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

src/helpers/test/knn.py

@@ -52,7 +52,7 @@ class CVSuiteTestKNN:
 
         for row in data:
             tree = row.pop(0)
-            # photoId = row.pop(1)
+            photoId = row.pop(1)
             id = Tree[tree.upper()]
             
             # print("Tree name =", tree, " id =", id.value)
@@ -104,14 +104,16 @@ class CVSuiteTestKNN:
         if self.trained:
             raise EnvironmentError("Model already trained!")
         else:
+            print(data)
+            print(data.shape)
             self.knn.train(data, cv.ml.ROW_SAMPLE, tags)
             
             # 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)
+    def predict(self, data, nr = 3):
+        return self.knn.findNearest(data, nr)
     
 if __name__ == "__main__":
     args = parser.parse_args()

src/suite.py

@@ -14,7 +14,12 @@ import json
 # OpenCV
 import numpy as np
 import cv2
-from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler, MaxAbsScaler
+from sklearn.preprocessing import (
+    MinMaxScaler,
+    StandardScaler,
+    RobustScaler,
+    MaxAbsScaler,
+)
 import joblib
 
 # GUI
@@ -26,9 +31,11 @@ from helpers.statistics import imgStats
 from helpers.logger import CVSuiteLogger, C_DBUG, C_WARN
 from helpers.canvas import CVSuiteCanvas
 from helpers.sift import getSiftData
+from helpers.tags import Tree
 
 # Tests
 from helpers.test.knn import CVSuiteTestKNN
+
 # from helpers.test.decision_tree import CVSuiteTestDecisionTree
 
 ## UI config load
@@ -41,6 +48,7 @@ CONFIG_PATH = "./src/config/config.json"
 config_file = open(CONFIG_PATH, encoding="utf-8")
 config_json = json.load(config_file)
 
+
 ## UI class setup
 class CVSuite:
     def __init__(self, master=None):
@@ -97,7 +105,6 @@ class CVSuite:
         # Attempt to load scaler
         if config_json["scaler"] != "":
             self.scaler = joblib.load(config_json["scaler"])
-            print(self.scaler)
         else:
             self.scaler = None
 
@@ -108,7 +115,9 @@ class CVSuite:
             self.test_knn = None
 
         if config_json["models"]["dectree"] != "":
-            self.test_dectree = CVSuiteTestDecisionTree(config_json["models"]["dectree"])
+            self.test_dectree = CVSuiteTestDecisionTree(
+                config_json["models"]["dectree"]
+            )
         else:
             self.test_dectree = None
 
@@ -303,26 +312,39 @@ class CVSuite:
         output.configure(state="normal")
         output.delete(1.0, "end")
 
-        # Normalise data
+        # Remove tag and photoId
         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))
+        # Add actual name        
+        output.insert("end", f"Actual:\n\t{tag.upper()}\n")
 
-        print(data)
+        # Normalise data using loaded scalers
+        for idx, value in enumerate(data):
+            d = np.array(value)
+            data[idx] = self.scaler[idx].transform(d.astype(np.float32).reshape(1, -1))[0][0]
+        
+        data = np.array([data], dtype=np.float32)
         
         if self.test_knn is not None:
             # Do knn test
             output.insert("end", "KNN Result:\n")
-            pass
+
+            ret, results, neighbours ,dist = self.test_knn.predict(data)
+
+            for idx, res_id in enumerate(neighbours[0]):
+                output.insert("end", f" {idx}:\t{Tree(res_id).name}\n")
+    
+            print(C_DBUG, "KNN Result:")
+            print("\t\tresult: \t{}".format(results) )
+            print("\t\tneighbours:\t{}".format(neighbours) )
+            print("\t\tdistance:\t{}".format(dist) )
         else:
             print(C_WARN, "KNN Model not configured!")
 
         if self.test_dectree is not None:
             print(self.test_dectree.predict(data))
             output.insert("end", "Decision Tree Result:\n")
-            pass
         else:
             print(C_WARN, "Decison Tree Model not configured!")
 
@@ -367,11 +389,11 @@ class CVSuite:
                 print("Full update forced!")
 
         if self.updatePath():
-            print(C_DBUG, F"Processing {self.img_name}")
+            print(C_DBUG, f"Processing {self.img_name}")
 
-            self.mainwindow.title(F"{TITLE} - {self.img_name}")
+            self.mainwindow.title(f"{TITLE} - {self.img_name}")
             self.log.add("Tree", self.img_name.split("_")[0])
-            self.log.add("ID", self.img_name.split("_")[1].split('.')[0])
+            self.log.add("ID", self.img_name.split("_")[1].split(".")[0])
 
             # Get all user vars
             ct1 = self.canny_thr1.get()
@@ -467,11 +489,9 @@ class CVSuite:
             self.log.add("SIFT total response", siftData[5])
             self.log.add("SIFT average response", siftData[6])
 
-            # Run tests
-            self.runTest(self.log.data)
-
             # Write results to CSV file
             if not part_update:
+                self.runTest(self.log.data)
                 self.log.update()
             else:
                 self.log.clear()  # Prevent partial updates from breaking log
@@ -480,6 +500,7 @@ class CVSuite:
             plt.show(block=False)  ## Graphs
             self.canvas.draw(size)  ## Images
 
+
 if __name__ == "__main__":
     app = CVSuite()
     app.run()