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()