diff --git a/src_folder/BackEnd/CameraDetection/CameraDetection.py b/src_folder/BackEnd/CameraDetection/CameraDetection.py
deleted file mode 100644
index 468d5a3..0000000
--- a/src_folder/BackEnd/CameraDetection/CameraDetection.py
+++ /dev/null
@@ -1,165 +0,0 @@
-import cv2
-import numpy as np
-
-#  from Track import nothing
-# # Farbwerte für die Erkennung (Beispiel: Rot, Grün, Blau)
-colors = [(0, 0, 255), (0, 255, 0), (255, 0, 0)]
-color_names = ["Rot", "Gruen", "Blau"]
-
-# video cap
-
-
-# Farbgrenzen für die Erkennung
-lower_red = np.array([80, 160, 150])
-upper_red = np.array([255, 255, 255])
-
-lower_green = np.array([40, 50, 160])
-upper_green = np.array([80, 255, 255])
-
-lower_blue = np.array([95, 180, 90])
-upper_blue = np.array([130, 255, 255])
-
-CURRENT_SCORES= {'score_red': 0,
-                 'score_green': 0,
-                 'score_blue': 0
-                }
-
-# Funktion zur Farberkennung
-def erkennung_farben(img):
-    hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
-    results = []
-
-    count_red = 0
-    count_green = 0
-    count_blue = 0
-
-    for i, color in enumerate(colors):
-        if i == 0:
-            lower = lower_red
-            upper = upper_red
-        elif i == 1:
-            lower = lower_green
-            upper = upper_green
-        elif i == 2:
-            lower = lower_blue
-            upper = upper_blue
-
-        mask = cv2.inRange(hsv_img, lower, upper)
-
-        # Farbfläche finden
-        contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-        center = None
-        count = 0
-
-        for contour in contours:
-            if count < 3:
-                if cv2.contourArea(contour) > 100:
-                    # Schwerpunkt der Kontur berechnen
-                    M = cv2.moments(contour)
-                    if M["m00"] > 0:
-                        cX = int(M["m10"] / M["m00"])
-                        cY = int(M["m01"] / M["m00"])
-                        center = (cX, cY)
-                        count += 1
-                        # Rechteck zeichnen
-                        x, y, w, h = cv2.boundingRect(contour)
-                        cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
-                        cv2.putText(img, color_names[i], (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, color, 2)
-
-                        # Farbanzahl erhöhen
-                        if i == 0:
-                            count_red += 1
-                        elif i == 1:
-                            count_green += 1
-                        elif i == 2:
-                            count_blue += 1
-
-
-
-        results.append(center)
-
-    return img, results, count_red, count_green, count_blue
-
-# Funktion zur Positionsermittlung
-def ermittle_position(results, img_width):
-    positions = []
-
-    # pos_to_num = {"Rechts": 3,
-    #               "Mitte": 2,
-    #               "Links": 1,
-    #               "Nicht gefunden": 0}
-
-    for result in results:
-        if result is None:
-            position = 0
-        else:
-            x = result[0]
-            if x < img_width / 3:
-                position = 3
-            elif x < 2 * img_width / 3:
-                position = 2
-            else:
-                position = 1
-
-        positions.append(position)
-    return positions
-
-# def count_colors(my_game: Game, red: int, green: int, blue: int) -> None:
-#     currrent_scores = {'score_red': red,
-#                         'score_green': green,
-#                         'score_blue': blue
-#                         }
-#     my_game.set_scoreboard(currrent_scores)
-    
-
-def main():
-    # Videoquelle öffnen (kann auch eine Bilddatei sein)
-    video = cv2.VideoCapture(0)  # Hier "0" für die Kamera verwenden
-    
-    while True:
-
-        ret, frame = video.read(0)
-
-        # Fehlerbehandlung, wenn kein Bild gelesen werden kann
-        if not ret:
-            break
-        # Farben erkennen
-        farben_img, ergebnisse, count_red, count_green, count_blue = erkennung_farben(frame)
-
-        # Add counted colours to the scoreboard in the correct field
-        
-        # count_colors(count_red, count_green, count_blue)
-         
-
-        # Anzahl der Farben anzeigen
-        # cv2.putText(farben_img, f"Rot: {count_red}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, colors[0], 2)
-        # cv2.putText(farben_img, f"Gruen: {count_green}", (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 1, colors[1], 2)
-        # cv2.putText(farben_img, f"Blau: {count_blue}", (10, 90), cv2.FONT_HERSHEY_SIMPLEX, 1, colors[2], 2)
-
-        # Positionen ermitteln
-        img_width = frame.shape[1]
-        positionen = ermittle_position(ergebnisse, img_width)
-
-        # Positionen anzeigen
-        for i, position in enumerate(positionen):
-            cv2.putText(farben_img, f"{color_names[i]}: {position}", (10, 150 + 30 * i),
-                        cv2.FONT_HERSHEY_SIMPLEX, 1, colors[i], 2)
-
-        # Linien zeichnen
-        cv2.line(farben_img, (img_width // 3, 0), (img_width // 3, frame.shape[0]), (0, 0, 0), 2)
-        cv2.line(farben_img, (2 * img_width // 3, 0), (2 * img_width // 3, frame.shape[0]), (0, 0, 0), 2)
-
-            # Bild anzeigen
-        cv2.imshow("Farberkennung", farben_img)
-
-            # Auf "q" drücken, um die Schleife zu beenden
-        if cv2.waitKey(1) & 0xFF == ord('q'):
-            break
-
-        # Videoquelle und Fenster schließen
-    video.release()
-    cv2.destroyAllWindows()
-
-
-if __name__ == "__main__":
-    main()
\ No newline at end of file
diff --git a/src_folder/Game_Images/current_score.png b/src_folder/Game_Images/current_score.png
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diff --git a/src_folder/Presentation/Interaktion Praktikum Präsentation.pptx b/src_folder/Presentation/Interaktion Praktikum Präsentation.pptx
new file mode 100644
index 0000000..62f619c
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