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semester_4/osnove_racunalniskega_vida/Naloga_5/implement.py

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+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+from skimage.feature import hog
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.metrics import accuracy_score
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import classification_report
+from sklearn import svm
+from sklearn import tree
+import pickle
+import os
+
+def lbp(image):
+    height, width = image.shape[:2]
+    lbp_image = np.zeros_like(image, dtype=np.uint8)
+    
+    for y in range(1, height-1):
+        for x in range(1, width-1):
+            center = image[y, x]
+            code = 0
+            
+            # Compare the pixel values of the neighborhood with the center pixel
+            code |= (image[y-1, x-1] > center) << 7
+            code |= (image[y-1, x] > center) << 6
+            code |= (image[y-1, x+1] > center) << 5
+            code |= (image[y, x+1] > center) << 4
+            code |= (image[y+1, x+1] > center) << 3
+            code |= (image[y+1, x] > center) << 2
+            code |= (image[y+1, x-1] > center) << 1
+            code |= (image[y, x-1] > center) << 0
+            
+            lbp_image[y, x] = code
+    
+    return lbp_image
+
+def load_data(pictures, marks):
+    pictures = np.load(pictures)
+    marks = np.load(marks)
+    
+    return pictures, marks
+
+def get_data(folder_path):
+    img_data = []
+    marks = []
+    i = 0
+    for root, dirs, files in os.walk(folder_path):
+        for file in files:
+            file_path = os.path.join(root, file)
+
+            if 'c' in file_path:
+                marks.append(1)
+            else:
+                marks.append(0)
+
+            img = cv2.imread(file_path)
+            img = cv2.resize(img, (100, 100))
+            gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+            lbpHist = cv2.calcHist([lbp(gray)], [0], None, [256], [0, 256])
+            lbpHist = lbpHist.astype(np.float64)
+            lbpHist = lbpHist.flatten()
+
+            hogFeatures = hog(gray, orientations=9, pixels_per_cell=(10, 10), cells_per_block=(2, 2), block_norm='L2-Hys')
+
+            features = np.concatenate((lbpHist, hogFeatures), axis=0)
+            img_data.append(features)
+            print(i)
+            i += 1
+
+
+    img_data = np.array(img_data)
+    marks = np.array(marks)
+    return img_data, marks
+
+def save_data(img_data, marks, img_data_path, marks_data_path):
+    np.save(img_data_path, img_data)
+    np.save(marks_data_path, marks)
+
+def train_and_save_model(img_data, marks):
+    
+    #---------------------KNN-------------------
+    knn = KNeighborsClassifier(n_neighbors=5)
+    knn.fit(img_data, marks)
+    knnPickle = open('Models\\knn.bin', 'wb')
+    pickle.dump(knn, knnPickle)
+    knnPickle.close()
+
+    #------------------SVM----------------------
+    svmM = svm.SVC()
+    svmM.fit(img_data, marks)
+    svmPickle = open('Models\\svm.bin', 'wb')
+    pickle.dump(svmM, svmPickle)
+    svmPickle.close()
+
+    #------------------DT----------------------
+    dt = tree.DecisionTreeClassifier()
+    dt.fit(img_data, marks)
+    dtPickle = open('Models\\dt.bin', 'wb')
+    pickle.dump(dt, dtPickle)
+    dtPickle.close()
+
+def load_model():
+
+    #---------------------KNN-------------------
+    knnPickle = open('Models\\knn.bin', 'rb')
+    knn = pickle.load(knnPickle)
+    knnPickle.close()
+
+    #------------------SVM----------------------
+    svmPickle = open('Models\\svm.bin', 'rb')
+    svm = pickle.load(svmPickle)
+    svmPickle.close()
+
+    #------------------DT----------------------
+    dtPickle = open('Models\\dt.bin', 'rb')
+    dt = pickle.load(dtPickle)
+    dtPickle.close()
+
+    return knn, svm, dt
+
+
+
+
+
+
+
+
+

semester_4/osnove_racunalniskega_vida/Naloga_5/test.py

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+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+from skimage.feature import hog
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.metrics import accuracy_score
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import classification_report
+from sklearn import svm
+from sklearn import tree
+import pickle
+
+from implement import train_and_save_model, save_data, get_data, load_data, load_model
+
+print("Test")
+
+img_data_path = "Data\\test_img_data.npy"
+marks_data_path = "Data\\test_marks.npy"
+train_baza_path = "Baza\\Pet100"
+
+print("Loading data")
+if False:
+    img_data, marks = get_data(train_baza_path)
+    save_data(img_data, marks, img_data_path, marks_data_path)
+else:
+    img_data, marks = load_data(img_data_path, marks_data_path)
+
+print("Loding model")
+knn, svm, dt = load_model()
+
+print("Calculating predictions")
+knn_pred = knn.predict(img_data)
+svm_pred = svm.predict(img_data)
+dt_pred = dt.predict(img_data)
+
+predictions = []
+for i in range(len(knn_pred)):
+    if knn_pred[i] == svm_pred[i] == dt_pred[i]:
+        predictions.append(knn_pred[i])
+    elif knn_pred[i] == svm_pred[i]:
+        predictions.append(knn_pred[i])
+    elif knn_pred[i] == dt_pred[i]:
+        predictions.append(knn_pred[i])
+    elif svm_pred[i] == dt_pred[i]:
+        predictions.append(svm_pred[i])
+
+predictions = np.array(predictions)
+
+print("KNN: ", accuracy_score(marks, knn_pred))
+print("SVM: ", accuracy_score(marks, svm_pred))  
+print("DT: ", accuracy_score(marks, dt_pred))
+print("Accuracy: ", accuracy_score(marks, predictions))
+
+print("End")

semester_4/osnove_racunalniskega_vida/Naloga_5/train.py

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+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+from skimage.feature import hog
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.metrics import accuracy_score
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import classification_report
+from sklearn import svm
+from sklearn import tree
+import pickle
+
+from implement import train_and_save_model, save_data, get_data, load_data
+
+print("Train")
+
+img_data_path = "Data\\train_img_data.npy"
+marks_data_path = "Data\\train_marks.npy"
+train_baza_path = "Baza\\Pet1000"
+
+print("loading data")
+if False:
+    img_data, marks = get_data(train_baza_path)
+    save_data(img_data, marks, img_data_path, marks_data_path)
+else:
+    img_data, marks = load_data(img_data_path, marks_data_path)
+
+
+print("Training")
+train_and_save_model(img_data, marks)
+print("End")