Initial commit

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 ]

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_())