Udpate tracker

Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>
Merge with master
2020-02-27 18:04:52 +01:00 · 2020-02-27 17:41:26 +01:00 · 2020-02-27 17:00:07 +01:00 · 2020-01-15 19:11:14 +01:00 · 2020-01-15 19:07:05 +01:00 · 2020-01-15 19:01:23 +01:00
331 changed files with 8298 additions and 42606 deletions
@@ -1,4 +1,10 @@
 *~
+demo/demo/data/img_crop/
+demo/demo/data/img_disparity/
+demo/demo/data/map/
+demo/demo/data/masks_orient/
+demo/demo/data/pmat_new/
+demo/demo/data/masks_v2/
 build/
 .vscode/
 *.bin
@@ -8,17 +14,6 @@ build/
 *.h5
 *.tar.gz
 *.weights
+*.zip
 .idea/
 *.hdf5
-*.pk
-*.table
-cmake-build-release/
-demo/COCO_val2017
-demo/BDD100K_val
-/.vs
-cmake-build-minsizerel/*
-scripts/COCO_val2017/*
-scripts/COCO_val2017.zip
-scripts/all_labels.txt
-/cmake/cuda_script
-/cmake-build-debug/
@@ -0,0 +1,6 @@
+[submodule "tracker_CLASS"]
+	path = tracker_CLASS
+	url = https://github.com/mive93/tracker_CLASS.git
+[submodule "masa_protocol"]
+	path = masa_protocol
+	url = https://git.hipert.unimore.it/rcavicchioli/masa_protocol.git
@@ -1,69 +1,8 @@
-cmake_minimum_required(VERSION 3.15)
-project(tkDNN)
+cmake_minimum_required(VERSION 3.5)
+
+project (tkDNN)
 set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} ${CMAKE_CURRENT_SOURCE_DIR}/cmake)
-set(CMAKE_CXX_STANDARD 14)
-
-option(ENABLE_OPENCV_CUDA_CONTRIB "Enable OpenCV CUDA Contrib" OFF )
-
-if(NOT CMAKE_BUILD_TYPE)
-  set(CMAKE_BUILD_TYPE "Release" CACHE STRING "default build" FORCE)
-endif(NOT CMAKE_BUILD_TYPE)
-
-find_package(CUDA 9.0 REQUIRED)
-if (CUDA_FOUND)
-    set(OUTPUTFILE ${CMAKE_CURRENT_SOURCE_DIR}/cmake/cuda_script) # No suffix required
-    execute_process(COMMAND "rm ${OUTPUTFILE}")
-    set(CUDAFILE ${CMAKE_CURRENT_SOURCE_DIR}/cmake/getCudaArch.cu)
-    execute_process(COMMAND ${CUDA_NVCC_EXECUTABLE} -lcuda ${CUDAFILE} -o ${OUTPUTFILE})
-    execute_process(COMMAND ${OUTPUTFILE}
-            RESULT_VARIABLE CUDA_RETURN_CODE
-            OUTPUT_VARIABLE ARCH)
-
-    if(${CUDA_RETURN_CODE} EQUAL 0)
-        set(CUDA_SUCCESS "TRUE")
-    else()
-        set(CUDA_SUCCESS "FALSE")
-    endif()
-
-    if (${CUDA_SUCCESS})
-        message(STATUS "CUDA Architecture: ${ARCH}")
-        message(STATUS "CUDA Version: ${CUDA_VERSION_STRING}")
-        message(STATUS "CUDA Path: ${CUDA_TOOLKIT_ROOT_DIR}")
-        message(STATUS "CUDA Libararies: ${CUDA_LIBRARIES}")
-        message(STATUS "CUDA Performance Primitives: ${CUDA_npp_LIBRARY}")
-        set(CUDA_NVCC_FLAGS "${ARCH}")
-    else()
-        message(WARNING ${ARCH})
-    endif()
-endif()
-
-SET(CUDA_SEPARABLE_COMPILATION ON)
-
-if(UNIX)
-    if(CMAKE_BUILD_TYPE MATCHES Release)
-        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}  -fPIC -Wno-deprecated-declarations -Wno-unused-variable -O3")
-        set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} --maxrregcount=32)
-    endif()
-
-    if(CMAKE_BUILD_TYPE MATCHES Debug)
-        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}  -fPIC -Wno-deprecated-declarations -Wno-unused-variable -g3")
-        set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} --maxrregcount=32 -G -g)
-    endif()
-endif()
-
-if(WIN32)
-    if(CMAKE_BUILD_TYPE MATCHES Release)
-        set(CMAKE_CXX_FLAGS "/O2 /FS /EHsc /MD")
-        set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} --maxrregcount=32)
-    endif()
-
-    if(CMAKE_BUILD_TYPE MATCHES Debug)
-        set(CMAKE_CXX_FLAGS "/Od /FS /EHsc /MDd")
-        set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} --maxrregcount=32 -G -g)
-    endif()
-set(CMAKE_WINDOWS_EXPORT_ALL_SYMBOLS ON)
-endif(WIN32)
-
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -fPIC")
 include_directories(${CMAKE_CURRENT_SOURCE_DIR}/include/tkDNN)

 # project specific flags
@@ -71,77 +10,54 @@ if(DEBUG)
    add_definitions(-DDEBUG)
 endif()

-if(TKDNN_PATH)
-    message("SET TKDNN_PATH:" ${TKDNN_PATH})
-    add_definitions(-DTKDNN_PATH="${TKDNN_PATH}")
-else()
-    add_definitions(-DTKDNN_PATH="${CMAKE_CURRENT_SOURCE_DIR}")
-endif()
-

 #-------------------------------------------------------------------------------
 # CUDA
 #-------------------------------------------------------------------------------
-set(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS}" --compiler-options '-fPIC')
-
+find_package(CUDA 9.0 REQUIRED)
+SET(CUDA_SEPARABLE_COMPILATION ON)
+#set(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} -arch=sm_30 --compiler-options '-fPIC'")

 find_package(CUDNN REQUIRED)
-include_directories(${CUDNN_INCLUDE_DIR})
-
-find_package(yaml-cpp REQUIRED)
-

 # compile
-file(GLOB tkdnn_CUSRC "src/kernels/*.cu" "src/sorting.cu" "src/pluginsRT/*.cpp")
+file(GLOB tkdnn_CUSRC "src/kernels/*.cu")
 cuda_include_directories(${CMAKE_CURRENT_SOURCE_DIR}/include ${CUDA_INCLUDE_DIRS} ${CUDNN_INCLUDE_DIRS})
 cuda_add_library(kernels SHARED ${tkdnn_CUSRC})
-target_link_libraries(kernels ${CUDA_CUBLAS_LIBRARIES} ${CUDA_LIBRARIES} ${CUDNN_LIBRARIES} yaml-cpp)
-


 #-------------------------------------------------------------------------------
 # External Libraries
 #-------------------------------------------------------------------------------
-find_package(Eigen3 REQUIRED)
-message("Eigen DIR: " ${EIGEN3_INCLUDE_DIR})
-include_directories(${EIGEN3_INCLUDE_DIR})
-
 find_package(OpenCV REQUIRED)
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DOPENCV")
-if(ENABLE_OPENCV_CUDA_CONTRIB)
-    if (OpenCV_FOUND)
-        find_package(OpenCV COMPONENTS cudawarping cudaarithm)
-        if(OpenCV_cudawarping_FOUND AND OpenCV_cudaarithm_FOUND)
-            add_compile_definitions(OPENCV_CUDACONTRIB)
-            message("OpenCV Cuda Contrib modules found")
-        else()
-            message("OpenCV Cuda Contrib modules not found")
-            set(ENABLE_OPENCV_CUDA_CONTRIB OFF)
-        endif()
-    endif()
-endif()
-# if(OpenCV_CUDA_VERSION)
-#     add_compile_definitions(OPENCV_CUDACONTRIB)
-# endif()

-# gives problems in cross-compiling, probably malformed cmake config
+include_directories(/usr/include/gdal)

 #-------------------------------------------------------------------------------
 # Build Libraries
 #-------------------------------------------------------------------------------
 file(GLOB tkdnn_SRC "src/*.cpp")
-set(tkdnn_LIBS kernels ${CUDA_LIBRARIES} ${CUDA_CUBLAS_LIBRARIES} ${CUDNN_LIBRARIES} ${OpenCV_LIBS} yaml-cpp)
+set(tkdnn_LIBS kernels ${CUDA_LIBRARIES} ${CUDA_CUBLAS_LIBRARIES} ${CUDNN_LIBRARIES} ${OpenCV_LIBS})
+
+file(GLOB class_SRC "src/class_src/*.cpp")
+
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall -std=c++11 -O3")
+include_directories(${CMAKE_CURRENT_SOURCE_DIR}/include ${CUDA_INCLUDE_DIRS} ${OPENCV_INCLUDE_DIRS} ${NVINFER_INCLUDES} "~/repos/cereal/include" ${CMAKE_CURRENT_SOURCE_DIR}/tracker_CLASS/c++/src /usr/include/python2.7)
+
+set(class_LIBS ${OpenCV_LIBS} -lgdal yaml-cpp python2.7)

-set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
-include_directories(${CMAKE_CURRENT_SOURCE_DIR}/include ${CUDA_INCLUDE_DIRS} ${OPENCV_INCLUDE_DIRS} ${NVINFER_INCLUDES})
 add_library(tkDNN SHARED ${tkdnn_SRC})
-target_link_libraries(tkDNN ${tkdnn_LIBS} ${CUDA_CUBLAS_LIBRARIES})
+target_link_libraries(tkDNN ${tkdnn_LIBS})
+
+add_library(CLASS SHARED ${class_SRC})
+target_link_libraries(CLASS ${class_LIBS})
+

 #static 
 #add_library(tkDNN_static STATIC ${tkdnn_SRC})
 #target_link_libraries(tkDNN_static ${tkdnn_LIBS})

-# SMALL NETS
 add_executable(test_simple tests/simple/test_simple.cpp)
 target_link_libraries(test_simple tkDNN)

@@ -151,93 +67,62 @@ target_link_libraries(test_mnist tkDNN)
 add_executable(test_mnistRT tests/mnist/test_mnistRT.cpp)
 target_link_libraries(test_mnistRT tkDNN)

+## YOLO NETS
+add_executable(test_yolo tests/yolo/yolo.cpp)
+target_link_libraries(test_yolo tkDNN)
+
+add_executable(test_yolo_voc tests/yolo_voc/yolo_voc.cpp)
+target_link_libraries(test_yolo_voc tkDNN)
+
+add_executable(test_yolo_tiny tests/yolo_tiny/yolo_tiny.cpp)
+target_link_libraries(test_yolo_tiny tkDNN)
+
+add_executable(test_yolo_relu tests/yolo_relu/yolo_relu.cpp)
+target_link_libraries(test_yolo_relu tkDNN)
+
+
+add_executable(test_yolo_224 tests/yolo_224/yolo_224.cpp)
+target_link_libraries(test_yolo_224 tkDNN)
+
+add_executable(test_yolo_berkeley tests/yolo_berkeley/yolo_berkeley.cpp)
+target_link_libraries(test_yolo_berkeley tkDNN)
+
+add_executable(test_yolo3_coco4 tests/yolo3_coco4/yolo3_coco4.cpp)
+target_link_libraries(test_yolo3_coco4 tkDNN)
+
+add_executable(test_yolo3_berkeley tests/yolo3_berkeley/yolo3_berkeley.cpp)
+target_link_libraries(test_yolo3_berkeley tkDNN)
+
+add_executable(test_yolo3_tetrapack tests/yolo3_tetrapack/yolo3_tetrapack.cpp)
+target_link_libraries(test_yolo3_tetrapack tkDNN)
+
+add_executable(test_yolo3_tetrapack_resize tests/yolo3_tetrapack_resize/yolo3_tetrapack_resize.cpp)
+target_link_libraries(test_yolo3_tetrapack_resize tkDNN)
+
+add_executable(test_yolo3_BCDS6 tests/yolo3_BCDS6/yolo3_BCDS6.cpp)
+target_link_libraries(test_yolo3_BCDS6 tkDNN)
+add_executable(test_yolo3_flir tests/yolo3_flir/yolo3_flir.cpp)
+target_link_libraries(test_yolo3_flir tkDNN)
+
 add_executable(test_imuodom tests/imuodom/imuodom.cpp)
 target_link_libraries(test_imuodom tkDNN)
-
-# DARKNET
-file(GLOB darknet_SRC "tests/darknet/*.cpp")
-foreach(test_SRC ${darknet_SRC})
-    get_filename_component(test_NAME "${test_SRC}" NAME_WE)
-    set(test_NAME test_${test_NAME})
-    add_executable(${test_NAME} ${test_SRC})
-    target_link_libraries(${test_NAME} tkDNN)
-    install(TARGETS ${test_NAME} DESTINATION bin)
-endforeach()
-
-# MOBILENET
-add_executable(test_mobilenetv2ssd tests/mobilenet/mobilenetv2ssd/mobilenetv2ssd.cpp)
-target_link_libraries(test_mobilenetv2ssd tkDNN)
-
-add_executable(test_bdd-mobilenetv2ssd tests/mobilenet/bdd-mobilenetv2ssd/bdd-mobilenetv2ssd.cpp)
-target_link_libraries(test_bdd-mobilenetv2ssd tkDNN)
-
-add_executable(test_mobilenetv2ssd512 tests/mobilenet/mobilenetv2ssd512/mobilenetv2ssd512.cpp)
-target_link_libraries(test_mobilenetv2ssd512 tkDNN)
-
-# BACKBONES
-add_executable(test_resnet101 tests/backbones/resnet101/resnet101.cpp)
-target_link_libraries(test_resnet101 tkDNN)
-
-add_executable(test_dla34 tests/backbones/dla34/dla34.cpp)
-target_link_libraries(test_dla34 tkDNN)
-
-# CENTERNET
-add_executable(test_resnet101_cnet tests/centernet/resnet101_cnet/resnet101_cnet.cpp)
-target_link_libraries(test_resnet101_cnet tkDNN)
-
-add_executable(test_dla34_cnet tests/centernet/dla34_cnet/dla34_cnet.cpp)
-target_link_libraries(test_dla34_cnet tkDNN)
-
-add_executable(test_dla34_cnet3d tests/centernet/dla34_cnet3d/dla34_cnet3d.cpp)
-target_link_libraries(test_dla34_cnet3d tkDNN)
-
-# CENTERTRACK
-
-add_executable(test_dla34_ctrack tests/centertrack/dla34_ctrack/dla34_ctrack.cpp)
-target_link_libraries(test_dla34_ctrack tkDNN)
-
-# SHELFNET
-add_executable(test_shelfnet tests/shelfnet/shelfnet.cpp)
-target_link_libraries(test_shelfnet tkDNN)
-
-add_executable(test_shelfnet_berkeley tests/shelfnet/shelfnet_berkeley.cpp)
-target_link_libraries(test_shelfnet_berkeley tkDNN)
-
-add_executable(test_shelfnet_mapillary tests/shelfnet/shelfnet_mapillary.cpp)
-target_link_libraries(test_shelfnet_mapillary tkDNN)
-
-add_executable(test_shelfnet_coco tests/shelfnet/shelfnet_coco.cpp)
-target_link_libraries(test_shelfnet_coco tkDNN)
-
-# MONODEPTH2
-add_executable(test_monodepth2_640 tests/monodepth2/monodepth2_640.cpp)
-target_link_libraries(test_monodepth2_640 tkDNN)
-
-add_executable(test_monodepth2_1024 tests/monodepth2/monodepth2_1024.cpp)
-target_link_libraries(test_monodepth2_1024 tkDNN)
+################################################################################


-# DEMOS
 add_executable(test_rtinference tests/test_rtinference/rtinference.cpp)
 target_link_libraries(test_rtinference tkDNN)

-add_executable(map_demo demo/demo/map.cpp)
-target_link_libraries(map_demo tkDNN)
+add_executable(yolo3_demo demo/demo/demo.cpp 
+                        tracker_CLASS/c++/src/ekf.cpp
+                        tracker_CLASS/c++/src/trackutils.cpp
+                        tracker_CLASS/c++/src/plot.cpp
+                        tracker_CLASS/c++/src/tracker.cpp )

-add_executable(demo demo/demo/demo.cpp)
-target_link_libraries(demo tkDNN)
+                        
+target_link_libraries(yolo3_demo tkDNN CLASS)

-add_executable(demo3D demo/demo/demo3D.cpp)
-target_link_libraries(demo3D tkDNN)

-add_executable(demoTracker demo/demo/demoTracker.cpp)
-target_link_libraries(demoTracker tkDNN)

-add_executable(seg_demo demo/demo/seg_demo.cpp)
-target_link_libraries(seg_demo tkDNN)
-
-add_executable(demoDepth demo/demo/demoDepth.cpp)
-target_link_libraries(demoDepth tkDNN)

 #-------------------------------------------------------------------------------
 # Install
@@ -248,11 +133,23 @@ target_link_libraries(demoDepth tkDNN)
 #endif()
 message("install dir:" ${CMAKE_INSTALL_PREFIX})
 install(DIRECTORY include/ DESTINATION include/)
-install(TARGETS tkDNN  DESTINATION lib)
-install(TARGETS test_simple test_mnist test_mnistRT test_rtinference demo map_demo DESTINATION bin)
+install(TARGETS tkDNN kernels DESTINATION lib)
 install(DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/cmake/" # source directory
        DESTINATION "share/tkDNN/cmake/" # target directory
 )
-install(DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/tests/" # source directory
-        DESTINATION "share/tkDNN/tests" # target directory
-)
+
+
+#-------------------------------------------------------------------------------
+# Prepare for test
+#-------------------------------------------------------------------------------
+set(TEST_DATA true CACHE BOOL "If true download deps")
+if( ${TEST_DATA} )
+	message("Launching pre-build dependency installer script...")
+
+    execute_process (COMMAND bash -c "bash build_models.sh download"
+        WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/tests)
+
+	set(TEST_DATA false CACHE BOOL "If true download deps" FORCE)
+	message("Finished dowloading test weights")
+endif()
+
@@ -1,339 +0,0 @@
-                    GNU GENERAL PUBLIC LICENSE
-                       Version 2, June 1991
-
- Copyright (C) 1989, 1991 Free Software Foundation, Inc.,
- 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- Everyone is permitted to copy and distribute verbatim copies
- of this license document, but changing it is not allowed.
-
-                            Preamble
-
-  The licenses for most software are designed to take away your
-freedom to share and change it.  By contrast, the GNU General Public
-License is intended to guarantee your freedom to share and change free
-software--to make sure the software is free for all its users.  This
-General Public License applies to most of the Free Software
-Foundation's software and to any other program whose authors commit to
-using it.  (Some other Free Software Foundation software is covered by
-the GNU Lesser General Public License instead.)  You can apply it to
-your programs, too.
-
-  When we speak of free software, we are referring to freedom, not
-price.  Our General Public Licenses are designed to make sure that you
-have the freedom to distribute copies of free software (and charge for
-this service if you wish), that you receive source code or can get it
-if you want it, that you can change the software or use pieces of it
-in new free programs; and that you know you can do these things.
-
-  To protect your rights, we need to make restrictions that forbid
-anyone to deny you these rights or to ask you to surrender the rights.
-These restrictions translate to certain responsibilities for you if you
-distribute copies of the software, or if you modify it.
-
-  For example, if you distribute copies of such a program, whether
-gratis or for a fee, you must give the recipients all the rights that
-you have.  You must make sure that they, too, receive or can get the
-source code.  And you must show them these terms so they know their
-rights.
-
-  We protect your rights with two steps: (1) copyright the software, and
-(2) offer you this license which gives you legal permission to copy,
-distribute and/or modify the software.
-
-  Also, for each author's protection and ours, we want to make certain
-that everyone understands that there is no warranty for this free
-software.  If the software is modified by someone else and passed on, we
-want its recipients to know that what they have is not the original, so
-that any problems introduced by others will not reflect on the original
-authors' reputations.
-
-  Finally, any free program is threatened constantly by software
-patents.  We wish to avoid the danger that redistributors of a free
-program will individually obtain patent licenses, in effect making the
-program proprietary.  To prevent this, we have made it clear that any
-patent must be licensed for everyone's free use or not licensed at all.
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-modification follow.
-
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-   TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION
-
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-a notice placed by the copyright holder saying it may be distributed
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-
-    tkDNN 
-    Copyright (C) 2017 Francesco Gatti
-
-    This program is free software; you can redistribute it and/or modify
-    it under the terms of the GNU General Public License as published by
-    the Free Software Foundation; either version 2 of the License, or
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-Public License instead of this License.
@@ -1,215 +1,58 @@
 # tkDNN
-tkDNN is a Deep Neural Network library built with cuDNN and tensorRT primitives, specifically thought to work on NVIDIA Jetson Boards. It has been tested on TK1(branch cudnn2), TX1, TX2, AGX Xavier, Nano and several discrete GPUs.
-The main goal of this project is to exploit NVIDIA boards as much as possible to obtain the best inference performance. It does not allow training. 
+tkDNN is a Deep Neural Network library built with cuDNN primitives specifically thought to work on NVIDIA TK1(and all successive) board.<br>
+The main scope is to do high performance inference on already trained models.

-
-If you use tkDNN in your research, please cite the [following paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9212130&casa_token=sQTJXi7tJNoAAAAA:BguH9xCIY48MxbtDS3LXzIXzO-9sWArm7Hd7y7BwaLmqRuM_Gx8bOYizFPNMNtpo5K0kB-P-). For use in commercial solutions, write at gattifrancesco@hotmail.it and micaela.verucchi@unimore.it or refer to https://hipert.unimore.it/ .
-
-```
-@inproceedings{verucchi2020systematic,
-  title={A Systematic Assessment of Embedded Neural Networks for Object Detection},
-  author={Verucchi, Micaela and Brilli, Gianluca and Sapienza, Davide and Verasani, Mattia and Arena, Marco and Gatti, Francesco and Capotondi, Alessandro and Cavicchioli, Roberto and Bertogna, Marko and Solieri, Marco},
-  booktitle={2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)},
-  volume={1},
-  pages={937--944},
-  year={2020},
-  organization={IEEE}
-}
-```
-
-### What's new
-#### 20 July 2021
- [x] Support to sematic segmentation [README](docs/README_seg.md)
- [x] Support 2D/3D Object Detection and Tracking [README](docs/README_2d3dtracking.md)
-#### 24 November 2021
- [x] Support to sematic segmentation on cuda 11
- [x] Support to TensorRT8. (thanks to [Harshvardhan Chandirasekar](https://github.com/perseusdg))
-#### 30 March 2022
- [x] Support to monocular depth esitmation [README](docs/README_depth.md) (thanks to [Harshvardhan Chandirasekar](https://github.com/perseusdg))
-
-
-
-## FPS Results
-Inference FPS of yolov4 with tkDNN, average of 1200 images with the same dimension as the input size, on 
-  * RTX 2080Ti (CUDA 10.2, TensorRT 7.0.0, Cudnn 7.6.5);
-  * Xavier AGX, Jetpack 4.3 (CUDA 10.0, CUDNN 7.6.3, tensorrt 6.0.1 );
-  * Xavier NX, Jetpack 4.4  (CUDA 10.2, CUDNN 8.0.0, tensorrt 7.1.0 ). 
-  * Tx2, Jetpack 4.2 (CUDA 10.0, CUDNN 7.3.1, tensorrt 5.0.6 );
-  * Jetson Nano, Jetpack 4.4  (CUDA 10.2, CUDNN 8.0.0, tensorrt 7.1.0 ). 
-
-| Platform   | Network    | FP32, B=1 | FP32, B=4	| FP16, B=1 |	FP16, B=4 |	INT8, B=1 |	INT8, B=4 | 
-| :------:   | :-----:    | :-----:   | :-----:   | :-----:   |	:-----:   |	:-----:   |	:-----:   | 
-| RTX 2080Ti | yolo4 320  | 118.59	  | 237.31	  | 207.81	  | 443.32	  | 262.37	  | 530.93    | 
-| RTX 2080Ti | yolo4 416  | 104.81	  | 162.86	  | 169.06	  | 293.78	  | 206.93	  | 353.26    | 
-| RTX 2080Ti | yolo4 512  | 92.98	    | 132.43	  | 140.36	  | 215.17	  | 165.35	  | 254.96    | 
-| RTX 2080Ti | yolo4 608  | 63.77	    | 81.53	    | 111.39	  | 152.89	  | 127.79	  | 184.72    | 
-| AGX Xavier | yolo4 320  |	26.78	    | 32.05	    | 57.14	    | 79.05	    | 73.15	    | 97.56     |
-| AGX Xavier | yolo4 416  |	19.96	    | 21.52	    | 41.01	    | 49.00	    | 50.81	    | 60.61     |
-| AGX Xavier | yolo4 512  |	16.58	    | 16.98	    | 31.12	    | 33.84	    | 37.82	    | 41.28     |
-| AGX Xavier | yolo4 608  |	9.45 	    | 10.13	    | 21.92	    | 23.36	    | 27.05	    | 28.93     |
-| Xavier NX  | yolo4 320  |	14.56	    | 16.25	    | 30.14	    | 41.15	    | 42.13	    | 53.42     |
-| Xavier NX  | yolo4 416  |	10.02	    | 10.60	    | 22.43	    | 25.59	    | 29.08	    | 32.94     |
-| Xavier NX  | yolo4 512  |	8.10	    | 8.32	    | 15.78	    | 17.13	    | 20.51	    | 22.46     |
-| Xavier NX  | yolo4 608  |	5.26	    | 5.18	    | 11.54	    | 12.06	    | 15.09	    | 15.82     |
-| Tx2        | yolo4 320	| 11.18	    | 12.07	    | 15.32	    | 16.31     | -         | -         |
-| Tx2        | yolo4 416	| 7.30	    | 7.58	    | 9.45	    | 9.90      | -         | -         |
-| Tx2        | yolo4 512	| 5.96	    | 5.95	    | 7.22	    | 7.23      | -         | -         |
-| Tx2        | yolo4 608	| 3.63	    | 3.65	    | 4.67	    | 4.70      | -         | -         |
-| Nano       | yolo4 320	| 4.23	    | 4.55	    | 6.14	    | 6.53      | -         | -         |
-| Nano       | yolo4 416	| 2.88	    | 3.00	    | 3.90	    | 4.04      | -         | -         |
-| Nano       | yolo4 512	| 2.32	    | 2.34	    | 3.02	    | 3.04      | -         | -         |
-| Nano       | yolo4 608	| 1.40	    | 1.41	    | 1.92	    | 1.93      | -         | -         |
-
-## MAP Results
-Results for COCO val 2017 (5k images), on RTX 2080Ti, with conf threshold=0.001
-
-|                      | CodaLab       | CodaLab   | CodaLab       | CodaLab     | tkDNN map     | tkDNN map |
-| -------------------- | :-----------: | :-------: | :-----------: | :---------: | :-----------: | :-------: |
-|                      | **tkDNN**     | **tkDNN** | **darknet**   | **darknet** | **tkDNN**     | **tkDNN** |
-|                      | MAP(0.5:0.95) | AP50      | MAP(0.5:0.95) | AP50        | MAP(0.5:0.95) | AP50      |
-| Yolov3 (416x416)     | 0.381         | 0.675     | 0.380         | 0.675       | 0.372         | 0.663     |
-| yolov4 (416x416)     | 0.468         | 0.705     | 0.471         | 0.710       | 0.459         | 0.695     |
-| yolov3tiny (416x416) | 0.096         | 0.202     | 0.096         | 0.201       | 0.093         | 0.198     |
-| yolov4tiny (416x416) | 0.202         | 0.400     | 0.201         | 0.400       | 0.197         | 0.395     |
-| Cnet-dla34 (512x512) | 0.366         | 0.543     | \-            | \-          | 0.361         | 0.535     |
-| mv2SSD (512x512)     | 0.226         | 0.381     | \-            | \-          | 0.223         | 0.378     |
-
-## Index
- [tkDNN](#tkdnn)
-  - [Index](#index)
-  - [Dependencies](#dependencies)
-  - [How to compile this repo](#how-to-compile-this-repo)
-  - [Workflow](#workflow)
-  - [Exporting weights](#exporting-weights)
-  - [Run the demos](#run-the-demos)
-  - [tkDNN on Windows 10 or Windows 11](#tkdnn-on-windows-10-or-windows-11)
-  - [Existing tests and supported networks](#existing-tests-and-supported-networks)
-  - [References](#references)
-  
+this branch actually work on every NVIDIA GPU that support the dependencies:
+* CUDA 10.0
+* CUDNN 7.603
+* TENSORRT 6.01
+* OPENCV 4.1

 ## Dependencies
-This branch works on every NVIDIA GPU that supports the following (latest tested) dependencies:
-* CUDA 11.3 (or >= 10.2) 
-* cuDNN 8.2.1 (or >= 8.0.4)
-* TensorRT 8.0.3 (or >=7.2)
-* OpenCV 4.5.4 (or >=4)
-* cmake 3.21 (or >= 3.15)
-* yaml-cpp 0.5.2
-* eigen3 3.3.4
-* curl 7.58

 ```
-sudo apt install libyaml-cpp-dev curl libeigen3-dev
-
-```
-
-#### About OpenCV
-To compile and install OpenCV4 with contrib us the script ```install_OpenCV4.sh```. It will download and compile OpenCV in Download folder.
-```
-bash scripts/install_OpenCV4.sh
-```
-If you have OpenCV compiled with cuda and contrib and want to use it with tkDNN pass  ```ENABLE_OPENCV_CUDA_CONTRIB=ON``` flag when compiling tkDBB
-. If the flag is not passed,the preprocessing of the networks is computed on the CPU, otherwise on the GPU. In the latter case some milliseconds are saved in the end-to-end latency. 
-
-## How to compile this repo
-Build with cmake. If using Ubuntu 18.04 a new version of cmake is needed (3.15 or above).
-On both linux and windows ,the ```CMAKE_BUILD_TYPE``` variable needs to be defined as either ```Release``` or ```Debug```.
-```
-git clone https://github.com/ceccocats/tkDNN
-cd tkDNN
-mkdir build
-cd build
-cmake -DCMAKE_BUILD_TYPE=Release .. 
-make
+sudo apt install libgdal-dev libeigen3-dev python-matplotlib libyaml-cpp-dev libcereal-dev python2.7-dev
 ```

 ## Workflow
-Steps needed to do inference on tkDNN with a custom neural network. 
-* Build and train a NN model with your favorite framework.
-* Export weights and bias for each layer and save them in a binary file (one for layer).
-* Export outputs for each layer and save them in a binary file (one for layer).
-* Create a new test and define the network, layer by layer using the weights extracted and the output to check the results. 
-* Do inference.
+The recommended workflow follow these step:
+* Build and train a model in Keras (on any PC)
+* Export weights and bias 
+* Define the model on tkDNN
+* Do inference (on TK1)

-## Exporting weights
+## Compile the library
+Build with cmake
+```
+mkdir build
+cd build
+cmake ..
+# use -DTEST_DATA=False to skip dataset download
+make
+```
+during the cmake configuration it will be dowloaded the weights needed for running
+the tests

-For specific details on how to export weights see [HERE](./docs/exporting_weights.md).
-
-## Run the demos 
-
-For specific details on how to run:
- 2D object detection demos, details on FP16, INT8 and batching see [HERE](./docs/demo.md).
- segmentation demos see [HERE](./docs/README_seg.md).
- monocular depth estimation see [HERE](./docs/README_depth.md).
- 2D/3D object detection and tracking demos see [HERE](./docs/README_2d3dtracking.md).
- mAP demo to evaluate 2D object detectors see [HERE](./docs/mAP_demo.md).
+## Test
+Assumiung you have correctly builded the library these are the test ready to exec:
+* test_simple: a simple convolutional and dense network (CUDNN only)
+* test_mnist: the famous mnist netwok (CUDNN and TENSORRT)
+* test_mnistRT: the mnist network hardcoded in using tensorRT apis (TENSORRT only)
+* test_yolo: YOLO detection network (CUDNN and TENSORRT)
+* test_yolo_tiny: smaller version of YOLO (CUDNN and TENSRRT)
+* test_yolo3_berkeley: our yolo3 version trained with BDD100K dateset 

+## yolo3 berkeley demo detection
+For the live detection you need to precompile the tensorRT file by luncing the desidered network test, this is the recommended process:
+```
+export TKDNN_MODE=FP16   # set the half floating point optimization
+rm yolo3_berkeley.rt		 # be sure to delete(or move) old tensorRT files
+./test_yolo3_berkeley              # run the yolo test (is slow)
+# with f16 inference the result will be a bit incorrect
+```
+this will genereate a yolo3_berkeley.rt file that can be used for live detection:
+```
+./yolo3_demo                               # launch detection on a demo video
+./yolo3_demo yolo3_berkeley.rt /dev/video0 # launch detection on device 0
+```
 ![demo](https://user-images.githubusercontent.com/11562617/72547657-540e7800-388d-11ea-83c6-49dfea2a0607.gif)
-
-## tkDNN on Windows 10 or Windows 11
-
-For specific details on how to run tkDNN on Windows 10/11 see [HERE](./docs/windows.md).
-
-## Existing tests and supported networks
-
-| Test Name         | Network                                       | Dataset                                                       | N Classes | Input size    | Weights                                                                   |
-| :---------------- | :-------------------------------------------- | :-----------------------------------------------------------: | :-------: | :-----------: | :------------------------------------------------------------------------ |
-| yolo              | YOLO v2<sup>1</sup>                           | [COCO 2014](http://cocodataset.org/)                          | 80        | 608x608       | [weights](https://cloud.hipert.unimore.it/s/nf4PJ3k8bxBETwL/download)                                                                   |
-| yolo_224          | YOLO v2<sup>1</sup>                           | [COCO 2014](http://cocodataset.org/)                          | 80        | 224x224       | weights                                                                   |
-| yolo_berkeley     | YOLO v2<sup>1</sup>                           | [BDD100K  ](https://bair.berkeley.edu/blog/2018/05/30/bdd/)   | 10        | 416x736       | weights                                                                   |
-| yolo_relu         | YOLO v2 (with ReLU, not Leaky)<sup>1</sup>    | [COCO 2014](http://cocodataset.org/)                          | 80        | 416x416       | weights                                                                   |
-| yolo_tiny         | YOLO v2 tiny<sup>1</sup>                      | [COCO 2014](http://cocodataset.org/)                          | 80        | 416x416       | [weights](https://cloud.hipert.unimore.it/s/m3orfJr8pGrN5mQ/download)                                                                   |
-| yolo_voc          | YOLO v2<sup>1</sup>                           | [VOC      ](http://host.robots.ox.ac.uk/pascal/VOC/)          | 21        | 416x416       | [weights](https://cloud.hipert.unimore.it/s/DJC5Fi2pEjfNDP9/download)                                                                   |
-| yolo3             | YOLO v3<sup>2</sup>                           | [COCO 2014](http://cocodataset.org/)                          | 80        | 416x416       | [weights](https://cloud.hipert.unimore.it/s/jPXmHyptpLoNdNR/download)     |
-| yolo3_512   | YOLO v3<sup>2</sup>                                 | [COCO 2017](http://cocodataset.org/)                          | 80        | 512x512       | [weights](https://cloud.hipert.unimore.it/s/RGecMeGLD4cXEWL/download)     |
-| yolo3_berkeley    | YOLO v3<sup>2</sup>                           | [BDD100K  ](https://bair.berkeley.edu/blog/2018/05/30/bdd/)   | 10        | 320x544       | [weights](https://cloud.hipert.unimore.it/s/o5cHa4AjTKS64oD/download)                                                                   |
-| yolo3_coco4       | YOLO v3<sup>2</sup>                           | [COCO 2014](http://cocodataset.org/)                          | 4         | 416x416       | [weights](https://cloud.hipert.unimore.it/s/o27NDzSAartbyc4/download)                                                                   |
-| yolo3_flir        | YOLO v3<sup>2</sup>                           | [FREE FLIR](https://www.flir.com/oem/adas/adas-dataset-form/) | 3         | 320x544       | [weights](https://cloud.hipert.unimore.it/s/62DECncmF6bMMiH/download)                                                                   |
-| yolo3_tiny        | YOLO v3 tiny<sup>2</sup>                      | [COCO 2014](http://cocodataset.org/)                          | 80        | 416x416       | [weights](https://cloud.hipert.unimore.it/s/LMcSHtWaLeps8yN/download)     |
-| yolo3_tiny512     | YOLO v3 tiny<sup>2</sup>                      | [COCO 2017](http://cocodataset.org/)                          | 80        | 512x512       | [weights](https://cloud.hipert.unimore.it/s/8Zt6bHwHADqP4JC/download)     |
-| dla34             | Deep Leayer Aggreagtion (DLA) 34<sup>3</sup>  | [COCO 2014](http://cocodataset.org/)                          | 80        | 224x224       | weights                                                                   |
-| dla34_cnet        | Centernet (DLA34 backend)<sup>4</sup>         | [COCO 2017](http://cocodataset.org/)                          | 80        | 512x512       | [weights](https://cloud.hipert.unimore.it/s/KRZBbCQsKAtQwpZ/download)     |
-| mobilenetv2ssd    | Mobilnet v2 SSD Lite<sup>5</sup>              | [VOC      ](http://host.robots.ox.ac.uk/pascal/VOC/)          | 21        | 300x300       | [weights](https://cloud.hipert.unimore.it/s/x4ZfxBKN23zAJQp/download)     |
-| mobilenetv2ssd512 | Mobilnet v2 SSD Lite<sup>5</sup>              | [COCO 2017](http://cocodataset.org/)                          | 81        | 512x512       | [weights](https://cloud.hipert.unimore.it/s/pdCw2dYyHMJrcEM/download)     |
-| resnet101         | Resnet 101<sup>6</sup>                        | [COCO 2014](http://cocodataset.org/)                          | 80        | 224x224       | weights                                                                   |
-| resnet101_cnet    | Centernet (Resnet101 backend)<sup>4</sup>     | [COCO 2017](http://cocodataset.org/)                          | 80        | 512x512       | [weights](https://cloud.hipert.unimore.it/s/5BTjHMWBcJk8g3i/download)     |
-| csresnext50-panet-spp    | Cross Stage Partial Network <sup>7</sup>     | [COCO 2014](http://cocodataset.org/)                          | 80        | 416x416       | [weights](https://cloud.hipert.unimore.it/s/Kcs4xBozwY4wFx8/download)     |
-| yolo4             | Yolov4 <sup>8</sup>                           | [COCO 2017](http://cocodataset.org/)                          | 80        | 416x416       | [weights](https://cloud.hipert.unimore.it/s/d97CFzYqCPCp5Hg/download)     |
-| yolo4_320         | Yolov4 <sup>8</sup>                           | [COCO 2017](http://cocodataset.org/)                          | 80        | 320x320       | [weights](https://cloud.hipert.unimore.it/s/d97CFzYqCPCp5Hg/download)     |
-| yolo4_512         | Yolov4 <sup>8</sup>                           | [COCO 2017](http://cocodataset.org/)                          | 80        | 512x512       | [weights](https://cloud.hipert.unimore.it/s/d97CFzYqCPCp5Hg/download)     |
-| yolo4_608         | Yolov4 <sup>8</sup>                           | [COCO 2017](http://cocodataset.org/)                          | 80        | 608x608       | [weights](https://cloud.hipert.unimore.it/s/d97CFzYqCPCp5Hg/download)     |
-| yolo4_berkeley             | Yolov4 <sup>8</sup>                           | [BDD100K  ](https://bair.berkeley.edu/blog/2018/05/30/bdd/)                          | 10        | 544x320       | [weights](https://cloud.hipert.unimore.it/s/nkWFa5fgb4NTdnB/download)     |
-| yolo4tiny             | Yolov4 tiny <sup>9</sup>                           | [COCO 2017](http://cocodataset.org/)                          | 80        | 416x416       | [weights](https://cloud.hipert.unimore.it/s/iRnc4pSqmx78gJs/download)     |
-| yolo4x             | Yolov4x-mish  <sup>9</sup>                          | [COCO 2017](http://cocodataset.org/)                          | 80        | 640x640       | [weights](https://cloud.hipert.unimore.it/s/5MFjtNtgbDGdJEo/download)     |
-| yolo4tiny_512           | Yolov4 tiny <sup>9</sup>                           | [COCO 2017](http://cocodataset.org/)                          | 80        | 512x512       | [weights](https://cloud.hipert.unimore.it/s/iRnc4pSqmx78gJs/download)     |
-| yolo4x-cps            | Scaled Yolov4 <sup>10</sup>                          | [COCO 2017](http://cocodataset.org/)                          | 80        | 512x512       | [weights](https://cloud.hipert.unimore.it/s/AfzHE4BfTeEm2gH/download)     |
-| shelfnet              | ShelfNet18_realtime<sup>11</sup>                      | [Cityscapes](https://www.cityscapes-dataset.com/)                          | 19        | 1024x1024       | [weights](https://cloud.hipert.unimore.it/s/mEDZMRJaGCFWSJF/download)                                                                   |
-| shelfnet_berkeley              | ShelfNet18_realtime<sup>11</sup>                           | [DeepDrive](https://bdd-data.berkeley.edu/)                          | 20        | 1024x1024       | [weights](https://cloud.hipert.unimore.it/s/m92e7QdD9gYMF7f/download)                                                                   |
-| dla34_cnet3d        | Centernet3D (DLA34 backend)<sup>4</sup>         | [KITTI 2017](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d)                          | 1        | 512x512       | [weights](https://cloud.hipert.unimore.it/s/2MDyWGzQsTKMjmR/download)     |
-| dla34_ctrack        | CenterTrack (DLA34 backend)<sup>12</sup>         | [NuScenes 3D](https://www.nuscenes.org/)                          | 7        | 512x512       | [weights](https://cloud.hipert.unimore.it/s/rjNfgGL9FtAXLHp/download)     |
-| monodepth2        | Monodepth2 <sup>13</sup>         | [KITTI DEPTH](http://www.cvlibs.net/datasets/kitti/raw_data.php)                          | -        | 640x192       | [weights-mono](https://cloud.hipert.unimore.it/s/iYw9QwgP6CsqxLR/download)     |
-| monodepth2        | Monodepth2 <sup>13</sup>         | [KITTI DEPTH](http://www.cvlibs.net/datasets/kitti/raw_data.php)                          | -        | 640x192       | [weights-stereo](https://cloud.hipert.unimore.it/s/XmwbWNXDfqyQ4EL/download)     |
-
-
-## References
-
-1. Redmon, Joseph, and Ali Farhadi. "YOLO9000: better, faster, stronger." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
-2. Redmon, Joseph, and Ali Farhadi. "Yolov3: An incremental improvement." arXiv preprint arXiv:1804.02767 (2018).
-3. Yu, Fisher, et al. "Deep layer aggregation." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.
-4. Zhou, Xingyi, Dequan Wang, and Philipp Krähenbühl. "Objects as points." arXiv preprint arXiv:1904.07850 (2019).
-5. Sandler, Mark, et al. "Mobilenetv2: Inverted residuals and linear bottlenecks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.
-6. He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
-7. Wang, Chien-Yao, et al. "CSPNet: A New Backbone that can Enhance Learning Capability of CNN." arXiv preprint arXiv:1911.11929 (2019).
-8. Bochkovskiy, Alexey, Chien-Yao Wang, and Hong-Yuan Mark Liao. "YOLOv4: Optimal Speed and Accuracy of Object Detection." arXiv preprint arXiv:2004.10934 (2020).
-9. Bochkovskiy, Alexey, "Yolo v4, v3 and v2 for Windows and Linux" (https://github.com/AlexeyAB/darknet)
-10. Wang, Chien-Yao, Alexey Bochkovskiy, and Hong-Yuan Mark Liao. "Scaled-YOLOv4: Scaling Cross Stage Partial Network." arXiv preprint arXiv:2011.08036 (2020).
-11. Zhuang, Juntang, et al. "ShelfNet for fast semantic segmentation." Proceedings of the IEEE International Conference on Computer Vision Workshops. 2019.
-12. Zhou, Xingyi, Vladlen Koltun, and Philipp Krähenbühl. "Tracking objects as points." European Conference on Computer Vision. Springer, Cham, 2020.
-13. Godard, Clément, et al. "Digging into self-supervised monocular depth estimation." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019.
-
-## Contributors
-The main contibutors, in chronological order, are: 
- [Francesco Gatti](https://github.com/ceccocats), francesco.gatti@hipert.it
- [Micaela Verucchi](https://github.com/mive93), micaela.verucchi@unimore.it
- [Davide Sapienza](https://github.com/sapienzadavide), davide.sapienza@unimore.it
- [Harshvardhan Chandirasekar](https://github.com/perseusdg), f20180523@goa.bits-pilani.ac.in
@@ -1,66 +1,33 @@
-# find the library
-if(CUDA_FOUND)
-  find_cuda_helper_libs(cudnn)
-  set(CUDNN_LIBRARY ${CUDA_cudnn_LIBRARY} CACHE FILEPATH "location of the cuDNN library")
-  unset(CUDA_cudnn_LIBRARY CACHE)
+# Find the header files

-  find_cuda_helper_libs(nvinfer)
-  set(NVINFER_LIBRARY ${CUDA_nvinfer_LIBRARY} CACHE FILEPATH "location of the nvinfer library")
-  unset(CUDA_nvinfer_LIBRARY CACHE)
-endif()
-
-# find the include
-if(CUDNN_LIBRARY)
-  find_path(CUDNN_INCLUDE_DIR
-    cudnn.h
-    PATHS ${CUDA_TOOLKIT_INCLUDE}
-    DOC "location of cudnn.h"
-    NO_DEFAULT_PATH
-  )
-
-  if(NOT CUDNN_INCLUDE_DIR)
-    find_path(CUDNN_INCLUDE_DIR
-      cudnn.h
-      DOC "location of cudnn.h"
-    )
-  endif()
-
-  message("-- Found CUDNN: " ${CUDNN_LIBRARY})
-  message("-- Found CUDNN include: " ${CUDNN_INCLUDE_DIR})
-endif()
-
-if(NVINFER_LIBRARY)
-  find_path(NVINFER_INCLUDE_DIR
-    NvInfer.h
-    PATHS ${CUDA_TOOLKIT_INCLUDE}
-    DOC "location of NvInfer.h"
-    NO_DEFAULT_PATH
-  )
-
-  if(NOT NVINFER_INCLUDE_DIR)
-    find_path(NVINFER_INCLUDE_DIR
-        NvInfer.h
-        DOC "location of NvInfer.h"
-    )
-  endif()
-
-  message("-- Found NVINFER: " ${NVINFER_LIBRARY})
-  message("-- Found NVINFER include: " ${NVINFER_INCLUDE_DIR})
-endif()
-
-
-include(FindPackageHandleStandardArgs)
-find_package_handle_standard_args(CUDNN
-  FOUND_VAR CUDNN_FOUND
-  REQUIRED_VARS
-  CUDNN_LIBRARY
-  CUDNN_INCLUDE_DIR
-  VERSION_VAR CUDNN_VERSION
+find_path(CUDNN_INCLUDE_DIR
+        ${CMAKE_SYSROOT}/usr/local/include
+        ${CMAKE_SYSROOT}/usr/include
+        /usr/local/nvidia/tensorrt/include/
+        NO_DEFAULT_PATH
 )

-if(CUDNN_FOUND)
-  set(CUDNN_LIBRARIES ${CUDNN_LIBRARY} ${NVINFER_LIBRARY})
-  set(CUDNN_INCLUDE_DIRS ${CUDNN_INCLUDE_DIR} ${NVINFER_INCLUDE_DIR})
-endif()
+set(OLD_ROOT ${CMAKE_FIND_ROOT_PATH})
+list(APPEND CMAKE_FIND_ROOT_PATH /)
+list(APPEND CMAKE_FIND_LIBRARY_SUFFIXES .so.7)
+list(APPEND CMAKE_FIND_LIBRARY_SUFFIXES .so.5)
+find_library(CUDNN_LIB
+    NAMES cudnn
+    PATHS
+    /usr/local/driveworks/targets/${CMAKE_SYSTEM_PROCESSOR}-Linux/lib
+    /usr/lib/${CMAKE_SYSTEM_PROCESSOR}-linux-gnu/
+    NO_DEFAULT_PATH
+)
+find_library(CUDNN_NVLIB
+    NAMES "nvinfer"
+    PATHS
+    /usr/local/driveworks/targets/${CMAKE_SYSTEM_PROCESSOR}-Linux/lib
+    /usr/lib/${CMAKE_SYSTEM_PROCESSOR}-linux-gnu/
+    NO_DEFAULT_PATH
+)
+set(CMAKE_FIND_ROOT_PATH ${OLD_ROOT})

-set(CUDNN_FOUND true)
+set(CUDNN_LIBRARIES ${CUDNN_LIB} ${CUDNN_NVLIB})
+message("-- Found CUDNN: "  ${CUDNN_LIB})
+message("-- Found NVINFER: "  ${CUDNN_NVLIB})
+set(CUDNN_FOUND true)
@@ -1,20 +0,0 @@
-#include <stdio.h>
-
-int main(int argc, char **argv){
-    cudaDeviceProp dP;
-    float min_cc = 5.0;
-
-    int rc = cudaGetDeviceProperties(&dP, 0);
-    if(rc != cudaSuccess) {
-        cudaError_t error = cudaGetLastError();
-        printf("CUDA error: %s", cudaGetErrorString(error));
-        return rc; /* Failure */
-    }
-    if((dP.major+(dP.minor/10)) < min_cc) {
-        printf("Min Compute Capability of %2.1f required:  %d.%d found\n Not Building CUDA Code", min_cc, dP.major, dP.minor);
-        return 1; /* Failure */
-    } else {
-        printf("-arch=sm_%d%d", dP.major, dP.minor);
-        return 0; /* Success */
-    }
-}
@@ -1,7 +0,0 @@
-classes     : 80    #number of classes
-map_points  : 101   #number of recall points (0 for all, 101 for COCO, 11 PascalVOC)
-map_levels  : 10    #number of IoU step for the AP
-map_step    : 0.05  #step of IoU
-IoU_thresh  : 0.5   #starting IoU threshold
-conf_thresh : 0.001   #threshold on the condifence of the bbox
-verbose     : false #print on screen information
@@ -1,7 +0,0 @@
-classes     : 3    #number of classes
-map_points  : 101   #number of recall points (0 for all, 101 for COCO, 11 PascalVOC)
-map_levels  : 10    #number of IoU step for the AP
-map_step    : 0.05  #step of IoU
-IoU_thresh  : 0.5   #starting IoU threshold
-conf_thresh : 0.0   #threshold on the condifence of the bbox
-verbose     : false #print on screen information
@@ -0,0 +1,22 @@
+%YAML:1.0
+---
+calibration_time: "Mon 06 May 2019 11:32:13 PM CEST"
+image_width: 1920
+image_height: 1080
+board_width: 8
+board_height: 6
+square_size: 2.4799999237060547e+01
+flags: 0
+camera_matrix: !!opencv-matrix
+   rows: 3
+   cols: 3
+   dt: d
+   data: [ 1.6158690952190570e+03, 0., 9.4702812371722337e+02, 0.,
+       1.6123979985757153e+03, 5.1995630055718266e+02, 0., 0., 1. ]
+distortion_coefficients: !!opencv-matrix
+   rows: 5
+   cols: 1
+   dt: d
+   data: [ -4.0971199964304100e-01, 1.8755404192050384e-01,
+       -5.3059322427743867e-03, -1.0380603625304912e-03, 0. ]
+avg_reprojection_error: 3.4351035832972515e-01
@@ -0,0 +1,22 @@
+%YAML:1.0
+---
+calibration_time: "Tue 07 May 2019 10:32:53 AM CEST"
+image_width: 3072
+image_height: 1728
+board_width: 8
+board_height: 6
+square_size: 2.4799999237060547e+01
+flags: 0
+camera_matrix: !!opencv-matrix
+   rows: 3
+   cols: 3
+   dt: d
+   data: [ 4.7264390181579711e+03, 0., 1.5059850098280642e+03, 0.,
+       4.6793092340700096e+03, 6.7300681982359868e+02, 0., 0., 1. ]
+distortion_coefficients: !!opencv-matrix
+   rows: 5
+   cols: 1
+   dt: d
+   data: [ -4.2669569210605879e-01, 6.6337608795749903e-01,
+       -1.3881256269106437e-03, 5.2468063845700682e-03, 0. ]
+avg_reprojection_error: 3.1312290189919406e-01
@@ -0,0 +1,22 @@
+%YAML:1.0
+---
+calibration_time: "Tue 07 May 2019 10:14:44 AM CEST"
+image_width: 1920
+image_height: 1080
+board_width: 8
+board_height: 6
+square_size: 2.4799999237060547e+01
+flags: 0
+camera_matrix: !!opencv-matrix
+   rows: 3
+   cols: 3
+   dt: d
+   data: [ 1.6902498656747011e+03, 0., 9.7959318966703324e+02, 0.,
+       1.7552884617253583e+03, 5.3327953707582492e+02, 0., 0., 1. ]
+distortion_coefficients: !!opencv-matrix
+   rows: 5
+   cols: 1
+   dt: d
+   data: [ -5.4891909767312119e-01, 2.5555919841568631e-01,
+       -4.3831358875660656e-03, -1.3934378903760349e-02, 0. ]
+avg_reprojection_error: 1.1758482932800183e+00
@@ -0,0 +1,22 @@
+%YAML:1.0
+---
+calibration_time: "Mon 06 May 2019 11:32:13 PM CEST"
+image_width: 1920
+image_height: 1080
+board_width: 8
+board_height: 6
+square_size: 2.4799999237060547e+01
+flags: 0
+camera_matrix: !!opencv-matrix
+   rows: 3
+   cols: 3
+   dt: d
+   data: [ 1.6158690952190570e+03, 0., 9.4702812371722337e+02, 0.,
+       1.6123979985757153e+03, 5.1995630055718266e+02, 0., 0., 1. ]
+distortion_coefficients: !!opencv-matrix
+   rows: 5
+   cols: 1
+   dt: d
+   data: [ -4.0971199964304100e-01, 1.8755404192050384e-01,
+       -5.3059322427743867e-03, -1.0380603625304912e-03, 0. ]
+avg_reprojection_error: 3.4351035832972515e-01
@@ -0,0 +1,22 @@
+%YAML:1.0
+---
+calibration_time: "Tue 07 May 2019 09:56:50 AM CEST"
+image_width: 1920
+image_height: 1080
+board_width: 8
+board_height: 6
+square_size: 2.4799999237060547e+01
+flags: 0
+camera_matrix: !!opencv-matrix
+   rows: 3
+   cols: 3
+   dt: d
+   data: [ 1.6229477302581809e+03, 0., 1.0277357980566628e+03, 0.,
+       1.6485741394129034e+03, 5.5596919291027621e+02, 0., 0., 1. ]
+distortion_coefficients: !!opencv-matrix
+   rows: 5
+   cols: 1
+   dt: d
+   data: [ -3.7853584845653426e-01, 7.8553352913896368e-02,
+       -6.5552938633907229e-03, -1.6436824648695104e-02, 0. ]
+avg_reprojection_error: 8.4629096638637347e-01
@@ -0,0 +1,22 @@
+%YAML:1.0
+---
+calibration_time: "Fri 03 May 2019 11:56:13 PM CEST"
+image_width: 3072
+image_height: 1728
+board_width: 8
+board_height: 6
+square_size: 2.4799999237060547e+01
+flags: 0
+camera_matrix: !!opencv-matrix
+   rows: 3
+   cols: 3
+   dt: d
+   data: [ 5.8796921906556563e+03, 0., 1.3036708932691290e+03, 0.,
+       5.9435402023228071e+03, 8.1110067822514861e+02, 0., 0., 1. ]
+distortion_coefficients: !!opencv-matrix
+   rows: 5
+   cols: 1
+   dt: d
+   data: [ -5.4688862790206871e-01, 5.1913397860290666e-01,
+       -2.1076612628273591e-03, 1.6869796115416984e-02, 0. ]
+avg_reprojection_error: 6.7667474319420251e-01
@@ -0,0 +1,22 @@
+%YAML:1.0
+---
+calibration_time: "Sat 04 May 2019 12:00:38 AM CEST"
+image_width: 3072
+image_height: 1728
+board_width: 8
+board_height: 6
+square_size: 2.4799999237060547e+01
+flags: 0
+camera_matrix: !!opencv-matrix
+   rows: 3
+   cols: 3
+   dt: d
+   data: [ 4.6903033136815602e+03, 0., 1.6303445000881884e+03, 0.,
+       4.7582671272189546e+03, 4.3596515032334111e+02, 0., 0., 1. ]
+distortion_coefficients: !!opencv-matrix
+   rows: 5
+   cols: 1
+   dt: d
+   data: [ -3.4366857232996317e-01, 2.2799325522263861e-01,
+       2.0765840315530557e-02, -4.0088654509745098e-03, 0. ]
+avg_reprojection_error: 3.9811872397860709e-01
@@ -0,0 +1,22 @@
+%YAML:1.0
+---
+calibration_time: "Tue 07 May 2019 10:20:53 AM CEST"
+image_width: 3072
+image_height: 1728
+board_width: 8
+board_height: 6
+square_size: 2.4799999237060547e+01
+flags: 0
+camera_matrix: !!opencv-matrix
+   rows: 3
+   cols: 3
+   dt: d
+   data: [ 2.5005410461483498e+03, 0., 1.5319405824251596e+03, 0.,
+       2.5001544574623872e+03, 7.8267345299919543e+02, 0., 0., 1. ]
+distortion_coefficients: !!opencv-matrix
+   rows: 5
+   cols: 1
+   dt: d
+   data: [ -3.7379752112038928e-01, 1.6246299444310250e-01,
+       8.0371978716752837e-04, -9.6108499236087584e-04, 0. ]
+avg_reprojection_error: 3.6334262234685299e-01
@@ -0,0 +1,22 @@
+%YAML:1.0
+---
+calibration_time: "Tue 07 May 2019 10:37:47 AM CEST"
+image_width: 960
+image_height: 720
+board_width: 8
+board_height: 6
+square_size: 2.4799999237060547e+01
+flags: 0
+camera_matrix: !!opencv-matrix
+   rows: 3
+   cols: 3
+   dt: d
+   data: [ 5.0439587680799593e+02, 0., 4.8997081391816727e+02, 0.,
+       5.0714582349015507e+02, 3.5481348085748095e+02, 0., 0., 1. ]
+distortion_coefficients: !!opencv-matrix
+   rows: 5
+   cols: 1
+   dt: d
+   data: [ -2.7916140864065331e-01, 6.5465070220501562e-02,
+       -1.9231901334709591e-03, -2.6191562264760264e-03, 0. ]
+avg_reprojection_error: 5.7283635087126605e-01
@@ -0,0 +1,22 @@
+%YAML:1.0
+---
+calibration_time: "Tue 07 May 2019 10:41:12 AM CEST"
+image_width: 960
+image_height: 720
+board_width: 8
+board_height: 6
+square_size: 2.4799999237060547e+01
+flags: 0
+camera_matrix: !!opencv-matrix
+   rows: 3
+   cols: 3
+   dt: d
+   data: [ 5.1663651913150818e+02, 0., 4.7267297458218127e+02, 0.,
+       5.1291090124818436e+02, 3.8505850298928243e+02, 0., 0., 1. ]
+distortion_coefficients: !!opencv-matrix
+   rows: 5
+   cols: 1
+   dt: d
+   data: [ -2.8051872523046845e-01, 6.0895269981008610e-02,
+       -9.7920840355269542e-03, -4.9804820350633240e-04, 0. ]
+avg_reprojection_error: 5.4967308787122626e-01
@@ -0,0 +1,22 @@
+%YAML:1.0
+---
+calibration_time: "Tue 07 May 2019 10:50:02 AM CEST"
+image_width: 960
+image_height: 720
+board_width: 8
+board_height: 6
+square_size: 2.4799999237060547e+01
+flags: 0
+camera_matrix: !!opencv-matrix
+   rows: 3
+   cols: 3
+   dt: d
+   data: [ 4.9724079419911664e+02, 0., 4.9277930193807083e+02, 0.,
+       4.9700744926387819e+02, 3.6581239154403062e+02, 0., 0., 1. ]
+distortion_coefficients: !!opencv-matrix
+   rows: 5
+   cols: 1
+   dt: d
+   data: [ -2.7582961261093608e-01, 6.6908017283259263e-02,
+       -2.1580546593114500e-03, -1.7921711595441153e-03, 0. ]
+avg_reprojection_error: 3.7129088933918375e-01
@@ -0,0 +1,22 @@
+%YAML:1.0
+---
+calibration_time: "Tue 07 May 2019 10:53:27 AM CEST"
+image_width: 960
+image_height: 720
+board_width: 8
+board_height: 6
+square_size: 2.4799999237060547e+01
+flags: 0
+camera_matrix: !!opencv-matrix
+   rows: 3
+   cols: 3
+   dt: d
+   data: [ 4.9372152821507876e+02, 0., 4.7585791077351445e+02, 0.,
+       4.9644139996881893e+02, 3.5961856724726260e+02, 0., 0., 1. ]
+distortion_coefficients: !!opencv-matrix
+   rows: 5
+   cols: 1
+   dt: d
+   data: [ -2.9023109325424973e-01, 8.3150964750672046e-02,
+       -6.1378621304345154e-04, 8.4481910933416999e-04, 0. ]
+avg_reprojection_error: 3.4691001942524069e-01
@@ -0,0 +1,22 @@
+%YAML:1.0
+---
+calibration_time: "Sat 04 May 2019 12:35:58 AM CEST"
+image_width: 3840
+image_height: 2160
+board_width: 8
+board_height: 6
+square_size: 2.4799999237060547e+01
+flags: 0
+camera_matrix: !!opencv-matrix
+   rows: 3
+   cols: 3
+   dt: d
+   data: [ 2.1723071272381276e+03, 0., 1.9718118689531000e+03, 0.,
+       2.2377541672328439e+03, 9.3157209524899565e+02, 0., 0., 1. ]
+distortion_coefficients: !!opencv-matrix
+   rows: 5
+   cols: 1
+   dt: d
+   data: [ -3.8516162509048857e-01, 1.8961757063227327e-01,
+       1.8297248985443184e-02, -8.9166274086698288e-03, 0. ]
+avg_reprojection_error: 9.9886914863900311e-01
@@ -0,0 +1,22 @@
+%YAML:1.0
+---
+calibration_time: "Sun 05 May 2019 08:52:13 PM CEST"
+image_width: 3072
+image_height: 1728
+board_width: 8
+board_height: 6
+square_size: 2.4799999237060547e+01
+flags: 0
+camera_matrix: !!opencv-matrix
+   rows: 3
+   cols: 3
+   dt: d
+   data: [ 2.9841357325808735e+03, 0., 1.5379802472694901e+03, 0.,
+       2.9784613885271938e+03, 8.9330228722164566e+02, 0., 0., 1. ]
+distortion_coefficients: !!opencv-matrix
+   rows: 5
+   cols: 1
+   dt: d
+   data: [ -5.1096356919758967e-01, 1.4543132746407733e-01,
+       -3.1254001577433334e-02, -1.4769334036191385e-02, 0. ]
+avg_reprojection_error: 9.3544537534095662e-01
@@ -0,0 +1,22 @@
+%YAML:1.0
+---
+calibration_time: "Sun 05 May 2019 09:24:33 PM CEST"
+image_width: 3072
+image_height: 1728
+board_width: 8
+board_height: 6
+square_size: 2.4799999237060547e+01
+flags: 0
+camera_matrix: !!opencv-matrix
+   rows: 3
+   cols: 3
+   dt: d
+   data: [ 2.4796388675592771e+03, 0., 1.5358283835422017e+03, 0.,
+       2.4440198814655632e+03, 8.9911455540136217e+02, 0., 0., 1. ]
+distortion_coefficients: !!opencv-matrix
+   rows: 5
+   cols: 1
+   dt: d
+   data: [ -3.9478938399374452e-01, 1.6288159087710818e-01,
+       -1.8565610712959927e-02, -7.0112574756757643e-03, 0. ]
+avg_reprojection_error: 4.5805459213906724e-01
@@ -0,0 +1,22 @@
+%YAML:1.0
+---
+calibration_time: "Tue 07 May 2019 10:03:44 AM CEST"
+image_width: 3072
+image_height: 1728
+board_width: 8
+board_height: 6
+square_size: 2.4799999237060547e+01
+flags: 0
+camera_matrix: !!opencv-matrix
+   rows: 3
+   cols: 3
+   dt: d
+   data: [ 2.6027348174982544e+03, 0., 1.4808496083807213e+03, 0.,
+       2.6008830910556521e+03, 6.7577068120137187e+02, 0., 0., 1. ]
+distortion_coefficients: !!opencv-matrix
+   rows: 5
+   cols: 1
+   dt: d
+   data: [ -3.4912899377320661e-01, 1.5704840296202566e-01,
+       6.4926875404798358e-03, 5.7293259996249049e-03, 0. ]
+avg_reprojection_error: 4.0040122960491076e-01
@@ -0,0 +1,3 @@
+-7.327951322519857 -89.11104695972499 9201.012307315486 
+-3.2739347567332553 -42.14373356189513 3668.5149400959867 
+-0.0009310035308911926 -0.010684335913469737 0.9999999999999999 
@@ -0,0 +1,3 @@
+-5.575118981764042 19.15613757077127 7116.209593619794 
+-3.2016502214672586 11.087267568165238 4660.384266971425 
+-0.0007553414127200226 0.002676057142671632 1.0 
@@ -0,0 +1,3 @@
+-0.6209787370656474 -57.192772931734694 6553.189887121747 
+-0.655304496315561 -28.195100762686465 3423.351597211246 
+-0.0001275987755308679 -0.007884524572727637 1.0 
@@ -0,0 +1,3 @@
+21.441694474392808 72.45500438933801 4652.4523150318 
+9.195810184174585 36.76655098601718 1801.6321216143722 
+0.0027471614789484795 0.009610371445379061 1.0 
@@ -0,0 +1,3 @@
+23.89713091621312 60.523880117656084 7778.591483347838 
+11.708054761838232 41.819183783017614 1126.9348661897782 
+0.0029807026524197224 0.008783281479831609 0.9999999999999999 
@@ -0,0 +1,3 @@
+-2.986713098145805 -21.641128011999868 6870.903103366085 
+-2.2937864581163416 -14.410613254705751 4594.877638262097 
+-0.0004752252182089383 -0.003112785793525571 1.0 
@@ -0,0 +1,3 @@
+0.797119441300711 35.495698816749574 2225.384878217832 
+0.3510443326339413 59.809425152982726 1419.4212456173686 
+-1.8197506232485688e-05 0.01820976131616732 1.0 
@@ -0,0 +1,3 @@
+2.213864892663308 -21.831117643802187 4060.61306610222 
+0.9075230321117119 -7.525265027425343 1268.217122301812 
+0.0005431549026467866 -0.0054930502425022745 1.0 
@@ -0,0 +1,3 @@
+-8.65061527352736 -23.247834477892475 4009.9545358742134 
+-2.2093309523396223 -8.398154406654037 1529.4606465341913 
+-0.001952580524058866 -0.006089308376531831 1.0 
@@ -0,0 +1,3 @@
+-8.665173353556622 -19.056833294770822 3827.027102449301 
+-2.728678410673174 -6.695956964089541 1523.1971944608492 
+-0.0020218867007343213 -0.004877696027397352 1.0 
@@ -0,0 +1,3 @@
+-0.5624066154780419 -26.36968028701744 3384.7920236393156 
+-0.6590389521668137 -8.709883019993336 1162.7049573736972 
+-0.00023421305979212285 -0.006824826866955653 0.9999999999999999 
@@ -0,0 +1,3 @@
+-0.9691844827251948 -16.63913290283819 3991.384826153086 
+-0.47672518258283236 -11.365071723693541 1770.7201067235435 
+-0.0003215600914247826 -0.005142032883105236 1.0 
@@ -0,0 +1,3 @@
+-1.5172087045737013 5.226213011053854 2493.822558690447 
+-1.4892268949636678 4.265359557115566 3382.185465939972 
+-0.000492856899218649 0.0016917382715537009 1.0 
@@ -0,0 +1,3 @@
+3.671630456405992 102.56885593938215 3570.381264275829 
+1.5985035927893978 46.57491080817888 -1491.4874345505793 
+0.0006446393888135914 0.027894339637393125 1.0 
@@ -0,0 +1,3 @@
+0.44666021794890903 -32.932455985363575 3557.76647014547 
+1.0444994361599629 -37.23760709667856 4404.4517853552425 
+0.00029114742043039743 -0.010721230871024154 1.0 
@@ -0,0 +1,3 @@
+0.1982518156851575	-13.22038303683631	595.3838470607648	
+-0.4806350794284377	-6.567788488139482	598.1732743332492	
+-0.00016595895699412456	-0.007499178419813009	1.0
@@ -0,0 +1,3 @@
+-0.4122368700442484 -10.479982981650977 812.1821012303 
+-0.6536846365005352 -4.951476039607947 512.0831604767536 
+-0.00042630219831388434 -0.006003223898658603 1.0 
@@ -1,156 +1,603 @@
-#include <iostream>
-#include <signal.h>
-#include <stdlib.h>     /* srand, rand */
-//#include <unistd.h>
-#include <mutex>
+#include <time.h>

-#include "CenternetDetection.h"
-#include "MobilenetDetection.h"
+#include "utils.h"
 #include "Yolo3Detection.h"
+#include "message.h"
+#include "visualization.h"
+#include "configuration.h"
+
+#include "tracker.h"
+#include "../masa_protocol/include/send.hpp"
+#include "../masa_protocol/include/serialize.hpp"
+
+// #include <assert.h>
+// #include <unistd.h>
+// #include <mutex>
+// #include <ctime>
+// #include <pthread.h>
+// #include <signal.h>
+// #include <chrono>
+// #include <math.h>
+// #include <typeinfo>
+// #include <iostream>
+
+#define MAX_DETECT_SIZE 100

 bool gRun;
+std::chrono::steady_clock::time_point local_clock_start;
+std::mutex mutexgRun;
+std::string obj_class[10]{"person", "car", "truck", "bus", "motor", "bike", "rider", "traffic light", "traffic sign", "train"};
+//mutex for some opencv operations
+std::mutex mutex_cv;
+Show_t updates;
+bool SAVE_RESULT = false;

-void sig_handler(int signo) {
-    std::cout<<"request gateway stop\n";
+void sig_handler(int signo)
+{
+    std::cout << "request gateway stop\n";
+    mutexgRun.lock();
    gRun = false;
+    mutexgRun.unlock();
 }

-int main(int argc, char *argv[]) {
+void *readVideoCapture(void *x_void_ptr)
+{
+    std::cout << "readVideoCapture start...\n";

-    signal(SIGINT, sig_handler);
-
-    // get config file path and read it
-    #ifdef __linux__ 
-        std::string config_file = "../demo/demoConfig.yaml";
-    #elif _WIN32
-        std::string config_file = "..\\..\\..\\demo\\demoConfig.yaml";
-    #endif
-    if(argc > 1)
-        config_file = argv[1]; 
-    
-    YAML::Node conf =  YAMLloadConf(config_file);
-    if(!conf)
-        FatalError("Problem with config file");
-
-    // read settings from config file
-    std::string net = YAMLgetConf<std::string>(conf, "net", "yolo4tiny_fp32.rt");
-    if(!fileExist(net.c_str()))
-        FatalError("The given network does not exist. Create the rt first.");
-
-    #ifdef __linux__
-        std::string input = YAMLgetConf<std::string>(conf, "input", "../demo/yolo_test.mp4");
-    #elif _WIN32
-        std::string input = YAMLgetConf<std::string>(conf, "win_input", "..\\..\\..\\demo\\yolo_test.mp4");
-    #endif
-        if(!fileExist(input.c_str()))
-        FatalError("The given input video does not exist.");
-
-    char ntype          = YAMLgetConf<char>(conf, "ntype", 'y');
-    int n_classes       = YAMLgetConf<int>(conf, "n_classes", 80);
-    int n_batch         = YAMLgetConf<int>(conf, "n_batch", 1);
-    if(n_batch < 1 || n_batch > 64)
-        FatalError("Batch dim not supported");
-    float conf_thresh   = YAMLgetConf<float>(conf, "conf_thresh", 0.3);
-    bool show           = YAMLgetConf<bool>(conf, "show", true);
-    bool save           = YAMLgetConf<bool>(conf, "save", false);
-
-    std::cout   <<"Net settings - net: "<< net
-                <<", ntype: "<< ntype
-                <<", n_classes: "<< n_classes
-                <<", n_batch: "<< n_batch
-                <<", conf_thresh: "<< conf_thresh<<"\n"; 
-    std::cout   <<"Demo settings - input: "<< input
-                <<", show: "<< show
-                <<", save: "<< save<<"\n\n"; 
-    
-    // create detection network
-    tk::dnn::Yolo3Detection yolo;
-    tk::dnn::CenternetDetection cnet;
-    tk::dnn::MobilenetDetection mbnet;
-
-    tk::dnn::DetectionNN *detNN;  
-
-    switch(ntype)
+    Frame_t *info_f = (Frame_t *)x_void_ptr;
+    mutex_cv.lock();
+    cv::VideoCapture cap(info_f->input, cv::CAP_FFMPEG);
+    mutex_cv.unlock();
+    cv::Mat frame_loc, frame0;
+    int frame_nbr_loc = 0;
+    // bool to_show = false;
+    if (!cap.isOpened())
    {
-        case 'y':
-            detNN = &yolo;
-            break;
-        case 'c':
-            detNN = &cnet;
-            break;
-        case 'm':
-            detNN = &mbnet;
-            n_classes++;
-            break;
-        default:
-        FatalError("Network type not allowed (3rd parameter)\n");
+        mutexgRun.lock();
+        gRun = false;
+        mutexgRun.unlock();
    }
-
-    detNN->init(net,n_classes,n_batch,conf_thresh);
-
-    // open video stream
-    cv::VideoCapture cap(input);
-    if(!cap.isOpened())
-        gRun = false; 
    else
-        std::cout<<"camera started\n";
+        std::cout << "camera started\n";

-    cv::VideoWriter resultVideo;
-    if(save) {
-        int w = cap.get(cv::CAP_PROP_FRAME_WIDTH);
-        int h = cap.get(cv::CAP_PROP_FRAME_HEIGHT);
-        resultVideo.open("result.mp4", cv::VideoWriter::fourcc('M','P','4','V'), 30, cv::Size(w, h));
+    // cap.set(cv::CAP_PROP_BUFFERSIZE,3);
+    // std::cout<<"buf size: "<<cap.get(CV_CAP_PROP_BUFFERSIZE)<<std::endl;
+    auto start_t = std::chrono::steady_clock::now();
+    auto step_t = std::chrono::steady_clock::now();
+    auto end_t = std::chrono::steady_clock::now();
+    auto current_timestamp = std::chrono::steady_clock::now();
+
+    // compute fps and find camera's clock
+    double shift, mean_time = 0;
+    std::cout << "Frames per second using video.get(cv::CAP_PROP_FPS) : " << cap.get(cv::CAP_PROP_FPS) << std::endl;
+    std::cout << "readVideoCapture computes frame rate...\n";
+    // //compute frame rate
+    int i = 0;
+    int num_f = 120;
+    // the first 20 frames are null
+    while (i < 21)
+    {
+        cap >> frame_loc;
+        i++;
    }

-    if(show)
-        cv::namedWindow("detection", cv::WINDOW_NORMAL);
+    i = 0;
+    start_t = std::chrono::steady_clock::now();
+    while (i < num_f)
+    {
+        step_t = std::chrono::steady_clock::now();
+        cap >> frame_loc;
+        mean_time = mean_time + std::chrono::duration_cast<std::chrono::milliseconds>(std::chrono::steady_clock::now() - step_t).count();
+        std::cout << " step " << i << " : " << std::chrono::duration_cast<std::chrono::milliseconds>(std::chrono::steady_clock::now() - step_t).count() << " ms" << std::endl;
+        i++;
+    }
+    end_t = std::chrono::steady_clock::now();
+
+    std::cout << "Capturing " << num_f << " frames" << std::endl;
+    std::cout << " Time taken : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t - start_t).count() << " ms" << std::endl;
+
+    /*
+    mean_time indicates the milliseconds from a frame and the next. (frame rate)
+    local_clock_sync is the camera clock.
+    shift is the difference from local camera clock and local process clock.
+    a frame is allowed if its local timestamp minus its local clock is less then a tollerance, 
+    otherwise it will be considered old.
+    */
+    auto local_clock_sync = std::chrono::steady_clock::now();
+    mean_time = mean_time / num_f;
+    shift = ((double)std::chrono::duration_cast<std::chrono::milliseconds>(local_clock_sync - local_clock_start).count()) / mean_time;
+    shift = (shift - (int)shift) * mean_time;
+    std::cout << ".-------------------------------\n";
+    std::cout << " mean time: " << mean_time << std::endl;
+    std::cout << " shift: " << shift << std::endl;
+    std::cout << " TIMEDIFFERENCE:  " << std::chrono::duration_cast<std::chrono::milliseconds>(local_clock_sync - local_clock_start).count() << std::endl;
+    std::cout << "\n\n\n\n";
+    std::cout << "readVideoCapture start to capture...\n";
+    while (gRun)
+    {
+        // mutex_cv.lock();
+        cap >> frame_loc;
+        // mutex_cv.unlock();
+        current_timestamp = std::chrono::steady_clock::now();
+        shift = std::chrono::duration_cast<std::chrono::milliseconds>(current_timestamp - local_clock_sync).count();
+        std::cout << " RELATIVE TIMESTAMP FRAME : " << shift << " ms" << std::endl;
+        shift = shift / mean_time;
+        shift = (shift - (int)shift) * mean_time;
+        shift = (shift - mean_time / 2 >= 0) ? -(mean_time - shift) : shift;
+        std::cout << "DELAY frame_" << frame_nbr_loc << " : " << shift << " ms" << std::endl;
+        // TODO: here introduce a tollerance to discard old frame
+
+        // std::cout<< "CV_CAP_PROP_POS_MSEC:   "<< cap.get( cv::CAP_PROP_POS_MSEC) <<std::endl;
+        // std::cout<< "CV_CAP_PROP_POS_FRAMES:  "<< cap.get( cv::CAP_PROP_POS_FRAMES) <<std::endl;  // <-- the v4l2 'sequence' field
+        // std::cout<< "cv::CAP_PROP_FPS:  "<< cap.get( cv::CAP_PROP_FPS)<<std::endl;
+        // std::cout << "Format: " << cap.get(CV_CAP_PROP_FORMAT) << "\n";
+        // CAP_PROP_POS_MSEC Current position of the video file in milliseconds or video capture timestamp.
+        std::cout << "id: " << cap.get(cv::CAP_PROP_POS_MSEC) << std::endl;
+        // CAP_PROP_FRAME_COUNT Number of frames in the video file.
+        std::cout << "id: " << cap.get(cv::CAP_PROP_FRAME_COUNT) << std::endl;
+
+        if (!frame_loc.data)
+        {
+            usleep(1000000);
+            mutex_cv.lock();
+            cap.open(info_f->input);
+            printf("cap reinitialize\n");
+            mutex_cv.unlock();
+            continue;
+        }
+
+        end_t = std::chrono::steady_clock::now();
+        std::cout << " VC-TIME 1 : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t - start_t).count() << " ms" << std::endl;
+        start_t = end_t;
+
+        info_f->sem_vc.lock();
+        info_f->frame = frame_loc.clone();
+        info_f->frame_nbr = frame_nbr_loc;
+        info_f->sem_vc.unlock();
+        // usleep(50000);
+        end_t = std::chrono::steady_clock::now();
+        std::cout << " VC-TIME 2 : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t - start_t).count() << " ms" << std::endl;
+        start_t = end_t;
+        frame_nbr_loc++;
+    }
+    return (void *)0;
+}
+
+void *computationTask(void *x_void_ptr)
+{
+    Camera_t *camera = (Camera_t *)x_void_ptr;
+    pthread_t visual, originalshow, detectionshow, topviewshow, disparityshow;
+    pthread_t videocap;
+    tk::dnn::Yolo3Detection yolo = *(camera->yolo);
+    //create video capture thread
+    Frame_t info_f;
+    info_f.input = camera->input;
+    if (pthread_create(&videocap, NULL, readVideoCapture, (void *)&info_f))
+    {
+        fprintf(stderr, "Error creating thread\n");
+        return (void *)1;
+    };
+
+    bool to_show = camera->to_show;
+    double adfGeoTransform[6];
+    for (int i = 0; i < 6; i++)
+        adfGeoTransform[i] = camera->adfGeoTransform[i];
+
+    ModFrame_t info_show;
+    if (to_show)
+    {
+        // initialize updates struct
+        updates.update_o = false;
+        updates.update_de = false;
+        updates.update_t = false;
+        updates.update_di = false;
+        if (pthread_create(&visual, NULL, show_updates, (void *)NULL))
+        {
+            fprintf(stderr, "Error creating thread\n");
+            return (void *)1;
+        };
+        if (pthread_create(&originalshow, NULL, originalFrame, (void *)&info_f))
+        {
+            fprintf(stderr, "Error creating thread\n");
+            return (void *)1;
+        };
+        if (pthread_create(&disparityshow, NULL, disparityFrame, (void *)&info_f))
+        {
+            fprintf(stderr, "Error creating thread\n");
+            return (void *)1;
+        };
+        info_show.H = cv::Mat(cv::Size(3, 3), CV_64FC1);
+        if (pthread_create(&detectionshow, NULL, detectionFrame, (void *)&info_show))
+        {
+            fprintf(stderr, "Error creating thread\n");
+            return (void *)1;
+        };
+        if (pthread_create(&topviewshow, NULL, topviewFrame, (void *)&info_show))
+        {
+            fprintf(stderr, "Error creating thread\n");
+            return (void *)1;
+        };
+    }
+    char *pmatrix = camera->pmatrix;
+    /*projection matrix from camera to map*/
+    cv::Mat H(cv::Size(3, 3), CV_64FC1);
+    read_projection_matrix(H, pmatrix);
+    assert(cv::countNonZero(H) > 0);
+    // std::cout<<H<<std::endl;
+    // return (void*)0;
+    /*Camera calibration*/
+    cv::Mat cameraMat, distCoeff;
+    readCameraCalibrationYaml(camera->cameraCalib, cameraMat, distCoeff);
+    std::cout << cameraMat << std::endl;
+    std::cout << distCoeff << std::endl;
+
+    /*GPS information*/
+    std::vector<ObjCoords> coords;
+
+    /*socket*/
+    Communicator Comm(SOCK_DGRAM);
+    Comm.open_client_socket((char *)"127.0.0.1", 8888);
+
+    Message *m = new Message;
+    m->cam_idx = camera->CAM_IDX;
+    m->lights.clear();
+    /*Conversion for tracker, from gps to meters and viceversa*/
+    // mutex_cv.lock();
+    geodetic_converter::GeodeticConverter gc;
+    gc.initialiseReference(44.655540, 10.934315, 0);
+    // mutex_cv.unlock();
+    double east, north, up;
+    // double lat, lon, alt;
+    /*Mask info*/
+    cv::Mat mask = cv::imread(camera->maskfile, cv::IMREAD_GRAYSCALE);
+    cv::Mat maskOrient = cv::imread(camera->maskFileOrient);
+    // cv::Mat maskOrient = cv::imread(camera->maskFileOrient, 0);
+
+    /*for(int i=0; i< mask.cols; i++)
+    {
+        for(int j=0; j< mask.rows; j++)
+        {
+            std::cout<<maskOrient.at<cv::Vec3b>(i,j)    <<std::endl;
+        }
+    }
+    
+   
+    return 0;*/
+    /*tracker infos*/
+    std::vector<Tracker> trackers;
+    std::vector<Data> cur_frame;
+    int initial_age = -5;
+    int age_threshold = -8;
+    int n_states = 5;
+    float dt = 0.03;
+
+    int frame_nbr = 0;
+
+    //save video
+    /*cv::VideoWriter outputVideo;
+    cv::Size S = cv::Size((int)cap.get(cv::CAP_PROP_FRAME_WIDTH), //Acquire input size
+                          (int)cap.get(cv::CAP_PROP_FRAME_HEIGHT));
+    outputVideo.open("test.avi", static_cast<int>(cap.get(cv::CAP_PROP_FOURCC)), cap.get(cv::CAP_PROP_FPS), S, true);*/
+
+    cv::Mat map1, map2;
+    auto start_t = std::chrono::steady_clock::now();
+    auto step_t = std::chrono::steady_clock::now();
+    auto end_t = std::chrono::steady_clock::now();
+    // auto step_t_segmentation = std::chrono::steady_clock::now();
+    // auto end_t_segmentation = std::chrono::steady_clock::now();
+
+    //TODO: move in a thread
+    // // information for the disparity map
+    // std::vector <cv::Rect> pre_rois;
+    // cv::Mat pre_frame;
+    cv::Mat orig_frame;
+    // cv::Mat canny, pre_canny, canny_RGB, pre_canny_RGB;
+    // cv::Mat canny_img;
+
+    // box variable
+    tk::dnn::box b;
+    int x0, h, y1; //w, x1, y0;
+    int objClass;
+    std::string det_class;
+    ;
+    // float prob;
+    cv::Scalar intensity;
+
+
+    // cv::VideoWriter resultVideo;
+    // if(SAVE_RESULT) {
+    //     int w = cap.get(cv::CAP_PROP_FRAME_WIDTH);
+    //     int h = cap.get(cv::CAP_PROP_FRAME_HEIGHT);
+    //     resultVideo.open("result.mp4", cv::VideoWriter::fourcc('M','P','4','V'), 30, cv::Size(w, h));
+    // }

    cv::Mat frame;
-    std::vector<cv::Mat> batch_frame;
-    std::vector<cv::Mat> batch_dnn_input;
+    cv::Mat frame_crop;
+    cv::Mat dnn_input;
+    bool first_iteration = true;

-    // start detection loop
-    gRun = true;
-    while(gRun) {
-        batch_dnn_input.clear();
-        batch_frame.clear();
-        
-        for(int bi=0; bi< n_batch; ++bi){
-            cap >> frame; 
-            if(!frame.data) 
-                break;
-            
-            batch_frame.push_back(frame);
+    while (gRun)
+    {
+        TIMER_START
+        start_t = std::chrono::steady_clock::now();
+        step_t = start_t;

-            // this will be resized to the net format
-            batch_dnn_input.push_back(frame.clone());
-        } 
-        if(!frame.data) 
-            break;
-    
-        //inference
-        detNN->update(batch_dnn_input, n_batch);
-        detNN->draw(batch_frame);
+        info_f.sem_vc.lock();
+        frame = info_f.frame.clone();
+        if (info_f.frame_nbr - frame_nbr > 1)
+            std::cout << "more than one - f_n (diff " << info_f.frame_nbr - frame_nbr << ")\n";
+        frame_nbr = info_f.frame_nbr;
+        info_f.sem_vc.unlock();
+        std::cout << "f_n: " << frame_nbr << std::endl;
+        // if (!frame.data)
+        if (frame_nbr == 0)
+        {
+            usleep(1000000);
+            printf("no frame received\n");
+            continue;
+        }
+        orig_frame = frame.clone();
+        // mutex_cv.lock();
+        if (first_iteration)
+            cv::initUndistortRectifyMap(cameraMat, distCoeff, cv::Mat(), cameraMat, frame.size(), CV_16SC2, map1, map2);
+        cv::Mat temp = frame.clone();
+        cv::remap(temp, frame, map1, map2, 1);
+        //undistort(temp, frame, cameraMat, distCoeff);
+        // mutex_cv.unlock();

-        if(show){
-            for(int bi=0; bi< n_batch; ++bi){
-                cv::imshow("detection", batch_frame[bi]);
-                cv::waitKey(1);
+        // this will be resized to the net format
+        dnn_input = frame.clone();
+        // TODO: async infer
+        yolo.update(dnn_input);
+        int num_detected = yolo.detected.size();
+        if (num_detected > MAX_DETECT_SIZE)
+            num_detected = MAX_DETECT_SIZE;
+
+        coords.clear();
+        end_t = std::chrono::steady_clock::now();
+        std::cout << " TIME 1 : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t - step_t).count() << " ms" << std::endl;
+        step_t = end_t;
+        // draw dets
+        std::cout << "camera: " << camera->CAM_IDX << " - num detected: " << num_detected << std::endl;
+
+        //TODO: move in a thread
+        // //preprocessing frame
+        // step_t_segmentation = std::chrono::steady_clock::now();
+        // // src_gray
+        // canny_img = img_laplacian(orig_frame,0);
+        // cv::Canny(canny_img, canny, 100, 100*2 );
+        // // sprintf(buf_frame_crop_name,"../demo/demo/data/img_disparity/%d_%d_canny.jpg",frame_nbr, 999);
+        // // cv::imwrite(buf_frame_crop_name, canny);
+        // end_t_segmentation = std::chrono::steady_clock::now();
+        // std::cout << " - TIME END pre canny : "<<std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms"<<std::endl;
+        // step_t_segmentation = end_t_segmentation;
+        // // std::cout<<"o: "<<orig_frame.cols<<" - "<<orig_frame.rows<<std::endl;
+        // // std::cout<<"canny: "<<canny.cols<<" - "<<canny.rows<<std::endl;
+        // // std::cout<<"pre: "<<pre_canny.cols<<" - "<<pre_canny.rows<<std::endl;
+        // if(!first_iteration)
+        // {
+        //     // backtorgb = cv::cvtColor(pre_canny,cv::COLOR_GRAY2RGB)
+        //     cv::cvtColor(pre_canny, pre_canny_RGB, cv::COLOR_GRAY2RGB);
+        //     cv::cvtColor(canny, canny_RGB, cv::COLOR_GRAY2RGB);
+        //     disparity_frame = frame_disparity(pre_canny_RGB, canny_RGB, frame_nbr, 999, 0);
+        //     std::cout<<"size: "<<disparity_frame.rows<<" - "<<disparity_frame.cols<<std::endl;
+        //     if (disparity_frame.rows == 0 || disparity_frame.cols == 0)
+        //         return -1;
+        //     if (disparity_frame.empty())
+        //     { // only fools don't check...
+        //         std::cout << "image not loaded !" << std::endl;
+        //         return -1;
+        //     }
+        //     end_t_segmentation = std::chrono::steady_clock::now();
+        //     std::cout << " TIME canny : frame_disparity : "<<std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms"<<std::endl;
+        //     step_t_segmentation = end_t_segmentation;
+
+        //     // //--------------------------------
+        //     // //frame box disparity on the original image
+        //     // step_t_segmentation = std::chrono::steady_clock::now();
+        //     // frame_box_disparity(pre_frame, frame, pre_rois, frame_nbr);
+        //     // // reset pre_rois for the new roi of the current frame
+        //     // // pre_rois.erase(pre_rois.begin(), pre_rois.end());
+        //     // end_t_segmentation = std::chrono::steady_clock::now();
+        //     // std::cout << " TIME Frame disparity : "<<std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms"<<std::endl;
+        //     // step_t_segmentation = end_t_segmentation;
+
+        //     // //frame box disparity on the preprocessed image
+        //     // cv::cvtColor(pre_canny, pre_canny_RGB, cv::COLOR_GRAY2RGB);
+        //     // cv::cvtColor(canny, canny_RGB, cv::COLOR_GRAY2RGB);
+        //     // frame_box_disparity(pre_canny_RGB, canny_RGB, pre_rois, frame_nbr);
+        //     // // reset pre_rois for the new roi of the current frame
+        //     // pre_rois.erase(pre_rois.begin(), pre_rois.end());
+        //     // end_t_segmentation = std::chrono::steady_clock::now();
+        //     // std::cout << " TIME Canny Frame disparity : "<<std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms"<<std::endl;
+        //     // step_t_segmentation = end_t_segmentation;
+        //     // //---------------------------------
+        // }
+
+        // compute some metrics on the whole frame
+        // segmentation(pre_frame, frame, frame_nbr, 0, 0);
+
+        for (int i = 0; i < num_detected; i++)
+        {
+            b = yolo.detected[i];
+            x0 = b.x;
+            // w = b.w;
+            // x1 = b.x + w;
+            // y0 = b.y;
+            h = b.h;
+            y1 = b.y + h;
+            objClass = b.cl;
+            det_class = obj_class[b.cl];
+            // prob = b.prob;
+
+            intensity = mask.at<uchar>(cv::Point(int(x0 + b.w / 2), y1));
+
+            if (intensity[0])
+            {
+
+                if (objClass < 6)
+                {
+
+                    // find the rectangular on the frame (sub-figure)
+                    // roi.x = (x0 > 0)? x0 : 0;
+                    // roi.y = (y0 > 0)? y0 : 0;
+                    // // std::cout<<"x "<<roi.x<<" - y "<<roi.y<<std::endl;
+                    // roi.width = (roi.x+w >= frame.cols)? frame.cols-1-roi.x : w;
+                    // roi.height = (roi.y+h >= frame.rows)? frame.rows-1-roi.y : h;
+                    // std::cout<<"w "<<roi.width<<" - h "<<roi.height<<std::endl;
+                    // std::cout<<"wf "<<frame.cols<<" - hf "<<frame.rows<<std::endl;
+                    // std::cout<<"---"<<std::endl;
+                    // std::cout<<"x "<<roi.x<<" to "<<roi.width+roi.x<<" wf "<<frame.cols<<std::endl;
+                    // std::cout<<"y "<<roi.y<<" to "<<roi.height+roi.y<<" hf "<<frame.rows<<std::endl;
+                    //update pre_roi for the next frame
+                    // pre_rois.push_back(roi);
+
+                    // segmentation(frame(roi), frame(roi), frame_nbr, i, 1);
+
+                    /////
+                    convert_coords(coords, x0 + b.w / 2, y1, objClass, H, adfGeoTransform);
+
+                    // //std::cout<<objClass<<" ("<<prob<<"): "<<x0<<" "<<y0<<" "<<x1<<" "<<y1<<"\n";
+                    // cv::rectangle(frame, cv::Point(x0, y0), cv::Point(x1, y1), camera->yolo.colors[objClass], 2);
+                    // // draw label
+                    // int baseline = 0;
+                    // float fontScale = 0.5;
+                    // int thickness = 2;
+                    // cv::Size textSize = getTextSize(det_class, cv::FONT_HERSHEY_SIMPLEX, fontScale, thickness, &baseline);
+                    // cv::rectangle(frame, cv::Point(x0, y0), cv::Point((x0 + textSize.width - 2), (y0 - textSize.height - 2)), camera->yolo.colors[b.cl], -1);
+                    // cv::putText(frame, det_class, cv::Point(x0, (y0 - (baseline / 2))), cv::FONT_HERSHEY_SIMPLEX, fontScale, cv::Scalar(255, 255, 255), thickness);
+                }
            }
        }
-        if(n_batch == 1 && save)
-            resultVideo << frame;
+
+        end_t = std::chrono::steady_clock::now();
+        std::cout << " TIME 2 : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t - step_t).count() << " ms" << std::endl;
+        step_t = end_t;
+        //convert from latitude and longitude to meters for ekf
+        cur_frame.clear();
+        for (size_t i = 0; i < coords.size(); i++)
+        {
+            gc.geodetic2Enu(coords[i].lat_, coords[i].long_, 0, &east, &north, &up);
+            cur_frame.push_back(Data(east, north, frame_nbr, coords[i].class_));
+        }
+        if (first_iteration)
+        {
+            // if there aren't detections and it is the first iteration, we can't initialize the tracker, so continue
+            if (cur_frame.empty())
+                continue;
+            for (auto f : cur_frame)
+                trackers.push_back(Tracker(f, initial_age, dt, n_states));
+        }
+        else
+        {
+            Track(cur_frame, dt, n_states, initial_age, age_threshold, trackers);
+        }
+        std::cout << "There are " << trackers.size() << " trackers" << std::endl;
+        //prepare message with tracker info
+        if (trackers.size() != 0)
+        {
+            // mutex_cv.lock();
+            addRoadUserfromTracker(trackers, m, gc, maskOrient, adfGeoTransform, H);
+            // mutex_cv.unlock();
+            //prepare the message with detection info
+            //prepare_message(m, coords, CAM_IDX);
+            //send message
+            if (!m->objects.empty())
+                Comm.send_message(m);
+        }
+
+        if (to_show)
+        {
+            //populate the ModFrame_t
+            info_show.sem.lock();
+            info_show.original_frame = frame.clone();
+            // std::vector<Tracker> trackers;
+            info_show.trackers = trackers;
+            // geodetic_converter::GeodeticConverter gc;
+            info_show.gc = gc;
+            for (int i = 0; i < 6; i++)
+                info_show.adfGeoTransform[i] = adfGeoTransform[i];
+            // cv::Mat H;
+            info_show.H = H.clone();
+            info_show.yolo = yolo;
+            // std::copy(camera->yolo.begin(), camera->yolo.end(), info_show.yolo.begin());
+            info_show.mask = mask.clone();
+            info_show.sem.unlock();
+        }
+
+        // update pre_frame for the disparity map
+        // pre_frame = orig_frame.clone();
+        // pre_canny = canny.clone();
+        if (first_iteration)
+            first_iteration = false;
+
+        frame_nbr++;
+        std::cout << camera->CAM_IDX << " camera thread: ";
+        TIMER_STOP
    }
-
-    std::cout<<"detection end\n";   
-    
-    double mean = 0; 
-    std::cout<<COL_GREENB<<"\n\nTime stats:\n";
-    std::cout<<"Min: "<<*std::min_element(detNN->stats.begin(), detNN->stats.end())<<" ms\n";
-    std::cout<<"Max: "<<*std::max_element(detNN->stats.begin(), detNN->stats.end())<<" ms\n";    
-    for(int i=0; i<detNN->stats.size(); i++) mean += detNN->stats[i]; mean /= detNN->stats.size();
-    std::cout<<"Avg: "<<mean<<" ms\t"<<1000/(mean)<<" FPS\n"<<COL_END;   
-
-    return 0;
+    return (void *)0;
 }

+int main(int argc, char *argv[])
+{
+
+    std::cout << "detection\n";
+    signal(SIGINT, sig_handler);
+    srand(time(NULL));
+
+    Parameters_t par;
+
+    if(!read_parameters(argc, argv, &par))
+        return -1;
+    
+    tk::dnn::Yolo3Detection yolo[par.n_cameras];
+    for(int i=0; i<par.n_cameras; i++)
+    {
+        yolo[i].init(par.net);
+        yolo[i].thresh = 0.25;    
+        
+        // if(SAVE_RESULT)
+        //     resultVideo << frame;
+    }
+    // tk::dnn::Yolo3Detection yolo;
+    // yolo.init(net);
+    // yolo.thresh = 0.25;    
+    
+    gRun = true;
+    // start the local clock. It is used to check the incoming frames (by different cameras) 
+    local_clock_start = std::chrono::steady_clock::now();
+
+    /*GPS information*/
+    double *adfGeoTransform = (double *)malloc(6 * sizeof(double));
+    readTiff(par.tiffile, adfGeoTransform);
+    // Camera_t cameras[par.n_cameras]; 
+    for(int i=0; i<par.n_cameras; i++)
+    {
+        for(int j = 0; j < 6; j++ )
+            par.cameras[i].adfGeoTransform[j] = adfGeoTransform[j];
+        par.cameras[i].yolo = &yolo[i];
+        // par.cameras[i].yolo.init(par.net);
+        // par.cameras[i].yolo.thresh = 0.25;
+        // cameras[i].yolo = yolo[i];
+        // cameras[i].yolo = yolo;
+        
+    }
+    pthread_t camera_task[par.n_cameras];
+    for(int i=0; i<par.n_cameras; i++)
+    {
+        std::cout<<"creating thread\n";
+        if(pthread_create(&camera_task[i], NULL, computationTask, (void*)&(par.cameras[i])))
+        {
+            fprintf(stderr, "error creating thread\n");
+            return 1;
+        }
+    }
+    for(int i=0; i<par.n_cameras; i++)
+    {
+        pthread_join(camera_task[i], NULL);
+    }
+    std::cout <<" free adfGeoT \n";
+    free(adfGeoTransform);
+    std::cout << "detection end\n";
+    return 0;
+}
@@ -1,159 +0,0 @@
-#include <iostream>
-#include <signal.h>
-#include <stdlib.h>     /* srand, rand */
-//#include <unistd.h>
-#include <mutex>
-
-#include "demo_utils.h"
-#include "CenternetDetection3D.h"
-
-bool gRun;
-bool SAVE_RESULT = false;
-
-void sig_handler(int signo) {
-    std::cout<<"request gateway stop\n";
-    gRun = false;
-}
-
-int main(int argc, char *argv[]) {
-
-    std::cout<<"detection\n";
-    signal(SIGINT, sig_handler);
-
-
-    std::string net = "dla34_cnet3d_fp32.rt";
-    if(argc > 1)
-        net = argv[1]; 
-    #ifdef __linux__ 
-        std::string input = "../demo/yolo_test.mp4";
-    #elif _WIN32
-        std::string input = "..\\..\\..\\demo\\yolo_test.mp4";
-    #endif
-
-    if(argc > 2)
-        input = argv[2]; 
-    std::string calib_params = "";
-    if(argc > 3)
-        calib_params = argv[3]; 
-    char ntype = 'c';
-    if(argc > 4)
-        ntype = argv[4][0]; 
-    int n_classes = 3;
-    if(argc > 5)
-        n_classes = atoi(argv[5]); 
-    int n_batch = 1;
-    if(argc > 6)
-        n_batch = atoi(argv[6]); 
-        
-    bool show = true;
-    if(argc > 7)
-        show = atoi(argv[7]); 
-    float conf_thresh=0.3;
-    if(argc > 8)
-        conf_thresh = atof(argv[8]);     
-
-    if(n_batch < 1 || n_batch > 64)
-        FatalError("Batch dim not supported");
-
-    if(!show)
-	    SAVE_RESULT = true;
-
-    tk::dnn::CenternetDetection3D cnet;
-
-    tk::dnn::DetectionNN3D *detNN;  
-
-    switch(ntype)
-    {
-        case 'c':
-            detNN = &cnet;
-            break;
-        default:
-        FatalError("Network type not allowed (3rd parameter)\n");
-    }
-    std::vector<cv::Mat> calibs;
-    if(!calib_params.empty() && calib_params!="NULL") {
-        std::cout<<"calib_params: "<<calib_params<<std::endl;
-        cv::Mat calib;
-        // the calibration matrix must be a 3x3 matrix
-        readCalibrationMatrix(calib_params, calib);
-        for(int bi=0; bi< n_batch; ++bi)
-            calibs.push_back(calib);
-    }
-    detNN->init(net, n_classes, n_batch, conf_thresh, calibs);
-
-    gRun = true;
-
-    cv::VideoCapture cap(input);
-    if(!cap.isOpened())
-        gRun = false; 
-    else
-        std::cout<<"camera started\n";
-
-    cv::VideoWriter resultVideo;
-    if(SAVE_RESULT) {
-        int w = cap.get(cv::CAP_PROP_FRAME_WIDTH);
-        int h = cap.get(cv::CAP_PROP_FRAME_HEIGHT);
-        resultVideo.open("result.mp4", cv::VideoWriter::fourcc('M','P','4','V'), 30, cv::Size(w, h));
-    }
-    cv::Mat frame;
-    if(show)
-	    cv::namedWindow("detection", cv::WINDOW_NORMAL);
-    
-    std::vector<cv::Mat> batch_frame;
-    std::vector<cv::Mat> batch_dnn_input;
-
-    while(gRun) {
-        batch_dnn_input.clear();
-        batch_frame.clear();
-        
-        for(int bi=0; bi< n_batch; ++bi){
-            cap >> frame; 
-            if(!frame.data) 
-                break;
-            batch_frame.push_back(frame);
-
-            // this will be resized to the net format
-            batch_dnn_input.push_back(frame.clone());
-        } 
-        if(!frame.data) 
-            break;  
- 
-        //inference
-        detNN->update(batch_dnn_input, n_batch, false, nullptr, false);
-        detNN->draw(batch_frame);
-
-        if(show){
-            for(int bi=0; bi< n_batch; ++bi){
-                cv::imshow("detection", batch_frame[bi]);
-                cv::waitKey(1);
-            }
-        }
-        if(n_batch == 1 && SAVE_RESULT)
-            resultVideo << frame;
-    }
-
-    std::cout<<"detection end\n";   
-    double mean = 0; 
-    
-    std::cout<<COL_GREENB<<"\n\nTime preprocessing stats:\n";
-    std::cout<<"Min: "<<*std::min_element(detNN->pre_stats.begin(), detNN->pre_stats.end())<<" ms\n";    
-    std::cout<<"Max: "<<*std::max_element(detNN->pre_stats.begin(), detNN->pre_stats.end())<<" ms\n";    
-    for(int i=0; i<detNN->pre_stats.size(); i++) mean += detNN->pre_stats[i]; mean /= detNN->pre_stats.size();
-    std::cout<<"Avg: "<<mean<<" ms\n"<<COL_END;   
-    mean=0;
-    std::cout<<COL_GREENB<<"\n\nTime stats:\n";
-    std::cout<<"Min: "<<*std::min_element(detNN->stats.begin(), detNN->stats.end())<<" ms\n";    
-    std::cout<<"Max: "<<*std::max_element(detNN->stats.begin(), detNN->stats.end())<<" ms\n";    
-    for(int i=0; i<detNN->stats.size(); i++) mean += detNN->stats[i]; mean /= detNN->stats.size();
-    std::cout<<"Avg: "<<mean<<" ms\n"<<COL_END;   
-    mean=0;
-    std::cout<<COL_GREENB<<"\n\nTime postprocessing stats:\n";
-    std::cout<<"Min: "<<*std::min_element(detNN->post_stats.begin(), detNN->post_stats.end())<<" ms\n";    
-    std::cout<<"Max: "<<*std::max_element(detNN->post_stats.begin(), detNN->post_stats.end())<<" ms\n";    
-    for(int i=0; i<detNN->post_stats.size(); i++) mean += detNN->post_stats[i]; mean /= detNN->post_stats.size();
-    std::cout<<"Avg: "<<mean<<" ms\n"<<COL_END;   
-    
-    
-    return 0;
-}
-
@@ -1,106 +0,0 @@
-#include <iostream>
-#include <signal.h>
-#include <stdlib.h>     /* srand, rand */
-//#include <unistd.h>
-#include <mutex>
-
-#include "tkDNN/DepthNN.h"
-
-bool gRun;
-
-void sig_handler(int signo) {
-    std::cout<<"request gateway stop\n";
-    gRun = false;
-}
-
-int main(int argc, char *argv[]) {
-
-    signal(SIGINT, sig_handler);
-
-    std::string net = "monodepth2_fp32.rt";
-    if(argc > 1)
-        net = argv[1]; 
-    #ifdef __linux__ 
-        std::string input = "../demo/yolo_test.mp4";
-    #elif _WIN32
-        std::string input = "..\\..\\..\\demo\\yolo_test.mp4";
-    #endif
-    if(argc > 2)
-        input = argv[2]; 
-    bool show = true;
-    if(argc > 3)
-        show = atoi(argv[3]); 
-    bool save = true;
-    if(argc > 4)
-        save = atoi(argv[4]); 
-
-    std::cout   <<"Net settings - net: "<< net
-                <<"\n"; 
-    std::cout   <<"Demo settings - input: "<< input
-                <<", show: "<< show
-                <<", save: "<< save<<"\n\n"; 
-    
-    tk::dnn::DepthNN depthNN;  
-
-    // create depth network 
-    int n_batch = 1;
-    depthNN.init(net, n_batch);
-
-    // open video stream
-    cv::VideoCapture cap(input);
-    if(!cap.isOpened())
-        gRun = false; 
-    else
-        std::cout<<"camera started\n";
-
-    cv::VideoWriter resultVideo;
-    if(save) {
-        int w = depthNN.output_w;
-        int h = depthNN.output_h;
-        resultVideo.open("result.mp4", cv::VideoWriter::fourcc('M','P','4','V'), 30, cv::Size(w, h));
-    }
-
-    if(show)
-        cv::namedWindow("depth", cv::WINDOW_NORMAL);
-
-    cv::Mat frame;
-    std::vector<cv::Mat> batch_frame;
-    std::vector<cv::Mat> batch_dnn_input;
-
-    // start detection loop
-    gRun = true;
-    while(gRun) {
-        batch_dnn_input.clear();
-        batch_frame.clear();
-
-        //read frame
-        cap >> frame; 
-        if(!frame.data) 
-            break;
-        batch_frame.push_back(frame);
-        batch_dnn_input.push_back(frame.clone());
-    
-        //inference
-        depthNN.update(batch_dnn_input, 1);
-        if(show){
-            cv::imshow("depth", depthNN.depthMats[0]);
-            cv::waitKey(1);
-
-        }
-
-        if(save)
-            resultVideo << depthNN.depthMats[0];
-    }
-
-    std::cout<<"detection end\n";   
-    
-    double mean = 0; 
-    std::cout<<COL_GREENB<<"\n\nTime stats depth:\n";
-    std::cout<<"Min: "<<*std::min_element(depthNN.stats.begin(), depthNN.stats.end())<<" ms\n";    
-    std::cout<<"Max: "<<*std::max_element(depthNN.stats.begin(), depthNN.stats.end())<<" ms\n";    
-    for(int i=0; i<depthNN.stats.size(); i++) mean += depthNN.stats[i]; mean /= depthNN.stats.size();
-    std::cout<<"Avg: "<<mean<<" ms\t"<<1000/(mean)<<" FPS\n";   
-
-    return 0;
-}
-
@@ -1,160 +0,0 @@
-#include <iostream>
-#include <signal.h>
-#include <stdlib.h>     /* srand, rand */
-//#include <unistd.h>
-#include <mutex>
-
-#include "demo_utils.h"
-#include "CenterTrack.h"
-
-bool gRun;
-bool SAVE_RESULT = false;
-
-void sig_handler(int signo) {
-    std::cout<<"request gateway stop\n";
-    gRun = false;
-}
-
-int main(int argc, char *argv[]) {
-
-    std::cout<<"detection\n";
-    signal(SIGINT, sig_handler);
-
-
-    std::string net = "dla34_cnet3d_track_fp32.rt";
-    if(argc > 1)
-        net = argv[1]; 
-    #ifdef __linux__ 
-        std::string input = "../demo/yolo_test.mp4";
-    #elif _WIN32
-        std::string input = "..\\..\\..\\demo\\yolo_test.mp4";
-    #endif
-
-    if(argc > 2)
-        input = argv[2]; 
-    std::string calib_params = "";
-    if(argc > 3)
-        calib_params = argv[3];
-    char ntype = 'c';
-    if(argc > 4)
-        ntype = argv[4][0]; 
-    int n_classes = 3;
-    if(argc > 5)
-        n_classes = atoi(argv[5]); 
-    int n_batch = 1;
-    if(argc > 6)
-        n_batch = atoi(argv[6]); 
-    bool show = true;
-    if(argc > 7)
-        show = atoi(argv[7]); 
-    float conf_thresh=0.3;
-    if(argc > 8)
-        conf_thresh = atof(argv[8]);   
-    bool t3d = true;
-    if(argc > 9)
-        t3d = atoi(argv[9]);
-    if(n_batch < 1 || n_batch > 64)
-        FatalError("Batch dim not supported");
-
-    if(!show)
-	    SAVE_RESULT = true;
-
-    tk::dnn::CenterTrack ctrack;
-
-    tk::dnn::TrackingNN *trackNN;  
-
-    switch(ntype)
-    {
-        case 'c':
-            trackNN = &ctrack;
-            break;
-        default:
-        FatalError("Network type not allowed (3rd parameter)\n");
-    }
-    std::vector<cv::Mat> calibs;
-    if(!calib_params.empty() && calib_params!="NULL") {
-        std::cout<<"calib_params: "<<calib_params<<std::endl;
-        cv::Mat calib;
-        // the calibration matrix must be a 3x3 matrix
-        readCalibrationMatrix(calib_params, calib);
-        for(int bi=0; bi< n_batch; ++bi)
-            calibs.push_back(calib);
-    }
-    trackNN->init(net, n_classes, n_batch, conf_thresh, t3d, calibs);
-
-    gRun = true;
-
-    cv::VideoCapture cap(input);
-    if(!cap.isOpened())
-        gRun = false; 
-    else
-        std::cout<<"camera started\n";
-
-    cv::VideoWriter resultVideo;
-    if(SAVE_RESULT) {
-        int w = cap.get(cv::CAP_PROP_FRAME_WIDTH);
-        int h = cap.get(cv::CAP_PROP_FRAME_HEIGHT);
-        resultVideo.open("result.mp4", cv::VideoWriter::fourcc('M','P','4','V'), 30, cv::Size(w, h));
-    }
-    cv::Mat frame;
-    if(show)
-	    cv::namedWindow("detection", cv::WINDOW_NORMAL);
-    
-    std::vector<cv::Mat> batch_frame;
-    std::vector<cv::Mat> batch_dnn_input;
-
-    while(gRun) {
-        batch_dnn_input.clear();
-        batch_frame.clear();
-        
-        for(int bi=0; bi< n_batch; ++bi){
-            cap >> frame; 
-            if(!frame.data) 
-                break;
-            batch_frame.push_back(frame);
-
-            // this will be resized to the net format
-            batch_dnn_input.push_back(frame.clone());
-        } 
-        if(!frame.data) 
-            break;  
- 
-        //inference
-        trackNN->update(batch_dnn_input, n_batch, false, nullptr, false);
-        trackNN->draw(batch_frame);
-
-        if(show){
-            for(int bi=0; bi< n_batch; ++bi){
-                cv::imshow("detection", batch_frame[bi]);
-                cv::waitKey(1);
-            }
-        }
-        if(n_batch == 1 && SAVE_RESULT)
-            resultVideo << frame;
-    }
-
-    std::cout<<"detection end\n";   
-    double mean = 0; 
-    
-    std::cout<<COL_GREENB<<"\n\nTime preprocessing stats:\n";
-    std::cout<<"Min: "<<*std::min_element(trackNN->pre_stats.begin(), trackNN->pre_stats.end())<<" ms\n";    
-    std::cout<<"Max: "<<*std::max_element(trackNN->pre_stats.begin(), trackNN->pre_stats.end())<<" ms\n";    
-    for(int i=0; i<trackNN->pre_stats.size(); i++) mean += trackNN->pre_stats[i]; mean /= trackNN->pre_stats.size();
-    std::cout<<"Avg: "<<mean<<" ms\n"<<COL_END;   
-    mean=0;
-    std::cout<<COL_GREENB<<"\n\nTime stats:\n";
-    std::cout<<"Min: "<<*std::min_element(trackNN->stats.begin(), trackNN->stats.end())<<" ms\n";    
-    std::cout<<"Max: "<<*std::max_element(trackNN->stats.begin(), trackNN->stats.end())<<" ms\n";    
-    for(int i=0; i<trackNN->stats.size(); i++) mean += trackNN->stats[i]; mean /= trackNN->stats.size();
-    std::cout<<"Avg: "<<mean<<" ms\n"<<COL_END;   
-    mean=0;
-    std::cout<<COL_GREENB<<"\n\nTime postprocessing stats:\n";
-    std::cout<<"Min: "<<*std::min_element(trackNN->post_stats.begin(), trackNN->post_stats.end())<<" ms\n";    
-    std::cout<<"Max: "<<*std::max_element(trackNN->post_stats.begin(), trackNN->post_stats.end())<<" ms\n";    
-    for(int i=0; i<trackNN->post_stats.size(); i++) mean += trackNN->post_stats[i]; mean /= trackNN->post_stats.size();
-    std::cout<<"Avg: "<<mean<<" ms\n"<<COL_END;   
-    
-    
-    return 0;
-}
-
@@ -1,264 +0,0 @@
-
-#include <iostream>
-#include <signal.h>
-#include <stdlib.h>     /* srand, rand */
-#ifdef __linux__
-#include <unistd.h>
-#endif
-
-#include <mutex>
-#include "utils.h"
-
-#include <opencv2/core/core.hpp>
-#include <opencv2/highgui/highgui.hpp>
-#include <opencv2/videoio.hpp>
-#include <opencv2/imgproc/imgproc.hpp>
-
-#include "Yolo3Detection.h"
-#include "CenternetDetection.h"
-#include "MobilenetDetection.h"
-
-#include "evaluation.h"
-
-#include <map>
-
-void convertFilename(std::string &filename,const std::string l_folder, const std::string i_folder, const std::string l_ext,const std::string i_ext)
-{
-    filename.replace(filename.find(l_folder),l_folder.length(),i_folder);
-    filename.replace(filename.find(l_ext),l_ext.length(),i_ext);
-}
-
-int main(int argc, char *argv[]) 
-{
-    char ntype = 'y';
-    const char *config_filename = "../demo/config.yaml";
-    const char * net = "yolo4tiny_fp32.rt";
-    const char * labels_path = "../demo/COCO_val2017/all_labels.txt";
-    int n_batches = 1;
-    float confidence_thresh = 0.3;
-    bool show = false;
-    bool write_dets = false;
-    bool write_res_on_file = true;
-    bool write_coco_json = false;
-    int n_images = 5000;
-
-    bool verbose;
-    int classes, map_points, map_levels;
-    float map_step, IoU_thresh, conf_thresh;
-    double vm_total = 0, rss_total = 0;
-    double vm, rss;
-
-    //read args
-    if(argc > 1)
-        net = argv[1]; 
-    if(argc > 2)
-        ntype = argv[2][0];
-    if(argc > 3)
-        labels_path = argv[3]; 
-    if(argc > 4)
-        config_filename = argv[4]; 
-    if(argc > 5)
-        n_batches = atoi(argv[5]); 
-    if(argc > 6)
-        confidence_thresh = atof(argv[6]); 
-
-    std::cout<<"conf t: "<<confidence_thresh<<std::endl;
-
-    //check if files needed exist
-    if(!fileExist(config_filename))
-        FatalError("Wrong config file path.");
-    if(!fileExist(net))
-        FatalError("Wrong net file path.");
-    if(!fileExist(labels_path))
-        FatalError("Wrong labels file path.");
-
-    //read mAP parameters
-    tk::dnn::readmAPParams( config_filename, classes,  map_points, map_levels, map_step,
-                IoU_thresh, conf_thresh, verbose);
-
-    //extract network name from rt path
-    std::string net_name;
-    removePathAndExtension(net, net_name);
-    std::cout<<"Network: "<<net_name<<std::endl;
-
-    //open files (if needed)
-    std::ofstream times, memory, coco_json;
-
-    if(write_coco_json){
-        coco_json.open(net_name+"_COCO_res.json");
-        coco_json << "[\n";
-    }
-
-    if(write_res_on_file){
-        times.open("times_"+net_name+"_"+ std::to_string(n_batches)+"_"+std::to_string(confidence_thresh)+".csv");
-        memory.open("memory.csv", std::ios_base::app);
-        memory<<net_name+"_"+ std::to_string(n_batches)+"_"+std::to_string(confidence_thresh)<<";";
-    }
-
-    // instantiate detector
-    tk::dnn::Yolo3Detection yolo;
-    tk::dnn::CenternetDetection cnet;
-    tk::dnn::MobilenetDetection mbnet;
-    tk::dnn::DetectionNN *detNN;  
-    int n_classes = classes;   
-    switch(ntype){
-        case 'y':
-            detNN = &yolo;
-            break;
-        case 'c':
-            detNN = &cnet;
-            break;
-        case 'm':
-            detNN = &mbnet;
-            n_classes++;
-            break;
-        default:
-            FatalError("Network type not allowed (3rd parameter)\n");
-    }
-    detNN->init(net,n_classes, 1, conf_thresh);
-
-    //read images 
-    std::ifstream all_labels(labels_path);
-    std::string l_filename;
-    std::vector<tk::dnn::Frame> images;
-    std::vector<tk::dnn::box> detected_bbox;
-
-    std::cout<<"Reading groundtruth and generating detections"<<std::endl;
-
-    if(show)
-        cv::namedWindow("detection", cv::WINDOW_NORMAL);
-
-    bool file_ok = false;
-
-    int images_done;
-    for (images_done=0 ; images_done < n_images ;) {
-        
-
-        int cur_batches = 0;
-        std::vector<cv::Mat> batch_frames;
-        std::vector<cv::Mat> batch_dnn_input;
-            
-        std::vector<tk::dnn::Frame> cur_frames;
-        for(;cur_batches<n_batches && images_done < n_images;cur_batches++, ++images_done){
-
-            std::getline(all_labels, l_filename);
-            file_ok = all_labels ? true : false ;
-            if (!file_ok)
-                break;
-
-            tk::dnn::Frame f;
-            f.lFilename = l_filename;
-            f.iFilename = l_filename;
-            convertFilename(f.iFilename, "labels", "images", ".txt", ".jpg");
-
-            // read frame
-            if(!fileExist(f.iFilename.c_str()))
-                FatalError("Wrong image file path.");
-
-            cv::Mat frame = cv::imread(f.iFilename.c_str(), cv::IMREAD_COLOR);
-            batch_frames.push_back(frame);
-            f.height = frame.rows;
-            f.width = frame.cols;
-
-            if(!frame.data) 
-                break;
-            batch_dnn_input.push_back(frame.clone());
-
-            // read and save groundtruth labels
-            if(fileExist(f.lFilename.c_str()))
-            {
-                std::ifstream labels(f.lFilename);
-                for(std::string line; std::getline(labels, line); ){
-                    std::istringstream in(line); 
-                    tk::dnn::BoundingBox b;
-                    in >> b.cl >> b.x >> b.y >> b.w >> b.h;  
-                    b.prob = 1;
-                    b.truthFlag = 1;
-                    f.gt.push_back(b);
-
-                    if(show)// draw rectangle for groundtruth
-                        cv::rectangle(batch_frames[cur_batches], cv::Point((b.x-b.w/2)*f.width, (b.y-b.h/2)*f.height), cv::Point((b.x+b.w/2)*f.width,(b.y+b.h/2)*f.height), cv::Scalar(0, 255, 0), 2);             
-                }
-            } 
-
-            cur_frames.push_back(f);
-        }
-        if (!file_ok)
-            break;
-
-        //inference 
-        detNN->update(batch_dnn_input,cur_batches,write_res_on_file, &times, write_coco_json);
-        detNN->draw(batch_frames);
-
-        for(int j=0;j<cur_frames.size(); ++j){
-            if(write_coco_json)
-            printJsonCOCOFormat(&coco_json, cur_frames[j].iFilename.c_str(), detNN->batchDetected[j], classes,  cur_frames[j].width, cur_frames[j].height);        
-
-            std::ofstream myfile;
-            if(write_dets)
-                myfile.open ("det/"+cur_frames[j].lFilename.substr(cur_frames[j].lFilename.find("labels/") + 7));
-
-            // save detections labels
-            for(auto d:detNN->batchDetected[j]){
-                //convert detected bb in the same format as label
-                //<x_center>/<image_width> <y_center>/<image_width> <width>/<image_width> <height>/<image_width>
-                tk::dnn::BoundingBox b;
-                b.x = (d.x + d.w/2) / cur_frames[j].width;
-                b.y = (d.y + d.h/2) / cur_frames[j].height;
-                b.w = d.w / cur_frames[j].width;
-                b.h = d.h / cur_frames[j].height;
-                b.prob = d.prob;
-                b.cl = d.cl;
-                cur_frames[j].det.push_back(b);
-
-                if(write_dets)
-                    myfile << d.cl << " "<< d.prob << " "<< b.x << " "<< b.y << " "<< b.w << " "<< b.h <<"\n";
-
-                if(show)// draw rectangle for detection
-                    cv::rectangle(batch_frames[j], cv::Point(d.x, d.y), cv::Point(d.x + d.w, d.y + d.h), cv::Scalar(0, 0, 255), 2);             
-                }
-
-            if(write_dets)
-                myfile.close();
-      
-            images.push_back(cur_frames[j]);
-        
-            if(show){
-                cv::imshow("detection", batch_frames[j]);
-                cv::waitKey(0);
-            }
-            
-        }
-        std::cout <<COL_ORANGEB<< "Images done:\t" << images_done<< "\tcur batch:\t"<<cur_batches<< "\n"<<COL_END;
-        
-        getMemUsage(vm, rss);
-        vm_total += vm;
-        rss_total += rss;
-
-        
-    }
-
-    if(write_coco_json){
-        coco_json.seekp (coco_json.tellp() - std::streampos(2));
-        coco_json << "\n]\n";
-        coco_json.close();
-    }
-
-    std::cout << "Avg VM[MB]: " << vm_total/images_done/1024.0 << ";Avg RSS[MB]: " << rss_total/images_done/1024.0 << std::endl;
-
-    //compute mAP
-    double AP = tk::dnn::computeMapNIoULevels(images,classes,IoU_thresh,confidence_thresh, map_points, map_step, map_levels, verbose, write_res_on_file, net_name+"_"+ std::to_string(n_batches)+"_"+std::to_string(confidence_thresh));
-    std::cout<<"mAP "<<IoU_thresh<<":"<<IoU_thresh+map_step*(map_levels-1)<<" = "<<AP<<std::endl;
-
-    //compute average precision, recall and f1score
-    tk::dnn::computeTPFPFN(images,classes,IoU_thresh,confidence_thresh, verbose, write_res_on_file, net_name +"_"+ std::to_string(n_batches)+"_"+std::to_string(confidence_thresh));
-
-    if(write_res_on_file){
-        memory<<vm_total/images_done/1024.0<<";"<<rss_total/images_done/1024.0<<"\n";
-        times.close();
-        memory.close();
-    }
-
-    return 0;
-}
-
@@ -1,150 +0,0 @@
-#include <iostream>
-#include <signal.h>
-#include <stdlib.h>     /* srand, rand */
-#ifdef __linux__
-#include <unistd.h>
-#endif 
-#include <mutex>
-
-#include "SegmentationNN.h"
-
-bool gRun;
-bool SAVE_RESULT = true;
-
-void sig_handler(int signo) {
-    std::cout<<"request gateway stop\n";
-    gRun = false;
-}
-
-void writePred(const std::string& images_names, const std::string& gt_folder, const std::string& out_folder, tk::dnn::SegmentationNN& segNN, int& width, int& height, bool show=false){
-    std::ifstream all_gt(images_names);
-    std::string filename;
-    cv::Mat frame;
-    for (; std::getline(all_gt, filename); ) {
-        std::cout<<filename<<std::endl;
-        frame = cv::imread(gt_folder + filename);
-        height = frame.rows;
-        width = frame.cols;
-        segNN.updateOriginal(frame, false);
-        if(show)
-            segNN.draw();
-        cv::imwrite(out_folder + filename, segNN.segmented[0]);
-    }
-}
-
-int main(int argc, char *argv[]) {
-
-    std::cout<<"detection\n";
-    signal(SIGINT, sig_handler);
-
-
-    std::string net = "shelfnet_fp32.rt";
-    if(argc > 1)
-        net = argv[1]; 
-    std::string input = "../demo/yolo_test.mp4";
-    if(argc > 2)
-        input = argv[2]; 
-    int n_batch = 1;
-    if(argc > 3)
-        n_batch = atoi(argv[3]); 
-    int n_classes = 19;
-    if(argc > 4)
-        n_classes = atoi(argv[4]); 
-    bool resize = false;
-    if(argc > 5)
-        resize = atoi(argv[5]); 
-    int baseline_resize = 1024;
-    if(argc > 6)
-        baseline_resize = atoi(argv[6]); 
-    bool show = true;
-    if(argc > 7)
-        show = atoi(argv[7]); 
-    bool write_pred = false;
-    if(argc > 8)
-        write_pred = atoi(argv[8]); 
-
-    if(resize && (baseline_resize < 0 || baseline_resize > 5000)) 
-        FatalError("Problem with baseline resize")
-    if(n_batch < 1 || n_batch > 64)
-        FatalError("Batch dim not supported");
-
-    //net initialization 
-    tk::dnn::SegmentationNN segNN;
-    segNN.init(net, n_classes, n_batch);
-
-    int height = 0, width = 0;
-    int basewidth=baseline_resize,  hsize; 
-    
-    if(write_pred){
-        std::string gt_folder = "../demo/CityScapes_val/images/";
-        std::string images_names = "../demo/CityScapes_val/all_images.txt";
-        std::string out_folder = "seg/";
-
-        writePred(images_names, gt_folder, out_folder, segNN, width, height, show);
-    }
-    else{
-        if(!show)
-            SAVE_RESULT = true;
-
-        gRun = true;
-
-        cv::VideoCapture cap(input);
-        if(!cap.isOpened())
-            gRun = false; 
-        else
-            std::cout<<"camera started\n";
-
-        cv::VideoWriter resultVideo;
-        if(SAVE_RESULT) {
-            int w,h;
-            if(resize){
-                w = basewidth;
-                h = int((float(cap.get(cv::CAP_PROP_FRAME_HEIGHT))*float(basewidth/float(cap.get(cv::CAP_PROP_FRAME_WIDTH)))));
-            }
-            else{
-                w = cap.get(cv::CAP_PROP_FRAME_WIDTH);
-                h = cap.get(cv::CAP_PROP_FRAME_HEIGHT);
-            }
-            resultVideo.open("result.mp4", cv::VideoWriter::fourcc('M','P','4','V'), 30, cv::Size(w, h));
-        }
-
-        cv::Mat frame;
-        while(gRun) {
-            cap >> frame; 
-            if(!frame.data) 
-                break;
-
-            if(resize){
-                hsize  = int((float(frame.rows)*float(basewidth/float(frame.cols))));
-                cv::resize(frame, frame, cv::Size(basewidth, hsize));
-            }
-
-            height = frame.rows;
-            width = frame.cols;
-
-            //inference
-            segNN.updateOriginal(frame, true);
-            if(show)
-                segNN.draw();
-
-            if(SAVE_RESULT)
-                resultVideo << segNN.segmented[0];
-        }
-    }
-
-    std::cout<<"segmentation end\n";   
-    double mean = 0, mean_pre = 0, mean_post = 0;
-    
-    std::cout<<COL_GREENB<<"\n\nTime stats for size ["<<width<<","<<height<<"] :\n";
-    
-    for(int i=0; i<segNN.stats.size(); i++) mean += segNN.stats[i]; mean /= segNN.stats.size();
-    for(int i=0; i<segNN.stats_pre.size(); i++) mean_pre += segNN.stats_pre[i]; mean_pre /= segNN.stats_pre.size();
-    for(int i=0; i<segNN.stats_post.size(); i++) mean_post += segNN.stats_post[i]; mean_post /= segNN.stats_post.size();
-    std::cout<<"Avg pre:\t"<<mean_pre<<" ms\t"<<1000/(mean_pre)<<" FPS\n";   
-    std::cout<<"Avg inf:\t"<<mean<<" ms\t"<<1000/(mean)<<" FPS\n";   
-    std::cout<<"Avg post:\t"<<mean_post<<" ms\t"<<1000/(mean_post)<<" FPS\n\n";   
-    std::cout<<"Avg tot:\t"<<(mean_pre + mean_post + mean) <<" ms\t"<<1000/((mean_pre + mean_post + mean))<<" FPS\n"<<COL_END;   
-    
-    return 0;
-}
-
@@ -1,14 +0,0 @@
-# video input
-input     : "../demo/yolo_test.mp4"
-win_input : "..\\..\\..\\demo\\yolo_test.mp4"
-
-# network config
-net         : "yolo4_berkeley_fp32.rt"
-ntype       : 'y'
-n_classes   : 80
-n_batch     : 1
-conf_thresh : 0.3
-
-# demo config
-show  : true
-save  : false
@@ -1,7 +0,0 @@
-FROM ceccocats/tkdnn:latest
-LABEL maintainer "Francesco Gatti"
-
-RUN cd && git clone https://github.com/ceccocats/tkDNN.git && cd tkDNN && mkdir build && cd build \ 
-    && cmake .. && make -j12
-
-
@@ -1,140 +0,0 @@
-FROM nvidia/cudagl:11.3.1-devel-ubuntu20.04
-
-LABEL maintainer "TKDNN AUTHORS"
-LABEL Description="tkDNN+cudagl"
-LABEL com.tkdnn.nvidia.version="11.3.1"
-
-ENV DEBIAN_FRONTEND noninteractive
-ENV CC gcc
-ENV CXX g++
-
-RUN apt-get update && apt-get install -y \
-libblkid-dev && apt-get clean && rm -rf /var/lib/apt/lists/*
-
-RUN apt-get update && apt-get install -y \
-libcudnn8-dev=8.2.1.32-1+cuda11.3 \
-libcudnn8=8.2.1.32-1+cuda11.3 \
-libnvinfer-dev=8.0.3-1+cuda11.3 \
-libnvinfer8=8.0.3-1+cuda11.3 && apt-get clean && rm -rf /var/lib/apt/lists/*
-
-RUN apt-get update && apt-get install -y --no-install-recommends \
-libblkid-dev \
-locales \
-lsb-release \
-mesa-utils \
-git \
-nano \
-terminator \
-wget \
-curl \
-libssl-dev \
-htop \
-dbus-x11 \
-libqt5opengl5-dev \
-libgtk-3-dev \
-libvtk7-dev \
-libv4l-dev \
-tar \
-libgoogle-glog-dev \
-libgflags-dev \
-gfortran-9 \
-libtbb-dev \
-libgstreamer1.0-dev \
-libgstreamer-plugins-base1.0-dev \
-libdc1394-22-dev \
-libavresample-dev \
-libatlas-cpp-0.6-dev \
-python3-dev \
-gdb \
-python3-pip \
-unzip libtbb-dev && \
-apt-get clean && rm -rf /var/lib/apt/lists/*
-
-RUN apt-get update && apt-get install -y --no-install-recommends \
-software-properties-common && apt-get clean && rm -rf /var/lib/apt/lists/*
-
-RUN apt-add-repository universe
-RUN apt-get update && apt-get install -y python3-pip  python3 openssh-server ssh pyqt5-dev sip-dev && apt-get clean && rm -rf /var/lib/apt/lists/*
-RUN pip3 install --upgrade pip
-RUN pip3 install --upgrade virtualenv
-RUN pip3 install --upgrade paramiko
-RUN pip3 install --ignore-installed --upgrade numpy protobuf
-
-
-RUN cd ~ && mkdir build
-RUN cd ~/build && wget https://github.com/Kitware/CMake/releases/download/v3.21.4/cmake-3.21.4.tar.gz  && \
-tar -xvf cmake-3.21.4.tar.gz && cd cmake-3.21.4 && ./configure --prefix=/usr/local --qt-gui --parallel=12 && \
-make -j8 && make install
-
-RUN apt-get update && apt-get install -y automake autoconf pkg-config libevent-dev libncurses5-dev bison && \
-apt-get clean && rm -rf /var/lib/apt/lists/
-
-RUN git clone https://github.com/tmux/tmux.git && \
-cd tmux && git checkout tags/3.2 && ls -la && sh autogen.sh && ./configure && make -j8 && make install
-
-RUN apt-get update && apt-get install -y zsh && apt-get clean && rm -rf /var/lib/apt/lists/*
-RUN wget https://github.com/robbyrussell/oh-my-zsh/raw/master/tools/install.sh -O - | zsh || true
-RUN chsh -s /usr/bin/zsh root
-RUN git clone https://github.com/sindresorhus/pure /root/.oh-my-zsh/custom/pure
-RUN ln -s /root/.oh-my-zsh/custom/pure/pure.zsh-theme /root/.oh-my-zsh/custom/
-RUN ln -s /root/.oh-my-zsh/custom/pure/async.zsh /root/.oh-my-zsh/custom/
-RUN sed -i -e 's/robbyrussell/refined/g' /root/.zshrc
-RUN sed -i '/plugins=(/c\plugins=(git git-flow adb pyenv tmux)' /root/.zshrc
-
-RUN mkdir -p /root/.config/terminator/
-COPY assets/terminator_config /root/.config/terminator/config
-
-RUN echo "/usr/local/nvidia/lib" >> /etc/ld.so.conf.d/nvidia.conf && \
-    echo "/usr/local/nvidia/lib64" >> /etc/ld.so.conf.d/nvidia.conf && \
-    echo "/usr/local/cuda/lib64" >> /etc/ld.so.conf.d/nvidia.conf
-
-
-ENV PATH /usr/local/nvidia/bin:/usr/local/cuda/bin:${PATH}
-ENV LD_LIBRARY_PATH /usr/local/nvidia/lib:/usr/local/nvidia/lib64:/usr/local/cuda/lib64:/usr/lib:/usr/lib/x86_64-linux-gnu:/usr/local/lib:${LD_LIBRARY_PATH}
-ENV NVIDIA_VISIBLE_DEVICES all
-ENV NVIDIA_DRIVER_CAPABILITIES compute,utility,graphics
-
-
-
-RUN cd ~/build && wget https://github.com/opencv/opencv/archive/4.5.4.tar.gz && tar -xf 4.5.4.tar.gz && rm 4.5.4.tar.gz
-RUN cd ~/build && wget https://github.com/opencv/opencv_contrib/archive/4.5.4.tar.gz && tar -xf 4.5.4.tar.gz && rm 4.5.4.tar.gz
-RUN cd ~/build && \
-    cd opencv-4.5.4 && mkdir build && cd build && \
-    cmake -D CMAKE_BUILD_TYPE=RELEASE \
-    -D CMAKE_INSTALL_PREFIX=/usr/local \
-    -D INSTALL_PYTHON_EXAMPLES=OFF \
-    -D INSTALL_C_EXAMPLES=OFF \
-    -D OPENCV_EXTRA_MODULES_PATH='~/build/opencv_contrib-4.5.4/modules' \
-    -D BUILD_EXAMPLES=OFF \
-    -D BUILD_TESTS=OFF \
-    -D BUILD_PERF_TESTS=OFF \
-    -D BUILD_DOCS=OFF \
-    -D WITH_CUDA=ON \
-    -D WITH_OPENGL=ON \
-    -D WITH_NVCUVID=ON \
-    -D CUDA_ARCH_BIN=7.2 \
-    -D CUDA_ARCH_PTX=7.2 \
-    -D ENABLE_FAST_MATH=ON \
-    -D CUDA_FAST_MATH=ON \
-    -D WITH_CUBLAS=ON \
-    -D WITH_CUDNN=ON \
-    -D WITH_OPENMP=ON \
-    -D WITH_NONFREE=ON \
-    -D WITH_LIBV4L=ON \
-    -D WITH_GSTREAMER=ON \
-    -D WITH_GSTREAMER_0_10=OFF \
-    -D WITH_TBB=ON \
-    ../ && make -j12 && make install && ldconfig
-
-RUN cd ~ && rm -rf build
-
-RUN cd ~ && mkdir Development && cd Development && \
-git clone https://github.com/ceccocats/tkDNN.git && cd tkDNN && \
-mkdir build && cd build && \
-cmake -DCMAKE_BUILD_TYPE=Release .. && \
-make -j6
-
-RUN apt-get clean && rm -rf /var/lib/apt/lists/*
-COPY assets/entrypoint_setup.sh /
-ENTRYPOINT ["/entrypoint_setup.sh"]
-CMD ["terminator"]
@@ -1,18 +0,0 @@
-# Use the prebuilt image
-```
-# build image
-docker build -t tkdnn:build -f Dockerfile . 
-```
-
-# Build Base Docker image
-```
-# make nvidia docker working
-# follow this guide: https://github.com/NVIDIA/nvidia-docker
-
-# build image
-docker build -t ceccocats/tkdnn:latest -f Dockerfile.base .
-
-# run image
-./docker_launch.sh
-```
-
@@ -1,123 +0,0 @@
-#! /bin/bash
-
-CMD=
-
-# Functions
-# TOOD: Check if we can use: getent passwd $USER to extract all variables
-# TODO: Check for valid inputs, cause now it will go through even with bad inputs
-check_envs () {
-    DOCKER_CUSTOM_USER_OK=true;
-    if [ -z ${DOCKER_USER_NAME+x} ]; then 
-        DOCKER_CUSTOM_USER_OK=false;
-        return;
-    fi
-    
-    if [ -z ${DOCKER_USER_ID+x} ]; then 
-        DOCKER_CUSTOM_USER_OK=false;
-        return;
-    else
-        if ! [ -z "${DOCKER_USER_ID##[0-9]*}" ]; then 
-            echo -e "\033[1;33mWarning: User-ID should be a number. Falling back to defaults.\033[0m"
-            DOCKER_CUSTOM_USER_OK=false;
-            return;
-        fi
-    fi
-    
-    if [ -z ${DOCKER_USER_GROUP_NAME+x} ]; then 
-        DOCKER_CUSTOM_USER_OK=false;
-        return;
-    fi
-
-    if [ -z ${DOCKER_USER_GROUP_ID+x} ]; then 
-        DOCKER_CUSTOM_USER_OK=false;
-        return;
-    else
-        if ! [ -z "${DOCKER_USER_GROUP_ID##[0-9]*}" ]; then 
-            echo -e "\033[1;33mWarning: Group-ID should be a number. Falling back to defaults.\033[0m"
-            DOCKER_CUSTOM_USER_OK=false;
-            return;
-        fi
-    fi
-}
-
-setup_env_user () {
-    USER=$1
-    USER_ID=$2
-    GROUP=$3
-    GROUP_ID=$4
-
-    ## Create user
-    useradd -m $USER
-
-    ## Copy zsh/sh configs
-    cp /root/.profile /home/$USER/
-    cp /root/.bashrc /home/$USER/
-    cp /root/.zshrc /home/$USER/
-    ## Copy terminator configs
-    mkdir -p /home/$USER/.config/terminator
-    cp /root/.config/terminator/config /home/$USER/.config/terminator/config
-    cp /root/.config/terminator/background.png /home/$USER/.config/terminator/background.png
-    cp -rf /root/.oh-my-zsh /home/$USER/
-    cp -rf /root/tkDNN /home/$USER/
-    rm -rf /home/$USER/.oh-my-zsh/custom/pure.zsh-theme /home/$USER/.oh-my-zsh/custom/async.zsh
-    ln -s /home/$USER/.oh-my-zsh/custom/pure/pure.zsh-theme /home/$USER/.oh-my-zsh/custom/
-    ln -s /home/$USER/.oh-my-zsh/custom/pure/async.zsh /home/$USER/.oh-my-zsh/custom/
-    sed -i -e 's@ZSH=\"/root@ZSH=\"/home/$USER@g' /home/$USER/.zshrc
-    # Copy SSH keys & fix owner
-    if [ -d "/root/.ssh" ]; then
-        cp -rf /root/.ssh /home/$USER/
-        chown -R $USER:$GROUP /home/$USER/.ssh
-    fi
-    
-    ## Fix owner
-    chown $USER:$GROUP /home/$USER
-    chown -R $USER:$GROUP /home/$USER/.config
-    chown $USER:$GROUP /home/$USER/.profile
-    chown $USER:$GROUP /home/$USER/.bashrc
-    chown $USER:$GROUP /home/$USER/.zshrc
-    chown -R $USER:$GROUP /home/$USER/.oh-my-zsh
-    chown -R $USER:$GROUP /home/$USER/tkDNN
-
-    ## This a trick to keep the evnironmental variables of root which is important!
-    echo "if ! [ \"$DOCKER_USER_NAME\" = \"$(id -un)\" ]; then" >> /root/.bashrc
-    echo "    cd /home/$DOCKER_USER_NAME" >> /root/.bashrc
-    echo "    su $DOCKER_USER_NAME" >> /root/.bashrc
-    echo "fi" >> /root/.bashrc
-
-    echo "if ! [ \"$DOCKER_USER_NAME\" = \"$(id -un)\" ]; then" >> /root/.zshrc
-    echo "    cd /home/$DOCKER_USER_NAME" >> /root/.zshrc
-    echo "    su $DOCKER_USER_NAME" >> /root/.zshrc
-    echo "fi" >> /root/.zshrc
-
-    ## Setup Password-file
-    PASSWDCONTENTS=$(grep -v "^${USER}:" /etc/passwd)
-    GROUPCONTENTS=$(grep -v -e "^${GROUP}:" -e "^docker:" /etc/group)
-
-    (echo "${PASSWDCONTENTS}" && echo "${USER}:x:$USER_ID:$GROUP_ID::/home/$USER:/bin/bash") > /etc/passwd
-    (echo "${GROUPCONTENTS}" && echo "${GROUP}:x:${GROUP_ID}:") > /etc/group
-    (if test -f /etc/sudoers ; then echo "${USER}  ALL=(ALL)   NOPASSWD: ALL" >> /etc/sudoers ; fi)
-}
-
-
-# ---Main---
-
-# Create new user
-## Check Inputs
-check_envs
-
-## Determine user & Setup Environment
-if [ $DOCKER_CUSTOM_USER_OK == true ]; then
-    echo "  -->DOCKER_USER Input is set to '$DOCKER_USER_NAME:$DOCKER_USER_ID:$DOCKER_USER_GROUP_NAME:$DOCKER_USER_GROUP_ID'";
-    echo -e "\033[0;32mSetting up environment for user=$DOCKER_USER_NAME\033[0m"
-    setup_env_user $DOCKER_USER_NAME $DOCKER_USER_ID $DOCKER_USER_GROUP_NAME $DOCKER_USER_GROUP_ID
-else
-    echo "  -->DOCKER_USER* variables not set. Using 'root'.";
-    echo -e "\033[0;32mSetting up environment for user=root\033[0m"
-    DOCKER_USER_NAME="root"
-fi
-
-# Change shell to zsh
-chsh -s /usr/bin/zsh $DOCKER_USER_NAME
-
-# Run CMD from Docker
-"$@"
@@ -1,18 +0,0 @@
-[global_config]
-  title_transmit_bg_color = "#2e3436"
-[keybindings]
-[layouts]
-  [[default]]
-    [[[child1]]]
-      parent = window0
-      type = Terminal
-    [[[window0]]]
-      parent = ""
-      type = Window
-[plugins]
-[profiles]
-  [[default]]
-    background_color = "#282828"
-    cursor_color = "#aaaaaa"
-    foreground_color = "#f3f3f3"
-    palette = "#000000:#aa0000:#00aa00:#c4a000:#3465a4:#75507b:#06989a:#d3d7cf:#88807c:#f15d22:#73c48f:#ffce51:#48b9c7:#ad7fa8:#34e2e2:#eeeeec"
@@ -1,9 +0,0 @@
-xhost local:root
-docker run --rm -it --runtime=nvidia --privileged --net=host --cap-add sys_ptrace -d --ipc=host \
-v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY \
-v $HOME/.Xauthority:/home/$(id -un)/.Xauthority -e XAUTHORITY=/home/$(id -un)/.Xauthority \
-e DOCKER_USER_NAME=$(id -un) \
-e DOCKER_USER_ID=$(id -u) \
-e DOCKER_USER_GROUP_NAME=$(id -gn) \
-e DOCKER_USER_GROUP_ID=$(id -g) \
-v $HOME/.ssh:/home/$(id -un)/.ssh  ceccocats/tkdnn
@@ -1,92 +0,0 @@
-# 2D/3D Object Detection and Tracking
-
-Currently tkDNN supports only CenterTrack as 3DOD & 2D/3D Tracker network.
-
-## 3D Object Detection
-
-To run the 3D object detection demo follow these steps (example with CenterNet based on DLA34):
-```
-rm dla34_cnet3d_fp32.rt        # be sure to delete(or move) old tensorRT files
-./test_dla34_cnet3d            # run the yolo test (is slow)
-./demo3D dla34_cnet3d_fp32.rt ../demo/yolo_test.mp4 NULL c
-```
-The demo3D program takes the same parameters of the demo program:
-```
-./demo3D <network-rt-file> <path-to-video> <calibration-file> <kind-of-network> <number-of-classes> <n-batches> <show-flag> <conf-thresh>
-```
-where
-
-* ```<calibration-file>``` is the camera calibration file (opencv format). It is important that the file contains entry "camera_matrix" with sub-entry "rows", "cols", "data". If you do not want to pass the calibration file, pass "NULL" instead.
-
-![demo](https://user-images.githubusercontent.com/11939259/126784875-c4285497-d369-424f-abda-58274cd747ac.gif)
-
-## Object Detection and Tracking
-
-To run the 3D object detection & tracking demo follow these steps (example with CenterTrack based on DLA34):
-```
-rm dla34_ctrack_fp32.rt  # be sure to delete(or move) old tensorRT files
-./test_dla34_ctrack      # run the yolo test (is slow)
-./demoTracker dla34_ctrack_fp32.rt ../demo/yolo_test.mp4 NULL c
-```
-
-The demoTracker program takes the same parameters of the demo program:
-```
-./demoTracker <network-rt-file> <path-to-video> <calibration-file> <kind-of-network> <number-of-classes> <n-batches> <show-flag> <conf-thresh> <2D/3D-flag>
-```
-
-where
-
-* ```<calibration-file>``` is the camera calibration file (opencv format). It is important that the file contains entry "camera_matrix" with sub-entry "rows", "cols", "data". If you do not want to pass the calibration file, pass "NULL" instead.
-*  ```<2D/3D-flag>``` if set to 0 the demo will be in the 2D mode, while if set to 1 the demo will be in the 3D mode (Default is 1 - 3D mode).
-
-![demo](https://user-images.githubusercontent.com/11939259/126784878-513fa9e8-864a-4c24-b4bd-199737184708.gif)
-
-## FPS Results
-
-Inference FPS of shelfnet with tkDNN, average of 1200 images on:
-  * RTX 2080Ti (CUDA 10.2, TensorRT 7.0.0, Cudnn 7.6.5);
-  * Xavier AGX, Jetpack 4.3 (CUDA 10.0, CUDNN 7.6.3, tensorrt 6.0.1 );
-
-### 3D OD and Tracking
-
-| Platform   | Test                        | Phase   | FP32, ms  | FP32, FPS | FP16, ms  |	FP16, FPS | INT8, ms  | INT8, FPS | 
-| :------:   | :-----:                     | :-----: | :-----:   | :-----:   | :-----:   |	:-----:   | :-----:   | :-----:   | 
-| RTX 2080Ti | CenterTrack3D 512x512 (B=1) | pre     | 4.43883   |  225.285  |  4.42951  |  225.759   |  4.44278  |  225.084  |
-| RTX 2080Ti | CenterTrack3D 512x512 (B=1) | inf     | 9.03454   |  110.686  |  6.02013  |  166.109   |  5.31611  |  188.108  |
-| RTX 2080Ti | CenterTrack3D 512x512 (B=1) | post    | 0.96631   |  1034.87  |  0.96824  |  1032.80   |  0.95066  |  1051.90  |
-| RTX 2080Ti | CenterTrack3D 512x512 (B=1) | tot     | 14.4397   |  69.2535  |  11.4179  |  87.5818   |  10.7095  |  93.3750  |
-| RTX 2080Ti | CenterTrack3D 512x512 (B=4) | pre     | 4.60075   |  217.356  |  4.28658  |  233.286   |  4.29473  |  232.844  | 
-| RTX 2080Ti | CenterTrack3D 512x512 (B=4) | inf     | 8.48365   |  117.874  |  5.25150  |  190.422   |  4.58463  |  218.120  |  
-| RTX 2080Ti | CenterTrack3D 512x512 (B=4) | post    | 0.99484   |  1005.19  |  0.91776  |  1089.61   |  0.89853  |  1112.93  |  
-| RTX 2080Ti | CenterTrack3D 512x512 (B=4) | tot     | 14.0792   |  71.0266  |  10.4558  |  95.6405   |  9.77788  |  102.272  |  
-| AGX Xavier | CenterTrack3D 512x512 (B=1) | pre     | 34.9915   |  28.5784  |  33.5976  |  29.7440   |  34.4425  |  29.0339  |
-| AGX Xavier | CenterTrack3D 512x512 (B=1) | inf     | 76.3579   |  13.0962  |  52.4759  |  19.0564   |  51.4610  |  19.4322  |
-| AGX Xavier | CenterTrack3D 512x512 (B=1) | post    | 3.38576   |  295.355  |  3.26010  |  306.739   |  3.19770  |  312.725  |
-| AGX Xavier | CenterTrack3D 512x512 (B=1) | tot     | 114.735   |  8.71574  |  89.3336  |  11.1940   |  89.1012  |  11.2232  |
-| AGX Xavier | CenterTrack3D 512x512 (B=4) | pre     | 32.8933   |  30.4014  |  32.7950  |  30.4925   |  32.9603  |  30.3396  | 
-| AGX Xavier | CenterTrack3D 512x512 (B=4) | inf     | 74.2840   |  13.4618  |  50.3858  |  19.8469   |  49.2030  |  20.3240  |  
-| AGX Xavier | CenterTrack3D 512x512 (B=4) | post    | 3.14888   |  317.574  |  3.13615  |  318.862   |  3.02550  |  330.524  |  
-| AGX Xavier | CenterTrack3D 512x512 (B=4) | tot     | 110.326   |  9.06404  |  86.3169  |  11.5852   |  85.1888  |  11.7386  |  
-
-
-### 2D OD and Tracking
-
-| Platform   | Test                        | Phase   | FP32, ms  | FP32, FPS | FP16, ms  |	FP16, FPS | INT8, ms  | INT8, FPS | 
-| :------:   | :-----:                     | :-----: | :-----:   | :-----:   | :-----:   |	:-----:   | :-----:   | :-----:   | 
-| RTX 2080Ti | CenterTrack2D 512x512 (B=1) | pre     | 4.44386   |  225.030  |  4.43828  |  225.313   |  4.47747  |  223.340  |
-| RTX 2080Ti | CenterTrack2D 512x512 (B=1) | inf     | 9.08365   |  110.088  |  6.04842  |  165.332   |  5.34787  |  186.990  |
-| RTX 2080Ti | CenterTrack2D 512x512 (B=1) | post    | 0.98593   |  1014.27  |  0.97745  |  1023.07   |  0.96595  |  1035.25  |
-| RTX 2080Ti | CenterTrack2D 512x512 (B=1) | tot     | 14.5134   |  68.9018  |  11.4642  |  87.2281   |  10.7913  |  92.6672  |
-| RTX 2080Ti | CenterTrack2D 512x512 (B=4) | pre     | 4.41188   |  226.661  |  4.50800  |  221.828   |  4.29238  |  232.971  | 
-| RTX 2080Ti | CenterTrack2D 512x512 (B=4) | inf     | 8.29015   |  120.625  |  5.38630  |  185.656   |  4.58500  |  218.103  |  
-| RTX 2080Ti | CenterTrack2D 512x512 (B=4) | post    | 0.96847   |  1032.55  |  0.97997  |  1020.44   |  0.91791  |  1089.43  |  
-| RTX 2080Ti | CenterTrack2D 512x512 (B=4) | tot     | 13.6705   |  73.1502  |  10.8743  |  91.9602   |  9.79528  |  102.090  |  
-| AGX Xavier | CenterTrack2D 512x512 (B=1) | pre     | 33.4745   |  29.8735  |  33.4847  |  29.8643   |  33.5022  |  29.8488  |
-| AGX Xavier | CenterTrack2D 512x512 (B=1) | inf     | 76.2077   |  13.1220  |  52.5111  |  19.0436   |  51.6057  |  19.3777  |
-| AGX Xavier | CenterTrack2D 512x512 (B=1) | post    | 3.26055   |  306.697  |  3.26806  |  305.992   |  3.21988  |  310.571  |
-| AGX Xavier | CenterTrack2D 512x512 (B=1) | tot     | 111.943   |  8.93312  |  89.2639  |  11.2027   |  88.3278  |  11.3215  |
-| AGX Xavier | CenterTrack2D 512x512 (B=4) | pre     | 32.8323   |  30.4579  |  32.8595  |  30.4326   |  32.8195  |  30.4697  | 
-| AGX Xavier | CenterTrack2D 512x512 (B=4) | inf     | 74.3075   |  13.4576  |  50.3555  |  19.8588   |  49.1805  |  20.3333  |  
-| AGX Xavier | CenterTrack2D 512x512 (B=4) | post    | 3.12360   |  320.143  |  3.13570  |  318.908   |  3.04943  |  327.931  |  
-| AGX Xavier | CenterTrack2D 512x512 (B=4) | tot     | 110.263   |  9.06920  |  86.3507  |  11.5807   |  85.0494  |  11.7579  |  
-
@@ -1,54 +0,0 @@
-# Monocular depth estimation with tkDNN
-
-Currently tkDNN supports only Monodepth2 as monocular depth esitmation network.
-
-
-## Run the demo
-
-To run the depth estimation demo follow these steps (example with monodepth2):
-```
-rm monodepth2_fp32.rt        # be sure to delete(or move) old tensorRT files
-./test_monodepth2            # run the yolo test (is slow)
-./demoDepth monodepth2_fp32.rt ../demo/yolo_test.mp4 
-```
-In general the demo program takes the following parameters:
-```
-./demoDepth <network-rt-file> <path-to-video> <show-flag> <save-flag>
-```
-where
-*  ```<network-rt-file>``` is the rt file generated by a test
-*  ```<<path-to-video>``` is the path to a video file or a camera input  
-*  ```<show-flag>``` if set to 0 the demo will not show the visualization, it will otherwise (default=1)
-*  ```<save-flag>``` if set to 1 the demo will save the video into result.mp4, it won't otherwise (default=1)
-
-NB) By default it is used FP32 inference
-
-
-![demo](https://user-images.githubusercontent.com/11939259/160845358-0d6ab15d-c5f4-46ae-b9da-bfaf3903389d.gif "Results on yolo_test.mp4")  
-
-
-<!-- ## FPS Results
-
-Inference FPS of shelfnet with tkDNN, average of 1200 images on:
-  * RTX 2080Ti (CUDA 10.2, TensorRT 7.0.0, Cudnn 7.6.5);
-  * Xavier AGX, Jetpack 4.3 (CUDA 10.0, CUDNN 7.6.3, tensorrt 6.0.1 );
-
-| Platform   | Test                     | Phase   | FP32, ms  | FP32, FPS | FP16, ms  |	FP16, FPS  | INT8, ms |	INT8, FPS | 
-| :------:   | :-----:                  | :-----: | :-----:   | :-----:   | :-----:   |	:-----:    | :-----:  |	:-----:   | 
-| RTX 2080Ti | shelfnet 1024x1024 (B=1) | pre     | 6.11863   |  163.435  |   5.81465 |  171.979   |  5.88699 |   169.866 |
-| RTX 2080Ti | shelfnet 1024x1024 (B=1) | inf     | 11.5464   |  86.6074  |   7.35396 |  135.981   |  6.37623 |   156.832 |
-| RTX 2080Ti | shelfnet 1024x1024 (B=1) | post    | 4.09058   |  244.464  |   3.91961 |  255.128   |  4.07343 |   245.493 |
-| RTX 2080Ti | shelfnet 1024x1024 (B=1) | tot     | 21.7556   |  45.9652  |   17.0882 |  58.5199   |  16.3366 |   61.2121 |
-| RTX 2080Ti | shelfnet 2048x2048 (B=4) | pre     | 25.435    |  39.3158  |   25.2953 |  39.5331   |  25.9303 |   38.565  | 
-| RTX 2080Ti | shelfnet 2048x2048 (B=4) | inf     | 36.5015   |  27.3961  |   17.0534 |  58.6395   |  15.6061 |   64.0773 |  
-| RTX 2080Ti | shelfnet 2048x2048 (B=4) | post    | 17.3917   |  57.4985  |   17.1649 |  58.2583   |  17.5539 |   56.9675 |  
-| RTX 2080Ti | shelfnet 2048x2048 (B=4) | tot     | 79.3283   |  12.6058  |   59.5136 |  16.8029   |  59.0903 |   16.9233 |  
-| AGX Xavier | shelfnet 1024x1024 (B=1) | pre     | 8.0174    |  124.729  |   7.5117  |  133.126   |  7.47333 |   133.809 |
-| AGX Xavier | shelfnet 1024x1024 (B=1) | inf     | 72.4173   |  13.8089  |   37.505  |  26.6631   |  31.3286 |   31.9197 |
-| AGX Xavier | shelfnet 1024x1024 (B=1) | post    | 8.89958   |  112.365  |   8.83576 |  113.176   |  9.42655 |   106.083 |
-| AGX Xavier | shelfnet 1024x1024 (B=1) | tot     | 89.3342   |  11.1939  |   53.8525 |  18.5692   |  48.2285 |   20.7346 |
-| AGX Xavier | shelfnet 2048x2048 (B=4) | pre     | 47.1454   |  21.211   |   21.6475 |  46.1947   |  21.4201 |   46.6851 | 
-| AGX Xavier | shelfnet 2048x2048 (B=4) | inf     | 266.537   |  3.75183  |   128.321 |  7.79293   |  107.621 |   9.29185 |  
-| AGX Xavier | shelfnet 2048x2048 (B=4) | post    | 44.0711   |  22.6906  |   40.1732 |  24.8922   |  39.873  |   25.0796 |  
-| AGX Xavier | shelfnet 2048x2048 (B=4) | tot     | 357.753   |  2.79522  |   190.142 |  5.25922   |  168.914 |   5.92016 |   -->
-
@@ -1,68 +0,0 @@
-# Semantic Segmentation with tkDNN
-
-Currently tkDNN supports only ShelfNet as semantic segmentation network.
-
-
-## Run the demo
-
-To run the semantic segmentation demo follow these steps (example with shelfnet):
-```
-rm shelfnet_fp32.rt        # be sure to delete(or move) old tensorRT files
-export TKDNN_BATCHSIZE=4   # be sure you have batch size > than 1 if you want to run inference on images bigger than 1024
-./test_shelfnet            # run the yolo test (is slow)
-./demo shelfnet_fp32.rt ../demo/yolo_test.mp4 1 19
-```
-In general the demo program takes the following parameters:
-```
-./seg_demo <network-rt-file> <path-to-video> <n-batches> <number-of-classes> <resize-flag> <baseline-resize> <show-flag> <write-pred>
-```
-where
-*  ```<network-rt-file>``` is the rt file generated by a test
-*  ```<<path-to-video>``` is the path to a video file or a camera input  
-*  ```<n-batches>``` number of batches to use in inference (N.B. you should first export TKDNN_BATCHSIZE to the required n_batches and create again the rt file for the network).
-*  ```<number-of-classes>```is the number of classes the network is trained on
-*  ```<resize-flag>``` if set to 0 the demo will not resize the input frames, but use it as it is, otherwise it will resize it.
-*  ```<baseline-resize>``` is ```<resize-flag>``` is set to 1, then the input frames will be proportionally resized using ```<baseline-resize>``` as width baseline.
-*  ```<show-flag>``` if set to 0 the demo will not show the visualization but save the video into result.mp4 (if n-batches ==1)
-*  ```<write-pred>``` if set to 0 (default) the demo will run, otherwise the evaluation of a dataset will run and the output of the segmentation will be saved. Attention: this is under development and paths are embedded, so change them in the code in advance.
-
-NB) By default it is used FP32 inference
-NB) The batching is not used to work on more streams, rather to work on more tiles of the same image. Shelfnet never resized the input image, therefore for images greater than 1024x1024 tiles of 1024x1024 are given in input to the network in batch. 
-
-![demo](https://user-images.githubusercontent.com/11939259/126784236-38d24fc3-02df-4514-81c4-497e87e40b65.gif "Results on yolo_test.mp4")  
-
-For other demo videos refer to [this playlist](https://www.youtube.com/playlist?list=PLv0nEQYDD45y5EdSiywwCGPBmJVUzIWwe).
-
-NB) The gif and the videos are obtained with Mapillary Vistas weights, that we cannot publicly share due to its license restrictions. However, you can train Shelfnet using Mapillary and [this](https://git.hipert.unimore.it/mverucchi/shelfnet) fork of the original repo.
-
-
-## FPS Results
-
-Inference FPS of shelfnet with tkDNN, average of 1200 images on:
-  * RTX 2080Ti (CUDA 10.2, TensorRT 7.0.0, Cudnn 7.6.5);
-  * Xavier AGX, Jetpack 4.3 (CUDA 10.0, CUDNN 7.6.3, tensorrt 6.0.1 );
-
-| Platform   | Test                     | Phase   | FP32, ms  | FP32, FPS | FP16, ms  |	FP16, FPS  | INT8, ms |	INT8, FPS | 
-| :------:   | :-----:                  | :-----: | :-----:   | :-----:   | :-----:   |	:-----:    | :-----:  |	:-----:   | 
-| RTX 2080Ti | shelfnet 1024x1024 (B=1) | pre     | 6.11863   |  163.435  |   5.81465 |  171.979   |  5.88699 |   169.866 |
-| RTX 2080Ti | shelfnet 1024x1024 (B=1) | inf     | 11.5464   |  86.6074  |   7.35396 |  135.981   |  6.37623 |   156.832 |
-| RTX 2080Ti | shelfnet 1024x1024 (B=1) | post    | 4.09058   |  244.464  |   3.91961 |  255.128   |  4.07343 |   245.493 |
-| RTX 2080Ti | shelfnet 1024x1024 (B=1) | tot     | 21.7556   |  45.9652  |   17.0882 |  58.5199   |  16.3366 |   61.2121 |
-| RTX 2080Ti | shelfnet 2048x2048 (B=4) | pre     | 25.435    |  39.3158  |   25.2953 |  39.5331   |  25.9303 |   38.565  | 
-| RTX 2080Ti | shelfnet 2048x2048 (B=4) | inf     | 36.5015   |  27.3961  |   17.0534 |  58.6395   |  15.6061 |   64.0773 |  
-| RTX 2080Ti | shelfnet 2048x2048 (B=4) | post    | 17.3917   |  57.4985  |   17.1649 |  58.2583   |  17.5539 |   56.9675 |  
-| RTX 2080Ti | shelfnet 2048x2048 (B=4) | tot     | 79.3283   |  12.6058  |   59.5136 |  16.8029   |  59.0903 |   16.9233 |  
-| AGX Xavier | shelfnet 1024x1024 (B=1) | pre     | 8.0174    |  124.729  |   7.5117  |  133.126   |  7.47333 |   133.809 |
-| AGX Xavier | shelfnet 1024x1024 (B=1) | inf     | 72.4173   |  13.8089  |   37.505  |  26.6631   |  31.3286 |   31.9197 |
-| AGX Xavier | shelfnet 1024x1024 (B=1) | post    | 8.89958   |  112.365  |   8.83576 |  113.176   |  9.42655 |   106.083 |
-| AGX Xavier | shelfnet 1024x1024 (B=1) | tot     | 89.3342   |  11.1939  |   53.8525 |  18.5692   |  48.2285 |   20.7346 |
-| AGX Xavier | shelfnet 2048x2048 (B=4) | pre     | 47.1454   |  21.211   |   21.6475 |  46.1947   |  21.4201 |   46.6851 | 
-| AGX Xavier | shelfnet 2048x2048 (B=4) | inf     | 266.537   |  3.75183  |   128.321 |  7.79293   |  107.621 |   9.29185 |  
-| AGX Xavier | shelfnet 2048x2048 (B=4) | post    | 44.0711   |  22.6906  |   40.1732 |  24.8922   |  39.873  |   25.0796 |  
-| AGX Xavier | shelfnet 2048x2048 (B=4) | tot     | 357.753   |  2.79522  |   190.142 |  5.25922   |  168.914 |   5.92016 |  
-
-
-## Known issues
-
-When creating the rt file all the checks returns errors. It is due to a different resize function and handling of the original ShelfNet outputs.
-However, the network is supposed to work. 
@@ -1,120 +0,0 @@
-# 2D Object Detection with tkDNN
-
-## Supported Networks
-
-* Yolo4, Yolo4-csp, Yolo4x, Yolo4_berkeley, Yolo4tiny
-* Yolo3, Yolo3_berkeley, Yolo3_coco4, Yolo3_flir, Yolo3_512, Yolo3tiny, Yolo3tiny_512
-* Yolo2, Yolo2_voc, Yolo2tiny
-* Csresnext50-panet-spp, Csresnext50-panet-spp_berkeley
-* Resnet101_cnet, Dla34_cnet
-* Mobilenetv2ssd, Mobilenetv2ssd512, Bdd-mobilenetv2ssd
-
-## Index
-
- - [2D Object Detection](#2d-object-detection)
- - [FP16 inference](#fp16-inference)
- - [INT8 inference](#int8-inference)
- - [Batching](#batching)
-
-### 2D Object Detection
-This is an example using yolov4.
-
-To run the an object detection first create the .rt file by running:
-```
-rm yolo4_fp32.rt        # be sure to delete(or move) old tensorRT files
-./test_yolo4            # run the yolo test (is slow)
-```
-If you get problems in the creation, try to check the error activating the debug of TensorRT in this way:
-```
-cmake .. -DCMAKE_BUILD_TYPE=Debug -DDEBUG=True
-make
-```
-
-Once you have successfully created your rt file, run the demo:
-```
-./ demo <path-to-config>
-```
-In general the demo program takes 1 parameter, the ```<path-to-config>``` that is the path to che configuration file. The parameter is optional and its default value is ```"../demo/demoConfig.yaml"```.
-
-The config file is a yaml file with the following attributes:
-* ```net``` is the rt file generated by a test
-* ```input``` is the path to a video file or a camera input  (on Linux)
-* ```win_input``` is the path to a video file or a camera input  (on Windows)
-* ```ntype``` is the type of network. Thee types are currently supported: ```y``` (YOLO family), ```c``` (CenterNet family) and ```m``` (MobileNet-SSD family)
-* ```n_classes``` is the number of classes the network is trained on
-* ```n_batch``` number of batches to use in inference (N.B. you should first export TKDNN_BATCHSIZE to the required n_batches and create again the rt file for the network).
-* ```conf_thresh``` confidence threshold for the detector. Only bounding boxes with threshold greater than conf-thresh will be displayed.
-* ```show``` if set to 0 the demo will not show the visualization (if n-batches ==1)
-* ```save``` if set to 1 the demo will save the video of the demo into result.mp4 (if n-batches ==1)
-
-N.B. By default it is used FP32 inference
-
-
-![demo](https://user-images.githubusercontent.com/11562617/72547657-540e7800-388d-11ea-83c6-49dfea2a0607.gif)
-
-
-### FP16 inference
-
-To run the demo with FP16 inference follow these steps (example with yolov3):
-```
-export TKDNN_MODE=FP16  # set the half floating point optimization
-rm yolo4_fp16.rt        # be sure to delete(or move) old tensorRT files
-./test_yolo4            # run the yolo test (is slow)
-#set net: yolo4_fp16.rt in the config file
-./demo
-```
-N.B. Using FP16 inference will lead to some errors in the results (first or second decimal). 
-
-### INT8 inference
-
-To run the demo with INT8 inference three environment variables need to be set:
-
-  * ```export TKDNN_MODE=INT8```: set the 8-bit integer optimization
-  * ```export TKDNN_CALIB_IMG_PATH=/path/to/calibration/image_list.txt``` : image_list.txt has in each line the absolute path to a calibration image
-  * ```export TKDNN_CALIB_LABEL_PATH=/path/to/calibration/label_list.txt```: label_list.txt has in each line the absolute path to a calibration label
-  
-You should provide image_list.txt and label_list.txt, using training images. However, if you want to quickly test the INT8 inference you can run (from this repo root folder)
-```
-bash scripts/download_validation.sh COCO
-```
-to automatically download COCO2017 validation (inside demo folder) and create those needed file. Use BDD instead of COCO to download BDD validation. 
-
-Then a complete example using yolo3 and COCO dataset would be:
-```
-export TKDNN_MODE=INT8
-export TKDNN_CALIB_LABEL_PATH=../demo/COCO_val2017/all_labels.txt
-export TKDNN_CALIB_IMG_PATH=../demo/COCO_val2017/all_images.txt
-rm yolo4_int8.rt        # be sure to delete(or move) old tensorRT files
-./test_yolo4            # run the yolo test (is slow)
-#set net: yolo4_int8.rt in the config file
-./demo
-```
-N.B. 
-
- * Using INT8 inference will lead to some errors in the results. 
- * The test will be slower: this is due to the INT8 calibration, which may take some time to complete. 
- * INT8 calibration requires TensorRT version greater than or equal to 6.0
- * Only 100 images are used to create the calibration table by default (set in the code).
-
-### Batching
-
-#### BatchSize bigger than 1
-```
-export TKDNN_BATCHSIZE=2
-# build tensorRT files
-```
-This will create a TensorRT file with the desired **max** batch size.
-The test will still run with a batch of 1, but the created tensorRT can manage the desired batch size.
-
-#### Test batch Inference
-This will test the network with random input and check if the output of each batch is the same.
-```
-./test_rtinference <network-rt-file> <number-of-batches>
-# <number-of-batches> should be less or equal to the max batch size of the <network-rt-file>
-
-# example
-export TKDNN_BATCHSIZE=4           # set max batch size
-rm yolo3_fp32.rt                   # be sure to delete(or move) old tensorRT files
-./test_yolo3                       # build RT file
-./test_rtinference yolo3_fp32.rt 4 # test with a batch size of 4
-```
@@ -1,127 +0,0 @@
-# tkDNN export weights
-
-## Index 
-
- - [How to export weights](#how-to-export-weights)
-    - [1)Export weights from darknet](#1export-weights-from-darknet)
-    - [2)Export weights for DLA34 and ResNet101](#2export-weights-for-dla34-and-resnet101)
-    - [3)Export weights for CenterNet](#3export-weights-for-centernet)
-    - [4)Export weights for MobileNetSSD](#4export-weights-for-mobilenetssd)
-    - [5)Export weights for CenterTrack](#5export-weights-for-centertrack)
-    - [6)Export weights for ShelfNet](#6export-weights-for-shelfnet)
- - [Darknet Parser](#darknet-parser)
-
-## How to export weights
-
-Weights are essential for any network to run inference. For each test a folder organized as follow is needed (in the build folder):
-```
-    test_nn
-        |---- layers/ (folder containing a binary file for each layer with the corresponding wieghts and bias)
-        |---- debug/  (folder containing a binary file for each layer with the corresponding outputs)
-```
-Therefore, once the weights have been exported, the folders layers and debug should be placed in the corresponding test.
-
-### 1)Export weights from darknet
-To export weights for NNs that are defined in darknet framework, use [this](https://git.hipert.unimore.it/fgatti/darknet.git) fork of darknet and follow these steps to obtain a correct debug and layers folder, ready for tkDNN.
-
-```
-git clone https://git.hipert.unimore.it/fgatti/darknet.git
-cd darknet
-make
-mkdir layers debug
-./darknet export <path-to-cfg-file> <path-to-weights> layers
-```
-N.B. Use compilation with CPU (leave GPU=0 in Makefile) if you also want debug. 
-
-### 2)Export weights for DLA34 and ResNet101 
-To get weights and outputs needed to run the tests dla34 and resnet101 use the Python script and the Anaconda environment included in the repository.   
-
-Create Anaconda environment and activate it:
-```
-conda env create -f file_name.yml
-source activate env_name
-python <script name>
-```
-### 3)Export weights for CenterNet
-To get the weights needed to run Centernet tests use [this](https://github.com/sapienzadavide/CenterNet.git) fork of the original Centernet. 
-```
-git clone https://github.com/sapienzadavide/CenterNet.git
-```
-* follow the instruction in the README.md and INSTALL.md
-
-```
-python demo.py --input_res 512 --arch resdcn_101 ctdet --demo /path/to/image/or/folder/or/video/or/webcam --load_model ../models/ctdet_coco_resdcn101.pth --exp_wo --exp_wo_dim 512
-python demo.py --input_res 512 --arch dla_34 ctdet --demo /path/to/image/or/folder/or/video/or/webcam --load_model ../models/ctdet_coco_dla_2x.pth --exp_wo --exp_wo_dim 512
-```
-### 4)Export weights for MobileNetSSD
-To get the weights needed to run Mobilenet tests use [this](https://github.com/mive93/pytorch-ssd) fork of a Pytorch implementation of SSD network. 
-
-```
-git clone https://github.com/mive93/pytorch-ssd
-cd pytorch-ssd
-conda env create -f env_mobv2ssd.yml
-python run_ssd_live_demo.py mb2-ssd-lite <pth-model-fil> <labels-file>
-```
-### 5)Export weights for CenterTrack
-To get the weights needed to run CenterTrack tests use [this](https://github.com/sapienzadavide/CenterTrack.git) fork of the original CenterTrack. 
-```
-git clone https://github.com/sapienzadavide/CenterTrack.git
-```
-* follow the instruction in the README.md and INSTALL.md
-
-```
-python demo.py tracking,ddd --load_model ../models/nuScenes_3Dtracking.pth --dataset nuscenes --pre_hm --track_thresh 0.1 --demo /path/to/image/or/folder/or/video/or/webcam --test_focal_length 633 --exp_wo --exp_wo_dim 512 --input_h 512 --input_w 512
-```
-
-### 6)Export weights for ShelfNet
-To get the weights needed to run Shelfnet tests use [this](https://git.hipert.unimore.it/mverucchi/shelfnet) fork of a Pytorch implementation of Shelfnet network. 
-
-```
-git clone https://git.hipert.unimore.it/mverucchi/shelfnet
-cd shelfnet 
-cd ShelfNet18_realtime
-conda env create --file shelfnet_env.yml
-conda activate shelfnet
-mkdir layer debug
-python export.py
-```
-
-### 6)Export weights for monodepth2
-To get the weights needed to run Shelfnet tests use [this](https://github.com/perseusdg/monodepth2) fork of a Pytorch implementation of monodepth2 network. 
-
-```
-git clone https://github.com/perseusdg/monodepth2
-cd monodepth2
-mkdir models # Download the official weights and put depth.pth and encorder.pth inside this new folder
-conda env create --file monodepth.yaml
-conda activate monodepth2
-python exporter.py # you will find the weights inside the tkDNN_bin folder
-```
-
-## Darknet Parser
-tkDNN implement and easy parser for darknet cfg files, a network can be converted with *tk::dnn::darknetParser*:
-```
-// example of parsing yolo4
-tk::dnn::Network *net = tk::dnn::darknetParser("yolov4.cfg", "yolov4/layers", "coco.names");
-net->print();
-```
-All models from darknet are now parsed directly from cfg, you still need to export the weights with the described tools in the previous section.
-<details>
-  <summary>Supported layers</summary>
-  convolutional
-  maxpool
-  avgpool
-  shortcut
-  upsample
-  route
-  reorg
-  region
-  yolo
-</details>
-<details>
-  <summary>Supported activations</summary>
-  relu
-  leaky
-  mish
-  logistic
-</details>
@@ -1,32 +0,0 @@
-# Run the mAP demo
-
-To compute mAP, precision, recall and f1score to evaluate 2D object detectors, run the map_demo.
-
-A validation set is needed. 
-To download COCO_val2017 (80 classes) run (form the root folder): 
-```
-bash scripts/download_validation.sh COCO
-```
-To download Berkeley_val (10 classes) run (form the root folder): 
-```
-bash scripts/download_validation.sh BDD
-```
-
-To compute the map, the following parameters are needed:
-```
-./map_demo <network rt> <network type [y|c|m]> <labels file path> <config file path>
-```
-where 
-* ```<network rt>```: rt file of a chosen network on which compute the mAP.
-* ```<network type [y|c|m]>```: type of network. Right now only y(yolo), c(centernet) and m(mobilenet) are allowed
-* ```<labels file path>```: path to a text file containing all the paths of the ground-truth labels. It is important that all the labels of the ground-truth are in a folder called 'labels'. In the folder containing the folder 'labels' there should be also a folder 'images', containing all the ground-truth images having the same same as the labels. To better understand, if there is a label path/to/labels/000001.txt there should be a corresponding image path/to/images/000001.jpg. 
-* ```<config file path>```: path to a yaml file with the parameters needed for the mAP computation, similar to demo/config.yaml
-
-Example:
-
-```
-cd build
-./map_demo dla34_cnet_FP32.rt c ../demo/COCO_val2017/all_labels.txt ../demo/config.yaml
-```
-
-This demo also creates a json file named ```net_name_COCO_res.json``` containing all the detections computed. The detections are in COCO format, the correct format to submit the results to [CodaLab COCO detection challenge](https://competitions.codalab.org/competitions/20794#participate).
@@ -1,102 +0,0 @@
-# tkDNN on Windows 
-
-## Index
-
- - [Dependencies-Windows](#dependencies-windows)
- - [Compiling tkDNN on Windows](#compiling-tkdnn-on-windows)
- - [Run the demo on Windows](#run-the-demo-on-windows)
-    - [FP16 inference windows](#fp16-inference-windows)
-    - [INT8 inference windows](#int8-inference-windows)
- - [Run tkDNN on WSL2 with cuda](#tkdnn-on-cuda-wsl)
- - [Known issues with tkDNN on Windows](#known-issues-with-tkdnn-on-windows)
-
-### Dependencies-Windows 
-This branch should work on every NVIDIA GPU supported in windows with the following dependencies:
-
-* WINDOWS 10 1803/WINDOWS 11 or HIGHER 
-* CUDA 11.2 
-* CUDNN 8.1.1
-* TENSORRT 7.2.3 
-* OPENCV 4.2  
-* MSVC 16.9+
-* YAML-CPP 
-* EIGEN3
-* 7ZIP (ADD TO PATH)
-* NINJA 1.10
-
-
-All the above mentioned dependencies except 7ZIP can be installed using Microsoft's [VCPKG](https://github.com/microsoft/vcpkg.git) .
-After bootstrapping VCPKG the dependencies can be built and installed using the following command :
-
-```
-opencv4(normal) - vcpkg.exe install opencv4[tbb,jpeg,tiff,opengl,openmp,png,ffmpeg,eigen]:x64-windows yaml-cpp:x64-windows eigen3:x64-windows --x-install-root=C:\opt --x-buildtrees-root=C:\temp_vcpkg_build
-
-opencv4(cuda) - vcpkg.exe install opencv4[cuda,nonfree,contrib,eigen,tbb,jpeg,tiff,opengl,openmp,png,ffmpeg]:x64-windows yaml-cpp:x64-windows eigen3:x64-windows --x-install-root=C:\opt --x-buildtrees-root=C:\temp_vcpkg_build
-```
-To build opencv4 with cuda and cudnn version corresponding to your cuda version,vcpkg's cudnn portfile needs to be modified by adding ```$ENV{CUDA_PATH}```  at lines 16 and 17 in the portfile.cmake 
-
-After VCPKG finishes building and installing all the packages delete C:\temp_vcpkg_build and add C:\opt\x64-windows\bin and C:\opt\x64-windows\debug\bin to path 
-
-### Compiling tkDNN on Windows 
-
-tkDNN is built with cmake(3.15+) on windows along with ninja.Msbuild and NMake Makefiles are drastically slower when compiling the library compared to windows
-```
-git clone https://github.com/ceccocats/tkDNN.git
-cd tkdnn-windows
-mkdir build
-cd build
-cmake -DCMAKE_BUILD_TYPE=Release -G"Ninja" ..
-ninja -j4
-```
-
-### Run the demo on Windows 
-
-This example uses yolo4_tiny.\
-To run the object detection file create .rt file bu running:
-```
-.\test_yolo4tiny.exe
-```
-
-Once the rt file has been successfully create,run the demo using the following command:
-```
-.\demo.exe yolo4_fp32.rt ..\demo\yolo_test.mp4 y 80 ..\tests\darknet\cfg\yolo4.cfg ..\tests\darknet\names\cococ.names 
-```
- For general info on more demo paramters,check Run the demo section on top 
- To run the test_all_tests.sh on windows,use git bash or msys2 
-
-### FP16 inference windows 
-
-This is an untested feature on windows.To run the object detection demo with FP16 interference follow the below steps(example with yolo4tiny):
-```
-set TKDNN_MODE=FP16
-del /f yolo4tiny_fp16.rt
-.\test_yolo4tiny.exe
-.\demo.exe yolo4tiny_fp16.rt ..\demo\yolo_test.mp4
-```
-
-### INT8 inference windows 
-To run object detection demo with INT8 (example with yolo4tiny):
-```
-set TKDNN_MODE=INT8
-set TKDNN_CALIB_LABEL_PATH=..\demo\COCO_val2017\all_labels.txt
-set TKDNN_CALIB_IMG_PATH=..\demo\COCO_val2017\all_images.txt
-del /f  yolo4tiny_int8.rt        # be sure to delete(or move) old tensorRT files
-.\test_yolo4tiny.exe           # run the yolo test (is slow)
-.\demo.exe yolo4tiny_int8.rt ..\demo\yolo_test.mp4 y
-
-```
-
-### Run tkDNN on WSL2 with cuda
-tkDNN works on wsl2 with cuda,although not all networks (centernet,mobilenet) work properly.
-If you encounter issues with running the network as a result of driver not found or cuda launch error,running the following command should solve the issue
-```cp /usr/lib/wsl/lib/lib* /usr/lib/x86_64-linux-gnu/ ```
-
-
-
-### Known issues with tkDNN on Windows
-
-In theory all models (centernet,mobilenet,darknet,centertrack,cnet3d and shelfnet) should work on Windows.
-
-On pascal cards(sm 6x) ,nvidia cuda wsl driver 510.06 don't work well with tkDNN both on windows and cuda wsl , Nvidia drivers >465+ and < 500 are completely supported .
-
-
@@ -0,0 +1,44 @@
+#include <iostream>
+#include <sstream>
+#include <fstream>
+#include <iomanip>
+#include <stdlib.h>
+#include <cstring>
+#include <cstdlib>
+#include <time.h>
+#include <chrono>
+
+#include "cuda.h"
+#include "cuda_runtime_api.h"
+#include <cublas_v2.h>
+#include <cudnn.h>
+
+#include <opencv2/core/core.hpp>
+#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+
+//saliency
+#include <opencv2/core/utility.hpp>
+//#include <opencv2/saliency.hpp>
+#include <opencv2/highgui.hpp>
+
+#define SAVE false
+#define SAVE_TO(name, fn, i, var) {sprintf(buf_frame_crop_name,name,fn,i);\
+    cv::imwrite(buf_frame_crop_name, var);}
+
+
+
+// cv::Mat img_threshold(cv::Mat frame_crop);
+// cv::Mat img_background(cv::Mat frame_crop);
+// cv::Mat img_dist_transform(cv::Mat frame_crop);
+// cv::Mat img_watershed(cv::Mat frame_crop);
+void image_segmentation(cv::Mat frame_crop, int frame_nbr, int i);
+void image_gradients(cv::Mat frame_crop, int frame_nbr, int i);
+void image_find_contours(cv::Mat frame_crop, int frame_nbr, int i);
+void image_saliency(cv::Mat frame_crop, int frame_nbr, int i);
+void frame_box_disparity(cv::Mat pre_frame, cv::Mat frame, std::vector <cv::Rect>  pre_rois, int frame_nbr);
+void segmentation(cv::Mat pre_frame, cv::Mat frame_crop, int frame_nbr, int i, int mode);
+
+//canny
+cv::Mat img_laplacian(cv::Mat frame_crop, int ret);
+cv::Mat frame_disparity(cv::Mat pre_frame, cv::Mat frame, int frame_nbr, int i, int ret);
@@ -0,0 +1,32 @@
+#ifndef CALIBRATION_H
+#define CALIBRATION_H
+
+#include "gdal.h"
+#include <gdal_priv.h>
+#include <gdal/gdal.h>
+#include "gdal/gdal_priv.h"
+#include "gdal/cpl_conv.h"
+
+#include <yaml-cpp/yaml.h>
+#include <opencv2/calib3d.hpp>
+#include <opencv2/core.hpp>
+
+#include <iostream>
+#include <cstring>
+
+struct ObjCoords
+{
+    double lat_;
+    double long_;
+    int class_;
+};
+
+void readTiff(char *filename, double *adfGeoTransform);
+void readCameraCalibrationYaml(const std::string &cameraCalib, cv::Mat &cameraMat, cv::Mat &distCoeff);
+void pixel2coord(int x, int y, double &lat, double &lon, double *adfGeoTransform);
+void coord2pixel(double lat, double lon, int &x, int &y, double *adfGeoTransform);
+void fillMatrix(cv::Mat &H, double *matrix, bool show = false);
+void read_projection_matrix(cv::Mat &H, char *path);
+void convert_coords(std::vector<ObjCoords> &coords, int x, int y, int detected_class, cv::Mat H, double *adfGeoTransform);
+
+#endif /*CALIBRATION_H*/
@@ -0,0 +1,45 @@
+#ifndef CAMERAUTILS_H
+#define CAMERAUTILS_H
+
+#include <vector>
+#include <mutex>
+#include <opencv2/core/core.hpp>
+#include "tracker.h"
+#include "Yolo3Detection.h"
+
+struct Camera_t
+{
+    int CAM_IDX;
+    char *input;
+    char *pmatrix;
+    char *maskfile;
+    char *cameraCalib;
+    char *maskFileOrient;
+    bool to_show;
+    tk::dnn::Yolo3Detection *yolo;
+    double adfGeoTransform[6];
+};
+
+struct Frame_t
+{
+    char *input;
+    cv::Mat frame;
+    int frame_nbr;
+    // sem_vc for mainthread, videocapturethread, originalthread and disparitythread
+    std::mutex sem_vc;
+};
+
+struct ModFrame_t
+{
+    std::vector<Tracker> trackers;
+    geodetic_converter::GeodeticConverter gc;
+    double adfGeoTransform[6];
+    cv::Mat H;
+    cv::Mat original_frame;
+    tk::dnn::Yolo3Detection yolo;
+    cv::Mat mask;
+    // sem for mainthread, detectionthread and topviewthread
+    std::mutex sem;
+};
+
+#endif /*CAMERAUTILS_H*/
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Micaela Verucchi	615b4c8a52	Udpate tracker Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2020-02-27 18:04:52 +01:00
Micaela Verucchi	fe37bcdf1e	Merge with master Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2020-02-27 17:41:26 +01:00
Micaela Verucchi	6911752e3e	Update tracker Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>	2020-02-27 17:00:07 +01:00
xavier	33844c1ab2	Batchnorm eps fix, works on jetpack 4.3	2020-01-15 19:11:14 +01:00
xavier	7233b065a8	Compiles with opencv4 -pt 2	2020-01-15 19:07:05 +01:00
xavier	2fa9f691ab	Compiles with opencv4 Signed-off-by: xavier <micaelaverucchi@gmail.com>	2020-01-15 19:01:23 +01:00
Davide Sapienza	cfb457fdec	Fix bug in velocity conversion. This commit fixes a wrong operation in the velocity conversion. A reduced speed (because we are in a urban track) is now stored into a uint8. Thus granularity is now half km/h. Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>	2019-11-11 18:07:03 +01:00
Davide Sapienza	1af2b792b8	Fix segmentation fault on yolo3Detection object copy This commit fixes a segmentation fault appeared in yolo3 network updating. Now in Camera_t structure type there is a network pointer. Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>	2019-10-18 19:05:42 +02:00
Davide Sapienza	d5ae26dfef	Add configuration file to read CLASS parameters This commit changes the parameters reading. It introduces getopt to read input parameters from command line and it uses a configuration yaml file to read the input parameters for the network, the map and the cameras. This commit fixes a bug in message sending to the aggregator. Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>	2019-10-15 10:24:43 +02:00
Micaela Verucchi	35787cc771	Refactoring and modularization Signed-off-by: Micaela Verucchi <micaela.verucchi@unimore.it>	2019-10-04 11:12:01 +02:00
Davide Sapienza	bb7d382d96	Handle cases captured by asserts This commit removes asserts and handles their cases.	2019-09-27 15:37:14 +02:00
Davide Sapienza	4a7b290fdf	Updating the submodule 'tracker_CLASS' to the latest version	2019-09-27 14:58:26 +02:00
Davide Sapienza	5a1c7fb83d	Move several cameras into a single process. This commit pairs a camera with a thread. In this way, the single process can to manage several camera. Every camera thread create one video capture thread to read its input stream. Only one camera thread can start the visualization. This commit fixes the wrong data reading from file of the read_projection_matrix function. This commit fixes the wrong orientation mask accessing of the addRoadUserfromTracker function. In the code there are two sections to test.	2019-09-26 16:39:16 +02:00
Davide Sapienza	b1a3620061	Fix visualization thread This commit splits some operations into different threads. Some threads compute the visualization preprocessing for the live, detection, top view and disparity visualization. Only one thread has the role to display the different views.	2019-09-09 14:40:43 +02:00
Davide Sapienza	5bbb3f3480	Include frame disparity visualization	2019-09-03 19:08:33 +02:00
Davide Sapienza	88e0f9393a	Include flag to save preprocessed images	2019-09-03 19:03:55 +02:00
Davide Sapienza	2d62d2524c	Fix the visualization thread This commit moves the computation of the visualization into the 'showImages' function (display thread). The main thread workload and the time consuming for each frame are reduced. Please enter the commit message for your changes. Lines starting	2019-08-30 17:13:29 +02:00
Davide Sapienza	7f667af48f	Add some frame filters This commit adds some box frame filters for the edge detection (semantic segmentation) and the frame disparity operation, both on the single frame box and on the whole image.	2019-08-30 10:07:52 +02:00
Davide Sapienza	2bcf9ab53b	Update mask images	2019-08-30 10:06:12 +02:00
Davide Sapienza	ed83dfd99b	Edit .gitignore: it excludes generated files	2019-08-30 09:47:58 +02:00
mive93	5f444825ad	optimized undistortion	2019-05-16 19:48:59 +02:00
mive93	12fd8d0109	tracker modified	2019-05-16 12:49:23 +02:00
Micaela Verucchi	32b6d51949	update readme with dependencies	2019-05-15 09:18:40 +02:00
mive93	86da302163	added send of trackers infos	2019-05-14 08:38:52 +02:00
mive93	ff5e376873	added file for cameras calibration	2019-05-13 15:12:02 +02:00
mive93	a6d19d3698	order	2019-05-08 20:08:20 +02:00
mive93	e38d8e82ca	new send and submodule masa_protocol added	2019-05-08 11:34:18 +02:00
mive93	caf4ddbce2	merge with master	2019-05-08 10:24:22 +02:00
mive93	8627c5feeb	reading from yaml file	2019-05-07 22:43:39 +02:00
mive93	be31ae10d2	calibration	2019-05-07 22:13:24 +02:00
mive93	f51a35ac5a	Merge branch 'class' of https://github.com/ceccocats/tkDNN into class	2019-05-07 21:07:23 +02:00
mive93	428858eaae	mask	2019-05-07 21:07:18 +02:00
Micaela Verucchi	68ecd15125	Merge branch 'class' of https://github.com/ceccocats/tkDNN into class	2019-05-07 18:57:07 +00:00
Micaela Verucchi	3311196edb	commit submodule	2019-05-07 18:56:36 +00:00
Micaela Verucchi	5c7301f7f4	Merge branch 'class' of https://github.com/ceccocats/tkDNN into class	2019-05-06 22:27:25 +02:00
Micaela Verucchi	d2456b1d8a	added BCDS test	2019-05-06 22:27:20 +02:00
Tomasz Kloda	7d1d31ac45	re-added thread for visualisation	2019-04-28 11:46:38 +00:00
Tomasz Kloda	1b9fe1ea61	added arrows(to fix), deleted old traj in top view	2019-04-27 15:59:55 +00:00
Tomasz Kloda	753699104a	submodule fix	2019-04-27 11:39:41 +00:00
Tomasz Kloda	978833fd6e	Revert "visualization via thread" This reverts commit `ca9d18c69e`.	2019-04-27 11:27:51 +00:00
Tomasz Kloda	17c5b7a818	readme modified	2019-04-27 08:31:54 +00:00
mive93	ca9d18c69e	visualization via thread	2019-04-20 17:53:03 +02:00
Micaela Verucchi	41ba8afa6d	view from top added	2019-04-20 17:06:37 +02:00
Micaela Verucchi	ea1f0cc193	colors to path	2019-04-20 16:19:32 +02:00
Micaela Verucchi	9b413b77ab	tracking integrated	2019-04-20 15:48:52 +02:00
Micaela Verucchi	9a4a65a3c3	added data	2019-04-20 13:48:55 +02:00
Micaela Verucchi	bd45b016bb	minor	2019-04-20 12:37:36 +02:00
Francesco Gatti	28c012cade	added tracker	2019-04-20 12:33:05 +02:00
Francesco Gatti	9b03bfcbd7	merged	2019-04-19 16:33:35 +02:00
Francesco Gatti	3c32d0c876	georeferencing	2019-04-19 16:21:56 +02:00
mive93	40c67e8536	dla commented	2019-04-15 11:54:16 +02:00
mive93	38a1956404	Merge branch 'class' of https://github.com/ceccocats/tkDNN into class	2019-04-15 11:45:33 +02:00
mive93	21a698bb63	added server and serialization	2019-04-15 11:38:54 +02:00
Francesco Gatti	46c32edb94	dimension inverted in tetrapack_resize test	2019-03-06 17:02:08 +01:00
Francesco Gatti	1c4aa3c5d7	Merge branch 'class' of https://github.com/ceccocats/tkDNN into class	2019-03-06 16:35:02 +01:00
Francesco Gatti	d631169821	tetrapak test added	2019-03-06 16:29:41 +01:00
mive93	61b6621d2c	Updated to have more launching parameters	2019-02-22 13:02:22 +01:00
mive93	39df47574e	Merge branch 'master' into class	2019-02-20 17:04:44 +01:00
mive93	1c8122f22d	class stuff	2019-02-20 17:00:48 +01:00