) using +numpy.dot(M, v) for shape (4, \*) column vectors, respectively +numpy.dot(v, M.T) for shape (\*, 4) row vectors ("array of points"). + +This module follows the "column vectors on the right" and "row major storage" +(C contiguous) conventions. The translation components are in the right column +of the transformation matrix, i.e. M[:3, 3]. +The transpose of the transformation matrices may have to be used to interface +with other graphics systems, e.g. with OpenGL's glMultMatrixd(). See also [16]. + +Calculations are carried out with numpy.float64 precision. + +Vector, point, quaternion, and matrix function arguments are expected to be +"array like", i.e. tuple, list, or numpy arrays. + +Return types are numpy arrays unless specified otherwise. + +Angles are in radians unless specified otherwise. + +Quaternions w+ix+jy+kz are represented as [w, x, y, z]. + +A triple of Euler angles can be applied/interpreted in 24 ways, which can +be specified using a 4 character string or encoded 4-tuple: + + *Axes 4-string*: e.g. 'sxyz' or 'ryxy' + + - first character : rotations are applied to 's'tatic or 'r'otating frame + - remaining characters : successive rotation axis 'x', 'y', or 'z' + + *Axes 4-tuple*: e.g. (0, 0, 0, 0) or (1, 1, 1, 1) + + - inner axis: code of axis ('x':0, 'y':1, 'z':2) of rightmost matrix. + - parity : even (0) if inner axis 'x' is followed by 'y', 'y' is followed + by 'z', or 'z' is followed by 'x'. Otherwise odd (1). + - repetition : first and last axis are same (1) or different (0). + - frame : rotations are applied to static (0) or rotating (1) frame. + +References +---------- +(1) Matrices and transformations. Ronald Goldman. + In "Graphics Gems I", pp 472-475. Morgan Kaufmann, 1990. +(2) More matrices and transformations: shear and pseudo-perspective. + Ronald Goldman. In "Graphics Gems II", pp 320-323. Morgan Kaufmann, 1991. +(3) Decomposing a matrix into simple transformations. Spencer Thomas. + In "Graphics Gems II", pp 320-323. Morgan Kaufmann, 1991. +(4) Recovering the data from the transformation matrix. Ronald Goldman. + In "Graphics Gems II", pp 324-331. Morgan Kaufmann, 1991. +(5) Euler angle conversion. Ken Shoemake. + In "Graphics Gems IV", pp 222-229. Morgan Kaufmann, 1994. +(6) Arcball rotation control. Ken Shoemake. + In "Graphics Gems IV", pp 175-192. Morgan Kaufmann, 1994. +(7) Representing attitude: Euler angles, unit quaternions, and rotation + vectors. James Diebel. 2006. +(8) A discussion of the solution for the best rotation to relate two sets + of vectors. W Kabsch. Acta Cryst. 1978. A34, 827-828. +(9) Closed-form solution of absolute orientation using unit quaternions. + BKP Horn. J Opt Soc Am A. 1987. 4(4):629-642. +(10) Quaternions. Ken Shoemake. + http://www.sfu.ca/~jwa3/cmpt461/files/quatut.pdf +(11) From quaternion to matrix and back. JMP van Waveren. 2005. + http://www.intel.com/cd/ids/developer/asmo-na/eng/293748.htm +(12) Uniform random rotations. Ken Shoemake. + In "Graphics Gems III", pp 124-132. Morgan Kaufmann, 1992. +(13) Quaternion in molecular modeling. CFF Karney. + J Mol Graph Mod, 25(5):595-604 +(14) New method for extracting the quaternion from a rotation matrix. + Itzhack Y Bar-Itzhack, J Guid Contr Dynam. 2000. 23(6): 1085-1087. +(15) Multiple View Geometry in Computer Vision. Hartley and Zissermann. + Cambridge University Press; 2nd Ed. 2004. Chapter 4, Algorithm 4.7, p 130. +(16) Column Vectors vs. Row Vectors. + http://steve.hollasch.net/cgindex/math/matrix/column-vec.html + +Examples +-------- +>>> alpha, beta, gamma = 0.123, -1.234, 2.345 +>>> origin, xaxis, yaxis, zaxis = [0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1] +>>> I = identity_matrix() +>>> Rx = rotation_matrix(alpha, xaxis) +>>> Ry = rotation_matrix(beta, yaxis) +>>> Rz = rotation_matrix(gamma, zaxis) +>>> R = concatenate_matrices(Rx, Ry, Rz) +>>> euler = euler_from_matrix(R, 'rxyz') +>>> numpy.allclose([alpha, beta, gamma], euler) +True +>>> Re = euler_matrix(alpha, beta, gamma, 'rxyz') +>>> is_same_transform(R, Re) +True +>>> al, be, ga = euler_from_matrix(Re, 'rxyz') +>>> is_same_transform(Re, euler_matrix(al, be, ga, 'rxyz')) +True +>>> qx = quaternion_about_axis(alpha, xaxis) +>>> qy = quaternion_about_axis(beta, yaxis) +>>> qz = quaternion_about_axis(gamma, zaxis) +>>> q = quaternion_multiply(qx, qy) +>>> q = quaternion_multiply(q, qz) +>>> Rq = quaternion_matrix(q) +>>> is_same_transform(R, Rq) +True +>>> S = scale_matrix(1.23, origin) +>>> T = translation_matrix([1, 2, 3]) +>>> Z = shear_matrix(beta, xaxis, origin, zaxis) +>>> R = random_rotation_matrix(numpy.random.rand(3)) +>>> M = concatenate_matrices(T, R, Z, S) +>>> scale, shear, angles, trans, persp = decompose_matrix(M) +>>> numpy.allclose(scale, 1.23) +True +>>> numpy.allclose(trans, [1, 2, 3]) +True +>>> numpy.allclose(shear, [0, math.tan(beta), 0]) +True +>>> is_same_transform(R, euler_matrix(axes='sxyz', *angles)) +True +>>> M1 = compose_matrix(scale, shear, angles, trans, persp) +>>> is_same_transform(M, M1) +True +>>> v0, v1 = random_vector(3), random_vector(3) +>>> M = rotation_matrix(angle_between_vectors(v0, v1), vector_product(v0, v1)) +>>> v2 = numpy.dot(v0, M[:3,:3].T) +>>> numpy.allclose(unit_vector(v1), unit_vector(v2)) +True + +""" + +from __future__ import division, print_function + +import math + +import numpy + +__version__ = '2013.04.16' +__docformat__ = 'restructuredtext en' +__all__ = [] + + +def identity_matrix(): + """Return 4x4 identity/unit matrix. + + >>> I = identity_matrix() + >>> numpy.allclose(I, numpy.dot(I, I)) + True + >>> numpy.sum(I), numpy.trace(I) + (4.0, 4.0) + >>> numpy.allclose(I, numpy.identity(4)) + True + + """ + return numpy.identity(4) + + +def translation_matrix(direction): + """Return matrix to translate by direction vector. + + >>> v = numpy.random.random(3) - 0.5 + >>> numpy.allclose(v, translation_matrix(v)[:3, 3]) + True + + """ + M = numpy.identity(4) + M[:3, 3] = direction[:3] + return M + + +def translation_from_matrix(matrix): + """Return translation vector from translation matrix. + + >>> v0 = numpy.random.random(3) - 0.5 + >>> v1 = translation_from_matrix(translation_matrix(v0)) + >>> numpy.allclose(v0, v1) + True + + """ + return numpy.array(matrix, copy=False)[:3, 3].copy() + + +def reflection_matrix(point, normal): + """Return matrix to mirror at plane defined by point and normal vector. + + >>> v0 = numpy.random.random(4) - 0.5 + >>> v0[3] = 1. + >>> v1 = numpy.random.random(3) - 0.5 + >>> R = reflection_matrix(v0, v1) + >>> numpy.allclose(2, numpy.trace(R)) + True + >>> numpy.allclose(v0, numpy.dot(R, v0)) + True + >>> v2 = v0.copy() + >>> v2[:3] += v1 + >>> v3 = v0.copy() + >>> v2[:3] -= v1 + >>> numpy.allclose(v2, numpy.dot(R, v3)) + True + + """ + normal = unit_vector(normal[:3]) + M = numpy.identity(4) + M[:3, :3] -= 2.0 * numpy.outer(normal, normal) + M[:3, 3] = (2.0 * numpy.dot(point[:3], normal)) * normal + return M + + +def reflection_from_matrix(matrix): + """Return mirror plane point and normal vector from reflection matrix. + + >>> v0 = numpy.random.random(3) - 0.5 + >>> v1 = numpy.random.random(3) - 0.5 + >>> M0 = reflection_matrix(v0, v1) + >>> point, normal = reflection_from_matrix(M0) + >>> M1 = reflection_matrix(point, normal) + >>> is_same_transform(M0, M1) + True + + """ + M = numpy.array(matrix, dtype=numpy.float64, copy=False) + # normal: unit eigenvector corresponding to eigenvalue -1 + w, V = numpy.linalg.eig(M[:3, :3]) + i = numpy.where(abs(numpy.real(w) + 1.0) < 1e-8)[0] + if not len(i): + raise ValueError("no unit eigenvector corresponding to eigenvalue -1") + normal = numpy.real(V[:, i[0]]).squeeze() + # point: any unit eigenvector corresponding to eigenvalue 1 + w, V = numpy.linalg.eig(M) + i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0] + if not len(i): + raise ValueError("no unit eigenvector corresponding to eigenvalue 1") + point = numpy.real(V[:, i[-1]]).squeeze() + point /= point[3] + return point, normal + + +def rotation_matrix(angle, direction, point=None): + """Return matrix to rotate about axis defined by point and direction. + + >>> R = rotation_matrix(math.pi/2, [0, 0, 1], [1, 0, 0]) + >>> numpy.allclose(numpy.dot(R, [0, 0, 0, 1]), [1, -1, 0, 1]) + True + >>> angle = (random.random() - 0.5) * (2*math.pi) + >>> direc = numpy.random.random(3) - 0.5 + >>> point = numpy.random.random(3) - 0.5 + >>> R0 = rotation_matrix(angle, direc, point) + >>> R1 = rotation_matrix(angle-2*math.pi, direc, point) + >>> is_same_transform(R0, R1) + True + >>> R0 = rotation_matrix(angle, direc, point) + >>> R1 = rotation_matrix(-angle, -direc, point) + >>> is_same_transform(R0, R1) + True + >>> I = numpy.identity(4, numpy.float64) + >>> numpy.allclose(I, rotation_matrix(math.pi*2, direc)) + True + >>> numpy.allclose(2, numpy.trace(rotation_matrix(math.pi/2, + ... direc, point))) + True + + """ + sina = math.sin(angle) + cosa = math.cos(angle) + direction = unit_vector(direction[:3]) + # rotation matrix around unit vector + R = numpy.diag([cosa, cosa, cosa]) + R += numpy.outer(direction, direction) * (1.0 - cosa) + direction *= sina + R += numpy.array([[ 0.0, -direction[2], direction[1]], + [ direction[2], 0.0, -direction[0]], + [-direction[1], direction[0], 0.0]]) + M = numpy.identity(4) + M[:3, :3] = R + if point is not None: + # rotation not around origin + point = numpy.array(point[:3], dtype=numpy.float64, copy=False) + M[:3, 3] = point - numpy.dot(R, point) + return M + + +def rotation_from_matrix(matrix): + """Return rotation angle and axis from rotation matrix. + + >>> angle = (random.random() - 0.5) * (2*math.pi) + >>> direc = numpy.random.random(3) - 0.5 + >>> point = numpy.random.random(3) - 0.5 + >>> R0 = rotation_matrix(angle, direc, point) + >>> angle, direc, point = rotation_from_matrix(R0) + >>> R1 = rotation_matrix(angle, direc, point) + >>> is_same_transform(R0, R1) + True + + """ + R = numpy.array(matrix, dtype=numpy.float64, copy=False) + R33 = R[:3, :3] + # direction: unit eigenvector of R33 corresponding to eigenvalue of 1 + w, W = numpy.linalg.eig(R33.T) + i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0] + if not len(i): + raise ValueError("no unit eigenvector corresponding to eigenvalue 1") + direction = numpy.real(W[:, i[-1]]).squeeze() + # point: unit eigenvector of R33 corresponding to eigenvalue of 1 + w, Q = numpy.linalg.eig(R) + i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0] + if not len(i): + raise ValueError("no unit eigenvector corresponding to eigenvalue 1") + point = numpy.real(Q[:, i[-1]]).squeeze() + point /= point[3] + # rotation angle depending on direction + cosa = (numpy.trace(R33) - 1.0) / 2.0 + if abs(direction[2]) > 1e-8: + sina = (R[1, 0] + (cosa-1.0)*direction[0]*direction[1]) / direction[2] + elif abs(direction[1]) > 1e-8: + sina = (R[0, 2] + (cosa-1.0)*direction[0]*direction[2]) / direction[1] + else: + sina = (R[2, 1] + (cosa-1.0)*direction[1]*direction[2]) / direction[0] + angle = math.atan2(sina, cosa) + return angle, direction, point + + +def scale_matrix(factor, origin=None, direction=None): + """Return matrix to scale by factor around origin in direction. + + Use factor -1 for point symmetry. + + >>> v = (numpy.random.rand(4, 5) - 0.5) * 20 + >>> v[3] = 1 + >>> S = scale_matrix(-1.234) + >>> numpy.allclose(numpy.dot(S, v)[:3], -1.234*v[:3]) + True + >>> factor = random.random() * 10 - 5 + >>> origin = numpy.random.random(3) - 0.5 + >>> direct = numpy.random.random(3) - 0.5 + >>> S = scale_matrix(factor, origin) + >>> S = scale_matrix(factor, origin, direct) + + """ + if direction is None: + # uniform scaling + M = numpy.diag([factor, factor, factor, 1.0]) + if origin is not None: + M[:3, 3] = origin[:3] + M[:3, 3] *= 1.0 - factor + else: + # nonuniform scaling + direction = unit_vector(direction[:3]) + factor = 1.0 - factor + M = numpy.identity(4) + M[:3, :3] -= factor * numpy.outer(direction, direction) + if origin is not None: + M[:3, 3] = (factor * numpy.dot(origin[:3], direction)) * direction + return M + + +def scale_from_matrix(matrix): + """Return scaling factor, origin and direction from scaling matrix. + + >>> factor = random.random() * 10 - 5 + >>> origin = numpy.random.random(3) - 0.5 + >>> direct = numpy.random.random(3) - 0.5 + >>> S0 = scale_matrix(factor, origin) + >>> factor, origin, direction = scale_from_matrix(S0) + >>> S1 = scale_matrix(factor, origin, direction) + >>> is_same_transform(S0, S1) + True + >>> S0 = scale_matrix(factor, origin, direct) + >>> factor, origin, direction = scale_from_matrix(S0) + >>> S1 = scale_matrix(factor, origin, direction) + >>> is_same_transform(S0, S1) + True + + """ + M = numpy.array(matrix, dtype=numpy.float64, copy=False) + M33 = M[:3, :3] + factor = numpy.trace(M33) - 2.0 + try: + # direction: unit eigenvector corresponding to eigenvalue factor + w, V = numpy.linalg.eig(M33) + i = numpy.where(abs(numpy.real(w) - factor) < 1e-8)[0][0] + direction = numpy.real(V[:, i]).squeeze() + direction /= vector_norm(direction) + except IndexError: + # uniform scaling + factor = (factor + 2.0) / 3.0 + direction = None + # origin: any eigenvector corresponding to eigenvalue 1 + w, V = numpy.linalg.eig(M) + i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0] + if not len(i): + raise ValueError("no eigenvector corresponding to eigenvalue 1") + origin = numpy.real(V[:, i[-1]]).squeeze() + origin /= origin[3] + return factor, origin, direction + + +def projection_matrix(point, normal, direction=None, + perspective=None, pseudo=False): + """Return matrix to project onto plane defined by point and normal. + + Using either perspective point, projection direction, or none of both. + + If pseudo is True, perspective projections will preserve relative depth + such that Perspective = dot(Orthogonal, PseudoPerspective). + + >>> P = projection_matrix([0, 0, 0], [1, 0, 0]) + >>> numpy.allclose(P[1:, 1:], numpy.identity(4)[1:, 1:]) + True + >>> point = numpy.random.random(3) - 0.5 + >>> normal = numpy.random.random(3) - 0.5 + >>> direct = numpy.random.random(3) - 0.5 + >>> persp = numpy.random.random(3) - 0.5 + >>> P0 = projection_matrix(point, normal) + >>> P1 = projection_matrix(point, normal, direction=direct) + >>> P2 = projection_matrix(point, normal, perspective=persp) + >>> P3 = projection_matrix(point, normal, perspective=persp, pseudo=True) + >>> is_same_transform(P2, numpy.dot(P0, P3)) + True + >>> P = projection_matrix([3, 0, 0], [1, 1, 0], [1, 0, 0]) + >>> v0 = (numpy.random.rand(4, 5) - 0.5) * 20 + >>> v0[3] = 1 + >>> v1 = numpy.dot(P, v0) + >>> numpy.allclose(v1[1], v0[1]) + True + >>> numpy.allclose(v1[0], 3-v1[1]) + True + + """ + M = numpy.identity(4) + point = numpy.array(point[:3], dtype=numpy.float64, copy=False) + normal = unit_vector(normal[:3]) + if perspective is not None: + # perspective projection + perspective = numpy.array(perspective[:3], dtype=numpy.float64, + copy=False) + M[0, 0] = M[1, 1] = M[2, 2] = numpy.dot(perspective-point, normal) + M[:3, :3] -= numpy.outer(perspective, normal) + if pseudo: + # preserve relative depth + M[:3, :3] -= numpy.outer(normal, normal) + M[:3, 3] = numpy.dot(point, normal) * (perspective+normal) + else: + M[:3, 3] = numpy.dot(point, normal) * perspective + M[3, :3] = -normal + M[3, 3] = numpy.dot(perspective, normal) + elif direction is not None: + # parallel projection + direction = numpy.array(direction[:3], dtype=numpy.float64, copy=False) + scale = numpy.dot(direction, normal) + M[:3, :3] -= numpy.outer(direction, normal) / scale + M[:3, 3] = direction * (numpy.dot(point, normal) / scale) + else: + # orthogonal projection + M[:3, :3] -= numpy.outer(normal, normal) + M[:3, 3] = numpy.dot(point, normal) * normal + return M + + +def projection_from_matrix(matrix, pseudo=False): + """Return projection plane and perspective point from projection matrix. + + Return values are same as arguments for projection_matrix function: + point, normal, direction, perspective, and pseudo. + + >>> point = numpy.random.random(3) - 0.5 + >>> normal = numpy.random.random(3) - 0.5 + >>> direct = numpy.random.random(3) - 0.5 + >>> persp = numpy.random.random(3) - 0.5 + >>> P0 = projection_matrix(point, normal) + >>> result = projection_from_matrix(P0) + >>> P1 = projection_matrix(*result) + >>> is_same_transform(P0, P1) + True + >>> P0 = projection_matrix(point, normal, direct) + >>> result = projection_from_matrix(P0) + >>> P1 = projection_matrix(*result) + >>> is_same_transform(P0, P1) + True + >>> P0 = projection_matrix(point, normal, perspective=persp, pseudo=False) + >>> result = projection_from_matrix(P0, pseudo=False) + >>> P1 = projection_matrix(*result) + >>> is_same_transform(P0, P1) + True + >>> P0 = projection_matrix(point, normal, perspective=persp, pseudo=True) + >>> result = projection_from_matrix(P0, pseudo=True) + >>> P1 = projection_matrix(*result) + >>> is_same_transform(P0, P1) + True + + """ + M = numpy.array(matrix, dtype=numpy.float64, copy=False) + M33 = M[:3, :3] + w, V = numpy.linalg.eig(M) + i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0] + if not pseudo and len(i): + # point: any eigenvector corresponding to eigenvalue 1 + point = numpy.real(V[:, i[-1]]).squeeze() + point /= point[3] + # direction: unit eigenvector corresponding to eigenvalue 0 + w, V = numpy.linalg.eig(M33) + i = numpy.where(abs(numpy.real(w)) < 1e-8)[0] + if not len(i): + raise ValueError("no eigenvector corresponding to eigenvalue 0") + direction = numpy.real(V[:, i[0]]).squeeze() + direction /= vector_norm(direction) + # normal: unit eigenvector of M33.T corresponding to eigenvalue 0 + w, V = numpy.linalg.eig(M33.T) + i = numpy.where(abs(numpy.real(w)) < 1e-8)[0] + if len(i): + # parallel projection + normal = numpy.real(V[:, i[0]]).squeeze() + normal /= vector_norm(normal) + return point, normal, direction, None, False + else: + # orthogonal projection, where normal equals direction vector + return point, direction, None, None, False + else: + # perspective projection + i = numpy.where(abs(numpy.real(w)) > 1e-8)[0] + if not len(i): + raise ValueError( + "no eigenvector not corresponding to eigenvalue 0") + point = numpy.real(V[:, i[-1]]).squeeze() + point /= point[3] + normal = - M[3, :3] + perspective = M[:3, 3] / numpy.dot(point[:3], normal) + if pseudo: + perspective -= normal + return point, normal, None, perspective, pseudo + + +def clip_matrix(left, right, bottom, top, near, far, perspective=False): + """Return matrix to obtain normalized device coordinates from frustum. + + The frustum bounds are axis-aligned along x (left, right), + y (bottom, top) and z (near, far). + + Normalized device coordinates are in range [-1, 1] if coordinates are + inside the frustum. + + If perspective is True the frustum is a truncated pyramid with the + perspective point at origin and direction along z axis, otherwise an + orthographic canonical view volume (a box). + + Homogeneous coordinates transformed by the perspective clip matrix + need to be dehomogenized (divided by w coordinate). + + >>> frustum = numpy.random.rand(6) + >>> frustum[1] += frustum[0] + >>> frustum[3] += frustum[2] + >>> frustum[5] += frustum[4] + >>> M = clip_matrix(perspective=False, *frustum) + >>> numpy.dot(M, [frustum[0], frustum[2], frustum[4], 1]) + array([-1., -1., -1., 1.]) + >>> numpy.dot(M, [frustum[1], frustum[3], frustum[5], 1]) + array([ 1., 1., 1., 1.]) + >>> M = clip_matrix(perspective=True, *frustum) + >>> v = numpy.dot(M, [frustum[0], frustum[2], frustum[4], 1]) + >>> v / v[3] + array([-1., -1., -1., 1.]) + >>> v = numpy.dot(M, [frustum[1], frustum[3], frustum[4], 1]) + >>> v / v[3] + array([ 1., 1., -1., 1.]) + + """ + if left >= right or bottom >= top or near >= far: + raise ValueError("invalid frustum") + if perspective: + if near <= _EPS: + raise ValueError("invalid frustum: near <= 0") + t = 2.0 * near + M = [[t/(left-right), 0.0, (right+left)/(right-left), 0.0], + [0.0, t/(bottom-top), (top+bottom)/(top-bottom), 0.0], + [0.0, 0.0, (far+near)/(near-far), t*far/(far-near)], + [0.0, 0.0, -1.0, 0.0]] + else: + M = [[2.0/(right-left), 0.0, 0.0, (right+left)/(left-right)], + [0.0, 2.0/(top-bottom), 0.0, (top+bottom)/(bottom-top)], + [0.0, 0.0, 2.0/(far-near), (far+near)/(near-far)], + [0.0, 0.0, 0.0, 1.0]] + return numpy.array(M) + + +def shear_matrix(angle, direction, point, normal): + """Return matrix to shear by angle along direction vector on shear plane. + + The shear plane is defined by a point and normal vector. The direction + vector must be orthogonal to the plane's normal vector. + + A point P is transformed by the shear matrix into P" such that + the vector P-P" is parallel to the direction vector and its extent is + given by the angle of P-P'-P", where P' is the orthogonal projection + of P onto the shear plane. + + >>> angle = (random.random() - 0.5) * 4*math.pi + >>> direct = numpy.random.random(3) - 0.5 + >>> point = numpy.random.random(3) - 0.5 + >>> normal = numpy.cross(direct, numpy.random.random(3)) + >>> S = shear_matrix(angle, direct, point, normal) + >>> numpy.allclose(1, numpy.linalg.det(S)) + True + + """ + normal = unit_vector(normal[:3]) + direction = unit_vector(direction[:3]) + if abs(numpy.dot(normal, direction)) > 1e-6: + raise ValueError("direction and normal vectors are not orthogonal") + angle = math.tan(angle) + M = numpy.identity(4) + M[:3, :3] += angle * numpy.outer(direction, normal) + M[:3, 3] = -angle * numpy.dot(point[:3], normal) * direction + return M + + +def shear_from_matrix(matrix): + """Return shear angle, direction and plane from shear matrix. + + >>> angle = (random.random() - 0.5) * 4*math.pi + >>> direct = numpy.random.random(3) - 0.5 + >>> point = numpy.random.random(3) - 0.5 + >>> normal = numpy.cross(direct, numpy.random.random(3)) + >>> S0 = shear_matrix(angle, direct, point, normal) + >>> angle, direct, point, normal = shear_from_matrix(S0) + >>> S1 = shear_matrix(angle, direct, point, normal) + >>> is_same_transform(S0, S1) + True + + """ + M = numpy.array(matrix, dtype=numpy.float64, copy=False) + M33 = M[:3, :3] + # normal: cross independent eigenvectors corresponding to the eigenvalue 1 + w, V = numpy.linalg.eig(M33) + i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-4)[0] + if len(i) < 2: + raise ValueError("no two linear independent eigenvectors found %s" % w) + V = numpy.real(V[:, i]).squeeze().T + lenorm = -1.0 + for i0, i1 in ((0, 1), (0, 2), (1, 2)): + n = numpy.cross(V[i0], V[i1]) + w = vector_norm(n) + if w > lenorm: + lenorm = w + normal = n + normal /= lenorm + # direction and angle + direction = numpy.dot(M33 - numpy.identity(3), normal) + angle = vector_norm(direction) + direction /= angle + angle = math.atan(angle) + # point: eigenvector corresponding to eigenvalue 1 + w, V = numpy.linalg.eig(M) + i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0] + if not len(i): + raise ValueError("no eigenvector corresponding to eigenvalue 1") + point = numpy.real(V[:, i[-1]]).squeeze() + point /= point[3] + return angle, direction, point, normal + + +def decompose_matrix(matrix): + """Return sequence of transformations from transformation matrix. + + matrix : array_like + Non-degenerative homogeneous transformation matrix + + Return tuple of: + scale : vector of 3 scaling factors + shear : list of shear factors for x-y, x-z, y-z axes + angles : list of Euler angles about static x, y, z axes + translate : translation vector along x, y, z axes + perspective : perspective partition of matrix + + Raise ValueError if matrix is of wrong type or degenerative. + + >>> T0 = translation_matrix([1, 2, 3]) + >>> scale, shear, angles, trans, persp = decompose_matrix(T0) + >>> T1 = translation_matrix(trans) + >>> numpy.allclose(T0, T1) + True + >>> S = scale_matrix(0.123) + >>> scale, shear, angles, trans, persp = decompose_matrix(S) + >>> scale[0] + 0.123 + >>> R0 = euler_matrix(1, 2, 3) + >>> scale, shear, angles, trans, persp = decompose_matrix(R0) + >>> R1 = euler_matrix(*angles) + >>> numpy.allclose(R0, R1) + True + + """ + M = numpy.array(matrix, dtype=numpy.float64, copy=True).T + if abs(M[3, 3]) < _EPS: + raise ValueError("M[3, 3] is zero") + M /= M[3, 3] + P = M.copy() + P[:, 3] = 0.0, 0.0, 0.0, 1.0 + if not numpy.linalg.det(P): + raise ValueError("matrix is singular") + + scale = numpy.zeros((3, )) + shear = [0.0, 0.0, 0.0] + angles = [0.0, 0.0, 0.0] + + if any(abs(M[:3, 3]) > _EPS): + perspective = numpy.dot(M[:, 3], numpy.linalg.inv(P.T)) + M[:, 3] = 0.0, 0.0, 0.0, 1.0 + else: + perspective = numpy.array([0.0, 0.0, 0.0, 1.0]) + + translate = M[3, :3].copy() + M[3, :3] = 0.0 + + row = M[:3, :3].copy() + scale[0] = vector_norm(row[0]) + row[0] /= scale[0] + shear[0] = numpy.dot(row[0], row[1]) + row[1] -= row[0] * shear[0] + scale[1] = vector_norm(row[1]) + row[1] /= scale[1] + shear[0] /= scale[1] + shear[1] = numpy.dot(row[0], row[2]) + row[2] -= row[0] * shear[1] + shear[2] = numpy.dot(row[1], row[2]) + row[2] -= row[1] * shear[2] + scale[2] = vector_norm(row[2]) + row[2] /= scale[2] + shear[1:] /= scale[2] + + if numpy.dot(row[0], numpy.cross(row[1], row[2])) < 0: + numpy.negative(scale, scale) + numpy.negative(row, row) + + angles[1] = math.asin(-row[0, 2]) + if math.cos(angles[1]): + angles[0] = math.atan2(row[1, 2], row[2, 2]) + angles[2] = math.atan2(row[0, 1], row[0, 0]) + else: + #angles[0] = math.atan2(row[1, 0], row[1, 1]) + angles[0] = math.atan2(-row[2, 1], row[1, 1]) + angles[2] = 0.0 + + return scale, shear, angles, translate, perspective + + +def compose_matrix(scale=None, shear=None, angles=None, translate=None, + perspective=None): + """Return transformation matrix from sequence of transformations. + + This is the inverse of the decompose_matrix function. + + Sequence of transformations: + scale : vector of 3 scaling factors + shear : list of shear factors for x-y, x-z, y-z axes + angles : list of Euler angles about static x, y, z axes + translate : translation vector along x, y, z axes + perspective : perspective partition of matrix + + >>> scale = numpy.random.random(3) - 0.5 + >>> shear = numpy.random.random(3) - 0.5 + >>> angles = (numpy.random.random(3) - 0.5) * (2*math.pi) + >>> trans = numpy.random.random(3) - 0.5 + >>> persp = numpy.random.random(4) - 0.5 + >>> M0 = compose_matrix(scale, shear, angles, trans, persp) + >>> result = decompose_matrix(M0) + >>> M1 = compose_matrix(*result) + >>> is_same_transform(M0, M1) + True + + """ + M = numpy.identity(4) + if perspective is not None: + P = numpy.identity(4) + P[3, :] = perspective[:4] + M = numpy.dot(M, P) + if translate is not None: + T = numpy.identity(4) + T[:3, 3] = translate[:3] + M = numpy.dot(M, T) + if angles is not None: + R = euler_matrix(angles[0], angles[1], angles[2], 'sxyz') + M = numpy.dot(M, R) + if shear is not None: + Z = numpy.identity(4) + Z[1, 2] = shear[2] + Z[0, 2] = shear[1] + Z[0, 1] = shear[0] + M = numpy.dot(M, Z) + if scale is not None: + S = numpy.identity(4) + S[0, 0] = scale[0] + S[1, 1] = scale[1] + S[2, 2] = scale[2] + M = numpy.dot(M, S) + M /= M[3, 3] + return M + + +def orthogonalization_matrix(lengths, angles): + """Return orthogonalization matrix for crystallographic cell coordinates. + + Angles are expected in degrees. + + The de-orthogonalization matrix is the inverse. + + >>> O = orthogonalization_matrix([10, 10, 10], [90, 90, 90]) + >>> numpy.allclose(O[:3, :3], numpy.identity(3, float) * 10) + True + >>> O = orthogonalization_matrix([9.8, 12.0, 15.5], [87.2, 80.7, 69.7]) + >>> numpy.allclose(numpy.sum(O), 43.063229) + True + + """ + a, b, c = lengths + angles = numpy.radians(angles) + sina, sinb, _ = numpy.sin(angles) + cosa, cosb, cosg = numpy.cos(angles) + co = (cosa * cosb - cosg) / (sina * sinb) + return numpy.array([ + [ a*sinb*math.sqrt(1.0-co*co), 0.0, 0.0, 0.0], + [-a*sinb*co, b*sina, 0.0, 0.0], + [ a*cosb, b*cosa, c, 0.0], + [ 0.0, 0.0, 0.0, 1.0]]) + + +def affine_matrix_from_points(v0, v1, shear=True, scale=True, usesvd=True): + """Return affine transform matrix to register two point sets. + + v0 and v1 are shape (ndims, \*) arrays of at least ndims non-homogeneous + coordinates, where ndims is the dimensionality of the coordinate space. + + If shear is False, a similarity transformation matrix is returned. + If also scale is False, a rigid/Euclidean transformation matrix + is returned. + + By default the algorithm by Hartley and Zissermann [15] is used. + If usesvd is True, similarity and Euclidean transformation matrices + are calculated by minimizing the weighted sum of squared deviations + (RMSD) according to the algorithm by Kabsch [8]. + Otherwise, and if ndims is 3, the quaternion based algorithm by Horn [9] + is used, which is slower when using this Python implementation. + + The returned matrix performs rotation, translation and uniform scaling + (if specified). + + >>> v0 = [[0, 1031, 1031, 0], [0, 0, 1600, 1600]] + >>> v1 = [[675, 826, 826, 677], [55, 52, 281, 277]] + >>> affine_matrix_from_points(v0, v1) + array([[ 0.14549, 0.00062, 675.50008], + [ 0.00048, 0.14094, 53.24971], + [ 0. , 0. , 1. ]]) + >>> T = translation_matrix(numpy.random.random(3)-0.5) + >>> R = random_rotation_matrix(numpy.random.random(3)) + >>> S = scale_matrix(random.random()) + >>> M = concatenate_matrices(T, R, S) + >>> v0 = (numpy.random.rand(4, 100) - 0.5) * 20 + >>> v0[3] = 1 + >>> v1 = numpy.dot(M, v0) + >>> v0[:3] += numpy.random.normal(0, 1e-8, 300).reshape(3, -1) + >>> M = affine_matrix_from_points(v0[:3], v1[:3]) + >>> numpy.allclose(v1, numpy.dot(M, v0)) + True + + More examples in superimposition_matrix() + + """ + v0 = numpy.array(v0, dtype=numpy.float64, copy=True) + v1 = numpy.array(v1, dtype=numpy.float64, copy=True) + + ndims = v0.shape[0] + if ndims < 2 or v0.shape[1] < ndims or v0.shape != v1.shape: + raise ValueError("input arrays are of wrong shape or type") + + # move centroids to origin + t0 = -numpy.mean(v0, axis=1) + M0 = numpy.identity(ndims+1) + M0[:ndims, ndims] = t0 + v0 += t0.reshape(ndims, 1) + t1 = -numpy.mean(v1, axis=1) + M1 = numpy.identity(ndims+1) + M1[:ndims, ndims] = t1 + v1 += t1.reshape(ndims, 1) + + if shear: + # Affine transformation + A = numpy.concatenate((v0, v1), axis=0) + u, s, vh = numpy.linalg.svd(A.T) + vh = vh[:ndims].T + B = vh[:ndims] + C = vh[ndims:2*ndims] + t = numpy.dot(C, numpy.linalg.pinv(B)) + t = numpy.concatenate((t, numpy.zeros((ndims, 1))), axis=1) + M = numpy.vstack((t, ((0.0,)*ndims) + (1.0,))) + elif usesvd or ndims != 3: + # Rigid transformation via SVD of covariance matrix + u, s, vh = numpy.linalg.svd(numpy.dot(v1, v0.T)) + # rotation matrix from SVD orthonormal bases + R = numpy.dot(u, vh) + if numpy.linalg.det(R) < 0.0: + # R does not constitute right handed system + R -= numpy.outer(u[:, ndims-1], vh[ndims-1, :]*2.0) + s[-1] *= -1.0 + # homogeneous transformation matrix + M = numpy.identity(ndims+1) + M[:ndims, :ndims] = R + else: + # Rigid transformation matrix via quaternion + # compute symmetric matrix N + xx, yy, zz = numpy.sum(v0 * v1, axis=1) + xy, yz, zx = numpy.sum(v0 * numpy.roll(v1, -1, axis=0), axis=1) + xz, yx, zy = numpy.sum(v0 * numpy.roll(v1, -2, axis=0), axis=1) + N = [[xx+yy+zz, 0.0, 0.0, 0.0], + [yz-zy, xx-yy-zz, 0.0, 0.0], + [zx-xz, xy+yx, yy-xx-zz, 0.0], + [xy-yx, zx+xz, yz+zy, zz-xx-yy]] + # quaternion: eigenvector corresponding to most positive eigenvalue + w, V = numpy.linalg.eigh(N) + q = V[:, numpy.argmax(w)] + q /= vector_norm(q) # unit quaternion + # homogeneous transformation matrix + M = quaternion_matrix(q) + + if scale and not shear: + # Affine transformation; scale is ratio of RMS deviations from centroid + v0 *= v0 + v1 *= v1 + M[:ndims, :ndims] *= math.sqrt(numpy.sum(v1) / numpy.sum(v0)) + + # move centroids back + M = numpy.dot(numpy.linalg.inv(M1), numpy.dot(M, M0)) + M /= M[ndims, ndims] + return M + + +def superimposition_matrix(v0, v1, scale=False, usesvd=True): + """Return matrix to transform given 3D point set into second point set. + + v0 and v1 are shape (3, \*) or (4, \*) arrays of at least 3 points. + + The parameters scale and usesvd are explained in the more general + affine_matrix_from_points function. + + The returned matrix is a similarity or Euclidean transformation matrix. + This function has a fast C implementation in transformations.c. + + >>> v0 = numpy.random.rand(3, 10) + >>> M = superimposition_matrix(v0, v0) + >>> numpy.allclose(M, numpy.identity(4)) + True + >>> R = random_rotation_matrix(numpy.random.random(3)) + >>> v0 = [[1,0,0], [0,1,0], [0,0,1], [1,1,1]] + >>> v1 = numpy.dot(R, v0) + >>> M = superimposition_matrix(v0, v1) + >>> numpy.allclose(v1, numpy.dot(M, v0)) + True + >>> v0 = (numpy.random.rand(4, 100) - 0.5) * 20 + >>> v0[3] = 1 + >>> v1 = numpy.dot(R, v0) + >>> M = superimposition_matrix(v0, v1) + >>> numpy.allclose(v1, numpy.dot(M, v0)) + True + >>> S = scale_matrix(random.random()) + >>> T = translation_matrix(numpy.random.random(3)-0.5) + >>> M = concatenate_matrices(T, R, S) + >>> v1 = numpy.dot(M, v0) + >>> v0[:3] += numpy.random.normal(0, 1e-9, 300).reshape(3, -1) + >>> M = superimposition_matrix(v0, v1, scale=True) + >>> numpy.allclose(v1, numpy.dot(M, v0)) + True + >>> M = superimposition_matrix(v0, v1, scale=True, usesvd=False) + >>> numpy.allclose(v1, numpy.dot(M, v0)) + True + >>> v = numpy.empty((4, 100, 3)) + >>> v[:, :, 0] = v0 + >>> M = superimposition_matrix(v0, v1, scale=True, usesvd=False) + >>> numpy.allclose(v1, numpy.dot(M, v[:, :, 0])) + True + + """ + v0 = numpy.array(v0, dtype=numpy.float64, copy=False)[:3] + v1 = numpy.array(v1, dtype=numpy.float64, copy=False)[:3] + return affine_matrix_from_points(v0, v1, shear=False, + scale=scale, usesvd=usesvd) + + +def euler_matrix(ai, aj, ak, axes='sxyz'): + """Return homogeneous rotation matrix from Euler angles and axis sequence. + + ai, aj, ak : Euler's roll, pitch and yaw angles + axes : One of 24 axis sequences as string or encoded tuple + + >>> R = euler_matrix(1, 2, 3, 'syxz') + >>> numpy.allclose(numpy.sum(R[0]), -1.34786452) + True + >>> R = euler_matrix(1, 2, 3, (0, 1, 0, 1)) + >>> numpy.allclose(numpy.sum(R[0]), -0.383436184) + True + >>> ai, aj, ak = (4*math.pi) * (numpy.random.random(3) - 0.5) + >>> for axes in _AXES2TUPLE.keys(): + ... R = euler_matrix(ai, aj, ak, axes) + >>> for axes in _TUPLE2AXES.keys(): + ... R = euler_matrix(ai, aj, ak, axes) + + """ + try: + firstaxis, parity, repetition, frame = _AXES2TUPLE[axes] + except (AttributeError, KeyError): + _TUPLE2AXES[axes] # validation + firstaxis, parity, repetition, frame = axes + + i = firstaxis + j = _NEXT_AXIS[i+parity] + k = _NEXT_AXIS[i-parity+1] + + if frame: + ai, ak = ak, ai + if parity: + ai, aj, ak = -ai, -aj, -ak + + si, sj, sk = math.sin(ai), math.sin(aj), math.sin(ak) + ci, cj, ck = math.cos(ai), math.cos(aj), math.cos(ak) + cc, cs = ci*ck, ci*sk + sc, ss = si*ck, si*sk + + M = numpy.identity(4) + if repetition: + M[i, i] = cj + M[i, j] = sj*si + M[i, k] = sj*ci + M[j, i] = sj*sk + M[j, j] = -cj*ss+cc + M[j, k] = -cj*cs-sc + M[k, i] = -sj*ck + M[k, j] = cj*sc+cs + M[k, k] = cj*cc-ss + else: + M[i, i] = cj*ck + M[i, j] = sj*sc-cs + M[i, k] = sj*cc+ss + M[j, i] = cj*sk + M[j, j] = sj*ss+cc + M[j, k] = sj*cs-sc + M[k, i] = -sj + M[k, j] = cj*si + M[k, k] = cj*ci + return M + + +def euler_from_matrix(matrix, axes='sxyz'): + """Return Euler angles from rotation matrix for specified axis sequence. + + axes : One of 24 axis sequences as string or encoded tuple + + Note that many Euler angle triplets can describe one matrix. + + >>> R0 = euler_matrix(1, 2, 3, 'syxz') + >>> al, be, ga = euler_from_matrix(R0, 'syxz') + >>> R1 = euler_matrix(al, be, ga, 'syxz') + >>> numpy.allclose(R0, R1) + True + >>> angles = (4*math.pi) * (numpy.random.random(3) - 0.5) + >>> for axes in _AXES2TUPLE.keys(): + ... R0 = euler_matrix(axes=axes, *angles) + ... R1 = euler_matrix(axes=axes, *euler_from_matrix(R0, axes)) + ... if not numpy.allclose(R0, R1): print(axes, "failed") + + """ + try: + firstaxis, parity, repetition, frame = _AXES2TUPLE[axes.lower()] + except (AttributeError, KeyError): + _TUPLE2AXES[axes] # validation + firstaxis, parity, repetition, frame = axes + + i = firstaxis + j = _NEXT_AXIS[i+parity] + k = _NEXT_AXIS[i-parity+1] + + M = numpy.array(matrix, dtype=numpy.float64, copy=False)[:3, :3] + if repetition: + sy = math.sqrt(M[i, j]*M[i, j] + M[i, k]*M[i, k]) + if sy > _EPS: + ax = math.atan2( M[i, j], M[i, k]) + ay = math.atan2( sy, M[i, i]) + az = math.atan2( M[j, i], -M[k, i]) + else: + ax = math.atan2(-M[j, k], M[j, j]) + ay = math.atan2( sy, M[i, i]) + az = 0.0 + else: + cy = math.sqrt(M[i, i]*M[i, i] + M[j, i]*M[j, i]) + if cy > _EPS: + ax = math.atan2( M[k, j], M[k, k]) + ay = math.atan2(-M[k, i], cy) + az = math.atan2( M[j, i], M[i, i]) + else: + ax = math.atan2(-M[j, k], M[j, j]) + ay = math.atan2(-M[k, i], cy) + az = 0.0 + + if parity: + ax, ay, az = -ax, -ay, -az + if frame: + ax, az = az, ax + return ax, ay, az + + +def euler_from_quaternion(quaternion, axes='sxyz'): + """Return Euler angles from quaternion for specified axis sequence. + + >>> angles = euler_from_quaternion([0.99810947, 0.06146124, 0, 0]) + >>> numpy.allclose(angles, [0.123, 0, 0]) + True + + """ + return euler_from_matrix(quaternion_matrix(quaternion), axes) + + +def quaternion_from_euler(ai, aj, ak, axes='sxyz'): + """Return quaternion from Euler angles and axis sequence. + + ai, aj, ak : Euler's roll, pitch and yaw angles + axes : One of 24 axis sequences as string or encoded tuple + + >>> q = quaternion_from_euler(1, 2, 3, 'ryxz') + >>> numpy.allclose(q, [0.435953, 0.310622, -0.718287, 0.444435]) + True + + """ + try: + firstaxis, parity, repetition, frame = _AXES2TUPLE[axes.lower()] + except (AttributeError, KeyError): + _TUPLE2AXES[axes] # validation + firstaxis, parity, repetition, frame = axes + + i = firstaxis + 1 + j = _NEXT_AXIS[i+parity-1] + 1 + k = _NEXT_AXIS[i-parity] + 1 + + if frame: + ai, ak = ak, ai + if parity: + aj = -aj + + ai /= 2.0 + aj /= 2.0 + ak /= 2.0 + ci = math.cos(ai) + si = math.sin(ai) + cj = math.cos(aj) + sj = math.sin(aj) + ck = math.cos(ak) + sk = math.sin(ak) + cc = ci*ck + cs = ci*sk + sc = si*ck + ss = si*sk + + q = numpy.empty((4, )) + if repetition: + q[0] = cj*(cc - ss) + q[i] = cj*(cs + sc) + q[j] = sj*(cc + ss) + q[k] = sj*(cs - sc) + else: + q[0] = cj*cc + sj*ss + q[i] = cj*sc - sj*cs + q[j] = cj*ss + sj*cc + q[k] = cj*cs - sj*sc + if parity: + q[j] *= -1.0 + + return q + + +def quaternion_about_axis(angle, axis): + """Return quaternion for rotation about axis. + + >>> q = quaternion_about_axis(0.123, [1, 0, 0]) + >>> numpy.allclose(q, [0.99810947, 0.06146124, 0, 0]) + True + + """ + q = numpy.array([0.0, axis[0], axis[1], axis[2]]) + qlen = vector_norm(q) + if qlen > _EPS: + q *= math.sin(angle/2.0) / qlen + q[0] = math.cos(angle/2.0) + return q + + +def quaternion_matrix(quaternion): + """Return homogeneous rotation matrix from quaternion. + + >>> M = quaternion_matrix([0.99810947, 0.06146124, 0, 0]) + >>> numpy.allclose(M, rotation_matrix(0.123, [1, 0, 0])) + True + >>> M = quaternion_matrix([1, 0, 0, 0]) + >>> numpy.allclose(M, numpy.identity(4)) + True + >>> M = quaternion_matrix([0, 1, 0, 0]) + >>> numpy.allclose(M, numpy.diag([1, -1, -1, 1])) + True + + """ + q = numpy.array(quaternion, dtype=numpy.float64, copy=True) + n = numpy.dot(q, q) + if n < _EPS: + return numpy.identity(4) + q *= math.sqrt(2.0 / n) + q = numpy.outer(q, q) + return numpy.array([ + [1.0-q[2, 2]-q[3, 3], q[1, 2]-q[3, 0], q[1, 3]+q[2, 0], 0.0], + [ q[1, 2]+q[3, 0], 1.0-q[1, 1]-q[3, 3], q[2, 3]-q[1, 0], 0.0], + [ q[1, 3]-q[2, 0], q[2, 3]+q[1, 0], 1.0-q[1, 1]-q[2, 2], 0.0], + [ 0.0, 0.0, 0.0, 1.0]]) + + +def quaternion_from_matrix(matrix, isprecise=False): + """Return quaternion from rotation matrix. + + If isprecise is True, the input matrix is assumed to be a precise rotation + matrix and a faster algorithm is used. + + >>> q = quaternion_from_matrix(numpy.identity(4), True) + >>> numpy.allclose(q, [1, 0, 0, 0]) + True + >>> q = quaternion_from_matrix(numpy.diag([1, -1, -1, 1])) + >>> numpy.allclose(q, [0, 1, 0, 0]) or numpy.allclose(q, [0, -1, 0, 0]) + True + >>> R = rotation_matrix(0.123, (1, 2, 3)) + >>> q = quaternion_from_matrix(R, True) + >>> numpy.allclose(q, [0.9981095, 0.0164262, 0.0328524, 0.0492786]) + True + >>> R = [[-0.545, 0.797, 0.260, 0], [0.733, 0.603, -0.313, 0], + ... [-0.407, 0.021, -0.913, 0], [0, 0, 0, 1]] + >>> q = quaternion_from_matrix(R) + >>> numpy.allclose(q, [0.19069, 0.43736, 0.87485, -0.083611]) + True + >>> R = [[0.395, 0.362, 0.843, 0], [-0.626, 0.796, -0.056, 0], + ... [-0.677, -0.498, 0.529, 0], [0, 0, 0, 1]] + >>> q = quaternion_from_matrix(R) + >>> numpy.allclose(q, [0.82336615, -0.13610694, 0.46344705, -0.29792603]) + True + >>> R = random_rotation_matrix() + >>> q = quaternion_from_matrix(R) + >>> is_same_transform(R, quaternion_matrix(q)) + True + + """ + M = numpy.array(matrix, dtype=numpy.float64, copy=False)[:4, :4] + if isprecise: + q = numpy.empty((4, )) + t = numpy.trace(M) + if t > M[3, 3]: + q[0] = t + q[3] = M[1, 0] - M[0, 1] + q[2] = M[0, 2] - M[2, 0] + q[1] = M[2, 1] - M[1, 2] + else: + i, j, k = 1, 2, 3 + if M[1, 1] > M[0, 0]: + i, j, k = 2, 3, 1 + if M[2, 2] > M[i, i]: + i, j, k = 3, 1, 2 + t = M[i, i] - (M[j, j] + M[k, k]) + M[3, 3] + q[i] = t + q[j] = M[i, j] + M[j, i] + q[k] = M[k, i] + M[i, k] + q[3] = M[k, j] - M[j, k] + q *= 0.5 / math.sqrt(t * M[3, 3]) + else: + m00 = M[0, 0] + m01 = M[0, 1] + m02 = M[0, 2] + m10 = M[1, 0] + m11 = M[1, 1] + m12 = M[1, 2] + m20 = M[2, 0] + m21 = M[2, 1] + m22 = M[2, 2] + # symmetric matrix K + K = numpy.array([[m00-m11-m22, 0.0, 0.0, 0.0], + [m01+m10, m11-m00-m22, 0.0, 0.0], + [m02+m20, m12+m21, m22-m00-m11, 0.0], + [m21-m12, m02-m20, m10-m01, m00+m11+m22]]) + K /= 3.0 + # quaternion is eigenvector of K that corresponds to largest eigenvalue + w, V = numpy.linalg.eigh(K) + q = V[[3, 0, 1, 2], numpy.argmax(w)] + if q[0] < 0.0: + numpy.negative(q, q) + return q + + +def quaternion_multiply(quaternion1, quaternion0): + """Return multiplication of two quaternions. + + >>> q = quaternion_multiply([4, 1, -2, 3], [8, -5, 6, 7]) + >>> numpy.allclose(q, [28, -44, -14, 48]) + True + + """ + w0, x0, y0, z0 = quaternion0 + w1, x1, y1, z1 = quaternion1 + return numpy.array([-x1*x0 - y1*y0 - z1*z0 + w1*w0, + x1*w0 + y1*z0 - z1*y0 + w1*x0, + -x1*z0 + y1*w0 + z1*x0 + w1*y0, + x1*y0 - y1*x0 + z1*w0 + w1*z0], dtype=numpy.float64) + + +def quaternion_conjugate(quaternion): + """Return conjugate of quaternion. + + >>> q0 = random_quaternion() + >>> q1 = quaternion_conjugate(q0) + >>> q1[0] == q0[0] and all(q1[1:] == -q0[1:]) + True + + """ + q = numpy.array(quaternion, dtype=numpy.float64, copy=True) + numpy.negative(q[1:], q[1:]) + return q + + +def quaternion_inverse(quaternion): + """Return inverse of quaternion. + + >>> q0 = random_quaternion() + >>> q1 = quaternion_inverse(q0) + >>> numpy.allclose(quaternion_multiply(q0, q1), [1, 0, 0, 0]) + True + + """ + q = numpy.array(quaternion, dtype=numpy.float64, copy=True) + numpy.negative(q[1:], q[1:]) + return q / numpy.dot(q, q) + + +def quaternion_real(quaternion): + """Return real part of quaternion. + + >>> quaternion_real([3, 0, 1, 2]) + 3.0 + + """ + return float(quaternion[0]) + + +def quaternion_imag(quaternion): + """Return imaginary part of quaternion. + + >>> quaternion_imag([3, 0, 1, 2]) + array([ 0., 1., 2.]) + + """ + return numpy.array(quaternion[1:4], dtype=numpy.float64, copy=True) + + +def quaternion_slerp(quat0, quat1, fraction, spin=0, shortestpath=True): + """Return spherical linear interpolation between two quaternions. + + >>> q0 = random_quaternion() + >>> q1 = random_quaternion() + >>> q = quaternion_slerp(q0, q1, 0) + >>> numpy.allclose(q, q0) + True + >>> q = quaternion_slerp(q0, q1, 1, 1) + >>> numpy.allclose(q, q1) + True + >>> q = quaternion_slerp(q0, q1, 0.5) + >>> angle = math.acos(numpy.dot(q0, q)) + >>> numpy.allclose(2, math.acos(numpy.dot(q0, q1)) / angle) or \ + numpy.allclose(2, math.acos(-numpy.dot(q0, q1)) / angle) + True + + """ + q0 = unit_vector(quat0[:4]) + q1 = unit_vector(quat1[:4]) + if fraction == 0.0: + return q0 + elif fraction == 1.0: + return q1 + d = numpy.dot(q0, q1) + if abs(abs(d) - 1.0) < _EPS: + return q0 + if shortestpath and d < 0.0: + # invert rotation + d = -d + numpy.negative(q1, q1) + angle = math.acos(d) + spin * math.pi + if abs(angle) < _EPS: + return q0 + isin = 1.0 / math.sin(angle) + q0 *= math.sin((1.0 - fraction) * angle) * isin + q1 *= math.sin(fraction * angle) * isin + q0 += q1 + return q0 + + +def random_quaternion(rand=None): + """Return uniform random unit quaternion. + + rand: array like or None + Three independent random variables that are uniformly distributed + between 0 and 1. + + >>> q = random_quaternion() + >>> numpy.allclose(1, vector_norm(q)) + True + >>> q = random_quaternion(numpy.random.random(3)) + >>> len(q.shape), q.shape[0]==4 + (1, True) + + """ + if rand is None: + rand = numpy.random.rand(3) + else: + assert len(rand) == 3 + r1 = numpy.sqrt(1.0 - rand[0]) + r2 = numpy.sqrt(rand[0]) + pi2 = math.pi * 2.0 + t1 = pi2 * rand[1] + t2 = pi2 * rand[2] + return numpy.array([numpy.cos(t2)*r2, numpy.sin(t1)*r1, + numpy.cos(t1)*r1, numpy.sin(t2)*r2]) + + +def random_rotation_matrix(rand=None): + """Return uniform random rotation matrix. + + rand: array like + Three independent random variables that are uniformly distributed + between 0 and 1 for each returned quaternion. + + >>> R = random_rotation_matrix() + >>> numpy.allclose(numpy.dot(R.T, R), numpy.identity(4)) + True + + """ + return quaternion_matrix(random_quaternion(rand)) + + +class Arcball(object): + """Virtual Trackball Control. + + >>> ball = Arcball() + >>> ball = Arcball(initial=numpy.identity(4)) + >>> ball.place([320, 320], 320) + >>> ball.down([500, 250]) + >>> ball.drag([475, 275]) + >>> R = ball.matrix() + >>> numpy.allclose(numpy.sum(R), 3.90583455) + True + >>> ball = Arcball(initial=[1, 0, 0, 0]) + >>> ball.place([320, 320], 320) + >>> ball.setaxes([1, 1, 0], [-1, 1, 0]) + >>> ball.setconstrain(True) + >>> ball.down([400, 200]) + >>> ball.drag([200, 400]) + >>> R = ball.matrix() + >>> numpy.allclose(numpy.sum(R), 0.2055924) + True + >>> ball.next() + + """ + def __init__(self, initial=None): + """Initialize virtual trackball control. + + initial : quaternion or rotation matrix + + """ + self._axis = None + self._axes = None + self._radius = 1.0 + self._center = [0.0, 0.0] + self._vdown = numpy.array([0.0, 0.0, 1.0]) + self._constrain = False + if initial is None: + self._qdown = numpy.array([1.0, 0.0, 0.0, 0.0]) + else: + initial = numpy.array(initial, dtype=numpy.float64) + if initial.shape == (4, 4): + self._qdown = quaternion_from_matrix(initial) + elif initial.shape == (4, ): + initial /= vector_norm(initial) + self._qdown = initial + else: + raise ValueError("initial not a quaternion or matrix") + self._qnow = self._qpre = self._qdown + + def place(self, center, radius): + """Place Arcball, e.g. when window size changes. + + center : sequence[2] + Window coordinates of trackball center. + radius : float + Radius of trackball in window coordinates. + + """ + self._radius = float(radius) + self._center[0] = center[0] + self._center[1] = center[1] + + def setaxes(self, *axes): + """Set axes to constrain rotations.""" + if axes is None: + self._axes = None + else: + self._axes = [unit_vector(axis) for axis in axes] + + def setconstrain(self, constrain): + """Set state of constrain to axis mode.""" + self._constrain = bool(constrain) + + def getconstrain(self): + """Return state of constrain to axis mode.""" + return self._constrain + + def down(self, point): + """Set initial cursor window coordinates and pick constrain-axis.""" + self._vdown = arcball_map_to_sphere(point, self._center, self._radius) + self._qdown = self._qpre = self._qnow + if self._constrain and self._axes is not None: + self._axis = arcball_nearest_axis(self._vdown, self._axes) + self._vdown = arcball_constrain_to_axis(self._vdown, self._axis) + else: + self._axis = None + + def drag(self, point): + """Update current cursor window coordinates.""" + vnow = arcball_map_to_sphere(point, self._center, self._radius) + if self._axis is not None: + vnow = arcball_constrain_to_axis(vnow, self._axis) + self._qpre = self._qnow + t = numpy.cross(self._vdown, vnow) + if numpy.dot(t, t) < _EPS: + self._qnow = self._qdown + else: + q = [numpy.dot(self._vdown, vnow), t[0], t[1], t[2]] + self._qnow = quaternion_multiply(q, self._qdown) + + def next(self, acceleration=0.0): + """Continue rotation in direction of last drag.""" + q = quaternion_slerp(self._qpre, self._qnow, 2.0+acceleration, False) + self._qpre, self._qnow = self._qnow, q + + def matrix(self): + """Return homogeneous rotation matrix.""" + return quaternion_matrix(self._qnow) + + +def arcball_map_to_sphere(point, center, radius): + """Return unit sphere coordinates from window coordinates.""" + v0 = (point[0] - center[0]) / radius + v1 = (center[1] - point[1]) / radius + n = v0*v0 + v1*v1 + if n > 1.0: + # position outside of sphere + n = math.sqrt(n) + return numpy.array([v0/n, v1/n, 0.0]) + else: + return numpy.array([v0, v1, math.sqrt(1.0 - n)]) + + +def arcball_constrain_to_axis(point, axis): + """Return sphere point perpendicular to axis.""" + v = numpy.array(point, dtype=numpy.float64, copy=True) + a = numpy.array(axis, dtype=numpy.float64, copy=True) + v -= a * numpy.dot(a, v) # on plane + n = vector_norm(v) + if n > _EPS: + if v[2] < 0.0: + numpy.negative(v, v) + v /= n + return v + if a[2] == 1.0: + return numpy.array([1.0, 0.0, 0.0]) + return unit_vector([-a[1], a[0], 0.0]) + + +def arcball_nearest_axis(point, axes): + """Return axis, which arc is nearest to point.""" + point = numpy.array(point, dtype=numpy.float64, copy=False) + nearest = None + mx = -1.0 + for axis in axes: + t = numpy.dot(arcball_constrain_to_axis(point, axis), point) + if t > mx: + nearest = axis + mx = t + return nearest + + +# epsilon for testing whether a number is close to zero +_EPS = numpy.finfo(float).eps * 4.0 + +# axis sequences for Euler angles +_NEXT_AXIS = [1, 2, 0, 1] + +# map axes strings to/from tuples of inner axis, parity, repetition, frame +_AXES2TUPLE = { + 'sxyz': (0, 0, 0, 0), 'sxyx': (0, 0, 1, 0), 'sxzy': (0, 1, 0, 0), + 'sxzx': (0, 1, 1, 0), 'syzx': (1, 0, 0, 0), 'syzy': (1, 0, 1, 0), + 'syxz': (1, 1, 0, 0), 'syxy': (1, 1, 1, 0), 'szxy': (2, 0, 0, 0), + 'szxz': (2, 0, 1, 0), 'szyx': (2, 1, 0, 0), 'szyz': (2, 1, 1, 0), + 'rzyx': (0, 0, 0, 1), 'rxyx': (0, 0, 1, 1), 'ryzx': (0, 1, 0, 1), + 'rxzx': (0, 1, 1, 1), 'rxzy': (1, 0, 0, 1), 'ryzy': (1, 0, 1, 1), + 'rzxy': (1, 1, 0, 1), 'ryxy': (1, 1, 1, 1), 'ryxz': (2, 0, 0, 1), + 'rzxz': (2, 0, 1, 1), 'rxyz': (2, 1, 0, 1), 'rzyz': (2, 1, 1, 1)} + +_TUPLE2AXES = dict((v, k) for k, v in _AXES2TUPLE.items()) + + +def vector_norm(data, axis=None, out=None): + """Return length, i.e. Euclidean norm, of ndarray along axis. + + >>> v = numpy.random.random(3) + >>> n = vector_norm(v) + >>> numpy.allclose(n, numpy.linalg.norm(v)) + True + >>> v = numpy.random.rand(6, 5, 3) + >>> n = vector_norm(v, axis=-1) + >>> numpy.allclose(n, numpy.sqrt(numpy.sum(v*v, axis=2))) + True + >>> n = vector_norm(v, axis=1) + >>> numpy.allclose(n, numpy.sqrt(numpy.sum(v*v, axis=1))) + True + >>> v = numpy.random.rand(5, 4, 3) + >>> n = numpy.empty((5, 3)) + >>> vector_norm(v, axis=1, out=n) + >>> numpy.allclose(n, numpy.sqrt(numpy.sum(v*v, axis=1))) + True + >>> vector_norm([]) + 0.0 + >>> vector_norm([1]) + 1.0 + + """ + data = numpy.array(data, dtype=numpy.float64, copy=True) + if out is None: + if data.ndim == 1: + return math.sqrt(numpy.dot(data, data)) + data *= data + out = numpy.atleast_1d(numpy.sum(data, axis=axis)) + numpy.sqrt(out, out) + return out + else: + data *= data + numpy.sum(data, axis=axis, out=out) + numpy.sqrt(out, out) + + +def unit_vector(data, axis=None, out=None): + """Return ndarray normalized by length, i.e. Euclidean norm, along axis. + + >>> v0 = numpy.random.random(3) + >>> v1 = unit_vector(v0) + >>> numpy.allclose(v1, v0 / numpy.linalg.norm(v0)) + True + >>> v0 = numpy.random.rand(5, 4, 3) + >>> v1 = unit_vector(v0, axis=-1) + >>> v2 = v0 / numpy.expand_dims(numpy.sqrt(numpy.sum(v0*v0, axis=2)), 2) + >>> numpy.allclose(v1, v2) + True + >>> v1 = unit_vector(v0, axis=1) + >>> v2 = v0 / numpy.expand_dims(numpy.sqrt(numpy.sum(v0*v0, axis=1)), 1) + >>> numpy.allclose(v1, v2) + True + >>> v1 = numpy.empty((5, 4, 3)) + >>> unit_vector(v0, axis=1, out=v1) + >>> numpy.allclose(v1, v2) + True + >>> list(unit_vector([])) + [] + >>> list(unit_vector([1])) + [1.0] + + """ + if out is None: + data = numpy.array(data, dtype=numpy.float64, copy=True) + if data.ndim == 1: + data /= math.sqrt(numpy.dot(data, data)) + return data + else: + if out is not data: + out[:] = numpy.array(data, copy=False) + data = out + length = numpy.atleast_1d(numpy.sum(data*data, axis)) + numpy.sqrt(length, length) + if axis is not None: + length = numpy.expand_dims(length, axis) + data /= length + if out is None: + return data + + +def random_vector(size): + """Return array of random doubles in the half-open interval [0.0, 1.0). + + >>> v = random_vector(10000) + >>> numpy.all(v >= 0) and numpy.all(v < 1) + True + >>> v0 = random_vector(10) + >>> v1 = random_vector(10) + >>> numpy.any(v0 == v1) + False + + """ + return numpy.random.random(size) + + +def vector_product(v0, v1, axis=0): + """Return vector perpendicular to vectors. + + >>> v = vector_product([2, 0, 0], [0, 3, 0]) + >>> numpy.allclose(v, [0, 0, 6]) + True + >>> v0 = [[2, 0, 0, 2], [0, 2, 0, 2], [0, 0, 2, 2]] + >>> v1 = [[3], [0], [0]] + >>> v = vector_product(v0, v1) + >>> numpy.allclose(v, [[0, 0, 0, 0], [0, 0, 6, 6], [0, -6, 0, -6]]) + True + >>> v0 = [[2, 0, 0], [2, 0, 0], [0, 2, 0], [2, 0, 0]] + >>> v1 = [[0, 3, 0], [0, 0, 3], [0, 0, 3], [3, 3, 3]] + >>> v = vector_product(v0, v1, axis=1) + >>> numpy.allclose(v, [[0, 0, 6], [0, -6, 0], [6, 0, 0], [0, -6, 6]]) + True + + """ + return numpy.cross(v0, v1, axis=axis) + + +def angle_between_vectors(v0, v1, directed=True, axis=0): + """Return angle between vectors. + + If directed is False, the input vectors are interpreted as undirected axes, + i.e. the maximum angle is pi/2. + + >>> a = angle_between_vectors([1, -2, 3], [-1, 2, -3]) + >>> numpy.allclose(a, math.pi) + True + >>> a = angle_between_vectors([1, -2, 3], [-1, 2, -3], directed=False) + >>> numpy.allclose(a, 0) + True + >>> v0 = [[2, 0, 0, 2], [0, 2, 0, 2], [0, 0, 2, 2]] + >>> v1 = [[3], [0], [0]] + >>> a = angle_between_vectors(v0, v1) + >>> numpy.allclose(a, [0, 1.5708, 1.5708, 0.95532]) + True + >>> v0 = [[2, 0, 0], [2, 0, 0], [0, 2, 0], [2, 0, 0]] + >>> v1 = [[0, 3, 0], [0, 0, 3], [0, 0, 3], [3, 3, 3]] + >>> a = angle_between_vectors(v0, v1, axis=1) + >>> numpy.allclose(a, [1.5708, 1.5708, 1.5708, 0.95532]) + True + + """ + v0 = numpy.array(v0, dtype=numpy.float64, copy=False) + v1 = numpy.array(v1, dtype=numpy.float64, copy=False) + dot = numpy.sum(v0 * v1, axis=axis) + dot /= vector_norm(v0, axis=axis) * vector_norm(v1, axis=axis) + return numpy.arccos(dot if directed else numpy.fabs(dot)) + + +def inverse_matrix(matrix): + """Return inverse of square transformation matrix. + + >>> M0 = random_rotation_matrix() + >>> M1 = inverse_matrix(M0.T) + >>> numpy.allclose(M1, numpy.linalg.inv(M0.T)) + True + >>> for size in range(1, 7): + ... M0 = numpy.random.rand(size, size) + ... M1 = inverse_matrix(M0) + ... if not numpy.allclose(M1, numpy.linalg.inv(M0)): print(size) + + """ + return numpy.linalg.inv(matrix) + + +def concatenate_matrices(*matrices): + """Return concatenation of series of transformation matrices. + + >>> M = numpy.random.rand(16).reshape((4, 4)) - 0.5 + >>> numpy.allclose(M, concatenate_matrices(M)) + True + >>> numpy.allclose(numpy.dot(M, M.T), concatenate_matrices(M, M.T)) + True + + """ + M = numpy.identity(4) + for i in matrices: + M = numpy.dot(M, i) + return M + + +def is_same_transform(matrix0, matrix1): + """Return True if two matrices perform same transformation. + + >>> is_same_transform(numpy.identity(4), numpy.identity(4)) + True + >>> is_same_transform(numpy.identity(4), random_rotation_matrix()) + False + + """ + matrix0 = numpy.array(matrix0, dtype=numpy.float64, copy=True) + matrix0 /= matrix0[3, 3] + matrix1 = numpy.array(matrix1, dtype=numpy.float64, copy=True) + matrix1 /= matrix1[3, 3] + return numpy.allclose(matrix0, matrix1) + + +def _import_module(name, package=None, warn=True, prefix='_py_', ignore='_'): + """Try import all public attributes from module into global namespace. + + Existing attributes with name clashes are renamed with prefix. + Attributes starting with underscore are ignored by default. + + Return True on successful import. + + """ + import warnings + from importlib import import_module + try: + if not package: + module = import_module(name) + else: + module = import_module('.' + name, package=package) + except ImportError: + if warn: + warnings.warn("failed to import module %s" % name) + else: + for attr in dir(module): + if ignore and attr.startswith(ignore): + continue + if prefix: + if attr in globals(): + globals()[prefix + attr] = globals()[attr] + elif warn: + warnings.warn("no Python implementation of " + attr) + globals()[attr] = getattr(module, attr) + return True + + +#_import_module('_transformations') + +if __name__ == "__main__": + import doctest + import random # used in doctests + numpy.set_printoptions(suppress=True, precision=5) + doctest.testmod() + diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp index aa1dd9e137e..8140e12631f 100644 --- a/src/caffe/solver.cpp +++ b/src/caffe/solver.cpp @@ -189,10 +189,10 @@ void Solver::Solve(const char* resume_file) { Snapshot(); } - if (param_.test_interval() && iter_ % param_.test_interval() == 0 - && (iter_ > 0 || param_.test_initialization())) { - TestAll(); - } + //if (param_.test_interval() && iter_ % param_.test_interval() == 0 + // && (iter_ > 0 || param_.test_initialization())) { + // TestAll(); + //} const bool display = param_.display() && iter_ % param_.display() == 0; net_->set_debug_info(display && param_.debug_info()); @@ -279,12 +279,19 @@ void Solver::Solve(const char* resume_file) { float x_adj = (qx*grid_dim + grid_dim / 2) / scaling; float y_adj = (qy*grid_dim + grid_dim / 2) / scaling; + //std::cout<<*(bb_start+(((n*64+z)*quad_height+qy)*quad_width+qx))<<" "; + //std::cout<<*(bb_start+(((n*64+z+16)*quad_height+qy)*quad_width+qx))<<" "; + //std::cout<<*(bb_start+(((n*64+z+32)*quad_height+qy)*quad_width+qx))<<" "; + //std::cout<<*(bb_start+(((n*64+z+48)*quad_height+qy)*quad_width+qx))<::Solve(const char* resume_file) { net_->Forward(bottom_vec, &loss); LOG(INFO) << "Iteration " << iter_ << ", loss = " << loss; } - if (param_.test_interval() && iter_ % param_.test_interval() == 0) { - TestAll(); - } + //if (param_.test_interval() && iter_ % param_.test_interval() == 0) { + // TestAll(); + //} LOG(INFO) << "Optimization Done."; } From 8dd759560b00a15e2383e15adcb486031b65da54 Mon Sep 17 00:00:00 2001 From: Tao Wang Date: Mon, 1 Dec 2014 11:16:21 -0800 Subject: [PATCH 6/6] adding 'lane' foler under models. --- models/brody/deploy.prototxt | 238 ++++++++------------------ models/brody/solver.prototxt | 8 +- models/brody/train_val_brody.prototxt | 132 ++++---------- 3 files changed, 108 insertions(+), 270 deletions(-) diff --git a/models/brody/deploy.prototxt b/models/brody/deploy.prototxt index 85e72f10774..14634682829 100644 --- a/models/brody/deploy.prototxt +++ b/models/brody/deploy.prototxt @@ -1,15 +1,13 @@ -name: "BrodyNet" +# This is not brody's net. +name: "AlexNet" input: "data" input_dim: 10 input_dim: 3 -input_dim: 480 -input_dim: 640 - +input_dim: 227 +input_dim: 227 layers { name: "conv1" type: CONVOLUTION - bottom: "data" - top: "conv1" blobs_lr: 1 blobs_lr: 2 weight_decay: 1 @@ -18,15 +16,9 @@ layers { num_output: 96 kernel_size: 11 stride: 4 - weight_filler { - type: "gaussian" - std: 0.01 - } - bias_filler { - type: "constant" - value: 0.1 - } } + bottom: "data" + top: "conv1" } layers { name: "relu1" @@ -34,33 +26,31 @@ layers { bottom: "conv1" top: "conv1" } -layers { - name: "pool1" - type: POOLING - bottom: "conv1" - top: "pool1" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 2 - } -} layers { name: "norm1" type: LRN - bottom: "pool1" - top: "norm1" lrn_param { local_size: 5 alpha: 0.0001 beta: 0.75 } + bottom: "conv1" + top: "norm1" +} +layers { + name: "pool1" + type: POOLING + pooling_param { + pool: MAX + kernel_size: 3 + stride: 2 + } + bottom: "norm1" + top: "pool1" } layers { name: "conv2" type: CONVOLUTION - bottom: "norm1" - top: "conv2" blobs_lr: 1 blobs_lr: 2 weight_decay: 1 @@ -70,15 +60,9 @@ layers { pad: 2 kernel_size: 5 group: 2 - weight_filler { - type: "gaussian" - std: 0.01 - } - bias_filler { - type: "constant" - value: 0.1 - } } + bottom: "pool1" + top: "conv2" } layers { name: "relu2" @@ -86,33 +70,31 @@ layers { bottom: "conv2" top: "conv2" } -layers { - name: "pool2" - type: POOLING - bottom: "conv2" - top: "pool2" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 2 - } -} layers { name: "norm2" type: LRN - bottom: "pool2" - top: "norm2" lrn_param { local_size: 5 alpha: 0.0001 beta: 0.75 } + bottom: "conv2" + top: "norm2" +} +layers { + name: "pool2" + type: POOLING + pooling_param { + pool: MAX + kernel_size: 3 + stride: 2 + } + bottom: "norm2" + top: "pool2" } layers { name: "conv3" type: CONVOLUTION - bottom: "norm2" - top: "conv3" blobs_lr: 1 blobs_lr: 2 weight_decay: 1 @@ -121,15 +103,9 @@ layers { num_output: 384 pad: 1 kernel_size: 3 - weight_filler { - type: "gaussian" - std: 0.01 - } - bias_filler { - type: "constant" - value: 0.1 - } } + bottom: "pool2" + top: "conv3" } layers { name: "relu3" @@ -140,8 +116,6 @@ layers { layers { name: "conv4" type: CONVOLUTION - bottom: "conv3" - top: "conv4" blobs_lr: 1 blobs_lr: 2 weight_decay: 1 @@ -151,15 +125,9 @@ layers { pad: 1 kernel_size: 3 group: 2 - weight_filler { - type: "gaussian" - std: 0.01 - } - bias_filler { - type: "constant" - value: 0.1 - } } + bottom: "conv3" + top: "conv4" } layers { name: "relu4" @@ -170,8 +138,6 @@ layers { layers { name: "conv5" type: CONVOLUTION - bottom: "conv4" - top: "conv5" blobs_lr: 1 blobs_lr: 2 weight_decay: 1 @@ -181,15 +147,9 @@ layers { pad: 1 kernel_size: 3 group: 2 - weight_filler { - type: "gaussian" - std: 0.01 - } - bias_filler { - type: "constant" - value: 0.1 - } } + bottom: "conv4" + top: "conv5" } layers { name: "relu5" @@ -200,140 +160,86 @@ layers { layers { name: "pool5" type: POOLING - bottom: "conv5" - top: "pool5" pooling_param { pool: MAX kernel_size: 3 stride: 2 } + bottom: "conv5" + top: "pool5" } layers { - name: "fc6-conv" - type: CONVOLUTION - bottom: "pool5" - top: "fc6-conv" + name: "fc6" + type: INNER_PRODUCT blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0 - convolution_param { + inner_product_param { num_output: 4096 - kernel_size: 6 - pad: 3 - weight_filler { - type: "gaussian" - std: 0.01 - } - bias_filler { - type: "constant" - value: 1 - } } + bottom: "pool5" + top: "fc6" } layers { name: "relu6" type: RELU - bottom: "fc6-conv" - top: "fc6-conv" + bottom: "fc6" + top: "fc6" } layers { name: "drop6" type: DROPOUT - bottom: "fc6-conv" - top: "fc6-conv" dropout_param { - dropout_ratio: 0.0 + dropout_ratio: 0.5 } + bottom: "fc6" + top: "fc6" } layers { - name: "fc7-conv" - type: CONVOLUTION - bottom: "fc6-conv" - top: "fc7-conv" + name: "fc7" + type: INNER_PRODUCT blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0 - convolution_param { + inner_product_param { num_output: 4096 - kernel_size: 1 - weight_filler { - type: "gaussian" - std: 0.01 - } - bias_filler { - type: "constant" - value: 1 - } } + bottom: "fc6" + top: "fc7" } layers { name: "relu7" type: RELU - bottom: "fc7-conv" - top: "fc7-conv" + bottom: "fc7" + top: "fc7" } layers { name: "drop7" type: DROPOUT - bottom: "fc7-conv" - top: "fc7-conv" dropout_param { - dropout_ratio: 0.0 + dropout_ratio: 0.5 } + bottom: "fc7" + top: "fc7" } - layers { - name: "bb-output" - type: CONVOLUTION - bottom: "fc7-conv" - top: "bb-output" + name: "fc8" + type: INNER_PRODUCT blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0 - convolution_param { - num_output: 64 - kernel_size: 1 - weight_filler { - type: "gaussian" - std: 0.01 - } - bias_filler { - type: "constant" - value: 1.0 - } - } -} - -layers { - name: "pixel-conv" - type: CONVOLUTION - bottom: "fc7-conv" - top: "pixel-conv" - blobs_lr: 1 - blobs_lr: 2 - weight_decay: 1 - weight_decay: 0 - convolution_param { - num_output: 16 - kernel_size: 1 - weight_filler { - type: "gaussian" - std: 0.005 - } - bias_filler { - type: "constant" - value: 1.0 - } + inner_product_param { + num_output: 1000 } + bottom: "fc7" + top: "fc8" } - -# Pixel level logistic prediction. layers { - name: "pixel-prob" - type: SIGMOID - bottom: "pixel-conv" - top: "pixel-prob" + name: "prob" + type: SOFTMAX + bottom: "fc8" + top: "prob" } diff --git a/models/brody/solver.prototxt b/models/brody/solver.prototxt index c1501649dbc..25793c049b7 100644 --- a/models/brody/solver.prototxt +++ b/models/brody/solver.prototxt @@ -1,14 +1,14 @@ net: "models/brody/train_val_brody.prototxt" test_iter: 1000 test_interval: 1000 -base_lr: 0.0000001 +base_lr: 0.00000001 lr_policy: "step" gamma: 0.1 stepsize: 100000 -display: 10 +display: 20 max_iter: 1450000 -momentum: 0.9 +momentum: 0.2 weight_decay: 0.00005 -snapshot: 10000 +snapshot: 2000 snapshot_prefix: "models/brody/caffe_brody_train" solver_mode: GPU diff --git a/models/brody/train_val_brody.prototxt b/models/brody/train_val_brody.prototxt index b7acd942f86..e98103aa5a7 100644 --- a/models/brody/train_val_brody.prototxt +++ b/models/brody/train_val_brody.prototxt @@ -3,11 +3,12 @@ name: "BrodyNet" # Training input. layers { name: "data" - type: VIDEO_DATA + type: DATA top: "data" - video_data_param { - source: "/scail/group/deeplearning/driving_data/twangcat/schedules/q50_multilane_planar_train_schedule1_batch20_2cam.txt" - batch_size: 20 + data_param { + source: "driving_img_train" + backend: LMDB + batch_size: 25 } transform_param { mean_file: "driving_img_mean.binaryproto" @@ -18,11 +19,12 @@ layers { # Bounding box label and pixel label. layers { name: "label" - type: MULTILANE_LABEL + type: DATA top: "label" - multilane_label_param { - source: "/scail/group/deeplearning/driving_data/twangcat/schedules/q50_multilane_planar_train_schedule1_batch20_2cam.txt" - batch_size: 20 + data_param { + source: "driving_label_train" + backend: LMDB + batch_size: 25 } include: { phase: TRAIN } } @@ -33,9 +35,9 @@ layers { type: DATA top: "data" data_param { - source: "/deep/group/driving_data/twangcat/lmdb/driving_img_test" + source: "driving_img_test" backend: LMDB - batch_size: 20 + batch_size: 25 } transform_param { mean_file: "driving_img_mean.binaryproto" @@ -49,9 +51,9 @@ layers { type: DATA top: "label" data_param { - source: "/deep/group/driving_data/twangcat/lmdb/driving_label_test" + source: "driving_label_test" backend: LMDB - batch_size: 20 + batch_size: 25 } include: { phase: TEST } } @@ -63,13 +65,9 @@ layers { bottom: "label" top: "pixel-label" top: "bb-label" - top: "height-label" - top: "norm-label" slice_param { slice_dim: 1 slice_point: 16 - slice_point: 80 - slice_point: 96 } } @@ -88,19 +86,6 @@ layers { } } -layers { - name: "height-block" - type: CONCAT - bottom: "height-label" - bottom: "height-label" - bottom: "height-label" - bottom: "height-label" - top: "height-block" - concat_param { - concat_dim: 1 - } -} - layers { name: "conv1" type: CONVOLUTION @@ -120,7 +105,7 @@ layers { } bias_filler { type: "constant" - value: 0.1 + value: 0 } } } @@ -165,14 +150,14 @@ layers { num_output: 256 pad: 2 kernel_size: 5 - group: 2 + group: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" - value: 0.1 + value: 1 } } } @@ -223,7 +208,7 @@ layers { } bias_filler { type: "constant" - value: 0.1 + value: 0 } } } @@ -246,14 +231,14 @@ layers { num_output: 384 pad: 1 kernel_size: 3 - group: 2 + group: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" - value: 0.1 + value: 1 } } } @@ -276,14 +261,14 @@ layers { num_output: 256 pad: 1 kernel_size: 3 - group: 2 + group: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" - value: 0.1 + value: 1 } } } @@ -319,7 +304,7 @@ layers { pad: 3 weight_filler { type: "gaussian" - std: 0.01 + std: 0.005 } bias_filler { type: "constant" @@ -339,7 +324,7 @@ layers { bottom: "fc6-conv" top: "fc6-conv" dropout_param { - dropout_ratio: 0.0 + dropout_ratio: 0.5 } } layers { @@ -356,7 +341,7 @@ layers { kernel_size: 1 weight_filler { type: "gaussian" - std: 0.01 + std: 0.005 } bias_filler { type: "constant" @@ -376,7 +361,7 @@ layers { bottom: "fc7-conv" top: "fc7-conv" dropout_param { - dropout_ratio: 0.0 + dropout_ratio: 0.5 } } @@ -385,16 +370,16 @@ layers { type: CONVOLUTION bottom: "fc7-conv" top: "bb-output" - blobs_lr: 100 - blobs_lr: 200 - weight_decay: 0.00001 + blobs_lr: 0.001 + blobs_lr: 2 + weight_decay: 1 weight_decay: 0 convolution_param { num_output: 64 kernel_size: 1 weight_filler { type: "gaussian" - std: 0.01 + std: 0.005 } bias_filler { type: "constant" @@ -456,63 +441,10 @@ layers { } # Squared loss on the bounding boxes. -#layers { -# name: "bb-loss" -# type: EUCLIDEAN_LOSS -# bottom: "bb-masked-output" -# bottom: "bb-label" -# top: "bb-loss" -# loss_weight: 0.01 -#} - -# L1 error loss layers { - name: "bb-diff" - type: ELTWISE + name: "bb-loss" + type: EUCLIDEAN_LOSS bottom: "bb-masked-output" bottom: "bb-label" - eltwise_param { - operation: SUM - coeff: 1.0 - coeff: -1.0 - } - top: "bb-diff" -} - -#layers { -# name: "bb-loss" -# type: ABSVAL -# bottom: "bb-diff" -# top: "bb-loss" -# # 1 / (20 * 15 * 64) -# loss_weight: 0.00000000001 -#} - -layers { - name: "bb-loss-pow2" - type: POWER - bottom: "bb-diff" - top: "bb-loss-pow2" - # 1 / (20 * 15 * 64) - power_param { - power: 2 - } -} - -layers { - name: "bb-loss-normalize" - type: ELTWISE - bottom: "bb-loss-pow2" - bottom: "height-block" - eltwise_param { - operation: PROD - } top: "bb-loss" - loss_weight: 0.001 } - -#layers { -# name: "bb-loss-silence" -# type: SILENCE -# bottom: "bb-loss" -#}