Ready Model

classes/Concept.py

@@ -0,0 +1,27 @@
+from enum import Enum, auto
+
+class Concept(Enum):
+	UNKNOWN = auto()
+	VORFAHRT_GEWAEHREN = auto()
+	VORFAHRT_STRASSE = auto()
+	STOP = auto()
+	RECHTS_ABBIEGEN = auto()
+	LINKS_ABBIEGEN = auto()
+	RECHTS_VOR_LINKS = auto()
+
+	def identify_by_str(name: str):
+		match name:
+			case 'VORFAHRT_GEWAEHREN':
+				return Concept.VORFAHRT_GEWAEHREN
+			case 'VORFAHRT_STRASSE':
+				return Concept.VORFAHRT_STRASSE
+			case 'STOP':
+				return Concept.STOP
+			case 'RECHTS_ABBIEGEN': 
+				return Concept.RECHTS_ABBIEGEN
+			case 'LINKS_ABBIEGEN':
+				return Concept.LINKS_ABBIEGEN
+			case 'RECHTS_VOR_LINKS':
+				return Concept.RECHTS_VOR_LINKS
+			case _:
+				return Concept.UNKNOWN

classes/DataLoader.py

@@ -0,0 +1,95 @@
+import os
+import csv
+import cv2
+import tensorflow as tf
+import numpy as np
+
+class DataSetLoader:
+    def __init__(self, p: str, x: int, y: int) -> None:
+        if not os.path.exists(p) or p is None:
+            raise Exception("Path is not correct")
+        else:
+            self.path_to_dataset = p
+        
+        self.x_size = x if x is not None else 25
+        self.y_size = y if y is not None else 25
+
+
+    def get_concept(self, path) -> int:
+        if "fahrtrichtung_links" in path:
+            return 1
+        elif "fahrtrichtung_rechts" in path:
+            return 2
+        elif "rechts_vor_links" in path:
+            return 3
+        elif "stop" in path:
+            return 4
+        elif "vorfahrt_gewaehren" in path:
+            return 5
+        elif "vorfahrtsstrasse" in path:
+            return 6
+        else:
+            return 7
+
+
+    def get_classified_csv(self):
+        images = []
+        for dirpath, dnames, fnames in os.walk(self.path_to_dataset):
+            for fname in fnames:
+                image_path = os.path.join(dirpath, fname)
+                concept = self.get_concept(image_path)
+                if any(ext in fname for ext in ['.bmp', '.jpeg', '.jpg', '.png']):
+                    images.append((image_path, concept))
+
+        with open(os.path.abspath(os.path.join(__file__, "..", "..", "all_data_cropped.csv")), "w") as csv_output:
+            csv_writer = csv.writer(csv_output, delimiter=";")
+            csv_writer.writerow(["name", "concept"])
+            for image in images:
+                csv_writer.writerow(image)
+
+
+    def create_dataset_from_csv(self, path: str):
+        images = []
+        concepts = []
+
+        with open(path, "r") as csv_data:
+            csv_reader = csv.reader(csv_data, delimiter=";")
+            next(csv_reader)
+            for row in csv_reader:
+                images.append(row[0])
+                concepts.append(int(row[1]))
+
+        all_images = []
+        all_labels = []
+
+        for idx, (img_path, label) in enumerate(zip(images, concepts)):
+            try:
+                image = self.load_image_with_opencv(img_path)
+                all_images.append(image)
+                all_labels.append(label)
+                tf.print(idx)
+            except Exception as e:
+                print(f"Error loading image {img_path}: {e}")
+                continue  # Skip this image if error occurs
+
+        all_images = np.array(all_images)
+        all_labels = np.array(all_labels)
+
+        dataset = tf.data.Dataset.from_tensor_slices((all_images, all_labels))
+
+        return dataset
+
+
+    def load_image_with_opencv(self, file_path):
+        image = cv2.imread(file_path)
+
+        if image is None:
+            raise Exception(f"Could not load image: {file_path}")
+        
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+        image = cv2.resize(image, (self.x_size, self.y_size))
+
+        image = image.astype(np.float32) / 255.0
+
+        return image