diff --git a/src/experiments/algorithms/colour_FE.py b/src/experiments/algorithms/colour_FE.py
index ce6c0f9..b0bdb4a 100644
--- a/src/experiments/algorithms/colour_FE.py
+++ b/src/experiments/algorithms/colour_FE.py
@@ -4,20 +4,40 @@ from cv2.mat_wrapper import Mat as Mat
 import os
 import numpy as np
 import csv
+from enum import Enum
 
 CSV_PATH = "src\\experiments\\algorithms\\data\\"
 IMG_PATH = "dataset\\accasia_1210048 (2023_09_28 12_19_26 UTC).JPG"
 BARK_TYPES = 8
 
+class Tree(Enum):
+    ACCASIA = 0
+    BERK = 1
+    EIK = 2
+    ELS = 3
+    ESDOORN = 4
+    ES = 5
+    LINDE = 6
+    PLATAAN = 7
+
+class ColourSpace(Enum):
+    BGR = 0
+    HSV = 1
+
 class colourTester:
     def __init__(self) -> None:
-        pass
+        self.bgr_curves = [[[], [], []] for x in range(BARK_TYPES)]
+        self.bgr_ids = ["", "", ""]
+        self.hsv_curves = [[[], [], []] for x in range(BARK_TYPES)]
+        self.hsv_ids = ["", "", ""]
+        self.labels = ["" for x in range(BARK_TYPES)]
 
     def loadImage(self) -> None:
         img = cv2.imread(self.im_path, 1)
         assert img is not None, \
             "Failed to load an image, check path"
-        self.img = img
+        self.bgr_img = img
+        self.hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
         return
     
     def setImPath(self, path : str) -> None:
@@ -50,17 +70,42 @@ class colourTester:
         return (mean, std)
     
     def loadCurves(self) -> None:
-        labels = []
-        ids = []
-        data = [[[], [], []] for x in range(BARK_TYPES)]
         with open(self.csv_bgr_path, 'r') as bgr_file:
             reader = csv.reader(bgr_file, delimiter=',')
-            # fix whatever is happening here
-                
-        print(data)
+            j = 0
+            i = -1
+            for row in reader:
+                j+=1
+                i += 1 if (j-1) % 3 == 0 else 0
+                self.labels[i] = row[0]
+                self.bgr_ids[(j-1)%3] = row[1]
+                for col in row[2:]:
+                    self.bgr_curves[i][(j-1)%3].append(col)
+
+        with open(self.csv_hsv_path, 'r') as hsv_file:
+            reader = csv.reader(hsv_file, delimiter=',')
+            j = 0
+            i = -1
+            for row in reader:
+                j+=1
+                i += 1 if (j-1) % 3 == 0 else 0
+                self.labels[i] = row[0]
+                self.hsv_ids[(j-1)%3] = row[1]
+                for col in row[2:]:
+                    self.hsv_curves[i][(j-1)%3].append(col)
         return
     
-    def testImage(self) -> None:
+    def correlate(self, mean : float, tree : Tree, space : ColourSpace) -> [float, float, float]:
+        results = [0, 0, 0]
+        if space == ColourSpace.BGR:
+            curve = self.bgr_curves
+        elif space == ColourSpace.HSV:
+            curve = self.hsv_curves
+        for i in range(3):
+            results[i] = float(curve[tree.value][i][int(mean[i])])
+        return results
+    
+    def testImage(self) -> Tree:
         # load an image
         self.loadImage()
         
@@ -68,16 +113,49 @@ class colourTester:
         self.loadCurves()
 
         # extract data
-        mean, std = self.getData(self.img)
+        bgr_mean, bgr_std = self.getData(self.bgr_img)
+        hsv_mean, hsv_std = self.getData(self.hsv_img)
 
-        # correlate with curves 
-        # dictionary? list? with 0 - 1 scores
-        pass
+        # correlate with curves
+        av_results = np.zeros(BARK_TYPES)
+        for tree in Tree:
+            bgr_res = self.correlate(bgr_mean, tree, ColourSpace.BGR)
+            hsv_res = self.correlate(hsv_mean, tree, ColourSpace.HSV)
+            av_results[tree.value] = (np.mean(bgr_res)+np.mean(hsv_res) / 2)
+            # print(str(IMG_PATH.split('_')[0])+';'+tree.name + ';' + str(np.mean(bgr_res)) + ';' + str(np.mean(hsv_res)))
+        return np.argmax(av_results)
+
+    def testDataset(self, path : str) -> None:
+        n_t = 0
+        n_f = 0
+        tree_type_cor = np.zeros(BARK_TYPES)
+        tree_type_inc = np.zeros(BARK_TYPES)
+        res = np.zeros(BARK_TYPES)
+        i = -1
+        last_name = ""
+        for file in os.listdir(path):
+            file_path = os.path.join(path, file)
+            self.setImPath(file_path)
+            name = file_path.split('_')[0].split('\\')[1].upper()
+            result = Tree(self.testImage()).name
+            
+            if last_name != name:
+                i += 1
+                last_name = name
+
+            if(name == result):
+                tree_type_cor[i] += 1
+            else:
+                tree_type_inc[i] += 1
+            
+        res = tree_type_cor / (tree_type_cor + tree_type_inc) * 100
+        for i in range(BARK_TYPES):
+            print("%s: %d%% correct"%(Tree(i).name, res[i]))
+        return 
 
 if __name__ == "__main__":
     tester = colourTester()
-    tester.setImPath(IMG_PATH)
     tester.setCsvBgrPath(CSV_PATH + "bgr_curve.csv")
     tester.setCsvHsvPath(CSV_PATH + "hsv_curve.csv")
-    tester.testImage()
+    tester.testDataset("dataset")
     pass
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