From ced9e792bf1edf26e24fd3ce5314710698c44531 Mon Sep 17 00:00:00 2001 From: Faerbit Date: Tue, 17 Jan 2017 22:39:27 +0100 Subject: [PATCH] Initial commit. --- CMakeLists.txt | 39 ++ Makefile | 12 + Readme.md | 57 ++ colors.h | 1032 +++++++++++++++++++++++++++++ config.json | 375 +++++++++++ controller.cpp | 452 +++++++++++++ controller.h | 103 +++ cvimagewidget.h | 123 ++++ data/imgs/.gitignore | 1 + data/imgs/nut_0.jpg | Bin 0 -> 33704 bytes data/imgs/nut_1.jpg | Bin 0 -> 22929 bytes data/imgs/nut_10.jpg | Bin 0 -> 26400 bytes data/imgs/nut_11.jpg | Bin 0 -> 29872 bytes data/imgs/nut_12.jpg | Bin 0 -> 24230 bytes data/imgs/nut_13.jpg | Bin 0 -> 24695 bytes data/imgs/nut_14.jpg | Bin 0 -> 25924 bytes data/imgs/nut_15.jpg | Bin 0 -> 28624 bytes data/imgs/nut_16.jpg | Bin 0 -> 25882 bytes data/imgs/nut_17.jpg | Bin 0 -> 26129 bytes data/imgs/nut_18.jpg | Bin 0 -> 24733 bytes data/imgs/nut_19.jpg | Bin 0 -> 24037 bytes data/imgs/nut_2.jpg | Bin 0 -> 30544 bytes data/imgs/nut_20.jpg | Bin 0 -> 22671 bytes data/imgs/nut_21.jpg | Bin 0 -> 28736 bytes data/imgs/nut_22.jpg | Bin 0 -> 22662 bytes data/imgs/nut_23.jpg | Bin 0 -> 29873 bytes data/imgs/nut_24.jpg | Bin 0 -> 35274 bytes data/imgs/nut_25.jpg | Bin 0 -> 27656 bytes data/imgs/nut_26.jpg | Bin 0 -> 20123 bytes data/imgs/nut_3.jpg | Bin 0 -> 25771 bytes data/imgs/nut_4.jpg | Bin 0 -> 27536 bytes data/imgs/nut_5.jpg | Bin 0 -> 22544 bytes data/imgs/nut_6.jpg | Bin 0 -> 25184 bytes data/imgs/nut_7.jpg | Bin 0 -> 27032 bytes data/imgs/nut_8.jpg | Bin 0 -> 28512 bytes data/imgs/nut_9.jpg | Bin 0 -> 26970 bytes data/input.csv | 28 + data/nuts.net | 36 + data/output.csv | 28 + detectionprocessing.cpp | 162 +++++ detectionprocessing.h | 43 ++ filenames.cpp | 7 + filenames.h | 14 + main.cpp | 14 + mainwindow.cpp | 264 ++++++++ mainwindow.h | 53 ++ mainwindow.ui | 691 +++++++++++++++++++ neuralnet.cpp | 273 ++++++++ neuralnet.h | 38 ++ processing.cpp | 183 +++++ processing.h | 50 ++ processingstep.h | 100 +++ rateprocessing.cpp | 311 +++++++++ rateprocessing.h | 46 ++ test_data/.directory | 4 + test_data/IMG_20170117_012527.jpg | Bin 0 -> 5707010 bytes 56 files changed, 4539 insertions(+) create mode 100644 CMakeLists.txt create mode 100644 Makefile create mode 100644 Readme.md create mode 100644 colors.h create mode 100644 config.json create mode 100644 controller.cpp create mode 100644 controller.h create mode 100644 cvimagewidget.h create mode 100644 data/imgs/.gitignore create mode 100644 data/imgs/nut_0.jpg create mode 100644 data/imgs/nut_1.jpg create mode 100644 data/imgs/nut_10.jpg create mode 100644 data/imgs/nut_11.jpg create mode 100644 data/imgs/nut_12.jpg create mode 100644 data/imgs/nut_13.jpg create mode 100644 data/imgs/nut_14.jpg create mode 100644 data/imgs/nut_15.jpg create mode 100644 data/imgs/nut_16.jpg create mode 100644 data/imgs/nut_17.jpg create mode 100644 data/imgs/nut_18.jpg create mode 100644 data/imgs/nut_19.jpg create mode 100644 data/imgs/nut_2.jpg create mode 100644 data/imgs/nut_20.jpg create mode 100644 data/imgs/nut_21.jpg create mode 100644 data/imgs/nut_22.jpg create mode 100644 data/imgs/nut_23.jpg create mode 100644 data/imgs/nut_24.jpg create mode 100644 data/imgs/nut_25.jpg create mode 100644 data/imgs/nut_26.jpg create mode 100644 data/imgs/nut_3.jpg create mode 100644 data/imgs/nut_4.jpg create mode 100644 data/imgs/nut_5.jpg create mode 100644 data/imgs/nut_6.jpg create mode 100644 data/imgs/nut_7.jpg create mode 100644 data/imgs/nut_8.jpg create mode 100644 data/imgs/nut_9.jpg create mode 100644 data/input.csv create mode 100644 data/nuts.net create mode 100644 data/output.csv create mode 100644 detectionprocessing.cpp create mode 100644 detectionprocessing.h create mode 100644 filenames.cpp create mode 100644 filenames.h create mode 100644 main.cpp create mode 100644 mainwindow.cpp create mode 100644 mainwindow.h create mode 100644 mainwindow.ui create mode 100644 neuralnet.cpp create mode 100644 neuralnet.h create mode 100644 processing.cpp create mode 100644 processing.h create mode 100644 processingstep.h create mode 100644 rateprocessing.cpp create mode 100644 rateprocessing.h create mode 100644 test_data/.directory create mode 100644 test_data/IMG_20170117_012527.jpg diff --git a/CMakeLists.txt b/CMakeLists.txt new file mode 100644 index 0000000..0e706c6 --- /dev/null +++ b/CMakeLists.txt @@ -0,0 +1,39 @@ +cmake_minimum_required(VERSION 2.8.11) + +project(haselnuss) + +set(CMAKE_INCLUDE_CURRENT_DIR ON) + +# set qt specific options +set(CMAKE_AUTOMOC ON) +set(CMAKE_AUTOUIC ON) + +# set c++ 14 +set(CMAKE_CXX_STANDARD 14) + +find_package(Qt5Widgets REQUIRED) +find_package(Qt5Core REQUIRED) +find_package(OpenCV REQUIRED) +find_package(fann REQUIRED) +find_package(PkgConfig REQUIRED) +pkg_search_module(JSONCPP REQUIRED jsoncpp) + +# Source Files +FILE(GLOB SOURCE_FILES "${CMAKE_SOURCE_DIR}/*.cpp") +# Header files +FILE(GLOB HEADER_FILES "${CMAKE_SOURCE_DIR}/*.h") + +set(INCLUDE_DIRS ${INCLUDE_DIRS} "${CMAKE_SOURCE_DIR}") +set(INCLUDE_DIRS ${INCLUDE_DIRS} "${FANN_INCLUDE_DIR}") + +INCLUDE_DIRECTORIES("${INCLUDE_DIRS}") + +# Libraries +set(LIBRARIES ${LIBRARIES} ${QT_LIBRARIES}) +set(LIBRARIES ${LIBRARIES} ${OpenCV_LIBS}) +set(LIBRARIES ${LIBRARIES} ${FANN_LIBRARIES}) +set(LIBRARIES ${LIBRARIES} ${JSONCPP_LIBRARIES}) + +add_executable(haselnuss ${SOURCE_FILES} ${HEADER_FILES}) +qt5_use_modules(haselnuss Widgets) +target_link_libraries(haselnuss ${LIBRARIES}) diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..ea6ff03 --- /dev/null +++ b/Makefile @@ -0,0 +1,12 @@ +all: run + +.PHONY: build +build: + mkdir -p build + cd build; cmake -G Ninja -DCMAKE_BUILD_TYPE=Debug ..; ninja + +run: build + ./build/haselnuss + +clean: + rm -rf build diff --git a/Readme.md b/Readme.md new file mode 100644 index 0000000..00b0729 --- /dev/null +++ b/Readme.md @@ -0,0 +1,57 @@ +#Rate my Haselnuss +Visually guess the taste of a hazelnut + +##Disclaimer + +This program has been created as part of my image processing practical course +at the FH Aachen. Therefore some parts of this are in german. If you're +interested in this and have trouble understanding it, do not hesitate to +contact me: I'll gladly translate it for you to english. + +##Idee +Dieses Programm versucht Haselnüsse visuell nach ihrem Geschmack zu +klassifizieren, insbesondere die Erkennung der schlecht schmeckenden soll +gelingen. + + +##Technische Vorraussetzungen +Dieses Programm benutzt CMake und kann wie jedes andere CMake-Projekt +kompiliert werden: +``` +mkdir build +cd build +cmake .. +make +``` + +Grundsätzlich sollte dieses Programm platformunabhängig sein. Getestet ist +dieses jedoch nur unter Linux. Insbesondere die direkte Aufnahme der Fotos über +die Android Debug Bridge (ADB) funktioniert nur unter Linux. + +Folgende Libraries werden benötigt: +* Qt5 +* OpenCV 3.1 +* fann 2.2 (Fast Artificial Neural Network Library, + [http://leenissen.dk/fann/wp/]()) +* JsonCPP [https://github.com/open-source-parsers/jsoncpp]() + +##Aufnahmebedingungen +Aufnahmebedingungen für Eingabedaten: +* Heller, einfarber Hintergrund mit einem H-Wert (im HSV-Farbraum) von über 70 +* Lockere Anordnung +* Senkrechte Aufnahme aus ca. 18 cm Entfernung. +* Senkrechte Beleuchtung (z.B. Blitz) +* Auflösung des Bildsensors: 4160x3120 + +Alle Zahlenwerte können geändert werden, wenn die Konfiguration entsprechend +angepasst wird. Die aktuelle Konfiguration ist allerdings nur unter diesen +Bedingungen getestet. Abweichende Bedingungen können schlechtere Ergebnisse +liefern. + +##Bedienung +Die Bedienung des Programmes sollte relativ selbsterklärend sein. Man läd ein +Bild in die Applikation und erhält eine Klassifikation der Nüsse visuell +angezeigt. Unter dem Konfigurationstab können die Erkennungs- und +Bewertungsschritte nachvollzogen und angepasst werden. +Einige Zahlenwerte können hier ebenfalls konfiguriert werden. + diff --git a/colors.h b/colors.h new file mode 100644 index 0000000..5b38398 --- /dev/null +++ b/colors.h @@ -0,0 +1,1032 @@ +#ifndef COLORS_H +#define COLORS_H + +#include +#include + +std::vector colors = { + cv::Vec3b(0, 0, 0), + cv::Vec3b(255, 255, 0), + cv::Vec3b(28, 230, 255), + cv::Vec3b(255, 52, 255), + cv::Vec3b(255, 74, 70), + cv::Vec3b(0, 137, 65), + cv::Vec3b(0, 111, 166), + cv::Vec3b(163, 0, 89), + cv::Vec3b(255, 219, 229), + cv::Vec3b(122, 73, 0), + cv::Vec3b(0, 0, 166), + cv::Vec3b(99, 255, 172), + cv::Vec3b(183, 151, 98), + cv::Vec3b(0, 77, 67), + cv::Vec3b(143, 176, 255), + cv::Vec3b(153, 125, 135), + cv::Vec3b(90, 0, 7), + cv::Vec3b(128, 150, 147), + cv::Vec3b(254, 255, 230), + cv::Vec3b(27, 68, 0), + cv::Vec3b(79, 198, 1), + cv::Vec3b(59, 93, 255), + cv::Vec3b(74, 59, 83), + cv::Vec3b(255, 47, 128), + cv::Vec3b(97, 97, 90), + cv::Vec3b(186, 9, 0), + cv::Vec3b(107, 121, 0), + cv::Vec3b(0, 194, 160), + cv::Vec3b(255, 170, 146), + cv::Vec3b(255, 144, 201), + cv::Vec3b(185, 3, 170), + cv::Vec3b(209, 97, 0), + cv::Vec3b(221, 239, 255), + cv::Vec3b(0, 0, 53), + cv::Vec3b(123, 79, 75), + cv::Vec3b(161, 194, 153), + cv::Vec3b(48, 0, 24), + cv::Vec3b(10, 166, 216), + cv::Vec3b(1, 51, 73), + cv::Vec3b(0, 132, 111), + cv::Vec3b(55, 33, 1), + cv::Vec3b(255, 181, 0), + cv::Vec3b(194, 255, 237), + cv::Vec3b(160, 121, 191), + cv::Vec3b(204, 7, 68), + cv::Vec3b(192, 185, 178), + cv::Vec3b(194, 255, 153), + cv::Vec3b(0, 30, 9), + cv::Vec3b(0, 72, 156), + cv::Vec3b(111, 0, 98), + cv::Vec3b(12, 189, 102), + cv::Vec3b(238, 195, 255), + cv::Vec3b(69, 109, 117), + cv::Vec3b(183, 123, 104), + cv::Vec3b(122, 135, 161), + cv::Vec3b(120, 141, 102), + cv::Vec3b(136, 85, 120), + cv::Vec3b(250, 208, 159), + cv::Vec3b(255, 138, 154), + cv::Vec3b(209, 87, 160), + cv::Vec3b(190, 196, 89), + cv::Vec3b(69, 102, 72), + cv::Vec3b(0, 134, 237), + cv::Vec3b(136, 111, 76), + cv::Vec3b(52, 54, 45), + cv::Vec3b(180, 168, 189), + cv::Vec3b(0, 166, 170), + cv::Vec3b(69, 44, 44), + cv::Vec3b(99, 99, 117), + cv::Vec3b(163, 200, 201), + cv::Vec3b(255, 145, 63), + cv::Vec3b(147, 138, 129), + cv::Vec3b(87, 83, 41), + cv::Vec3b(0, 254, 207), + cv::Vec3b(176, 91, 111), + cv::Vec3b(140, 208, 255), + cv::Vec3b(59, 151, 0), + cv::Vec3b(4, 247, 87), + cv::Vec3b(200, 161, 161), + cv::Vec3b(30, 110, 0), + cv::Vec3b(121, 0, 215), + cv::Vec3b(167, 117, 0), + cv::Vec3b(99, 103, 169), + cv::Vec3b(160, 88, 55), + cv::Vec3b(107, 0, 44), + cv::Vec3b(119, 38, 0), + cv::Vec3b(215, 144, 255), + cv::Vec3b(155, 151, 0), + cv::Vec3b(84, 158, 121), + cv::Vec3b(255, 246, 159), + cv::Vec3b(32, 22, 37), + cv::Vec3b(114, 65, 143), + cv::Vec3b(188, 35, 255), + cv::Vec3b(153, 173, 192), + cv::Vec3b(58, 36, 101), + cv::Vec3b(146, 35, 41), + cv::Vec3b(91, 69, 52), + cv::Vec3b(253, 232, 220), + cv::Vec3b(64, 78, 85), + cv::Vec3b(0, 137, 163), + cv::Vec3b(203, 126, 152), + cv::Vec3b(164, 232, 4), + cv::Vec3b(50, 78, 114), + cv::Vec3b(106, 58, 76), + cv::Vec3b(131, 171, 88), + cv::Vec3b(0, 28, 30), + cv::Vec3b(209, 247, 206), + cv::Vec3b(0, 75, 40), + cv::Vec3b(200, 208, 246), + cv::Vec3b(163, 164, 137), + cv::Vec3b(128, 108, 102), + cv::Vec3b(34, 40, 0), + cv::Vec3b(191, 86, 80), + cv::Vec3b(232, 48, 0), + cv::Vec3b(102, 121, 109), + cv::Vec3b(218, 0, 124), + cv::Vec3b(255, 26, 89), + cv::Vec3b(138, 219, 180), + cv::Vec3b(30, 2, 0), + cv::Vec3b(91, 78, 81), + cv::Vec3b(200, 149, 197), + cv::Vec3b(50, 0, 51), + cv::Vec3b(255, 104, 50), + cv::Vec3b(102, 225, 211), + cv::Vec3b(207, 205, 172), + cv::Vec3b(208, 172, 148), + cv::Vec3b(126, 211, 121), + cv::Vec3b(1, 44, 88), + cv::Vec3b(122, 123, 255), + cv::Vec3b(214, 142, 1), + cv::Vec3b(53, 51, 57), + cv::Vec3b(120, 175, 161), + cv::Vec3b(254, 178, 198), + cv::Vec3b(117, 121, 124), + cv::Vec3b(131, 115, 147), + cv::Vec3b(148, 58, 77), + cv::Vec3b(181, 244, 255), + cv::Vec3b(210, 220, 213), + cv::Vec3b(149, 86, 189), + cv::Vec3b(106, 113, 74), + cv::Vec3b(0, 19, 37), + cv::Vec3b(2, 82, 95), + cv::Vec3b(10, 163, 247), + cv::Vec3b(233, 129, 118), + cv::Vec3b(219, 213, 221), + cv::Vec3b(94, 188, 209), + cv::Vec3b(61, 79, 68), + cv::Vec3b(126, 100, 5), + cv::Vec3b(2, 104, 78), + cv::Vec3b(150, 43, 117), + cv::Vec3b(141, 133, 70), + cv::Vec3b(150, 149, 197), + cv::Vec3b(231, 115, 206), + cv::Vec3b(216, 106, 120), + cv::Vec3b(62, 137, 190), + cv::Vec3b(202, 131, 78), + cv::Vec3b(81, 138, 135), + cv::Vec3b(91, 17, 60), + cv::Vec3b(85, 129, 59), + cv::Vec3b(231, 4, 196), + cv::Vec3b(0, 0, 95), + cv::Vec3b(169, 115, 153), + cv::Vec3b(75, 129, 96), + cv::Vec3b(89, 115, 138), + cv::Vec3b(255, 93, 167), + cv::Vec3b(247, 201, 191), + cv::Vec3b(100, 49, 39), + cv::Vec3b(81, 58, 1), + cv::Vec3b(107, 148, 170), + cv::Vec3b(81, 160, 88), + cv::Vec3b(164, 91, 2), + cv::Vec3b(29, 23, 2), + cv::Vec3b(226, 0, 39), + cv::Vec3b(231, 171, 99), + cv::Vec3b(76, 96, 1), + cv::Vec3b(156, 105, 102), + cv::Vec3b(100, 84, 123), + cv::Vec3b(151, 151, 158), + cv::Vec3b(0, 106, 102), + cv::Vec3b(57, 20, 6), + cv::Vec3b(244, 215, 73), + cv::Vec3b(0, 69, 210), + cv::Vec3b(0, 108, 49), + cv::Vec3b(221, 182, 208), + cv::Vec3b(124, 101, 113), + cv::Vec3b(159, 178, 164), + cv::Vec3b(0, 216, 145), + cv::Vec3b(21, 160, 138), + cv::Vec3b(188, 101, 233), + cv::Vec3b(255, 255, 254), + cv::Vec3b(198, 220, 153), + cv::Vec3b(32, 59, 60), + cv::Vec3b(103, 17, 144), + cv::Vec3b(107, 58, 100), + cv::Vec3b(245, 225, 255), + cv::Vec3b(255, 160, 242), + cv::Vec3b(204, 170, 53), + cv::Vec3b(55, 69, 39), + cv::Vec3b(139, 180, 0), + cv::Vec3b(121, 120, 104), + cv::Vec3b(198, 0, 90), + cv::Vec3b(59, 0, 10), + cv::Vec3b(200, 98, 64), + cv::Vec3b(41, 96, 124), + cv::Vec3b(64, 35, 52), + cv::Vec3b(125, 90, 68), + cv::Vec3b(204, 184, 124), + cv::Vec3b(184, 129, 131), + cv::Vec3b(170, 81, 153), + cv::Vec3b(181, 214, 195), + cv::Vec3b(163, 132, 105), + cv::Vec3b(159, 148, 240), + cv::Vec3b(167, 69, 113), + cv::Vec3b(184, 148, 166), + cv::Vec3b(113, 187, 140), + cv::Vec3b(0, 180, 51), + cv::Vec3b(120, 158, 201), + cv::Vec3b(109, 128, 186), + cv::Vec3b(149, 63, 0), + cv::Vec3b(94, 255, 3), + cv::Vec3b(228, 255, 252), + cv::Vec3b(27, 225, 119), + cv::Vec3b(188, 177, 229), + cv::Vec3b(118, 145, 47), + cv::Vec3b(0, 49, 9), + cv::Vec3b(0, 96, 205), + cv::Vec3b(210, 0, 150), + cv::Vec3b(137, 85, 99), + cv::Vec3b(41, 32, 29), + cv::Vec3b(91, 50, 19), + cv::Vec3b(167, 111, 66), + cv::Vec3b(137, 65, 46), + cv::Vec3b(26, 58, 42), + cv::Vec3b(73, 75, 90), + cv::Vec3b(168, 140, 133), + cv::Vec3b(244, 171, 170), + cv::Vec3b(163, 243, 171), + cv::Vec3b(0, 198, 200), + cv::Vec3b(234, 139, 102), + cv::Vec3b(149, 138, 159), + cv::Vec3b(189, 201, 210), + cv::Vec3b(159, 160, 100), + cv::Vec3b(190, 71, 0), + cv::Vec3b(101, 129, 136), + cv::Vec3b(131, 164, 133), + cv::Vec3b(69, 60, 35), + cv::Vec3b(71, 103, 93), + cv::Vec3b(58, 63, 0), + cv::Vec3b(6, 18, 3), + cv::Vec3b(223, 251, 113), + cv::Vec3b(134, 142, 126), + cv::Vec3b(152, 208, 88), + cv::Vec3b(108, 143, 125), + cv::Vec3b(215, 191, 194), + cv::Vec3b(60, 62, 110), + cv::Vec3b(216, 61, 102), + cv::Vec3b(47, 93, 155), + cv::Vec3b(108, 94, 70), + cv::Vec3b(210, 91, 136), + cv::Vec3b(91, 101, 108), + cv::Vec3b(0, 181, 127), + cv::Vec3b(84, 92, 70), + cv::Vec3b(134, 96, 151), + cv::Vec3b(54, 93, 37), + cv::Vec3b(37, 47, 153), + cv::Vec3b(0, 204, 255), + cv::Vec3b(103, 78, 96), + cv::Vec3b(252, 0, 156), + cv::Vec3b(146, 137, 107), + cv::Vec3b(30, 35, 36), + cv::Vec3b(222, 201, 178), + cv::Vec3b(157, 73, 72), + cv::Vec3b(133, 171, 180), + cv::Vec3b(52, 33, 66), + cv::Vec3b(208, 150, 133), + cv::Vec3b(164, 172, 172), + cv::Vec3b(0, 255, 255), + cv::Vec3b(174, 156, 134), + cv::Vec3b(116, 42, 51), + cv::Vec3b(14, 114, 197), + cv::Vec3b(175, 216, 236), + cv::Vec3b(192, 100, 185), + cv::Vec3b(145, 2, 140), + cv::Vec3b(254, 237, 191), + cv::Vec3b(255, 183, 137), + cv::Vec3b(156, 184, 228), + cv::Vec3b(175, 255, 209), + cv::Vec3b(42, 54, 76), + cv::Vec3b(79, 74, 67), + cv::Vec3b(100, 112, 149), + cv::Vec3b(52, 187, 255), + cv::Vec3b(128, 119, 129), + cv::Vec3b(146, 0, 3), + cv::Vec3b(179, 165, 167), + cv::Vec3b(1, 134, 21), + cv::Vec3b(241, 255, 200), + cv::Vec3b(151, 111, 92), + cv::Vec3b(255, 59, 193), + cv::Vec3b(255, 95, 107), + cv::Vec3b(7, 125, 132), + cv::Vec3b(245, 109, 147), + cv::Vec3b(87, 113, 218), + cv::Vec3b(78, 30, 42), + cv::Vec3b(131, 0, 85), + cv::Vec3b(2, 211, 70), + cv::Vec3b(190, 69, 45), + cv::Vec3b(0, 144, 94), + cv::Vec3b(190, 0, 40), + cv::Vec3b(110, 150, 227), + cv::Vec3b(0, 118, 153), + cv::Vec3b(254, 201, 109), + cv::Vec3b(156, 106, 125), + cv::Vec3b(63, 161, 184), + cv::Vec3b(137, 61, 227), + cv::Vec3b(121, 180, 214), + cv::Vec3b(127, 212, 217), + cv::Vec3b(103, 81, 187), + cv::Vec3b(178, 141, 45), + cv::Vec3b(226, 122, 5), + cv::Vec3b(221, 156, 184), + cv::Vec3b(170, 188, 122), + cv::Vec3b(152, 0, 52), + cv::Vec3b(86, 26, 2), + cv::Vec3b(143, 127, 0), + cv::Vec3b(99, 80, 0), + cv::Vec3b(205, 125, 174), + cv::Vec3b(138, 94, 45), + cv::Vec3b(255, 179, 225), + cv::Vec3b(107, 100, 102), + cv::Vec3b(198, 211, 0), + cv::Vec3b(1, 0, 226), + cv::Vec3b(136, 236, 105), + cv::Vec3b(143, 204, 190), + cv::Vec3b(33, 0, 28), + cv::Vec3b(81, 31, 77), + cv::Vec3b(227, 246, 227), + cv::Vec3b(255, 142, 177), + cv::Vec3b(107, 79, 41), + cv::Vec3b(163, 127, 70), + cv::Vec3b(106, 89, 80), + cv::Vec3b(31, 42, 26), + cv::Vec3b(4, 120, 77), + cv::Vec3b(16, 24, 53), + cv::Vec3b(230, 224, 208), + cv::Vec3b(255, 116, 254), + cv::Vec3b(0, 164, 95), + cv::Vec3b(143, 93, 248), + cv::Vec3b(75, 0, 89), + cv::Vec3b(65, 47, 35), + cv::Vec3b(216, 147, 158), + cv::Vec3b(219, 157, 114), + cv::Vec3b(96, 65, 67), + cv::Vec3b(181, 186, 206), + cv::Vec3b(152, 158, 183), + cv::Vec3b(210, 196, 219), + cv::Vec3b(165, 135, 175), + cv::Vec3b(119, 215, 150), + cv::Vec3b(127, 140, 148), + cv::Vec3b(255, 155, 3), + cv::Vec3b(85, 81, 150), + cv::Vec3b(49, 221, 174), + cv::Vec3b(116, 182, 113), + cv::Vec3b(128, 38, 71), + cv::Vec3b(42, 55, 63), + cv::Vec3b(1, 74, 104), + cv::Vec3b(105, 102, 40), + cv::Vec3b(76, 123, 109), + cv::Vec3b(0, 44, 39), + cv::Vec3b(122, 69, 34), + cv::Vec3b(59, 88, 89), + cv::Vec3b(229, 211, 129), + cv::Vec3b(255, 243, 255), + cv::Vec3b(103, 159, 160), + cv::Vec3b(38, 19, 0), + cv::Vec3b(44, 87, 66), + cv::Vec3b(145, 49, 175), + cv::Vec3b(175, 93, 136), + cv::Vec3b(199, 112, 106), + cv::Vec3b(97, 171, 31), + cv::Vec3b(140, 242, 212), + cv::Vec3b(197, 217, 184), + cv::Vec3b(159, 255, 251), + cv::Vec3b(191, 69, 204), + cv::Vec3b(73, 57, 65), + cv::Vec3b(134, 59, 96), + cv::Vec3b(185, 0, 118), + cv::Vec3b(0, 49, 119), + cv::Vec3b(197, 130, 210), + cv::Vec3b(193, 179, 148), + cv::Vec3b(96, 43, 112), + cv::Vec3b(136, 120, 104), + cv::Vec3b(186, 191, 176), + cv::Vec3b(3, 0, 18), + cv::Vec3b(209, 172, 254), + cv::Vec3b(127, 222, 254), + cv::Vec3b(75, 92, 113), + cv::Vec3b(163, 160, 151), + cv::Vec3b(230, 109, 83), + cv::Vec3b(99, 123, 93), + cv::Vec3b(146, 190, 165), + cv::Vec3b(0, 248, 179), + cv::Vec3b(190, 221, 255), + cv::Vec3b(61, 181, 167), + cv::Vec3b(221, 50, 72), + cv::Vec3b(182, 228, 222), + cv::Vec3b(66, 119, 69), + cv::Vec3b(89, 140, 90), + cv::Vec3b(185, 76, 89), + cv::Vec3b(129, 129, 213), + cv::Vec3b(148, 136, 139), + cv::Vec3b(254, 214, 189), + cv::Vec3b(83, 109, 49), + cv::Vec3b(110, 255, 146), + cv::Vec3b(228, 232, 255), + cv::Vec3b(32, 226, 0), + cv::Vec3b(255, 208, 242), + cv::Vec3b(76, 131, 161), + cv::Vec3b(189, 115, 34), + cv::Vec3b(145, 92, 78), + cv::Vec3b(140, 71, 135), + cv::Vec3b(2, 81, 23), + cv::Vec3b(162, 170, 69), + cv::Vec3b(45, 27, 33), + cv::Vec3b(169, 221, 176), + cv::Vec3b(255, 79, 120), + cv::Vec3b(82, 133, 0), + cv::Vec3b(0, 154, 46), + cv::Vec3b(23, 252, 228), + cv::Vec3b(113, 85, 90), + cv::Vec3b(82, 93, 130), + cv::Vec3b(0, 25, 90), + cv::Vec3b(150, 120, 116), + cv::Vec3b(85, 85, 88), + cv::Vec3b(11, 33, 44), + cv::Vec3b(30, 32, 43), + cv::Vec3b(239, 191, 196), + cv::Vec3b(111, 151, 85), + cv::Vec3b(111, 117, 134), + cv::Vec3b(80, 29, 29), + cv::Vec3b(55, 45, 0), + cv::Vec3b(116, 29, 22), + cv::Vec3b(94, 179, 147), + cv::Vec3b(181, 180, 0), + cv::Vec3b(221, 74, 56), + cv::Vec3b(54, 61, 255), + cv::Vec3b(173, 101, 82), + cv::Vec3b(102, 53, 175), + cv::Vec3b(131, 107, 186), + cv::Vec3b(152, 170, 127), + cv::Vec3b(70, 72, 54), + cv::Vec3b(50, 44, 62), + cv::Vec3b(124, 185, 186), + cv::Vec3b(91, 105, 101), + cv::Vec3b(112, 125, 61), + cv::Vec3b(122, 0, 29), + cv::Vec3b(110, 70, 54), + cv::Vec3b(68, 58, 56), + cv::Vec3b(174, 129, 255), + cv::Vec3b(72, 144, 121), + cv::Vec3b(137, 115, 52), + cv::Vec3b(0, 144, 135), + cv::Vec3b(218, 113, 60), + cv::Vec3b(54, 22, 24), + cv::Vec3b(255, 111, 1), + cv::Vec3b(0, 102, 121), + cv::Vec3b(55, 14, 119), + cv::Vec3b(75, 58, 131), + cv::Vec3b(201, 226, 230), + cv::Vec3b(196, 65, 112), + cv::Vec3b(255, 69, 38), + cv::Vec3b(115, 190, 84), + cv::Vec3b(196, 223, 114), + cv::Vec3b(173, 255, 96), + cv::Vec3b(0, 68, 125), + cv::Vec3b(220, 206, 201), + cv::Vec3b(189, 148, 121), + cv::Vec3b(101, 110, 91), + cv::Vec3b(236, 82, 0), + cv::Vec3b(255, 110, 194), + cv::Vec3b(122, 97, 126), + cv::Vec3b(221, 174, 162), + cv::Vec3b(119, 131, 127), + cv::Vec3b(165, 51, 39), + cv::Vec3b(96, 142, 255), + cv::Vec3b(181, 153, 215), + cv::Vec3b(165, 1, 73), + cv::Vec3b(78, 0, 37), + cv::Vec3b(201, 177, 169), + cv::Vec3b(3, 145, 154), + cv::Vec3b(27, 42, 37), + cv::Vec3b(229, 0, 241), + cv::Vec3b(152, 46, 11), + cv::Vec3b(182, 113, 128), + cv::Vec3b(224, 88, 89), + cv::Vec3b(0, 96, 57), + cv::Vec3b(87, 143, 155), + cv::Vec3b(48, 82, 48), + cv::Vec3b(206, 147, 76), + cv::Vec3b(179, 194, 190), + cv::Vec3b(192, 186, 192), + cv::Vec3b(181, 6, 211), + cv::Vec3b(23, 12, 16), + cv::Vec3b(76, 83, 79), + cv::Vec3b(34, 68, 81), + cv::Vec3b(62, 65, 65), + cv::Vec3b(120, 114, 109), + cv::Vec3b(182, 96, 43), + cv::Vec3b(32, 4, 65), + cv::Vec3b(221, 181, 136), + cv::Vec3b(73, 114, 0), + cv::Vec3b(197, 170, 182), + cv::Vec3b(3, 60, 97), + cv::Vec3b(113, 178, 245), + cv::Vec3b(169, 224, 136), + cv::Vec3b(73, 121, 176), + cv::Vec3b(162, 195, 223), + cv::Vec3b(120, 65, 73), + cv::Vec3b(45, 43, 23), + cv::Vec3b(62, 14, 47), + cv::Vec3b(87, 52, 76), + cv::Vec3b(0, 145, 190), + cv::Vec3b(228, 81, 209), + cv::Vec3b(75, 75, 106), + cv::Vec3b(92, 1, 26), + cv::Vec3b(124, 128, 96), + cv::Vec3b(255, 148, 145), + cv::Vec3b(76, 50, 93), + cv::Vec3b(0, 92, 139), + cv::Vec3b(229, 253, 164), + cv::Vec3b(104, 209, 182), + cv::Vec3b(3, 38, 65), + cv::Vec3b(20, 0, 35), + cv::Vec3b(134, 131, 169), + cv::Vec3b(207, 255, 0), + cv::Vec3b(167, 44, 62), + cv::Vec3b(52, 71, 90), + cv::Vec3b(177, 187, 154), + cv::Vec3b(180, 160, 79), + cv::Vec3b(141, 145, 142), + cv::Vec3b(161, 104, 166), + cv::Vec3b(129, 61, 58), + cv::Vec3b(66, 82, 24), + cv::Vec3b(218, 131, 134), + cv::Vec3b(119, 97, 51), + cv::Vec3b(86, 57, 48), + cv::Vec3b(132, 152, 174), + cv::Vec3b(144, 193, 211), + cv::Vec3b(181, 102, 107), + cv::Vec3b(155, 88, 94), + cv::Vec3b(133, 100, 101), + cv::Vec3b(173, 124, 144), + cv::Vec3b(226, 188, 0), + cv::Vec3b(227, 170, 224), + cv::Vec3b(178, 194, 254), + cv::Vec3b(253, 0, 57), + cv::Vec3b(0, 155, 117), + cv::Vec3b(255, 244, 109), + cv::Vec3b(232, 126, 172), + cv::Vec3b(223, 227, 230), + cv::Vec3b(132, 133, 144), + cv::Vec3b(170, 146, 151), + cv::Vec3b(131, 161, 147), + cv::Vec3b(87, 121, 119), + cv::Vec3b(62, 113, 88), + cv::Vec3b(198, 66, 137), + cv::Vec3b(234, 0, 114), + cv::Vec3b(196, 168, 203), + cv::Vec3b(85, 200, 153), + cv::Vec3b(231, 143, 207), + cv::Vec3b(0, 69, 71), + cv::Vec3b(246, 226, 227), + cv::Vec3b(150, 103, 22), + cv::Vec3b(55, 143, 219), + cv::Vec3b(67, 94, 106), + cv::Vec3b(218, 0, 4), + cv::Vec3b(27, 0, 15), + cv::Vec3b(91, 156, 143), + cv::Vec3b(110, 43, 82), + cv::Vec3b(1, 17, 21), + cv::Vec3b(227, 232, 196), + cv::Vec3b(174, 59, 133), + cv::Vec3b(234, 28, 169), + cv::Vec3b(255, 158, 107), + cv::Vec3b(69, 125, 139), + cv::Vec3b(146, 103, 139), + cv::Vec3b(0, 205, 187), + cv::Vec3b(156, 204, 4), + cv::Vec3b(0, 46, 56), + cv::Vec3b(150, 197, 127), + cv::Vec3b(207, 246, 180), + cv::Vec3b(73, 40, 24), + cv::Vec3b(118, 110, 82), + cv::Vec3b(32, 55, 14), + cv::Vec3b(227, 209, 159), + cv::Vec3b(46, 60, 48), + cv::Vec3b(178, 234, 206), + cv::Vec3b(243, 189, 164), + cv::Vec3b(162, 78, 61), + cv::Vec3b(151, 111, 217), + cv::Vec3b(140, 159, 168), + cv::Vec3b(124, 43, 115), + cv::Vec3b(78, 95, 55), + cv::Vec3b(93, 84, 98), + cv::Vec3b(144, 149, 111), + cv::Vec3b(106, 167, 118), + cv::Vec3b(219, 203, 246), + cv::Vec3b(218, 113, 255), + cv::Vec3b(152, 124, 149), + cv::Vec3b(82, 50, 60), + cv::Vec3b(187, 60, 66), + cv::Vec3b(88, 77, 57), + cv::Vec3b(79, 193, 95), + cv::Vec3b(162, 185, 193), + cv::Vec3b(121, 219, 33), + cv::Vec3b(29, 89, 88), + cv::Vec3b(189, 116, 78), + cv::Vec3b(22, 11, 0), + cv::Vec3b(32, 34, 26), + cv::Vec3b(107, 130, 149), + cv::Vec3b(0, 224, 228), + cv::Vec3b(16, 36, 1), + cv::Vec3b(27, 120, 42), + cv::Vec3b(218, 169, 181), + cv::Vec3b(176, 65, 93), + cv::Vec3b(133, 146, 83), + cv::Vec3b(151, 160, 148), + cv::Vec3b(6, 227, 196), + cv::Vec3b(71, 104, 140), + cv::Vec3b(124, 103, 85), + cv::Vec3b(7, 92, 0), + cv::Vec3b(117, 96, 213), + cv::Vec3b(125, 159, 0), + cv::Vec3b(195, 109, 150), + cv::Vec3b(77, 145, 62), + cv::Vec3b(95, 66, 118), + cv::Vec3b(252, 228, 200), + cv::Vec3b(48, 48, 82), + cv::Vec3b(79, 56, 27), + cv::Vec3b(229, 165, 50), + cv::Vec3b(112, 102, 144), + cv::Vec3b(170, 154, 146), + cv::Vec3b(35, 115, 99), + cv::Vec3b(115, 1, 62), + cv::Vec3b(255, 144, 121), + cv::Vec3b(167, 154, 116), + cv::Vec3b(2, 155, 219), + cv::Vec3b(255, 1, 105), + cv::Vec3b(199, 210, 231), + cv::Vec3b(202, 136, 105), + cv::Vec3b(128, 255, 205), + cv::Vec3b(187, 31, 105), + cv::Vec3b(144, 176, 171), + cv::Vec3b(125, 116, 169), + cv::Vec3b(252, 199, 219), + cv::Vec3b(153, 55, 91), + cv::Vec3b(0, 171, 77), + cv::Vec3b(171, 174, 209), + cv::Vec3b(190, 157, 145), + cv::Vec3b(230, 229, 167), + cv::Vec3b(51, 44, 34), + cv::Vec3b(221, 88, 123), + cv::Vec3b(245, 255, 247), + cv::Vec3b(93, 48, 51), + cv::Vec3b(109, 56, 0), + cv::Vec3b(255, 0, 32), + cv::Vec3b(181, 123, 179), + cv::Vec3b(215, 255, 230), + cv::Vec3b(197, 53, 169), + cv::Vec3b(38, 0, 9), + cv::Vec3b(106, 135, 129), + cv::Vec3b(168, 171, 180), + cv::Vec3b(212, 82, 98), + cv::Vec3b(121, 75, 97), + cv::Vec3b(70, 33, 178), + cv::Vec3b(141, 164, 219), + cv::Vec3b(199, 200, 144), + cv::Vec3b(111, 233, 173), + cv::Vec3b(162, 67, 167), + cv::Vec3b(178, 176, 129), + cv::Vec3b(24, 27, 0), + cv::Vec3b(40, 97, 84), + cv::Vec3b(76, 164, 59), + cv::Vec3b(106, 149, 115), + cv::Vec3b(168, 68, 29), + cv::Vec3b(92, 114, 123), + cv::Vec3b(115, 134, 113), + cv::Vec3b(208, 207, 203), + cv::Vec3b(137, 123, 119), + cv::Vec3b(31, 63, 34), + cv::Vec3b(65, 69, 167), + cv::Vec3b(218, 152, 148), + cv::Vec3b(161, 117, 122), + cv::Vec3b(99, 36, 60), + cv::Vec3b(173, 170, 255), + cv::Vec3b(0, 205, 226), + cv::Vec3b(221, 188, 98), + cv::Vec3b(105, 142, 177), + cv::Vec3b(32, 132, 98), + cv::Vec3b(0, 183, 224), + cv::Vec3b(97, 74, 68), + cv::Vec3b(155, 187, 87), + cv::Vec3b(122, 92, 84), + cv::Vec3b(133, 122, 80), + cv::Vec3b(118, 107, 126), + cv::Vec3b(1, 72, 51), + cv::Vec3b(255, 131, 71), + cv::Vec3b(122, 142, 186), + cv::Vec3b(39, 71, 64), + cv::Vec3b(148, 100, 68), + cv::Vec3b(235, 216, 230), + cv::Vec3b(100, 98, 65), + cv::Vec3b(55, 57, 23), + cv::Vec3b(106, 212, 80), + cv::Vec3b(129, 129, 123), + cv::Vec3b(212, 153, 227), + cv::Vec3b(151, 148, 64), + cv::Vec3b(1, 26, 18), + cv::Vec3b(82, 101, 84), + cv::Vec3b(181, 136, 92), + cv::Vec3b(164, 153, 165), + cv::Vec3b(3, 173, 137), + cv::Vec3b(179, 0, 139), + cv::Vec3b(227, 196, 181), + cv::Vec3b(150, 83, 31), + cv::Vec3b(134, 113, 117), + cv::Vec3b(116, 86, 158), + cv::Vec3b(97, 125, 159), + cv::Vec3b(231, 4, 82), + cv::Vec3b(6, 126, 175), + cv::Vec3b(166, 151, 182), + cv::Vec3b(183, 135, 168), + cv::Vec3b(156, 255, 147), + cv::Vec3b(49, 29, 25), + cv::Vec3b(58, 148, 89), + cv::Vec3b(110, 116, 110), + cv::Vec3b(176, 197, 174), + cv::Vec3b(132, 237, 247), + cv::Vec3b(237, 52, 136), + cv::Vec3b(117, 76, 120), + cv::Vec3b(56, 70, 68), + cv::Vec3b(199, 132, 123), + cv::Vec3b(0, 182, 197), + cv::Vec3b(127, 166, 112), + cv::Vec3b(193, 175, 158), + cv::Vec3b(42, 127, 255), + cv::Vec3b(114, 165, 140), + cv::Vec3b(255, 192, 127), + cv::Vec3b(157, 235, 221), + cv::Vec3b(217, 124, 142), + cv::Vec3b(126, 124, 147), + cv::Vec3b(98, 230, 116), + cv::Vec3b(181, 99, 158), + cv::Vec3b(255, 168, 97), + cv::Vec3b(194, 165, 128), + cv::Vec3b(141, 156, 131), + cv::Vec3b(183, 5, 70), + cv::Vec3b(55, 43, 46), + cv::Vec3b(0, 152, 255), + cv::Vec3b(152, 89, 117), + cv::Vec3b(32, 32, 76), + cv::Vec3b(255, 108, 96), + cv::Vec3b(68, 80, 131), + cv::Vec3b(133, 2, 170), + cv::Vec3b(114, 54, 31), + cv::Vec3b(150, 118, 163), + cv::Vec3b(72, 68, 73), + cv::Vec3b(206, 214, 194), + cv::Vec3b(59, 22, 74), + cv::Vec3b(204, 167, 99), + cv::Vec3b(44, 127, 119), + cv::Vec3b(2, 34, 123), + cv::Vec3b(163, 126, 111), + cv::Vec3b(205, 230, 220), + cv::Vec3b(205, 255, 251), + cv::Vec3b(190, 129, 26), + cv::Vec3b(247, 113, 131), + cv::Vec3b(237, 230, 226), + cv::Vec3b(205, 198, 180), + cv::Vec3b(255, 224, 158), + cv::Vec3b(58, 114, 113), + cv::Vec3b(255, 123, 89), + cv::Vec3b(78, 78, 1), + cv::Vec3b(74, 198, 132), + cv::Vec3b(139, 200, 145), + cv::Vec3b(188, 138, 150), + cv::Vec3b(207, 99, 83), + cv::Vec3b(220, 222, 92), + cv::Vec3b(94, 170, 221), + cv::Vec3b(246, 160, 173), + cv::Vec3b(226, 105, 170), + cv::Vec3b(163, 218, 228), + cv::Vec3b(67, 110, 131), + cv::Vec3b(0, 46, 23), + cv::Vec3b(236, 251, 255), + cv::Vec3b(161, 194, 182), + cv::Vec3b(80, 0, 63), + cv::Vec3b(113, 105, 91), + cv::Vec3b(103, 196, 187), + cv::Vec3b(83, 110, 255), + cv::Vec3b(93, 90, 72), + cv::Vec3b(137, 0, 57), + cv::Vec3b(150, 147, 129), + cv::Vec3b(55, 21, 33), + cv::Vec3b(94, 70, 101), + cv::Vec3b(170, 98, 195), + cv::Vec3b(141, 111, 129), + cv::Vec3b(44, 97, 53), + cv::Vec3b(65, 6, 1), + cv::Vec3b(86, 70, 32), + cv::Vec3b(230, 144, 52), + cv::Vec3b(109, 166, 189), + cv::Vec3b(229, 142, 86), + cv::Vec3b(227, 166, 139), + cv::Vec3b(72, 177, 118), + cv::Vec3b(210, 125, 103), + cv::Vec3b(181, 178, 104), + cv::Vec3b(127, 132, 39), + cv::Vec3b(255, 132, 230), + cv::Vec3b(67, 87, 64), + cv::Vec3b(234, 228, 8), + cv::Vec3b(244, 245, 255), + cv::Vec3b(50, 88, 0), + cv::Vec3b(75, 107, 165), + cv::Vec3b(173, 206, 255), + cv::Vec3b(155, 138, 204), + cv::Vec3b(136, 81, 56), + cv::Vec3b(88, 117, 193), + cv::Vec3b(126, 115, 17), + cv::Vec3b(254, 165, 202), + cv::Vec3b(159, 139, 91), + cv::Vec3b(165, 91, 84), + cv::Vec3b(137, 0, 106), + cv::Vec3b(175, 117, 111), + cv::Vec3b(42, 32, 0), + cv::Vec3b(116, 153, 161), + cv::Vec3b(255, 181, 80), + cv::Vec3b(0, 1, 30), + cv::Vec3b(209, 81, 28), + cv::Vec3b(104, 129, 81), + cv::Vec3b(188, 144, 138), + cv::Vec3b(120, 200, 235), + cv::Vec3b(133, 2, 255), + cv::Vec3b(72, 61, 48), + cv::Vec3b(196, 34, 33), + cv::Vec3b(94, 167, 255), + cv::Vec3b(120, 87, 21), + cv::Vec3b(12, 234, 145), + cv::Vec3b(255, 250, 237), + cv::Vec3b(179, 175, 157), + cv::Vec3b(62, 61, 82), + cv::Vec3b(90, 155, 194), + cv::Vec3b(156, 47, 144), + cv::Vec3b(141, 87, 0), + cv::Vec3b(173, 215, 156), + cv::Vec3b(0, 118, 139), + cv::Vec3b(51, 125, 0), + cv::Vec3b(197, 151, 0), + cv::Vec3b(49, 86, 220), + cv::Vec3b(148, 69, 117), + cv::Vec3b(236, 255, 220), + cv::Vec3b(210, 76, 178), + cv::Vec3b(151, 112, 60), + cv::Vec3b(76, 37, 127), + cv::Vec3b(158, 3, 102), + cv::Vec3b(136, 255, 236), + cv::Vec3b(181, 100, 129), + cv::Vec3b(57, 109, 43), + cv::Vec3b(86, 115, 95), + cv::Vec3b(152, 131, 118), + cv::Vec3b(155, 177, 149), + cv::Vec3b(169, 121, 92), + cv::Vec3b(228, 197, 211), + cv::Vec3b(159, 79, 103), + cv::Vec3b(30, 43, 57), + cv::Vec3b(102, 67, 39), + cv::Vec3b(175, 206, 120), + cv::Vec3b(50, 46, 223), + cv::Vec3b(134, 180, 135), + cv::Vec3b(194, 48, 0), + cv::Vec3b(171, 232, 107), + cv::Vec3b(150, 101, 109), + cv::Vec3b(37, 14, 53), + cv::Vec3b(166, 0, 25), + cv::Vec3b(0, 128, 207), + cv::Vec3b(202, 239, 255), + cv::Vec3b(50, 63, 97), + cv::Vec3b(164, 73, 220), + cv::Vec3b(106, 157, 59), + cv::Vec3b(255, 90, 228), + cv::Vec3b(99, 106, 1), + cv::Vec3b(209, 108, 218), + cv::Vec3b(115, 96, 96), + cv::Vec3b(255, 186, 173), + cv::Vec3b(211, 105, 180), + cv::Vec3b(255, 222, 214), + cv::Vec3b(108, 109, 116), + cv::Vec3b(146, 125, 94), + cv::Vec3b(132, 93, 112), + cv::Vec3b(91, 98, 193), + cv::Vec3b(47, 74, 54), + cv::Vec3b(228, 95, 53), + cv::Vec3b(255, 59, 83), + cv::Vec3b(172, 132, 221), + cv::Vec3b(118, 41, 136), + cv::Vec3b(112, 236, 152), + cv::Vec3b(64, 133, 67), + cv::Vec3b(44, 53, 51), + cv::Vec3b(46, 24, 45), + cv::Vec3b(50, 57, 37), + cv::Vec3b(25, 24, 27), + cv::Vec3b(47, 46, 44), + cv::Vec3b(2, 60, 50), + cv::Vec3b(155, 158, 226), + cv::Vec3b(88, 175, 173), + cv::Vec3b(92, 66, 77), + cv::Vec3b(122, 197, 166), + cv::Vec3b(104, 93, 117), + cv::Vec3b(185, 188, 189), + cv::Vec3b(131, 67, 87), + cv::Vec3b(26, 123, 66), + cv::Vec3b(46, 87, 170), + cv::Vec3b(229, 81, 153), + cv::Vec3b(49, 110, 71), + cv::Vec3b(205, 0, 197), + cv::Vec3b(106, 0, 77), + cv::Vec3b(127, 187, 236), + cv::Vec3b(243, 86, 145), + cv::Vec3b(215, 197, 74), + cv::Vec3b(98, 172, 183), + cv::Vec3b(203, 161, 188), + cv::Vec3b(162, 138, 154), + cv::Vec3b(108, 63, 59), + cv::Vec3b(255, 228, 125), + cv::Vec3b(220, 186, 227), + cv::Vec3b(95, 129, 109), + cv::Vec3b(58, 64, 74), + cv::Vec3b(125, 191, 50), + cv::Vec3b(230, 236, 220), + cv::Vec3b(133, 44, 25), + cv::Vec3b(40, 83, 102), + cv::Vec3b(184, 203, 156), + cv::Vec3b(14, 13, 0), + cv::Vec3b(75, 93, 86), + cv::Vec3b(107, 84, 63), + cv::Vec3b(226, 113, 114), + cv::Vec3b(5, 104, 236), + cv::Vec3b(46, 181, 0), + cv::Vec3b(210, 22, 86), + cv::Vec3b(239, 175, 255), + cv::Vec3b(104, 32, 33), + cv::Vec3b(45, 32, 17), + cv::Vec3b(218, 76, 255), + cv::Vec3b(112, 150, 142), + cv::Vec3b(255, 123, 125), + cv::Vec3b(74, 25, 48), + cv::Vec3b(232, 194, 130), + cv::Vec3b(231, 219, 188), + cv::Vec3b(166, 132, 134), + cv::Vec3b(31, 38, 60), + cv::Vec3b(54, 87, 78), + cv::Vec3b(82, 206, 121), + cv::Vec3b(173, 170, 169), + cv::Vec3b(138, 159, 69), + cv::Vec3b(101, 66, 210), + cv::Vec3b(0, 251, 140), + cv::Vec3b(93, 105, 123), + cv::Vec3b(204, 210, 127), + cv::Vec3b(148, 165, 161), + cv::Vec3b(121, 2, 41), + cv::Vec3b(227, 131, 230), + cv::Vec3b(126, 164, 193), + cv::Vec3b(78, 68, 82), + cv::Vec3b(75, 44, 0), + cv::Vec3b(98, 11, 112), + cv::Vec3b(49, 76, 30), + cv::Vec3b(135, 74, 166), + cv::Vec3b(227, 0, 145), + cv::Vec3b(102, 70, 10), + cv::Vec3b(235, 154, 139), + cv::Vec3b(234, 195, 163), + cv::Vec3b(152, 234, 179), + cv::Vec3b(171, 145, 128), + cv::Vec3b(184, 85, 47), + cv::Vec3b(26, 43, 47), + cv::Vec3b(148, 221, 197), + cv::Vec3b(157, 140, 118), + cv::Vec3b(156, 131, 51), + cv::Vec3b(148, 169, 201), + cv::Vec3b(57, 41, 53), + cv::Vec3b(140, 103, 94), + cv::Vec3b(204, 233, 58), + cv::Vec3b(145, 113, 0), + cv::Vec3b(1, 64, 11), + cv::Vec3b(68, 152, 150), + cv::Vec3b(28, 163, 112), + cv::Vec3b(224, 141, 167), + cv::Vec3b(139, 74, 78), + cv::Vec3b(102, 119, 118), + cv::Vec3b(70, 146, 173), + cv::Vec3b(103, 189, 168), + cv::Vec3b(105, 37, 92), + cv::Vec3b(211, 191, 255), + cv::Vec3b(74, 81, 50), + cv::Vec3b(126, 146, 133), + cv::Vec3b(119, 115, 60), + cv::Vec3b(231, 160, 204), + cv::Vec3b(81, 162, 136), + cv::Vec3b(44, 101, 106), + cv::Vec3b(77, 92, 94), + cv::Vec3b(201, 64, 58), + cv::Vec3b(221, 215, 243), + cv::Vec3b(0, 88, 68), + cv::Vec3b(180, 162, 0), + cv::Vec3b(72, 143, 105), + cv::Vec3b(133, 129, 130), + cv::Vec3b(212, 233, 185), + cv::Vec3b(61, 115, 151), + cv::Vec3b(202, 232, 206), + cv::Vec3b(214, 0, 52), + cv::Vec3b(170, 103, 70), + cv::Vec3b(158, 85, 133), + cv::Vec3b(186, 98, 0) +}; + +#endif // COLORS_H diff --git a/config.json b/config.json new file mode 100644 index 0000000..88f9fc8 --- /dev/null +++ b/config.json @@ -0,0 +1,375 @@ +{ + "detection" : + [ + { + "input" : "color", + "name" : "Verkleinern", + "operation" : "_resize", + "param1" : + { + "default" : 512, + "lowerBound" : 1, + "name" : "Neue Seitenlänge", + "odd" : false, + "upperBound" : 10000 + }, + "param2" : + { + "default" : 0, + "lowerBound" : 0, + "name" : "Ignoriert", + "odd" : false, + "upperBound" : 0 + } + }, + { + "input" : "previous", + "name" : "HSV Select", + "operation" : "_thresh_color", + "param1" : + { + "default" : 0, + "lowerBound" : 0, + "name" : "untere Grenze", + "odd" : false, + "upperBound" : 360 + }, + "param2" : + { + "default" : 70, + "lowerBound" : 0, + "name" : "obere Grenze", + "odd" : false, + "upperBound" : 360 + } + }, + { + "input" : "previous", + "name" : "Dilatiere", + "operation" : "_dilate", + "param1" : + { + "default" : 11, + "lowerBound" : 1, + "name" : "Kernel Größe", + "odd" : true, + "upperBound" : 100 + }, + "param2" : + { + "default" : 1, + "lowerBound" : 1, + "name" : "Iterationen", + "odd" : false, + "upperBound" : 50 + } + }, + { + "input" : "previous", + "name" : "Zurück zur Originalgröße", + "operation" : "_resizeToOriginal", + "param1" : + { + "default" : 0, + "lowerBound" : 0, + "name" : "Ignoriert", + "odd" : false, + "upperBound" : 0 + }, + "param2" : + { + "default" : 0, + "lowerBound" : 0, + "name" : "Ignoriert", + "odd" : false, + "upperBound" : 0 + } + }, + { + "input" : "previous", + "name" : "Grenzwert", + "operation" : "_threshold", + "param1" : + { + "default" : 0, + "lowerBound" : 0, + "name" : "Ignoriert", + "odd" : false, + "upperBound" : 0 + }, + "param2" : + { + "default" : 0, + "lowerBound" : 0, + "name" : "Ignoriert", + "odd" : false, + "upperBound" : 0 + } + }, + { + "input" : "previous", + "name" : "Markiere Hintergrund", + "operation" : "_markBackground", + "param1" : + { + "default" : 0, + "lowerBound" : 0, + "name" : "Ignoriert", + "odd" : false, + "upperBound" : 0 + }, + "param2" : + { + "default" : 0, + "lowerBound" : 0, + "name" : "Ignoriert", + "odd" : false, + "upperBound" : 0 + } + }, + { + "input" : "previous", + "name" : "Invertieren", + "operation" : "_invert_markers", + "param1" : + { + "default" : 0, + "lowerBound" : 0, + "name" : "Ignoriert", + "odd" : false, + "upperBound" : 0 + }, + "param2" : + { + "default" : 0, + "lowerBound" : 0, + "name" : "Ignoriert", + "odd" : false, + "upperBound" : 0 + } + }, + { + "input" : "previous", + "name" : "Erodieren", + "operation" : "_erode_intermediate", + "param1" : + { + "default" : 11, + "lowerBound" : 1, + "name" : "Kernel Größe", + "odd" : true, + "upperBound" : 100 + }, + "param2" : + { + "default" : 2, + "lowerBound" : 1, + "name" : "Iterationen", + "odd" : false, + "upperBound" : 50 + } + }, + { + "input" : "previous", + "name" : "Zurück zur Originalgröße", + "operation" : "_resizeToOriginal", + "param1" : + { + "default" : 0, + "lowerBound" : 0, + "name" : "Ignoriert", + "odd" : false, + "upperBound" : 0 + }, + "param2" : + { + "default" : 0, + "lowerBound" : 0, + "name" : "Ignoriert", + "odd" : false, + "upperBound" : 0 + } + }, + { + "input" : "previous", + "name" : "Grenzwert", + "operation" : "_threshold", + "param1" : + { + "default" : 0, + "lowerBound" : 0, + "name" : "Ignoriert", + "odd" : false, + "upperBound" : 0 + }, + "param2" : + { + "default" : 0, + "lowerBound" : 0, + "name" : "Ignoriert", + "odd" : false, + "upperBound" : 0 + } + }, + { + "input" : "previous", + "name" : "Wasserscheide", + "operation" : "_watershed", + "param1" : + { + "default" : 0, + "lowerBound" : 0, + "name" : "Ignoriert", + "odd" : false, + "upperBound" : 0 + }, + "param2" : + { + "default" : 0, + "lowerBound" : 0, + "name" : "Ignoriert", + "odd" : false, + "upperBound" : 0 + } + } + ], + "rating" : { + "steps" : + [ + { + "input" : "color", + "name" : "Grenzwert", + "operation" : "_threshold", + "param1" : + { + "default" : 0, + "lowerBound" : 0, + "name" : "Ignoriert", + "odd" : false, + "upperBound" : 0 + }, + "param2" : + { + "default" : 0, + "lowerBound" : 0, + "name" : "Ignoriert", + "odd" : false, + "upperBound" : 0 + } + }, + { + "input" : "previous", + "name" : "Durschnittlicher H-Wert", + "operation" : "_mean_color", + "param1" : + { + "default" : 0, + "lowerBound" : 0, + "name" : "Ignoriert", + "odd" : false, + "upperBound" : 0 + }, + "param2" : + { + "default" : 0, + "lowerBound" : 0, + "name" : "Ignoriert", + "odd" : false, + "upperBound" : 0 + } + }, + { + "input" : "color", + "name" : "Grenzwert", + "operation" : "_threshold", + "param1" : + { + "default" : 0, + "lowerBound" : 0, + "name" : "Ignoriert", + "odd" : false, + "upperBound" : 0 + }, + "param2" : + { + "default" : 0, + "lowerBound" : 0, + "name" : "Ignoriert", + "odd" : false, + "upperBound" : 0 + } + }, + { + "input" : "previous", + "name" : "Fit Ellipse", + "operation" : "_fit_ellipse", + "param1" : + { + "default" : 220, + "lowerBound" : 1, + "name" : "Pixel pro Zentimeter", + "odd" : false, + "upperBound" : 10000 + }, + "param2" : + { + "default" : 0, + "lowerBound" : 0, + "name" : "Ignoriert", + "odd" : false, + "upperBound" : 0 + } + }, + { + "input" : "color", + "name" : "Grenzwert nach Otsu", + "operation" : "_thresh_otsu", + "param1" : + { + "default" : 0, + "lowerBound" : 0, + "name" : "Ignoriert", + "odd" : false, + "upperBound" : 0 + }, + "param2" : + { + "default" : 0, + "lowerBound" : 0, + "name" : "Ignoriert", + "odd" : false, + "upperBound" : 0 + } + }, + { + "input" : "previous", + "name" : "Bewerte kleine Flecken", + "operation" : "_rate_spots", + "param1" : + { + "default" : 220, + "lowerBound" : 1, + "name" : "Pixel pro Zentimeter", + "odd" : false, + "upperBound" : 10000 + }, + "param2" : + { + "default" : 0, + "lowerBound" : 0, + "name" : "Ignoriert", + "odd" : false, + "upperBound" : 0 + } + } + ], + "results" : + [ + "avg_h_val", + "size_diagonal", + "size_ratio", + "spot_avg_h_val", + "spot_avg_size" + ] + } +} diff --git a/controller.cpp b/controller.cpp new file mode 100644 index 0000000..4ef22aa --- /dev/null +++ b/controller.cpp @@ -0,0 +1,452 @@ +#include "controller.h" +#include +#include +#include +#include +#include +#include +#include +#include "filenames.h" + +using namespace std; +using namespace cv; + +Controller::Controller(QObject* parent) : QObject(parent), + detProc(), markers(detProc.getMarkers()) { + string errors; + ifstream file; + file.open(FileNames::configName); + bool ok = Json::parseFromStream(Json::CharReaderBuilder(), + file, &config, &errors); + file.close(); + if (!ok) { + cerr << "Error while reading " << FileNames::configName + << ":\n" << errors << "\n"; + exit(1); + } + + detProc.init(config["detection"]); + rateProc.init(config["rating"]["steps"]); + net.init(config["rating"]["results"]); +} + +Controller& Controller::inst() { + static Controller ctrlr; + return ctrlr; +} + +const Mat* Controller::getCurImg() { + return &cur_img; +} + +const Mat* Controller::getCurNut() { + return &nuts[current_nut].img; +} + +void Controller::openImage() { + filePath = QFileDialog::getOpenFileName(nullptr, + tr("Öffnen"), "", + tr("Bilder (*.png *.jpg)")); + if (filePath.toStdString() == "") { + return; + } + loadImage(); +} + +void Controller::loadImage() { + if ( !cur_img.empty() ){ + cur_img.copyTo(prev_img); + } + cur_img = imread(filePath.toStdString(), IMREAD_COLOR); + emit statusMessage("Bild geladen ..."); + process(); +} + +void Controller::process() { + if (valid_nut) { + if (nut_rating > 0) { + emit statusMessage("Bewerte gegessene Nuss..."); + rateProc.reset(); + rateProc.setRateCurNut(false); + rateProc.process(); + rateProc.setRateCurNut(true); + debugResultPrint(); + net.saveNut(); + } + else { + cout << "No score selected. Not saving Nut." << endl; + } + } + detProc.reset(); + emit newInputImage(cur_img); + emit statusMessage("Erkenne Nüsse ..."); + if (!detect()) { + return; + } + emit newInputImage(detProc.getResult()); + if (!prev_img.empty()) { + vector results = {}; + emit statusMessage("Extrahiere Nüsse ..."); + extractNuts(results); + if (matchNuts(results)) { + nuts = results; + valid_nut = true; + } + else { + valid_nut = false; + nuts = results; + } + } + else { + emit statusMessage("Extrahiere Nüsse ..."); + extractNuts(); + } + nut_rating = -1; + //debugPaint(); + emit statusMessage("Bewerte Nüsse ..."); + Mat withScores = Mat::zeros(cur_img.size(), CV_8UC3); + for (int i = 0; i>& contours = rateProc.getContours(); + vector rects(contours.size()); + for (unsigned int i = 0; i max_rect_size) { + max_rect_size = rects[i].height * rects[i].width; + max_rect_index = i; + } + } + drawContours(withScores, contours, max_rect_index, + Scalar(static_cast(score * 100.0f *0.708333f), 255, 255), + CV_FILLED, LINE_8, noArray(), INT_MAX, + Point(nuts[current_nut].min_y, nuts[current_nut].min_x)); + } + emit statusMessage("Zeige Ergebnisse an ..."); + cvtColor(withScores, withScores, CV_HSV2BGR); + withScores = 0.5 *withScores + 0.5 * cur_img; + paintScores(withScores); + emit statusMessage("Fertig."); +} + +void Controller::debugResultPrint() { + ostringstream sstr; + sstr << "Results: "; + for (const auto& result : rateProc.getResults()) { + sstr << result.first << ": " << result.second << ", "; + } + cout << sstr.str() << endl; +} + +void Controller::paintScores(Mat& img) { + for (unsigned int i = 0; i( + chrono::steady_clock::now() - start); + cout << "Detection took " << (float)duration.count()/1000.0 + << " seconds." << endl; + return success; +} + +bool Controller::matchNuts(const vector& cur_results) { + auto start = chrono::steady_clock::now(); + vector min_dists = vector(nuts.size()); + int count = 0; + for(const auto& nut : nuts) { + vector x_dist = vector(cur_results.size()); + for (unsigned int i = 0; i y_dist = vector(cur_results.size()); + for (unsigned int i = 0; i dist = vector(cur_results.size()); + magnitude(x_dist, y_dist, dist); + auto min = min_element(dist.begin(), dist.end()); + min_dists[count] = *min; + cout << "Minimum distance for nut " << count << + ": " << *min + << " (" + << (*min/ + (sqrt(pow(static_cast(cur_img.cols), 2) + + pow(static_cast(cur_img.rows), 2))))*100.0 + << "%)\n"; + count++; + } + bool success = false; + if (cur_results.size() == nuts.size() - 1) { + success = true; + int nut_index; + auto max = max_element(min_dists.begin(), min_dists.end()); + nut_index = distance(min_dists.begin(), max); + cout << "Nut " << nut_index << " has been taken away.\n"; + nuts[nut_index].img.copyTo(nut_to_rate); + emit newNutToRate(nut_to_rate); + } + else { + emit message("Keine Nuss erkannt"); + } + auto duration = chrono::duration_cast( + chrono::steady_clock::now() - start); + cout << "Matching took " << (float)duration.count()/1000.0 + << " seconds." << endl; + return success; +} + +void Controller::extractNuts(vector& result_nuts) { + auto start = chrono::steady_clock::now(); + if (result_nuts.size() > 0) { + return; + } + if (markers.empty()) { + if (!detProc.process()) { + return; + } + } + int nut_count = detProc.getNutCount(); + result_nuts = vector(nut_count); + for (unsigned int i = 0; i(i, j) -2; + if (index >= 0 && index < nut_count) { + result_nuts[index].min_x = min(result_nuts[index].min_x, i); + result_nuts[index].min_y = min(result_nuts[index].min_y, j); + result_nuts[index].max_x = max(result_nuts[index].max_x, i); + result_nuts[index].max_y = max(result_nuts[index].max_y, j); + } + } + } + for (unsigned int i = 0; i(i, j)-2; + if (index >= 0 && index < nut_count) { + result_nuts[index].img.at(i - result_nuts[index].min_x, + j - result_nuts[index].min_y) = cur_img.at(i, j); + } + } + } + auto duration = chrono::duration_cast( + chrono::steady_clock::now() - start); + cout << "Nut extraction took " << (float)duration.count()/1000.0 + << " seconds.\n"; + cout << "Detected " << result_nuts.size() << " nuts." << endl; + rateProc.reset(); + if (rateProc.isInteractive()) { + emit newNut(result_nuts[current_nut].img); + } +} + +void Controller::nextNut() { + if (nuts.size() == 0 || nuts[current_nut].img.empty()) { + return; + } + int size = nuts.size(); + current_nut = min(current_nut + 1, size-1); + newNut(); + if (rateProc.isInteractive()) { + emit newNut(nuts[current_nut].img); + emit rateProc.requestUpdate(); + } +} + +void Controller::previousNut() { + if (nuts.size() == 0 || nuts[current_nut].img.empty()) { + return; + } + current_nut = max(current_nut - 1, 0); + newNut(); + if (rateProc.isInteractive()) { + emit newNut(nuts[current_nut].img); + } +} + +void Controller::newNut() { + rateProc.reset(); + if (rateProc.isInteractive()) { + rateProc.start(true); + } +} + +string Controller::runCmd(string cmd) { + char buffer[128]; + string result = ""; + shared_ptr pipe(popen(cmd.c_str(), "r"), pclose); + if (!pipe) throw runtime_error("popen() failed!"); + while (!feof(pipe.get())) { + if (fgets(buffer, 128, pipe.get()) != NULL) + result += buffer; + } + return result; +} + +void Controller::saveDetectionConfig() { + int current_step = detProc.getCurrentStep(); + config["detection"][current_step]["param1"]["default"] = + detProc.getCurParams()[0].val; + config["detection"][current_step]["param2"]["default"] = + detProc.getCurParams()[1].val; + ofstream file ("config.json"); + file << Json::writeString(Json::StreamWriterBuilder(), config); + file.close(); +} + +void Controller::saveRatingConfig() { + int current_step = rateProc.getCurrentStep(); + config["rating"]["steps"][current_step]["param1"]["default"] = + rateProc.getCurParams()[0].val; + config["rating"]["steps"][current_step]["param2"]["default"] = + rateProc.getCurParams()[1].val; + ofstream file ("config.json"); + file << Json::writeString(Json::StreamWriterBuilder(), config); + file.close(); +} + +void Controller::reset() { + resetNuts(); + detProc.reset(); + rateProc.reset(); + cur_img.release(); + prev_img.release(); + Mat empty; + emit newInputImage(empty); + emit newNutToRate(empty); +} + +void Controller::resetNuts() { + current_nut = 0; + nut_rating = -1; + nuts = {}; +} + +void Controller::saveData() { + cout << "... saving data ... " << endl; + if (valid_nut) { + if (nut_rating > 0) { + net.saveNut(); + } + else { + cout << "No score selected. Not saving Nut." << endl; + } + } +} + +void Controller::currentRating(int score) { + nut_rating = score; +} + +int Controller::getCurRating() { + return nut_rating; +} + +int Controller::getN() { + return net.getN(); +} + +void Controller::neuralNetChange(int val) { + if (val == Qt::Unchecked) { + net.neuralNetEnabled(false); + process(); + } + else if (val == Qt::Checked) { + net.neuralNetEnabled(true); + process(); + } +} + +const cv::Mat* Controller::getNutToRate() { + return &nut_to_rate; +} diff --git a/controller.h b/controller.h new file mode 100644 index 0000000..fd00bd5 --- /dev/null +++ b/controller.h @@ -0,0 +1,103 @@ +#ifndef CONTROLLER_H +#define CONTROLLER_H + +#include "detectionprocessing.h" +#include "rateprocessing.h" +#include "neuralnet.h" +#include +#include +#include +#include +#include +#include + +struct Nut { + int min_x; + int min_y; + int max_x; + int max_y; + cv::Mat img; + float score; + + int center_x() const { + return min_x + ((max_x - min_x)/2); + } + + int center_y() const { + return min_y + ((max_y - min_y)/2); + } +}; + +class Controller : public QObject { + + Q_OBJECT + +public: + DetectionProcessing& getDetectionProcessing() { return detProc; } + RateProcessing& getRateProcessing() { return rateProc; } + void extractNuts(std::vector& result_nuts = inst().nuts); + const cv::Mat* getCurImg(); + const cv::Mat* getCurNut(); + const cv::Mat* getNutToRate(); + void process(); + void resetNuts(); + int getCurRating(); + int getN(); + //Singleton + static Controller& inst(); + Controller(Controller const&) = delete; + void operator=(Controller const&) = delete; + +signals: + void newNut(const cv::Mat& nut); + void newInputImage(const cv::Mat& img); + void newNutToRate(const cv::Mat& nut); + void message(std::string text); + void statusMessage(QString); + +public slots: + void nextNut(); + void previousNut(); + void openImage(); + void captureImage(); + void saveDetectionConfig(); + void saveRatingConfig(); + void reset(); + void saveData(); + void currentRating(int score); + void neuralNetChange(int val); + +// private methods +private: + std::string runCmd(std::string cmd); + void loadImage(); + bool detect(); + void newNut(); + bool matchNuts(const std::vector& cur_results); + void debugPaint(); + void debugResultPrint(); + void paintScores(cv::Mat& img); + +// members +private: + // Singleton + Controller(QObject* parent = nullptr); + + DetectionProcessing detProc; + RateProcessing rateProc; + NeuralNet net; + Json::Value config; + std::vector nuts{}; + int current_nut = 0; + const cv::Mat& markers; + cv::Mat prev_img; + cv::Mat cur_img; + static Controller *singleton; + QTemporaryDir transferFolder; + QString filePath; + cv::Mat nut_to_rate; + int nut_rating = -1; + bool valid_nut = false; +}; + +#endif // CONTROLLER_H diff --git a/cvimagewidget.h b/cvimagewidget.h new file mode 100644 index 0000000..7d27f09 --- /dev/null +++ b/cvimagewidget.h @@ -0,0 +1,123 @@ +#ifndef CVIMAGEWIDGET_H +#define CVIMAGEWIDGET_H +#include +#include +#include +#include +#include +#include + +class CVImageWidget : public QWidget +{ + Q_OBJECT +public: + explicit CVImageWidget(QWidget *parent = 0) : QWidget(parent) {} + + QSize sizeHint() const { return _qimage.size(); } + QSize minimumSizeHint() const { return QSize(400, 400); } + +public slots: + + void showImage(const cv::Mat& image) { + if (image.empty()) { + _qimage = QImage(); + repaint(); + return; + } + + cv::Mat tmp; + if (image.type() == CV_32S) { + image.convertTo(tmp, CV_8U); + } + else { + tmp = image.clone(); + } + + // Scale image to 512 pixels + float fac = 1.0; + if (size().width() > size().height()) { + fac = (float) size().height()/image.rows; + } + else { + fac = (float) size().width()/image.cols; + } + cv::Size new_size((int)image.cols*fac, (int) image.rows*fac) ; + cv::resize(tmp, _tmp, new_size); + // Convert the image to the RGB888 format + switch (_tmp.type()) { + case CV_8UC1: + cvtColor(_tmp, _tmp, CV_GRAY2RGB); + break; + case CV_8UC3: + cvtColor(_tmp, _tmp, CV_BGR2RGB); + break; + } + + // QImage needs the data to be stored continuously in memory + assert(_tmp.isContinuous()); + // Assign OpenCV's image buffer to the QImage. Note that the + // bytesPerLine parameter + // (http://qt-project.org/doc/qt-4.8/qimage.html#QImage-6) is 3*width + // because each pixel has three bytes. + _qimage = QImage(_tmp.data, _tmp.cols, _tmp.rows, _tmp.cols*3, + QImage::Format_RGB888); + repaint(); + } + + void resizeEvent(QResizeEvent* event) { + if (_tmp.empty()) { + return; + } + float fac = 1.0; + if (event->size().width() > event->size().height()) { + fac = static_cast(event->size().height())/ + static_cast(_tmp.rows); + } + else { + fac = static_cast(event->size().width())/ + static_cast(_tmp.cols); + } + cv::Size new_size((int)_tmp.cols*fac, (int) _tmp.rows*fac) ; + cv::resize(_tmp, _tmp, new_size); + // Assign OpenCV's image buffer to the QImage. Note that the + // bytesPerLine parameter + // (http://qt-project.org/doc/qt-4.8/qimage.html#QImage-6) is 3*width + // because each pixel has three bytes. + _qimage = QImage(_tmp.data, _tmp.cols, _tmp.rows, _tmp.cols*3, + QImage::Format_RGB888); + repaint(); + } + + void showTextImage(std::string text) { + int fontFace = cv::FONT_HERSHEY_SIMPLEX; + double fontScale = 2; + int thickness = 3; + cv::Mat img(600, 800, CV_8UC3, cv::Scalar::all(0)); + int baseline=0; + cv::Size textSize = cv::getTextSize(text, fontFace, + fontScale, thickness, &baseline); + baseline += thickness; + // center the text + cv::Point textOrg((img.cols - textSize.width)/2, + (img.rows + textSize.height)/2); + // then put the text itself + cv::putText(img, text, textOrg, fontFace, fontScale, + cv::Scalar::all(255), thickness, 8); + showImage(img); + } + + +protected: + + void paintEvent(QPaintEvent* /*event*/) { + // Display the image + QPainter painter(this); + painter.drawImage(QPoint(0,0), _qimage); + painter.end(); + } + + QImage _qimage; + cv::Mat _tmp; +}; + +#endif //CVIMAGEWIDGET_H diff --git a/data/imgs/.gitignore b/data/imgs/.gitignore new file mode 100644 index 0000000..b5a379d --- /dev/null +++ b/data/imgs/.gitignore @@ -0,0 +1 @@ +!*.jpg diff --git a/data/imgs/nut_0.jpg b/data/imgs/nut_0.jpg new file mode 100644 index 0000000000000000000000000000000000000000..d412a0d05968f0f37b05d0e9639228ff9a6e8d89 GIT binary patch literal 33704 zcmbTdbx<5p(>J=fLm;>lEVu=?Bm{SYyW3)my9IY!2p$q(v1M_0cXxMpw~)*8zE$_u zd++_@`@S>PT{AU3Q~jGW=bY|7-TxN;Z2;cMNy$n9;NSoNxVHoFZy6u~fQS1}`wxfz zPeVlb4@W{oL_kDFLPq{CLP0}CMnOYCMn=UzMMM8jdppC#K*#*g#eZJ%-;cs0As`^3 zqadUFm&pGX`_}=$MTK*N`-}kh0RWE+hky(BuLnT&rYF*WsrRPre>XUI1Vkicls7dn z-UMpjy{V6Y@TN4aISd;sBd2PNjA(tfS%Bv6~YpyM%h z4n{*KBqAmurDtIL$i)1KmycgSP)OpNq?ELbtlW2X4NWa=9bGeX3rj0&8(SAwH+K(D zFYg~Ap<&?>kx@y>Dd5z!pXnLD|Kt}GLW+t@s;X;h>*^aCo4UGtdi(kZ28X7mXJ+T- z7Z#T`x3+h7_x2ACk1nsSZ*K4IA0D6ng9{FT@ZYfB`2R01+&5hC|LFsi|KNgy_jn@& 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+20;2.23449875088078933e+01;2.20789957046508789e+00;8.45886528491973877e-01;7.81261270189904735e+01;5.70639237309947187e-02; +21;2.24425214007782117e+01;2.36169195175170898e+00;9.09197628498077393e-01;3.87564170377607482e+01;6.99308254502036403e-02; +22;2.55443989631836352e+01;2.14459204673767090e+00;9.73132252693176270e-01;5.02639359259216931e+01;5.56839998271064091e-02; +23;2.14019211549139357e+01;2.33309888839721680e+00;8.76630365848541260e-01;3.50631278883680224e+01;5.73160344904119351e-02; +24;2.55478804441722041e+01;2.03121805191040039e+00;8.49647045135498047e-01;3.12149577973704666e+01;3.17756131700512243e-02; +25;2.58053171628290663e+01;1.97867727279663086e+00;9.44334387779235840e-01;5.90922410389039996e+01;3.49965437046893188e-02; +26;1.79281499888120770e+01;2.26088070869445801e+00;8.75540912151336670e-01;3.12600847616190833e+01;3.33142616346284934e-02; diff --git a/data/nuts.net b/data/nuts.net new file mode 100644 index 0000000..8339e01 --- /dev/null +++ b/data/nuts.net @@ -0,0 +1,36 @@ +FANN_FLO_2.1 +num_layers=3 +learning_rate=0.700000 +connection_rate=1.000000 +network_type=0 +learning_momentum=0.000000 +training_algorithm=1 +train_error_function=1 +train_stop_function=0 +cascade_output_change_fraction=0.010000 +quickprop_decay=-0.000100 +quickprop_mu=1.750000 +rprop_increase_factor=1.200000 +rprop_decrease_factor=0.500000 +rprop_delta_min=0.000000 +rprop_delta_max=50.000000 +rprop_delta_zero=0.100000 +cascade_output_stagnation_epochs=12 +cascade_candidate_change_fraction=0.010000 +cascade_candidate_stagnation_epochs=12 +cascade_max_out_epochs=150 +cascade_min_out_epochs=50 +cascade_max_cand_epochs=150 +cascade_min_cand_epochs=50 +cascade_num_candidate_groups=2 +bit_fail_limit=3.49999994039535522461e-01 +cascade_candidate_limit=1.00000000000000000000e+03 +cascade_weight_multiplier=4.00000005960464477539e-01 +cascade_activation_functions_count=10 +cascade_activation_functions=3 5 7 8 10 11 14 15 16 17 +cascade_activation_steepnesses_count=4 +cascade_activation_steepnesses=2.50000000000000000000e-01 5.00000000000000000000e-01 7.50000000000000000000e-01 1.00000000000000000000e+00 +layer_sizes=6 7 2 +scale_included=0 +neurons (num_inputs, activation_function, activation_steepness)=(0, 0, 0.00000000000000000000e+00) (0, 0, 0.00000000000000000000e+00) (0, 0, 0.00000000000000000000e+00) (0, 0, 0.00000000000000000000e+00) (0, 0, 0.00000000000000000000e+00) (0, 0, 0.00000000000000000000e+00) (6, 3, 5.00000000000000000000e-01) (6, 3, 5.00000000000000000000e-01) (6, 3, 5.00000000000000000000e-01) (6, 3, 5.00000000000000000000e-01) (6, 3, 5.00000000000000000000e-01) (6, 3, 5.00000000000000000000e-01) (0, 3, 0.00000000000000000000e+00) (7, 0, 5.00000000000000000000e-01) (0, 0, 0.00000000000000000000e+00) +connections (connected_to_neuron, weight)=(0, -2.89685893058776855469e+00) (1, -2.21340131759643554688e+00) (2, -6.53899073600769042969e-01) (3, 5.05288600921630859375e-01) (4, -2.12488818168640136719e+00) (5, -3.21228432655334472656e+00) (0, -2.64115977287292480469e+00) (1, 4.38077020645141601562e+00) (2, -2.23532605171203613281e+00) (3, 2.82412099838256835938e+00) (4, -1.78801298141479492188e+00) (5, -2.15510916709899902344e+00) (0, -1.28378415107727050781e+00) (1, -4.66057968139648437500e+00) (2, -3.13344097137451171875e+00) (3, -3.34458440542221069336e-01) (4, 5.57140636444091796875e+00) (5, 4.77392911911010742188e+00) (0, -2.00398230552673339844e+00) (1, 1.77709951996803283691e-01) (2, -4.12612724304199218750e+00) (3, -4.50434893369674682617e-01) (4, 4.67733287811279296875e+00) (5, -7.28608146309852600098e-02) (0, -1.84619712829589843750e+00) (1, -1.72842276096343994141e+00) (2, -3.57149934768676757812e+00) (3, -1.81574237346649169922e+00) (4, 6.00842870771884918213e-02) (5, -2.67083120346069335938e+00) (0, -4.89023780822753906250e+00) (1, -6.48415279388427734375e+00) (2, -3.84839057922363281250e+00) (3, 3.94571590423583984375e+00) (4, -3.12683850526809692383e-01) (5, 3.10192298889160156250e+00) (6, -5.09613215923309326172e-01) (7, 3.94862794876098632812e+00) (8, 6.27435493469238281250e+00) (9, -4.65998220443725585938e+00) (10, -4.07627582550048828125e-01) (11, -2.93442416191101074219e+00) (12, -3.50238943099975585938e+00) diff --git a/data/output.csv b/data/output.csv new file mode 100644 index 0000000..8b33fbc --- /dev/null +++ b/data/output.csv @@ -0,0 +1,28 @@ +nut_id;score; +0;2; +1;4; +2;4; +3;4; +4;5; +5;3; +6;3; +7;1; +8;2; +9;3; +10;1; +11;2; +12;3; +13;4; +14;1; +15;4; +16;5; +17;3; +18;3; +19;4; +20;3; +21;3; +22;4; +23;2; +24;4; +25;4; +26;4; diff --git a/detectionprocessing.cpp b/detectionprocessing.cpp new file mode 100644 index 0000000..d0a27c7 --- /dev/null +++ b/detectionprocessing.cpp @@ -0,0 +1,162 @@ +#include "detectionprocessing.h" +#include "controller.h" +#include "colors.h" +#include +#include +#include +#include +#include +#include + +using namespace std; +using namespace cv; + +DetectionProcessing::DetectionProcessing(QObject* parent) + : Processing(parent) {} + +void DetectionProcessing::init(Json::Value json_steps) { + Processing::init(this->metaObject(), json_steps); +} + +Mat DetectionProcessing::_dilate(const Mat& input, int kernel_size, + int iterations) { + Mat out; + dilate(input, out, getStructuringElement(MORPH_ELLIPSE, + Size(kernel_size, kernel_size)), Point(-1, -1), iterations); + return out; +} + +Mat DetectionProcessing::_threshold(const Mat& input, int ignored, + int ignored2) { + Mat out; + threshold(input, out, 127, 255, THRESH_BINARY); + return out; +} + +Mat DetectionProcessing::_invert(const Mat& input, int ignored, int ignored2) { + Mat out = Scalar::all(255) - input; + return out; +} + +Mat DetectionProcessing::_invert_markers(const Mat& input, int ignored, + int ignored2) { + markers = Scalar::all(1) - markers; + return markers; +} + +Mat DetectionProcessing::_thresh_color(const Mat& input, int lowerThreshold, int + upperThreshold) { + Mat hsv; + cvtColor(input, hsv, COLOR_BGR2HSV); + intermediate = Mat (input.size(), CV_8U); + inRange(hsv, Scalar(lowerThreshold, 0, 0), Scalar(upperThreshold, + 255, 255), intermediate); + return intermediate; +} + +Mat DetectionProcessing::_erode(const Mat& input, int kernel_size, int + iterations) { + Mat out; + erode(input, out, getStructuringElement(MORPH_ELLIPSE, + Size(kernel_size, kernel_size)), Point(-1, -1), iterations); + return out; +} + +Mat DetectionProcessing::_erode_intermediate(const Mat& input, int kernel_size, + int iterations) { + Mat out; + erode(intermediate, out, getStructuringElement(MORPH_ELLIPSE, + Size(kernel_size, kernel_size)), Point(-1, -1), iterations); + return out; +} + +Mat DetectionProcessing::_markBackground(const Mat& input, int ignored, + int ignored2) { + markers = Mat::zeros(input.size(), CV_32S); + markers = Scalar::all(0); + Mat in = input.clone(); + vector> contours; + findContours(in, contours, RETR_LIST, CHAIN_APPROX_NONE); + for(int i = 0; i> contours; + findContours(in, contours, RETR_LIST, CHAIN_APPROX_SIMPLE); + nut_count = 0; + for(int i = 0; i(i,j); + if( index == -1 ) + wshed.at(i,j) = Vec3b(255,255,255); + else if( index <= 0 || index > nut_count ) + wshed.at(i,j) = Vec3b(0,0,0); + else + wshed.at(i,j) = colors[(index - 1) % colors.size()]; + } + } + wshed = wshed*0.5 + src*0.5; + return wshed; +} + +Mat DetectionProcessing::_resize(const Mat& input, int new_length, + int ignored) { + Mat out; + float fac = 1.0; + if (input.rows > input.cols) { + fac = (float) new_length/input.rows; + } + else { + fac = (float) new_length/input.cols; + } + Size new_size((int)input.cols*fac, (int) input.rows*fac) ; + resize(input, out, new_size, INTER_NEAREST); + return out; +} + +Mat DetectionProcessing::_resizeToOriginal(const Mat& input, int ignored, + int ignored2) { + Mat out; + float fac = 1.0; + if (input.rows > input.cols) { + fac = (float) Controller::inst().getCurImg()->rows/input.rows; + } + else { + fac = (float) Controller::inst().getCurImg()->cols/input.cols; + } + Size new_size((int)input.cols*fac, (int) input.rows*fac) ; + resize(input, out, new_size, INTER_NEAREST); + return out; +} + +const cv::Mat& DetectionProcessing::getResult() { + return steps.back().getOutput(); +} + +const cv::Mat& DetectionProcessing::getMarkers() { + return markers; +} + +const cv::Mat* DetectionProcessing::getInput() { + return Controller::inst().getCurImg(); +} + +int DetectionProcessing::getNutCount() { + return nut_count; +} diff --git a/detectionprocessing.h b/detectionprocessing.h new file mode 100644 index 0000000..d3941b0 --- /dev/null +++ b/detectionprocessing.h @@ -0,0 +1,43 @@ +#ifndef DETECTIONPROCESSING_H +#define DETECTIONPROCESSING_H + +#include +#include "processing.h" +#include + +class DetectionProcessing : public Processing { + + Q_OBJECT + +public: + DetectionProcessing(QObject *parent = 0); + void init(Json::Value json_steps); + const cv::Mat& getResult(); + const cv::Mat& getMarkers(); + int getNutCount(); + +// Processing methods +public slots: + cv::Mat _dilate(const cv::Mat& input , int kernel_size, int iterations); + cv::Mat _threshold(const cv::Mat& input, int ignored, int ignored2); + cv::Mat _thresh_color(const cv::Mat& input, int ignored, int ignored2); + cv::Mat _erode(const cv::Mat& input , int kernel_size, int iterations); + cv::Mat _erode_intermediate(const cv::Mat& input , int kernel_size, int iterations); + cv::Mat _watershed(const cv::Mat& input, int ignored, int ignored2); + cv::Mat _invert(const cv::Mat& input, int ignored, int ignored2); + cv::Mat _invert_markers(const cv::Mat& input, int ignored, int ignored2); + cv::Mat _resize(const cv::Mat& input, int new_length, int ignored); + cv::Mat _resizeToOriginal(const cv::Mat& input, int ignored, int ignored2); + cv::Mat _markBackground(const cv::Mat& input, int ignored, int ignored2); + +private: + const cv::Mat* getInput(); + +// Members +private: + cv::Mat markers; + int nut_count; + cv::Mat intermediate; +}; + +#endif // DETECTIONPROCESSING_H diff --git a/filenames.cpp b/filenames.cpp new file mode 100644 index 0000000..4dd9844 --- /dev/null +++ b/filenames.cpp @@ -0,0 +1,7 @@ +#include "filenames.h" + +const std::string FileNames::configName = "config.json"; +const std::string FileNames::inputName = "data/input.csv"; +const std::string FileNames::outputName = "data/output.csv"; +const std::string FileNames::imgPath = "data/imgs/"; +const std::string FileNames::neuralNetName = "data/nuts.net"; diff --git a/filenames.h b/filenames.h new file mode 100644 index 0000000..6d51cfa --- /dev/null +++ b/filenames.h @@ -0,0 +1,14 @@ +#ifndef FILENAMES_H +#define FILENAMES_H + +#include + +struct FileNames { + static const std::string configName; + static const std::string inputName; + static const std::string outputName; + static const std::string imgPath; + static const std::string neuralNetName; +}; + +#endif // FILENAMES_H diff --git a/main.cpp b/main.cpp new file mode 100644 index 0000000..ae3bcae --- /dev/null +++ b/main.cpp @@ -0,0 +1,14 @@ +#include + +#include +#include +#include + +int main(int argc, char** argv) { + QApplication app(argc, argv); + MainWindow window; + + window.show(); + + return app.exec(); +} diff --git a/mainwindow.cpp b/mainwindow.cpp new file mode 100644 index 0000000..be23185 --- /dev/null +++ b/mainwindow.cpp @@ -0,0 +1,264 @@ +#include "mainwindow.h" +#include "ui_mainwindow.h" +#include +#include +#include + +using namespace std; + +MainWindow::MainWindow(QWidget *parent) : controller(Controller::inst()), + detProc(controller.getDetectionProcessing()), + rateProc(controller.getRateProcessing()), + QMainWindow(parent), + ui(new Ui::MainWindow) { + ui->setupUi(this); + //QDir::setCurrent(qApp->applicationDirPath()); + connect(ui->loadButton, SIGNAL( clicked() ), + this, SLOT( load() )); + connect(ui->nextButton, SIGNAL( clicked() ), + &detProc, SLOT( nextStep() )); + connect(ui->previousButton, SIGNAL( clicked() ), + &detProc, SLOT( previousStep() )); + connect(ui->saveButton, SIGNAL( clicked() ), + &detProc, SLOT( saveCur() )); + connect(ui->saveConfigButton, SIGNAL( clicked() ), + &controller, SLOT( saveDetectionConfig() )); + connect(ui->captureButton, SIGNAL( clicked() ), + this, SLOT( capture() )); + connect(ui->resetButton, SIGNAL( clicked() ), + &controller, SLOT( reset() )); + captureShortcut = new QShortcut(QKeySequence("Return"), + ui->liveTab); + connect(captureShortcut, SIGNAL( activated() ), + ui->captureButton, SLOT ( animateClick() )); + captureShortcut2 = new QShortcut(QKeySequence("Space"), + ui->liveTab); + connect(captureShortcut2, SIGNAL( activated() ), + ui->captureButton, SLOT ( animateClick() )); + connect(&detProc, SIGNAL( newInputImage(const cv::Mat&) ), + ui->inputView, SLOT( showImage(const cv::Mat&))); + connect(&detProc, SIGNAL( newOutputImage(const cv::Mat&) ), + ui->outputView, SLOT( showImage(const cv::Mat&))); + connect(ui->param1SpinBox, SIGNAL ( valueChanged(int) ), + &detProc, SLOT( setParam1(int) )); + connect(ui->param2SpinBox, SIGNAL ( valueChanged(int) ), + &detProc, SLOT( setParam2(int) )); + connect(&detProc, SIGNAL( requestUpdate() ), + this, SLOT( updateDetection() )); + connect(&rateProc, SIGNAL( requestUpdate() ), + this, SLOT( updateRating() )); + connect(ui->rate_nextButton, SIGNAL( clicked() ), + &rateProc, SLOT( nextStep() )); + connect(ui->rate_previousButton, SIGNAL( clicked() ), + &rateProc, SLOT( previousStep() )); + connect(ui->rate_saveButton, SIGNAL( clicked() ), + &rateProc, SLOT( saveCur() )); + connect(ui->rate_saveConfigButton, SIGNAL( clicked() ), + &controller, SLOT( saveRatingConfig() )); + connect(&rateProc, SIGNAL( newInputImage(const cv::Mat&) ), + ui->rate_inputView, SLOT( showImage(const cv::Mat&))); + connect(&rateProc, SIGNAL( newOutputImage(const cv::Mat&) ), + ui->rate_outputView, SLOT( showImage(const cv::Mat&))); + connect(ui->rate_param1SpinBox, SIGNAL ( valueChanged(int) ), + &rateProc, SLOT( setParam1(int) )); + connect(ui->rate_param2SpinBox, SIGNAL ( valueChanged(int) ), + &rateProc, SLOT( setParam2(int) )); + connect(ui->configTabWidget, SIGNAL( currentChanged(int) ), + this, SLOT( modeConfig(int) )); + connect(ui->superTabWidget, SIGNAL( currentChanged(int) ), + this, SLOT( mode(int) )); + connect(ui->previousNut, SIGNAL( clicked() ), + &controller, SLOT( previousNut() )); + connect(ui->nextNut, SIGNAL( clicked() ), + &controller, SLOT( nextNut() )); + connect(&controller, SIGNAL( newNut(const cv::Mat&) ), + ui->rate_inputView , SLOT( showImage(const cv::Mat&) )); + connect(&controller, SIGNAL( newInputImage(const cv::Mat&) ), + ui->liveView , SLOT( showImage(const cv::Mat&) )); + connect(&controller, SIGNAL( newNutToRate(const cv::Mat&) ), + ui->liveNut, SLOT( showImage(const cv::Mat&) )); + connect(&controller, SIGNAL( message(std::string ) ), + ui->liveNut, SLOT( showTextImage(std::string) )); + connect(ui->rateButton1, SIGNAL(clicked()), + this, SLOT( select1())); + connect(ui->rateButton2, SIGNAL(clicked()), + this, SLOT( select2())); + connect(ui->rateButton3, SIGNAL(clicked()), + this, SLOT( select3())); + connect(ui->rateButton4, SIGNAL(clicked()), + this, SLOT( select4())); + connect(ui->rateButton5, SIGNAL(clicked()), + this, SLOT( select5())); + connect(&controller, SIGNAL( statusMessage(QString) ), + ui->statusLabel, SLOT( setText(QString) )); + connect(ui->neuralNetCheckBox, SIGNAL( stateChanged(int) ), + &controller, SLOT( neuralNetChange(int) )); + updateDetection(); + updateRating(); + updateN(); +} + +void MainWindow::select1() { + controller.currentRating(1); +} + +void MainWindow::select2() { + controller.currentRating(2); +} + +void MainWindow::select3() { + controller.currentRating(3); +} + +void MainWindow::select4() { + controller.currentRating(4); +} + +void MainWindow::select5() { + controller.currentRating(5); +} + +void MainWindow::load() { + resetRateButtons(); + controller.openImage(); + updateN(); +} + +void MainWindow::updateN() { + ostringstream sstr; + sstr << "N = " << to_string(controller.getN()); + ui->NLabel->setText(QString::fromStdString(sstr.str())); +} + +void MainWindow::capture() { + resetRateButtons(); + ui->captureButton->setEnabled(false); + ui->captureButton->blockSignals(true); + captureShortcut->blockSignals(true); + captureShortcut2->blockSignals(true); + qApp->processEvents(); + controller.captureImage(); + ui->captureButton->setEnabled(true); + captureShortcut->blockSignals(false); + captureShortcut2->blockSignals(false); + ui->captureButton->blockSignals(false); + updateN(); +} + +void MainWindow::resetRateButtons() { + ui->rateButton1->setAutoExclusive(false); + ui->rateButton2->setAutoExclusive(false); + ui->rateButton3->setAutoExclusive(false); + ui->rateButton4->setAutoExclusive(false); + ui->rateButton5->setAutoExclusive(false); + ui->rateButton1->setChecked(false); + ui->rateButton2->setChecked(false); + ui->rateButton3->setChecked(false); + ui->rateButton4->setChecked(false); + ui->rateButton5->setChecked(false); + ui->rateButton1->setAutoExclusive(true); + ui->rateButton2->setAutoExclusive(true); + ui->rateButton3->setAutoExclusive(true); + ui->rateButton4->setAutoExclusive(true); + ui->rateButton5->setAutoExclusive(true); +} + +void MainWindow::updateDetection() { + ui->progressBar->setValue(detProc.getProgress()); + ui->operationLabel->setText(QString::fromStdString("Aktuelle Operation: \"" + + detProc.getStepName() + "\"")); + vector params = detProc.getCurParams(); + ui->param1Label->setText(QString::fromStdString("Parameter 1: \"" + + params[0].name + "\"")); + ui->param2Label->setText(QString::fromStdString("Parameter 2: \"" + + params[1].name + "\"")); + ui->param1SpinBox->blockSignals(true); + ui->param2SpinBox->blockSignals(true); + ui->param1Slider->setMinimum(params[0].lowerBound); + ui->param1Slider->setMaximum(params[0].upperBound); + ui->param1Slider->setValue(params[0].val); + ui->param2Slider->setMinimum(params[1].lowerBound); + ui->param2Slider->setMaximum(params[1].upperBound); + ui->param2Slider->setValue(params[1].val); + ui->param1SpinBox->setMinimum(params[0].lowerBound); + ui->param1SpinBox->setMaximum(params[0].upperBound); + ui->param1SpinBox->setValue(params[0].val); + ui->param2SpinBox->setMinimum(params[1].lowerBound); + ui->param2SpinBox->setMaximum(params[1].upperBound); + ui->param2SpinBox->setValue(params[1].val); + ui->param1SpinBox->blockSignals(false); + ui->param2SpinBox->blockSignals(false); +} + +void MainWindow::updateRating() { + ui->rate_progressBar->setValue(rateProc.getProgress()); + ui->rate_operationLabel->setText(QString::fromStdString( + "Aktuelle Operation: \"" + + rateProc.getStepName() + "\"")); + vector params = rateProc.getCurParams(); + ui->rate_param1Label->setText(QString::fromStdString("Parameter 1: \"" + + params[0].name + "\"")); + ui->rate_param2Label->setText(QString::fromStdString("Parameter 2: \"" + + params[1].name + "\"")); + ui->rate_param1SpinBox->blockSignals(true); + ui->rate_param2SpinBox->blockSignals(true); + ui->rate_param1Slider->setMinimum(params[0].lowerBound); + ui->rate_param1Slider->setMaximum(params[0].upperBound); + ui->rate_param1Slider->setValue(params[0].val); + ui->rate_param2Slider->setMinimum(params[1].lowerBound); + ui->rate_param2Slider->setMaximum(params[1].upperBound); + ui->rate_param2Slider->setValue(params[1].val); + ui->rate_param1SpinBox->setMinimum(params[0].lowerBound); + ui->rate_param1SpinBox->setMaximum(params[0].upperBound); + ui->rate_param1SpinBox->setValue(params[0].val); + ui->rate_param2SpinBox->setMinimum(params[1].lowerBound); + ui->rate_param2SpinBox->setMaximum(params[1].upperBound); + ui->rate_param2SpinBox->setValue(params[1].val); + ui->rate_param1SpinBox->blockSignals(false); + ui->rate_param2SpinBox->blockSignals(false); + ostringstream sstr; + sstr << "Ergebnisse: "; + for (auto&& result : rateProc.getResults()) { + sstr << result.first << ": " << result.second << ", "; + } + ui->resultLabel->setText(QString::fromStdString(sstr.str())); +} + +void MainWindow::mode(int tabIndex) { + if (tabIndex == 0) { + detProc.setInteractive(false); + rateProc.setInteractive(false); + controller.process(); + } + else if (tabIndex == 1) { + modeConfig(ui->configTabWidget->currentIndex()); + } +} + +void MainWindow::modeConfig(int tabIndex) { + if (tabIndex == 0) { + detProc.setInteractive(true); + rateProc.setInteractive(false); + updateDetection(); + detProc.start(); + } + else if (tabIndex == 1) { + detProc.setInteractive(false); + rateProc.setInteractive(true); + updateRating(); + controller.resetNuts(); + controller.extractNuts(); + rateProc.start(true); + } +} + +void MainWindow::closeEvent(QCloseEvent* event) { + controller.saveData(); +} + +MainWindow::~MainWindow() +{ + delete ui; + delete captureShortcut; + delete captureShortcut2; +} diff --git a/mainwindow.h b/mainwindow.h new file mode 100644 index 0000000..2317b93 --- /dev/null +++ b/mainwindow.h @@ -0,0 +1,53 @@ +#ifndef MAINWINDOW_H +#define MAINWINDOW_H + +#include +#include +#include +#include +#include "detectionprocessing.h" +#include "rateprocessing.h" +#include "controller.h" + +namespace Ui { + class MainWindow; +} + +class MainWindow : public QMainWindow +{ + Q_OBJECT + +public: + explicit MainWindow(QWidget *parent = 0); + ~MainWindow(); + +private slots: + void updateDetection(); + void updateRating(); + void modeConfig(int tabIndex); + void mode(int tabIndex); + void capture(); + void load(); + void select1(); + void select2(); + void select3(); + void select4(); + void select5(); + +private: + void closeEvent(QCloseEvent* event); + void resetRateButtons(); + void updateN(); + +// Members +private: + Ui::MainWindow* ui; + Controller& controller; + DetectionProcessing& detProc; + RateProcessing& rateProc; + QTimer* autoSaveTimer; + QShortcut* captureShortcut; + QShortcut* captureShortcut2; +}; + +#endif // MAINWINDOW_H diff --git a/mainwindow.ui b/mainwindow.ui new file mode 100644 index 0000000..19a011b --- /dev/null +++ b/mainwindow.ui @@ -0,0 +1,691 @@ + + + MainWindow + + + + 0 + 0 + 1097 + 807 + + + + + 0 + 0 + + + + + 1035 + 560 + + + + Haselnüsse + + + + + + + + + + 0 + + + + Live + + + + + + + + + 0 + 0 + + + + + + + + + + + 0 + 0 + + + + + + + + + 3 + 0 + + + + Geschmack + + + + + + &1 + + + 1 + + + + + + + &2 + + + 2 + + + + + + + &3 + + + 3 + + + + + + + &4 + + + 4 + + + + + + + &5 + + + 5 + + + + + + + + + + + + + + + + + 0 + 0 + + + + Status: + + + + + + + + 9 + 0 + + + + ... lade ... + + + + + + + N = 0 + + + + + + + + + + + Bild laden + + + + + + + Bild aufnehmen + + + + + + + + + + Zurücksetzen + + + + + + + + 0 + 0 + + + + Benutze Neurales Netz + + + true + + + + + + + + + + Konfiguration + + + + + + 0 + + + + Nüsse erkennen + + + + + + + + Vorheriger Schritt + + + Ctrl+S + + + + + + + Nächster Schritt + + + Right + + + + + + + Speichern + + + + + + + + + true + + + 24 + + + + + + + Aktuelle Operation: + + + + + + + + + Input: + + + + + + + Output: + + + + + + + + + + + + 0 + 0 + + + + + + + + + 0 + 0 + + + + + + + + + + + + Parameter 1: + + + + + + + 255 + + + 10 + + + Qt::Horizontal + + + + + + + 1 + + + 255 + + + 1 + + + 10 + + + + + + + Parameter 2: + + + + + + + 255 + + + 3 + + + Qt::Horizontal + + + + + + + 1 + + + 255 + + + 3 + + + + + + + Konfiguration speichern + + + + + + + + + + Nüsse bewerten + + + + + + + + vorherige Nuss + + + + + + + nächste Nuss + + + + + + + Vorheriger Schritt + + + Ctrl+S + + + + + + + Nächster Schritt + + + Right + + + + + + + true + + + Speichern + + + + + + + + + true + + + 24 + + + + + + + Aktuelle Operation: + + + + + + + + + Input: + + + + + + + Output: + + + + + + + + + + + + 0 + 0 + + + + + + + + + 0 + 0 + + + + + + + + + + + + Parameter 1: + + + + + + + 255 + + + 10 + + + Qt::Horizontal + + + + + + + 1 + + + 255 + + + 1 + + + 10 + + + + + + + Parameter 2: + + + + + + + 255 + + + 3 + + + Qt::Horizontal + + + + + + + 1 + + + 255 + + + 3 + + + + + + + Konfiguration speichern + + + + + + + + + Ergebnis: + + + + + + + + + + + + + + + + + CVImageWidget + QWidget +

cvimagewidget.h
+ 1 + + + + + + rate_param1Slider + valueChanged(int) + rate_param1SpinBox + setValue(int) + + + 272 + 729 + + + 408 + 733 + + + + + rate_param1SpinBox + valueChanged(int) + rate_param1Slider + setValue(int) + + + 411 + 718 + + + 226 + 728 + + + + + rate_param2SpinBox + valueChanged(int) + rate_param2Slider + setValue(int) + + + 845 + 726 + + + 797 + 732 + + + + + rate_param2Slider + valueChanged(int) + rate_param2SpinBox + setValue(int) + + + 704 + 726 + + + 840 + 719 + + + + + diff --git a/neuralnet.cpp b/neuralnet.cpp new file mode 100644 index 0000000..8af6bdb --- /dev/null +++ b/neuralnet.cpp @@ -0,0 +1,273 @@ +#include "neuralnet.h" +#include "controller.h" +#include "filenames.h" +#include +#include +#include + +using namespace std; +using namespace FANN; + +void NeuralNet::saveNut() { + if (Controller::inst().getCurRating() == -1) { + cerr << "Error: current rating -1 during saving." << endl; + exit(1); + } + ostringstream input_line; + input_line << current_index << ";"; + input_line.precision(numeric_limits::max_digits10); + const auto& results = Controller::inst().getRateProcessing().getResults(); + for (const auto& result : result_names) { + auto pos = results.find(result); + if (pos == results.end()) { + cerr << "Could not find " << result << " in results." << endl; + exit(1); + } + inputTrainData.push_back(pos->second); + input_line << scientific << pos->second << ";"; + } + ofstream input_file; + input_file.open(FileNames::inputName, ios_base::app); + input_file << input_line.str() << "\n"; + + outputTrainData.push_back(Controller::inst().getCurRating()); + ofstream output_file; + output_file.open(FileNames::outputName, ios_base::app); + output_file << current_index << ";"; + output_file << Controller::inst().getCurRating() << ";" << "\n"; + + ostringstream img_file; + img_file << FileNames::imgPath + << "nut_" << to_string(current_index) + << ".jpg"; + imwrite(img_file.str(), *Controller::inst().getNutToRate()); + current_index++; + if (current_index % 5 == 0) { + trainAndSave(); + } +} + +void NeuralNet::trainAndSave() { + setlocale(LC_ALL, "en_GB.UTF-8"); + getScalingFactors(); + scaleData(); + training_data data; + data.set_train_data(scaledOutputTrainData.size(), result_names.size(), + &scaledInputTrainData[0], 1, &scaledOutputTrainData[0]); + net.train_on_data(data, 100000, 1000, 0.01); + if (!net.save(FileNames::neuralNetName)) { + cerr << "Error saving neural net:" + << net.get_errstr() << endl; + exit(1); + } +} + +void NeuralNet::scaleData() { + scaledInputTrainData = vector(inputTrainData.size()); + for(int i = 0; i(scaling_input_data[i])) * + get <1>(scaling_input_data[i]); + } + } + scaledOutputTrainData = vector(outputTrainData.size()); + for(int i = 0; i>(result_names.size()); + for(int i = 0; i& input) { + float input_arr[input.size()]; + for (unsigned int i = 0; i 0) { + input_arr[i] = + (pos->second - get<0>(scaling_input_data[i])) * + get<1>(scaling_input_data[i]); + } + else { + input_arr[i] = pos->second; + } + } + float ret = 0.0f; + if (use_neural_net) { + float* output; + output = net.run(input_arr); + ret = output[0]; + } + else { + ret = + 0.25 * input_arr[0] + + 0.125* input_arr[1] + + 0.125* (1.0f/input_arr[2]) + + 0.25 * input_arr[3] + + 0.25 * input_arr[4]; + } + return min(1.0f, max(ret, 0.0f)); +} + +void NeuralNet::init(Json::Value jsonResults) { + for(int i = 0; i +#include +#include +#include +#include + +class NeuralNet { +public: + void saveNut(); + void init(Json::Value jsonResults); + int getN(); + float run(const std::map& input); + void neuralNetEnabled(bool val); + +private: + void createHeaders(); + bool filesExist(); + void trainAndSave(); + void getScalingFactors(); + void scaleData(); + +private: + int current_index = 0; + std::vector inputTrainData = {}; + std::vector scaledInputTrainData = {}; + std::vector outputTrainData = {}; + std::vector scaledOutputTrainData = {}; + std::vector result_names = {}; + FANN::neural_net net; + // tuple of min and scaling factor for each input + std::vector> scaling_input_data = {}; + bool use_neural_net = true; +}; + +#endif // NEURALNET_H diff --git a/processing.cpp b/processing.cpp new file mode 100644 index 0000000..003d6e6 --- /dev/null +++ b/processing.cpp @@ -0,0 +1,183 @@ +#include "processing.h" +#include "controller.h" +#include +#include +#include +#include +#include +#include + +using namespace std; +using namespace cv; + + +Processing::Processing(QObject *parent) : QObject(parent) {} + +void Processing::init(const QMetaObject* metaObj, Json::Value json_steps) { + for(int i = 0; imethodCount(); i++) { + QMetaMethod method = metaObj->method(i); + if (method.name().toStdString() == json_op_name) { + operation = method; + found = true; + } + } + if (!found) { + cerr << "Processing method " << json_op_name << + " not found.\n"; + exit(1); + } + Json::Value json_param1 = json_steps[i]["param1"]; + Parameter param1; + param1.val = json_param1.get("default", 0).asInt(); + param1.lowerBound = json_param1.get("lowerBound", 0).asInt(); + param1.upperBound = json_param1.get("upperBound", 0).asInt(); + param1.name = json_param1.get("name", "").asString(); + param1.odd = json_param1.get("odd", false).asBool(); + Json::Value json_param2 = json_steps[i]["param2"]; + Parameter param2; + param2.val = json_param2.get("default", 0).asInt(); + param2.lowerBound = json_param2.get("lowerBound", 0).asInt(); + param2.upperBound = json_param2.get("upperBound", 0).asInt(); + param2.name = json_param2.get("name", "").asString(); + param2.odd = json_param2.get("odd", false).asBool(); + vector params = {param1, param2}; + InputType type; + if (json_steps[i]["input"] == "previous") { + type = previous; + } + else if (json_steps[i]["input"] == "color") { + type = color; + } + ProcessingStep step(this, json_name, operation, params, type); + steps.push_back(step); + } +} + +void Processing::start(bool force) { + if (!steps[current_step].hasInputData()) { + return; + } + if (interactive) { + emit newInputImage(steps[current_step].getInput()); + } + if (steps[current_step].getOutput().empty() || force) { + processImage(); + } + else { + emit newOutputImage(steps[current_step].getOutput()); + } +} + +void Processing::reset() { + current_step = 0; + for (auto&& step : steps) { + step.reset(); + } + InputType type = steps[0].getInputType(); + if (type == color) { + steps[0].setInput(Controller::inst().getCurImg()); + } +} + +void Processing::processImage() { + steps[current_step].execute(); + if (interactive) { + emit newOutputImage(steps[current_step].getOutput()); + } +} + +void Processing::setParam1(int val) { + steps[current_step].setParam1(val); + processImage(); +} + +void Processing::setParam2(int val) { + steps[current_step].setParam2(val); + processImage(); +} + +void Processing::nextStep() { + current_step = min((int)steps.size() - 1, current_step+1); + InputType type = steps[current_step].getInputType(); + if (type == previous) { + steps[current_step].setInput(&steps[current_step - + 1].getOutput()); + if (interactive) { + emit newInputImage(steps[current_step].getInput()); + } + } + else if (type == color) { + steps[current_step].setInput(getInput()); + if (interactive && steps[current_step].hasInputData()) { + emit newInputImage(steps[current_step].getInput()); + } + } + if (interactive) { + emit requestUpdate(); + } + if (steps[current_step].hasInputData()) { + processImage(); + } + else { + cerr << "No input data for step: " + + steps[current_step].getStepName() << endl; + } +} + +void Processing::previousStep() { + current_step = max(0, current_step-1); + if (interactive) { + emit requestUpdate(); + if (steps[current_step].hasData()) { + emit newInputImage(steps[current_step].getInput()); + emit newOutputImage(steps[current_step].getOutput()); + } + } +} + +int Processing::getProgress() { + double prog = (double)(current_step+1)/steps.size(); + return ceil(prog*100); +} + +string Processing::getStepName() { + return steps[current_step].getStepName(); +} + +vector Processing::getCurParams() { + return steps[current_step].getParams(); +} + +void Processing::saveCur() { + steps[current_step].save(); +} + +void Processing::setInteractive(bool val) { + interactive = val; +} + +bool Processing::isInteractive() { + return interactive; +} + +int Processing::getCurrentStep() { + return current_step; +} + +bool Processing::process() { + if (! steps[0].hasInputData()) { + return false; + } + + // also do step 0 + current_step = -1; + for (unsigned int i = 0; i +#include +#include + +#include "processingstep.h" + +class Processing : public QObject { + + Q_OBJECT + +public: + Processing(QObject *parent = 0); + std::vector getCurParams(); + std::string getStepName(); + int getProgress(); + void setInteractive(bool val); + bool isInteractive(); + void start(bool force = false); + void reset(); + int getCurrentStep(); + bool process(); + +signals: + void newInputImage(const cv::Mat& newImage); + void newOutputImage(const cv::Mat& newImage); + void requestUpdate(); + +public slots: + void setParam1(int val); + void setParam2(int val); + void nextStep(); + void previousStep(); + void saveCur(); + void processImage(); + +protected: + virtual const cv::Mat* getInput() = 0; + +// Members +protected: + std::vector steps {}; + int current_step = 0; + Json::Value config; + void init(const QMetaObject* metaObj, Json::Value json_steps); + bool interactive = false; +}; + +#endif // PROCESSING_H diff --git a/processingstep.h b/processingstep.h new file mode 100644 index 0000000..a5a2a97 --- /dev/null +++ b/processingstep.h @@ -0,0 +1,100 @@ +#ifndef PROCESSINGSTEP_H +#define PROCESSINGSTEP_H + +#include +#include +#include +#include + +struct Parameter { + int val; + int lowerBound; + int upperBound; + std::string name; + bool odd; +}; + +enum InputType { + previous, + color +}; + +class ProcessingStep { + +public: + ProcessingStep(QObject* processing, + std::string stepName, + QMetaMethod operation, + std::vector params, + InputType inputType + ) { + this->processing = processing; + this->stepName = stepName; + this->operation = operation; + this->params = params; + this->inputType = inputType; + } + + std::vector getParams() { return params; } + std::string getStepName() { return stepName; } + void setParam1 (int val) { + if (params[0].odd && val % 2 == 0) { + val--; + } + params[0].val = val; + } + + int getParam1() { return params[0].val; } + + int getParam2() { return params[1].val; } + + void setParam2 (int val) { + if (params[1].odd && val % 2 == 0) { + val--; + } + params[1].val = val; + } + void execute() { + if (input->empty()) { + std::cerr << "Input empty for processing step: " << stepName << std::endl; + return; + } + operation.invoke(processing, + Q_RETURN_ARG(cv::Mat, output), + Q_ARG(const cv::Mat&, *input), + Q_ARG(int, params[0].val), + Q_ARG(int, params[1].val) + ); + } + void save() { + imwrite(stepName + ".jpg", output); + } + + const cv::Mat& getOutput() { return output; } + const cv::Mat& getInput() { return *input; } + + void setInput(const cv::Mat* input) { this->input = input; } + + InputType getInputType() { return inputType; } + + bool hasInputData() { return input != nullptr && !input->empty(); } + + bool hasData() { return input != nullptr && !input->empty() + && !output.empty(); } + + void reset() { + input = nullptr; + output.release(); + } + +private: + const cv::Mat* input = nullptr; + cv::Mat output; + std::string stepName; + QMetaMethod operation; + std::vector params; + InputType inputType; + QObject* processing; +}; + +#endif diff --git a/rateprocessing.cpp b/rateprocessing.cpp new file mode 100644 index 0000000..2a9855d --- /dev/null +++ b/rateprocessing.cpp @@ -0,0 +1,311 @@ +#include "detectionprocessing.h" +#include "controller.h" +#include +#include +#include +#include +#include +#include +#include +#include + +using namespace std; +using namespace cv; + +RateProcessing::RateProcessing(QObject* parent) + : Processing(parent) {} + +void RateProcessing::init(Json::Value json_steps) { + Processing::init(this->metaObject(), json_steps); +} + + +Mat RateProcessing::_dilate(const Mat& input, int kernel_size, + int iterations) { + Mat out; + dilate(input, out, getStructuringElement(MORPH_ELLIPSE, + Size(kernel_size, kernel_size)), Point(-1, -1), iterations); + return out; +} + +Mat RateProcessing::_threshold(const Mat& input, int ignored, + int ignored2) { + Mat gray; + cvtColor(input, gray, COLOR_BGR2GRAY); + Mat out; + threshold(gray, out, 1, 255, THRESH_BINARY); + return out; +} + +Mat RateProcessing::_thresh_otsu(const Mat& input, int ignored, + int ignored2) { + Mat gray; + cvtColor(input, gray, COLOR_BGR2GRAY); + Mat out; + threshold(gray, out, 0, 255, THRESH_BINARY | THRESH_OTSU); + return out; +} + +Mat RateProcessing::_invert(const Mat& input, int ignored, int ignored2) { + Mat out = Scalar::all(255) - input; + return out; +} + +Mat RateProcessing::_thresh_color(const Mat& input, int lowerThreshold, int + upperThreshold) { + Mat hsv; + cvtColor(input, hsv, COLOR_BGR2HSV); + Mat out(input.size(), CV_8U); + inRange(hsv, Scalar(lowerThreshold, 0, 0), Scalar(upperThreshold, + 255, 255), out); + return out; +} + +Mat RateProcessing::_mean_color(const Mat& input, int ignored, int ignored2) { + Mat hsv_nut; + cvtColor(*getInput(), hsv_nut, CV_BGR2HSV); + Scalar res = mean(hsv_nut, input); + results["avg_h_val"] = res[0]; + if (isInteractive()) { + emit requestUpdate(); + } + return hsv_nut; +} + +Mat RateProcessing::_erode(const Mat& input, int kernel_size, int + iterations) { + Mat out; + erode(input, out, getStructuringElement(MORPH_ELLIPSE, + Size(kernel_size, kernel_size)), Point(-1, -1), iterations); + return out; +} + +Mat RateProcessing::_hough(const Mat& input, int canny_1, int canny_2) { + vector circles; + Mat gray; + cvtColor(input, gray, COLOR_BGR2GRAY); + HoughCircles(gray, circles, HOUGH_GRADIENT, 1, input.rows/4, canny_1, + canny_2); + Mat out; + input.copyTo(out); + for (int i = 0; i> contours; + Mat in; + float fac = 1.0; + if (input.rows > input.cols) { + fac = (float) max_length/input.rows; + } + else { + fac = (float) max_length/input.cols; + } + Size new_size((int)input.cols*fac, (int) input.rows*fac) ; + resize(input, in, new_size); + findContours(in, contours, RETR_LIST, CHAIN_APPROX_TC89_KCOS); + Mat out; + if (isInteractive()) { + out = Mat::zeros(in.size(), CV_8UC3); + for(int i = 0; i < contours.size(); i++) { + drawContours(out, contours, i, Scalar::all(255), 1, 8); + } + } + return out; +} + +Mat RateProcessing::_find_contours(const Mat& input, int ignored, + int ignored2) { + Mat in = input.clone(); + vector> contours; + Mat out; + findContours(in, contours, RETR_LIST, CHAIN_APPROX_NONE); + if (isInteractive()) { + out = Mat::zeros(input.size(), CV_8UC3); + for(int i = 0; i < contours.size(); i++) { + drawContours(out, contours, i, Scalar::all(255), 1, 8); + } + } + return out; +} + + +Mat RateProcessing::_fit_ellipse(const Mat& input, int ignored, int ignored2) { + Mat in = input.clone(); + //vector> contours; + contours = {}; + Mat out; + findContours(in, contours, RETR_LIST, CHAIN_APPROX_NONE); + if (isInteractive()) { + out = Mat::zeros(input.size(), CV_8UC3); + } + vector boxes = {}; + for(int i = 0; i < contours.size(); i++) { + int count = contours[i].size(); + if( count < 6 ) + continue; + Mat pointsf; + Mat(contours[i]).convertTo(pointsf, CV_32F); + RotatedRect box = fitEllipse(pointsf); + if( MAX(box.size.width, box.size.height) > + MIN(box.size.width, box.size.height)*30 ) + continue; + boxes.push_back(box); + if (isInteractive()) { + drawContours(out, contours, i, Scalar::all(255), 1, 8); + ellipse(out, box, Scalar(0,0,255)); + Point2f vtx[4]; + box.points(vtx); + for( int j = 0; j < 4; j++ ) { + line(out, vtx[j], vtx[(j+1)%4], Scalar(0,255,0), 1, LINE_AA); + } + } + } + RotatedRect& max_box = boxes[0]; + int max_area = 0; + for (auto&& box : boxes) { + if ((box.size.width * box.size.height) > max_area) { + max_box = box; + max_area = box.size.width * box.size.height; + } + } + results["size_ratio"] = max_box.size.width/max_box.size.height; + results["size_diagonal"] = + sqrt(max_box.size.height * max_box.size.height + + max_box.size.height * max_box.size.height) + / static_cast(steps[current_step].getParam1()); + if (isInteractive()) { + emit requestUpdate(); + } + return out; +} + +Mat RateProcessing::_rate_spots(const Mat& input, int ignored, + int ignored2) { + Mat in = input.clone(); + vector> contours; + Mat out; + findContours(in, contours, RETR_LIST, CHAIN_APPROX_NONE); + if (isInteractive()) { + out = Mat::zeros(input.size(), CV_8UC3); + } + vector boxes = {}; + // image and mask of spots + vector> spots = {}; + for(int i = 0; i < contours.size(); i++) { + int count = contours[i].size(); + if( count < 6 ) + continue; + Mat pointsf; + Mat(contours[i]).convertTo(pointsf, CV_32F); + RotatedRect box = fitEllipse(pointsf); + if( MAX(box.size.width, box.size.height) > + MIN(box.size.width, box.size.height)*30 ) + continue; + boxes.push_back(box); + if (isInteractive()) { + drawContours(out, contours, i, Scalar::all(255), 1, 8); + ellipse(out, box, Scalar(0,0,255)); + Point2f vtx[4]; + box.points(vtx); + for( int j = 0; j < 4; j++ ) { + line(out, vtx[j], vtx[(j+1)%4], Scalar(0,255,0), 1, LINE_AA); + } + } + // extract spot from image + Rect roi = boundingRect(contours[i]); + Mat mask = Mat::zeros(input.size(), CV_8UC1); + drawContours(mask, contours, i, Scalar(255), CV_FILLED); + Mat contourRegion; + Mat imageROI; + getInput()->copyTo(imageROI, mask); + contourRegion = imageROI(roi); + Mat maskRegion = mask(roi); + spots.push_back(make_tuple(contourRegion, maskRegion)); + } + + // find largest ellipse to ignore it, because its no spot but the nut itself + int max_box_index = -1; + int max_area = 0; + for (unsigned int i = 0; i max_area) { + max_box_index = i; + max_area = boxes[i].size.width * boxes[i].size.height; + } + } + double box_avg_size = 0.0f; + for (unsigned int i = 0; i 1) { + box_avg_size /= static_cast(boxes.size() - 1); + } + else { + box_avg_size = 0.0f; + } + results["spot_avg_size"] = box_avg_size/steps[current_step].getParam1(); + double spot_avg_h_val = 0.0f; + for (unsigned int i = 0; i(spots[i]), hsv_spot, CV_BGR2HSV); + Scalar res = mean(hsv_spot, get<1>(spots[i])); + spot_avg_h_val += res[0]; + } + if (spots.size() > 1) { + spot_avg_h_val /= static_cast(spots.size() - 1); + } + else { + spot_avg_h_val = 0.0f; + } + results["spot_avg_h_val"] = spot_avg_h_val; + if (isInteractive()) { + emit requestUpdate(); + } + return out; +} + +void RateProcessing::reset() { + current_step = 0; + InputType type = steps[0].getInputType(); + if (type == color) { + steps[0].setInput(Controller::inst().getCurNut()); + } + results.clear(); + rateCurNut = true; +} + +map RateProcessing::getResults() { + return results; +} + +const cv::Mat* RateProcessing::getInput() { + if (rateCurNut) { + return Controller::inst().getCurNut(); + } + else { + return Controller::inst().getNutToRate(); + } +} + +const vector>& RateProcessing::getContours() { + return contours; +} + +void RateProcessing::setRateCurNut(bool val) { + rateCurNut = val; +} diff --git a/rateprocessing.h b/rateprocessing.h new file mode 100644 index 0000000..2311b50 --- /dev/null +++ b/rateprocessing.h @@ -0,0 +1,46 @@ +#ifndef RATEPROCESSING_H +#define RATEPROCESSING_H + +#include +#include "processing.h" +#include +#include + +class RateProcessing : public Processing { + + Q_OBJECT + +public: + RateProcessing(QObject *parent = 0); + void init(Json::Value json_steps); + void reset(); + std::map getResults(); + const std::vector>& getContours(); + void setRateCurNut(bool val); + +// Processing methods +public slots: + cv::Mat _dilate(const cv::Mat& input , int kernel_size, int iterations); + cv::Mat _threshold(const cv::Mat& input, int ignored, int ignored2); + cv::Mat _thresh_otsu(const cv::Mat& input, int ignored, int ignored2); + cv::Mat _thresh_color(const cv::Mat& input, int ignored, int ignored2); + cv::Mat _erode(const cv::Mat& input , int kernel_size, int iterations); + cv::Mat _invert(const cv::Mat& input, int iter, int ignored); + cv::Mat _hough(const cv::Mat& input, int canny_1, int canny_2); + cv::Mat _fit_ellipse(const cv::Mat& input, int ignored, int ignored2); + cv::Mat _rate_spots(const cv::Mat& input, int ignored, int ignored2); + cv::Mat _find_contours(const cv::Mat& input, int ignored, int ignored2); + cv::Mat _find_contours_resized(const cv::Mat& input, int max_length, int ignored2); + cv::Mat _mean_color(const cv::Mat& input, int ignored, int ignored2); + +private: + const cv::Mat* getInput(); + +// Members +private: + bool rateCurNut = true; + std::map results; + std::vector> contours; +}; + +#endif // RATEPROCESSING_H diff --git a/test_data/.directory b/test_data/.directory new file mode 100644 index 0000000..7e8a08f --- /dev/null +++ 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