src/experiments/knn/knn.py

@@ -3,9 +3,9 @@ import numpy as np
 import matplotlib.pyplot as plt
 import seaborn as sns
 import csv
+from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler, MaxAbsScaler
 from enum import Enum
 import random
-from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler, MaxAbsScaler
 from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, matthews_corrcoef
 
 class Tree(Enum):
@@ -19,8 +19,7 @@ class Tree(Enum):
     PLATAAN = 7
 
 # Open file
-# file = open('dataset\\csv\\result-2023-10-14T16.13.30.csv', "r")
+file = open('dataset\\csv\\result-2023-10-14T16.13.30.csv', "r")
-file = open('./out/result-2023-10-10T15.08.36.csv', "r")
 data = list(csv.reader(file, delimiter=","))
 file.close()
 
@@ -33,7 +32,7 @@ tags_int = []
 
 for row in data:
     tree = row.pop(0)
-    # photoId = row.pop(1)
+    row.pop(1) # TODO: Doe dit niet
     id = Tree[tree.upper()]
     
     # print("Tree name =", tree, " id =", id.value)
@@ -63,26 +62,26 @@ for idx, col in enumerate(data[0]):
     column = np.array(column).reshape(len(column))
     
     # DEBUG Print resulting column
-    # print("NORM", header[idx + 1], "\n", column)
+    print("NORM", header[idx + 1], "\n", column)
     
     # Replace original data array
     data[:, idx] = column
 
 # # Get a random number for testing
 # validateId = random.randint(0, tags_len - 1)
-# tag_true = []
+tag_true = []
-# tag_predict = []
+tag_predict = []
 
-# print(tags_len)
+print(tags_len)
 
-# for validateId in range(0, tags_len - 1):
+for validateId in range(0, tags_len - 1):
-#     # Remove object from train set
+    # Remove object from train set
-#     validateTag = tags_int[validateId]
+    validateTag = tags_int[validateId]
-#     validateObj =np.array([data[validateId]])
+    validateObj =np.array([data[validateId]])
-#     np.delete(tags_int, validateId)
+    np.delete(tags_int, validateId)
-#     np.delete(data, validateTag)
+    np.delete(data, validateTag)
 
-#     tag_true.append(validateTag)
+    tag_true.append(validateTag)
 
     # print(validateTag, validateObj)
 
@@ -91,29 +90,27 @@ for idx, col in enumerate(data[0]):
     print(data.dtype, type(data), tags_int.dtype, type(tags_int))
     knn.train(data, cv.ml.ROW_SAMPLE, tags_int)
 
-    knn.save('./out/models/knn_nosift.pkl')
-
     # print (data)
     # print('--------------------')
     # print (validateObj)
 
-    # ret, results, neighbours ,dist = knn.findNearest(validateObj, 3)
+    ret, results, neighbours ,dist = knn.findNearest(validateObj, 3)
-    # tag_predict.append(results[0][0])
+    tag_predict.append(results[0][0])
     
     # print( "result: {}\n".format(results) )
     # print( "neighbours: {}\n".format(neighbours) )
     # print( "distance: {}\n".format(dist) )
     
 
-# # Create a heatmap
+# Create a heatmap
-# sns.heatmap(confusion_matrix(tag_true, tag_predict), annot=True)
+sns.heatmap(confusion_matrix(tag_true, tag_predict), annot=True)
-# plt.title( "Confusion Matrix KNN" )
+plt.title( "Confusion Matrix KNN" )
-# plt.show()
+plt.show()
 
 # Score
-# print("Accuracy score", accuracy_score(tag_true, tag_predict))
+print("Accuracy score", accuracy_score(tag_true, tag_predict))
-# print("Precision score (macro)", precision_score(tag_true, tag_predict, average='macro'))
+print("Precision score (macro)", precision_score(tag_true, tag_predict, average='macro'))
-# print("Precision score (micro)", precision_score(tag_true, tag_predict, average='micro'))
+print("Precision score (micro)", precision_score(tag_true, tag_predict, average='micro'))
-# print("Recall score (macro)", recall_score(tag_true, tag_predict, average='macro'))
+print("Recall score (macro)", recall_score(tag_true, tag_predict, average='macro'))
-# print("Recall score (micro)", recall_score(tag_true, tag_predict, average='micro'))
+print("Recall score (micro)", recall_score(tag_true, tag_predict, average='micro'))
-# print("MCC", matthews_corrcoef(tag_true, tag_predict))
+print("MCC", matthews_corrcoef(tag_true, tag_predict))

src/helpers/gui/main.ui

@@ -323,7 +323,6 @@
         <child>
           <object class="tk.Text" id="testdata" named="True">
             <property name="height">15</property>
-            <property name="state">disabled</property>
             <property name="text" translatable="yes">No tests have been run yet</property>
             <property name="undo">false</property>
             <property name="width">25</property>

src/helpers/test/knn.py

@@ -1,103 +0,0 @@
-import cv2 as cv
-import numpy as np
-import csv
-from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler, MaxAbsScaler
-import argparse
-from enum import Enum
-
-parser = argparse.ArgumentParser(prog='KNN Train CLI')
-parser.add_argument('-i', '--input', help='Input CSV file', required=True)
-parser.add_argument('-o', '--output', help='Output model file', required=True)
-
-class Tree(Enum):
-    ACCASIA = 0
-    BERK = 1
-    EIK = 2
-    ELS = 3
-    ESDOORN = 4
-    ES = 5
-    LINDE = 6
-    PLATAAN = 7
-
-class CVSuiteTestKNN: 
-    def __init__(self, model = None):
-        if model is None:
-            self.knn = cv.ml.KNearest_create()
-            self.trained = False
-        else:
-            self.knn = cv.ml.KNearest_load(model)
-            self.trained = True
-            
-    def trainCSV(self, path, output):
-        '''
-        Takes preprocessed data from CVSuite, normalises it and trains the model
-        Function expects first two columns of the dataset to be tag and photoId, the first row should be the CSV header
-        '''
-        file = open(path, mode='r')
-        data = list(csv.reader(file, delimiter=","))
-        file.close()        
-        
-        header = data.pop(0)
-        print("CSV tags: ", header)
-        
-        # Get classifier tags
-        tags_int = []
-
-        for row in data:
-            tree = row.pop(0)
-            # photoId = row.pop(1)
-            id = Tree[tree.upper()]
-            
-            # print("Tree name =", tree, " id =", id.value)
-            tags_int.append(id.value)
-
-        # 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
-        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()
-            scaler = MaxAbsScaler()
-
-            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
-        
-        # Pass data to train function
-        self.train(data, tags_int, output)
-        
-    def train(self, data, tags, output):
-        '''
-        Data should be normalised before being passed to this function
-        This function should not be run from within the suite
-        '''
-        if self.trained:
-            throw("Model already trained!")
-        else:
-            self.knn.train(data, cv.ml.ROW_SAMPLE, tags)
-            self.knn.save(output)
-    
-    def predict(self, data):
-        return self.knn.predict(data)
-    
-if __name__ == "__main__":
-    args = parser.parse_args()
-    test = CVSuiteTestKNN()
-    test.trainCSV(args.input, args.output)

src/suite.py

@@ -19,9 +19,6 @@ from helpers.logger import CVSuiteLogger, C_DBUG
 from helpers.canvas import CVSuiteCanvas
 from helpers.sift import getSiftData
 
-# Tests
-from helpers.test.knn import CVSuiteTestKNN
-
 ## UI config load
 PROJECT_PATH = pathlib.Path(__file__).parent
 PROJECT_UI = "./src/helpers/gui/main.ui"
@@ -84,9 +81,6 @@ class CVSuite:
         )
         builder.connect_callbacks(self)
 
-        # Model tests
-        self.test_knn = CVSuiteTestKNN(config_json["models"]["knn"])
-
         # Load values from config after UI has been initialised
         self.img_path.set(config_json["path"])
         self.img_size.set(config_json["size"])
@@ -273,15 +267,6 @@ class CVSuite:
             self.log.add(f"Mean {label}", mean[idx])
             self.log.add(f"Std {label}", std[idx])
 
-    def runTest(self, event=None):
-        output = self.builder.get_object("testdata")
-        output.configure(state="normal")
-        output.delete(1.0, "end")
-        
-        output.insert("end", "test\n")
-        
-        output.configure(state="disabled")
-
     def updatePath(self):
         """
         Only update image name and path
@@ -421,9 +406,6 @@ class CVSuite:
             self.log.add("SIFT total response", siftData[5])
             self.log.add("SIFT average response", siftData[6])
 
-            # Run tests
-            self.runTest()
-
             # Write results to CSV file
             if not part_update:
                 self.log.update()
@@ -434,6 +416,7 @@ class CVSuite:
             plt.show(block=False)  ## Graphs
             self.canvas.draw(size)  ## Images
 
+
 if __name__ == "__main__":
     app = CVSuite()
     app.run()