diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..82241ad
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,2 @@
+cascade/
+*.vec
\ No newline at end of file
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..2d1f3f2
--- /dev/null
+++ b/README.md
@@ -0,0 +1,4 @@
+# Размеченный набор данных для тренировки каскада Хаара.
+
+
+**14.02.2018** 114 положительных и 20 отрицательных семплов.
\ No newline at end of file
diff --git a/annotate.bat b/annotate.bat
new file mode 100644
index 0000000..a417903
--- /dev/null
+++ b/annotate.bat
@@ -0,0 +1 @@
+opencv_annotation.exe --annotations=positive\desc.txt --images=positive\img\
\ No newline at end of file
diff --git a/annotate_neg.bat b/annotate_neg.bat
new file mode 100644
index 0000000..ea7bb2a
--- /dev/null
+++ b/annotate_neg.bat
@@ -0,0 +1 @@
+opencv_annotation.exe --annotations=negative\desc.txt --images=negative\img\
\ No newline at end of file
diff --git a/cascade_114.xml b/cascade_114.xml
new file mode 100644
index 0000000..0c7d201
--- /dev/null
+++ b/cascade_114.xml
@@ -0,0 +1,1856 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<cascade>
+  <stageType>BOOST</stageType>
+  <featureType>HAAR</featureType>
+  <height>24</height>
+  <width>24</width>
+  <stageParams>
+    <boostType>GAB</boostType>
+    <minHitRate>9.9500000476837158e-01</minHitRate>
+    <maxFalseAlarm>2.0000000298023224e-01</maxFalseAlarm>
+    <weightTrimRate>9.4999999999999996e-01</weightTrimRate>
+    <maxDepth>1</maxDepth>
+    <maxWeakCount>100</maxWeakCount></stageParams>
+  <featureParams>
+    <maxCatCount>0</maxCatCount>
+    <featSize>1</featSize>
+    <mode>BASIC</mode></featureParams>
+  <stageNum>30</stageNum>
+  <stages>
+    <!-- stage 0 -->
+    <_>
+      <maxWeakCount>4</maxWeakCount>
+      <stageThreshold>6.1191022396087646e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 7 -1.0194615274667740e-01</internalNodes>
+          <leafValues>
+            9.5918369293212891e-01 -6.3636362552642822e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 5 -4.0728724561631680e-03</internalNodes>
+          <leafValues>
+            1. -5.2663099765777588e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 115 1.2253245338797569e-02</internalNodes>
+          <leafValues>
+            8.4069854021072388e-01 -8.1297862529754639e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 19 -5.7850056327879429e-04</internalNodes>
+          <leafValues>
+            9.6993935108184814e-01 -5.9242463111877441e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 1 -->
+    <_>
+      <maxWeakCount>3</maxWeakCount>
+      <stageThreshold>-1.7814858257770538e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 17 -3.2096032053232193e-02</internalNodes>
+          <leafValues>
+            9.2233008146286011e-01 -8.8235294818878174e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 51 -6.5044114307966083e-05</internalNodes>
+          <leafValues>
+            9.3704944849014282e-01 -6.2369889020919800e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 2 -3.2705262303352356e-02</internalNodes>
+          <leafValues>
+            9.7520345449447632e-01 -4.7677981853485107e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 2 -->
+    <_>
+      <maxWeakCount>4</maxWeakCount>
+      <stageThreshold>8.1029498577117920e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 124 -1.2769214808940887e-02</internalNodes>
+          <leafValues>
+            -1. 8.3486241102218628e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 26 -9.0138064697384834e-03</internalNodes>
+          <leafValues>
+            9.7876518964767456e-01 -4.5648041367530823e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 68 3.4802396839950234e-05</internalNodes>
+          <leafValues>
+            -3.6495596170425415e-01 9.5406961441040039e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 123 1.0581551119685173e-02</internalNodes>
+          <leafValues>
+            -5.2215665578842163e-01 8.7185049057006836e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 3 -->
+    <_>
+      <maxWeakCount>5</maxWeakCount>
+      <stageThreshold>4.0472313761711121e-02</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 81 -3.1068045645952225e-03</internalNodes>
+          <leafValues>
+            9.5652174949645996e-01 -2.8571429848670959e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 57 2.0577483810484409e-03</internalNodes>
+          <leafValues>
+            7.1598923206329346e-01 -6.3747322559356689e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 70 -2.4233385920524597e-03</internalNodes>
+          <leafValues>
+            8.1838792562484741e-01 -5.7543998956680298e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 53 -1.7738107126206160e-03</internalNodes>
+          <leafValues>
+            9.1049617528915405e-01 -5.4297876358032227e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 90 -4.9254018813371658e-03</internalNodes>
+          <leafValues>
+            -6.1935108900070190e-01 7.2861611843109131e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 4 -->
+    <_>
+      <maxWeakCount>3</maxWeakCount>
+      <stageThreshold>7.8384047746658325e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 97 -7.8761000186204910e-03</internalNodes>
+          <leafValues>
+            8.4615385532379150e-01 -5.0000000000000000e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 24 -3.6609177477657795e-03</internalNodes>
+          <leafValues>
+            9.0939432382583618e-01 -4.3718609213829041e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 13 -7.7339172363281250e-02</internalNodes>
+          <leafValues>
+            -9.7170764207839966e-01 5.7594394683837891e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 5 -->
+    <_>
+      <maxWeakCount>4</maxWeakCount>
+      <stageThreshold>5.2353471517562866e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 54 -4.1675469838082790e-03</internalNodes>
+          <leafValues>
+            9.1489362716674805e-01 -2.3076923191547394e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 15 -3.6844521760940552e-02</internalNodes>
+          <leafValues>
+            7.8007924556732178e-01 -6.4769864082336426e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 103 -7.1153987664729357e-04</internalNodes>
+          <leafValues>
+            9.5806866884231567e-01 -4.3098264932632446e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 100 -4.9817760009318590e-04</internalNodes>
+          <leafValues>
+            -7.0172888040542603e-01 7.3305737972259521e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 6 -->
+    <_>
+      <maxWeakCount>4</maxWeakCount>
+      <stageThreshold>3.0710747838020325e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 21 -1.1664564907550812e-01</internalNodes>
+          <leafValues>
+            -8.3333331346511841e-01 8.3333331346511841e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 82 -7.3953056707978249e-03</internalNodes>
+          <leafValues>
+            7.9573929309844971e-01 -8.0899965763092041e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 128 4.5996299013495445e-03</internalNodes>
+          <leafValues>
+            -5.0174516439437866e-01 1.</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 85 -1.1761346831917763e-03</internalNodes>
+          <leafValues>
+            8.4644663333892822e-01 -5.8789223432540894e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 7 -->
+    <_>
+      <maxWeakCount>4</maxWeakCount>
+      <stageThreshold>7.6983821392059326e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 135 -9.7279846668243408e-03</internalNodes>
+          <leafValues>
+            -3.3333334326744080e-01 8.7878787517547607e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 14 -2.3044910281896591e-02</internalNodes>
+          <leafValues>
+            9.5836859941482544e-01 -2.3611436784267426e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 74 -1.7800333444029093e-03</internalNodes>
+          <leafValues>
+            8.3148199319839478e-01 -4.5252162218093872e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 101 -7.9730397555977106e-04</internalNodes>
+          <leafValues>
+            -6.1479663848876953e-01 6.9011461734771729e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 8 -->
+    <_>
+      <maxWeakCount>5</maxWeakCount>
+      <stageThreshold>7.5326335430145264e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 136 1.4038953930139542e-02</internalNodes>
+          <leafValues>
+            7.8571426868438721e-01 -1.</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 3 -7.7194213867187500e-02</internalNodes>
+          <leafValues>
+            1. -3.1633466482162476e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 32 -1.7028313130140305e-02</internalNodes>
+          <leafValues>
+            9.7271174192428589e-01 -3.7967106699943542e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 20 -1.7401198419975117e-05</internalNodes>
+          <leafValues>
+            9.7514635324478149e-01 -3.1910774111747742e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 38 -9.3333079712465405e-04</internalNodes>
+          <leafValues>
+            1. -3.1159153580665588e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 9 -->
+    <_>
+      <maxWeakCount>4</maxWeakCount>
+      <stageThreshold>6.5016674995422363e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 42 -3.2504081726074219e-01</internalNodes>
+          <leafValues>
+            -1. 7.8571426868438721e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 102 2.5070074480026960e-04</internalNodes>
+          <leafValues>
+            -4.3042686581611633e-01 1.</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 47 -3.9426828734576702e-03</internalNodes>
+          <leafValues>
+            8.6331462860107422e-01 -5.3992539644241333e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 88 -1.5344347048085183e-05</internalNodes>
+          <leafValues>
+            8.3480477333068848e-01 -5.0501507520675659e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 10 -->
+    <_>
+      <maxWeakCount>4</maxWeakCount>
+      <stageThreshold>3.2409232854843140e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 99 -6.1300307512283325e-02</internalNodes>
+          <leafValues>
+            -8.0000001192092896e-01 8.0000001192092896e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 71 8.2518381532281637e-04</internalNodes>
+          <leafValues>
+            8.6509734392166138e-01 -4.6485468745231628e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 104 -3.6617890000343323e-02</internalNodes>
+          <leafValues>
+            7.2037822008132935e-01 -5.6055676937103271e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 105 -3.9236986776813865e-04</internalNodes>
+          <leafValues>
+            8.1955176591873169e-01 -5.0799059867858887e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 11 -->
+    <_>
+      <maxWeakCount>5</maxWeakCount>
+      <stageThreshold>5.2400612831115723e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 66 3.1007808446884155e-01</internalNodes>
+          <leafValues>
+            7.5438594818115234e-01 -1.</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 36 -2.7915482860407792e-05</internalNodes>
+          <leafValues>
+            1. -3.4249910712242126e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 48 1.1072430061176419e-04</internalNodes>
+          <leafValues>
+            -3.0998235940933228e-01 1.</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 109 -3.2647023908793926e-04</internalNodes>
+          <leafValues>
+            8.4530580043792725e-01 -3.8354799151420593e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 6 -5.2382797002792358e-03</internalNodes>
+          <leafValues>
+            8.0564963817596436e-01 -4.0658101439476013e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 12 -->
+    <_>
+      <maxWeakCount>5</maxWeakCount>
+      <stageThreshold>2.1174523234367371e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 80 -3.2128132879734039e-03</internalNodes>
+          <leafValues>
+            8.9361703395843506e-01 -1.5384615957736969e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 39 -2.2903287783265114e-02</internalNodes>
+          <leafValues>
+            -7.5699198246002197e-01 6.4205741882324219e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 73 -7.1159969083964825e-03</internalNodes>
+          <leafValues>
+            8.8974249362945557e-01 -4.5264312624931335e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 37 1.4371569704962894e-05</internalNodes>
+          <leafValues>
+            -3.9928361773490906e-01 8.7402701377868652e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 59 -1.5665931277908385e-04</internalNodes>
+          <leafValues>
+            1. -4.7200244665145874e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 13 -->
+    <_>
+      <maxWeakCount>5</maxWeakCount>
+      <stageThreshold>1.1491265296936035e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 67 6.5334253013134003e-03</internalNodes>
+          <leafValues>
+            8.0180180072784424e-01 -1.</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 77 -3.5587808815762401e-04</internalNodes>
+          <leafValues>
+            -2.9145056009292603e-01 1.</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 117 3.8948595523834229e-02</internalNodes>
+          <leafValues>
+            -6.9925945997238159e-01 6.1840617656707764e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 50 6.4011402428150177e-03</internalNodes>
+          <leafValues>
+            6.6090732812881470e-01 -7.3287850618362427e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 119 -1.4200755686033517e-05</internalNodes>
+          <leafValues>
+            9.6139496564865112e-01 -5.3820288181304932e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 14 -->
+    <_>
+      <maxWeakCount>4</maxWeakCount>
+      <stageThreshold>8.2505524158477783e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 0 -2.3115582764148712e-02</internalNodes>
+          <leafValues>
+            -1. 7.5438594818115234e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 23 -5.0797220319509506e-03</internalNodes>
+          <leafValues>
+            1. -3.8661360740661621e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 93 -5.6326277554035187e-03</internalNodes>
+          <leafValues>
+            8.7289577722549438e-01 -3.6189204454421997e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 116 1.3386184036789928e-05</internalNodes>
+          <leafValues>
+            -4.1561290621757507e-01 8.8146233558654785e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 15 -->
+    <_>
+      <maxWeakCount>5</maxWeakCount>
+      <stageThreshold>1.0768294334411621e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 22 8.7885763496160507e-03</internalNodes>
+          <leafValues>
+            7.6991152763366699e-01 -1.</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 92 1.8156839068979025e-03</internalNodes>
+          <leafValues>
+            6.6584479808807373e-01 -7.1413332223892212e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 18 -3.0675576999783516e-02</internalNodes>
+          <leafValues>
+            5.8492934703826904e-01 -7.7677690982818604e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 45 -5.6896253954619169e-04</internalNodes>
+          <leafValues>
+            9.4850182533264160e-01 -4.5835724472999573e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 132 5.7190689258277416e-03</internalNodes>
+          <leafValues>
+            -4.6498358249664307e-01 8.9447909593582153e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 16 -->
+    <_>
+      <maxWeakCount>4</maxWeakCount>
+      <stageThreshold>6.2197548151016235e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 33 -2.7249091863632202e-01</internalNodes>
+          <leafValues>
+            -1. 7.8571426868438721e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 35 -8.9669218286871910e-03</internalNodes>
+          <leafValues>
+            8.8649171590805054e-01 -4.5166960358619690e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 126 5.5545461364090443e-03</internalNodes>
+          <leafValues>
+            -5.0043833255767822e-01 8.7396764755249023e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 61 -2.9420750215649605e-03</internalNodes>
+          <leafValues>
+            8.7035274505615234e-01 -5.4979223012924194e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 17 -->
+    <_>
+      <maxWeakCount>5</maxWeakCount>
+      <stageThreshold>1.1221034526824951e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 31 -1.3594087958335876e-01</internalNodes>
+          <leafValues>
+            -7.7777779102325439e-01 7.8378379344940186e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 78 -4.3383683077991009e-05</internalNodes>
+          <leafValues>
+            8.5233044624328613e-01 -3.1708166003227234e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 106 -2.5698443874716759e-02</internalNodes>
+          <leafValues>
+            6.4835226535797119e-01 -6.2684595584869385e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 63 1.0367551294621080e-04</internalNodes>
+          <leafValues>
+            -5.0289368629455566e-01 8.6489045619964600e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 91 -5.9701339341700077e-04</internalNodes>
+          <leafValues>
+            -6.5946936607360840e-01 6.4416360855102539e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 18 -->
+    <_>
+      <maxWeakCount>4</maxWeakCount>
+      <stageThreshold>3.8028842210769653e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 95 2.5974204763770103e-02</internalNodes>
+          <leafValues>
+            8.1081077456474304e-02 9.2771083116531372e-01</leafValues></_>
+        <_>
+          <internalNodes>
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diff --git a/create_samples.bat b/create_samples.bat
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index 0000000..0e53a89
--- /dev/null
+++ b/create_samples.bat
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+opencv_createsamples -vec traning.vec -info positive\desc.txt -bg neg_desc.txt -w 24 -h 24
\ No newline at end of file
diff --git a/negative/desc.txt b/negative/desc.txt
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@@ -0,0 +1 @@
+opencv_traincascade -data cascade -vec traning.vec -bg negative/desc.txt -numPos 100 -numNeg 20 -numStages 30 -featureType HAAR -w 24 -h 24 -minHitRate 0.995 -maxFalseAlarmRate 0.2 -precalcValBufSize 1024 -precalcValBufSize 1024 -numThreads 4
\ No newline at end of file