KNNNNNNNNNNNNNNN

src/experiments/knn/knn.py

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+import cv2 as cv
+import numpy as np
+import matplotlib.pyplot as plt
+import csv
+from sklearn.preprocessing import MinMaxScaler
+from enum import Enum
+import random
+
+class Tree(Enum):
+    ACCASIA = 0
+    BERK = 1
+    EIK = 2
+    ELS = 3
+    ESDOORN = 4
+    ES = 5
+    LINDE = 6
+    PLATAAN = 7
+
+# Open file
+file = open('./out/result-2023-10-10T15.08.36.csv', "r")
+data = list(csv.reader(file, delimiter=","))
+file.close()
+
+# Remove CSV header
+header = data.pop(0)
+print("CSV tags: ", header)
+
+# Get classifier tags
+tags_int = []
+for row in data:
+    tree = row.pop(0)
+    id = Tree[tree.upper()]
+    
+    # print("Tree name =", tree, " id =", id.value)
+    tags_int.append(id.value)
+    
+# Get a random number for testing
+validateId = random.randint(0, len(tags_int) - 1)
+
+# Make into numpy array cus OpenCV is dumb af
+tags_int = np.array(tags_int, dtype=np.int32)
+
+# Transform array for normalisation
+data = np.array(data, dtype=np.float32)
+
+for idx, col in enumerate(data[0]):
+    # Get column from data
+    column = data[:, idx]
+    
+    # Shape it to 2 dimentional
+    column = np.array(column).reshape(-1, 1)
+    
+    # Perform Min - Max scaling
+    scaler = MinMaxScaler()
+    column = scaler.fit_transform(column)
+    
+    # Reshape it back cus scaler is dumb af
+    column = np.array(column).reshape(len(column))
+    
+    # DEBUG Print resulting column
+    # print("NORM", header[idx + 1], "\n", column)
+    
+    # Replace original data array
+    data[:, idx] = column
+
+validateTag = tags_int[validateId]
+validateObj =np.array([data[validateId]])
+np.delete(tags_int, validateId)
+np.delete(data, validateTag)
+
+print(validateTag, validateObj)
+
+knn = cv.ml.KNearest_create()
+knn.train(data, cv.ml.ROW_SAMPLE, tags_int)
+
+# print (data)
+# print('--------------------')
+# print (validateObj)
+
+ret, results, neighbours ,dist = knn.findNearest(validateObj, 3)
+
+print( "result: {}\n".format(results) )
+print( "neighbours: {}\n".format(neighbours) )
+print( "distance: {}\n".format(dist) )