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

README.md

@@ -80,7 +80,7 @@ $ python ./src/suite.py
     - Based on your system configuration, this might take a while
 3. Run the CVSuiteTestKNN CLI tool:
 ```sh
-$ python ./src/helpers/test/knn.py -i ./out/result-(date/time).csv -o ./out/models/model_knn.yaml 
+$ python ./src/helpers/test/knn.py -i ./out/result-(date/time).csv -o ./out/models/ 
 ```
 4. Edit your `config.json` to include the newly created model
 

src/helpers/test/knn.py

@@ -5,6 +5,9 @@ from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler, Ma
 import argparse
 from enum import Enum
 import yaml
+import joblib
+import datetime
+import os
 
 parser = argparse.ArgumentParser(prog='KNN Train CLI')
 parser.add_argument('-i', '--input', help='Input CSV file', required=True)
@@ -34,6 +37,7 @@ class CVSuiteTestKNN:
     def trainCSV(self, path, output):
         '''
         Takes preprocessed data from CVSuite, normalises it and trains the model
+        Output should be a folder path
         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')
@@ -85,6 +89,10 @@ class CVSuiteTestKNN:
             # Replace original data array
             data[:, idx] = column
         
+        # Dump the scalers
+        now = datetime.datetime.now()
+        joblib.dump(self.scale, os.path.join(output, F"scale_{now.strftime('%Y-%m-%dT%H.%M.%S')}.pkl"))
+        
         # Pass data to train function
         self.train(data, tags_int, output)
         
@@ -97,7 +105,10 @@ class CVSuiteTestKNN:
             raise EnvironmentError("Model already trained!")
         else:
             self.knn.train(data, cv.ml.ROW_SAMPLE, tags)
-            self.knn.save(output)
+            
+            # Save it
+            now = datetime.datetime.now()
+            self.knn.save(os.path.join(output, F"model_knn_{now.strftime('%Y-%m-%dT%H.%M.%S')}.yaml"))
     
     def predict(self, data):
         return self.knn.predict(data)

src/suite.py

@@ -15,6 +15,7 @@ import json
 import numpy as np
 import cv2
 from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler, MaxAbsScaler
+import joblib
 
 # GUI
 import pygubu
@@ -28,7 +29,7 @@ from helpers.sift import getSiftData
 
 # Tests
 from helpers.test.knn import CVSuiteTestKNN
-from helpers.test.decision_tree import CVSuiteTestDecisionTree
+# from helpers.test.decision_tree import CVSuiteTestDecisionTree
 
 ## UI config load
 PROJECT_PATH = pathlib.Path(__file__).parent
@@ -93,8 +94,15 @@ class CVSuite:
         )
         builder.connect_callbacks(self)
 
+        # Attempt to load scaler
+        if config_json["scaler"] != "":
+            self.scaler = joblib.load(config_json["scaler"])
+            print(self.scaler)
+        else:
+            self.scaler = None
+
         # Model tests
-        if config_json["models"]["knn"] != "":
+        if self.scaler is not None and config_json["models"]["knn"] != "":
             self.test_knn = CVSuiteTestKNN(config_json["models"]["knn"])
         else:
             self.test_knn = None
@@ -299,9 +307,10 @@ class CVSuite:
         tag = data.pop(0)
         photoId = data.pop(1)
         
+        for idx, value in enumerate(data):
+            data[idx] = self.scaler[idx].transform(np.array(value).reshape(-1, 1))
+        
         print(data)
-        for value in data:
-            print(value)
         
         if self.test_knn is not None:
             # Do knn test