Merge branch 'main' of https://arnweb.nl/gitea/arne/EV5_Beeldherk_Bomen

src/experiments/knn/knn.py

@@ -1,10 +1,12 @@
 import cv2 as cv
 import numpy as np
 import matplotlib.pyplot as plt
+import seaborn as sns
 import csv
 from sklearn.preprocessing import MinMaxScaler
 from enum import Enum
 import random
+from sklearn.metrics import confusion_matrix
 
 class Tree(Enum):
     ACCASIA = 0
@@ -27,6 +29,7 @@ print("CSV tags: ", header)
 
 # Get classifier tags
 tags_int = []
+
 for row in data:
     tree = row.pop(0)
     id = Tree[tree.upper()]
@@ -34,10 +37,8 @@ for row in data:
     # 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_len = len(tags_int)
 tags_int = np.array(tags_int, dtype=np.int32)
 
 # Transform array for normalisation
@@ -63,14 +64,27 @@ for idx, col in enumerate(data[0]):
     # Replace original data array
     data[:, idx] = column
 
+# # Get a random number for testing
+# validateId = random.randint(0, tags_len - 1)
+tag_true = []
+tag_predict = []
+
+print(tags_len)
+
+for validateId in range(0, tags_len - 1):
+    # Remove object from train set
     validateTag = tags_int[validateId]
     validateObj =np.array([data[validateId]])
     np.delete(tags_int, validateId)
     np.delete(data, validateTag)
 
-print(validateTag, validateObj)
+    tag_true.append(validateTag)
+
+    # print(validateTag, validateObj)
 
     knn = cv.ml.KNearest_create()
+    print(tags_int)
+    print(data.dtype, type(data), tags_int.dtype, type(tags_int))
     knn.train(data, cv.ml.ROW_SAMPLE, tags_int)
 
     # print (data)
@@ -78,7 +92,14 @@ knn.train(data, cv.ml.ROW_SAMPLE, tags_int)
     # print (validateObj)
 
     ret, results, neighbours ,dist = knn.findNearest(validateObj, 3)
+    tag_predict.append(results[0][0])
     
-print( "result: {}\n".format(results) )
-print( "neighbours: {}\n".format(neighbours) )
-print( "distance: {}\n".format(dist) )
+    # print( "result: {}\n".format(results) )
+    # print( "neighbours: {}\n".format(neighbours) )
+    # print( "distance: {}\n".format(dist) )
+    
+
+# Create a heatmap
+sns.heatmap(confusion_matrix(tag_true, tag_predict), annot=True)
+plt.title( "Confusion Matrix KNN" )
+plt.show()