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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")