diff --git a/lab2_1/lab2_1.py b/lab2_1/lab2_1.py
new file mode 100644
index 0000000..b3e7937
--- /dev/null
+++ b/lab2_1/lab2_1.py
@@ -0,0 +1,162 @@
+import os
+import sys
+import cv2
+import numpy as np
+from matplotlib import pyplot as plt
+
+
+def menu():
+    print("Выберите пункт меню:\n1.Linear filter\n2.Blur\n3.Median blur\n4.GaussianBlur\n5.Erode")
+    print("6.Dilate\n7.MORPH_OPERATIONS\n8.Sobel\n9.Laplacian\n10.Canny\n11.CalcHist\n12.EqualizeHist")
+    print("13.Save\n14.Add Figure\n15.Del Figure\n16.Exit")
+
+
+def main():
+    image1 = cv2.imread("D:\\MACH\\LAB_2\\image0.jpg")
+    image2 = image1.copy()
+    im = image1.copy()
+    while True:
+        menu()
+        try:
+            s = int(input())
+            if s == 1:
+                image2 = image1.copy()
+                kernel = np.array([[-0.1, 0.2, -0.1], [0.2, 3.0, 0.2], [-0.1, 0.2, -0.1]])
+                image2 = cv2.filter2D(image2, -1, kernel)
+                cv2.imshow("Image_load", image1)
+                cv2.imshow("Result_Image", image2)
+                cv2.waitKey(0)
+            if s == 2:
+                image2 = image1.copy()
+                image2 = cv2.blur(image2, (5, 5))
+                cv2.imshow("Image_load", image1)
+                cv2.imshow("Result_Image", image2)
+                cv2.waitKey(0)
+            if s == 3:
+                image2 = image1.copy()
+                image2 = cv2.medianBlur(image2, 5)
+                cv2.imshow("Image_load", image1)
+                cv2.imshow("Result_Image", image2)
+                cv2.waitKey(0)
+            if s == 4:
+                image2 = image1.copy()
+                image2 = cv2.GaussianBlur(image2, (9, 9), cv2.BORDER_DEFAULT)
+                cv2.imshow("Image_load", image1)
+                cv2.imshow("Result_Image", image2)
+                cv2.waitKey(0)
+            if s == 5:
+                image2 = image1.copy()
+                image2 = cv2.erode(image2, np.ones((11, 11)))
+                cv2.imshow("Image_load", image1)
+                cv2.imshow("Result_Image", image2)
+                cv2.waitKey(0)
+            if s == 6:
+                image2 = image1.copy()
+                image2 = cv2.dilate(image2, np.ones((11, 11)))
+                cv2.imshow("Image_load", image1)
+                cv2.imshow("Result_Image", image2)
+                cv2.waitKey(0)
+            if s == 7:
+                image2 = image1.copy()
+                image3 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)
+                cv2.imshow("Image_load", image1)
+                kernel = np.ones((6, 6), np.uint8)
+                image3 = cv2.morphologyEx(image3, cv2.MORPH_OPEN, kernel, iterations=1)
+                cv2.imshow("MORPH_OPEN", image3)
+                image3 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)
+                image3 = cv2.morphologyEx(image3, cv2.MORPH_CLOSE, kernel, iterations=1)
+                cv2.imshow("MORPH_CLOSE", image3)
+                image3 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)
+                image3 = cv2.morphologyEx(image3, cv2.MORPH_GRADIENT, kernel, iterations=1)
+                cv2.imshow("MORPH_GRADIENT", image3)
+                image3 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)
+                image3 = cv2.morphologyEx(image3, cv2.MORPH_TOPHAT, kernel, iterations=1)
+                cv2.imshow("MORPH_TOPHAT", image3)
+                image3 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)
+                image3 = cv2.morphologyEx(image3, cv2.MORPH_BLACKHAT, kernel, iterations=1)
+                cv2.imshow("MORPH_BLACKHAT", image3)
+                image3 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)
+                cv2.waitKey(0)
+            if s == 8:
+                image2 = image1.copy()
+                image3 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)
+                image3 = cv2.GaussianBlur(image3, (3, 3), 0)
+                im3 = cv2.Sobel(image3, cv2.CV_64F, 1, 0, ksize=5)
+                im4 = cv2.Sobel(image3, cv2.CV_64F, 0, 1, ksize=5)
+                im5 = cv2.Sobel(image3, cv2.CV_8UC1, 0, 1, ksize=5)
+                cv2.imshow("Image_load", image1)
+                cv2.imshow("Result_Image_X", im3)  # x
+                cv2.imshow("Result_Image_Y", im4)  # y
+                cv2.imshow("Result_Gradient", im5)  # gradient
+                cv2.waitKey(0)
+            if s == 9:
+                image2 = image1.copy()
+                image3 = cv2.cvtColor(image2, cv2.COLOR_RGB2GRAY)
+                image3 = cv2.GaussianBlur(image3, (3, 3), 0)
+                im3 = cv2.Laplacian(image3, cv2.CV_64F)
+                cv2.imshow("Image_load", image1)
+                cv2.imshow("Result_Image", im3)  # x
+                cv2.waitKey(0)
+            if s == 10:
+                image2 = image1.copy()
+                image3 = cv2.cvtColor(image2, cv2.COLOR_RGB2GRAY)
+                image3 = cv2.blur(image2, (5, 5))
+                image3 = cv2.Canny(image3, 100, 100)
+                cv2.imshow("Image_load", image1)
+                cv2.imshow("Result_Image", image3)  # x
+                cv2.waitKey(0)
+            if s == 11:
+                image2 = image1.copy()
+                color = ('b', 'g', 'r')
+                cv2.imshow("Image_load", image1)
+                for i, col in enumerate(color):
+                    histr = cv2.calcHist([image2], [i], None, [256], [0, 256])
+                    plt.plot(histr, color=col)
+                    plt.xlim([0, 256])
+                plt.show()
+                plt.close()
+                cv2.waitKey(0)
+            if s == 12:
+                image2 = image1.copy()
+                image2 = cv2.cvtColor(image2, cv2.COLOR_RGB2GRAY)
+                image2 = cv2.equalizeHist(image2)
+                cv2.imshow("Image_load", image1)
+                cv2.imshow("Result_Image", image2)
+                cv2.waitKey(0)
+            if s == 13:
+                cv2.imshow("Image_load", image1)
+                cv2.imshow("Result_Image", image2)
+                cv2.imwrite("D:\\im1.jpg", image1)
+                cv2.imwrite("D:\\im2.jpg", image2)
+                cv2.waitKey(0)
+            if s == 14:
+                image2 = image1.copy()
+                cv2.imshow("Image_load", image1)
+                image2 = cv2.line(image2, (15, 15), (270, 270), (76, 187, 23), 10)  # линия
+                image2 = cv2.rectangle(image2, (20, 20), (100, 100), (0, 0, 255), 10)  # нарисуем четырехугольник
+                image2 = cv2.circle(image2, (100, 100), 40, (0, 255, 0), 10)  # нарисуем круг
+                cv2.imshow("Result_Image", image2)
+                image1 = image2
+                cv2.waitKey(0)
+            if s == 15:
+                cv2.imwrite("1.jpg", im)
+                cv2.imwrite("2.jpg", image1)
+                img = cv2.imread("1.jpg")
+                mask = cv2.imread("2.jpg", 0)
+                res = cv2.bitwise_and(img, img, mask=mask)
+                image2 = res.copy()
+                image1 = res.copy()
+                cv2.imshow("Result_Image", res)
+
+                cv2.waitKey(0)
+                os.remove("1.jpg")
+                os.remove("2.jpg")
+            if s == 16:
+                cv2.destroyAllWindows()
+                sys.exit(0)
+        except ValueError:
+            print("Неверный пункт меню!!!! Выберите другое!")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/lab2_2/Operate.txt b/lab2_2/Operate.txt
new file mode 100644
index 0000000..7dfcb1c
--- /dev/null
+++ b/lab2_2/Operate.txt
@@ -0,0 +1,21 @@
+1.Image
+2.Linear filter
+3.Blur
+4.Median blur
+5.GaussianBlur
+6.Erode
+7.Dilate
+8.1.MORPH_MORPH_OPEN
+8.2.MORPH_MORPH_CLOSE
+8.3.MORPH_MORPH_GRADIENT
+8.4.MORPH_MORPH_TOPHAT
+8.5.MORPH_MORPH_BLACKHAT
+9.Sobel_X
+10.Sobel_Y
+11.Sobel_Gradient
+12.Laplacian
+13.Canny
+14.CalcHist
+15.EqualizeHist
+16.Add Figure
+17.Del Figure
diff --git a/lab2_2/lab2_2.py b/lab2_2/lab2_2.py
new file mode 100644
index 0000000..4326d71
--- /dev/null
+++ b/lab2_2/lab2_2.py
@@ -0,0 +1,271 @@
+import sys
+import cv2
+import numpy 
+from PyQt5.QtGui import QImage, QPixmap
+from PyQt5.QtWidgets import QWidget, QApplication, QLabel, QVBoxLayout, QHBoxLayout, QPushButton, QFileDialog,QComboBox,QMessageBox
+from PyQt5.QtCore import Qt
+from matplotlib import pyplot as plt
+class Example(QWidget):
+    def __init__(self):
+        super().__init__()
+        self.image = None
+        self.label = QLabel()
+        self.initUI()
+
+    def initUI(self):
+        self.label.setText('OpenCV Image')
+        self.label.setAlignment(Qt.AlignCenter)
+
+        btn_open = QPushButton('Открыть изображение')
+        btn_open.clicked.connect(self.openImage)
+
+        btn_procesar = QPushButton('Сохранить изображение')
+        btn_procesar.clicked.connect(self.saveImage)
+
+
+        f=open("Operate.txt","r")
+        attr=f.read().splitlines()
+        f.close()
+        self.oper=QComboBox()
+        self.oper.addItems(attr)
+        self.oper.currentIndexChanged.connect(self.combobox)
+
+        top_bar = QHBoxLayout()
+        top_bar.addWidget(btn_open)
+        top_bar.addWidget(btn_procesar)
+        top_bar.addWidget(self.oper)
+
+        root = QVBoxLayout(self)
+        root.addLayout(top_bar)
+        root.addWidget(self.label)
+        self.resize(540, 574)
+        self.setWindowTitle('ST_2_2')
+        self.show()
+
+    def openImage(self):
+        self.filename, _ = QFileDialog.getOpenFileName(None, 'Buscar Imagen', '.', 'Image Files (*.png *.jpg *.jpeg *.bmp)')
+        if self.filename:
+            with open(self.filename, "rb") as file:
+                data = numpy.array(bytearray(file.read()))
+                self.image = cv2.imdecode(data, cv2.IMREAD_UNCHANGED)    
+                self.mostrarImagen(self.image)
+            self.image2=self.image
+            
+            
+    
+
+    def saveImage(self):
+        filename = QFileDialog.getSaveFileName(self,"QFileDialog.getSaveFileName()","","Image Files (*.jpg)")
+        img=self.image2
+        cv2.imwrite(filename[0],img)
+        
+
+
+    def mostrarImagen(self,s):
+        size = s.shape
+        step = s.size / size[0]
+        qformat = QImage.Format_Indexed8
+        if len(size) == 3:
+            if size[2] == 4:
+                qformat = QImage.Format_RGBA8888
+            else:
+                qformat = QImage.Format_RGB888
+        img = QImage(s, size[1], size[0], step, qformat)
+        img = img.rgbSwapped()
+        self.label.setPixmap(QPixmap.fromImage(img))
+        self.resize(self.label.pixmap().size())
+        
+
+    def combobox(self, index):     
+        if index==0:
+            self.i0()
+        if index==1:
+            self.i1()
+        if index==2:
+            self.i2()
+        if index==3:
+            self.i3()
+        if index==4:
+            self.i4()
+        if index==5:
+            self.i5()
+        if index==6:
+            self.i6()
+        if index==7:
+            self.i7()
+        if index==8:
+            self.i8()
+        if index==9:
+            self.i9()
+        if index==10:
+            self.i10()
+        if index==11:
+            self.i11()
+        if index==12:
+            self.i12()
+        if index==13:
+            self.i13()
+        if index==14:
+            self.i14()
+        if index==15:
+            self.i15()
+        if index==16:
+            self.i16()
+        if index==17:
+            self.i17()
+        if index==18:
+            self.i18()
+        if index==19:
+            self.i19()
+        if index==20:
+            self.i20()
+
+
+    def i0(self):
+        self.image2=self.image
+        self.mostrarImagen(self.image)
+
+    def i1(self):
+        self.image2=self.image
+        kernel = numpy.array([[-0.1,0.2,-0.1],[0.2,3.0,0.2],[-0.1,0.2,-0.1]])
+        self.image2=cv2.filter2D(self.image2,-1,kernel)
+        self.mostrarImagen(self.image2)
+        
+    
+    def i2(self):
+        self.image2=self.image
+        self.image2 = cv2.blur(self.image2,(5,5))
+        self.mostrarImagen(self.image2)
+        
+
+    def i3(self):
+        self.image2=self.image
+        self.image2 = cv2.medianBlur(self.image2,5)
+        self.mostrarImagen(self.image2)
+    
+    def i4(self):
+        self.image2=self.image
+        self.image2 = cv2.GaussianBlur(self.image2, (9,9),cv2.BORDER_DEFAULT)
+        self.mostrarImagen(self.image2)
+    
+    def i5(self):
+        self.image2=self.image
+        self.image2 = cv2.erode(self.image2, numpy.ones((11, 11)))
+        self.mostrarImagen(self.image2)
+    
+    def i6(self):
+        self.image2=self.image
+        self.image2 = cv2.dilate(self.image2, numpy.ones((11, 11)))
+        self.mostrarImagen(self.image2)
+    
+    def i7(self):
+        self.image2=self.image
+        kernel = numpy.ones((6,6),numpy.uint8)
+        self.image2=cv2.morphologyEx(self.image2, cv2.MORPH_OPEN, kernel,iterations = 1)
+        self.mostrarImagen(self.image2)
+    
+    def i8(self):
+        self.image2=self.image
+        kernel = numpy.ones((6,6),numpy.uint8)
+        self.image2=cv2.morphologyEx(self.image2, cv2.MORPH_CLOSE, kernel,iterations = 1)
+        self.mostrarImagen(self.image2)
+    
+    def i9(self):
+        self.image2=self.image
+        kernel = numpy.ones((6,6),numpy.uint8)
+        self.image2=cv2.morphologyEx(self.image2, cv2.MORPH_GRADIENT, kernel,iterations = 1)
+        self.mostrarImagen(self.image2)
+    
+    def i10(self):
+        self.image2=self.image
+        kernel = numpy.ones((6,6),numpy.uint8)
+        self.image2=cv2.morphologyEx(self.image2, cv2.MORPH_TOPHAT, kernel,iterations = 1)
+        self.mostrarImagen(self.image2)
+    
+    def i11(self):
+        self.image2=self.image
+        kernel = numpy.ones((6,6),numpy.uint8)
+        self.image2=cv2.morphologyEx(self.image2, cv2.MORPH_BLACKHAT, kernel,iterations = 1)
+        self.mostrarImagen(self.image2)
+    
+
+    def i12(self):
+        self.image2=self.image
+        self.image2 = cv2.cvtColor(self.image2,cv2.COLOR_BGR2GRAY)
+        self.image2 = cv2.GaussianBlur(self.image2,(3,3),0)
+        self.image2 = cv2.Sobel(self.image2,cv2.CV_64F,1,0,ksize=5) 
+        self.mostrarImagen(self.image2)
+    
+    def i13(self):
+        self.image2=self.image
+        self.image2 = cv2.cvtColor(self.image2,cv2.COLOR_BGR2GRAY)
+        self.image2 = cv2.GaussianBlur(self.image2,(3,3),0)
+        self.image2 = cv2.Sobel(self.image2,cv2.CV_64F,0,1,ksize=5) 
+        self.mostrarImagen(self.image2)
+    
+    def i14(self):
+        self.image2=self.image
+        self.image2 = cv2.cvtColor(self.image2,cv2.COLOR_BGR2GRAY)
+        self.image2 = cv2.GaussianBlur(self.image2,(3,3),0)
+        self.image2 = cv2.Sobel(self.image2,cv2.CV_8UC1,0,1,ksize=5) 
+        self.mostrarImagen(self.image2)
+    
+    def i15(self):
+        self.image2=self.image
+        self.image2 = cv2.cvtColor(self.image2,cv2.COLOR_RGB2GRAY)
+        self.image2 = cv2.GaussianBlur(self.image2,(3,3),0)
+        self.image2 = cv2.Laplacian(self.image2,cv2.CV_64F)
+        self.mostrarImagen(self.image2)
+    
+    def i16(self):
+        self.image2=self.image
+        self.image2 = cv2.cvtColor(self.image2,cv2.COLOR_RGB2GRAY)
+        self.image2 = cv2.blur(self.image2,(5,5))
+        self.image2 = cv2.Canny(self.image2, 100, 100)
+        self.mostrarImagen(self.image2)
+
+    def i17(self):
+        self.image2=self.image
+        color = ('b','g','r')
+        for i,col in enumerate(color):
+            histr = cv2.calcHist([self.image2],[i],None,[256],[0,256])
+            plt.plot(histr,color = col)
+            plt.xlim([0,256])
+        plt.show()
+        
+
+    def i18(self):
+        self.image2=self.image
+        self.image2 = cv2.cvtColor(self.image2,cv2.COLOR_RGB2GRAY)
+        self.image2 = cv2.equalizeHist(self.image2)
+        self.mostrarImagen(self.image2)
+
+    def i19(self):
+        
+        self.image2=self.image
+        self.image2 = cv2.line(self.image2,(15,15),(270,270),(76,187,23),10)#линия
+        self.image2 = cv2.rectangle(self.image2,(20,20),(100,100),(0,0,255),10)#нарисуем четырехугольник
+        self.image2 = cv2.circle(self.image2,(100,100),40,(0,255,0),10)#нарисуем круг
+        self.image=self.image2
+        self.mostrarImagen(self.image2)
+
+    def i20(self):
+        if self.filename:
+            with open(self.filename, "rb") as file:
+                data = numpy.array(bytearray(file.read()))
+                self.image = cv2.imdecode(data, cv2.IMREAD_UNCHANGED)    
+                self.mostrarImagen(self.image)
+        self.mostrarImagen(self.image)
+        self.image2=self.image
+
+    def closeEvent(self, event):
+        close = QMessageBox.question(self,"Выход","Вы хотите завершить работу?",QMessageBox.Yes | QMessageBox.No)
+        if close == QMessageBox.Yes:
+            event.accept()
+        else:
+            event.ignore()
+
+if __name__ == '__main__':
+    app = QApplication(sys.argv)
+    win = Example()
+    sys.exit(app.exec_())
\ No newline at end of file
diff --git a/lab4/1.yml b/lab4/1.yml
new file mode 100644
index 0000000..c864de8
--- /dev/null
+++ b/lab4/1.yml
@@ -0,0 +1,81 @@
+%YAML:1.0
+---
+opencv_ml_svm:
+   format: 3
+   svmType: C_SVC
+   kernel:
+      type: RBF
+      gamma: 1.
+   C: 1.
+   term_criteria: { epsilon:1.1920928955078125e-07, iterations:1000 }
+   var_count: 1
+   class_count: 2
+   class_labels: !!opencv-matrix
+      rows: 2
+      cols: 1
+      dt: i
+      data: [ -1, 1 ]
+   sv_total: 50
+   support_vectors:
+      - [ 0. ]
+      - [ 0. ]
+      - [ 1.40129846e-45 ]
+      - [ 0. ]
+      - [ 1.40129846e-45 ]
+      - [ 1.40129846e-45 ]
+      - [ 0. ]
+      - [ 0. ]
+      - [ 2.80259693e-45 ]
+      - [ 0. ]
+      - [ 2.80259693e-45 ]
+      - [ 2.80259693e-45 ]
+      - [ 2.80259693e-45 ]
+      - [ 2.80259693e-45 ]
+      - [ 4.20389539e-45 ]
+      - [ 2.80259693e-45 ]
+      - [ 1.40129846e-45 ]
+      - [ 0. ]
+      - [ 0. ]
+      - [ 0. ]
+      - [ 0. ]
+      - [ 1.40129846e-45 ]
+      - [ 0. ]
+      - [ 2.80259693e-45 ]
+      - [ 1.40129846e-45 ]
+      - [ 0. ]
+      - [ 0. ]
+      - [ 0. ]
+      - [ 0. ]
+      - [ 1.40129846e-45 ]
+      - [ 2.80259693e-45 ]
+      - [ 2.80259693e-45 ]
+      - [ 1.40129846e-45 ]
+      - [ 1.40129846e-45 ]
+      - [ 0. ]
+      - [ 0. ]
+      - [ 0. ]
+      - [ 0. ]
+      - [ 0. ]
+      - [ 0. ]
+      - [ 1.40129846e-45 ]
+      - [ 0. ]
+      - [ 1.40129846e-45 ]
+      - [ 0. ]
+      - [ 2.80259693e-45 ]
+      - [ 0. ]
+      - [ 1.40129846e-45 ]
+      - [ 2.80259693e-45 ]
+      - [ 1.40129846e-45 ]
+      - [ 0. ]
+   decision_functions:
+      -
+         sv_count: 50
+         rho: 0.
+         alpha: [ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
+             1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., -1., -1., -1.,
+             -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1.,
+             -1., -1., -1., -1., -1., -1., -1., -1., -1., -1. ]
+         index: [ 12, 15, 2, 16, 4, 5, 21, 24, 8, 31, 10, 11, 23, 13, 14,
+             29, 30, 32, 33, 40, 42, 44, 46, 47, 48, 19, 26, 27, 28, 1,
+             3, 25, 0, 7, 34, 35, 36, 37, 38, 39, 9, 41, 6, 43, 17, 45,
+             18, 20, 22, 49 ]
diff --git a/lab4/lab4.py b/lab4/lab4.py
new file mode 100644
index 0000000..4131c40
--- /dev/null
+++ b/lab4/lab4.py
@@ -0,0 +1,214 @@
+#import numpy as np
+#import cv2 
+#img = cv2.imread("D:\MACH\LAB_4\crocodile\image_0001.jpg")
+#gray= cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
+#sift = cv2.SIFT_create()
+#kp = sift.detect(gray,None)
+#img=cv2.drawKeypoints(gray,kp,img)
+#cv2.imshow("R",img)
+#cv2.waitKey(0)
+#cv2.destroyAllWindows()
+import sys
+import cv2
+import numpy as np
+import random
+import math
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score
+from PyQt5.QtGui import QImage, QPixmap
+from PyQt5.QtWidgets import (QWidget, QApplication, QLabel, QVBoxLayout, QHBoxLayout, QPushButton, 
+                             QFileDialog,QComboBox,QMessageBox,QTextBrowser)
+from PyQt5.QtCore import Qt
+class Example(QWidget):
+    def __init__(self):
+        super().__init__()
+        self.image = None
+        self.textbrowser = QTextBrowser()
+        self.initUI()
+
+    def initUI(self):
+
+        self.btn_open = QPushButton('Изображения folder1')
+        self.btn_open.clicked.connect(self.openImages1)
+
+        self.btn_open1 = QPushButton('Изображения folder2')
+        self.btn_open1.clicked.connect(self.openImages2)
+
+
+        self.btn_ = QPushButton('Обучить и создать матрицы')
+        self.btn_.clicked.connect(self.matrix_and_train)
+
+
+        self.btn1 = QPushButton('Детектировать тестовые изображения')
+        self.btn1.clicked.connect(self.detect_image)
+
+        self.btn2 = QPushButton('Сформировать лес и натренировать полученную модель')
+        self.btn2.clicked.connect(self.form_les_and_train)
+
+        self.btn_.setVisible(False)
+        self.btn1.setVisible(False)
+        self.btn2.setVisible(False)
+
+
+        top_bar = QHBoxLayout()
+        top_bar.addWidget(self.btn_open)
+        top_bar.addWidget(self.btn_open1)
+        top_bar.addWidget(self.btn_)
+        top_bar.addWidget(self.btn1)
+        top_bar.addWidget(self.btn2)
+        root = QVBoxLayout(self)
+        root.addLayout(top_bar)
+        root.addWidget(self.textbrowser)
+        
+        self.spisok=list()
+        self.spisok2=list()
+        self.spisok3=list()
+
+
+        self.train=list()
+        self.matrix=list()
+        self.resize(540, 574)
+        self.setWindowTitle('ST_4')
+        self.show()
+
+    def openImages1(self):
+        filenames1 = QFileDialog.getOpenFileNames(None, 'Открыть изображения', '.', 'Image Files (*.png *.jpg *.jpeg *.bmp)')
+        lk=filenames1[0]
+        self.erroropened(lk,"1")
+        self.mass(lk,1)
+        lk.clear()
+
+
+    def openImages2(self):
+        filenames2 = QFileDialog.getOpenFileNames(None, 'Открыть изображения', '.', 'Image Files (*.png *.jpg *.jpeg *.bmp)')
+        lk=filenames2[0]
+        self.erroropened(lk,"2")
+        self.mass(lk,2)
+        lk.clear()
+        
+    def erroropened(self,s,s1):
+        if len(s)!=0:
+            q=QMessageBox.information(self,"Информация","Изображения из "+s1+" папки получены!")
+        else:
+            q=QMessageBox.information(self,"Информация","Вы не выбрали изображения!")
+
+
+    def mass(self,p,s1):
+        if s1==1:
+            self.spisok3.clear()
+            for v in range(len(p)):
+                self.spisok.append(str(p[v]))
+                self.spisok3.append(str(p[v])+"SKT")
+            self.appendos("Тестовый набор картинок",self.spisok)
+        if s1==2:
+            for v in range(len(p)):
+                self.spisok2.append(str(p[v]))
+                self.spisok3.append(str(p[v])+"NO")
+            self.spisok3=list(set(self.spisok3))
+            self.appendos("Набор картинок для тренировки",self.spisok2)
+            self.btn_open.setVisible(False)
+            self.btn_open1.setVisible(False)
+            self.btn_.setVisible(True)
+        q=QMessageBox.information(self,"Информация","Количество изображений тестовой категории: "+str(len(self.spisok))+"\nКоличество изображений основной категории: "+str(len(self.spisok2))+"\nОбщее количество изображений: "+str(int(len(self.spisok)+int(len(self.spisok2)))))
+    
+    def matrix_and_train(self):
+        for v in range(len(self.spisok3)):
+            s=str(self.spisok3[v])
+            if s.endswith("SKT"):
+                self.train.append(round(float(0.5),1))
+                self.matrix.append(round(float(1.0),1))
+            if s.endswith("NO"):
+                q=round(float(random.uniform(1.0,3.0)),1)                
+                self.train.append(q)
+                self.matrix.append(round(float(-1.0),1))
+                
+        self.appendos("Ваши тренировочные данные",self.train)
+        self.appendos("Ваша матрица",self.matrix)
+        train=np.array([self.train],dtype=int)
+        labels = np.array(self.matrix,dtype=int)
+        self.svm = cv2.ml.SVM_create()
+        self.svm.train(train, cv2.ml.COL_SAMPLE, labels)
+        self.svm.save("1.yml")
+        self.textbrowser.append("Модель сохранена!")
+        close = QMessageBox.question(self,"Поздравляем!","Ваша модель натренирована!",QMessageBox.Yes | QMessageBox.No)
+        if close == QMessageBox.Yes:
+            pass
+        self.btn_.setVisible(False)
+        self.btn1.setVisible(True)
+
+    def appendos(self,s1,s2):
+        self.textbrowser.append(s1)
+        for v in range(len(s2)):
+            self.textbrowser.append(str(s2[v]))
+
+    def detect_image(self):
+        self.model = RandomForestClassifier(n_estimators=100,bootstrap = True,max_features = 'sqrt')
+        self.spisok3.clear()
+        for v in range(len(self.spisok)):
+            lkst=list()            
+            img = cv2.imread(str(self.spisok[v]))
+            gray= cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
+            sift = cv2.SIFT_create()
+            kp = sift.detect(gray,None)
+            for keyPoint in kp:
+                #self.spisok3.append(keyPoint.pt[0])
+                #self.spisok3.append(keyPoint.pt[1])
+                #print(keyPoint.pt[0])
+                #print(keyPoint.pt[1])
+                lkst.append(keyPoint.pt[0])
+                lkst.append(keyPoint.pt[1])
+                
+            #self.spisok3.append(lkst)
+            train=np.array([lkst],dtype=int)
+            labels = np.array([lkst],dtype=int)
+            self.model.fit(train, labels)
+            self.textbrowser.append("Модель натренирована на рисунке "+str(v))
+        #print(len(self.spisok3))
+        self.textbrowser.append("Выявлены контурные точки на тестовых изображениях!")
+        
+            #s = keyPoint.size
+            #print(x,y,s)
+        self.btn1.setVisible(False)
+        self.btn2.setVisible(True)
+
+    def form_les_and_train(self):
+        
+   #print(self.spisok3)
+        for v in range(len(self.spisok2)):
+            lk=list()
+            img = cv2.imread(str(self.spisok2[v]))
+            gray= cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
+            sift = cv2.SIFT_create()
+            kp = sift.detect(gray,None)
+            for keyPoint in kp:
+                #self.spisok3.append(keyPoint.pt[0])
+                #self.spisok3.append(keyPoint.pt[1])
+                #print(keyPoint.pt[0])
+                #print(keyPoint.pt[1])
+                lkst.append(keyPoint.pt[0])
+                lkst.append(keyPoint.pt[1])
+            train =np.array([lkst],dtype=int)
+            self.model.predict(train)
+            #self.model.
+            print("RED")
+        #train = np.array([self.spisok3],dtype=int)
+        #labels = np.array([self.spisok3],dtype=int)
+        #model = RandomForestClassifier(n_estimators=100,bootstrap = True,max_features = 'sqrt')
+        #model.fit(train, labels)
+        #tree = DecisionTreeClassifier()
+        #tree.fit(self.spisok3, self.spisok3)
+        #accuracy = accuracy_score(len(self.spisok3), len(self.spisok3))
+        #print('Model Accuracy:',accuracy)
+
+
+    def closeEvent(self, event):
+        close = QMessageBox.question(self,"Выход","Вы хотите завершить работу?",QMessageBox.Yes | QMessageBox.No)
+        if close == QMessageBox.Yes:
+            event.accept()
+        else:
+            event.ignore()
+
+if __name__ == '__main__':
+    app = QApplication(sys.argv)
+    win = Example()
+    sys.exit(app.exec_())
\ No newline at end of file
diff --git a/lab4/sift_keypoints.jpg b/lab4/sift_keypoints.jpg
new file mode 100644
index 0000000..d852d0e
Binary files /dev/null and b/lab4/sift_keypoints.jpg differ
diff --git a/lab6_1/lab6_1.py b/lab6_1/lab6_1.py
new file mode 100644
index 0000000..59e38c0
--- /dev/null
+++ b/lab6_1/lab6_1.py
@@ -0,0 +1,87 @@
+import os
+import sys
+import cv2
+import numpy as np
+from matplotlib import pyplot as plt
+def main():
+    lstone=list()
+    lsttwo=list()
+    image1 = cv2.imread("D:\\MACH\\MACH\\LAB_2\\image0.jpg")
+    image2=image1
+    image3=image1
+    image4=image1
+    image5=image1
+    image6=image1
+    image7=image1
+    image2=erode_cv(image2,11)
+    image3=dilate_cv(image3,11)
+    image4=median_cv(image4,5)
+    image5=erode_ipp(image5,14)
+    image6=dilate_ipp(image6,14)
+    image7=median_ipp(image7,7)
+
+    cv2.imshow("Image_load",image1)
+    cv2.imshow("erode_cv",image2)
+    cv2.imshow("dilate_cv",image3)
+    cv2.imshow("median_cv",image4)
+    cv2.imshow("erode_ipp",image5)
+    cv2.imshow("dilate_ipp",image6)
+    cv2.imshow("median_ipp",image7)
+    cv2.waitKey(0)
+
+    lstone=sravit_metod(image2)
+    lsttwo=sravit_metod(image5)
+    lsttree=lsttwo[len(lstone):]
+    print("Erode")
+    print("Количество отличий : "+str(len(lsttwo)-len(lstone))+" \nРазница")
+    print(lsttree)
+
+    lstone=sravit_metod(image3)
+    lsttwo=sravit_metod(image5)
+    lsttree=lstone[len(lsttwo):]
+    print("Dilate")
+    print("Количество отличий : "+str(len(lstone)-len(lsttwo))+" \nРазница")
+    print(lsttree)
+
+    lstone=sravit_metod(image4)
+    lsttwo=sravit_metod(image7)
+    lsttree=lstone[len(lsttwo):]
+    print("Median")
+    print("Количество отличий : "+str(len(lstone)-len(lsttwo))+" \nРазница")
+    print(lsttree)
+
+def erode_cv(s1,s2):
+    s1 = cv2.erode(s1, np.ones((s2, s2)))
+    return s1
+def dilate_cv(s1,s2):
+    s1 = cv2.dilate(s1, np.ones((s2, s2)))
+    return s1
+def median_cv(s1,s2):
+    s1 = cv2.medianBlur(s1,s2)
+    return s1
+def erode_ipp(s1,s2):
+    s1 = cv2.erode(s1, np.ones((s2, s2)))
+    return s1
+def dilate_ipp(s1,s2):
+    s1 = cv2.dilate(s1, np.ones((s2, s2)))
+    return s1
+def median_ipp(s1,s2):
+    s1 = cv2.medianBlur(s1,s2)
+    return s1
+
+def sravit_metod(s1):
+    cv2.imwrite("im1.jpg",s1)
+    s1 = cv2.imread("im1.jpg", 0)
+    th, threshed = cv2.threshold(s1, 100, 255,cv2.THRESH_BINARY_INV|cv2.THRESH_OTSU)
+    cnts = cv2.findContours(threshed, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)[-2]
+    t1= 3
+    t2 = 20
+    xcnts = []
+    for cnt in cnts:
+        if t1<cv2.contourArea(cnt) <t2:
+            xcnts.append(cnt)
+    os.remove("im1.jpg")
+    return xcnts
+
+if __name__=="__main__":
+    main()
diff --git a/lab6_2/lab6_2.py b/lab6_2/lab6_2.py
new file mode 100644
index 0000000..f9358bc
--- /dev/null
+++ b/lab6_2/lab6_2.py
@@ -0,0 +1,96 @@
+import os
+import sys
+import cv2
+import numpy as np
+from matplotlib import pyplot as plt
+def main():
+    lstone=list()
+    lsttwo=list()
+    image1 = cv2.imread("D:\\MACH\\MACH\\LAB_2\\image0.jpg")
+    image2=image1
+    image3=image1
+    image4=image1
+    image5=image1
+    image6=image1
+    image7=image1
+    image2=erode_cv(image2,11)
+    image3=dilate_cv(image3,11)
+    image4=median_cv(image4,5)
+    image5=erode_ipp(image5,14)
+    image6=dilate_ipp(image6,14)
+    image7=median_ipp(image7,7)
+
+
+    x = np.linspace(-5, 2, 100)                     # от -5 до 2 сделать 100 точек
+    # показать рисунок
+    
+
+
+    
+    lstone=sravit_metod(image2)
+    lsttwo=sravit_metod(image5)
+    lsttree=lsttwo[len(lstone):]
+    y1 =len(lstone)*x
+    y2 =len(lsttree)*x
+    
+    
+    lstone=sravit_metod(image3)
+    lsttwo=sravit_metod(image5)
+    
+    y3 =len(lstone)*x
+    y4 =len(lsttwo)*x
+    
+    lstone=sravit_metod(image4)
+    lsttwo=sravit_metod(image7)
+    lsttree=lstone[len(lsttwo):]
+    y5 =len(lstone)*x
+    y6 =len(lsttwo)*x
+    
+    fig, ax = plt.subplots()                        
+    ax.plot(x, y1, color="blue", label="erode_cv")  
+    ax.plot(x, y2, color="red", label="erode_ipp")  
+    ax.plot(x, y3, color="green", label="dilate_cv")  
+    ax.plot(x, y4, color="yellow", label="dilate_ipp") 
+    ax.plot(x, y5, color="orange", label="median_cv")  
+    ax.plot(x, y6, color="grey", label="median_ipp")   
+    ax.set_xlabel("x")                              
+    ax.set_ylabel("y")                              
+    ax.legend()                                     
+    plt.show()                                      
+
+def erode_cv(s1,s2):
+    s1 = cv2.erode(s1, np.ones((s2, s2)))
+    return s1
+def dilate_cv(s1,s2):
+    s1 = cv2.dilate(s1, np.ones((s2, s2)))
+    return s1
+def median_cv(s1,s2):
+    s1 = cv2.medianBlur(s1,s2)
+    return s1
+def erode_ipp(s1,s2):
+    s1 = cv2.erode(s1, np.ones((s2, s2)))
+    return s1
+def dilate_ipp(s1,s2):
+    s1 = cv2.dilate(s1, np.ones((s2, s2)))
+    return s1
+def median_ipp(s1,s2):
+    s1 = cv2.medianBlur(s1,s2)
+    return s1
+
+def sravit_metod(s1):
+    cv2.imwrite("im1.jpg",s1)
+    s1 = cv2.imread("im1.jpg", 0)
+    th, threshed = cv2.threshold(s1, 100, 255,cv2.THRESH_BINARY_INV|cv2.THRESH_OTSU)
+    cnts = cv2.findContours(threshed, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)[-2]
+    t1= 3
+    t2 = 20
+    xcnts = []
+    for cnt in cnts:
+        if t1<cv2.contourArea(cnt) <t2:
+            xcnts.append(cnt)
+    os.remove("im1.jpg")
+    return xcnts
+
+if __name__=="__main__":
+    main()
+
diff --git a/lab8/1.xml b/lab8/1.xml
new file mode 100644
index 0000000..e743a48
--- /dev/null
+++ b/lab8/1.xml
@@ -0,0 +1,3654 @@
+<?xml version="1.0"?>
+<!-- Add more negative to training. -->
+<opencv_storage>
+<cars3 type_id="opencv-haar-classifier">
+  <size>
+    20 20</size>
+  <stages>
+    <_>
+      <!-- stage 0 -->
+      <trees>
+        <_>
+          <!-- tree 0 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>
+                  6 12 8 8 -1.</_>
+                <_>
+                  6 16 8 4 2.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>0.0452074706554413</threshold>
+            <left_val>-0.7191650867462158</left_val>
+            <right_val>0.7359663248062134</right_val></_></_>
+        <_>
+          <!-- tree 1 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>
+                  1 12 18 1 -1.</_>
+                <_>
+                  7 12 6 1 3.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>-0.0161712504923344</threshold>
+            <left_val>0.5866637229919434</left_val>
+            <right_val>-0.5909150242805481</right_val></_></_>
+        <_>
+          <!-- tree 2 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>
+                  7 18 5 2 -1.</_>
+                <_>
+                  7 19 5 1 2.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>0.0119725503027439</threshold>
+            <left_val>-0.3645753860473633</left_val>
+            <right_val>0.8175076246261597</right_val></_></_>
+        <_>
+          <!-- tree 3 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>
+                  5 12 11 4 -1.</_>
+                <_>
+                  5 14 11 2 2.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>0.0554178208112717</threshold>
+            <left_val>-0.5766019225120544</left_val>
+            <right_val>0.8059020042419434</right_val></_></_></trees>
+      <stage_threshold>-1.0691740512847900</stage_threshold>
+      <parent>-1</parent>
+      <next>-1</next></_>
+    <_>
+      <!-- stage 1 -->
+      <trees>
+        <_>
+          <!-- tree 0 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>
+                  1 12 18 2 -1.</_>
+                <_>
+                  7 12 6 2 3.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>-0.0243058893829584</threshold>
+            <left_val>0.5642552971839905</left_val>
+            <right_val>-0.7375097870826721</right_val></_></_>
+        <_>
+          <!-- tree 1 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>
+                  3 1 14 6 -1.</_>
+                <_>
+                  3 3 14 2 3.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>-0.0302439108490944</threshold>
+            <left_val>0.5537161827087402</left_val>
+            <right_val>-0.5089462995529175</right_val></_></_>
+        <_>
+          <!-- tree 2 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>
+                  4 8 12 9 -1.</_>
+                <_>
+                  4 11 12 3 3.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>-0.1937028020620346</threshold>
+            <left_val>0.7614368200302124</left_val>
+            <right_val>-0.3485977053642273</right_val></_></_>
+        <_>
+          <!-- tree 3 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>
+                  8 18 12 2 -1.</_>
+                <_>
+                  14 18 6 1 2.</_>
+                <_>
+                  8 19 6 1 2.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>0.0120156398043036</threshold>
+            <left_val>-0.4035871028900146</left_val>
+            <right_val>0.6296288967132568</right_val></_></_>
+        <_>
+          <!-- tree 4 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>
+                  0 12 6 6 -1.</_>
+                <_>
+                  2 12 2 6 3.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>2.9895049519836903e-03</threshold>
+            <left_val>-0.4086846113204956</left_val>
+            <right_val>0.4285241067409515</right_val></_></_>
+        <_>
+          <!-- tree 5 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>
+                  6 11 9 8 -1.</_>
+                <_>
+                  6 15 9 4 2.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>0.1299877017736435</threshold>
+            <left_val>-0.2570166885852814</left_val>
+            <right_val>0.5929297208786011</right_val></_></_>
+        <_>
+          <!-- tree 6 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>
+                  1 6 10 2 -1.</_>
+                <_>
+                  1 6 5 1 2.</_>
+                <_>
+                  6 7 5 1 2.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>-6.0164160095155239e-03</threshold>
+            <left_val>0.5601549744606018</left_val>
+            <right_val>-0.2849527895450592</right_val></_></_></trees>
+      <stage_threshold>-1.0788700580596924</stage_threshold>
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+                  11 16 1 1 2.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>-4.2610289528965950e-04</threshold>
+            <left_val>-0.4836213886737823</left_val>
+            <right_val>0.0490311309695244</right_val></_></_></trees>
+      <stage_threshold>-1.0918780565261841</stage_threshold>
+      <parent>11</parent>
+      <next>-1</next></_></stages></cars3>
+</opencv_storage>
\ No newline at end of file
diff --git a/lab8/lab8.py b/lab8/lab8.py
new file mode 100644
index 0000000..b5b6994
--- /dev/null
+++ b/lab8/lab8.py
@@ -0,0 +1,18 @@
+import cv2
+import numpy as np
+from PIL import Image, ImageDraw
+def main():
+    image = Image.open('D:\\MACH\\MACH\\LAB_2\\img1.jpg')
+    image_arr = np.asarray(image)
+
+    hog = cv2.HOGDescriptor()
+    hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
+    detections, weights = hog.detectMultiScale(image_arr)
+    detections_rectangles = detections.tolist()
+    draw = ImageDraw.Draw(image)
+    for x, y, w, h in detections_rectangles:
+        draw.rectangle(
+            [x, y, x + w, y + h], outline=(255, 0, 0))
+    image.show()
+if __name__ == '__main__':
+    main()